CN115049232B - Method and system for judging station area abnormity - Google Patents
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Abstract
The embodiment of the invention discloses a method and a system for judging the abnormity of a transformer area, which are used for acquiring transformer area data of each transformer area aiming at each transformer area, wherein the transformer area data comprise total power consumption of the transformer area, sub power consumption of each power consumer in the transformer area, transformer area archive data and electric energy meter archive data; processing the platform area data of each platform area based on an error analysis model to obtain a data analysis result; judging whether the station area corresponding to the data analysis result is abnormal or not based on each data analysis result; if so, judging the abnormal analysis result of the distribution room; and generating state information corresponding to the distribution area based on the abnormal analysis result. According to the technical scheme of the embodiment of the invention, the generation of the state information of the transformer area based on the transformer area data is realized, the accuracy of the generation of the state information is improved, and convenience is provided for the overhaul of working personnel.
Description
Technical Field
The invention relates to the technical field of power grid technologies, in particular to a method and a system for judging station area abnormity.
Background
At present, each household almost has electricity utilization requirements, and household appliances, lamps and the like consume electric energy. And determining the electric energy consumption of each power consumer by reading the electricity indicating value of the electric energy meter configured for each power consumer. When the electric energy meter has a fault, the electric indication value cannot be correctly displayed, and the benefits of the electric consumers and the power supply companies are influenced. Therefore, the staff is required to read the electric energy indicating value in a specified period so as to find out whether the electric energy meter has a fault or not in time.
In general, a worker is required to read a field indicating value of an electric energy meter of a user in each distribution area, and then the situation that whether the electric energy meter has a fault or is stolen or not is determined by comparing the field indicating value with the indicating value of the previous day or several days. For each district with wide geographical range and large number of power consumers, the electric energy meter fault diagnosis method consumes a large amount of human resources and time, requires certain working experience of workers, and has uncertainty in judging electric energy meter faults or judging whether electricity stealing occurs or not.
Therefore, a method capable of automatically determining the abnormal condition of the transformer area is urgently needed, the accuracy of determining the abnormal condition of the transformer area is improved, and the overhaul speed of workers is further increased.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention is proposed in view of the above-mentioned problems of the conventional method for determining a station area abnormality.
Therefore, an object of the present invention is to provide a method for determining a block anomaly, which realizes determination of the block anomaly and improves the accuracy and efficiency of the determination.
In order to solve the technical problems, the invention provides the following technical scheme: the method for judging the station area abnormity comprises the steps of obtaining initial station area data of a target station area, and carrying out format conversion and cleaning pretreatment on the initial station area data, wherein the station area data comprise total station area power consumption, sub power consumption of each power consumer in the station area, station area archive data and electric energy meter archive data; judging and correcting the preprocessed transformer area data, and processing the transformer area data by using an error analysis-based model to obtain a data analysis result; judging whether the target platform area is abnormal or not based on the data analysis result; and when the target transformer area is determined to have abnormality, generating an abnormality analysis result of the target transformer area, and generating state information of the target transformer area according to the abnormality analysis result.
As a preferable aspect of the station area abnormality determination method according to the present invention, wherein: in the judgment and correction of the station area data, the station area data is judged whether to be an enhanced station area or not by adopting a type-based judgment rule, and when the target station area is determined to be the enhanced station area, the data of the enhanced station area is corrected by adopting an enhanced model.
As a preferable scheme of the method for determining the abnormal area of the distribution room, the method comprises the following steps: the error analysis model comprises a plurality of analysis submodels for processing the data of the target platform area to obtain corresponding data analysis sub-results: the first analysis submodel is used for processing the total power consumption of the transformer area, each sub power consumption and the electric quantity metering period to obtain a relative error result corresponding to the electric energy meter; the second analysis submodel is used for processing the electric energy meter archive data to obtain a user-variable relation result; the third analysis submodel is used for processing the clock display time corresponding to the sub power consumption of each power consumer to obtain a clock abnormal result; and the fourth analysis submodel is used for processing the sub electricity consumption of each electricity consumer to obtain a suspected electricity stealing result.
