91 results about "State evolution" patented technology

The invention relates to the technical field of artificial intelligence and computer vision, discloses a subway key component fault detection method and system based on an AI visual large model, and aims to solve the problems of low detection precision, weak generalization ability, insufficient multi-mode understanding, poor real-time performance and lack of state evolution modeling in the prior art. The method comprises the following steps: acquiring images of key components through a multi-view industrial camera array, and performing distortion correction, illumination normalization and noise suppression; a pre-trained visual large model is utilized to extract deep space features, and modeling is carried out on a continuous frame feature sequence through bidirectional LSTM to capture a time sequence change trend. By introducing the large-scale visual large model and spatio-temporal joint modeling, the identification capability of tiny defects is improved, the discrimination stability is enhanced, high-precision and low-delay automatic detection is realized, the false alarm rate and the omission ratio are remarkably reduced, and the detection efficiency and the system maintainability are improved.

The invention relates to a fabricated building management method and system based on BIM and digital twinning. The method comprises the steps that a BIM project model is constructed; generating project plan information; building a project digital twinborn model based on the BIM project model and the project plan information; driving a project flow process to be executed; receiving feedback information of the Internet of Things to perform space-time constraint rule verification, and driving state evolution update of the digital twin model; when the risk information is recognized, the risk type is judged, and an adaptive regulation and control strategy is executed; according to the method, a bidirectional mapping mechanism of the BIM project model and the digital twinborn model is constructed, time-space constraint verification and state evolution updating are carried out on the construction process in combination with real-time data of the Internet of Things, an adaptive regulation and control strategy is triggered when the risk is identified, real-time monitoring and dynamic optimization of the whole process of the fabricated building are realized, and the construction efficiency is improved. The method has the advantages of improving the collaboration of the construction process, reducing the rework rate and guaranteeing the project progress and quality.

The invention discloses a power grid equipment fault diagnosis method based on time sequence modeling and knowledge enhancement reasoning, and the method comprises the steps: extracting multi-modal features of multi-source data of equipment, and carrying out the fusion of the multi-modal features to obtain a fusion feature vector; on the basis of the fusion feature vector, combining static structured parameters of the equipment, and carrying out combined modeling to obtain a state evolution coding vector; based on the fusion feature vector and the state evolution coding vector, multi-path reasoning based on knowledge enhancement is adopted, and the fault confidence degree of each reasoning path is obtained; and carrying out interpretable fusion and adaptive optimization on each fault confidence coefficient to obtain an optimal fault diagnosis strategy. According to the method, the whole process of running state evolution is accurately depicted through fusion of multi-modal features, structuring and time sequence joint modeling; knowledge-enhanced multi-path reasoning is adopted, effective integration of data driving and expert experience knowledge is realized, interpretable fusion and adaptive optimization are carried out, and the accuracy and reliability of fault diagnosis are greatly improved.

The invention discloses a coal mine hidden danger event intelligent reasoning method based on a knowledge graph, and belongs to the technical field of coal mine safety, and the method specifically comprises the steps: obtaining a to-be-deduced entity with a real-time data flow recognition state parameter exceeding a threshold value range; calling a state transition rule set according to the entity type and constructing an independent deduction process; executing state transition calculation in each deduction process to generate a state evolution sequence; state combination matching is carried out based on the relation mode of the static knowledge graph, and entity state pairs meeting relation triggering conditions are recognized; establishing a cross-process data channel between the related deduction processes and performing parameter conversion; state deduction is executed again based on the input parameters, and deduction network topology is constructed; and finally, a complete hidden danger evolution path is obtained through reverse tracking parameter transfer relation integration. According to the method, dynamic deduction of the coal mine hidden danger forming process and accurate identification of the linkage risk conduction path are realized, and the accuracy of potential safety hazard early warning is effectively improved.

The invention discloses a beam guidance optimization method based on position prediction in unmanned aerial vehicle communication, and relates to the technical field of unmanned aerial vehicle communication optimization, and the method comprises the steps: obtaining the flight path data of an unmanned aerial vehicle, carrying out the adaptive segmentation according to the flight path data, and constructing a state evolution model for each flight path; performing short-term prediction and medium-term residual error calibration prediction according to the state evolution model, and constructing a prediction correction term to generate a prediction point set; mapping the prediction point set to a direction coordinate system of a ground communication array to construct an angle evolution tensor, and screening to obtain a candidate beam direction set; and screening a standby beam direction with the maximum path coverage redundancy from the candidate beam direction set. According to the method, a beam guiding optimization mechanism with state identification, adaptive prediction, space tolerance, historical correction and multi-path dynamic regulation and control capabilities can be realized, and the beam control precision, the failure recovery capability and the communication link stability in a complex airspace motion state during communication of the unmanned aerial vehicle are remarkably improved.

