Abnormal alarm method and device for power utilization information collection terminal based on state monitoring

Abstract

The application provides a state monitoring-based power utilization information acquisition terminal abnormality alarm method and device, relates to the technical field of abnormality alarm, and comprises the following steps: acquiring an integrated deployment structure of a power utilization information acquisition terminal; performing state monitoring on each power utilization information acquisition terminal, determining a transmission link of any state monitoring data set and each deployment element on the transmission link according to a 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 the state as an abnormal candidate transmission link if the state is abnormal; performing link overlap analysis on the abnormal candidate transmission link, performing abnormal element reverse tracing according to an abnormal transmission link cluster; and triggering a concentrator to generate an element alarm signal for reporting according to the abnormal deployment element. The application can solve the technical problem that the accuracy of abnormality alarm is low in the prior art, and achieves the technical effect of improving the accuracy of abnormality detection.

Description

Technical Field

[0001] This application relates to the field of abnormal alarm technology, and in particular to an abnormal alarm method and device for power consumption information collection terminals based on status monitoring. Background Technology

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

[0003] Currently, existing technologies still have significant shortcomings in anomaly diagnosis. Most methods focus on identifying operational anomalies within the terminal equipment itself, neglecting the potential correlations between multiple terminals on the same communication link or shared deployment components, resulting in insufficient ability to identify centralized anomalies. Therefore, existing technologies cannot effectively identify structural or systemic risks in shared deployment components such as communication gateways, power buses, and cache modules, affecting the accuracy of alarms and the timeliness of maintenance. To address these issues, there is an urgent need to propose an overlap analysis and anomaly clustering modeling method for transmission links to support the rapid location of potential deployment component failures when multiple terminal anomalies occur simultaneously.

[0004] In summary, existing technologies suffer from a lack of modeling and analysis capabilities for the structural relationships between multiple electricity information collection terminals. This leads to the inability to detect faults in shared deployment components 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 anomaly alarm of electricity information collection terminal based on condition monitoring, in order to solve the technical problem in the prior art that the lack of modeling and analysis capabilities for the structural relationship between multiple electricity information collection terminals makes it impossible to detect the failure of shared deployment components involved in centralized anomalies, which further affects the accuracy of anomaly alarms, the timeliness of fault location and the efficiency of system operation and maintenance.

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

[0007] Firstly, this application provides a method for anomaly alarm of electricity consumption information collection terminals based on status monitoring, implemented by an anomaly alarm device for electricity consumption information collection terminals based on status monitoring. The method includes: acquiring an integrated deployment structure of electricity consumption information collection terminals, the integrated deployment structure including multiple electricity consumption information collection terminals and connected integrated deployment elements; performing status monitoring on each electricity consumption information collection terminal to acquire a status monitoring dataset; determining the transmission link of any status monitoring dataset and each deployment element on the transmission link according to the receiving end of the status monitoring dataset; having the corresponding receiving end perform anomaly detection on the transmitted status monitoring dataset, marking any anomaly as a candidate transmission link; performing link overlap analysis on the candidate transmission links to extract anomaly transmission link clusters; performing reverse tracing of the anomaly elements according to the anomaly transmission link clusters to locate the anomaly deployment elements within the anomaly transmission link clusters; and triggering a concentrator to generate an element alarm signal based on the anomaly deployment element for reporting.

[0008] Preferably, the method for abnormal alarm of power consumption information collection terminal based on status monitoring further includes: wherein the status monitoring dataset 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 the transmission link identifier between the receiving end and the status monitoring dataset of each power consumption information collection terminal; and performing link element parsing based on the transmission link identifier of the transmission logic association table to determine each deployed element on the transmission link.

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

[0010] Preferably, the abnormal alarm method for the power consumption information collection terminal based on status monitoring further includes: outputting abnormal candidate transmission links and submitting an information record table for the abnormal candidate transmission links; wherein, the information record table records items including 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 abnormality occurrence time window; and calling the information record 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 status monitoring further 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 abnormal candidate transmission link set; extracting connected components from the overlap analysis matrix to extract abnormal transmission link clusters, wherein each abnormal transmission link cluster contains abnormal candidate transmission links with an overlap degree greater than a preset overlap threshold.

[0012] Preferably, the abnormal alarm method for the power consumption information collection terminal based on status monitoring further includes: selecting any two links from 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 method for abnormal alarm of power consumption information collection terminal based on status monitoring further includes: outputting a complete set of components according to the abnormal transmission link cluster; defining the abnormal overlap of each component, wherein the abnormal overlap is the proportion of the component appearing in the abnormal transmission link cluster; defining the terminal abnormal weight, wherein the terminal abnormal weight is the number of abnormal collection terminals in the abnormal transmission link cluster; and performing reverse tracing of abnormal components based on the abnormal overlap of each component according to the terminal abnormal weight, identifying components with an abnormality index as abnormal deployment components.

