CN117849700B - Modular electric energy metering system capable of controlling measurement - Google Patents
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Abstract
The invention discloses a modular electric energy metering system capable of controlling measurement, relates to the technical field of electric energy metering, and aims to solve the problem of inaccurate electric energy metering caused by inaccurate electric energy data acquisition. According to the invention, the identification model of each load can be constructed to rapidly acquire the target electric energy characteristics corresponding to the target time sequence data of each key characteristic in the key characteristic data, so that the operation characteristics of the load can be accurately determined, the data acquisition efficiency and accuracy are improved, the errors can be timely found and corrected through periodic calibration, the accuracy of the data is improved, the quality of the data can be effectively ensured through data periodic calibration, the positions of abnormal data and parameters to be adjusted are confirmed, and the positions and parameters are sent to a display terminal for intelligent control regulation or manual regulation by a worker, so that the rapidness and accuracy of electric energy metering abnormality repair are further improved.
Description
Technical Field
The invention relates to the technical field of electric energy metering, in particular to a modular electric energy metering system capable of controlling measurement.
Background
Electric energy metering refers to the process of measuring electric energy using an electric energy metering device.
The chinese patent with publication number CN202676757U discloses a modularized electric energy metering box, mainly through designing the main switch room, the ammeter case, the information acquisition room into modularization respectively, when needs increase the ammeter case, only need increase ammeter case module to realize the extension of whole electric energy metering box, above-mentioned patent has solved the problem of electric energy metering modularization, but still has following problem in actual operation:
1. The acquisition port is not subjected to further abnormality investigation, so that the electric energy metering is inaccurate due to inaccurate data acquisition after the port is abnormal.
2. The load amounts of different attribute data in the electric energy data are not further confirmed, so that the statistical efficiency of the electric energy data is reduced.
3. The acquired electric energy metering data is not subjected to further abnormality analysis and control, so that timely repair according to the abnormality is not possible.
Disclosure of Invention
The invention aims to provide a modular electric energy metering system capable of controlling measurement, wherein an identification model of each load quantity is constructed, so that target electric energy characteristics corresponding to target time sequence data of each key characteristic in key characteristic data can be quickly obtained, further, the running characteristics of the load quantity can be accurately determined, the data acquisition efficiency and accuracy are improved, errors can be timely found and corrected through periodic calibration, the accuracy of the data is improved, the quality of the data can be effectively ensured through the periodic calibration of the data, the difference between the data acquired in real time and historical data can be more intuitively found according to curve overlapping comparison, the position of abnormal data and parameters to be adjusted are confirmed and sent to a display terminal for intelligent control regulation or manual regulation of staff, the rapidness and the accuracy of electric energy metering abnormality repair are further improved, and the problems in the prior art can be solved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a modular power metering system for controllable measurement, comprising:
the electric energy data acquisition monitoring unit is used for:
Collecting electric energy data required to be subjected to electric energy metering, monitoring an acquisition port of each electric energy data, and positioning faults of abnormal acquisition ports;
the electric energy data feature extraction unit is used for:
Confirming the load of each data in the collected electric energy data, classifying the load according to the load of each data, and obtaining the final operation characteristic of each data according to the classification;
the electric energy metering data monitoring unit is used for:
Confirming the electric energy consumption data of the electric energy data according to the operation characteristic data, obtaining electric energy metering data according to the electric energy consumption, and marking the obtained electric energy metering data as metering data to be processed;
the monitoring data analysis control unit is used for:
and carrying out periodic calibration on the metering data to be processed, carrying out data comparison on the metering data to be processed after the calibration is completed and the historical metering data, judging the operation parameters of the metering data to be processed according to the comparison result, and controlling and adjusting the metering data to be processed according to the operation parameters.
Preferably, the electric energy data acquisition monitoring unit includes:
the data acquisition module is used for:
The data needed for electric energy metering comprises voltage data, current data, time data and phase angle data;
Confirming the port of each electric energy metering data, and confirming the reference port parameter of each port according to the port of the electric energy metering data;
The port monitoring module is used for:
confirming a monitoring index of each port according to the reference port parameters, and generating a monitoring plug-in according to a preset monitoring plug-in generation rule based on the monitoring index, wherein the monitoring plug-in carries a dynamic function library;
obtaining configuration parameters of ports according to the monitoring plug-in, adjusting the reference port parameters through each preset function in the dynamic function library, and obtaining the monitoring ports corresponding to the monitoring plug-in according to the adjustment results;
When the monitoring ports monitor the ports, extracting the monitoring logs of each port according to the preset time interval of the monitoring plug-in, and confirming the monitoring data characteristics in each monitoring log;
Classifying and counting the obtained monitoring data characteristics, and obtaining sub-monitoring data of each piece of electric power data after classifying and counting;
Comparing the sub-monitoring data of each power data with preset target monitoring data;
Judging whether the sub-monitoring data of each power data is within the range of the preset target monitoring data according to the comparison result, if not, judging that the sub-monitoring data is abnormal monitoring data, and confirming the fault position of the abnormal port according to the abnormal monitoring data.
Preferably, the power data feature extraction unit includes:
a time sequence characteristic data confirming module for:
redundancy and dimension reduction processing are carried out on the collected electric energy data, and the processed actual electric energy data are obtained;
Extracting features of actual electric energy data, and acquiring an initial feature set of the electric energy data according to an extraction result;
key features related to the electric energy load in the initial feature set are called and marked as key feature data;
Obtaining topological structure information and preset operation mode information of the electric energy data and node attributes of each port node;
determining the electric energy topology weight value of each port node according to the node attribute of each port node, the topology structure information of the electric energy data and the preset operation mode information;
And obtaining a basic value of each load in the electric energy data according to the electric energy topology weight value, and obtaining time sequence characteristic data corresponding to the electric energy data according to the basic value of each load.
