Switch loop data extraction method, device and equipment based on phase locking

Abstract

The invention discloses a switch loop data extraction method, device and equipment based on phase locking, and the method comprises the steps: carrying out the interval change analysis of operation data in a ring main unit, and recognizing a communication abnormal node through a phase locking technology; associating a mechanical vibration source based on the communication abnormal nodes, detecting a vibration conduction path, extracting current fluctuation characteristics, and determining position deviation; the operation current intensity is adjusted in combination with fluctuation characteristic parameters, and accurate off-position control is achieved through reverse braking force; constructing a dynamic threshold reference based on frequency domain analysis, and generating a complete operation record according to the dynamic threshold reference and the accurate parking data; performing typed grouping and transmission optimization on the data, and controlling data switching by adopting a gradually migrated beat sequence; and finally, through signal reconstruction and data fusion technologies, a switch loop data set with continuous time sequence and complete space is generated. The method can effectively cope with communication interference, mechanical vibration and other complex environmental factors, and improves the integrity and accuracy of data acquisition.

Description

Technical Field

[0001] The present invention relates to the technical field of power system data processing, and in particular to a method, device and equipment for extracting switch loop data based on phase locking. Background Art

[0002] Ring main units (RMUs) are a crucial component of distribution networks. The operational status of their switch circuits directly impacts the safety and reliability of power systems. The rapid development of smart grids is driving higher demands for the accurate collection and real-time monitoring of switchgear operating data. However, existing switch circuit data collection technologies suffer from issues such as inaccurate communication quality detection, difficulty identifying vibration interference, and low data transmission efficiency. These issues lead to missing, distorted, and incomplete operational data.

[0003] Traditional data collection methods primarily employ fixed-time sampling strategies and static threshold anomaly detection mechanisms. These methods lack in-depth analysis of communication link quality and are unable to accurately identify the root causes of communication failures. When addressing the impact of mechanical equipment vibration on data collection, existing technologies typically employ simple filtering methods or fixed compensation strategies, making them difficult to adapt to complex and changing vibration environments. Furthermore, current data transmission technologies often employ fixed transmission modes and unified processing flows, lacking the ability to optimize configuration based on data characteristics. This makes transmission congestion and data loss more likely to occur in scenarios with large data volumes and high real-time requirements. Furthermore, existing data recovery technologies offer limited means for data interruption and loss during transmission, resulting in limited reconstruction accuracy and difficulty ensuring the integrity and continuity of the final data set. Summary of the Invention

[0004] The present invention provides a switch loop data extraction method, device and equipment based on phase locking, which aims to solve the problems of data missing, distortion and incompleteness caused by communication anomalies and mechanical vibration interference during the ring network cabinet switch loop data acquisition process. Phase locking analysis is used to identify communication abnormal nodes, vibration conduction detection locates the interference source and extracts current fluctuation characteristics, dynamic compensation technology is used to adjust the operating current to achieve precise parking control, dynamic threshold benchmark is constructed based on frequency domain analysis to generate complete operation records, transmission efficiency is optimized through data grouping and dynamic scheduling, and progressive migration technology is used to achieve smooth data switching. Finally, a complete and continuous switch loop data set is generated through signal reconstruction and data fusion, providing high-quality data support for intelligent operation monitoring of ring network cabinets and improvement of distribution network reliability.

[0005] A first aspect of the present invention provides a switching loop data extraction method based on phase locking, comprising the following steps: Collecting operating data in the ring main unit, analyzing the interval changes of the operating data and identifying abnormal communication nodes; Associating the abnormal communication node with a mechanical vibration source to obtain a vibration source position, extracting current fluctuation characteristics based on the vibration source position, and determining a position deviation using the current fluctuation characteristics; extracting fluctuation characteristic parameters based on the current fluctuation characteristics and the position deviation, adjusting the operating current intensity using the fluctuation characteristic parameters to obtain an adjusted current value, and applying a reverse braking force to the switching device using the adjusted current value to obtain precise parking position data; constructing a dynamic threshold benchmark based on the fluctuation characteristic parameters, and generating a complete operation record according to the dynamic threshold benchmark and the precise parking data; According to the complete operation record, the current data group and the position data group are grouped by data type to obtain a current data group and a position data group, a load state of each transmission channel is detected to obtain a load parameter, and the current data group and the position data group are dynamically scheduled using the load parameter to obtain a transmission execution state; constructing a beat sequence of progressive migration using the current data set, performing migration step processing according to the beat sequence to obtain a migration control instruction, performing spatial positioning anchor point analysis on the position data set to obtain a guide path, executing progressive switching according to the migration control instruction and the guide path, and obtaining a switching record; Signal reconstruction is performed in combination with the switching record and the current data group to obtain continuous current data, filling data is obtained based on the transmission execution status and the position data group, and the continuous current data and the filling data are fused to obtain a complete data set.

[0006] A second aspect of the present invention provides a switching loop data extraction device based on phase locking, comprising: An anomaly detection module is used to collect operating data in the ring network cabinet, analyze the interval changes of the operating data and identify communication abnormal nodes; A vibration analysis module, configured to obtain a vibration source position by associating the abnormal communication node with a mechanical vibration source, extract a current fluctuation feature based on the vibration source position, and determine a position deviation using the current fluctuation feature; a brake control module, configured to extract fluctuation characteristic parameters based on the current fluctuation characteristics and the position deviation, adjust the operating current intensity using the fluctuation characteristic parameters to obtain an adjusted current value, and apply a reverse braking force to the switch device using the adjusted current value to obtain precise parking position data; a record generation module, configured to construct a dynamic threshold reference based on the fluctuation characteristic parameters, and generate a complete operation record according to the dynamic threshold reference and the precise parking data; a scheduling management module, configured to group the complete operation record by data type to obtain a current data group and a position data group, detect a load state of each transmission channel to obtain a load parameter, and dynamically schedule the current data group and the position data group using the load parameter to obtain a transmission execution state; a switching execution module, configured to construct a beat sequence for progressive migration using the current data set, perform migration step processing according to the beat sequence to obtain a migration control instruction, perform spatial positioning anchor point analysis on the position data set to obtain a guide path, execute progressive switching according to the migration control instruction and the guide path, and obtain a switching record; A data fusion module is used to combine the switching record and the current data group to perform signal reconstruction to obtain continuous current data, obtain filling data based on the transmission execution status and the position data group, and fuse the continuous current data and the filling data to obtain a complete data set.

[0007] The third aspect of the present invention proposes a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of a phase-locked switching loop data extraction method disclosed in the first aspect are implemented.

[0008] The beneficial effects of the present invention are reflected in the following aspects: First, the time interval of the operating data is accurately analyzed through the phase locking technology, the distribution pattern of the communication abnormal nodes is identified, and the association mechanism between the communication abnormal nodes and the mechanical vibration source is established. Through the spatial distribution map and the vibration conduction path detection, the current fluctuation characteristics are accurately extracted and the position deviation is quantitatively calculated. Combined with dynamic current adjustment and reverse braking force control, the stopping accuracy of the switching device is effectively improved, and the technical problems of low accuracy and insufficient vibration interference suppression ability of the traditional fixed threshold detection method are solved. Second, the progressive migration beat sequence construction technology based on the current data group is adopted. Through beat feature extraction, timing migration pattern establishment and migration stage division, the rhythmic control of data transmission is realized. Combined with the spatial anchor point analysis and guidance path construction of the position data group, the coordinated cooperation of migration control instructions and guidance paths is realized to achieve progressive data switching, which effectively improves the efficiency and stability of data transmission and solves the problems of poor adaptability and frequent transmission congestion of the traditional fixed transmission mode. Third, the signal interruption point identification and timing alignment technology based on switching execution records, combined with signal reconstruction processing using multiple interpolation methods, can effectively recover the current signal data lost or interrupted during transmission, and through the intelligent fusion of continuity data and fill-in data, generate a temporally continuous and spatially complete data set, solving the technical difficulties of low interpolation accuracy and poor data integrity of traditional data recovery methods, and improving the quality and availability of the final data set.

[0009] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings herein illustrate specific examples of the technical solutions described in the present invention, and together with the specific implementation methods constitute a part of the specification, and are used to explain the technical solutions, principles and effects of the present invention.

[0011] Unless otherwise specified or defined, the same reference numerals in different drawings represent the same or similar technical features, and the same or similar technical features may also be represented by different reference numerals.

[0012] Figure 1 It is a flow chart of a method for extracting switching loop data based on phase locking according to the present invention.

[0013] Figure 2 This is a structural block diagram of a switching loop data extraction device based on phase locking in the present invention.

[0014] Figure 3 It is a structural schematic diagram of a computer device of the present invention. DETAILED DESCRIPTION

[0015] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0016] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0017] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0018] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0019] The technical solutions of the embodiments of this application are introduced below.

