Power supply loop automatic configuration method and system

Abstract

The invention discloses an automatic configuration method and system for a power supply loop, and relates to the technical field of intelligent power distribution. After a plug device is rotationally locked, an identity is read in a near field, and corresponding voltage and power templates are called; inputting the power template and bus real-time current into an edge algorithm to obtain residual capacity and generate an initial power distribution matrix; when the residual capacity is higher than a safety threshold value, a phase sequence, a breaking value and overcurrent delay are issued to the intelligent circuit breaker, and zero-voltage window spark-free closing is realized; during operation, load change is predicted, time dislocation and power coupling parameters are calculated, a peak regulation coefficient is obtained through fuzzy reasoning, and a non-critical load migration stabilizing peak value is triggered; and finally, synchronizing the identity, the power distribution matrix and peak regulation parameters to the digital twin model, and realizing self-learning, energy efficiency improvement and system stability improvement by means of a parallel simulation optimization rule and second-level multiplexing during similar access.

Description

Technical Field

[0001] This invention relates to the field of intelligent power distribution technology, specifically to an automatic power circuit configuration method and system. Background Technology

[0002] In heavy-load scenarios of new energy (such as urban rail traction, battery swapping stations, offshore wind power centralized control, and petrochemical complexes), high-capacity DC buses have replaced traditional multi-level AC power distribution as the mainstream energy supply architecture. Existing technologies generally adopt a "centralized monitoring and control - zoned power distribution" approach: after receiving samples at fixed intervals, the host computer generates a distribution meter using static safety factors and linear power balance equations, and completes the closing through ordinary circuit breakers. To accommodate various heterogeneous loads such as grid-connected inverters, energy storage modules, and fast charging stacks, the industry common practice is to preset a uniform overcurrent margin on the circuit breaker side and hard limit branch power using conservation logic; simultaneously, non-critical loads are manually reduced during peak load periods using single-level prediction or empirical thresholds. To pursue visualized management, some solutions introduce cloud monitoring or digital dashboards, but these often only allow playback of historical curves or alarm events, lacking the ability to deeply embed real-time data and perform on-site adaptive optimization. As bus power levels move towards megawatt levels and load sides exhibit a superposition of "triple waveforms"—second-level pulses, ten-minute-level tides, and hourly-level troughs—traditional centralized, static threshold-based power distribution strategies have revealed significant bottlenecks in terms of concurrency, safety margins, and equipment aging management. Write bandwidth is locked by a single thread, strategy updates rely on manual parameter tuning, load migration requires shutdown verification, and equipment health and power scheduling are isolated from each other, leading to frequent mismatches in distribution meters and difficulty in implementing protection logic during large-scale access.

[0003] When high-impact loads such as fast charging stacks, high-speed energy storage, or mine hoists are concentrated online in the same time slot, static thresholds cannot identify the power complementarity between loads, easily misjudging random spikes as persistent loads and triggering intelligent circuit breakers to refuse to close. If on-duty personnel are forced to relax the thresholds, the bus voltage drop will cause inverters to trip in tandem. Furthermore, the existing system only displays the operation logs offline and lacks vectorized retrieval capabilities for similar equipment. This means that when similar units are repeatedly connected, the template still needs to be recalculated, resulting in a cold start delay of several seconds. Algorithm iterations also require production shutdown for verification, and it is impossible to use a digital twin environment for safe playback. As a result, distribution matrix mismatch and arcing accidents occur during peak periods, while energy waste and excessive wear of circuit breakers occur during off-peak periods. After long-term operation, it is difficult to trace the legitimacy and effect of any single weight update.

[0004] Therefore, the present invention provides a method and system for automatic configuration of power circuits. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides an automatic power circuit configuration method and system. It reads the user's identity in the near field and calls the corresponding voltage and power templates. The power templates are then compared with the real-time bus current input using an edge algorithm to calculate the remaining capacity and generate an initial distribution matrix. When the remaining capacity exceeds a safety threshold, phase sequence, breaking value, and overcurrent delay are sent to the intelligent circuit breaker to achieve spark-free closing within a zero-voltage window. During operation, load changes are predicted, time misalignment and power coupling parameters are calculated, and a peak-shaving coefficient is obtained through fuzzy inference, triggering the migration of non-critical loads to smooth peak values. Finally, the user's identity, distribution matrix, and peak-shaving parameters are synchronized to a digital twin model. Parallel simulation is used to optimize rules, and the model is reused in seconds when similar connections are made, achieving self-learning and energy efficiency improvement, thus solving the technical problems described in the background section.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] An automatic power circuit configuration method includes,

[0010] When the detection plug-in device completes rotation locking, the access unit identity is read via near-field communication and the voltage template and power template associated with the identity are called and stored in the cache area;

[0011] The power template and bus current sample are fed into the edge algorithm to obtain the remaining capacity, and an initial distribution matrix matching the identity is generated and the safety margin value is calculated and output as parameters.

[0012] When the remaining capacity is higher than the safety threshold, the phase sequence, breaking current threshold and overcurrent delay parameters are sent to the target smart circuit breaker to achieve sparkless closing in the zero voltage window.

[0013] During operation, the controller predicts the load changes of the bus and branches, outputs time misalignment parameters and power coupling parameters, obtains the peak-shaving coefficient through fuzzy inference, and migrates the non-critical loads when the threshold is reached.

[0014] The identity vector, power distribution matrix, time misalignment and power coupling parameters, and peak shaving coefficient are synchronized to the digital twin model, and the output of the reuse template is recorded in the audit after parallel simulation and optimization.

[0015] Furthermore, the mechanical locking reliability parameter is calculated from the ratio of rotational torque energy to angular velocity energy. After the reliability parameter reaches the adaptive threshold, the magnetic field difference between the output of the longitudinal induction coil and the output of the transverse Hall array is compared. The near-field communication reading action is triggered only when both satisfy the set threshold.

[0016] Furthermore, after the identity is read, the identity is split into the manufacturer domain, device family domain and sequence domain, and a three-dimensional vectorized identity is generated according to the preset bit weight. The voltage template and power template are retrieved through a multi-branch index tree based on spatial partitioning. After the retrieval result passes the consistency hash verification, it is written to the high-speed register using a differential hot loading method.

[0017] Furthermore, the edge algorithm extracts the high-frequency harmonic power density and low-frequency fundamental power density of the bus through dual-channel multi-rate sampling, and fuses them into the bus equivalent current in an energy-conserving manner. Then, combined with the power template vector after phase sequence replacement and linear calibration, the remaining capacity parameters are calculated according to the nonlinear amplification penalty model.

[0018] Furthermore, the voltage template matrix and the calibrated power template vector are combined to form a candidate distribution matrix, and a first-order norm sparsity penalty is added under the minimum Frobenius distance target. An iterative soft thresholding algorithm is used to generate a sparsified initial distribution matrix and output a safety margin value.

[0019] Furthermore, before issuing instructions to the intelligent circuit breaker, the center time and width of the zero-voltage window are predicted. Then, the phase sequence instruction matrix, breaking current threshold vector, and overcurrent delay vector are merged into a single frame and a one-time hash verification field is added. After handshake confirmation, sparkless closing is performed within the window.

[0020] Furthermore, after closing the circuit, the contact micro-displacement capacitance, current-carrying Hall effect sensor, and three-mode transparent-reflective fiber optic sensor are used to synchronously detect the contact closing time and arc index. When the arc index exceeds the limit, the controller adaptively recalculates the next zero-voltage window and executes the opening-closing process. If there are consecutive failures, the controller readjusts the power distribution matrix and records the fault events.

[0021] Furthermore, the load prediction uses a hybrid model of high-frequency, mid-frequency, and low-frequency three-way convolutional networks and gated recursive units to generate a unified prediction curve. The time misalignment parameter is calculated by morphological peak-valley alignment, and the power coupling parameter is calculated by mutual information combined with cointegration test.

[0022] Furthermore, the time misalignment parameter and power coupling parameter are assigned to an adaptive fuzzy inference model to obtain the peak shaving coefficient. When the peak shaving coefficient reaches the threshold, a migration instruction table is generated based on the non-critical load priority index and the bus impedance matrix, and the migration action of disconnecting first and then connecting is executed in parallel within the same zero voltage window.

[0023] Furthermore, the high-consistency mirror synchronization adopts a multi-shard lock-free ring cache and asynchronous write thread to store the identity vector, power distribution matrix family, time misalignment parameter, power coupling parameter and peak shaving coefficient in the database in the form of an entity-version-field graph model, and uses a logical clock plus a two-phase commit mechanism to ensure cross-shard data consistency.

[0024] Furthermore, a dual-objective loss is constructed using a digital twin platform to evaluate peak shaving residuals and energy consumption integrals, driving the Bayesian optimizer to update fuzzy rule weights online. If the Euclidean distance between the newly accessed unit's uploaded identity vector and the historical vector is lower than the cloning threshold, the corresponding voltage template, power template, and optimizer weights are directly reused, and hash signature verification and chain auditing are performed on all update operations.

[0025] An automatic power circuit configuration system includes,

[0026] When the detection plug-in device completes rotation locking, the identity reading module reads the access unit identity via near-field communication and calls the voltage template and power template associated with the identity, storing them in the cache area;

[0027] The capacity assessment module sends the power template and bus current sampling into the edge algorithm to obtain the remaining capacity, generates an initial distribution matrix that matches the identity, and calculates the safety margin value and outputs parameters.

[0028] When the remaining capacity is higher than the safety threshold, the closing control module sends the phase sequence, breaking current threshold and overcurrent delay parameters to the target smart circuit breaker to achieve sparkless closing within the zero voltage window.

