Power distribution control device load optimization distribution system considering multi-load fluctuation

Abstract

The application provides a power distribution control equipment load optimization distribution system considering multi-load fluctuation, relates to the technical field of power distribution network remote control and data acquisition scheduling, and the method acquires a gateway active power sequence and an equipment operation time consumption statistical set; performs trend difference calculation on the power sequence to extract a load growth rate judgment value, and performs progress comparison on the time consumption statistical set to extract an equipment maintenance priority feature; performs weighted deduction calculation on the double-source feature to output a comprehensive current-limiting scheduling priority score; and generates accurate load reduction driving signaling and span differentiated recovery queuing state identification based on the score. The method solves the pain points that multi-modal concurrent load surges are easy to cause gateway instantaneous overload and that existing scheduling lacks sensing flexibility, and realizes the best dynamic balance of the physical safety bottom line and macro business benefit of the power grid.

Description

Technical Field

[0001] This invention relates to the field of remote control and dispatching technology for power distribution networks, specifically to a load optimization and allocation system for power distribution control equipment that takes into account multiple load fluctuations. Background Technology

[0002] With the deep evolution of the Industrial Internet and smart parks, the dense connection of multimodal electrical equipment to the power distribution network has led to increasingly complex dynamic load management challenges for local power distribution gateways. The transformation from data acquisition to remote collaborative control of the power distribution network in smart power distribution has become a core development trend for ensuring the stability of modern industrial-grade power topologies. Existing power distribution data acquisition and control architectures still have limitations in optimizing allocation strategies under multi-load fluctuations: when faced with severe fluctuations caused by the concurrent startup of hundreds of devices, existing technologies can only passively issue tripping commands after the current substantially exceeds the limit. Their millisecond-level response lag easily leads to the power distribution gateway being overwhelmed by transient load surges; for example, a smart power distribution control method and device for a distribution cabinet, published in CN117543835A. While this solution improves the automation level of electrical state response, its limitation of relying solely on the prediction of equipment electrical properties becomes apparent under current-limited conditions. It cannot quantify the "task completion progress" and "production schedule completion rate" of the target production node. This kind of intolerant and careless power outage can easily kill core production nodes, causing secondary economic losses due to production link disruptions while preserving grid parameters. Summary of the Invention

[0003] The purpose of this invention is to provide a load optimization and allocation system for power distribution control equipment that considers multiple load fluctuations. It introduces the "load growth rate judgment value," which characterizes the physical overload risk of the gateway, and the "equipment preservation priority characteristic," which characterizes macroscopic value, into the same computational space to construct a core processing hub. By performing cross-weighted deduction calculations, it completes the transformation from single-source blind circuit breaker protection to multi-source comprehensive priority dynamic adjudication, achieving the isolation of extreme surge conditions and flexible recovery based on time-series mapping. This addresses the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A load optimization and allocation system for power distribution control equipment that considers multiple load fluctuations includes:

[0006] Multi-source dual-track load data sensing unit: used to acquire the periodic node total active power sequence reported by the distribution gateway node, and the daily average operating time statistics of the controlled equipment extracted by the statistics module;

[0007] Overload Slope and Production Completion Calculation Unit: Used to perform trend difference calculation on the total active power sequence of the periodic nodes, and extract the load growth rate judgment value to characterize the overload risk of the distribution gateway; and to perform task progress comparison calculation on the daily average operating time statistics set of the controlled equipment, and extract the equipment maintenance priority feature to characterize the business production completion.

[0008] Cross-domain feature weighted priority adjudication unit: used to perform weighted deduction calculation on the load growth rate judgment value and the equipment preservation priority feature, and output a comprehensive flow limiting scheduling priority score;

[0009] The derating cutoff and flexible queuing execution unit is used to generate derating drive signaling for the controlled equipment based on the comprehensive current limiting scheduling priority score. This derating drive signaling is configured to drive a remote power distribution control node to perform a target power state transition matching a preset derating gradient.

[0010] A recovery queue status identifier is generated for the controlled device. The recovery queue status identifier is configured to trigger a remote power distribution control node to perform dynamic recovery of the unrestricted power supply maintenance state for the controlled device after a preset time window.

[0011] Compared with the prior art, the beneficial effects of the present invention are:

[0012] This solution extracts the load growth rate determination value by performing trend difference calculation on the total active power sequence of periodic nodes, and introduces a nonlinear exponential surge amplification factor in conjunction with gateway capacity saturation. This mechanism transforms the traditional "over-limit cut-off" into "dynamic penalty pre-intervention based on ramp slope and load margin," effectively overcoming the physical delay resistance of existing SCADA system mechanical actions, improving the sensitivity of gateway surge protection, and ensuring the absolute safety of transformer thermodynamic operating boundaries.

[0013] This solution extracts the daily average operating time statistics of controlled equipment, calculates the equipment maintenance priority characteristics to characterize the completion of business scheduling, and uses this as the basic scheduling baseline for weighted deduction calculations with individual disaster penalty. This method effectively avoids the "one-size-fits-all" approach of existing technologies that inadvertently kill high-value production nodes that are about to meet targets during emergency power outages, reduces the scrap rate of semi-finished products caused by power outages, and achieves synergistic gains that maximize economic benefits.

[0014] This invention, based on a comprehensive current-limiting scheduling priority score, uses a time mapping function to perform reciprocal transformation calculations to generate recovery queuing status identifiers with differentiated time window spans. This mechanism smoothly transforms the originally disordered, high-frequency secondary grid connection impacts into an ordered time-series polling sequence where lower-scoring (low-priority, high-risk equipment) devices are queued for longer periods. This effectively overcomes the secondary arcing short-circuit hazard caused by concentrated reclosing after traditional power outages, enhancing the robustness of the distribution network. Attached Figure Description

[0015] Figure 1 This is a diagram illustrating the execution environment and core technology roadmap architecture of the present invention.

[0016] Figure 2 This is a schematic diagram illustrating the verification results of numerical verification calculations for nonlinear penalty mapping and decision isolation features;

[0017] Figure 3 A schematic diagram of the technical route for the load optimization allocation method of the power distribution control equipment of the present invention;

[0018] Figure 4 This is a dynamic topology diagram of the present invention in a smart manufacturing park environment. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various elements, but unless otherwise stated, these elements are not limited by these terms. These terms are used only to distinguish one element from another.

[0021] Example 1:

[0022] Please see Figures 1 to 4 The present invention provides a technical solution:

[0023] A load optimization and allocation system for power distribution control equipment that considers multiple load fluctuations, executed by the processor of the power distribution control equipment, includes the following:

[0024] Multi-source dual-track load data sensing unit: used to acquire the periodic node total active power sequence reported by the distribution gateway node, and the daily average operating time statistics of the controlled equipment extracted by the statistics module;

[0025] The process of obtaining the periodic node total active power sequence reported by the distribution gateway node includes:

[0026] Extract the basic transient power acquisition data of the power distribution gateway node within a preset time window;

[0027] The system uses a pre-defined algorithm model to perform data smoothing and denoising on the basic transient power acquisition data of the equipment to obtain a steady-state power feature sequence.

[0028] The steady-state power feature sequence is subjected to mean aggregation based on a preset time slice dimension to generate the total active power sequence of the periodic nodes.

[0029] The process of obtaining the daily average operating time statistics of the controlled equipment extracted by the statistics module includes:

[0030] Read the historical power on / off status records of the controlled device within a preset historical period;

[0031] Remove invalid time segments representing abnormal shutdown boundaries from the historical power-on / off status records of the equipment, and extract the effective operating time span;

[0032] Perform cross-cycle cumulative summation calculation on the effective operating time span to construct a statistical set of the average daily operating time of the controlled equipment.

[0033] The periodic node total active power sequence is denoted as It represents the overall energy throughput trajectory of the local power grid within a macroscopic time slice. In this embodiment, the preset time window span corresponding to this sequence is preferably set to 5 to 15 minutes.