As a preferable aspect of the station area abnormality determination method according to the present invention, wherein: and the abnormal analysis result of the target platform area is used for judging abnormal sub-results corresponding to various abnormal types according to the abnormal types corresponding to the data analysis sub-results one by one, wherein the abnormal analysis result comprises at least one abnormal sub-result.
As a preferable scheme of the method for determining the abnormal area of the distribution room, the method comprises the following steps: the step of determining the abnormal sub-result is as follows: if the relative error calculated based on the total electricity consumption and/or the sub electricity consumption is larger than the error threshold, judging that the abnormal sub-result is out-of-tolerance abnormal; if the clock of at least one electric energy meter is abnormal, judging that the abnormal sub-result is abnormal, and displaying an electric indication value by the electric energy meter, wherein the electric indication value is used for determining the sub-electricity consumption; if the total electricity consumption of the current distribution area is less than the sum of the sub electricity consumptions of all the users, judging that the abnormal sub result is abnormal in the user-to-user relationship; and if at least one sub-electricity consumption quantity in the current distribution area is smaller than the preset electricity quantity value, judging that the abnormal sub-result is suspected electricity stealing abnormity.
As a preferable aspect of the station area abnormality determination method according to the present invention, wherein: inputting the data of the target station area into an abnormal station area analysis model after the data of the target station area, judging whether the target station area is abnormal or not based on the abnormal station area analysis model, if so, generating an abnormal analysis result of the target station area when the target station area is determined to be abnormal, and generating the state information of the target station area based on the abnormal analysis result.
As a preferable aspect of the station area abnormality determination method according to the present invention, wherein: and monitoring the total electric energy meter and the sub electric energy meters of the target platform area, and generating early warning information when the total electric energy meter and/or the sub electric energy meters are abnormal.
As a preferable aspect of the station area abnormality determination method according to the present invention, wherein: the enhanced station zone comprises: at least one of a large-scale platform area, a newly-built platform area, a light-load platform area and a multi-user platform area.
As a preferable aspect of the station area abnormality determination method according to the present invention, wherein: the abnormal transformer area analysis model comprises at least one of a transformer area general table abnormality analysis submodel, a transformer area acquisition rate abnormality analysis submodel, a transformer area overload analysis submodel, a transformer area light load analysis submodel and an edge transformer area analysis submodel.
As a preferable aspect of the station area abnormality determination method according to the present invention, wherein: a station area abnormality determination system includes: the distribution area data acquisition module is used for acquiring distribution area data of each distribution area aiming at each distribution area, wherein the distribution area data comprises total power consumption of the distribution area, sub power consumption of each power consumer in the distribution area, distribution area archive data and electric energy meter archive data; the type determining module is used for processing the station area data of each station area based on an error analysis model to obtain a data analysis result; the abnormality determination module is used for determining whether the station area corresponding to each data analysis result is abnormal or not based on each data analysis result; if so, judging the abnormal analysis result of the distribution room; and the state information determining module is used for generating state information corresponding to the distribution area based on the abnormal analysis result.
The invention has the beneficial effects that: the method comprises the steps of obtaining station area data of each station area, processing the station area data of each station area based on an error analysis model aiming at the station area data of each station area to obtain a data analysis result, judging whether the station area corresponding to the data analysis result is abnormal or not based on the data analysis result, judging the abnormal analysis result of the station area if the station area corresponding to the data analysis result is abnormal, and generating state information of the station area based on the abnormal analysis result. According to the technical scheme of the embodiment of the invention, the generation of the state information of the transformer area based on the transformer area data is realized, the accuracy of the generation of the state information is improved, and convenience is provided for the overhaul of working personnel.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor. Wherein:
fig. 1 is a flowchart of a method for determining a station area anomaly according to the present invention.