Preferably, the electric energy data feature extraction unit further comprises
The abnormal acquisition alarm module is used for judging the degree of data missing of the electric energy data before the electric energy data is subjected to redundancy and dimension reduction processing, and determining whether to perform abnormal data acquisition alarm or not according to the judging result;
Wherein, gather unusual alarm module, include:
The information extraction module is used for extracting specific data information corresponding to the voltage data, the current data, the time data and the phase angle data in the electric energy data;
the proportion parameter acquisition module is used for carrying out missing value proportion calculation on the voltage data, the current data, the time data and the phase angle data according to specific data information corresponding to the voltage data, the current data, the time data and the phase angle data, so as to obtain missing value proportion parameters corresponding to the voltage data, the current data, the time data and the phase angle data;
the proportion index obtaining module is used for obtaining the comprehensive missing proportion index of the electric energy data according to missing value proportion parameters corresponding to the voltage data, the current data, the time data and the phase angle data; wherein, the comprehensive deletion proportion index is obtained by the following formula:
；
Wherein E represents a comprehensive deletion proportion index; e 0 represents a preset index reference value; n represents the number of data types in the electric energy data; b i represents a missing value proportion parameter of the ith data type; b 0i denotes a missing value ratio threshold value of the i-th data type; b max represents the missing value scale parameter maximum value among the n data types; b min represents the missing value scale parameter minimum value among the n data types; b p represents the missing value proportion threshold average value among the n data types;
The first abnormality alarm module is used for carrying out data acquisition abnormality alarm when the comprehensive missing proportion index exceeds a preset comprehensive index threshold value;
and the secondary abnormal alarm module is used for evaluating specific missing parameters corresponding to the voltage data, the current data, the time data and the phase angle data when the comprehensive missing proportion index does not exceed a preset comprehensive index threshold value, and judging whether to perform data acquisition abnormal alarm or not according to an evaluation result.
Preferably, the second-level abnormality alarm module includes:
The data extraction module is used for extracting detection data sets corresponding to the voltage data, the current data, the time data and the phase angle data, and identifying missing values corresponding to the voltage data, the current data, the time data and the phase angle data from the detection data sets corresponding to the voltage data, the current data, the time data and the phase angle data by utilizing an identification tool;
The total data set generation module is used for calling the missing values corresponding to the voltage data, the current data, the time data and the phase angle data and the detection data generation time corresponding to the missing values, and generating a total data set of the missing values corresponding to the voltage data, the current data, the time data and the phase angle data;
The sub-data set generation module is used for dividing the total missing value data set corresponding to the voltage data, the current data, the time data and the phase angle data according to a preset time period to obtain a missing value sub-data set corresponding to the total missing value data set corresponding to the voltage data, the current data, the time data and the phase angle data;
The information calling module is used for calling the missing value parameter information contained in each missing value sub-data set corresponding to the missing value total data set corresponding to the voltage data, the current data, the time data and the phase angle data and the detection data generation time corresponding to the missing value;
the missing data importance degree evaluation subparameter is used for acquiring missing data importance degree evaluation subparameters of each missing value subpassage set at the moment of generating the missing value parameter information contained in each missing value subpassage set and the detection data corresponding to the missing value; wherein, the missing data importance degree evaluation subparameter is obtained by the following formula:
；
wherein S z represents a missing data importance degree evaluation sub-parameter; s z01 represents a first parameter; s z02 represents a second parameter; m represents the number of missing values contained in the missing value sub-data set; x i represents a specific data parameter value of the i-th missing value; x z represents the data value of the data median of the data type corresponding to the missing value sub-dataset; t p represents the average time interval of occurrence of missing values of the data type corresponding to the missing value sub-dataset; t i represents the generation time of the detection data corresponding to the ith missing value in the missing value subset;
the missing data importance degree comprehensive evaluation parameter acquisition module is used for acquiring missing data importance degree comprehensive evaluation parameters of the electric energy data according to missing data importance degree evaluation subparameters corresponding to each missing value subparameter corresponding to the missing value total data set corresponding to the voltage data, the current data, the time data and the phase angle data;
And the second abnormal alarm module is used for carrying out data acquisition abnormal alarm when the importance degree evaluation parameter of the missing data of the electric energy data exceeds a preset important evaluation parameter threshold value.
Preferably, the missing data importance degree comprehensive evaluation parameter obtaining module includes:
The evaluation sub-parameter calling module is used for calling the missing data importance degree evaluation sub-parameters of each missing value sub-data set corresponding to the voltage data, the current data, the time data and the phase angle data;
The missing data importance degree evaluation parameter acquisition module is used for acquiring missing data importance degree evaluation parameters of the missing value total data set corresponding to the voltage data, the current data, the time data and the phase angle data by utilizing the missing data importance degree evaluation subparameter of each missing value subparameter corresponding to the voltage data, the current data, the time data and the phase angle data; the missing data importance degree evaluation parameter is obtained through the following formula:
；
S y represents missing data importance degree evaluation parameters corresponding to voltage data, current data, time data and phase angle data; k represents the number of the missing value sub-data sets correspondingly contained in the voltage data, the current data, the time data and the phase angle data; m i represents the number of missing values contained in the ith missing value sub-dataset; m 0 represents a preset number threshold of missing values; s zi represents a missing data importance degree evaluation sub-parameter corresponding to the ith missing value sub-dataset;
The missing data importance degree comprehensive evaluation parameter calculation module is used for obtaining missing data importance degree comprehensive evaluation parameters of the electric energy data by utilizing missing data importance degree evaluation parameters corresponding to the voltage data, the current data, the time data and the phase angle data, wherein the missing data importance degree comprehensive evaluation parameters are obtained through the following formula:
；
S c represents a missing data importance degree comprehensive evaluation parameter; lambda i represents the evaluation coefficient of the ith data type; s yi represents a missing data importance evaluation parameter of the ith data type; s zmax represents the maximum value of the missing data importance degree evaluation sub-parameter of the missing value sub-data set included in each data type.
