A monitoring method and system for low-voltage power boxes

Abstract

The present application relates to the technical field of abnormal equipment monitoring, and particularly relates to a monitoring method and system for low-voltage power boxes, which solves the technical problem of improper monitoring resource configuration and difficult abnormal tracing caused by the difference between the monitoring requirements and environmental interference of the power boxes in the prior art. The method comprises the following steps: acquiring load monitoring data and environmental data of a plurality of low-voltage power boxes in a park; extracting monitoring requirement features according to the load monitoring data, extracting environmental interference features according to the environmental data, and classifying the plurality of low-voltage power boxes according to the monitoring requirement features and the environmental interference features; determining the centralized management compatibility difficulty of each category according to the monitoring requirement features and the environmental interference features of the low-voltage power boxes in each category; building a multi-level resource management architecture of the park according to the centralized management compatibility difficulty of the plurality of categories, and performing hierarchical monitoring on the plurality of low-voltage power boxes according to the multi-level resource management architecture.

Description

Technical Field

[0001] This invention relates to the field of abnormal equipment monitoring technology, specifically to a monitoring method and system for a low-voltage power box. Background Technology

[0002] As the terminal equipment of the power system, low-voltage power distribution boxes have evolved from simple electrical protection devices into comprehensive intelligent platforms integrating modern sensing, communication, automatic control, and power electronics technologies. By integrating fieldbus, industrial Ethernet, and monitoring and metering terminals, modern low-voltage power distribution boxes can collect electrical variable parameters such as current, voltage, and power factor in real time, and have remote communication, fault early warning, and automatic switching control capabilities, providing key support for refined energy management and reactive power compensation of the power grid.

[0003] Within the same industrial park, multiple low-voltage power distribution boxes are typically distributed to support the power needs of different load types. These power distribution boxes are interconnected by interwoven lines, which can allow abnormal problems to propagate and spread along the connection paths. Furthermore, low-voltage power distribution boxes lack the dedicated communication lines of high-voltage power distribution boxes and must rely on existing networks or wireless communication technologies for centralized monitoring. The diverse installation environments and varying load uses of these boxes result in the simultaneous and intertwined interference from external environmental factors and the different monitoring requirements of various loads, creating a complex and ever-changing situation for unified monitoring and management.

[0004] The lack of a unified quantitative and integrated analysis method for the differences in monitoring needs and environmental interference among low-voltage power boxes in the park leads to a low degree of matching between the configuration of monitoring resources and the actual needs of each power box. This results in technical problems such as high monitoring costs, delayed anomaly feedback, and inability to accurately locate the real source of anomalies. Summary of the Invention

[0005] To address the technical problems of improper monitoring resource allocation and difficulty in anomaly tracing caused by differences in monitoring requirements and environmental interference in existing technologies for power boxes, the present invention aims to provide a monitoring method and system for low-voltage power boxes. The specific technical solution adopted is as follows: Firstly, a monitoring method for low-voltage power boxes is provided, comprising: acquiring load monitoring data and environmental data of multiple low-voltage power boxes within a park; extracting monitoring requirement features based on the load monitoring data, extracting environmental interference features based on the environmental data, and classifying the multiple low-voltage power boxes according to the monitoring requirement features and environmental interference features; determining the centralized management compatibility difficulty of each category based on the monitoring requirement features and environmental interference features of the low-voltage power boxes within each category; constructing a multi-level resource management architecture for the park based on the centralized management compatibility difficulty of multiple categories, and performing hierarchical monitoring of the multiple low-voltage power boxes according to the multi-level resource management architecture.

[0006] Based on the above technical solution, the low-voltage power box monitoring method provided by this invention acquires load monitoring data and environmental data of low-voltage power boxes within a park, extracts monitoring demand characteristics and environmental interference characteristics from them, and classifies the power boxes accordingly, enabling refined differentiation of monitoring management based on the inherent differences of different categories. Furthermore, the compatibility difficulty of centralized management is determined based on the monitoring demand characteristics and environmental interference characteristics of power boxes within each category, achieving a quantitative assessment of the degree of obstruction to unified management for each category. Subsequently, a multi-level resource management architecture is built based on the compatibility difficulty of centralized management, and hierarchical monitoring is implemented, ensuring that the allocation of monitoring resources matches the actual management difficulty of each category. This reduces overall monitoring costs and complexity while improving anomaly feedback speed and monitoring efficiency.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the method for classifying multiple low-voltage power boxes based on monitoring demand characteristics and environmental interference characteristics specifically includes: analyzing the monitoring demand characteristics of any two low-voltage power boxes to obtain the monitoring demand similarity between any two low-voltage power boxes; analyzing the environmental interference characteristics of any two low-voltage power boxes to obtain the environmental similarity between any two low-voltage power boxes; weightedly fusing the monitoring demand similarity and environmental similarity to obtain a fused similarity used to characterize the comprehensive management similarity between any two low-voltage power boxes; and using the fused similarity as a distance metric to perform clustering operations on the multiple low-voltage power boxes to divide them into multiple categories.

[0008] In conjunction with the first aspect above, in one possible implementation, the aforementioned monitoring requirement characteristics include at least: load type, current fluctuation coefficient, and normal operating power range; the method for analyzing the monitoring requirement characteristics of any two low-voltage power boxes among multiple low-voltage power boxes to obtain the monitoring requirement similarity between any two low-voltage power boxes specifically includes: determining the load type matching degree, current fluctuation coefficient similarity, and normal operating power range overlap rate based on the load type, current fluctuation coefficient, and normal operating power range of any two low-voltage power boxes; and weightedly fusing the load type matching degree, current fluctuation coefficient similarity, and normal operating power range overlap rate to obtain the monitoring requirement similarity.

[0009] In conjunction with the first aspect above, in one possible implementation, the aforementioned environmental interference characteristics include at least: temperature, humidity, electromagnetic interference intensity, and obstruction level; the method for analyzing the environmental interference characteristics of any two low-voltage power boxes among multiple low-voltage power boxes to obtain the environmental similarity between any two low-voltage power boxes specifically includes: determining the temperature difference, humidity difference, electromagnetic interference intensity difference, and obstruction level difference based on the temperature, humidity, electromagnetic interference intensity, and obstruction level of any two low-voltage power boxes; weightedly fusing the temperature difference, humidity difference, electromagnetic interference intensity difference, and obstruction level difference to obtain the environmental difference distance; and negatively mapping the environmental difference distance to obtain the environmental similarity.

