Dynamic detection alarm intelligent electricity testing ground wire hanging method based on auxiliary ground wire hanging

Abstract

The invention provides an intelligent electricity testing and ground wire hanging method in the electric power field. The intelligent electricity testing and ground wire hanging method in the electric power field comprises the steps of S1, overall scheme design and a core structure; s2, integration of the intelligent safety monitoring module; s3, dynamic alarm and state feedback technology realization; and S4, performing data-driven fault diagnosis and maintenance optimization. Compared with the prior art, the device has the following beneficial effects that the adaptability limitation of a traditional fixed device on working conditions is broken through, automatic height adjustment is achieved, the working efficiency and operation convenience are greatly improved, friction loss is reduced, the energy conversion efficiency is improved, the service life of equipment is prolonged, a multi-dimensional risk assessment system is constructed, and the working efficiency is improved. And contact pressure abnormity, mechanical faults and insulation performance degradation risks are accurately identified, the false alarm rate is reduced, and the fault response time is shortened. Closed-loop management from preventive maintenance to precise maintenance is realized, the operation and maintenance cost is comprehensively reduced, and the long-term reliability of power operation is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power, and in particular to a dynamic detection and alarm intelligent voltage detection and grounding wire suspension method based on auxiliary grounding wire suspension. Background Technology

[0002] In electrified railway systems, the overhead contact line, as a core power supply facility, plays a crucial role in transmitting electrical energy from traction substations to electric locomotives. However, traditional overhead contact line systems have significant limitations in structural design and operation and maintenance. First, existing overhead contact line grounding poles mostly adopt a fixed structure, which is difficult to adapt to the operational needs of overhead contact lines at different heights. This forces operators to frequently change tools or make manual adjustments, severely restricting operational efficiency and flexibility. Second, the voltage testing process relies on manual visual judgment of the voltage status, lacking a real-time data feedback mechanism, making it susceptible to environmental interference or visual errors, and posing a risk of misjudgment. Furthermore, the contact pressure control methods between the grounding clamp and the conductor are limited, failing to dynamically monitor the contact status, and posing a long-term potential safety hazard of increased resistance, localized overheating, or even electrical fires due to poor contact.

[0003] In terms of safety monitoring, traditional equipment generally lacks the ability to respond to dynamic operating conditions. For example, contact pressure monitoring often uses fixed threshold judgments, making it difficult to adapt to environmental noise interference such as temperature changes and mechanical vibrations; vibration analysis relies on manual inspections or simple threshold warnings, failing to accurately identify early mechanical fault characteristics; motor load protection strategies are fixed and cannot cope with load fluctuations under complex operating conditions; environmental parameter monitoring is limited to discrete sensors and lacks multi-dimensional data correlation analysis capabilities. These problems lead to insufficient system safety, low operation and maintenance efficiency, and an inability to meet the demands of modern railway transportation for efficient, reliable, and intelligent operation and maintenance. Therefore, there is an urgent need for a new type of overhead contact line technology that integrates voltage detection, grounding, dynamic monitoring, and intelligent early warning to break through the technical bottlenecks of traditional models and comprehensively improve operational safety and equipment reliability. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent voltage detection and grounding wire method in the power field, which solves the problems of traditional fixed structures being unable to adapt to multiple working conditions, low voltage detection reliability, and safety hazards caused by poor contact. It realizes integrated operation of voltage detection and grounding, real-time dynamic monitoring and intelligent early warning, and improves work efficiency, system safety and the level of intelligent equipment operation and maintenance.

[0005] This invention provides an intelligent voltage detection and grounding wire connection method in the power industry, the method comprising:

[0006] S1: Overall scheme design and core structure;

[0007] S2: Integration of intelligent safety monitoring module;

[0008] S3: Implementation of dynamic alarm and status feedback technology;

[0009] S4: Data-driven fault diagnosis and maintenance optimization;

[0010] In step S1, the overall scheme design and core structure include: proposing a novel integrated device that combines voltage detection, grounding, and intelligent monitoring functions. This device, through multiple technological innovations, significantly improves the adaptability, intelligence level, and safety performance of the equipment, specifically in the following aspects:

[0011] (1) Design of telescopic voltage testing pole

[0012] To address the issue of traditional fixed grounding poles being unable to adapt to various height requirements, this invention innovatively introduces a multi-section nested telescopic pole structure, integrating a high-precision voltage detector at the top of the pole. This structure achieves automatic lifting and lowering adjustment via a built-in motor, enabling rapid matching of contact wire positions at different heights, thereby significantly improving operational flexibility and efficiency. Furthermore, in terms of structural mechanics design, the formula for calculating the driving force is:

[0013] F=k·ΔL+m·a

[0014] Where F is the required driving force, k is the material stiffness coefficient, ΔL is the telescopic displacement, m is the mass of the telescopic rod, and a is the acceleration. This formula ensures that the telescopic mechanism possesses sufficient stability and load-bearing capacity under various operating conditions.

[0015] (2) Grounding hook innovation

[0016] To improve the reliability of contact between the grounding clamp and the conductor, the grounding hook of this device uses a high-performance alloy material and embeds a miniature pressure sensor for real-time monitoring of the contact pressure between the grounding clamp and the conductor. This design not only helps to detect poor contact in a timely manner but also provides accurate data for subsequent analysis. The formula for calculating the contact pressure is:

[0017]

[0018] Where P represents contact pressure, F contact Let A be the applied force and A be the actual contact area. This formula allows for a quantitative assessment of the contact condition, further ensuring operational safety.

