Electric tower inclination early warning method based on multiple sensors

Abstract

The invention discloses an electric tower inclination early warning method based on multiple sensors, which is implemented by an electric tower inclination early warning system based on multiple sensors, wherein the electric tower inclination early warning system is composed of an intelligent thundersight system, a power supply system, a clinometer and a weather instrument which are matched with one another. The early warning method comprises the following steps of: 1, acquiring data of an inclination sensor: measuring inclination angles of the electric tower in x-axis and y-axis directions by using the inclination sensor, calculating the inclination displacement of the electric tower by acquiring data of the inclination angles of the electric tower in real time, and calculating the inclination displacement of the electric tower by adopting a plurality of sensor monitoring modes through data analysis and fusion; according to the invention, a health state evaluation model of the electric tower can be established, real-time monitoring is carried out by means of an advanced sensor technology, early warning is given in time and emergency measures are taken in advance when the electric tower is inclined or weather conditions are bad, so that the risk of accidents is reduced, and the monitoring capability of the state of the electric tower can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric tower monitoring, and particularly to a method for warning of electric tower inclination based on multiple sensors. Background Art

[0002] With the increasing complexity of the power system and the continuous expansion of the high-voltage power grid, the safety and stability of power transmission have become the focus of social attention. As a core component of the power system, the stability of the transmission tower is directly related to the safe operation of the entire power grid. However, when the electric tower faces external environmental changes, such as severe weather (strong wind, heavy rain, ice and snow, etc.), geological disasters (such as landslides, mudslides, etc.) or mechanical damage, it is prone to inclination, shaking, and even collapse. These abnormal conditions may not only cause power outages, but also increase the maintenance and emergency repair costs, and even pose serious safety hazards.

[0003] Most traditional electric tower monitoring methods rely on manual inspections, periodically checking the electric towers. This method not only has a high working intensity, but also cannot detect sudden abnormal conditions in a timely manner.

[0004] There are also solutions that use sensor monitoring devices such as inclination sensors, tension sensors, and meteorological sensors alone. However, these solutions cannot comprehensively guarantee the transmission line electric towers. When using a single sensor to judge crises such as inclination and give early warnings, inclination may occur without a lead time.

[0005] Therefore, the present invention combines multiple sensor technologies and proposes a method for warning of electric tower inclination based on multiple sensors, aiming to improve the real-time performance, accuracy, and reliability of electric tower monitoring, and provide a strong guarantee for the safe operation of the power system.

[0006] In view of the above problems, the present invention is improved. Summary of the Invention

[0007] The present invention proposes a method for warning of electric tower inclination based on multiple sensors, which solves the above problems existing in the prior art during use.

[0008] The technical solution of the present invention is implemented as follows: A method for warning of electric tower inclination based on multiple sensors, which is implemented by an electric tower inclination warning system based on multiple sensors composed of an intelligent radar-vision system, a power supply system, an inclinometer, and a meteorological instrument. The warning steps are as follows:

[0009] Data acquisition of the inclination sensor:

[0010] The present invention uses an inclination sensor to measure the inclination angles of the electric tower in the x-axis and y-axis directions. By collecting the data of the inclination angles of the electric tower in real time, the inclination displacement of the electric tower can be calculated. When the inclination angle of the electric tower exceeds the set safety threshold, the system will trigger an alarm mechanism to prompt the operation and maintenance personnel to conduct inspections and maintenance;

[0011] Assume the height of the electric tower is H (i.e., the distance from the base to the top of the electric tower). Then, according to the inclination angle and trigonometric function relationship, the horizontal displacements Dx and Dy of the electric tower base can be calculated respectively by the following formulas:

[0012] D x = H × tan(θ x )

[0013] D y = H × tan(θ y );

[0014] If you want to calculate the total horizontal displacement D of the electric tower, you can combine the displacement components of the x-axis and y-axis and use the Pythagorean theorem (i.e., the Pythagoras theorem) to obtain the total displacement:

[0015]

[0016] Then, judge whether the alarm threshold of the electric tower displacement is reached to confirm whether to issue a warning report;

[0017] Wind speed monitoring and early warning:

[0018] The meteorological sensor monitors the wind speed in real time and uploads the data to the monitoring system. Combining with the designed wind speed of the electric tower itself (i.e., the wind resistance design parameters of the electric tower), early warning can be carried out according to the current wind speed data. When the monitored wind speed exceeds the wind speed bearing capacity designed by the electric tower, the system will issue a warning signal to prompt that the electric tower may be affected by the wind force, resulting in inclination or damage;

[0019] Before judging the real-time wind speed early warning, data collection is required to obtain real-time and historical wind speed data, including the wind speed magnitude, wind direction, and change frequency. Obtaining the material, designed height, weight, foundation depth, structure type (such as guyed tower, free-standing tower) of the electric tower, as well as the ultimate bearing capacity of the electric tower under different wind speeds, and geographical location (mountainous area, plain, etc.), soil characteristics, etc., will all affect the stability of the electric tower. Use the aerodynamic formula to calculate the lateral pressure of the wind on the electric tower: F = 0.5 × Cd × A × ρ × V2;

[0020] F: The acting force of the wind on the electric tower;

[0021] Cd: Drag coefficient (related to the structural shape of the electric tower);

[0022] A: The windward area of the electric tower;

[0023] ρ: air density;

[0024] V: wind speed;

[0025] Combined with historical data, set a wind speed threshold for the power tower. When the wind speed exceeds this threshold or reaches the warning level of the model, trigger a warning signal;

[0026] The setting of the warning level and the threshold of the wind speed force can usually be divided into the following warning levels according to the force F and the bearing limit of the power tower structure:

[0027] Level 1, Green level (Normal)

[0028] Condition: F < Fsafe;

[0029] Description: The lateral force borne by the power tower at the current wind speed is lower than its normal bearing capacity, the structure is safe, and no action is required;

[0030] Wind speed: less than 15 m / s (or set according to the specific structure);

[0031] Level 2, Yellow level (Attention)

[0032] Condition: Fsafe ≤ F < Fcaution;

[0033] Description: The force on the power tower due to the wind increases significantly, approaching the lower limit of the bearing capacity, but the structure can still bear it safely; Suggested measure: Closely monitor the change of wind speed, especially whether the wind speed further increases;

