Building electrical safety data acquisition and analysis method

Abstract

The invention relates to the field of electrical measurement, in particular to a building electrical safety data acquisition and analysis method. The method comprises the steps of abstracting a building electrical system into a dynamic graph, collecting voltage data of nodes in the dynamic graph in real time, and performing normalization processing to obtain normalized voltage data; marking nodes in the dynamic graph based on the normalized voltage data to obtain a node set; based on the node set and the normalized voltage data, calculating an abnormal topology clustering index of the nodes through a node abnormal topology clustering analysis algorithm; and performing fault detection and positioning based on the abnormal topology clustering index of the node. The problems that traditional building electrical safety management is poor in adaptability to a complex topological structure, and fault propagation effect analysis is insufficient are solved. The real-time monitoring capability on dynamic change is limited; and the identification difficulty of the multi-node cascading failure is large.

Description

Technical Field

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

[0002] Building electrical safety is one of the core issues in the operation and management of modern buildings. With the acceleration of the urbanization process and the popularization of intelligent buildings, the complexity and scale of electrical systems are increasing continuously, and the threats posed by electrical safety accidents (such as electrical fires, equipment failures, electric leakage, etc.) to personal safety, property safety, and social stability are becoming increasingly prominent. Therefore, how to monitor the operation status of building electrical systems in real time, collect and analyze data, and timely discover potential safety hazards has become a key problem to be solved urgently in the field of building electrical safety.

[0003] Traditional building electrical safety management mainly relies on regular inspections, manual maintenance, and post-event processing, and has the following limitations: limited real-time monitoring ability for dynamic changes, difficult to detect instantaneous faults or potential hazards; wide distribution of electrical equipment in complex buildings, difficult to comprehensively cover all key nodes; poor adaptability to complex topological structures, insufficient analysis of fault propagation effects; difficult to identify multi-node cascading faults, difficult to quickly locate the fault point, resulting in low emergency response efficiency, etc. Modern building electrical safety management is gradually developing towards intelligence and digitization. Through the comprehensive application of technologies such as sensors, the Internet of Things, and data analysis, it can achieve comprehensive monitoring of the operation status of electrical systems, real-time early warning of abnormalities, and prediction and prevention of faults, and will play a greater role in the future, providing 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 solve the technical problems of poor adaptability of traditional building electrical safety management to complex topological structures, insufficient analysis of fault propagation effects, limited real-time monitoring ability for dynamic changes, and difficult to identify multi-node cascading faults.

[0005] A method for collecting and analyzing building electrical safety data according to the present invention specifically includes the following technical solutions: A method for collecting and analyzing building electrical safety data includes the following steps: S1. Abstract the building electrical system as a dynamic graph, collect the voltage data of the nodes in the dynamic graph in real time, and perform normalization processing to obtain the normalized voltage data; based on the normalized voltage data, mark the nodes in the dynamic graph to obtain a node set; S2. Based on the node set and the normalized voltage data, through the node abnormal topology clustering analysis algorithm, introduce the exponential function, and combine the voltage abnormal deviation, the topology weighting factor, and the clustering factor to calculate the abnormal topology clustering index of the node. The specific formula is: , where, represents the abnormal topology clustering index of node at time ; represents the set of neighbor nodes of node ; represents the length of the time window; represents the normalized voltage data of node at time ; is the voltage abnormal deviation between and the historical normal reference value of node ; represents the topology weighting factor; represents the degree of connection of node ; represents the degree of connection of neighbor node of node ; represents the clustering factor of node and neighbor node at time ; represents the indicator function; represents the node set at time ; Traverse the node set, compare the abnormal topology clustering indices of each node, and obtain the node index with the largest abnormal topology clustering index as the fault source node index for fault detection and location.

[0006] Preferably, the S1 specifically includes: Based on the normalized voltage data, combine the historical normal reference value to calculate the absolute value of the voltage abnormal deviation; compare the absolute value of the voltage abnormal deviation with the preset node abnormal threshold: when the absolute value of the voltage abnormal deviation is greater than the node abnormal threshold, it indicates that the node voltage deviates from the normal state, and mark the node as abnormal; when the absolute value of the voltage abnormal deviation is less than or equal to the node abnormal threshold, mark the node as normal; traverse all nodes in the dynamic graph, and add all the marked nodes to the node set.

