Multi-specification line clamp precision and conductivity integrated aging test system

Abstract

The application relates to the technical field of power equipment state monitoring, and particularly discloses a multi-specification line clamp precision and conductivity integrated aging test system, which comprises a multidimensional data acquisition system, which is used for acquiring multi-dimensional state characteristic data of a line clamp, and triggering a double-layer verification mechanism for the abnormal state of the line clamp when detecting a suspected abnormal signal based on the multi-dimensional state characteristic data; and a power grid linkage analysis system, which is used for calculating a dynamic prediction result of the line clamp through a three-dimensional coupling model combined with real-time operation data of a power grid after the double-layer verification mechanism confirms that the line clamp is in an actual abnormal state. The double-layer verification mechanism of local density scoring and high-frequency secondary sampling is introduced, contradictions between monitoring sensitivity and false alarm rate are solved, and false early warnings caused by normal system fluctuations are greatly filtered on the premise of guaranteeing early micro-hidden danger capturing capacity.

Description

Technical Field

[0001] This invention relates to the field of power equipment condition monitoring technology, and in particular to an integrated aging test system for the accuracy and conductivity of multi-specification wire clamps. Background Technology

[0002] With the continuous expansion of the power grid and the increase in high-load operation conditions, the multi-specification clamps in the transmission lines are prone to nonlinear aging phenomena such as geometric creep and increased contact resistance under the long-term stress of multiple physical fields such as electricity, heat, mechanical and external environment, which can lead to clamp overheating or even melting failure.

[0003] Existing wire clamp condition monitoring methods mostly rely on single-dimensional feature acquisition and static threshold alarms, which cannot accurately characterize the multi-stress coupling degradation patterns of wire clamps under complex operating conditions. This traditional monitoring and maintenance mechanism faces irreconcilable technical conflicts in practical applications:

[0004] On the one hand, there is a conflict between monitoring sensitivity and false alarm rate in the anomaly identification stage. Increasing sensitivity makes it easy to be disturbed by normal fluctuations in the power grid and generate a large number of false alarms, while reducing sensitivity will miss early weak precursor signals.

[0005] On the other hand, when the online clamp is on the verge of sudden failure, there is a physical conflict between the linear prediction model and the nonlinear avalanche damage. The traditional linear cumulative damage model cannot truly reflect the thermal quenching effect caused by the simultaneous occurrence of extreme weather and extreme loads, leading to an overly optimistic prediction of the remaining life.

[0006] Even more critically, when dealing with extreme emergency situations, if the monitoring system detects that a certain clamp is facing an extremely high risk of melting and forcibly disconnects the line it is on, the huge load to be transferred will be instantly transferred to the adjacent line. If the real-time thermal stability margin of the target transfer line is not considered in advance, this mechanical local protection action can easily cause the adjacent line to trip instantly due to overload, thereby triggering a domino-like cascading collapse of the regional power grid and a major blackout. Summary of the Invention

[0007] This invention aims to at least partially address one of the technical problems in related technologies. Therefore, the objective of this invention is to propose an integrated aging test system for the accuracy and conductivity of multi-specification wire clamps, thereby improving the overall resilience and safety of power grid operation.

[0008] To achieve the above objectives, a first aspect of the present invention provides an integrated aging test system for the accuracy and conductivity of multi-specification wire clamps, comprising:

[0009] A multi-dimensional data acquisition system is used to collect multi-dimensional state feature data of wire clamps, and when a suspected abnormal signal is detected based on the multi-dimensional state feature data, a two-layer verification mechanism for the abnormal state of the wire clamp is triggered.

[0010] The power grid linkage analysis system is used to calculate the dynamic prediction result of the clamp by combining a three-dimensional coupling model with real-time power grid operation data after the dual-layer verification mechanism confirms that the clamp is in an actual abnormal state.

[0011] The scheduling and coordination decision-making system is used to generate operation and maintenance decisions that are coordinated with the power grid operation based on the dynamic prediction results, so as to perform early warning or power outage maintenance of the line clamp;

[0012] The process of generating the suspected abnormal signal and confirming it using the two-layer verification mechanism includes:

[0013] Calculate the average distance between the current feature sample and its historical neighboring feature samples based on the multidimensional state feature data, and calculate the local density score based on the average distance;

[0014] If the local density score is less than the preset density threshold, the suspected abnormal signal is generated, and multiple sets of the multidimensional state feature data are continuously collected at a higher sampling frequency than the current one for secondary calculation.

[0015] If the local density score obtained from the second calculation is still less than the density threshold, then the clamp is confirmed to be in the actual abnormal state.

[0016] To achieve the above objectives, a second aspect of the present invention proposes an integrated aging test method for the accuracy and conductivity of multi-specification wire clamps, the method comprising:

[0017] Collect multi-dimensional state feature data of the clamp, and when a suspected abnormal signal is detected based on the multi-dimensional state feature data, trigger a two-layer verification mechanism for the abnormal state of the clamp.

[0018] After the dual-layer verification mechanism confirms that the clamp is in an actual abnormal state, the dynamic prediction result of the clamp is calculated by combining the three-dimensional coupling model with the real-time operation data of the power grid.

[0019] Based on the dynamic prediction results, operation and maintenance decisions are generated in coordination with the power grid operation to perform early warning or power outage maintenance of the line clamps;

[0020] The process of generating the suspected abnormal signal and confirming it using the two-layer verification mechanism includes:

[0021] Calculate the average distance between the current feature sample and its historical neighboring feature samples based on the multidimensional state feature data, and calculate the local density score based on the average distance;

[0022] If the local density score is less than the preset density threshold, the suspected abnormal signal is generated, and multiple sets of the multidimensional state feature data are continuously collected at a higher sampling frequency than the current one for secondary calculation.

[0023] If the local density score obtained from the second calculation is still less than the density threshold, then the clamp is confirmed to be in the actual abnormal state.

[0024] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the above-described integrated aging test method for the accuracy and conductivity of multi-specification wire clamps.

[0025] The multi-specification wire clamp accuracy and conductivity integrated aging test system of this invention effectively overcomes many technical conflicts in the prior art and realizes high-precision, globally safe and highly adaptive wire clamp aging monitoring and collaborative operation and maintenance.

[0026] First, by introducing a two-layer verification mechanism of local density scoring and high-frequency secondary sampling, the contradiction between monitoring sensitivity and false alarm rate is resolved. While ensuring the ability to capture early minor hidden dangers, false warnings caused by normal system fluctuations are greatly filtered out.

[0027] Secondly, by using the correction module and the associated monitoring array, the limitations of traditional linear prediction are broken, and the quenching damage index induced by extreme weather can be accurately captured and quantified, achieving nonlinear accelerated convergence calculation and effectively seizing the intervention time window before sudden failure.

[0028] Finally, by introducing a cascading fault prevention and active physical cooling mechanism, the thermal stability margin of the target line is actively checked before triggering an emergency power outage transfer. When the margin is insufficient, the generation of a grid safety interlock signal, the issuance of a local disconnection command, and the activation of the active cooling execution device are combined to force cooling. This not only avoids the risk of grid cascading collapse caused by blind load transfer, but also provides critical physical safety time for the dispatching system to transfer power across regions. It completely eliminates the dispatching conflict between local equipment protection and global grid stability, and significantly improves the overall resilience and safety of grid operation. Attached Figure Description

[0029] Figure 1 This is a schematic diagram illustrating the implementation of the multi-specification wire clamp accuracy and conductivity integrated aging test system provided by the present invention;

[0030] Figure 2 This is a scatter plot of the local density scoring and double-layer anomaly verification mechanism based on multi-dimensional feature spatial distance in the multi-specification wire clamp accuracy and conductivity integrated aging test system provided by the present invention.

[0031] Figure 3 This is a comparison curve of the dynamic geometric critical value attenuation of the wire clamp under standard working conditions and extreme multi-stress coupling working conditions in the multi-specification wire clamp accuracy and conductivity integrated aging test system provided by the present invention.

[0032] Figure 4 This is a normalized weighted three-dimensional response surface plot of the surface and internal temperature difference gradient and acoustic emission impact count on the quenching damage index in the multi-specification wire clamp accuracy and conductivity integrated aging test system provided by the present invention.

[0033] Figure 5 This is a comparison curve of linear decay prediction and nonlinear avalanche rapid convergence lifetime based on the natural logarithm base in the multi-specification wire clamp accuracy and conductivity integrated aging test system provided by the present invention.

[0034] Figure 6 This is a diagram showing the nonlinear evolution of wire clamp temperature rise blocking and geometric creep rate delay before and after intervention by the active cooling actuator in the multi-specification wire clamp accuracy and conductivity integrated aging test system provided by this invention.

[0035] Figure 7 This invention provides a full-time waveform diagram of the dynamic evolution of the real-time thermal stability margin of the target transfer line and the release of the power grid safety interlocking interception in the integrated aging test system for the accuracy and conductivity of multi-specification wire clamps.

[0036] Figure 8 This is a flowchart illustrating the integrated aging test method for the accuracy and conductivity of multi-specification wire clamps provided by the present invention.

[0037] Figure 9 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0038] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0039] The following description, with reference to the accompanying drawings, describes an integrated aging test system, method, and electronic device for multi-specification wire clamp accuracy and conductivity according to embodiments of the present invention.

[0040] Example 1:

[0041] like Figure 1 As shown, this embodiment provides an integrated aging test system for the accuracy and conductivity of multi-specification wire clamps, including a multi-dimensional data acquisition system, a power grid linkage analysis system, and a dispatching collaborative decision-making system.

