Power transmission line thermochromic wire clamp heating early warning method and system

Abstract

The invention relates to the technical field of circuit detection, discloses a power transmission line thermochromic wire clamp heating early warning method and system, 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 risk of false alarm and missing alarm caused by equipment aging, the intelligent decision support module shortens the fault handling response time, the data closed-loop mechanism ensures the reliability of the system in the whole life cycle, and the reliability of the system in the whole life cycle is improved through multi-sensor cooperative monitoring and edge calculation processing. And the influence of environmental factors on data acquisition is reduced. The two-channel transmission architecture guarantees the data transmission integrity under different network conditions, the CRC verification mechanism effectively recognizes and corrects transmission errors, a high-quality data basis is provided for a subsequent early warning model, the abnormal data recollection mechanism avoids data missing caused by single collection failure, and continuous and stable operation of the monitoring system is ensured.

Description

Technical Field

[0001] This invention relates to the field of circuit testing technology, specifically to a method and system for early warning of overheating and discoloration of transmission line clamps. Background Technology

[0002] In power systems, conductor connectors are critical transmission nodes, and their operational status directly impacts the safety of the power grid. Color-changing clamps, with their temperature-sensitive properties, are widely used because they can visually reflect changes in the contact surface temperature. However, with the rapid development of ultra-high-voltage power grids, traditional manual inspection methods are no longer sufficient to meet the technical requirements for real-time status monitoring of these critical components.

[0003] Existing temperature monitoring technologies for transmission line connectors suffer from the following main technical deficiencies. First, in terms of data acquisition, conventional monitoring systems lack sufficient anti-interference capabilities and are significantly affected by electromagnetic interference and extreme weather conditions in actual operating environments, leading to large errors in monitoring data. For example, a certain type of commercial monitoring device can have a temperature measurement deviation of ±5℃ under strong wind conditions. Second, regarding early warning models, existing solutions mostly employ fixed threshold early warning mechanisms, which are difficult to adapt to dynamic changes such as equipment aging and load fluctuations. Actual operation data shows that the false alarm rate and false negative 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 operators with accurate fault diagnosis and handling suggestions. Grid operation statistics show that approximately 30% of clamp faults escalate due to untimely or inappropriate handling.

[0004] Therefore, we propose a method and system for early warning of heat discoloration of transmission line clamps to solve the problems mentioned above. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for early warning of overheating and discoloration of transmission line clamps, so as to solve the circuit detection problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for early warning of overheating of transmission line heat-induced color-changing clamps, the specific steps of which 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 operational data and the long-term performance of the equipment, the early warning model is regularly evaluated and updated.

[0007] Preferably, in step S1, the real-time data acquisition and transmission are carried out in the following specific manner: 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, while the backup channel transmits key status parameters through LoRa self-organizing network. Establish a time-series database in the cloud, perform CRC verification on the received data and store it, and generate a data integrity report for subsequent analysis.

[0008] Preferably, in step S2, the environmental and equipment data fusion is performed in the following specific manner: 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. The advanced fusion layer establishes an LSTM network based on the Attention mechanism to extract deep coupling features between device and environmental parameters.

[0009] Preferably, 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.

[0010] Preferably, in step S4, the real-time early warning and risk assessment are performed in the following ways: 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.

[0011] Preferably, 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.

[0012] Preferably, 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 automatically update model parameters, perform dual tests to verify the optimization effect and generate optimization suggestions.

[0013] Preferably, in step S7, the continuous monitoring and model optimization are performed in the following ways: 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.

[0014] This application also provides a power transmission line heat discoloration clamp heating early warning system, including a multi-parameter sensing and acquisition module, an edge computing module, a data fusion and 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.

[0015] The beneficial effects of this invention are: 1. This application effectively solves the problem of data acquisition distortion in strong electromagnetic environments and improves the accuracy of condition assessment through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarms and missed alarms caused by equipment aging, the intelligent decision support module shortens the fault handling response time, and the data closed-loop mechanism ensures the reliability of the system throughout its entire life cycle.