[0034] The daily average operating time of the controlled equipment is recorded as follows: It represents the historical baseline operating workload of all multimodal electrical equipment (such as flexible production lines, charging stations, etc.) under long-term assessment. The preset historical period is preferably set to 30 calendar days to ensure sufficient data breadth to fully smooth out occasional business fluctuations caused by daily production plans.

[0035] The gateway capacity saturation parameter is denoted as It represents the degree to which the current local power grid carrying capacity approaches the physical safety limit of the transformer. Its value range is limited to [0,1] to ensure that it meets the requirements of dimensionless calculation when used as the basis for subsequent dynamic surge amplification factor calculation.

[0036] 1) Specific implementation instructions for the multi-source dual-track load data sensing unit:

[0037] A data acquisition stream is constructed using standardized access nodes. The distribution gateway node is configured to listen to the time-series waveform data of smart meters in real time based on industrial communication protocols. Based on this, the basic transient power acquisition data of the distribution gateway node within a preset time window is extracted. A preset second-order Butterworth low-pass filter algorithm model is called to perform data smoothing and denoising processing on the basic transient power acquisition data to avoid data spike interference generated by high-frequency sampling of smart meter sensors. This filter model is configured to have flat passband characteristics to prevent distortion of the effective value of active power during the filtering process. The cutoff frequency threshold is preferably set to 0.5Hz based on the experience of anti-interference of the power grid fundamental frequency, with an actual configuration range of 0.1Hz to 2Hz. This is to eliminate abnormal high-frequency signals that are greater than the normal power frequency fluctuation, thereby removing the high-frequency transient fluctuation components caused by communication crosstalk or power grid harmonics in the time domain characteristics, and obtaining a pure steady-state power characteristic sequence. The steady-state power characteristic sequence is then subjected to mean aggregation based on a preset time slice dimension, mapping the discrete second-level sampling points to the energy integral parameter of the macroscopic period, and finally generating a periodic node total active power sequence with strong anti-interference capability. .

[0038] The system synchronously reads the historical power-on / off status records of controlled devices within a preset historical period (nearly 30 calendar days) through the system logic gateway. These records include service start / stop identifiers and heartbeat timestamps. Pre-set anomaly removal rules are then activated. By comparing the heartbeat disconnection duration with the standard repair cycle, invalid time segments representing abnormal downtime boundaries caused by errors, maintenance, or communication jitter in the historical power-on / off status records are identified and removed. This extracts the effective operating time span purely serving production operations. A cumulative summation calculation is performed across the effective operating time span, and by extracting the daily average distribution mean within that period, a statistical set of the controlled devices' daily average operating time with high confidence and reflecting objective production scheduling pressure is constructed. Simultaneously, based on the statistical set of the average daily operating time of the controlled equipment... By analyzing the scattered characteristic distribution within historical steady-state production cycles and fitting a linear relationship curve between output and time using a pre-defined least squares model, the lower limit of time required to meet the park's minimum daily order target is derived and solidified as a benchmark for average daily business performance. This serves as the anchoring basis for subsequent real-time comparisons.

[0039] Overload Slope and Production Completion Calculation Unit: Used to perform trend difference calculation on the total active power sequence of the periodic nodes, and extract the load growth rate judgment value to characterize the overload risk of the distribution gateway; and to perform task progress comparison calculation on the daily average operating time statistics set of the controlled equipment, and extract the equipment maintenance priority feature to characterize the business production completion.

[0040] The process of extracting the load growth rate judgment value used to characterize the overload risk of the power distribution gateway, and the process of extracting the equipment maintenance priority feature used to characterize the completion degree of business scheduling, are specifically configured as follows:

[0041] The current moment power feature and the first moment power feature in the total active power sequence of the periodic nodes are subjected to a difference calculation logical operation, and the difference result is compared with the span value of a preset time window to calculate the load growth rate determination value.

[0042] Extract the daily running time feature from the daily average operating time statistics set of the controlled equipment, and perform a division ratio mapping between the daily running time feature and the pre-configured daily average business compliance time benchmark to calculate the equipment maintenance priority feature.

[0043] 2) Specific implementation instructions for the overload slope and production completion calculation unit:

[0044] The load growth rate determination value is denoted as It represents the mathematical slope by which the current power distribution gateway approaches the overload critical point. Its steady-state trigger reference value is preferably limited to 0.15 kW / s. This reference is set to prevent the misjudgment of minor start-ups and shutdowns of conventional equipment as high-risk surge surges.

[0045] The priority characteristic of equipment maintenance is denoted as Its characteristics represent the objective survival priority of controlled equipment, which cannot be interrupted, based on the current production completion rate and production progress, when responding to power grid dispatch intervention.

[0046] To calculate this feature based on the active power ramp-up trend, the following mapping logic is executed: ;

[0047] The above mapping logic solution uses the trigger point of the current real-time scheduling command as the absolute time anchor point, and the total active power sequence at periodic nodes. In the process, the power features of the current time are locked at the end of the preset time sliding window (the latest valid sampling point closest to the current time). Simultaneously, backtracking is performed to extract the power features of the first and last moments at the starting point of the preset time sliding window (the historical boundary point at the farthest end within the sliding window span). Perform a difference operation on the two to extract the absolute increment representing the pure change in active power; then compare this absolute increment with the time span value of the aforementioned preset time window. Performing the ratio calculation is equivalent to calculating the first derivative of the active load within a unit time window, thereby determining the load growth rate judgment value used to characterize the steepness of the physical overload risk. .in To characterize the time parameter of the observation window scale, in this embodiment, its preferred value is defined as the range of 5 to 15 minutes.

[0048] Use the current running window as a statistical set of the average daily operating time of the controlled equipment. The dynamic continuation of the monitoring allows for the independent acquisition of the daily running time characteristics of the controlled equipment within the current business assessment cycle based on the real-time equipment status monitoring interface. The following normalization mapping mechanism is executed: ;

[0049] The normalization mapping mechanism is to normalize the features of the running time of the day. Compared with the pre-configured daily average business compliance time benchmark generated by extrapolating from the daily average operating time statistics of controlled equipment By performing a division ratio mapping, the absolute physical time value is scaled to a uniform dimensionless preset interval of [0,1], thereby calculating the equipment maintenance priority feature that represents the current task completion progress and production scheduling completion degree of the business node. Among them, the benchmark for average daily business performance. This represents the minimum operating time required for the controlled equipment to maintain the park's minimum production capacity plan. The daily average business performance benchmark is derived and extracted based on the following offline calibration mechanism: The basic production capacity logs of the business line to which the controlled equipment belongs within the historical steady-state production cycle are retrieved in advance; based on the above log data, a scatter plot feature distribution of daily output and effective equipment operating time is extracted; a linear relationship curve between output and time is fitted using a pre-set least squares model, and the lower limit time corresponding to the coordinate mapping point that meets the park's minimum daily order target is solidified as the daily average business performance benchmark. .

[0050] When equipment preservation priority features When the value approaches 1, it indicates that the controlled equipment has nearly completed its daily production schedule and has a high completion rate. Once the power is cut off, it is easy to cause a batch of semi-finished products to be scrapped. Therefore, its equipment preservation priority in the system scheduling is high, and its survival priority is high. Conversely, if the equipment preservation priority is close to 0, it indicates that the controlled equipment has just started up and can be the first node to be sacrificed by power failure during the high-risk period of physical surge. Its preservation priority is low.

[0051] Cross-domain feature weighted priority adjudication unit: used to perform weighted deduction calculation on the load growth rate judgment value and the equipment preservation priority feature, and output a comprehensive flow limiting scheduling priority score;

[0052] The process of performing a weighted deduction calculation on the load growth rate determination value and the equipment preservation priority feature, and outputting a comprehensive flow-limiting scheduling priority score, includes:

[0053] The device preservation priority characteristics are used as the baseline for basic scheduling priority.