Preferably, the electrical energy data feature extraction unit further includes:
the operation characteristic data confirming module is used for:
confirming time sequence characteristic data of each load in the electric energy data;
determining the electric energy characteristic information of each load according to the time sequence data of the load;
training a preset network model by taking the electric energy characteristic information as a model output sample to obtain an identification model of each load;
acquiring target electric energy characteristics corresponding to target time sequence series data of each key characteristic in the key characteristic data according to the identification model of each load;
acquiring a first operation characteristic of each load according to the target electric energy characteristic of each key characteristic;
And confirming the electric energy change rule of each load according to the change condition of the first operation characteristic;
confirming the load quantity with the similarity of the electric energy change rule larger than or equal to a preset threshold value as the similar load;
And classifying the loads of the same type according to the electric energy data types, and obtaining the final electric energy operation characteristics of each electric energy data type after classification.
Preferably, the electric energy metering data monitoring unit comprises:
the power consumption data confirming module is used for:
Confirming the power consumption times in the final power operation characteristics;
The power consumption times are the number of power consumption data acquired in a preset unit time, and the preset unit time is set according to actual conditions;
And confirming the data quantity corresponding to each power consumption data according to the power consumption times, and marking the data quantity as metering data to be calculated.
Preferably, the electric energy metering data monitoring unit further comprises:
The electric energy metering data confirming module is used for:
Carrying out data preprocessing on metering data to be calculated;
carrying out electric quantity calculation according to the metering data to be calculated after the data preprocessing is completed;
obtaining average metering data in metering data to be calculated according to the calculated electric quantity data;
Wherein the average metering data are average power data, average current data and accumulated electric quantity data;
and labeling the average metering data as metering data to be processed.
Preferably, the monitoring data analysis control unit includes:
A data period calibration module for:
confirming the area and the load change condition of each attribute data in the metering data to be processed;
carrying out calibration period confirmation according to the area and the load change condition of each attribute data;
Setting a calibration period according to the area and the load change condition of each attribute data;
And obtaining target processing metering data after the calibration of the metering data to be processed is completed.
Preferably, the monitoring data analysis control unit further includes:
the calibration data abnormality judgment module is used for:
comparing the target processing metering data with the historical metering data;
The method comprises the steps of performing curve data conversion on target processing metering data and historical metering data respectively;
Performing curve overlapping comparison on curve data of the target processing metering data and curve data of the historical metering data;
Obtaining a non-overlapping area after curve overlapping comparison;
Confirming the area range of the non-overlapped area, and when the area range is not in the preset qualified range, labeling the abnormal data of the target processing metering data of the non-overlapped area;
And confirming the adjustment parameters and the adjustment areas according to the marked abnormal data, and displaying the confirmed adjustment parameters and adjustment areas on the display terminal.
Compared with the prior art, the invention has the following beneficial effects:
1. According to the modular electric energy metering system capable of controlling measurement, the reference port parameters are adjusted through the preset functions in the dynamic function library, so that the accuracy of data acquisition of each port can be realized to the greatest extent, and the convenience of confirming the abnormal position in the later stage can be realized.
2. According to the modular electric energy metering system capable of controlling measurement, provided by the invention, the identification model of each load can be constructed, so that the target electric energy characteristic corresponding to the target time sequence data of each key characteristic in the key characteristic data can be rapidly obtained, the running characteristic of the load can be accurately determined, and the data acquisition efficiency and accuracy are improved.
3. According to the modularized electric energy metering system capable of controlling measurement, the errors can be timely found and corrected through periodic calibration, so that the accuracy of data is improved, the quality of the data can be effectively guaranteed through the periodic calibration of the data, the difference between the data acquired in real time and the historical data can be more intuitively found according to curve overlapping comparison, the position of abnormal data and the parameters to be adjusted are confirmed and sent to a display terminal for intelligent control regulation or manual regulation of staff, and the rapidness and the accuracy of electric energy metering abnormality repair are further improved.
Drawings
FIG. 1 is a schematic diagram of the overall module principle of the present invention;
FIG. 2 is a schematic flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the problem that in the prior art, when electric energy data is acquired, further abnormality investigation is not performed on an acquisition port, so that electric energy metering is inaccurate due to inaccurate data acquisition after the port is abnormal, please refer to fig. 1 and 2, the embodiment provides the following technical scheme:
a modular power metering system for controllable measurement, comprising:
the electric energy data acquisition monitoring unit is used for:
Collecting electric energy data required to be subjected to electric energy metering, monitoring an acquisition port of each electric energy data, and positioning faults of abnormal acquisition ports;
the electric energy data feature extraction unit is used for:
Confirming the load of each data in the collected electric energy data, classifying the load according to the load of each data, and obtaining the final operation characteristic of each data according to the classification;
the electric energy metering data monitoring unit is used for:
Confirming the electric energy consumption data of the electric energy data according to the operation characteristic data, obtaining electric energy metering data according to the electric energy consumption, and marking the obtained electric energy metering data as metering data to be processed;
the monitoring data analysis control unit is used for:
and carrying out periodic calibration on the metering data to be processed, carrying out data comparison on the metering data to be processed after the calibration is completed and the historical metering data, judging the operation parameters of the metering data to be processed according to the comparison result, and controlling and adjusting the metering data to be processed according to the operation parameters.
Specifically, the accuracy of data acquisition of each port can be realized to the greatest extent through the electric energy data acquisition monitoring unit, and abnormal position confirmation convenience is realized in the later stage, abnormal position information can be acquired more rapidly, the operation characteristics of the loads of the same type can be uniformly counted through classifying and processing the load quantity through the electric energy data characteristic extraction unit, operation characteristic statistics is not required to be carried out on the single load, the working efficiency is improved, and the frequency of electric energy consumption data acquired in the preset unit time and the data quantity corresponding to the electric energy consumption data acquired each time by the final electric energy operation characteristic can be acquired in real time through the electric energy metering data monitoring unit. The real-time performance and the accuracy of the data are ensured, the positions of the abnormal data and the parameters to be adjusted are confirmed through the monitoring data analysis control unit, and the abnormal data are sent to the display terminal for intelligent control regulation or manual regulation by staff.