[0016] 2. This application addresses the problem of large monitoring data errors under strong electromagnetic interference environments. Through multi-sensor collaborative monitoring and edge computing processing, the impact of environmental factors on data acquisition is reduced. The dual-channel transmission architecture ensures data transmission integrity under different network conditions, and the CRC check mechanism effectively identifies and corrects transmission errors, providing a high-quality data foundation for subsequent early warning models. The abnormal data re-acquisition mechanism avoids data loss due to single acquisition failures, ensuring the continuous and stable operation of the monitoring system. Attached Figure Description

[0017] Figure 1 This is a diagram illustrating the method steps of the present invention.

[0018] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1 A method for early warning of overheating and discoloration of transmission line clamps, the specific steps of which 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 operational data and the long-term performance of the equipment, the early warning model is regularly evaluated and updated.

[0021] In this embodiment, conductor connectors are critical components of transmission lines, and their operational status directly impacts grid safety. Color-changing clamps visually reflect changes in contact surface temperature through their temperature sensitivity; however, with the rapid development of ultra-high-voltage power grids, traditional manual inspection methods are insufficient to meet real-time monitoring needs. Conventional monitoring systems suffer from weak electromagnetic interference resistance and large data errors under extreme weather conditions. Fixed threshold early warning mechanisms cannot adapt to equipment aging and load fluctuations, resulting in high false alarm and false negative rates. Furthermore, existing systems lack intelligent analysis capabilities, making it difficult to provide maintenance personnel with accurate fault handling suggestions; approximately 30% of clamp faults escalate due to improper handling.

[0022] To address the aforementioned issues and the core shortcomings of existing technologies, such as insufficient data acquisition accuracy, poor adaptability of early warning models, and lack of decision support, it is necessary to construct a data acquisition system with high anti-interference capabilities, develop dynamically adjustable early warning models, and establish an intelligent decision support mechanism. Analysis revealed that the coupled influence of environmental factors and equipment status has not been fully explored, traditional fixed threshold methods cannot reflect parameter drift caused by equipment aging, and the lack of a closed-loop optimization mechanism leads to gradual degradation of model performance. Therefore, this paper proposes to improve data quality through multi-source data fusion, construct an adaptive model using machine learning, and introduce a continuous optimization mechanism to ensure the long-term effectiveness of the system.

[0023] This application proposes technical solutions for real-time data acquisition and transmission, environmental and equipment data fusion, adaptive early warning model construction, real-time early warning and risk assessment, intelligent feedback and decision support, maintenance data accumulation and in-depth analysis, and continuous monitoring and model optimization.

[0024] High-precision sensors refer to temperature, vibration, and current monitoring devices with electromagnetic interference resistance. Specifically, they can be implemented using distributed temperature sensor arrays combined with vibration sensors and current transformers to accurately capture temperature gradients and mechanical vibration characteristics of clamp contact surfaces. Data fusion algorithms are computational methods for processing multi-source heterogeneous data. Specifically, they can be implemented using improved wavelet packet transform combined with dynamic filters to eliminate environmental noise and extract deep correlation features between equipment and environmental parameters. Machine learning algorithms are intelligent models capable of processing time-series data. Specifically, they can be implemented using an ensemble learning architecture that integrates LSTM and random forest modules to establish a nonlinear mapping relationship between equipment status and fault risk. Dynamic safety thresholds are warning boundaries that automatically adjust with equipment aging. Specifically, they can be implemented through historical data modeling combined with environmental correction factors to adapt to parameter changes caused by equipment performance degradation.

[0025] A multi-parameter monitoring network collects real-time data on clamp temperature, vibration, and current. Edge computing is used for data preprocessing before establishing dual-channel redundant transmission. An environmental monitoring unit synchronously collects meteorological parameters, which are then fused with equipment data through a spatiotemporal alignment mechanism. A composite feature matrix is ​​constructed based on historical data and real-time features, and an ensemble learning model is used to calculate risk probability. When an abnormal state is detected, case-based reasoning technology is used to generate handling suggestions, and maintenance data is simultaneously fed back to the model optimization module. Complete operational data is stored using blockchain technology, and transfer learning is used to analyze equipment degradation trends, ultimately forming a closed-loop system for data collection, analysis, early warning, decision-making, and optimization.

[0026] Conventional solutions use a single temperature parameter for monitoring, while this solution captures the combined state of temperature gradient, vibration spectrum, and current carrying capacity through a multi-parameter monitoring network; existing technologies use fixed thresholds for early warning, while this solution introduces an environmental correction factor to achieve dynamic threshold adjustment; traditional systems lack data closure, while this solution continuously improves system performance through maintenance data accumulation and model optimization mechanisms.