[0054] Determine whether the load growth rate determination value is greater than or equal to a preset safe and stable limit; in response to the load growth rate determination value being greater than or equal to the safe and stable limit, map the load growth rate determination value to a dynamic penalty weight based on a preset proportional conversion coefficient;

[0055] Extract the current transient independent power of the controlled equipment, and perform a division ratio calculation between the current transient independent power and the current real-time total active power of the distribution network node to calculate the individual load disaster contribution rate used to characterize the surge responsibility share of a single machine;

[0056] The dynamic penalty weight is multiplied by the individual load disaster contribution rate to generate a device-level exclusive penalty feature;

[0057] The basic scheduling priority baseline is subtracted from the specific penalty feature to perform an aggregation operation, and the comprehensive rate limiting scheduling priority score is output. The comprehensive rate limiting scheduling priority score is inversely proportional to the specific penalty feature and directly proportional to the business scheduling completion rate.

[0058] The process of mapping the load growth rate determination value to a dynamic penalty weight using a proportional conversion factor is specifically configured as follows:

[0059] Extract the current real-time total active power of the distribution network node, and perform a division ratio calculation between the current real-time total active power and the pre-configured gateway physical capacity upper limit identifier to calculate the gateway capacity saturation parameter used to characterize the local power grid carrying capacity margin;

[0060] Determine whether the gateway capacity saturation parameter is greater than or equal to a preset high-risk capacity warning threshold;

[0061] In response to the gateway capacity saturation parameter being greater than or equal to the capacity high-risk warning threshold, a preset nonlinear exponential mapping unit is used to perform a joint amplification operation on the load growth rate determination value and the gateway capacity saturation parameter to calculate the dynamic surge amplification factor, which is then used to replace the basic proportional conversion coefficient.

[0062] Based on the dynamic surge amplification factor, the load growth rate determination value is nonlinearly mapped to the dynamic penalty weight; to ensure that the physical risk characteristics are given an overwhelming penalty weight under high grid load conditions;

[0063] In response to the gateway capacity saturation parameter being less than the capacity high-risk warning threshold, the basic static linear proportional conversion flow is maintained, and the load growth rate judgment value is mapped only to a constant-level dynamic penalty weight to isolate the nonlinear exponential surge effect.

[0064] 3) Specific implementation instructions for the cross-domain feature weighted priority adjudication unit:

[0065] The safe and stable boundary is denoted as It characterizes the tolerance of the power distribution gateway to normal load fluctuations by passively absorbing them through its own capacitance and line elasticity. In this embodiment, its preferred value is set to 0.08 kW / s. The logic behind this value setting is to filter out normal start-up and shutdown glitches of park lighting or basic temperature control equipment.

[0066] The high-risk capacity warning threshold is denoted as It represents the nonlinear inflection point coefficient as the local transformer bearing capacity approaches the thermodynamic melting point. In this preferred embodiment, its value is set to 85% of the full-load rated power.

[0067] The dynamic surge amplification factor is denoted as Its characterization is a nonlinear braking ratio imposed on the purely physical slope characteristics when the grid capacity is on the verge of depletion.

[0068] The dynamic penalty weight is denoted as It represents the negative deduction component that the controlled equipment must bear in the integrated scheduling due to the risk of global downtime caused by the controlled equipment.

[0069] The overall priority score for flow limiting and scheduling is recorded as follows: Its representation is the dimensionless integrated scheduling benchmark certificate finally calculated.

[0070] The preset proportional conversion factor is denoted as Its characterization is that when the grid capacity is within the normal safe range, it transforms the load ramp-up slope on the physical side into a static linear mapping multiplier of the business penalty weight. In this embodiment, its preferred value is set to 1. Before reaching the high-risk capacity warning threshold, the system only assigns a basic penalty according to the actual physical increment slope at a ratio of 1:1, thereby isolating excessive mathematical amplification effects and avoiding overreaction and accidental damage to the normal cross-start and stop of conventional equipment in the park; only after the high-risk transition condition is met is this constant coefficient completely intercepted and replaced by the nonlinear surge amplification factor.

[0071] Furthermore: Prioritize equipment maintenance features Extract and directly use it as the baseline for basic scheduling priority, logically establishing the core allocation principle that "equipment with higher business scheduling completion rate (lower tolerance) has a higher initial survival priority." Then, perform the following judgment and non-linear weight evolution: read the load growth rate judgment value obtained before the global configuration library judgment. Is it greater than or equal to the safe and stable limit? Response to load growth rate determination value If the limit is not met, the system directly assigns it a static constant conversion factor with a minimum value;

[0072] The minimum static conversion constant characterizes the absolute noise reduction multiplier set by the system to filter high-frequency sampling noise and normal service start-up / shutdown spikes when the local power grid load rise slope does not constitute a substantial intrusion wave. In this embodiment, its preferred value is set to 0.01.

[0073] Response to load growth rate determination value Greater than or equal to the safe and stable limit The system automatically intercepts the default static linear conversion closed-loop logic and triggers deep topology evaluation: specifically, it extracts the current real-time total active power parameter of the distribution gateway node in real time through the remote SCADA interface. Perform dimensionless normalization mapping processing, specifically transforming the real-time total active power parameter... With the gateway physical capacity limit identifier pre-configured by the power grid ledger Perform division percentage calculation and simultaneously execute hardware overflow prevention upper limit clamping operation. This calculation eliminates the physical dimensions of regional capacitance differences and forcibly locks the parameters to extreme values ​​when a transient inrush current substantially exceeds the limit. This allows for the calculation of the gateway capacity saturation parameter, which objectively characterizes the system's transient load-bearing capacity and converges to a preset dimensionless interval [0,1]. .

[0074] Determine the gateway capacity saturation parameter Is it greater than or equal to the preset high-risk capacity warning threshold? Among them, the high-risk capacity warning threshold The quantification value range is limited to between 80% and 90%, and in this embodiment, 85% is preferred.

[0075] Within the normal security range, the response is based on the gateway capacity saturation parameter. Less than this high-risk warning threshold The system maintains a basic static linear proportional conversion flow, only assigning a constant-level penalty increment; specifically, it introduces a preset proportional conversion coefficient. The system intercepts the activation flow of the nonlinear exponential mapping unit and determines the load growth rate value. Conversion factor with the preset ratio Performing only linear multiplication operations, the dynamic penalty weights are calculated to a constant value. .

[0076] Responding to the gateway capacity saturation parameter Greater than or equal to the high-risk warning threshold The nonlinear transition condition is satisfied, and the aforementioned linear conversion process is forcibly intercepted and blocked using a preset nonlinear exponential mapping unit. Instead, the following dynamic penalty solution logic is executed: ; ;

[0077] The above solution logic is as follows: using the natural logarithm as the base. As a base, the gateway capacity saturation parameter With preset sensitivity braking coefficient The product of these terms is used as an exponential term to perform nonlinear activation calculations, thereby solving for the dynamic surge amplification factor. This replaces the basic linear proportional conversion factor; the previously obtained load growth rate determination value is used. With the activated dynamic surge amplification factor Perform a multiplication operation and output the dynamic penalty weight after nonlinear addition. Among them, the sensitivity braking coefficient The sensitivity gain amplification factor, which characterizes the power grid's sensitivity to the nonlinear penalty slope under extreme operating conditions, is quantized within the range of [1.5, 3.0]. An offline measurement mechanism based on extreme data boundaries is introduced for calibrating the sensitivity braking coefficient: by injecting historical extreme surge waveform datasets (containing millisecond-level concurrent start-stop records of hundreds of devices) into the simulation sandbox, the system continuously monitors and calculates the physical protection action response time generated downstream of different parameter values; using the ability to issue commands to drive solid-state relays to achieve disconnection and closed-loop operation within a 50-millisecond fault-tolerant safety window under 99% of simulated surge conditions as the convergence rule, the optimal numerical range of this coefficient is finally iteratively determined.