[0019] (3) Drive system optimization

[0020] To improve overall operating efficiency and reduce energy consumption, the device employs a dual-guide wheel structure combined with a flexible hose drive, effectively reducing frictional losses during operation. The efficiency of this drive system can be evaluated using the following formula:

[0021]

[0022] Where η represents the driving efficiency, P out For output power, P in This refers to the input power. By optimizing the transmission path and material selection, the system achieves a higher energy conversion rate, further enhancing the equipment's practicality and energy-saving performance.

[0023] In step S2, the integration of the intelligent safety monitoring module includes: improving the level of intelligence and early warning capabilities by integrating the intelligent safety monitoring module.

[0024] (1) Dynamic monitoring of contact pressure and adaptive threshold adjustment

[0025] Traditional pressure monitoring methods rely on fixed thresholds for judgment, which are easily affected by environmental noise such as temperature changes and mechanical vibrations, leading to frequent false alarms or missed alarms. This invention embeds a miniature pressure sensor inside the grounding hook and combines it with a dynamic threshold algorithm to achieve adaptive adjustment.

[0026] P threshold (t)=μ P +k·σ P +α·ΔP history (t)

[0027] Where, μ P The average pressure; σ P ΔP represents the standard deviation, reflecting the environmental noise level; k is the threshold expansion coefficient, supporting on-site calibration to adapt to different operating conditions; α is the weight of the historical rate of change, ΔP history (t) represents the pressure change trend over the past t seconds.

[0028] By introducing the historical trend term ΔP history (t), effectively eliminating errors caused by material thermal expansion; parameters k and α support configuration for business scenarios, for example, k can be increased in high vibration environments to reduce false alarm rate.

[0029] (2) Vibration characteristic analysis and fault mode identification

[0030] Traditional vibration monitoring relies solely on fixed frequency thresholds, making it difficult to distinguish normal vibrations from mechanical fault characteristics, such as bearing wear or gear loosening. By installing a triaxial accelerometer at the hook joint and combining wavelet transform and spectral analysis, abnormal frequency features are extracted. The calculation formula is as follows:

[0031]

[0032] Among them, A i (f c ) represents the current time at the characteristic frequency f cAmplitude at point A; base (f c ) is the reference amplitude; ω i The weighting coefficients are the characteristic frequencies.

[0033] This invention sets a weighting coefficient ω i It supports online learning and improves sensitivity to fault characteristics through deep learning models; it adopts a normalized deviation calculation method. Eliminate the impact of individual differences in equipment.

[0034] (3) Dynamic evaluation of motor load status

[0035] Traditional current detection uses a fixed overload threshold, which is insufficient to handle fluctuations in motor load, such as the transient current during telescopic boom startup, leading to frequent false triggers. This invention connects a Hall sensor in series in the motor power supply circuit and uses an Exponentially Weighted Moving Average (EWMA) algorithm to dynamically adjust the overload threshold. The formula is as follows:

[0036]

[0037] Among them, I rated This is the rated current of the motor; Var(I) history ) represents the historical current variance; β∈[0,1] represents the smoothing coefficient; γ represents the safety margin coefficient, which can be configured according to the business scenario.

[0038] This invention sets a dynamic threshold I threshold (t) It can adapt to load fluctuations and significantly reduce false triggering; in special environments such as high temperature, it automatically increases the γ value and reserves heat dissipation margin.

[0039] (4) Environmental parameter sensing and insulation performance prediction

[0040] Traditional environmental monitoring relies on a single sensor, which cannot comprehensively assess the coupled effects between temperature, humidity, and the surface condition of insulating rods. This invention integrates a temperature and humidity sensor with a surface condition monitoring module to construct an environmental risk assessment model.

[0041]

[0042] Where T and H are the current temperature and humidity, respectively; T safe H safe For safety threshold; ω T ω H For weighting coefficients, support industry standard adaptation; S surface The surface condition score of the insulating rod is given; δ is the correlation strength coefficient.

[0043] Surface state score S of the present invention surface To fill the gaps in traditional monitoring; weighting coefficient ω Tω H It can quickly switch to different regional standards, improving system applicability.

[0044] (5) Multi-source data fusion and wireless communication optimization

[0045] Traditional monitoring methods suffer from the problem of "data silos," lack multi-source information fusion capabilities, and have high energy consumption due to wireless communication. This invention introduces a multi-source information fusion algorithm and a low-power communication protocol optimization mechanism, as shown in the following formula:

[0046]

[0047] E tx =K1·L+K2·d 2

[0048] Among them, R total For comprehensive risk scoring; ω i The weights are dynamic and optimized in real time through an online learning algorithm; E tx d is the energy consumption per transmission; L is the data packet length; d is the transmission distance; K1 and K2 are constant coefficients.

[0049] In step S3, dynamic alarm and status feedback technology is implemented.