[0027] This application effectively solves the problem of data acquisition distortion in strong electromagnetic environments and improves the accuracy of condition assessment through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarms and missed alarms caused by equipment aging, the intelligent decision support module shortens the fault handling response time, and the data closed-loop mechanism ensures the reliability of the system throughout its entire life cycle.

[0028] Example 2: Please refer to Figure 1In 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, while the backup channel transmits key status parameters through LoRa self-organizing network. Establish a time-series database in the cloud, perform CRC verification on the received data and store it, and generate a data integrity report for subsequent analysis.

[0029] In this embodiment: This application further proposes to deploy a distributed temperature sensor array at key temperature measurement points of the color-changing clamp body and adjacent fittings, and to configure vibration sensors and current transformers 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 the temperature gradient distribution of the contact surface, mechanical vibration spectrum characteristics, and current carrying value. A three-level data processing flow is executed in the edge terminal device, using sliding window mean filtering to eliminate random noise, removing abnormal data based on a preset threshold range, synchronizing multi-source data through timestamp alignment, and triggering a resampling mechanism for data that fails verification to ensure the validity of transmitted data. A dual-channel redundant transmission architecture is constructed, with the main channel using a 4G / 5G network to transmit compressed feature data packets, and the backup channel transmitting key status parameters through a LoRa self-organizing network. A time-series database is established in the cloud, and CRC verification is performed on the received data and stored. At the same time, a data integrity report is generated for subsequent analysis and retrieval.

[0030] A distributed temperature sensor array refers to multiple temperature measurement units arranged along key locations such as contact surfaces. This can be implemented using a patch thermocouple array to capture the non-uniformity of temperature distribution. A sliding window mean filtering method calculates the data mean within a set time window, which can be implemented using a fixed-length time window to eliminate random interference signals. A dual-channel redundant transmission architecture establishes two sets of communication links, one primary and one backup. This can be implemented using a heterogeneous network combination to ensure data transmission reliability in extreme environments. CRC checksum refers to a cyclic redundancy check algorithm, which can be implemented using the CRC-32 standard to verify the integrity of transmitted data packets.

[0031] A three-dimensional monitoring network of temperature, vibration, and current sensors is deployed at key connection points of transmission lines to capture transient characteristics of equipment operation at a sampling frequency of no less than 1Hz. Edge computing terminals perform three levels of processing on the raw data: first, sliding window filtering is applied to eliminate random noise; second, abnormal data is removed based on preset thresholds; and finally, multi-source data synchronization is achieved through timestamp alignment. An automatic resampling mechanism is triggered when data verification fails to ensure the validity of the input data. Data transmission adopts a dual-channel mode: the primary channel transmits compressed data packets via a mobile network, while the backup channel transmits key parameters via a low-power wide-area network. The cloud-based receiver performs CRC verification, stores the data in a time-series database, and generates an integrity report for subsequent analysis.

[0032] Conventional monitoring systems use only a single temperature sensor with a low sampling frequency, making it impossible to capture changes in temperature gradients at the contact surface. Existing data processing workflows lack mechanisms for identifying and re-sampling abnormal data, leading to errors in transmitted data. Traditional single-channel transmission architectures are prone to data loss in harsh environments and lack integrity verification mechanisms. This solution, through a multi-parameter monitoring network, a three-level data processing workflow, and dual-channel redundant transmission, effectively improves the accuracy of data acquisition and the reliability of transmission.

[0033] This application addresses the problem of large monitoring data errors under strong electromagnetic interference environments. Through multi-sensor collaborative monitoring and edge computing processing, it reduces the impact of environmental factors on data acquisition. The dual-channel transmission architecture ensures data transmission integrity under different network conditions, and the CRC check mechanism effectively identifies and corrects transmission errors, providing a high-quality data foundation for subsequent early warning models. The abnormal data re-acquisition mechanism avoids data loss due to single acquisition failures, ensuring the continuous and stable operation of the monitoring system.

[0034] Example 3: Please refer to Figure 1 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. The advanced fusion layer establishes an LSTM network based on the Attention mechanism to extract deep coupling features between device and environmental parameters.