[0042] A multi-dimensional data acquisition system is used to collect multi-dimensional state characteristic data of the clamps, and when a suspected abnormal signal is detected based on the multi-dimensional state characteristic data, a two-layer verification mechanism for the abnormal state of the clamps is triggered; a power grid linkage analysis system is used to calculate the dynamic prediction result of the clamps by combining a three-dimensional coupling model with real-time power grid operation data after the two-layer verification mechanism confirms that the clamps are in an actual abnormal state; a scheduling and collaborative decision-making system is used to generate operation and maintenance decisions coordinated with power grid operation based on the dynamic prediction results, so as to execute early warning or power outage maintenance of the clamps.

[0043] To ensure the comprehensiveness and accuracy of the aforementioned testing system's sensing capabilities from the source, it is essential to collect all physical characteristics of the clamp under complex operating conditions without any blind spots. Specifically, the multi-dimensional state characteristic data collected by the multi-dimensional data acquisition system includes: geometric asymptotic characteristic data, electrical performance data, electrical micro-precursor characteristic data, multi-physics environment data, and power grid characteristic data.

[0044] Among them, geometric asymptotic feature data refers to the macroscopic and microscopic deformation parameters of the wire clamp under long-term alternating mechanical and thermal stress. Geometric asymptotic feature data includes, but is not limited to, the effective contact area loss rate of the wire clamp contact interface, the absolute deformation of key stress nodes of the wire clamp, and the geometric creep rate of the wire clamp material. To obtain extremely high-precision geometric asymptotic feature data, the multi-dimensional data acquisition system is equipped with a three-dimensional laser scanning module based on the multi-beam interference principle and a contact interface micrometer.

[0045] Electrical performance data refers to the core electrical parameters reflecting the current conductivity of the wire clamp, mainly including the contact voltage drop across the two ends of the wire clamp, the overall circuit resistance, and the dynamic impedance spectrum. These electrical performance data directly determine whether the wire clamp will generate additional Joule heating due to poor contact.

[0046] Electrical micro-precursor characteristics refer to extremely brief and weak abnormal electromagnetic or thermodynamic features generated at the local microscopic level before macroscopic electrical failure occurs in wire clamps. Specifically, these characteristics include the trigger frequency of high-frequency micro-arc pulses, the current amplitude of the micro-arc pulses, and the local micro-temperature rise rate at the contact surface. These precursor signals typically last only a few microseconds to a few milliseconds, making them excellent indicators for detecting early-stage problems in wire clamps.

[0047] Multiphysics environment data refers to the external environmental stress parameters within the spatial range of the clamp, encompassing real-time ambient temperature, relative humidity, mechanical vibration frequency and amplitude caused by wind load, and concentration indicators of corrosive gases or salt spray in the air. Power grid characteristic data refers to the operational attributes of the transmission line where the clamp is located within the overall power grid topology, such as the line's allocated rated current carrying capacity, historical peak load records, and the importance level of its current topology node.

[0048] It is also important to note that, since the multidimensional state feature data originates from different types of sensors, and precursor signals such as micro-arcs are transient high-frequency signals, any minute time alignment error can lead to serious logical errors in the subsequent 3D coupled model when extracting causal relationships. Therefore, the multidimensional data acquisition system uses an atomic clock as a reference, simultaneously activating all acquisition devices for hardware synchronization via a trigger circuit, and employs a timestamp calibration algorithm to perform secondary software correction on the timestamps of the raw data from each acquisition device.

[0049] Specifically, atomic clocks possess extremely high oscillation frequency stability, providing a fundamental time reference with nanosecond-level accuracy. The trigger circuit utilizes high-speed optocoupler isolation devices and low-delay differential signal transmission lines to synchronously distribute the global clock signal generated by the atomic clock to all front-end acquisition devices, including temperature sensors, laser scanners, micro-ohmmeters, and micro-arc detectors, in a star topology. When the global trigger level arrives, all acquisition devices latch the current physical quantity within the same nanosecond-level time window, thus achieving stringent hardware synchronization.

[0050] Although hardware synchronization can eliminate errors at the start of data acquisition, the processing speed of analog-to-digital converters within different sensors and data bus queuing delays can still cause slight offsets in data packet timestamps. Therefore, a timestamp calibration algorithm must be introduced. The core formula of the timestamp calibration algorithm is:

[0051] ;

[0052] In the formula, This represents the actual absolute timestamp after secondary software correction; This represents the timestamp of the original record uploaded by each underlying acquisition device along with the data packet; This indicates the amount of nonlinear clock drift compensation caused by the temperature drift of the crystal oscillator inside the device; This represents the amount of fixed network delay compensation caused by the queuing and transmission of data commands in the communication bus. Through this timestamp calibration algorithm, multidimensional state feature data are strictly aligned to the same time segment, forming a rigorous five-dimensional multi-scale data array.

[0053] After completing the high-precision multi-dimensional state characteristic data acquisition, the system needs to perform real-time screening of massive amounts of data to determine whether there are early potential problems with the clamps. Considering that there are a large number of normal load switching inrush currents and lightning electromagnetic interference in actual power grid operation, false alarms in the monitoring system are very likely to be triggered. Therefore, this invention introduces a two-layer verification mechanism based on local density. The generation of the suspected abnormal signal and the confirmation process of the two-layer verification mechanism include:

[0054] The average distance between the current feature sample and its historical neighboring feature samples is calculated based on the multidimensional state feature data, and a local density score is calculated based on the average distance. If the local density score is less than a preset density threshold, a suspected abnormal signal is generated, and multiple sets of the multidimensional state feature data are continuously collected at a sampling frequency higher than the current frequency for secondary calculation. If the local density score obtained from the secondary calculation is still less than the density threshold, the clamp is confirmed to be in the actual abnormal state.

[0055] Specifically, the system first extracts multidimensional state feature data from the current time segment to form the current feature sample vector, and then extracts multiple historical feature sample vectors from the historical database within the past sliding time window as historical adjacent feature samples. The system uses a variant algorithm combining multidimensional Euclidean distance and Mahalanobis distance to calculate the distance between samples. The calculation formula is as follows:

[0056] ;

[0057] In the formula, Indicates the current feature sample and the first The weighted spatial distance between historical adjacent feature samples; This represents the total number of feature dimensions in multidimensional state feature data. Indicates the first The contribution weight coefficients of each feature dimension in anomaly detection; Indicates the current feature sample is at the th . Feature values ​​in each dimension; Indicates the first The historical adjacent feature samples in the first Feature values ​​in each dimension.

[0058] Furthermore, the system selects several historical neighboring feature samples that are closest to the current feature sample, calculates the arithmetic mean of these distances, defines it as the average distance, and uses variables... The system then calculates the local density score using an inverse proportional function mapping, with the following formula:

[0059] ;

[0060] In the formula, This is known as local density scoring. The physical meaning of this score is: if the current clamp state is highly consistent with the historical normal state, then... Extremely small A value close to 1 indicates that the data point is located in a high-density clustering area of ​​normal samples; if there is a slight contact failure or accelerated creep inside the clamp, the current feature sample will deviate from its normal trajectory in multidimensional space. Significantly increased, leading to The ratings plummeted.

[0061] It's also important to note that the system has a pre-defined density threshold based on the statistical distribution of historical baseline data. This threshold is used when the local density score is calculated in real-time. When the density score first falls below the preset threshold, the system determines that the clamp is deviating from its normal state. To prevent this deviation from being a false alarm caused by transient electromagnetic interference, the system generates a suspected abnormal signal. This signal does not immediately trigger an alarm; instead, it serves as an internal instruction, forcing the multi-dimensional data acquisition system to break its conventional timed sampling mechanism and instantly increase the sampling frequency by 10 to 100 times. The system continuously captures multiple sets of the latest multi-dimensional state feature data at this extremely high sampling rate and recalculates them using the aforementioned formula. If the local density score calculated from multiple high-frequency samplings remains consistently below the density threshold, it indicates that the deviation is not only significant in the spatial dimension but also exhibits continuity and stability in the temporal dimension. Only then does the system officially confirm that the clamp is in the actual abnormal state. Therefore, this two-layer verification mechanism effectively addresses the technical challenge of balancing sensitivity and false alarm rate in traditional single-threshold alarm systems.

[0062] For example, once the clamp is confirmed to be in an actual abnormal state, the system will immediately activate the powerful power grid linkage analysis system. The process by which the power grid linkage analysis system calculates the dynamic prediction results of the clamp includes: generating quantitative results by extracting spatial correlation features and temporal evolution laws from the multidimensional state feature data. The quantitative results include the probability of triggering an electrical sudden failure in the current geometric progression stage, the dynamic geometric critical value for triggering an electrical sudden failure, and the remaining time from the current state to the electrical sudden failure.

[0063] Specifically, the power grid linkage analysis system incorporates a three-dimensional coupled model that includes a convolutional neural network and a long short-term memory network architecture. The model first uses convolutional layers to scan the receptive field of the multi-dimensional state feature data matrix, capturing spatial correlation features such as the relationship between reduced contact area and increased local temperature rise rate. Subsequently, the long short-term memory network processes historical time-series data, memorizing the temporal evolution of clamp geometric creep over time. After focusing on the core features with the greatest impact on failure through a self-attention mechanism, the model outputs three highly significant quantitative results for engineering guidance. The probability of triggering a sudden electrical failure at the current geometric progression stage is used to measure the urgency of the current state; the dynamic geometric critical value for triggering a sudden electrical failure defines the physical bottom line for irreversible melting and damage to the clamp; and the remaining time directly provides power grid dispatchers with a valuable countdown for emergency repairs.