[0078] This embodiment introduces a dynamic adjustment mechanism: specifically, during the grid idle and low saturation phase, the system maintains a small exponential gain to ensure efficiency priority; while during the grid high load critical condition phase, the system assigns a penalty weight to the same physical ramp slope through the above logic, forcibly cutting off the adverse effects caused by the high-frequency start-stop of multimodal loads. The system extracts the equipment preservation priority features from the completed preceding calculations and maps them to the baseline of the basic scheduling priority. And obtain the current transient independent power of the corresponding controlled device. Calculate its relationship with the global real-time total active power. The proportion is used as the individual load's contribution rate to disaster, and a subtraction aggregation operation is performed: It uses the power ratio of controlled devices to apply a global dynamic penalty weight. Weighted summaries are used to generate differentiated, specific penalty features. The baseline scheduling priority, representing survival priority, is then directly subtracted from these specific penalty features by the integrated adjudication unit. This calculation allows for negative results. The engineering purpose is to break the scalar collapse paradox of globally uniform deduction by negatively lowering the scores of violating nodes with extremely high instantaneous power consumption under physical inrush conditions. This preserves a high-resolution penalty ranking scale, calculates and outputs an integrated flow-limiting scheduling priority score that includes a negative domain. Objectively, it achieves a comprehensive flow-limiting scheduling priority score. The optimal dynamic balancing mechanism is inversely proportional to the risk of overload in the power distribution gateway and directly proportional to the completion of business scheduling.

[0079] The derating cutoff and flexible queuing execution unit is used to generate a derating drive signaling for the controlled device based on the comprehensive current limiting scheduling priority score. The derating drive signaling is configured to drive the remote power distribution control node to perform a target power state transition matching a preset derating gradient. It also generates a recovery queuing state identifier for the controlled device. The recovery queuing state identifier is configured to trigger the remote power distribution control node to perform dynamic recovery of the unrestricted power supply maintenance state for the controlled device after a preset time window.

[0080] The process of generating load reduction drive signaling for the controlled device is specifically configured as follows:

[0081] Based on the comprehensive flow limiting scheduling priority score of all controlled devices, a ranking list of devices for load reduction control is constructed.

[0082] From the equipment load reduction control sorting list, select the set of low-performing equipment with scores in the preset bottom range;

[0083] Generate the load reduction drive signaling directed to the set of low-priority devices to trigger the remote power distribution control node to cut off or limit the current power supply hold-up state of the set of low-priority devices.

[0084] 4) Specific implementation instructions for the reduced threshold cutoff and flexible queuing execution unit:

[0085] The equipment load reduction control sorting list is recorded as follows: It represents the dynamic survival weight sequence of all energy-consuming nodes currently within the controlled range globally. The set of low-optimal devices is denoted as... Its representation, after the aforementioned dual-track game, is the group of sacrificial nodes that currently contributes the least to macroeconomic business output and poses the greatest threat to power grid physical security. The load reduction drive signaling is denoted as... It represents the underlying binary control message used to perform forced physical actions across communication networks.

[0086] The system aggregates the comprehensive rate-limiting scheduling priority scores of all controlled devices within the current scheduling slice. Based on this score data sequence, the system scheduler performs numerical traversal comparison and dimensional sorting mapping operations. Using a preset descending order reorganization algorithm (calling basic sorters such as quicksort or heapsort to allocate higher-scoring device nodes to the safe storage area at the beginning of the sequence and pushing low-scoring violation nodes to the truncation area at the end of the sequence), all device nodes are serialized and stored according to their scores, automatically constructing the device load reduction control sorting list representing the global survival weight. The system performs data slicing along the index pointers of this structured sorted list: from the device deload control sorted list. A reverse search is initiated at the tail end. Based on the pre-configured last-position interception ratio coefficient (preferably set to the extreme value range of the global last 15% to 20% in this embodiment), the identifiers of all controlled object nodes whose comprehensive flow limiting scheduling priority scores are in the last position range are extracted. The last-segment objects that are determined to have high business tolerance or are currently inducing severe power grid surges are logically stripped, and their network addressing parameters are packaged and aggregated to generate a low-priority device set for subsequent power outage intervention. .

[0087] A set of low-optimal devices is generated based on a pre-defined standard industrial communication protocol parsing framework. Load reduction drive signaling This signaling is configured to initialize in a silent suspended state and is dynamically triggered by the system when the equipment derating control sorting list is sliced. Its message structure embeds a pre-configured derating gradient state flag: when the flag resolves to a "hard cut-off" active state, it directly triggers a physical disconnection of the relay inside the remote power distribution control node; when the flag resolves to a "flexible current limiting" active state, it triggers the solid-state switch to adjust the corresponding output pulse width duty cycle parameter. This is transmitted via SCADA telemetry links or other industrial buses, thereby cutting off or limiting low-priority device sets according to gradients. The current power supply status is maintained. For flexible current limiting, the system is internally configured with a score-based tiered derating execution logic, specifically configured as follows:

[0088] Read the overall rate limiting scheduling priority score of each device in the low-priority device set. And determine the specific inferior interval it falls into; respond to the overall flow control priority score. The system falls into the first inferior interval; the score is in a slightly inferior state of [0.15, 0.30]. The system drives the solid-state switch to reduce the output pulse width duty cycle from 100% full load state according to the preset derating gradient step size coefficient. Reduced to 75% of the first restriction operating state;

[0089] Response based on comprehensive flow limiting scheduling priority score It falls into the second inferior interval; the score is in the moderately low superior state of [0.05, 0.15), and the system drives the solid-state switch to further reduce the duty cycle to the second restricted operating state of 50%;

[0090] Response based on comprehensive flow limiting scheduling priority score Falling into the third inferior interval; with a score at the extremely low-optimal edge of (0,0.05), the system drives the solid-state switch to reduce the duty cycle to the third restricted operating state of 25% in order to maintain the basic power supply to the device controller without driving the main motor.

[0091] In this embodiment, the derating gradient step size coefficient The specific quantification value is preferably set to 25%. If the gradient parameter is divided too finely or set to deviate from the lower limit, the solid-state switch will be forced to perform extremely high-frequency fine-tuning when dealing with high-frequency inrush current, causing switching losses and junction overheating risks. Moreover, it will be unable to form a sufficient power cliff to quickly curb the overload slope of the power distribution gateway. Conversely, if the parameter exceeds the upper limit, the flexible current limiting mechanism will degenerate into a quasi-hard cutoff model, causing the flexible production line in the transition state to suffer excessive voltage drop abrupt changes, which may easily lead to undervoltage lockup of the equipment driver.

[0092] The system transmits the aforementioned duty cycle control parameters via SCADA telemetry links or other industrial buses, thereby cutting off or limiting the set of low-optimal devices according to gradient constraints. The current power supply status is maintained.

[0093] The process of generating the recovery queuing status identifier for the controlled device is specifically configured as follows:

[0094] Determine whether the overall rate limiting scheduling priority score is greater than zero;

[0095] In response to the comprehensive flow limiting scheduling priority score being greater than zero, the comprehensive flow limiting scheduling priority score is converted into a reciprocal of a preset time mapping function to calculate the specific span parameter of the preset time window for the corresponding controlled device, so as to enable devices with lower priority scores to obtain a longer queuing waiting time.

[0096] In response to the overall flow-limiting scheduling priority score being less than or equal to zero, the preset time window is forcibly assigned the preset longest backoff queuing period. Specific implementation includes:

[0097] The time mapping function is denoted as Its characterization is a nonlinear transformation rule that projects discrete system scheduling scores onto the real physical time axis. In this preferred embodiment, it is configured as a reciprocal decay function with a lower bound constraint, designed to prevent low-performing devices with extremely high scores from being suspended indefinitely. The time window span parameter is denoted as... It represents the absolute freeze time that a controlled device must undergo from being forcibly restricted from power supply to being allowed to re-initiate a grid connection request. Its preferred value range is defined as 30 seconds to 600 seconds. The queuing status is restored as... Its representation is a timing activation token with an accompanying wake-up countdown.

