A power transmission line heat color-changing clamp heating early warning method and system

Abstract

The application relates to the technical field of circuit detection, and discloses a power transmission line heat-caused discoloration clamp heating early warning method and system. The application effectively solves the problem of data collection distortion in a strong electromagnetic environment, improves the accuracy of state evaluation through multi-source data fusion. A dynamically adjusted early warning model reduces the false alarm and missed alarm risks caused by equipment aging, an intelligent decision support module shortens the fault disposal response time, a data closed loop mechanism ensures the reliability of the system in the whole life cycle, through multi-sensor cooperative monitoring and edge computing processing, the influence of environmental factors on data collection is reduced. A double-channel transmission architecture guarantees the data transmission integrity under different network conditions, a CRC check mechanism effectively identifies and corrects transmission errors, provides a high-quality data basis for the subsequent early warning model, and an abnormal data re-sampling mechanism avoids data loss caused by single collection failure, and ensures the continuous and stable operation of the monitoring system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of circuit detection, in particular to a power transmission line heat-induced color-changing clamp heating early warning method and system. BACKGROUND

[0002] In the power system, the conductor connecting hardware is a key transmission node, and its working state directly affects the safe operation of the power grid. The color-changing clamp with temperature-sensitive characteristics is widely used because it can intuitively reflect the temperature change of the contact surface. However, with the rapid development of ultra-high voltage power grids, the traditional manual inspection method has been difficult to meet the technical needs of real-time state monitoring of such key components.

[0003] The existing temperature monitoring technology for power transmission line connecting components mainly has the following technical defects. First, in terms of data acquisition, the conventional monitoring system has insufficient anti-interference ability, and is significantly affected by electromagnetic interference and extreme weather in the actual operating environment, resulting in large errors in the monitoring data. For example, the temperature measurement deviation of a certain type of commercial monitoring device under strong wind conditions can reach ±5℃. Secondly, in terms of early warning models, the existing solutions mostly use a fixed threshold early warning mechanism, which is difficult to adapt to dynamic changes such as equipment aging and load fluctuations. Actual operation data shows that the false alarm rate and the missed alarm rate of such methods are as high as 15% and 12% respectively. In terms of operation and maintenance decision-making, most systems lack effective intelligent analysis functions, making it difficult to provide accurate fault diagnosis and disposal suggestions for operating personnel. Power grid operation statistics show that about 30% of the clamp failures are caused by delayed or inappropriate disposal, leading to the expansion of the fault.

[0004] Therefore, we propose a power transmission line heat-induced color-changing clamp heating early warning method and system to solve the above problems. SUMMARY

[0005] The purpose of the present application is to provide a power transmission line heat-induced color-changing clamp heating early warning method and system to solve the problem of circuit detection in the background art.

[0006] To achieve the above purpose, the present application provides the following technical solution: a power transmission line heat-induced color-changing clamp heating early warning method, the specific steps are as follows:

[0007] S1, real-time data acquisition and transmission, using high-precision sensors to monitor the working state of the color-changing clamp in the power transmission line in real time, and transmitting the collected data through wireless communication technology;

[0008] S2, environmental and equipment data fusion, collecting and fusing environmental data and equipment state data, using data fusion algorithm to process the input data, removing noise and uncertainty;

[0009] S3, adaptive early warning model construction, based on historical data, real-time monitoring data and environmental factors, using machine learning algorithm to construct adaptive early warning model;

[0010] S4, real-time early warning and risk judgment, after obtaining real-time data, the system judges the working state of the clamp according to the adaptive early warning model, when the equipment temperature or load exceeds the set safety threshold, the system sends out early warning signal;

[0011] S5, intelligent feedback and decision support, the system provides specific operation suggestions according to the current state and historical data of the equipment, including adjusting load, cooling and checking equipment;

[0012] S6, maintenance data accumulation and deep analysis, record each early warning event and its processing process, including maintenance record, fault handling and equipment replacement information, regularly analyze the aging trend and failure mode of the equipment, further optimize the early warning model;

[0013] S7, continuous monitoring and model optimization, based on real-time running data and long-term performance of the equipment, regularly evaluate and update the early warning model.

[0014] Preferably, in step S1, the real-time data acquisition and transmission is specifically as follows:

[0015] S1.1, distributed temperature sensor array is arranged at the key temperature measuring points of the color-changing clamp body and the adjacent fittings, vibration sensor and current transformer are configured at the line connection part to build a multi-parameter monitoring network, each sensor collects the running state parameters of the clamp in real time with a sampling frequency not less than 1Hz, including contact surface temperature gradient distribution, mechanical vibration spectrum characteristics and current value;

[0016] S1.2, three-level data processing flow is executed in the edge terminal device, sliding window mean filter is used to eliminate random noise, abnormal data is removed based on preset threshold range, multi-source data synchronization is realized through timestamp alignment, re-sampling mechanism is triggered for the data failed in verification to ensure the effectiveness of the transmission data;

[0017] S1.3, dual-channel redundant transmission architecture is constructed, main channel uses 4G / 5G network to transmit compressed feature data packet, standby channel transmits key state parameters through LoRa ad hoc network, time series database is established in the cloud, received data is subjected to CRC check and storage, and data integrity report is generated for subsequent analysis and calling.

[0018] Preferably, in step S2, the environmental and equipment data fusion is specifically as follows:

[0019] S2.1, Establish a meteorological environment monitoring unit, deploy temperature and humidity sensors, anemometers and pyranometers with electromagnetic interference prevention characteristics, collect environmental parameters every 5 minutes, establish a time synchronization mechanism with the device state monitoring system, achieve μs level time alignment through PTP protocol, build a spatial correlation model, map the environmental monitoring point coordinates to the device topology structure, and ensure data spatial consistency;

[0020] S2.2, Remove high-frequency noise of environmental sensors using improved wavelet packet transform, design a sliding window dynamic filter for state data, adjust the window width adaptively according to the load change rate, establish an abnormal data marking rule library, and automatically mark and isolate data that exceeds the physical constraint range;

[0021] S2.3, The primary fusion layer applies D-S evidence theory to process isomeric data conflicts, the intermediate fusion layer uses improved Kalman filter to realize time and space scale matching, and the high-level fusion layer establishes an LSTM network based on Attention mechanism to extract deep coupling features of devices and environmental parameters.

[0022] Preferably, in step S3, the adaptive early warning model is constructed in the following specific way:

[0023] S3.1, Extract the spatio-temporal correlation data of device state, environmental parameters and fault records from the historical database, use feature importance analysis to select key feature indicators, and construct a composite feature matrix containing time domain statistical features, frequency domain features and environmental coupling features;

[0024] S3.2, Organize the training sample set based on the sliding time window mechanism, use the integrated learning architecture to fuse the LSTM time series prediction module and the random forest classification module, introduce the online learning mechanism, and continuously optimize the model parameters through incremental updating;

[0025] S3.3, Establish a dynamic early warning threshold calculation model, design a hierarchical early warning mechanism, integrate a feedback adjustment module, and automatically adjust the model sensitivity according to the early warning accuracy.