[0014] Preferably, the abnormal alarm method for the power consumption information collection terminal based on status monitoring further includes: after extracting the abnormal transmission link cluster, performing cluster validity verification on the abnormal transmission link cluster; when the validity verification passes, performing reverse tracing of 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 a component alarm signal.

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

[0016] Secondly, this application also provides an abnormal alarm device for a power consumption information collection terminal based on status monitoring, used to execute the abnormal alarm method for a power consumption information collection terminal based on status monitoring as described in the first aspect, comprising: an integrated deployment structure acquisition module, used to acquire the integrated deployment structure of the power consumption information collection terminal, the integrated deployment structure including multiple power consumption information collection terminals and connected integrated deployment elements; a status monitoring dataset acquisition module, used to perform status monitoring on each power consumption information collection terminal, acquire a status monitoring dataset, and determine the transmission link of any status monitoring dataset and each deployment element on the transmission link according to the receiving end of the status monitoring dataset; an anomaly detection module, used to perform anomaly detection on the transmitted status monitoring dataset by the corresponding receiving end, and mark the abnormal state as an abnormal candidate transmission link; a link overlap analysis module, used to perform link overlap analysis on the abnormal candidate transmission links, extract abnormal transmission link clusters, perform reverse tracing of abnormal elements according to the abnormal transmission link clusters, and locate the abnormal deployment elements within the abnormal transmission link clusters; and an element alarm signal reporting module, used to trigger a concentrator to generate an element alarm signal and report it according to the abnormal deployment element.

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

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the abnormal alarm method for the power consumption information collection terminal based on condition monitoring in this application.

[0021] Figure 2This is a schematic diagram of the abnormal alarm device for the power consumption information acquisition terminal based on condition monitoring in this application.

[0022] Figure labeling: Integrated deployment structure acquisition module 11, status monitoring dataset acquisition module 12, anomaly detection module 13, link overlap analysis module 14, component alarm signal reporting module 15. Detailed Implementation

[0023] This application provides a method and device for anomaly alarm of electricity information collection terminals based on condition monitoring. It solves the technical problem in existing technologies where the lack of modeling and analysis capabilities for the structural relationships between multiple electricity information collection terminals leads to the inability to detect faults in shared deployment components involved in centralized anomalies, further affecting the accuracy of anomaly alarms, the timeliness of fault location, and the efficiency of system operation and maintenance. The application achieves the technical goals of anomaly cluster modeling based on link overlap relationships and reverse tracing and locating shared deployment components, thereby improving the accuracy of anomaly detection, shortening fault response time, and enhancing the level of intelligent operation and maintenance.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides an anomaly alarm method for a power consumption information collection terminal based on condition monitoring, which is applied to an anomaly alarm device for a power consumption information collection terminal based on condition monitoring, and specifically includes the following steps:

[0026] S1: Obtain the integrated deployment structure of the electricity consumption information collection terminal, the integrated deployment structure including multiple electricity consumption information collection terminals and connected integrated deployment elements.

[0027] Specifically, an electricity consumption information collection terminal refers to an intelligent device installed on the power user 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, including information such as the user's voltage, current, active power, reactive power, and power factor. The integrated deployment structure of the electricity consumption information collection terminal refers to the overall network topology and device deployment information related to the electricity consumption information collection terminal collected from the power system, thereby obtaining the connection relationships and transmission paths between each electricity consumption information collection terminal and other devices in the entire power system.

[0028] The integrated deployment structure includes multiple electricity information collection terminals and connected integrated deployment components. These components include communication links, access modules, and aggregation devices. A communication link refers to the physical line or wireless transmission path traversed during data transmission from the electricity information collection terminals. An access module is an intermediary component connecting the electricity information collection terminals to the upper-level network, such as a carrier module or RS485 converter. Aggregation devices, such as concentrators or communication master stations, act as relays and aggregators before data is uploaded from multiple electricity information collection terminals. By establishing this integrated deployment structure, the entire process of data collection, transmission, and reception from each electricity information collection terminal, along with its corresponding path nodes, can be obtained.

[0029] S2: Perform status monitoring on each electricity consumption information collection terminal, obtain status monitoring dataset, and determine the transmission link of any status monitoring dataset and each deployed element on the transmission link according to the receiving end of the status monitoring dataset.

[0030] Specifically, status monitoring is performed on each electricity consumption information collection terminal, that is, the operating status of the electricity consumption information collection terminal is acquired in real time or periodically. By checking whether there are any abnormalities between the electricity consumption information collection terminal and its external environment, such as unstable power supply, communication failure or data delay, it is determined whether the electricity consumption information collection terminal is in normal working condition, thereby obtaining quantifiable operating data, that is, status monitoring dataset.

[0031] Based on the receiving end of the status monitoring dataset, determine the transmission link that each piece of data traverses from the electricity information collection terminal to the receiving end. That is, all communication paths between the electricity 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 regulator / transformer modules, battery packs, power buses, etc.), data buffering and relay modules (such as local cache chips, gateway forwarding caches, etc.), and program management and synchronization modules (such as main control MCU, centralized controller, remote upgrade module, etc.).

