A method for collecting and analyzing building electrical safety data

Abstract

The present invention relates to the field of electrical measurement, and in particular to a method for collecting and analyzing building electrical safety data. The method includes: abstracting the building electrical system into a dynamic graph, collecting voltage data from nodes in the dynamic graph in real time, and performing normalization processing to obtain normalized voltage data; marking the nodes in the dynamic graph based on the normalized voltage data to obtain a node set; calculating the node abnormal topology clustering index based on the node set and the normalized voltage data using a node abnormal topology clustering analysis algorithm; and performing fault detection and location based on the node abnormal topology clustering index. This method addresses the technical issues of traditional building electrical safety management, such as poor adaptability to complex topologies, insufficient analysis of fault propagation effects, limited real-time monitoring capabilities for dynamic changes, and difficulty identifying multi-node cascading failures.

Description

Technical Field

[0001] The present invention relates to the field of electrical measurement, and in particular to a method for collecting and analyzing building electrical safety data. Background Art

[0002] Building electrical safety is a core issue in modern building operation and management. With the acceleration of urbanization and the prevalence of intelligent buildings, the complexity and scale of electrical systems are increasing. The threats posed by electrical safety incidents (such as electrical fires, equipment failures, and leakage) to personal safety, property security, and social stability are becoming increasingly prominent. Therefore, how to monitor the operating status of building electrical systems in real time, collect and analyze data, and promptly identify potential safety hazards has become a key issue that needs to be addressed in the field of building electrical safety.

[0003] Traditional building electrical safety management mainly relies on regular inspections, manual maintenance and post-processing, and has the following limitations: limited real-time monitoring capabilities for dynamic changes, making it difficult to detect instantaneous faults or potential hidden dangers; electrical equipment in complex buildings is widely distributed, making it difficult to fully cover all key nodes; poor adaptability to complex topological structures, and insufficient analysis of fault propagation effects; multi-node chain failures are difficult to identify, making it difficult to quickly locate the fault point, resulting in inefficient emergency response; modern building electrical safety management is gradually developing in the direction of intelligence and digitalization. Through the comprehensive application of sensors, the Internet of Things, data analysis and other technologies, it can achieve comprehensive monitoring of the operating status of the electrical system, real-time warning of anomalies, and prediction and prevention of faults. It will play a greater role in the future and provide more reliable technical guarantees for building electrical safety. Summary of the Invention

[0004] The present invention provides a method for collecting and analyzing building electrical safety data to address the technical problems of traditional building electrical safety management, such as poor adaptability to complex topological structures, insufficient analysis of fault propagation effects, limited real-time monitoring capabilities for dynamic changes, and difficulty in identifying multi-node cascading faults.

[0005] The present invention provides a method for collecting and analyzing building electrical safety data, which specifically includes the following technical solutions:

[0006] A method for collecting and analyzing building electrical safety data includes the following steps:

[0007] S1. Abstract the building electrical system into a dynamic graph, collect voltage data of nodes in the dynamic graph in real time, and perform normalization processing to obtain normalized voltage data; based on the normalized voltage data, mark the nodes in the dynamic graph to obtain a node set;

[0008] S2. Based on the node set and normalized voltage data, the node abnormal topology clustering analysis algorithm is used to introduce an exponential function. In combination with the voltage anomaly deviation, the topology weighting factor, and the clustering factor, the abnormal topology clustering index of the node is calculated. The specific formula is:

[0009] ,

[0010] in, Representation node At the moment The abnormal topological clustering index of Representation node The set of neighbor nodes of Indicates the length of the time window; Representation node At the moment Normalized voltage data With node Historical normal baseline value Abnormal voltage deviation; represents the topological weighting factor; Representation node connectivity; Representation node Neighbor nodes connectivity; Representation node and neighbor nodes At the moment The clustering factor of represents the indicator function; Indicates at time The node set of

[0011] Traverse the node set, compare the abnormal topology clustering index of each node, and obtain the node index with the largest abnormal topology clustering index, which is used as the fault source node index for fault detection and location.

[0012] Preferably, the S1 specifically includes:

[0013] Based on the normalized voltage data and combined with the historical normal baseline value, the absolute value of the voltage anomaly deviation is calculated; the absolute value of the voltage anomaly deviation is compared with the preset node anomaly threshold: when the absolute value of the voltage anomaly deviation is greater than the node anomaly threshold, it indicates that the node voltage deviates from the normal state and the node is marked as abnormal; when the absolute value of the voltage anomaly deviation is less than or equal to the node anomaly threshold, the node is marked as normal; all nodes in the dynamic graph are traversed and all marked nodes are added to the node set.

