Circuit breaker dispatching operation method and device driven by power distribution information mainboard

Abstract

The invention provides a circuit breaker dispatching operation method and device driven by a power distribution information mainboard, and the method comprises the steps: collecting the current fluctuation data and operation records of a circuit breaker node in a power distribution network, recognizing a high-energy density node based on the energy accumulation and dispersion characteristics, and constructing potential field gradient distribution; performing spectral analysis on the operation record, and coupling a result with potential field gradient distribution to generate a critical excitation point and a fluctuation trigger sequence; phase jump parameters are extracted, spiral coding processing is carried out, and a multi-dimensional control signal and a hierarchical driving strategy are constructed; circuit breaker operation is divided into an instantaneous pulse level and a continuous waveform level, and differential control is realized through signal bifurcation processing; phase locking is carried out to form a synchronous operation flow, and phase mutation is identified through multi-point phase sampling and a compensation signal is generated; finally, a pulse string scheduling instruction is generated based on the synchronous operation flow and the phase compensation signal, intelligent scheduling control of the circuit breaker is realized, and the operation stability and the control precision of a power distribution network are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation control, and in particular to a circuit breaker dispatching operation method and device driven by a power distribution information mainboard. Background Art

[0002] The distribution network, a critical component of the power system, directly impacts the reliability and safety of power supply. Circuit breakers, core control devices within the distribution network, ensure coordinated and accurate operation, crucial for maintaining system stability. However, existing circuit breaker control technology, primarily based on traditional protection logic and pre-programmed operation, lacks intelligent perception and dynamic response capabilities for real-time system status.

[0003] Traditional distribution network control methods have significant shortcomings: they cannot effectively handle complex grid state changes, lack in-depth analysis of device operating data, and struggle to achieve precise coordination between multiple devices. This leads to delayed system response and low control accuracy in the face of faults or abnormalities, potentially triggering chain reactions. Therefore, a new intelligent scheduling and operation technology for distribution network circuit breakers is needed to improve the overall operational efficiency and safety of the distribution network. Summary of the Invention

[0004] The present invention provides a circuit breaker scheduling operation method and device driven by a power distribution information motherboard. The method aims to fully explore and utilize the deep information of the distribution network operation data, integrate multi-dimensional control strategies, and conduct comprehensive and accurate analysis and processing of key control factors such as current fluctuations, operation records, and phase changes, thereby revealing the inherent correlation laws between factors and ultimately forming a multi-level, multi-granular, and highly coordinated intelligent control instruction sequence, providing a comprehensive and efficient control solution for the distribution network.

[0005] A first aspect of the present invention provides a circuit breaker dispatching operation method driven by a power distribution information mainboard, comprising the following steps: Collecting current fluctuation data and operation records of each circuit breaker node in the power distribution network, extracting energy concentration and dispersion characteristics based on the current fluctuation data, identifying high energy density nodes based on the energy concentration and dispersion characteristics, and constructing a potential field gradient distribution using the high energy density nodes; Performing spectrum analysis on the operation record to generate a resonant frequency set, coupling and superimposing the resonant frequency set with the potential field gradient distribution to generate a critical excitation point, and generating a fluctuation trigger sequence based on the critical excitation point; Extracting phase jump parameters from the current fluctuation data, inputting the phase jump parameters into a power distribution information mainboard for spiral encoding processing to generate a rotation drive vector, using the rotation drive vector to construct a multi-dimensional control signal within the mainboard, and constructing a hierarchical drive strategy based on priority reorganization of the multi-dimensional control signal; The circuit breaker operation is divided into an instantaneous pulse level and a continuous waveform level, and the hierarchical drive strategy and the fluctuation trigger sequence are subjected to signal bifurcation processing to generate a main dispatching branch and a free branch. The power distribution information mainboard performs resource dispatching on the instantaneous pulse level through the main dispatching branch to obtain eddy current drive, and performs timing control on the continuous waveform level through the free branch to form a ripple propagation path; Phase-locking the eddy current drive and the ripple propagation path to form a synchronous operation flow, performing signal diffusion analysis on the synchronous operation flow to generate a decentralized control distribution, setting a signal convergence node according to a timing priority based on the decentralized control distribution, constructing a control signal buffer in the signal convergence node, and generating an elastic release signal according to the control signal buffer; executing the elastic release signal to perform multi-point phase sampling to obtain phase distribution data, extracting phase difference spectrum lines based on the phase distribution data, identifying phase mutation intervals according to the phase difference spectrum lines, and generating a phase compensation activation signal using the phase mutation intervals; Based on the synchronous operation flow and the phase compensation activation signal, a pulse train scheduling instruction is generated according to timing control to complete the circuit breaker scheduling operation driven by the power distribution information mainboard.

[0006] A second aspect of the present invention provides a circuit breaker dispatching and operating device driven by a power distribution information mainboard, comprising: A data acquisition module, configured to collect current fluctuation data and operation records of each circuit breaker node in the power distribution network, extract energy concentration and dispersion characteristics based on the current fluctuation data, identify high energy density nodes based on the energy concentration and dispersion characteristics, and construct a potential field gradient distribution using the high energy density nodes; a spectrum analysis module, configured to perform spectrum analysis on the operation record to generate a resonant frequency set, couple and superimpose the resonant frequency set with the potential field gradient distribution to generate a critical excitation point, and generate a fluctuation trigger sequence based on the critical excitation point; an encoding processing module for extracting phase jump parameters from the current fluctuation data, inputting the phase jump parameters into the power distribution information mainboard for spiral encoding processing to generate a rotational drive vector, utilizing the rotational drive vector to construct a multi-dimensional control signal within the mainboard, and constructing a hierarchical drive strategy based on priority reorganization of the multi-dimensional control signal; A signal bifurcation module is used to divide the circuit breaker operation into an instantaneous pulse level and a continuous waveform level, perform signal bifurcation processing on the hierarchical drive strategy and the fluctuation trigger sequence to generate a main dispatching branch and a free branch. The power distribution information mainboard performs resource dispatch on the instantaneous pulse level through the main dispatching branch to obtain eddy current drive, and performs timing control on the continuous waveform level through the free branch to form a ripple propagation path; a synchronous control module, configured to phase-lock the eddy current drive and the ripple propagation path to form a synchronous operation flow, perform signal diffusion analysis on the synchronous operation flow to generate a decentralized control distribution, set a signal convergence node according to a timing priority based on the decentralized control distribution, construct a control signal buffer in the signal convergence node, and generate an elastic release signal based on the control signal buffer; a phase compensation module, configured to perform multi-point phase sampling on the elastic release signal to obtain phase distribution data, extract phase difference spectral lines based on the phase distribution data, identify phase mutation intervals based on the phase difference spectral lines, and generate a phase compensation activation signal using the phase mutation intervals; An execution control module is used to generate a pulse train scheduling instruction according to timing control based on the synchronous operation flow and the phase compensation activation signal, thereby completing the circuit breaker scheduling operation driven by the power distribution information mainboard.

[0007] The beneficial effects of the present invention are reflected in the following aspects: 1. By analyzing the energy concentration and dispersion characteristics of current fluctuation data, high-energy density nodes in the distribution network are accurately identified and located. The construction of potential field gradient distribution enables the distribution network to track energy flow trends in real time, effectively improving the sensitivity of abnormal state detection and the timeliness of fault warning, and transforming traditional passive monitoring into active perception. 2. The coupling of spectrum analysis technology and potential field distribution processing enables the automatic identification of periodic regularities and abnormal patterns in operation records. Spiral encoding and multi-dimensional signal processing technology convert complex phase information into actionable control vectors, allowing dispatch instructions to accurately adapt to different operating conditions, significantly improving the adaptability and execution accuracy of dispatch operations. 3. The hierarchical drive strategy enables differentiated processing of instantaneous pulse-level and continuous waveform-level operations. Phase locking technology ensures precise synchronization between multiple circuit breakers, and the elastic release mechanism optimizes the timing distribution of dispatch signals. Real-time detection and active compensation of phase mutations effectively eliminate unstable factors in the dispatch process, allowing the distribution network to maintain stable operation in the face of complex disturbances, and overall improving the anti-interference ability and execution reliability of dispatch operations.

[0008] 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

[0009] 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.

[0010] 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.

[0011] Figure 1 It is a flow chart of a circuit breaker dispatching operation method driven by a power distribution information mainboard of the present invention.

[0012] Figure 2 This is a structural block diagram of a circuit breaker dispatching and operating device driven by a power distribution information mainboard of the present invention. DETAILED DESCRIPTION

[0013] 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.

[0014] 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.

[0015] 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.

[0016] The technical solutions of the embodiments of this application are introduced below.

[0017] like Figure 1 As shown, an embodiment of the present invention provides a circuit breaker scheduling operation method driven by a power distribution information mainboard, comprising the following steps S110 to S170: Step S110: collect current fluctuation data and operation records of each circuit breaker node in the distribution network, extract energy concentration and dispersion characteristics based on the current fluctuation data, identify high energy density nodes according to the energy concentration and dispersion characteristics, and use the high energy density nodes to construct a potential field gradient distribution.

[0018] Specifically, real-time operational data from each node in the distribution network is collected through the SCADA interface of the distribution automation system and the embedded sensors of the intelligent circuit breakers. The current fluctuation data collection frequency is set at 1kHz to ensure the capture of transient changes and harmonic components. Each circuit breaker node is equipped with a three-phase current transformer with an accuracy of 0.2s and a measurement range of 0-6000A. Data collection utilizes a circular buffer mechanism with a size of 10MB, storing the last 10 seconds of high-frequency sampling data and a one-hour trend data storage frequency of 100Hz. Operation records, including the circuit breaker's open / close status, action timestamp (with 1ms accuracy), operation type (local / remote / protection action), and cumulative operation count, are uploaded in real time via GOOSE messages based on the IEC61850 protocol. Node identification uses a three-level coding system: substation-feeder-switch. For example, "220kV-F12-CB03" represents circuit breaker 3 on feeder 12 of a 220kV substation. During data preprocessing, outliers are removed. The triple standard deviation criterion is used to identify and mark sampling points outside the normal range. Kalman filtering is also used to eliminate measurement noise. GPS timing is used for time synchronization, ensuring that the network-wide data time scale deviation is less than 1ms. Data integrity is verified using the CRC32 algorithm, keeping the packet loss rate below 0.1%.