[0026] Preferably, in step S4, the real-time early warning and risk judgment are as follows:

[0027] S4.1, Real-time calculation of device state feature vector using sliding time window technology, input real-time features into the pre-constructed early warning model through the model inference interface, output multi-dimensional evaluation results including risk probability value, abnormal type and confidence;

[0028] S4.2, Construct a dynamic safety threshold curve based on device historical operation data, introduce an environmental correction factor to adjust the threshold in real time, and implement a multi-parameter joint criterion;

[0029] S4.3, establish a three-level early warning system, design a composite alarm strategy, and realize multi-channel synchronous release of early warning information.

[0030] Preferably, in step S5, the intelligent feedback and decision support is specifically as follows:

[0031] S5.1, match the historical fault feature library based on case-based reasoning technology, construct a fault cause probability graph using Bayesian network, and output a diagnostic report containing fault probability, risk level, and impact range;

[0032] S5.2, construct a device health state knowledge graph, develop a self-adaptive decision rule engine, and generate a decision tree containing optional operation schemes and their expected effects;

[0033] S5.3, design a visual operation guidance interface, generate a structured emergency plan document, and provide a voice interactive assistant decision function.

[0034] Preferably, in step S6, the data accumulation and deep analysis is specifically as follows:

[0035] S6.1, construct a time series database to store the complete running history of the equipment, use blockchain technology to ensure data tamper-proofing and traceability, realize standardized storage and associated indexing of multi-source heterogeneous data;

[0036] S6.2, use transfer learning technology to construct a device degradation trend prediction model, develop a fault mode mining algorithm based on association rules, and generate an analysis report containing fault features, occurrence rules, and evolution trend;

[0037] S6.3, design a model performance continuous monitoring index system, establish an incremental learning mechanism to realize automatic updating of model parameters, execute double test verification to optimize the effect and generate optimization suggestions.

[0038] Preferably, in step S7, the continuous monitoring and model optimization is specifically as follows:

[0039] S7.1, deploy dynamic monitoring probes to collect key indicators of the model in real time, construct an ELK-based performance monitoring platform, and automatically generate model evaluation reports including ROC curve and confusion matrix;

[0040] S7.2, implement online learning algorithm based on FTRL, design adaptive learning rate adjustment strategy, and execute parameter space Bayesian optimization to find the optimal hyperparameter combination;

[0041] S7.3, establish a model version management library, implement a new and old model test framework, and adopt a gradual release strategy.

[0042] The application also provides a power transmission line heat color-changing clamp heating early warning system, comprising a multi-parameter sensing acquisition module, an edge computing module, a data fusion processing module, an intelligent early warning decision module, an operation and maintenance decision module, a data asset management module, and a model operation and maintenance management module;

[0043] The multi-parameter sensing acquisition module uses high-precision sensors to monitor the working state of the color-changing clamp in the power transmission line in real time, and transmits the collected data through wireless communication technology.

[0044] The edge computing module collects and fuses environmental data and equipment state data, and uses a data fusion algorithm to process the input data to remove noise and uncertainty.

[0045] The data fusion processing module uses machine learning algorithms to build an adaptive early warning model based on historical data, real-time monitoring data, and environmental factors.

[0046] After obtaining real-time data, the intelligent early warning decision module judges the working state of the clamp according to the adaptive early warning model, and when the equipment temperature or load exceeds the set safety threshold, the system issues a warning signal.

[0047] After obtaining real-time data, the operation and maintenance decision module judges the working state of the clamp according to the adaptive early warning model, and when the equipment temperature or load exceeds the set safety threshold, the system issues a warning signal.

[0048] The data asset management module records each early warning event and its processing process, including maintenance records, fault handling, and equipment replacement information, regularly analyzes the aging trend and failure mode of the equipment, and further optimizes the early warning model.

[0049] The model operation and maintenance management module regularly evaluates and updates the early warning model based on real-time operation data and long-term performance of the equipment.

[0050] The beneficial effects of the application are:

[0051] 1. The application effectively solves the problem of data collection distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false positives and false negatives caused by equipment aging, the intelligent decision support module shortens the fault disposal response time, and the data closed-loop mechanism ensures the reliability of the system throughout its life cycle.

[0052] 2、The application solves the problem of large monitoring data error in strong electromagnetic interference environment, reduces the influence of environmental factors on data collection through multi-sensor cooperative monitoring and edge computing processing. The double-channel transmission architecture guarantees the data transmission integrity under different network conditions, and the CRC check mechanism effectively identifies and corrects transmission errors, providing a high-quality data basis for subsequent early warning models. The abnormal data re-sampling mechanism avoids data loss caused by single collection failure, ensuring the continuous and stable operation of the monitoring system. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The method steps of the application.

[0054] Figure 2 The system flowchart of the application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0056] Embodiment one: please refer to Figure 1 A power transmission line heat discoloration clamp heat warning method, the specific steps are as follows:

[0057] S1, real-time data acquisition and transmission, using high-precision sensors to monitor the working state of the discoloration clamp in the power transmission line in real time, and transmitting the collected data through wireless communication technology;

[0058] S2, environmental and equipment data fusion, collecting and fusing environmental data and equipment state data, using data fusion algorithm to process input data, removing noise and uncertainty;

[0059] S3, adaptive early warning model construction, based on historical data, real-time monitoring data and environmental factors, using machine learning algorithm to construct adaptive early warning model;

[0060] S4, real-time early warning and risk judgment, after obtaining real-time data, the system judges the working state of the clamp according to the adaptive early warning model, when the equipment temperature or load exceeds the set safety threshold, the system sends out a warning signal;

[0061] S5, intelligent feedback and decision support, the system provides specific operation suggestions according to the current state and historical data of the equipment, including adjusting the load, cooling and checking the equipment;

[0062] S6, maintain data accumulation and in-depth analysis, record each early warning event and its processing process, including maintenance records, fault handling and equipment replacement information, regularly analyze the aging trend and failure mode of the equipment, and further optimize the early warning model;

[0063] S7, continuous monitoring and model optimization, based on real-time operation data and long-term performance of the equipment, regularly evaluate and update the early warning model.

[0064] In this embodiment: the wire connection fittings are key components of the power transmission line, and their working state directly affects the safety of the power grid. The color-changing clamp intuitively reflects the temperature change of the contact surface through temperature-sensitive characteristics, but under the background of rapid development of ultra-high voltage power grids, traditional manual inspection methods cannot meet the real-time monitoring needs. The conventional monitoring system has weak anti-electromagnetic interference ability, large data error in extreme weather, and other problems, and the fixed threshold early warning mechanism cannot adapt to equipment aging and load fluctuations, resulting in high false alarm rate and missed alarm rate. In addition, the existing system lacks intelligent analysis function, and it is difficult to provide accurate fault disposal suggestions for operation and maintenance personnel, and about 30% of the clamp failures are caused by improper disposal, which leads to the expansion of the problem.