[0014] Preferably, the S2 specifically includes:

[0015] In the implementation process of the node anomaly topology clustering analysis algorithm, an indicator function is introduced based on the node set. When a node is marked as abnormal, the indicator function outputs 1, and when the node is marked as normal, the indicator function outputs 0.

[0016] Preferably, the S2 specifically includes:

[0017] In the implementation of the node abnormal topology clustering analysis algorithm, a time window is introduced. Based on the normalized voltage data, the voltage abnormal deviation of the node at each sampling moment within the time window is calculated.

[0018] Preferably, the S2 specifically includes:

[0019] In the implementation of the node anomaly topology clustering analysis algorithm, the common neighbor nodes of the node and its neighbor nodes are identified, and the clustering factor of the node and its neighbor nodes is calculated based on the connectivity between the node and its neighbor nodes. The specific formula is:

[0020] ,

[0021] in, Representation node and neighbor nodes Common neighbor nodes The number of nodes satisfy and , represents the edges in the dynamic graph, Representation node The set of neighbor nodes of Representation node and its neighboring nodes The intersection of the neighbor node sets; Representation node and its neighboring nodes The minimum value of connectivity.

[0022] The beneficial effects of the technical solution of the present invention are:

[0023] 1. By calculating the relative importance of a node and its neighboring nodes, a topology weighting factor is generated. This highlights the key role of highly connected nodes, such as distribution boxes, in fault propagation, avoids the excessive influence of low-connectivity nodes (such as terminal loads), and improves the accuracy of fault location, especially in building electrical systems with complex topologies, demonstrating higher reliability.

[0024] 2. By calculating the ratio of common neighbors between a node and its neighboring nodes and generating a clustering factor, the interconnection strength of the local network is quantified. This enhances sensitivity to faults in highly interconnected areas and can capture complex fault modes such as multi-node chain anomalies. This overcomes the limitations of single voltage deviation analysis and improves fault detection capabilities in multi-node, multi-connection scenarios.

[0025] 3. Based on the topological structure of the dynamic graph, the abnormal topological clustering index of all abnormal nodes is calculated synchronously. By calculating the square of the voltage anomaly deviation and introducing topological weighting factors and clustering factors, significant abnormal signals are strengthened, the impact of key nodes such as distribution boxes is highlighted, and the fault clustering effect in highly interconnected areas is captured, ultimately achieving efficient and accurate fault detection and location. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of the building electrical safety data collection and analysis method described in the present invention. DETAILED DESCRIPTION

[0027] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0028] Unless defined otherwise, 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 belongs.

[0029] The following describes in detail a specific solution of a building electrical safety data collection and analysis method provided by the present invention with reference to the accompanying drawings.

[0030] Refer to the attached Figure 1 , which shows a flow chart of a building electrical safety data collection and analysis method provided by an embodiment of the present invention, the method comprising the following steps:

[0031] S1. Abstract the building electrical system into a dynamic graph, collect voltage data of nodes in the dynamic graph in real time, and perform normalization processing to obtain normalized voltage data; based on the normalized voltage data, mark the nodes in the dynamic graph to obtain a node set;

[0032] Abstract the building electrical system into a dynamic diagram ,in Represents the nodes in the dynamic graph, i.e., detection points, such as distribution boxes, load points, etc. Representing edges in a dynamic graph, i.e., electrical association edges between nodes, such as cables or electrical connections, where the dynamic graph is dynamically updated over time;

[0033] The voltage data of the nodes in the dynamic diagram are collected in real time by sensors. As the core parameter of electrical safety analysis, the voltage data is normalized to unify the dimensions and generate normalized voltage data. , Represents a node in a dynamic graph, The normalization process adopts the existing minimum-maximum normalization process method, which is a technical means well known to those skilled in the art and will not be described in detail here;

[0034] For each node, calculate the normalized voltage data at the current moment Compared with historical normal benchmark values The absolute value of the voltage anomaly deviation between the two periods; the absolute value of the voltage anomaly deviation reflects the degree to which the node voltage deviates from the normal state; the historical normal baseline value represents the typical normalized voltage data of the node during normal operation, and is calculated by averaging the normalized voltage data of the historical non-abnormal period; the normalized voltage data of the historical non-abnormal period comes from an existing database; the historical non-abnormal period is set according to expert experience, and the recommended value is 24 hours (1 day); further, the node abnormality threshold is set according to expert experience , used to determine whether the voltage deviation is significant, the recommended value is 0.2;