[0064] It is important to understand that traditional clamp failure prediction models are often based on assumptions of constant load and ideal weather conditions, and the given critical values ​​are static theoretical values. However, in real power grids, severe fluctuations such as strong winds, heavy rain, grid overload, and line short circuits can instantly destroy the clamp's withstand capability, causing catastrophic breakage before reaching the static theoretical critical value. Therefore, this invention uses a multi-dimensional dynamic correction formula to forcibly inject real-time macroscopic fluctuations of the power grid into the calculation of the clamp's microscopic failure critical value. Specifically, the dynamic geometric critical value is calculated by multiplying the initial geometric critical value under standard operating conditions, the load influence coefficient, the short-circuit impact correction factor, the voltage fluctuation coefficient, the topology adjustment influence coefficient, and a meteorological correction function including wind speed and rainfall intensity. The formula for calculating the dynamic geometric critical value is as follows:

[0065] ;

[0066] In the formula, Indicates the wire clamp is The dynamic geometric critical value that can be withstood under specific complex working conditions at all times; This represents the initial geometric critical value of the clamp under standard operating conditions at the factory; the subsequent series of coefficients are the power grid impact factors, used to penalize or compensate for the critical value.

[0067] Specifically, the load impact coefficient, short-circuit impact correction factor, voltage fluctuation coefficient, and topology adjustment impact coefficient used by the power grid linkage analysis system are calculated based on standardized and quantified real-time power grid operation data. This includes: calculating the load impact coefficient based on the deviation ratio between the actual current and rated current of the clamp; calculating the short-circuit impact correction factor based on the number of short-circuit fault impacts and the fault duration; calculating the voltage fluctuation coefficient based on the voltage fluctuation value of the power grid node; and determining the topology adjustment impact coefficient based on the power grid topology adjustment status and corresponding impact coefficient.

[0068] For example, the calculation logic for the load influence factor is as follows: Let the actual current of the clamp be... The rated current carrying capacity of the clamp is Then the deviation from the proportion The formula for calculating the load influence factor is:

[0069] ;

[0070] In the formula, This is a weighted parameter sensitive to material heat load. When the actual current exceeds the rated current... When the Joule temperature is positive, it increases exponentially, causing the material to soften at high temperatures. The system utilizes this formula to... A value greater than 1 is used to adjust the expected load-bearing limit of the wire clamp in the model.

[0071] For example, the calculation logic of the short-circuit impact correction factor is as follows: The huge electrodynamic force generated by the short-circuit current will instantly tear apart the mechanical engagement structure of the clamp. Let the number of short-circuit fault impacts within one observation period be... The duration of the most recent fault was The baseline tolerance time is The formula for calculating the short-circuit impact correction factor is:

[0072] ;

[0073] In the formula, This is the electrodynamic damage factor. This formula ensures that for each short-circuit impact, the remaining mechanical tensile strength threshold of the clamp is reduced by one.

[0074] For example, the voltage fluctuation coefficient calculation logic is as follows: If the voltage at a grid node experiences frequent and severe drops or transient overvoltages, it will break down the oxide film on the surface of the clamp, accelerating electrochemical corrosion. Based on the voltage fluctuations at the grid node... With rated voltage The ratio of voltage fluctuation coefficient The calculation formula is

[0075] ;

[0076] In the formula, The aging coefficient is accelerated by partial discharge.

[0077] For example, the topology adjustment influence coefficient This is a discrete variable based on the power grid dispatch action state machine. When the power grid topology is in a stable operating state, the influence coefficient is assumed to be a base value of 1. When the power grid performs large-scale power flow transfer or main transformer switching operations, the resulting operational overvoltages and instantaneous load redistribution pose a significant challenge to the clamps. The system will adjust the load based on the severity of the power grid topology adjustment. The value is then updated upwards to the corresponding penalty multiplier.

[0078] Meteorological correction functions that include wind speed and rainfall intensity This function aims to characterize the wind-polarized tearing effect on wire clamps caused by severe external weather and the accelerated oxidation effect caused by rainwater intrusion. It receives wind speed data and precipitation data (in millimeters) from weather stations and outputs correction coefficients through an internal lookup table or polynomial fitting. Multiplying all these coefficients together completes a cross-scale dimensionality reduction calculation from the macroscopic power grid situation to the microscopic wire clamp critical value, completely reversing the blind optimism of traditional forecasting models.

[0079] Next, once the power grid linkage analysis system outputs accurate dynamic prediction results, the dispatching collaborative decision-making system takes over the overall process. Specifically, the power grid dispatching plan accessed by the dispatching collaborative decision-making system includes peak load periods, maintenance windows, and line topology adjustment plans for a preset future time period; the rules for generating operation and maintenance decisions include a multi-condition logic judgment strategy, and the specific judgment process is as follows:

[0080] When the probability of the electrical sudden failure is confirmed to be less than the first warning threshold, the original sampling frequency is maintained for continuous monitoring; when the probability is greater than or equal to the first warning threshold and less than the second warning threshold, and it is during a non-peak load period and there is no maintenance window, a second-level warning is issued and the maintenance window is requested.

[0081] When the probability is greater than or equal to the second warning threshold and less than the third warning threshold, and it is during a non-peak load period, a level three warning is issued and a temporary power outage for maintenance is arranged.

[0082] When the probability is greater than or equal to the third warning threshold, an emergency power outage operation is triggered, and the clamp replacement and synchronous load transfer are performed first; wherein, the first warning threshold is less than the second warning threshold, and the second warning threshold is less than the third warning threshold.

[0083] This multi-condition logic-based decision-making strategy fully respects the ironclad rule that power supply to the grid cannot be arbitrarily interrupted. Instead of resorting to the crude method of shutting down the grid at the first sign of an anomaly, the system is deeply integrated with the grid dispatch plan. The system connects to the dispatch terminal during peak load periods for the next 24 or 72 hours, as well as pre-planned maintenance windows. When the probability of failure is extremely low, just below the first warning threshold, it indicates that the clamp is still in the very early stages of geometric creep, and the system remains silent and performs routine monitoring without interfering with any grid operation.

[0084] When the probability of failure exceeds the first warning threshold but remains below the second warning threshold, a potential hazard has been identified. If, by this time, the power grid is not in a peak load period with tight power supply, and there are no relevant maintenance windows scheduled in the near future, the system will proactively issue a second-level warning order to the superior dispatch center and automatically generate maintenance applications using artificial intelligence algorithms, thus preemptively securing future line maintenance resources and preventing problems before they occur.

[0085] When the failure probability further deteriorates, exceeding the second warning threshold but falling below the third warning threshold, the micro-cracks or contact resistance inside the clamp have already undergone substantial damage. At this point, if it is still during a non-peak load period, the system will no longer wait for the regular maintenance window, but will immediately issue a very high-level Level 3 warning, forcing the maintenance team to prepare tools immediately and arranging a brief temporary power outage maintenance order, nipping the danger in the bud before it finally erupts.

[0086] When the failure probability skyrockets, ignoring all prior interventions, and eventually exceeds or equals the third warning threshold, it means the dynamic geometric critical value of the clamp has been breached. Internal through-arc or physical fracture can occur within minutes or even seconds. At this point, any waiting is catastrophic. The system will directly trigger the highest-level emergency power outage command, forcibly cutting off the main circuit current to the clamp, preventing a large-scale wildfire or electric shock accident caused by a broken wire falling. Simultaneously, the system, through the scheduling network, prioritizes synchronous load transfer to adjacent healthy lines, ensuring that the power outage time for downstream critical users is reduced to milliseconds. Then, it guides drones or repair personnel to the site to perform clamp replacement. This tiered defense, step-by-step strategy greatly enhances the resilience of the power grid.

[0087] Optionally, to ensure the aforementioned three-dimensional coupled model obtains sufficient training samples before going online, the system must be able to replicate decades of natural aging processes in a laboratory environment. The aging test system also includes a multiphysics coupled aging chamber for constructing an aging environment for the wire clamp. The multiphysics coupled aging chamber includes: a thermal and humidity stress module for adjusting ambient temperature and humidity; a mechanical vibration module for adjusting vibration frequency and amplitude, and supporting single-frequency, multi-frequency superposition, or random vibration modes; an electrical stress module for adjusting AC current and voltage, and supporting constant current, constant voltage, or short-circuit impact modes; and a chemical stress module for simulating the concentration of salt spray, sulfur dioxide, and water vapor mixtures, and supporting cyclic modes of corrosion and drying.

[0088] Specifically, the thermal and humidity stress module combines advanced polyurethane insulation materials with industrial-grade compressor refrigeration and high-power heating tubes to create a rapidly changing temperature field and extreme humidity environment around the clamp; the mechanical vibration module base is equipped with a large electromagnetic vibrator, which can accurately simulate high-frequency, low-amplitude wind vibration of the conductor caused by a light breeze, as well as low-frequency, large-amplitude undulation caused by a typhoon, and even supports random frequency band white noise vibration to simulate turbulent wind loads under complex terrain; the electrical stress module consists of a programmable high-power AC / DC power supply and a high-current generator, which can continuously inject current several times higher than the rated value into the test clamp, or apply a terrifying short-circuit impact overcurrent to the clamp within milliseconds through a specially designed short-circuit switch array, perfectly replicating the real lightning short-circuit fault of the power grid; the chemical stress module uses an ultrasonic atomizing nozzle network to precisely spray a configured high-concentration sodium chloride salt spray and sulfur dioxide harmful gas into the clamp's meshing gap, and accelerates the expulsion of hidden intergranular corrosion defects inside the clamp through a programmed alternating corrosion and drying cycle mode. By using a multi-physics field coupled aging chamber, the system successfully extracted massive amounts of extreme degradation data covering the entire life cycle.

[0089] Optionally, the aging test system also includes a digital twin verification system, within which a wired virtual model is established to realize a two-way verification mechanism between physical testing and virtual simulation, specifically including:

[0090] The geometric progressive feature data and electrical micro-precursor feature data collected in the physical environment are input into the virtual model of the clamp. The model parameters of the virtual model of the clamp are corrected by comparing the simulation results with the physical test results. The virtual model of the clamp is used to simulate the progressive to sudden failure process under different geometric creep paths and power grid conditions, and to generate virtual supplementary samples for the instant of sudden failure.