[0065] In order to solve the above problems, in view of the core defects of insufficient data acquisition accuracy, poor adaptability of early warning model, and lack of decision support in the prior art, it is necessary to build a data acquisition system with high anti-interference ability, develop a dynamically adjusted early warning model, and establish an intelligent decision support mechanism. Through analysis, it is found that the coupling influence of environmental factors and equipment state has not been fully tapped, the traditional fixed threshold method cannot reflect the parameter drift caused by equipment aging, and the lack of closed-loop optimization mechanism leads to gradual degradation of model performance. Based on this, it is proposed to improve data quality through multi-source data fusion, use machine learning to build an adaptive model, and introduce a continuous optimization mechanism to ensure the long-term effectiveness of the system.

[0066] The present application proposes a technical scheme of real-time data acquisition and transmission, environment and equipment data fusion, adaptive early warning model construction, real-time early warning and risk judgment, intelligent feedback and decision support, maintenance data accumulation and in-depth analysis, and continuous monitoring and model optimization.

[0067] The high-precision sensor refers to a temperature, vibration and current monitoring device with electromagnetic interference resistance, which can be specifically implemented by using a distributed temperature sensor array in cooperation with a vibration sensor and a current transformer to accurately capture the temperature gradient and mechanical vibration characteristics of the contact surface of the clamp. The data fusion algorithm refers to a calculation method for processing multi-source heterogeneous data, which can be specifically implemented by using an improved wavelet packet transform combined with a dynamic filter to eliminate environmental noise and extract deep correlation characteristics of equipment and environmental parameters. The machine learning algorithm refers to an intelligent model capable of processing time series data, which can be specifically implemented by using an integrated learning architecture to fuse an LSTM and a random forest module to establish a nonlinear mapping relationship between the device state and the fault risk. The dynamic safety threshold refers to a warning boundary that is automatically adjusted with the aging state of the device, which can be specifically implemented by modeling historical data combined with an environmental correction factor to adapt to parameter changes caused by device performance degradation.

[0068] The clamp temperature, vibration and current data are collected in real time through the multi-parameter monitoring network, and a double-channel redundant transmission is established after data preprocessing by edge computing. The environmental monitoring unit synchronously collects meteorological parameters, and performs fusion processing on the device data through a space-time alignment mechanism. A composite feature matrix is constructed based on historical data and real-time features, and an integrated learning model is used to realize risk probability calculation. When an abnormal state is detected, a disposal suggestion is generated in combination with case reasoning technology, and operation and maintenance data are fed back to the model optimization module. The complete operation data are stored through the blockchain technology, and the device degradation trend is analyzed by using the transfer learning, and finally a closed-loop system of data acquisition, analysis, early warning, decision-making and optimization is formed.

[0069] The conventional scheme uses a single temperature parameter for monitoring, while the present scheme captures the comprehensive state of the temperature gradient, vibration spectrum and current value through a multi-parameter monitoring network; the existing technology uses a fixed threshold for early warning, and the present scheme introduces an environmental correction factor to realize dynamic threshold adjustment; the traditional system lacks data closure, and the present scheme continuously improves the system performance through maintenance data accumulation and model optimization mechanism.

[0070] The present application effectively solves the problem of data acquisition distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the false alarm and missed alarm risk caused by device aging, the intelligent decision support module shortens the fault disposal response time, and the data closed-loop mechanism ensures the reliability of the system in the whole life cycle.

[0071] Embodiment two: please refer to Figure 1 , in step S1, the real-time data acquisition and transmission are specifically as follows:

[0072] S1.1, a distributed temperature sensor array is arranged at the key temperature measurement points of the color-changing wire clamp body and the adjacent fittings, vibration sensors and current transformers are configured at the line connection parts, a multi-parameter monitoring network is constructed, each sensor collects the wire clamp operating state parameters in real time at a sampling frequency of not less than 1 Hz, including the contact surface temperature gradient distribution, mechanical vibration spectrum characteristics and current value;

[0073] S1.2, a three-level data processing flow is executed in the edge terminal device, random noise is eliminated by using a sliding window mean filter, abnormal data is removed based on a preset threshold range, multi-source data synchronization is achieved through timestamp alignment, a re-sampling mechanism is triggered for the data that fails the verification, and the effectiveness of the transmission data is ensured;

[0074] S1.3, a dual-channel redundant transmission architecture is constructed, the main channel transmits compressed feature data packets through a 4G / 5G network, the standby channel transmits key state parameters through a LoRa ad hoc network, a time series database is established in the cloud, CRC verification is performed on the received data and stored, and a data integrity report is generated for subsequent analysis and calling.

[0075] In the embodiment, the application further proposes that a distributed temperature sensor array is arranged at the key temperature measurement points of the color-changing wire clamp body and the adjacent fittings, vibration sensors and current transformers are configured at the line connection parts, a multi-parameter monitoring network is constructed, each sensor collects the wire clamp operating state parameters in real time at a sampling frequency of not less than 1 Hz, including the contact surface temperature gradient distribution, mechanical vibration spectrum characteristics and current value; a three-level data processing flow is executed in the edge terminal device, random noise is eliminated by using a sliding window mean filter, abnormal data is removed based on a preset threshold range, multi-source data synchronization is achieved through timestamp alignment, a re-sampling mechanism is triggered for the data that fails the verification, and the effectiveness of the transmission data is ensured; a dual-channel redundant transmission architecture is constructed, the main channel transmits compressed feature data packets through a 4G / 5G network, the standby channel transmits key state parameters through a LoRa ad hoc network, a time series database is established in the cloud, CRC verification is performed on the received data and stored, and a data integrity report is generated for subsequent analysis and calling.

[0076] The distributed temperature sensor array refers to a plurality of temperature measurement units arranged along the key positions such as the contact surface, which can be implemented by a patch type thermocouple array, and is used to capture the non-uniformity characteristics of the temperature distribution. The sliding window mean filter refers to calculating the data mean value by setting a time window, which can be implemented by a fixed length time window, and is used to eliminate random interference signals. The dual-channel redundant transmission architecture refers to establishing a main and standby communication link, which can be implemented by a heterogeneous network combination, and is used to ensure the reliability of data transmission in extreme environments. The CRC verification refers to the cyclic redundancy check algorithm, which can be implemented by the CRC-32 standard, and is used to verify the integrity of the data packet transmission.

[0077] A three-dimensional monitoring network is formed by deploying temperature, vibration, and current sensors at key connection points of the power transmission line to capture the transient characteristics of the equipment operating state through a sampling frequency of no less than 1 Hz. The edge computing terminal performs three-level processing on the raw data: firstly, a sliding window filter is applied to eliminate random noise, secondly, abnormal data is removed according to a preset threshold, and finally, multi-source data synchronization is achieved through timestamp alignment. When data verification fails, a re-sampling mechanism is automatically triggered to ensure the validity of the input data. Data transmission adopts a primary and backup dual-channel mode, the primary channel transmits compressed data packets through the mobile network, the backup channel transmits key parameters through a low-power wide-area network, and the cloud receiving end implements CRC verification before storing the data in a time series database and generating an integrity report for subsequent analysis.