[0010] In conjunction with the first aspect above, in one possible implementation, the method for determining the centralized management compatibility difficulty of each category of low-voltage power boxes based on their monitoring requirement characteristics and environmental interference characteristics specifically includes: determining the monitoring interference degree based on the monitoring requirement characteristics of each low-voltage power box; determining the environmental interference degree based on the environmental interference characteristics of each low-voltage power box; and analyzing the coupling state of superposition or conflict between monitoring requirements and environmental interference based on the monitoring interference degree and environmental interference degree of low-voltage power boxes in each category, thereby determining the centralized management compatibility difficulty.

[0011] In conjunction with the first aspect above, in one possible implementation, the aforementioned monitoring requirement characteristics include at least: required sampling frequency, actual available sampling frequency, and current fluctuation coefficient; the method for determining the monitoring interference level based on the monitoring requirement characteristics of each low-voltage power box specifically includes: statistically analyzing the degree of shortage of the actual available sampling frequency of each low-voltage power box relative to the required sampling frequency; determining the monitoring interference level based on the degree of shortage of the actual available sampling frequency and the current fluctuation coefficient; the aforementioned environmental interference characteristics include at least electromagnetic interference intensity, temperature, and humidity; the method for determining the environmental interference level based on the environmental interference characteristics of each low-voltage power box specifically includes: statistically analyzing the degree to which the electromagnetic interference intensity of each low-voltage power box exceeds a preset electromagnetic interference safety threshold, and the degree to which the temperature and humidity exceed preset allowable fluctuation ranges; determining the environmental interference level based on the degree to which the electromagnetic interference intensity, temperature, and humidity exceed the limits.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the method of building a multi-level resource management architecture for the park based on the centralized management compatibility difficulty of multiple categories specifically includes: determining the management priority corresponding to each category based on the centralized management compatibility difficulty of each category and the number of low-voltage power boxes; dividing multiple low-voltage power boxes in the park into multiple logical levels at the data routing and computing power scheduling level based on the management priority; the multiple logical levels include a first-level central node, a second-level sub-node, and a third-level edge node.

[0013] In conjunction with the first aspect mentioned above, in one possible implementation, the method for performing hierarchical monitoring of multiple low-voltage power boxes based on a multi-level resource management architecture specifically includes: performing local sampling and on-site decision-making for the low-voltage power boxes corresponding to the third-level edge nodes, and uploading alarm signals when an anomaly is detected; performing timed sampling and cloud backup for the low-voltage power boxes corresponding to the second-level sub-nodes, and pushing alarm information within a preset time after an anomaly is triggered; and performing real-time monitoring and anomaly tracing analysis for the low-voltage power boxes corresponding to the first-level central node.

[0014] In conjunction with the first aspect mentioned above, in one possible implementation, the above-mentioned anomaly tracing analysis method specifically includes: obtaining the physical wiring topology map of multiple low-voltage power boxes within the park; if the target low-voltage power box triggers an anomaly alarm, then the physical wiring topology map is invoked to determine the associated power boxes that have upstream and downstream connections with the target low-voltage power box; if the anomaly characteristics of the associated power box are consistent with those of the target low-voltage power box, and the anomaly characteristics of the upstream associated power box appear earlier than those of the target low-voltage power box, then it is determined that the anomaly is propagated from upstream to downstream, and the first upstream low-voltage power box with the same anomaly characteristics is located as the true anomaly source; if the anomaly characteristics of the associated power box are consistent with those of the target low-voltage power box, and the anomaly characteristics of the target low-voltage power box appear earlier than those of the upstream associated power box, then it is determined that the anomaly is caused by the target low-voltage power box itself or a sudden change in downstream load, the target low-voltage power box is located as the starting point of the anomaly analysis, and the tracing continues downstream; if the anomaly characteristics of the associated power box are inconsistent with those of the target low-voltage power box, or if the associated power box has no anomaly, then the target low-voltage power box itself is determined to be abnormal and located.

[0015] Secondly, a monitoring system for low-voltage power boxes is provided, comprising: a data acquisition module for acquiring load monitoring data and environmental data of multiple low-voltage power boxes within a park; a feature classification module for extracting monitoring requirement features based on load monitoring data, extracting environmental interference features based on environmental data, and classifying multiple low-voltage power boxes according to the monitoring requirement features and environmental interference features; a compatibility analysis module for determining the centralized management compatibility difficulty of each category of low-voltage power boxes based on the monitoring requirement features and environmental interference features of each category; and a multi-level architecture management module for building a multi-level resource management architecture for the park based on the centralized management compatibility difficulty of multiple categories, and performing hierarchical monitoring of multiple low-voltage power boxes according to the multi-level resource management architecture.

[0016] The present invention has the following beneficial effects: By acquiring load monitoring data and environmental data from low-voltage power boxes within the park, monitoring demand characteristics and environmental interference characteristics are extracted, and the power boxes are categorized accordingly. This allows for refined differentiation of monitoring management based on the inherent differences between different categories. Furthermore, the compatibility difficulty of centralized management is determined based on the monitoring demand characteristics and environmental interference characteristics of power boxes within each category, enabling a quantitative assessment of the degree of obstruction to unified management for each category. Subsequently, a multi-level resource management architecture is built based on the compatibility difficulty of centralized management, and hierarchical monitoring is implemented. This ensures that the allocation of monitoring resources matches the actual management difficulty of each category, reducing overall monitoring costs and complexity while improving anomaly feedback speed and monitoring efficiency. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A system structure diagram of a monitoring system for a low-voltage power box provided in one embodiment of the present invention; Figure 2 A flowchart illustrating a method for monitoring a low-voltage power box according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of a monitoring device for a low-voltage power box according to an embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a monitoring method and system for a low-voltage power box according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details a specific scheme for a monitoring method and system for a low-voltage power box provided by the present invention.

[0022] Please see Figure 1The diagram illustrates a system structure of a monitoring system for a low-voltage power box according to an embodiment of the present invention. The monitoring system for the low-voltage power box includes: a data acquisition module 1, a feature classification module 2, a compatibility analysis module 3, and a multi-level architecture management module 4.

[0023] Optionally, the monitoring system for the low-voltage power box also includes: an anomaly location module 5.

[0024] Among them, the data acquisition module 1 is the data foundation unit of the entire system. It is mainly responsible for collecting and preprocessing load monitoring data and environmental data of all low-voltage power boxes in the park. This module can collect data through physical devices such as smart meters, current transformers, temperature and humidity sensors, electromagnetic interference detectors, shading status sensors and communication modules. It can simultaneously collect load monitoring data such as real-time electrical parameters, load type, load fluctuation range, and monitoring thresholds of the power boxes, as well as environmental data such as temperature and humidity, electromagnetic interference intensity, shading level, wind speed and rainfall. At the same time, it will perform noise reduction and standardization preprocessing on the collected raw data to eliminate data noise and dimensional differences, and finally output well-organized load monitoring data and environmental data, providing a unique raw analysis data source for the feature classification module 2.