The electric energy data acquisition monitoring unit includes:
the data acquisition module is used for:
The data needed for electric energy metering comprises voltage data, current data, time data and phase angle data;
Confirming the port of each electric energy metering data, and confirming the reference port parameter of each port according to the port of the electric energy metering data;
The port monitoring module is used for:
confirming a monitoring index of each port according to the reference port parameters, and generating a monitoring plug-in according to a preset monitoring plug-in generation rule based on the monitoring index, wherein the monitoring plug-in carries a dynamic function library;
obtaining configuration parameters of ports according to the monitoring plug-in, adjusting the reference port parameters through each preset function in the dynamic function library, and obtaining the monitoring ports corresponding to the monitoring plug-in according to the adjustment results;
When the monitoring ports monitor the ports, extracting the monitoring logs of each port according to the preset time interval of the monitoring plug-in, and confirming the monitoring data characteristics in each monitoring log;
Classifying and counting the obtained monitoring data characteristics, and obtaining sub-monitoring data of each piece of electric power data after classifying and counting;
Comparing the sub-monitoring data of each power data with preset target monitoring data;
Judging whether the sub-monitoring data of each power data is within the range of the preset target monitoring data according to the comparison result, if not, judging that the sub-monitoring data is abnormal monitoring data, and confirming the fault position of the abnormal port according to the abnormal monitoring data.
Specifically, the acquisition ports of the voltage data, the current data, the time data and the phase angle data are confirmed through the data acquisition module, the reference port parameter of each acquisition port is confirmed, the effectiveness of acquiring the data of each port can be realized according to the confirmed sharp port parameter, meanwhile, the reference port parameter is adjusted to the configuration parameters through each preset function in the dynamic function library, the accuracy of acquiring the data of each port and the convenience of confirming the abnormal position in the later stage can be realized to the greatest extent, the abnormal position information can be acquired more quickly, and whether the ports are abnormal or not can be effectively judged through monitoring the acquisition ports of the voltage data, the current data, the time data and the phase angle data, so that the accuracy and the reliability of acquiring the electric energy data are further improved.
In order to solve the problem that in the prior art, when electric energy metering is performed, the load amounts of different attribute data in the electric energy data are not further confirmed, so that the statistical efficiency of the electric energy data is reduced, please refer to fig. 1 and 2, the embodiment provides the following technical scheme:
an electrical energy data feature extraction unit comprising:
a time sequence characteristic data confirming module for:
redundancy and dimension reduction processing are carried out on the collected electric energy data, and the processed actual electric energy data are obtained;
Extracting features of actual electric energy data, and acquiring an initial feature set of the electric energy data according to an extraction result;
key features related to the electric energy load in the initial feature set are called and marked as key feature data;
Obtaining topological structure information and preset operation mode information of the electric energy data and node attributes of each port node;
determining the electric energy topology weight value of each port node according to the node attribute of each port node, the topology structure information of the electric energy data and the preset operation mode information;
And obtaining a basic value of each load in the electric energy data according to the electric energy topology weight value, and obtaining time sequence characteristic data corresponding to the electric energy data according to the basic value of each load.
The operation characteristic data confirming module is used for:
confirming time sequence characteristic data of each load in the electric energy data;
determining the electric energy characteristic information of each load according to the time sequence data of the load;
training a preset network model by taking the electric energy characteristic information as a model output sample to obtain an identification model of each load;
acquiring target electric energy characteristics corresponding to target time sequence series data of each key characteristic in the key characteristic data according to the identification model of each load;
acquiring a first operation characteristic of each load according to the target electric energy characteristic of each key characteristic;
And confirming the electric energy change rule of each load according to the change condition of the first operation characteristic;
confirming the load quantity with the similarity of the electric energy change rule larger than or equal to a preset threshold value as the similar load;
And classifying the loads of the same type according to the electric energy data types, and obtaining the final electric energy operation characteristics of each electric energy data type after classification.
Specifically, the initial characteristic data of the electric energy data can be quickly subjected to characteristic normalization processing through the time sequence characteristic data confirmation module, so that the data characteristic of each dimension can be counted, conditions are laid for subsequent load operation characteristic judgment, the collected electric energy data is subjected to redundancy and dimension reduction processing, the collected electric energy data can be ensured to be displayed in a more visual data form, the change trend and periodicity of the data can be deeply understood through analysis of the time sequence data, the inherent mechanism and influence factors of the data are better understood, the target electric energy characteristic corresponding to the target time sequence data of each key characteristic in the key characteristic data can be quickly obtained through the operation characteristic data confirmation module, the load operation characteristic can be accurately determined, the data acquisition efficiency and accuracy are improved, the load operation characteristics of the same type can be uniformly counted through classifying the load, the operation characteristic statistics for a single load is not needed, and the work efficiency is improved.
Specifically, the electric energy data feature extraction unit further includes
The abnormal acquisition alarm module is used for judging the degree of data missing of the electric energy data before the electric energy data is subjected to redundancy and dimension reduction processing, and determining whether to perform abnormal data acquisition alarm or not according to the judging result;
Wherein, gather unusual alarm module, include:
The information extraction module is used for extracting specific data information corresponding to the voltage data, the current data, the time data and the phase angle data in the electric energy data;
the proportion parameter acquisition module is used for carrying out missing value proportion calculation on the voltage data, the current data, the time data and the phase angle data according to specific data information corresponding to the voltage data, the current data, the time data and the phase angle data, so as to obtain missing value proportion parameters corresponding to the voltage data, the current data, the time data and the phase angle data;
the proportion index obtaining module is used for obtaining the comprehensive missing proportion index of the electric energy data according to missing value proportion parameters corresponding to the voltage data, the current data, the time data and the phase angle data; wherein, the comprehensive deletion proportion index is obtained by the following formula:
；
Wherein E represents a comprehensive deletion proportion index; e 0 represents a preset index reference value; n represents the number of data types in the electric energy data; b i represents a missing value proportion parameter of the ith data type; b 0i denotes a missing value ratio threshold value of the i-th data type; b max represents the missing value scale parameter maximum value among the n data types; b min represents the missing value scale parameter minimum value among the n data types; b p represents the missing value proportion threshold average value among the n data types;
The first abnormality alarm module is used for carrying out data acquisition abnormality alarm when the comprehensive missing proportion index exceeds a preset comprehensive index threshold value;
and the secondary abnormal alarm module is used for evaluating specific missing parameters corresponding to the voltage data, the current data, the time data and the phase angle data when the comprehensive missing proportion index does not exceed a preset comprehensive index threshold value, and judging whether to perform data acquisition abnormal alarm or not according to an evaluation result.