[0050] (1) Mathematical modeling and parameter optimization of alarm mechanism

[0051] The control module dynamically adjusts its response strategy based on sensor data. The three-level alarm mechanism requires mathematical modeling based on the physical characteristics of the power scenario, as shown in the following formula:

[0052] Level 1 alarm threshold calculation

[0053] Pressure threshold P min and P max The settings need to take into account the conductor cross-sectional area A, the elastic modulus E of the contact material, and the safety factor K:

[0054]

[0055] Where F min F max For the safe contact force range, K∈[1.2, 1.5] is the redundancy coefficient. When the measured pressure P real <P min A level one alarm is triggered.

[0056] Dynamic weight adjustment of level 2 alarm

[0057] The weighted function of motor current I and pressure duration t:

[0058]

[0059] Where α and β are weighting coefficients, I rated t is the rated current of the motor. threshold This represents the threshold for the duration of insufficient pressure. A level two alarm is triggered when S > 1, and the weighting coefficient can be dynamically optimized based on historical fault data.

[0060] Vibration spectrum feature extraction for Level 3 alarm

[0061] The determination of hook jamming is based on the spectral energy distribution of the vibration signal. After performing a Fast Fourier Transform on the triaxial accelerometer data, the abnormal frequency f is calculated. abnormal Energy percentage:

[0062]

[0063] If E abnormal If the value is greater than θ (threshold θ is calibrated using the equipment vibration baseline), a level three alarm is triggered. The frequency range Δf can be adjusted according to the fatigue characteristics of the hook material.

[0064] (2) Mathematical model of state feedback control

[0065] The control module adjusts the drive system response through status feedback, and its core formula is as follows:

[0066] State feedback control law

[0067] Based on the system state x(t), such as pressure P, position L, and current I, design the feedback gain matrix K to ensure that the dynamic characteristics of the closed-loop system meet safety requirements:

[0068] u(t) = -Kx(t) + r(t)

[0069] Where u(t) is the control input and r(t) is the reference input. The gain matrix K is designed using the pole placement method to ensure that the poles of the closed-loop system are located in the desired region.

[0070] Pressure-position coordinated control

[0071] To avoid positional deviation of the telescopic rod due to insufficient pressure, a pressure-position coupling control model is introduced:

[0072]

[0073] Where L0 is the initial target position, P ref The pressure reference value is γ∈[0,1], which is the compensation coefficient, and L max This represents the maximum length of the telescopic rod. The model achieves automatic position compensation when pressure is insufficient by adjusting γ.

[0074] Dynamic adjustment of current overload protection

[0075] The relationship between motor current I and speed ω can be dynamically adjusted using the following formula:

[0076]

[0077] Where η∈[0,1] is the deceleration coefficient, when I>I rated Reduce the speed to prevent overheating.

[0078] (3) Multi-sensor data fusion and state observer

[0079] To improve alarm accuracy, a state observer is used to fuse data from multiple sensors, as shown in the following formula:

[0080] A nonlinear system model is established using pressure P, position L, and vibration signal V as state variables:

[0081]

[0082] Where x k =[P k L k V k ] T w k v k This addresses process noise and observation noise. Iterative updates of the state estimate using EKF reduce the impact of sensor errors on alarm triggering.

[0083] Using the sliding window mean and variance Determine contact stability:

[0084]

[0085] When C fluctuation >C threshold When this occurs, a pressure instability alarm is triggered. The window size N can be adjusted according to the sampling frequency f. s Adjustment:

[0086]

[0087] In step S4, data-driven fault diagnosis and maintenance optimization; (1) acquiring vibration signal V(t) through a triaxial accelerometer and extracting spectral features through fast Fourier transform (FFT):

[0088]

[0089] Calculate the key frequency f abnormal Energy percentage E abnormal :

[0090]

[0091] Combining historical trend data E history Predicting remaining lifespan T using a linear regression model life :

[0092]

[0093] Where E threshold β is the fault threshold, β is the wear rate coefficient, and Δt is the sampling interval.

[0094] (2) Motor overload protection and dynamic speed regulation

[0095] Monitor the motor current I(t) and calculate the instantaneous overload factor S:

[0096]

[0097] Where η∈[0,1] is the speed reduction coefficient, which is optimized through the motor thermodynamic model:

[0098]

[0099] T motor T represents the current temperature. safe As a safety threshold, T max This is the maximum permissible temperature.

[0100] (3) Intelligent maintenance strategy generation and execution

[0101] Generate maintenance recommendations based on the diagnostic results;

[0102] Telescopic pole replacement recommendation: When T life <T replace When the preset replacement cycle is reached, a replacement instruction will be sent.

[0103] Lubrication of gear sets: If the friction characteristic frequency f in the vibration spectrum... friction The increase exceeded Δf limit This triggers a lubrication reminder.