[0034] Wind speed: 15 - 25 m / s (can be adjusted according to the power tower design);

[0035] Level 3, Orange level (Warning)

[0036] Condition: Fcaution ≤ F < Fdanger;

[0037] Description: The force borne by the power tower is close to the critical bearing range, showing signs of slight vibration or deformation;

[0038] Suggested measure: Immediately notify the maintenance personnel and make emergency preparations; If possible, evacuate the area near the power tower; Wind speed: 25 - 35 m / s (the specific range can be slightly adjusted according to the type of power tower);

[0039] Level 4, Red level (Danger)

[0040] Condition: F ≥ Fdanger F Description: The force borne by the power tower has reached or exceeded its design bearing limit, and there is a risk of collapse;

[0041] Recommended measures: Immediately activate the emergency plan, block the area around the power tower, and arrange professional maintenance personnel to check the tower. Try to avoid people approaching before the wind speed drops;

[0042] Wind speed: greater than 35m / s (adjusted according to tower design);

[0043] Determine the specific force thresholds Fsafe, Fcaution, and Fdanger+;

[0044] These force thresholds can be set according to the specific design and load-bearing capacity of the tower. The following is a typical determination method:

[0045] F_safe: The maximum permissible force under normal use conditions is calculated based on the yield strength and safety factor of the tower material;

[0046] F_caution: close to the yield strength of the tower material, but not yet reaching the limit, usually Fsafe×1.5; F_danger: reaching the structural limit of the tower, that is, the ultimate bearing capacity of the material, usually Fsafe×2 or higher;

[0047] Rainfall monitoring and early warning:

[0048] In addition to wind speed, meteorological sensors can also monitor rainfall and transmit its data to the monitoring system. By analyzing rainfall data, when rainfall reaches a certain threshold, the system can issue an early warning of the additional load that heavy rain may bring. For example, heavy rain may increase the risk of tower tilting or cause slippery soil, further threatening the stability of the tower.

[0049] Before issuing an early warning based on rainfall, corresponding data collection is also required. Real-time and historical rainfall data, including rainfall intensity, cumulative rainfall, rainfall duration, etc., are collected to understand the structural characteristics of the tower, such as the material, foundation depth, foundation design, etc., and to obtain information such as soil type, moisture content, and soil density around the tower. The saturation state and permeability of the soil will directly affect its bearing capacity during rainfall. For example, the geographical location of the tower, the surrounding slope (whether it is on a slope), and the geological type, etc. Towers in mountainous and hilly areas may face higher risks during rainfall.

[0050] Calculate the rainstorm danger index Hr based on data collection, and then divide the warning level;

[0051] Hr = f(P, T, G, C), where P is the rainfall intensity, such as how much rainfall per hour; T is the rainfall duration; G is the terrain factor. For flat areas or areas that are not prone to waterlogging, it can be set to 1. For areas with slopes that are prone to landslides or waterlogging, it can be set to, for example, 1.3 or other values according to the actual situation; C is the disaster resistance ability of power grid facilities. For relatively old facilities, it can be set to 0.7, 0.8, etc. For new equipment, it can be set to 0.95 or 1;

[0052] When Hr reaches 80 or above, it is a high - risk warning; when Hr reaches above 60 and less than 80, it is a relatively high - risk warning; when Hr reaches above 40 and less than 60, it is a medium - risk warning; when Hr reaches above 20 and less than 40, it is a low - risk warning;

[0053] Through sensor information such as rainfall and wind speed, historical data is recorded and a data model is formed to predict subsequent weather conditions. When strong winds, heavy rains, or heavy snows are predicted, early warnings are issued. Based on the accumulated historical meteorological data, different machine learning algorithms are used to predict disaster weather:

[0054] Regression model: Use methods such as linear regression or polynomial regression to predict the relationship between future meteorological conditions and disaster weather;

[0055] Time - series prediction: Methods such as ARIMA (Autoregressive Integrated Moving Average Model) and LSTM (Long Short - Term Memory Network) can handle the time - series characteristics in meteorological data and predict meteorological changes in the next few hours or days;

[0056] Classification model: Through classification algorithms such as decision trees, support vector machines (SVM), and random forests, based on the input meteorological data, it is predicted whether a certain disaster weather (such as strong winds, heavy rains) will occur;

[0057] Deep - learning model: A hybrid model based on convolutional neural networks (CNN) and recurrent neural networks (RNN) can also be used for complex weather prediction, especially for weather prediction problems with high - dimensional and non - linear relationships;

[0058] Model training process:

[0059] I. Data preparation: Use the historical dataset (including information such as temperature, humidity, wind speed, etc.) as input features, and use weather event types (such as strong winds, heavy rains, etc.) as output labels;

[0060] II. Training and validation: Use part of the historical data as the training set and another part as the validation set, and adjust the model parameters to improve the accuracy;

[0061] III. Model evaluation: Use indicators such as cross - validation, precision, recall rate, F1 - value, etc. to evaluate the model effect and ensure the generalization ability of the model;

[0062] Through real-time data collection based on meteorological sensors and predictions of machine learning models, early warnings for disaster weather can be effectively achieved. With the accumulation of data and continuous optimization of the models, the accuracy of the predictions will continue to improve, providing reliable disaster warning support for multiple fields such as society, agriculture, and transportation. This system can not only reduce losses caused by weather disasters but also improve the emergency response efficiency, ensuring the safety and health of the public.

[0063] Icing monitoring:

[0064] Record the xy values of the positions on the transmission line to be monitored during installation, as well as the magnification factor required for the camera, and perform pre-configuration. When the temperature is less than 0 degrees and the humidity is greater than 85%, or when the temperature is greater than 0 degrees but less than 3 degrees and the wind speed is greater than 5 m / s, a "cold wave effect" occurs, and icing may occur. When one of the above conditions is met, turn on the camera to record the preset positions configured during installation and synchronize for AI icing recognition. Once icing is recognized, report and give an early warning of the position where icing occurs on the corresponding transmission line; Data fusion and comprehensive evaluation:

[0065] By comprehensively analyzing data from tilt sensors and meteorological sensors, the system can real-time evaluate the stability of the electric tower and predict possible tilt or failure risks in advance. The data fusion technology integrates multi-source sensor data to provide an accurate assessment of the electric tower's state, further improving the accuracy and timeliness of early warnings. The early warning method of the present invention effectively prevents electric tower failures caused by factors such as bad weather by real-time monitoring and analyzing the tilt state of the electric tower and external meteorological conditions, ensuring the safe and stable operation of the power system. The electric tower tilt early warning system based on multiple sensors as described above in the present invention further: The intelligent radar-vision system includes an installation column installed on the ground, and a fixing member is detachably installed on the surface of the fixing member, and a cross bar is fixedly installed on one side of the fixing member.