[0029] The dynamic peak shaving module predicts the load changes of the bus and branches during operation, outputs time misalignment parameters and power coupling parameters, obtains the peak shaving coefficient through fuzzy inference, and migrates the non-critical loads when the threshold is reached.

[0030] The twin synchronization module synchronizes the identity vector, power distribution matrix, time misalignment and power coupling parameters and peak shaving coefficient to the digital twin model, outputs a reuse template through parallel simulation and optimization, and records it in the audit.

[0031] (III) Beneficial Effects

[0032] This invention provides an automatic power circuit configuration method and system, which has the following beneficial effects:

[0033] By integrating physical locking, radio frequency communication and identity verification into a single millisecond-level action through rotation locking credibility calculation, magnetic field redundancy confirmation and security handshake, we can truly achieve access-to-identification and identification-to-template invocation. This avoids the delay caused by traditional multi-level polling and eliminates false triggering caused by bounce or magnetic noise.

[0034] The edge algorithm, through the combined processes of multi-rate sampling, phase sequence mapping and nonlinear amplification, estimates the remaining capacity while retaining peak information without being overly conservative. Combined with sparsified initial value solution, it concentrates limited power on key branches, thereby shortening load start-up time and reducing bus voltage drop fluctuations.

[0035] Based on zero-voltage window prediction, three-dimensional command one-time packaging and exponential peak reduction curve, this solution simultaneously solves the three major problems of timing, data integrity and surge suppression in the sparkless closing process; the three-mode sensor closed-loop monitoring can also correct the arc risk in real time, so that it can be quickly re-aligned after a failure.

[0036] The combination of multi-scale load forecasting, time misalignment quantization, and power coupling measurement enables the controller to accurately determine the timing of peak shaving; the adaptive fuzzy inference model replaces hard threshold decision-making with continuous output, making the load migration action highly synchronized with the actual load changes; parallel queuing and fast rollback ensure that the migration process is almost imperceptible to the business, achieving strong synergy at the three levels of forecasting, decision-making, and execution.

[0037] With its high-parallel writing, semantic annotation, and scene playback capabilities, the digital twin platform not only provides real-time holographic mapping but also enables online self-adjustment of algorithm weights and second-level template reuse through bi-objective Bayesian optimization and identity vector cloning. Meanwhile, it ensures that every automatic update is traceable through hash signatures and hash chains. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the automatic power circuit configuration method of the present invention;

[0039] Figure 2 This is a schematic diagram of the automatic power circuit configuration system of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Please see Figure 1 This invention provides an automatic power circuit configuration method, including,

[0042] In the field of high-reliability DC bus systems, the delay between the rotation-plugging action and the loading of distribution parameters determines the safety margin and load flexibility of the entire automated distribution chain. Once the rotation-locking process is complete, the controller must immediately identify the connected unit and load the voltage template matrix and power template vector before it can perform edge calculations on the real-time bus current to provide the remaining capacity and initial distribution scheme. If the entry point cannot guarantee millisecond-level determinism, it will amplify bus voltage ripple, trigger false closing of smart circuit breakers, and cause prediction drift in the digital twin model.

[0043] Step 1: At the instant the plug-in device rotates and locks, the unique identity of the access unit is confirmed within milliseconds, and the corresponding voltage and power templates are called, laying the foundation for subsequent power distribution data.

[0044] Step 101: Accurate determination of rotational locking state and initialization of near-field communication

[0045] Rotational torque and angle closed-loop detection:

[0046] The rotation-plugging action goes through three phases from the moment the plug is forced to the moment it is fully locked: acceleration, braking, and slight rebound. The reverse braking torque has a complex nonlinear relationship with the instantaneous angular velocity.

[0047] The controller first uses the embedded microelectromechanical gyroscope to acquire the angular velocity curve in real time, and simultaneously retrieves the output from the strain gauge bridge array located on the inner wall of the rotating shaft, converting it into a reverse braking torque curve. Subsequently, the digital signal processing unit uses a double-integral algorithm to accumulate the time areas of torque and angular velocity respectively, and calculates the ratio between the two, defining the result as the rotational locking reliability parameter. This parameter reflects the degree of coupling between energy loss and displacement contribution during the rotation process; a larger value indicates a more complete torque-angular velocity match, and the mechanical zero point is closer to the theoretical locking position.

[0048] The controller compares the rotational locking reliability parameter with the pre-adaptively tuned locking threshold in real time. When the reliability is not lower than the threshold, the mechanical locking is confirmed and downstream logic is triggered. During this process, all sensor data undergoes first-order low-pass filtering and median filtering, with the filtering coefficients dynamically adjusted according to temperature and wear to ensure that the judgment sensitivity is maintained even under long-term service conditions.

[0049] In high-frequency plugging and unplugging applications in industrial applications, wear of the friction pair causes the initial compensation pad thickness to decrease monthly. If only an angle-positioned switch is used, a half-locked state may be mistakenly identified as a fully locked state, leading to premature opening of the anti-arc gate. By constructing an energy-displacement ratio index, the wear-induced measurement error at the displacement end is mapped back to the difference in energy dissipation at the torque end, forming a stable negative phase complementary mechanism. This transforms the uncertainty of wear into a quantifiable energy distribution difference, decoupling the judgment from the mechanism aging. This mechanism is particularly suitable for high-load, high-frequency plugging and unplugging environments such as aerospace ground test benches, and can significantly reduce arcing accidents caused by mis-locking.

[0050] Confirmation of redundancy in longitudinal and transverse magnetic fields:

[0051] The inherent strong magnetic field within the high-current DC bus can cause random drift in the induced impedance of the near-field communication antenna. A longitudinal induction coil is positioned along the outer edge of the rotating shaft to measure the main magnetic flux density along the insertion / removal direction. Simultaneously, a three-element Hall array is installed at a 90-degree angle to the coil to measure the transverse magnetic field component. The controller synchronously reads the two magnetic field data at a fixed period. First, temperature drift compensation is applied to the longitudinal flux density; then, amplitude correction is performed on the transverse data using a calibration coefficient; finally, the absolute difference between the two is calculated as the magnetic field difference parameter. If this difference is below the synchronization threshold, it indicates that the longitudinal and transverse components are in a steady-state consistent range; the dipole peak caused by magnetic field disturbances has attenuated to within the range allowed by the communication protocol. Under these conditions, the system allows the RF antenna to enter a switchable standby-wake-up state.

[0052] When multiple fast-charging ports are connected in parallel to the DC bus, closing any branch will instantaneously generate a high-amplitude asymmetrical magnetic field around the bus copper busbar. If near-field communication is initiated during the peak of the magnetic field, impedance imbalance in the matching circuit and insufficient amplitude modulation depth will occur, leading to bit sampling jitter. By measuring along both longitudinal and transverse paths and thresholding the mutual difference, the asymmetrical magnetic field during discharge can be treated as short-term high-frequency noise, thus providing a magnetostatically stable window for subsequent communication at the physical level. This solution eliminates the need for redesigning traditional shielding covers and bases, reducing the overall assembly complexity and weight.

[0053] Near-field communication wake-up timing adaptive optimization:

[0054] After completing the dual-threshold comparison of the rotation lock confidence parameter and the magnetic field difference parameter, the controller enters the RF gating stage. First, the digital signal processing unit calls the carrier-interference power ratio real-time estimation function to obtain the current RF signal-to-noise ratio (SNR). Second, the embedded scheduler maps the SNR to a jump delay bias based on an empirical exponential model. This bias, together with the set minimum physical delay, determines the optimal near-field communication delay. Third, after reaching the optimal delay point, the controller sends a wake-up frame and continuously monitors the preamble bit error rate (BER). If the BER exceeds the protocol upper limit, the delay step size is immediately increased and the signal is retransmitted until the BER falls into the target range or the process reverts to relocking. Through this dynamic delay-retransmission chain, the BER is reduced to the order of 10 to the power of negative six in active, harsh electromagnetic environments, ensuring that identity reading is error-free.

[0055] Electric bus battery swapping stations simultaneously raise and lower battery trays for multiple vehicles. When the energy storage modules in the trays are put online, severe flyback interference occurs, with low-frequency noise lasting for hundreds of microseconds. If the wake-up frame completely overlaps with the noise peak, near-field communication will fail multiple times, triggering timeout backoff and causing the dispatching robotic arm to stop. The delay-adaptive strategy automatically avoids noise peaks by evaluating the carrier-interference power ratio in real time. Even in the extreme case of three vehicles being put on the rack simultaneously in parallel, identity reading can still be completed stably without affecting the automatic code matching process.

[0056] One-time hash secure handshake and identity verification:

[0057] Once the physical layer of communication is established, the master controller first generates a 64-bit random number, which is then input into a one-way hash function along with the identity key to obtain a one-time hash value. The access unit tag uses the same input and an independent hardware accelerator to calculate its hash value, which is then returned to the master controller. The master controller compares the hash values ​​from both sides; if they match, the identity is confirmed as legitimate, and a triplet of timestamp, hardware identifier, and random number is written to the audit log. The entire process is executed only once and will not be repeated in subsequent communication windows to prevent replay attacks. The hash algorithm used is a Blake3-128-bit variant to reduce implementation complexity and meet low-power requirements.

[0058] Traditional electronic locks mostly use symmetric encryption for authentication, but symmetric schemes require storing long-term keys within the tag, making them vulnerable to leakage during dismantling and attack scenarios. One-time hashing schemes simply input a random number and the identity into a one-way function; the tag implementation does not require storing a session key. Even if an attacker intercepts the hash value, it cannot be replayed in subsequent insertions or removals. The audit logs in this scheme are written to the peer-level blockchain in an append-only manner, ensuring that external regulatory agencies can trace the insertion and removal history, thus complying with critical infrastructure security compliance requirements.