[0104] The present invention has the following beneficial effects:

[0105] This invention significantly improves the safety and intelligence of power operations through multiple technological innovations. In terms of structural design, the telescopic voltage detector combined with a high-precision voltage detector overcomes the limitations of traditional fixed devices in adapting to working conditions, achieving highly automatic adjustment and greatly improving operational efficiency and ease of use. The grounding hook innovatively uses high-performance alloy materials and a miniature pressure sensor to monitor contact pressure in real time and quantitatively assess the connection status, effectively avoiding safety hazards such as increased resistance and localized overheating caused by poor contact. The drive system optimizes the transmission path through a dual-guide wheel and flexible hose design, reducing friction loss, improving energy conversion efficiency, and extending equipment lifespan. At the intelligent monitoring level, it integrates dynamic threshold adjustment algorithms, vibration characteristic analysis, motor load assessment, and environmental parameter prediction models to construct a multi-dimensional risk assessment system, accurately identifying abnormal contact pressure, mechanical faults, and insulation degradation risks. A three-level alarm mechanism combined with a state feedback control model achieves graded response and adaptive adjustment from minor anomalies to emergency faults, significantly reducing false alarm rates and shortening fault response time. Multi-source data fusion and wireless communication optimization technologies break down data silos, improving system collaboration and early warning capabilities. Ultimately, the data-driven fault diagnosis model, generated through lifespan prediction and intelligent maintenance strategies, enables closed-loop management from preventative maintenance to precision repair, comprehensively reducing operation and maintenance costs and ensuring the long-term reliability of power operations. Attached Figure Description

[0106] Figure 1 This is a flowchart of a dynamic detection and alarm intelligent voltage detection method based on auxiliary grounding wire suspension. Detailed Implementation

[0107] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0108] This invention provides a dynamic detection and alarm intelligent voltage detection and grounding wire method based on auxiliary grounding wire suspension in the power field.

[0109] refer to Figure 1 As shown, the intelligent voltage detection and grounding wire method in the power field includes:

[0110] S1: Overall scheme design and core structure;

[0111] In current power contact network operations, traditional equipment suffers from numerous technical limitations, hindering work efficiency and safety. Firstly, existing contact network grounding poles are mostly fixed structures, unable to be flexibly adjusted in height according to actual site conditions. This forces operators to frequently change tools or manually adjust under different working conditions, severely impacting work efficiency. Secondly, voltage testing generally relies on manual visual judgment of voltage status, lacking a precise and real-time data feedback mechanism. This makes it prone to misjudgments due to visual errors or environmental interference, potentially leading to safety accidents. Furthermore, the limited means of controlling the contact pressure between the grounding clamp and the conductor make it difficult to guarantee a good electrical connection, posing potential risks such as increased resistance, localized overheating, and even electrical fires due to poor contact.

[0112] To address the aforementioned problems, this invention proposes a novel integrated device that combines voltage detection, grounding, and intelligent monitoring functions. Through several technological innovations, this device significantly improves the adaptability, intelligence level, and safety performance of the equipment, specifically in the following aspects:

[0113] (1) Design of telescopic voltage testing pole

[0114] To address the issue of traditional fixed grounding poles being unable to adapt to various height requirements, this invention innovatively introduces a multi-section nested telescopic pole structure, integrating a high-precision voltage detector at the top of the pole. This structure achieves automatic lifting and lowering adjustment via a built-in motor, enabling rapid matching of contact wire positions at different heights, thereby significantly improving operational flexibility and efficiency. Furthermore, in terms of structural mechanics design, the formula for calculating the driving force is:

[0115] F=k·ΔL+m·a

[0116] Where F is the required driving force, k is the material stiffness coefficient, ΔL is the telescopic displacement, m is the mass of the telescopic rod, and a is the acceleration. This formula ensures that the telescopic mechanism possesses sufficient stability and load-bearing capacity under various operating conditions.

[0117] (2) Grounding hook innovation

[0118] To improve the reliability of contact between the grounding clamp and the conductor, the grounding hook of this device uses a high-performance alloy material and embeds a miniature pressure sensor for real-time monitoring of the contact pressure between the grounding clamp and the conductor. This design not only helps to detect poor contact in a timely manner but also provides accurate data for subsequent analysis. The formula for calculating the contact pressure is:

[0119]

[0120] Where P represents contact pressure, F contactLet A be the applied force and A be the actual contact area. This formula allows for a quantitative assessment of the contact condition, further ensuring operational safety.

[0121] (3) Drive system optimization

[0122] To improve overall operating efficiency and reduce energy consumption, the device employs a dual-guide wheel structure combined with a flexible hose drive, effectively reducing frictional losses during operation. The efficiency of this drive system can be evaluated using the following formula:

[0123]

[0124] Where η represents the driving efficiency, P out For output power, P in This refers to the input power. By optimizing the transmission path and material selection, the system achieves a higher energy conversion rate, further enhancing the equipment's practicality and energy-saving performance.

[0125] S2: Integration of intelligent safety monitoring module;

[0126] In power operation scenarios, traditional monitoring methods have many limitations, which seriously restrict the improvement of equipment safety and operation and maintenance efficiency. For example, traditional fixed grounding poles lack dynamic monitoring capabilities and cannot detect changes in contact pressure between the grounding clamp and the conductor in real time, thus posing a long-term risk of poor contact; vibration monitoring relies on manual inspection or simple threshold judgment, making it difficult to identify early characteristics of mechanical faults; current detection mostly adopts fixed threshold protection strategies, which cannot adapt to load fluctuations under complex operating conditions; and environmental parameter monitoring generally relies on discrete sensors, lacking the ability to perform correlation analysis on multi-dimensional data.

[0127] To address the aforementioned issues, an integrated intelligent safety monitoring module can be used to enhance the level of intelligence and early warning capabilities.