[0019] Based on the collected current fluctuation dataset, time-frequency domain analysis methods were used to extract energy concentration and dispersion characteristics. First, a short-time Fourier transform (SFT) was performed on the current time series. A Hamming window with a window length of 256 samples and a 50% overlap was selected as the window function to obtain a time-frequency spectrum matrix. The energy density E(f) = |P(t,f)|² of each frequency component was calculated in the frequency domain, focusing on the energy distribution of the fundamental (50 Hz) and its 2nd-7th harmonics. The energy concentration index, defined as J = ∑(E(f)·f²) / ∑E(f), reflects the degree of energy concentration in the high-frequency range. A J value greater than 100 indicates abnormal energy concentration. Energy dispersion was quantified by calculating the spectral entropy H = -∑(p(f)·log(p(f))), where p(f) is the normalized power spectral density. Values ​​of H less than 2.5 indicate concentrated energy distribution, while values ​​greater than 4.0 indicate highly dispersed energy. Transient energy mutation detection utilizes wavelet packet decomposition, using the db4 wavelet basis and five decomposition layers. The moment of energy mutation is identified by the modulus maxima of the detail coefficients at each layer. Directional characteristics are extracted by calculating the rate of change of active power (P) and reactive power (Q). A dP / dt exceeding 50 kW / s is defined as rapid energy accumulation, while a dQ / dt below -50 kW / s is defined as rapid energy dissipation. dQ / dt reflects the dynamic changes in reactive power compensation demand. Three-phase imbalance, a stability indicator, is calculated based on the collected three-phase current vectors using the formula ε = max(|Ia-Ib|,|Ib-Ic|,|Ic-Ia|) / avg(Ia,Ib,Ic), where Ia, Ib, and Ic are the instantaneous values ​​of the three-phase currents. Imbalance exceeding 0.15 is marked as significant.

[0020] High-energy-density nodes in the distribution network are identified based on the extracted energy concentration and dispersion feature vectors. A node energy density evaluation system was established, comprehensively considering four key features: power change rate, energy concentration, spectral entropy, and three-phase imbalance. The power change rate reflects the transient impact intensity of the node, with a weight of 30%; energy concentration reflects the spectral concentration characteristics, with a weight of 30%; spectral entropy reflects the degree of energy concentration through reverse scoring, with a weight of 25%; and three-phase imbalance quantifies the impact on system stability, with a weight of 15%. After normalizing the eigenvalues ​​of each node, a comprehensive energy density score is calculated based on the weighted ratio. The power change rate is standardized to 50 kilowatts per second, and the energy concentration is based on 100. The spectral entropy is reverse mapped to give higher scores to nodes with higher concentration, and the imbalance is squared to highlight the impact of severe imbalance. A dynamic threshold strategy is used to identify high-energy-density nodes, with the threshold set at the mean of the comprehensive score of all nodes in the network plus 1.5 standard deviations. Taking into account the time-varying nature of load, the threshold is appropriately increased by 15% during peak hours to accommodate the energy concentration characteristics of load-intensive areas. When the node comprehensive score exceeds the dynamic threshold, the node is identified as a high energy density node.

[0021] High-energy-density nodes are used to construct the potential field gradient distribution. Based on an electrostatic field analogy, the potential function for a single node is defined as φ(r) = D·exp(-|r-r0| / λ), where D is the node energy density, r0 is the node location, and λ is set as a characteristic length of 0.5 kilometers. During the potential field construction process, each computational grid point records the node ID of the source contributing to its potential value, facilitating the tracing of the causes of high-potential field regions. For strongly correlated node pairs, the original potential field is enhanced by 20% multiplied by the correlation coefficient to strengthen the coupling effect between nodes. The total potential field calculation takes into account node characteristic differences: high-energy-density nodes have their gradient steepness enhanced by shortening the characteristic length; high-imbalance nodes exhibit anisotropic attenuation; high-spectral entropy nodes utilize large-scale filtering to preserve diffusion characteristics; and fixed boundary potential values ​​are set for regions with significant reactive power variations. The gradient calculation uses the central difference method on a 50-meter grid to analyze the energy flow intensity (0-50 kW / km), flow direction, and divergence distribution. By integrating all the characteristic information and correlations of high-energy-density nodes, the potential field gradient distribution is ultimately obtained.

[0022] Step S120 , performing spectrum analysis on the operation record to generate a resonant frequency set, coupling and superimposing the resonant frequency set with the potential field gradient distribution to generate a critical excitation point, and generating a fluctuation trigger sequence based on the critical excitation point.

[0023] Specifically, spectral analysis of operation records is performed to generate a set of resonant frequencies. First, discrete operation records are converted into continuous time series. An operation density function is constructed with a sampling interval of 1 second. Opening and closing operations are assigned values ​​of +1 and -1, respectively, and no operation is assigned a value of 0. For each circuit breaker node, the operation sequence for the past 24 hours is extracted to form a time series vector of length 86,400. Spectral analysis is performed using a fast Fourier transform (FFT) with a window length of 3600 points (1 hour) and a 50% overlap for sliding analysis. Spectral calculations focus on the low-frequency band of 0.001-1 Hz, corresponding to operation patterns with periods ranging from 1000 seconds to 1 second. Resonant frequencies are identified by searching for significant peaks in the spectrum, with the peak detection threshold set at three times the average power spectral density. Typical resonant frequencies include: 0.0116 Hz (corresponding to 1 / 24 of the daily period), reflecting hourly load variations; 0.000694 Hz (corresponding to the daily period), reflecting day-night switching; and 0.0001157 Hz (corresponding to the weekly period), reflecting workday patterns. Multi-node resonance analysis identifies the synchronous operation mode of the entire network by calculating the mutual correlation coefficient of the spectrum of different nodes. The frequency components with a correlation coefficient greater than 0.7 are marked as the system resonance frequency. The weight of the operation type is differentiated: protection action is given 1.5 times the weight, remote operation is given 1.2 times the weight, and local operation is given 1.0 times the weight, reflecting the degree of system impact of different operations. The cumulative number of operations is used to correct the spectrum amplitude. The spectrum contribution of nodes with frequent operations is correspondingly enhanced. The analysis results form a resonance frequency set F={f1,f2,...,fn}, each frequency contains the frequency value, amplitude, phase and participating node list.

[0024] In some embodiments, coupling and superimposing the resonant frequency set with the potential field gradient distribution to generate a critical excitation point includes: performing frequency symbiosis analysis on the resonant frequency set to generate a symbiotic frequency pair; performing phase change matching on the symbiotic frequency pair and the potential field gradient distribution to form a phase change region; performing critical state identification in the phase change region to output critical state parameters; and calibrating the critical excitation point based on the critical state parameters.

[0025] Exemplarily, the frequency symbiosis analysis of the resonant frequency set to generate a symbiotic frequency pair includes: identifying a dominant frequency and an auxiliary frequency from the resonant frequency set; performing a mutual correlation analysis on the dominant frequency and the auxiliary frequency to establish a correlation coefficient; establishing a frequency pairing rule based on the correlation coefficient; and generating a symbiotic frequency pair using the frequency pairing rule.

[0026] First, the dominant frequency is identified based on the spectral amplitude and the number of participating nodes in the resonant frequency set. The dominant frequency is defined as the frequency component with the top 20% amplitude and at least five participating nodes. Auxiliary frequencies are the remaining significant frequency components, typically harmonic or modulating with the dominant frequency. Frequency classification takes into account physical significance: fundamental frequencies (0.01-0.1 Hz) reflect the system's inherent oscillations, harmonic frequencies (0.1-1 Hz) reflect nonlinear responses, and ultra-low frequencies (<0.01 Hz) correspond to long-period fluctuations. Dominant frequencies typically include daily and hourly cycles and power frequency-related components, while auxiliary frequencies include their harmonics and combinations. A dominant-auxiliary frequency mapping table is established, recording the set of potential auxiliary frequencies for each dominant frequency. Next, the correlation between the dominant and auxiliary frequencies is analyzed to quantify the strength of the coupling between the frequencies. This is comprehensively evaluated using multiple methods: cross-correlation functions calculate time-domain correlations, coherence functions assess frequency-domain consistency, and mutual information quantifies nonlinear dependencies. Time-varying correlations are captured using a sliding window analysis with a window length set to 10 times the dominant frequency period. Phase correlation is a key consideration; a constant or linearly varying phase difference indicates a strong locking relationship. Amplitude correlation is assessed using envelope correlation; synchronized increases and decreases indicate energy coupling. Spatial correlation analyzes the correlation distribution of the same frequency pair at different nodes. The correlation coefficient ranges from [0 to 1], with values ​​greater than 0.7 defining strong correlation, 0.4-0.7 as moderate correlation, and less than 0.4 as weak correlation. Frequency pairing rules are then established based on the correlation coefficient. Harmonic relationships are first considered, prioritizing integer multiples of frequencies, such as the natural pairing of f with 2f and 3f. Sum and difference frequency relationships are then considered, with combination pairings formed when f3 ≈ f1 ± f2. Close frequency pairing applies when the frequency difference is less than 10% of the smaller frequency, reflecting mode competition. The correlation coefficient threshold varies with the pairing type: harmonic pairing requires a correlation coefficient greater than 0.8, combination pairing requires a correlation coefficient greater than 0.6, and close pairing requires a correlation coefficient greater than 0.7. Temporal stability constraints require that pairings persist for at least 12 hours during the observation period. Spatial consistency constraints ensure that pairings occur simultaneously at multiple nodes. Priority setting gives priority to pairings with clear physical meaning, avoiding mismatches caused by accidental correlations. The pairing rules form a decision tree structure to support fast pairing judgment. Finally, the established frequency pairing rules are used to generate symbiotic frequency pairs. A greedy algorithm is used to match frequency pairs in sequence according to the priority order of the pairing rules. Each dominant frequency is paired with up to three auxiliary frequencies to avoid excessive complexity. Pairing verification uses an energy conservation test to ensure that the energy exchange of frequency pairs conforms to physical laws. Typical symbiotic frequency pairs include: circadian rhythm pairs formed by the daily cycle (0.0000116Hz) and its 24th harmonic (0.000278Hz), harmonic pairs formed by the power frequency-related 50Hz and 100Hz and 150Hz, and beat frequency pairs formed by low-frequency oscillations. The pairing results record the frequency value, correlation coefficient, pairing type, spatial distribution and time evolution characteristics.The generated symbiotic frequency pair set contains 15-30 pairs of main frequency combinations, covering the main oscillation modes of the system.