The technical effects of the technical scheme are as follows: by judging the degree of data missing before the data redundancy and dimension reduction processing, which data are missing can be more accurately identified, so that the result deviation caused by processing the missing data is avoided. The missing value proportion of the four data types of voltage, current, time and phase angle is comprehensively evaluated, so that the missing condition of the data can be more comprehensively known, and more accurate decision making is facilitated. The comprehensive missing proportion index is calculated in real time, and the data acquisition abnormality can be found in time, so that an alarm is given in time, and the subsequent analysis error caused by the data abnormality is avoided. According to the comprehensive missing proportion index and other parameters, different abnormal alarm strategies can be selected, so that different situations can be dealt with more flexibly. Through a multistage abnormal alarm mechanism, the reliability of alarm can be improved, and false alarm or missing alarm is avoided. Therefore, the technical scheme can improve the accuracy, the integrity, the instantaneity, the flexibility and the reliability of data processing on the performance index.
Specifically, the second-level abnormality alarm module includes:
The data extraction module is used for extracting detection data sets corresponding to the voltage data, the current data, the time data and the phase angle data, and identifying missing values corresponding to the voltage data, the current data, the time data and the phase angle data from the detection data sets corresponding to the voltage data, the current data, the time data and the phase angle data by utilizing an identification tool;
The total data set generation module is used for calling the missing values corresponding to the voltage data, the current data, the time data and the phase angle data and the detection data generation time corresponding to the missing values, and generating a total data set of the missing values corresponding to the voltage data, the current data, the time data and the phase angle data;
The sub-data set generation module is used for dividing the total missing value data set corresponding to the voltage data, the current data, the time data and the phase angle data according to a preset time period to obtain a missing value sub-data set corresponding to the total missing value data set corresponding to the voltage data, the current data, the time data and the phase angle data;
The information calling module is used for calling the missing value parameter information contained in each missing value sub-data set corresponding to the missing value total data set corresponding to the voltage data, the current data, the time data and the phase angle data and the detection data generation time corresponding to the missing value;
the missing data importance degree evaluation subparameter is used for acquiring missing data importance degree evaluation subparameters of each missing value subpassage set at the moment of generating the missing value parameter information contained in each missing value subpassage set and the detection data corresponding to the missing value; wherein, the missing data importance degree evaluation subparameter is obtained by the following formula:
；
wherein S z represents a missing data importance degree evaluation sub-parameter; s z01 represents a first parameter; s z02 represents a second parameter; m represents the number of missing values contained in the missing value sub-data set; x i represents a specific data parameter value of the i-th missing value; x z represents the data value of the data median of the data type corresponding to the missing value sub-dataset; t p represents the average time interval of occurrence of missing values of the data type corresponding to the missing value sub-dataset; t i represents the generation time of the detection data corresponding to the ith missing value in the missing value subset;
the missing data importance degree comprehensive evaluation parameter acquisition module is used for acquiring missing data importance degree comprehensive evaluation parameters of the electric energy data according to missing data importance degree evaluation subparameters corresponding to each missing value subparameter corresponding to the missing value total data set corresponding to the voltage data, the current data, the time data and the phase angle data;
And the second abnormal alarm module is used for carrying out data acquisition abnormal alarm when the importance degree evaluation parameter of the missing data of the electric energy data exceeds a preset important evaluation parameter threshold value.
The technical effects of the technical scheme are as follows: by extracting the detection dataset and identifying missing values from the dataset, it is possible to more accurately determine which data is missing. And the importance degree evaluation subparameter and the comprehensive evaluation parameter of the missing data are combined, so that the importance degree of the missing data can be more accurately judged, and the occurrence of false alarm or missing alarm is avoided. The missing values of the four data types of voltage, current, time and phase angle are comprehensively evaluated, the information is extracted from the total data set and the sub data set, the situation of data missing can be more comprehensively known, and more accurate decisions can be made.
By generating the total data set and the sub data sets of the missing values and calling the missing value parameter information and the detection data generation time in each sub data set, the abnormal data acquisition can be found in time, so that an alarm can be given in time, and the subsequent analysis errors caused by the abnormal data can be avoided. According to the importance degree evaluation sub-parameters and the comprehensive evaluation parameters of the missing data, different abnormal alarm strategies can be selected, so that different situations can be dealt with more flexibly. Through a multistage abnormal alarm mechanism, the reliability of alarm can be improved, and false alarm or missing alarm is avoided. Meanwhile, the importance degree of the missing data can be judged more accurately by acquiring the importance degree evaluation sub-parameters and the comprehensive evaluation parameters of the missing data of each sub-data set, and the reliability of the alarm is further improved. Therefore, the technical scheme can improve the accuracy, the integrity, the instantaneity, the flexibility and the reliability of data processing on the performance index.
On the other hand, the missing values are detected and identified, so that the situation of the data set can be more comprehensively known, and analysis errors caused by data missing can be avoided. Meanwhile, the missing values are processed through data filling, interpolation and other methods, so that the data integrity can be further improved. By using an automation tool to judge the degree of data missing and process the missing value, the efficiency of data processing can be greatly improved, and the time of data processing can be shortened. In addition, through a multi-stage abnormality alarm mechanism and data preprocessing, abnormal values and noise in the data can be further reduced, and the quality of the data is improved. Meanwhile, through the multistage abnormal alarm mechanism and parameter setting, the early warning accuracy can be improved, and false alarm or missing alarm conditions are avoided.