The invention discloses a predictive maintenance method for an implicit state of power equipment, and belongs to the technical field of intelligent operation and maintenance of the power equipment. According to the method, multi-mode information such as SCADA data, acoustic vibration data, infrared thermal image data and partial discharge data is collected, space-time alignment and attention mechanism fusion are carried out, and a hidden state code representing the internal health state of equipment is extracted; a dynamic state deduction model combining physical constraint and data driving is constructed, and prediction of the future state evolution trajectory of the equipment is achieved; and performing a virtual maintenance experiment in the digital twin based on a prediction result, and generating an optimal maintenance strategy through multi-objective optimization. According to the method, the problems that the hidden state of the equipment cannot be sensed and the prediction capability is lacked in the prior art are solved, the conversion from passive maintenance to predictive maintenance is realized, and the accuracy and foresight of operation and maintenance of the power equipment are improved.

The invention belongs to the technical field of intelligent patrol of substations, and particularly relates to a positive and negative sample collaborative patrol method and system based on time sequence state evolution analysis. The objective of the invention is to solve the problems of high state jump misjudgment rate, high slow change fault omission rate and poor environmental adaptability caused by independent operation of positive and negative sample algorithms and lack of time sequence state evolution analysis in an existing substation intelligent patrol system. According to the main scheme, the method comprises the steps that firstly, a system obtains and preprocesses equipment image flow and historical data, and a standardized time sequence set containing timestamps, state tags and change vectors is constructed; recent data is aggregated for each equipment state to form a time sequence state chain, and operation logic is embedded to identify illegal hopping; then, through a double-stage cooperation mechanism, a preliminary screening stage inhibits low-confidence-coefficient false alarm, and a trend stage fits a slowly-varying slope to trigger early warning; and finally, dynamically weighting and fusing positive and negative sample outputs according to environmental parameters, and generating a credible score and making a decision. The method is suitable for intelligent operation and maintenance scenes under complex working conditions.

The invention discloses a raw material uniformity mixing control method based on wet flue gas desulfurization gypsum slurry, which comprises the following steps: constructing a time sequence working condition data set by collecting process data such as feeding rate, slurry concentration, particle size distribution and stirring parameters, and extracting main dynamic characteristics of shearing and agglomeration based on an SINDy algorithm; the method comprises the following steps: establishing a state evolution model, inputting a current working condition into the model to generate a state evolution path, collecting shear strength and aggregation degree observation data at key nodes of the path, and identifying an implicit state and an estimation error of a system by adopting a horizon estimation algorithm and combining a historical process trajectory and a control constraint condition. When the prediction deviation exceeds the limit, model structure adjustment and parameter updating are triggered, regulation and control output quantity is generated based on the model deviation, a control instruction is output, dynamic optimization control over the slurry mixing uniformity is achieved, working condition changes can be dynamically adapted, and the slurry mixing uniformity and the process stability are improved.

The invention relates to an RGB-T target tracking method and system based on spatio-temporal state evolution, and the method comprises the steps: constructing a double-branch RGB-T target tracking model, enabling RGB and TIR branches to share the weight of a ViT encoder, employing an iterative processing frame, and transmitting spatio-temporal context information between frames through an updatable context memory Tokens. In each iteration, after RGB and TIR modal features are extracted respectively, cross-modal time sequence context modeling is carried out through a modal perception time sequence Mamba module, and the module realizes long-time target perception representation learning through a cross-modal coupling state transition mechanism and a prompt guide strategy; and carrying out intra-modal and inter-modal feature fusion in a spatial dimension through a cross-modal Mama aggregation module, and finally outputting a target position through a prediction head. According to the method, lasting cross-modal state evolution can be realized with linear complexity, the spatial-temporal characteristics of visible light and infrared light are effectively fused, and the tracking robustness and accuracy are improved in complex scenes such as rapid target movement, shielding or modal degradation.