[0025] Feature classification module 2 receives the standardized data output from data acquisition module 1 and is the core classification processing unit of the system. It can perform feature extraction and classification operations through physical devices such as edge computing units and embedded data processing chips. This module extracts monitoring requirement features from load monitoring data and environmental interference features from environmental data. It further calculates the monitoring requirement similarity and environmental similarity of any two power boxes, and then weights and fuses the two similarities to obtain a fused similarity. The fused similarity is used as the metric to complete the clustering and classification of power boxes, dividing them into multiple homogeneous power box categories. The classification results are directly transmitted to compatibility analysis module 3 to provide a classification basis for the subsequent calculation of compatibility difficulty in centralized management.

[0026] The compatibility analysis module 3 operates based on the power box category classification results output by the feature classification module 2. It can perform compatibility quantification calculation through physical devices such as the central processing unit and dedicated data analysis server. For each category of power boxes, this module first determines the monitoring interference degree of a single power box based on the monitoring requirement characteristics, and then determines the environmental interference degree of a single power box based on the environmental interference characteristics. It then analyzes the superposition or conflict coupling state of the two types of interference, and finally accurately calculates the centralized management compatibility difficulty of each category. The calculated compatibility difficulty data will be transmitted to the multi-level architecture management module 4 as the core quantitative basis for building a multi-level resource management architecture.

[0027] The multi-level architecture management module 4 uses the centralized management compatibility difficulty output by the compatibility analysis module 3 as the core parameter. It can build a hierarchical management and control architecture through physical devices such as the park main server, regional sub-control terminals, and edge acquisition controllers. This module determines the corresponding management priority based on the number of power boxes in each category. According to the priority, the power boxes are divided into a three-level logical architecture: a first-level central node, a second-level sub-node, and a third-level edge node. At the same time, it performs differentiated hierarchical monitoring based on the architecture and configures corresponding sampling, data transmission, and decision rules for power boxes at different levels to achieve reasonable allocation of monitoring resources. The real-time monitoring data and abnormal signals generated by this module will be pushed to the abnormal location module 5 simultaneously.

[0028] The anomaly location module 5 is the system's precise fault handling unit. It can achieve anomaly tracing and location through physical devices such as topology analysis unit and anomaly alarm terminal. This module obtains the physical wiring topology map of the low-voltage power boxes in the park. After receiving the anomaly alarm signal transmitted by the multi-level architecture management module 4, it retrieves the upstream and downstream related power box data of the target low-voltage power box, compares the anomaly characteristics, distinguishes between interference conduction and its own anomaly, and finally locates the real anomaly source, completing the monitoring closed loop of the entire system.

[0029] Please see Figure 2 The diagram illustrates a flowchart of a monitoring method for a low-voltage power box according to an embodiment of the present invention. The monitoring method for the low-voltage power box includes: S1. Obtain load monitoring data and environmental data from multiple low-voltage power boxes within the park.

[0030] In some implementations, load monitoring data is collected and preprocessed. Smart meters and current transformers are deployed in each low-voltage power box as load monitoring data acquisition devices. To fully capture the transient waveforms of power frequency AC and the rapidly changing characteristics such as the peak starting current of motor-type loads, the device's preset raw sampling frequency is set to 1kHz. This sampling frequency is much higher than the Nyquist frequency (100Hz) of the 50Hz power frequency signal, enabling distortion-free recording of the real-time current waveform. The collected load monitoring data includes real-time current, active power, reactive power, load type labels (e.g., load type is identified by numbers, lighting load is labeled as 1, motor load as 2, heating load as 3), load fluctuation range (e.g., daily maximum current, daily minimum current, daily maximum power, daily minimum power), current actual available sampling frequency (this frequency is affected by the park network load and electromagnetic interference, and will change dynamically with the network status), required sampling frequency (based on the preset sampling frequency of the load type, the setting rule is to match the rate of change of the load current, for example, the required sampling frequency of motor load can be set to 15 times / second, and the required sampling frequency of lighting load can be set to 5 times / second), and monitoring threshold requirements (e.g., the nighttime low power abnormal monitoring threshold for lighting load, and the peak starting current monitoring threshold for motor load).

[0031] Environmental data is collected and preprocessed. Temperature and humidity sensors, electromagnetic interference detectors, and obstruction status sensors are deployed at the installation location of each low-voltage power box as environmental data acquisition devices. The preset sampling frequency of the devices is set to 1 time / 5 minutes. The rule for setting this sampling frequency is to match the change cycle of the park's environmental parameters, ensuring that the slow change characteristics of the environment can be captured while avoiding excessive data volume. For example, the hourly average change rate of temperature and humidity is relatively low, and this sampling frequency can effectively reflect its long-term change trend. The collected environmental data includes hourly average temperature, hourly average humidity, real-time electromagnetic interference intensity, and installation obstruction level (e.g., no obstruction is marked as 0, partial obstruction as marked as 1, and full obstruction as marked as 2). For low-voltage power boxes installed in the park's edge areas and outdoors, wind speed sensors and rainfall intensity sensors are additionally deployed, with a preset sampling frequency of 1 time / minute. The rule for setting this sampling frequency is to match the change cycle of outdoor meteorological parameters to capture the impact of short-term strong winds and rainfall on the operating environment of the power boxes.

[0032] The collected raw data undergoes denoising processing. For time-series data such as current and power in the load monitoring data, a preset wavelet denoising algorithm is used. Specifically, this algorithm uses a db4 wavelet basis for three-level decomposition to remove high-frequency electromagnetic interference noise. The rules for setting the wavelet basis and the number of decomposition levels are as follows: the db4 wavelet basis has good time-frequency localization characteristics, making it suitable for processing non-stationary signals in power systems; and the three-level decomposition effectively separates high-frequency noise from low-frequency effective signals. For example, after denoising the collected current data of a certain type of motor load, high-frequency spike noise caused by electromagnetic interference can be eliminated, preserving the true current fluctuation characteristics. Environmental data is processed using a preset sliding window filtering algorithm. The preset window size is 5, and the rule for setting the window size is to achieve a balance between smoothing instantaneous errors and preserving the trend of environmental data changes. For example, when a single abnormal jump occurs in the collected temperature data of a certain power box, the sliding window filter can correct the abnormal value by averaging the adjacent data points, without affecting the overall temperature change trend.