[0020] like Figure 1 As shown, an embodiment of the present invention provides a switching loop data extraction method based on phase locking, comprising the following steps S110 to S170: Step S110 , collecting operation data in the ring main unit, analyzing the interval changes of the operation data and identifying nodes with abnormal communication.

[0021] Specifically, a high-precision sensor network is configured. The current sensor uses the Hall effect principle, with a measurement accuracy of ±0.1%. The sampling frequency is set to 10kHz, and the measurement range covers 10mA-1000A. The voltage sensor uses a voltage divider resistor network, with a measurement accuracy of ±0.2%, a sampling frequency of 5kHz, and a measurement range of 0-35kV. The switch position sensor uses a photoelectric encoder with a position resolution of 0.1mm and a response time of less than 1ms. For example, a typical 10kV ring network cabinet contains 6 switch circuits, each of which simultaneously monitors three-phase current, line voltage, and circuit breaker position, forming a data acquisition network with 18 current channels, 18 voltage channels, and 6 position channels. Multi-circuit parallel acquisition achieves time synchronization through GPS timing signals to ensure the time consistency of data in each circuit. Data preprocessing includes steps such as zero-point drift correction, filtering and noise reduction, and outlier removal. The acquisition period is set to 100ms to ensure full tracking of the switch operation process. The operation data contains the measurement value and the corresponding communication timestamp, ultimately forming structured operation data containing the current, voltage, switch position information of each switch circuit and its transmission timestamp.

[0022] In some embodiments, the performing interval change analysis on the operating data to identify communication abnormal nodes includes: extracting the data transmission time interval based on the operating data; performing phase lock analysis on the time interval to obtain the locked phase and the unlocked phase; constructing a synchronization reference frequency based on the locked phase, and performing frequency calibration on the unlocked phase based on the synchronization reference frequency to obtain a calibrated phase deviation; and marking the communication abnormal nodes according to the calibrated phase deviation.

[0023] The ring main unit communication system uses a fixed-cycle data transmission protocol. During normal operation, data packets should arrive at a constant interval. Communication link failures, electromagnetic interference, or node clock desynchronization can cause transmission delay fluctuations, manifesting as abnormal time interval variations. The data transmission interval is extracted based on the communication timestamp information in the operating data. The difference between the timestamps of the current measurements is calculated. For example, if the timestamps of two consecutive current measurements are T1 = 10:30:15.123 and T2 = 10:30:15.223, the transmission interval is 100ms. The correlation between time interval anomalies and actual switching operations is verified by combining the changing characteristics of current and voltage data. When a time interval anomaly is detected, the corresponding current and voltage fluctuations are simultaneously analyzed to distinguish data changes caused by communication failures from normal switching operations. A median filter is used to filter the interval sequence to remove transient anomalies caused by clock jitter. Interval data normalization aligns the time intervals of different circuits to the same time base, facilitating subsequent unified analysis. The frequency domain features of the interval sequence are extracted using a fast Fourier transform (FFT) to identify periodic components of the interval variations. For example, a 50Hz dominant frequency component was found in the frequency domain analysis of the interval sequence of a certain loop, indicating the presence of power frequency interference. The statistical characteristics of the time intervals include parameters such as mean, standard deviation, skewness, and kurtosis, which directly reflect the stability of the communication system. The threshold for abnormal intervals is set based on the 3σ criterion, and intervals outside the normal range are marked as abnormal.

[0024] Phase lock analysis is performed based on the phase information of the time interval sequence. The time interval sequence is converted into an analytical signal using the Hilbert transform, and the instantaneous phase φ(t) = arctan(Im[Δt_analytic] / Re[Δt_analytic]), where Δt_analytic is the analytical signal obtained after the Hilbert transform of the time interval sequence Δt, is extracted. This conversion process maps the time interval information in the time domain to the phase domain for analysis. For nodes that detect a 50Hz frequency component, the phase lock determination threshold is adjusted accordingly, relaxing the phase difference change threshold from π / 4 to π / 3 to accommodate power frequency interference environments. Phase lock is determined based on the stability of the phase difference. A locked state is considered achieved when the phase difference change for N consecutive sampling points is less than the adjusted threshold. Locked phases are identified through phase difference variance analysis. Phases with variances less than a preset threshold are marked as locked phases. These locked phases represent the stable operating range of the communication system. Lost-lock phases are identified by detecting phase jumps, which are defined as phase differences between adjacent sampling points exceeding π / 2. For example, in phase lock analysis, loops 1-3 maintain a stable phase relationship between sampling points 100-150, identifying them as locked. However, loop 4 experiences a phase jump of π at sampling point 120, identifying them as unlocked. The statistical characteristics of unlocked phases correlate with the aforementioned time interval statistics, with nodes with greater skewness having a higher probability of unlocking. Phase noise analysis is performed using power spectral density calculations. Based on phase domain conversion and lock state analysis, the complete distribution of locked and unlocked phases can be accurately identified.

[0025] A reference frequency for system synchronization is established based on the locked phase, and this reference is used to calibrate the frequency of the unlocked phase. A weighted average method is used for the locked phases: the reference frequency f_ref = Σ(wi × f_i) / Σwi, where f_i is the frequency corresponding to the i-th locked phase and w_i is the weight coefficient. The weight coefficient is determined based on the stability of the locked phase, with phases with higher stability being assigned a greater weight to ensure the reliability of the reference frequency. A low-pass filter is used to smooth the reference frequency to ensure reference stability. The frequency calibration algorithm uses the least squares method, with the goal of aligning the frequency of the unlocked phase as closely as possible to the reference frequency. For nodes with severe phase jumps, a jump compensation mechanism is introduced during the calibration process, adjusting the calibration gain based on the jump amplitude. The frequency deviation is calculated as the difference between the unlocked phase frequency and the reference frequency. Frequency calibration is performed iteratively, and the calibration gain is adaptively adjusted based on feedback from the calibration results. The calibrated phase deviation is calculated using the phase difference method. Statistical analysis of the deviation includes metrics such as mean deviation, standard deviation, and maximum deviation. This calibration process ultimately generates accurate phase deviation data that quantifies communication quality.

[0026] Communication anomalous nodes are marked based on the calibrated phase deviation. A statistical analysis method is used, with the threshold T_anomaly = μ_deviation + k × σ_deviation, where μ_deviation is the mean deviation, σ_deviation is the standard deviation, and k is the threshold coefficient, typically set to 2.5. Anomalous nodes are classified based on the severity of the deviation: minor anomalies correspond to deviations of 0.05-0.10 radians, moderate anomalies correspond to deviations of 0.10-0.15 radians, and severe anomalies correspond to deviations greater than 0.15 radians. Anomalous nodes are verified by correlating switch position data. When a node is marked as anomalous, the presence of switch action at the corresponding moment is checked to eliminate false anomalies caused by normal operation. Spatial clustering analysis of anomalous nodes is performed using the DBSCAN algorithm to identify concentrated distribution areas of anomalous nodes. Temporal correlation analysis is performed by calculating the temporal patterns of anomaly occurrences to identify periodic and sudden anomalies.

[0027] Step S120 , obtaining the vibration source position by associating the abnormal communication node with the mechanical vibration source, extracting the current fluctuation characteristics based on the vibration source position, and determining the position deviation using the current fluctuation characteristics.

[0028] In some embodiments, obtaining the vibration source position by associating the communication abnormal node with the mechanical vibration source includes: constructing a spatial distribution map based on the communication abnormal node; performing vibration source association analysis on the distribution map to determine the vibration impact range; and locating the vibration source position according to the impact range.

[0029] A spatial distribution map is constructed based on communication anomaly nodes. Using the three-dimensional coordinates of these nodes, the anomaly nodes are precisely located within the spatial coordinate volume of the ring main unit (RMU). This coordinate volume is established with the RMU center as the origin, with the X-axis pointing east, the Y-axis pointing north, and the Z-axis pointing upward. Coordinate accuracy is achieved to the centimeter level. For example, the anomaly nodes "Moderate - Loop 03-2024-01-01 10:30:15," "Minor - Loop 04-2024-01-01 10:30:16," and "Severe - Loop 05-2024-01-01 10:30:17" obtained from S110 correspond to the spatial coordinates (1.5m, 2.3m, 1.8m), (1.8m, 2.5m, 1.9m), and (2.1m, 2.7m, 2.0m), respectively. The anomaly level is related to vibration intensity: severe anomalies correspond to high vibration areas, while minor anomalies correspond to low vibration areas. The spatial mapping of anomaly intensity uses a three-dimensional interpolation method to convert discrete anomaly node data into a continuous spatial intensity distribution. The spatial gradient is calculated by performing a differential operation on the intensity distribution, with the gradient vector pointing in the direction of the fastest intensity change.