[0059] The rotational locking reliability parameter calculated by energy-displacement fusion projects the uncertainty of mechanical wear onto the real-time measurable energy domain, achieving full-lifetime self-adaptation. The cross-field redundancy confirmation mechanism uses the difference threshold to freeze the magnetostatic stability window, enabling the coil and Hall sensor information to mutually verify each other and eliminate single-path measurement deviations. The near-field communication delay adaptive strategy binds the real-time signal-to-noise ratio at the RF layer, accurately shifting the start-up window to the interference low-density area, significantly reducing the RF negotiation failure rate. The one-time hash handshake elevates the security of the integrated hardware and software anti-replay scenario to the level of audit traceability. After combination, the entire link completes the mechanical-electromagnetic-RF-cryptographic multi-domain convergence within milliseconds, further maintaining stable output in high-intensity magnetic and high-mechanical shock environments, improving the availability and security level of the power distribution system.

[0060] Step 102: Quick Template Matching and Structured Cache Loading

[0061] Multidimensional identity vectorization encoding:

[0062] The access unit identity consists of three parts: the vendor domain, the device family domain, and the sequence domain, totaling 128 bits. To reduce tree depth in large-scale parallel retrieval, the controller first decomposes the three domains into independent unsigned integers, and then multiplies each integer element-wise with a pre-calibrated bit weight vector to obtain a three-dimensional weight vector, which is named the vectorized identity.

[0063] In the bit-weighted vector, the manufacturer field has the smallest weight, and the sequence field has the largest weight, to meet the business requirement of determining the highest priority of serial numbers within the device family. After vectorization, the controller uses the vector as coordinates to project it into a three-dimensional index space, allowing for nearest neighbor lookups using a tree structure without scanning the entire hash chain. The entire mapping process is completed within the on-chip hardware multiplier with a latency of less than one microsecond, and is piped using single instruction multiple data instructions to ensure no coding bottlenecks occur when multiple plug-in devices are online simultaneously.

[0064] Energy storage rooms typically deploy hundreds or thousands of battery clusters in batches. If traditional binary hash key methods are used, each identity key must be read completely, and hardware hash table expansion introduces unpredictable rearrangements. Vectorized encoding parses identities into coordinates, decoupling the retrieval process from the identity length. Even if a 256-bit identity format is subsequently adopted, only the coordinate dimensions need to be expanded or the weights adjusted, without needing to rebuild the entire index table. Therefore, this solution has long-term evolution capabilities.

[0065] Parallel search based on template index of KD tree:

[0066] After vectorized identity generation, the controller invokes the built-in KD-tree search algorithm to perform a retrieval based on the nearest neighbor count. The index contains over one million templates, distributed in a distinct cluster pattern in three-dimensional space. The KD-tree is recursively partitioned based on the spatial median during construction, and the search proceeds downwards along the tree to quickly locate the sub-region containing the target coordinates. To shorten the worst-case path length, the construction algorithm inserts a fixed three-dimensional centroid plane during the initial partitioning, maintaining an approximately linear relationship between the tree layer number and the logarithmic function. The retrieval results directly return the physical addresses of the voltage template matrix and power template vector in read-only memory, along with the template version number and hash value for subsequent consistency verification.

[0067] Many templates exhibit family-sequence clustering in storage media. KD-trees utilize spatial partitioning to converge templates of the same family to adjacent nodes, significantly reducing the number of traversals. At a node density of millions, the average query path is only nineteen levels, nearly two orders of magnitude shorter than traditional linked list searches. During parallel retrieval, the hardware scheduling engine can distribute query requests from different access devices to multiple tree traversal cores, forming true multi-core parallelism, with the acceleration effect increasing linearly with the number of access units.

[0068] Template Consistency Hash Verification:

[0069] After the index returns the physical address, the controller reads the template header through the direct memory access interface to obtain the hash digest recorded during the factory process. Subsequently, the controller invokes the on-chip accelerator to recalculate the hash of the template content and compare it with the recorded value. If they match, the process passes; otherwise, arbitration logic is triggered and a fault event is recorded. The hash digest uses a Blake3-128-bit variant, which combines high speed and collision resistance, making it more suitable than SHA-3 fixed-length hash digests in embedded scenarios. Hash verification can detect bit flips caused by electromagnetic interference or write errors, and can also identify human tampering.

[0070] In environments with strong pulsed currents, ferroelectric random access memory (FRAM) is prone to sporadic write noise. Relying solely on error correction codes can only handle a few bit flips, and it falls short when faced with large data misalignments. Hash checksums cover the entire template data surface, independent of specific locations, making bit flips and block errors easier to detect.

[0071] Version management and incremental hot reloading:

[0072] After the hash verification passes, the controller compares the template version number with the master version number. If the version is outdated, the incremental loading process begins. The controller first calculates the index of the difference section between the template and the master version, and then copies only the difference section from the high-speed solid-state storage where the master version resides to the controller's high-speed register area. Incremental loading uses a transaction log to record modification operations to prevent version fragmentation due to power outages during loading. After loading is complete, the controller recalculates the template hash and updates the version number, writing the difference section index and update log to the event queue for easy offline auditing. The incremental transmission volume remains below ten percent of the original template size, effectively reducing the bus communication broadcast occupancy rate and shortening the overall template switching time.

[0073] When multiple access units share the same bus, the template may be upgraded multiple times during the field service cycle. If the complete template is sent with each upgrade, it will put continuous pressure on the controller buffer. Incremental loading ensures that the transmission volume is proportional to the amount of change, and even in the maintenance window where multiple people are upgrading in parallel, it is not easy to trigger a flood broadcast storm. The transaction log method ensures that a complete and rollbackable template copy exists at any time, improving system maintainability.

[0074] Vectorized identity encoding schemes break free from the linear collision dilemma of traditional concatenated hash keys, mapping millions of templates to a three-dimensional indexable space and reducing retrieval depth; KD-tree parallel search utilizes spatial clustering characteristics to compress the search path to the order of magnitude and naturally supports multi-core parallelism, fundamentally solving the problem of concurrent contention among multiple access units; Blake triple hash verification covers the entire template data surface, achieving dual detection of bit flips and block errors, and combined with incremental hot loading, reduces the upgrade bandwidth requirement to one-tenth of the original scheme, and ensures template consistency through a transaction log mechanism.

[0075] An information channel is formed from west to east, encompassing energy-displacement fusion, magnetic field redundancy, dynamic delay adaptation, one-time hash verification, multi-dimensional identity encoding, KD-tree indexing, hash full verification, and incremental hot loading. Within the specified window, this channel completes mechanical locking confirmation, rapid identity reading, template positioning, integrity verification, and rollback loading. This provides a deterministic and traceable data entry point for the real-time capacity assessment and initial power distribution scheme generation in step two. Simultaneously, it injects a highly reliable initial state into the subsequent digital twin model, ensuring the system maintains secure, maintainable, and auditable operational attributes even under long-term evolution and extreme industrial and commercial environments.

[0076] At this point, the controller has mastered the voltage template matrix parameters and power template vector parameters, and continuously receives real-time bus current sampling streams through a high-speed analog-to-digital conversion sequence. Step two focuses on: 1. Obtaining bus equivalent current parameters that accurately reflect energy distribution characteristics through multi-rate sampling and frequency domain clipping techniques; 2. Rearranging and linearly calibrating the power template vector parameters using phase sequence permutation matrix parameters and field calibration coefficients to generate calibrated power template vector parameters; 3. Calculating the remaining capacity parameters and constructing initial parameters for the sparse distribution matrix using the nonlinear amplification penalty approach; 4. Outputting the initial distribution matrix parameters under three layers of protection: safety threshold convolution filtering, constraint set projection, and soft saturation function.

[0077] Ensure that the controller can receive power distribution commands that can be directly issued within a two-millisecond time window, while keeping the safety margin of local bus nodes, electromagnetic compatibility limits and circuit breaker operating delays within the compliant range.

[0078] Step 2: Integrate the power template with the real-time bus current to accurately calculate the remaining capacity and output a safe and feasible initial distribution matrix.

[0079] Step 201: Real-time data fusion and remaining capacity calculation

[0080] Frequency domain clipping of multi-rate sampling links:

[0081] In industrial scenarios, bus current signals often exhibit both high-amplitude pulse spikes and slow drift of the low-frequency fundamental frequency. A single sampling rate cannot simultaneously capture peak values ​​and maintain baseline accuracy. Therefore, the controller is equipped with two analog-to-digital conversion channels: a high-frequency channel at 64 kHz to lock in spike details, and a low-frequency channel at 2 kHz to track the fundamental frequency trend.

[0082] To avoid timescale drift, the controller incorporates a unified window function in the firmware of both channels to ensure consistent slice boundaries. Next, the digital signal processor filters out baseband components from high-frequency samples using a Chebyshev type-1 filter bank, sums and squares the remaining results, and then averages the results to obtain the average parameters of the high-frequency energy components. The same process is used to obtain the average parameters of the low-frequency energy components. Subsequently, the controller combines the two parameters using the square root of the sum of squares to form the equivalent bus current parameters, ensuring energy conservation. Through this energy conservation constraint, spikes are treated as high-frequency energy, rather than being mistakenly identified as continuous load, thus providing an unbiased input for remaining capacity calculation.

