Power distribution method, system, storage medium and device for intelligent power distribution cabinet

Abstract

The present application provides a power distribution method, system, storage medium and equipment for an intelligent power distribution cabinet, which relates to the field of food safety management technology. The method includes: collecting the power parameters corresponding to each power-consuming device and extracting the historical change characteristics of the power parameters; calculating the time series correlation coefficients between each power parameter, and constructing a feature correlation matrix based on the time series correlation coefficients; based on the historical change characteristics and the feature correlation matrix, predicting the power prediction parameters corresponding to each power-consuming device within a preset time period in the future; determining the abnormal power parameters and the corresponding range of abnormal power-consuming devices based on the power prediction parameters; and generating a target power distribution strategy based on the abnormal power parameters and the range of abnormal power-consuming devices. The implementation of the technical solution provided by the present application improves the overall power distribution efficiency.

Description

Technical Field

[0001] The present application relates to the field of intelligent power distribution technology, and in particular to a power distribution method, system, storage medium and device for an intelligent power distribution cabinet. Background Art

[0002] With the rapid development of intelligent manufacturing, power distribution systems within industrial parks are becoming increasingly complex, and the power supply relationships between various electrical devices are becoming highly coupled. In practical applications, abnormal power parameters in one device can be transmitted through the power supply network, triggering cascading failures in other devices and causing widespread downtime across the entire production system.

[0003] Currently, industrial parks typically manage power distribution using a monitoring method based on preset thresholds. This method primarily monitors power parameters such as voltage and current of each device in real time. When a parameter exceeds a preset threshold, the corresponding protective measures are triggered. For example, if the current of a device exceeds a threshold, the device's power supply is directly cut off. However, this approach lacks flexibility and lags, making it difficult to detect potential anomalies in a timely manner. This can lead to delayed adjustments to power distribution strategies and low power distribution efficiency. Summary of the Invention

[0004] The present application provides a power distribution method, system, storage medium and equipment for an intelligent power distribution cabinet, which improves the overall power distribution efficiency.

[0005] In a first aspect, the present application provides a power distribution method for an intelligent power distribution cabinet, the method comprising:

[0006] Collecting power parameters corresponding to each electrical device and extracting historical change characteristics of the power parameters;

[0007] Calculating the time series correlation coefficients between the power parameters, and constructing a feature correlation matrix based on the time series correlation coefficients;

[0008] Based on the historical change characteristics and the characteristic association matrix, predicting the power prediction parameters corresponding to each of the electrical devices within a preset time period in the future;

[0009] Determine abnormal power parameters and the range of abnormal electrical equipment affected accordingly based on the power prediction parameters;

[0010] A target power distribution strategy is generated based on the abnormal power parameter and the range of the abnormal powered equipment.

[0011] By adopting the above technical solution, by collecting the power parameters of each electrical device and extracting its historical change characteristics, and combining the calculated time series correlation coefficient to construct a feature correlation matrix, it is possible to effectively capture the power coupling relationship between devices, and predict the power prediction parameters in the future preset time period based on the historical change characteristics and the feature correlation matrix, so as to timely discover potential abnormal risks. By determining the abnormal power parameters and their impact range, and generating the target power distribution strategy accordingly, the power distribution plan is adjusted in advance, avoiding the passive response problem caused by the single reliance on preset thresholds in the traditional method. By analyzing the correlation and change rules between power parameters, this solution establishes a complete processing chain from parameter monitoring, abnormality prediction to strategy generation, thereby improving the distribution system's ability to predict abnormal situations and processing flexibility, effectively reducing the probability of cascading failures, and improving the overall distribution efficiency.

[0012] In a second aspect of the present application, a power distribution system of an intelligent power distribution cabinet is provided, the system comprising:

[0013] A feature extraction module is used to collect power parameters corresponding to each electrical device and extract historical change characteristics of the power parameters;

[0014] A correlation calculation module, configured to calculate a time series correlation coefficient between the power parameters and construct a characteristic correlation matrix based on the time series correlation coefficient;

[0015] A power prediction module, configured to predict power prediction parameters corresponding to each of the electrical devices within a preset time period in the future based on the historical change characteristics and the characteristic association matrix;

[0016] An abnormal parameter determination module, configured to determine abnormal power parameters and a corresponding range of affected abnormal electrical equipment based on the power prediction parameters;

[0017] A power distribution strategy generating module is used to generate a target power distribution strategy based on the abnormal power parameters and the range of abnormal power-consuming equipment.

[0018] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0019] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0020] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0021] This application collects the power parameters of each electrical device and extracts its historical change characteristics, and constructs a feature correlation matrix based on the calculated time series correlation coefficient. It can effectively capture the power coupling relationship between devices, and predict the power prediction parameters in the future preset time period based on the historical change characteristics and the feature correlation matrix. It can timely discover potential abnormal risks, and by determining the abnormal power parameters and their impact range, and generating the target power distribution strategy accordingly, it realizes the early adjustment of the power distribution plan and avoids the passive response problem caused by the single reliance on preset thresholds in traditional methods. By analyzing the correlation and change patterns between power parameters, this solution establishes a complete processing chain from parameter monitoring, abnormality prediction to strategy generation, improving the distribution system's ability to predict abnormal situations and processing flexibility, effectively reducing the probability of cascading failures, and improving overall distribution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a power distribution method for an intelligent power distribution cabinet provided in an embodiment of the present application;

[0023] Figure 2 This is a module diagram of a power distribution system of an intelligent power distribution cabinet provided in an embodiment of the present application;

[0024] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0025] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0028] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0030] Please refer to Figure 1 , a flow chart of a power distribution method of an intelligent power distribution cabinet is proposed. The method can be implemented by a computer program, a single-chip microcomputer, or run on a power distribution system of an intelligent power distribution cabinet. The computer program can be integrated into a computer device or run as an independent tool application. Specifically, the method includes steps 10 to 50, which are as follows:

[0031] Step 10: Collect the power parameters corresponding to each electrical device and extract the historical change characteristics of the power parameters.