[0033] The denoised data is standardized. All data is normalized and mapped to the numerical range [0, 1] to eliminate dimensional differences between different dimensions. Normalization can be performed using a maximum-minimum (max-min) linear normalization method. The maximum and minimum values ​​of each parameter are set according to the following rule: the maximum and minimum values ​​of the parameter collected from all low-voltage power boxes in the park within a preset statistical period (e.g., the past 7 days) are used as reference extreme values ​​for normalization. This rule ensures that the normalized data reflects the relative level of each power box in the overall distribution of the park. For example, the maximum value of a certain monitoring parameter, current, is 100A and the minimum value is 0A. Furthermore, to prevent system crashes caused by a zero denominator in the normalization calculation, a preset minimum positive number is added to the denominator. The minimum positive number is set to be much smaller than the typical fluctuation range of the parameter in the park; for example, it can be set to 10. -6 This value will not significantly affect the normalization result, and can effectively avoid the case where the denominator is zero.

[0034] Specifically, during the standardization process, if a measured parameter value exceeds the preset maximum or minimum value for that parameter within the park, the measured value will be truncated. When the measured value is greater than the preset maximum value, it will be replaced with the preset maximum value; when the measured value is less than the preset minimum value, it will be replaced with the preset minimum value. Performing normalization calculations after truncation avoids interference from outlier data in subsequent analysis, while ensuring that the normalized data remains within the [0, 1] interval.

[0035] S2. Extract monitoring requirement features from load monitoring data, extract environmental interference features from environmental data, and classify multiple low-voltage power boxes according to monitoring requirement features and environmental interference features.

[0036] In some implementations, the monitoring requirement characteristics of any two low-voltage power boxes among multiple low-voltage power boxes are analyzed to obtain the monitoring requirement similarity between the two boxes. Specifically, the monitoring requirement characteristics include at least: load type, current fluctuation coefficient, and normal operating power range. The load type is represented by a preset identifier, for example, lighting loads are identified as 1, motor loads as 2, and heating loads as 3. The current fluctuation coefficient is calculated based on the daily current fluctuation range, by dividing the difference between the maximum and minimum current of the day by the average current of the day. Based on the load type, current fluctuation coefficient, and normal operating power range of any two low-voltage power boxes, the load type matching degree, current fluctuation coefficient similarity, and normal operating power range overlap rate are determined. The rule for setting the load type matching degree is: the matching degree is 1 when the load type identifiers of the two power boxes are the same, and 0 when they are different. The absolute value of the difference between the current fluctuation coefficients of two low-voltage power boxes is calculated and mapped to a similarity value in the interval (0, 1) using a preset negative correlation mapping (e.g., the exponential decay function exp(-absolute difference)). This yields the current fluctuation coefficient similarity. The normal operating power range overlap rate is the ratio of the intersection to the union of the power monitoring thresholds of the two power boxes, representing the overlap ratio of the power operating ranges. Both the current fluctuation coefficient similarity and the normal operating power range overlap rate are closer to 1, indicating greater similarity. Then, the load type matching degree, current fluctuation coefficient similarity, and normal operating power range overlap rate are weighted and fused to obtain the monitoring requirement similarity. The weight setting rule is adjusted according to the load priority of the park, with the sum of the weights being 1. For example, in a general scenario, the weight of load type matching degree is set to 0.5, the weight of current fluctuation coefficient similarity degree is 0.3, and the weight of normal operating power range overlap rate is 0.2. In industrial parks, the weight of load type matching degree can be increased to 0.6, while other weights are reduced to highlight the core impact of industrial load types.

[0037] The environmental interference characteristics of any two low-voltage power boxes in a set of multiple low-voltage power boxes are analyzed to obtain the environmental similarity between them. Specifically, the environmental interference characteristics include at least: temperature, humidity, electromagnetic interference intensity, and obstruction level. All characteristics are standardized values, ranging from [0, 1]. Based on the temperature, humidity, electromagnetic interference intensity, and obstruction level of any two low-voltage power boxes, the differences in temperature, humidity, electromagnetic interference intensity, and obstruction level are determined. The difference value is the absolute value of the difference between the standardized values ​​of the corresponding features. The temperature, humidity, electromagnetic interference intensity, and obstruction level differences are weighted and fused to obtain the environmental difference distance. The weighting rule is adjusted according to the degree of influence of environmental characteristics on monitoring, with the sum of weights being 1. For example, in a general scenario, the weight of temperature difference is set to 0.4, humidity difference to 0.2, electromagnetic interference intensity difference to 0.3, and obstruction level difference to 0.1. In electromagnetically dense areas, the weight of electromagnetic interference intensity difference can be increased to 0.4, while other weights are reduced to highlight the impact of electromagnetic interference. Environmental similarity is obtained by negatively mapping the environmental difference distance (e.g., 1 - environmental difference distance).

[0038] The similarity of monitoring needs and environmental similarity are weighted and fused to obtain a fused similarity score that characterizes the overall management similarity between any two low-voltage power boxes. The weighting rule is adjusted according to the type of park, with the sum of the weights being 1. For example, in a general scenario, the weight of monitoring need similarity is set to 0.6 and the weight of environmental similarity is set to 0.4; in an office park, both the weights of monitoring need similarity and environmental similarity can be set to 0.5; and in an industrial park, the weight of monitoring need similarity can be set to 0.7, highlighting the core influencing factors in the corresponding scenario.

[0039] Clustering distance is obtained by negatively mapping the fusion similarity. For example, the clustering distance is calculated by subtracting the difference in fusion similarity from 1, resulting in a smaller clustering distance for higher similarity. Using this clustering distance as a distance metric, clustering is performed on multiple low-voltage power boxes to divide them into multiple categories. Specifically, the density-based spatial clustering of applications with noise (DBSCAN) algorithm can be used to cluster multiple low-voltage power boxes. The DBSCAN algorithm requires setting two parameters: neighborhood radius and minimum number of samples. The neighborhood radius can be set according to the distribution of clustering distances (e.g., taking the inflection point of the k-th nearest neighbor distance after ascending sorting of all sample distances). The minimum number of samples is usually set to 2 or 3 to ensure the reasonableness of the minimum cluster size. After the algorithm executes, it automatically divides the samples into multiple density-connected clusters and marks sparse samples that cannot be assigned to any cluster as noise points, which are managed independently. Finally, multiple power box clusters are obtained, with the monitoring needs and environmental interference of the power boxes within each cluster being highly similar, and significant differences between clusters.

[0040] S3. Based on the monitoring requirements and environmental interference characteristics of low-voltage power boxes in each category, determine the difficulty of centralized management compatibility for each category.