[0030] Vibration source correlation analysis is performed on the distribution map to determine the vibration impact range. Divergence analysis of the gradient vector field is used. Areas with negative divergence indicate vector convergence and correspond to the location of the vibration source. The intensity distribution in the spatial distribution map undergoes principal component analysis to identify the primary direction of vibration impact. The direction of the first principal component generally corresponds to the primary direction of vibration propagation. The vibration impact range is determined based on contour analysis of the intensity distribution. Concentric contours centered on the vibration source define the boundaries of the impact range. The impact range is quantified using an ellipse fitting method, where the major and minor axes of the ellipse represent the impact distance of the vibration in different directions. For example, based on the analysis of the spatial distribution map, the impact range ellipse of a vibration source has a major axis of 8 meters and a minor axis of 5 meters, with a major axis angle of 30°, indicating that the vibration has the greatest impact in the northeast direction. The vibration attenuation law is based on the relationship between distance and intensity, and attenuation generally follows an inverse square law or exponential decay.

[0031] The vibration source is located based on the impact range. The geometric center of the impact range is weighted and calculated, with the location coordinate P_source = Σ(I_i × P_i) / ΣI_i, where I_i is the intensity value at the i-th location and P_i is the corresponding location coordinate. Weight assignment considers both intensity and vibration attenuation. Nodes farther away receive reduced weights based on the attenuation law. The weight is adjusted to I_i × exp(-λ × d_i), where λ is the attenuation coefficient and d_i is the distance from the node to the center. The attenuation coefficient λ is determined based on the ring main unit's structural material and vibration frequency. It is set between 0.1 and 0.2 for metal frame structures and 0.2 and 0.3 for hybrid structures to ensure that the weight attenuation matches the actual vibration propagation characteristics. Improving positioning accuracy is achieved through iterative optimization. Starting with the initial positioning result, the location coordinates are gradually optimized to minimize the objective function. Initial positioning uses the unweighted geometric center as the starting point. The iterative step size is adaptively adjusted based on the gradient magnitude. Convergence is considered achieved when the position change is less than 1 cm. The optimization process considers the principal component direction constraint to ensure that the positioning results are adjusted along the main vibration propagation direction. The principal component direction is determined by the eigenvectors of the covariance matrix of the intensity distribution, and the optimization search is preferentially performed along the direction of the first principal component. The objective function adopts a weighted least squares form to minimize the deviation between the predicted intensity and the actual intensity.

[0032] Current fluctuation characteristics are extracted based on the location of the vibration source. Based on the location of the vibration source, the structural and material characteristics of the ring main unit (RMU) are analyzed to determine the vibration transmission path. Considering structural elements such as the metal frame, insulation materials, and connecting components, the vibration transmission characteristics of different materials vary significantly, and there is conduction impedance at the connecting bolts. The conduction path is calculated using the finite element method. The RMU structure is discretized, a conduction matrix based on the stiffness and damping characteristics between adjacent nodes is established, and the vibration propagation delay between each node is calculated. The vibration intensity distribution along the vibration transmission path is used to analyze the mechanism by which vibration affects current measurement. Vibration affects current through direct mechanical coupling and electromagnetic induction coupling. Direct mechanical coupling refers to vibration directly acting on the current sensor, resulting in signal modulation. Electromagnetic induction coupling refers to vibration-induced electromotive force (EMF) generated in a magnetic field due to minute conductor displacements. The current fluctuation amplitude is calculated based on the vibration intensity and sensor sensitivity: ΔI = S_sensor × V_vibration × cos(θ), where S_sensor is the sensor sensitivity, V_vibration is the vibration velocity, and θ is the angle between the vibration direction and the sensor's sensitive axis. Combined with the raw current data collected by the S110, the current changes before and after the vibration are compared to verify the fluctuation characteristics. When periodic fluctuations are detected in the current data during the communication anomaly period, and the fluctuation frequency is consistent with the vibration frequency, the current fluctuation caused by the vibration is confirmed. The fluctuation frequency is extracted through spectrum analysis, and the current fluctuation component corresponding to the vibration frequency is identified. Mechanical vibration produces a sideband modulation effect near the power frequency. The time delay characteristics of the conduction path are analyzed by calculating the phase relationship between the current fluctuation and the vibration signal. The spatial distribution characteristics are determined by analyzing the fluctuation differences of sensors in different locations. Sensors closer to the vibration source have stronger fluctuations. Ultimately, the key current fluctuation characteristic parameters such as amplitude, frequency, and phase are extracted.

[0033] Position deviation is determined using current fluctuation characteristics. The formula for calculating position deviation is Δd = K_pos(f) × A_wave × sin(φ_wave + φ_offset), where K_pos(f) is the frequency-dependent position sensitivity coefficient, A_wave is the current fluctuation amplitude, φ_wave is the fluctuation phase, and φ_offset is the phase offset. The sensitivity coefficient increases with frequency. Low-frequency vibration has little effect on position, while high-frequency vibration has a significant impact. The direction of position deviation is determined based on the phase information of the current fluctuation. A positive phase corresponds to a positive deviation, and a negative phase corresponds to a negative deviation. Combined with switch position data collected by the S110, the calculated position deviation is verified to be consistent with the actual position measurement. When the calculated deviation value matches the abnormal offset detected by the position sensor, the accuracy of the position deviation analysis is confirmed. The effect of frequency on phase offset is determined using the phase-frequency characteristic curve. The direction of position deviation is determined based on the phase information of the current fluctuation. The deviation compensation amount is equal to the negative value of the predicted deviation, ultimately determining the position deviation of the switchgear during operation.

[0034] In step S130, the fluctuation characteristic parameters are extracted in combination with the current fluctuation characteristics and the position deviation, the operating current intensity is adjusted using the fluctuation characteristic parameters, and the adjusted current value is obtained. The adjusted current value is used to apply a reverse braking force to the switching device to obtain precise parking data.

[0035] Specifically, a comprehensive analysis of current fluctuation characteristics and position deviation is performed to extract fluctuation characteristic parameters reflecting the operating status of the switchgear. This is achieved through numerical correlation analysis between the amplitude, frequency, and phase of the current fluctuation characteristics and the position deviation. The correlation strength between the current fluctuation amplitude ΔI and the position deviation Δd is calculated using the correlation coefficient R_correlation = Cov(ΔI, Δd) / [σ(ΔI) × σ(Δd)], where Cov is the covariance and σ is the standard deviation. The correlation analysis uses a sliding window technique with a window length of 100 sampling points to ensure the stability of the statistical results. The covariance calculation takes into account the time delay effect of the signal, and the optimal delay time is determined using the cross-correlation function. For example, for a switching circuit with a current fluctuation amplitude of 2.5A and a position deviation of 1.2mm, a correlation coefficient of 0.85 was obtained based on historical data analysis, indicating a strong correlation between the two. The stability of the fluctuation frequency is assessed by calculating the frequency variance. Frequency extraction uses a fast Fourier transform to identify the dominant frequency component and harmonic components. Phase consistency analysis is performed by calculating the difference between the current fluctuation phase and the position deviation phase. Phase extraction uses the Hilbert transform to obtain the instantaneous phase. Fluctuation characteristic parameters include three core parameters: the fluctuation intensity factor, the frequency stability factor, and the phase synchronization factor. Each factor is normalized to ensure a value between 0 and 1. The comprehensive fluctuation characteristic parameters are calculated using a weighted summation method, with weights assigned based on the influence of each factor on parking accuracy. In-depth correlation analysis between current fluctuation characteristics and position deviation completes the quantitative extraction of the fluctuation characteristic parameter W_total.

[0036] The operating current is adjusted based on the magnitude of the fluctuation characteristic parameter W_total, achieving precise control by establishing a parameter-current mapping relationship. Based on the square relationship between electromagnetic force and current, increasing the current strengthens the electromagnetic attraction, improving the stability and accuracy of the switching operation. The adjustment strategy employs differentiated control based on various factors: the fluctuation intensity factor influences the adjustment amplitude; when the fluctuation intensity factor is greater than 0.7, the basic adjustment amount is increased; the frequency stability factor influences the adjustment rate; when the frequency is unstable, a slow adjustment is adopted to avoid resonance; and the phase synchronization factor influences the adjustment timing; when the phase is out of sync, the adjustment is delayed to allow for stability. The adjusted current value is calculated as I_adjusted = I_nominal × (1 + K_adjust × W_total), where I_nominal is the nominal operating current and K_adjust is the adjustment coefficient. When the correlation coefficient R_correlation is greater than 0.8, the strong coupling adjustment mode is adopted, and the adjustment coefficient K_adjust is increased by 20%; when R_correlation is less than 0.5, the weak coupling mode is adopted, and the adjustment coefficient is reduced by 30%. The direction of current adjustment is determined by the sign of the fluctuation characteristic parameter. A positive value indicates that the current needs to be increased to overcome the additional resistance, while a negative value indicates that the current needs to be reduced to avoid overshoot. The adjustment range is limited by the device's rated parameters and safety margin to ensure that the adjusted current does not exceed the device's tolerance.