[0032] Among them, electrical equipment in the embodiment of this application refers to various types of equipment that require power supply in an industrial production environment, including but not limited to production equipment (such as CNC machine tools, injection molding machines), power equipment (such as air compressors, water pumps), environmental control equipment (such as air conditioning, ventilation systems), etc.

[0033] In the embodiment of the present application, power parameters refer to various electrical quantities that reflect the operating status of electrical equipment, including but not limited to voltage value, current value, active power, reactive power, power factor, harmonic content, etc.

[0034] In the embodiment of this application, historical change characteristics refer to the changing patterns of power parameters in the past period of time, including but not limited to statistical characteristics such as the mean, standard deviation, maximum value, minimum value, fluctuation cycle, mutation point, and trend characteristics of the parameters changing over time.

[0035] Specifically, a power collection device is installed at the power supply end of each electrical device. The power collection device includes a voltage sensor and a current sensor. The voltage sensor collects the voltage value of the electrical device in real time, and the current sensor collects the current value of the electrical device in real time. Active power, reactive power, and power factor are calculated based on the voltage and current values. The power collection device samples the power parameters at a preset sampling period (e.g., 1 minute) and transmits the sampled data to a data processing unit. After obtaining the power parameters, their historical variation characteristics need to be extracted to facilitate subsequent analysis of their variation patterns. The past 30 days of power parameter sampling data can be selected as the analysis sample. First, the raw sampled data is preprocessed, including data padding, outlier removal, and data normalization. Then, the mean, standard deviation, maximum, and minimum values ​​of the processed power parameters within each sampling period are calculated to obtain the parameter statistical characteristics. Next, the power parameter sequence is segmented using a sliding time window method, with a window length set to 24 hours. The parameter fluctuation period and mutation point location within each time window are calculated to obtain the parameter periodic characteristics. Finally, based on statistical features and periodic features, a historical change feature set containing multidimensional features is constructed.

[0036] Step 20: Calculate the time series correlation coefficients between the power parameters and construct a feature correlation matrix based on the time series correlation coefficients.

[0037] Specifically, complex intercorrelations often exist between power parameters, and these relationships can affect the overall operating status of the power distribution system. To accurately describe and quantify these correlations, it is necessary to calculate the time-series correlation coefficients between the power parameters and construct a characteristic correlation matrix. Within a time window of a preset length (e.g., 24 hours), the fluctuation characteristics of each power parameter are extracted, including statistical features such as the parameter's mean, standard deviation, and peak value. These features can reflect the temporal variation patterns of the parameters. Subsequently, by calculating the difference series of power parameters within adjacent time windows, dynamic parameter variation information is obtained. Based on this difference series, statistical methods such as the Pearson correlation coefficient are used to calculate the synchronous variation coefficients between the power parameters. This coefficient reflects the degree of consistency in the variation trends of different parameters. Furthermore, the time-series correlation coefficient is calculated based on the synchronous variation coefficients and the fluctuation characteristics to quantify the strength of the correlation between the parameters. After obtaining the time-series correlation coefficients, to construct a more reasonable characteristic correlation matrix, the time-series correlation coefficients are first normalized and mapped to the [0, 1] interval to obtain the normalized coefficients. At the same time, the fluctuation amplitude of each power parameter is calculated. Larger fluctuation amplitudes indicate a greater impact on the system, and corresponding weight coefficients are determined accordingly. The normalization coefficients are combined with the weight coefficients to construct an initial correlation matrix. To improve the reliability of the correlation matrix, the correlation strength in the initial correlation matrix is ​​tested, and correlation items with a correlation strength below a preset threshold (e.g., 0.3) are eliminated, ultimately resulting in a characteristic correlation matrix.

[0038] Based on the above embodiment, as an optional embodiment, the step of calculating the time series correlation coefficient between the power parameters may further include the following steps:

[0039] Step 2011: extracting the fluctuation characteristics of each power parameter within a time window of a preset length.

[0040] Specifically, a time window of preset length (for example, 4 hours) is selected as the basic analysis unit, and feature extraction is performed on power parameters such as voltage, current, and power in each time window. First, the sampled data of each power parameter is preprocessed, including removing outliers and smoothing the data to reduce the influence of random noise. Then, the statistical characteristics of the power parameters are calculated in each time window, including the mean μ, standard deviation σ, peak factor Kp (the ratio of the maximum value to the mean), waveform factor Kf (the ratio of the root mean square value to the arithmetic mean), etc. These characteristic values ​​together constitute the fluctuation characteristic vector in the time window. In order to enhance the expressive power of the features, the rate of change and trend factor of the parameters are also introduced, where the rate of change is obtained by calculating the difference between adjacent sampling points, and the trend factor is characterized by the slope obtained by least squares fitting.

[0041] Step 2012: Calculate the difference sequence of power parameters in adjacent time windows.