[0091] Specifically, the digital twin verification system utilizes finite element analysis software and an electromagnetic transient calculation platform within a high-performance computing cluster to meticulously construct a highly realistic virtual model of wire clamps, including bolts, cable trays, and crimped fittings, at a 1:1 scale. The process of physically verifying the virtual model is a closed loop of model calibration. As data sources such as temperature gradients and resistance changes collected from the physical environment—i.e., the real power grid or aging chamber—continuously flow into the system, the twin system applies these realistic boundary conditions to the virtual model.

[0092] If the stress distribution calculated by the virtual model deviates from the location of the macroscopic crack in the physical entity, the system will automatically adjust parameters such as the material's elastic modulus and thermal conductivity within the virtual model using a backpropagation algorithm until the fitting error between the virtual and reality models falls below the tolerance limit. Virtual supplementary physics is the key to breaking down data barriers. In the actual aging cycle of wire clamps, the instantaneous data of catastrophic sudden failures is extremely difficult to capture and carries a high degree of danger and destructiveness. The system utilizes a calibrated, robust wire clamp virtual model, injecting unlimited combinations of extreme virtual harsh working conditions into a supercomputer to simulate countless possible fracture and melting evolution paths. This generates valuable virtual supplementary samples of sudden failure moments in batches, greatly enriching the training feature library of the three-dimensional coupled model.

[0093] Optionally, the aging test system also includes an edge application system for real-time analysis of the abnormal state of the clamp on-site and bidirectional data synchronization with the power grid linkage analysis system. The edge application system includes a micro acquisition module, a lightweight algorithm module, a wireless communication module, and a local early warning module. The lightweight algorithm module is equipped with a three-dimensional coupled model with compressed parameters, performs local analysis tasks including geometric progressive stage determination, micro-precursor feature extraction, and critical point prediction, and outputs audible and visual warnings or SMS warnings through the local early warning module.

[0094] Specifically, the edge application system is compactly encapsulated in a highly protected metal anti-interference shell, directly suspended or clipped near the high-voltage clamps of the power transmission line. The miniature acquisition module integrates a miniature infrared temperature probe, strain gauge, and high-frequency current transformer, directly acquiring first-hand physical intelligence on the front lines. However, due to the stringent power consumption constraints of solar power in the field, the edge cannot run a full version of the massive neural network. Therefore, the lightweight algorithm module employs knowledge distillation and network pruning techniques to compress and refine the massive three-dimensional coupled model in the cloud into a small, low-computing-power-requirement simplified miniature model. This miniature model still retains the core geometric progressive stage decision logic and critical point prediction framework, enabling microsecond-level local anomaly analysis even in isolated network environments.

[0095] The local early warning module is equipped with a high-brightness strobe LED array and a high-decibel buzzer. Once the lightweight algorithm determines that the clamp is about to fail, the local early warning module will flash a red warning beam and emit a piercing alarm sound in the forest to warn people and vehicles below that are trying to approach. At the same time, it will push an emergency alarm SMS to the mobile terminal of the maintenance personnel through wireless communication modules such as low-power wide area network, so as to achieve seamless data fusion between the cloud and the edge.

[0096] For example, let's set up a visualization scenario: simulate the monitored power transmission line clamp located in the main power transmission network of a coastal heavy industrial area.

[0097] On the afternoon of a peak summer electricity consumption day, the multi-dimensional data acquisition system obtained the feature sample vector data set for the current moment. First, the calculation process of the two-layer verification mechanism is demonstrated. In the standardized feature space, the Euclidean distance between the current feature sample and the three most recent historical neighboring feature samples extracted within the past sliding time window is calculated.

[0098] Assuming that after substituting the values ​​into the aforementioned multidimensional spatial distance formula, the three weighted spatial distances obtained are 0.35, 0.40, and 0.45, the system then calculates the arithmetic mean of these three distances, i.e., the average distance. .

[0099] Next, the system will average the distance Substitute the values ​​into the local density scoring formula for calculation:

[0100] ;

[0101] The system pre-stores a historical safety density threshold of 0.800. Since the currently calculated local density score of 0.714 is clearly less than the density threshold of 0.800, the system immediately determines that the clamp has an abnormal tendency to detach and generates a suspected abnormal signal. At this point, the first layer of the dual-layer verification mechanism is triggered. The system immediately issues a strong command, increasing the sampling frequency of the clamp node sensor array from once every 5 minutes to once every second, continuously capturing 10 sets of new feature data. After high-frequency secondary calculation, the latest average distance is obtained as 0.42, and the secondary local density score is... Since 0.704 remains firmly below the density threshold of 0.800, the system decisively filters out the possibility of occasional noise interference, ultimately confirming that the clamp is indeed in an actual abnormal state, and the anomaly is established.

[0102] like Figure 2 The scatter plot shown below illustrates the system's process from spatial distance calculation to density score mapping and high-frequency secondary sampling, based on the local density scoring and two-layer anomaly verification mechanism using multi-dimensional feature space distance. The horizontal axis of the figure represents the sampling time series, the left vertical axis represents the average distance in the feature space, and the right vertical axis represents the local density score.

[0103] The blue scatter dots in the figure represent normal clusters collected at low frequency within the historical time window. At this time, the clamp status is relatively stable, the average distance in the feature space is small, and the corresponding black density score change curve runs smoothly at a high level, which is higher than the safe density threshold represented by the magenta dashed line in the figure.

[0104] When microscopic potential hazards appear in the wire clamp, yellow suspected loose points appear in the graph. The system calculates that the average distance increases significantly at this time, causing the black curve to drop sharply and fall below the safety density threshold for the first time, thus triggering the first layer of the two-layer verification mechanism. Subsequently, the system adjusts the sampling mechanism to perform high-frequency capture. The red scattered points in the graph represent dense secondary confirmed points. In the continuous high-frequency secondary calculation, the average distance remains at a high level, so that the black density score change curve remains stable at a low level and continues to be below the safety density threshold.

[0105] By observing the spatial distance differences and density distribution of the blue normal points, yellow suspected points, and red high-frequency confirmed points on the graph, as well as the relative positional changes of the black curve and the magenta dashed line, the attached figure objectively reflects the working process of the two-layer verification mechanism in using spatial deviation and temporal continuity to filter transient interference signals, thus verifying the effectiveness of this scheme in balancing monitoring sensitivity and false alarm rate.

[0106] Following this, the power grid linkage analysis system took over the process, activating the dynamic prediction core calculation engine to derive the dynamic geometric critical values. The system then consulted the factory database to determine the initial geometric critical values ​​of this type of clamp under standard laboratory constant temperature and constant current conditions. Set as the safe tolerance limit for contact area reduction, with a value of 100.0 relative units.

[0107] At this moment, the system retrieves real-time measurement data from the power grid dispatching backend to perform comprehensive environmental coefficient calculations. First, it calculates the load impact coefficient. The current transmitted current in the line where the current clamp is located. The current surged to 1200 amps, while the safety rated current specified on the clamp nameplate... Calculate the deviation ratio for only 1000 amperes. .

[0108] Setting material heat load sensitivity weight parameters Substituting into the formula, we obtain the load influence coefficient. .

[0109] Secondly, calculate the short-circuit impact correction factor. Retrieving the line protection operation records, it was found that this clamp experienced one external fault impact within the past six months, i.e., the number of impacts... Duration The standard tolerance time is 0.15 seconds. The time is 0.10 seconds. Set the power disruption factor. Substituting into the formula, we get .

[0110] Next, due to the extremely stable grid voltage, the voltage fluctuation coefficient... The base value is 1.00. Furthermore, there has been no substation network reconfiguration topology change, so the topology adjustment impact coefficient is [not specified]. We also take the base value of 1.00.

[0111] However, due to its coastal location, the meteorological station issued an emergency warning that the current location was being affected by the outer cloud system of a typhoon, with wind speeds reaching 15 meters per second, accompanied by torrential rain. Upon receiving these extreme weather parameters, the meteorological correction function module, through nonlinear mapping, determined that the wind deflection and the rapid cooling effect of the rain were extremely high, and directly output a penalty index, causing the meteorological correction function to... .

[0112] Subsequently, the system substitutes all the precisely calculated power grid impact factors into the core dynamic geometric critical value multiplication correction formula:

[0113] ;

[0114] Calculation results show that the safety tolerance of the line clamp, which was originally as high as 100.0 relative units in the laboratory environment, has its dynamic geometric critical value increased to 133.515 relative units under the combined stress of real high temperature overload, lightning strike damage and typhoon rain. This means that the physical space that the clamp can withstand for further deterioration has been drastically compressed by more than 30%.

[0115] like Figure 3 The graph showing the comparison of dynamic geometric critical value decay of the clamp under standard and extreme multi-stress coupling conditions illustrates the impact of complex power grid fluctuations on the physical critical state of the clamp. The horizontal axis of the graph represents the operating time series, and the vertical axis represents the geometric critical value in relative units.

[0116] The blue dashed line in the figure represents the static geometric critical value of the clamp under standard laboratory conditions. This curve remains at a relatively fixed level, reflecting the theoretical evaluation benchmark in conventional static prediction models. In contrast, the red solid line in the figure represents the dynamic critical value curve derived by this invention based on a multi-stress coupling model.

[0117] It can be observed that as the operating time progresses, when the clamp is subjected to extreme weather conditions such as high temperature and full load, external short-circuit fault impact, and typhoon and rainstorm, the red solid line exhibits a significant step-up waveform, eventually reaching a relatively high unit value. This step-up red dynamic curve contrasts with the stable blue static horizontal line, revealing the evolution process of the clamp's physical safety space being compressed under the superposition of multiple physical stresses.

[0118] The accompanying figure, through curve comparison and waveform transformation, reflects the rationality of the multi-factor dynamic correction logic of the present invention in describing the change law of the critical value of the clamp under extreme working conditions, and demonstrates the technical improvement of the test system in dynamic prediction.

