Power consumption information acquisition terminal abnormity alarm method and device based on state monitoring

Abstract

The invention provides an electricity utilization information acquisition terminal abnormity alarm method and device based on state monitoring, and relates to the technical field of abnormity alarm, and the method comprises the steps: obtaining an integrated deployment structure of an electricity utilization information acquisition terminal; monitoring the state of each electricity consumption information acquisition terminal, and determining a transmission link of any state monitoring data set and each deployment element on the transmission link according to the receiving end of the state monitoring data set; performing anomaly detection on the transmitted state monitoring data set by a corresponding receiving end, and if the state is abnormal, marking as an abnormal candidate transmission link; performing link overlap analysis on the abnormal candidate transmission links, and performing abnormal element reverse traceability according to the abnormal transmission link cluster; and triggering the concentrator to generate an element alarm signal for reporting according to the abnormally deployed element. According to the invention, the technical problem that the accuracy of abnormal alarm is low in the prior art can be solved, and the technical effect of improving the accuracy of abnormal detection is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of abnormality alarms, and in particular to a method and device for abnormality alarms of power consumption information collection terminals based on status monitoring. Background Art

[0002] In the current power system, electricity consumption information collection terminals are widely deployed at user electricity metering sites. They undertake functions such as real-time collection of electricity data, communication reporting, and fault reporting. They are key equipment for realizing the linkage between the perception layer and communication layer of the smart grid.

[0003] At present, existing technologies still have significant defects in anomaly diagnosis. Most methods focus on identifying operational anomalies of the terminal equipment itself, ignoring the possible correlation between multiple terminals on the same communication link or shared deployment components, resulting in insufficient recognition of centralized anomalies. Therefore, existing technologies cannot effectively identify structural or systemic hidden dangers in shared deployment components such as communication gateways, power buses, cache modules, etc., affecting the accuracy of alarms and the timeliness of maintenance. In response to the above problems, it is urgent to propose an overlap analysis and anomaly clustering modeling method for transmission links to support the rapid location of potential deployment component failures when multi-terminal anomalies occur in a concentrated manner.

[0004] In summary, the existing technology has a technical problem: due to the lack of modeling and analysis capabilities for the structural relationships between multiple electricity consumption information collection terminals, it is unable to detect the shared deployment component failures involved in centralized anomalies, further affecting the accuracy of anomaly alarms, the timeliness of fault location, and the efficiency of system operation and maintenance. Summary of the Invention

[0005] The purpose of this application is to provide a method and device for abnormal alarm of electricity consumption information collection terminal based on state monitoring, so as to solve the technical problem in the prior art that due to the lack of modeling and analysis capabilities of the structural correlation between multiple electricity consumption information collection terminals, the shared deployment component failures involved in centralized abnormalities cannot be discovered, which further affects the accuracy of abnormal alarms, the timeliness of fault location and the efficiency of system operation and maintenance.

[0006] In view of the above problems, the present application provides a method and device for abnormal alarm of electricity consumption information collection terminal based on status monitoring.

[0007] In the first aspect, the present application provides an abnormal alarm method for an electricity information collection terminal based on state monitoring, which is implemented by an abnormal alarm device for an electricity information collection terminal based on state monitoring, including: obtaining an integrated deployment structure of an electricity information collection terminal, the integrated deployment structure including multiple electricity information collection terminals and connected integrated deployment components; performing state monitoring on each electricity information collection terminal, obtaining a state monitoring data set, and determining the transmission link of any state monitoring data set and each deployment component on the transmission link according to the receiving end of the state monitoring data set; performing abnormality detection on the transmitted state monitoring data set by the corresponding receiving end, and marking an abnormal candidate transmission link if the state is abnormal; performing link overlap analysis on the abnormal candidate transmission link, extracting an abnormal transmission link cluster, performing reverse tracing of abnormal components according to the abnormal transmission link cluster, and locating abnormal deployment components within the abnormal transmission link cluster; triggering a concentrator to generate an component alarm signal for reporting based on the abnormal deployment component.

[0008] Preferably, the abnormal alarm method of the power consumption information collection terminal based on state monitoring also includes: wherein the state monitoring data set includes power supply status data, communication quality data, data transmission behavior data and heartbeat status data; establishing a transmission logic association table, the transmission logic association table is the transmission link identifier between the receiving end and the state monitoring data set of each power consumption information collection terminal; link element parsing is performed according to the transmission link identifier of the transmission logic association table to determine the deployment elements on the transmission link.

[0009] Preferably, the abnormal alarm method of the electricity consumption information collection terminal based on state monitoring also includes: extracting the operating characteristics of the state monitoring data set, including data signal fluctuation, ACK response rate, data missingness and data delay; using a sliding window trend algorithm to obtain a historical state monitoring data set, performing feature deviation anomaly analysis on the operating characteristics of the historical state monitoring data set and the operating characteristics of the state monitoring data set, and outputting a feature deviation value; if the feature deviation value is greater than a preset deviation threshold, marking the transmission link corresponding to the state monitoring data set as an abnormal state and outputting it as an abnormal candidate transmission link.

[0010] Preferably, the abnormal alarm method of the electricity consumption information collection terminal based on state monitoring also includes: outputting abnormal candidate transmission links and submitting an information recording table of the abnormal candidate transmission links; wherein the record items of the information recording table include the terminal list to which each abnormal candidate transmission link belongs, the sequence of deployment elements involved in the transmission path, the abnormality type and the time window in which the abnormality occurs; calling the information recording table to perform link overlap analysis on the abnormal candidate transmission links.