[0032] S3: The corresponding receiving end performs anomaly detection on the transmitted status monitoring dataset. If the status is abnormal, it is marked as an abnormal candidate transmission link.

[0033] Specifically, after receiving status monitoring datasets uploaded from various electricity information collection terminals, the receiving device corresponding to each electricity information collection terminal performs anomaly detection processing on the status monitoring datasets. The status monitoring datasets contain information on the operational performance of the electricity information collection terminals, such as whether the power supply voltage is stable, whether data is uploaded on time, and whether there is packet loss or delay in communication, thereby determining whether the currently received data significantly deviates from historical performance or a reasonable range. During the detection process, techniques such as feature extraction, moving averages, and trend comparisons may be used to identify abnormal signals such as a sudden drop in ACK response rate, prolonged lack of heartbeat packet response, and drastic communication fluctuations. Once the receiving end identifies a status anomaly, it marks the entire transmission link corresponding to that status monitoring dataset as an anomaly candidate.

[0034] S4: 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 the abnormal deployment components within the abnormal transmission link clusters.

[0035] Specifically, link overlap analysis is performed on candidate abnormal transmission links. This involves calculating the structural overlap of multiple communication links marked as potentially abnormal to identify any shared deployment components. Link overlap analysis typically involves comparing the repetition of nodes (i.e., deployment components) within the links. For example, if two links both pass through the same communication gateway or power module, it indicates structural overlap. If multiple links share the same set of critical components, it's possible that a collective failure is caused by these components.

[0036] Next, anomalous transmission link clusters are extracted, and anomalous links with high overlap are grouped together to form a set of links that are highly correlated both structurally and behaviorally. The links in the link clusters share a large number of deployment components, and most of them have experienced anomalies, representing a concentrated manifestation of a certain type of structural anomaly.

[0037] Then, reverse tracing is performed on the abnormal transmission link clusters. This means identifying the components most likely to cause multiple link anomalies by analyzing the recurrence of deployed components within the link clusters. Reverse tracing is an analytical method that deduces the cause from the phenomenon, pinpointing systemic components that may trigger localized concentrated anomalies. For example, if a communication concentrator appears in multiple abnormal links, accounting for 80%, it is very likely that the component itself is malfunctioning.

[0038] Finally, by tracing the source of abnormal deployment components, the analysis results can be used to determine whether a specific device or module may be damaged or failed. This helps maintenance personnel quickly find potential fault points without having to check all terminals or links one by one, thus improving the efficiency and accuracy of fault handling.

[0039] S5: Based on the abnormal deployment element, the concentrator is triggered to generate an element alarm signal and report it.

[0040] Specifically, based on the abnormal deployment of components, the concentrator generates a component alarm signal and reports it. The concentrator then processes the signal, generating a corresponding component-level alarm signal and sending it to the higher-level management platform or maintenance personnel. Abnormally deployed components refer to communication components, power modules, data relay devices, or program control units that are relied upon by multiple abnormal links and are suspected of causing a failure. The concentrator has the capability to communicate with multiple data acquisition terminals and perform preliminary data processing.

[0041] Alarm signals are standardized anomaly notifications issued by the concentrator, containing information such as the type and location of the faulty component, the number of terminals involved, and the abnormal time period. This information is then quickly transmitted to the operation and maintenance system or on-duty personnel. Reporting refers to the transmission of alarm information through the communication network to the remote control platform or backend database system, providing a basis for subsequent fault handling, operation and maintenance scheduling, and system log recording, thereby assisting in the rapid location of the fault.

[0042] Furthermore, this application also includes: wherein the status monitoring dataset 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 the transmission link identifier between the receiving end and the status monitoring dataset of each power consumption information collection terminal; and performing link element parsing based on the transmission link identifier of the transmission logic association table to determine each deployed element on the transmission link.

[0043] Specifically, the status monitoring dataset includes power supply status data (e.g., whether there is regulated output, voltage fluctuation), communication quality data (e.g., signal strength, packet loss rate), data transmission behavior data (e.g., reporting cycle, number of retransmissions), and heartbeat status data (e.g., small acknowledgment data packets sent periodically to determine whether the electricity information collection terminal is online).

[0044] A transmission logic association table is established to record the transmission path information between each receiving end, each electricity information collection terminal and its status monitoring data, and to determine the transmission link from the electricity information collection terminal to the receiving end. This facilitates subsequent tracing of the specific path in the data transmission process, thereby locating potential abnormal nodes and marking the obtained transmission link identifier.

[0045] Next, based on the transmission link identifiers recorded in the transmission logic association table, link element resolution can be performed, which involves identifying the communication components, power management components, buffer and relay modules, and program management modules involved in the transmission link, and determining each deployed element on the transmission link. Table 1 shows a partial record of the most recent link element resolution.