[0078] The conventional monitoring system only uses a single temperature sensor and has a low sampling frequency, which cannot capture the temperature gradient changes of the contact surface. The existing data processing process lacks abnormal data identification and re-sampling mechanism, resulting in errors in the transmitted data. The traditional single-channel transmission architecture is prone to data loss in harsh environments and lacks an integrity verification mechanism. The present scheme effectively improves the accuracy of data acquisition and the reliability of transmission through a multi-parameter monitoring network, a three-level data processing flow, and a dual-channel redundant transmission.

[0079] The present application solves the problem of large monitoring data errors in a strong electromagnetic interference environment. Through multi-sensor cooperative monitoring and edge computing processing, the influence of environmental factors on data acquisition is reduced. The dual-channel transmission architecture ensures the integrity of data transmission under different network conditions, and the CRC verification mechanism effectively identifies and corrects transmission errors, providing a high-quality data foundation for subsequent early warning models. The abnormal data re-sampling mechanism avoids data loss caused by single acquisition failure, ensuring the continuous and stable operation of the monitoring system.

[0080] Embodiment three: please refer to Figure 1 , in step S2, the environment and device data fusion specific ways are as follows:

[0081] S2.1, a meteorological environment monitoring unit is established, temperature and humidity sensors with anti-electromagnetic interference characteristics, an anemometer, and a solar radiation meter are deployed, environmental parameters are collected at a period of 5 minutes, a time synchronization mechanism is established with the device state monitoring system, a μs-level timestamp alignment is achieved through PTP protocol, a spatial correlation model is constructed, the environment monitoring point coordinates are mapped to the device topology structure, and the data spatial consistency is ensured;

[0082] S2.2, an improved wavelet packet transform is used to remove high-frequency noise of the environment sensor, a state data sliding window dynamic filter is designed, the window width is adaptively adjusted according to the load change rate, an abnormal data marking rule library is established, and data exceeding the physical constraint range is automatically marked and isolated;

[0083] S2.3, the primary fusion layer applies D-S evidence theory to process isomeric data conflicts, the middle fusion layer adopts improved Kalman filter to realize space-time scale matching, and the high-level fusion layer establishes an LSTM network based on an Attention mechanism to extract deep coupling features of the device and the environmental parameters.

[0084] In the embodiment, the application further proposes a specific implementation mode of the environmental and device data fusion in step S2, including establishing a meteorological environment monitoring unit, deploying temperature and humidity sensors, wind speed and direction instruments, and solar radiation meters with anti-electromagnetic interference characteristics, collecting environmental parameters at a fixed period, establishing a time synchronization mechanism with the device state monitoring system, realizing time scale alignment through a protocol, constructing a space correlation model, mapping the environmental monitoring point coordinates to the device topology structure, using an improved wavelet packet transform to remove high-frequency noise of the environmental sensors, designing a state data sliding window dynamic filter with a window width that is adaptively adjusted according to the load change rate, and establishing an abnormal data marking rule library; the primary fusion layer applies D-S evidence theory to process isomeric data conflicts, the middle fusion layer adopts improved Kalman filter to realize space-time scale matching, and the high-level fusion layer establishes an LSTM network based on an Attention mechanism to extract deep coupling features of the device and the environmental parameters.

[0085] The improved wavelet packet transform refers to a method of reconstructing a signal through multi-scale decomposition, which can be implemented by using a Daubechies wavelet basis function, and is used to eliminate high-frequency noise interference in the data collected by the environmental sensors. The D-S evidence theory refers to a mathematical tool for processing uncertain information, which can be used to fuse multi-source data by using a basic probability assignment function to solve the sensor data conflict problem. The improved Kalman filter refers to a filtering algorithm in which adaptive noise covariance estimation is introduced, which can realize space-time data matching through state transition matrix correction. The LSTM network based on the Attention mechanism refers to a long short-term memory network combined with an attention mechanism, which can use a multi-head attention module to capture the nonlinear correlation between the device and the environmental parameters.

[0086] The meteorological environment monitoring unit deploys an anti-electromagnetic interference sensor array to collect environmental parameters periodically, and realizes accurate time scale alignment with the device state data through a time synchronization protocol. In the data preprocessing stage, the improved wavelet packet transform performs noise reduction processing on the original environmental data, and the dynamic filter automatically adjusts the window width according to the load change, effectively eliminating transient interference. In the data fusion process, the primary fusion layer eliminates multi-source data conflicts through the D-S evidence theory, the middle fusion layer realizes alignment of data at different space-time scales by using the improved Kalman filter, and the high-level fusion layer strengthens key feature extraction through the attention mechanism, and finally forms a feature vector reflecting the coupling relationship between the device and the environment.

[0087] The existing scheme does not consider electromagnetic interference protection when collecting environmental data, and lacks accurate time synchronization mechanism, resulting in poor spatio-temporal correlation of data. The scheme effectively improves the spatial consistency of data by deploying special protection sensors and establishing a us-level time alignment mechanism. Traditional filtering methods use fixed parameters to process noise, and the scheme significantly improves the noise suppression capability through dynamic window adjustment and adaptive wavelet packet decomposition. Conventional data fusion mostly uses a single algorithm, and the scheme constructs a three-level fusion architecture, combines evidence theory, improved filtering and deep learning technology, and realizes multi-level feature extraction.

[0088] The application solves the problem of spatio-temporal inconsistency between environmental monitoring data and equipment state data, reduces the influence of electromagnetic interference and random noise on data quality, and improves the fusion accuracy of multi-source heterogeneous data. By constructing a multi-level fusion architecture, the coupled features of equipment operating state and environmental factors are effectively extracted, providing more accurate feature input for subsequent warning models, thereby improving the timeliness and reliability of the heating warning.

[0089] Embodiment four: please refer to Figure 1 In step S3, the adaptive warning model is constructed as follows:

[0090] S3.1, extract the spatio-temporal correlation data of equipment state, environmental parameters and fault records from the historical database, use feature importance analysis to screen key feature indicators, and construct a composite feature matrix containing time domain statistical features, frequency domain features and environmental coupling features;

[0091] S3.2, organize the training sample set based on the sliding time window mechanism, use the integrated learning architecture to fuse the LSTM time series prediction module and the random forest classification module, introduce the online learning mechanism, and continuously optimize the model parameters through incremental update;

[0092] S3.3, establish a dynamic warning threshold calculation model, design a hierarchical warning mechanism, integrate a feedback adjustment module, and automatically adjust the model sensitivity according to the warning accuracy.

[0093] In this embodiment, the application further proposes the specific way of constructing an adaptive warning model: extract the spatio-temporal correlation data of equipment state, environmental parameters and fault records from the historical database, use feature importance analysis to screen key feature indicators, and construct a composite feature matrix containing time domain statistical features, frequency domain features and environmental coupling features; organize the training sample set based on the sliding time window mechanism, use the integrated learning architecture to fuse the LSTM time series prediction module and the random forest classification module, introduce the online learning mechanism, and continuously optimize the model parameters through incremental update; establish a dynamic warning threshold calculation model, design a hierarchical warning mechanism, integrate a feedback adjustment module, and automatically adjust the model sensitivity according to the warning accuracy.