[0128] (1) Dynamic monitoring of contact pressure and adaptive threshold adjustment

[0129] Traditional pressure monitoring methods rely on fixed thresholds for judgment, which are easily affected by environmental noise such as temperature changes and mechanical vibrations, leading to frequent false alarms or missed alarms. This invention embeds a miniature pressure sensor inside the grounding hook and combines it with a dynamic threshold algorithm to achieve adaptive adjustment.

[0130] P threshold (t)=μ P +k·σ P +α·ΔP history (t)

[0131] Where, μ P The average pressure; σ PΔP represents the standard deviation, reflecting the environmental noise level; k is the threshold expansion coefficient, supporting on-site calibration to adapt to different operating conditions; α is the weight of the historical rate of change, ΔP history (t) represents the pressure change trend over the past t seconds.

[0132] By introducing the historical trend term ΔP history (t), effectively eliminating errors caused by material thermal expansion; parameters k and α support configuration for business scenarios, for example, k can be increased in high vibration environments to reduce false alarm rate.

[0133] (2) Vibration characteristic analysis and fault mode identification

[0134] Traditional vibration monitoring relies solely on fixed frequency thresholds, making it difficult to distinguish normal vibrations from mechanical fault characteristics, such as bearing wear or gear loosening. By installing a triaxial accelerometer at the hook joint and combining wavelet transform and spectral analysis, abnormal frequency features are extracted. The calculation formula is as follows:

[0135]

[0136] Among them, A i (f c ) represents the current time at the characteristic frequency f c Amplitude at point A; base (f c ) is the reference amplitude; ω i The weighting coefficients are the characteristic frequencies.

[0137] Set the weighting coefficient ω i It supports online learning and improves sensitivity to fault characteristics through deep learning models; it adopts a normalized deviation calculation method. Eliminate the impact of individual differences in equipment.

[0138] (3) Dynamic evaluation of motor load status

[0139] Traditional current detection uses a fixed overload threshold, which is insufficient to handle fluctuations in motor load, such as the transient current during telescopic boom startup, leading to frequent false triggers. This invention connects a Hall sensor in series in the motor power supply circuit and uses an Exponentially Weighted Moving Average (EWMA) algorithm to dynamically adjust the overload threshold. The formula is as follows:

[0140]

[0141] Among them, I rated This is the rated current of the motor; Var(I) history ) represents the historical current variance; β∈[0,1] represents the smoothing coefficient; γ represents the safety margin coefficient, which can be configured according to the business scenario.

[0142] By setting a dynamic threshold I threshold(t) It can adapt to load fluctuations and significantly reduce false triggering; in special environments such as high temperature, it automatically increases the γ value and reserves heat dissipation margin.

[0143] (4) Environmental parameter sensing and insulation performance prediction

[0144] Traditional environmental monitoring relies on a single sensor, which cannot comprehensively assess the coupled effects between temperature, humidity, and the surface condition of insulating rods. This invention integrates a temperature and humidity sensor with a surface condition monitoring module to construct an environmental risk assessment model.

[0145]

[0146] Where T and H are the current temperature and humidity, respectively; T safe H safe For safety threshold; ω T ω H For weighting coefficients, support industry standard adaptation; S surface The surface condition score of the insulating rod is given; δ is the correlation strength coefficient.

[0147] Surface condition score S surface To fill the gaps in traditional monitoring; weighting coefficient ω T ω H It can quickly switch to different regional standards, improving system applicability.

[0148] (5) Multi-source data fusion and wireless communication optimization

[0149] Traditional monitoring methods suffer from the problem of "data silos," lack multi-source information fusion capabilities, and have high energy consumption due to wireless communication. This invention introduces a multi-source information fusion algorithm and a low-power communication protocol optimization mechanism, as shown in the following formula:

[0150]

[0151] E tx =K1·L+K2·d 2

[0152] Among them, R total For comprehensive risk scoring; ω i The weights are dynamic and optimized in real time through an online learning algorithm; E tx d is the energy consumption per transmission; L is the data packet length; d is the transmission distance; K1 and K2 are constant coefficients.

[0153] S3: Implementation of dynamic alarm and status feedback technology;

[0154] (1) Mathematical modeling and parameter optimization of alarm mechanism

[0155] The control module dynamically adjusts its response strategy based on sensor data. The three-level alarm mechanism requires mathematical modeling based on the physical characteristics of the power scenario, as shown in the following formula:

[0156] Level 1 alarm threshold calculation

[0157] Pressure threshold P min and P max The settings need to take into account the conductor cross-sectional area A, the elastic modulus E of the contact material, and the safety factor K:

[0158]

[0159] Where F min F max For the safe contact force range, K∈[1.2, 1.5] is the redundancy coefficient. When the measured pressure P real <P min A level one alarm is triggered.

[0160] Dynamic weight adjustment of level 2 alarm

[0161] The weighted function of motor current I and pressure duration t:

[0162]

[0163] Where α and β are weighting coefficients, I rated t is the rated current of the motor. threshold This represents the threshold for the duration of insufficient pressure. A level two alarm is triggered when S > 1, and the weighting coefficient can be dynamically optimized based on historical fault data.

[0164] Vibration spectrum feature extraction for Level 3 alarm

[0165] The determination of hook jamming is based on the spectral energy distribution of the vibration signal. After performing a Fast Fourier Transform on the triaxial accelerometer data, the abnormal frequency f is calculated. abnormal Energy percentage:

[0166]

[0167] If E abnormal If the value is greater than θ (threshold θ is calibrated using the equipment vibration baseline), a level three alarm is triggered. The frequency range Δf can be adjusted according to the fatigue characteristics of the hook material.