[0098] In this embodiment, after the forced disconnection action is performed at the remote power distribution control node, the reverse wake-up flow logic is synchronously activated for the same batch of restricted low-priority equipment: specifically, by retrieving the comprehensive current limiting scheduling priority score calculated in the previous step for the controlled object. Determine the priority score for integrated flow control scheduling. Is it a non-positive number? ). In response to (This indicates that the controlled device triggered an inrush that exceeded the limit, and its penalty weight has covered and cleared the service preservation baseline.) The system directly intercepts the subsequent countdown calculation and forcibly assigns the controlled device the preset maximum backoff queuing period. To perform physical isolation; in response to (For conventionally restricted devices), the system executes the following combined time-mapping function. The calculation logic: It scores the overall rate limiting and scheduling priority. Supplemented with an anti-overflow constant As a denominator term, it is related to the preset time scaling factor. Perform an inverse proportional division mapping operation; where the time scaling factor is... The physical meaning is to map the dimensionless integrated flow-limiting scheduling priority score to a dimensionless conversion reference parameter of absolute physical time (seconds). In this embodiment, the time scaling factor... The preferred value is 150. In practical applications, based on the total transformer cooling load characteristics of different industrial parks, it can be flexibly configured within the continuous range of [50, 300]. By introducing this parameter and limiting it to the above range, a balance is achieved between the transformer disaster prevention dead zone and the recovery of upper-level production efficiency. The overflow prevention constant is also included. The introduction of this feature aims to prevent the use of integrated flow control scheduling priority scores. When the value approaches zero, the mathematical calculation engine throws a division-by-zero exception and crashes. In this embodiment, the overflow prevention constant is preferably set to an absolute value of 0.001. Then, the result of amplifying this reciprocal is compared with the baseline sleep duration. Perform aggregation and summation, and introduce a preset maximum backoff queuing period. By performing comparison and truncation calculations, the specific span parameter that uniquely corresponds to the preset time window of the controlled device is finally calculated. The baseline of basic dormancy duration. The value is set to 30 seconds. This setting is intended to ensure the minimum physical dead time required for local transformer heat dissipation and internal magnetic field reconstruction, preventing arcing short circuits caused by instantaneous reclosing; the longest backoff queuing period. The preferred value is set to 600 seconds.

[0099] In this embodiment, the lower the priority score of the controlled device in the comprehensive flow limiting scheduling, the longer its calculated queuing waiting time parameter. This enables deeper physical isolation and delayed wake-up of high-risk / low-priority loads over a longer period in the system control flow. Based on this, a recovery queuing status identifier for the controlled device, including a wake-up countdown, is generated. And it is issued. This recovery queuing status identifier is managed as a timing activation token in the system's global state registry. It is configured to remain in a blocking monitoring state when the target device is in a forced restricted state, in response to the specific span parameter corresponding to the remote power distribution control node detecting the depletion of the internal clock. This trigger condition causes the recovery queue status flag to seamlessly transition from a blocked state to an active state. This active state then triggers the physical driver to automatically close the relay to perform dynamic, shockless recovery for the controlled device's normal power consumption state.

[0100] While performing the step of obtaining the daily average operating time statistics of the controlled equipment extracted by the statistics module, the method also configures a degradation monitoring unit in parallel:

[0101] Continuously monitor the data update status of the daily average operating time statistics set of the controlled device;

[0102] In response to a data update failure frequency greater than or equal to a preset steady-state operating baseline threshold, the feature extraction flow for business scheduling completion is forcibly cut off, the dynamic calculation of the comprehensive flow limiting scheduling priority score is stopped, and the system automatically switches to the preset pure physical topology defense logic to maintain the basic overload protection of the power distribution gateway node.

[0103] The process of automatically switching to the preset pure physical topology defense logic is specifically configured as follows:

[0104] Retrieve a preset local power grid spatial ledger and extract the network topology physical distance parameters of each controlled device from the distribution gateway node, wherein the network topology physical distance parameters characterize the risk of line loss and end voltage drop;

[0105] The load growth rate determination value and the network topology physical distance parameter are imported into the backup decision unit to perform weighted aggregation calculation, and the physical downgrading priority feature used to replace the comprehensive flow limiting scheduling priority score is calculated.

[0106] Based on the physical download priority characteristics, in descending order, the load reduction drive signaling is generated sequentially for the corresponding controlled equipment to achieve a gradual, flexible power outage contraction from high-risk, high-loss nodes at the end of the distribution network to core hub nodes. Specific implementation details are as follows:

[0107] The steady-state operating reference threshold is denoted as It represents the maximum tolerance limit of the remote control system for communication jitter from cross-domain data sources on the business side. In this embodiment, it is preferably set to "no response for 3 consecutive communication scheduling cycles". The network topology physical distance parameter is denoted as... It characterizes the physical cable extension length and topology node level impedance between the terminal controlled equipment and the core transformer of the distribution gateway. The physical downsizing priority characteristic is denoted as... Its representation is the power outage sacrifice ranking parameter derived purely from the risk of electrical line loss and voltage drop in the extreme case of complete data loss.

[0108] This embodiment accumulates the daily average operating time statistics of the controlled equipment in real time. The frequency of data packet acquisition failures. Determine whether the currently counted failure frequency is greater than or equal to a preset steady-state operating baseline threshold. If the above failure conditions are met, it is determined that the remote business system logic gateway has substantially crashed or the industrial fieldbus has encountered high-frequency electromagnetic interference, and the feature extraction and mapping flow for business scheduling completion is interrupted; the preset pure physical topology defense logic is activated simultaneously.

[0109] Access and retrieve the preset local power grid spatial static ledger, which serves as an objective record of the infrastructure layer. Perform cross-table retrieval mapping based on the communication node identifier as the unique addressing primary key. Extract the network topology physical distance parameters of each online controlled end-device node from the distribution gateway hub node in the real spatial wiring topology map. The load growth rate will be determined by the stable load growth rate obtained from the smart meter array. Together with the extracted network topology physical distance parameters Import the standby decision unit and perform the following weighted aggregation calculation: It determines the load growth rate, which represents a surge in active power. With the preset slope disaster prevention weight constant Perform the first branch multiplication mapping; based on the electric field physics law that reactive power loss and the risk of voltage drop at the end increase exponentially with increasing distance from the transformer, adjust the network topology physical distance parameters. Perform nonlinear dimensionality reduction with squared magnification, and then compare the result with a preset distance penalty decay constant. Perform the weighted multiplication operation of the second branch; perform aggregation and summation calculation on the intermediate state values ​​calculated from the above two branches, thereby successfully calculating the physical downgrade priority feature. Slope disaster prevention weight constant With distance penalty decay constant The conversion coefficients are configured to eliminate differences in data dimensions. By pre-extracting topological impedance logs and load surge slope logs from historical power outages in the local power grid; and using a hard physical boundary constraint that ensures the voltage drop at the end node does not exceed 10% of the standard rated voltage, a multiple linear regression algorithm is used to determine the coefficient ratios that can map these two heterogeneous indicators to a unified and dimensionless comprehensive voltage drop risk assessment benchmark, ultimately locking in the correct values. and The curing ratio (preferred ratio in this embodiment is 1:0.045).

[0110] Based on the physical downgrade priority characteristics In descending order, load reduction drive signaling is generated sequentially, pointing to the corresponding controlled device. This is based on the overall rate limiting scheduling priority score. When the value approaches the theoretical maximum of 1, it indicates that the target node has a high degree of business scheduling completion within the current scheduling slice, and the slope of the local power grid load surge it induces is low. Under this state, the device is given the highest survival priority to avoid the batch scrapping of semi-finished products due to accidental disconnection.