[0083] In fast battery swapping stations for electric mining trucks, the busbar experiences pulse peaks with doubled amplitude due to parallel high-current connections. If the single-point sampling results are directly substituted into the capacity calculation, the controller will incorrectly determine that the busbar is close to saturation and refuse to add new loads. Multi-rate sampling links isolate transient spikes into high-frequency energy and revert to the true power using the square root of the sum of squares during the energy synthesis stage, thus preserving a safety margin while avoiding excessive conservatism.

[0084] Phase sequence mapping and linear calibration of power template vectors:

[0085] Power template vector parameters are stored in the manufacturing stage with a default phase sequence, but in the field wiring, phase interchange often occurs due to space constraints or human habits. First, the phase sequence code parameters of the plug-in unit are obtained through near-field communication. Then, the phase sequence permutation matrix parameters are constructed to map and rearrange the power template vector parameters one by one, generating rearranged power template vector parameters.

[0086] Subsequently, the controller references the bus port calibration coefficients and performs phase-by-phase linear scaling based on the measured values ​​of copper bus voltage drop and temperature rise, outputting calibration power template vector parameters. Rearrangement resolves the risk of phase sequence mismatch, and linear scaling eliminates amplitude errors caused by on-site voltage drops; these two steps ensure complete consistency between the template values ​​and the actual physical connections.

[0087] In underground coal mine lighting-power hybrid systems, if the lighting phase and power phase are interchanged, the lighting branch will be incorrectly assigned to the power side, causing the lighting protection factor to be unnecessarily inflated. The phase sequence replacement matrix parameters reorder the template sequence to ensure that the protection budget is consistent with the actual phase; linear calibration corrects the amplitude of each phase power based on the actual temperature rise of the copper busbars on site, ensuring that the template is consistent with the field impedance.

[0088] Solving for the nonlinear amplification penalty of the remaining capacity parameter:

[0089] The controller imports the calibration power template vector parameters and the bus equivalent current parameters into the hybrid calculation module. First, it aggregates the power of all branches to obtain the aggregated power parameters. Then, it compares the bus equivalent current parameters with the design nominal current reference. Based on an empirical exponential coefficient, it nonlinearly amplifies the current proportion to obtain the amplification factor parameter. The controller subtracts the aggregated power parameter multiplied by the amplification factor parameter from the design rated power limit to calculate the remaining capacity parameter. If the remaining capacity parameter is negative, it is immediately clamped to zero. The core of the nonlinear amplification penalty principle is that when the actual bus current is close to the rated reference, any new load must be added with greater caution. Therefore, the amplification factor parameter increases the weight of existing power, thus triggering an insufficient capacity alarm earlier.

[0090] During peak summer loads, data center cooling pump units often operate at over 90% of their rated current. A simple linear difference calculation would assume there's still a 10% margin, but the cooling pump load could rapidly increase due to instantaneous temperature rise. Nonlinear amplification penalty uses an empirical exponent to amplify the 90% current to a percentage exceeding 90%, ensuring the controller stops adding new load before the cooling pump load actually reaches its limit.

[0091] Sparsity construction of the initial values ​​of the power distribution matrix:

[0092] Once the remaining capacity parameters are locked, the controller combines the voltage template matrix parameters with the calibrated power template vector parameters to form candidate distribution matrix parameters. Subsequently, the controller defines a squared distance metric between the candidate and target matrices and applies a first-order norm penalty factor to encourage matrix elements to converge toward a sparse distribution. The algorithm performs two rounds of iterative soft-thresholding processing, ultimately outputting initial parameters for the sparse distribution matrix. This sparsity strategy concentrates limited power onto a small number of critical channels, rather than distributing it evenly across all channels, avoiding the awkward situation where all loads are allocated but none are sufficient to start.

[0093] Airport ground power supplies need to provide shore power to multiple aircraft in standby mode during periods of tight flight scheduling. If the power supply were to distribute power evenly across all aircraft stands at once, each stand would receive only a very small allocation, insufficient to power air conditioning and auxiliary equipment, resulting in all stands waiting. The selective initial parameters of a sparse power distribution matrix allocate sufficient power to the stands most urgently needing to start, improving overall operational efficiency.

[0094] In use, real-time data fusion, through multi-rate sampling and energy conservation synthesis methods, ensures that the equivalent current parameters of the bus retain both peak information and do not misjudge continuous loads; phase sequence mapping and linear calibration ensure that the power template vector parameters are completely consistent on-site and at the template end, eliminating the need for manual verification; nonlinear amplification penalty makes the risk amplification characteristics of the remaining capacity parameters take into account both the absolute value of the current and the current proportion; the sparsity strategy, through first-order norm penalty, makes the initial value parameters of the distribution matrix present a centralized distribution form, which is more in line with the requirements of start-up loads.

[0095] Step 202: Safety threshold verification and initial power distribution matrix improvement

[0096] Safety threshold convolutional filter:

[0097] The controller reads the node safety margin vector parameters, where each component represents the remaining margin calculated for the corresponding bus node based on impedance, heat dissipation conditions, and regulatory limits. Subsequently, the controller uses the node safety margin vector parameters as the convolution kernel to perform linear convolution on the initial parameters of the sparse distribution matrix, generating a local safety margin matrix parameter. Any negative value in the matrix element indicates that the corresponding node will be overloaded under the current allocation scheme. The controller uses this to mark risk locations, providing boundary information for subsequent constraint projection. The convolution kernel length is consistent with the combined series-parallel inductance and resistance parameters of the nodes, allowing spatial coupling effects to be naturally integrated into the calculation.

[0098] During welding operations in large shipyards, multiple inverter welding machines may operate simultaneously in the same section. The series-parallel coupling of inductors and resistors in the copper busbars can cause localized amplification of node voltage drops. Safety threshold convolutional filters utilize linear convolution to embed this spatial amplification behavior into safety margin judgments, preventing undetected hotspots from creating safety hazards.

[0099] Multi-factor constrained ensemble projection:

[0100] To simultaneously meet electromagnetic compatibility limits, temperature rise limits, and circuit breaker operating delay requirements, the controller constructs a coefficient matrix and corresponding limit vectors containing three types of constraint coefficients, projecting the initial parameters of the sparse distribution matrix onto the feasible set defined by linear inequalities. The projection employs a least squares-Lagrange multiplier solution method, completing both distance minimization and constraint satisfaction in a single iteration, outputting the projected distribution matrix parameters. This approach condenses complex and multifaceted regulatory provisions into a single mathematical operation, avoiding the iterative oscillations and time overhead caused by traditional item-by-item tailoring.

[0101] Urban rail transit traction inverter systems have extremely strict limitations on harmonic voltage distortion rates. If the power distribution scheme only focuses on total power and ignores harmonic parameters, it may lead to excessive harmonics during vehicle operation. Multi-factor constraint ensemble projection simultaneously satisfies harmonics, temperature rise, and time delay in a single projection step, reducing the uncertainty caused by manual configuration and multiple rounds of trial calculations.

[0102] Soft saturation function and continuous clipping:

[0103] Even after projection, the power of a few channels may still approach their limits, especially in dynamic fluctuation environments where there is a risk of exceeding limits. Therefore, the controller applies a soft saturation function element-by-element to the projected distribution matrix parameters: it remains unchanged when the element value is below a smoothing threshold; when the element value exceeds the threshold, it shrinks inversely, with the shrinkage curve maintaining a continuous first derivative. The function output is the initial distribution matrix parameter. Soft saturation ensures that the matrix surface has no hard inflections, preventing the controller from generating a step output during real-time load fluctuations, thereby reducing the probability of the downstream inverter entering self-protection mode.

[0104] Offshore wind power distribution platforms are affected by wave disturbances, causing periodic fluctuations in load power. If the elements of the distribution matrix are hard-truncated, the element values ​​will be truncated to zero due to exceeding limits when the wave peaks, and will quickly return to full value when the wave troughs, forming a sawtooth output and causing closed-loop oscillation between the power supply and load. Soft saturation functions allow the output curve to transition smoothly, naturally attenuating the oscillation amplitude.

[0105] The safety threshold convolutional filter directly injects spatial coupling characteristics into the overload judgment process, enabling early identification of local hot spots; multi-factor constraint projection solidifies multiple regulations into a single mathematical operation, significantly reducing the scheme verification time; and the soft saturation function avoids control loop oscillations through differentiable continuous contraction.

[0106] At the end of step two, the controller has calculated the remaining capacity parameters and output the initial power distribution matrix parameters accordingly; at the same time, the safety threshold convolutional filter indicates that all bus nodes still have sufficient margin and can enter the sparkless power-on stage.

[0107] The design goal of step three is to enable the target smart circuit breaker to close in the shortest possible time without generating any visible arc, and to fully feed back the actual closing behavior and health information to the controller after the action is completed, so that the subsequent load prediction-migration algorithm can simultaneously consider the aging factors of the equipment.

[0108] Step 3: Based on the remaining capacity, send the phase sequence, breaking value and overcurrent delay to the target intelligent circuit breaker to achieve sparkless closing within the zero voltage window.

[0109] Step 301: Generation and issuance of circuit breaker sparkless closing command

[0110] Sparkless closing window prediction:

[0111] The controller first retrieves the most recent zero-crossing where the phase voltage changed from positive to negative in the real-time sampling sequence and records this instant as the real-time zero-crossing time parameter.

[0112] To compensate for the phase lag caused by the equivalent inductance and equivalent resistance of the bus, the controller calls the compensation time shift stored in the on-chip lookup table area, adds the compensation time shift to the real-time zero-crossing time parameter, and generates the zero-voltage window center time parameter. Subsequently, the controller calculates the window width parameter using a piecewise proportional conversion method based on the damping coefficient mapped from the bus fundamental period and quality factor. This results in a safe closing time window centered on the zero-voltage window center time parameter and with the window width parameter as half its width. To improve robustness, the controller simultaneously records the predicted values ​​of the previous and next zero-crossings as candidate time slots in case of mismatch and writes all data into the internal time reference table. Through this process, the zero-voltage window can be quickly calculated without real-time integration in hardware, avoiding the risk of false zero-crossing judgments in strong harmonic scenarios.