[0037] In some embodiments, the use of the adjusted current value to apply a reverse braking force to the switching device to obtain precise parking data includes: calculating motion inertia parameters based on the adjusted current value; determining the timing of applying the braking force according to the inertia parameters; applying a reverse braking force to the switching device based on the braking force application timing to obtain a braking response; and generating precise parking data according to the braking response.

[0038] The motion inertia parameters are calculated based on the adjusted current value I_adjusted. The relationship between current and motion speed is based on the motor's torque-current characteristic curve. Torque is proportional to current, and torque further determines acceleration and motion speed. The current torque is inferred from the adjusted current value, and the moment of inertia is inverted based on historical motion data: moment of inertia J = T / (dω / dt), where T is torque and dω / dt is angular acceleration. The center of mass is calculated using a weighted average method, taking into account the mass and position coordinates of each component. The equation of motion is based on Newton's second law, considering the balance between driving torque, damping torque, and moment of inertia. The conversion between angular velocity and linear velocity is achieved through the geometric relationship of the switch contact's motion trajectory, converting the contact's circular motion into linear displacement. The frequency characteristic analysis of the inertia parameters considers the natural vibration modes of the switch device, and the differences in the effective moment of inertia at different frequencies. Based on the dynamic transformation of the adjusted current value and inertia characteristic analysis, the two core motion inertia parameters, moment of inertia and motion speed, are obtained.

[0039] The optimal timing for applying the braking force is determined based on inertia parameters, enabling precise control of the braking process. The braking distance is calculated using the formula S_brake = v_current² / (2 × a_brake), where v_current is the current velocity and a_brake is the braking acceleration. The relationship between braking acceleration and braking force is determined by dividing the braking torque by the inertia moment, calculated based on the adjusted current value. Braking timing is determined when the distance between the current position and the target position equals the braking distance. For example, if the current switch velocity is 50 mm / s and the braking acceleration is 100 mm / s², braking will begin when the switch reaches the required braking distance from the target position. The timing safety margin is set based on control accuracy and response delay, with a 10% safety margin added to the actual braking distance. A multi-stage braking strategy uses different braking intensities according to different stages of motion, with light braking applied initially and stronger braking applied as the vehicle approaches the target. Key braking force application timing parameters, including the braking initiation distance and the braking initiation timing T_timing, are determined through precise application of inertia parameters and dynamic calculations.

[0040] Based on the timing of braking force application, a reverse braking force is applied to the switch device to obtain the braking response. Braking force is applied strictly according to the predetermined braking start timing (T_timing). When the switch moves to a distance equal to the braking distance from the target position (T_timing), braking current is immediately initiated. Based on the adjustment direction, the braking current gradually increases from the adjusted value during positive adjustment and gradually decreases from the adjusted value during negative adjustment. The braking current curve uses an S-shaped curve to ensure smooth braking force application and avoid shock. Real-time calculation of the braking torque is based on the adjusted current value and magnetic field strength. Braking response is monitored synchronously using multiple sensors: position sensors monitor position changes, velocity sensors monitor velocity changes, and acceleration sensors monitor acceleration changes. The braking response curve records include multi-dimensional data such as position-time, velocity-time, and acceleration-time curves. Braking effectiveness is evaluated by comparing actual braking response with theoretical expectations. Evaluation metrics include braking time, braking accuracy, and velocity decay rate.

[0041] Based on multi-dimensional data from the braking response, including position-time, velocity-time, and acceleration-time curves, precise parking data is extracted and integrated. This precise parking data includes: parking error (calculated from the difference between the final position and the target position in the position-time curve), actual braking time (determined from the moment the velocity drops to zero in the velocity-time curve), velocity decay rate (obtained from the slope of the velocity-time curve), braking smoothness (assessed from the degree of fluctuation in the acceleration-time curve), and braking accuracy (compared with the final position sensor reading and the target value). A complete precise parking data set is generated through comprehensive analysis of data from multiple sensors. For example, the precise parking data for a particular braking operation is: {parking error: 0.05mm, braking time: 180ms, velocity decay rate: 278mm / s², braking smoothness: ±5mm / s², braking accuracy: 99.5%}. Statistical analysis of multiple braking response data forms the distribution characteristics of precise parking data, including statistical parameters such as mean, standard deviation, and maximum deviation, and ultimately generates precise parking data for evaluating the parking performance of switchgear and optimizing control strategies.

[0042] Step S140: construct a dynamic threshold benchmark based on the fluctuation characteristic parameters, and generate a complete operation record based on the dynamic threshold benchmark and the precise parking data.

[0043] In some embodiments, constructing a dynamic threshold benchmark based on the fluctuation characteristic parameters includes: performing frequency domain analysis on the fluctuation characteristic parameters to obtain frequency domain characteristics; identifying dominant frequency components based on the frequency domain characteristics; calculating a threshold adjustment coefficient based on the dominant frequency components; and constructing a dynamic threshold benchmark based on the adjustment coefficient.

[0044] The fluctuation characteristic parameter W_total exhibits periodic variations in the time series. This periodicity reflects the operating rhythm and interference pattern of the ring main unit. Frequency domain analysis of the fluctuation characteristic parameter W_total is performed, and the frequency characteristics of the parameter variations are identified through spectral transformation. Frequency domain analysis uses the Fast Fourier Transform (FFT) method to convert the time domain fluctuation characteristic parameter sequence into a frequency domain spectral distribution. During the transformation, the sampling frequency is set to 100 Hz and the number of transformation points is selected to 1024, ensuring a frequency resolution of 0.1 Hz. The Hanning window function is selected to effectively reduce spectral leakage. Spectral preprocessing includes DC component removal and normalization. DC component removal is achieved by subtracting the sequence mean, and normalization ensures comparability of signals of different amplitudes. Frequency domain feature extraction includes key parameters such as power spectral density, spectral center of gravity, and spectral bandwidth. Power spectral density reflects the energy distribution of each frequency component, spectral center of gravity reflects the center position of the spectrum, and spectral bandwidth reflects the degree of spectral dispersion. Spectral calculation uses overlapping segmentation with a segment length of 256 points and an overlap ratio of 50% to improve the reliability of spectral estimation.

[0045] Based on the distribution patterns and energy concentration of frequency domain features, the frequency components that play a dominant role in threshold construction are identified. A combined method of peak detection and energy analysis is used to identify dominant frequencies by identifying significant peaks in the power spectral density. The peak detection threshold is set at three times the average power spectral density, and the minimum interval between peaks is set at 1 Hz to avoid identifying overly dense spurious peaks. Energy analysis is performed by calculating the energy contribution ratio of each frequency component, which is the ratio of the power at that frequency to the total power. Dominant frequencies are selected based on energy contribution ranking, with frequency components with an energy contribution ratio greater than 5% being selected as dominant. The spectral center of gravity is used to determine the shift in the frequency distribution. A shift toward higher frequencies indicates that the system is affected by high-frequency interference. The spectral bandwidth is used to assess the degree of frequency dispersion. A narrow bandwidth indicates a single interference source, while a wide bandwidth indicates the presence of multiple interference sources. The stability of frequency components is assessed through consistency analysis across multiple measurements, with frequency components with good consistency being prioritized. The physical significance of frequency components is analyzed by considering the inherent characteristics of the device and the characteristics of the excitation source. Natural frequencies correspond to structural resonances of the device, while excitation frequencies correspond to external interference sources.

[0046] The threshold adjustment coefficient is calculated based on the dominant frequency components. Frequency weights are assigned using the square root of the energy contribution rate, ensuring that high-energy frequencies receive a greater influence weight. Energy contribution rates are normalized by standardizing the total energy, making the contribution rates under different measurement conditions comparable. The comprehensive adjustment coefficient is derived by weighting the dominant frequency components and performing a weighted summation of all dominant frequency components. The calculation of the adjustment coefficient takes into account the interaction and coupling effects between frequencies, and improves calculation accuracy through cross-term correction. The impact strength of the dominant frequency is assessed based on its contribution to the total spectral energy. The higher the contribution, the greater the contribution to the adjustment coefficient. The range of the adjustment coefficient is limited to 0.5-2.0 to avoid instability caused by over-adjustment. Quantitative analysis of the dominant frequency components completes the precise calculation of the threshold adjustment coefficient.