[0011] Preferably, the abnormal alarm method for the power consumption information collection terminal based on state monitoring also includes: structuring the abnormal candidate transmission links, each node on the abnormal candidate transmission link represents a deployment element; collecting the set of abnormal candidate transmission links after structuring; constructing an overlap analysis matrix of the set of abnormal candidate transmission links, performing connected domain extraction based on the overlap analysis matrix, and extracting abnormal transmission link clusters, wherein each abnormal transmission link cluster contains abnormal candidate transmission links whose overlap degree is greater than a preset overlap threshold.

[0012] Preferably, the abnormal alarm method for the power consumption information collection terminal based on condition monitoring also includes: selecting any two links in the abnormal candidate transmission link set and calculating the number of shared deployment elements; calculating the overlap index of any two links based on the number of shared deployment elements, wherein the overlap index is the ratio of the number of shared deployment elements to the path length of the shorter of the two links; and obtaining the overlap analysis matrix corresponding to the abnormal candidate transmission link set based on the overlap index.

[0013] Preferably, the abnormal alarm method for the power consumption information collection terminal based on state monitoring also includes: outputting the full set of components according to the abnormal transmission link cluster; defining the abnormal overlap of each component, the abnormal overlap is the proportion of the component appearing in the abnormal transmission link cluster; defining the terminal abnormal weight, the terminal abnormal weight is the number of abnormal collection terminals in the abnormal transmission link cluster; performing reverse tracing of the abnormal components for the abnormal overlap of each component according to the terminal abnormal weight, and identifying components with a value greater than a preset abnormal index as abnormal deployment components.

[0014] Preferably, the abnormal alarm method of the power consumption information collection terminal based on status monitoring also includes: after extracting the abnormal transmission link cluster, clustering validity verification is performed on the abnormal transmission link cluster, and when the validity verification passes, the abnormal component is reversely traced according to the abnormal transmission link cluster; when the validity verification fails, the abnormal transmission link cluster is excluded, and the nodes of each link in the abnormal transmission link cluster are abnormally identified, triggering the concentrator to generate a component alarm signal.

[0015] Preferably, the abnormal alarm method for the power consumption information collection terminal based on state monitoring also includes: wherein, the cluster validity verification includes calculating the number of abnormal collection terminals and the overlapping frequency of deployment components of each abnormal transmission link cluster; when the number of abnormal collection terminals and the overlapping frequency of deployment components are both greater than or equal to the corresponding expected thresholds, the cluster validity verification is passed; when either the number of abnormal collection terminals or the overlapping frequency of deployment components is less than the corresponding expected threshold, the cluster validity verification fails.

[0016] In a second aspect, the present application further provides an abnormal alarm device for an electricity consumption information collection terminal based on state monitoring, which is used to execute the abnormal alarm method for an electricity consumption information collection terminal based on state monitoring as described in the first aspect, comprising: an integrated deployment structure acquisition module, used to acquire the integrated deployment structure of the electricity consumption information collection terminal, the integrated deployment structure comprising multiple electricity consumption information collection terminals and connected integrated deployment components; a state monitoring data set acquisition module, used to perform state monitoring on each electricity consumption information collection terminal, acquire a state monitoring data set, and determine the transmission link of any state monitoring data set and each deployment component on the transmission link according to the receiving end of the state monitoring data set; an abnormality detection module, used to perform abnormality detection on the transmitted state monitoring data set by the corresponding receiving end, and if the state is abnormal, mark it as an abnormal candidate transmission link; a link overlap analysis module, used to perform link overlap analysis on the abnormal candidate transmission link, extract an abnormal transmission link cluster, reversely trace the abnormal component according to the abnormal transmission link cluster, and locate the abnormal deployment component within the abnormal transmission link cluster; and a component alarm signal reporting module, used to trigger the concentrator to generate a component alarm signal for reporting based on the abnormal deployment component.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goals of abnormal cluster modeling based on link overlap relationships and reverse tracing and positioning of shared deployment components, the technical effects of improving the accuracy of abnormality detection, shortening fault response time, and enhancing the level of intelligent operation and maintenance are achieved.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0020] Figure 1 This is a flow chart of the abnormal alarm method of the power consumption information collection terminal based on status monitoring in this application.

[0021] Figure 2This is a structural diagram of the abnormal alarm device of the electricity consumption information collection terminal based on state monitoring in this application.

[0022] Description of reference numerals: integrated deployment structure acquisition module 11 , status monitoring data set acquisition module 12 , anomaly detection module 13 , link overlap analysis module 14 , component alarm signal reporting module 15 . DETAILED DESCRIPTION

[0023] This application provides a method and device for abnormal alarms for power consumption information collection terminals based on condition monitoring. This solves the technical problem in the prior art of being unable to detect shared deployment component failures related to centralized anomalies due to a lack of modeling and analysis capabilities for the structural relationships between multiple power consumption information collection terminals, further affecting the accuracy of abnormal alarms, the timeliness of fault location, and the efficiency of system operation and maintenance. This application achieves the technical goals of abnormal cluster modeling based on link overlap relationships and reverse tracing and locating shared deployment components, achieving the technical effects of improving anomaly detection accuracy, shortening fault response time, and enhancing the level of intelligent operation and maintenance.

[0024] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0025] For example, see the attached Figure 1 The present application provides a method for abnormal alarm of an electricity consumption information collection terminal based on state monitoring, which is applied to an abnormal alarm device of an electricity consumption information collection terminal based on state monitoring, and specifically includes the following steps: S1: Acquire an integrated deployment structure of power consumption information collection terminals, where the integrated deployment structure includes multiple power consumption information collection terminals and connected integrated deployment components.