[0113] In a parallel cluster environment of high-voltage DC fast charging piles, simultaneous commutation of multiple branches can cause instantaneous zero-crossing point offset, which is difficult to synchronize using traditional timer-driven methods. By employing real-time zero-crossing retrieval combined with time shift compensation, the phase delay caused by copper busbar parasitic parameters can be shortened from milliseconds to tens of microseconds, ensuring that the zero-voltage window truly covers the moment of contact closure and greatly improving the spark-free hit rate.

[0114] Ternary instruction package:

[0115] Once the zero-voltage window is locked, the controller begins constructing a one-time command frame. First, a command frame header field is written for synchronization detection. Second, the zero-voltage window center time parameter and window width parameter are written sequentially in a fixed-length manner to ensure accurate time window resolution at the circuit breaker end. Subsequently, phase sequence command matrix parameters, breaking current threshold vector parameters, and overcurrent delay vector parameters are inserted in a fixed order. All three types of service fields are in big-endian format and include an eight-bit channel priority tag to prevent them from being overwritten by lower-priority commands in high-concurrency scenarios. Finally, the controller performs a one-time hash operation on the entire command frame to obtain a hash check field, which is appended to the frame tail. This single-frame command structure can quickly check for bit errors using cyclic redundancy checks and uses the hash check field to ensure that service fields are not tampered with during transmission. After the command frame is pushed into the bus physical layer transmission queue, the controller immediately enters the transmission timing state and waits for handshake confirmation.

[0116] Many previous solutions transmitted phase sequence, breaking current, and overcurrent delay in multiple frames. If a misalignment occurred between frames, the circuit breaker would refuse to close due to incomplete parameters. The three-element instruction packaging strategy combines the three core parameters into an indivisible data body, reducing the number of state machine transitions. At the same time, the hash check field provides end-to-end integrity protection for the power-on instruction, preventing the risk of instruction distortion still being executed in high electromagnetic interference environments.

[0117] Impact current resistance curve embedding:

[0118] During command frame generation, the controller performs exponential peak-shaving calculations on the breaking current threshold vector parameter to generate a peak-shaving coefficient vector parameter. This calculation references two inputs: the remaining capacity parameter and the bus rated power. When the remaining capacity is ample, the peak-shaving coefficient vector parameter is biased towards one, indicating a larger allowable current flow; when the remaining capacity is limited, the peak-shaving coefficient vector parameter is significantly less than one, used to suppress the initial surge. Subsequently, based on the peak-shaving coefficient vector parameter, the controller appends a compressed description of the surge current resistance curve parameter to the end of the command frame. After closing, the circuit breaker firmware adjusts the contact closing pressure segment by segment according to this curve, controlling the rise rate of the current flow. By directly writing the surge suppression rules into the command frame, the controller avoids the need to store a large number of curve templates locally on the circuit breaker, while ensuring real-time coupling between surge suppression behavior and remaining capacity.

[0119] On offshore drilling platforms, the stator windings of the hoisting motor generate a surge current tens of times higher than normal upon initial energization. Without a suppression mechanism, this will directly lower the DC bus voltage, causing other loads to lose power. The peak shaving coefficient vector parameter is inversely related to the remaining capacity parameter, meaning that surges are forcibly suppressed more during periods of grid strain, preventing avalanche-like power outages at the system level.

[0120] Real-time command issuance and handshake confirmation:

[0121] At a pre-defined guiding time point, approximately 1000 degrees Celsius away from the center of the zero-voltage window, the controller broadcasts a synchronization code to the bus. Upon detecting the synchronization code, the target circuit breaker immediately locks the communication channel and sends back a handshake confirmation bit. The accuracy of the confirmation bit is evaluated by the frame checker of the programmable gate array. If the error rate is below a preset threshold, the circuit breaker buffers subsequent service fields; if the error rate exceeds the limit, the controller terminates the current transmission and automatically queues the data for the next zero-voltage window to retry.

[0122] Once the handshake is confirmed, the circuit breaker precisely times according to the zero-voltage window center time and window width parameters described in the instruction frame, preparing to close the contacts when the window opens. The entire handshake process lasts no more than two microseconds, allowing ample time for subsequent action detection.

[0123] In fully automated battery swapping stations, the insertion and removal cycle of the robotic arm is measured in seconds, while the circuit breaker closing window is measured in microseconds. By using a guide code and handshake confirmation, the time scale difference between bus communication and physical movement is bridged with a synchronization signal, ensuring that the circuit breaker will only enter the closing countdown after the mechanical movement is completed, thus blocking the possibility of erroneous closing from the source.

[0124] Zero-voltage window prediction robustly locks in the true zero-voltage moment through real-time zero-crossing search and compensation time shift calculation, significantly improving the spark-free hit rate; the three-element instruction packaging merges the three core parameters of phase sequence, breaking threshold, and overcurrent delay into an indivisible data body, while introducing a hash check field to complete integrity protection and reduce the risk of communication frame errors and illegal tampering; the embedded surge current curve allows the surge suppression strategy to be dynamically coupled with the remaining capacity parameter, which can be adaptively adjusted according to the system margin, avoiding excessive conservatism or excessive risk-taking; real-time instruction issuance and handshake confirmation complete data interaction at the microsecond level, ensuring that the circuit breaker action and the zero-voltage window are strictly synchronized.

[0125] Step 302: Circuit breaker operation detection and closed-loop correction

[0126] Contact travel - Hall effect - fiber optic tri-mode detection:

[0127] After the command is executed, the controller needs to know in real time whether the circuit breaker contacts have closed successfully within the zero-voltage window and whether an arc has occurred. To this end, three complementary sensing units are integrated inside the circuit breaker housing: first, a micro-displacement capacitive sensor, responsible for converting contact travel into capacitance changes and marking a timestamp for each micrometer of travel; second, an integrated Hall element, used to monitor the instantaneous magnetic field strength difference caused by sudden changes in current flow, thereby confirming that current has flowed after contact closure; and third, a transflective fiber optic sensor, specifically designed to capture visible-near-infrared photon counts within the closed cavity, serving as direct evidence of the presence or absence of an arc. These three signals are uniformly tagged by the local clock and written into a circular buffer, then pushed to the controller side according to a fixed priority. The controller side uses a Kalman filter to fuse and estimate possible jitter and missing values, ultimately obtaining the contact closure time parameters and arc index parameters. If the arc index parameter is zero, it indicates that no arc occurred during the closure process; if it is greater than zero, the realignment process begins.

[0128] In cryogenic storage terminals, a sudden increase in lubricant viscosity can slow down contact closure speed. The displacement channel allows the system to determine if slow contact movement is due to mechanical obstruction; the Hall channel provides a time indicator of current flow; and the fiber optic channel directly determines the presence of an electric arc from the number of visible photons. Tri-mode fusion can distinguish between two mismatch scenarios: slow but safe, and fast but arcing, providing data interpretation for subsequent decision-making.

[0129] Zero-voltage window realignment and compensation:

[0130] When the three-mode test results show that the contact closing time parameter deviates from the original window center time parameter and a non-zero arc index parameter appears, the controller immediately starts the realignment process.

[0131] First, the previous bus phase voltage sampling data is reused, and the center time parameters for the next zero-voltage window are recalculated based on the newly observed contact delay. Simultaneously, the peak-shaving coefficient vector parameters remain unchanged to reduce the computational load of generating the next command. The controller then repackages the command frame, but adds a trip-reclosing flag field to the frame header to instruct the circuit breaker to open the contacts to release residual energy before waiting for the next window. Since the previous failure exposed the contact delay, this re-alignment incorporates this delay into the new timing plan, significantly improving the success rate of the second closing operation.

[0132] Strong winds inside onshore wind turbine nacelles cause cable swaying, resulting in mechanical delays that often exhibit a continuous offset. By incorporating the initially observed delay into a new time slot calculation, the closing action can be precisely aligned to the next window without increasing the number of switching operations.

[0133] Local power distribution matrix readjustment and fault assignment:

[0134] If the arc index parameter is still detected to be higher than the tolerance value after two consecutive realignments, the controller determines that the circuit breaker has potential mechanical or contact damage. At this time, in order to prevent the faulty branch from continuing to occupy the bus margin, the controller redirects the power share allocated to the faulty circuit breaker in the initial distribution matrix parameters to the healthy circuit breaker channel with the lowest impedance on the same bus, forming a local distribution matrix readjustment result, according to the principle of minimum impedance. Subsequently, the controller writes the circuit breaker fault code and the complete sequence of the three-mode sensors into the event log and pushes it to the digital twin model simultaneously, so that the operation and maintenance team can schedule maintenance without affecting real-time power supply.

[0135] In long-term operation scenarios of urban integrated utility tunnels, if a single circuit breaker intermittently arcs and repeatedly occupies busbar power, it can create a hidden leakage risk. Local readjustment immediately transfers power to the healthy channel, ensuring uninterrupted power supply to critical loads such as lighting and security monitoring, while generating visual alarms on the digital twin side to improve maintenance efficiency.

