CN120044302A - Intelligent electric energy meter capable of monitoring and reporting operation errors - Google Patents
Intelligent electric energy meter capable of monitoring and reporting operation errors Download PDFInfo
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Abstract
本发明涉及电力状态监控技术领域，具体为一种运行误差可监测上报的智能电能表，电能表包括误差运行参数监控模块、误差时空关联建模模块、误差传播路径分析模块、误差动态调控模块、误差适应性验证模块、误差传递状态汇总模块。本发明中，通过实时捕获运行偏差并标记异常时间段及节点，提升异常事件的定位与记录能力，构建异常分布全景，支持路径优化，对权重值和影响因子分析筛选高误差路径，优化误差传播层次结构，动态调整负载比例和连接路径，配置多路径降低传递风险，增强系统鲁棒性，实时监测和异常验证提高状态响应和误差修正精度，归档分析提取关键信息生成上报记录，实现电能表误差监控的智能化与高效化。
The present invention relates to the technical field of power state monitoring, and specifically to an intelligent electric energy meter whose operation error can be monitored and reported, and the electric energy meter includes an error operation parameter monitoring module, an error spatiotemporal correlation modeling module, an error propagation path analysis module, an error dynamic control module, an error adaptability verification module, and an error transmission state summary module. In the present invention, by real-time capture of operation deviations and marking of abnormal time periods and nodes, the positioning and recording capabilities of abnormal events are improved, a panoramic view of abnormal distribution is constructed, path optimization is supported, weight values and influencing factors are analyzed to screen high-error paths, the error propagation hierarchy is optimized, the load ratio and connection path are dynamically adjusted, multiple paths are configured to reduce transmission risks, and system robustness is enhanced. Real-time monitoring and abnormal verification improve the accuracy of state response and error correction, and archive analysis extracts key information to generate reporting records, thereby realizing intelligent and efficient electric energy meter error monitoring.
Description
Technical Field
The invention relates to the technical field of power state monitoring, in particular to an intelligent electric energy meter with a monitoring and reporting function of operation errors.
Background
The technical field of power state monitoring mainly focuses on comprehensive monitoring and management of the running state of a power system, including the collection of the running parameters of power equipment, power quality analysis, fault early warning and diagnosis, and the transmission and processing of real-time data, and is characterized in that by intelligent and automatic technology, the key nodes in the power system are monitored with high precision, the stability and the safety of power supply are ensured, meanwhile, the operation efficiency is improved, and typical application scenes comprise power grid state monitoring, load analysis, power equipment operation fault diagnosis and the like, and the method is widely used in modern power systems.
The intelligent electric energy meter capable of monitoring and reporting the operation errors is advanced equipment integrating metering, monitoring and data reporting functions, and is mainly used for accurately metering electric energy consumption, simultaneously monitoring the operation state of the equipment in real time, particularly the automatic detection and reporting functions of the errors, and can help users to know the working condition of the electric energy meter in time and improve the accuracy and reliability of electric energy metering. The intelligent electric energy meter is widely applied to industrial, commercial and household electric field scenes and is an important component of modern power system management.
In the prior art, in the anomaly analysis of data acquisition and monitoring nodes, a depth depiction and dynamic regulation means for parameter anomaly distribution are lacked, so that a system can only realize simple detection and early warning of anomalies, but accurate anomaly time interval and node association information are difficult to provide, electric energy meter monitoring mainly focuses on static records of single parameters, the analysis of spatial distribution and influence factors of anomaly propagation paths is ignored, effective identification and optimization of high-duty-ratio paths are difficult to realize, and the defects of real-time load regulation and multipath transmission mechanisms are difficult to realize, so that the instability of the system is easily caused under the condition of uneven load distribution or single-path faults in the prior art, the dynamic monitoring and adaptability verification capability of the anomaly paths is weak, and an archiving analysis and efficient feedback mechanism based on actual operation data is lacked, so that key node problems cannot be captured and processed in time due to the limitation, and the reliability and accuracy of electric energy meter monitoring are reduced.
Disclosure of Invention
In order to solve the technical problems that in the prior art, in the abnormal analysis of data acquisition and monitoring nodes, due to the lack of a deep characterization and dynamic regulation means for parameter abnormal distribution, a system can only realize simple detection and early warning of abnormality, but accurate abnormal time interval and node association information are difficult to provide, electric energy meter monitoring mainly focuses on static records of single parameters, the analysis of space distribution and influence factors of abnormal propagation paths is ignored, effective identification and optimization of high-duty-ratio paths are difficult to realize, and the loss of real-time load regulation and multi-path transmission mechanisms is difficult to realize, so that the system instability is easily caused under the condition of uneven load distribution or single-path faults in the prior art, the dynamic monitoring and adaptability verification capability of the abnormal paths is weak, and the archiving analysis and the efficient feedback mechanism based on actual operation data are lacked, and the limitation causes that key node problems can not be captured and processed in time, so that the monitoring reliability and accuracy of the electric energy meter are reduced. The technical scheme is as follows:
In one aspect, an intelligent electric energy meter capable of monitoring and reporting operation errors is provided, and the electric energy meter comprises:
the error operation parameter monitoring module periodically collects current values, voltage values and load power values of the electric energy meter, performs abnormal state extraction, time sequencing and interval division, and establishes parameter abnormal sequence records;
the error space-time correlation modeling module extracts abnormal node distances and adjacency relations based on the parameter abnormal sequence records, performs space grouping annotation and time-frequency amplitude analysis, and generates an error space-time distribution matrix;
The error propagation path analysis module extracts node weight values and influence factors based on the error space-time distribution matrix, accumulates node propagation relations, screens high error paths and constructs an error propagation path map;
the error dynamic regulation and control module regulates the load proportion and the connection path based on the error transmission path map, screens standby nodes, distributes low-risk transmission paths and generates a dynamic load distribution table;
the error adaptability verification module is used for acquiring load data, monitoring a current and voltage change range, verifying path stability, recording response conditions and acquiring node adaptability verification results based on the dynamic load distribution table;
And the error transfer status summarizing module collects and files error data based on the node adaptability verification result, analyzes the node error change condition, extracts key data points and generates an operation error monitoring reporting record table.