[0042] Specifically, after obtaining the fluctuation characteristics of each time window, in order to analyze the dynamic change law of the power parameters, it is necessary to calculate the difference sequence between adjacent time windows, and divide the time axis into several continuous time windows according to the preset length, recorded as W1, W2, ..., Wn. For each power parameter, the difference between each characteristic quantity between adjacent time windows is calculated. For example, for time windows Wi and Wi+1, the mean difference Δμi=μi+1-μi, the standard deviation difference Δσi=σi+1-σi, and the difference of other characteristic quantities are calculated. These differences constitute the difference sequence {Δμi, Δσi, ΔKpi, ΔKfi} that describes the dynamic change of the parameters. In order to improve the comparability of the difference sequence, the differences are standardized so that the differences of different types of characteristic quantities have the same dimension.

[0043] Step 2013: Calculate the synchronous variation coefficients between the power parameters based on the difference sequence.

[0044] Specifically, for any two power parameters A and B, their difference sequences within a continuous time window are first denoted as DA = {ΔμA, ΔσA, ΔKpA, ΔKfA} and DB = {ΔμB, ΔσB, ΔKpB, ΔKfB}. The correlation between these two difference sequences, namely the synchronization coefficient Sync(A,B), is calculated using a modified Pearson correlation coefficient method. During this calculation, a time-decay weight function w(t) = e^(-λt) is introduced, where λ is the decay coefficient and t is the time distance. This emphasizes the importance of recent data. The synchronization coefficient is calculated as: Sync(A,B) = Σ[w(t)·(DA(t)-μDA)(DB(t)-μDB)] / sqrt(Σ[w(t)·(DA(t)-μDA)²]·Σ[w(t)·(DB(t)-μDB)²]), where μDA and μDB are the weighted averages of the difference sequences DA and DB, respectively.

[0045] Step 2014: Calculate the time series correlation coefficient based on the synchronous variation coefficient and the fluctuation characteristics.

[0046] Specifically, after obtaining the synchronous variation coefficient, it is necessary to calculate the final time series correlation coefficient in combination with the fluctuation characteristics of the power parameters. The fluctuation characteristics reflect the inherent variation law of the parameters, while the synchronous variation coefficient characterizes the dynamic correlation between the parameters. The combination of the two can more comprehensively describe the relationship between the parameters. Set the fluctuation characteristic weight factor α (0<α<1), and record the fluctuation characteristic similarity as Sim(A,B). The calculation formula of the time series correlation coefficient R(A,B) is: R(A,B)=α·Sim(A,B)+(1-α)·Sync(A,B). Among them, the fluctuation characteristic similarity Sim(A,B) is obtained by calculating the cosine similarity of the characteristic vectors of the two parameters in a single time window. In order to improve the stability of the calculation, the similarity of multiple consecutive time windows is smoothed by sliding average.

[0047] Based on the above embodiment, as an optional embodiment, the step of constructing a feature correlation matrix according to the time series correlation coefficient may further include the following steps:

[0048] Step 2021: normalize the time series correlation coefficient to obtain a normalized coefficient.

[0049] Specifically, since the time series correlation coefficients calculated between different power parameters may have differences in dimensions and scales, in order to make the correlation strengths between the parameters comparable, the time series correlation coefficients need to be normalized. For the time series correlation coefficient R(i,j) between any two power parameters i and j, the Min-Max normalization method is used, that is: N(i,j)=(R(i,j)-Rmin) / (Rmax - Rmin), where Rmax and Rmin are the maximum and minimum values ​​of all time series correlation coefficients, respectively. In order to avoid the influence of extreme values ​​on the normalization results, a saturation threshold mechanism is introduced. When R(i,j) exceeds the preset range, it is limited to a reasonable range. At the same time, in order to improve the robustness of normalization, a piecewise linear mapping method is adopted, and different mapping coefficients are used in different value intervals, so that the coefficient distribution after normalization is more uniform and reasonable.

[0050] Step 2022: Calculate the fluctuation amplitude of each power parameter and determine the weight coefficient according to the fluctuation amplitude.

[0051] Specifically, while obtaining the normalization coefficient, the importance of each power parameter needs to be assessed. This can be achieved by calculating the parameter's fluctuation amplitude. For each power parameter, its fluctuation characteristic sequence is first extracted within a preset observation period (e.g., 7 days), including the instantaneous fluctuation amplitude v(t), the root mean square value Vrms, and the peak value Vpeak. The calculation of the fluctuation amplitude requires considering the frequency characteristics of the parameter variation. Therefore, a frequency domain analysis method is introduced, using a fast Fourier transform (FFT) to obtain the parameter's spectral distribution. Based on the spectral analysis results, the comprehensive fluctuation amplitude A is calculated as A = β1·Vrms+β2·Vpeak+β3·Σ(ak·|Xk|), where β1, β2, and β3 are combination coefficients, ak is the weight coefficient for different frequency components, and |Xk| is the corresponding frequency amplitude. Based on the calculated fluctuation amplitude, the softmax function is used to determine the weight coefficient for each parameter: W(i) = exp(γ·A(i)) / Σexp(γ·A(j)), where γ is a modulation factor used to control the concentration of the weight distribution. To make the weight coefficients adaptable over time, a sliding time window update mechanism is used to regularly recalculate the fluctuation amplitude and weight coefficients. At the same time, a smoothing factor is introduced to perform exponential smoothing on the weight coefficients to avoid the impact of sudden changes in weights on the system.

[0052] Step 2023: Construct an initial correlation matrix based on the normalization coefficients and weight coefficients.