Specifically, the missing data importance degree comprehensive evaluation parameter acquisition module comprises:
The evaluation sub-parameter calling module is used for calling the missing data importance degree evaluation sub-parameters of each missing value sub-data set corresponding to the voltage data, the current data, the time data and the phase angle data;
The missing data importance degree evaluation parameter acquisition module is used for acquiring missing data importance degree evaluation parameters of the missing value total data set corresponding to the voltage data, the current data, the time data and the phase angle data by utilizing the missing data importance degree evaluation subparameter of each missing value subparameter corresponding to the voltage data, the current data, the time data and the phase angle data; the missing data importance degree evaluation parameter is obtained through the following formula:
；
S y represents missing data importance degree evaluation parameters corresponding to voltage data, current data, time data and phase angle data; k represents the number of the missing value sub-data sets correspondingly contained in the voltage data, the current data, the time data and the phase angle data; m i represents the number of missing values contained in the ith missing value sub-dataset; m 0 represents a preset number threshold of missing values; s zi represents a missing data importance degree evaluation sub-parameter corresponding to the ith missing value sub-dataset;
The missing data importance degree comprehensive evaluation parameter calculation module is used for obtaining missing data importance degree comprehensive evaluation parameters of the electric energy data by utilizing missing data importance degree evaluation parameters corresponding to the voltage data, the current data, the time data and the phase angle data, wherein the missing data importance degree comprehensive evaluation parameters are obtained through the following formula:
；
S c represents a missing data importance degree comprehensive evaluation parameter; lambda i represents the evaluation coefficient of the ith data type; s yi represents a missing data importance evaluation parameter of the ith data type; s zmax represents the maximum value of the missing data importance degree evaluation sub-parameter of the missing value sub-data set included in each data type.
The technical effects of the technical scheme are as follows: the importance degree of each missing value can be evaluated more accurately by calling the missing data importance degree evaluation sub-parameters of each missing value sub-data set and calculating the missing data importance degree evaluation parameters by using the parameters. The evaluation method comprehensively considers a plurality of parameters, including specific data parameter values of the missing values, appearance time intervals and the like, and can judge the importance degree of the missing data more accurately.
The comprehensive evaluation parameters of the importance degree of the missing data can be further calculated by using the evaluation coefficient of each data type and the evaluation parameters of the importance degree of the missing data. The comprehensive evaluation method considers the characteristics and the importance of different data types, can comprehensively evaluate the missing data from multiple dimensions, and improves the accuracy and the reliability of evaluation. Parameters such as the evaluation coefficient, the number threshold of the missing values and the like in the technical scheme can be adaptively adjusted according to actual needs. The self-adaptive adjustment capability enables the technical scheme to be better adapted to different situations and requirements, and flexibility and applicability of the self-adaptive adjustment capability are improved.
Meanwhile, by utilizing a preset formula and algorithm, the technical scheme can efficiently calculate the importance degree evaluation parameter and the comprehensive evaluation parameter of the missing data. The high-efficiency computing capability can greatly shorten the time of data processing and analysis, and improves the data processing efficiency. And through the multistage abnormal alarm mechanism and parameter setting, the early warning accuracy can be improved, and false alarm or missing alarm can be avoided. Meanwhile, the abnormal condition of the data can be judged more accurately by accurately evaluating the importance degree of the missing data, and the early warning accuracy is further improved.
An electric energy metering data monitoring unit comprising:
the power consumption data confirming module is used for:
Confirming the power consumption times in the final power operation characteristics;
The power consumption times are the number of power consumption data acquired in a preset unit time, and the preset unit time is set according to actual conditions;
And confirming the data quantity corresponding to each power consumption data according to the power consumption times, and marking the data quantity as metering data to be calculated.
The electric energy metering data confirming module is used for:
Carrying out data preprocessing on metering data to be calculated;
carrying out electric quantity calculation according to the metering data to be calculated after the data preprocessing is completed;
obtaining average metering data in metering data to be calculated according to the calculated electric quantity data;
Wherein the average metering data are average power data, average current data and accumulated electric quantity data;
and labeling the average metering data as metering data to be processed.
Specifically, the electric energy consumption data in the final electric energy operation characteristic is confirmed through the electric energy consumption data confirmation module, the energy use condition of the electric energy consumption data can be better known, the problem of energy waste is found, corresponding measures are taken to improve the energy efficiency, the electric energy cost is reduced, the production efficiency is improved, the electric energy metering data are acquired through the electric energy metering data confirmation module, the electricity consumption condition in the final electric energy operation characteristic can be better known, the problem of energy waste is found, corresponding energy saving measures are taken, the electric energy consumption and the carbon emission are reduced, the energy saving and emission reduction are promoted, and the frequency of the electric energy consumption data acquired in the preset unit time and the data quantity corresponding to the electric energy consumption data can be acquired each time through the electric energy consumption data confirmation module and the electric energy metering data confirmation module. This ensures real-time and accurate data.
In order to solve the problem that in the prior art, the acquired electric energy metering data cannot be repaired in time due to no further abnormality analysis and control, referring to fig. 1 and 2, the present embodiment provides the following technical solutions:
a monitoring data analysis control unit comprising:
A data period calibration module for:
confirming the area and the load change condition of each attribute data in the metering data to be processed;
carrying out calibration period confirmation according to the area and the load change condition of each attribute data;
Setting a calibration period according to the area and the load change condition of each attribute data;
And obtaining target processing metering data after the calibration of the metering data to be processed is completed.