[0066] The electric tower tilt early warning system based on multiple sensors as described above in the present invention further: An adjustable installation hoop is sleeved on the surface of the cross bar, and a plurality of mounting rods are adjustably and fixedly installed on the surface of the installation hoop.

[0067] The electric tower tilt early warning system based on multiple sensors as described above in the present invention further: The mounting rod includes a main rod installed on the installation hoop, an end rod is provided at one end of the main rod away from the installation hoop, a regulating screw is fixedly connected to the end of the end rod facing the main rod, and the end of the regulating screw away from the end rod is inserted and installed in the main rod.

[0068] The electric tower tilt early warning system based on multiple sensors as described above in the present invention further: A bracket is fixedly installed at one end of the cross bar, and radars are fixedly installed at the bottoms on both sides of the bracket.

[0069] For the electric tower tilt warning system based on multiple sensors as described above, further: An installation platform is fixedly installed at the top of the bracket, and a pan-tilt is fixedly installed at the top of the installation platform.

[0070] For the electric tower tilt warning system based on multiple sensors as described above, further: A control terminal is fixedly installed at the output end of the pan-tilt, and a first camera and a second camera are respectively fixedly installed on both sides of the control terminal.

[0071] For the electric tower tilt warning system based on multiple sensors as described above, further: The power supply system includes a triangular bracket arranged on the ground, and a power supply device is detachably installed on one side of the triangular bracket. For the electric tower tilt warning system based on multiple sensors as described above, further: A plurality of solar panels are detachably installed on one side of the triangular bracket, and the plurality of solar panels are all arranged at an inclination of 45 degrees. For the electric tower tilt warning system based on multiple sensors as described above, further: A circuit protection device is fixedly installed inside the triangular bracket.

[0072] In summary, the beneficial effects of the present invention are as follows:

[0073] 1. The present invention adopts a monitoring method using multiple sensors. Through data analysis and fusion, it can establish a health status evaluation model of the electric tower, and perform real-time monitoring with the help of advanced sensor technology. When the electric tower tilts or the meteorological conditions are severe, it can issue early warnings in a timely manner and take emergency measures in advance, thereby reducing the risk of accidents and effectively improving the monitoring ability of the electric tower status.

[0074] 2. Precise warning and reliability improvement: By combining the data of the tilt sensor and the meteorological sensor, the system can more accurately evaluate the stability of the electric tower. For example, when the wind speed or rainfall reaches a certain threshold, the data of the tilt sensor can provide the current physical tilt state, and this real-time information can verify whether there is a tilt tendency, thereby improving the warning accuracy.

[0075] 3. Real-time risk identification and fault prevention: The tilt sensor can identify the immediate displacement or tilt of the electric tower, while the meteorological sensor provides environmental conditions (such as wind speed and rainfall). This combination can timely detect and warn of potential structural problems. For example, under strong wind or heavy rain conditions, if an increase in tilt is detected, the system can immediately issue a risk signal to prevent further damage.

[0076] 4. Extend the equipment life and optimize maintenance: By combining meteorological data, the warning system can help power companies prioritize the inspection or maintenance of electric towers in high-risk areas after bad weather, avoid long-term structural stress caused by tilt problems, and thus extend the service life of the equipment.

[0077] 5. Reduce false alarms and unnecessary maintenance: Using a tilt sensor alone may cause false alarms due to small daily deviations, but combined with meteorological data, it can better determine whether these changes are caused by the external environment, thereby reducing unnecessary maintenance and false alarm rates.

[0078] 6. The present invention arranges mounting posts, fixings and cross bars. The mounting posts are installed vertically on the ground. All equipment is directly or indirectly installed on the mounting posts, and is also indirectly or directly installed on the cross bars. The fixings can be removed from the mounting posts. Therefore, the installation height of the fixings can also be adjusted according to actual use requirements, thereby meeting various use requirements of users and providing convenience for users' work.

[0079] 7. The present invention is used to install the mounting rod through the mounting hoop, and the mounting hoop is installed by sleeve connection and then fastened with bolts. In this way, the installation position of the mounting hoop can be adjusted according to actual use requirements, the installation position of the mounting rod can also be adjusted along with the mounting hoop, and the installation of the inclinometer and the meteorological instrument can also be adjusted, providing convenience for the user's work.

[0080] 8. The present invention adopts the arrangement of a mounting rod, which is an extended mounting component. The mounting rod can be used to mount the inclinometer and the meteorological instrument on the main frame, and can also be kept away from the ground to avoid damage by small animals, thereby providing convenience for the user's work. In addition, the mounting rod can be freely adjusted in length, thereby adjusting the height of the installed equipment to meet more usage requirements. When adjusting, the nut on the adjusting screw is loosened, and then the end rod or the adjusting screw is rotated to extend the end rod and the adjusting screw outward. After adjustment, the nut on the adjusting screw is tightened to tighten it. It should be noted that the inclinometer and the meteorological instrument may not be installed on the mounting rod, and it can be selected according to actual needs.

[0081] 9. The present invention sets a bracket, which is used to install equipment components, and the setting of the bracket can increase more installation space and installation positions, so as to facilitate users to install more corresponding equipment, ensure the accuracy of the early warning system, and provide convenience for users' work.

[0082] 10. Through the settings of the mounting platform, pan-tilt head, control terminal, Camera 1 and Camera 2, the control terminal controls the pan-tilt head, and then based on the object position detected by the radar by the pan-tilt head, Camera 1 and Camera 2 rotate to adjust the shooting position and angle to capture the shooting object. Thus, it can monitor the changes in the working environment with the radar according to the actual working environment, adjust and judge the true size of the object and the actual state of the object in the video, so as to judge whether the electric tower is tilted, and then issue a warning in time, so that the user can make a timely response. The mounting platform is used to stably mount the pan-tilt head, control terminal, Camera 1 and Camera 2, ensuring the stability of the work of the pan-tilt head, control terminal, Camera 1 and Camera 2, and providing convenience for the user's work.