[0041] In some implementations, the monitoring interference level is determined based on the monitoring requirements of each low-voltage power box, which reflects the degree of conflict between the sampling frequency requirements caused by load fluctuations in the low-voltage power box and the actual communication capability.

[0042] In specific implementation, the monitoring requirement characteristics include at least: required sampling frequency, actual available sampling frequency, and current fluctuation coefficient. The required sampling frequency and actual available sampling frequency are standardized values, ranging from [0, 1]. The degree of shortage of the actual available sampling frequency relative to the required sampling frequency is calculated for each low-voltage power box. If the actual available sampling frequency is less than the required sampling frequency, the shortage is represented by the difference between the required sampling frequency and the actual available sampling frequency, characterizing the degree of conflict between actual communication capabilities and monitoring requirements. If the actual available sampling frequency is greater than or equal to the required sampling frequency, the shortage is zero. Based on the shortage degree and the current fluctuation coefficient, the monitoring interference degree is determined. The current fluctuation coefficient characterizes the severity of load fluctuations on sampling accuracy. The current fluctuation coefficient is normalized by dividing it by (1 + current fluctuation coefficient), resulting in a normalized current fluctuation coefficient. Then, the shortage degree is multiplied by the normalized current fluctuation coefficient to quantify the combined effect of load fluctuations and sampling frequency conflict, reflecting the coupling relationship of the amplified dual factors, thus obtaining the monitoring interference degree.

[0043] The environmental interference degree is determined based on the environmental interference characteristics of each low-voltage power box, which is used to reflect the degree of interference of environmental factors on the monitoring data of the low-voltage power box.

[0044] In the specific implementation, environmental interference characteristics include electromagnetic interference intensity, temperature, and humidity. All characteristics are standardized values, ranging from [0, 1]. The extent to which the electromagnetic interference intensity of each low-voltage power box exceeds a preset electromagnetic interference safety threshold is statistically analyzed. If the electromagnetic interference intensity is less than or equal to the preset electromagnetic interference safety threshold, the exceedance is zero. If the electromagnetic interference intensity is greater than the preset electromagnetic interference safety threshold, the exceedance is represented by the difference between the electromagnetic interference intensity and the preset electromagnetic interference safety threshold, reflecting the degree to which the electromagnetic interference exceeds the safe level. Simultaneously, the extent to which temperature and humidity exceed preset allowable fluctuation ranges are statistically analyzed, reflecting the degree to which temperature and humidity deviate from normal conditions. If the measured value is less than the lower limit of the preset allowable fluctuation range, the exceedance is represented by the difference between the lower limit and the measured value. If the measured value is greater than the upper limit of the preset allowable fluctuation range, the exceedance is represented by the difference between the measured value and the upper limit of the preset allowable fluctuation range. If the measured value is within the preset allowable fluctuation range, the exceedance is zero.

[0045] Among them, the preset electromagnetic interference safety threshold, the preset upper and lower limits of the temperature allowable fluctuation range, and the preset upper and lower limits of the humidity allowable fluctuation range are all consistent with the same normalization standard as the environmental interference characteristics. The values ​​are limited to the standardized range of [0,1]. The closer the value is to 1, the higher the intensity of the corresponding environmental parameter. The preset calibration is based on the long-term environmental monitoring statistics of the park. For example, in general office parks, the preset electromagnetic interference safety threshold is 0.35, the preset allowable temperature fluctuation range is [0.2, 0.65], and the preset allowable humidity fluctuation range is [0.25, 0.7]; in industrial production parks, the preset electromagnetic interference safety threshold is 0.5, the preset allowable temperature fluctuation range is [0.25, 0.75], and the preset allowable humidity fluctuation range is [0.20, 0.65]; in outdoor open-air parks, the preset electromagnetic interference safety threshold is 0.4, the preset allowable temperature fluctuation range is [0.15, 0.85], and the preset allowable humidity fluctuation range is [0.15, 0.8]; and in coastal high-humidity parks, the preset electromagnetic interference safety threshold is 0.38, the preset allowable temperature fluctuation range is [0.2, 0.7], and the preset allowable humidity fluctuation range is [0.3, 0.85].

[0046] The environmental interference level is determined based on the degree of exceedance of electromagnetic interference intensity, temperature, and humidity. Specifically, since electromagnetic interference, temperature interference, and humidity interference are three independent parallel interference sources, each affecting monitoring data individually from three dimensions: wireless communication, equipment insulation, and electronic component stability, exceeding any one of these will generate environmental interference, and exceeding multiple factors will result in superimposed interference. Therefore, a weighted summation method is used for calculation. The weights of the three interference items are set based on the actual impact of environmental factors on the wireless monitoring and operation of low-voltage power boxes, and are calibrated using a variable control method for laboratory environments. The sum of the weights is 1. For example, in a calibration experiment, the weights for electromagnetic interference intensity, temperature, and humidity were 0.5, 0.3, and 0.2 in a general office park; 0.6, 0.25, and 0.15 in an industrial production park, emphasizing the weight of electromagnetic interference's impact on monitoring; 0.45, 0.35, and 0.2 in an outdoor open-air park, highlighting the impact of high-temperature environments on equipment and monitoring; and 0.4, 0.2, and 0.4 in a coastal high-humidity park, increasing the weight of humidity interference.

[0047] Based on the monitoring interference and environmental interference of low-voltage power boxes in each category, analyze the coupling state of superposition or conflict between monitoring needs and environmental interference, and determine the difficulty of centralized management compatibility.

[0048] In practice, the comprehensive interference degree of each low-voltage power box within the category is first calculated to quantify the combined impact of monitoring requirements and environmental interference on the equipment. The comprehensive interference degree can be obtained by weighted summation of monitoring interference degree and environmental interference degree, with the weight used to adjust the relative importance of the two types of interference. In general scenarios, this can be set to 0.5 to balance the impact of the two types of interference. Then, the average and standard deviation of the comprehensive interference degree of all low-voltage power boxes within the category are calculated. The average value represents the average comprehensive interference level, and the standard deviation represents the interference diversity. Finally, the average comprehensive interference level and interference diversity are weighted and fused to obtain the centralized management compatibility difficulty of this category. The specific calculation formula can be: In the formula, The average value of the comprehensive interference of the k-th type of low-voltage power box reflects the combined level of the overall severity of monitoring requirements within the category and the severity of the external environment. The larger the value, the stronger the unified response capability required for centralized management and the greater the difficulty. The standard deviation of the comprehensive interference of the k-th type of low-voltage power box reflects the degree of dispersion of each device in the category in terms of the comprehensive characteristics of monitoring needs and environmental interference. The larger the value, the more different types of interference modes exist at the same time (for example, some devices are biased towards monitoring needs conflict, and some devices are biased towards severe environmental interference). The more compatible strategies are needed when centrally managing, the greater the management difficulty. The preset superposition interference weights have a value range of [0, 1], which is applicable to general scenarios. A setting of 0.7 aligns with the reality that superimposed interference is the primary influencing factor. This setting can be adjusted based on the type of industrial park; for example, it can be increased in electromagnetically dense parks. The value highlights the impact of superimposed interference.