[0035] In this embodiment: This application further proposes a specific implementation method for the fusion of environmental and equipment data in step S2, including establishing a meteorological environment monitoring unit, deploying temperature and humidity sensors, wind speed and direction instruments and solar radiometers with anti-electromagnetic interference characteristics, collecting environmental parameters at fixed periods, establishing a time synchronization mechanism with the equipment status monitoring system, achieving time scale alignment through a protocol, constructing a spatial association model, and mapping the coordinates of environmental monitoring points to the equipment topology; using an improved wavelet packet transform to remove high-frequency noise from environmental sensors, designing a dynamic filter for the status data sliding window with the window width adaptively adjusted according to the load change rate, and establishing an abnormal data marking rule base; the primary fusion layer applies 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 the deep coupling features between equipment and environmental parameters.

[0036] Improved wavelet packet transform refers to a method of reconstructing signals through multi-scale decomposition, specifically implemented using Daubechies wavelet basis functions, to eliminate high-frequency noise interference in environmental sensor data. DS evidence theory is a mathematical tool for handling uncertain information; specifically, it can use basic probability assignment functions to fuse multi-source data and resolve sensor data conflicts. Improved Kalman filtering is a filtering algorithm that introduces adaptive noise covariance estimation; specifically, it can achieve spatiotemporal data matching through state transition matrix correction. LSTM networks based on the attention mechanism are long short-term memory networks that combine attention mechanisms; specifically, they can use multi-head attention modules to capture the nonlinear correlation between device and environmental parameters.

[0037] The meteorological and environmental monitoring unit deploys an electromagnetic interference-resistant sensor array to periodically collect environmental parameters and achieves precise time-scale alignment with equipment status data through a time synchronization protocol. In the data preprocessing stage, an improved wavelet packet transform is used to reduce noise in the raw environmental data, while a dynamic filter automatically adjusts the window width according to load changes, effectively eliminating transient interference. During data fusion, the primary fusion layer eliminates conflicts between multi-source data using DS evidence theory, the intermediate fusion layer uses an improved Kalman filter to align data at different spatiotemporal scales, and the advanced fusion layer enhances key feature extraction through an attention mechanism, ultimately forming a feature vector reflecting the coupling relationship between the equipment and the environment.

[0038] Existing solutions fail to consider electromagnetic interference protection during environmental data acquisition and lack precise time synchronization mechanisms, resulting in poor spatiotemporal correlation of data. This solution effectively improves data spatial consistency by deploying dedicated protective sensors and establishing a μs-level timescale alignment mechanism. Traditional filtering methods use fixed parameters to handle noise; this solution significantly improves noise suppression capabilities through dynamic window adjustment and adaptive wavelet packet decomposition. Conventional data fusion often employs a single algorithm; this solution constructs a three-level fusion architecture, combining evidence theory, improved filtering, and deep learning techniques to achieve multi-level feature extraction.

[0039] This application addresses the spatiotemporal inconsistency between environmental monitoring data and equipment status data, reduces the impact of electromagnetic interference and random noise on data quality, and improves the fusion accuracy of multi-source heterogeneous data. By constructing a multi-layered fusion architecture, it effectively extracts the coupling characteristics between equipment operating status and environmental factors, providing more accurate feature inputs for subsequent early warning models, thereby improving the timeliness and reliability of overheating early warning.

[0040] Example 4: Please refer to Figure 1 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.

[0041] In this embodiment, this application further proposes a specific method for constructing an adaptive early warning model: Spatiotemporal correlation data of equipment status, environmental parameters, and fault records are extracted from historical databases; key feature indicators are screened using feature importance analysis; a composite feature matrix containing time-domain statistical features, frequency-domain features, and environmental coupling features is constructed; a training sample set is organized based on a sliding time window mechanism; an ensemble learning architecture is used to fuse the LSTM time-series prediction module and the random forest classification module; an online learning mechanism is introduced; and model parameters are continuously optimized through incremental updates; a dynamic early warning threshold calculation model is established; a hierarchical early warning mechanism is designed; a feedback adjustment module is integrated; and the model sensitivity is automatically adjusted based on the early warning accuracy.

[0042] A composite feature matrix refers to a multidimensional feature set that integrates equipment operating status, environmental conditions, and historical fault data. Specifically, it can be achieved by using the Pearson correlation coefficient method to screen key indicators and by concatenating the time-domain mean and variance, frequency-domain wavelet coefficients, and environmental temperature and humidity parameters into a matrix. This matrix reflects the coupling relationship between equipment operating status and environmental factors.