[0083] 11. Through the settings of the triangular bracket and the power supply device, the power supply device, solar panel and circuit protection device are all mounted on the triangular bracket to improve the stability of the work of the power supply device, solar panel and circuit protection device. The power supply device is used for storing and supplying electricity, and the power supply device will supply electricity to other working devices in the system to ensure the normal operation of the warning system and provide convenience for the user's work.

[0084] 12. Through the setting of the solar panel, the solar panel is used for solar power generation. In this way, the entire warning system can realize self-power generation and electricity storage, achieving self-sufficiency and avoiding the situation of being unable to work due to power failure, providing convenience for the user's work.

[0085] 13. Through the setting of the circuit protection device, the circuit protection device is used to adjust the current and voltage, and can play a role in protecting the entire power supply system, ensuring the normal operation of the warning system and providing convenience for the user's work. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0087] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0088] Figure 2 It is an implementation flowchart of the warning method of the present invention;

[0089] Figure 3 It is a structural block diagram of the warning system of the present invention;

[0090] Figure 4 It is a distribution diagram of the warning system device of the present invention;

[0091] Figure 5 Schematic diagram of the three-dimensional structure of the intelligent radar-vision system;

[0092] Figure 6 Schematic diagram of the three-dimensional structure of the power supply system;

[0093] Figure 7 Schematic diagram of the three-dimensional structure of the fitting rod of the present invention;

[0094] Figure 8 Schematic diagram of the short focal length linear spectrum of the camera of the present invention;

[0095] Figure 9 Schematic diagram of the standard focal length linear spectrum of the camera of the present invention;

[0096] Figure 10 Schematic diagram of the short focal length linear spectrum of the camera of the present invention;

[0097] Figure 11 Flow chart of the system for scanning and identifying objects of the present invention;

[0098] Figure 12 Flow chart of the system warning of the present invention.

[0099] In the figure: 1, installation column; 2, fixing part; 3, cross bar; 4, installation hoop; 5, fitting rod; 6, bracket; 7, radar; 8, installation platform; 9, pan-tilt; 10, control terminal; 11, camera one; 12, triangular bracket; 13, power supply device; 14, solar panel; 15, circuit protection device; 16, camera two; 51, main rod; 52, end rod; 53, adjusting screw. Specific embodiments

[0100] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying Figures 1-12 , and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0101] Embodiment

[0102] An electric tower tilt warning method based on multiple sensors, which is implemented by an electric tower tilt warning system based on multiple sensors composed of an intelligent radar-vision system, a power supply system, an inclinometer and a weather meter. The warning steps are as follows:

[0103] Tilt sensor data acquisition:

[0104] The present invention utilizes an inclination sensor to measure the inclination angles of a power tower in the x-axis and y-axis directions. By collecting the data of the power tower's inclination angles in real time, the inclination displacement of the power tower can be calculated. When the inclination angle of the power tower exceeds the set safety threshold, the system will trigger an alarm mechanism to prompt the operation and maintenance personnel to conduct inspections and maintenance;

[0105] Assume the height of the power tower is H (i.e., the distance from the base to the top of the power tower). Then, according to the inclination angle and trigonometric function relationship, the horizontal displacements Dx and Dy of the power tower base can be calculated respectively by the following formulas:

[0106] D x = H × tan(θ x )

[0107] D y = H × tan(θ y );

[0108] If you want to calculate the total horizontal displacement D of the power tower, you can combine the displacement components of the x-axis and y-axis and use the Pythagorean theorem (i.e., Pythagoras' theorem) to obtain the total displacement:

[0109]

[0110] Then, judge whether the alarm threshold of the power tower displacement is reached to confirm whether to issue a warning report;

[0111] Wind speed monitoring and early warning:

[0112] The meteorological sensor monitors the wind speed in real time and uploads the data to the monitoring system. Combining with the designed wind speed of the power tower itself (i.e., the wind resistance design parameters of the power tower), early warning can be carried out according to the current wind speed data. When the monitored wind speed exceeds the wind speed bearing capacity designed for the power tower, the system will issue a warning signal to indicate that the power tower may be affected by the wind force, resulting in inclination or damage;

[0113] Before making a real-time early warning judgment on the wind speed, data collection is required to obtain real-time and historical wind speed data, including the wind speed magnitude, wind direction, and change frequency, to obtain the material, designed height, weight, foundation depth, structure type (such as guyed tower, free-standing tower) of the power tower, as well as the ultimate bearing capacity of the power tower under different wind speeds, and geographical location (mountainous area, plain, etc.), soil characteristics, etc., which will all affect the stability of the power tower. Use the aerodynamic formula to calculate the lateral pressure of the wind on the power tower: F = 0.5 × Cd × A × ρ × V2;

[0114] F: The acting force of the wind on the power tower;

[0115] Cd: Drag coefficient (related to the structural shape of the power tower);

[0116] A: The windward area of the power tower;

[0117] ρ: air density;

[0118] V: wind speed;

[0119] Combined with historical data, set a wind speed threshold for the power tower. When the wind speed exceeds this threshold or reaches the warning level of the model, trigger a warning signal;

[0120] The warning level and the threshold setting of the wind speed force can usually be divided into the following warning levels according to the force F and the bearing limit of the power tower structure:

[0121] Level 1, Green level (Normal)

[0122] Condition: F < Fsafe;

[0123] Description: The lateral force borne by the power tower at the current wind speed is lower than its normal bearing capacity, the structure is safe, and no action is required;

[0124] Wind speed: less than 15 m / s (or set according to the specific structure);

[0125] Level 2, Yellow level (Attention)

[0126] Condition: Fsafe ≤ F < Fcaution;

[0127] Description: The force on the power tower due to the wind increases significantly, approaching the lower limit of the bearing capacity, but the structure can still bear it safely; Suggested measure: Closely monitor the change of wind speed, especially whether the wind speed further increases;

[0128] Wind speed: 15 - 25 m / s (can be adjusted according to the power tower design);