[0047] A dynamic threshold baseline is constructed based on the adjustment coefficient. When vibration is strong, the threshold is increased accordingly, while when vibration is weak, the threshold is decreased accordingly, ensuring that the judgment criteria align with the actual operating conditions. The threshold adjustment coefficient is applied to the correction calculation of the basic threshold. The dynamic threshold is obtained by multiplying the basic threshold and the adjustment coefficient. The basic threshold is determined based on the mean and standard deviation of historical statistical data, using the mean plus twice the standard deviation. The frequency-adaptive nature of the threshold is achieved through real-time updating of the adjustment coefficient, which is dynamically calculated based on the current dominant frequency component. The time evolution of the threshold is updated using exponential smoothing, with the old and new thresholds weighted averaged according to the set smoothing coefficient. Threshold boundary checks ensure that the dynamic threshold remains within a reasonable range. The upper limit is set at 1.5 times the basic threshold, and the lower limit is set at 0.7 times the basic threshold. Dynamic threshold stability analysis is performed by continuously monitoring the variance of threshold changes. If the variance is excessive, the smoothing coefficient is automatically increased.

[0048] A complete operation record is generated based on the dynamic threshold benchmark and precise parking data. The quality of operation execution is evaluated by comparing various indicators based on the precise parking data with the dynamic threshold benchmark. The parking error assessment compares the parking error in the precise parking data with the dynamic threshold benchmark, calculating the relative deviation to obtain a position accuracy score. The braking time assessment compares the actual braking time with the time threshold to obtain a time efficiency score. The braking smoothness assessment, based on acceleration change data, compares it with the smoothness threshold to obtain a smoothness score. The formula for calculating the comprehensive operation quality score is C_total = w1 × E_position + w2 × E_time + w3 × E_smooth, where E_position is the position accuracy score, E_time is the time efficiency score, and E_smooth is the smoothness score. The weights w1 = 0.5, w2 = 0.3, and w3 = 0.2. A complete operation record is generated by integrating all relevant data in chronological order. The record structure includes: operation timestamp (accurate to the millisecond), operation type (switch action type), fluctuation characteristic parameter values ​​(W_total and its three components), dynamic threshold baseline values, precise stop data (stop error, braking time, speed decay rate, braking smoothness, braking accuracy), quality assessment results (individual scores and overall score), and anomaly flags (whether the dynamic threshold is exceeded). The data is stored in a structured format to facilitate subsequent query and analysis. For example, a complete operation record for a particular operation might be: {Time: 2024-01-01 10:30:15.235, Type: Circuit Breaker Closing, W_total: 0.68, Dynamic Threshold: 0.18, Stop Error: 0.05mm, Braking Time: 180ms, Overall Score: 0.92, Status: Normal}. The operation record integrity check ensures that all required fields contain valid data, and missing data is filled using interpolation or default values. The resulting complete operation record provides detailed data support for switchgear operation analysis and fault diagnosis.

[0049] Step S150 , grouping the complete operation record by data type to obtain current data groups and position data groups, detecting the load status of each transmission channel to obtain load parameters, and dynamically scheduling the current data groups and position data groups using the load parameters to obtain the transmission execution status.

[0050] Specifically, the ring main unit control system includes multiple transmission channels for transmitting control instructions and monitoring data between the central controller and each switch actuator. Complete operation records are classified and grouped according to data type characteristics, extracting current and position data groups for transmission optimization. Data grouping is performed based on the data type identifier and measurement characteristics in the complete operation records. Current data is identified using the current identifier in the data record, which includes current-related information such as three-phase current values, current RMS values, and current harmonic components. Position data is identified using the position identifier, which includes position-related information such as switch position coordinates, motion trajectory, and stop accuracy. The grouping process uses a data type filtering method to automatically classify data based on the type field of the data record. The current data group consists of time series data such as current amplitude, current frequency, and current phase. The position data group consists of spatial data such as position coordinate, position deviation, and motion speed. Data integrity verification is performed by counting the number and temporal coverage of each data group to ensure the integrity of the grouping results. For example, a complete operation record contains 1000 data records, including 600 current-related records and 400 location-related records, forming the current data group and the location data group, respectively. Data deduplication is achieved by comparing timestamps and numerical values ​​to remove duplicate data records. Group indexes are established based on timestamp sorting to facilitate subsequent rapid retrieval and processing. This typed grouping of the complete operation record yields structured current and location data groups.

[0051] After data grouping is completed, the current load status of each transmission channel needs to be evaluated to select the optimal transmission path. Load parameters are obtained by comprehensively evaluating key channel metrics such as data flow, transmission delay, and error rate. Data flow is monitored by counting the amount of data transmitted per unit time. Flow calculation uses a sliding window method with a window length of 10 seconds. Transmission delay is measured by calculating the difference between the send and receive timestamps. Delay statistics include parameters such as average delay, maximum delay, and delay jitter. The error rate is calculated by the ratio of error packets to total packets, and error detection uses a checksum verification method. Channel bandwidth utilization is calculated based on the ratio of actual transmission rate to the rated channel bandwidth. A utilization exceeding 80% is considered heavily loaded. Load assessment uses a multi-dimensional scoring method: the load score L_score = w1 × flow score + w2 × delay score + w3 × error rate score, with weights w1 = 0.4, w2 = 0.4, and w3 = 0.2. Channel priority is determined based on the load score, with high-priority channels allocated to important data transmission. Real-time update of load parameters is achieved through periodic monitoring with an update period of 5 seconds. Comprehensive load monitoring of each transmission channel establishes a complete set of load parameters.

[0052] Load parameters are used to dynamically schedule current and position data groups to determine their transmission execution status. First, a dynamic scheduling plan is developed by matching the channel load scores based on the load parameters with the data group's transmission requirements. Data transmission requirements are assessed through a comprehensive analysis of factors such as data group size, priority, and real-time requirements. Current data groups with high real-time requirements are assigned to low-latency transmission channels, while position data groups with high reliability requirements are assigned to low-error-rate transmission channels. High-frequency data (frequency components exceeding 50 Hz in the current data group) are prioritized for high-bandwidth channels, while precise positioning data (data with position deviations less than 0.1 mm) in the position data group are prioritized for high-stability channels. Operational data with a low C_total score is prioritized for transmission, facilitating rapid problem analysis. Next, dynamic scheduling is performed using load balancing principles to distribute data transmission tasks as evenly as possible across transmission channels. The optimization objectives are to minimize overall transmission latency and maximize transmission reliability. A greedy strategy is used to prioritize high-priority data to the optimal channel. The dynamic scheduling plan is adjusted based on real-time changes in channel status and the allocation strategy is recalculated. Then, based on the channel allocation information in the dynamic scheduling scheme, the corresponding transmission channels are started according to a predetermined startup sequence. The startup sequence arrangement takes into account data priorities and dependencies. The channel initialization process includes steps such as channel parameter configuration, buffer allocation, and transmission protocol settings. Finally, the transmission execution is monitored by real-time tracking of indicators such as data transmission progress, transmission rate, and transmission quality. The transmission progress is calculated based on the ratio of the amount of data transmitted to the total amount of data. The transmission quality is evaluated based on data integrity checks and error recovery statistics. The exception handling mechanism is implemented through strategies such as transmission timeout, error retry, and channel switching. The execution status record contains complete information such as the start time, transmission time, completion time, and transmission results. Ultimately, the transmission execution status reflecting the transmission performance of each channel is obtained.

[0053] Step S160: Use the current data group to construct a beat sequence for progressive migration, perform migration step processing according to the beat sequence to obtain a migration control instruction, perform spatial positioning anchor point analysis on the position data group to obtain a guide path, perform progressive switching according to the migration control instruction and the guide path, and obtain a switching record.

[0054] Specifically, when switching between channels, the ring main unit (RMU) needs to gradually migrate control authority and operating data from the current control channel to the target channel to avoid electrical shock caused by sudden changes. Traditional direct switching methods can cause sudden current surges and control signal interruptions at the moment of switching, potentially leading to malfunctioning switches or equipment damage. Therefore, it is necessary to establish a beat sequence and spatial guidance path for gradual migration to achieve a smooth transition of control authority and seamless data migration, ensuring safe and stable operation of the RMU during channel switching.

[0055] In some embodiments, the use of the current data group to construct a beat sequence of progressive migration includes: extracting beat features from the current data group; constructing a timing migration pattern based on the beat features; dividing the migration stages according to the migration pattern; and generating a beat sequence of progressive migration based on the migration stages.