[0046] Table 1: Partial Records of the Most Recent Link Component Resolution

[0047]

[0048] Furthermore, this application also includes: extracting the operational characteristics of the status monitoring dataset, including data signal fluctuations, ACK response rate, data missingness, and data latency; using a sliding window trend algorithm to obtain historical status monitoring datasets, performing feature deviation anomaly analysis between the operational characteristics of the historical status monitoring datasets and the operational characteristics of the status monitoring datasets, and outputting feature deviation values; if the feature deviation value is greater than a preset deviation threshold, marking the transmission link corresponding to the status monitoring dataset as a status anomaly and outputting it as an anomaly candidate transmission link.

[0049] Specifically, the operational characteristics of the status monitoring dataset refer to indicators reflecting the operational quality and stability of the data uploaded by the electricity information collection terminals. Operational characteristics are quantitative descriptions of data behavior, including data signal fluctuations, ACK response rate, data missingness, and data latency. Data signal fluctuations reflect the degree of change in data values ​​over time; for example, a drastic change in the voltage reading of a terminal within a short period may indicate instability. The ACK response rate is the proportion of data sent by the electricity information collection terminal that receives confirmation from the receiving end, used to determine the reliability of communication; a low ACK response rate indicates severe data packet loss. Data missingness indicates the frequency of not receiving data at the expected upload time; if 96 uploads are expected in a day but only 80 are received, the missing rate is 16.7%. Data latency measures the average time required for terminal data to be sent and received; a significant increase in latency may indicate link congestion or slow equipment processing.

[0050] Next, the sliding window trend algorithm, a time series processing method, captures changes in data trends over continuous time periods. This algorithm compares the current state monitoring dataset with its historical data, setting a time window, such as the past 7 days, to extract operational characteristic values ​​from the electricity consumption information collection terminals, forming historical samples. These samples are then compared with the currently observed operational characteristics. This comparison allows for the calculation of the characteristic deviation value, i.e., the degree of difference between the current state and the historical normal state.

[0051] If the deviation value is large, exceeding the preset deviation threshold, it indicates that the current operating behavior deviates from the normal mode and there may be an anomaly. The transmission link through which the corresponding status monitoring dataset passes is marked as having an anomaly. This marking does not immediately assert a link failure, but rather lists it as a candidate transmission link for anomalies—a link with potential problems. Further overlay analysis of anomalies from multiple terminals is needed to confirm its true status, thus avoiding false alarms caused by occasional fluctuations or isolated failures.

[0052] Furthermore, this application also includes: outputting abnormal candidate transmission links and submitting an information record table for the abnormal candidate transmission links; wherein, the information record table records items including 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 abnormality occurrence time window; and calling the information record table to perform link overlap analysis on the abnormal candidate transmission links.

[0053] Specifically, after identifying an anomaly and marking the transmission link as a candidate anomaly transmission link, the data is further output and organized into an information record table to provide a data foundation for subsequent analysis, facilitating source tracing analysis and structural judgment of the anomaly.

[0054] The information log table is a structured data table used to systematically archive detailed information about each candidate transmission link for anomalies. It includes three aspects: First, the list of associated terminals, which is the number of all electricity information collection terminals that upload data through this link, used to determine whether the anomaly is concentrated; second, the sequence of deployed components involved in the transmission path, referring to various communication devices, power supply modules, data buffer nodes, etc. that constitute this link, such as concentrators, communication buses, or cache chips, which helps to locate components that may fail later; and third, the anomaly type and the time window in which the anomaly occurred. The anomaly type may include data loss, communication delay, ACK loss, etc., while the time window is used to locate the period in which the anomaly occurred, such as data upload interruption occurring from 10:00 to 11:00 on the same day.

[0055] Subsequently, the data from the information record table is retrieved to perform link overlap analysis on all recorded candidate abnormal transmission links. Link overlap analysis determines whether anomalies are concentrated at critical nodes by comparing whether multiple links share the same deployed components. For example, if abnormal transmission links of different terminals all contain a certain communication module or power supply equipment, then that module or equipment is very likely the root cause of the anomalies in multiple terminals.

[0056] Furthermore, this application also includes: structuring the abnormal candidate transmission links, where each node on the abnormal candidate transmission links represents a deployment element; collecting a set of structured abnormal candidate transmission links; constructing an overlap analysis matrix of the abnormal candidate transmission link set; extracting connected components from the overlap analysis matrix to extract abnormal transmission link clusters, wherein each abnormal transmission link cluster contains abnormal candidate transmission links with an overlap degree greater than a preset overlap threshold.

[0057] Specifically, structuring candidate transmission links involves breaking down each marked as anomalous into an ordered sequence of nodes, using these nodes as the basic units in a graph structure. Each node represents a deployment element, i.e., a physical or logical component that plays a role in data transmission. This structuring transforms the originally irregular and difficult-to-compare transmission paths into a computable and analyzable graph structure, laying the foundation for subsequent overlap calculations and cluster analysis.