[0111] When the overall flow restriction scheduling priority score When approaching the theoretical minimum (penetrating to the negative domain), the objective characteristic is that although the controlled equipment has a certain basic survival right, the transient power surge it causes has reached or broken through the thermodynamic melting threshold of the distribution transformer, resulting in a dynamic penalty weight. It exhibits exponential expansion, eroding the operational baseline. This extreme trend indicates a potentially fatal surge in system performance.

[0112] Gateway capacity saturation parameter With the final dynamic penalty weight Crossing the high-risk capacity warning threshold The correlation then becomes nonlinear and positive, thus showing a relationship with the overall flow-limiting scheduling priority score. It exhibits a non-linear negative correlation. Gateway capacity saturation parameter. The degree to which the load-bearing capacity of the mapped transformer approximates the physical limit.

[0113] Load growth rate determination value With dynamic penalty weights It shows a linear positive correlation, and further correlates with the overall flow-limiting scheduling priority score. They are negatively correlated. The steeper the slope of the first derivative of load growth, the more severe the transient inrush current in the power grid. The load growth rate is used as the determining factor. Designed as a negative correlation deduction basis, this system is equipped with the ability to identify and isolate sharply rising waveforms in advance, changing the lag of the traditional solution of "responding after exceeding the limit" and supporting the technical effect of "early warning and interception".

[0114] Equipment maintenance priority features Priority score of integrated flow limiting and scheduling There is a basic positive correlation. The longer the current operating time of the controlled equipment, the closer it is to the benchmark, and its production scheduling completion rate shows a monotonically increasing trend in industrial manufacturing patterns. This embodiment is configured in a local power distribution gateway digital twin monitoring scenario that includes multimodal concurrent loads.

[0115] In this embodiment, a gateway physical capacity limit flag is set. 1000; safe and stable boundary The value is 0.08; the high-risk capacity warning threshold. It is 0.85; steady-state operating reference threshold. Three consecutive scheduling failures; based on offline calibration, sensitivity braking coefficient. The time scaling factor is constant at 2. The baseline for base sleep duration is 150. The longest backoff queuing period is 30. The overflow prevention constant is 600. The value is 0.001. Slope disaster prevention weight constant. Set to 1, distance penalty decay constant Set to 0.045.

[0116] The frequency of data packet acquisition failures on the business side is accumulated in real time. The system responds when this frequency falls below the steady-state operating baseline threshold. Three measurements were taken to determine if the system was in a robust state, and the transient waveforms monitored by the distribution gateway nodes were extracted in parallel. After filtering, a periodic power sequence was generated, and the load growth rate determination value was calculated. Simultaneously, the dimensionless equipment maintenance priority characteristics were deduced. Compare gateway capacity saturation parameters. If the value does not reach 0.85, static coefficient product is performed; once it is greater than or equal to 0.85, a braking coefficient based on the natural logarithm base and sensitivity is triggered. The exponential amplifier operation is performed. After calculating the specific penalty feature, a mathematical subtraction operation is performed between it and the basic business line to output the comprehensive rate limiting scheduling priority score. And translate it into the specific span parameters of the time window. If the number of packet acquisition failures is greater than or equal to 3, the system determines that it has entered a communication blocking state and immediately forcibly truncates the above-mentioned dual-track differential flow, automatically retrieving the network topology physical distance parameters from the local spatial ledger. Based on the above digital twin model settings, the response data calculated according to the dynamic changes of the input parameters is shown below.

[0117] Table 1: Examples of System Integrated Rate Limiting and Disaster Prevention Control Indicators under Multimodal Concurrent Load Surge and Communication Blind Spot Conditions

[0118] Scene Description Communication failure frequency Calculate link state Real-time total active power of distribution gateway Gateway capacity saturation parameter Load growth rate determination value Equipment maintenance priority features Dynamic surge amplification factor Comprehensive rate limiting and scheduling priority score Physical downgrade priority characteristics Final control action and specific span parameters of the time window Scenario 1: Normal stable trough period 0 Dual-track business is stable 500 0.5 0.05 0.9 1 0.895 not applicable Flexible queuing, issuing time sequence identifiers (span 197.4) Scenario 2: Period of Rising Regular Electricity Consumption 0 Dual-track business is stable 700 0.7 0.1 0.2 1 0.18 not applicable Intercepting boundary violations, forcibly assigning a maximum limit (span 600). Scenario 3: High-load criticality - high-optimal node 1 Dual-track business is stable 860 0.86 0.2 0.95 5.58 0.8384 not applicable To resist erosion and maintain a suspended state, a marker (span 208.7) was issued. Scenario 4: High-load critical - low-optimal node 0 Dual-track business is stable 860 0.86 0.2 0.1 5.58 -0.1232 not applicable Negative fields trigger physical isolation, resulting in a hard cutoff (span 600). Scenario 5: Extreme surges breach defenses 2 Dual-track business is stable 950 0.95 0.5 0.5 6.68 -0.168 not applicable Negative fields trigger physical isolation, resulting in a hard cutoff (span 600). Scenario 6: Forced stripping from the edge of meltdown 0 Dual-track business is stable 980 0.98 0.8 0.99 7.1 -0.146 not applicable Priority is reset to prevent meltdown, and a hard cut is made (span 600). Scenario 7: Degradation due to business communication blind spots 3 Blind spot physical degradation 860 not applicable 0.5 Forced blocking not applicable Computational flow circuit breaker 1.22 Activate the blackout contraction defense line and cut it off according to this characteristic value (topological distance 4).

[0119] Scenario 3 and Scenario 4 implement cross-validation of business priorities under the same high-load infrastructure; Scenario 6 verifies the unilateral suppressive effect of exponential penalties on business privileges. Scenario 7 introduces a backup topology defense mechanism detached from big data to verify the accuracy of the downsizing algorithm. The data in the table shows the specific mathematical process by which the reciprocal mapping mechanism triggers the physical circuit breaker defense when the comprehensive rate limiting scheduling priority score is ≤0, clamping the wake-up time to the maximum upper limit.

[0120] Define the physical priority erosion coefficient as ; Response to the overall flow-limiting scheduling priority score If the condition of being greater than or equal to 0 is met, the equipment preservation priority feature will be applied. Priority score of integrated flow limiting and scheduling Perform a subtraction operation to generate an absolute penalty difference parameter; then combine the absolute penalty difference parameter with the equipment preservation priority feature. The percentage calculation is performed by division, and the final result is multiplied by a fixed constant of 100% to convert it into a percentage format. This is in response to the overall rate limiting and scheduling priority score. If the condition of being less than 0 is met, the physical priority erosion coefficient will be forcibly increased. Direct clamping locks at 100% in the extreme state. This parameter quantitatively characterizes the slope ramp risk induced by a single end node under complex multimodal high-connectivity power grid surge conditions.

[0121] Compare the computational flow between Scenario 3 and Scenario 4. When the total grid power reaches 860, the gateway capacity saturation parameter... The score jumped to 0.86, exceeding the preset warning threshold of 0.85. Under this intensified penalty, the controlled node in Scenario 3, based on its business scheduling completion rate, maintained a score of 0.8384 after deducting the amplified individual penalty fluctuation, and was allocated a reasonable cooling-off waiting time window of 208.7 seconds; while the low-value newly started device in Scenario 4 had an initial priority of only 0.1, and under the same physical penalty coefficient, its score was instantly driven into the negative domain.

[0122] Traditional quota protection mechanisms can only passively trigger physical disconnection when the gateway capacity reaches 100%. In Scenario 4 of this embodiment, this system relies on the pre-intervention of the derivative slope characteristics to legally calculate the high-risk signal and execute the load reduction signaling when the local load saturation reaches 0.86 (86% capacity utilization).