[0053] Specifically, to fully describe the correlation between power parameters, it is necessary to organically combine normalization coefficients and weight coefficients to construct an initial correlation matrix. Specifically, for n power parameters, an n×n initial correlation matrix M is constructed, where the matrix element M(i,j) represents the correlation strength between parameter i and parameter j. The initial correlation strength is calculated using a weighted combination method as follows:

[0054] M(i,j)=η·N(i,j)+(1-η)·[W(i)+W(j)] / 2, where η is a balancing factor (0<η<1) that adjusts the relative importance of the normalization coefficient N(i,j) and the weight coefficients W(i) and W(j). To ensure the symmetry of the correlation matrix, the calculated matrix elements are symmetric: M(i,j)=M(j,i)=[M(i,j)+M(j,i)] / 2. Furthermore, to eliminate the influence of the parameters themselves, the matrix diagonal elements are set to 1.

[0055] Step 2024: Detect the correlation strength in the initial correlation matrix, and remove correlation items with correlation strength lower than the correlation strength threshold to obtain a feature correlation matrix.

[0056] Specifically, after constructing the initial correlation matrix, the correlation strength needs to be tested and optimized to improve the matrix's reliability and practicality. First, a correlation strength threshold θ (e.g., 0.3) is set. This threshold can be dynamically adjusted based on system characteristics and actual needs. For each off-diagonal element M(i, j) in the matrix, if its value is less than the threshold θ, the correlation is considered weak, and the corresponding matrix element is set to 0, that is, the correlation item is eliminated. To avoid the information loss that may be caused by simple threshold truncation, a fuzzy threshold mechanism is introduced. That is, a transition interval [θ-δ, θ+δ] is set near the threshold θ. For the correlation strength falling within this interval, a nonlinear attenuation function is used to smooth it according to its distance from the threshold.

[0057] After removing weakly correlated terms, a transitive correlation test is required to maintain the structural stability of the matrix. If there is a strong correlation between parameters i and j, or j and k, and the correlation between i and k is removed, the correlation between i and k needs to be reassessed. This involves introducing the transitive correlation τ(i, j, k) = min[M(i, j), M(j, k)] . When τ(i, j, k) is greater than a secondary threshold θ', the correlation between i and k is retained and its correlation strength is updated. This approach prevents important indirect correlations from being mistakenly removed. Finally, the optimized matrix is ​​normalized to obtain the final feature correlation matrix.

[0058] Step 30: Based on the historical change characteristics and the characteristic correlation matrix, predict the power prediction parameters corresponding to each electrical device within a preset time period in the future.

[0059] Specifically, in an optional embodiment, an LSTM deep learning model can be used to construct a basic time series forecasting model. This model input includes the historical power parameter sequences and temporal characteristics of each electrical device. Based on this, the characteristic correlation matrix M is introduced into the forecasting process as a correlation constraint. Specifically, for any parameter pair (i, j), the predicted values ​​P(i) and P(j) must satisfy the correlation strength constraint: |P(i)-P(j)| ≤ λ·(1-M(i, j)), where λ is the relaxation factor. By solving this constrained optimization problem, a forecast result that satisfies the correlation is obtained. To improve forecast reliability, a dynamic error compensation mechanism is introduced to correct the forecast value based on the statistical characteristics of the most recent forecast error. This method ensures that the forecast results are consistent with historical variation patterns while ensuring the stability of the correlation between parameters. Practice has shown that the forecast error can be controlled within 5%, providing a reliable basis for the intelligent operation and maintenance of power distribution systems.

[0060] Based on the above embodiment, as an optional embodiment, the step of predicting the power prediction parameters corresponding to each electrical device within a preset time period in the future based on the historical change characteristics and the characteristic correlation matrix may further include the following steps:

[0061] Step 301: Divide the power parameters into core parameter groups and subordinate parameter groups according to the correlation strength in the feature correlation matrix.

[0062] Specifically, the power parameters are first grouped based on the characteristic correlation matrix. The correlation strength distribution of each parameter in the characteristic correlation matrix is ​​analyzed, and the correlation centrality index C(i) = ΣjM(i, j) for each parameter is calculated, where M(i, j) is an element in the correlation matrix. A correlation threshold ξ is set. When the correlation centrality C(i) of a parameter is greater than ξ, it is classified as a core parameter group; otherwise, it is classified as a dependent parameter group. This grouping method can identify key parameters with strong influence in the system and help reduce the complexity of the prediction problem.

[0063] Step 302: Calculate the independent prediction value of the core parameter group based on the time series prediction model constructed based on the historical change characteristics of the core parameter group.

[0064] Specifically, for the core parameter group, given the strong independence of its parameter changes, a deep learning model is used for independent prediction. A multi-layer LSTM network is constructed as a time series prediction model. The input layer contains historical data sequences and auxiliary features (such as time tags and environmental factors). Multiple LSTM layers extract time series features, and finally a fully connected layer generates the predicted output. To improve the model's generalization, a dropout mechanism and L2 regularization are introduced during training. The model's loss function uses a modified Huber loss, which can handle both small and large errors. This model can generate independent predicted values ​​(VPs) for the core parameter group.

[0065] Step 303: Establish an association mapping model based on the association relationship between the core parameter group and the subordinate parameter group in the feature association matrix.

[0066] Specifically, a correlation submatrix, Ms, between the core parameter group and the subordinate parameter group is extracted from the feature correlation matrix. The elements of this submatrix, Ms(i,j), represent the strength of the correlation between core parameter i and subordinate parameter j. Based on this correlation submatrix, a nonlinear mapping function, f(x)=Σ[wk·φ(||x-ck||)], is constructed, where wk is the weight coefficient, ck is the basis function center point, and φ(·) is the radial basis function. The parameters of the mapping function are optimized using training data to accurately describe the conversion relationship between the parameters.

[0067] Step 304: Calculate the association prediction value of the dependent parameter group based on the association mapping model and the independent prediction value of the core parameter group.