[0007] Preferably, the S2 specifically includes: In the implementation process of the node anomaly topology clustering analysis algorithm, based on the node set, an indicator function is introduced. When a node is marked as abnormal, the indicator function outputs 1, and when a node is marked as normal, the indicator function outputs 0.

[0008] Preferably, the S2 specifically includes: In the implementation process of the node anomaly topology clustering analysis algorithm, a time window is introduced, and based on the normalized voltage data, the voltage anomaly deviation of the node at each sampling moment within the time window is calculated.

[0009] Preferably, the S2 specifically includes: In the implementation process of the node anomaly topology clustering analysis algorithm, the common neighbor nodes of the node and its neighbor nodes are identified, and combined with the connection degrees of the node and its neighbor nodes, the clustering factor between the node and its neighbor nodes is calculated. The specific formula is: , where represents the number of common neighbor nodes of node and neighbor node . The node satisfies and and . represents the edge in the dynamic graph, represents the neighbor node set of node , represents the intersection of the neighbor node sets of node and its neighbor node ; represents the minimum value of the connection degrees of node and its neighbor node .

[0010] The beneficial effects of the technical solution of the present invention are: 1. By calculating the relative importance between the node and its neighbor nodes, a topology weighting factor is generated, highlighting the key role of high-connection-degree nodes such as distribution boxes in the fault propagation, avoiding the excessive influence of low-connection-degree nodes (such as end loads), improving the accuracy of fault location, and showing higher reliability especially in the building electrical system with complex topology structures.

[0011] 2. By calculating the common neighbor ratio between the node and its neighbor nodes, a clustering factor is generated to quantify the interconnection strength of the local network, enhancing the sensitivity to faults in high-interconnection regions, being able to capture complex fault modes such as multi-node chain anomalies, making up for the limitations of single voltage deviation analysis, and improving the fault detection ability in multi-node and multi-connection scenarios.

[0012] 3. Based on the topological structure of the dynamic graph, synchronously calculate the abnormal topological clustering index of all abnormal nodes, and strengthen the significant abnormal signals by calculating the square of the voltage abnormal deviation, introducing the topological weighting factor and the clustering factor, highlighting the influence of key nodes such as distribution boxes, and capturing the fault aggregation effect in the highly interconnected area, so as to finally achieve efficient and accurate fault detection and location. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flowchart of a method for collecting and analyzing building electrical safety data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0016] The following specifically describes the specific solution of a method for collecting and analyzing building electrical safety data provided by the present invention with reference to the accompanying drawings.