[0094] The composite feature matrix refers to a multi-dimensional feature set integrating equipment operating state, environmental conditions and historical failure data. Specifically, the Pearson correlation coefficient method can be used to screen key indicators, and time domain mean variance, frequency domain wavelet coefficient and environmental temperature and humidity parameters can be matrix spliced to achieve this.

[0095] The sliding time window mechanism refers to a sample organization method of dynamically intercepting continuous monitoring data according to a preset time length. Specifically, a variable window width strategy can be used, such as automatically adjusting the window span according to the load fluctuation frequency, so that the training sample maintains time sequence continuity. This mechanism can adapt to the dynamic changes of the equipment operating state.

[0096] The integrated learning architecture refers to a hybrid model structure that combines the advantages of different algorithms. Specifically, the LSTM network can be used to extract time sequence features, and the random forest can be used to process discrete classification problems. The output results are integrated through a weighted voting mechanism. This architecture can simultaneously process the time sequence characteristics of the clip temperature change and the classification identification of sudden abnormalities.

[0097] The dynamic early warning threshold calculation model refers to a decision criterion that automatically adjusts with the degree of equipment aging and environmental conditions. Specifically, the exponential weighted moving average method can be used to establish a baseline, and an environmental correction coefficient can be added to generate a dynamic threshold curve. This model can eliminate the problem of inadequate adaptability of fixed thresholds to the equipment degradation process.

[0098] In the model construction stage, first, the feature engineering integrates multi-element data such as equipment temperature gradient, vibration spectrum, and environmental temperature and humidity to form a composite feature matrix. The sliding window mechanism is used to generate training samples with time sequence correlation. Then, a hybrid model of LSTM and random forest is built, and the model parameters are updated in real time through an online learning mechanism. In the early warning stage, the dynamic threshold model adjusts the decision criteria in real time according to the equipment running time and environmental parameters, and the feedback adjustment module automatically corrects the model sensitivity based on historical early warning accuracy, forming a closed-loop optimization system.

[0099] The traditional early warning model only uses a single temperature threshold and lacks environmental parameter correction, and cannot adapt to the changes in temperature rise characteristics caused by equipment aging. This scheme captures the multi-dimensional correlation between equipment and environment through a composite feature matrix, and uses a dynamic threshold model to achieve adaptive adjustment of the early warning criteria, solving the problem of false positives in the fixed threshold mechanism in long-term running scenarios. At the same time, the integrated learning architecture can more effectively handle the mixed mode of gradual change and mutation in the clip heating process compared to a single algorithm model.

[0100] The application can accurately identify early signs of abnormal temperature changes of the contact surface of the clamp, effectively distinguish normal load fluctuations from real failure precursors, and reduce false alarm situations caused by equipment aging or environmental mutations. The dynamic threshold mechanism can automatically correct the judgment criteria as the clamp service period, avoiding the risk of missed reports caused by the fixed threshold of traditional methods. The hybrid model architecture takes into account the dual needs of temperature trend prediction and sudden anomaly detection, improving the adaptability to complex operating conditions.

[0101] Embodiment five: please refer to Figure 1 In step S4, the specific ways of real-time early warning and risk judgment are as follows:

[0102] S4.1, real-time calculation of equipment state feature vector is carried out by using sliding time window technology, real-time features are input into the pre-constructed early warning model through model reasoning interface, and multi-dimensional evaluation results including risk probability value, abnormal type and confidence are output;

[0103] S4.2, dynamic safety threshold curve is constructed based on historical operation data of the equipment, environmental correction factor is introduced to adjust the threshold in real time, and multi-parameter joint criterion is implemented;

[0104] S4.3, three-level early warning system is established, composite alarm strategy is designed, and multi-channel synchronous release of early warning information is realized.

[0105] In this embodiment: the application further proposes that after obtaining real-time data, the system judges the working state of the clamp according to the adaptive early warning model, and when the temperature or load of the equipment exceeds the set safety threshold, the specific ways of the system issuing early warning signals include: real-time calculation of equipment state feature vector is carried out by using sliding time window technology, real-time features are input into the pre-constructed early warning model through model reasoning interface, and multi-dimensional evaluation results including risk probability value, abnormal type and confidence are output; dynamic safety threshold curve is constructed based on historical operation data of the equipment, environmental correction factor is introduced to adjust the threshold in real time, and multi-parameter joint criterion is implemented; three-level early warning system is established, composite alarm strategy is designed, and multi-channel synchronous release of early warning information is realized.

[0106] Sliding time window technology refers to a method of extracting continuous data segments at fixed time intervals for feature calculation. Specifically, it can be implemented using an adjustable time window with a length of 5 to 30 seconds. This technology can capture short-term fluctuations in equipment status. Dynamic safety threshold curves are warning lines that change over time based on historical equipment operating parameters. Specifically, they can be generated by fitting the temperature-load relationship curve under normal operating conditions. Their function is to reflect changes in safety boundaries caused by equipment aging. Environmental correction factors are adjustment coefficients used to quantify the impact of environmental parameters on equipment safety thresholds. Specifically, they can be calculated using a multiple regression model under different environmental combinations to compensate for the interference of extreme weather on threshold judgment. Multi-parameter joint criteria refer to logical rules for risk judgment based on multiple dimensions such as temperature, vibration, and current. Specifically, they can be designed as a weighted scoring mechanism to improve early warning reliability by eliminating misjudgments based on single parameters. A three-level early warning system refers to a differentiated alarm mechanism based on risk levels. Specifically, it can set three response levels: primary warning, intermediate alarm, and emergency response, to achieve optimal resource allocation.

[0107] The equipment status feature vector extracts the temperature gradient distribution, vibration spectrum characteristics, and current fluctuation parameters within the current time window through a sliding time window. After standardization, these parameters are input into the early warning model for inference calculations. The model outputs multidimensional evaluation results including anomaly type classification probability, risk level score, and model confidence index. The dynamic safety threshold curve dynamically adjusts the upper and lower limits of the threshold based on the temperature-load correspondence in historical equipment operating data, combined with current environmental correction factors. For example, it automatically lowers the temperature warning value in high-temperature environments. The multi-parameter joint criterion establishes correlation rules between temperature change rate, vibration energy spectral density, and current harmonic content. An early warning is triggered when two of the three parameters simultaneously exceed the limit. In the three-level early warning system, the primary warning triggers the equipment status review process, the intermediate alarm initiates the remote diagnostic program, and the emergency response level directly activates the protection device and notifies maintenance personnel.

[0108] Traditional fixed-threshold early warning methods cannot adapt to performance degradation caused by equipment aging, while dynamic safety threshold curves automatically update safety boundaries by continuously tracking the historical status of equipment. Conventional single-parameter criteria are susceptible to occasional interference, leading to false alarms, while multi-parameter joint criteria significantly improve judgment accuracy through cross-validation. Existing single early warning levels are insufficient to distinguish the severity of faults; a three-level early warning system can implement differentiated response strategies based on risk levels.