The parameter anomaly sequence record comprises an anomaly time period, an anomaly node mark and anomaly parameter value distribution, the error space-time distribution matrix comprises inter-node distance grouping, space correlation marking, time sequence frequency analysis and time sequence amplitude analysis, the error transmission path map comprises node transmission weight, influence factor distribution, propagation path direction and path hierarchical structure, the dynamic load distribution table comprises key node load proportion, standby node configuration, low risk path distribution and multipath switching scheme, the node adaptability verification result comprises key node anomaly mark, path response state, current-voltage variation range monitoring result and path connection stability verification, and the operation error monitoring report record table comprises error data archiving record, inter-node error variation analysis, adaptability verification information extraction and key data point statistics.
As a further aspect of the present invention, the error operation parameter monitoring module includes:
The data capturing submodule periodically reads current, voltage and power data based on the working state of the electric energy meter, performs data acquisition according to a set time interval, records signal loss and fluctuation, and stores acquired data after verification to obtain running state data;
the abnormality detection submodule compares current, voltage and power ranges based on the running state data, screens abnormal data, marks frequent and sudden fluctuation, sorts and eliminates invalid abnormal points to obtain an abnormal data set;
The abnormal time sequence analysis submodule sorts abnormal data according to the abnormal data set and the time stamp, divides a fixed time interval, calculates abnormal frequency of the interval, marks frequent abnormal time periods, extracts nodes and associates events, and establishes parameter abnormal sequence records.
As a further aspect of the present invention, the error space-time correlation modeling module includes:
the node space relation analysis submodule extracts node coordinate data based on the parameter abnormal sequence record, calculates node distance in a pairing mode, normalizes a distance value, marks unmatched nodes as abnormal points and generates a node distance matrix;
the space association grouping submodule extracts adjacent relation node pairs based on the node distance matrix, groups and marks nodes according to a distance threshold value, maps grouping information to the matrix, sorts and filters abnormal node pairs, and generates a space association marking result;
And the cross node analysis submodule analyzes the time sequence change trend based on the space association labeling result, analyzes node time association in combination with the grouping data, marks abnormal association nodes and generates an error space-time distribution matrix.
As a further aspect of the present invention, the error propagation path analysis module includes:
The weight value extraction submodule analyzes the node numerical relation based on the error space-time distribution matrix, extracts the weight value of the transmission path, matches and adjusts the weight value data, and generalizes the node association relation to obtain the path transmission weight data;
the path weight superposition submodule extracts a path node sequence based on the path transfer weight data, accumulates node weight values, screens node combinations with high weight, sorts and screens node paths, checks the integrity of the accumulation process and generates a high-weight propagation path set;
The node transmission construction submodule analyzes the node transmission sequence by adopting a dynamic time warping algorithm based on the high-weight transmission path set, extracts the transmission direction, classifies and reorganizes the node transmission structure, sorts and organizes the continuous topological structure, sorts the transmission paths of the nodes into the continuous topological structure, and generates an error transmission path map.
As a further aspect of the present invention, the formula of the dynamic time warping algorithm is as follows:
Wherein R ij represents an abnormal association score value between the node i and the node j, S i and S j represent spatial association strength values of the node i and the node j, T ij represents a time sequence difference value of the node i and the node j, C i and C j represent classification characteristic values of the node i and the node j in the packet data, α is a weight coefficient of spatial association, β is an adjustment coefficient of time sequence change, and γ is a weight coefficient of packet characteristics.
As a further aspect of the present invention, the error dynamic regulation module includes:
The load distribution adjustment submodule extracts key node load proportion data based on the error transfer path map, screens out super-threshold nodes, repartitions connection path load data, distributes adjustment loads to adjacent nodes and acquires load adjustment distribution data;
The standby node screening submodule analyzes the nodes with low load values based on the load adjustment distribution data, screens the residual capacity nodes as standby nodes, marks the paths as standby paths, updates the node-path association structure and generates standby node allocation results;
and the path load configuration submodule extracts the path information transmitted by the standby node based on the standby node allocation result, redistributes high load path data, transfers load to the standby path, updates the load value and verifies the structure, and generates a dynamic load allocation table.
As a further aspect of the present invention, the error adaptability verification module includes:
The node load monitoring submodule analyzes node load data based on the dynamic load distribution table, tracks a load change range, marks out over-range nodes, records the corresponding relation between a load value and a time point and acquires the dynamic node load data;
the path stability verification submodule analyzes the marked node connection path based on the dynamic node load data, checks the path load recovery time and stability, screens the continuous change path and acquires path response state data;
The node adaptability recording submodule analyzes the key nodes of the abnormal path based on the path response state data, marks the overload nodes in a classified mode, calculates the adaptability adjustment load value of the nodes, records the node load and delay characteristics, optimizes the node hierarchical structure and generates a node adaptability verification result.
As a further aspect of the present invention, an adaptive adjustment load value of the node is calculated according to the formula:
Wherein L adj represents an adaptive load value of the node, L i represents a current load value of the ith node, μ represents an average value of the node load, D i represents a delay time of the ith node, μ d represents an average value of the node delay, C node represents a connection number of the node, and W 1、W2、W3 is a weight parameter.