The invention relates to the technical field of natural resource monitoring and evaluation, and discloses a natural resource dynamic monitoring and evaluation system based on multi-source spatio-temporal data fusion, comprising a semantic mapping module for analyzing a business document to generate a constraint rule and constructing a basic spatio-temporal tensor; the perception strategy module monitors state node evolution and generates an acquisition strategy instruction to drive an external sensor to obtain enhanced perception data; the state evolution module deduces an expected legal state sequence and reconstructs an actual physical state sequence in combination with enhanced perception data; the energy quality fingerprint module establishes nonlinear mapping by using the time sequence energy quality data, and calculates a fingerprint consistency index to verify the authenticity of a physical state; and the deviation risk module calculates a space-time semantic distance and a propagation risk value based on the topological network in combination with the consistency index, and generates a resource audit report. According to the invention, through event-driven active perception and cross-modal energy quality implicit verification, the problems of insufficient monitoring data space-time precision and difficult camouflage compliance identification are effectively solved.

The invention discloses a circuit breaker adaptive protection method based on reinforcement learning, and the method comprises the following steps: collecting the original operation data of a circuit breaker, and carrying out the preprocessing; a DreamerV2 model is constructed; carrying out modeling on a state evolution process by utilizing a world model sub-module in the DreamerV2 model; generating observation reconstruction loss and reward estimation loss according to the potential state sequence and the hidden state sequence; making a decision on the current potential state through a strategy optimization module, and outputting a circuit breaker control action; the control action is applied to the circuit breaker, and a reward value composed of an actual execution result and a feedback state is obtained; and constructing a strategy optimization objective function and a joint loss function to update the strategy network and the world model sub-module respectively. According to the method, the DreamV2 reinforcement learning model is introduced, self-adaptive optimization of the circuit breaker control strategy is achieved, and the method has the advantages of being rapid in response and high in strategy precision.

This invention provides a robotic zero-value insulation detection system based on intelligent sensing, belonging to the field of electrical variable measurement technology. By constructing and solving a state evolution model driven by both intrinsic health state and future external stress, and by generating future health state trajectories, it improves the accuracy and foresight of predicting critical failure events, reducing the risk of unplanned outages caused by sudden insulator failures. Secondly, by conducting in-depth attribution analysis on the dominant stresses that lead to the deterioration of the state trajectory, it provides decision support for power grid operation scheduling and maintenance strategies, enabling resources to be more effectively allocated to the highest-risk links. It enhances the robustness and intelligence of the entire detection system in complex and ever-changing operating environments, promoting the transformation of transmission line operation and maintenance models towards higher-order predictive maintenance and proactive risk management.

This invention provides an intelligent operation and maintenance system for prefabricated substations based on big data, belonging to the field of prefabricated substation operation and maintenance technology. The data access unit unifies the time alignment of electrical operation, environmental monitoring, and historical operation and maintenance data, constructing a constrained time-series data structure based on equipment topology; the state evolution modeling unit extracts multi-scale features, generating three sets of evolution parameters: impedance, capacity, and load; the control variable reconstruction unit generates enhanced control input vectors through component mapping and iterative feedback; the real-time control execution unit outputs reactive power regulation and voltage control commands accordingly; and the human-machine feedback unit embeds manually corrected information and updates control parameters. This invention solves the problems of lack of topological constraints in multi-source data and the difficulty of embedding human experience into automated control, improving the intelligence level, control accuracy, and adaptability of prefabricated substation operation and maintenance.

The application discloses a kind of communication power supply operating state intelligent monitoring methods based on machine learning, comprising the following steps: S1, obtains multiple-source monitoring data and pre-processes;S2, sequence segmentation is carried out using sliding time window and statistical feature vector is extracted, forms state variable set;S3, each state variable is encoded to construct state node and establish weighted directed connection, and construct operating state evolution diagram;S4, state transition matrix is constructed, main transition feature is extracted and low rank is approximately reconstructed, sequence learning is carried out to construct state update function;S5, to state node, multiple-step recursion is carried out, and the Euclidean distance between recursive state and risk boundary is calculated, to determine state monitoring result;S6, error is calculated and state update function parameter is iteratively updated.The application can realize the multiple-step recursion prediction and risk trend identification of communication power supply operating state, improve the accuracy and stability of communication power supply operating state monitoring.

