142 results about "Fault prevention" patented technology

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

Disclosed is a digital twin-based fault diagnosis method for logistics mobile equipment, capable of achieving online monitoring of equipment state and fault diagnosis by means of fusion of digital twin technology and sensor data, thereby allowing for proactive fault prevention and precise maintenance, improving the reliability and operation efficiency of logistics equipment and prolonging the service life thereof. The technical solution of the method comprises: step 1, on the basis of logistics mobile equipment, establishing a corresponding digital twin model; step 2, acquiring real-time data of a battery entity for adaptive model updating; step 3, inputting fault information into a bidirectional GRU model having a multi-head attention mechanism; step 4, performing outlier detection on the basis of an improved fuzzy isolation forest algorithm, comprising introducing a type-2 fuzzy theory into a fuzzy isolation forest algorithm and making improvements; and step 5, using the obtained fault data to implement corrective measures on the basis of risk levels.

The invention relates to the technical field of equipment monitoring and management, and particularly discloses an AI oT-based distributed industrial equipment monitoring and management system and method, and the system comprises a distributed equipment monitoring module which collects operation parameters, performance indexes and environment data in real time through a plurality of types of sensors disposed on industrial equipment, a dynamic hierarchical edge architecture is utilized to process data and evaluate a static health state, and task intelligent layering and cloud collaboration are supported. And the multi-modal perception analysis module generates an equipment association graph based on the collaborative task, extracts multi-modal features by using a cross-node collaborative algorithm, and realizes dynamic health assessment by combining a CNN-LSTM fusion model and equipment implicit dependency relationship analysis. And the distributed equipment management module fuses equipment dynamic and static health data, cooperates with a task association graph and a fault tracing model, and realizes efficient resource configuration and fault prevention. The system guarantees intelligent monitoring and management of the whole life cycle of industrial equipment through a cooperation mechanism of edge intelligence and cloud deep analysis.

The invention relates to a water conservancy facility operation and maintenance method based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data of a water conservancy facility, and obtaining a standardized multi-dimensional data matrix; multi-modal features of the standardized multi-dimensional data matrix are extracted through a graph neural network fusion algorithm, and a fusion feature vector is obtained; performing fault diagnosis on the fusion feature vector through a knowledge graph reasoning algorithm automatically constructed by a domain ontology to obtain fault diagnosis information; based on the fault diagnosis information, performing facility state evolution analysis through a space-time coupling degradation prediction model to obtain a space-time degradation prediction result; according to the space-time degradation prediction result, state evaluation of the water conservancy facilities is carried out through a dual-objective optimized dynamic weight distribution algorithm, and an operation and maintenance state evaluation result is obtained; and on the basis of the operation and maintenance state evaluation result, performing cross-basin cascade fault prevention and control on the water conservancy facilities to obtain a cascade fault prevention and control scheme. The method improves the prediction precision of the degradation of the water conservancy facilities, and enhances the anti-risk capability of the water conservancy infrastructure.

The invention discloses a distributed photovoltaic equipment detection method and system based on big data analysis, through multi-source data acquisition including equipment operation data, state data, electric energy quality data and environment data, comprehensive working information of a photovoltaic power station can be obtained, rich data support is provided for subsequent analysis, and the detection efficiency is improved. Data cleaning and standardization technologies are adopted, the quality and consistency of data are ensured, the accuracy of subsequent model training and fault diagnosis is improved, fault types are given through a multi-model fusion diagnosis method in combination with feature indexes and a detection method, and a fault knowledge base is constructed, so that the accuracy of fault diagnosis is improved, and the fault diagnosis efficiency is improved. And valuable experience and data support are provided for future fault prevention and maintenance.

The invention discloses a communication base station electric power safety monitoring alarm system based on the Internet of Things technology, and relates to the technical field of Internet of Things communication base station electric power safety monitoring. A multi-mode sensing fusion and self-calibration sensor is used for data collection, electric power data are accurately collected, transmission depends on terahertz communication in combination with hybrid encryption and self-adaptive dormancy, and the communication base station electric power safety monitoring alarm system is high in reliability. Safety and low consumption are ensured, edge intelligence and federated learning are analyzed, knowledge graph is used for auxiliary diagnosis, alarm is used for setting a dynamic threshold value, intelligent linkage is achieved, equipment management depends on digital twinning and a block chain, and stable operation of the equipment in the whole life cycle is guaranteed. According to the invention, accurate power data acquisition, high-speed safe transmission, intelligent analysis and processing, multi-level linkage alarm, remote safety control of equipment, linkage environment monitoring and fault prevention can be realized, the power safety of the communication base station can be comprehensively guaranteed, and the communication stability can be improved.