[0058] Next, all structured candidate transmission links for anomalies are collected to form a set of candidate transmission links for anomalies. An overlap analysis matrix is ​​then constructed to describe the sharing relationships between all anomaly links. Each element of the overlap analysis matrix represents the degree of overlap of the deployed components shared between any two anomaly links. The overlap degree is calculated by dividing the number of deployed components shared by the two links in the overlap analysis matrix by the length of the shorter link; this value measures the similarity between the two links. If the overlap degree is higher than a pre-set overlap threshold, such as 0.6, it indicates that the two links have a significant overlap in their transmission paths and may share the same source of failure.

[0059] Finally, connected component extraction is performed on the overlap analysis matrix. This involves aggregating link pairs with an overlap greater than a set threshold to form multiple clusters of anomalous transmission links. Each cluster contains several highly overlapping anomalous transmission links, which are highly likely to be anomalous simultaneously due to sharing certain key components. Therefore, they are considered a unified problem domain requiring further tracing and investigation.

[0060] Furthermore, this application also includes: selecting any two links from the set of abnormal candidate transmission links 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 set of abnormal candidate transmission links based on the overlap index.

[0061] Specifically, any two links from the set of abnormal candidate transmission links are selected and compared in a pairwise manner to calculate the number of shared deployment elements. The number of shared deployment elements refers to the number of elements commonly contained in the paths of the two links.

[0062] 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 greater likelihood of cascading failures due to an anomaly in the same component.

[0063] Then, based on the overlap index 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 times n. The overlap analysis matrix provides basic data support for subsequent connected component extraction and abnormal cluster identification. It can help discover whether highly overlapping and highly coupled structural regions have formed between multiple links, and can be further used to analyze whether there are common cause failure points.

[0064] Furthermore, this application also includes: outputting a complete set of elements according to the abnormal transmission link cluster; defining the abnormal overlap of each element, wherein the abnormal overlap is the proportion of the element appearing in the abnormal transmission link cluster; defining the terminal abnormal weight, wherein the terminal abnormal weight is the number of abnormal acquisition terminals in the abnormal transmission link cluster; and performing reverse tracing of abnormal elements based on the abnormal overlap of each element according to the terminal abnormal weight, identifying elements with an abnormality index greater than a preset abnormality index as abnormal deployment elements.

[0065] Specifically, based on the complete set of output components for each abnormal transmission link cluster, all deployed components in each cluster identified as having severe structural overlap are extracted. The complete set of components represents all devices related to the abnormal links in the current network, including communication equipment, power modules, cache chips, controllers, etc.

[0066] Next, the number of times a component appears in an abnormal link is divided by the total number of links in that abnormal cluster to define the abnormal overlap of each component, quantifying how frequently the component appears in the abnormal transmission link cluster. For example, if a deployed component appears 7 times in a cluster containing 10 abnormal links, then the abnormal overlap is 0.7, which can reflect whether a certain component may be a common cause of multiple link anomalies.

[0067] Furthermore, a terminal anomaly weight is defined, which measures the overall fault intensity of a cluster by the number of abnormal terminals involved in each abnormal transmission link cluster. The larger the terminal anomaly weight value, the more acquisition terminals are involved in the cluster, indicating that the anomaly may have a wider impact.

[0068] Then, by reverse-tracing the abnormal overlap of each component based on the terminal anomaly weight, it is possible to identify whether a deployed component is abnormal by considering not only the frequency of the component appearing in the abnormal link, but also the terminal weight of the abnormal cluster to which it belongs. By combining these two indicators, core components that cause serious interference to the system can be identified more accurately.

[0069] Finally, when the overall anomaly index of a component exceeds a preset threshold, the component can be identified as an abnormal deployment component. Such components are considered potential sources of anomalies or fault centers and should be given priority in alarm, maintenance, and fault tolerance design. For example, by setting an anomaly overlap threshold of 0.6 and an anomaly weight threshold of 10 terminals, if a component meets both conditions simultaneously, it can be identified as a high-risk deployment component.

[0070] Furthermore, this application also includes: after extracting the abnormal transmission link cluster, performing cluster validity verification on the abnormal transmission link cluster; when the validity verification passes, performing reverse tracing of 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 a component alarm signal.

[0071] Specifically, after extracting anomalous transmission link clusters, the cluster validity is verified. Multiple anomalous link combinations formed by the current aggregation are evaluated to determine whether the links truly exhibit structural overlap and statistical significance. Cluster validity verification analyzes indicators such as overlap distribution, link structural correlation, and terminal spatial density to determine whether a cluster can represent a true potential anomalous region. For example, if all links in a cluster share most of the key components and the data fluctuation patterns between the links are highly consistent, the cluster can be considered valid.