[0129] Level 3, Orange level (Warning)

[0130] Condition: Fcaution ≤ F < Fdanger;

[0131] Description: The force borne by the power tower is close to the critical bearing range, showing signs of slight vibration or deformation;

[0132] Suggested measure: Immediately notify the maintenance personnel and make emergency preparations; If possible, evacuate the area near the power tower; Wind speed: 25 - 35 m / s (the specific range can be slightly adjusted according to the power tower type);

[0133] Level 4, Red level (Danger)

[0134] Condition: F ≥ Fdanger F Description: The force borne by the power tower has reached or exceeded its design bearing limit, and there is a risk of collapse;

[0135] Suggested measures: Immediately activate the emergency plan, block off the area around the power tower, arrange for professional maintenance personnel to check the condition of the power tower, and avoid having people approach as much as possible before the wind speed drops;

[0136] Wind speed: greater than 35 m / s (adjusted according to the power tower design);

[0137] Determine the specific force thresholds Fsafe, Fcaution, Fdanger+;

[0138] These force thresholds can be set according to the specific design and load-bearing capacity of the power tower. The following is a typical determination method:

[0139] F_safe: Calculate the maximum allowable force under normal operating conditions based on the yield strength of the power tower material and the safety factor;

[0140] F_caution: Close to the yield strength of the power tower material but not yet reaching the limit, usually Fsafe × 1.5; F_danger: Reaching the structural limit of the power tower, i.e., the ultimate load-bearing capacity of the material, usually Fsafe × 2 or higher;

[0141] Rainfall monitoring and early warning:

[0142] In addition to wind speed, the meteorological sensor can also monitor rainfall and transmit its data to the monitoring system. By analyzing the rainfall data, when the rainfall reaches a certain threshold, the system can issue a warning regarding the possible additional load brought by heavy rain. For example, heavy rain may increase the risk of the power tower tilting or the soil becoming slippery, further threatening the stability of the power tower;

[0143] Before issuing a warning based on rainfall, corresponding data collection also needs to be done, collecting real-time and historical rainfall data, including rainfall intensity, cumulative rainfall, rainfall duration, etc., understanding the structural characteristics of the power tower such as its material, foundation depth, foundation design, etc., obtaining information on the soil type, moisture content, soil density, etc. around the power tower. The saturation state and water permeability of the soil will directly affect its load-bearing capacity during rainfall. For example, the geographical location of the power tower, the surrounding slope (whether on a slope), geological type, etc. The risk faced by power towers in mountainous and hilly areas may be higher during rainfall;

[0144] Calculate the heavy rain hazard index Hr based on the data collection, and then conduct early warning level classification;

[0145] Hr = f(P, T, G, C), where P is the rainfall intensity, such as how much rainfall per hour; T is the rainfall duration; G is the terrain factor. For flat areas that are not prone to waterlogging, it can be set to 1. For areas with slopes that are prone to landslides or waterlogging, it can be set to, for example, 1.3 or other values according to the actual situation; C is the disaster resistance ability of power grid facilities. For relatively old facilities, it can be set to 0.7, 0.8, etc., and for new equipment, it can be set to 0.95 or 1;

[0146] When Hr reaches 80 or above, it is a high - risk warning; when Hr reaches above 60 and less than 80, it is a relatively high - risk warning; when Hr reaches above 40 and less than 60, it is a medium - risk warning; when Hr reaches above 20 and less than 40, it is a low - risk warning;

[0147] Through sensor information such as rainfall and wind speed, historical data is recorded and a data model is formed to predict subsequent weather conditions. When strong winds, heavy rains, or heavy snows are predicted, early warnings are issued. Based on the accumulated historical meteorological data, different machine learning algorithms are used to predict disaster weather:

[0148] Regression model: Use methods such as linear regression or polynomial regression to predict the relationship between future meteorological conditions and disaster weather;

[0149] Time - series prediction: Methods such as ARIMA (Autoregressive Integrated Moving Average Model) and LSTM (Long Short - Term Memory Network) can handle the temporal characteristics in meteorological data and predict meteorological changes in the next few hours or days;

[0150] Classification model: Through classification algorithms such as decision trees, support vector machines (SVM), and random forests, based on the input meteorological data, it is predicted whether a certain disaster weather (such as strong winds, heavy rains) will occur;

[0151] Deep - learning model: A hybrid model based on convolutional neural networks (CNN) and recurrent neural networks (RNN) can also be used for complex weather prediction, especially for weather prediction problems with high - dimensional and non - linear relationships;

[0152] Model training process:

[0153] I. Data preparation: Use historical data sets (including information such as temperature, humidity, wind speed, etc.) as input features and use weather event types (such as strong winds, heavy rains, etc.) as output labels;

[0154] II. Training and validation: Use part of the historical data as the training set and another part as the validation set, and adjust the model parameters to improve the accuracy;

[0155] III. Model evaluation: Use indicators such as cross - validation, precision, recall rate, F1 value, etc. to evaluate the model effect and ensure the generalization ability of the model;

[0156] Through real-time data collection based on meteorological sensors and predictions of machine learning models, early warnings for disaster weather can be effectively achieved. With the accumulation of data and the continuous optimization of the models, the accuracy of the predictions will continue to improve, providing reliable disaster warning support for multiple fields such as society, agriculture, and transportation. This system can not only reduce losses caused by weather disasters but also improve the emergency response efficiency, ensuring the safety and health of the public.

[0157] Icing monitoring:

[0158] Record the xy values of the positions on the transmission line to be monitored during installation, as well as the magnification factor required for the camera, and perform pre-configuration. When the temperature is less than 0 degrees and the humidity is greater than 85%, or when the temperature is greater than 0 degrees but less than 3 degrees and the wind speed is greater than 5 m / s, a "cold wave effect" occurs, which may cause icing. When one of the above conditions is met, turn on the camera to record the preset positions configured during installation and synchronize for AI icing recognition. Once icing is recognized, report and give an early warning of the position where icing occurs on the corresponding transmission line; Data fusion and comprehensive evaluation:

[0159] By comprehensively analyzing data from tilt sensors and meteorological sensors, the system can evaluate the stability of the electric tower in real time and predict possible tilt or failure risks in advance. The data fusion technology integrates multi-source sensor data to provide an accurate assessment of the electric tower's state, further improving the accuracy and timeliness of the early warning. The early warning method of the present invention effectively prevents electric tower failures caused by factors such as bad weather by real-time monitoring and analyzing the tilt state of the electric tower and external meteorological conditions, ensuring the safe and stable operation of the power system. The intelligent radar-vision system includes an installation column 1 installed on the ground, and a fixing member 2 is detachably installed on the surface of the fixing member 2. One side of the fixing member 2 is fixedly installed with a cross bar 3.