[0136] Action safety confirmation and feedback closed loop:

[0137] Once the contact closing time parameter falls within the new window range and the arc index parameter is suppressed to zero, the circuit breaker immediately generates a successful operation confirmation frame. The confirmation frame includes the actual closing time parameter, the maximum current carrying rate during peak shaving, and the estimated remaining life parameter calculated based on the cumulative number of contact wear cycles. Upon receiving the confirmation frame, the controller writes the successful operation identifier to the operating database and synchronously updates the estimated remaining life parameter to the circuit breaker health assessment matrix. The health assessment matrix serves as the input to the edge prediction model in step four, used to dynamically adjust the non-critical load migration threshold.

[0138] Port shore power systems require differentiated scheduling based on the aging of equipment. By synchronizing the remaining lifespan estimation parameters to the health assessment matrix, the edge prediction model can proactively reduce the allocation priority of aging circuit breakers during the next round of peak shaving, allowing them to have more opportunities to rest during high-load periods and extending the overall system lifespan.

[0139] The contact travel-Hall-fiber tri-mode detection integrates mechanical, magnetic field, and photonic dimensions to simultaneously collect physical motion, current on / off status, and arc status, enabling rapid location of the root cause of closing mismatch; zero-voltage window realignment directly compensates for the first deviation when calculating the next window, reducing repeated iterations and energy surges; local power distribution matrix readjustment instantly migrates power according to the principle of minimum impedance before the fault is repaired, ensuring continuous power supply to critical loads; and action safety confirmation writes the actual closing time parameters and remaining life estimation parameters into the health assessment matrix, achieving equipment management from a full life cycle perspective.

[0140] By incorporating parameters such as the zero-voltage window center time, window width, phase sequence command matrix, breaking current threshold vector, overcurrent delay vector, peak clipping coefficient vector, contact closing time, arc index, local distribution matrix readjustment results, and remaining lifetime estimation parameters, a prediction-command-suppression-detection-correction-feedback coupling chain is formed. The front end generates and issues sparkless closing commands, while the back end performs immediate detection after execution and readjusts or recalibrates as needed. Finally, health information is embedded into the digital twin model, providing a reliable equipment state vector for subsequent load prediction and dynamic migration.

[0141] After the sparkless power-on process is completed in the first three steps, the power distribution system enters a steady-state operation phase. However, the actual power curve on the load side does not remain static but is constantly driven by multiple factors such as shift changes, ambient temperature, process cycle time, and user behavior, exhibiting multi-scale fluctuations. If the controller continues to use a fixed distribution matrix without dynamic correction, it may trigger bus voltage drop when the load suddenly increases or cause energy waste when the load drops sharply.

[0142] Step 4: Continuously predict load fluctuations and generate peak-shaving coefficients through fuzzy inference to trigger seamless migration of non-critical loads to dynamically smooth bus peaks.

[0143] Step 401: Multi-scale load forecasting and correlation parameter extraction

[0144] Construction of multi-scale prediction models:

[0145] First, the total bus power curve and branch power matrix for the most recent 24 hours are synchronized to a unified millisecond timescale, and then fed into a three-way convolutional network in batches via a sliding window function. The first-level convolution of the network uses a very small receptive field, covering only twenty sampling points, to accurately capture peak information within one second; the second-level convolution expands the receptive field to five hundred sampling points, specifically capturing periodic fluctuations of around ten minutes; the third-level convolution further expands to thirty thousand sampling points to examine hourly trends caused by day-night cycles or shift work. The output of each level of convolution is connected to a set of gated recursive units. During the encoding-decoding process, the recursive units use a forget gate mechanism to filter irrelevant noise, while binding the hidden state to a timestamp, so that the model retains the exact location of high-frequency pulses while avoiding confusion with low-frequency trends. The three branches are linearly superimposed at the end through energy weighting, and the superposition weights are adaptively adjusted by backpropagation of the training error. Finally, the controller outputs a unified prediction curve at the end of each rolling window and pushes the curve along with the real-time curve into the alignment algorithm.

[0146] When switching production batches, the air compressor initially triggers a spike on the order of seconds, followed by a slow increase in steam load over ten-minute periods, and finally, the entire unit experiences an hourly dip during shift changes. Single-scale models often fail to address all aspects and only perform well within a specific timeframe. The three-frequency convolution-recursive hybrid structure splits and then fuses signals from different time bands, taking into account all features with a dedicated data acquisition and centralized decision-making approach, thus avoiding the amplification of overall bias by optimizing only local risks.

[0147] Derivation of time misalignment parameters:

[0148] After obtaining the predicted and real-time curves, the controller invokes an improved dynamic time warping algorithm. Instead of performing full-amplitude alignment on the two complete curves, the algorithm first extracts all obvious peaks and valleys using continuous wavelet transform, and then performs local elastic matching using peak-valley pairs as anchor points. The matching results are used to calculate the time difference between each pair of anchor points, and after removing outliers above three times the median, the average is calculated. This average is defined as the time misalignment parameter. If the average is positive, it indicates that the predicted curve is lagging overall; if it is negative, it indicates that the predicted curve is leading overall; if the absolute value is below a preset threshold, the two curves are considered sufficiently consistent in time.

[0149] In cleanroom air conditioning systems, the temperature feedback loop introduces a slow adjustment delay to the power generation load. If the model prediction fails to compensate for this delay, the peak value is often one minute later than the actual value, causing the migration action to lag behind the on-site fluctuations. The time misalignment parameter provides a quantitative indicator of this phase mismatch and directly participates in the subsequent peak shaving logic, enabling the controller to automatically increase the migration ratio or initiate actions in advance when the prediction lags.

[0150] Power coupling parameter calculation:

[0151] The controller then pairs the real-time power matrix with the predicted power matrix by branch, using mutual information to measure the nonlinear correlation between branches. To distinguish between true coupling and trend resonance, a cointegration test is performed on each pair of power sequences; mutual information scores are only retained if the cointegration statistic falls within the significance interval. Finally, the controller takes the arithmetic mean of the mutual information scores for all significantly coupled branches; this mean is defined as the power coupling parameter. A higher value indicates more pronounced synchronous fluctuations between branches; a lower value indicates a complementary relationship between loads.

[0152] In mine double-skip hoisting systems, the lifting and lowering power complements each other. Mutual information testing reveals negative coupling, while cointegration testing fails, resulting in low power coupling parameters. Subsequent fuzzy inference then reduces the peak-shaving coefficient to prevent unnecessary migration. Conversely, in the lighting and sound circuitry of large concerts, synchronization increases significantly, causing parameters to rise and triggering active peak shaving.

[0153] The multi-scale prediction model allows second-level peaks and hour-level trends to coexist in the same prediction sequence, eliminating the need for manual merging at the back end; the time misalignment parameter uses local peaks and valleys as anchors to pinpoint phase mismatch to the second level, ensuring that the migration window is not missed due to model lag; the power coupling parameter uses mutual information and cointegration dual filtering to retain true synchronous fluctuations while eliminating spurious correlations, providing a reliable scale for peak shaving.

[0154] Step 402: Fuzzy Inference Peak Shaving and Load Migration Execution

[0155] Design of an adaptive fuzzy inference model:

[0156] The controller maps the time misalignment parameter and power coupling parameter to five membership intervals: minimum, slightly minimum, medium, slightly maximum, and maximum, respectively, and generates twenty-five fuzzy rules by row and column combination. Each rule provides a low, medium, or high-level peak-shaving output. To ensure the rules are adaptive in different scenarios, the controller maintains a peak-shaving effect history table in the digital twin model, recording the correspondence between the peak-shaving coefficient and the peak-shaving residual for each iteration. The controller uses a gradient descent method to fine-tune the output weights of each rule, minimizing the residual in subsequent iterations. The final output peak-shaving coefficient varies continuously between zero and one, avoiding the jitter-pause cycle caused by hard thresholds while preserving human-readable rule interpretability. When the peak-shaving coefficient exceeds the tuning threshold, the system immediately enters the priority sorting process.

[0157] In large exhibition halls, peak loads for lighting and air conditioning tend to occur simultaneously. If the model predictions coincide with the real-time curves but have low complementarity, it is necessary to quickly migrate some non-critical lighting to backup branches. Fuzzy inference models can output high peak-shaving coefficients when there is little time misalignment but high coupling, and output low peak-shaving coefficients when both parameters are low, thereby maintaining system stability.

[0158] Non-critical load prioritization and migration planning:

[0159] The controller traverses the non-critical load database, reading four indicators: power consumption, business importance, cold start time, and equipment health. It then calculates a migration priority index using the analytic hierarchy process (AHP). Subsequently, based on the branch impedance matrix, it calculates the potential voltage drop increase on the current bus for each target branch and sorts the migration targets by the minimum voltage drop. The controller combines the priority index and voltage drop sorting to generate a migration instruction table: each instruction includes the source branch address, target branch address, migration start time, and migration duration. The migration start time is uniformly aligned to the upcoming zero-voltage window, and the migration duration depends on the load's cold start frequency, ensuring that no undervoltage occurs during the entire process of the load transitioning from power-off on the old branch to power-on on the new branch.

[0160] In data centers, backplane fans have low power but are numerous; hard disk arrays have high power but poor cold start tolerance. The priority index mechanism will relocate backplane fans first to reduce the risk of frequent power outages of high-power devices; impedance matrix mapping ensures that the voltage drop difference between the new branch and the source branch is minimized, avoiding the generation of new local hot spots.

[0161] Migration parallel execution and bus synchronization:

[0162] The controller issues power-off and power-on commands in batches 100 microseconds before the zero-voltage window arrives. To reduce overall latency, the system uses a parallel queuing strategy: all commands are ordered according to the target branch and then enter bus communication in parallel. Power-off commands precede power-on commands by a fixed time difference to ensure disconnection before connection. The controller monitors the bus voltage in real time. Once the voltage drop exceeds the 1% warning threshold, a pause flag is immediately triggered, and the migrated loads are quickly rolled back to their original branches. If the migration is complete and the voltage drop is stable, the system writes "migration successful" in the event log.