[0056] Beat feature extraction is performed on the current data set. Time domain feature extraction includes identifying key moments such as current peaks, valleys, rising edges, and falling edges. Peak detection uses a local maximum search method, with the detection threshold set at 1.5 times the current mean. Valley detection uses a local minimum search method, complementing peak detection. Rising and falling edges are detected by the sign change of the current rate of change; a valid edge is identified when the rate of change exceeds a threshold. Frequency domain feature extraction is performed using a fast Fourier transform to identify the primary frequency components of the current variation. The beat frequency is determined based on the peak position of the power spectral density; the frequency corresponding to the peak is the beat frequency. The beat amplitude is calculated based on the difference between the time domain peak and valley values; the amplitude reflects the strength of the beat. Beat regularity is assessed using variance analysis of the beat intervals; beats with smaller variance are more regular. Beat synchronization analysis considers the beat consistency between multiphase currents; beats with better synchronization are more suitable as migration references. In-depth analysis of the current data set extracts beat characteristic parameters that reflect the rhythm of the variation.

[0057] Based on the extracted beat features, a migration pattern is constructed to describe the temporal regularity of data migration. A multi-level temporal model is employed, consisting of three layers: a fast migration layer, a medium migration layer, and a slow migration layer. The fast migration layer corresponds to high-frequency beat features and handles rapidly changing data migration needs. The medium migration layer corresponds to medium-frequency beat features and handles routine data migration operations. The slow migration layer corresponds to low-frequency beat features and handles data maintenance migration under stable conditions. Data with good beat regularity is prioritized in the fast migration layer, leveraging its stable beat characteristics for efficient migration. Data with poor beat regularity is prioritized in the slow migration layer, which uses smoothing to mitigate the impact of irregular beats. Pattern parameters are configured based on statistical analysis of beat features. The frequency parameter uses the mean beat frequency, and the amplitude parameter uses the effective value of the beat amplitude. Temporal relationships are established through correlation analysis between beat features. Features with high correlation are grouped into the same temporal pattern. Pattern adaptive adjustment is based on real-time changes in beat features, with pattern parameters updated when significant changes occur. Pattern validation is achieved through regression testing of historical data to verify the predictive accuracy and adaptability of the pattern, and the temporal modeling of beat characteristics constructs a temporal migration pattern to guide data migration.

[0058] Data migration execution is divided into phases based on the hierarchical structure and frequency characteristics of the sequential migration model. Phase division is based on factors such as migration complexity, data priority, and system load. The initial phase corresponds to the slow migration layer, primarily responsible for system preparation and basic data migration. The transition phase corresponds to the medium-speed migration layer, primarily responsible for batch migration of key data. The sprint phase corresponds to the fast migration layer, primarily responsible for the rapid migration of high-priority data. Phase transitions are based on migration progress and system status, with preparations for the next phase initiated when the current phase is 80% complete. Phase durations are calculated based on data volume and migration rate, with time allocation following a 2:5:3 ratio. Phase parallelism is designed to allow for partial overlap between adjacent phases, improving overall migration efficiency. A phase monitoring mechanism, implemented through phase milestones and checkpoints, ensures that each phase executes as planned. Phase adjustment strategies are dynamically optimized based on real-time execution, triggering adjustments when a phase performs abnormally. The phased decomposition of the sequential migration model establishes a structured migration phase plan.

[0059] Based on the divided migration phases and corresponding execution strategies, a migration beat sequence with progressive characteristics is generated. The sequence structure is designed using a hierarchical progressive model, with beat intensity and frequency gradually increasing with each phase. The beat sequence in the initial phase uses a low-frequency, small-amplitude beat pattern to ensure a smooth system startup. The beat sequence in the transition phase uses a medium-frequency and amplitude beat pattern to establish a stable migration rhythm. The beat sequence in the sprint phase uses a high-frequency, large-amplitude beat pattern to achieve fast and efficient data migration. The gradual adjustment of beat parameters uses linear or exponential functions for smooth transitions, avoiding system shocks caused by sudden beat changes. A sequence synchronization mechanism ensures that the beat sequences of each channel remain consistent, preventing data conflicts and contention. The beat sequence is encoded using a combination of timestamps and intensity values, facilitating precise execution and control. Through beat conversion and progressive design during the migration phase, a complete progressive migration beat sequence is ultimately generated.

[0060] Based on the beat strength and timing distribution characteristics of the beat sequence, the migration step size is precisely processed to generate migration control instructions. The step size is determined using a logarithmic mapping of the beat strength: strong beats correspond to large step sizes, while weak beats correspond to small step sizes. The step size calculation formula is S_step = S_base × log(1 + I_beat / I_ref), where S_base is the base step size, I_beat is the beat strength, and I_ref is the reference strength. The migration direction is determined based on the increasing or decreasing trend of the beat sequence: an increasing trend corresponds to a positive migration, while a decreasing trend corresponds to a negative migration. Step size optimization considers the balance between transmission efficiency and stability. Excessively large step sizes may lead to unstable transmission, while excessively small step sizes affect transmission efficiency. The segmented migration control process divides the entire migration process into multiple stages, each employing a different step size strategy. The control instruction encoding contains complete information, including step size, migration direction, execution time, and priority. The execution order of instructions is based on the temporal order of the beat sequence, ensuring a continuous and smooth migration process. The format of control instructions adopts structured coding to support fast parsing and execution. The step size quantization of beat sequence and control strategy design generate accurate migration control instructions.

[0061] In some embodiments, performing spatial positioning anchor point analysis on the position data group to obtain a guidance path includes: extracting spatial coordinate information based on the position data group; performing cluster analysis on the coordinate information to determine the anchor point position; constructing a path connection matrix based on the anchor point position; and generating a guidance path according to the connection matrix.

[0062] Spatial coordinate information, including three-dimensional coordinate values ​​and related attribute information, is extracted from the location data set. Coordinate system unification converts coordinate data from different sources into a unified reference coordinate system, ensuring coordinate data consistency. Coordinate accuracy is calibrated through calibration using known reference points to eliminate measurement errors and systematic biases. Coordinate data filtering uses a combination of median and mean filtering to remove noise and outliers from the coordinate data. Spatiotemporal association is established by associating coordinate information with corresponding timestamps to form a spatiotemporal coordinate sequence. Coordinate resolution optimization adjusts the coordinate accuracy level based on application requirements, balancing accuracy and computational efficiency. Coordinate integrity is verified by counting the number and distribution density of valid coordinate points to ensure coordinate data integrity. Coordinate quality is assessed through a comprehensive evaluation based on metrics such as coordinate accuracy, stability, and continuity. Coordinate parsing and preprocessing of the location data set extracts high-quality spatial coordinate information.

[0063] Coordinate information is clustered to determine anchor point locations. The K-means clustering method is used to group spatial coordinates based on similarity. The number of clusters is determined based on the spatial distribution characteristics and density analysis of the coordinate data, and the optimal number of clusters is selected using the elbow rule. Euclidean distance is used as the distance metric to calculate the spatial distance between coordinate points. Cluster centers are initialized using the K-means++ method to ensure a reasonable distribution of initial cluster centers. The clustering iteration process is achieved by minimizing the sum of squared distances within clusters, and the iteration termination condition is that the change in cluster center is less than a threshold. Clustering quality is comprehensively evaluated using multiple evaluation metrics to ensure the rationality of the clustering results. Anchor point locations are determined based on the center coordinates of each cluster. The cluster center is the candidate anchor point location. Anchor point screening considers factors such as representativeness, stability, and accessibility, and the most suitable location is selected as the final anchor point. Anchor point attributes are recorded, including location coordinates, coverage, and importance weight. Clustering and optimization analysis of spatial coordinates determine the anchor point locations for the transmission network.

[0064] A path connectivity matrix is ​​constructed based on the anchor point locations. The matrix dimension is equal to the number of anchor points, and the matrix elements represent the connection weights between corresponding anchor points. Connection weights are calculated based on the Euclidean distance between anchor points, with shorter distances giving higher weights. Connectivity is determined by considering whether a direct physical path exists between anchor points. Weights between unconnected anchor points are set to infinity. Path quality is assessed based on multiple factors, including distance, reliability, and transmission capacity. Paths with higher quality receive higher connection weights. Matrix symmetry is ensured by ensuring that the matrix element M(i, j) = M(j, i), reflecting the bidirectional nature of connections between anchor points. Matrix sparsity is optimized by setting a distance threshold. Direct connections between anchor points exceeding the threshold are not established, reducing matrix complexity. The connectivity matrix is ​​normalized to a range of 0–1, facilitating subsequent path calculations. Matrix validation is achieved by checking the matrix's connectivity and rationality, ensuring that all anchor points are reachable via the path. The matrix update mechanism supports dynamic adjustment when anchor point locations change. Connectivity modeling at anchor points constructs a complete path connectivity matrix.