[0119] At this point, the three-dimensional coupled model performs a deep mapping comparison between the actual degradation measurement value of the current feature sample and this extremely stringent dynamic geometric critical value of 133.515, and finally generates key quantitative results in the output layer: the probability of triggering electrical sudden failure in the current geometric asymptotic stage has reached 72%, and the remaining time from the current state to the occurrence of catastrophic fracture is calculated to be less than 2.5 hours.

[0120] Finally, armed with this crucial data, the logical decision-making state machine of the scheduling and collaborative decision-making system began operating at high speed. The system first reads the parameters of the three preset security defense watersheds:

[0121] The first warning threshold is set at 30%, the second warning threshold at 60%, and the third warning threshold at 80%. This fully complies with the aforementioned logical hard constraint that the first warning threshold is less than the second warning threshold and the second warning threshold is less than the third warning threshold.

[0122] The system determined that the current predicted failure probability of 72% was greater than the second warning threshold of 60%, but had not yet reached the final critical threshold of 80%. At the same time, the system quickly scanned the load forecast curve of the dispatch duty table and found that although the current load was extremely high, the industrial park's day shift would end in one hour, and the power grid would officially leave the peak load period.

[0123] Based on all the above prerequisites, the dispatching and collaborative decision-making system accurately hit the third rule path among multiple logical judgment strategies. The system not only avoided triggering an emergency power outage panic or passively waiting for a lengthy maintenance planning period, but also immediately issued a sharp, level-three warning pop-up to the control center. Furthermore, without any manual intervention, the system automatically generated a highly targeted temporary power outage maintenance dispatch operation ticket, requiring maintenance personnel to bring professional live-line working insulated tools and spare clamps, and to immediately arrive at the site to perform precise emergency repair and replacement tasks within the golden time window when the load curve just begins to decline after 1 / 10 of an hour.

[0124] Example 2:

[0125] In Example 1, the system constructs a dynamic prediction model based on the product of multiple power grid influencing factors. This model has extremely high prediction accuracy in most conventional power grid fluctuation scenarios, and its underlying logic is based on the assumption that each stress failure is independent and linearly superimposed.

[0126] However, the actual operating environment of high-voltage power grids is unpredictable, especially during peak summer or extremely cold weather, when wire clamps often face the most fatal multi-stress coupling failure scenarios in physics. When a wire clamp is under extreme full load or even overload, its internal core temperature is extremely high; if it encounters a sudden rainstorm or blizzard, the outer surface of the wire clamp will be instantly and forcibly cooled. This extreme internal heat and external cold environment will tear a huge temperature gradient inside the metal wire clamp, triggering a thermal quenching effect at the microcrystalline lattice level. The difference in the coefficient of thermal expansion of different metal materials will cause a large number of microcracks to instantly initiate and expand inside.

[0127] Under this avalanche-like destruction mechanism, conventional linear multiplication models will fail severely, resulting in a predicted remaining time that is much longer than the actual survival time.

[0128] To address this issue, the power grid linkage analysis system in the aging test system also includes a correction module, specifically a multi-stress coupled nonlinear avalanche correction module. In addition, the aging test system also includes a micro-array for combined infrared and acoustic emission monitoring that is normally in a dormant state.

[0129] For example, the multi-stress coupled nonlinear avalanche correction module is a high-priority emergency calculation engine independently deployed within the cloud-based power grid linkage analysis system. This module normally operates silently in the background, not interfering with the regular 3D coupled model prediction process. Only when Boolean logic gates under specific physical conditions are simultaneously broken down will this correction module, with the highest system privileges, take over the calculation of dynamic geometric critical values, forcibly rewriting the originally mild linear meteorological correction function into an extremely steep nonlinear decay term. The module is designed to buy the power grid dispatch system even just a few minutes of emergency decision-making time in the event of a catastrophic physical upheaval.

[0130] To complement the aforementioned nonlinear algorithms in the cloud, the system adds special sensing capabilities to the physical world at the front end. The infrared and acoustic emission joint monitoring microarray, normally in a dormant state, is secured to the outer armor layer of the monitored clamp using high-strength, weather-resistant insulating straps. This microarray is essentially a highly integrated IoT sensing node that combines a high-frequency piezoelectric ceramic chip with a miniature uncooled infrared focal plane detector. It is designed to be in a dormant state under normal conditions because the power consumption of infrared imaging and ultra-high-frequency acoustic sampling is extremely high. If it were to remain continuously powered on for 24 hours, the thin solar panels and inductive power extraction plates at the edge would be insufficient to support its energy consumption. Therefore, through a strict dormancy mechanism, the core analog-to-digital converter and wireless RF transmission components inside the array are in a deep sleep mode at the microampere level, with only an ultra-low-power external wake-up interrupt pin remaining on standby, thus resolving the contradiction between high-frequency monitoring and harsh power supply conditions in the field.

[0131] This embodiment precisely defines the wake-up timing of the high-power sensor triggering mechanism. When the power grid linkage analysis system detects that the load influence coefficient is greater than the set first load threshold, and at the same time the wind speed or rainfall intensity in the meteorological correction function meets the set sudden cooling meteorological threshold, the multi-stress coupled nonlinear avalanche correction module generates a concurrent wake-up command.

[0132] It is important to note that the aforementioned triggering mechanism constitutes a stringent logical AND gate condition. The first load threshold represents a high-energy active state entered by the wire clamp due to the accumulation of Joule heat from the current. Typically, this value is triggered when the current exceeds 1.2 times the rated safe current. Simultaneously, the external parameter in the meteorological correction function must exceed the warning line. The sudden cooling meteorological threshold is specifically defined as a wind speed suddenly increasing to over 15 meters per second within 10 minutes, or a rainfall intensity reaching a torrential rain level of over 20 millimeters per hour. When the two extreme physical fields of high internal heat generation and strong external cooling collide violently at the same time, the physical preconditions for thermal shock damage are fully met. At this point, the multi-stress coupled nonlinear avalanche correction module instantly generates a concurrent wake-up command with an extremely high priority identifier, penetrating conventional communication queuing protocols and reaching the edge with a network latency of less than 50 milliseconds.

[0133] Once the hardware node receives the instruction, it begins to perform transient capture of massive amounts of data. The infrared and acoustic emission joint monitoring microarray is activated in response to the concurrent wake-up instruction, and simultaneously collects the temperature gradient value between the inside and outside of the clamp and the acoustic emission impact count of the internal microcracks caused by the thermal cooling effect.

[0134] Specifically, after the array is activated, the miniature uncooled infrared focal plane detector rapidly scans the infrared spectrum of the metal radiation outside the clamp using its own lens array, and calculates the real-time extreme cold temperature of the clamp's surface based on the preset emissivity of the aluminum alloy surface. Simultaneously, the system retrieves internal contact temperature rise data monitored by a conventional multi-dimensional data acquisition system as a core temperature reference. By subtracting the surface quenching temperature from the core high temperature and then dividing by the equivalent metal thickness of the clamp, the core physical quantity describing the thermal stress tearing intensity—the surface-internal temperature gradient value—can be accurately calculated. The larger this value, the more severe the thermal expansion and contraction torsional stress experienced by the clamp's cross-section.

[0135] Optionally, acoustic emission impact counting of internal microcracks is the most direct evidence for capturing microscopic fractures in materials. When a metal lattice is forcibly torn apart under a large temperature gradient, the fracture surface releases transient elastic stress waves with frequencies between 100 kHz and 1 MHz. High-frequency piezoelectric ceramic wafers inside the array capture these stress waves and convert them into weak electrical pulse signals. A digital signal processor inside the microarray filters and amplifies this signal, setting a fixed decibel threshold. Whenever the peak value of the elastic wave exceeds this threshold, a counter records an impact. The total number of impacts accumulated within a set sampling time window is the acoustic emission impact count of the internal microcracks. This count objectively reflects the severity of the fracture of the metal bonds inside the wire clamp at a microscopic level.

[0136] After acquiring these two highly representative physical quantities, the cloud-based system begins high-dimensional dimensionality reduction and fusion calculations. The multi-stress coupled nonlinear avalanche correction module performs normalized weighted calculations on the surface-interior temperature difference gradient value and the acoustic emission impact count of the internal microcracks to obtain the quenching damage index.

[0137] It is important to note that since the dimensions of the temperature gradient value between the inside and outside of the surface are completely different from those of the acoustic emission impact count of the internal microcracks—the former being a continuous thermodynamic physical quantity and the latter a discrete pulse statistical quantity—a rigorous dimensionless normalization process is necessary. The specific formula for this normalized weighted calculation is defined as follows:

[0138] ;

[0139] In the formula, This represents the quenching damage index mentioned above. Weighting coefficients representing the temperature gradient; The weighting coefficients represent the acoustic emission impacts, and it is guaranteed that the sum of the two weighting coefficients equals the value 1; This represents the real-time surface-to-internal temperature gradient value calculated by fusing infrared and internal temperature data. This represents the standard critical temperature gradient reference value that triggers plastic deformation in materials; This represents the real-time acoustic emission impact count of internal microcracks. This represents the standard acoustic emission impact reference count value for macroscopic cracking of materials. Through this algorithm, the multi-stress coupled nonlinear avalanche correction module unifies macroscopic thermodynamics and microscopic acoustic phenomena into a dimensionless numerical indicator for disaster early warning.

[0140] like Figure 4 The normalized weighted three-dimensional response surface plot of the surface-interior temperature gradient and acoustic emission impact count on the quenching damage index, shown in the figure, illustrates the numerical response relationship of the system when handling the fusion calculation of cross-scale physical quantities. The horizontal axis of the figure represents the surface-interior temperature gradient, the vertical axis represents the acoustic emission impact count of the internal microcracks, and the vertical axis represents the quenching damage index obtained after normalization calculation.