[0168] (2) Mathematical model of state feedback control

[0169] The control module adjusts the drive system response through status feedback, and its core formula is as follows:

[0170] State feedback control law

[0171] Based on the system state x(t), such as pressure P, position L, and current I, design the feedback gain matrix K to ensure that the dynamic characteristics of the closed-loop system meet safety requirements:

[0172] u(t) = -Kx(t) + r(t)

[0173] Where u(t) is the control input and r(t) is the reference input. The gain matrix K is designed using the pole placement method to ensure that the poles of the closed-loop system are located in the desired region.

[0174] Pressure-position coordinated control

[0175] To avoid positional deviation of the telescopic rod due to insufficient pressure, a pressure-position coupling control model is introduced:

[0176]

[0177] Where L0 is the initial target position, P ref The pressure reference value is γ∈[0,1], which is the compensation coefficient, and L max This represents the maximum length of the telescopic rod. The model achieves automatic position compensation when pressure is insufficient by adjusting γ.

[0178] Dynamic adjustment of current overload protection

[0179] The relationship between motor current I and speed ω can be dynamically adjusted using the following formula:

[0180]

[0181] Where η∈[0,1] is the deceleration coefficient, when I>I rated Reduce the speed to prevent overheating.

[0182] (3) Multi-sensor data fusion and state observer

[0183] To improve alarm accuracy, a state observer is used to fuse data from multiple sensors, as shown in the following formula:

[0184] A nonlinear system model is established using pressure P, position L, and vibration signal V as state variables:

[0185]

[0186] Where x k =[P k L k V k ] T w k v k This addresses process noise and observation noise. Iterative updates of the state estimate using EKF reduce the impact of sensor errors on alarm triggering.

[0187] Using the sliding window mean and variance Determine contact stability:

[0188]

[0189] When C fluctuation >C threshold When this occurs, a pressure instability alarm is triggered. The window size N can be adjusted according to the sampling frequency f. s Adjustment:

[0190]

[0191] S4: Data-driven fault diagnosis and maintenance optimization;

[0192] (1) Vibration signal V(t) is acquired by a triaxial accelerometer, and spectral features are extracted by Fast Fourier Transform (FFT):

[0193]

[0194] Calculate the key frequency f abnormal Energy percentage E abnormal :

[0195]

[0196] Combining historical trend data E history Predicting remaining lifespan T using a linear regression model life :

[0197]

[0198] Where E threshold β is the fault threshold, β is the wear rate coefficient, and Δt is the sampling interval.

[0199] (2) Motor overload protection and dynamic speed regulation

[0200] Monitor the motor current I(t) and calculate the instantaneous overload factor S:

[0201]

[0202] Where η∈[0,1] is the speed reduction coefficient, which is optimized through the motor thermodynamic model:

[0203]

[0204] T motor T represents the current temperature. safe As a safety threshold, T max This is the maximum permissible temperature.

[0205] (3) Intelligent maintenance strategy generation and execution

[0206] Generate maintenance recommendations based on the diagnostic results;

[0207] Telescopic pole replacement recommendation: When T life <T replace When the preset replacement cycle is reached, a replacement instruction will be sent.

[0208] Lubrication of gear sets: If the friction characteristic frequency f in the vibration spectrum... friction The increase exceeded Δf limit This triggers a lubrication reminder.

[0209] Multiple technological innovations have significantly improved the safety and intelligence of power operations. In terms of structural design, the telescopic voltage detector, combined with a high-precision voltage detector, overcomes the limitations of traditional fixed devices in adapting to working conditions, achieving highly automatic adjustment and greatly improving operational efficiency and ease of use. The grounding hook innovatively uses high-performance alloy materials and miniature pressure sensors to monitor contact pressure in real time and quantitatively assess the connection status, effectively avoiding safety hazards such as increased resistance and localized overheating caused by poor contact. The drive system optimizes the transmission path through a dual-guide wheel and flexible hose design, reducing friction loss, improving energy conversion efficiency, and extending equipment lifespan. At the intelligent monitoring level, a multi-dimensional risk assessment system is constructed by integrating dynamic threshold adjustment algorithms, vibration characteristic analysis, motor load assessment, and environmental parameter prediction models to accurately identify abnormal contact pressure, mechanical faults, and insulation degradation risks. A three-level alarm mechanism combined with a state feedback control model enables graded response and adaptive adjustment from minor anomalies to emergency faults, significantly reducing false alarm rates and shortening fault response time. Multi-source data fusion and wireless communication optimization technologies break down the limitations of "data silos," improving system collaboration and early warning capabilities. Ultimately, the data-driven fault diagnosis model, generated through lifespan prediction and intelligent maintenance strategies, enables closed-loop management from preventative maintenance to precision repair, comprehensively reducing operation and maintenance costs and ensuring the long-term reliability of power operations.

[0210] Matters not covered in this invention are common knowledge.

[0211] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent electric line inspection and ground line hanging method based on auxiliary ground line suspension and dynamic detection alarm, characterized in that: Specifically comprising the following steps: S1: overall scheme design and core structure; S2: integration of intelligent safety monitoring module; S3: dynamic alarm and state feedback technology implementation; S4: data-driven fault diagnosis and maintenance optimization.