Fig. 2 is a flowchart of a station area abnormality determination system according to the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, the references herein to "one embodiment" or "an embodiment" refer to a particular feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Furthermore, the present invention is described in detail with reference to the drawings, and in the detailed description of the embodiments of the present invention, the cross-sectional view illustrating the structure of the device is not enlarged partially according to the general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Example 1
Referring to fig. 1, a flowchart of a method for determining a block area anomaly according to an embodiment of the present invention is provided, where the embodiment is applicable to a case of determining a block area anomaly, and the method may be implemented by a block area anomaly determination apparatus, which may be implemented in a form of software and/or hardware, and includes the following steps:
the method comprises the steps of firstly, obtaining station area data of a target station area, wherein the station area refers to the power supply range or area of a transformer (a transformer) in an electric power system, and the station area data comprises total power consumption of the station area, sub power consumption of each power consumer in the station area, station area archive data and electric energy meter archive data. The total power usage of the area may be determined by the total representative value on the transformer. The sub-power usage of each power consumer may be determined from the representation of the power consumer's electricity meter. The station area archive data comprises station area identification, station area position information, station area range, power consumer identification included in the station area, the number of power consumers and the like. The electric energy meter archive data comprises total electric energy meter archive data and sub-electric energy meter archive data, each archive data comprises an electric energy meter identification, an electric energy meter indicating value, an electric energy meter clock, an electric energy meter model and the like, and the electric energy meter clock refers to the time displayed on the electric energy meter, such as beijing time 2022 year 3 month 2 day 12. Optionally, the expression form of the platform area identifier, the electric energy meter identifier and/or the power consumer identifier may be special characters, english letters, numbers and the like. Alternatively, the period of collecting the total power consumption and the sub power consumption of the electricity distribution site may be 24 hours, and the period of collecting the electric energy meter archive data of the electricity distribution site may be 1 week.
Specifically, the platform area data of each platform area is obtained, and optionally, the platform area data is periodically obtained according to a preset acquisition cycle, so as to process the platform area data to obtain an abnormality determination result of each platform area.
Further, the method further includes preprocessing the acquired initial station area data of each station area, for example, performing format conversion, data type conversion, data cleaning, and the like on the acquired data, so that the station area data obtained after preprocessing can be processed in the subsequent steps. The data cleansing is to remove data such as the recognized power indicating number of 0 or the power indicating number of random codes, and to generate the status information of the station area corresponding to the second abnormality result based on the second abnormality result and the abnormality analysis result described below by using the removed data as the second abnormality result. For example, the removed data includes an electric energy meter identifier, a station data acquisition time, an electric energy meter corresponding user identifier, a station identifier where the electric energy meter is located, and the like, and the removed data is used as a second abnormal result to generate state information based on the second abnormal result and the abnormal analysis result, so that the staff can overhaul the corresponding electric energy meter based on the state information.
And secondly, judging and correcting the preprocessed transformer area data, and processing the transformer area data by using an error analysis-based model to obtain a data analysis result.
The error analysis model is a model for processing the data of the transformer area to obtain a data analysis result. Alternatively, the error analysis model may be a model for calculating a relative error of the electric quantity, a model for calculating a clock error, or the like. And the data analysis result refers to the fact that the station area data are processed according to the error analysis model to obtain an analysis result.
Specifically, the station area data of each station area is processed through the error analysis model to obtain a data analysis result, and the subsequent station area abnormity judgment is performed based on the data analysis result.
Further, in the embodiment of the present invention, before processing the platform region data based on the error analysis model, the method further includes: judging whether the station area is an enhanced station area or not based on a category judgment rule aiming at the station area data of each station area; if yes, modifying the platform area data based on the enhanced model corresponding to the enhanced platform area to obtain modified data; processing the platform area data based on the error analysis model, comprising: the correction data is processed based on an error analysis model.