As a further aspect of the present invention, the error transfer status rollup module includes:
The error data profiling sub-module extracts node error information, sorts and sorts time points and node numbers based on the node adaptability verification result, eliminates repeated records, files according to time and node sequence, and generates node error archiving records;
The key error extraction submodule extracts error data among nodes based on the node error filing record, marks out over-range data points, sorts node numbers and time sequence distribution, sorts error values and marks associated paths, and generates a key error data point set;
And the error monitoring and summarizing submodule analyzes a distribution rule and a node path based on the key error data point set, records the node and path error accumulation condition, sorts the association relationship into a structured table, integrates the node state and the error information, and generates an operation error monitoring and reporting record table.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
The method has the advantages that through periodic acquisition of data such as current, voltage and load power of the electric energy meter and extraction of abnormal state data, running deviation can be captured in real time, abnormal time periods and related nodes can be marked, positioning and recording capacity of abnormal events is enhanced through accurate data capture and time distinction, space association grouping is conducted by utilizing distance values and adjacent relations among the abnormal nodes, correlation of cross nodes is analyzed by combining frequency and amplitude of time sequences, panoramic expression of abnormal distribution is achieved, and basic support is provided for follow-up path optimization. The method has the advantages that the weight values and the influence factors among the nodes are analyzed, the node propagation direction and the hierarchical relation are combined for accumulation, the critical paths with higher error occupation are screened out, the visualization level of error sources is improved, the hierarchical structure of error propagation is optimized, the load proportion data and the connection paths are dynamically adjusted, the risk of single-path transmission is reduced through multi-path configuration, the robustness of the system is guaranteed, the node load is monitored in real time, the abnormal paths are verified, the state response is more timely, and the accuracy of error correction is guaranteed. By archiving and analyzing the error data, the abnormal information of the key nodes is extracted, the report records are generated, the transparency and the analysis value of the monitoring data of the electric energy meter are improved, and the intelligent, accurate and efficient monitoring of the error of the electric energy meter is realized in a data-driven dynamic management and regulation mode.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an intelligent ammeter with operation error monitoring and reporting functions, which is provided by the embodiment of the invention;
FIG. 2 is a schematic diagram of a power meter frame according to the present invention;
FIG. 3 is a flow chart of the error operating parameter monitoring module of the present invention;
FIG. 4 is a flow chart of the error space-time correlation modeling module of the present invention;
FIG. 5 is a flow chart of the error propagation path analysis module of the present invention;
FIG. 6 is a flow chart of the error dynamic adjustment module of the present invention;
FIG. 7 is a flow chart of the error adaptability verification module in the present invention;
FIG. 8 is a flow chart of the error delivery status rollup module of the present invention.
Detailed Description
The technical scheme of the invention is described below with reference to the accompanying drawings.
In embodiments of the invention, words such as "exemplary," "such as" and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the term use of an example is intended to present concepts in a concrete fashion. Furthermore, in embodiments of the present invention, the meaning of "and/or" may be that of both, or may be that of either, optionally one of both.
In the embodiments of the present invention, "image" and "picture" may be sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized. "of", "corresponding (corresponding, relevant)" and "corresponding (corresponding)" are sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized.
In embodiments of the present invention, sometimes a subscript such as W 1 may be written in a non-subscript form such as W1, and the meaning of the expression is consistent when de-emphasizing the distinction.
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention provides an intelligent electric energy meter with a monitoring and reporting operation error, which is shown in fig. 1-2, wherein the intelligent electric energy meter with the monitoring and reporting operation error is shown as intention, and the system comprises:
The error operation parameter monitoring module periodically collects current values, voltage values and load power values of the electric energy meter, extracts operation state data exceeding an abnormal threshold value, marks a time period and associated nodes of frequent abnormality through sequencing and interval division of time distribution, and establishes parameter abnormality sequence records;
The error space-time correlation modeling module extracts distance values and adjacent relations among abnormal nodes based on the parameter abnormal sequence records, performs grouping annotation on space correlation among the nodes, analyzes cross node correlation by combining frequency and amplitude of time sequences, and generates an error space-time distribution matrix;
the error propagation path analysis module extracts weight values and influence factors of transfer relations among the nodes based on the error space-time distribution matrix, integrates and superimposes the weight values on propagation paths of the influence nodes, screens paths with high error occupation ratio, draws node propagation directions and hierarchical relations, and constructs an error transfer path map;
the error dynamic regulation and control module regulates proportion data and connection paths of load distribution of key nodes based on an error transfer path map, screens standby nodes, redistributes connection values, performs load configuration and switching operation, distributes a plurality of low-risk transfer paths and generates a dynamic load distribution table;
The error adaptability verification module is used for acquiring node load data based on a dynamic load distribution table, dynamically monitoring the variation range of current and voltage, verifying the stability of path connection, extracting key nodes with abnormal states, recording path response conditions and acquiring a node adaptability verification result;
The error transfer status summarizing module collects and files error data of the monitoring nodes of the electric energy meter based on the node adaptability verification result, analyzes the error change condition among the nodes, records and analyzes error information generated in the node adaptability verification process, extracts key data points in the error information, and generates an operation error monitoring reporting record table.
The parameter anomaly sequence record comprises an anomaly time period, an anomaly node mark and anomaly parameter value distribution, the error space-time distribution matrix comprises inter-node distance grouping, space correlation marking, time sequence frequency analysis and time sequence amplitude analysis, the error transmission path map comprises node transmission weight, influence factor distribution, propagation path direction and path hierarchical structure, the dynamic load distribution table comprises key node load proportion, standby node configuration, low risk path distribution and a multi-path switching scheme, the node adaptability verification result comprises key node anomaly marks, path response states, current and voltage change range monitoring results and path connection stability verification, and the operation error monitoring report record table comprises error data archiving records, inter-node error change analysis, adaptability verification information extraction and key data point statistics.