The invention relates to the technical field of carton forming control, in particular to a packaging carton forming control system and method, and the system comprises a structure analysis module, a sequence judgment module, a state matching module, an action constraint module and an instruction generation module. According to the method, the paperboard size and the fold line position are subjected to structured analysis, the section sequence relation is established, the forming sequence can be adaptively adjusted along with the change of the carton structure, the folding process is mapped into the state evolution relation, and action constraint judgment is conducted in combination with the real-time position of an execution mechanism; the broken line actions form a mutual exclusion coordination relation in space and time, so that mechanical interference and sequence conflicts among different forming stages are avoided, forming instructions have continuity and consistency, stable operation is kept under the conditions of specification switching and complex structures, manual intervention requirements are reduced, and the overall forming reliability is improved.

This invention relates to the field of data processing and discloses a method and system for project risk identification, early warning, and control. The method includes: acquiring state parameters of logical state nodes in a multi-dimensional data logical network, constructing state evolution trajectories, and calculating decay acceleration within adjacent time windows; identifying parallel monitoring node pairs sharing a common underlying resource pool and calculating the correlation of decay acceleration; when the correlation is negative and the difference exceeds a threshold, generating directed edges pointing to asymmetric resource offset occupancy in the topology graph and outputting early warning and control instructions. This invention identifies the asymmetry in the decay rate of evolution between parallel nodes, constructs asynchronous decay scissor difference judgment rules, achieves reverse mapping of resource scheduling imbalance, eliminates the blind spots of cross-path resource dependency perception in traditional monitoring systems, locks down the risk source before local fluctuations trigger global cascading failures, and improves the timeliness of early warning.

The invention discloses an intelligent water affair management system based on deep learning, and the system comprises a data collection and preprocessing module which is used for collecting the data of a water supply network and constructing a standardized water affair input tensor; the topology modeling module is used for generating a water supply pipe network diagram structure and diagram structure representation thereof; the state initialization module is used for fusing the input tensor and the graph structure to generate an initial state vector; the state evolution calculation module is used for establishing an augmented state evolution equation and carrying out adaptive integration to generate a prediction state sequence and source sink estimation; the multi-objective decision module is used for constructing a comprehensive cost function and generating a pump station and valve control instruction; the physical constraint projection module is used for executing water pressure, flow velocity and water age constraint projection and outputting an executable scheduling instruction; and the dynamic simulation feedback module is used for performing digital twin simulation and updating the model and sparse estimation weight. According to the invention, fine control and dynamic feedback optimization of the water affair system are realized, and the operation efficiency and the scheduling intelligence level are improved.

The invention provides a big data mining method and device applied to LPDDR performance detection, and the method comprises the steps: receiving an original performance record stream, carrying out the data reconstruction, and generating a time-aligned data reconstruction set; and performing association rule mining on the data reconstruction set, extracting a frequent co-occurrence relationship between performance states in a time dimension, and screening to generate a performance influence association mode set. And mapping the set to a state transition space, constructing a directed weighted performance state evolution graph, and calculating and identifying a key state transition path through graph density. And generating a detection adjustment instruction based on the path, and transmitting the detection adjustment instruction back to a control unit of the detection system so as to reconfigure the sampling frequency and the detection item execution sequence. According to the method, accurate self-adaptive adjustment of the LPDDR detection process is realized by mining the deep association mode of the performance data.

The application discloses a high-dimensional data computing architecture, method, system and device based on recursive projection compression and closed audit. After the method obtains high-dimensional input data, at least one recursive projection compression is performed to form an intermediate audit state representation and / or a decision state representation; a state space deviation function and a state evolution trend prediction operator are calculated on the state representation; dimension hedging processing is performed between different recursive levels, different projection dimensions, different loop representations or different boundary representations, and combined with boundary legality audit, resource closed audit, capacity constraint audit, loop consistency audit, platform flatness audit and stability window audit, a control amount, a compensation amount, a reconstruction amount or a branch adjustment amount is generated; a deterministic early termination is triggered through consistency comparison of parallel audit loops, and in the preferred embodiment, the integrity of the compression result is verified through reverse checking. The application is suitable for industrial monitoring, omics data analysis and other high-dimensional complex system state audit scenarios.