The category determination rule is a rule that can determine whether a station zone corresponding to the station zone data is an enhanced station zone according to the station zone data. Optionally, the category determination rule includes a first sub-rule for determining a large cell, a second sub-rule for determining a newly-built cell, a third sub-rule for determining a multi-user cell, and a fourth sub-rule for determining a light cell. It should be understood that the first sub-rule, the second sub-rule, the third sub-rule and the fourth sub-rule are only used for distinguishing different sub-rules and are not in a sequential order. The enhanced station zone includes: at least one of a large-scale platform area, a newly-built platform area, a light-load platform area and a multi-user platform area. The large-scale transformer area comprises a transformer area with the area larger than a preset range. The newly-built transformer area comprises a transformer area of which the time from the construction time to the transformer area data acquisition is shorter than the preset construction time. The multi-user cell comprises a cell in which the number of users is larger than the preset number of users. The light load distribution area comprises a distribution area with the load rate smaller than a preset low load threshold value. The correction data refers to adjusting the distribution room data so that the error analysis model can process the adjusted data. For example, the error analysis model has an upper limit of 200 for the processed data amount, and the enhanced station area has an amount of 500 for the sub power consumption, and the error analysis model cannot process these data, so the data of the enhanced station area needs to be corrected so that the error analysis model can operate normally.
Specifically, the station area data is processed according to the category determination rule, and whether the station area corresponding to the station area data is an enhanced station area is determined. Optionally, the zone area archive data and the electric energy meter archive data are processed according to the category determination rule, and whether the zone area corresponding to the zone area data is a large-scale zone area, a newly-built zone area and/or a multi-user zone area is determined. And when the station area is determined to be the enhanced station area, processing the station area data based on the enhanced model to obtain the corrected data, so that the corrected data can be input into the error analysis model to further perform subsequent processing. For example, the zone area data in the zone archive data is determined according to the first sub-rule, and whether the zone corresponding to the zone archive data is a large zone is determined. If so, merging the sub-electricity consumption of the electricity consumers in the large-scale distribution area according to a preset rule based on the enhanced model, for example, merging the electricity consumers with the sub-electricity consumption less than 10 degrees into one electricity consumer so that the number of the electricity consumers in the large-scale distribution area conforms to the processing range of the error analysis model.
Further, after obtaining the zone data of each zone, the method further includes: inputting the station area data into an abnormal station area analysis model aiming at each station area data, and judging whether the station area corresponding to the station area data is abnormal or not based on the abnormal station area analysis model; the abnormal transformer area analysis model comprises at least one of a transformer area general table abnormality analysis submodel, a transformer area acquisition rate abnormality analysis submodel, a transformer area overload analysis submodel, a transformer area light load analysis submodel and an edge transformer area analysis submodel; if so, obtaining an initial abnormal result of the transformer area; generating state information corresponding to the distribution room based on the anomaly analysis result, including: and generating state information corresponding to the distribution area based on the initial abnormity result and the abnormity analysis result.
The sub-model for analyzing the table area summary table abnormity is used for judging the table area summary table abnormity, for example, the summary table archive data in the electric energy meter archive data is abnormal, the table area data at the moment cannot be processed by the subsequent abnormity analysis model, and the table area is judged to be the table area summary table abnormity. The sub-model for analyzing the abnormal acquisition rate of the distribution room is used for judging the acquisition rate of the distribution room, for example, the acquisition rate of the sub-power consumption in the distribution room is determined based on the information of each sub-power consumption in the distribution room and the power consumption users in the archive data of the distribution room, and when the acquisition rate is smaller than the preset acquisition rate, the abnormal acquisition rate of the distribution room is judged. And the transformer area overload analysis sub-model is used for processing transformer area data and judging whether the transformer area is overloaded or not. For example, within a preset time period, when the load rate obtained based on the zone data is greater than a preset high load rate, it is determined that the zone corresponding to the zone data is an overloaded zone. Similarly, the station area light load analysis sub-model is used for processing station area data and judging whether the station area is light loaded. For example, when the load rate obtained based on the station area data is smaller than the low load rate within the preset time period, the light-load station area corresponding to the station area data is determined. The edge station area analysis sub-model is used for judging whether the station area is an edge station area, for example, whether the station area is the edge station area is judged according to the position information in the station area archive data. Optionally, the abnormal block area analysis model further includes a block area total table number analysis sub-model, which is used for calculating the number of block area total tables, and when the number of total tables is smaller than a preset threshold, it is determined that the number of block area total tables is abnormal.