[0123] In the scenario described in Scenario 7, when the detected frequency of business communication failures reaches the threshold of three times, the system does not fall into a deadlock of unresponsive control but instead executes a millisecond-level circuit breaker. The system calls the reserved distance defense module to extract the network topology physical distance parameters. Value 4 performs square arithmetic derivation and applies a weighting constant of 0.045. Simultaneously, it aggregates this with a slope constant of 0.5 directly read from the meter layer to independently calculate the physical downgrade priority characteristic. It is 1.22.

[0124] Figure 1 The physical application objects on the left represent the smart power distribution gateway node and its matrix of multimodal controlled devices. The technical roadmap derived from this physical object consists of the following: the first rectangle, "Multi-source Dual-track Load Data Sensing," corresponds to the pre-processing action of the system acquiring the periodic node total active power sequence reported by the power distribution gateway node and the daily average operating time statistics of the controlled equipment; the second flowchart, "Overload Slope and Sunk Cost (Production Completion) Calculation," maps the independent calculation stage where the system performs trend difference calculation on the power sequence to extract the load growth rate judgment value, and performs task progress comparison on the time statistics to extract the equipment preservation priority characteristics; the third flowchart, "Cross-domain Feature Weighted Priority Adjudication," intuitively shows the core decision-making logic of the system performing weighted deduction calculation on the above dual-domain features and outputting a comprehensive current limiting scheduling priority score; and the final fourth flowchart, "Rated Cut-off and Flexible Queuing Execution," corresponds to the terminal action execution layer that generates and issues load reduction driving signaling matching the preset rated gradient based on the priority score, and triggers the target equipment to perform dynamic recovery queuing status identification after a preset time window.

[0125] Figure 2 This diagram illustrates the deterministic mathematical response of the system under high grid load conditions. The X-axis in the diagram represents the gateway capacity saturation parameter of the local distribution gateway. The left-hand principal Y-axis represents the dynamic surge amplification factor generated internally by the system. The right-hand sub-Y-axis represents the overall flow-limiting scheduling priority score. The vertical marker T1 actually represents the high-risk capacity warning threshold of 0.85; the horizontal baseline marker M1 represents the constant mapping value of 1 under normal steady state; and the horizontal defense line marker B0 represents the zero-point value of the inverse calculation that triggers the hardware forced circuit breaker.

[0126] Figure 2 Numerical calculations provide a direct and quantifiable understanding of the deterministic dual-track evolution of the system state. When the input parameter fluctuates within the normal range of 0.50 to 0.70 along the X-axis, the system performs a static linear cardinal mapping, and the dynamic surge amplification factor represented by the left-hand Y-axis... The baseline M1 is maintained at a constant steady state. When the input parameter crosses the boundary T1 with a precise value of 0.85, the control topology undergoes a nonlinear trajectory change: the product penalty factor rapidly deviates from the steady state and enters an exponential expansion range. Due to the suppression of this calculation amplitude, the high-optimal node curve (green dot matrix) with high business scheduling completion rate is not broken until the 0.98 node; while the low-optimal node curve (blue dot matrix) encounters a sharp downward squeeze after starting from 0.7, and first crosses the defense line benchmark B0 corresponding to the value 0 when the X-axis reaches 0.86. The performance jump process of "physical risk weight implementing unilateral absolute suppression of low-value business scheduling completion rate in advance" is quantified to achieve a dynamic balance between flexible resource utilization and extreme value isolation of physical security.

[0127] Figure 4 During system operation, the processor of the computing device acquires the periodic total active power sequence of nodes reported by the distribution gateway nodes, and the daily average operating time statistics of controlled equipment extracted by the statistics module. The above macro-level dual-track data streams converge into the comprehensive adjudication unit, which extracts the load growth rate judgment value to characterize the overload risk of the distribution gateway, and the equipment maintenance priority feature to characterize the completion of business scheduling. The system performs weighted deduction calculation to output a comprehensive current limiting scheduling priority score, and generates load reduction drive signaling and restoration queuing status identifier downstream based on this score. This instruction is sent to the remote distribution control node and cuts off the blind batch power outage path, thereby driving the remote end to perform power state transition and timing restoration matching the preset gradient for the multimodal macro-level power consumption cluster under extreme concurrent surge, achieving the best balance between thermal disaster prevention of park transformers and protection of core flexible production lines from impact.

[0128] Figure 4In the middle, PGN stands for Distribution Gateway Node, which is the power supply infrastructure gateway deployed in the smart manufacturing park. It is responsible for reporting the total active power sequence of macro nodes to the digital twin screen in real time and is the starting point for monitoring transient surges throughout the park. STM stands for Statistics Module, which is the big data processing hub on the business side. It is responsible for extracting the effective operating time span of each macro-controlled device from the park's production scheduling system and constructing a daily average operating time statistics set, providing preliminary data support for quantifying the completion of business production scheduling. CAU stands for Comprehensive Decision Unit, which integrates the physical slope characteristics of PGN with the business characteristics of STM. When the capacity saturation approaches the extreme value, it activates the nonlinear penalty mapping to calculate the comprehensive current limiting scheduling priority score that determines the survival of macro devices. BAU stands for Backup Decision Unit, which is the topology-level fallback defense line for the park to deal with extreme communication outages. When the business data flow is interrupted, it extracts the network topology physical distance parameters based on the local power grid spatial ledger and performs weighted aggregation with the physical slope to generate physical downsizing priority features. RDC stands for Remote Distribution Control Node, which is the main macro control execution switch at the park level. It receives load reduction signals and queue recovery flags, and is responsible for implementing flexible derating or disconnection isolation actions on the densely connected multimodal industrial load matrix below. TCE represents large-scale temperature control cluster, which is one of the macroscopic manifestations of multimodal power consumption equipment in the park. As a high-energy-consuming environmental control unit, it is controlled by the dynamic current limiting scheduling instructions of RDC. FPL represents flexible production line, which is a macroscopic physical node in the park that carries high-value manufacturing tasks. Due to its high degree of uninterrupted production completion, it is given a high priority for equipment preservation. HFC represents high-frequency concurrent charging terminal, which is a high-frequency concurrent power carrier distributed in the park. Its large-scale instantaneous start-up can easily induce the risk of overload and shutdown of the power distribution gateway, and it is a key control object for the system to perform nonlinear surge interception.

[0129] The computational logic involved in this application can be constructed using algorithms such as regression analysis in machine learning, establishing a mathematical model by analyzing the inherent trends and interrelationships of the collected parameters. This process can be implemented using specialized computational tools (such as Python's Scikit-learn library or the R language environment). Throughout all calculations, to eliminate the influence of different physical dimensions and ensure that data is compared and analyzed on the same scale, the input parameters in each formula are dimensionless. The dimensionless techniques used include, but are not limited to, max-min normalization or Z-score standardization.

[0130] To decouple the core algorithm from specific application strategies and ensure the configurability and ease of debugging of the technical solution, all configurable operating parameters in the specific implementation path of this invention are read through a standardized "configuration interface". The data source of this configuration interface is a "data storage module" (e.g., a non-transitory computer-readable storage medium, such as a configuration file, database entry, or cloud configuration service), which is configured to store configuration data in key-value pair format.

[0131] It should be emphasized that the foregoing embodiments are merely illustrative of preferred implementations of the present invention and are not intended to limit the scope of protection of the present invention. This application also provides a computer-readable storage medium having computer program instructions stored thereon.