[0160] Specifically, with the settings of the installation column 1, the fixing member 2, and the cross bar 3, the installation column 1 is erected and installed on the ground, and all equipment is directly or indirectly installed on the installation column 1 and also indirectly or directly installed on the cross bar 3. The fixing member 2 can be detached from the installation column 1, so the installation height of the fixing member 2 can also be adjusted according to actual usage requirements, thus meeting various usage needs of users and providing convenience for users' work.

[0161] An adjustable installation hoop 4 is sleeved on the surface of the cross bar 3, and a plurality of fitting rods 5 are adjustably and fixedly installed on the surface of the installation hoop 4.

[0162] Specifically, the mounting hoop 4 is used to install the matching rod 5, and the mounting hoop 4 is installed by sleeve connection and then fastening with bolts. In this way, the installation position of the mounting hoop 4 can be adjusted according to actual use requirements, and the installation position of the matching rod 5 can also be adjusted along with the mounting hoop 4. The installation of the inclinometer and the meteorological instrument can also be adjusted, providing convenience for the user's work.

[0163] The fitting rod 5 includes a main rod 51 installed on the mounting hoop 4, an end rod 52 is provided at one end of the main rod 51 away from the mounting hoop 4, an adjusting screw 53 is fixedly connected to one end of the end rod 52 facing the main rod 51, and the end of the adjusting screw 53 away from the end rod 52 is inserted and installed in the main rod 51.

[0164] Specifically, the mounting rod 5 is an extended mounting component. The mounting rod 5 can be used to mount the inclinometer and the meteorological instrument on the main frame, and can also be kept away from the ground to avoid damage by small animals, providing convenience for the user's work and use. The mounting rod 5 can also be freely adjusted in length, thereby adjusting the height of the installed equipment to meet more usage requirements. When adjusting, loosen the nut on the adjusting screw 53, and then rotate the end rod 52 or the adjusting screw 53 to extend the end rod 52 and the adjusting screw 53 outward. After adjustment, tighten the nut on the adjusting screw 53 to tighten it. It should be noted that the inclinometer and the meteorological instrument may not be installed on the mounting rod 5, and you can choose according to actual needs.

[0165] A bracket 6 is fixedly mounted on one end of the crossbar 3 , and radars 7 are fixedly mounted on the bottoms of both sides of the bracket 6 .

[0166] Specifically, the setting of bracket 6 is used to install equipment components, and the setting of bracket 6 can increase more installation space and installation positions, so as to facilitate users to install more corresponding equipment, ensure the accuracy of the early warning system, and provide convenience for users' work.

[0167] A mounting platform 8 is fixedly installed on the top of the bracket 6, a pan / tilt head 9 is fixedly installed on the top of the mounting platform 8, a control terminal 10 is fixedly installed on the output end of the pan / tilt head 9, a camera 11 and a camera 2 16 are fixedly installed on both sides of the control terminal 10, and both cameras 11 and 16 are provided with fill lights.

[0168] Specifically, the installation platform 8, the pan-tilt head 9, the control terminal 10, the first camera 11 and the second camera 16 are arranged such that the control terminal 10 controls the pan-tilt head 9, and then based on the object position detected by the radar 7 by the pan-tilt head 9, the first camera 11 and the second camera 16 rotate to adjust the shooting position and angle to capture the shooting object, thereby realizing monitoring the changes in the working environment by the radar according to the actual working environment, adjusting and judging the true size of the object in the video and the actual state of the object, so as to judge whether the electric tower is tilted, and then giving an early warning in a timely manner so that the user can make a reaction in a timely manner. The installation platform 8 is used to stably install the pan-tilt head 9, the control terminal 10, the first camera 11 and the second camera 16, ensuring the stability of the operation of the pan-tilt head 9, the control terminal 10, the first camera 11 and the second camera 16, and providing convenience for the user's work.

[0169] The alarm working process of the intelligent radar-vision system is as follows

[0170] 0201. Start;

[0171] 0202. The radar 7 detects a target and reports it to the control terminal 10;

[0172] 0203. Has the target crossed the warning line? If N, end; if Y, continue;

[0173] 0204. The pan-tilt head 9 rotates to the target direction, adjusts the zoom value according to the distance, and starts recording;

[0174] 0205. Invoke AI to process the video and identify the object type and the width and height in pixels;

[0175] 0206. Calculate the actual size of the object according to the zoom values of the first camera 11 and the second camera 16;

[0176] 0207. Is the height exceeding the warning value? If N, end; if Y, continue;

[0177] 0208. Send out a safety warning message;

[0178] 0209. End.

[0179] The power supply system includes a triangular bracket 12 arranged on the ground, and a power supply device 13 is detachably installed on one side of the triangular bracket 12.

[0180] Specifically, for the arrangement of the triangular bracket 12 and the power supply device 13, the power supply device 13, the solar panel 14 and the circuit protection device 15 are all installed on the triangular bracket 12 to improve the stability of the operation of the power supply device 13, the solar panel 14 and the circuit protection device 15. The power supply device 13 is used for storing and supplying electricity, and the power supply device 13 will supply electricity to other working devices in the system to ensure the normal operation of the warning system and provide convenience for the user's work.

[0181] On one side of the triangular bracket 12, a plurality of solar panels 14 are detachably installed, and the plurality of solar panels 14 are all arranged at an inclination of forty-five degrees.

[0182] Specifically, with the setting of the solar panels 14, the solar panels 14 are used for solar power generation. In this way, the entire warning system can achieve self-power generation and power storage, realizing self-sufficiency, avoiding the situation of being unable to work due to power failure, and providing convenience for users' work and use.

[0183] Inside the triangular bracket 12, a circuit protection device 15 is fixedly installed.