[0163] In cloud computing rooms, a near-simultaneous power outage of a hard drive array can cause a momentary drop in current, while an immediate power-on can trigger a surge. Parallel queuing, combined with a fixed time difference, encapsulates the power outage and power-on within the same zero-voltage window, shortening the peak-to-valley interval and suppressing voltage drop fluctuations.

[0164] Digital twin synchronization and rule self-evolution:

[0165] After the migration is complete, the controller writes the time misalignment parameters, power coupling parameters, peak shaving coefficient, migration instruction table, and actual peak shaving residuals into the digital twin model all at once. The twin side generates a weight adjustment vector and writes back the fuzzy rule weights by comparing the expected residuals with the actual residuals. Simultaneously, the circuit breaker action counts involved in the migration are incremented, and the health assessment matrix is ​​updated. The digital twin uses this data to perform Monte Carlo simulations in the background to evaluate the impact of different rule adjustments on the peak shaving effect, and synchronizes the optimal weight set back to the controller during low-load periods at night, achieving integrated offline-online iteration.

[0166] Port shore power equipment is best suited for large-scale simulations during off-peak shift changes. The digital twin model uses the accumulated migration data throughout the day to try multiple rule combinations, and then pushes the optimal combination online the next day. This ensures that rule optimization does not consume computing power during peak hours and that online strategies are always based on the latest operating data.

[0167] The adaptive fuzzy inference model dynamically evolves rule weights with peak-shaving residuals through online gradient fine-tuning, enabling rules to be shaped in reverse by effects. Priority generation integrates business importance, power level, cold start time, and health into a migration index, avoiding migration based solely on power level while neglecting business continuity. Parallel queuing, bus monitoring, and rapid rollback form a three-in-one system, providing real-time self-protection capabilities during the migration process. Digital twin synchronization and offline Monte Carlo simulation deeply couple rule evolution with equipment health management. It can quickly suppress bus spikes during peak hours and continuously learn during off-peak hours, forming an adaptive and maintainable advantage that traditional static peak-shaving algorithms cannot match.

[0168] The timing misalignment parameter and power coupling parameter provide a two-dimensional scale for the peak shaving factor; the peak shaving factor directly determines the migration instruction table, ensuring that each load migration is based on sufficient data evidence; the migration results are continuously collected by the digital twin model and the decision logic for the next round is changed through weighted write-back.

[0169] In the first four steps, the controller has achieved a closed-loop capability of instant recognition, instant matching, instant peak reduction, and instant stabilization. However, if this capability is to be reproduced in milliseconds in future similar access units and the algorithm itself is to continue to grow, all high-value information generated on-site must be projected into the digital twin platform without any omissions.

[0170] Step 5: Write the identity vector, power distribution matrix and peak-shaving parameters into the digital twin model with high consistency, drive the algorithm to self-evolve and provide second-level template reuse for similar access.

[0171] Step 501, Mirror Synchronization - Semantic Mapping

[0172] High-parallelism message queue write mechanism:

[0173] The controller divides the data into several independent sub-streams on the internal bus according to parameter categories. Each sub-stream is assigned a dedicated circular buffer and an asynchronous write thread. The write thread uses an event-driven model: when the circular buffer fill rate reaches a set percentage, the thread packages the buffer fragments into a fixed-byte message and sends it to the digital twin receiver at once.

[0174] Because the circular cache employs a lock-free, single-producer-single-consumer design, cache writes and sends do not compete for each other, allowing multiple write threads to work in parallel at the same time. The digital twin quickly assigns the arriving fragments to their correct positions using the fragment number in the message header, and then aligns their timing based on the sequence number within the message. The key to this mechanism is that write throughput is approximately linearly positively correlated with the number of parallel fragments, while the write cycle time is primarily determined by network round-trip latency, no longer limited by the single-threaded processing rate.

[0175] For a centralized control center that simultaneously receives real-time data from over twenty inverters and over ten energy storage units, serial writing would cause tail queuing with any network jitter, amplifying the overall latency. By binding the ring cache and asynchronous threads to the sharded queue, even if some threads pause due to network fluctuations, the remaining threads can continue pushing, keeping the average write latency stably at the sub-millisecond level, meeting the high refresh rate requirements of the twin database.

[0176] Parametric meta-modeling and semantic annotation:

[0177] All written data is not directly stored on disk in a flat table format, but first enters a hybrid object-relational graph. The top layer of the graph consists of entity nodes, used to hold objects such as identity embedding vectors, power distribution matrix families, and prediction-peak-shaving parameter families. Below the entity nodes, version nodes are generated in iterative batches. Below the version nodes, field nodes are extracted. Each field node, upon insertion, is written with a three-element label consisting of a full Chinese name identifier, an international unit of measurement identifier, and a data type identifier, and an additional vectorized representation of over 100 dimensions is generated for similarity retrieval or clustering in the vector indexing engine. This meta-modeling process logically separates what the data contains from what the data represents, achieving decoupling between semantics and byte representation.

[0178] Retrieving the historical relationship between peak-shaving coefficients and peak-shaving residuals in a distributed data lake traditionally requires comparing keywords from document titles, paths, and field aliases. After meta-modeling, all relevant fields and their versions can be returned simply by quickly querying the quantity representing the peak-shaving effect in the vector space, greatly improving data reuse efficiency.

[0179] Time-consistent reconciliation and version control:

[0180] Because parallel writes in fragmented format naturally lead to out-of-order delivery, a logical clock stamp is inserted into the message header. The digital twin employs a two-phase commit strategy when consuming messages: first, each fragment uploaded is marked as ready to commit; then, once all fragments are present and the logical clock difference between fragments does not exceed the tolerance range, the entire batch is converted to committed at once. If any fragments are missing or the clock difference exceeds the tolerance within the timeout window, the digital twin immediately rolls back this batch of data and requests the controller to resend it. In this way, every write is guaranteed to be persisted to disk under a globally consistent snapshot, avoiding the scenario where the identity has been updated but the power distribution matrix remains misaligned during future playback.

[0181] In multi-link wireless backhaul, short-term packet loss is very common. Two-phase commit utilizes the atomic principle of "all data must be received before committing," which rigidly prevents some fields from being entered into the database first and some fields from being entered later, thereby ensuring that the reference relationship between versions is always valid.

[0182] Runtime scene replay cache:

[0183] The controller slices the total bus power curve and the corresponding distribution matrix index at a time granularity of one-hundredth of a second and labels them with playback tags in the twin database. When developers need to verify new algorithms offline, they only need to retrieve the slice sequence for a specific day or week, eliminating the need for lengthy trial and error on-site. The twin uses on-site data to build a rapid simulation model, then compares the simulated power curve with the actual on-site curve point by point, defining the integral of the squared difference between the two as the playback error. If the error meets a preset threshold, the simulation model is considered sufficiently accurate and can be used as a testbed for the optimizer; if the error exceeds the threshold, the twin automatically retrieves the latest fields and recalibrates the parameters until the error converges.

[0184] For urban rail transit traction power grids, nighttime shutdown windows are insufficient for large-scale online experiments. Replay caching allows maintenance teams to quickly reenact peak periods in an offline environment, repeatedly deriving new peak-shaving strategies and voltage drop thresholds in the shadow bus. After passing simulation acceptance, the system can be brought online during the midday off-peak hours, greatly reducing trial-and-error costs.

[0185] Parallel queue writing breaks down the original single-channel bottleneck into several independent working threads, achieving quasi-linear scaling of throughput and number of devices; meta-modeling allows massive heterogeneous fields to have a unified coordinate system at the semantic level, doubling retrieval efficiency; two-phase commit uses a logical clock as an anchor to ensure that there is no longer a risk of dangling references between versions; and replay caching encapsulates real-world working conditions into reusable scripts, providing a risk-free testing ground for offline algorithm iteration.

[0186] Step 502, Feedback Learning - Direct Reuse

[0187] Multi-objective weight tuning mechanism:

[0188] The digital twin platform scans all peak-shaving actions from the previous day at midnight, calculating two evaluation indicators: peak-shaving residual integral and energy consumption excess integral. It dynamically assigns different weights to these two indicators based on electricity prices, penalties, or operational strategies, and then weights them together to obtain a comprehensive loss value. The platform then performs a Bayesian iteration on the existing fuzzy rule weights: first, it samples a batch of candidate weights, uses a replay cache to quickly evaluate the residual and energy consumption, and then updates the online controller with the lowest comprehensive loss. The entire process is completed within a virtual environment and has no real-time impact on the bus.

[0189] The peak shaving targets for photovoltaic power generation and wind power generation during the off-season have completely different weights. By using dual-indicator weighting, the twin platform can adjust the rules immediately when the operation strategy changes, allowing the controller to focus on energy consumption during the day and peak suppression at night, thus achieving adaptive behavior at the strategy level.

[0190] Vectorized cloning configuration process:

[0191] Once the newly connected unit has rotated into position and uploaded its identity embedding vector, the controller performs a vector retrieval in the twin database: first, it spatially indexes all historical identity vectors, then calculates similarity using Euclidean distance. If the minimum distance is below the cloning threshold, it directly packages and distributes the voltage template, power template, initial power distribution matrix, and latest optimizer weights of similar historical units, replacing the conventional three-step process of template retrieval, remaining capacity calculation, and sparse optimization. This allows similar devices to be in a deeply optimized state upon first power-on.