[0068] Specifically, after obtaining the association mapping model, the independent predicted value VP of the core parameter group is used as input, and the associated predicted value VR = f(VP) of the subordinate parameter group is calculated through a mapping function. To improve prediction reliability, an error feedback mechanism is introduced to dynamically correct the mapping results based on historical prediction errors. This association mapping model not only reduces computational complexity but also improves the rationality of prediction results by constraining the associations between parameters, providing reliable technical support for distribution system state prediction. This hierarchical prediction method based on association relationships ensures both prediction accuracy and improved algorithm execution efficiency.

[0069] Step 305: The independent prediction value and the associated prediction value are combined in a weighted fusion manner to generate power prediction parameters for each electrical device.

[0070] Specifically, to obtain complete power forecast parameters for each power consumer, a fusion calculation method is used to generate the final forecast results. For each power consumer, its power forecast parameters include key indicators such as active power, reactive power, voltage, and current. A weighted fusion approach is used to calculate the forecast values ​​for each of these parameters. For parameters in the core parameter group (such as active power P and voltage V), the forecast value is equal to the weighted sum of the independent forecast value VP and the associated forecast value VR, i.e., P = α·VP + (1-α)·VR. For parameters in the dependent parameter group (such as reactive power Q and current I), the forecast value depends primarily on the associated forecast value, calculated based on the core parameter forecast value via an association mapping model. The resulting power forecast parameter set is {P, Q, V, I}, where each parameter contains time series values ​​within a preset time period. For example, if the preset time period is the next 24 hours, each parameter will contain 96 forecast point values ​​at 15-minute intervals.

[0071] Step 40: Determine abnormal power parameters and the corresponding range of affected abnormal power-consuming equipment based on the power prediction parameters.

[0072] Specifically, a topological diagram reflecting the power supply relationships between electrical devices is first created. The predicted power parameters are then compared with the preset normal operating range to identify abnormal power parameters that exceed the corresponding normal operating range. The range of abnormal electrical devices can then be determined based on the electrical devices affected by the abnormal power parameters.

[0073] Based on the above embodiment, as an optional embodiment, the step of determining abnormal power parameters and the corresponding range of affected abnormal electrical equipment based on the power prediction parameters may further include the following steps:

[0074] Step 401: Establish a power supply topology diagram between various electrical devices.

[0075] Specifically, the electrical equipment in the power distribution system is represented as nodes in the graph, and the power supply connection relationships between the equipment are represented as directed edges to construct a directed graph G(V, E). Each node vi ∈ V contains the basic attribute information of the equipment, such as equipment type, rated capacity, etc.; each edge eij ∈ E represents the power supply path from equipment i to equipment j and records its key characteristic parameters, such as line impedance, rated current, etc.

[0076] Step 402: Compare the power prediction parameters with the preset normal operation range to determine the abnormal power parameters.

[0077] Specifically, based on the established topological relationship graph, compare the power prediction parameters with the preset normal operation range. For the power prediction parameter set {P, Q, V, I} of each equipment, set its normal operation range as [Pmin, Pmax], [Qmin, Qmax], [Vmin, Vmax], [Imin, Imax]. When the predicted value of any parameter exceeds the corresponding normal operation range, mark it as an abnormal power parameter. For example, when the predicted voltage V < Vmin or V > Vmax, determine that the voltage parameter is abnormal.

[0078] Step 403: Identify the associated equipment that has a power supply connection with the equipment corresponding to the abnormal power parameter according to the power supply topological relationship graph.

[0079] Specifically, after determining the abnormal parameters, analyze the abnormal propagation path through the topological relationship graph. Assume that the abnormal equipment node is va, and use the graph traversal algorithm to identify the set of associated equipment Ra that has a power supply connection with va. The breadth-first search algorithm can be used to traverse the graph G starting from va to identify all reachable nodes. For each associated node vi ∈ Ra, calculate its electrical distance dia from the abnormal node va, and this distance value reflects the attenuation degree of the abnormal influence. According to the power supply hierarchy relationship of the associated equipment, further divide Ra into the upstream equipment set Ru (power supply equipment) and the downstream equipment set Rd (power receiving equipment), and analyze the influence characteristics of the abnormality on the upstream and downstream equipment respectively.

[0080] Step 404: Calculate the power parameter offset value of the associated equipment, and the power parameter offset value is the deviation degree of the power parameter of the associated equipment from the corresponding preset standard value.

[0081] Specifically, for each associated device vi, calculate the normalized offset of its power parameters {P, Q, V, I} relative to the preset standard values ​​{Ps, Qs, Vs, Is}. The offset is calculated using the weighted Euclidean distance method: Di = sqrt(wp(P-Ps)² / Ps²+wq(Q-Qs)² / Qs²+wv(V-Vs)² / Vs²+wi(I-Is)² / Is²), where wp, wq, wv, and wi are the weight coefficients of each parameter, reflecting the importance of different parameters on the device's operating status.

[0082] Step 405: Filter out the electrical equipment combinations whose power parameter offset values ​​are greater than the offset threshold as the abnormal electrical equipment range.

[0083] Specifically, based on the calculated offset value, an adaptive offset threshold η is set to screen abnormal devices. The threshold η is determined by comprehensively considering system operational requirements and historical statistical characteristics: η = μd + k·σd, where μd and σd are the mean and standard deviation of historical offset values, respectively, and k is a safety factor. When a device's offset value Di exceeds η, it is included in the list of abnormal power users. For critical devices near the source of the anomaly, the threshold is appropriately lowered to improve monitoring sensitivity.

[0084] Step 50: Generate a target power distribution strategy based on the abnormal power parameters and the range of abnormal power-consuming devices.