Specifically, as shown in fig. 2 and 3, the error operation parameter monitoring module includes:
The data capturing submodule periodically reads current, voltage and power data based on the working state of the electric energy meter, performs data acquisition according to a set time interval, records signal loss and fluctuation, and stores acquired data after verification to obtain running state data;
When each acquisition is performed, an electric energy meter reading instruction is started through control logic, a sampling period and a sampling window are set, original data acquired each time are written into a temporary storage area, high-frequency components in the period are separated according to acquired current and voltage data, a low-frequency stable part is screened as effective data, instantaneous power, average power and fluctuation range are calculated for power data, the change trend of a power factor is recorded, a signal strength threshold is set, a signal loss event and the duration of the signal loss event are recorded, the signal fluctuation amplitude is recorded in a grading manner, the specific moment when the time label is lost and fluctuation occurs is adopted, the integrity of the signal record is ensured, after each acquisition is completed, the accuracy of the acquired data is checked through a data check rule, for example, point-by-point check is performed on the physical upper and lower limit ranges of current and voltage, point value data which are obviously not in accordance with reality are removed, the acquisition error rate is counted, after the check is performed, the effective data is written into the permanent storage area, running state data is formed according to the sequence of time stamps, the data is organized into a plurality of file storage groups, and the data is convenient for subsequent processing and use.
The abnormality detection sub-module compares current, voltage and power ranges based on the running state data, screens abnormal data, marks frequent and sudden fluctuation, sorts and eliminates invalid abnormal points to obtain an abnormal data set;
Comparing each group of numerical values in the running state data with a preset normal range by a method of item-by-item inspection, marking the numerical values exceeding the range as preliminary abnormal points, judging whether the abnormal points belong to burst abnormality or frequent fluctuation by combining the data of a plurality of sampling points before and after each abnormal point, judging the burst abnormality by calculating the absolute variation of the values of adjacent points, confirming the frequent fluctuation by counting the standard deviation of the variation times and the amplitude of the adjacent sampling points, eliminating abnormal data caused by instantaneous sampling errors of an electric energy meter or short-time signal interference, for example, eliminating the duration of the abnormal points, eliminating isolated abnormal points in a single sampling period, carrying out cluster analysis on the marked frequent fluctuation data, classifying the fluctuation phenomenon with similar characteristics into one group, eliminating low-amplitude fluctuation which does not contribute to analysis, classifying the marked invalid abnormal points and eliminating the abnormal data from the abnormal data, ensuring the accuracy of subsequent analysis, recording the statistical information in the screening process, forming an auxiliary statistical file of the abnormal data, and finally obtaining the classified and cleaned abnormal data set.
The abnormal time sequence analysis sub-module sorts abnormal data according to the abnormal data set and the time stamp, divides a fixed time interval, calculates abnormal frequency of the interval, marks frequent abnormal time periods, extracts nodes and associates events, and establishes parameter abnormal sequence records;
Setting fixed time intervals, for example, dividing intervals according to minutes or hours, counting abnormal data in each time interval, calculating the occurrence frequency of the abnormal data, obtaining the abnormal frequency by accumulating count values of the abnormal data in each interval, marking the interval with higher frequency value as a focused attention interval, classifying the abnormal data in the interval according to event properties, for example, current abnormality, voltage abnormality and power abnormality, recording the distribution condition of each abnormality in the interval, extracting the node with highest occurrence frequency as an important parameter of time sequence analysis, combining a time label, associating the frequently-occurring abnormal interval with an external event, for example, using an electrical peak or equipment maintenance period, forming a preliminary association record of the event and the abnormality, carrying out sequence clustering on abnormal points repeatedly occurring in a time sequence, dividing the abnormal phenomenon with similar characteristics into a class, extracting the characteristics of each class, summarizing, establishing parameter abnormality sequence records for all abnormal time sequence data, marking the abnormal class, occurrence time, frequency and intensity, and arranging according to the interval, and providing complete abnormal base data for further deep analysis.
Specifically, as shown in fig. 2 and 4, the error space-time correlation modeling module includes:
The node space relation analysis submodule extracts node coordinate data based on the parameter abnormal sequence record, calculates node distance in a pairing mode, normalizes the distance value, marks unmatched nodes as abnormal points and generates a node distance matrix;
Dividing the coordinates of each node into independent coordinate pairs according to the space distribution rule of the nodes, storing the independent coordinate pairs as computable structured data, pairing to calculate Euclidean distance between the nodes, traversing the coordinate pairs of all the nodes according to a fixed pairing sequence, recording the distance calculation value of each pair of the nodes to a temporary matrix, normalizing the obtained distance data after all the node pairing is finished, scaling the distance value to a [0,1] interval by a maximum and minimum normalization method, storing the normalized value in association with the original distance data, screening unmatched nodes by analyzing the node existence state in the parameter abnormal sequence record, directly marking the isolated nodes which do not appear in any node pairing as abnormal points, recording the abnormal state of the abnormal points, integrating the normalized distance value with the abnormal point marking information, generating a node distance matrix which contains the distance value between all the nodes and the abnormal marking state of unmatched nodes, and providing input data support for a subsequent analysis module.
The space association grouping sub-module extracts adjacent relation node pairs based on the node distance matrix, groups and marks nodes according to a distance threshold value, maps grouping information to the matrix, sorts and filters abnormal node pairs, and generates a space association marking result;
Selecting node pairs smaller than a set distance threshold value as adjacent relation node pairs according to a normalized value of the node distance, recording adjacent states of the node pairs, sorting each group of adjacent relation node pairs according to node numbers and distance values so as to facilitate subsequent grouping and labeling operations, grouping the adjacent nodes according to the distance threshold value, acquiring identification information of each group of nodes through recursion searching of nodes in the same group, ensuring that all nodes in the same group have transitive connection, mapping grouping information to a node matrix, updating grouping attribution information of each node, simultaneously distributing unique grouping identifiers for each group of nodes, screening and filtering the node pairs in the group, wherein the node pairs marked as abnormal are ensured to only contain valid nodes in each group association, rechecking grouping results, eliminating isolated groups and invalid groups, generating a space association labeling result, including a node list of each group and adjacent relation data of each pair of nodes, and simultaneously storing labeling information of abnormal nodes.