[0109] This application can dynamically adjust the safety threshold according to the actual status of the equipment, avoiding false alarms or missed alarms caused by equipment aging; it can effectively identify real fault signals through cross-validation of multi-dimensional parameters, reducing the probability of misjudgment caused by environmental interference; the hierarchical early warning mechanism can optimize the allocation of operation and maintenance resources, ensuring that abnormal events of different severity receive corresponding levels of handling and response.

[0110] Embodiment six: please refer to Figure 1 In step S5, the specific ways of intelligent feedback and decision support are as follows.

[0111] S5.1, match the historical fault feature library based on case-based reasoning technology, construct the fault cause probability graph by using the Bayesian network, and output the diagnostic report containing the fault possibility, risk level and influence range;

[0112] S5.2, construct the device health state knowledge graph, develop the adaptive decision rule engine, and generate the decision tree containing the optional operation scheme and its expected effect;

[0113] S5.3, design the visual operation guidance interface, generate the structured emergency plan document, and provide the voice interactive auxiliary decision function.

[0114] In this embodiment, the specific implementation of the intelligent feedback and decision support stage is further proposed, including matching the historical fault feature library based on case-based reasoning technology, constructing the fault cause probability graph by using the Bayesian network and outputting the diagnostic report; constructing the device health state knowledge graph and developing the adaptive decision rule engine to generate the decision tree; designing the visual operation guidance interface and generating the structured emergency plan document, while providing the voice interactive auxiliary decision function.

[0115] Case-based reasoning technology refers to the technology of matching the current device state characteristics by searching the historical fault case library, which can be realized by using similarity calculation algorithm, and is used for quickly positioning similar fault scenes. The Bayesian network refers to the technology of constructing the causal relationship reasoning framework based on the probabilistic graph model, which can be realized by using the conditional probability table, and is used for quantifying the relevance of different fault causes. The device health state knowledge graph refers to the database that expresses the correlation of device state in the form of graph structure, which can be realized by using ontology modeling technology, and is used for integrating the complex correlation of device operating parameters and environmental factors. The adaptive decision rule engine refers to the calculation module that can dynamically adjust the decision logic according to real-time data, which can be realized by using fuzzy reasoning system, and is used for generating operation schemes suitable for different working conditions. The visual operation guidance interface refers to the man-machine interaction platform that converts complex data into graphical display, which can be realized by using WebGL three-dimensional rendering technology, and is used for intuitively presenting the device state and operation guidance.

[0116] After obtaining real-time monitoring data, the system first retrieves the case set with the highest similarity from the historical fault library through the case-based reasoning engine, calculates the conditional probability distribution of each fault cause based on the Bayesian network, and generates a multi-dimensional diagnostic report containing a possibility ranking. Subsequently, based on the device health knowledge graph, a state evaluation model is established, and through the decision rule engine, a pre-set operation rule library is traversed to generate an operation decision tree containing the expected cooling effect and risk coefficient. Finally, through the visualization interface, the diagnostic conclusion and operation suggestions are converted into a three-dimensional topological graph display, and a standardized emergency plan document is generated and the voice interaction module is started, providing step-by-step operation guidance for on-site personnel.

[0117] Compared with existing technologies, traditional systems often use fixed decision trees or simple rule matching mechanisms, which cannot dynamically adapt to parameter drift problems caused by equipment aging. However, through the collaborative action of Bayesian networks and knowledge graphs, the present scheme can real-time correct fault probability weights and effectively reuse historical experience through case-based reasoning. Existing technologies usually rely on static plan libraries, while the present scheme can generate targeted operation plans according to the current load level and environmental conditions through the dynamic adjustment mechanism of the decision rule engine. Existing systems often use tables or simple charts to display information, while the present scheme significantly improves the understandability of complex information and the real-time nature of operation guidance through the combination of three-dimensional visualization and voice interaction.

[0118] The present application realizes the intelligent upgrading of fault diagnosis and disposal decision, effectively solves the poor adaptability of disposal schemes caused by the rigid decision logic of traditional systems, overcomes the response delay defects caused by the dependence on artificial experience, and significantly improves the emergency disposal efficiency and operation accuracy under abnormal working conditions.

[0119] Embodiment Seven: Please refer to Figure 1 In step S6, the specific ways of maintaining data accumulation and deep analysis are as follows:

[0120] S6.1, Construct a time series database to store the complete running history of the equipment, use blockchain technology to ensure data tamper-proofing and traceability, realize standardized storage and association indexing of multi-source heterogeneous data;

[0121] S6.2, Use transfer learning technology to construct a device degradation trend prediction model, develop a fault mode mining algorithm based on association rules, and generate an analysis report containing fault features, occurrence rules, and evolution trends;

[0122] S6.3, Design a model performance continuous monitoring index system, establish an incremental learning mechanism to realize automatic updating of model parameters, execute double testing to verify optimization effect and generate optimization suggestions.

[0123] In this embodiment: the present application further proposes a power transmission line heat color-changing clamp heating early warning method, the specific steps are as follows: the complete operation history of the time series database storage device is constructed, the blockchain technology is used to ensure data tamper-proof and traceability, and the standardized storage and associated index of multi-source heterogeneous data are realized; the migration learning technology is used to construct the device degradation trend prediction model, the fault mode mining algorithm based on association rules is developed, and the analysis report containing fault characteristics, occurrence law and evolution trend is generated; the model performance continuous monitoring index system is designed, the incremental learning mechanism is established to realize the automatic updating of model parameters, the double test verification is executed to optimize the effect and generate optimization suggestions.

[0124] The time series database refers to a database system that stores device operation parameters in chronological order, which can be implemented by InfluxDB or TimescaleDB, and is used to record the time series changes of device temperature, vibration and current parameters. The blockchain technology refers to the use of distributed ledger technology to ensure data tamper-proof, which can be implemented by Hyperledger Fabric framework, and performs data verification and storage operations through smart contracts to solve the problem of traditional database vulnerability to malicious tampering. The transfer learning technology refers to a machine learning method that transfers existing domain knowledge to new tasks, which can use a pre-trained neural network model to transfer parameters and solve the problem of poor generalization of the prediction model caused by insufficient device degradation data. The association rule mining algorithm refers to an algorithm that discovers frequent item sets in a data set, which can be implemented by the Apriori algorithm to extract the association law between clamp temperature anomalies and mechanical vibrations from historical fault data. The incremental learning mechanism refers to a mechanism in which the model dynamically updates parameters when receiving new data, which can be implemented by an online gradient descent algorithm to make the early warning model continuously adapt to the data distribution changes caused by device aging. The double test verification refers to a verification method that simultaneously retains new and old model versions for comparison, which can be implemented by the AB test framework to ensure that the model optimization process does not reduce the original performance.

[0125] The temperature, vibration and current parameters generated during the operation of the device are written into the time series database in real time, and the blockchain node generates a hash value for each batch of data and stores it in a distributed manner. When analyzing the device degradation trend, the transfer learning model extracts common features from the fault data of other similar devices and trains them with the limited samples of the current device. The association rule mining algorithm periodically scans the historical database to identify the association mode between temperature anomalies and mechanical looseness. The model performance monitoring system continuously tracks the prediction accuracy and response delay indicators, and triggers the incremental learning process when performance degradation is detected to adjust the model parameters using the latest data. The optimized model needs to run in parallel with the old version in the test environment, and the optimization effect is confirmed by comparing the early warning accuracy and false alarm rate.