Claims

1. A load optimization and allocation system for power distribution control equipment considering multiple load fluctuations, characterized in that, Specifically, it includes: Multi-source dual-track load data sensing unit: used to acquire the periodic node total active power sequence reported by the distribution gateway node, and the daily average operating time statistics of the controlled equipment extracted by the statistics module; Overload Slope and Production Completion Calculation Unit: Used to perform trend difference calculation on the total active power sequence of the periodic nodes, and extract the load growth rate judgment value to characterize the overload risk of the distribution gateway; and to perform task progress comparison calculation on the daily average operating time statistics set of the controlled equipment, and extract the equipment maintenance priority feature to characterize the business production completion. Cross-domain feature weighted priority adjudication unit: used to perform weighted deduction calculation on the load growth rate judgment value and the equipment preservation priority feature, and output a comprehensive flow limiting scheduling priority score; The derating cutoff and flexible queuing execution unit is used to generate derating drive signaling for the controlled equipment based on the comprehensive current limiting scheduling priority score. This derating drive signaling is configured to drive a remote power distribution control node to perform a target power state transition matching a preset derating gradient. A recovery queue status identifier is generated for the controlled device. The recovery queue status identifier is configured to trigger a remote power distribution control node to perform dynamic recovery of the unrestricted power supply maintenance state for the controlled device after a preset time window.

2. The load optimization and allocation system for power distribution control equipment considering multiple load fluctuations according to claim 1, characterized in that: The process of obtaining the periodic node total active power sequence reported by the distribution gateway node includes: Extract the basic transient power acquisition data of the power distribution gateway node within a preset time window; The system uses a pre-defined algorithm model to perform data smoothing and denoising on the basic transient power acquisition data of the equipment to obtain a steady-state power feature sequence. The steady-state power feature sequence is subjected to mean aggregation based on a preset time slice dimension to generate the total active power sequence of the periodic nodes.

3. The load optimization and allocation system for power distribution control equipment considering multiple load fluctuations according to claim 2, characterized in that: The process of obtaining the daily average operating time statistics of the controlled equipment extracted by the statistics module includes: Read the historical power on / off status records of the controlled device within a preset historical period; Remove invalid time segments representing abnormal shutdown boundaries from the historical power-on / off status records of the equipment, and extract the effective operating time span; Perform cross-cycle cumulative summation calculation on the effective operating time span to construct a statistical set of the average daily operating time of the controlled equipment.

4. The load optimization and allocation system for power distribution control equipment considering multiple load fluctuations according to claim 3, characterized in that: The process of extracting the load growth rate judgment value used to characterize the overload risk of the power distribution gateway, and the process of extracting the equipment maintenance priority feature used to characterize the completion degree of business scheduling, are specifically configured as follows: The current moment power feature and the first moment power feature in the total active power sequence of the periodic nodes are subjected to a difference calculation logical operation, and the difference result is compared with the span value of a preset time window to calculate the load growth rate determination value. Extract the daily running time feature from the daily average operating time statistics set of the controlled equipment, and perform a division ratio mapping between the daily running time feature and the pre-configured daily average business compliance time benchmark to calculate the equipment maintenance priority feature.

5. The load optimization and allocation system for power distribution control equipment considering multiple load fluctuations according to claim 4, characterized in that: The process of performing a weighted deduction calculation on the load growth rate determination value and the equipment preservation priority feature, and outputting a comprehensive flow-limiting scheduling priority score, includes: The device preservation priority characteristics are used as the baseline for basic scheduling priority. Determine whether the load growth rate determination value is greater than or equal to a preset safe and stable limit; in response to the load growth rate determination value being greater than or equal to the safe and stable limit, map the load growth rate determination value to a dynamic penalty weight based on a preset proportional conversion coefficient; Extract the current transient independent power of the controlled equipment, and perform a division ratio calculation between the current transient independent power and the current real-time total active power of the distribution network node to calculate the individual load disaster contribution rate used to characterize the surge responsibility share of a single machine; The dynamic penalty weight is multiplied by the individual load disaster contribution rate to generate a device-level exclusive penalty feature; The basic scheduling priority baseline is subtracted from the specific penalty feature to perform an aggregation operation, and the comprehensive rate limiting scheduling priority score is output. The comprehensive rate limiting scheduling priority score is inversely proportional to the specific penalty feature and directly proportional to the business scheduling completion rate.

6. The load optimization and allocation system for power distribution control equipment considering multiple load fluctuations according to claim 5, characterized in that: The process of mapping the load growth rate determination value to a dynamic penalty weight using a proportional conversion factor is specifically configured as follows: Extract the current real-time total active power of the distribution network node, and perform a division ratio calculation between the current real-time total active power and the pre-configured gateway physical capacity upper limit identifier to calculate the gateway capacity saturation parameter used to characterize the local power grid carrying capacity margin; Determine whether the gateway capacity saturation parameter is greater than or equal to a preset high-risk capacity warning threshold; In response to the gateway capacity saturation parameter being greater than or equal to the capacity high-risk warning threshold, a preset nonlinear exponential mapping unit is used to perform a joint amplification operation on the load growth rate determination value and the gateway capacity saturation parameter to calculate the dynamic surge amplification factor, which is then used to replace the basic proportional conversion coefficient. Based on the dynamic surge amplification factor, the load growth rate determination value is nonlinearly mapped to the dynamic penalty weight; In response to the gateway capacity saturation parameter being less than the capacity high-risk warning threshold, the basic static linear proportional conversion flow is maintained, and the load growth rate judgment value is mapped only to a constant-level dynamic penalty weight to isolate the nonlinear exponential surge effect.

7. The load optimization and allocation system for power distribution control equipment considering multiple load fluctuations according to claim 6, characterized in that: The process of generating load reduction drive signaling for the controlled device is specifically configured as follows: Based on the comprehensive flow limiting scheduling priority score of all controlled devices, a ranking list of devices for load reduction control is constructed. From the equipment load reduction control sorting list, select the set of low-performing equipment with scores in the preset bottom range; Generate the load reduction drive signaling directed to the set of low-priority devices to trigger the remote power distribution control node to cut off or limit the current power supply hold-up state of the set of low-priority devices.

8. The load optimization and allocation system for power distribution control equipment considering multiple load fluctuations according to claim 7, characterized in that: The process of generating the recovery queuing status identifier for the controlled device is specifically configured as follows: Determine whether the overall rate limiting scheduling priority score is greater than zero; In response to the comprehensive flow limiting scheduling priority score being greater than zero, the comprehensive flow limiting scheduling priority score is converted into a reciprocal of a preset time mapping function to calculate the specific span parameter of the preset time window for the corresponding controlled device, so as to enable devices with lower priority scores to obtain a longer queuing waiting time. In response to the overall flow control priority score being less than or equal to zero, the preset time window is forcibly assigned the preset longest backoff queuing period.

9. The load optimization and allocation system for power distribution control equipment considering multiple load fluctuations according to claim 8, characterized in that: While performing the step of obtaining the daily average operating time statistics of the controlled equipment extracted by the statistics module, a degradation monitoring unit is also configured in parallel: Continuously monitor the data update status of the daily average operating time statistics set of the controlled device; In response to a data update failure frequency greater than or equal to a preset steady-state operating baseline threshold, the feature extraction flow for business scheduling completion is forcibly cut off, the dynamic calculation of the comprehensive flow limiting scheduling priority score is stopped, and the system automatically switches to the preset pure physical topology defense logic to maintain the basic overload protection of the power distribution gateway node.

10. The load optimization and allocation system for power distribution control equipment considering multiple load fluctuations according to claim 9, characterized in that: The process of automatically switching to the preset pure physical topology defense logic is specifically configured as follows: Retrieve a preset local power grid spatial ledger and extract the network topology physical distance parameters of each controlled device from the distribution gateway node, wherein the network topology physical distance parameters characterize the risk of line loss and end voltage drop; The load growth rate determination value and the network topology physical distance parameter are imported into the backup decision unit to perform weighted aggregation calculation, and the physical downgrading priority feature used to replace the comprehensive flow limiting scheduling priority score is calculated. Based on the physical load reduction priority characteristics in descending order, the load reduction drive signaling is generated sequentially to the corresponding controlled equipment, so as to realize the step-by-step flexible power outage contraction from high-risk and high-loss nodes at the end of the power distribution network to the core hub nodes.

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