The cross node analysis sub-module analyzes the time sequence variation trend based on the space association labeling result, analyzes the node time association in combination with the grouping data, marks the abnormal association nodes and generates an error space-time distribution matrix;
The method comprises the steps of carrying out linear interpolation and differential operation on node time sequence data in each group, extracting time correlation characteristics among nodes, analyzing node time correlation by combining the group data, counting the similarity of time variation trend of each node and adjacent nodes, marking nodes with different time variation trends as abnormal correlation nodes by calculating correlation indexes of node time variation, analyzing the time variation concentration degree of the nodes in the group according to time sequence data distribution of each group of nodes, marking the node classification with variation amplitude which is obviously deviated from an average value as the abnormal correlation nodes, combining the node time correlation characteristics and abnormal node marking states, generating an error time-space distribution matrix, wherein the matrix comprises the time correlation states, the error distribution characteristics and the time-space abnormal distribution characteristics of the nodes, and providing input support for further analysis of error propagation and time-space correlation.
Specifically, as shown in fig. 2 and 5, the error propagation path analysis module includes:
the weight value extraction submodule analyzes the node numerical relation based on the error space-time distribution matrix, extracts the transmission path weight value, matches and adjusts the weight value data, and generalizes the node association relation to obtain the path transmission weight data;
And each node value in the matrix is used as initial input of a weight value, the time and space association relation among the nodes is utilized to analyze the numerical value transmission proportion among adjacent nodes one by one, the weight value of each transmission path is extracted, the transmission influence degree of each path is calculated by comparing the error distribution and time change trend of the adjacent nodes, the numerical range of the transmission weight value is regulated according to the distribution rule, the association strength among the nodes is logically reflected by the weight value, the regulated weight value data is matched, the weight value recalibration is carried out on the weight value abnormal points appearing in the same time period or the same space region, the association relation among the nodes is induced according to the weight regulation result, the participation weight of each group of nodes in the path is extracted, the complete path transmission weight data is induced and arranged, and the structured storage is provided for the subsequent path weight value superposition analysis.
The path weight superposition sub-module extracts a path node sequence based on path transmission weight data, accumulates node weight values, screens node combinations with high weight, sorts and screens node paths, checks the integrity of the accumulation process, and generates a high-weight propagation path set;
Analyzing the transmission sequence among path nodes, accumulating the weight value of each node on the path to form the total weight of the path, recording the intermediate calculation value in the accumulation process, screening the node combination with the highest weight value by comparing the accumulation results of all paths, screening the influence degree of key nodes in the path according to the weight sequence, recording the specific position of the key nodes in the transmission path, further checking the screened high-weight path, checking whether the accumulation process of each path is complete or not segment by segment, ensuring that no weight value omission or error superposition occurs, and finally generating a high-weight propagation path set by combining the screening results, wherein the path set comprises the weight distribution and the accumulation value of all nodes in the path sequence.
The node transmission construction submodule analyzes a node transmission sequence, extracts a transmission direction, classifies and reorganizes a node transmission structure, sorts and organizes a continuous topological structure based on a high-weight transmission path set, sorts and organizes transmission paths of the nodes into a continuous topological structure, and generates an error transmission path map;
the formula of the dynamic time warping algorithm is as follows:
Wherein, R ij represents abnormal association scoring value between node i and node j, S i and S j represent spatial association strength value of node i and node j respectively, T ij represents time sequence difference value of node i and node j, C i and C j represent classification characteristic value of node i and node j in packet data respectively, alpha is weight coefficient of spatial association, beta is adjustment coefficient of time sequence change, gamma is packet characteristic weight coefficient;
Parameter meaning and setting value:
And S i and S j, the spatial correlation strength values of the node i and the node j reflect the weights of the spatial correlation strength values in the spatial correlation labels. Obtained by quantitatively analyzing the geographical position and the spatial relationship of the node, and setting S i＝0.8,Sj =0.5;
And T ij, quantifying the time variation trend deviation degree of the two nodes according to the difference value of the time sequences of the node i and the node j. Setting T ij =2.5 by calculating a Dynamic Time Warping (DTW) distance of the two-node time series;
And C i and C j, namely classifying characteristic values of the node i and the node j in the packet data, and reflecting the packet characteristics of the node. Obtained by performing quantization coding on the attributes of the nodes, and setting C i＝1,Cj =2;
And alpha, a weight coefficient of spatial correlation, which is used for adjusting the influence intensity of the spatial difference in the correlation score. Setting α=1.2 according to correlation analysis determination of the history data;
And beta is an adjustment coefficient of time sequence change and is used for balancing the contribution of time difference to the scoring result. Setting β=0.8 by analysis of variance determination of time series data;
and gamma, grouping characteristic weight coefficient, which is used for enhancing the sensitivity of grouping characteristic to abnormal association detection. Setting γ=0.5 according to the discrete degree determination of the grouping characteristic;
Substituting the parameters into a formula to calculate:
calculating the absolute value of the spatial correlation intensity difference, |s i-Sj |= |0.8-0.5|=0.3;
Calculating the absolute value of the difference of the classification characteristic values, wherein I C i-Cj I= |1-2 I=1;
Calculating a weighted sum in a denominator: beta.T ij+γ·|Ci-Cj |=0.8· 2.5+0.5·1=2+0.5=2.5;
The square root of the denominator is calculated:
Calculating molecules: α·|s i-Sj | = 1.2.0.3=0.36;
Calculating an abnormality association score value R ij:
The result R ij apprxeq 0.228 indicates that a certain degree of abnormal association exists between the node i and the node j, and the grading value can be used for further analyzing and marking the abnormal association nodes to generate an error transfer path map.