[0192] In bus battery swapping scenarios, identical battery trays are frequently plugged and unplugged. Template distribution based on cloning thresholds quickly reduces the access time per vehicle from seconds to hundreds of microseconds, while ensuring that each tray inherits the peak-shaving experience of the most recent identical tray, significantly reducing scheduling jitter.

[0193] Flexible transfer learning anti-forgetting mechanism:

[0194] When introducing new scene data, the Bayesian optimizer may overwrite experience from older scenes. To address this, the platform calculates the Euclidean distance between the old and new parameters and adds an elastic regularization term to the loss function. The regularization strength is correlated with scene similarity through a ramp function; the lower the scene similarity, the stronger the regularization, thus protecting existing knowledge. The platform also periodically replays representative older scenes. If a significant deterioration in peak-shaving residuals and energy consumption integrals is detected, a rollback process is automatically initiated, resetting the weights to the previous stable version.

[0195] The urban power grid operates under drastically different conditions in summer and winter. If the sample size increases dramatically in summer and becomes sparse in winter, the optimizer will favor summer weights. Elastic regularization ensures that winter weights are retained by penalizing drastic changes in parameters; periodic replays verify the performance of winter scenarios, and if a decline occurs, the promotion of new weights is immediately stopped to avoid a sudden drop in service quality.

[0196] Trusted execution and security auditing mechanisms:

[0197] Each time a rule weight is updated or a template clone is distributed, an unforgeable signature is generated using a hash function and a random number before leaving the twin server. Upon receiving the update, the controller first verifies the signature locally before writing it to a read-only audit list. If the signature does not match or the timestamp and random number have already been used, execution is rejected and an alarm is triggered. The audit list uses a one-way hash chain, with each record containing a timestamp, instruction digest, signature, and digest hash, ensuring that any tampering will compromise the chain's integrity.

[0198] In financial data centers, power distribution systems are critical infrastructure, and any malicious replacement of parameters could lead to power outages. Hashchain auditing ensures that all updates are traceable and tamper-proof, allowing for rapid identification of the source of unauthorized operations even if the internal network is compromised.

[0199] Multi-objective weight optimization allows the algorithm to switch its focus in real time according to operational strategies; vectorized cloning instantly transfers historical experience to similar devices, completely eliminating the cold start blind spot; elastic transfer learning avoids disaster forgetting through regularization and replay double insurance; trusted execution and auditing build a security boundary for automatic distribution.

[0200] Step five seamlessly maps real-time operational data to the digital twin model, enabling the twin model to drive the controller to complete rule self-evolution and template reuse within seconds. Similar access devices receive mature power distribution templates upon going online; networked devices continuously benefit from algorithm self-tuning; all updates are traceable and rollbackable, truly realizing a closed-loop automatic power circuit configuration that achieves experience assetization, algorithm self-growth, and zero-trust operation.

[0201] Please see Figure 2 This invention provides an automatic power circuit configuration system, comprising,

[0202] When the detection plug-in device completes rotation locking, the identity reading module reads the access unit identity via near-field communication and calls the voltage template and power template associated with the identity, storing them in the cache area;

[0203] The capacity assessment module sends the power template and bus current sampling into the edge algorithm to obtain the remaining capacity, generates an initial distribution matrix that matches the identity, and calculates the safety margin value and outputs parameters.

[0204] When the remaining capacity is higher than the safety threshold, the closing control module sends the phase sequence, breaking current threshold and overcurrent delay parameters to the target smart circuit breaker to achieve sparkless closing within the zero voltage window.

[0205] The dynamic peak shaving module predicts the load changes of the bus and branches during operation, outputs time misalignment parameters and power coupling parameters, obtains the peak shaving coefficient through fuzzy inference, and migrates the non-critical loads when the threshold is reached.

[0206] The twin synchronization module synchronizes the identity vector, power distribution matrix, time misalignment and power coupling parameters and peak shaving coefficient to the digital twin model, outputs a reuse template through parallel simulation and optimization, and records it in the audit.

[0207] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0208] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0209] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0210] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0211] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for automatically configuring a power supply circuit, characterized in that: include, When the detection plug-in device completes rotation locking, the access unit identity is read via near-field communication and the voltage template and power template associated with the identity are called and stored in the cache area; The power template and bus current sample are fed into the edge algorithm to obtain the remaining capacity, and an initial distribution matrix matching the identity is generated and the safety margin value is calculated and output as parameters. When the remaining capacity is higher than the safety threshold, the phase sequence, breaking current threshold and overcurrent delay parameters are sent to the target smart circuit breaker to achieve sparkless closing in the zero voltage window. During operation, the load changes of the bus and branches are predicted, and the time misalignment parameters and power coupling parameters are output. The peak-shaving coefficient is obtained through fuzzy inference, and the non-critical loads are migrated when the threshold is reached. The identity vector, power distribution matrix, time misalignment and power coupling parameters, and peak shaving coefficient are synchronized to the digital twin model, and the output of the reuse template is recorded in the audit after parallel simulation and optimization.

2. The automatic power circuit configuration method according to claim 1, characterized in that: The mechanical locking reliability parameter is calculated from the ratio of rotational torque energy to angular velocity energy. After the reliability parameter reaches the adaptive threshold, the magnetic field difference between the output of the longitudinal induction coil and the output of the transverse Hall array is compared. The near-field communication reading action is triggered only when both meet the set threshold.

3. The automatic power circuit configuration method according to claim 2, characterized in that: After the identity is read, the identity is split into the manufacturer domain, device family domain and sequence domain. A three-dimensional vectorized identity is generated according to the preset bit weight. The voltage template and power template are retrieved through a multi-branch index tree based on spatial partitioning. After the retrieval result passes the consistency hash verification, it is written to the high-speed register using the differential hot loading method.

4. The automatic power circuit configuration method according to claim 3, characterized in that: The edge algorithm extracts the high-frequency harmonic power density and low-frequency fundamental power density of the bus through dual-channel multi-rate sampling, and fuses them into the bus equivalent current in an energy-conserving manner. Then, combined with the power template vector after phase sequence replacement and linear calibration, the remaining capacity parameters are calculated according to the nonlinear amplification penalty model.

5. The automatic power circuit configuration method according to claim 4, characterized in that: The voltage template matrix and the calibrated power template vector are combined to form a candidate distribution matrix. A first-order norm sparsity penalty is added under the minimum Frobenius distance target. An iterative soft thresholding algorithm is used to generate a sparsified initial distribution matrix and output the safety margin value.

6. The automatic power circuit configuration method according to claim 5, characterized in that: Before issuing commands to the intelligent circuit breaker, the center time and width of the zero-voltage window are predicted. Then, the phase sequence command matrix, breaking current threshold vector and overcurrent delay vector are merged into a single frame and a one-time hash verification field is added. After handshake confirmation, sparkless closing is performed within the window.

7. The automatic power circuit configuration method according to claim 6, characterized in that: After closing, the contact micro-displacement capacitance, current-carrying Hall effect sensor, and three-mode transparent-reflective fiber optic sensor are used to synchronously detect the contact closing time and arc index. When the arc index exceeds the limit, the controller adaptively recalculates the next zero-voltage window and executes the opening-closing process. If there are consecutive failures, the controller readjusts the power distribution matrix and records the fault event.

8. The automatic power circuit configuration method according to claim 7, characterized in that: Load prediction uses a hybrid model of high-frequency, medium-frequency, and low-frequency three-way convolutional networks and gated recursive units to generate a unified prediction curve. It calculates time misalignment parameters by morphological peak-valley alignment and calculates power coupling parameters by mutual information combined with cointegration test. After the time misalignment parameter and power coupling parameter are assigned to the adaptive fuzzy inference model, the peak shaving coefficient is obtained. When the peak shaving coefficient reaches the threshold, a migration instruction table is generated based on the non-critical load priority index and the bus impedance matrix, and the migration action of disconnecting first and then connecting is executed in parallel within the same zero voltage window.

9. The automatic power circuit configuration method according to claim 8, characterized in that: High consistency mirror synchronization uses a multi-sharded lock-free ring cache and asynchronous write thread to store the identity vector, power distribution matrix family, time misalignment parameter, power coupling parameter and peak shaving coefficient in the database in the form of entity-version-field graph model, and uses a logical clock plus a two-phase commit mechanism to ensure cross-shard data consistency. By constructing a dual-objective loss for peak shaving residuals and energy consumption integrals through a digital twin platform, the Bayesian optimizer is driven to update the fuzzy rule weights online. If the Euclidean distance between the newly accessed unit's uploaded identity vector and the historical vector is lower than the cloning threshold, the corresponding voltage template, power template, and optimizer weights are directly reused, and hash signature verification and chain auditing are performed on all update operations.

10. An automatic power circuit configuration system, characterized in that: include, When the detection plug-in device completes rotation locking, the identity reading module reads the access unit identity via near-field communication and calls the voltage template and power template associated with the identity, storing them in the cache area; The capacity assessment module sends the power template and bus current sampling into the edge algorithm to obtain the remaining capacity, generates an initial distribution matrix that matches the identity, and calculates the safety margin value and outputs parameters. When the remaining capacity is higher than the safety threshold, the closing control module sends the phase sequence, breaking current threshold and overcurrent delay parameters to the target smart circuit breaker to achieve sparkless closing within the zero voltage window. The dynamic peak shaving module predicts the load changes of the bus and branches during operation, outputs time misalignment parameters and power coupling parameters, obtains the peak shaving coefficient through fuzzy inference, and migrates the non-critical loads when the threshold is reached. The twin synchronization module synchronizes the identity vector, power distribution matrix, time misalignment and power coupling parameters and peak shaving coefficient to the digital twin model, outputs a reuse template through parallel simulation and optimization, and records it in the audit.

Images