[0049] Part One This represents the overall interference level, reflecting the impact of the overall interference level within the category on compatibility difficulty. Part Two To address the diverse nature of interference, the standard deviation of the overall interference level reflects the degree of dispersion of each device within a category in terms of monitoring needs and the comprehensive characteristics of environmental interference. The larger the standard deviation, the more likely that there are both high-interference and low-interference devices within that category. This means that the differences in strategies that need to be compatible during centralized management are greater, and the management difficulty also increases accordingly.

[0050] Specifically, if the category only includes a single low-voltage electrical box, When the value is 0, the difficulty of centralized management and compatibility is determined by the overall interference level of the device itself.

[0051] S4. Based on the difficulty of centralized management and compatibility of multiple categories, build a multi-level resource management architecture for the park, and perform hierarchical monitoring of multiple low-voltage power boxes according to the multi-level resource management architecture.

[0052] In some implementations, the management priority for each category is determined based on its centralized management compatibility difficulty and the number of low-voltage power boxes. This priority characterizes the allocation level of monitoring resources for different categories of low-voltage power boxes. Higher centralized management compatibility difficulty indicates a more complex coupling between the internal monitoring needs of this type of equipment and environmental interference, requiring more computing, communication, and storage resources for refined management. A larger number of low-voltage power boxes within a category indicates greater overall importance of this type of equipment in the campus power supply network. Therefore, higher centralized management compatibility difficulty and a larger number of low-voltage power boxes within a category are more suitable for and require unified centralized monitoring, thus necessitating higher management priority and more monitoring resources. The formula for calculating management priority can be: In the formula, The management priority of the k-th type of low-voltage power box represents the level of monitoring resource allocation for this category; The difficulty of centralized management compatibility for the k-th type of low-voltage power distribution box; Let K be the number of low-voltage electrical boxes of type k. The difficulty of centralized management compatibility for the i-th type of low-voltage power distribution box; Let be the number of low-voltage electrical boxes of type i; and These are weights for management difficulty and equipment quantity, respectively, with a sum of 1. The default value for both is 0.5, which can be adjusted according to the actual needs of the park. For example, for parks with high security requirements, the weight can be set to... The value is set at 0.7 to ensure that high-difficulty categories receive sufficient resources; for parks prioritizing scale, the value can be appropriately increased. G is the preset priority upper limit. The setting rule is to amplify the normalized result to an integer range that is easy to set the level threshold. In general scenarios, this value is fixed at 10, but it can be adjusted according to the size of the park. For example, a large park can be adjusted to 100 to facilitate the setting of more detailed levels.

[0053] The maximum value among all K categories is selected by the maximum value function max to normalize the centralized management compatibility difficulty and the number of low-voltage power boxes, so as to eliminate the difference in dimensions. Then, the two dimensions are weighted and merged, and finally multiplied by the priority upper limit to map the result to the interval [0, priority upper limit]. The larger the value, the higher the management priority.

[0054] Based on management priorities, multiple low-voltage power boxes within the industrial park are divided into several logical levels at the data routing and computing power scheduling level. These logical levels include a primary central node, secondary sub-nodes, and a tertiary edge node. In specific implementation, different categories of low-voltage power boxes are assigned to corresponding logical levels according to preset priority thresholds. The preset thresholds are determined based on the scale of equipment, monitoring resources, and communication capabilities within the industrial park. In general scenarios, three levels are defined: categories with a management priority greater than or equal to 8 are assigned to primary central nodes; categories with a management priority greater than or equal to 5 but less than 8 are assigned to secondary sub-nodes; and categories with a management priority less than 5 are assigned to tertiary edge nodes. For example, in an industrial park, the management priority of power boxes for core production equipment (high compatibility difficulty and a large number of devices) is 9, and they are assigned to primary central nodes; the management priority of power boxes for auxiliary motors (medium compatibility difficulty and a moderate number of devices) is 6, and they are assigned to secondary sub-nodes; and the management priority of outdoor independent power boxes (low compatibility difficulty and a small number of devices) is 3, and they are assigned to tertiary edge nodes. At the physical level, the low-voltage power boxes are still connected to the park network nearby. Differentiated scheduling is only performed at the data routing and computing power scheduling level according to the divided logical hierarchy, without changing the physical network topology.

[0055] In some implementations, differentiated monitoring strategies are configured for low-voltage power boxes at different logical levels to achieve hierarchical monitoring.

[0056] In practice, local sampling and on-site decision-making are performed on the low-voltage power boxes corresponding to the third-level edge nodes. The local sampling frequency of 1kHz can be used for sampling. In order to save communication and cloud computing resources, local real-time analysis and anomaly judgment are performed without relying on data upload. Alarm signals are only uploaded when an anomaly is judged, without the need to upload the full monitoring data.

[0057] Timed sampling and cloud backup are performed on the low-voltage power boxes corresponding to the secondary sub-nodes. The local sampling frequency of 1kHz can be used for sampling, and the monitoring data can be uploaded to the cloud for backup at a medium frequency of, for example, once every 5 minutes. After an anomaly is triggered, an alarm information is pushed within a preset time. The preset time is set to 10 seconds. The rule for setting this time threshold is to ensure that the abnormal information is pushed to the operation and maintenance personnel in a timely manner, while avoiding frequent pushes caused by false alarms.

[0058] Real-time monitoring and anomaly tracing analysis are performed on the low-voltage power boxes corresponding to the primary central nodes. The local sampling frequency of 1kHz can be used for sampling, and the monitoring data can be uploaded in real time at a high frequency of, for example, once per second. High-performance servers and redundant communication links are configured to perform global aggregation of monitoring data, anomaly warning and line correlation verification, ensuring that any transient anomaly of the core equipment can be captured and accurately located.