Specifically, as shown in fig. 2 and 6, the error dynamic adjustment module includes:
The load distribution adjustment submodule extracts key node load proportion data based on an error transfer path map, screens out super-threshold nodes, repartitions connection path load data, distributes adjustment loads to adjacent nodes and acquires load adjustment distribution data;
Selecting nodes with higher load proportion from the map, screening out nodes exceeding a set load threshold by calculating the total load value and the duty ratio born by the nodes, analyzing the load data in the connecting paths of the super-threshold nodes, repartitioning the load proportion of the paths according to the distribution condition of the loads in the paths, adjusting the load distribution priority, transferring the load part of the super-threshold nodes to the adjacent nodes according to the residual bearing capacity of the adjacent nodes in the load adjustment process, simultaneously recording the paths and the transfer quantity of the load transfer, ensuring the balance after the load redistribution, and after the adjustment is completed, counting the load proportion of each node and each path again to obtain the load adjustment distribution data, and verifying the rationality and the balance of the load adjustment.
The standby node screening submodule analyzes the nodes with low load values based on the load adjustment distribution data, screens the residual capacity nodes as standby nodes, marks the paths as standby paths, updates the node-path association structure and generates a standby node distribution result;
Screening nodes with load values obviously lower than an average load level and residual bearing capacity, marking the nodes as standby nodes, calculating the residual capacity of each standby node, sorting the nodes according to the residual capacity, selecting the node with the largest residual capacity as a priority standby node, marking the node connection path meeting standby conditions as a standby path in combination with path load adjustment, updating the association structure between the nodes and the paths to ensure that the standby nodes and the paths can meet dynamic allocation requirements, and generating a standby node allocation result, wherein the result comprises a standby node list, standby path identifiers and residual capacity distribution.
The path load configuration submodule extracts the transmission path information of the standby node based on the standby node distribution result, redistributes high load path data, transfers load to the standby path, updates the load value and verifies the structure, and generates a dynamic load distribution table;
Analyzing data in a high-load path, selecting a path with load exceeding an adjustment target range for load redistribution, combining residual capacity of a standby node and associated path information, transferring part of load data on the high-load path to the standby path, distributing the adjusted load data according to path priority and standby capacity sequence, updating load values of each node and path after the transfer is completed, simultaneously verifying an adjusted structure, checking integrity and stability in a load distribution process, ensuring that no new overload node or disconnection condition is introduced in the load transfer, generating a dynamic load distribution table, recording real-time load distribution condition of each path and load change value of each node, providing complete load dynamic management basis, and providing support for load optimization and scheduling.
Specifically, as shown in fig. 2 and 7, the error adaptability verification module includes:
The node load monitoring submodule analyzes node load data based on a dynamic load distribution table, tracks a load change range, marks out over-range nodes, records the corresponding relation between a load value and a time point and acquires the dynamic node load data;
Extracting load values of nodes at different time points, tracking the load change range of the nodes, marking out-of-range nodes by comparing the node load values with a set normal load range, analyzing out-of-range load time periods and frequencies of the nodes, recording the relation between the overload values and corresponding time points for each out-of-range node by combining time labels in a dynamic load distribution table, forming an overload time sequence, counting the change amplitude and change rate of the dynamic change process of the node load to identify change rules or burst anomalies, sorting the dynamic load change conditions into a node load change chart after all node data analysis is completed, forming dynamic node load data, ensuring that the data can clearly show real-time load states and historical load change tracks of the nodes, and providing load input data for subsequent path stability verification.
The path stability verification submodule analyzes the marked node connection path based on the dynamic node load data, checks the path load recovery time and stability, screens the continuous change path and acquires path response state data;
Analyzing marked overscope node connection paths, extracting load change conditions of related paths, analyzing time sequences of load transmission among nodes for each path, judging load recovery time of the paths by counting the time length of the load in the paths to a normal range, checking the fluctuation range and frequency of the path load, confirming the load stability of the paths by comparing the standard deviation and the maximum value of the path fluctuation, screening out paths with long load change duration or remarkable fluctuation amplitude, classifying the paths as continuously changed paths, recording state data of each path, including fluctuation start-stop time, fluctuation amplitude, recovery time and the like, sorting the path state data into path response state data, and marking dynamic load characteristics of each path.
The node adaptability recording submodule analyzes key nodes of an abnormal path based on path response state data, marks overload nodes in a classified mode, calculates an adaptability adjustment load value of the nodes, records node load and delay characteristics, optimizes a node hierarchical structure and generates a node adaptability verification result;
calculating an adaptive adjustment load value of the node according to the formula:
Wherein, L adj represents the adaptive adjustment load value of the node, L i represents the current load value of the ith node, mu represents the average value of the node load, D i represents the delay time of the ith node, mu d represents the average value of the node delay, C node represents the connection number of the node, and W 1、W2、W3 is a weight parameter;
formula details and formula calculation derivation process:
The formula is used for calculating an adaptive adjustment load value of the node, and the obtained result is used for measuring the comprehensive adaptability of the node load and delay, so that a quantization basis is provided for node level optimization;
L i represents the current load value of the ith node, the unit is the number of requests per second, the value is obtained through real-time acquisition of path response state data, and the value is set to 120 (the number of requests per second);
Mu represents the average value of all node loads, the unit is the number of requests per second, the average value calculation is performed by using all node load values in the path, and the node load values 100, 120, 140, 110 and 130 are calculated by assuming that the node load values are obtained from the path data
D i denotes the delay time of the ith node in milliseconds, which is obtained by the node response delay monitoring system and is set to 30 milliseconds;
Mu d represents the average value of all node delays, in milliseconds, calculated by averaging the delay data of all nodes in the path, assuming delay time data of 20, 30, 40, 25, 35, calculated
C node represents the connection number of the node, represents the number of other nodes directly connected to the node in the path, and is obtained through path topology structure analysis and is set to be 4;
w 1、W2、W3 is a weight parameter, and the relative importance of load adjustment, the priority of delay optimization and regularization treatment of denominator are respectively controlled, so that the stability and accuracy of the optimization process are ensured;
w 1 = 0.5, for adjusting the extent of influence of the deviation of the load from the average on the overall load adaptability, the weight setting referencing the sensitivity of the load variation to the quality of service;
W 2 = 0.8 for adjusting the influence of the deviation of the delay from the average delay on the adaptation, the weight setting being based on the direct influence of the delay on the path efficiency;
W 3 =2, for regularizing the influence of the denominator part, the setting basis is to avoid instability of the calculation result caused by too small denominator;
Calculating a load deviation square root term:
calculating a delay deviation square root term:
Calculating denominator W 3+log(1+Cnode) =2+log (1+4) =2+log (5) ≡2+0.698= 2.698;
The comprehensive computing node adaptively adjusts the load:
The result L adj is about 2.871 shows that the adaptive adjustment load value of the node measures the comprehensive balance of the current load and the delay, the higher the value is, the closer the load and the delay distribution of the node are to the overall level, the better the adaptability is, and the result can be used as a reference basis for further optimizing the node hierarchical structure.