[0027] Phase transition matching is performed on the identified symbiotic frequency pairs and the potential field gradient distribution to identify phase transition regions where the system state suddenly changes. The matching process calculates the equivalent energy density for each symbiotic frequency pair, taking into account the amplitude and phase relationship between the master and slave frequencies. When the master frequency amplitude is A1 and the slave frequency amplitude is A2, the equivalent energy density is taken as the geometric mean of the two, multiplied by the phase coupling factor. The second-order derivative of the potential field gradient is used to identify unstable regions of the potential field. Saddle points, where the eigenvalues ​​have opposite signs, are most sensitive to phase transitions and often correspond to weak links in the distribution network. The phase transition criterion defines the order parameter as the ratio of the frequency energy to the potential field energy. When it approaches 1, the system is in a critical state, and the degree of deviation from 1 reflects the system stability margin. Spatial scanning is performed at a resolution of 100 meters, and the local phase transition probability is calculated at each grid point. Gaussian kernel smoothing is used to reduce numerical noise. Phase transition regions are primarily distributed at the boundaries where the potential field gradient changes sharply, with a typical scale of 200-500 meters and an irregular elliptical or strip-like distribution. Time evolution analysis found that the phase change region has a slow drift characteristic, with a movement speed of about 10-20 meters per hour.

[0028] Critical state identification is performed within the phase transition region, and critical state parameters are output. A multi-parameter joint assessment is employed, with key indicators including local energy concentration, frequency coherence, phase synchronization, and perturbation sensitivity. Energy concentration is calculated by integrating the potential field within the region. Energy criticality is indicated when it exceeds twice the surrounding average value. The integration radius is set at 100 meters to cover the primary impact range. Frequency coherence reflects the degree of locking of the symbiotic frequency pairs. A phase difference remaining within ±π / 6 for more than 10 cycles is considered strong coherence, indicating that the system has entered a self-organized state. Phase synchronization quantifies the synchronization of oscillations at different spatial locations. It is calculated using the Kuramoto order parameter. A synchronization index greater than 0.8 indicates the formation of regional resonance. Perturbation sensitivity is assessed using the maximum Lyapunov exponent. A positive value indicates a sensitive dependence of the system on initial conditions, while a value close to zero indicates the edge of a critical state. Additional indicators include the growth rate of information entropy and the change in correlation dimension, which characterize the evolution of system complexity from an information theoretic perspective. Multi-indicator fusion adopts the framework of evidence theory to deal with the uncertainty and conflict between indicators, and outputs critical state parameters including the confidence level, critical energy value, excitation probability, local energy density, coherence time and correlation length of each indicator.

[0029] Based on the identified critical state parameters, the critical excitation point is precisely calibrated. The excitation point calibration comprehensively considers four core elements: spatial position, excitation threshold, trigger conditions, and impact range. Spatial positioning uses the weighted centroid method to calculate the excitation center, with the weights determined by the confidence level of each parameter. The positioning accuracy reaches 50 meters, meeting the requirements of project implementation. The excitation threshold is dynamically set based on the critical energy and excitation probability, following the logarithmic relationship of E_th=E_c×(1+0.2×ln(P_e)). The higher the probability, the higher the threshold to reduce the false alarm rate. The impact range is determined based on the correlation length. A circular area with a radius of 2 times the correlation length and centered at the excitation point is used. When the critical energy is higher, the range is correspondingly expanded. The trigger condition is designed with triple protection: the energy condition requires that the local energy density continuously exceed the threshold for 5 seconds, the time condition requires that the critical state be maintained for more than the coherence time, and the spatial condition requires that the impact range exceed 2 times the correlation length. The five-level classification of excitation points is based on the probability of excitation: Level 1 (>0.8) requires immediate response, Level 2 (0.6-0.8) initiates early warning, Level 3 (0.4-0.6) requires enhanced monitoring, Level 4 (0.2-0.4) is recorded and filed, and Level 5 (<0.2) is considered a potential risk point.

[0030] Based on the calibrated critical trigger points, a fluctuation trigger sequence is generated to guide the coordinated operation of circuit breakers. The trigger sequence design adheres to the principles of "prevention first, hierarchical response, and coordinated linkage," with differentiated trigger strategies formulated based on the trigger point level and spatial relationship. For level 1 trigger points, an immediate trigger signal is generated, requiring pre-positioning of the relevant circuit breakers within 1 second. Level 2 trigger points have a 5-second warning period during which parameter trends are continuously monitored. Level 3 and below triggers employ conditional triggering, activating only when monitored indicators deteriorate further. Spatial coordination takes into account the overlapping influence ranges of trigger points. For groups of trigger points within 500 meters of each other, synchronous triggering is used to avoid mutual interference. For groups 500-1000 meters apart, sequential triggering is used, with intervals of 2-3 seconds between each action. Timing optimization is based on energy propagation speed, prioritizing upstream high-energy nodes to block energy transfer downstream. Trigger intensity is graded based on trigger energy: strong (tripping), medium (current limiting), and weak (alarming), to avoid overreaction. Sequence encoding utilizes a triplet format of priority, timestamp, and action type, enabling millisecond-level execution accuracy. Finally, a fluctuation trigger sequence including trigger time, target node, action type and priority is generated.

[0031] Step S130: extract phase jump parameters from the current fluctuation data, input the phase jump parameters into the power distribution information mainboard for spiral encoding processing to generate a rotation drive vector, use the rotation drive vector to construct a multi-dimensional control signal in the mainboard, and construct a hierarchical drive strategy based on the priority reorganization of the multi-dimensional control signal.

[0032] Specifically, phase jump parameters are extracted from current fluctuation data. First, a Hilbert transform is performed on the three-phase current vectors to obtain the instantaneous phase sequence φa(t), φb(t), and φc(t) for each phase. Phase jump detection uses a differential algorithm to calculate the phase change Δφ(t) = φ(t) - φ(t-1) between adjacent sampling points. A jump event is marked when the change exceeds π / 6 radians. Jump parameters extracted include the jump amplitude (in radians), the jump time (with millisecond accuracy), the jump duration, and the jump direction (positive / negative). Three-phase correlation analysis identifies synchronous and asynchronous jumps. Synchronous jumps are defined as all three phases experiencing a jump within 50 ms, reflecting a systemic disturbance; asynchronous jumps may be caused by localized faults. Jump frequency statistics show that the jump rate is less than 0.1 times / second during normal operation and can reach 1-5 times / second under abnormal conditions. Spatial distribution characteristics are obtained by comparing phases at multiple nodes. The phase difference between adjacent nodes reflects the direction of power flow. Analysis of the transition propagation speed revealed that phase disturbances propagate through the network at 200-300 m / s, consistent with the propagation speed of electromagnetic waves in power transmission lines. Parameter normalization maps the transition amplitude to the range [-1, 1] for ease of subsequent encoding. This ultimately generates phase transition parameters containing timestamp, node ID, transition type, amplitude, direction, and associated information.

[0033] In some embodiments, the phase jump parameter is input into the power distribution information mainboard for spiral encoding processing to generate a rotation drive vector, including: multi-dimensional space mapping of the phase jump parameter to establish spatial coordinates; constructing a spiral encoding trajectory based on the spatial coordinates; folding the encoding space along the spiral encoding trajectory to generate a folding vector; and generating a rotation drive vector through the folding vector.

[0034] After receiving phase transition parameters, the power distribution information mainboard performs multidimensional spatial mapping, converting discrete parameters into a continuous spatial representation. The mainboard's built-in mapping unit is designed to take the physical meaning of the parameters into account: the first dimension represents the transition amplitude, with a mapping range of [-π, π]; the second dimension represents the transition instant, using relative time coordinates; the third dimension represents the transition duration, mapped on a logarithmic scale to highlight short transitions; and the fourth dimension encodes the transition direction and type. For synchronous transitions, a fifth dimension is added to represent the three-phase coordination. The sixth dimension encodes node ID information, using a hash function to map the string identifier "220kV-F12-CB03" to a unique numeric value to maintain node traceability. The seventh dimension represents association information, quantifying the strength of the association between nodes as a continuous value in the interval [0, 1], with strong associations (synchronous transitions) approaching 1 and weak associations (independent transitions) approaching 0. The mapping function uses a nonlinear transformation, f(x) = sign(x)·|x|^0.7, to enhance the resolution of small-amplitude transitions. The time dimension uses cyclic mapping, with the period set to an integer multiple of the power frequency period to maintain phase continuity. Spatial normalization ensures that each dimension has equal weight, preventing a single parameter from dominating the encoding result. Soft limiting is used for outlier processing to compress extreme values ​​to a reasonable range. The mainboard's multi-node processing module uses node IDs for spatial positioning. Adjacent nodes determine interpolation weights based on their association information. Strongly associated nodes are assigned a high weight (0.8), while weakly associated nodes are assigned a lower weight (0.3), forming a continuous field that takes into account the network topology.

[0035] A spiral encoding trajectory is constructed based on established spatial coordinates, achieving orderly parameter organization and efficient compression. First, the specific parameters of the spiral trajectory are determined using seven-dimensional spatial coordinates. The first three dimensions directly map to the spiral's starting position, the fourth and fifth dimensions control the spiral's orientation and tilt angles, the sixth dimension determines the spiral density distribution, and the seventh dimension influences the local curvature of the spiral. An Archimedean spiral is used as the basic form, with a radius r = a + b·θ, where θ is the polar angle. The starting radius a is determined by the first-dimensional coordinate value, and the pitch coefficient b is adaptively adjusted based on the second-dimensional coordinate. Three-dimensional extension is achieved by adding a vertical component, with a height h = c·θ, where the lift coefficient c is determined by the third-dimensional coordinate. The trajectory's starting point is selected at the transition event with the highest first-dimensional coordinate value, ensuring that important information is encoded first. Path planning optimizes the connection strategy based on the sixth and seventh dimensions, minimizing overall curvature between adjacent points. Adaptive pitch is adjusted based on local parameter density, reducing the pitch in dense areas to increase sampling density. Trajectory smoothing is achieved through Bezier curve fitting, with control point selection ensuring C2 continuity. Boundary processing utilizes reflecting boundary conditions, ensuring smooth traversal when the trajectory approaches a boundary. The multi-layer spiral design allows for the hierarchical encoding of complex parameter sets, with each layer corresponding to a different time scale or spatial extent.