[0141] The sloping colored surface in the figure, which transitions from cool to warm tones, is the damage index response surface. The deepening of its color and the increase of its spatial height reflect the calculation process of the overall evaluation index increasing in a superimposed manner as the temperature difference increases and micro-fractures increase under the condition of internal heat and external cold.

[0142] The magenta semi-transparent horizontal suspended plane marked in the figure represents the pre-set material yield limit warning plane. Based on the extreme working condition simulation results in the implementation method, when the surface-to-internal temperature gradient and acoustic emission impact count reach high values, the height of the corresponding red solid coordinate point found on the colored surface exceeds the semi-transparent warning plane representing the safety bottom line.

[0143] This three-dimensional spatial display visually represents the normalized weighted calculation process, objectively illustrating the calculation logic of the correction module in quantifying the degree of weather-induced damage.

[0144] As the severe weather persisted, the quenching damage index began to rise continuously, eventually triggering the most disruptive algorithmic trajectory change operation in the entire system. When the quenching damage index exceeded the set material yield threshold, the multi-stress coupled nonlinear avalanche correction module replaced the weather correction function in the calculation process with an exponential decay term based on the natural logarithm base, performing nonlinear accelerated convergence calculation on the dynamic geometric critical value to reduce the remaining time from the current state to the electrical sudden failure.

[0145] Specifically, the material yield threshold is a physical constant pre-determined through numerous destructive experiments, marking that the wire clamp material has passed the elastic recovery stage and completely entered the irreversible accelerated damage phase. Once... The value exceeded the limit, meaning that the conventional linear penalty could no longer describe the collapse velocity of the clamp. At this point, the multi-stress coupled nonlinear avalanche correction module decisively discarded the original meteorological correction function from Example 1. Instead, a nonlinear extreme-velocity decay function based on the natural constant base was used. The modified dynamic geometric critical value calculation formula completely evolved into:

[0146]

[0147] In the formula, the base of the natural logarithm is... The negative quenching damage index, used as an exponential term, endows the entire computational model with a physical property exhibiting an exponential, cliff-like decline. This means that whenever a batch of microcracks forms inside the clamp, or whenever the temperature gradient increases by 1 percentage point, the geometrically critical residual capacity that the clamp can withstand is proportionally and directly weakened. This nonlinear accelerated convergence calculation will force the clamp's condition assessment result, which originally had several days of life remaining, to collapse in an extremely short time. Subsequently, after receiving this extremely compressed dynamic geometric critical value, the three-dimensional coupled model will output a precipitous reduction in the remaining time from the current state to the sudden electrical failure, thus completely shattering the dispatchers' hopes.

[0148] like Figure 5The graph showing the comparison between the linear decay prediction and the nonlinear avalanche rapid convergence lifetime based on the natural logarithm base reveals the adjustment of the correction module to the physical lifetime prediction of the clamp under multi-stress coupled failure scenarios. The horizontal axis of the graph represents the duration of extreme conditions, and the vertical axis represents the predicted remaining physical lifetime.

[0149] The blue dashed line in the figure represents the traditional linear decay prediction lifetime curve. This curve shows a relatively gentle linear downward trend, reflecting the lifetime assessment results given by the conventional prediction model based on the static critical value before the introduction of nonlinear correction.

[0150] Conversely, the solid red line in the figure represents the nonlinear, rapidly converging lifetime curve generated by introducing the exponential decay term in this invention. It can be observed that under extreme concurrent stress, when the quenching damage index of the material exceeds the yield threshold warning level, the solid red line exhibits a significant accelerated decline waveform. The red curve, representing the actual decay, rapidly converges the predicted remaining lifetime to a lower level within a shorter time series.

[0151] The waveform transformation from a gradual to an accelerated decline, along with the hyperbola comparison, illustrates the sensitivity of the nonlinear convergence algorithm in capturing specific physical acceleration and degradation processes, reflecting the application value of this prediction model in assisting the power grid dispatching system to obtain emergency decision-making time in advance.

[0152] Example 3:

[0153] Based on the multi-dimensional feature acquisition and analysis and nonlinear avalanche prediction constructed in Examples 1 and 2, this example mainly focuses on the global safety interlocking and pre-emptive physical intervention technology solution designed for the integrated aging test system for multi-specification wire clamp accuracy and conductivity when facing extreme physical failure risks, in order to completely resolve the scheduling conflict between local equipment protection and global power grid stability.

[0154] Specifically, the scheduling and collaborative decision-making system further includes a cascaded fault prevention module, and the aging test system further includes an active cooling actuator installed on the surface of the clamp. The cascaded fault prevention module is a high-level logic decision unit embedded in the core control layer of the scheduling and collaborative decision-making system, possessing the highest veto authority exceeding that of conventional power outage commands. The active cooling actuator is a miniaturized solid-state cooling device designed based on the principle of semiconductor thermoelectric effect, which is directly attached and fixed to the surface of the core contact area of ​​the clamp, which is highly susceptible to heat generation and creep, using thermally insulating fasteners.

[0155] For example, when the probability of a sudden electrical failure of a clamp is determined by the system to be greater than or equal to the third warning threshold, according to conventional logic, the system should immediately trigger an emergency power outage and simultaneously transfer the load to an adjacent line to protect the clamp that is about to blow. However, before executing the emergency power outage and the synchronous load transfer, the cascaded fault prevention module obtains the real-time thermal stability margin of the target transfer line. The target transfer line refers to an adjacent healthy transmission line designated by the dispatch center to accept the overflow power flow after the current faulty line is disconnected, under the current power grid topology.

[0156] Real-time thermal stability margin refers to the maximum additional safe current-carrying capacity that the target transfer line can currently withstand without triggering over-limit tripping of relay protection devices. To accurately quantify this indicator, the cascaded fault prevention module must retrieve real-time power flow snapshots of the regional power grid and perform rigorous calculations using a specific tolerance stripping algorithm. The complete formula for calculating real-time thermal stability margin is defined as follows:

[0157] ;

[0158] In the formula, This represents the real-time thermal stability margin of the calculated target transfer path; This represents the maximum allowable current carrying capacity of the target transfer line under current ambient temperature and wind speed conditions. This represents the reserved current redundancy as a safety precaution mandated by power grid dispatching regulations. Using the above formula, the system can objectively assess the current reserves of adjacent lines, preventing secondary disasters caused by indiscriminate relocation.

[0159] After obtaining accurate margin data, the system immediately enters the critical game-theoretic decision-making stage. If the real-time thermal stability margin is less than the load to be transferred during the synchronous load transfer, and a cascading overload trip risk is determined, the cascading fault prevention module intercepts the emergency power outage operation and the synchronous load transfer, and generates a power grid safety interlock signal.

[0160] The load to be transferred is the total actual current being carried by the line containing the currently failing clamp, and which must be transferred. When the spare capacity of the target transfer line, i.e., its real-time thermal stability margin, is insufficient to accommodate this massive load to be transferred, forcibly executing the transfer will cause a massive backflow that instantly breaks through the thermal stability limit of the target transfer line, triggering its protective switch to trip. This tripping will violently push the load to the next line, causing a domino effect of widespread power outages. Therefore, the cascaded fault prevention module decisively exercises its highest veto power at this moment, intercepting the operation command to cut off the switch at the software level, and simultaneously broadcasting a high-priority power grid safety interlock signal to the entire network, declaring that the system has entered a critical interlock self-rescue mode.

[0161] While simple blocking prevented a complete grid collapse, the clamps on the current line were still on the verge of melting due to high temperatures. Therefore, immediate, two-pronged rescue measures were necessary. Specifically, in response to the grid safety blocking signal, the dispatching and coordination decision-making system issued a local disconnection command to the downstream non-core nodes of the line to which the clamp belonged to reduce the actual current of the line, and simultaneously sent a forced cooling command to the active cooling actuator.

[0162] It's important to note that downstream non-core nodes refer to electricity consumption areas with lower priority in the grid load importance rating, such as landscape lighting loads in large commercial complexes and heat storage loads in non-continuous production factories. Issuing a partial disconnection command means that the system, without affecting the main grid, utilizes the end-point control capabilities of the smart distribution network to precisely disconnect these non-critical loads. This operation can immediately reduce the actual current flowing through critical clamps, reducing the continuous generation of Joule heat at its source.

[0163] Simultaneously, physical intervention at the front end is also initiated. The active cooling actuator responds to the forced cooling command by physically cooling the wire clamp to reduce its temperature rise and slow down its geometric creep rate. The active cooling actuator contains a multi-stage semiconductor Peltier cold-end array. Upon receiving the forced cooling command, the internal energy storage capacitor instantly releases a large current to drive the Peltier element, causing its cold-end temperature to plummet below zero within seconds. This extremely cold energy is then rapidly injected into the high-temperature metal core of the wire clamp through a specially designed high-thermal-conductivity graphene interface material.

[0164] For example, to clearly define the mathematical relationship between the physical cooling effect and the extension of the clamp's lifespan, the system incorporates a cooling intervention simulation algorithm. The formula for calculating the reduction in temperature rise is:

[0165] ;

[0166] In the formula, This represents the absolute value of the temperature drop of the clamp body after the active cooling actuator has been activated; Represents the rated maximum cooling power of the semiconductor cooling array; The coefficient representing the efficiency of cold energy transfer at the thermal interface; This represents the specific heat capacity of the metal material used in the wire clamp; This represents the core quality of the heat-receiving clamp; This represents the duration for which the forced cooling command remains active.