[0026] Specifically, an electricity consumption information collection terminal refers to an intelligent device installed at the power user's side for collecting, storing, and transmitting electricity consumption information. As a front-end sensing node in a smart grid or distribution automation system, it is responsible for acquiring data from devices such as electricity meters in real time or periodically, such as the user's voltage, current, active power, reactive power, power factor, and other information. The integrated deployment structure for acquiring electricity consumption information collection terminals refers to the overall network topology and device deployment information related to the electricity consumption information collection terminals collected from the power system, thereby obtaining the connection relationship and transmission path between each electricity consumption information collection terminal and other devices in the entire power system.

[0027] The integrated deployment structure includes multiple electricity consumption information collection terminals and connected integrated deployment components. Integrated deployment components include communication links, access modules, and aggregation devices. A communication link refers to the physical line or wireless transmission path that data from an electricity consumption information collection terminal passes through during transmission. An access module is an intermediary component that connects the electricity consumption information collection terminal to the upper-level network, such as a carrier module or RS485 converter. Aggregation devices, such as concentrators or communication master stations, act as a transit and aggregation device before multiple electricity consumption information collection terminals upload data. By establishing an integrated deployment structure, the entire process of data collection, transmission, and reception for each electricity consumption information collection terminal, as well as the corresponding path nodes, can be obtained.

[0028] S2: Performing status monitoring on each power consumption information collection terminal to obtain a status monitoring data set, and determining a transmission link of any status monitoring data set and each deployed component on the transmission link according to a receiving end of the status monitoring data set.

[0029] Specifically, the status of each electricity consumption information collection terminal is monitored, that is, the operating status of the electricity consumption information collection terminal is obtained in real time or periodically, and whether there are any abnormalities between the electricity consumption information collection terminal and the external environment it depends on, such as unstable power supply, communication failure or data delay, is determined to determine whether the electricity consumption information collection terminal is in normal working condition, and then quantifiable operating data, namely the status monitoring data set, is obtained.

[0030] According to the receiving end of the status monitoring data set, the transmission link that each piece of data passes through from the power consumption information collection terminal to the receiving end is determined, that is, all communication paths between the power consumption information collection terminal and the receiving end, including communication components (such as concentrators, communication gateways, UART expansion modules, communication buses, etc.), power management components (shared voltage stabilization / transformation modules, battery packs, power buses, etc.), data cache and transfer modules (such as local cache chips, gateway forwarding caches, etc.) and program management and synchronization modules (such as master MCU, centralized controller, remote upgrade module), etc.

[0031] S3: The corresponding receiving end performs anomaly detection on the transmitted status monitoring data set. If the status is abnormal, it is marked as an abnormal candidate transmission link.

[0032] Specifically, after receiving the status monitoring data set uploaded from each electricity information collection terminal, the receiving end device corresponding to the electricity information collection terminal performs anomaly detection processing on the status monitoring data set. The status monitoring data set contains the operating performance of the electricity information collection terminal, such as whether the power supply voltage is stable, whether the data is uploaded on time, whether there is packet loss or delay in communication, etc., and then determines whether the currently received data deviates significantly from historical performance or a reasonable range. The detection process may use methods such as feature extraction, sliding mean, trend comparison, etc. to detect abnormal signals such as a sudden drop in ACK response rate, a long period of no response to the heartbeat packet, and severe communication fluctuations. After the receiving end identifies the status anomaly, the entire transmission link corresponding to the status monitoring data set is marked as an abnormal candidate.

[0033] S4: performing link overlap analysis on the abnormal candidate transmission links, extracting abnormal transmission link clusters, performing reverse tracing of abnormal components according to the abnormal transmission link clusters, and locating abnormal deployment components within the abnormal transmission link clusters.

[0034] Specifically, link overlap analysis is performed on candidate transmission links for abnormalities. The structural overlap of multiple communication links marked as potentially abnormal is calculated to identify any commonly dependent deployment components. Link overlap analysis is typically performed by comparing the duplication of nodes (i.e., deployment components) within a link. For example, if two links pass through the same communication gateway or power module, this indicates structural overlap. If multiple links share the same set of critical components, a collective failure may be caused by these components.

[0035] Next, we extract clusters of abnormal transmission links and group together abnormal links with high overlap, forming a set of links that are highly correlated both structurally and behaviorally. Links in a cluster share a large number of deployment components and are often anomalies, representing a concentrated manifestation of a certain type of structural anomaly.

[0036] Then, reverse tracing is performed based on the abnormal transmission link cluster to identify the abnormal component. This means analyzing the recurrence of deployed components within the link cluster to identify the component most likely to cause multiple link anomalies. Reverse tracing is an analytical method that traces the cause from the phenomenon, pinpointing the systemic component that may be causing the localized, concentrated anomaly. For example, if a communication concentrator appears in multiple abnormal links and accounts for 80% of them, it is likely that the component itself is experiencing an anomaly.

[0037] Finally, by tracing the source and locating abnormally deployed components, the analysis results can be used to determine whether specific equipment or modules may be damaged or failed. This can help operation and maintenance personnel quickly identify potential fault points without having to check all terminals or links one by one, improving the efficiency and accuracy of fault handling.

[0038] S5: The abnormally deployed component triggers the concentrator to generate a component alarm signal for reporting.

[0039] Specifically, abnormally deployed components trigger the concentrator to generate component-level alarm signals for reporting. The concentrator then processes these signals and generates corresponding component-level alarm signals, which are then sent to the higher-level management platform or maintenance personnel. Abnormally deployed components include communication components, power modules, data transfer devices, or program control units that are commonly relied upon by multiple abnormal links and are suspected of causing failures. The concentrator is capable of communicating with multiple data collection terminals and performing preliminary data processing.