[0036] The code space is folded along the spiral encoding trajectory to achieve a compact representation of high-dimensional information. Parameters are first sampled along the trajectory, with the sampling interval adaptively adjusted based on the local curvature, increasing the sampling density in regions of high curvature. The folding algorithm draws on the principles of protein folding and determines the optimal folded configuration through energy minimization. The folding energy E = E_bend + E_twist + E_compress corresponds to bending, torsional, and compression energies, respectively. The folding process utilizes a simulated annealing algorithm with an initial temperature T0 = 100 and a cooling rate of 0.95 to ensure convergence to the global optimum. Constraints include: the angle between adjacent segments must not exceed π / 2 to avoid over-folding; a minimum curvature radius constraint to prevent singularities; and volume conservation to maintain information integrity. The number of folding layers is typically 3-5, with the folding angle decreasing with each layer. The folded structure exhibits fractal characteristics and self-similarity at different scales. The vectorized representation uses the coordinates of the folded key points and the connection topology, achieving a compression ratio of up to 10:1. The folded vector preserves the temporal and spatial relationships of the original parameters while significantly reducing storage and transmission overhead.

[0037] The static folding structure is converted into a dynamic driving signal by generating a rotational driving vector through the folding vector. The rotation transformation is based on quaternion representation to avoid the universal lock problem and ensure smooth rotation. The angular velocity ω of the driving vector is proportional to the local curvature of the folding vector, and the high curvature area produces rapid rotation. The rotation axis selects the local normal vector of the folding vector to ensure that the rotational motion is consistent with the structural characteristics. The amplitude modulation is based on the energy density distribution of the folding vector. The area with high folding curvature corresponds to the high energy area of ​​the original jump event, giving a higher driving strength. Driving strength , where F is the folded vector field and k is the modulation coefficient. Phase encoding embeds time information into the rotating phase, achieving timing synchronization. The vector is decomposed into a fundamental frequency component and harmonic components. The fundamental frequency corresponds to the main drive, and the harmonics provide fine tuning. The time evolution of the drive vector is described by the differential equation dV / dt = Ω × V + F(t), where Ω is the angular velocity vector, F(t) is the external drive term, and V is the rotating drive vector. Stability analysis ensures that the drive vector does not diverge, which is verified by Lyapunov function. The final set of rotating drive vectors covers different spatial locations and frequency components.

[0038] A multidimensional control signal is constructed within the mainboard using rotating drive vectors. First, the rotating drive vectors are mapped to control channels on the mainboard, with each channel corresponding to a specific control function. Signal dimension design includes: amplitude for controlling power output, phase for adjusting power factor, frequency for managing harmonic content, and direction for specifying energy flow. Vector synthesis utilizes weighted superposition, with weights dynamically adjusted based on the fluctuation trigger sequence generated by the S120. Timing is orchestrated to account for control delay, with a lead of 50-200ms to compensate for system inertia. Spatial distribution is extended to the entire network through interpolation to ensure control signal continuity. Signal modulation utilizes a combination of pulse-width modulation (PWM) and vector modulation, with a carrier frequency of 10kHz and a modulation depth of 0.1-0.9. Multi-channel coordination is achieved through a cross-correlation matrix to avoid control conflicts. The feedback channel is designed for real-time correction with a sampling period of 1ms, forming a closed-loop control system. The resulting multidimensional control signal matrix is ​​N×M×T, where N is the number of nodes, M is the control dimension (typically 1 / 4-8), and T is the time step, fully covering the control requirements of the distribution network.

[0039] In some embodiments, the priority reorganization based on the multi-dimensional control signal to construct a hierarchical driving strategy includes: extracting the priority of the multi-dimensional control signal to establish the original priority; performing reverse processing based on the original priority to generate a reverse signal; comparing and analyzing the reverse signal with the original priority to form a reorganization parameter; and using the reorganization parameter to construct a hierarchical driving strategy.

[0040] Priority extraction is performed on multidimensional control signals to identify the urgency and importance of different control actions. Priority assessment is based on multiple factors: signal amplitude reflects control strength, with high-amplitude signals receiving higher priority; rate of change reflects urgency, with rapidly changing signals prioritized; spatial impact range determines importance, with signals affecting more nodes receiving higher priority; and correlation with the trigger sequence: strongly correlated signals inherit the trigger priority. The scoring function is P = α·A + β·dA / dt + γ·N + δ·C, where A is the amplitude, N is the number of affected nodes, and C is the correlation coefficient. The weighting coefficient is dynamically adjusted based on system status. Time window analysis identifies continuous and transient control requests and assigns them different priority categories. Priority quantification uses an integer scale of 1-10, with 10 being the highest priority. Concurrent control signals are prioritized to form an execution queue. Conflict detection identifies mutually exclusive control actions, ensuring that high-priority signals are not interfered with by lower-priority signals. The raw priority column, sorted in descending order by score, contains signal ID, priority value, timestamp, and control parameters.

[0041] The original priority level is reversed to explore the feasibility of an inverse control strategy. This reversal operation is not a simple reversal of the order, but rather an inverse design based on control theory. First, reversible and irreversible control actions are identified. Protection actions are marked as irreversible, while regulatory actions can be reversed. The reversal algorithm considers temporal causality to ensure that the reversed signal sequence still meets physical constraints. Phase inversion shifts the phase of the original signal by π radians to achieve an inverse control effect. Amplitude mapping uses a nonlinear transformation: f_inv(x)=1-x², where x is the original priority value and f_inv(x) is the reversed priority value. This shifts high-priority signals to low-priority signals after reversal. Timing reordering accounts for control delays, adjusting the timestamps of the reversed signals accordingly. Spatial symmetry exploits network topology to exchange control signals from central nodes with edge nodes. Frequency domain reversal is achieved through a Hilbert transform, preserving signal energy conservation. The reversed signal retains the statistical properties of the original signal, maintaining its mean and variance.

[0042] The reversed priority signal is compared and analyzed with the original priority to generate the reconfiguration parameters. A multi-dimensional evaluation is performed: time domain comparison calculates the cross-correlation function of the two signal sequences, with the peak position indicating the optimal alignment point; frequency domain comparison identifies differences in frequency components through power spectral density analysis; spatial comparison assesses the geographical distribution of control effectiveness, quantified using Kullback-Leibler divergence; and effectiveness comparison simulates the control effects of the two strategies, using system stability as the evaluation metric. The difference metric is defined as D = Σ|P_orig - P_inv| / N, where P_orig is the original priority, P_inv is the reversed priority, N is the total number of nodes, and D is the difference metric. The result is normalized to the interval [0,1]. Sensitivity analysis identifies control links that are sensitive to priority changes. Complementarity assessment reveals that the original and reversed strategies can work together in certain scenarios. Critical point detection identifies parameter regions where the two strategies are equally effective, which serve as the strategy switching boundary. The comparison results generate a reconfiguration parameter set, including the optimal mixing ratio, switching threshold, coordination mode, and prohibited combinations.

[0043] Hierarchical drive strategies are constructed using restructured parameters to achieve flexible and efficient distribution network control. Strategies are tiered based on the spatial scope and temporal scale of control: Level 1 strategies cover key nodes across the entire network with a response time of less than 100ms; Level 2 strategies target regional control with a response time of 100ms-1s; Level 3 strategies handle local optimization with a response time of 1-10s; and Level 4 strategies handle long-term regulation with a response time greater than 10s. Each level of strategy determines the optimal mix of original and reversed signals. Level 1 strategies favor original signals (80%:20%), while Level 4 strategies increase the reversed signal ratio (40%:60%). A switching threshold determines the specific conditions for strategy level transitions. When system state parameters exceed the set threshold, the system automatically switches to the corresponding strategy level. A collaborative mode coordinates strategies at different levels. When a higher-level strategy is activated, lower-level strategies enter a collaborative support mode to avoid control conflicts. A prohibition on combination rules prevents the simultaneous execution of conflicting control actions, ensuring system security and stability. Trigger conditions are designed based on system status and disturbance level. Level 3 and 4 strategies are executed under normal conditions, while Level 1 and 2 strategies are activated in emergencies. Strategy switching utilizes a smooth transition to avoid system oscillations caused by sudden control changes. Execution priority ensures that higher-level strategies can override the actions of lower-level strategies. The resulting hierarchical drive strategy includes strategy levels, trigger conditions, control parameters, execution sequence, and expected results, enabling intelligent hierarchical scheduling of the distribution network.

[0044] In step S140, the circuit breaker operation is divided into an instantaneous pulse level and a continuous waveform level, and the hierarchical driving strategy and the fluctuation trigger sequence are subjected to signal bifurcation processing to generate a main dispatching branch and a free branch. The power distribution information main board performs resource dispatching on the instantaneous pulse level through the main dispatching branch to obtain eddy current drive, and performs timing control on the continuous waveform level through the free branch to form a ripple propagation path.

[0045] Specifically, circuit breaker operation records are analyzed for their time domain characteristics, categorizing them into two levels based on their duration and operating characteristics. The instantaneous pulse level is defined as fast operations with an operating time of less than 100ms, including fault tripping (typically 20-50ms), quick-break protection (10-30ms), and instantaneous reclosing (80-100ms). These operations are characterized by fast response, concentrated energy, and localized impact, and are designated as Class P in the operation sequence. The sustained waveform level encompasses operations with an operating time greater than 100ms, including normal opening and closing (200-500ms), load transfer (1-5 seconds), and planned maintenance isolation (10-30 seconds), and is designated as Class W. The grading criteria comprehensively consider temporal characteristics, calculated by the difference in timestamps between operation records, energy characteristics derived from the current rate of change (di / dt > 1000A / s is classified as pulse-level), and spatial characteristics determined by the number of affected nodes. After classification, each operation carries a four-tuple attribute (level identifier P / W, time characteristic value, energy index, and spatial impact).

[0046] In some embodiments, the signal bifurcation processing of the hierarchical driving strategy and the fluctuation trigger sequence to generate the main scheduling branch and the free branch includes: performing signal cross analysis on the hierarchical driving strategy and the fluctuation trigger sequence to determine the intersection point; establishing a bifurcation path guide based on the intersection point; performing branch allocation processing through the bifurcation path guide to output allocation parameters; and using the allocation parameters to generate the main scheduling branch and the free branch.