[0126] The traditional scheme stores data in a single database and lacks a tamper-proofing mechanism, and has the problem of low data reliability. The conventional degradation prediction model relies on a large amount of labeled data, and the lack of fault samples in actual operation leads to insufficient prediction accuracy. Existing fault analysis relies on manual experience to summarize rules, and it is difficult to find implicit correlations between complex parameters. Model updating usually requires full retraining, which cannot adapt to the dynamic changes of device state.

[0127] The application realizes the reliable storage and efficient retrieval of device operation data, improves the generalization ability of the degradation trend prediction model in the sample insufficient scene, automatically finds the potential correlation rules between the temperature anomaly of the clamp and mechanical failure, ensures that the early warning model can continuously adapt to the data distribution shift caused by device aging, and guarantees the safety of the model optimization process through a double verification mechanism.

[0128] Embodiment eight: please refer to Figure 1 In step S7, the specific method of continuous monitoring and model optimization is as follows:

[0129] S7.1, deploy dynamic monitoring probes to collect model key indicators in real time, build an ELK-based performance monitoring platform, and automatically generate model evaluation reports including ROC curves and confusion matrices;

[0130] S7.2, implement an online learning algorithm based on FTRL, design an adaptive learning rate adjustment strategy, and perform parameter space Bayesian optimization to find the optimal hyperparameter combination;

[0131] S7.3, establish a model version management library, implement a new and old dual model test framework, and adopt a gradual release strategy.

[0132] In this embodiment: the application further proposes the specific implementation method in the continuous monitoring and model optimization stage, including deploying dynamic monitoring probes to collect model key indicators in real time, building an ELK-based performance monitoring platform and automatically generating model evaluation reports; implement an online learning algorithm based on FTRL, design an adaptive learning rate adjustment strategy and perform parameter space Bayesian optimization; establish a model version management library, implement a new and old dual model test framework and adopt a gradual release strategy.

[0133] Dynamic monitoring probes refer to lightweight data acquisition modules embedded in early warning models, which can be implemented using a microservice architecture to capture runtime metrics such as model inference latency and memory usage in real time. ELK-based performance monitoring platforms are monitoring systems comprised of Elasticsearch, Logstash, and Kibana, implemented through configured log collection pipelines and visualization dashboards to continuously track model prediction accuracy and stability. FTRL online learning algorithms are online training methods suitable for non-convex optimization, implemented using a sparse gradient update mechanism to iterate model parameters without affecting system real-time performance. Parameter space Bayesian optimization is a hyperparameter search method based on Gaussian processes, implemented by constructing surrogate models and acquisition functions to find optimal solutions in complex parameter combinations. Model version management libraries are storage systems supporting multiple versions, implemented using a Git-like mechanism to record parameter snapshots and performance benchmarks during model iteration.

[0134] During system operation, dynamic monitoring probes continuously collect key performance indicators (KPIs) during model inference and transmit log data to the ELK platform in real time for aggregation and analysis. The performance monitoring platform automatically generates an evaluation report containing ROC curves and confusion matrices, providing quantitative evidence of the model's health status. The online learning algorithm dynamically adjusts model parameters based on real-time data streams, balancing the weights of new data and historical knowledge through an adaptive learning rate mechanism. The Bayesian optimization module periodically scans the hyperparameter space, using historical optimization records to guide the search direction. The version management system establishes an independent branch for each model update, verifies the optimization effect through parallel testing of new and old models, and finally deploys the validated model version to the production environment using a gradual release strategy.

[0135] Traditional methods typically employ offline batch updates for model maintenance, which suffers from response lag and difficulties in version rollback. This solution combines dynamic monitoring with online learning to achieve real-time optimization of model parameters; it improves hyperparameter tuning efficiency by replacing grid search with Bayesian optimization; and it effectively reduces model update risks by supporting parallel verification of multiple versions through a version management system.

[0136] This application addresses the issue of decreased adaptability caused by the lag in updates to traditional early warning models, thereby improving the predictive accuracy of the models in dynamic operating environments. By establishing a systematic model monitoring and optimization mechanism, continuous performance improvement of the early warning model is achieved, while ensuring the controllability and traceability of the model iteration process, providing reliable technical support for power equipment status early warning.

[0137] The application also provides a power transmission line heat color-changing clamp heating early warning system, comprising a multi-parameter sensing acquisition module, an edge computing module, a data fusion processing module, an intelligent early warning decision module, an operation and maintenance decision module, a data asset management module, and a model operation and maintenance management module;

[0138] The multi-parameter sensing acquisition module uses high-precision sensors to monitor the working state of the color-changing clamp in the power transmission line in real time, and transmits the collected data through wireless communication technology;

[0139] The edge computing module collects and fuses environmental data and equipment state data, and uses a data fusion algorithm to process the input data to remove noise and uncertainty;

[0140] The data fusion processing module uses machine learning algorithms to build an adaptive early warning model based on historical data, real-time monitoring data, and environmental factors;

[0141] After obtaining real-time data, the intelligent early warning decision module judges the working state of the clamp according to the adaptive early warning model, and when the equipment temperature or load exceeds the set safety threshold, the system issues a warning signal;

[0142] After obtaining real-time data, the operation and maintenance decision module judges the working state of the clamp according to the adaptive early warning model, and when the equipment temperature or load exceeds the set safety threshold, the system issues a warning signal;

[0143] The data asset management module records each early warning event and its processing process, including maintenance records, fault handling, and equipment replacement information, regularly analyzes the aging trend and failure mode of the equipment, and further optimizes the early warning model;

[0144] The model operation and maintenance management module regularly evaluates and updates the early warning model based on real-time operation data and long-term performance of the equipment.

[0145] The contents not described in detail in the specification belong to the prior art known to those skilled in the art.