2. The dynamic detection alarm intelligent grounding line hanging method based on auxiliary grounding line suspension according to claim 1, characterized in that: In step S1, the overall scheme design and core structure include: a new integrated device integrating the functions of testing electricity, grounding and intelligent monitoring is proposed; through multiple technical innovations, the adaptability, intelligent level and safety performance of the device are significantly improved, which is embodied in the following aspects: (1) telescopic testing pole design To solve the problem that the traditional fixed ground line pole cannot adapt to various height requirements, the invention innovatively introduces a multi-section nested telescopic pole structure, and integrates a high-precision tester at the top of the pole body; the structure realizes automatic lifting adjustment through the built-in motor drive, can quickly match the position of the catenary at different heights, thereby greatly improving the operation flexibility and efficiency; at the same time, in the structural mechanics design, the calculation formula of the driving force is: F=k·ΔL+m·a Where F is the required driving force, k is the material stiffness coefficient, ΔL is the telescopic displacement, m is the telescopic rod mass, and a is the acceleration; the introduction of this formula ensures that the telescopic mechanism has sufficient stability and carrying capacity under various working conditions; (2) ground line hook innovation To improve the contact reliability between the grounding clamp and the wire, the ground line hook part of the device uses high-performance alloy materials and embeds a micro pressure sensor to monitor the contact pressure between the grounding clamp and the wire in real time; this design not only helps to timely detect poor contact conditions, but also provides accurate basis for subsequent data analysis; the calculation formula of the contact pressure is: Where P represents the contact pressure, F contact is the applied force, and A is the actual contact area; the contact state can be quantitatively evaluated by this formula, further ensuring work safety; (3) drive system optimization In order to improve the overall operation efficiency and reduce energy consumption, the device adopts a double guide wheel structure with flexible hose drive, which effectively reduces the friction loss in the movement process; the efficiency of the drive system can be evaluated by the following formula: where η represents the driving efficiency, P out is the output power, P in is the input power; through the optimization of the transmission path and material selection, the system realizes higher energy conversion rate, further improves the practicality and energy saving performance of the equipment.

3. The dynamic detection alarm intelligent testing and grounding line hanging method based on auxiliary grounding line suspension according to claim 1, characterized in that: In step S2, the integration of intelligent safety monitoring module includes: improving the intelligent level and early warning ability by integrating the intelligent safety monitoring module; (1) contact pressure dynamic monitoring and adaptive threshold adjustment The traditional pressure monitoring method relies on fixed threshold for judgment, which is easily disturbed by temperature changes, mechanical vibration and other environmental noise, resulting in frequent false alarms or missed alarms; this invention embeds a micro pressure sensor in the inner side of the ground line hook, and realizes adaptive adjustment combined with dynamic threshold algorithm; P threshold (t) = μ P + k · σ P + a · ΔP history (t) where μ P is the mean pressure; σ P is the standard deviation, reflecting the ambient noise level; k is a threshold expansion coefficient, supporting field calibration to adapt to different working conditions; α is the historical change rate weight, ΔP history (t) represents the pressure change trend in the past t seconds; By introducing a history trend term ΔP history (t), the error caused by material thermal expansion is effectively eliminated; parameters k and a support service scenario configuration, for example, k can be increased to reduce false alarm rate in a high vibration environment; (2) vibration feature analysis and fault mode recognition The traditional vibration monitoring only relies on fixed frequency threshold, which is difficult to distinguish between normal vibration and mechanical fault characteristics such as bearing wear and gear looseness; a three-axis accelerometer is installed at the joint of the hook, combined with wavelet transform and frequency spectrum analysis to extract abnormal frequency characteristics; the calculation formula is as follows: wherein A i (f c ) is the amplitude at the characteristic frequency f c at the current time; A base (f c ) is the reference amplitude; ω i is the weight coefficient of the characteristic frequency; Setting weight coefficient ω i Support online learning, improve the sensitivity to fault features through deep learning model; adopt normalized deviation calculation method Eliminate the influence of individual differences of equipment; (3) motor load state dynamic evaluation Traditional current detection uses a fixed overload threshold, which is difficult to cope with motor load fluctuations, such as transient current during telescopic rod startup, leading to frequent false triggers; the present invention uses a Hall sensor in series with the motor power supply circuit and dynamically adjusts the overload threshold using the Exponential Weighted Moving Average (EWMA) algorithm; the formula is as follows: Wherein, I rated is the motor rated current; Var(I history ) is the historical current variance; β ∈ [0, 1] is a smoothing coefficient; γ is a safety margin coefficient, which can be configured according to the business scenario; By setting a dynamic threshold I threshold (t) can adapt to load fluctuations, significantly reducing false triggering; in high temperature and other special environments, automatically increase the value of gamma, reserve heat allowance; (4) Environmental parameter perception and insulation performance prediction Traditional environmental monitoring relies on a single sensor and cannot comprehensively evaluate the coupling effects between temperature, humidity, and the surface state of the insulating rod; integrate temperature and humidity sensors with surface state monitoring modules to build an environmental risk assessment model: Wherein, T, H are current temperature and humidity respectively; T safe , H safe are safety threshold; ω T , ω H are weight coefficients, supporting industry standard adaptation; S surface is the surface state score of the insulating rod; δ is the correlation strength coefficient; Surface state score S surface Fill in the traditional monitoring blind area; weight coefficient ω T , ω H Can be quickly switched to different regional specifications, improve system applicability; (5) Multi-source data fusion and wireless communication optimization Traditional monitoring methods have the problem of "data island", lack multi-source information fusion capability, and have high wireless communication energy consumption; introduce multi-source information fusion algorithm and low-power communication protocol optimization mechanism, formula as follows: E tx = K1 · L + K2 · d 2 wherein R total is the comprehensive risk score; ω i is the dynamic weight, which is optimized in real time by an online learning algorithm; E tx is the energy consumption of a single transmission; L is the data packet length, d is the transmission distance; K1, K2 are constant coefficients.