[0047] Signal intersection analysis is performed between the hierarchical driving strategy and the fluctuation trigger sequence to determine intersection points. First, time alignment is performed to synchronize the execution timeline of the driving strategy with the timestamps of the trigger sequence, with a uniform time resolution of 10ms. The nature of the operation is identified based on the action type in the fluctuation trigger sequence. Tripping actions correspond to P-type pulse-level operations, while regulating actions correspond to W-type waveform-level operations. The trigger conditions of the driving strategy and the priority of the fluctuation trigger sequence are combined to determine the validity of the intersection. A combination of a high-priority sequence and strict trigger conditions generates a strong intersection point. For pulse-level operations marked as P-type, the focus is on examining their intersection with the first and second-level driving strategies, as these intersections require high-priority processing. Signal projection projects the multidimensional driving strategy onto the action space of the trigger sequence. W-type waveform-level operations primarily intersect with the third and fourth-level strategies. The numerical value of the control parameter affects the calculation of the crossover strength, with large parameter values ​​resulting in high-strength crossovers. Sequential constraints on the execution sequence ensure the timing of the crossover points. Crossover points that violate these constraints are marked as invalid. Expected effect metrics are used to evaluate the quality of crossover points, with those with high expected effects receiving higher weighting. Crossover detection uses a sliding window method, with the window length adaptively adjusted based on the operation level (50ms for P-class and 200ms for W-class). The crossover criterion is defined as a valid crossover point when the product of the strategy strength and the trigger strength exceeds a threshold of 0.6 and the duration meets the level requirements. Each crossover point inherits the operation level attributes, with time coordinates accurate to the millisecond level and spatial locations corresponding to specific circuit breaker nodes. The crossover strength is quantized into a value in the [0, 1] interval, and the crossover type is categorized as either enhancement or suppression.

[0048] Based on the identified intersections, bifurcation paths are guided. The bifurcation mechanism utilizes the period-doubling bifurcation rule from chaos theory. The bifurcation parameter r for P-type intersections is set to 3.6-3.8, resulting in rapid bifurcation; the r value for W-type intersections is 3.2-3.4, resulting in slow bifurcation. Path initialization begins at the intersection. P-type operations rapidly extend along the direction of the steepest energy gradient, while W-type operations choose directions with uniform energy distribution. The bifurcation angle is proportional to the energy index of the operation. High-energy P-type operations produce large-angle bifurcations (60-90 degrees), while low-energy W-type operations produce small-angle bifurcations (30-45 degrees). During path evolution, the spatial influence determines the path's extension range. P-type operations with a single node influence have concentrated paths, while W-type operations with multiple nodes influence have divergent paths. Branching decisions fully consider the characteristics of the operation. Enhanced P-type crossovers produce explosive bifurcations, while suppressed W-type crossovers produce convergent bifurcations.

[0049] Branch allocation is performed through bifurcated path guidance, rationally assigning operation signals of different levels to corresponding branches. Allocation rules prioritize the signal's operation-level attributes. P-class pulse-level signals receive the highest allocation priority, automatically receiving over 70% of the main branch's bandwidth resources. Energy allocation is dynamically adjusted based on the operation's energy metrics. P-class operations with high energy metrics (>1000A / s) can receive up to 85% of the main branch's resources. W-class waveform-level signals are primarily assigned to free branches, but critical W-class operations (such as load transfers affecting more than five nodes) also reserve 30% of the main branch's resources. When calculating allocation weights, temporal characteristics directly influence path selection. Ultra-fast P-class operations (less than 50ms) must traverse the main branch, while W-class operations (longer than 1 second) prioritize free branches. Spatial influence is converted into path complexity weights through network topology analysis. Operations with large impact ranges are allowed to transmit concurrently across multiple paths. A time-varying allocation strategy adjusts based on the real-time distribution of operation levels. When P-class operations are densely populated, the main branch's weight is increased to 0.9. Based on allocation weight calculation and bandwidth share analysis, the final output allocation parameters include: main scheduling branch bandwidth allocation parameters (10MHz), free branch bandwidth allocation parameters (1MHz), delay control parameters (main branch <5ms, free branch 50-200ms), cache allocation parameters (main branch 1MB, free branch 10MB) and synchronization mechanism parameters.

[0050] The main dispatching branch and the free branch are generated using allocation parameters. The main dispatching branch is optimized for transmitting Class P pulse-level signals. Its design parameters directly match the characteristics of pulse-level operation: ultra-low latency (<5ms) meets fast response requirements, and 10MHz bandwidth supports instantaneous transmission of high-energy index signals. A fast identification mechanism for Class P operation is pre-installed in the branch, allowing direct routing based on level identification, bypassing the complex parsing process. The free branch is optimized for the slowly varying characteristics of Class W waveform-level signals, allowing for a larger latency (50-200ms) while providing stable transmission quality. The 1MHz bandwidth is sufficient to carry progressive control signals. The buffer sizes of both branches are set based on the typical duration of the operation level. The main branch has a small buffer (1MB) with fast refresh to accommodate Class P operation, while the free branch has a large buffer (10MB) to support the long-term transmission of Class W operation. The branch synchronization mechanism specifically considers mixed operation scenarios. When Class P and Class W operations need to coordinate, level identification ensures correct convergence timing.

[0051] The power distribution information mainboard processes instantaneous pulse-level operations marked as P-class through the main dispatch branch, leveraging their rapid nature to generate eddy currents. The resource scheduler first extracts all P-class operations from the main branch. The time signature values ​​(<100ms) carried by these operations determine the urgency of scheduling. Computing resources are allocated to each P-class operation based on its energy index, with operations exceeding 2000A / s receiving exclusive CPU cores. Scheduling slots are sorted by the time signature values ​​of the P-class operations, with operations with the shortest duration (<20ms) prioritized. The eddy current generation algorithm is designed to target the concentrated energy characteristics of P-class operations, converting their high di / dt values ​​into the initial angular momentum of the eddy currents. A spatial influence parameter controls the eddy current's diffusion radius. Single-node P-class operations generate compact eddies (radius 100 meters), while multi-node P-class operations generate extended eddies (radius up to 500 meters). The eddy current intensity directly maps to the energy index, forming a rotating field proportional to the original pulse intensity. The eddies of multiple P-class operations are superimposed using timestamps to determine their phase, allowing synchronized P-class operations to produce a coherent enhancement effect. The differentiated processing of P-type and W-type operations achieves the coordinated unity of fast response and stable transmission, effectively improving the control accuracy and system adaptability of different types of operations.

[0052] Continuous waveform-level operations labeled as Class W are processed by free branches, leveraging their gradual characteristics to construct ripple propagation paths. A timing controller identifies the temporal characteristics of Class W operations (>100ms) and designs a multi-stage control sequence accordingly. Class W operations with long duration characteristics (>1 second) are further refined into stages. The initial amplitude of the ripples generated is determined by the energy index of the Class W operation. Its low energy density enables the ripples to spread gradually. The propagation speed is adjusted based on the spatial impact. Class W operations affecting multiple nodes have a reduced propagation speed (100m / s) to ensure complete coverage. The waveform evolution process incorporates the hierarchical characteristics of Class W operations. Normal opening and closing operations produce regular ripples, while load transfer operations generate asymmetric ripple patterns. The decay parameter is linked to the temporal characteristics. Long-duration Class W operations use a slow decay (α = 0.1 / km) to ensure that remote nodes can still perceive them. The ripples of multiple Class W operations are time-coordinated through the large buffer of the free branches, forming an orderly wave sequence. The resulting ripple propagation fully reflects the gradual impact characteristics of Class W operations.

[0053] In step S150, the eddy current drive and the ripple propagation path are phase-locked to form a synchronous operation flow, a signal diffusion analysis is performed on the synchronous operation flow to generate a distributed control distribution, a signal convergence node is set according to the timing priority based on the distributed control distribution, a control signal buffer area is constructed in the signal convergence node, and an elastic release signal is generated according to the control signal buffer area.

[0054] In some embodiments, the phase-locking of the eddy current drive and the ripple propagation path to form a synchronous operation flow includes: performing phase repositioning processing on the eddy current drive to establish a positioning phase; performing timing correction on the ripple propagation path based on the positioning phase to generate correction parameters; using the correction parameters to achieve phase time synchronization to form synchronization parameters; and forming a synchronous operation flow through the synchronization parameters.

[0055] The eddy current drive is phase-repositioned to convert its dynamic rotational phase into a stable reference. First, the instantaneous position (x_c, y_c) of the eddy current center is identified, which follows a spiral trajectory over time. A coordinate transformation converts the rotating coordinate system into a fixed coordinate system, eliminating the influence of the eddy current's own rotation on the phase measurement. Phase unwrapping addresses the 2π jump problem and ensures phase continuity. The reference point is selected at the location of maximum eddy current intensity, typically 100-200 meters from the eddy current center, offset from the trigger point for Class P operation. The positioning phase φ_loc is obtained through spatial averaging. Eight sampling points with equal angles within the eddy current's influence radius are selected and their phase mean is calculated. Phase stability is enhanced using a Kalman filter, whose prediction model is based on the evolution of the eddy current's angular velocity. Noise suppression uses a median filter with a window length of 5 sampling points to remove pulse interference. Positioning accuracy reaches a spatial resolution of 10 meters and a temporal resolution of 1 ms.

[0056] Based on the established positioning phase, the ripple propagation path is time-corrected to compensate for differences in the propagation characteristics of the two signals. This correction accounts for ripple diffusion delay, with the delay increasing by 0.5-1 second for every 100-meter increase in propagation distance. The ripple phase sampling point is selected at the wavefront and located by the gradient maximum. The timing offset Δt is determined through cross-correlation analysis, searching for the maximum correlation peak within a ±5-second range. The correction function uses piecewise linear interpolation, with linear correction in the near field (<500 meters) and nonlinear correction to account for attenuation in the far field (>500 meters). The long-term nature of Class W operation requires historical consistency in the correction process, and a sliding window is used to store the last 10 seconds of correction history. Dynamic correction is adjusted based on system load fluctuations. Under high load, ripple propagation slows, and the correction amount is increased by 20%. Spatial variability correction takes into account network topology, with end nodes receiving larger corrections than backbone nodes. Correction parameters include the time offset, spatial attenuation factor, and frequency scaling factor, fully describing the ripple path adjustment scheme.

[0057] Correction parameters are used to achieve phase and time synchronization between eddies and ripples, ensuring coordinated coordination between the two control modes. The synchronization algorithm uses a weighted least squares method, with the objective function minimizing the time integral of the phase difference. Weight design takes signal quality into account, assigning greater weights to periods with high signal-to-noise ratios. Weight updates utilize an adaptive algorithm, adjusting every 100 ms based on the real-time signal-to-noise ratio to ensure consistent tracking of high-quality signal segments. Frequency synchronization is achieved using a fractional-order phase-locked loop (PLL), which allows for non-integer frequency relationships. The PLL order is set to 0.7, achieving a balance between fast tracking and noise suppression. The convergence rate for phase synchronization is set to 0.1-0.5 seconds, faster than the ripple period but slower than the eddy period. Energy conservation is maintained during synchronization, and phase adjustments do not alter the total energy of the signal. Boundary condition processing ensures a smooth transition between synchronized and asynchronous regions. The transition zone width is set to 200 meters, and a cosine window function is used for gradual transition.