The calibration data abnormality judgment module is used for:
comparing the target processing metering data with the historical metering data;
The method comprises the steps of performing curve data conversion on target processing metering data and historical metering data respectively;
Performing curve overlapping comparison on curve data of the target processing metering data and curve data of the historical metering data;
Obtaining a non-overlapping area after curve overlapping comparison;
Confirming the area range of the non-overlapped area, and when the area range is not in the preset qualified range, labeling the abnormal data of the target processing metering data of the non-overlapped area;
And confirming the adjustment parameters and the adjustment areas according to the marked abnormal data, and displaying the confirmed adjustment parameters and adjustment areas on the display terminal.
Specifically, the acquired electric energy metering data is subjected to periodic calibration through the data periodic calibration module, and errors can be found and corrected in time through the periodic calibration, so that the accuracy of the data is improved, and the quality of the data can be effectively ensured through the data periodic calibration. During the calibration process, operations such as cleaning, de-duplication, and complementing are performed on the data to eliminate duplicate, erroneous, or incomplete data. This helps to improve the consistency and integrity of the data, thereby ensuring the quality of the data. The high-quality data can provide more accurate conclusion for subsequent data analysis, the calibrated electric energy metering data and the historical data are subjected to curve comparison through the calibration data anomaly judgment module, the difference between the data acquired in real time and the historical data can be more intuitively found according to curve overlapping comparison, the historical metering data are all qualified electric energy metering data, the target processing metering data in a non-overlapping area are subjected to anomaly data marking through curve overlapping, the positions of the anomaly data and parameters to be adjusted are confirmed, and the positions and parameters to be adjusted are sent to a display terminal for intelligent control regulation or manual regulation by a worker, so that the rapidness and the accuracy of electric energy metering anomaly repair are further improved.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A modular power metering system for controlled measurement, comprising:
the electric energy data acquisition monitoring unit is used for:
Collecting electric energy data required to be subjected to electric energy metering, monitoring an acquisition port of each electric energy data, and positioning faults of abnormal acquisition ports;
the electric energy data feature extraction unit is used for:
Confirming the load of each data in the collected electric energy data, classifying the load according to the load of each data, and obtaining the final operation characteristic of each data according to the classification;
the electric energy metering data monitoring unit is used for:
Confirming the electric energy consumption data of the electric energy data according to the operation characteristic data, obtaining electric energy metering data according to the electric energy consumption, and marking the obtained electric energy metering data as metering data to be processed;
the monitoring data analysis control unit is used for:
performing periodic calibration on the metering data to be processed, performing data comparison on the metering data to be processed after the calibration is completed and the historical metering data, judging the operation parameters of the metering data to be processed according to the comparison result, and performing control adjustment on the metering data to be processed according to the operation parameters;
The electric energy data characteristic extraction unit further comprises
The abnormal acquisition alarm module is used for judging the degree of data missing of the electric energy data before the electric energy data is subjected to redundancy and dimension reduction treatment, and determining whether to perform abnormal data acquisition alarm or not according to a judging result;
Wherein, gather unusual alarm module, include:
The information extraction module is used for extracting specific data information corresponding to the voltage data, the current data, the time data and the phase angle data in the electric energy data;
the proportion parameter acquisition module is used for carrying out missing value proportion calculation on the voltage data, the current data, the time data and the phase angle data according to specific data information corresponding to the voltage data, the current data, the time data and the phase angle data, so as to obtain missing value proportion parameters corresponding to the voltage data, the current data, the time data and the phase angle data;
The proportion index obtaining module is used for obtaining the comprehensive missing proportion index of the electric energy data according to missing value proportion parameters corresponding to the voltage data, the current data, the time data and the phase angle data;
The first abnormality alarm module is used for carrying out data acquisition abnormality alarm when the comprehensive missing proportion index exceeds a preset comprehensive index threshold value;
The secondary abnormal alarm module is used for evaluating specific missing parameters corresponding to the voltage data, the current data, the time data and the phase angle data when the comprehensive missing proportion index does not exceed a preset comprehensive index threshold value, and judging whether to perform data acquisition abnormal alarm or not according to an evaluation result;
The second grade abnormal alarm module includes:
The data extraction module is used for extracting detection data sets corresponding to the voltage data, the current data, the time data and the phase angle data, and identifying missing values corresponding to the voltage data, the current data, the time data and the phase angle data from the detection data sets corresponding to the voltage data, the current data, the time data and the phase angle data by utilizing an identification tool;
The total data set generation module is used for calling the missing values corresponding to the voltage data, the current data, the time data and the phase angle data and the detection data generation time corresponding to the missing values, and generating a total data set of the missing values corresponding to the voltage data, the current data, the time data and the phase angle data;
The sub-data set generation module is used for dividing the total missing value data set corresponding to the voltage data, the current data, the time data and the phase angle data according to a preset time period to obtain a missing value sub-data set corresponding to the total missing value data set corresponding to the voltage data, the current data, the time data and the phase angle data;
The information calling module is used for calling the missing value parameter information contained in each missing value sub-data set corresponding to the missing value total data set corresponding to the voltage data, the current data, the time data and the phase angle data and the detection data generation time corresponding to the missing value;
the missing data importance degree evaluation subparameter is used for acquiring missing data importance degree evaluation subparameters of each missing value subpassage set at the moment of generating the missing value parameter information contained in each missing value subpassage set and the detection data corresponding to the missing value;
the missing data importance degree comprehensive evaluation parameter acquisition module is used for acquiring missing data importance degree comprehensive evaluation parameters of the electric energy data according to missing data importance degree evaluation subparameters corresponding to each missing value subparameter corresponding to the missing value total data set corresponding to the voltage data, the current data, the time data and the phase angle data;
the second abnormal alarm module is used for carrying out data acquisition abnormal alarm when the importance degree evaluation parameter of the missing data of the electric energy data exceeds a preset important evaluation parameter threshold value;
The missing data importance degree comprehensive evaluation parameter acquisition module comprises:
The evaluation sub-parameter calling module is used for calling the missing data importance degree evaluation sub-parameters of each missing value sub-data set corresponding to the voltage data, the current data, the time data and the phase angle data;
The missing data importance degree evaluation parameter acquisition module is used for acquiring missing data importance degree evaluation parameters of the missing value total data set corresponding to the voltage data, the current data, the time data and the phase angle data by utilizing the missing data importance degree evaluation subparameter of each missing value subparameter corresponding to the voltage data, the current data, the time data and the phase angle data;
And the missing data importance degree comprehensive evaluation parameter calculation module is used for acquiring missing data importance degree comprehensive evaluation parameters of the electric energy data by utilizing the missing data importance degree evaluation parameters corresponding to the voltage data, the current data, the time data and the phase angle data.