[0167] It is also important to note that with the forced temperature interruption, the lattice slip of the clamp material will be forcibly frozen, and geometric creep will be greatly suppressed. The evaluation formula for delaying the rate of geometric creep is defined as:

[0168] ;

[0169] In the above creep retardation formula, This represents a new, slow creep rate exhibited by the clamp after physical cooling intervention; This represents the original high-speed creep rate before cooling, when the plant was in a critical state. The microscopic activation energy constant representing the metal material of the wire clamp required to induce creep; Represents the ideal gas constant; This represents the absolute high temperature value of the cooling front line. The absolute temperature value of the clamp after cooling is equal to the temperature before cooling minus the absolute value of the temperature drop. Through this series of rigorous physical barriers and exponential attenuation interventions, the system forcefully pulled the clamp back from the brink of melting, gaining an extremely valuable buffer period for the dispatch network.

[0170] like Figure 6 The diagram showing the nonlinear evolution of clamp temperature rise blocking and geometric creep rate delay before and after intervention by the active cooling actuator demonstrates the actual operational effect of the system implementing physical cooling intervention under abnormal operating conditions. The attached diagram uses a dual vertical axis design: the horizontal axis represents the intervention time series, the left vertical axis represents the core absolute temperature of the clamp, and the right vertical axis represents the geometric creep rate of the clamp.

[0171] In the graph, the solid red line represents the temperature change waveform, and the dashed blue line represents the creep rate change waveform. Before the physical intervention was initiated, the solid red line maintained a high temperature level, and the corresponding dashed blue line was also at a high creep rate, reflecting the deterioration trend of the clamp at that time.

[0172] With the issuance of the forced cooling command and the activation of the active cooling actuator, the red solid line shows a clear downward waveform. Within the set cooling time window, the temperature is effectively reduced and stabilized in a lower range. As the core temperature decreases, the blue dashed line, representing the geometric creep rate, exhibits an accelerated downward waveform under the action of the exponential decay mechanism, eventually converging and running smoothly at an extremely low level.

[0173] This waveform transformation from a high-level drop to a smooth operation, along with the physical correlation evolution of the hyperbola, visualizes the cooling and blocking control process within the system, objectively demonstrating the practical effectiveness of active physical cooling intervention in reducing the rate of wire clamp deterioration and extending the safety buffer period.

[0174] After performing load reduction and physical cooling operations to extend the system's lifespan, the system did not cease monitoring the overall situation. Specifically, the scheduling and collaborative decision-making system continuously and cyclically acquires the real-time thermal stability margin of the target transfer line until the real-time thermal stability margin is greater than or equal to the load to be transferred. At this point, the power grid safety interlock signal is released, and the emergency power outage operation and the synchronous load transfer are resumed.

[0175] It is also important to note that continuous cyclic acquisition refers to the system constantly recalculating the aforementioned real-time thermal stability margin formula at an extremely high frequency of every 10 seconds. Since the power flow of the power grid is dynamic, as nighttime temperatures drop, leading to an increase in the extreme value of dynamic capacity expansion on the lines, or as the arrival of low electricity demand in other areas, the spare capacity of the target transfer lines will gradually recover and increase. Once, at a certain time point, the system detects that the real-time thermal stability margin of the target transfer lines has finally expanded to fully accommodate the load to be transferred, it indicates that the entire system has met the physical conditions for a safe takeover.

[0176] At this moment, the cascade fault prevention module immediately cancels the interception and releases the power grid safety interlock signal. The previously frozen emergency power outage command is instantly and unimpededly transmitted to the substation circuit breaker. The dangerous line where the clamp is located is safely disconnected, and the load is smoothly transferred. A successful battle against cascade failure and precise equipment protection has come to a successful conclusion. This not only constitutes a technical closed loop in theory, but also has practical guiding significance in actual engineering applications.

[0177] like Figure 7 The attached figure shows the dynamic evolution of the real-time thermal stability margin of the target transfer line and the full-time waveform diagram of the grid safety interlock interception release, demonstrating the system's logical judgment process between load transfer and grid stability under complex operating conditions. The horizontal axis of the figure represents the grid operation sequence, and the vertical axis represents the current amplitude.

[0178] In the figure, the solid green line represents the dynamic evolution curve of the real-time thermal stability margin of the target transfer line, while the red step line represents the load to be transferred curve of the faulty line. During peak load times, the green curve is at a low point, indicating that the available capacity that adjacent lines can accept is limited, while the red line representing the load to be transferred is at a higher point.

[0179] Since the green curve is below the red line at this moment, it means that the available capacity cannot meet the transfer demand. At this moment, the system triggers the safety interlocking interception trigger point marked by the red solid dot, and executes the operation of intercepting the power outage transfer command.

[0180] With the issuance of the interlock command, the system performs a partial disconnection operation, causing the red line to undergo a downward step waveform change, as the load to be transferred is reduced and stabilized. As the operation progresses to the off-peak electricity period, the green curve shows an upward climbing waveform, and the real-time thermal stability margin gradually increases. When the green curve crosses the red line, the system triggers the safety interlock release and recovery point, marked by a solid blue dot, at the crossing point. This dynamic crossing waveform and the marking of the interception release node demonstrate the control logic of the system continuously monitoring the margin until the transfer conditions are met.

[0181] Example 4:

[0182] like Figure 8 As shown in the figure, this embodiment discloses an integrated aging test method for the accuracy and conductivity of multi-specification wire clamps. This method thoroughly resolves the three core conflicts pointed out in the background art, namely the conflict between monitoring sensitivity and false alarm rate, the conflict between linear prediction and nonlinear avalanche damage, and the scheduling conflict between local equipment protection and global power grid stability, through rigorous step flow and cross-dimensional calculation.

[0183] For example, this aging test method includes a primary sensing and screening step. The system collects multi-dimensional state characteristic data of the clamps, and when a suspected abnormal signal is detected based on the multi-dimensional state characteristic data, a two-layer verification mechanism for the abnormal state of the clamps is triggered.

[0184] It is also important to note that in actual high-voltage transmission networks, line clamps are constantly subjected to complex electromagnetic interference and random structural stress. Relying solely on a single static threshold to determine whether a line clamp is malfunctioning is highly susceptible to interference from inrush currents during normal load switching, resulting in a massive number of false alarms. To resolve this technical conflict between monitoring sensitivity and false alarm rate, this invention completely abandons the traditional direct alarm logic in its methodology, instead employing a unique and rigorous signal verification process.

[0185] Specifically, the process of generating the suspected abnormal signal and confirming it using the dual-layer verification mechanism includes: calculating the average distance between the current feature sample and its historical neighboring feature samples based on the multidimensional state feature data, and calculating a local density score based on the average distance. By calculating spatial distances in the multidimensional feature space and mapping them to dimensionless local density scores, the system can extremely sensitively capture the very early budding state of wire clamp geometric creep or micro-arcs, ensuring extremely high sensitivity in detecting potential hazards.

[0186] Furthermore, after obtaining the score, the system executes a conditional judgment step. If the local density score is less than a preset density threshold, a suspected abnormal signal is generated, and multiple sets of multi-dimensional state feature data are continuously collected at a sampling frequency higher than the current one for secondary calculation. This secondary calculation is the core defense against false warnings. Within a very short time window, the system synchronously and frequently captures the latest five-dimensional data column through hardware to conduct a continuous review of the deviation trend in the previous moment in the time dimension. If the local density score obtained from the secondary calculation is still less than the density threshold, the clamp is confirmed to be in the actual abnormal state. Through this smooth two-layer verification mechanism, this method not only successfully retains the ability to detect early minor hidden dangers, but also completely rejects false warnings caused by normal system fluctuations, achieving excellent beneficial results.

[0187] Optionally, once the potential hazard is definitively confirmed, the testing method immediately jumps from the front-end perception stage to the deep inference stage in the cloud-based system. This method stipulates that after the dual-layer verification mechanism confirms that the clamp is in an actual abnormal state, the dynamic prediction result of the clamp is calculated using a three-dimensional coupling model combined with real-time power grid operation data.

[0188] Under normal conditions, the three-dimensional coupled model incorporates grid load fluctuations, short-circuit impacts, and conventional meteorological factors as linear multipliers into the calculation of dynamic geometric critical values. However, once the system detects a significant temperature gradient between the inside and outside of the clamp and dense microcrack elastic wave impacts via the front-end infrared and acoustic emission microarrays, this method instantly activates nonlinear accelerated convergence logic. The three-dimensional coupled model automatically discards the original linear meteorological correction function and forcibly introduces an exponentially rapidly decaying term based on the natural logarithm base. This methodological nonlinear trajectory change enables the system to accurately quantify the quenching damage index under extreme concurrent conditions, effectively preemptively seizing and extracting a valuable intervention window from the catastrophic sudden failure evolution process, thereby outputting extremely accurate and rigorous dynamic prediction results.

[0189] For example, once the system has accurately determined the countdown and risk probability, the testing method enters the final stage of overall planning and action execution. Based on the dynamic prediction results, the system generates operation and maintenance decisions coordinated with the power grid operation to execute early warnings or power outage maintenance for the line clamps.

[0190] Specifically, the steps for generating operation and maintenance decisions aim to completely eliminate deep scheduling conflicts between local equipment protection and global power grid stability. According to this method, when the prediction result indicates that the failure probability of the clamp has exceeded the highest risk third warning threshold, and the system is preparing to issue an emergency power outage maintenance command, an indispensable verification step must be performed beforehand: verifying the real-time thermal stability margin of the target transfer line.

[0191] It is also important to note that if the margin check fails, this method strictly prohibits the system from performing mechanical local protection actions. Instead, the system will immediately generate a power grid safety interlock signal to intercept blind load transfers and prevent domino-effect regional blackouts caused by adjacent lines tripping due to instantaneous overload. During the interlock period, the operation and maintenance decision-making will shift from simple disconnection to physical rescue. The system will automatically issue a local disconnection command to disconnect downstream non-core loads and simultaneously drive the active cooling actuators installed on the surface of the line clamps for rapid physical cooling.