[0043] The sliding time window mechanism refers to a sample organization method that dynamically extracts continuous monitoring data according to a preset time length. Specifically, it can adopt a variable window width strategy, such as automatically adjusting the window span according to the frequency of load fluctuations, so as to maintain the temporal continuity of training samples. This mechanism can adapt to the dynamic changes in equipment operating status.

[0044] An ensemble learning architecture refers to a hybrid model structure that combines the advantages of different algorithms. Specifically, it can use an LSTM network to extract temporal features, a random forest to handle discrete classification problems, and a weighted voting mechanism to integrate the output results. This architecture can simultaneously handle the temporal characteristics of wire clamp temperature changes and the classification and identification of sudden anomalies.

[0045] The dynamic early warning threshold calculation model refers to a judgment standard that automatically adjusts according to the degree of equipment aging and environmental conditions. Specifically, an exponentially weighted moving average method can be used to establish a baseline, and an environmental correction coefficient can be superimposed to generate a dynamic threshold curve. This model can eliminate the problem of insufficient adaptability of fixed thresholds to the equipment degradation process.

[0046] In the model building phase, firstly, multi-dimensional data such as equipment temperature gradient, vibration spectrum, and ambient temperature and humidity are integrated through feature engineering to form a composite feature matrix. A sliding window mechanism is then used to generate training samples with temporal correlation. Subsequently, a hybrid model of LSTM and random forest is built, and model parameters are updated in real time through an online learning mechanism. In the early warning phase, the dynamic threshold model adjusts the judgment criteria in real time based on equipment runtime and environmental parameters. The feedback adjustment module automatically corrects the model sensitivity based on historical early warning accuracy, forming a closed-loop optimization system.

[0047] Traditional early warning models rely solely on a single temperature threshold and lack environmental parameter correction, making them ill-suited to changes in temperature rise characteristics caused by equipment aging. This solution captures the multidimensional relationship between equipment and the environment through a composite feature matrix and employs a dynamic threshold model to adaptively adjust the early warning standard, thus resolving the misjudgment problem of fixed threshold mechanisms in long-term operating scenarios. Furthermore, compared to single algorithm models, the ensemble learning architecture more effectively handles the mixed patterns of gradual and abrupt changes during wire clamp heating.

[0048] This application can accurately identify early signs of abnormal temperature changes on the contact surface of wire clamps, effectively distinguishing between normal load fluctuations and true fault precursors, and reducing false alarms caused by equipment aging or sudden environmental changes. The dynamic threshold mechanism automatically adjusts the judgment criteria as the wire clamp service life progresses, avoiding the risk of missed alarms caused by fixed thresholds in traditional methods. The hybrid model architecture addresses both temperature trend prediction and sudden anomaly detection, improving adaptability to complex operating conditions.

[0049] Example 5: Please refer to Figure 1 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.

[0050] In this embodiment: This application further proposes that after acquiring 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 in the following specific ways: using sliding time window technology to calculate the equipment status feature vector in real time, inputting the real-time features into the pre-built early warning model through the model inference interface, and outputting a multi-dimensional evaluation result including risk probability value, anomaly type and confidence level; constructing a dynamic safety threshold curve based on the equipment's historical operating data, introducing an environmental correction factor to adjust the threshold in real time, and implementing multi-parameter joint judgment criteria; establishing a three-level early warning system, designing a composite alarm strategy, and realizing the multi-channel synchronous release of early warning information.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] Example 6: Please refer to Figure 1 In step S5, the specific methods of intelligent feedback and decision support are as follows; 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.

[0056] In this embodiment: This application further proposes specific implementation methods in the intelligent feedback and decision support stage, including matching historical fault feature databases based on case reasoning technology, constructing a fault cause probability graph using Bayesian network and outputting a diagnostic report; constructing a knowledge graph of equipment health status and developing an adaptive decision rule engine to generate a decision tree; designing a visual operation guidance interface and generating a structured emergency plan document, while providing voice interactive auxiliary decision-making function.