[0017] Refer to the attached Figure 1 , which shows a flowchart of a method for collecting and analyzing building electrical safety data provided by an embodiment of the present invention. The method includes the following steps: S1. Abstract the building electrical system into a dynamic graph, collect the voltage data of the nodes in the dynamic graph in real time, and perform normalization processing to obtain the normalized voltage data; based on the normalized voltage data, mark the nodes in the dynamic graph to obtain a node set; Abstract the building electrical system into a dynamic graph , where represents the nodes in the dynamic graph, that is, the detection points, such as distribution boxes, load points, etc., represents the edges in the dynamic graph, that is, the electrical connection edges between nodes, such as cables or electrical connections, and the dynamic graph is dynamically updated over time; Collect the voltage data of the nodes in the dynamic graph in real time through sensors as the core parameters for electrical safety analysis. To unify the dimensions, normalize the voltage data to generate the normalized voltage data , represents the nodes in the dynamic graph, Represents time; the normalization process uses the existing min-max normalization method, which is a well-known technical means for those skilled in the art and will not be elaborated here; For each node, calculate the normalized voltage data at the current moment and the historical normal reference value the absolute value of the voltage abnormal deviation between them; the absolute value of the voltage abnormal deviation reflects the degree to which the node voltage deviates from the normal state; the historical normal reference value represents the typical normalized voltage data of the node during normal operation, which is obtained by calculating the average value of the normalized voltage data in the historical non-abnormal period; the normalized voltage data in the historical non-abnormal period comes from the existing database; the historical non-abnormal period is set according to the expert experience method, and the recommended value is 24 hours (1 day); further, set the node abnormal threshold according to the expert experience method , used to judge whether the voltage deviation is significant, and the recommended value is 0.2; Compare the absolute value of the voltage abnormal deviation of each node with the node abnormal threshold: if the absolute value of the voltage abnormal deviation is greater than the node abnormal threshold, it means that the node voltage deviates significantly from the normal state, then mark the node as abnormal; if the absolute value of the voltage abnormal deviation is less than or equal to the node abnormal threshold, then regard the node as normal; traverse all nodes and add all the marked nodes to the node set , expressed as follows: If , node is marked as abnormal; If , node is marked as normal; By marking the voltage abnormal nodes, it provides candidate fault sources for abnormal analysis; S2. Based on the node set and the normalized voltage data, through the node abnormal topology clustering analysis algorithm, calculate the abnormal topology clustering index of the node; based on the abnormal topology clustering index of the node, perform fault detection and location; Based on the node set and the normalized voltage data, through the node abnormal topology clustering analysis algorithm, through the comprehensive calculation of time window accumulation, topology weighting and clustering effect, generate the abnormal topology clustering index of the node; the specific implementation process of the node abnormal topology clustering analysis algorithm is as follows: Based on the node set, introduce an indicator function. When the node is marked as abnormal, the indicator function outputs 1, otherwise the indicator function outputs 0; Further, calculate the voltage abnormal deviation of each node at each sampling moment within the time window, which is the core of the abnormal topology clustering index of the node. To highlight serious faults, introducing time window analysis can ensure sensitivity to both short-term and continuous abnormalities and enhance robustness; Further, calculate the topological weighting factor between each node and its neighbor nodes: respectively obtain the connection degrees of the current node and its neighbor nodes, and divide the connection degree of the current node by the sum of the connection degrees of the current node and its neighbor nodes, with a range from 0 to 1, so as to be able to reflect the relative importance of the nodes in the dynamic graph; the topological weighting factor can highlight the abnormal contributions of key nodes (such as distribution boxes), and avoid the excessive influence of low-connection-degree nodes (such as end loads), in order to improve the fault location accuracy; Further, identify the common neighbor nodes of the current node and its neighbor nodes, and count the number of common neighbor nodes. Divide the number of common neighbor nodes of the current node and its neighbor nodes by the minimum value of the connection degrees of the current node and its neighbor nodes to calculate the clustering factor between the node and its neighbor nodes, with a range from 0 to 1, which is used to quantify the local interconnection strength of the dynamic graph; the clustering factor can enhance the sensitivity to faults in highly interconnected areas to make up for the limitations of single deviation analysis, in order to capture complex fault patterns (such as multi-node cascading anomalies); Based on graph theory (node connection degree, clustering factor), signal processing (square of voltage anomaly deviation, time window), and the fault propagation characteristics of the electrical system, by integrating voltage anomalies and topological effects, to quantify the node anomaly degree. The specific calculation formula for defining the abnormal topological clustering index of the node is: , wherein, represents the abnormal topological clustering index of node at time , which is used to reflect the severity of the node fault; represents the summation of all neighbor nodes of node , which is used to reflect the fault propagation effect; represents the set of neighbor nodes of node ; represents the summation over all sampling times within the time window , and by accumulating the voltage anomaly deviations within the time window, to capture the dynamic trend of the fault (such as continuous low voltage); represents the length of the time window, which is set according to the expert experience method, and the value range can be set to ; represents the square of the voltage anomaly deviation between the normalized voltage data of node at time and the historical normal reference value of node , which is used to quantify the intensity of the voltage anomaly. By using the square form to amplify significant deviations, to highlight the fault nodes; represents node The historical normal reference value is obtained by calculating the average of the normalized voltage data during the historical non - abnormal period. The data during the historical non - abnormal period comes from an existing database, and the non - abnormal period is set according to the expert experience method, with a recommended value of 24 hours (1 day). represents the topological weighting factor, with a range of used to quantify the node relative to its neighbor nodes in the relative importance in the dynamic graph topology. Nodes with high connectivity (i.e., key nodes, such as distribution boxes) can make the topological weighting factor close to 1 to amplify the abnormal topological clustering index of the node and reflect the fault impact of the key node; represents the connectivity of node , that is, the number of edges directly connected to node in the dynamic graph, which can reflect the connectivity of node in the dynamic graph. Nodes with high connectivity represent key nodes (such as distribution boxes); represents the connectivity of node , that is, the number of edges directly connected to node in the dynamic graph; represents the clustering factor of node and its neighbor node at time , with a range of used to quantify the proportion of node and its neighbor node sharing common neighbors, that is, the clustering effect of the local network, reflecting the propagation potential of faults in highly interconnected areas. The calculation formula is: , represents the number of common neighbor nodes of node and its neighbor node . The node satisfies and , represents the set of neighbor nodes of node , represents the intersection of the set of neighbor nodes of node and its neighbor node ; represents the minimum value of the connectivity of node and its neighbor node , used as the normalization denominator; represents the indicator function. If node is marked as abnormal, then . If node is marked as normal, then ; The introduction of topological weighting factors and clustering factors can adapt to the complex topology of building electrical systems (such as distribution boxes with high connectivity or sparse end loads), effectively capture the fault propagation effect, and improve the reliability of electrical safety data analysis in multi-node and multi-connection scenarios; Traverse the node set, compare the abnormal topology clustering indices 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: , where, represents the fault source node index at time ; represents the index of the node selected from the node set with the largest abnormal topology clustering index ; The above node abnormal topology clustering analysis algorithm, based on the topological structure of the dynamic graph, synchronously calculates the abnormal topology clustering indices of all abnormal nodes, and strengthens the significant abnormal signals by calculating the square of the voltage abnormal deviation, introducing topological weighting factors and clustering factors, highlighting the influence of key nodes such as distribution boxes, and capturing the fault aggregation effect in high-interconnection areas, ultimately achieving efficient and accurate fault detection and location, with significant practical value and promotion potential.