The invention relates to the technical field of electric energy meter burning prevention, in particular to an electric energy meter burning fault prevention system fusing multi-modal data. The system comprises a data acquisition module used for acquiring an environment temperature sequence and a voltage sequence, a current sequence and an internal temperature sequence of each phase of an electric energy meter; the data normality analysis module is used for acquiring the data normality probability of each phase at each moment; the data anomaly judgment module is used for fitting the voltage sequence and the current sequence of each phase to obtain a fitting curve of each phase, and then judging whether the current moment is a suspected anomaly moment or not; the time segmentation module is used for acquiring different time periods by using the environment temperature sequence; the temperature trend analysis module is used for acquiring the current stability degree, the current approaching degree and the temperature slope of each time period; and acquiring a predicted temperature slope based on the current stability degree, the current approaching degree and the temperature slope of each phase in each time period, and further performing early warning. The electric energy meter can be effectively prevented from being burnt.

The invention discloses a fault monitoring method and system using AI intelligent image recognition, and the method achieves the continuous monitoring of the subtle change of equipment through collecting the time-space information image data of the operation state of the equipment and extracting the dynamic characteristics of motion trail, deformation rate, spectrum change and the like. The dynamic features are associated with the historical state of the device, and a dynamic incidence matrix is constructed to analyze potential trend deviation points in a time sequence, so that possible faults are predicted, and not only occurred problems are identified. An abnormal mode matching rule is generated by calculating a prediction window and combining the prediction window with equipment operation environment parameters, meanwhile, a response threshold value is adjusted to adapt to equipment performance attenuation characteristics, and finally an early warning signal is generated through cross validation of the abnormal mode matching rule and the prediction window. According to the method, the abnormal development trend can be identified before the equipment has an obvious fault, early warning is given out in advance, the transformation from post-event identification to pre-event early warning is realized, and the fault prevention capability is remarkably enhanced.

The invention discloses a base station alarm early warning management method and system. The method comprises the following steps: acquiring a first operation state sequence of each of a plurality of first base stations in a target area in a first time period; for each first base station, analyzing the first operation state sequence corresponding to the first base station by using a pre-trained base station alarm type prediction model to obtain a first predicted base station alarm type of the first base station at the first moment; and determining the troubleshooting priority of each first base station according to the first predicted base station alarm type of each of the plurality of first base stations in the target area at the first moment, and managing the plurality of first base stations in the target area according to the troubleshooting priorities. According to the invention, the technical problem that the operation and maintenance efficiency and the fault defense capability of the base station are relatively low because the related base station operation and maintenance technology cannot carry out early warning on the base station alarm in advance is solved.

The invention discloses a handcart type high-voltage circuit breaker operating characteristic evaluation method based on big data analysis, and particularly relates to the field of data analysis, which comprises the steps of monitoring time division, data entry, data analysis and evaluation. All-time multi-dimensional monitoring is achieved through big data analysis, monitoring sub-areas are divided, dynamic data are collected at high frequency, transient changes are captured, the limitation of traditional single-point monitoring is broken through, and comprehensive data support is provided; quantitative indexes are constructed based on a multi-parameter fusion model, the electrical and mechanical system collaboration is comprehensively evaluated, the multi-dimensional performance is reflected, and the evaluation precision is improved; and a health interval judgment rule is established, so that real-time comprehensive evaluation can be realized, faults can be accurately positioned, anomalies can be recognized in advance, a basis is provided for maintenance, and the intelligent level of power operation and maintenance and the fault prevention capability are improved.