Specifically, as shown in fig. 2 and 8, the error transfer status rollup module includes:
the error data profiling sub-module extracts node error information, sorts and sorts time points and node numbers based on the node adaptability verification result, eliminates repeated records, files according to time and node sequence, and generates a node error archiving record;
Analyzing error values one by one according to a time stamp and a node number, classifying and sorting the error information into two types of static errors and dynamic errors, rearranging time points in records according to an occurrence time sequence, ensuring that error data can reflect a real time evolution process, carrying out uniqueness verification on the node numbers, eliminating redundant information caused by repeated records or data combination, ensuring that each node number only corresponds to the unique error record, screening repeated fluctuation information caused by error propagation or node transfer characteristics in the error records, eliminating invalid error fluctuation data, archiving all error information according to the time points and the node sequence after the duplication elimination and the sorting are completed, adding indexes to the archived records, ensuring that the error information of a specific node or a specific time period can be quickly retrieved, finally generating a node error archived record, and providing a complete data structure comprising the time sequence, the node numbers and the error values.
The key error extraction submodule extracts error data among nodes based on the node error filing record, marks out the overscope data points, sorts the node numbers and the time sequence distribution, sorts the error values and marks the associated paths, and generates a key error data point set;
Analyzing error transfer characteristics among nodes, screening data according to the amplitude of error values, marking data points which exceed a set error range, grouping error points according to node numbers in combination with the distribution characteristics of time sequences and node numbers for the marked out data points with the overscope, rearranging the error points into a time sequence distribution table, sequencing the error values of each group of nodes according to the amplitude of the error from large to small, marking each data point with an associated transfer path so as to analyze path characteristics in error transfer, screening data points with larger error amplitude and obvious influence range after finishing, classifying the data points into key error points, and forming a key error data point set which comprises node numbers, time sequence distribution, error value size and path marking information.
The error monitoring summarizing submodule analyzes a distribution rule and a node path based on a key error data point set, records the node and path error accumulation condition, arranges association relations into a structured table, integrates node states and error information, and generates an operation error monitoring reporting record table;
Analyzing node and path distribution rules in a data point set, recording an error accumulation value of each node and a propagation condition in a path one by one, analyzing the error accumulation total quantity of each path through accumulation calculation, marking a path with obvious error accumulation as an important monitoring object, arranging error data of the nodes and the paths into a structured table according to time sequence, node numbers and path identifiers so as to comprehensively analyze space and time distribution of the errors, integrating real-time state information and error information of the nodes by combining a node adaptability verification result and a key error data point set, including overload state, error propagation influence and accumulation characteristics of the nodes, and finally generating an operation error monitoring reporting record table which intuitively presents the error distribution, accumulation condition and key characteristics of the nodes and the paths and provides detailed data basis for a follow-up optimizing and improving system.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent ammeter that operation error can monitor and report, its characterized in that, the ammeter includes:
the error operation parameter monitoring module periodically collects current values, voltage values and load power values of the electric energy meter, performs abnormal state extraction, time sequencing and interval division, and establishes parameter abnormal sequence records;
the error space-time correlation modeling module extracts abnormal node distances and adjacency relations based on the parameter abnormal sequence records, performs space grouping annotation and time-frequency amplitude analysis, and generates an error space-time distribution matrix;
The error propagation path analysis module extracts node weight values and influence factors based on the error space-time distribution matrix, accumulates node propagation relations, screens high error paths and constructs an error propagation path map;
the error dynamic regulation and control module regulates the load proportion and the connection path based on the error transmission path map, screens standby nodes, distributes low-risk transmission paths and generates a dynamic load distribution table;
the error adaptability verification module is used for acquiring load data, monitoring a current and voltage change range, verifying path stability, recording response conditions and acquiring node adaptability verification results based on the dynamic load distribution table;
And the error transfer status summarizing module collects and files error data based on the node adaptability verification result, analyzes the node error change condition, extracts key data points and generates an operation error monitoring reporting record table.
2. The intelligent ammeter capable of monitoring and reporting the operation errors according to claim 1, wherein the parameter anomaly sequence records comprise anomaly time periods, anomaly node marks and anomaly parameter value distribution, the error space-time distribution matrix comprises inter-node distance grouping, space correlation marks, time sequence frequency analysis and time sequence amplitude analysis, the error transmission path map comprises node transmission weights, influence factor distribution, propagation path directions and path hierarchical structures, the dynamic load distribution table comprises key node load proportions, standby node configuration, low risk path distribution and a multi-path switching scheme, the node adaptability verification results comprise key node anomaly marks, path response states, current-voltage change range monitoring results and path connection stability verification, and the operation error monitoring and reporting record table comprises error data archiving records, inter-node error change analysis, adaptability verification information extraction and key data point statistics.