[0018] In summary, a method for collecting and analyzing building electrical safety data is completed.

[0019] The sequence of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0020] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

[0021] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A method for collecting and analyzing building electrical safety data, characterized in that: The following steps are involved: S1. Abstract the building electrical system into a dynamic graph, collect the voltage data of the nodes in the dynamic graph in real time, and perform normalization processing to obtain the 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, and the abnormal voltage deviation, topology weighting factor, and clustering factor are combined to calculate the abnormal topology clustering index of the node. 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 The 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 A collection of nodes; 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: The S1 specifically includes: Based on the normalized voltage data and combined with the historical normal baseline value, the absolute value of the voltage abnormal deviation is calculated; the absolute value of the voltage abnormal deviation is compared with the preset node abnormal threshold: when the absolute value of the voltage abnormal deviation is greater than the node abnormal threshold, it means that the node voltage deviates from the normal state and the node is marked as abnormal; when the absolute value of the voltage abnormal deviation is less than or equal to the node abnormal threshold, the node is marked as normal; traverse all nodes in the dynamic graph and add all marked nodes to the node set.

3. A building electrical safety data collection and analysis method according to claim 1, characterized in that: The S2 specifically includes: In the implementation process of the node anomaly topology clustering analysis algorithm, an indicator function is introduced based on the node set. 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.

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

5. A building electrical safety data collection and analysis method according to claim 4, characterized in that: The S2 specifically includes: In the implementation process of the node abnormal topology clustering analysis algorithm, the common neighbor nodes of the node and the neighbor nodes are identified, and the clustering factor of the node and the neighbor nodes is calculated by combining the connectivity of the node and the neighbor nodes. The specific formula 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 neighbor nodes The intersection of the neighbor node sets; Representation Node and its neighbor nodes The minimum value of connectivity.

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