According to the converter valve waste heat digital comprehensive analysis model and method, working parameters of a converter valve are monitored in real time, a physical mechanism and a data driving method are combined, and a hybrid modeling system is constructed for waste heat model building and dynamic correction. A multi-scale data fusion framework is adopted to carry out data processing and mode recognition, the accuracy of model prediction is evaluated through a residual analysis mechanism, and future temperature rise prediction is carried out. According to the method, a dynamic knowledge graph is constructed based on a digital twin platform, multi-dimensional report contents including a cooling system upgrading scheme, a valve tower inspection period adjustment strategy and the like are generated, and data traceability is ensured through block chain evidence storage. According to the method, the requirements of a current converter valve waste heat analysis method for real-time monitoring and accurate prediction are improved, a feasible mode for real-time monitoring and accurate prediction of converter valve waste heat is provided, the accuracy of equipment working state feedback and fault prevention measures is ensured, and the stability of a high-voltage direct-current power transmission system is further ensured.

The invention discloses an intelligent ditch gate dam self-adaptive adjustment method based on the Internet of Things, relates to the technical field of drainage control, and provides a set of integrated method of multi-source data fusion, predictive diagnosis and automatic fault tolerance for fault prevention and disposal of a gate dam execution mechanism in farmland irrigation. High-quality sensing data is obtained in real time through edge calculation and a wavelet noise reduction technology, and potential anomalies are marked in time; secondly, analyzing the state of an execution mechanism by combining a historical fault record and a health model, and sending out a maintenance suggestion in advance; if a serious fault occurs, the local controller automatically starts secondary adjustment and emergency linkage to temporarily compensate downstream water supply or flood diversion; and finally, the central dispatching center iteratively updates a dispatching strategy by using a global collaborative optimization and self-learning mechanism, and the system reliability and the resource utilization efficiency are enhanced. Therefore, closed-loop management of the gate dam execution mechanism is realized, and stable operation of large-scale farmland irrigation under load and outdoor environments is ensured.

The invention relates to the technical field of fire safety, in particular to an Internet of Things fire-fighting electrical early warning method and early warning system, and the method comprises the following steps: collecting the current and voltage data of a plurality of positions in a building in real time based on an Internet of Things sensor, recognizing abnormal fluctuation through frequency analysis, detecting the aging state of a wire, and generating a harmonic abnormality detection record. According to the invention, the current and voltage in the building are monitored in real time through the Internet of Things technology, detection and identification of wire aging and electric leakage events are realized, data support is provided for fault prevention, the early warning capability of the system is enhanced, the fire risk is identified in an early stage, and the equipment state is monitored in real time by using temperature and pressure sensors. The device performance degradation is evaluated through data analysis, timely response is made when overload, short circuit and overheating risks occur to the device, the fire risk level is calculated in combination with environment monitoring data, early warning is sent to management personnel in real time, the emergency response speed and efficiency are improved, and the fire prevention and control capacity is improved.

The invention relates to the technical field of industrial automation, and particularly discloses a fault prevention and diagnosis control system based on a red-mine intelligent mining shield tunneling machine, which is characterized in that time-space synchronous acquisition and fusion of multi-source heterogeneous sensor data are realized through a red-mine operating system, and a multi-dimensional state sequence containing equipment operating parameters and health indexes is constructed; establishing a probability world model based on historical data, predicting state transition probability distribution and quantifying uncertainty; dividing a safety area according to uncertainty, and verifying action safety through multi-step rolling prediction; and iteratively updating a decision strategy by adopting a security constraint strategy optimization algorithm, and dynamically updating model parameters in combination with an online learning mechanism, so that the problems of insufficient data fusion, lagging fault prediction and insufficient exploration action security in the prior art are solved; accurate sensing of the operation state of the shield tunneling machine, early warning of faults and safe autonomous decision making are achieved, and the operation reliability and the maintenance efficiency of equipment are improved.

The invention discloses a cable joint defect automatic identification method and system for cable construction, and relates to the technical field of installation, maintenance and detection of power cables, and the method comprises the steps: obtaining an internal parameter, an external parameter and a distortion coefficient of a camera; adjusting the position of a camera according to a calibration result, and carrying out multi-angle shooting on the cable joint; carrying out geometric correction and color correction on the shot image; fusing the plurality of images through an image synthesis technology; extracting a key area of the cable joint from the corrected image; and determining space coordinates. And performing defect identification and classification by adopting a deep learning model, and performing quantitative measurement on the defect size. According to the invention, common defects in the cable joint can be accurately identified, the problems of missing detection and false detection in traditional manual detection are avoided, the automatic detection capability in electric power facility construction and maintenance is improved, safety monitoring, fault prevention and intelligent maintenance of an electric power circuit are facilitated, and the detection efficiency is improved. And the construction quality of cable joints and the long-term reliability of electric power facilities are ensured.