[0058] Synchronization parameters form a complete synchronized operation flow, organically combining the advantages of P-type and W-type operations. Operation flow generation utilizes a vector synthesis method, with the eddy current component providing rapid response and the ripple component ensuring wide-area coverage. The synthesis ratio is dynamically adjusted based on the synchronization strength: a 7:3 ratio for full synchronization and an 8:2 ratio for partial synchronization. The ratio is adjusted using a piecewise linear function: maintaining a 7:3 ratio for synchronization strengths between 0.8 and 1.0, transitioning linearly to 8:2 for 0.5 and 0.8, and remaining constant at 8:2 for strengths below 0.5. Spatiotemporal coding embeds synchronization information into the operation flow, with each packet carrying a timestamp, spatial coordinates, and synchronization status. The encoding format utilizes a 64-bit fixed-length code: a 16-bit timestamp (millisecond accuracy), 32-bit spatial coordinates (16 bits each for X and Y), an 8-bit synchronization status, and an 8-bit checksum. A flow control mechanism prevents the bursty nature of P-type operations from overwhelming the progressive information of W-type operations. A dual token bucket mechanism is used, with a P-class token bucket rate of 5000 tokens / second and a W-class token bucket rate of 1000 tokens / second, ensuring fair transmission of both types of signals. Priority inheritance ensures that the urgency of the original operation is maintained in the synchronization flow.

[0059] A signal diffusion analysis of synchronous operation flows was conducted to investigate the propagation characteristics and impact range of control signals in the distribution network. Based on the reaction-diffusion equation, the constraints imposed by grid topology on signal propagation were considered. The diffusion coefficient D was set differently based on node type: D = 100 m² / s for substation nodes to promote rapid diffusion, D = 50 m² / s for feeder nodes to moderate diffusion, and D = 20 m² / s for terminal nodes to restrict diffusion. The source term strength is determined by the instantaneous power of the synchronous operation flow, with P-type components generating pulsed sources and W-type components forming continuous sources. Boundary conditions considered electrical isolation, with the switch disconnection position set as a reflecting boundary. The temporal evolution of the diffusion process was solved using the finite element method, with the spatial mesh matching the actual grid structure. Nonlinear effects were observed in areas of high signal intensity, with diffusion velocity saturating with signal strength. Anisotropic diffusion is enhanced along the power line and restricted perpendicularly. The analysis results generate a decentralized control distribution, including a spatial distribution of signal intensity, a propagation velocity field, a node influence weight distribution, and control domain partitioning. The signal intensity presents a multi-center distribution characteristic. P-type operation forms a local high-intensity center, and W-type operation produces a wide-area medium-intensity background, forming a complete decentralized control distribution.

[0060] Based on decentralized control distribution, signal aggregation nodes are assigned according to temporal priority. Local maxima are identified as candidate aggregation nodes based on the spatial distribution of signal strength. These locations naturally form signal aggregation centers. The propagation velocity field distribution determines the response speed requirements of each aggregation node. Aggregation nodes in high-speed propagation areas are assigned shorter processing delays. The node influence weight distribution guides the hierarchical division of aggregation nodes. High-weight areas are assigned first-level aggregation nodes to handle emergency signals, medium-weight areas are assigned second-level nodes to handle routine signals, and low-weight areas are assigned third-level nodes for global coordination. The control domain division results determine the jurisdiction and service boundaries of each aggregation node, avoiding overlap and gaps. Temporal priority is dynamically adjusted based on the arrival time and urgency of signals, with the first-arriving emergency signals receiving the highest processing priority. Node capacity is determined by the signal traffic within the aggregation area, with a typical configuration supporting concurrent processing of 10-50 signals. Spatial layout utilizes Voronoi diagrams to ensure that each area is served by the nearest aggregation node. Inter-node communication utilizes priority queues, allowing high-priority signals to preempt lower-priority channels. The aggregation topology is dynamically optimized based on system operating conditions, with aggregation node density increased during periods of high load.

[0061] A control signal cache is built into the aggregation node to provide storage support for orderly signal processing and flexible release. A multi-level design is employed: the first-level cache (L1) uses high-speed SRAM with a capacity of 256KB to store the most urgent P-class signals; the second-level cache (L2) uses DRAM with a capacity of 16MB to cache general control signals; and the third-level cache (L3) uses an SSD with a capacity of 1GB to store historical data and low-priority signals. The cache strategy is optimized based on signal characteristics, using FIFO for P-class signals to ensure low latency and LRU for W-class signals to support repeated access. Write control enables categorized signal storage, with synchronous operation flows allocated to different cache levels based on the ratio of their P-class to W-class components. Read optimization reduces latency through a prefetch mechanism, preloading relevant data based on the temporal relevance of signals. Cache coherence is maintained through version control, with each signal carrying a timestamp and sequence number. Overflow handling ensures that critical signals are not lost, while low-priority signals can be downgraded or discarded. Dynamic capacity adjustment adapts to signal traffic, temporarily expanding the L1 cache by 50% during peak periods.

[0062] In some embodiments, generating an elastic release signal based on the control signal buffer area includes: constructing a core signal release range based on the control signal buffer area; performing peripheral diffusion analysis on the core signal release range to obtain diffusion parameters; obtaining a buffer propagation chain based on the diffusion parameters; and generating an elastic release signal based on the buffer propagation chain.

[0063] Based on the control signal cache, a core signal release range is constructed to prioritize the release of key control signals. First, high-priority signals in the cache are identified, including all P-class signals in the L1 cache and mixed signals with a synchronization strength greater than 0.8 in the L2 cache. The spatial range is determined by the signal's influence radius. The core range for P-class signals is 1.5 times its eddy radius to ensure complete coverage. The temporal range is set as a prediction window of 100ms to 1s in the future, differentiated by signal type: 100ms for P-class signals, 500ms for mixed signals, and 1s for W-class signals. Signal screening utilizes a multi-criteria decision-making process, comprehensively considering priority, urgency, impact range, and resource requirements. The number of core signals is controlled within 80% of the system's processing capacity, with a 20% margin reserved to accommodate unexpected demand. Range boundaries are defined using fuzzy sets, allowing signals to dynamically adjust between core and non-core based on real-time conditions. The update cycle matches the signal type: every 10ms for P-class signals and every 100ms for W-class signals.

[0064] Diffusion parameters are derived from the core signal's peripheral diffusion range. Optimized for the core signal's high energy density, the diffusion field intensity I(r,t) maintains the I0 level within the core range and decays as I0exp(-r / λ) beyond the boundary. The core signal's diffusion coefficient is 50% higher than that of a standard signal, reflecting its preferential propagation characteristics. Time diffusion analysis reveals that a core P-type signal can cover a 500-meter radius within 100 ms, while a mixed signal with a synchronization strength >0.8 requires 300 ms to cover the same radius. Interactions are considered only for interference between core signals, ignoring weak coupling with background signals. Diffusion directionality is optimized based on the core signal's target node, forming a directional diffusion channel rather than a uniform diffusion pattern. Boundary effects specifically address signal reflections at the core range's edges, with absorbing boundaries designed to reduce energy backflow. Diffusion parameter extraction includes a core signal-specific diffusion coefficient tensor, D_core, which is 30-50% higher than the general coefficient; a fast decay coefficient α_fast for P-type signals; a slow decay coefficient α_slow for high-synchronization signals; and a directional coupling coefficient β_dir, which describes the directional propagation characteristics of the core signal. The spatial distribution of the parameters shows a gradient characteristic centered on the core range.

[0065] A buffered transmission chain is constructed based on diffusion parameters to design a multi-hop signal transmission path from source to destination. A minimum spanning tree algorithm is used to ensure connectivity among all nodes while minimizing transmission distance. Link capacity is allocated based on the core signal's dedicated diffusion coefficient tensor, D_core. Broadband links are allocated in directions with high diffusion coefficients, while directions with strong diffusion capabilities receive more bandwidth resources. The hop limit is set based on the signal type. P-class signals have a maximum of three hops to ensure low latency. The fast attenuation coefficient α_fast is used to compensate for inter-hop attenuation for P-class signals. W-class signals allow seven hops for wide-area coverage, while the slow attenuation coefficient α_slow is used to budget energy for long-distance transmission. Relay node selection considers node processing capacity and current load, prioritizing lightly loaded nodes. The buffering strategy utilizes the directional coupling coefficient β_dir to optimize the buffer configuration of each relay node. Links with strong directional propagation use small buffers for fast forwarding, while links with weak directional propagation have increased buffer capacity. Chain redundancy is achieved by establishing primary and backup paths, with automatic failover in the event of a primary path failure. Transmission scheduling utilizes time division multiplexing, with different priority signals allocated different time slot ratios. The dynamic characteristics of the chain adapt to network changes through regular reconstruction, and the reconstruction period is adjusted between 1-10 minutes according to the stability of the network.

[0066] Based on the buffered propagation chain, the final elastic release signal is generated, enabling adaptive delivery of control signals. The release strategy is adjusted based on real-time network status, accelerating release when the load is light and delaying release when the load is heavy. Release timing follows the priority principle, but introduces random perturbations to prevent simultaneous releases from causing network congestion. Signal shaping smoothes the signal before release. P-type signal spikes are controlled through clipping, and W-type signal glitches are eliminated through filtering. Batch release bundles similar signals for transmission, improving transmission efficiency. The typical batch size is 10-20 signals. The release rate is controlled using a token bucket algorithm, with an average rate of 1000 signals / second and a burst rate of up to 5000 signals / second. Energy modulation pre-compensates based on the attenuation characteristics of the propagation chain to ensure sufficient signal strength upon arrival. Coding optimization selects appropriate modulation schemes based on different link characteristics, using high-order modulation for reliable links and robust coding for unreliable links. The resulting elastic release signal combines the advantages of fast response and stable transmission.

[0067] Step S160 , executing the elastic release signal to perform multi-point phase sampling to obtain phase distribution data, extracting phase difference spectrum lines based on the phase distribution data, identifying phase mutation intervals according to the phase difference spectrum lines, and generating a phase compensation activation signal using the phase mutation intervals.