The application discloses a bus duct intelligent online monitoring method and system, relates to the technical field of power equipment operation monitoring, and comprises the following steps: S1, constructing a basic representation set of bus duct operation states; S2, performing state correlation mapping by using the operation state representation set; S3, performing state consistency constraint determination by using a bus duct state association structure; S4, performing state evolution continuity arrangement by using a state consistency determination result; and S5, performing abnormal evolution identification by using an evolvable state sequence. The application sets a state consistency constraint determination mechanism based on the bus duct state association structure, uses a proportional coefficient to describe an expected cooperative change relationship between associated states, generates a structured state consistency determination result, can identify abnormal deviation between the associated states without relying on a single parameter overrun, and thus improves the foresight and effectiveness of bus duct operation abnormality identification.

The application discloses a three-dimensional point cloud change detection method based on state interaction fusion Mamba network, which comprises the following steps: carrying out block and Z-order serialization on double-phase point clouds, and inputting the double-phase point clouds into a Fusion Mamba module; generating selective parameters of a state space model of another phase by using features of one phase, modulating a state evolution process through a bidirectional symmetry mechanism, realizing deep cross-phase interaction fusion, returning features and carrying out differential classification to obtain a change result; the application utilizes the linear complexity characteristic of the Mamba framework, replaces traditional feature splicing through state interaction at the parameter level, effectively models long-range dependence, and completes three-dimensional point cloud change detection; the application significantly reduces the calculation complexity to O (N), solves the problem of high memory occupation of the Transformer method, and improves the discrimination of double-phase feature fusion through deep state modulation, thereby realizing high-precision and low-resource-consumption change detection in a large-scale urban scene.

The invention relates to the technical field of solid oxide electrolytic cell control, and particularly discloses an S0EC electrolysis system multi-parameter cooperative control method based on a neural network. The method implements a rolling optimization process including inner ring state prediction and outer ring strategy generation. In each control period, firstly, a refined prediction sequence of a future system state is obtained based on a current operation parameter through a state evolution prediction network; the strategy generation network is combined with the prediction sequence and an external target instruction, and a dynamic weight coefficient is generated after adjustment of a dynamic strategy optimizer; the multi-target instruction solver uses the weight coefficient and the state prediction sequence to solve and execute an optimal cooperative control instruction sequence under the physical constraint of the system; and finally, synchronously updating the internal parameters of the prediction network and the strategy generation network on line by utilizing a prediction error generated after execution. According to the method, prospective collaborative optimization control on multiple parameters of the SOEC system is realized, and multiple targets such as operation efficiency, equipment service life and dynamic response are effectively balanced.

This invention discloses a computer-based method for evaluating the complexity of the working environment of unmanned ground vehicles (UGVs). First, it determines environmental complexity evaluation indicators and establishes an evaluation indicator system. Then, it acquires and normalizes multi-source environmental data using onboard sensors and a deep learning model. Next, it determines the weights of static environmental indicators using the Analytic Hierarchy Process (AHP) and calculates the static environmental complexity index using a nonlinear mapping function. Simultaneously, it establishes a potential energy function incorporating distance, speed, and predicted collision time, calculating the total environmental potential energy to output the dynamic environmental complexity index. For the state evolution complexity evaluation indicator, it constructs a state evolution complexity quantification model based on state change entropy, outputting the state evolution complexity index. Finally, it outputs the working environment complexity index by establishing a multi-dimensional coupled complexity model. This invention achieves real-time quantification and evaluation of working environment complexity, providing reliable data for autonomous decision-making, path planning, and safety control of UGVs.

The invention discloses a heterogeneous data intelligent evolution analysis system based on time sequence track characteristics, and relates to the technical field of artificial intelligence and intelligent decision making. Comprising the steps that a state data acquisition module periodically acquires multi-source state data of a target object; the state vectorization module is used for converting the multi-source data into a state data vector with a uniform format; the time sequence construction module arranges a plurality of state data vectors to form an object state time sequence; an evolution path extraction module extracts a state change relation from the time sequence to generate a current state evolution path; the deviation judgment module compares the current state evolution path with a predefined reference state evolution path and calculates an evolution deviation degree; the decision generation module generates a decision instruction according to the evolution deviation degree threshold value and the dominant trend type; and the decision feedback module corrects the path matching model or the reference state evolution path according to the updated data. According to the method, quantitative modeling, intelligent evolution analysis and adaptive decision optimization of a complex dynamic process are realized.