The invention relates to the technical field of communication engineering, in particular to a communication line operation and maintenance method and system based on fuzzy logic and a storage medium. The method comprises the following steps: acquiring real-time communication line parameters; performing fault prediction on the real-time communication line parameters through the dynamic fuzzy learning model to obtain fault prediction data; inputting the fault prediction data into a fuzzy reasoning model for analysis to obtain a fault possibility; and performing communication line operation and maintenance on the fault possibility according to the alarm threshold. Faults can be accurately predicted, adaptability is improved, response time is shortened, and active fault prevention is achieved.

The invention discloses a power grid operation monitoring device, and belongs to the technical field of power grid monitoring. The device comprises the working cabinet and the opening and closing mechanism, the opening and closing mechanism used for fault prevention is arranged in the middle of one side of the working cabinet, and the device has the advantages that the problem that an electric power system in the device is corroded and damaged easily due to long-time moisture invasion caused by excessive night moisture due to excessive day-night temperature difference is solved; therefore, the monitoring accuracy and the use effect of the device in a special region are ensured, and meanwhile, the closed leaf blinds in a low-temperature environment can also reduce the loss of internal temperature, so that the problem that a semiconductor device of the device is slow in response or makes a mistake easily due to too low temperature can be prevented; the monitoring and using effects of the device are ensured, and the opening and closing of the leaf blinds automatically respond depending on the temperature change, so that the device has the advantage of low-maintenance self-adaptive capability.

The invention discloses an air pre-heater anti-tripping method and system based on working condition recognition, and relates to the technical field of thermal equipment operation monitoring and fault prevention and control, and the method comprises the steps: collecting operation state parameters of an air pre-heater; constructing a working condition recognition model, and performing classification judgment and anomaly recognition on the operation state parameters of the air pre-heater; and based on the abnormal identification result, adjusting tripping logic and executing an anti-tripping control strategy. According to the method, continuous acquisition and quantitative representation of the key variables in the time sequence dimension in the equipment operation process are realized, so that basic data support is provided for subsequent working condition modeling and state recognition, the precision and timeliness of air pre-heater anomaly recognition are improved, and the accuracy and timeliness of air pre-heater anomaly recognition are improved based on the anomaly recognition result. The method has the advantages that tripping logic is adjusted, an anti-tripping control strategy is executed, the self-adaptability of tripping decision is improved, mistaken tripping is avoided, the safety of key equipment is guaranteed, and differential response and closed-loop execution of protection control are achieved.

The invention discloses an automobile maintenance engine fault dynamic diagnosis system and method. The system comprises a data acquisition module, a data preprocessing module, a dynamic monitoring and recording module, a sound signal extraction algorithm module, a wavelet transform algorithm module, a neural network module and a fault diagnosis and prediction module. The method comprises the following steps: step 1, data acquisition; step 2, data preprocessing; step 3, real-time monitoring and recording; step 4, sound signal extraction and analysis; step 5, fault feature extraction; step 6, constructing and training a neural network; step 7, fault diagnosis and prediction; the dynamic monitoring function is designed, the vibration frequency, surface abnormity and working condition parameters of the automobile engine can be comprehensively monitored, real-time alarming is carried out on abnormal conditions, potential risks existing in the engine can be found in time, powerful support is provided for fault prevention and processing, and the working efficiency is improved. And a fast cyclic modulation spectrum peak graph algorithm is designed through a multi-stage decomposition method.

The invention discloses a self-service luggage check-in equipment fault prediction method and system, a medium and a product. According to the method, a time sequence data sequence is constructed and divided by obtaining an operation data fragment of an internal sensor of equipment and an environment data fragment of an external sensor, the time sequence data sequence is input into an interference detection model, interfered data is corrected, then a correction time data sequence is constructed, and then a fault prediction model is input to obtain a result. And if the equipment is predicted to have a fault, positioning a fault risk part and sending a maintenance request to maintenance personnel. According to the scheme, through comprehensive data acquisition, data integrity guarantee and accurate fault prediction, the fault prediction accuracy and maintenance efficiency are effectively improved, the influence of external interference on a fault prediction result is reduced, and accurate equipment maintenance and fault prevention are realized.