[0068] Specifically, elastic release signals are deployed to multiple monitoring points on the distribution network to capture phase evolution during signal propagation. The sampling point placement strategy is based on network criticality and signal strength distribution, prioritizing deployment at high-energy-density nodes, aggregation nodes, and chain relay points. A typical configuration is 4-6 sampling points per square kilometer. Phase measurement utilizes synchronized phasor measurement units (PMUs), achieving a time synchronization accuracy of 1 microsecond and a phase measurement accuracy of 0.01 degrees. The sampling frequency is adaptive based on signal characteristics: 10kHz high-speed sampling is used to capture transient changes in P-type signal components, while 1kHz conventional sampling is sufficient for W-type signal components. Multi-point coordination utilizes GPS timing to achieve synchronized sampling across the entire network, ensuring temporal consistency of phase distribution data. Data preprocessing includes outlier removal, phase unwrapping, and noise filtering to generate a preliminary phase distribution dataset. Spatial interpolation utilizes the Kriging method to expand the phase data from discrete sampling points into a continuous phase field, improving the spatial coverage of the phase distribution data. Time series analysis identifies trend and cyclical components of the phase and isolates phase changes associated with elastic release. The final data is organized into a four-dimensional array φ(x, y, z, t) containing phase distribution data, where the three spatial dimensions reflect the network topology and the time dimension records the dynamic evolution.

[0069] In some embodiments, the extraction of phase differential spectrum lines based on the phase distribution data includes: identifying differential influencing factors from the phase distribution data, the differential influencing factors including opening and closing disturbance characteristics, node coupling strength and drive modulation depth; performing weighted quantization analysis on the differential influencing factors to generate quantization weights; constructing a spectrum extraction matrix based on the quantization weights; and generating phase differential spectrum lines using the spectrum extraction matrix.

[0070] Three key differential influencing factors were identified from phase distribution data. These factors directly affect the spatial and temporal rate of phase change. The switching disturbance signature reflects the instantaneous impact of circuit breaker operation on the phase. It is identified by detecting step changes in phase. A typical characteristic is a phase jump of ±π / 3 at the moment of switching, lasting 20-50ms. The disturbance intensity is proportional to the circuit breaker capacity, with large-capacity circuit breakers generating phase disturbances of up to ±π / 2. Node coupling strength describes the degree of phase correlation between adjacent nodes. It is obtained by calculating the spatial correlation function of the phases. Strongly coupled nodes (correlation coefficient > 0.8) maintain a phase difference within 20 degrees. The coupling strength decays exponentially with electrical distance, and the decay constant reflects the density of the network. The driven modulation depth quantifies the degree of modulation of the elastic release signal on the original phase. The modulation index is extracted through demodulation analysis. The modulation depth for P-type signals reaches 0.6-0.8, while for W-type signals it is 0.2-0.4. These three factors interact at different spatial and temporal scales, jointly determining the complex characteristics of phase differentials.

[0071] A weighted quantization analysis was performed on the identified differential influencing factors to determine their contribution to the phase difference. The quantized weights were calculated using the Analytic Hierarchy Process (AHP) to construct a judgment matrix to compare the relative importance of the factors. The switching perturbation feature, due to its strong instantaneous impact, received the highest weight of 0.45, reflecting the system's sensitivity to sudden changes. The node coupling strength was weighted 0.35, reflecting the role of network interconnection in phase propagation. The drive modulation depth was weighted 0.20, primarily affecting the gradual phase transition. Based on the weight distribution of the three factors, the combined weight w_i for each sampling point was calculated as 0.45 × switching perturbation factor + 0.35 × node coupling factor + 0.20 × drive modulation factor. Differentiated weighting strategies were established for different signal types: the high-frequency characteristics of P-type signals require an increased switching perturbation weight of 0.6, while the low-frequency characteristics of W-type signals require an increased modulation depth weight of 0.4. Finally, a quantized weight was generated, combining the combined weights of the sampling points and the signal type differentiation strategy.

[0072] The spectrum extraction matrix is ​​constructed based on the quantized weights to achieve efficient conversion from phase data to frequency domain features. The matrix design uses the weighted discrete Fourier transform (WDFT), and the weight function is directly embedded in the transform kernel. The matrix dimension is N×N, where N=1024 provides sufficient frequency resolution. The matrix elements , where Mij is the element in the i-th row and j-th column of the matrix, w i is the comprehensive weight of the i-th sampling point, j is the imaginary unit, ij represents the product of row and column indices, and N is the matrix dimension. Sparse optimization utilizes the local correlation of phase data to increase the sparsity of the matrix to 90%, significantly reducing the amount of calculation. Frequency selectivity is achieved by designing bandpass characteristics, focusing on extracting phase change frequency components of 0.1-100 Hz. Orthogonality is guaranteed by the Gram-Schmidt process to ensure the independent extraction of different frequency components. Numerical stability is improved by preconditioning technology, and the condition number is controlled within 100. Parallelization is achieved by dividing the matrix into blocks, accelerating the calculation on the GPU, and shortening the processing time to less than 10ms. The adaptive update of the matrix is ​​dynamically adjusted according to the signal characteristics. P-type signals enhance high-frequency resolution, and W-type signals optimize low-frequency accuracy.

[0073] The spectrum extraction matrix is ​​used to transform the weighted phase data to generate differential spectrum lines that reflect the phase change characteristics. The spectrum line calculation first performs spatial differentiation on the phase data. (where ∇ is the gradient operator and φ is the phase distribution function) and time difference (where ∂ is the partial derivative operator and t is the time variable), capturing the rate of change information of the phase. (Where S is the spatial difference spectrum, M is the spectrum extraction matrix, and · is the matrix multiplication operation) The difference data is projected into the frequency domain to obtain the spatial difference spectrum. The time difference spectrum is obtained via short-time Fourier transform, with the window length adaptive according to the signal type. Spectral line fusion uses complex representation, with the amplitude reflecting the difference strength and the phase indicating the direction of change. The main peak is identified through local maximum search, typically identifying 3-5 main frequency components. The Savitzky-Golay filter is used for spectral smoothing, maintaining peak characteristics while suppressing noise. The frequency resolution reaches 0.01Hz, which is sufficient to distinguish similar oscillation modes. The dynamic range is extended to 80dB through logarithmic transformation, and the characteristics of strong and weak signals are displayed simultaneously. The temporal evolution of the spectrum is displayed using a waterfall plot, which intuitively reflects the dynamic changes in the phase difference.

[0074] Phase-difference spectrum analysis identifies phase abrupt changes in the system, which indicate potential instability risks. The mutation detection algorithm, based on the statistical properties of the spectrum, flags an anomaly when the amplitude of a frequency component exceeds three standard deviations from the mean. Time-domain localization maps frequency-domain anomalies back to the time axis through inverse transformation, achieving a localization accuracy of ±5ms. Spatial localization, combining multi-point sampling data, triangulates the center of the phase abrupt change with a spatial accuracy of ±50 meters. Phase abrupt change intensity is graded based on the peak height of the spectrum: mild (3-5 times the mean), moderate (5-10 times), and severe (>10 times). Duration analysis distinguishes between transient (<100ms) and sustained (>100ms) phase abrupt changes, adjusting the handling strategy accordingly. Mutation pattern recognition, using a machine learning classifier, identifies three typical patterns: periodic, random, and cascading. Spatial correlation analysis reveals that 70% of the phase abrupt changes are spatially clustered, concentrated in high-load areas.

[0075] Targeted phase compensation activation signals are generated based on the identified phase mutation intervals to proactively suppress the development of phase instability. Compensation strategies are designed differently based on the mutation type: transient mutations are compensated with reverse pulses, while sustained mutations use progressive compensation. The compensation signal's phase is set to the opposite of the mutation phase to ensure maximum compensation. Amplitude calculation takes propagation attenuation into account, and appropriate boosting is applied at the source to ensure sufficient intensity upon reaching the mutation region. Timing is designed to initiate activation 50-100ms in advance to compensate for signal propagation delay. Spatial distribution is optimized based on the shape of the mutation region, with centralized compensation applied for point-like mutations and distributed compensation for surface-like mutations. A multi-stage compensation mechanism first applies a main compensation signal for rapid suppression, followed by a fine-tuning signal for refined correction. Compensation effectiveness is verified through real-time monitoring, with parameters automatically adjusted if performance falls short. Energy constraints ensure the compensation signal does not introduce new perturbations, keeping the total energy within 5% of the system capacity. The activation signal encodes complete parameters, including compensation location, intensity, phase, and duration, ultimately generating a phase compensation activation signal with targeted suppression capabilities.

[0076] Step S170 , generating a pulse train scheduling instruction according to timing control based on the synchronous operation flow and the phase compensation activation signal, and completing the circuit breaker scheduling operation driven by the power distribution information mainboard.

[0077] Specifically, the synchronous operation flow and phase compensation activation signal are integrated to construct and generate pulse train scheduling instructions. Based on the vector synthesis ratio of the synchronous operation flow, P-class and W-class operations are combined in a mixed ratio of 7:3 or 8:2. A 7:3 ratio is used in fully synchronized conditions, while an 8:2 ratio is adjusted for partial synchronization to enhance responsiveness. The phase compensation activation signal proactively intervenes in detected phase mutations, generating corresponding circuit breaker operation instructions based on the compensation signal's position, strength, phase, and duration parameters. Periodic mutations utilize inverse pulses of the same frequency, random mutations utilize broadband offset pulses, and cascaded mutations implement a hierarchical blocking pulse sequence. Instruction generation begins with timing alignment, utilizing the synchronous operation flow's 64-bit spatiotemporal encoding to unify the time base to millisecond-level accuracy. This is combined with the compensation signal's 50-100ms advance activation timing for precise scheduling. Priority arbitration prioritizes safety. When the compensation signal conflicts with the operation flow, compensation takes precedence to maintain system stability. The power distribution information mainboard's pulse train design utilizes variable-length encoding, dynamically allocating drive resources based on the token bucket control mechanism of the synchronous operation flow. Emergency commands use short pulses (1-3), routine commands use standard pulse trains (5-10), and complex commands allow for long pulse trains (up to 20). The pulse interval is set based on the circuit breaker's response characteristics: 50ms for fast circuit breakers and 100ms for regular circuit breakers. Command packing utilizes the batch release mechanism of the synchronous operation flow to consolidate commands with the same purpose. The power distribution information mainboard optimizes the packing strategy based on the spatial distribution characteristics of the compensation signal. A CRC32 checksum ensures command integrity. The mainboard uses an energy modulation mechanism to pre-compensate output pulses based on the attenuation characteristics of the propagation chain, ensuring sufficient drive strength when the command reaches the circuit breaker. The scheduling strategy comprehensively considers the circuit breaker's current state and historical operation records to generate a globally optimal scheduling solution. The resulting pulse train scheduling instructions effectively combine preventive control with emergency response, completing circuit breaker scheduling operations driven by the power distribution information mainboard.