[0059] Furthermore, the anomaly tracing analysis method includes: if the target low-voltage power box triggers an abnormal alarm, the physical wiring topology diagram is invoked to identify associated power boxes with upstream and downstream connections to the target low-voltage power box. If the abnormal characteristics of the associated power box and the target low-voltage power box are consistent, they are determined to be in the same interfered circuit. The occurrence times of the abnormal characteristics of the target low-voltage power box and the associated power box are further compared. If the occurrence time of the abnormal characteristics of the upstream associated power box is earlier than that of the target low-voltage power box, the anomaly is determined to be propagated from upstream to downstream, and the first upstream low-voltage power box with the same abnormal characteristics is identified as the true anomaly source. If the occurrence time of the abnormal characteristics of the target low-voltage power box is earlier than that of the upstream associated power box, the anomaly is determined to be caused by the target low-voltage power box itself or a sudden change in downstream load, and the target low-voltage power box is identified as the starting point of the anomaly analysis, continuing to trace downstream. If the abnormal characteristics of the associated power box and the target low-voltage power box are inconsistent, or if the associated power box has no abnormality, the target low-voltage power box itself is determined to be abnormal and its location is determined.

[0060] The determination of consistent abnormal characteristics can be achieved by comparing the similarity of electrical characteristic waveforms between the associated power box and the target low-voltage power box within a preset time window before and after the abnormal triggering time. Specifically, firstly, current or voltage sampling data of the target low-voltage power box and the associated power box within the same time period are collected. Effective values ​​are calculated according to a preset power frequency period (e.g., 20ms) and normalized (e.g., max-min linear normalization, using the maximum and minimum values ​​within the time period, where a preset minimum positive number can be added to the denominator to prevent division by zero), forming a normalized effective value sequence. The effective values ​​include at least the root mean square value, and may also include one or more of the peak value, average rectified value, and total harmonic distortion rate. Then, the dynamic time warping (DTW) algorithm is used to calculate the distance between the normalized effective value sequences of the target low-voltage power box and the associated power box within the same time period. This distance is divided by the length of the optimal path (i.e., the number of matching point pairs in the sequence) to obtain the waveform distance. When there are multiple effective values, the average waveform distance corresponding to all normalized effective value sequences is calculated as the final waveform distance. If the waveform distance is less than a preset distance threshold, and the Pearson correlation coefficient of the normalized effective value sequence (when there are multiple effective values, the average of the Pearson correlation coefficients corresponding to all normalized effective value sequences is calculated as the final Pearson correlation coefficient) is greater than a preset correlation threshold, then the two are determined to have the same abnormal characteristics. The preset time window can be set to the 5 sampling points before and 10 sampling points after the anomaly trigger time to fully capture the transient process of anomaly propagation. The preset distance threshold and preset correlation threshold can be calibrated based on similar anomaly events in historical data; the preset distance threshold can be set to 0.15, and the preset correlation threshold can be set to 0.9.

[0061] It's important to note that in the tiered monitoring strategy, the allocation of monitoring resources is not limited to data sampling frequency. It comprehensively considers the system's computing power, the complexity of analysis algorithms, and the authority for cross-node correlation analysis. While the primary central node performs real-time monitoring, it is actually allocated the system's most critical resources for correlation analysis and anomaly tracing: when complex anomalies occur, the primary central node can access the entire park's physical wiring topology map, comprehensively analyze data and alarm signals from secondary and tertiary edge nodes, and perform upstream and downstream interference propagation determination to accurately locate the true source of the anomaly. This represents the most powerful and highest-authority resource investment. Tertiary edge nodes perform local sampling and on-site decision-making, handling simple anomalies that only involve their own devices and have clear rules. Therefore, categories with higher management priority are granted higher anomaly analysis permissions and more complex global diagnostic computing power.

[0062] Based on the above technical solution, by acquiring load monitoring data and environmental data of low-voltage power boxes within the park, monitoring demand characteristics and environmental interference characteristics are extracted, and the power boxes are classified accordingly. This allows for refined differentiation of monitoring management based on the inherent differences between different categories. Furthermore, the compatibility difficulty of centralized management is determined based on the monitoring demand characteristics and environmental interference characteristics of power boxes within each category, enabling a quantitative assessment of the degree of obstruction to unified management for each category. Subsequently, a multi-level resource management architecture is built based on the compatibility difficulty of centralized management, and hierarchical monitoring is implemented. This ensures that the allocation of monitoring resources matches the actual management difficulty of each category, reducing overall monitoring costs and complexity while improving anomaly feedback speed and monitoring efficiency.

[0063] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0065] In this embodiment of the invention, the monitoring device for a low-voltage power box can be divided into functional units according to the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0066] This invention also provides a hardware structure diagram of a monitoring device for a low-voltage power box, see [link / reference]. Figure 3 The monitoring device 300 of the low-voltage power box includes a processor 301, and optionally, a memory 302 connected to the processor 301.

[0067] In the first possible implementation, see Figure 3The monitoring device 300 for the low-voltage power box also includes a transceiver 303. The processor 301, memory 302, and transceiver 303 are connected via a bus. The transceiver 303 is used to communicate with other devices or communication networks. Optionally, the transceiver 303 may include a transmitter and a receiver. The device in the transceiver 303 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of the present invention. The device in the transceiver 303 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of the present invention.

[0068] Based on the first possible implementation method Figure 3 The structural diagram shown can be used to illustrate the structure of the monitoring device for the low-voltage power box involved in the above embodiments.

[0069] in, Figure 3 This can also be illustrated by the system chip in the monitoring device of the low-voltage power box. In this case, the actions performed by the monitoring device of the low-voltage power box can be implemented by the system chip. The specific actions performed can be found above and will not be repeated here.

[0070] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In this invention, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several of the functions listed in this invention.

[0071] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its scope. Thus, if such modifications and modifications of the invention fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A monitoring method for a low-voltage power distribution box, characterized in that, include: Acquire load monitoring data and environmental data from multiple low-voltage power boxes within the park; Based on the load monitoring data, monitoring requirement features are extracted; based on the environmental data, environmental interference features are extracted; and multiple low-voltage power boxes are classified according to the monitoring requirement features and environmental interference features. Based on the monitoring needs and environmental interference characteristics of low-voltage power boxes in each category, the difficulty of centralized management compatibility for each category is determined. Based on the difficulty of centralized management and compatibility of multiple categories, a multi-level resource management architecture is built for the park, and hierarchical monitoring is performed on multiple low-voltage power boxes according to the multi-level resource management architecture.