Specifically, the data of the transformer area is processed according to the anomaly analysis model, and whether the transformer area is abnormal or not is judged. If yes, obtaining an initial abnormal result of the distribution area. Optionally, when the distribution room is different in distribution room general table, abnormal in edge distribution room and/or abnormal in distribution room acquisition rate, determining an initial abnormal result, and generating state information corresponding to the distribution room based on the initial abnormal result and the abnormal analysis result. It should be noted that, when it is determined that the distribution room is a distribution room summary table, an edge distribution room and/or a distribution room with abnormal collection rate, the corresponding distribution room data is determined as an initial abnormal result, and subsequent calculation of an error analysis model is not performed. And performing subsequent calculation of an error analysis model on the station area data determined as the light-load station area and other station area data without abnormality.
And thirdly, judging whether the target platform area is abnormal or not based on the data analysis result, generating an abnormal analysis result of the target platform area when the target platform area is determined to be abnormal, and generating state information of the target platform area according to the abnormal analysis result.
The abnormal analysis result refers to the result of abnormality of the distribution room and/or the electric energy meter in the distribution room. Specifically, whether the distribution room is abnormal or not is judged according to the data analysis result, and if yes, the abnormal analysis result of the distribution room is judged. Optionally, the data analysis result is determined according to a preset determination rule, and then whether the distribution room is abnormal is determined. For example, the data analysis result includes a relative error corresponding to the electric energy meter, and according to the error determination sub-rule in the determination rule, when the relative error corresponding to the electric energy meter is larger than an error threshold, it is determined that the station area corresponding to the data analysis result is abnormal, and the obtained abnormal analysis result is out of tolerance. Or the data analysis result comprises display time corresponding to the electric energy representation value, a sub-rule is judged according to the clock in the judgment rule, and when the display time of the electric energy meter does not accord with the display time of Beijing time, the station area corresponding to the data analysis result is judged to be abnormal, and the abnormal analysis result is clock abnormity. It should be understood that the anomaly analysis result in the embodiment of the present invention is a diagnosis result, and a worker needs to check on the spot to determine the anomaly analysis result.
And when the target distribution area is determined to have abnormality, generating an abnormality analysis result of the target distribution area, and generating state information of the target distribution area according to the abnormality analysis result, wherein the state information refers to information generated according to the abnormality analysis result, such as distribution area summary table archive information, a user variable relation distribution area list, a large distribution area list and the like. Of course, information of the electric energy meter in the zone area, such as an AA electric energy meter exception in the ZZ zone area, may also be included.
Specifically, the state information corresponding to the transformer area is generated according to the abnormal analysis result, and therefore workers can carry out targeted troubleshooting and the like on the transformer area with the problems according to the state information. Optionally, the abnormal analysis result is processed according to a preset display rule, the abnormal analysis result is regularly arranged according to the display rule, and then the state information is generated. Optionally, the status information is output or displayed on a display interface so that the staff member can view the status information.
Further, in the embodiment of the present invention, the total electric energy meter and the sub-electric energy meters included in each distribution room are monitored, and whether the total electric energy meter and/or the sub-electric energy meters are abnormal is determined; if yes, generating early warning information; generating state information corresponding to the distribution room based on the anomaly analysis result, including: and generating state information corresponding to the distribution room based on the early warning information and the abnormal analysis result.
The abnormality of the total electric energy meter and/or the sub-electric energy meters includes, but is not limited to, flying, stopping, reversing, and the drop of the electric energy consumption in a preset time period. The pre-alarm information includes, but is not limited to, pre-alarm time, diagnostic results, suggested treatment options, and the like. For example, 2022.3.2, 12.
Specifically, within a preset acquisition period, monitoring data is acquired for the total electric energy meter and the sub-electric energy meters, and optionally, the acquisition mode may be acquisition during acquisition of the data of the distribution room, or acquisition of the monitoring data may be performed by an independent acquisition system. And when the total electric energy meter and/or the sub-electric energy meters are judged to be abnormal based on the monitoring data, generating early warning information, and generating state information corresponding to the transformer area based on the early warning information and the abnormal analysis result. For example, when the early warning information includes that the XX electric energy meter in the YY station area has clock abnormality and the abnormality analysis result shows that the clock of the XX electric energy meter in the YY station area is abnormal, the generated state information is that the XX electric energy meter in the YY station area has clock abnormality. Optionally, the occupation ratios of the early warning information and the abnormal analysis result are set, and further, the state information is generated according to the respective occupation ratios and the contents.