[0040] Alarm signals are standardized notifications of abnormalities issued by the concentrator. They contain information such as the type and location of the faulty component, the number of terminals involved, and the time period of the abnormality. These information then quickly transmits diagnostic results to the operation and maintenance system or on-duty personnel. Reporting involves transmitting alarm information via a communication network to a remote control platform or backend database system, providing a basis for subsequent troubleshooting, operation and maintenance scheduling, and system logging, thereby helping to quickly locate the fault point.

[0041] Furthermore, the present application also includes: wherein the status monitoring data set includes power supply status data, communication quality data, data transmission behavior data and heartbeat status data; establishing a transmission logic association table, the transmission logic association table is the transmission link identifier between the receiving end and the status monitoring data set of each power consumption information collection terminal; link element parsing is performed according to the transmission link identifier of the transmission logic association table to determine the various deployed elements on the transmission link.

[0042] Specifically, the status monitoring data set includes power supply status data (such as whether there is a regulated output and voltage fluctuations), communication quality data (such as signal strength and packet loss rate), data transmission behavior data (such as reporting cycle and number of retransmissions), and heartbeat status data (such as small confirmation data packets sent periodically to determine whether the power consumption information collection terminal is online).

[0043] Establish a transmission logic association table to record the transmission path information between each receiving end, each electricity consumption information collection terminal and its status monitoring data, and determine the transmission link from the electricity consumption information collection terminal to the received data, so as to facilitate the subsequent tracing of the specific path in the data transmission process, thereby locating potential abnormal nodes and marking the transmission link identification.

[0044] Next, based on the transmission link identifiers recorded in the transmission logic association table, link element resolution can be performed. This involves identifying the communication components, power management components, cache and transfer modules, and program management modules involved in the transmission link, and determining the various deployed components on the transmission link. Table 1 shows a partial record of the most recent link element resolution.

[0045] Table 1: Partial record of the latest link element analysis

[0046] Furthermore, the present application also includes: extracting the operating characteristics of the status monitoring data set, including data signal fluctuations, ACK response rate, data missingness and data delay; using a sliding window trend algorithm to obtain a historical status monitoring data set, performing feature deviation anomaly analysis on the operating characteristics of the historical status monitoring data set and the operating characteristics of the status monitoring data set, and outputting a feature deviation value; if the feature deviation value is greater than a preset deviation threshold, marking the transmission link corresponding to the status monitoring data set as an abnormal state and outputting it as an abnormal candidate transmission link.

[0047] Specifically, the operational characteristics of a condition monitoring dataset refer to indicators reflecting operational quality and stability analyzed from data uploaded by electricity consumption information collection terminals. These operational characteristics are quantitative descriptions of data behavior, including data signal fluctuation, ACK response rate, data missingness, and data latency. Data signal fluctuation reflects the degree to which data values vary over time. For example, a terminal's voltage reading fluctuates dramatically over a short period of time, which may indicate unstable operation. The ACK response rate refers to the percentage of times the electricity consumption information collection terminal receives acknowledgments from the receiving end after sending data, used to assess communication reliability. A low ACK response rate indicates significant packet loss. Data missingness indicates how often data is not received at the expected upload time. If 96 uploads are expected in a day but only 80 are received, the missingness rate is 16.7%. Data latency measures the average time between the time terminal sends and the time it receives data. A significantly increased latency may indicate link congestion or slow device processing.

[0048] Next, the sliding window trend algorithm is a time series processing method that can capture changes in data trends over a continuous period of time. This algorithm compares the current state monitoring dataset with its historical data. A set time window, such as the past seven days, is used to extract the operating characteristics of the electricity consumption information collection terminal to form a historical sample. This sample is then compared with the currently observed operating characteristics. This comparison allows the calculation of the characteristic deviation value, which represents the degree of difference between the current state and the historical normal state.

[0049] If the deviation value is large, exceeding the preset deviation threshold, it indicates that the current operating behavior has deviated from the normal mode and may be abnormal. The transmission link through which the corresponding status monitoring data set passes is marked as abnormal. This marking does not immediately declare the link fault, but rather lists it as an abnormal candidate transmission link. In other words, a link with potential problems requires further overlapping analysis of abnormal conditions at multiple terminals to confirm its true status. This can avoid false alarms caused by occasional fluctuations or isolated failures.

[0050] Furthermore, the present application also includes: outputting abnormal candidate transmission links and submitting an information recording table of abnormal candidate transmission links; wherein the record items of the information recording table include a list of terminals to which each abnormal candidate transmission link belongs, a sequence of deployment elements involved in the transmission path, anomaly type, and anomaly occurrence time window; calling the information recording table to perform link overlap analysis on the abnormal candidate transmission links.

[0051] Specifically, after a status anomaly is identified and the transmission link is marked as an abnormal candidate transmission link, it is further output and organized into an information record table to provide a data basis for subsequent analysis, facilitating traceability analysis and structural judgment of the anomaly.

[0052] The information record table is a structured data table used to systematically archive detailed information for each candidate abnormal transmission link. It includes three aspects: the first is the list of terminals to which it belongs, that is, the numbers of all electricity information collection terminals that upload data through this link, which is used to determine whether the anomaly is centralized; the second is the sequence of deployed components involved in the transmission path, which refers to the various types of communication equipment, power supply modules, data buffer nodes, etc. that make up the link, such as concentrators, communication buses, or cache chips, which help to subsequently locate components that may have failed; the third is the anomaly type and the time window in which the anomaly occurred. Anomaly types may include data loss, communication delays, ACK loss, etc., while the time window is used to locate the time period when the anomaly occurred, such as a data upload interruption from 10:00 to 11:00 on the same day.

[0053] The data in this information record table is then used to perform link overlap analysis on all recorded candidate abnormal transmission links. Link overlap analysis compares multiple links to determine whether they share common deployment components and whether anomalies are concentrated at key nodes. For example, if a communication module or power supply device is shared among abnormal transmission links at different terminals, that module or device is highly likely the root cause of the multi-terminal anomaly.