2. The monitoring method for a low-voltage power box according to claim 1, characterized in that, Based on monitoring requirements and environmental interference characteristics, multiple low-voltage power boxes are classified, including: Analyze the monitoring requirement characteristics of any two low-voltage power boxes among multiple low-voltage power boxes to obtain the monitoring requirement similarity between any two low-voltage power boxes; Analyze the environmental interference characteristics of any two low-voltage power boxes among multiple low-voltage power boxes to obtain the environmental similarity between any two low-voltage power boxes; By weighted and fused the monitoring requirement similarity and environmental similarity, a fused similarity is obtained to characterize the comprehensive management similarity between any two low-voltage power boxes; Using the fusion similarity as a distance metric, clustering operations are performed on multiple low-voltage power boxes to obtain multiple categories.

3. The monitoring method for a low-voltage power box according to claim 2, characterized in that, The monitoring requirement characteristics include at least: load type, current fluctuation coefficient, and normal operating power range; analyze the monitoring requirement characteristics of any two low-voltage power boxes among multiple low-voltage power boxes to obtain the monitoring requirement similarity between any two low-voltage power boxes, including: Based on the load type, current fluctuation coefficient, and normal operating power range of any two low-voltage power boxes, determine the load type matching degree, current fluctuation coefficient similarity, and normal operating power range overlap rate. The similarity of the monitoring requirements is obtained by weighting and fusing the load type matching degree, the current fluctuation coefficient similarity, and the overlap rate of the normal operating power range.

4. The monitoring method for a low-voltage power box according to claim 2, characterized in that, The environmental interference characteristics include at least: temperature, humidity, electromagnetic interference intensity, and obstruction level; the environmental interference characteristics of any two low-voltage power boxes are analyzed to obtain the environmental similarity between any two low-voltage power boxes, including: Based on the temperature, humidity, electromagnetic interference intensity, and shielding level of any two low-voltage power boxes, determine the differences in temperature, humidity, electromagnetic interference intensity, and shielding level. The environmental difference distance is obtained by weighted fusion of temperature difference, humidity difference, electromagnetic interference intensity difference and shading level difference, and then the environmental difference distance is negatively correlated to obtain the environmental similarity.

5. The monitoring method for a low-voltage power box according to claim 1, characterized in that, Based on the monitoring needs and environmental interference characteristics of low-voltage power boxes within each category, the difficulty of centralized management compatibility for each category is determined, including: The monitoring interference level is determined based on the monitoring requirements of each low-voltage power box; The degree of environmental interference is determined based on the environmental interference characteristics of each low-voltage power box; Based on the monitoring interference and environmental interference of low-voltage power boxes in each category, the coupling state of superposition or conflict between monitoring requirements and environmental interference is analyzed to determine the compatibility difficulty of centralized management.

6. The monitoring method for a low-voltage power box according to claim 5, characterized in that, The monitoring requirement characteristics include at least: required sampling frequency, actual available sampling frequency, and current fluctuation coefficient; the monitoring interference level is determined based on the monitoring requirement characteristics of each low-voltage power box, including: The extent to which the actual available sampling frequency for each low-voltage power box is insufficient relative to the required sampling frequency is determined. The monitoring interference level is determined based on the degree of shortage of the actual available sampling frequency and the current fluctuation coefficient; The environmental interference characteristics include at least electromagnetic interference intensity, temperature, and humidity; the environmental interference level is determined based on the environmental interference characteristics of each low-voltage power box, including: The extent to which the electromagnetic interference intensity of each low-voltage power box exceeds the preset electromagnetic interference safety threshold, and the extent to which the temperature and humidity exceed the preset allowable fluctuation range, are statistically analyzed. The degree of environmental interference is determined based on the extent to which electromagnetic interference intensity, temperature, and humidity exceed the limits.

7. The monitoring method for a low-voltage power box according to claim 1, characterized in that, Based on the difficulty of centralized management and compatibility across multiple categories, a multi-level resource management architecture is established for the park, including: Based on the difficulty of centralized management compatibility for each category and the number of low-voltage power boxes, determine the management priority for each category; Based on the management priority, multiple low-voltage power boxes in the park are divided into multiple logical levels at the data routing and computing power scheduling level; the multiple logical levels include a first-level central node, a second-level sub-node, and a third-level edge node.

8. The monitoring method for a low-voltage power box according to claim 7, characterized in that, Based on the aforementioned multi-level resource management architecture, hierarchical monitoring is performed on multiple low-voltage power boxes, including: Local sampling and on-site decision-making are performed on the low-voltage power boxes corresponding to the three-level edge nodes, and alarm signals are uploaded when an anomaly is detected. The low-voltage power boxes corresponding to the secondary sub-nodes are sampled and backed up to the cloud at regular intervals, and alarm information is pushed within a preset time after an anomaly is triggered. Real-time monitoring and anomaly tracing analysis are performed on the low-voltage power boxes corresponding to the primary central node.

9. The monitoring method for a low-voltage power box according to claim 8, characterized in that, The anomaly tracing analysis includes: Obtain the physical wiring topology diagram of multiple low-voltage power boxes within the park; If the target low-voltage power box triggers an abnormal alarm, the physical wiring topology map is invoked to determine the associated power boxes that have upstream and downstream connections with the target low-voltage power box. If the abnormal characteristics of the associated power box are consistent with those of the target low-voltage power box, and the abnormal characteristics of the upstream associated power box appear earlier than those of the target low-voltage power box, then it is determined that the abnormality is transmitted from upstream to downstream, and the first low-voltage power box with the same abnormal characteristics upstream is located as the real source of the abnormality. If the abnormal characteristics of the associated power box are consistent with those of the target low-voltage power box, and the abnormal characteristics of the target low-voltage power box appear earlier than those of the upstream associated power box, then the abnormality is determined to be caused by the target low-voltage power box itself or by a sudden change in downstream load. The target low-voltage power box is located as the starting point for the abnormality analysis, and the source is traced downstream. If the abnormal characteristics of the associated power box and the target low-voltage power box are inconsistent, or if the associated power box is not abnormal, then the target low-voltage power box itself is determined to be abnormal and its location is determined.

10. A monitoring system for a low-voltage power distribution box, characterized in that, include: The data acquisition module is used to acquire load monitoring data and environmental data from multiple low-voltage power boxes within the park. The feature classification module is used to extract monitoring requirement features based on the load monitoring data, extract environmental interference features based on the environmental data, and classify multiple low-voltage power boxes based on the monitoring requirement features and environmental interference features. The compatibility analysis module is used to determine the compatibility difficulty of centralized management for each category based on the monitoring requirements and environmental interference characteristics of low-voltage power boxes within each category. The multi-level architecture management module is used to build a multi-level resource management architecture for the park based on the difficulty of centralized management of multiple categories, and to perform hierarchical monitoring of multiple low-voltage power boxes according to the multi-level resource management architecture.

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