[0035] Compare the absolute value of the voltage anomaly deviation of each node with the node anomaly threshold: if the absolute value of the voltage anomaly deviation is greater than the node anomaly threshold, it means that the node voltage deviates significantly from the normal state, and the node is marked as abnormal. If the absolute value of the voltage anomaly deviation is less than or equal to the node anomaly threshold, the node is considered normal; traverse all nodes and add all marked nodes to the node set , expressed as follows:

[0036] like ,node Mark as abnormal;

[0037] like ,node Mark as normal;

[0038] By marking abnormal voltage nodes, candidate fault sources are provided for abnormality analysis;

[0039] S2. Based on the node set and normalized voltage data, calculate the abnormal topology clustering index of the node through the node abnormal topology clustering analysis algorithm; perform fault detection and location based on the abnormal topology clustering index of the node;

[0040] Based on the node set and normalized voltage data, the node abnormal topology clustering analysis algorithm is used to generate the node abnormal topology clustering index through comprehensive calculation of time window accumulation, topology weighting and clustering effect. The specific implementation process of the node abnormal topology clustering analysis algorithm is as follows:

[0041] Based on the node set, an indicator function is introduced. When the node is marked as abnormal, the indicator function outputs 1, otherwise the indicator function outputs 0;

[0042] Furthermore, the voltage anomaly deviation of each node at each sampling moment within the time window is calculated as the core of the node anomaly topology clustering index to highlight serious faults. Introducing time window analysis can ensure sensitivity to both short-term and persistent anomalies and enhance robustness.

[0043] Furthermore, a topological weighting factor is calculated between each node and its neighboring nodes: the connectivity of the current node and its neighboring nodes is obtained separately, and the current node's connectivity is divided by the sum of the connectivity of the current node and its neighboring nodes. The value ranges from 0 to 1 to reflect the relative importance of the node in the dynamic graph. The topological weighting factor can highlight the abnormal contribution of key nodes (such as distribution boxes) and avoid the excessive influence of low-connectivity nodes (such as terminal loads), thereby improving fault location accuracy.

[0044] Furthermore, the common neighbor nodes of the current node and its neighbor nodes are identified and their number is counted. The number of common neighbor nodes between the current node and its neighbor nodes is divided by the minimum value of the connectivity between the current node and its neighbor nodes. The clustering factor between the node and its neighbor nodes is calculated, ranging from 0 to 1. This is used to quantify the local interconnectedness of the dynamic graph. The clustering factor can enhance the sensitivity to failures in highly interconnected areas, thereby compensating for the limitations of single deviation analysis and capturing complex failure modes (such as multi-node chain anomalies).

[0045] Based on graph theory (node connectivity, clustering factor), signal processing (squared voltage anomaly deviation, time window), and electrical system fault propagation characteristics, the degree of node anomaly is quantified by integrating voltage anomaly and topological effects. The specific calculation formula for the node abnormal topology clustering index is defined as follows:

[0046] ,

[0047] in, Representation node At the moment The abnormal topology clustering index is used to reflect the severity of node failure; Indicates the node All neighbor nodes of The sum is performed to reflect the propagation effect of the fault; Representation node The set of neighbor nodes of Represents the time window All sampling moments Summing is performed to capture the dynamic trend of the fault (such as continuous low voltage) by accumulating abnormal voltage deviations within the time window; Indicates the length of the time window, which is set according to expert experience. The value range can be set to ; Representation node At the moment Normalized voltage data With node Historical normal baseline value The square of the voltage anomaly deviation is used to quantify the intensity of the voltage anomaly. The significant deviation is amplified by the square form to highlight the fault node. Representation node The historical normal baseline value is calculated by averaging the normalized voltage data of the historical non-abnormal period. The data of the historical non-abnormal period comes from an existing database. The non-abnormal period is set according to expert experience, and 24 hours (1 day) is recommended. Represents the topological weighting factor, ranging from , used to quantize nodes Relative to neighboring nodes The relative importance of nodes in the dynamic graph topology, with high connectivity (i.e., key nodes such as distribution boxes), can make the topology weighting factor close to 1, thereby amplifying the abnormal topological clustering index of the nodes and reflecting the impact of failures of key nodes; Representation node The connectivity of nodes in the dynamic graph The number of directly connected edges can reflect the node Connectivity in dynamic graphs, nodes with high connectivity represent key nodes (e.g., distribution boxes); Representation node The connectivity of nodes in the dynamic graph The number of directly connected edges; Representation node and neighbor nodes At the moment The clustering factor ranges from , used to quantize nodes and neighbor nodes The proportion of shared common neighbors, i.e., the clustering effect of the local network, reflects the potential for fault propagation in highly interconnected areas and is calculated as: , Representation node and neighbor nodes Common neighbor nodes The number of nodes satisfy and , Representation node The set of neighbor nodes of Representation node and its neighboring nodes The intersection of the neighbor node sets; Representation node and its neighboring nodes The minimum value of the connectivity is used as the normalized denominator; Represents the indicator function, if the node Marked as abnormal, then , if the node Marked as normal, then ;