According to the technical scheme, the method and the device for analyzing the power distribution area data obtain the power distribution area data of each power distribution area, process the power distribution area data on the basis of the error analysis model aiming at the power distribution area data of each power distribution area to obtain a data analysis result, judge whether the power distribution area corresponding to the data analysis result is abnormal or not on the basis of the data analysis result, judge the abnormal analysis result of the power distribution area if the power distribution area corresponding to the data analysis result is abnormal, and generate the state information of the power distribution area on the basis of the abnormal analysis result. According to the technical scheme of the embodiment of the invention, the generation of the state information of the transformer area based on the transformer area data is realized, the accuracy of the generation of the state information is improved, and convenience is provided for the overhaul of workers.
Example 2
Referring to fig. 1, a second embodiment of the present invention is a second embodiment, which refines an error analysis model based on the alternatives of the above embodiments, and further, the error analysis model includes a plurality of analysis submodels to process data of the target station area to obtain corresponding data analysis sub-results. Wherein, the error analysis model includes: the first analysis submodel is used for processing the total power consumption of the transformer area, each sub power consumption and the electric quantity metering period to obtain a relative error result corresponding to the electric energy meter; the second analysis submodel is used for processing the electric energy meter archive data to obtain a user variation relation result; the third analysis submodel is used for processing the clock display time corresponding to the sub power consumption of each power consumer to obtain a clock abnormal result; and the fourth analysis submodel is used for processing the sub power consumption of each power consumer to obtain a suspected electricity stealing result.
The method comprises the following specific steps:
step one, processing the platform area data through each analysis submodel to obtain data analysis submodules corresponding to each analysis submodel, and obtaining data analysis results based on each data analysis submodules; and judging whether the station area corresponding to the data analysis result is abnormal or not based on each data analysis result, if the target station area is abnormal, judging abnormal sub-results corresponding to various abnormal types according to the abnormal types corresponding to the various data analysis sub-results one by one, wherein the abnormal analysis result comprises at least one abnormal sub-result.
And the data analysis sub-results correspond to the error analysis sub-models one to one. The error analysis submodel comprises a first analysis submodel, a second analysis submodel, a third analysis submodel and a fourth analysis submodel. The abnormal types comprise suspected electricity stealing, out of tolerance, clock abnormity and household change relation abnormity. It should be understood that the type of abnormality in the embodiment of the present invention is a diagnosis result, and thus, the electricity stealing type is a suspected electricity stealing type. Out-of-tolerance is the relative error corresponding to the power meter exceeding an error threshold.
Specifically, according to the abnormality type corresponding to each data analysis submodel, an abnormality sub-result corresponding to each abnormality type is determined, and an abnormality analysis result is obtained based on the abnormality sub-result.
Further, in the embodiment of the present invention, determining the abnormal sub-result corresponding to each abnormal type according to the abnormal type corresponding to each data analysis sub-result one to one includes: if the relative error calculated based on the total electricity consumption and/or the sub electricity consumption is larger than the error threshold, judging that the abnormal sub-result is out-of-tolerance abnormal; if the clock of at least one electric energy meter is abnormal, judging that the abnormal sub-result is clock abnormality; if the total electricity consumption of the current distribution area is less than the sum of the sub electricity consumptions of all the users, judging that the abnormal sub result is abnormal in the user-to-user relationship; and if at least one sub-electricity consumption quantity in the current distribution area is smaller than the preset electricity quantity value, judging that the abnormal sub-result is suspected electricity stealing abnormity.
The electric energy meter displays the electric indicating value, and the electric indicating value is used for determining the sub power consumption.