[0072] Next, once the validity verification passes, reverse tracing of the abnormal components is performed according to the cluster of abnormal transmission links. Reverse tracing refers to searching upwards from the structure of multiple links for the most likely critical deployment components that may have caused the anomaly, thereby identifying and evaluating shared devices in multiple terminal links to pinpoint the root cause of the failure. For example, in an effective cluster, if a communication gateway device appears repeatedly in 10 links, and the corresponding terminals on those links all experience abnormal communication delays, then that device is likely the root cause of the failure.

[0073] On the other hand, when the validity verification fails, it indicates that the cluster's structural coherence is insufficient, possibly due to multiple unrelated links being mistakenly aggregated together. Therefore, the entire abnormal transmission link cluster needs to be excluded and no longer used as the basis for reverse tracing. To avoid overlooking potential anomalies, each link in this cluster will be analyzed individually.

[0074] When clustering is excluded, anomaly identification is performed on each node of each link, and the status of each deployed component is assessed to check for issues such as signal interference, disconnection, and data packet loss. Once the identification results reach the alarm threshold, the concentrator will be triggered to generate a component alarm signal, notifying the operation and maintenance system to maintain or replace the target component. As the core aggregation node in the system, the concentrator undertakes the critical functions of data collection, judgment, and alarm generation. The alarm signals it generates are sent to the manual or automated operation and maintenance system through the management platform.

[0075] Furthermore, this application also includes: wherein, the cluster validity verification includes calculating the number of abnormal acquisition terminals and the frequency of overlapping deployment elements for each abnormal transmission link cluster; when both the number of abnormal acquisition terminals and the frequency of overlapping deployment elements are greater than or equal to the corresponding expected threshold, the cluster validity verification passes; when either the number of abnormal acquisition terminals or the frequency of overlapping deployment elements is less than the corresponding expected threshold, the cluster validity verification fails.

[0076] Specifically, during the cluster validity verification process, the number of abnormal data collection terminals contained in each abnormal transmission link cluster is calculated. This represents how many electricity information collection terminals in an aggregated link set have experienced abnormal status. The number of abnormal data collection terminals is a key indicator for measuring cluster density and the degree of concentrated anomalies. For example, if a cluster contains 20 terminals, and 15 of them experience communication failures or data loss, the cluster exhibits strong anomaly concentration.

[0077] Next, it is necessary to calculate the overlap frequency of deployment components, which refers to the frequency with which deployment components shared by multiple abnormal transmission links in the cluster occur within the cluster. Deployment components include communication modules, power modules, data buffer modules, etc., which affect the health of the links. The higher the overlap frequency, the more likely the links rely on the same infrastructure, and the failure may originate from shared components.

[0078] Subsequently, when both the number of abnormal data collection terminals and the frequency of overlapping deployment components are greater than or equal to the corresponding expected thresholds, the cluster validity verification is considered successful. The expected thresholds are pre-set empirical or model parameters, such as a threshold of 10 for the number of abnormal data collection terminals and a threshold of 0.6 for the frequency of overlapping deployment components. When the actual values ​​meet these two conditions, it indicates that the cluster is not only abnormally concentrated but also has a common cause relationship in its structure, making it suitable for subsequent anomaly tracing.

[0079] Conversely, if either the number of abnormally collected terminals or the frequency of overlapping deployment components falls below the set expected threshold, the cluster's validity verification is considered unsuccessful. For example, if a cluster contains 15 links but only 3 abnormal terminals, or the frequency of overlapping deployment components is only 0.2, it indicates that the cluster's aggregation characteristics are weak and it cannot serve as a reliable basis for abnormal aggregation. Therefore, the cluster should be abandoned and analysis should proceed to the single-link level.

[0080] In summary, the abnormal alarm method for power consumption information collection terminals based on state monitoring provided in this application has the following technical effects: by achieving the technical goal of abnormal cluster modeling based on link overlap relationship and reverse tracing and locating shared deployment components, it achieves the technical effects of improving the accuracy of abnormal detection, shortening the fault response time, and enhancing the level of intelligent operation and maintenance.

[0081] Example 2: Based on the same inventive concept as the power consumption information collection terminal anomaly alarm method based on status monitoring in the previous examples, this application also provides a power consumption information collection terminal anomaly alarm device based on status monitoring. Please refer to the appendix. Figure 2 The system includes: an integrated deployment structure acquisition module 11, used to acquire the integrated deployment structure of electricity information collection terminals, the integrated deployment structure including multiple electricity information collection terminals and connected integrated deployment elements; a status monitoring dataset acquisition module 12, used to perform status monitoring on each electricity information collection terminal, acquire a status monitoring dataset, and determine the transmission link of any status monitoring dataset and each deployment element on the transmission link according to the receiving end of the status monitoring dataset; an anomaly detection module 13, used to perform anomaly detection on the transmitted status monitoring dataset by the corresponding receiving end, and mark the abnormal status as an abnormal candidate transmission link; a link overlap analysis module 14, used to perform link overlap analysis on the abnormal candidate transmission links, extract abnormal transmission link clusters, perform reverse tracing of abnormal elements according to the abnormal transmission link clusters, and locate the abnormal deployment elements within the abnormal transmission link clusters; and an element alarm signal reporting module 15, used to trigger the concentrator to generate an element alarm signal and report it according to the abnormal deployment element.