[0184] Specifically, with the setting of the circuit protection device 15, the circuit protection device 15 is used to adjust the current and voltage, and can play a role in protecting the entire power supply system, ensuring that the warning system can work normally, and providing convenience for users' work and use.

[0185] Intelligent monitoring devices such as tilt sensors and meteorological sensors such as wind speed and rainfall sensors are widely used in the power industry. These sensors can collect data such as the tilt angle, wind speed, and precipitation of the electric tower in real time, providing accurate status monitoring and prediction information for power companies.

[0186] It should be noted that for the functions to be realized by each hardware in the present invention, there are a large number of mature technologies as support, which belong to the prior art. The essence of the present invention lies in optimizing and combining the existing hardware and its connection methods for a specific application scenario to meet the adaptation requirements in the specific application scenario and solve the problems raised in the background technology (without involving the improvement of the software inside the hardware).

[0187] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An electric tower tilt warning method based on multiple sensors, characterized in that: This early warning method is implemented by a multi-sensor-based electric tower inclination early warning system composed of an intelligent radar and vision system, a power supply system, an inclinometer, and a weather meter. The early warning steps are as follows: Inclination sensor data collection: The present invention uses an inclination sensor to measure the inclination angles of the electric tower in the x-axis and y-axis directions. By collecting the data of the electric tower's inclination angles in real time, the inclination displacement of the electric tower can be calculated. When the inclination angle of the electric tower exceeds the set safety threshold, the system will trigger an alarm mechanism to prompt the operation and maintenance personnel to conduct inspections and maintenance; Assume the height of the electric tower is H (i.e., the distance from the base to the top of the electric tower). Then, according to the inclination angle and trigonometric function relationship, the horizontal displacements Dx and Dy of the electric tower base can be calculated respectively by the following formulas: D x = H × tan(θ x ) D y = H × tan(θ y ); If you want to calculate the total horizontal displacement D of the electric tower, you can combine the displacement components of the x-axis and y-axis and use the Pythagorean theorem (i.e., the Pythagoras theorem) to obtain the total displacement: Then, determine whether the alarm threshold of the electric tower displacement is reached to confirm whether to issue an early warning report; Wind speed monitoring and early warning: The weather sensor monitors the wind speed in real time and uploads the data to the monitoring system. Combining the designed wind speed of the electric tower itself (i.e., the wind resistance design parameters of the electric tower), early warning can be carried out based on the current wind speed data. When the monitored wind speed exceeds the wind speed bearing capacity designed for the electric tower, the system will issue an early warning signal, indicating that the electric tower may be affected by the wind force, resulting in inclination or damage; Before making a real-time wind speed early warning judgment, data collection is required to obtain real-time and historical wind speed data, including wind speed magnitude, wind direction, and change frequency. Obtaining the material, designed height, weight, foundation depth, and structure type of the electric tower (such as guyed tower, free-standing tower), as well as the ultimate bearing capacity of the electric tower under different wind speeds, and geographical location (mountainous area, plain, etc.), soil characteristics, etc., will all affect the stability of the electric tower. Use the aerodynamic formula to calculate the lateral pressure of the wind on the electric tower: F = 0.5×Cd×A×ρ×V2; F: The acting force of the wind on the electric tower; Cd: Drag coefficient (related to the structural shape of the electric tower); A: The windward area of the electric tower; ρ: Air density; V: Wind speed; Combined with historical data, set a wind speed threshold for the electric tower. When the wind speed exceeds this threshold or reaches the early warning level of the model, trigger an early warning signal; The setting of the early warning level and the threshold of the wind speed acting force can usually be divided into the following early warning levels according to the acting force F and the bearing limit of the electric tower structure: Level 1, green level (normal) Condition: F < Fsafe; Description: The lateral acting force borne by the electric tower under the current wind speed is lower than its normal bearing capacity, the structure is safe, and no action is required; Wind speed: less than 15 m / s (or set according to the specific structure); Level 2, yellow level (attention) Condition: Fsafe ≤ F < Fcaution; Description: The influence of the wind force on the electric tower increases significantly, approaching the lower limit of the bearing capacity, but the structure can still bear it safely; Suggested measure: Closely monitor the change of the wind speed, especially whether the wind speed further increases; Wind speed: 15 - 25 m / s (can be adjusted according to the electric tower design); Level 3, orange level (warning) Condition: Fcaution ≤ F < Fdanger; Description: The force on the tower is close to the critical load range, and there are signs of slight vibration or deformation; Recommended actions: Immediately notify maintenance personnel and make emergency preparations; if possible, evacuate the area near the tower; Wind speed: 25-35m / s (the specific range can be fine-tuned according to the tower type); Level 4, Red (Dangerous) Condition: F≥FdangerF Description: The force on the tower has reached or exceeded its design bearing limit, and there is a risk of collapse; Recommended measures: Immediately activate the emergency plan, block the area around the power tower, and arrange professional maintenance personnel to check the tower. Try to avoid people approaching before the wind speed drops; Wind speed: greater than 35m / s (adjusted according to tower design); Determine the specific force thresholds Fsafe, Fcaution, and Fdanger+; These force thresholds can be set according to the specific design and load-bearing capacity of the tower. The following is a typical determination method: F_safe: The maximum permissible force under normal use conditions is calculated based on the yield strength and safety factor of the tower material; F_caution: close to the yield strength of the tower material, but not yet reaching the limit, usually Fsafe×1.5; F_danger: reaching the structural limit of the tower, that is, the ultimate bearing capacity of the material, usually Fsafe×2 or higher; Rainfall monitoring and early warning: In addition to wind speed, meteorological sensors can also monitor rainfall and transmit its data to the monitoring system. By analyzing rainfall data, when rainfall reaches a certain threshold, the system can issue an early warning of the additional load that heavy rain may bring. For example, heavy rain may increase the risk of tower tilting or cause slippery soil, further threatening the stability of the tower. Before issuing an early warning based on rainfall, corresponding data collection is also required. Real-time and historical rainfall data, including rainfall intensity, cumulative rainfall, rainfall duration, etc., are collected to understand the structural characteristics of the tower, such as the material, foundation depth, foundation design, etc., and to obtain information such as soil type, moisture content, and soil density around the tower. The saturation state and permeability of the soil will directly affect its bearing capacity during rainfall. For example, the geographical location of the tower, the surrounding slope (whether it is on a slope), and the geological type, etc. Towers in mountainous and hilly areas may face higher risks during rainfall. The rainstorm hazard index Hr is calculated based on the data collection, and then the warning level is divided; Hr = f(P, T, G, C), where P is the rainfall intensity, such as how much rainfall falls in one hour; T is the duration of rainfall; G is the terrain factor. For flat areas and areas that are not prone to waterlogging, it can be set to 1. For areas with slopes that are prone to landslides or waterlogging, it can be set to 1.3 or other according to actual conditions; C is the disaster resistance of power grid facilities. For older ones, it can be set to 0.7, 0.8, etc., and for new equipment, it can be set to 0.95 or 1; When Hr reaches 80 or above, it is a high-risk warning; when Hr reaches 60 or above and less than 80, it is a relatively high-risk warning; when Hr reaches 40 or above and less than 60, it is a medium-risk warning; when Hr reaches 20 and less than 40, it is a low-risk warning; Record historical data through sensor information such as rainfall and wind speed, and form a data model to predict subsequent weather conditions. When strong winds, heavy rains, or heavy snows are predicted, give early warnings. Based on the accumulated historical meteorological data, use different machine learning algorithms to predict disaster weather: Regression model: Use methods such as linear regression or polynomial regression to predict the relationship between future meteorological conditions and disaster weather; Time series prediction: Methods such as ARIMA (Autoregressive Integrated Moving Average Model) and LSTM (Long Short-Term Memory Network) can handle the temporal characteristics in meteorological data and predict meteorological changes in the next few hours or days; Classification model: Through classification algorithms such as decision trees, support vector machines (SVM), and random forests, predict whether a certain disaster weather (such as strong winds, heavy rains) will occur based on the input meteorological data; Deep learning model: A hybrid model based on convolutional neural networks (CNN) and recurrent neural networks (RNN) can also be used for complex weather prediction, especially for weather prediction problems with high-dimensional and non-linear relationships; Model training process: I. Data preparation: Use the historical dataset (including information such as temperature, humidity, and wind speed) as input features, and use weather event types (such as strong winds, heavy rains, etc.) as output labels; II. Training and validation: Use part of the historical data as the training set and another part as the validation set, and adjust the model parameters to improve the accuracy; III. Model evaluation: Use indicators such as cross-validation, precision, recall rate, and F1 value to evaluate the model effect and ensure the generalization ability of the model; Through real-time data collection based on meteorological sensors and predictions of machine learning models, early warnings for disaster weather can be effectively realized. With the accumulation of data and continuous optimization of the model, the prediction accuracy will continue to improve, providing reliable disaster warning support for multiple fields such as society, agriculture, and transportation. This system can not only reduce losses caused by weather disasters but also improve the emergency response efficiency and ensure the safety and health of the public; Icing monitoring: Record the xy values of the positions on the transmission line that need to be monitored during installation, as well as the magnification factor of the camera. Make pre-configurations. When the temperature is less than 0 degrees and the humidity is greater than 85%, or when the temperature is greater than 0 degrees but less than 3 degrees and the wind speed is greater than 5 m / s, a "cold wave effect" occurs, and icing may occur. When one of the above conditions is met, turn on the camera to record the preset positions configured during installation and synchronize for AI icing recognition. Once icing is recognized, report and give an early warning of the position where icing occurs on the corresponding transmission line; Data fusion and comprehensive evaluation: Through comprehensive analysis of data from tilt sensors and meteorological sensors, the system can real-time evaluate the stability of the electric tower and predict possible tilt or fault risks in advance. The data fusion technology integrates multi-source sensor data to provide an accurate assessment of the electric tower status, further improving the accuracy and timeliness of early warnings; The early warning method of the present invention can effectively prevent electric tower failures caused by factors such as bad weather by real-time monitoring and analyzing the tilt state of the electric tower and external meteorological conditions, ensuring the safe and stable operation of the power system.