[0078] In order to implement the circuit breaker scheduling operation method driven by the power distribution information mainboard corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 2 , Figure 2 The following is a block diagram of a circuit breaker dispatching and operating device 200 driven by a power distribution information mainboard according to an embodiment of the present application. For ease of illustration, only the parts relevant to this embodiment are shown. The circuit breaker dispatching and operating device 200 driven by a power distribution information mainboard according to an embodiment of the present application comprises: The data acquisition module 201 is used to collect current fluctuation data and operation records of each circuit breaker node in the power distribution network, extract energy concentration and dispersion characteristics based on the current fluctuation data, identify high energy density nodes based on the energy concentration and dispersion characteristics, and construct a potential field gradient distribution using the high energy density nodes; a spectrum analysis module 202 configured to perform spectrum analysis on the operation record to generate a resonant frequency set, couple and superimpose the resonant frequency set with the potential field gradient distribution to generate a critical excitation point, and generate a fluctuation trigger sequence based on the critical excitation point; The encoding processing module 203 is configured to extract phase jump parameters from the current fluctuation data, input the phase jump parameters into the power distribution information mainboard for spiral encoding processing to generate a rotation drive vector, use the rotation drive vector to construct a multi-dimensional control signal within the mainboard, and construct a hierarchical drive strategy based on the priority reorganization of the multi-dimensional control signal; The signal bifurcation module 204 is used to divide the circuit breaker operation into an instantaneous pulse level and a continuous waveform level, perform signal bifurcation processing on the hierarchical drive strategy and the fluctuation trigger sequence to generate a main dispatching branch and a free branch. The power distribution information main board performs resource dispatch on the instantaneous pulse level through the main dispatching branch to obtain eddy current drive, and performs timing control on the continuous waveform level through the free branch to form a ripple propagation path. a synchronization control module 205 for phase-locking the eddy current drive and the ripple propagation path to form a synchronous operation flow, performing signal diffusion analysis on the synchronous operation flow to generate a decentralized control distribution, setting signal convergence nodes according to timing priority based on the decentralized control distribution, constructing a control signal buffer in the signal convergence node, and generating an elastic release signal based on the control signal buffer; A phase compensation module 206 is configured to perform multi-point phase sampling on the elastic release signal to obtain phase distribution data, extract phase difference spectral lines based on the phase distribution data, identify phase mutation intervals based on the phase difference spectral lines, and generate a phase compensation activation signal using the phase mutation intervals; The execution control module 207 is used to generate a pulse train scheduling instruction according to the timing control based on the synchronous operation flow and the phase compensation activation signal, and complete the circuit breaker scheduling operation driven by the power distribution information mainboard.

[0079] The aforementioned circuit breaker dispatching and operating device 200 driven by a power distribution information motherboard can implement the aforementioned method embodiment of a circuit breaker dispatching and operating method driven by a power distribution information motherboard. 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 embodiments of this application can be referenced to the contents of the aforementioned method embodiment and are not further described in this embodiment.

[0080] 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.

[0081] 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 circuit breaker dispatching operation method driven by a power distribution information mainboard, characterized in that ,include: Collecting current fluctuation data and operation records of each circuit breaker node in the power distribution network, extracting energy concentration and dispersion characteristics based on the current fluctuation data, identifying high energy density nodes based on the energy concentration and dispersion characteristics, and constructing a potential field gradient distribution using the high energy density nodes; Performing spectrum analysis on the operation record to generate a resonant frequency set, coupling and superimposing the resonant frequency set with the potential field gradient distribution to generate a critical excitation point, and generating a fluctuation trigger sequence based on the critical excitation point; Extracting phase jump parameters from the current fluctuation data, inputting the phase jump parameters into a power distribution information mainboard for spiral encoding processing to generate a rotation drive vector, using the rotation drive vector to construct a multi-dimensional control signal within the mainboard, and constructing a hierarchical drive strategy based on priority reorganization of the multi-dimensional control signal; The circuit breaker operation is divided into an instantaneous pulse level and a continuous waveform level, and the hierarchical drive strategy and the fluctuation trigger sequence are subjected to signal bifurcation processing to generate a main dispatching branch and a free branch. The power distribution information mainboard performs resource dispatching on the instantaneous pulse level through the main dispatching branch to obtain eddy current drive, and performs timing control on the continuous waveform level through the free branch to form a ripple propagation path; Phase-locking the eddy current drive and the ripple propagation path to form a synchronous operation flow, performing signal diffusion analysis on the synchronous operation flow to generate a decentralized control distribution, setting a signal convergence node according to a timing priority based on the decentralized control distribution, constructing a control signal buffer in the signal convergence node, and generating an elastic release signal according to the control signal buffer; executing the elastic release signal to perform multi-point phase sampling to obtain phase distribution data, extracting phase difference spectrum lines based on the phase distribution data, identifying phase mutation intervals according to the phase difference spectrum lines, and generating a phase compensation activation signal using the phase mutation intervals; Based on the synchronous operation flow and the phase compensation activation signal, a pulse train scheduling instruction is generated according to timing control to complete the circuit breaker scheduling operation driven by the power distribution information mainboard.

2. The method according to claim 1, characterized in that The step of coupling and superimposing the resonant frequency set with the potential field gradient distribution to generate a critical excitation point comprises: Performing frequency symbiosis analysis on the resonant frequency set to generate symbiosis frequency pairs; Performing phase change matching on the symbiotic frequency pair and the potential field gradient distribution to form a phase change region; Performing critical state identification in the phase transition region and outputting critical state parameters; The critical excitation point is calibrated based on the critical state parameter.

3. The method according to claim 1, characterized in that The step of inputting the phase jump parameter into the power distribution information mainboard for spiral encoding processing to generate a rotation drive vector includes: Performing multi-dimensional spatial mapping on the phase jump parameter to establish spatial coordinates; constructing a spiral encoding trajectory based on the spatial coordinates; Performing code space folding along the spiral code trajectory to generate a folding vector; A rotational driving vector is generated by the folding vector.

4. The method according to claim 1, characterized in that The priority reorganization based on the multi-dimensional control signal to construct a hierarchical driving strategy includes: Performing priority extraction on the multi-dimensional control signal to establish an original priority; Performing reverse processing based on the original priority to generate a reverse signal; Comparing and analyzing the reverse order signal with the original priority to form a reorganization parameter; The reorganization parameters are used to construct a hierarchical driving strategy.

5. The method according to claim 1, characterized in that The signal bifurcation processing of the hierarchical driving strategy and the fluctuation trigger sequence to generate a main scheduling branch and a free branch includes: Perform signal cross analysis on the hierarchical driving strategy and the fluctuation trigger sequence to determine the cross point; establishing a bifurcated path guide based on the intersection; Perform branch allocation processing and output allocation parameters through the bifurcated path guidance; The allocation parameters are used to generate a main dispatching branch and a free branch.

6. The method according to claim 1, characterized in that The step of phase-locking the eddy current drive and the ripple propagation path to form a synchronous operation flow comprises: performing phase repositioning processing on the eddy current drive to establish a positioning phase; Performing timing correction on the ripple propagation path based on the positioning phase to generate correction parameters; Using the correction parameters to achieve phase time synchronization to form synchronization parameters; A synchronization operation flow is formed by the synchronization parameters.

7. The method according to claim 1, characterized in that , said generating an elastic release signal according to said control signal buffer area, comprising: Constructing a core signal release range based on the control signal buffer area; Performing peripheral diffusion analysis on the core signal release range to obtain diffusion parameters; Obtaining a buffer propagation chain according to the diffusion parameter; An elastic release signal is generated based on the buffer propagation chain.

8. The method according to claim 1, characterized in that , said extracting phase difference spectrum lines based on said phase distribution data comprises: identifying differential influencing factors from the phase distribution data, the differential influencing factors comprising an opening and closing disturbance characteristic, a node coupling strength, and a driving modulation depth; Performing weight quantification analysis on the differential impact factors to generate quantified weights; constructing a spectrum extraction matrix based on the quantization weights; The spectrum extraction matrix is ​​used to generate phase difference spectrum lines.

9. The method according to claim 2, characterized in that The step of performing frequency symbiosis analysis on the resonant frequency set to generate a symbiotic frequency pair includes: identifying a dominant frequency and an auxiliary frequency from the set of resonant frequencies; Performing a correlation analysis on the dominant frequency and the auxiliary frequency to establish a correlation coefficient; establishing a frequency pairing rule based on the correlation coefficient; The frequency pairing rule is used to generate co-occurrence frequency pairs.

10. A circuit breaker dispatching and operating device driven by a power distribution information mainboard, characterized in that ,include: A data acquisition module, configured to collect current fluctuation data and operation records of each circuit breaker node in the power distribution network, extract energy concentration and dispersion characteristics based on the current fluctuation data, identify high energy density nodes based on the energy concentration and dispersion characteristics, and construct a potential field gradient distribution using the high energy density nodes; a spectrum analysis module, configured to perform spectrum analysis on the operation record to generate a resonant frequency set, couple and superimpose the resonant frequency set with the potential field gradient distribution to generate a critical excitation point, and generate a fluctuation trigger sequence based on the critical excitation point; an encoding processing module for extracting phase jump parameters from the current fluctuation data, inputting the phase jump parameters into the power distribution information mainboard for spiral encoding processing to generate a rotational drive vector, utilizing the rotational drive vector to construct a multi-dimensional control signal within the mainboard, and constructing a hierarchical drive strategy based on priority reorganization of the multi-dimensional control signal; A signal bifurcation module is used to divide the circuit breaker operation into an instantaneous pulse level and a continuous waveform level, perform signal bifurcation processing on the hierarchical drive strategy and the fluctuation trigger sequence to generate a main dispatching branch and a free branch. The power distribution information mainboard performs resource dispatch on the instantaneous pulse level through the main dispatching branch to obtain eddy current drive, and performs timing control on the continuous waveform level through the free branch to form a ripple propagation path; a synchronous control module, configured to phase-lock the eddy current drive and the ripple propagation path to form a synchronous operation flow, perform signal diffusion analysis on the synchronous operation flow to generate a decentralized control distribution, set a signal convergence node according to a timing priority based on the decentralized control distribution, construct a control signal buffer in the signal convergence node, and generate an elastic release signal based on the control signal buffer; a phase compensation module, configured to perform multi-point phase sampling on the elastic release signal to obtain phase distribution data, extract phase difference spectral lines based on the phase distribution data, identify phase mutation intervals based on the phase difference spectral lines, and generate a phase compensation activation signal using the phase mutation intervals; An execution control module is used to generate a pulse train scheduling instruction according to timing control based on the synchronous operation flow and the phase compensation activation signal, thereby completing the circuit breaker scheduling operation driven by the power distribution information mainboard.

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