[0082] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on status monitoring is also used for: wherein the status monitoring dataset 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 the transmission link identifier between the receiving end and the status monitoring dataset of each power consumption information collection terminal; and performing link element parsing based on the transmission link identifier of the transmission logic association table to determine each deployed element on the transmission link.

[0083] Furthermore, the abnormal alarm device for the power consumption information acquisition terminal based on status monitoring is also used to: extract the operating characteristics of the status monitoring dataset, including data signal fluctuations, ACK response rate, data missingness, and data latency; obtain historical status monitoring datasets using a sliding window trend algorithm; perform feature deviation anomaly analysis between the operating characteristics of the historical status monitoring datasets and the operating characteristics of the status monitoring datasets; and output feature deviation values; if the feature deviation value is greater than a preset deviation threshold, mark the transmission link corresponding to the status monitoring dataset as having an abnormal status and output it as an abnormal candidate transmission link.

[0084] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on status monitoring is also used for: outputting abnormal candidate transmission links and submitting an information record table for the abnormal candidate transmission links; wherein, the information record table records items including 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 abnormality occurrence time window; and calling the information record table to perform link overlap analysis on the abnormal candidate transmission links.

[0085] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on status monitoring is also used for: performing structured processing on the abnormal candidate transmission links, where each node on the abnormal candidate transmission links represents a deployment element; collecting the structured abnormal candidate transmission link set; constructing an overlap analysis matrix of the abnormal candidate transmission link set; extracting connected components from the overlap analysis matrix; and extracting abnormal transmission link clusters, wherein each abnormal transmission link cluster contains abnormal candidate transmission links with an overlap degree greater than a preset overlap threshold.

[0086] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on status monitoring is also used for: 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.

[0087] Furthermore, the power consumption information collection terminal anomaly alarm device based on status monitoring is also used for: outputting a complete set of components according to the abnormal transmission link cluster; defining the 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 the terminal anomaly weight, wherein the terminal anomaly weight is the number of abnormal collection terminals in the abnormal transmission link cluster; and performing reverse tracing of abnormal components based on the abnormal overlap degree of each component according to the terminal anomaly weight, identifying components with an abnormality index greater than a preset anomaly index as abnormal deployment components.

[0088] Furthermore, the abnormal alarm device of the power consumption information collection terminal based on status monitoring is also used for: after extracting the abnormal transmission link cluster, performing cluster validity verification on the abnormal transmission link cluster; when the validity verification passes, performing reverse tracing of 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 a component alarm signal.

[0089] Furthermore, the abnormal alarm device for the power consumption information collection terminal based on status monitoring is also used for: wherein, the cluster validity verification includes calculating the number of abnormal collection terminals and the frequency of overlapping deployment elements for each abnormal transmission link cluster; when both the number of abnormal collection terminals and the frequency of overlapping deployment elements are greater than or equal to the corresponding expected threshold, the cluster validity verification passes; when either the number of abnormal collection terminals or the frequency of overlapping deployment elements is less than the corresponding expected threshold, the cluster validity verification fails.

[0090] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The abnormal alarm method and specific examples of the power consumption information collection terminal based on state monitoring in the aforementioned embodiment 1 are also applicable to the abnormal alarm device of the power consumption information collection terminal based on state monitoring in this embodiment. Through the foregoing 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. Therefore, for the sake of brevity, it will not be described in detail here.

[0091] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0092] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for abnormal alarm of electricity consumption information collection terminal based on condition monitoring, characterized in that, The methods include: An integrated deployment structure for acquiring electricity consumption information collection terminals is obtained, the integrated deployment structure including multiple electricity consumption information collection terminals and connected integrated deployment elements; Status monitoring is performed on each electricity consumption information collection terminal to obtain status monitoring datasets. The transmission link of any status monitoring dataset and each deployed element on the transmission link are determined according to the receiving end of the status monitoring dataset. The corresponding receiving end performs anomaly detection on the transmitted status monitoring dataset, and if the status is abnormal, it is marked as an abnormal candidate transmission link. Perform link overlap analysis on the abnormal candidate transmission links, extract abnormal transmission link clusters, and perform reverse tracing of abnormal components according to the abnormal transmission link clusters to locate abnormal deployment components within the abnormal transmission link clusters. The concentrator is triggered by the abnormal deployment element to generate an element alarm signal and report it. Perform link overlap analysis on the abnormal candidate transmission links to extract abnormal transmission link clusters. The method includes: The abnormal candidate transmission links are structured, and each node on the abnormal candidate transmission link represents a deployment element; Collect a set of anomalous candidate transmission links after structured processing; Construct an overlap analysis matrix for the set of abnormal candidate transmission links, extract connected components from the overlap analysis matrix, and extract abnormal transmission link clusters, wherein each abnormal transmission link cluster contains abnormal candidate transmission links with an overlap degree greater than a preset overlap threshold. The method for constructing the overlap analysis matrix of the abnormal candidate transmission link set includes: Select any two links from the set of abnormal candidate transmission links and calculate the number of shared deployment elements; The overlap index of any two links is calculated 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. Based on the overlap index, obtain the overlap analysis matrix corresponding to the set of abnormal candidate transmission links; The method for tracing abnormal components back to their source within the abnormal transmission link cluster to locate abnormally deployed components includes: According to the aforementioned abnormal transmission link cluster, output the complete set of components; Define the abnormal overlap degree for each element, whereby the abnormal overlap degree is the proportion of the element appearing in the abnormal transmission link cluster; Define a terminal anomaly weight, which is the number of abnormal acquisition terminals in the abnormal transmission link cluster; Based on the terminal anomaly weight, the anomaly overlap of each component is used to trace the abnormal components in reverse, and components with an abnormality index greater than the preset anomaly index are identified as abnormal deployment components.