[0054] Furthermore, the present application also includes: performing structured processing on the abnormal candidate transmission links, where each node on the abnormal candidate transmission links represents a deployment element; collecting a set of abnormal candidate transmission links after structured processing; constructing an overlap analysis matrix of the set of abnormal candidate transmission links, performing connected domain extraction based on the overlap analysis matrix, and extracting abnormal transmission link clusters, wherein each abnormal transmission link cluster contains abnormal candidate transmission links whose overlap degree is greater than a preset overlap threshold.

[0055] Specifically, structuring candidate transmission links involves breaking down each transmission path marked as abnormal into an ordered sequence of nodes, with nodes serving as the basic units in a graph structure. Each node represents a deployment element—a physical or logical component that plays a role in the data transmission process. This structuring transforms the previously irregular and difficult-to-compare transmission paths into a computable and analyzable graph structure, laying the foundation for subsequent overlap calculation and cluster analysis.

[0056] Next, all the abnormal candidate transmission links after structured processing are collected to form a set of abnormal candidate transmission links. An overlap analysis matrix is further constructed to describe the sharing relationship between all abnormal links. Each element of the overlap analysis matrix represents the degree of overlap of the deployment elements shared between any two abnormal links. Among them, the ratio of the number of deployment elements shared by two links in the overlap analysis matrix to the length of the shorter link is called the overlap degree, which is used to measure the similarity between the two links. If the overlap degree value is higher than a pre-set overlap threshold, such as 0.6, it means that the two links have a large overlap in the transmission path and may have the same fault source.

[0057] Finally, the overlap analysis matrix is used to extract connected domains. Link pairs with overlap greater than a set threshold are clustered to form multiple clusters of abnormal transmission links. Each cluster contains several highly overlapping abnormal transmission links, which are likely to be abnormal at the same time due to sharing key components. Therefore, they are considered a unified problem domain for further tracing and investigation.

[0058] Furthermore, the present application also includes: selecting any two links from the set of abnormal candidate transmission links and calculating the number of shared deployment elements; calculating an overlap index of any two links based on the number of shared deployment elements, wherein the overlap index is the ratio of the number of shared deployment elements to the path length of the shorter of the two links; and obtaining an overlap analysis matrix corresponding to the set of abnormal candidate transmission links based on the overlap index.

[0059] Specifically, we select any two links from the set of abnormal candidate transmission links, perform a pairwise comparison analysis, and calculate the number of shared deployment components. The number of shared deployment components refers to the number of components that the two links share in their paths.

[0060] Next, based on the number of shared deployment components, an overlap index is calculated by dividing the number of shared deployment components in the two links by the length of the shorter of the two paths. Path length refers to the total number of deployment components contained in the link. The overlap index reflects the degree of structural overlap between the two links. A higher value indicates a higher degree of sharing and a higher likelihood of linked failures due to a single component failure.

[0061] Then, based on the overlap metrics calculated between all links, a complete overlap analysis matrix is constructed. Each cell represents the overlap between a pair of links. The size of the overlap analysis matrix is related to the number of abnormal links. If there are n abnormal links, the overlap analysis matrix is n by n. The overlap analysis matrix provides basic data support for subsequent connected domain extraction and abnormal cluster identification. It can help identify whether there are structural areas of high overlap and high coupling between multiple links, and further analyze whether there are common cause failure points.

[0062] Furthermore, the present application also includes: outputting a full set of components according to the abnormal transmission link cluster; defining an abnormal overlap degree of each component, wherein the abnormal overlap degree is the proportion of the component appearing in the abnormal transmission link cluster; defining a terminal abnormal weight, wherein the terminal abnormal weight is the number of abnormal collection terminals in the abnormal transmission link cluster; performing reverse tracing of the abnormal components based on the abnormal overlap degree of each component according to the terminal abnormal weight, and identifying components with a value greater than a preset abnormal index as abnormal deployment components.

[0063] Specifically, the complete component set is output based on the abnormal transmission link cluster. All deployed components in each abnormal link cluster identified as having severe structural overlap are extracted. The complete component set represents the entire network's equipment related to the abnormal link, including communication equipment, power modules, cache chips, controllers, and so on.

[0064] Next, the number of times a component appears in an anomaly link is divided by the total number of links in the anomaly cluster to define the anomaly overlap for each component. This quantifies how frequently the component appears in an anomaly transmission link cluster. For example, if a deployment component appears seven times in a cluster of 10 anomaly links, the anomaly overlap is 0.7, indicating whether a component is likely a common cause of anomalies in multiple links.

[0065] Furthermore, we define a terminal anomaly weight, which measures the overall fault intensity of each cluster by the number of abnormal terminals involved in each abnormal transmission link cluster. A larger terminal anomaly weight indicates that the cluster involves more acquisition terminals, indicating that the anomaly may have a higher impact range.

[0066] Then, the anomaly overlap of each component is traced back to its source based on the terminal anomaly weight. This means that when identifying an anomaly in a deployed component, not only is the frequency of the component appearing in the anomaly link considered, but also the terminal weight of the anomaly cluster in which it resides. By combining these two metrics, the core components causing severe system interference can be more accurately identified.

[0067] Finally, if a component's comprehensive anomaly index exceeds a preset threshold, it is identified as an abnormally deployed component. Such components are considered possible anomaly sources or failure centers and should be prioritized in alarm, troubleshooting, and fault tolerance design. For example, if a 0.6 anomaly overlap threshold and a 10-terminal anomaly weight threshold are set, a component meeting both conditions will be identified as a high-risk deployment component.