3. The intelligent ammeter capable of monitoring and reporting operation errors according to claim 1, wherein the error operation parameter monitoring module comprises:
The data capturing submodule periodically reads current, voltage and power data based on the working state of the electric energy meter, performs data acquisition according to a set time interval, records signal loss and fluctuation, and stores acquired data after verification to obtain running state data;
the abnormality detection submodule compares current, voltage and power ranges based on the running state data, screens abnormal data, marks frequent and sudden fluctuation, sorts and eliminates invalid abnormal points to obtain an abnormal data set;
The abnormal time sequence analysis submodule sorts abnormal data according to the abnormal data set and the time stamp, divides a fixed time interval, calculates abnormal frequency of the interval, marks frequent abnormal time periods, extracts nodes and associates events, and establishes parameter abnormal sequence records.
4. The intelligent ammeter capable of monitoring and reporting operation errors according to claim 1, wherein the error space-time correlation modeling module comprises:
the node space relation analysis submodule extracts node coordinate data based on the parameter abnormal sequence record, calculates node distance in a pairing mode, normalizes a distance value, marks unmatched nodes as abnormal points and generates a node distance matrix;
the space association grouping submodule extracts adjacent relation node pairs based on the node distance matrix, groups and marks nodes according to a distance threshold value, maps grouping information to the matrix, sorts and filters abnormal node pairs, and generates a space association marking result;
And the cross node analysis submodule analyzes the time sequence change trend based on the space association labeling result, analyzes node time association in combination with the grouping data, marks abnormal association nodes and generates an error space-time distribution matrix.
5. The intelligent ammeter capable of monitoring and reporting operation errors according to claim 1, wherein the error propagation path analysis module comprises:
The weight value extraction submodule analyzes the node numerical relation based on the error space-time distribution matrix, extracts the weight value of the transmission path, matches and adjusts the weight value data, and generalizes the node association relation to obtain the path transmission weight data;
the path weight superposition submodule extracts a path node sequence based on the path transfer weight data, accumulates node weight values, screens node combinations with high weight, sorts and screens node paths, checks the integrity of the accumulation process and generates a high-weight propagation path set;
The node transmission construction submodule analyzes the node transmission sequence by adopting a dynamic time warping algorithm based on the high-weight transmission path set, extracts the transmission direction, classifies and reorganizes the node transmission structure, sorts and organizes the continuous topological structure, sorts the transmission paths of the nodes into the continuous topological structure, and generates an error transmission path map.
6. The intelligent ammeter capable of monitoring and reporting operation errors according to claim 5, wherein the formula of the dynamic time warping algorithm is as follows:
Wherein R ij represents an abnormal association score value between the node i and the node j, S i and S j represent spatial association strength values of the node i and the node j, T ij represents a time sequence difference value of the node i and the node j, C i and C j represent classification characteristic values of the node i and the node j in the packet data, α is a weight coefficient of spatial association, β is an adjustment coefficient of time sequence change, and γ is a weight coefficient of packet characteristics.
7. The intelligent ammeter capable of monitoring and reporting operation errors according to claim 1, wherein the error dynamic regulation module comprises:
The load distribution adjustment submodule extracts key node load proportion data based on the error transfer path map, screens out super-threshold nodes, repartitions connection path load data, distributes adjustment loads to adjacent nodes and acquires load adjustment distribution data;
The standby node screening submodule analyzes the nodes with low load values based on the load adjustment distribution data, screens the residual capacity nodes as standby nodes, marks the paths as standby paths, updates the node-path association structure and generates standby node allocation results;
and the path load configuration submodule extracts the path information transmitted by the standby node based on the standby node allocation result, redistributes high load path data, transfers load to the standby path, updates the load value and verifies the structure, and generates a dynamic load allocation table.
8. The intelligent ammeter capable of monitoring and reporting operation errors according to claim 1, wherein the error adaptability verification module comprises:
The node load monitoring submodule analyzes node load data based on the dynamic load distribution table, tracks a load change range, marks out over-range nodes, records the corresponding relation between a load value and a time point and acquires the dynamic node load data;
the path stability verification submodule analyzes the marked node connection path based on the dynamic node load data, checks the path load recovery time and stability, screens the continuous change path and acquires path response state data;
The node adaptability recording submodule analyzes the key nodes of the abnormal path based on the path response state data, marks the overload nodes in a classified mode, calculates the adaptability adjustment load value of the nodes, records the node load and delay characteristics, optimizes the node hierarchical structure and generates a node adaptability verification result.
9. The intelligent ammeter capable of monitoring and reporting operation errors according to claim 8, wherein the adaptive adjustment load value of the node is calculated according to the formula:
Wherein L adj represents an adaptive load value of the node, L i represents a current load value of the ith node, μ represents an average value of the node load, D i represents a delay time of the ith node, μ d represents an average value of the node delay, C node represents a connection number of the node, and W 1、W2、W3 is a weight parameter.
10. The intelligent ammeter capable of monitoring and reporting operation errors according to claim 1, wherein the error transfer status summarizing module comprises:
The error data profiling sub-module extracts node error information, sorts and sorts time points and node numbers based on the node adaptability verification result, eliminates repeated records, files according to time and node sequence, and generates node error archiving records;
The key error extraction submodule extracts error data among nodes based on the node error filing record, marks out over-range data points, sorts node numbers and time sequence distribution, sorts error values and marks associated paths, and generates a key error data point set;
And the error monitoring and summarizing submodule analyzes a distribution rule and a node path based on the key error data point set, records the node and path error accumulation condition, sorts the association relationship into a structured table, integrates the node state and the error information, and generates an operation error monitoring and reporting record table.
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| CN120237645A (en) * | 2025-05-30 | 2025-07-01 | 北京首兴安成电力工程有限公司 | Optimal dispatching method of energy storage in distribution network based on dynamic load forecasting |
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