4. The dynamic detection alarm intelligent electric line checking and grounding line hanging method based on auxiliary grounding line suspension according to claim 1, characterized in that: in step S3, dynamic alarm and state feedback technology are realized; (1) Mathematical modeling and parameter optimization of alarm mechanism The control module dynamically adjusts the response strategy through sensor data, and the three-level alarm mechanism needs to be mathematically modeled in combination with the physical characteristics of the power scene, the formula is as follows: First-level alarm threshold calculation Pressure value threshold P min and P max The setting of P must take into account the cross-sectional area A of the wire, the elastic modulus E of the contact material and a safety factor K: where F min , F max is the contact force safety range, K ∈ [1.2, 1.5] is the redundancy coefficient; when the measured pressure P real < P min , a first level alarm is triggered; Dynamic weight adjustment of second-level alarm Combined with the weighted function of motor current I and pressure duration t: wherein a, b are weight coefficients, I rated is the motor rated current, t threshold is the pressure deficiency duration threshold; when S>1 triggers the secondary alarm, the weight coefficient can be dynamically optimized according to historical fault data; Vibration spectrum feature extraction of third-level alarm The determination of the hook jam is based on the frequency spectrum energy distribution of the vibration signal; after the three-axis accelerometer data is subjected to fast Fourier transform, the energy proportion of the abnormal frequency f abnormal is calculated. where Δf = [f low , f high ] If E abnormal > θ (threshold θ calibrated by device vibration baseline), a three-level alarm is triggered; the frequency range Δf can be adjusted according to the fatigue characteristics of the hook material; (2) Mathematical model of state feedback control The control module adjusts the drive system response through state feedback, and its core formula is as follows: State feedback control law Based on the system state x(t), such as pressure P, position L, and current I, design the feedback gain matrix K to make the closed-loop system dynamic characteristics meet the safety requirements: u(t) = -Kx(t) + r(t) Where u(t) is the control input, and r(t) is the reference input; the gain matrix K is designed by the pole placement method to ensure that the closed-loop system poles are located in the desired region; Pressure-position cooperative control To avoid position deviation caused by insufficient pressure of the telescopic rod, a pressure-position coupling control model is introduced: where L0is the initial target position, P ref is the pressure reference value, γ ∈ [0, 1] is the compensation coefficient, L max is the maximum length of the telescopic rod; the model realizes automatic position compensation when the pressure is insufficient by adjusting γ; Dynamic adjustment of current overload protection The relationship between motor current I and speed ω can be dynamically adjusted by the following formula: where η ∈ [0, 1] is a speed reduction coefficient, which reduces the speed to prevent overheating when I > I rated max. (3) Multi-sensor data fusion and state observer To improve the alarm accuracy, a state observer is used to fuse multi-sensor data, the formula is as follows: A nonlinear system model is established with pressure P, position L, and vibration signal V as state variables: where x k = [P k , L k , V k ] T , w k , v k are process noise and observation noise; the state estimation value is updated by EKF iteration to reduce the influence of sensor error on alarm triggering; By sliding window mean and variance Judge contact stability: When C fluctuation > C threshold a pressure instability alarm is triggered; the window size N can be adjusted according to the sampling frequency f s :

5. The dynamic detection alarm intelligent electric line checking and grounding line hanging method based on auxiliary grounding line suspension according to claim 1, characterized in that: in step S4, data-driven fault diagnosis and maintenance optimization; (1) collect vibration signal V(t) through a three-axis accelerometer, and extract the frequency spectrum features through Fast Fourier Transform (FFT): Energy proportion E of the calculated critical frequency f abnormal abnormal :​ where Δf = [f low , f high ] combined historical trend data E history , the remaining life T life is predicted by a linear regression model. where E threshold is the failure threshold, β is the wear rate coefficient, and Δt is the sampling interval. (2) Motor overload protection and dynamic speed regulation Monitor the motor current I(t) and calculate the instantaneous overload coefficient S: Where η ∈ [0, 1] is the speed reduction coefficient, which is optimized through a motor thermodynamic model: T motor T is the current temperature, safe T is the safety threshold, max T is the maximum allowed temperature; (3) Intelligent maintenance strategy generation and execution Generate maintenance recommendations based on the diagnosis results; Suggestion of telescopic rod replacement: when T life <T replace (pre-set replacement period), push the replacement instruction; Lubricate gear set reminder: If the friction characteristic frequency f friction Amplification super-Δf limit , trigger lubrication reminder.

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