[0068] Furthermore, the present application also includes: after extracting the abnormal transmission link cluster, performing cluster validity verification on the abnormal transmission link cluster, and when the validity verification passes, performing reverse tracing of the abnormal components according to the abnormal transmission link cluster; when the validity verification fails, excluding the abnormal transmission link cluster, performing abnormal identification on the nodes of each link in the abnormal transmission link cluster, and triggering the concentrator to generate an element alarm signal.

[0069] Specifically, after extracting anomalous transmission link clusters, cluster validation is performed on these clusters. This involves evaluating the multiple anomalous link combinations formed by the cluster to determine whether the links exhibit reasonable and statistically significant structural overlap. Cluster validation analyzes metrics such as overlap distribution, link structural correlation, and terminal spatial density to determine whether a cluster represents a true potential anomaly region. For example, if all links in a cluster share a majority of key components and the data fluctuation patterns between links are highly consistent, the cluster is considered valid.

[0070] Next, if the validity verification passes, reverse tracing is performed based on the abnormal transmission link cluster to the abnormal component. Reverse tracing involves tracing back through the structure of multiple links to the key deployment component most likely to have caused the abnormality. This process then involves identifying and evaluating shared devices across multiple terminal links to locate the root cause of the fault. For example, in a valid cluster, if a communication gateway device appears repeatedly on all 10 links, and all the corresponding terminals on the links experience abnormal communication delays, then this device is likely the root cause of the fault.

[0071] On the other hand, if the validity verification fails, it indicates that the cluster's structure is insufficiently connected, possibly due to multiple unrelated links being mistakenly aggregated. Therefore, the entire anomalous transmission link cluster must be excluded and no longer used as the basis for reverse tracing. To address potential anomalies, each link in this cluster will be further analyzed individually.

[0072] If a cluster is excluded, the system identifies anomalies at the nodes of each link, assessing the status of each deployed component individually to check for signal interference, disconnections, packet loss, and other issues. If the identified results reach the alarm threshold, the concentrator will generate a component alarm signal, notifying the operations and maintenance system to perform maintenance or replacement of the targeted component. As the core aggregation node in the system, the concentrator assumes the key functions of data collection, judgment, and alarm generation. The alarm signals it generates are then sent to manual or automated operations and maintenance systems via the management platform.

[0073] Furthermore, the present application also includes: wherein, the cluster validity verification includes calculating the number of abnormal collection terminals and the overlapping frequency of deployment elements of each abnormal transmission link cluster; when the number of abnormal collection terminals and the overlapping frequency of deployment elements are both greater than or equal to the corresponding expected threshold, the cluster validity verification is passed; when either the number of abnormal collection terminals or the overlapping frequency of deployment elements is less than the corresponding expected threshold, the cluster validity verification fails.

[0074] Specifically, during cluster validation, the number of abnormal data collection terminals within each abnormal transmission link cluster is calculated. This indicates how many power consumption data collection terminals within a clustered set of links have experienced abnormal conditions. The number of abnormal data collection terminals is a key indicator of cluster density and the degree of concentrated abnormalities. For example, if a cluster contains 20 terminals and 15 of them experience communication failures or data loss, the cluster exhibits a high degree of abnormal concentration.

[0075] Next, we need to calculate the overlap frequency of deployment components. This refers to the frequency of deployment components shared by multiple faulty transmission links within the cluster. Deployment components, such as communication modules, power modules, and data cache modules, affect link health. A higher overlap frequency indicates that the links rely on the same infrastructure, and the fault may originate from a shared component.

[0076] Subsequently, cluster validity verification is considered passed when both the number of abnormal collection terminals and the frequency of overlapping deployed components are greater than or equal to their corresponding expected thresholds. Expected thresholds are pre-set empirical or model parameters, such as 10 for the number of abnormal collection terminals and 0.6 for the frequency of overlapping deployed components. When these two conditions are met, the cluster is considered not only abnormally concentrated but also structurally common, making it suitable for subsequent anomaly tracing.

[0077] Conversely, if either the number of abnormal collection terminals or the overlap frequency of deployment components falls below the desired threshold, the cluster is considered to have failed validation. For example, if a cluster contains 15 links but only has 3 abnormal terminals, or the overlap frequency of deployment components is only 0.2, the cluster's aggregation properties are weak and cannot serve as a reliable basis for anomaly aggregation. Therefore, the cluster should be abandoned and analysis should be conducted at the single-link level.

[0078] To sum up, the abnormal alarm method of the power consumption information collection terminal based on state monitoring provided by this application has the following technical effects: by realizing the technical goals of abnormal clustering modeling based on link overlap relationships and reverse tracing and positioning of shared deployment components, the technical effects of improving the accuracy of abnormality detection, shortening fault response time and enhancing the level of intelligent operation and maintenance are achieved.

[0079] In the second embodiment, based on the same inventive concept as the abnormal alarm method of the power consumption information collection terminal based on state monitoring in the above embodiment, the present application also provides an abnormal alarm device of the power consumption information collection terminal based on state monitoring, please refer to the attached Figure 2 , including: an integrated deployment structure acquisition module 11, used to obtain the integrated deployment structure of the power consumption information collection terminal, the integrated deployment structure includes multiple power consumption information collection terminals and connected integrated deployment components; a state monitoring data set acquisition module 12, used to perform state monitoring on each power consumption information collection terminal, obtain the state monitoring data set, and determine the transmission link of any state monitoring data set and the deployment components on the transmission link according to the receiving end of the state monitoring data set; an abnormality detection module 13, used to perform abnormality detection on the transmitted state monitoring data set by the corresponding receiving end, and if the state is abnormal, it is marked as an abnormal candidate transmission link; a link overlap analysis module 14, used to perform link overlap analysis on the abnormal candidate transmission link, extract abnormal transmission link clusters, reversely trace abnormal components according to the abnormal transmission link clusters, and locate abnormal deployment components within the abnormal transmission link clusters; a component alarm signal reporting module 15, used to trigger the concentrator to generate component alarm signals for reporting according to the abnormal deployment components.