[0146] Although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to part of the technical features, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for early warning of overheating in transmission line clamps that change color due to heat, characterized in that: The specific steps are as follows: S1. Real-time data acquisition and transmission: High-precision sensors are used to monitor the working status of color-changing clamps in power transmission lines in real time, and the acquired data is transmitted through wireless communication technology. S2. Environmental and equipment data fusion: Collect and fuse environmental data and equipment status data, and use data fusion algorithms to process the input data to remove noise and uncertainty; S3. Adaptive early warning model construction: Based on historical data, real-time monitoring data, and environmental factors, an adaptive early warning model is constructed using machine learning algorithms. S4. Real-time early warning and risk assessment: After acquiring real-time data, the system judges the working status of the clamp based on the adaptive early warning model. When the equipment temperature or load exceeds the set safety threshold, the system issues an early warning signal. S5, Intelligent Feedback and Decision Support: The system provides specific operational suggestions based on the current status of the equipment and historical data, including adjusting the load, cooling, and inspecting the equipment. S6. Maintain data accumulation and in-depth analysis, record each early warning event and its handling process, including maintenance records, fault handling and equipment replacement information, regularly analyze the aging trend and failure mode of equipment, and further optimize the early warning model; S7. Continuous monitoring and model optimization: Based on real-time operating data and the long-term performance of the equipment, the early warning model is regularly evaluated and updated. In step S1, the specific methods for real-time data acquisition and transmission are as follows: S1.1 Distributed temperature sensor arrays are deployed at key temperature measurement points of the color-changing clamp body and adjacent fittings. Vibration sensors and current transformers are configured at the line connection points to construct a multi-parameter monitoring network. Each sensor collects clamp operating status parameters in real time at a sampling frequency of not less than 1Hz, including contact surface temperature gradient distribution, mechanical vibration spectrum characteristics, and current carrying value. S1.2 Execute a three-level data processing flow in the edge terminal device, use sliding window mean filtering to eliminate random noise, remove abnormal data based on a preset threshold range, realize multi-source data synchronization through timestamp alignment, and trigger a re-sampling mechanism for data that fails verification to ensure the validity of transmitted data; S1.3 Construct a dual-channel redundant transmission architecture. The main channel uses 4G / 5G network to transmit compressed feature data packets, and the backup channel transmits key status parameters through LoRa self-organizing network. Establish a time series database in the cloud, perform CRC check on the received data and store it, and generate a data integrity report for subsequent analysis. In step S2, the specific method for fusing environmental and equipment data is as follows: S2.1 Establish a meteorological environment monitoring unit, deploy temperature and humidity sensors, wind speed and direction instruments and solar radiometers with electromagnetic interference protection characteristics, collect environmental parameters at a 5-minute cycle, establish a time synchronization mechanism with the equipment status monitoring system, achieve μs-level time scale alignment through PTP protocol, construct a spatial correlation model, map the coordinates of environmental monitoring points to the equipment topology, and ensure data spatial consistency. S2.

2. An improved wavelet packet transform is used to remove high-frequency noise from environmental sensors. A dynamic filter with a sliding window for state data is designed, and the window width is adaptively adjusted according to the load change rate. An abnormal data marking rule base is established to automatically mark and isolate data that exceeds the physical constraint range. S2.3 The primary fusion layer uses DS evidence theory to handle conflicts between homogeneous and heterogeneous data; the intermediate fusion layer uses an improved Kalman filter to achieve spatiotemporal scale matching; and the advanced fusion layer establishes an LSTM network based on the Attention mechanism to extract deep coupling features between device and environmental parameters. In step S3, the adaptive early warning model is constructed in the following specific way: S3.1 Extract spatiotemporal correlation data of equipment status, environmental parameters and fault records from historical databases, use feature importance analysis to screen key feature indicators, and construct a composite feature matrix that includes time domain statistical features, frequency domain features and environmental coupling features; S3.

2. The training sample set is organized based on the sliding time window mechanism. An integrated learning architecture is adopted to fuse the LSTM time series prediction module and the random forest classification module. An online learning mechanism is introduced to continuously optimize the model parameters through incremental updates. S3.3 Establish a dynamic early warning threshold calculation model, design a hierarchical early warning mechanism, integrate a feedback adjustment module, and automatically adjust the model sensitivity according to the early warning accuracy. In step S4, the specific methods for real-time early warning and risk assessment are as follows: S4.

1. The sliding time window technology is used to calculate the equipment status feature vector in real time. The real-time features are input into the pre-built early warning model through the model inference interface, and the multi-dimensional evaluation results containing risk probability value, anomaly type and confidence level are output. S4.

2. Construct a dynamic safety threshold curve based on historical equipment operation data, introduce an environmental correction factor to adjust the threshold in real time, and implement a multi-parameter joint criterion. S4.3 Establish a three-level early warning system, design a composite alarm strategy, and realize the simultaneous release of early warning information through multiple channels; In step S5, the intelligent feedback and decision support are implemented in the following ways; S5.1 Based on case-based reasoning technology, match the historical fault feature database, use Bayesian network to construct a fault cause probability map, and output a diagnostic report containing fault probability, risk level and impact range. S5.2 Construct a knowledge graph of equipment health status, develop an adaptive decision rule engine, and generate a decision tree containing optional operation schemes and their expected effects; S5.3 Design a visual operation guidance interface, generate structured emergency response plan documents, and provide voice interactive decision-making assistance functions; In step S6, the specific methods for maintaining data accumulation and in-depth analysis are as follows: S6.1 Construct a complete operating history of the time-series database storage device, and use blockchain technology to ensure data tamper-proof and traceability, and realize standardized storage and associated indexing of multi-source heterogeneous data; S6.2 Utilize transfer learning techniques to construct a predictive model for equipment degradation trends, develop a fault mode mining algorithm based on association rules, and generate an analysis report containing fault characteristics, occurrence patterns, and evolution trends. S6.3 Design a continuous monitoring index system for model performance, establish an incremental learning mechanism to realize automatic updating of model parameters, perform dual tests to verify the optimization effect and generate optimization suggestions; In step S7, the specific methods for continuous monitoring and model optimization are as follows: S7.1 Deploy dynamic monitoring probes to collect key model metrics in real time, build an ELK-based performance monitoring platform, and automatically generate model evaluation reports including ROC curves and confusion matrices. S7.2 Implement an online learning algorithm based on FTRL, design an adaptive learning rate adjustment strategy, and perform parameter space Bayesian optimization to find the optimal hyperparameter combination; S7.3 Establish a model version management library, implement a dual-model testing framework (new and old models), and adopt a progressive release strategy.

2. A power transmission line heat discoloration clamp overheating early warning system, characterized in that: The transmission line heat discoloration clamp heating early warning system is used to execute the transmission line heat discoloration clamp heating early warning method described in claim 1 above, including a multi-parameter sensing acquisition module, an edge computing module, a data fusion processing module, an intelligent early warning decision module, an operation and maintenance decision module, a data asset management module, and a model operation and maintenance management module; The multi-parameter sensing and acquisition module uses high-precision sensors to monitor the working status of the color-changing clamps in the transmission line in real time, and transmits the acquired data through wireless communication technology. The edge computing module collects and fuses environmental data and device status data, and uses a data fusion algorithm to process the input data to remove noise and uncertainty. The data fusion processing module uses machine learning algorithms to build an adaptive early warning model based on historical data, real-time monitoring data, and environmental factors. After acquiring real-time data, the intelligent early warning decision module judges the working status of the clamp according to the adaptive early warning model. When the equipment temperature or load exceeds the set safety threshold, the system issues an early warning signal. After the operation and maintenance decision module obtains real-time data, the system judges the working status of the clamp according to the adaptive early warning model. When the equipment temperature or load exceeds the set safety threshold, the system issues an early warning signal. The data asset management module records each early warning event and its processing, including maintenance records, fault handling, and equipment replacement information. It also regularly analyzes the aging trend and failure mode of the equipment to further optimize the early warning model. The model operation and maintenance management module regularly evaluates and updates the early warning model based on real-time operating data and the long-term performance of the equipment.

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