[0085] Specifically, a multi-objective optimization model is first constructed, with the following optimization objectives: minimizing the impact of the anomaly, maximizing power supply reliability, and minimizing energy loss. The optimization variables include the status of each switchgear {Si}, the operating parameters of adjustable devices {Pi, Qi}, and the load distribution plan {Li}. A hierarchical decision-making approach is used to generate a power distribution strategy based on the current system state. For example, at the first level, an emergency assessment is performed. Based on the type and severity of the abnormal power parameters, abnormal scenarios are classified into three types: those requiring immediate disconnection, those requiring load transfer, and those requiring parameter adjustment. For each type, a corresponding response strategy template is designed, including a device disconnection sequence, load transfer path, and parameter adjustment plan. At the second level, the strategy details are optimized and adjusted based on the scope of abnormal power devices. For example, for scenarios requiring load transfer, a feasible transfer path is determined through power flow calculation: the backup power capacity {Ci} within the target area is first identified. Then, based on the principle of minimum path loss, the optimal load distribution plan is calculated. The transfer plan must satisfy the constraints: ∑Li ≤ Ci (capacity constraint) and all node voltages must satisfy Vmin ≤ Vi ≤ Vmax (voltage constraint).

[0086] Based on the above embodiment, as an optional embodiment, the step of generating a target power distribution strategy based on abnormal power parameters and the range of abnormal power-consuming devices may further include the following steps:

[0087] Step 501: Divide the anomaly level according to the deviation degree of the abnormal power parameters.

[0088] Specifically, the anomaly level L is calculated based on the deviation degree D between the abnormal power parameters and the preset standard value. The deviation degree is calculated using a normalization method: D = |X - Xs| / Xs, where X is the actual value of the abnormal parameter and Xs is the standard value. According to the size of the deviation degree, the anomaly level is divided into three levels: when D ≤ 20%, it is a mild anomaly (L = 1); when 20% < D ≤ 50%, it is a moderate anomaly (L = 2); when D > 50%, it is a severe anomaly (L = 3).

[0089] Step 502: Determine the voltage regulation amplitude and current limit threshold based on the anomaly level.

[0090] Specifically, based on the determined anomaly level L, the corresponding voltage regulation amplitude ΔV and current limit threshold Imax are calculated. The calculation formula for the voltage regulation amplitude is: ΔV = α·L·Vn, where α is the regulation coefficient (the value range is 0.02 - 0.05) and Vn is the rated voltage; the calculation formula for the current limit threshold is: Imax = (1 - β·L)·In, where β is the limit coefficient (the value range is 0.1 - 0.2) and In is the rated current. This method of parameter determination based on the anomaly level can achieve an accurate match between the regulation intensity and the anomaly degree.

[0091] Step 503: Calculate the power distribution ratio of each abnormal power-consuming device within the range of abnormal power-consuming devices, and generate the device start-stop time sequence according to the power distribution ratio.

[0092] Specifically, for the power distribution within the range of abnormal power-consuming devices, a weighted distribution method based on device importance and power consumption characteristics is adopted. First, calculate the weight coefficient wi of each device: wi = ki·pi·ri, where ki is the importance coefficient, pi is the load characteristic coefficient, and ri is the operating state coefficient. Then calculate the power distribution ratio γi: γi = wi / ∑wi. Determine the start-stop order of the devices according to the distribution ratio: sort the devices from large to small according to γi to generate the start sequence {S1, S2,..., Sn}, where the start time interval Δt between adjacent devices = f(γi), and f(γi) is a time mapping function based on the power ratio.

[0093] Step 504: Generate a target power distribution strategy based on the voltage regulation amplitude, current limit threshold, and device start-stop time sequence.

[0094] Specifically, to achieve coordinated control of multiple devices and ensure stable system operation, a scientific and reasonable target power distribution strategy must be generated based on voltage regulation amplitude, current limit threshold, and device start-stop sequence. First, based on the device start-up time intervals in the device start-stop sequence, power-consuming devices with the same or similar start-up times are grouped into concurrently operating device groups. A time clustering threshold is set. When the start-up time difference of devices is less than the time clustering threshold, these devices are assigned to the same operating device group, thus forming a device group collection.

[0095] For each group of equipment running simultaneously, calculate its total power demand P Ti The calculation method is: P Ti =∑P j +ΔP i , where P j is the rated power of the jth device in the group, ΔP i It is the power margin considering the starting shock and load fluctuation. i The determination of needs to consider the equipment type and operating characteristics, usually the value is: ΔPi = max{λ·∑Pj, P max}, where λ is the margin coefficient (0.1-0.2), P max The starting power of the largest device in the group. Based on the voltage adjustment amplitude ΔV and the current limit threshold I max , determine the operating constraints for each group of equipment running simultaneously. The constraints include: voltage constraint V i ∈[V nom -ΔV, V nom +ΔV], current constraint I i ≤I max , power constraint P i ≤P Ti At the same time, considering the mutual influence between devices, the coupling constraint is introduced: ΔV ij ≤εv (voltage mutual influence limit between devices), ΔI ij ≤εi (the mutual influence limit of current between devices), these constraints together constitute the operation constraint set C i .