[0065] Guided paths are generated based on the connectivity matrix. The Dijkstra shortest path method is used to find the optimal connection path between anchor points. The starting and destination points of the path search are determined based on data transmission requirements, supporting both point-to-multipoint and multipoint-to-multipoint path planning. Path costs are calculated based on the weights in the connectivity matrix, and the path with the lowest cost is selected as the optimal path. Path constraints include maximum hop count, transmission capacity, and reliability requirements. Alternative paths are generated using the K-shortest path algorithm, providing multiple options for each pair of anchor points. The objective function for path optimization comprehensively considers multiple objectives, including path length, transmission delay, and reliability. Path load balancing is achieved by distributing data transmission across multiple paths to avoid overloading a single path. Dynamic path adjustments are based on real-time changes in network status, automatically switching to alternative paths when path quality degrades. Path optimization and multi-path planning based on the connectivity matrix generate a complete network of guided paths between anchor points.

[0066] Gradual handover is performed based on the migration control instructions and the guided path, generating handover records. The timing of handover is determined by the execution time of the migration control instruction and the arrival time of nodes along the guided path. The initiation of the handover process follows the priority ranking of the migration control instructions, with higher-priority instructions taking precedence. The handover path is selected based on the weight distribution of the guided paths and the current transmission status, while also taking into account the aforementioned transmission execution status. Channels with good performance and light loads are prioritized. The handover step size is strictly controlled according to the step size parameters of the migration control instruction to ensure a smooth handover process. Gradual handover is implemented by gradually increasing the handover frequency and amplitude, from small-scale trial handovers to full handover. Handover execution is monitored for key metrics such as handover success rate, handover latency, and data integrity. Exception handling mechanisms address handover failures by setting handover timeouts, fallback strategies, and backup paths. The complete structure of a handover record consists of: {command number, path identifier, handover start time, handover completion time, pre-handover status, post-handover status, data integrity check results, transmission performance metrics (latency, throughput, error rate), and exception flags}.

[0067] Step S170 , combining the switching record and the current data group to perform signal reconstruction to obtain continuous current data, obtaining filling data based on the transmission execution status and the position data group, and fusing the continuous current data and the filling data to obtain a complete data set.

[0068] In some embodiments, the signal reconstruction performed in combination with the switching record and the current data group to obtain continuous current data includes: identifying a signal interruption point based on the switching record; performing timing alignment on the interruption point and the current data group; and performing signal interpolation processing based on the timing alignment result to generate continuous current data.

[0069] Signal interruption points are identified based on handover records. Signal interruption point identification utilizes a comprehensive analysis of the complete structural information in handover records. Data integrity check results are analyzed to directly identify data transmission interruptions. When the check indicates data incompleteness or a check failure, the corresponding time period is marked as a signal interruption. Comparative analysis of the states before and after the handover identifies abnormal state transitions. When the state before the handover is "normal transmission" and the state after the handover is "transmission abnormality" or "partial failure," the handover point is identified as a potential interruption point. Inspection of abnormality flags provides the most direct basis for interruption identification. Handover records marked as "abnormal" indicate confirmed signal interruptions. Error rate analysis, a transmission performance metric, assesses signal quality. Handover periods with an error rate exceeding 5% are identified as interruptions caused by degraded signal quality. Handover time is extracted from the timestamp field of the handover record, including the handover start time and handover completion time. The difference between the two is the handover delay. Disruption types are categorized based on a comprehensive assessment of multiple indicators: instantaneous interruption (delay <1ms and error rate <1%), short interruption (delay 1-10ms or error rate 1-5%), and long interruption (delay >10ms or error rate >5%). The assessment of interruption intensity comprehensively considers factors such as the degree of data integrity check failure, the magnitude of state changes, and the severity of anomaly markings. The precise location of the interruption is determined by the path identification field. Switching to different paths corresponds to signal interruptions at different locations. Interruption points are marked using a combination of timestamp, interruption type, and severity. In-depth analysis of switching records accurately identifies the distribution of interruption points in the current signal.

[0070] The interruption points and current data sets are time-aligned. Time base unification converts the interruption point times and current sampling times to the same time reference system, eliminating the impact of clock discrepancies. Time accuracy is achieved through high-precision time synchronization, achieving microsecond-level calibration. The alignment algorithm uses a nearest neighbor matching method to find the current sampling point closest to the interruption point. Alignment deviation is calculated based on the time difference, and alignments with deviations less than half the sampling interval are considered valid. Batch alignment of multiple interruption points is achieved through parallel processing, improving alignment efficiency. Alignment results are verified through a time consistency check to ensure the correct timing relationships after alignment. Alignment quality is assessed based on alignment accuracy and alignment success rate, with alignments with high accuracy and success rate being prioritized. An alignment mapping table is created to record the correspondence between interruption points and current sampling points, supporting subsequent interpolation processing. Alignment results are formatted and stored using an indexed table structure, containing information such as the interruption point number, corresponding sampling point, alignment accuracy, and alignment status.

[0071] Signal interpolation is performed based on the timing alignment results to generate continuous current data. Signal interpolation uses the interruption point locations determined by timing alignment and the surrounding normal current data for numerical estimation. The interpolation method is selected based on the interruption type and duration: instantaneous interruptions use linear interpolation, short interruptions use cubic spline interpolation, and long interruptions use pattern-based interpolation. The calculation formula for linear interpolation is I_interp = I_start + (I_end - I_start) × t / T, where I_start and I_end are the current values ​​before and after the interruption, t is the interpolation time, and T is the interruption duration. Cubic spline interpolation considers the smoothness of current changes and ensures the continuity and differentiability of the interpolation curve by constructing a cubic polynomial. Pattern interpolation uses data patterns from historically similar interruptions and uses pattern matching to find the most similar current change pattern for replication. Phase continuity verification is incorporated into the interpolation process. The instantaneous phase of the interpolated data is calculated and compared with the locked phase identified by the S110. If the phase deviation exceeds π / 8, the interpolation strategy is adjusted to ensure that the reconstructed signal maintains the original phase characteristics. Interpolation accuracy is controlled by setting an interpolation error threshold. When the error exceeds the threshold, a more accurate interpolation method is used. Interpolated data is marked with a special identifier to distinguish between original and interpolated data. Interpolation quality is assessed through cross-validation, using a subset of known data to verify interpolation accuracy.

[0072] Filled data is obtained based on the transmission execution status and location data sets. Transmission execution status analysis utilizes comprehensive metrics such as transmission success rate, transmission delay, and data integrity for a comprehensive assessment. Missing data is identified through statistical analysis. Data is considered missing when the transmission success rate falls below 95% or the transmission delay exceeds a preset threshold. Spatial continuity checks for location data are performed based on the spatial coordinate sequence of the location data set, identifying spatial gaps and unusual jumps in location records. Spatial interpolation utilizes the inverse distance weighting method to infer the values ​​of missing locations based on data from neighboring locations. Temporal interpolation utilizes linear interpolation to infer the location values ​​at intermediate moments based on location data from preceding and subsequent moments. Filled data types include missing data completion, anomaly correction, and precision enhancement. Filled data is generated based on historical data from similar time periods, using pattern matching to identify the most similar data segments for replacement. Corrected data is generated based on anomaly detection and data correction to identify and correct obviously erroneous data points. Enhanced data is generated based on the need for improved accuracy, using data fusion technology to enhance data accuracy and reliability.

[0073] Continuous current data and padded data are fused to obtain a complete dataset. A weighted averaging method based on quality weights is used. The fusion weight is calculated as W_fusion = Q_data × C_confidence, where Q_data represents the data quality score and C_confidence represents the confidence coefficient. The weight of the continuous current data is determined based on its temporal continuity quality and phase verification results, with data that passes phase verification receiving a higher weight. The weight of the padded data is determined based on its confidence level and source reliability, with data from channels with good transmission performance receiving a higher weight. Data consistency is checked through cross-validation to ensure temporal and spatial consistency of the fused data. Conflicting data are handled using a priority strategy, with high-quality continuous current data taking precedence over low-confidence padded data. The fused data format uses a standardized data structure, including complete fields such as timestamp, data type, value, quality indicator, and source identifier. Data integrity is finally verified through statistical coverage. A dataset is considered complete if both temporal and spatial coverage exceed 98%. A comprehensive data quality assessment is conducted based on four dimensions: accuracy, completeness, consistency, and reliability. Final verification of the complete data set is based on the phase-locking signature identified in S110. The accuracy of the data reconstruction is verified by comparing the phase consistency of the fused data with the original locked phase. Data reconstruction quality is considered excellent when the phase signature of the fused data deviates from the locked phase in S110 by less than π / 8. Phase-locking verification ensures that the reconstructed data maintains the timing characteristics and phase relationships of the original signal, enhancing the credibility of the data set. Through the precise fusion of the continuous current data and the padded data, and verification of the phase-locking signature, complete extraction of the switching loop data based on phase locking is achieved.