2. The modular power metering system of controllable measurement of claim 1, wherein: the electric energy data acquisition monitoring unit includes:
the data acquisition module is used for:
The data needed for electric energy metering comprises voltage data, current data, time data and phase angle data;
Confirming the port of each electric energy metering data, and confirming the reference port parameter of each port according to the port of the electric energy metering data;
The port monitoring module is used for:
confirming a monitoring index of each port according to the reference port parameters, and generating a monitoring plug-in according to a preset monitoring plug-in generation rule based on the monitoring index, wherein the monitoring plug-in carries a dynamic function library;
obtaining configuration parameters of ports according to the monitoring plug-in, adjusting the reference port parameters through each preset function in the dynamic function library, and obtaining the monitoring ports corresponding to the monitoring plug-in according to the adjustment results;
When the monitoring ports monitor the ports, extracting the monitoring logs of each port according to the preset time interval of the monitoring plug-in, and confirming the monitoring data characteristics in each monitoring log;
Classifying and counting the obtained monitoring data characteristics, and obtaining sub-monitoring data of each piece of electric power data after classifying and counting;
Comparing the sub-monitoring data of each power data with preset target monitoring data;
Judging whether the sub-monitoring data of each power data is within the range of the preset target monitoring data according to the comparison result, if not, judging that the sub-monitoring data is abnormal monitoring data, and confirming the fault position of the abnormal port according to the abnormal monitoring data.
3. The modular power metering system of controllable measurement of claim 2, wherein: the electric energy data feature extraction unit includes:
a time sequence characteristic data confirming module for:
redundancy and dimension reduction processing are carried out on the collected electric energy data, and the processed actual electric energy data are obtained;
Extracting features of actual electric energy data, and acquiring an initial feature set of the electric energy data according to an extraction result;
key features related to the electric energy load in the initial feature set are called and marked as key feature data;
Obtaining topological structure information and preset operation mode information of the electric energy data and node attributes of each port node;
determining the electric energy topology weight value of each port node according to the node attribute of each port node, the topology structure information of the electric energy data and the preset operation mode information;
And obtaining a basic value of each load in the electric energy data according to the electric energy topology weight value, and obtaining time sequence characteristic data corresponding to the electric energy data according to the basic value of each load.
4. A modular power metering system for controlled measurement as claimed in claim 3, wherein: the electric energy data feature extraction unit further includes:
the operation characteristic data confirming module is used for:
confirming time sequence characteristic data of each load in the electric energy data;
determining the electric energy characteristic information of each load according to the time sequence data of the load;
training a preset network model by taking the electric energy characteristic information as a model output sample to obtain an identification model of each load;
acquiring target electric energy characteristics corresponding to target time sequence series data of each key characteristic in the key characteristic data according to the identification model of each load;
acquiring a first operation characteristic of each load according to the target electric energy characteristic of each key characteristic;
And confirming the electric energy change rule of each load according to the change condition of the first operation characteristic;
confirming the load quantity with the similarity of the electric energy change rule larger than or equal to a preset threshold value as the similar load;
And classifying the loads of the same type according to the electric energy data types, and obtaining the final electric energy operation characteristics of each electric energy data type after classification.
5. The modular power metering system of controllable measurement of claim 4, wherein: the electric energy metering data monitoring unit comprises:
the power consumption data confirming module is used for:
Confirming the power consumption times in the final power operation characteristics;
The power consumption times are the number of power consumption data acquired in a preset unit time, and the preset unit time is set according to actual conditions;
And confirming the data quantity corresponding to each power consumption data according to the power consumption times, and marking the data quantity as metering data to be calculated.
6. The modular power metering system of controllable measurement of claim 5, wherein: the electric energy metering data monitoring unit further comprises:
The electric energy metering data confirming module is used for:
Carrying out data preprocessing on metering data to be calculated;
carrying out electric quantity calculation according to the metering data to be calculated after the data preprocessing is completed;
obtaining average metering data in metering data to be calculated according to the calculated electric quantity data;
Wherein the average metering data are average power data, average current data and accumulated electric quantity data;
and labeling the average metering data as metering data to be processed.
7. The modular power metering system of controllable measurement of claim 6, wherein: the monitoring data analysis control unit includes:
A data period calibration module for:
confirming the area and the load change condition of each attribute data in the metering data to be processed;
carrying out calibration period confirmation according to the area and the load change condition of each attribute data;
Setting a calibration period according to the area and the load change condition of each attribute data;
And obtaining target processing metering data after the calibration of the metering data to be processed is completed.
8. The modular power metering system of controllable measurement of claim 7, wherein: the monitoring data analysis control unit further comprises:
the calibration data abnormality judgment module is used for:
comparing the target processing metering data with the historical metering data;
The method comprises the steps of performing curve data conversion on target processing metering data and historical metering data respectively;
Performing curve overlapping comparison on curve data of the target processing metering data and curve data of the historical metering data;
Obtaining a non-overlapping area after curve overlapping comparison;
Confirming the area range of the non-overlapped area, and when the area range is not in the preset qualified range, labeling the abnormal data of the target processing metering data of the non-overlapped area;
And confirming the adjustment parameters and the adjustment areas according to the marked abnormal data, and displaying the confirmed adjustment parameters and adjustment areas on the display terminal.
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| CN117421684A (en) * | 2023-12-14 | 2024-01-19 | 易知谷科技集团有限公司 | Abnormal data monitoring and analyzing method based on data mining and neural network |
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