2. The method for abnormal alarm of electricity information collection terminal based on status monitoring as described in claim 1, characterized in that, The method involves determining the transmission link for any state monitoring dataset and the deployment elements on that transmission link according to the receiving end of the state monitoring dataset. include: The status monitoring dataset includes power supply status data, communication quality data, data transmission behavior data, and heartbeat status data. Establish a transmission logic association table, which serves as the transmission link identifier between the receiving end and the status monitoring dataset of each electricity consumption information collection terminal; Based on the transmission link identifier in the transmission logic association table, link element parsing is performed to determine each deployment element on the transmission link.

3. The method for abnormal alarm of electricity consumption information collection terminal based on condition monitoring as described in claim 1, characterized in that, The corresponding receiving end performs anomaly detection on the transmitted status monitoring dataset, including the following methods: Extract the operational characteristics of the status monitoring dataset, including data signal fluctuations, ACK response rate, data missingness, and data latency; A sliding window trend algorithm is used to obtain historical status monitoring datasets. The operational characteristics of the historical status monitoring datasets are compared with the operational characteristics of the current status monitoring datasets to perform feature deviation anomaly analysis and output feature deviation values. If the feature deviation value is greater than the preset deviation threshold, the transmission link corresponding to the status monitoring dataset is marked as having an abnormal status and output as an abnormal candidate transmission link.

4. The method for abnormal alarm of electricity consumption information collection terminal based on status monitoring as described in claim 3, characterized in that, The output is the abnormal candidate transmission links, and the information record table of the abnormal candidate transmission links is submitted. The information record table includes the terminal list of each abnormal candidate transmission link, the sequence of deployment elements involved in the transmission path, the abnormality type, and the time window of the abnormality occurrence. The information record table is invoked to perform link overlap analysis on the abnormal candidate transmission links.

5. The method for abnormal alarm of electricity information collection terminal based on status monitoring as described in claim 1, characterized in that, After extracting the abnormal transmission link clusters, the cluster validity is verified. When the validity verification passes, the abnormal components are traced back to their source according to the abnormal transmission link clusters. 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 identified as abnormal, triggering the concentrator to generate an alarm signal.

6. The method for abnormal alarm of electricity information collection terminal based on condition monitoring as described in claim 5, characterized in that, in, Cluster validity verification includes calculating the number of abnormal acquisition terminals and the frequency of overlapping deployment elements for each abnormal transmission link cluster; When the number of abnormal collection terminals and the frequency of overlap of deployment elements are both greater than or equal to the corresponding expected threshold, the cluster validity verification is passed. If either the number of abnormal acquisition terminals or the frequency of overlap of deployment elements is less than the corresponding expected threshold, the cluster validity verification fails.

7. An abnormal alarm device for an electricity consumption information collection terminal based on condition monitoring, characterized in that, The steps for implementing the abnormal alarm method for the power consumption information collection terminal based on condition monitoring as described in any one of claims 1 to 6 include: An integrated deployment structure acquisition module is used to acquire the integrated deployment structure of electricity information collection terminals, wherein the integrated deployment structure includes multiple electricity information collection terminals and connected integrated deployment elements; The status monitoring dataset acquisition module is used to monitor the status of each electricity information collection terminal, acquire the status monitoring dataset, and determine the transmission link of any status monitoring dataset and each deployed element on the transmission link according to the receiving end of the status monitoring dataset. The anomaly detection module is used by the corresponding receiving end to perform anomaly detection on the transmitted status monitoring dataset. If the status is abnormal, it is marked as an abnormal candidate transmission link. The link overlap analysis module is used 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 link clusters, and locate abnormal deployment components within the abnormal link clusters. The component alarm signal reporting module is used to generate and report component alarm signals based on the abnormal deployment component triggering the concentrator.

Images