The invention relates to the technical field of state monitoring, in particular to a highway construction site state monitoring method and system based on the Internet of Things, and the method comprises the following steps: collecting the operation starting ending and waiting time sequence of multiple construction machines, carrying out the time alignment, carrying out the statistics of the operation starting times and the operation frequency, and forming an operation time sequence; and analyzing a state change relationship based on the duration, calculating operation and waiting transfer characteristics, generating a collaborative state by integrating the dispersion degree of multiple mechanical states, analyzing the change trend of the collaborative state according to a monitoring period, constructing a state evolution chain, and forming a continuously updated state monitoring result of the construction site. According to the method, through analysis of the operation time sequence and state transition relation, continuous description and change correlation recognition of the operation state of the construction machinery are achieved, dynamic cognition of the multi-machinery cooperation state is formed, the operation and waiting switching rule is reflected with the help of trend characteristics, more detailed state support is provided for construction progress evolution, and the construction progress evolution efficiency is improved. And the judgment accuracy and response efficiency of field management are improved.

The invention provides a metal forming process macro-micro online reconstruction method based on digital twinning. The method comprises the steps that a state evolution database is constructed; selecting a training set from the state evolution database and training a macro-micro evolution prediction model by using the training set, the macro-micro evolution prediction model comprising a space-time diagram neural network and a forward prediction network which are respectively used for performing macro-micro state evolution prediction of each substance point in each time step, predicting physical state evolution of each substance point between adjacent time steps; in a production process, acquiring a process parameter combination measurement value in real time and inputting the process parameter combination measurement value into the macro-micro evolution prediction model; and carrying out visual three-dimensional reconstruction on the physical state evolution process of each substance point based on the output of the macro-micro evolution prediction model. According to the method provided by the invention, three-dimensional reconstruction based on digital twinning can be performed on macro-micro state evolution of the whole area of the metal material in a high-density and high-precision manner.

The invention relates to an AI-driven kitchen garbage dynamic monitoring and efficient conversion method and system, and the method comprises the steps: collecting multi-modal real-time sensing signals of kitchen garbage in a processing container, constructing multi-modal state data, and extracting a dynamic feature set from the data, and the kitchen garbage is structured and recombined and fused with a historical state evolution track to generate a comprehensive state vector, an intelligent decision model is input to output a processing strategy adjustment instruction, operation is executed according to the instruction, and a judgment signal is generated to indicate whether the kitchen garbage meets efficient conversion conditions or not. According to the scheme, the kitchen garbage treatment process can be accurately monitored, regulated and controlled, and the conversion efficiency and stability are improved.

The invention relates to the technical field of intelligent manufacturing and industrial software, and discloses a production plan scheduling collaborative optimization method and system based on MOM and multi-source data, and the method comprises the steps: constructing an equipment initial time-varying capability constraint envelope; extracting processing task demand features and calculating a processing effect residual vector; superposing the residual vectors to an equipment state step by step based on a state evolution equation so as to update an envelope in real time, and executing inclusion detection of a demand feature and the envelope; when the detection result is that the detection result is not included, identifying a physical dimension causing envelope narrowing, and generating an active recovery virtual task insertion sequence to remodel an equipment state track until the inclusion requirement is met; and finally generating a machine instruction code to execute closed-loop control. According to the method, a bidirectional coupling mechanism of task execution and equipment physical state evolution is established, online active recovery of equipment capability is realized by using an active recovery strategy, and high precision and stability of heavy equipment manufacturing are guaranteed.

This invention provides a method and system for transmission line inspection and analysis based on digital twins. The method involves: using a digital twin object identifier as the core, uniformly solidifying the transmission line structure and constructing an initial static line structure model; based on continuously collecting multi-source sensing data and forming a time-series operational observation set, constructing a multi-physics coupled evolution model that simultaneously characterizes icing and melting, thermal equilibrium response, and mechanical evolution; identifying hidden risks by jointly discriminating the state evolution trajectory of key state variables and their historical reference distribution; and conducting parameterized perturbations and multi-scenario simulations to assess operational reliability risks. Finally, the risk development trend is reflected back into the spatial line model, generating forward-looking inspection and analysis results. This invention can perform line inspections on transmission lines that are under long-term alternating thermo-mechanical coupling conditions of icing and melting.