[0080] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on state monitoring is also used for: wherein the state monitoring data set includes power supply status data, communication quality data, data transmission behavior data and heartbeat status data; establishing a transmission logic association table, the transmission logic association table is the transmission link identifier between the receiving end and the state monitoring data set of each power consumption information collection terminal; performing link element parsing according to the transmission link identifier of the transmission logic association table to determine each deployed element on the transmission link.

[0081] Furthermore, the abnormal alarm device of the electricity consumption information collection terminal based on state monitoring is also used to: extract the operating characteristics of the state monitoring data set, including data signal fluctuation, ACK response rate, data missingness and data delay; use a sliding window trend algorithm to obtain a historical state monitoring data set, perform feature deviation anomaly analysis on the operating characteristics of the historical state monitoring data set and the operating characteristics of the state monitoring data set, and output a feature deviation value; if the feature deviation value is greater than a preset deviation threshold, mark the transmission link corresponding to the state monitoring data set as an abnormal state and output it as an abnormal candidate transmission link.

[0082] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on state monitoring is also used to: output as abnormal candidate transmission links, and submit an information recording table of the abnormal candidate transmission links; wherein the record items of the information recording table include the terminal list to which each abnormal candidate transmission link belongs, the sequence of deployment elements involved in the transmission path, the abnormality type and the time window in which the abnormality occurs; and call the information recording table to perform link overlap analysis on the abnormal candidate transmission links.

[0083] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on state monitoring is also used to: perform structured processing on the abnormal candidate transmission links, where each node on the abnormal candidate transmission link represents a deployment element; collect the set of abnormal candidate transmission links after structured processing; construct an overlap analysis matrix of the set of abnormal candidate transmission links, extract connected domains based on the overlap analysis matrix, and extract abnormal transmission link clusters, wherein each abnormal transmission link cluster contains abnormal candidate transmission links whose overlap degree is greater than a preset overlap threshold.

[0084] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on condition monitoring is also used to: select any two links from the set of abnormal candidate transmission links and calculate the number of shared deployment elements; calculate the overlap index of any two links based on the number of shared deployment elements, wherein the overlap index is the ratio of the number of shared deployment elements to the path length of the shorter of the two links; and obtain the overlap analysis matrix corresponding to the set of abnormal candidate transmission links based on the overlap index.

[0085] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on state monitoring is also used to: output the full set of components according to the abnormal transmission link cluster; define the abnormal overlap of each component, the abnormal overlap is the proportion of the component appearing in the abnormal transmission link cluster; define the terminal abnormal weight, the terminal abnormal weight is the number of abnormal collection terminals in the abnormal transmission link cluster; perform reverse tracing of the abnormal components for the abnormal overlap of each component according to the terminal abnormal weight, and identify components with a value greater than a preset abnormal index as abnormal deployment components.

[0086] Furthermore, the abnormal alarm device of the power consumption information collection terminal based on status monitoring is also used to: after extracting the abnormal transmission link cluster, perform cluster validity verification on the abnormal transmission link cluster, and when the validity verification passes, perform reverse tracing of abnormal components according to the abnormal transmission link cluster; when the validity verification fails, exclude the abnormal transmission link cluster, identify the abnormalities of the nodes of each link in the abnormal transmission link cluster, and trigger the concentrator to generate a component alarm signal.

[0087] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on state monitoring is also used for: wherein, the cluster validity verification includes calculating the number of abnormal collection terminals and the overlapping frequency of deployment components of each abnormal transmission link cluster; when the number of abnormal collection terminals and the overlapping frequency of deployment components are both greater than or equal to the corresponding expected thresholds, the cluster validity verification is passed; when either the number of abnormal collection terminals or the overlapping frequency of deployment components is less than the corresponding expected threshold, the cluster validity verification fails.

[0088] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The abnormal alarm method of the power consumption information collection terminal based on state monitoring and the specific examples in the aforementioned embodiment one are also applicable to the abnormal alarm device of the power consumption information collection terminal based on state monitoring in this embodiment. Through the aforementioned detailed description of the abnormal alarm method of the power consumption information collection terminal based on state monitoring, those skilled in the art can clearly understand the abnormal alarm device of the power consumption information collection terminal based on state monitoring in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0089] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0090] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. The abnormal alarm method of the power consumption information collection terminal based on state monitoring is characterized in that: Methods include: Acquire an integrated deployment structure of power consumption information collection terminals, the integrated deployment structure comprising a plurality of power consumption information collection terminals and connected integrated deployment elements; Performing status monitoring on each power consumption information collection terminal to obtain a status monitoring data set, and determining a transmission link of any status monitoring data set and each deployed component on the transmission link according to a receiving end of the status monitoring data set; The corresponding receiving end performs anomaly detection on the transmitted status monitoring data set, and marks the abnormal state as an abnormal candidate transmission link; Performing link overlap analysis on the abnormal candidate transmission links to extract abnormal transmission link clusters, performing reverse tracing of abnormal components according to the abnormal transmission link clusters, and locating abnormal deployment components within the abnormal transmission link clusters; The abnormally deployed component triggers the concentrator to generate a component alarm signal for reporting.