[0096] Under the premise of satisfying the operation constraints, the optimization algorithm is used to generate the power allocation plan for each equipment group. The optimization objectives include: minimizing the total energy consumption min(∑Pi), minimizing the voltage fluctuation min(max|ΔVi|), and maximizing the load balance max(min(γi)). By solving the multi-objective optimization problem, the optimal power allocation plan Mi = {P i1 , P i2 , ..., P ij}, where P ijrepresents the allocated power for the jth device in the group. Finally, the power allocation plan is converted into specific coordinated control instructions. For each device group Gi, a control instruction set Ci = {ci1, ci2, ..., cik} is generated. Each instruction contains: device ID, target operating state, and operating parameters (including power setpoint, voltage regulation value, startup time, etc.). The control instructions of all device groups are integrated in a time sequence to form a complete target power distribution strategy S = {C1, C2, ..., Cm}. The strategy also includes inter-group switching conditions and exception handling mechanisms to ensure the continuity and reliability of strategy execution.

[0097] This power distribution strategy achieves coordinated control of multiple devices through rational device grouping, precise power demand calculation, comprehensive constraint setting, and optimized power allocation. The strategy's grouped execution mechanism reduces system impact and improves operational stability; comprehensive consideration of constraints ensures system security; optimized power allocation improves energy efficiency; and standardized packaging of coordinated control instructions facilitates strategy execution and management.

[0098] See Figure 2 , is a module diagram of a power distribution system of an intelligent power distribution cabinet provided in an embodiment of the present application. The power distribution system of the intelligent power distribution cabinet may include: a feature extraction module, a correlation calculation module, a power prediction module, an abnormal parameter determination module, and a power distribution strategy generation module, wherein:

[0099] A feature extraction module is used to collect power parameters corresponding to each electrical device and extract historical change characteristics of the power parameters;

[0100] A correlation calculation module, configured to calculate a time series correlation coefficient between the power parameters and construct a characteristic correlation matrix based on the time series correlation coefficient;

[0101] A power prediction module, configured to predict power prediction parameters corresponding to each of the electrical devices within a preset time period in the future based on the historical change characteristics and the characteristic association matrix;

[0102] An abnormal parameter determination module, configured to determine abnormal power parameters and a corresponding range of affected abnormal electrical equipment based on the power prediction parameters;

[0103] A power distribution strategy generating module is used to generate a target power distribution strategy based on the abnormal power parameters and the range of abnormal power-consuming equipment.

[0104] Optionally, the correlation calculation module is further configured to extract the fluctuation characteristics of each power parameter within a time window of a preset length;

[0105] Calculate the difference sequence of power parameters in adjacent time windows;

[0106] Calculating synchronous variation coefficients between power parameters based on the difference sequence;

[0107] The time series correlation coefficient is calculated according to the synchronous variation coefficient and the fluctuation characteristics.

[0108] Optionally, the correlation calculation module is further configured to perform normalization processing on the time series correlation coefficient to obtain a normalized coefficient;

[0109] Calculating the fluctuation amplitude of each of the power parameters and determining a weight coefficient according to the fluctuation amplitude;

[0110] constructing an initial correlation matrix based on the normalization coefficient and the weight coefficient;

[0111] The correlation strength in the initial correlation matrix is ​​detected, and correlation items with correlation strength lower than a correlation strength threshold are eliminated to obtain a characteristic correlation matrix.

[0112] Optionally, the power prediction module is further configured to divide the power parameters into a core parameter group and a subordinate parameter group according to the correlation strength in the characteristic correlation matrix;

[0113] Calculating independent prediction values ​​of the core parameter group using a time series prediction model constructed based on historical variation characteristics of the core parameter group;

[0114] Establishing an association mapping model according to the association relationship between the core parameter group and the subordinate parameter group in the feature association matrix;

[0115] Calculating the association prediction value of the dependent parameter group based on the association mapping model and the independent prediction value of the core parameter group;

[0116] The independent prediction value and the associated prediction value are combined in a weighted fusion manner to generate power prediction parameters for each electrical device.

[0117] Optionally, the abnormal parameter determination module is further configured to establish a power supply topology diagram between the electrical devices;

[0118] Comparing the power prediction parameters with a preset normal operating range to determine abnormal power parameters;

[0119] Identifying, based on the power supply topology diagram, associated devices that are connected to the device corresponding to the abnormal power parameter;

[0120] Calculating a power parameter offset value of the associated device, where the power parameter offset value is a degree of deviation between the power parameter of the associated device and a corresponding preset standard value;

[0121] The electric device combinations whose power parameter offset values ​​are greater than the offset threshold are screened out as the abnormal electric device range.

[0122] Optionally, the power distribution strategy generation module is further configured to classify abnormality levels according to the degree of deviation of the abnormal power parameters;

[0123] determining a voltage regulation amplitude and a current limit threshold based on the abnormality level;

[0124] Calculating the power allocation ratio of each abnormal power-consuming device within the range of the abnormal power-consuming device, and generating a device start-stop timing sequence according to the power allocation ratio;

[0125] A target power distribution strategy is generated based on the voltage regulation amplitude, the current limit threshold, and the device start and stop timing.

[0126] Optionally, the power distribution strategy generation module is further configured to divide the power-consuming devices into simultaneously operating device groups according to the device start and stop sequence;

[0127] Calculating the total power requirements of each of the simultaneously operating device groups;

[0128] determining an operation constraint condition for each of the simultaneously operating device groups based on the voltage regulation amplitude and the current limit threshold;

[0129] generating a power allocation plan that satisfies the operating constraints;

[0130] A coordinated control instruction is generated according to the power allocation scheme of the device group, and the coordinated control instruction is combined into a target power distribution strategy.

[0131] It should be noted that the above embodiments provide systems that implement their functions using only the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0132] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a power distribution method of an intelligent distribution cabinet in the above embodiment. The specific execution process can be found in the specific description of the above embodiment and will not be repeated here.

[0133] Please refer to Figure 3 The present application also discloses an electronic device. Figure 3The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0134] The communication bus 302 is used to implement the connection and communication between these components.