2. The power transmission line safety warning system according to claim 1, wherein: The intelligent radar and vision system includes an installation column (1) installed on the ground, and a fixing member (2) is detachably installed on the surface of the fixing member (2). One side of the fixing member (2) is fixedly installed with a cross bar (3).

3. The electric tower tilt warning system based on multiple sensors according to claim 2, wherein: An adjustable installation hoop (4) is sleeved on the surface of the cross bar (3), and a plurality of fitting rods (5) are adjustably and fixedly installed on the surface of the installation hoop (4).

4. The electric tower tilt warning system based on multiple sensors according to claim 3, characterized in that: The fitting rod (5) includes a main rod (51) installed on the installation hoop (4). One end of the main rod (51) away from the installation hoop (4) is provided with an end rod (52). One end of the end rod (52) facing the main rod (51) is fixedly connected with an adjusting screw rod (53), and the end of the adjusting screw rod (53) away from the end rod (52) is inserted and installed in the main rod (51).

5. The electric tower inclination warning system based on multiple sensors according to claim 4, characterized in that: One end of the cross bar (3) is fixedly installed with a bracket (6), and radars (7) are fixedly installed at the bottoms of both sides of the bracket (6).

6. The electric tower inclination warning system based on multiple sensors according to claim 5, characterized in that: The top of the bracket (6) is fixedly installed with an installation platform (8), and a pan-tilt head (9) is fixedly installed on the top of the installation platform (8).

7. The electric tower inclination early warning system based on multiple sensors according to claim 6, characterized in that: The output end of the pan-tilt head (9) is fixedly installed with a control terminal (10), and a first camera (11) and a second camera (16) are respectively fixedly installed on both sides of the control terminal (10).

8. A tilt warning system for electric towers based on multiple sensors according to claim 1, characterized in that: The power supply system includes a triangular bracket (12) arranged on the ground, and a power supply device (13) is detachably installed on one side of the triangular bracket (12).

9. The electric tower tilt warning system based on multiple sensors according to claim 8, characterized in that: A plurality of solar panels (14) are detachably installed on one side of the triangular bracket (12), and the plurality of solar panels (14) are all arranged at an inclination of 45 degrees.

10. A tilt warning system for electric towers based on multiple sensors according to claim 9, characterized in that: A circuit protection device (15) is fixedly installed inside the triangular bracket (12).

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