2. The abnormal alarm method for the power consumption information collection terminal based on state monitoring according to claim 1 is characterized in that: According to the receiving end of the condition monitoring data set, the transmission link of any condition monitoring data set and each deployment element on the transmission link are determined. include: The status monitoring data set includes power supply status data, communication quality data, data transmission behavior data and heartbeat status data; Establishing a transmission logic association table, wherein the transmission logic association table is a transmission link identifier between the receiving end and the status monitoring data set of each power consumption information collection terminal; Link element parsing is performed according to the transmission link identifier of the transmission logic association table to determine each deployment element on the transmission link.

3. The abnormal alarm method of the power consumption information collection terminal based on state monitoring according to claim 1 is characterized in that: The corresponding receiving end performs anomaly detection on the transmitted status monitoring data set, and the method includes: Extracting operational characteristics of the condition monitoring data set, including data signal fluctuation, ACK response rate, data missingness, and data delay; A sliding window trend algorithm is used to obtain a historical state monitoring data set, and the operating characteristics of the historical state monitoring data set and the operating characteristics of the state monitoring data set are analyzed for feature deviation anomaly, and a feature deviation value is output; If the characteristic deviation value is greater than a preset deviation threshold, the transmission link corresponding to the status monitoring data set is marked as abnormal and output as an abnormal candidate transmission link.

4. The abnormal alarm method for the power consumption information collection terminal based on state monitoring according to claim 3 is characterized in that: The output is an abnormal candidate transmission link, and an information record table of the abnormal candidate transmission link is submitted; The information record table includes a list of terminals to which each abnormal candidate transmission link belongs, a sequence of deployed components involved in the transmission path, anomaly type, and anomaly occurrence time window; The information record table is called to perform link overlap analysis on the abnormal candidate transmission link.

5. The abnormal alarm method of the power consumption information collection terminal based on state monitoring according to claim 1 is characterized in that: Performing link overlap analysis on the abnormal candidate transmission links to extract abnormal transmission link clusters, the method comprising: Performing structural processing on the abnormal candidate transmission link, where each node on the abnormal candidate transmission link represents a deployment element; Collect the abnormal candidate transmission link set after structured processing; An overlap analysis matrix of the abnormal candidate transmission link set is constructed, and connected domains are extracted from the overlap analysis matrix to extract abnormal transmission link clusters, wherein each abnormal transmission link cluster contains abnormal candidate transmission links whose overlap degree is greater than a preset overlap threshold.

6. The abnormal alarm method for the power consumption information collection terminal based on state monitoring according to claim 5 is characterized in that: Constructing an overlap analysis matrix of the abnormal candidate transmission link set, the method comprising: Select any two links from the abnormal candidate transmission link set and calculate the number of shared deployment elements; Calculating an overlap index of any two links based on the number of shared deployment elements, wherein the overlap index is a ratio of the number of shared deployment elements to the path length of the shorter of the two links; Based on the overlap index, an overlap analysis matrix corresponding to the abnormal candidate transmission link set is obtained.

7. The abnormal alarm method for the power consumption information collection terminal based on state monitoring according to claim 1 is characterized in that: The method includes: performing reverse tracing of abnormal components according to the abnormal transmission link cluster to locate abnormally deployed components within the abnormal transmission link cluster. Outputting a complete set of components according to the abnormal transmission link cluster; Define the abnormal overlap of each component, where the abnormal overlap is the proportion of the component appearing in the abnormal transmission link cluster; Define a terminal abnormality weight, where the terminal abnormality weight is the number of abnormal collection terminals in the abnormal transmission link cluster; The abnormal overlap of each component is reversely traced according to the terminal abnormality weight, and components with a value greater than a preset abnormality index are identified as abnormal deployment components.

8. The abnormal alarm method for the power consumption information collection terminal based on state monitoring according to claim 1 is characterized in that: After extracting the abnormal transmission link cluster, performing cluster validity verification on the abnormal transmission link cluster, and performing reverse tracing of the abnormal component according to the abnormal transmission link cluster when the validity verification passes; When the validity verification fails, the abnormal transmission link cluster is excluded, abnormality identification is performed on the nodes of each link in the abnormal transmission link cluster, and the concentrator is triggered to generate a component alarm signal.

9. The abnormal alarm method for the power consumption information collection terminal based on state monitoring according to claim 8, characterized in that: in, Cluster validity verification includes calculating the number of abnormal collection terminals and the frequency of overlapping deployment components for each abnormal transmission link cluster; When the number of abnormal collection terminals and the overlapping frequency of the deployment elements are both greater than or equal to the corresponding expected thresholds, the cluster validity verification is passed; When either the number of abnormal collection terminals or the overlapping frequency of deployment elements is less than the corresponding expected threshold, the cluster validity verification fails.

10. The abnormal alarm device of the power consumption information collection terminal based on state monitoring is characterized in that: The steps for implementing the abnormal alarm method of the power consumption information collection terminal based on condition monitoring according to any one of claims 1 to 9 include: An integrated deployment structure acquisition module is used to acquire an integrated deployment structure of a power consumption information collection terminal, wherein the integrated deployment structure includes a plurality of power consumption information collection terminals and connected integrated deployment elements; A status monitoring data set acquisition module is used to perform status monitoring on each power consumption information collection terminal, acquire a status monitoring data set, and determine the transmission link of any status monitoring data set and each deployed component on the transmission link according to the receiving end of the status monitoring data set; Anomaly detection module, used for the corresponding receiving end to perform anomaly detection on the transmitted status monitoring data set, and if the status is abnormal, it is marked as an abnormal candidate transmission link; A link overlap analysis module is configured to perform link overlap analysis on the abnormal candidate transmission links, extract abnormal transmission link clusters, perform reverse tracing of abnormal components according to the abnormal transmission link clusters, and locate abnormal deployment components within the abnormal transmission link clusters; The component alarm signal reporting module is used to trigger the concentrator to generate a component alarm signal for reporting according to the abnormally deployed component.

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