[0048] The introduction of topology weighting factors and clustering factors can adapt to the complex topology of building electrical systems (such as highly connected distribution boxes or sparse end loads), effectively capture fault propagation effects, and improve the reliability of electrical safety data analysis in multi-node and multi-connection scenarios;

[0049] Traverse the node set, compare the abnormal topology clustering index of each node, and obtain the index of the node with the largest abnormal topology clustering index as the fault source node index. The calculation formula is as follows:

[0050] ,

[0051] in, Indicates time The fault source node index; Represents a set of slave nodes The abnormal topological clustering index selected in Maximum node index;

[0052] The above-mentioned node abnormal topology clustering analysis algorithm, based on the topological structure of the dynamic graph, synchronously calculates the abnormal topology clustering index of all abnormal nodes, and strengthens significant abnormal signals by calculating the square of the voltage abnormality deviation and introducing topological weighting factors and clustering factors. It highlights the influence of key nodes such as distribution boxes and captures the fault aggregation effect in highly interconnected areas, ultimately achieving efficient and accurate fault detection and location. It has significant practical value and promotion potential.

[0053] In summary, a building electrical safety data collection and analysis method is completed.

[0054] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0055] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0056] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A building electrical safety data collection and analysis method, characterized in that: The following steps are involved: S1. Abstract the building electrical system into a dynamic graph, collect voltage data of nodes in the dynamic graph in real time, and perform normalization processing to obtain normalized voltage data; Based on the normalized voltage data, the nodes in the dynamic graph are marked to obtain a node set; S2. Based on the node set and normalized voltage data, the node abnormal topology clustering analysis algorithm is used to introduce an exponential function. In combination with the voltage anomaly deviation, the topology weighting factor, and the clustering factor, the abnormal topology clustering index of the node is calculated. The specific formula is: ， in, Representation node At the moment The abnormal topological clustering index of Representation node The set of neighbor nodes of Indicates the length of the time window; Representation node At the moment Normalized voltage data With node Historical normal baseline value Abnormal voltage deviation; represents the topological weighting factor; Representation node connectivity; Representation node Neighbor nodes connectivity; Representation node and neighbor nodes At the moment The clustering factor of Represents the indicator function. When the node is marked as abnormal, the indicator function outputs 1, and when the node is marked as normal, the indicator function outputs 0; Indicates at time The node set of The specific formula of the clustering factor is: ， in, Representation node and neighbor nodes Common neighbor nodes The number of nodes satisfy and , represents the edges in the dynamic graph, Representation node The set of neighbor nodes of Representation node and its neighboring nodes The intersection of the neighbor node sets; Representation node and its neighboring nodes The minimum value of connectivity; Traverse the node set, compare the abnormal topology clustering index of each node, and obtain the node index with the largest abnormal topology clustering index, which is used as the fault source node index for fault detection and location.

2. A building electrical safety data collection and analysis method according to claim 1, characterized in that: Said S1 specifically includes: Based on the normalized voltage data and combined with the historical normal baseline value, the absolute value of the voltage anomaly deviation is calculated; the absolute value of the voltage anomaly deviation is compared with the preset node anomaly threshold: when the absolute value of the voltage anomaly deviation is greater than the node anomaly threshold, it indicates that the node voltage deviates from the normal state and the node is marked as abnormal; when the absolute value of the voltage anomaly deviation is less than or equal to the node anomaly threshold, the node is marked as normal; all nodes in the dynamic graph are traversed and all marked nodes are added to the node set.

3. A building electrical safety data collection and analysis method according to claim 1, characterized in that: Said S2 specifically includes: In the implementation of the node abnormal topology clustering analysis algorithm, a time window is introduced. Based on the normalized voltage data, the voltage abnormal deviation of the node at each sampling moment within the time window is calculated.

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