[0057] Case-based reasoning technology refers to the technique of matching current equipment status characteristics by retrieving historical fault case databases. This can be implemented using similarity calculation algorithms to quickly locate similar fault scenarios. Bayesian networks are techniques for constructing causal reasoning frameworks based on probabilistic graphical models. This can be implemented using conditional probability tables to quantify the correlation between different fault causes. Equipment health status knowledge graphs are databases that express equipment status relationships in a graph structure. This can be implemented using ontology modeling techniques to integrate the complex relationships between equipment operating parameters and environmental factors. Adaptive decision rule engines are computational modules that dynamically adjust decision logic based on real-time data. This can be implemented using fuzzy reasoning systems to generate operating plans adapted to different operating conditions. Visualized operation guidance interfaces are human-computer interaction platforms that convert complex data into graphical displays. This can be implemented using WebGL 3D rendering technology to intuitively present equipment status and operation instructions.

[0058] After acquiring real-time monitoring data, the system first uses a case reasoning engine to retrieve the set of cases with the highest similarity from the historical fault database. It then uses a Bayesian network to calculate the conditional probability distribution of each fault cause, generating a multi-dimensional diagnostic report with probability ranking. Subsequently, a status assessment model is established based on the equipment health knowledge graph. A decision rule engine traverses a preset operation rule base to generate an operation decision tree containing the expected cooling effect and risk coefficient. Finally, the diagnostic conclusions and operation suggestions are converted into a 3D topology diagram through a visual interface. Simultaneously, a standardized emergency plan document is generated, and a voice interaction module is activated to provide step-by-step operation guidance for on-site personnel.

[0059] Compared to existing technologies, traditional systems often employ fixed decision trees or simple rule-matching mechanisms, which cannot dynamically adapt to parameter drift caused by equipment aging. This solution, however, utilizes the synergy of Bayesian networks and knowledge graphs to adjust fault probability weights in real time and effectively reuse historical experience through case-based reasoning. Existing technologies typically rely on static contingency plans, while this solution, through a dynamic adjustment mechanism of its decision rule engine, can generate targeted operational plans based on current load levels and environmental conditions. Existing systems often use tables or simple charts to display information, while this solution, through a combination of 3D visualization and voice interaction, significantly improves the understandability of complex information and the real-time nature of operational guidance.

[0060] This application realizes the intelligent upgrade of fault diagnosis and handling decision-making, effectively solves the problem of poor adaptability of handling solutions caused by rigid decision-making logic in traditional systems, overcomes the response delay defect caused by reliance on human experience, and significantly improves the efficiency and accuracy of emergency handling under abnormal working conditions.

[0061] Example 7: Please refer to Figure 1 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 performance monitoring index system for the model, establish an incremental learning mechanism to automatically update model parameters, perform dual tests to verify the optimization effect and generate optimization suggestions.

[0062] In this embodiment: This application further proposes a method for early warning of overheating and discoloration of transmission line clamps, the specific steps of which are as follows: constructing a time-series database to store the complete operating history of the device, using blockchain technology to ensure data tamper-proof and traceability, and realizing standardized storage and associated indexing of multi-source heterogeneous data; using transfer learning technology to construct a device degradation trend prediction model, developing a fault mode mining algorithm based on association rules, and generating an analysis report containing fault characteristics, occurrence patterns and evolution trends; designing a continuous monitoring index system for model performance, establishing an incremental learning mechanism to realize automatic updating of model parameters, performing dual tests to verify the optimization effect and generating optimization suggestions.

[0063] Time-series databases are database systems that store equipment operating parameters in chronological order. Specifically, they can be implemented using InfluxDB or TimescaleDB, and are used to comprehensively record the time-series changes in equipment temperature, vibration, and current parameters. Blockchain technology uses distributed ledger technology to ensure data immutability. Specifically, it can be implemented using the Hyperledger Fabric framework, using smart contracts to perform data verification and storage operations, solving the problem of traditional databases being vulnerable to malicious tampering. Transfer learning technology is a machine learning method that transfers existing domain knowledge to new tasks. Specifically, it can use pre-trained neural network models for parameter transfer, solving the problem of poor generalization ability of prediction models due to insufficient equipment degradation data. Association rule mining algorithms are algorithms that discover frequent itemsets in a dataset. Specifically, they can be implemented using the Apriori algorithm, extracting the correlation between abnormal clamp temperature and mechanical vibration from historical fault data. Incremental learning mechanisms are mechanisms that dynamically update parameters when the model receives new data. Specifically, they can be implemented using online gradient descent algorithms, enabling the early warning model to continuously adapt to changes in data distribution caused by equipment aging. Dual testing verification refers to a verification method that simultaneously retains the old and new model versions for performance comparison. Specifically, it can be implemented using an A / B testing framework to ensure that the model optimization process does not degrade the original performance.