[0074] In order to implement the phase-locked switching loop data extraction method corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 2 , Figure 2 The structure block diagram of a phase-locked switching loop data extraction device 200 provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to this embodiment are shown. The phase-locked switching loop data extraction device 200 provided in an embodiment of the present application includes: The anomaly detection module 201 is used to collect operating data in the ring main unit, analyze the interval changes of the operating data and identify nodes with abnormal communication; A vibration analysis module 202 is configured to obtain a vibration source position by associating the abnormal communication node with a mechanical vibration source, extract current fluctuation characteristics based on the vibration source position, and determine a position deviation using the current fluctuation characteristics; a brake control module 203 configured to extract fluctuation characteristic parameters based on the current fluctuation characteristics and the position deviation, adjust the operating current intensity using the fluctuation characteristic parameters to obtain an adjusted current value, and apply a reverse braking force to the switch device using the adjusted current value to obtain precise parking position data; a record generation module 204 for constructing a dynamic threshold reference based on the fluctuation characteristic parameter, and generating a complete operation record according to the dynamic threshold reference and the precise parking data; a scheduling management module 205 for grouping the complete operation record by data type to obtain a current data group and a position data group, detecting a load state of each transmission channel to obtain a load parameter, and dynamically scheduling the current data group and the position data group using the load parameter to obtain a transmission execution status; a switching execution module 206 configured to construct a beat sequence for progressive migration using the current data set, perform migration step processing based on the beat sequence to obtain a migration control instruction, perform spatial positioning anchor point analysis on the position data set to obtain a guide path, execute progressive switching based on the migration control instruction and the guide path, and obtain a switching record; The data fusion module 207 is used to combine the switching record and the current data group to perform signal reconstruction to obtain continuous current data, obtain filling data based on the transmission execution status and the position data group, and fuse the continuous current data and the filling data to obtain a complete data set.

[0075] The aforementioned phase-locked switching loop data extraction device 200 can implement the phase-locked switching loop data extraction method of the aforementioned method embodiment. The optional options in the aforementioned method embodiment also apply to this embodiment and are not described in detail here. The remaining contents of the present application embodiment can be referenced to the contents of the aforementioned method embodiment and are not further described in this embodiment.

[0076] like Figure 3 As shown, the third embodiment of the present invention further provides a computer device, including a memory 301, a processor 302, and a computer program stored in the memory 301 and executable on the processor 302, characterized in that when the processor 302 executes the program, the steps of the phase-locked switching loop data extraction method described in the first embodiment of the present invention are implemented.

[0077] The purpose of the above embodiments is to exemplify and deduce the technical solution of the present invention, and to fully describe the technical solution, purpose and effect of the present invention. Its purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosed content of the present invention, and it does not limit the scope of protection of the present invention.

[0078] The above embodiments are not exhaustive and may include many other embodiments not listed above. Any replacements and improvements made without violating the concept of the present invention are within the scope of protection of the present invention.

Claims

1. A switching loop data extraction method based on phase locking, characterized in that: include: Collecting operating data in the ring main unit, analyzing the interval changes of the operating data and identifying abnormal communication nodes; Associating the abnormal communication node with a mechanical vibration source to obtain a vibration source position, extracting current fluctuation characteristics based on the vibration source position, and determining a position deviation using the current fluctuation characteristics; extracting fluctuation characteristic parameters based on the current fluctuation characteristics and the position deviation, adjusting the operating current intensity using the fluctuation characteristic parameters to obtain an adjusted current value, and applying a reverse braking force to the switching device using the adjusted current value to obtain precise parking position data; constructing a dynamic threshold benchmark based on the fluctuation characteristic parameters, and generating a complete operation record according to the dynamic threshold benchmark and the precise parking data; According to the complete operation record, the current data group and the position data group are grouped by data type to obtain a current data group and a position data group, a load state of each transmission channel is detected to obtain a load parameter, and the current data group and the position data group are dynamically scheduled using the load parameter to obtain a transmission execution state; constructing a beat sequence of progressive migration using the current data set, performing migration step processing according to the beat sequence to obtain a migration control instruction, performing spatial positioning anchor point analysis on the position data set to obtain a guide path, executing progressive switching according to the migration control instruction and the guide path, and obtaining a switching record; Signal reconstruction is performed in combination with the switching record and the current data group to obtain continuous current data, filling data is obtained based on the transmission execution status and the position data group, and the continuous current data and the filling data are fused to obtain a complete data set.

2. The method according to claim 1, characterized in that The performing interval change analysis on the operation data to identify nodes with abnormal communication includes: extracting a data transmission time interval based on the operating data; Performing phase lock analysis on the time interval to obtain a locked phase and an unlocked phase; Constructing a synchronous reference frequency based on the locked phase, and performing frequency calibration on the unlocked phase based on the synchronous reference frequency to obtain a calibrated phase deviation; The communication abnormality node is marked according to the calibrated phase deviation.

3. The method according to claim 1, characterized in that The obtaining of the vibration source position by associating the mechanical vibration source with the communication abnormal node includes: Constructing a spatial distribution map based on the abnormal communication nodes; Performing vibration source correlation analysis on the distribution map to determine the vibration impact range; The vibration source is located according to the impact range.

4. The method according to claim 1, wherein The step of applying the adjusted current value to the switch device to apply a reverse braking force to obtain accurate parking position data includes: calculating motion inertia parameters based on the adjusted current value; determining a braking force application timing according to the inertia parameter; applying a reverse braking force to the switch device based on the braking force application timing to obtain a braking response; Accurate parking position data is generated according to the braking response.

5. The method according to claim 1, characterized in that The constructing of a dynamic threshold benchmark based on the fluctuation characteristic parameter includes: Performing frequency domain analysis on the fluctuation characteristic parameters to obtain frequency domain characteristics; identifying a dominant frequency component based on the frequency domain features; Calculating a threshold adjustment coefficient according to the dominant frequency component; A dynamic threshold reference is constructed based on the adjustment coefficient.

6. The method according to claim 1, characterized in that The step of constructing a gradually migrating beat sequence using the current data set includes: performing beat feature extraction on the current data group; Constructing a temporal migration pattern based on the beat features; Dividing the migration phases according to the migration pattern; A beat sequence of progressive migration is generated based on the migration phases.

7. The method according to claim 1, characterized in that The performing spatial positioning anchor point analysis on the position data group to obtain a guidance path includes: extracting spatial coordinate information based on the position data set; Performing cluster analysis on the coordinate information to determine the anchor point position; constructing a path connection matrix based on the anchor point positions; A guidance path is generated according to the connection matrix.

8. The method according to claim 1, characterized in that The combining of the switching record and the current data group to perform signal reconstruction to obtain continuous current data includes: identifying a signal interruption point based on the switching record; performing time sequence alignment on the interruption point and the current data group; Signal interpolation is performed based on the timing alignment results to generate continuous current data.

9. A switching loop data extraction device based on phase locking, characterized in that: include: An anomaly detection module is used to collect operating data in the ring network cabinet, analyze the interval changes of the operating data and identify communication abnormal nodes; A vibration analysis module, configured to obtain a vibration source position by associating the abnormal communication node with a mechanical vibration source, extract a current fluctuation feature based on the vibration source position, and determine a position deviation using the current fluctuation feature; a brake control module, configured to extract fluctuation characteristic parameters based on the current fluctuation characteristics and the position deviation, adjust the operating current intensity using the fluctuation characteristic parameters to obtain an adjusted current value, and apply a reverse braking force to the switch device using the adjusted current value to obtain precise parking position data; a record generation module, configured to construct a dynamic threshold reference based on the fluctuation characteristic parameters, and generate a complete operation record according to the dynamic threshold reference and the precise parking data; a scheduling management module, configured to group the complete operation record by data type to obtain a current data group and a position data group, detect a load state of each transmission channel to obtain a load parameter, and dynamically schedule the current data group and the position data group using the load parameter to obtain a transmission execution state; a switching execution module, configured to construct a beat sequence for progressive migration using the current data set, perform migration step processing according to the beat sequence to obtain a migration control instruction, perform spatial positioning anchor point analysis on the position data set to obtain a guide path, execute progressive switching according to the migration control instruction and the guide path, and obtain a switching record; A data fusion module is used to combine the switching record and the current data group to perform signal reconstruction to obtain continuous current data, obtain filling data based on the transmission execution status and the position data group, and fuse the continuous current data and the filling data to obtain a complete data set.

10. A computer device, characterized in that: The method comprises a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the method according to any one of claims 1 to 8 when executing the computer program.

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