[0192] Through this series of steps, this method not only successfully safeguards the overall physical stability and topological security of the power grid, but also secures a crucial golden period of safety for the dispatch center to execute cross-regional macro-level power dispatch. This has extremely high industrial application value in resolving multiple physical conflicts and improving the overall operational resilience of the power grid.

[0193] Example 5:

[0194] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0195] like Figure 9 The diagram shows a structural schematic of an electronic device according to the present invention. The electronic device 100 includes a processor 101 and a memory 103. The processor 101 and the memory 103 are connected, for example, via a bus 102. Optionally, the electronic device 100 may further include a transceiver 104. It should be noted that in practical applications, the transceiver 104 is not limited to one unit, and the structure of this electronic device 100 does not constitute a limitation on the embodiments of the present invention.

[0196] Processor 101 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 101 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0197] Bus 102 may include a pathway for transmitting information between the aforementioned components. Bus 102 may be a PCI bus or an EISA bus, etc. Bus 102 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0198] The memory 103 stores a computer program corresponding to the integrated aging test method for the accuracy and conductivity of multi-specification wire clamps according to the above embodiments of the present invention. This computer program is executed by the processor 101. The processor 101 executes the computer program stored in the memory 103 to implement the content shown in the aforementioned method embodiments.

[0199] Among them, electronic devices 100 include, but are not limited to: mobile terminals such as laptops and PADs (tablet computers) and fixed terminals such as desktop computers. Figure 9 The electronic device 100 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0200] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A multi-specification wire clip precision and conductivity integrated aging test system, characterized in that, include: A multi-dimensional data acquisition system is used to collect multi-dimensional state feature data of wire clamps, and when a suspected abnormal signal is detected based on the multi-dimensional state feature data, a two-layer verification mechanism for the abnormal state of the wire clamp is triggered. The power grid linkage analysis system is used to calculate the dynamic prediction result of the clamp by combining a three-dimensional coupling model with real-time power grid operation data after the dual-layer verification mechanism confirms that the clamp is in an actual abnormal state. The scheduling and coordination decision-making system is used to generate operation and maintenance decisions that are coordinated with the power grid operation based on the dynamic prediction results, so as to perform early warning or power outage maintenance of the line clamp; The process of generating the suspected abnormal signal and confirming it using the two-layer verification mechanism includes: calculating an average distance between the current feature sample and the historical adjacent feature samples according to the multi-dimensional state feature data , calculating a local density score based on the average distance The formula can be expressed as: ;​ If the local density score is less than the preset density threshold, the suspected abnormal signal is generated, and multiple sets of the multidimensional state feature data are continuously collected at a higher sampling frequency than the current one for secondary calculation. If the local density score obtained from the second calculation is still less than the density threshold, then the wire clamp is confirmed to be in the actual abnormal state. The process by which the power grid linkage analysis system calculates the dynamic prediction results of the clamp includes: generating quantitative results by extracting spatial correlation features and temporal evolution laws from the multidimensional state feature data. The quantitative results include the probability of triggering electrical sudden failure in the current geometric progression stage, the dynamic geometric critical value for triggering electrical sudden failure, and the remaining time from the current state to electrical sudden failure.

2. The system of claim 1, wherein, The multidimensional state feature data collected by the multidimensional data acquisition system specifically includes: Geometric asymptotic feature data, electrical performance data, electrical micro-precursor feature data, multiphysics environmental data, and power grid feature data; The multi-dimensional data acquisition system uses an atomic clock as a reference, and simultaneously starts all acquisition devices for hardware synchronization through a trigger circuit. It also uses a timestamp calibration algorithm to perform secondary software correction on the timestamps of the raw data from each acquisition device.

3. The system of claim 1, wherein, When the power grid linkage analysis system calculates the quantitative results, the dynamic geometric critical value is obtained by multiplying the initial geometric critical value under standard operating conditions, the load influence coefficient, the short-circuit impact correction factor, the voltage fluctuation coefficient, the topology adjustment influence coefficient, and the meteorological correction function that includes wind speed and rainfall intensity.

4. The system of claim 3, wherein, The load impact coefficient, short-circuit impact correction factor, voltage fluctuation coefficient, and topology adjustment impact coefficient used in the power grid linkage analysis system are calculated based on standardized and quantified real-time power grid operation data, specifically including: The load influence coefficient is calculated based on the deviation ratio between the actual current and the rated current of the clamp. The short-circuit impact correction factor is calculated based on the number of short-circuit impacts and the duration of the short-circuit fault. The voltage fluctuation coefficient is calculated based on the voltage fluctuation value of the power grid node; The topology adjustment influence coefficient is obtained based on the power grid topology adjustment status and the corresponding influence coefficient.

5. The system of claim 3, wherein, The power grid linkage analysis system also includes a correction module, and the aging test system also includes a micro array for joint monitoring of infrared and acoustic emission that is normally in a dormant state. When the power grid linkage analysis system detects that the load impact coefficient is greater than the set first load threshold, and at the same time the wind speed or rainfall intensity in the meteorological correction function meets the set sudden cooling meteorological threshold, the correction module generates a concurrent wake-up command. The infrared and acoustic emission joint monitoring microarray is activated in response to the concurrent wake-up command, and synchronously collects the surface and internal temperature gradient value of the clamp and the acoustic emission impact count of the internal microcracks caused by the thermal cooling effect. The correction module performs a normalized weighted calculation on the temperature difference gradient value between the inside and outside and the acoustic emission impact count of the internal microcracks to obtain the quenching damage index. When the quenching damage index is greater than the set material yield limit, the correction module replaces the meteorological correction function in the calculation process with an exponential decay term based on the natural logarithm base, and performs nonlinear accelerated convergence calculation on the dynamic geometric critical value to reduce the remaining time from the current state to the electrical sudden failure.

6. The system of claim 3, wherein, The power grid dispatch plan accessed by the dispatch collaborative decision-making system includes peak load periods, maintenance windows, and line topology adjustment plans for a future preset time period. The rules for generating operation and maintenance decisions include a multi-condition logical judgment strategy, and the specific judgment process is as follows: When the probability of the electrical sudden failure is confirmed to be less than the first warning threshold, the original sampling frequency is maintained for continuous monitoring. When the probability is greater than or equal to the first warning threshold and less than the second warning threshold, and it is during a non-peak load period and there is no maintenance window, a level-two warning is issued and the maintenance window is requested. When the probability is greater than or equal to the second warning threshold and less than the third warning threshold, and it is during a non-peak load period, a level three warning is issued and a temporary power outage for maintenance is arranged. When the probability is greater than or equal to the third warning threshold, an emergency power outage operation is triggered, and the clamp replacement and synchronous load transfer are performed first. Wherein, the first warning threshold is less than the second warning threshold, and the second warning threshold is less than the third warning threshold.

7. The system of claim 6, wherein, The scheduling and collaborative decision-making system also includes a cascaded fault prevention module, and the aging test system also includes an active cooling actuator installed on the surface of the clamp. Before executing the emergency power outage triggering operation and the synchronous load transfer, the cascaded fault prevention module obtains the real-time thermal stability margin of the target transfer line; If the real-time thermal stability margin is less than the load to be transferred during the synchronous load transfer, and it is determined that there is a risk of cascading overload tripping, the cascading fault prevention module intercepts the emergency power outage operation and the synchronous load transfer, and generates a power grid safety interlock signal. In response to the power grid safety interlock signal, the dispatching and coordination decision-making system issues a local disconnection command to the downstream non-core node of the line to which the clamp belongs in order to reduce the actual current of the line, and at the same time sends a forced cooling command to the active cooling execution device. The active cooling actuator responds to the forced cooling command to physically cool the wire clamp, thereby reducing the temperature rise of the wire clamp and slowing down the geometric creep rate. The scheduling and collaborative decision-making system continuously and cyclically acquires the real-time thermal stability margin of the target transfer line until the real-time thermal stability margin is greater than or equal to the load to be transferred. Then, the power grid safety interlock signal is released, and the emergency power outage operation and the synchronous load transfer are resumed.

8. The system of claim 1, wherein, The aging test system further includes a multi-physics field coupled aging chamber for constructing an aging environment for the wire clamp. The multi-physics field coupled aging chamber includes: The thermal and humidity stress module is used to regulate ambient temperature and humidity; The mechanical vibration module is used to adjust the vibration frequency and amplitude, and supports single-frequency, multi-frequency superposition, or random vibration modes. The electrical stress module is used to regulate AC current and voltage, and supports constant current, constant voltage or short-circuit impact modes. The chemical stress module is used to simulate the concentration of salt spray, sulfur dioxide, and water vapor mixtures, and supports cyclic modes of corrosion and drying.

9. The system of claim 1, wherein, The aging test system also includes a digital twin verification system, within which a wired virtual model is established to realize a two-way verification mechanism between physical testing and virtual simulation, specifically including: The geometric progressive feature data and electrical micro-precursor feature data collected in the physical environment are input into the virtual model of the wire clamp. The model parameters of the virtual model of the wire clamp are corrected by comparing the simulation results with the physical test results. The virtual model of the clamp is used to simulate the gradual to sudden failure process under different geometric creep paths and power grid operating conditions, and to generate virtual supplementary samples corresponding to the instant of sudden failure.

10. The system of claim 1, wherein, The aging test system also includes an edge application system, which is used to analyze the abnormal status of the clamp in real time on site and to synchronize data bidirectionally with the power grid linkage analysis system. The edge application system includes a miniature acquisition module, a lightweight algorithm module, a wireless communication module, and a local early warning module. The lightweight algorithm module is equipped with a three-dimensional coupled model with compressed parameters, performs local analytical tasks including geometric progressive stage determination, micro-precursor feature extraction, and critical point prediction, and outputs audible and visual warnings or SMS warnings through the local early warning module.

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