The utility model relates to an overspeed switch testing device, and belongs to the technical field of testing equipment. According to the technical scheme, a frame (1) comprises an upper layer and a lower layer, a motor (4) is installed on the lower layer of the frame (1), a frequency converter (2) and an alarm device box (3) are installed on the upper layer of the frame (1), an overspeed switch (6) is fixedly connected with the output end of the motor (4) through a coupler (5), the input end of the motor (4) is connected with the frequency converter (2), and the overspeed switch (6) is connected with the alarm device box (3). The beneficial effects of the utility model are that the frequency converter is adjusted to control the rotating speed of the motor to reach an overspeed switch limit value so as to determine the overspeed switch performance, thereby effectively satisfying the inspection test requirements of the equipment safety protection device, providing effective technical support for equipment safety protection and equipment fault prevention, ensuring the complete protection function, and improving the reliability. Therefore, the equipment fault risk is reduced.

The invention discloses a three-phase transformer single-phase grounding short circuit leakage magnetic field simulation analysis method based on a combination of three single-phase transformers, and aims to solve the problem that leakage magnetic field distribution and electromagnetic force conditions of a three-phase transformer formed by the combination of the three single-phase transformers cannot be accurately simulated when a single-phase grounding short circuit fault occurs in the prior art. Constructing a three-dimensional finite element geometric model of the single-phase transformer according to parameters such as the actual size of the transformer; then importing ansys Maxwell software, and adjusting the position to generate a three-dimensional calculation model; setting material attributes of each field domain, creating a winding, and constructing an external circuit matched with different working conditions; then setting a solution domain and boundary conditions, carrying out reasonable mesh generation, and selecting a transient field for simulation solution; and finally, checking simulation results such as short-circuit current and magnetic flux leakage nephograms. The method can accurately analyze the single-phase grounding short circuit fault of the transformer, accurately simulate the fault condition, find hidden dangers in advance, and provide reliable support for fault prevention and scientific design.

The invention relates to the technical field of electric power detection, and provides a low-voltage and low-frequency load shedding function test method and a test device, in the on-site real-model test process, the test method and the test device are hung on a grid along with an FTU, the voltage and frequency change of the power grid and the action of the FTU are recorded in real time, a broken line graph is generated through upper computer software, and the action process of on-site low-voltage and low-frequency detection is displayed. Whether the device correctly executes the low-voltage and low-frequency load shedding action or not is visually observed, and the method has high practical significance (fault prevention), asset configuration optimization (service life prolonging) and final service for efficient, reliable and economical operation of a power grid.

The application provides a reliability analysis method for positioning accuracy of a shield tool changing robot, realizes sensitivity analysis of error parameters by constructing a kinematic model and an error proxy model, and accurately evaluates the failure probability of positioning accuracy. The assembly and machining errors are comprehensively considered, the error parameters are treated as random variables, the accuracy and efficiency of the analysis are improved. The mean and standard deviation of the random variable are used for sensitivity calculation, and a failure calculation method of positioning accuracy sensitivity is obtained, which provides an important basis for optimization design. The application not only helps to reduce the tool changing time, improve the construction efficiency and reduce the safety risk, but also provides an accurate and reliable tool changing robot manufacturing scheme for designers. The method has universality and flexibility, and can be widely applied to performance improvement and fault prevention of the shield tool changing robot. The implementation of the application will promote the development of shield construction technology and provide strong support for industrial automation and efficient production.

This invention provides a safety assurance method and system for unmanned aerial vehicles (UAVs) used for medical examinations of high-rise buildings in cities. The method includes: continuously monitoring the flight parameters of the UAV, sampling the flight parameter data at a frequency of ≥100Hz, and performing fusion processing using a Kalman filter algorithm; employing a fault diagnosis algorithm, based on a dual judgment mechanism of threshold and trend analysis, to analyze the collected flight parameter data in real time; comparing power redundancy and attitude stability indicators with preset thresholds to determine the fault level, and performing graded emergency handling according to the fault level, triggering corresponding handling and response strategies for non-catastrophic or catastrophic faults; after the UAV lands, confirming the safety of the landing point through visual SLAM or GPS positioning, and initiating onboard communication to report the status. This invention provides a complete solution from fault prevention to collision buffering through redundant design, graded emergency response, and dual-level protection, maximizing the protection effect and facilitating industrial implementation and verification.