[0135] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0136] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0137] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0138] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also optionally be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 The memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a power distribution method of an intelligent power distribution cabinet.

[0139] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a power distribution method of an intelligent power distribution cabinet stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0140] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0142] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0143] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0144] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0145] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0146] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A power distribution method for an intelligent power distribution cabinet, characterized in that: The method comprises: Collecting power parameters corresponding to each electrical device and extracting historical change characteristics of the power parameters; Calculating the time series correlation coefficients between the power parameters, and constructing a feature correlation matrix based on the time series correlation coefficients; Based on the historical change characteristics and the characteristic association matrix, predicting the power prediction parameters corresponding to each of the electrical devices within a preset time period in the future; Determine abnormal power parameters and the range of abnormal power-consuming equipment affected accordingly based on the power prediction parameters; A target power distribution strategy is generated based on the abnormal power parameter and the range of the abnormal powered equipment.

2. The power distribution method of the intelligent power distribution cabinet according to claim 1, characterized in that: The calculating of the time series correlation coefficient between the power parameters includes: Extracting the fluctuation characteristics of each of the power parameters within a time window of a preset length; Calculate the difference sequence of power parameters in adjacent time windows; Calculating synchronous variation coefficients between power parameters based on the difference sequence; A time series correlation coefficient is calculated according to the synchronous variation coefficient and the fluctuation characteristic.

3. The power distribution method of the intelligent power distribution cabinet according to claim 1, characterized in that: The constructing a feature correlation matrix according to the time series correlation coefficient includes: performing normalization processing on the time series correlation coefficient to obtain a normalized coefficient; Calculating the fluctuation amplitude of each of the power parameters and determining a weight coefficient according to the fluctuation amplitude; constructing an initial correlation matrix based on the normalization coefficient and the weight coefficient; The correlation strength in the initial correlation matrix is ​​detected, and correlation items with correlation strength lower than a correlation strength threshold are eliminated to obtain a characteristic correlation matrix.

4. The power distribution method of the intelligent power distribution cabinet according to claim 1, characterized in that: The predicting of the power prediction parameters corresponding to each of the electrical devices within a preset time period in the future based on the historical change characteristics and the characteristic association matrix includes: Dividing the power parameters into a core parameter group and a subordinate parameter group according to the correlation strength in the characteristic correlation matrix; Calculating independent prediction values ​​of the core parameter group using a time series prediction model constructed based on historical variation characteristics of the core parameter group; Establishing an association mapping model according to the association relationship between the core parameter group and the subordinate parameter group in the feature association matrix; Calculating the association prediction value of the dependent parameter group based on the association mapping model and the independent prediction value of the core parameter group; The independent prediction value and the associated prediction value are combined in a weighted fusion manner to generate power prediction parameters for each electrical device.

5. The power distribution method of the intelligent power distribution cabinet according to claim 1, characterized in that: The determining of abnormal power parameters and a corresponding range of affected abnormal electrical equipment based on the power prediction parameters includes: Establishing a power supply topology diagram between the electrical devices; Comparing the power prediction parameters with a preset normal operating range to determine abnormal power parameters; Identifying, based on the power supply topology diagram, associated devices that are connected to the device corresponding to the abnormal power parameter; Calculating a power parameter offset value of the associated device, where the power parameter offset value is a degree of deviation between the power parameter of the associated device and a corresponding preset standard value; The electric device combinations whose power parameter offset values ​​are greater than the offset threshold are screened out as the abnormal electric device range.

6. The power distribution method of the intelligent power distribution cabinet according to claim 1, characterized in that: The generating of a target power distribution strategy based on the abnormal power parameter and the range of abnormal power-consuming equipment includes: Classifying abnormality levels according to the degree of deviation of the abnormal power parameters; determining a voltage regulation amplitude and a current limit threshold based on the abnormality level; Calculating the power allocation ratio of each abnormal power-consuming device within the range of the abnormal power-consuming device, and generating a device start-stop timing sequence according to the power allocation ratio; A target power distribution strategy is generated based on the voltage regulation amplitude, the current limit threshold, and the device start and stop timing.

7. The power distribution method of the intelligent power distribution cabinet according to claim 6, characterized in that: The generating of the target power distribution strategy based on the voltage regulation amplitude, the current limit threshold, and the device start / stop timing includes: Dividing the electrical equipment into groups of equipment running simultaneously according to the start and stop sequence of the equipment; Calculating the total power requirements of each of the simultaneously operating device groups; determining an operation constraint condition for each of the simultaneously operating device groups based on the voltage regulation amplitude and the current limit threshold; generating a power allocation plan that satisfies the operating constraints; A coordinated control instruction is generated according to the power allocation scheme of the device group, and the coordinated control instruction is combined into a target power distribution strategy.

8. A power distribution system for an intelligent power distribution cabinet, characterized in that: The system comprises: A feature extraction module is used to collect power parameters corresponding to each electrical device and extract historical change characteristics of the power parameters; A correlation calculation module, configured to calculate a time series correlation coefficient between the power parameters and construct a characteristic correlation matrix based on the time series correlation coefficient; A power prediction module, configured to predict power prediction parameters corresponding to each of the electrical devices within a preset time period in the future based on the historical change characteristics and the characteristic association matrix; An abnormal parameter determination module, configured to determine abnormal power parameters and a corresponding range of affected abnormal electrical equipment based on the power prediction parameters; A power distribution strategy generating module is used to generate a target power distribution strategy based on the abnormal power parameters and the range of abnormal power-consuming equipment.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executed by a method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

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