[0064] Temperature, vibration, and current parameters generated during equipment operation are written to a time-series database in real time. Blockchain nodes generate hash values ​​for each batch of data and store them in a distributed manner. When it is necessary to analyze equipment degradation trends, the transfer learning model extracts common features from the failure data of other similar equipment and trains it using the limited samples of the current equipment. The association rule mining algorithm periodically scans the historical database to identify the association patterns between temperature anomalies and mechanical loosening. The model performance monitoring system continuously tracks prediction accuracy and response latency indicators. When a performance degradation is detected, an incremental learning process is triggered to adjust the model parameters using the latest data. The optimized model needs to be run in parallel with the old version in a test environment, and the optimization effect is confirmed by comparing the early warning accuracy and false alarm rate.

[0065] Traditional solutions use a single database to store data and lack anti-tampering mechanisms, resulting in low data reliability. Conventional degradation prediction models rely on large amounts of labeled data, but the scarcity of fault samples in actual operation leads to insufficient prediction accuracy. Existing fault analysis largely depends on manual experience to summarize patterns, making it difficult to discover implicit correlations between complex parameters. Model updates typically require full retraining, failing to adapt to dynamic changes in equipment status.

[0066] This application enables reliable storage and efficient retrieval of equipment operation data, improves the generalization ability of the degradation trend prediction model in scenarios with insufficient samples, automatically discovers the potential correlation between abnormal wire clamp temperature and mechanical failure, ensures that the early warning model can continuously adapt to the data distribution shift caused by equipment aging, and ensures the safety of the model optimization process through a dual verification mechanism.

[0067] Example 8: Please refer to Figure 1 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.

[0068] In this embodiment: This application further proposes a specific implementation method in the continuous monitoring and model optimization stage, including deploying dynamic monitoring probes to collect key model indicators in real time, building an ELK-based performance monitoring platform and automatically generating model evaluation reports; implementing an online learning algorithm based on FTRL, designing an adaptive learning rate adjustment strategy and performing parameter space Bayesian optimization; establishing a model version management library, implementing a dual-model testing framework for new and old models and adopting a progressive release strategy.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] This application also provides a power transmission line heat discoloration clamp heating early warning system, including a multi-parameter sensing and acquisition module, an edge computing module, a data fusion and 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 collected data through wireless communication technology. The edge computing module collects and merges environmental data and device status data, and uses data fusion algorithms to process the input data to remove noise and uncertainty; The data fusion and 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 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. 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 equipment aging trends and failure modes 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 operational data and the long-term performance of the equipment.

[0074] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0075] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

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 operational data and the long-term performance of the equipment, the early warning model is regularly evaluated and updated.

2. The method for early warning of overheating of transmission line heat discoloration clamps according to claim 1, characterized in that: 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, while the backup channel transmits key status parameters through LoRa self-organizing network. Establish a time-series database in the cloud, perform CRC verification on the received data and store it, and generate a data integrity report for subsequent analysis.

3. The method for early warning of overheating of transmission line heat discoloration clamps according to claim 2, characterized in that: 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. The advanced fusion layer establishes an LSTM network based on the Attention mechanism to extract deep coupling features between device and environmental parameters.

4. The method for early warning of overheating of transmission line heat discoloration clamps according to claim 3, characterized in that: 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.

5. The method for early warning of overheating of transmission line heat discoloration clamps according to claim 4, characterized in that: 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.

6. The method for early warning of overheating of transmission line heat discoloration clamps according to claim 5, characterized in that: 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.

7. The method for early warning of overheating of transmission line heat discoloration clamps according to claim 6, characterized in that: 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 performance monitoring index system for the model, establish an incremental learning mechanism to automatically update model parameters, perform dual tests to verify the optimization effect and generate optimization suggestions.

8. The method for early warning of overheating of transmission line heat discoloration clamps according to claim 7, characterized in that: 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.

9. 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 according to any one of claims 1 to 8, and includes 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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