This application provides a two-layer prevention and control method and system for grid cascading faults considering the integration of new energy sources. Based on the probability density function and output power function of wind farms and photovoltaic farms, the Monte Carlo method is used to calculate stochastic power flow. Assuming an initial fault occurs on a line and it is disconnected by backup protection, for the remaining lines other than the line with the initial fault, an expression for whether a cascading fault occurs in the grid is established based on the calculated power flow value and its corresponding setting value. The node injection power combination is obtained based on the calculated power flow value, and a grid safety margin is established based on the node injection power combination. An inner-layer optimization model is constructed with minimizing the grid safety margin as the first objective function, and an outer-layer optimization model is constructed with maximizing the grid safety margin and minimizing grid loss cost as the second objective function. The two-layer prevention and control model for cascading faults is solved to obtain a cascading fault prevention and control strategy, thereby enabling early identification and precise and effective prevention and control of cascading faults.

The invention belongs to the technical field of machine room terminal management, and particularly relates to a machine room terminal operation and maintenance fault prediction system based on a large language model, which comprises a machine room terminal state sensing output module, an operation and maintenance feature intelligent mining module, a large language model fault reasoning module, a terminal safety hidden danger quantification module and an operation and maintenance supervision center. According to the method, multi-source heterogeneous operation and maintenance data of a machine room terminal are comprehensively collected and preprocessed to extract core features strongly related to faults, the fault probability and types are accurately output by means of semantic understanding and generalization ability of a large language model and based on feature vectors, loss caused by fault handling delay is reduced, and the fault handling efficiency is improved. And multi-dimensional analysis is carried out through the terminal potential safety hazard output module, so that the potential safety hazard degree of the machine room terminal is accurately judged, active identification and accurate quantification of potential safety risks are realized, a fault prevention and control defense line is built in advance, safe and stable operation of the machine room terminal is ensured, and the working difficulty of supervisors is remarkably reduced.

The invention discloses a fault risk assessment method and device after commutation failure of a hierarchical access system, and belongs to the technical field of power system control, and the method comprises the steps: calculating the commutation voltage integral area needed by commutation of a high-end inverter and a low-end inverter after commutation failure of the hierarchical access system; if the commutation voltage integral area is lower than an area threshold value, obtaining a new energy low-power-on / off-network risk assessment index and a first direct current locking risk assessment index, and further calculating a new energy high-power-on / off-network risk assessment index; in consideration of an AC-DC-new energy reactive coupling relationship in a receiving-end power grid and a causal relationship between cascading failures, the cascading risk that a system may have new energy low-power-on / high-power-on off-network and DC blocking failures after a DC commutation failure occurs is evaluated, and theoretical guidance is provided for prevention and control of the cascading failures of the receiving-end power grid. Therefore, the operation safety of the power system is improved.

The invention relates to the technical field of power transmission line vibration prevention, in particular to a power grid safety guarantee monitoring method and system. The method comprises the following steps: continuously monitoring the wind vibration amplitude of a power transmission line, and evaluating the vertical vibration degree of a lead; detecting the looseness of the spacer according to the vertical vibration degree of the lead; obtaining a four-split power transmission line image; inputting the four-bundle power transmission line image into a pre-trained deep learning model to determine a wire strand wear degree; predicting the wire strand breakage probability based on the wire strand wear degree; determining hidden danger points according to the spacer looseness and the wire strand breakage probability; carrying out anti-vibration hammer slip analysis according to the hidden danger points, and determining the deviation degree; adjusting the installation position of the damper based on the deviation degree, calculating the sliding friction force, and adjusting the fastening torque of the spacer to execute the dynamic anti-vibration task of the power transmission line. Based on the power transmission line anti-vibration technology, the operation safety and the fault prevention rate of the power transmission line are improved.