Specifically, when the relative error calculated based on the total power consumption and/or the sub power consumption is larger than the error threshold, the abnormal sub-result is determined to be out-of-tolerance abnormal. It should be noted that the formula for calculating the relative error can be a formula in the prior art. The abnormal type also comprises clock abnormity, user variation relation abnormity and suspected electricity stealing abnormity. And obtaining an abnormal analysis result according to the abnormal sub-result, and further generating state information according to the abnormal analysis result.
And step two, generating state information corresponding to the distribution area based on the abnormal analysis result. Further, the initial station area data is obtained, and the station area data is preprocessed, wherein the preprocessing comprises data cleaning and data conversion to obtain the station area data. And then, processing an abnormal platform area analysis model on the platform area data, specifically, judging whether the platform area corresponding to the platform area data is a light-load platform area or not based on a platform area light-load analysis sub-model, and if so, marking the platform area as the light-load platform area. And judging whether the station area corresponding to the station area data is abnormal in the summary table or not based on the station area summary table abnormality analysis submodel, and if so, outputting the station area as a part of initial abnormality information. And judging whether the station area corresponding to the station area data is an overload station area or not based on the station area overload analysis submodel, and if so, outputting the station area as a part of the initial abnormal information. And judging whether the acquisition rate of the station area data is abnormal or not based on the station area acquisition rate abnormality analysis submodel, and if so, outputting the station area data as a part of the initial abnormality information. And judging whether the station area corresponding to the station area data is the edge station area or not based on the edge station area analysis sub-model, and if so, marking the station area as the edge station area. And removing initial abnormal information through an abnormal region analysis model. And then, judging the enhanced type station areas of the remaining station area data, wherein the enhanced type station areas comprise a large-scale station area, a multi-user station area, a light-load station area and a newly-built station area. If the enhanced type station area exists, the enhanced type station area is corrected through the enhanced model, for example, the number of the power consumers is corrected, and the number of the power consumers is combined into the number capable of being processed by the error analysis model according to preset conditions. And processing an error analysis model based on the correction data. Specifically, the total power consumption of the distribution room, the sub power consumption of each sub power consumption and the power calculation cycle are processed through the first analysis sub model, and a relative error result corresponding to the electric energy meter is obtained. And processing the electric energy meter archive data through the second analysis submodel to obtain an user-variable relation result. And processing the clock display time corresponding to the sub power consumption of each power consumer through the third analysis sub model to obtain a clock abnormal result. And processing the sub-electricity consumption of each electricity consumer through a fourth analysis sub-model to obtain a suspected electricity stealing result. And monitoring the abnormity of the electric energy meter based on an abnormal event online monitoring module, wherein the monitoring comprises the monitoring of flying, stopping, reversing, reverse electric quantity, indication abnormity and the like. The reverse electric quantity refers to the situation that the used electric quantity is reduced in a preset time period, and indication value abnormity, such as continuous indication value fluctuation, is increased and reduced. When the abnormal event of the electric energy meter is monitored, the station area state information is generated based on the abnormal event and the abnormal analysis result, and certainly, the abnormal analysis result can also be updated based on the abnormal event, so that the abnormal analysis result is more accurate. The station area state information comprises a light load station area list, a large station area list, a summary table archive list, a station area calculation rate and a station area list with abnormal user variation relation. Of course, the method may also include an over-tolerance abnormal information list of the electric energy meter in the transformer area, an abnormal clock list of the electric energy meter, a suspected electricity stealing list of the transformer area, and the like. The station area state information is displayed on a display or displayed at a terminal of a worker, so that the worker can check the electric energy meter in the station area, and after the worker overhauls the electric energy meter, a wiring error list can be added into the station area state information, so that the subsequent work check and the like can be performed.
Specifically, the station area data of each station area is obtained, and the station area data of each station area is processed based on each sub-model in the error analysis model to obtain data analysis sub-results corresponding to each sub-model, so as to obtain a total data analysis result, and whether the station area corresponding to the data analysis result is abnormal is determined based on each data analysis result. And when the station area is judged to be abnormal, obtaining an abnormal analysis result of the station area, and generating state information corresponding to the station area based on the abnormal analysis result. By the technical scheme of the embodiment of the invention, the station area data is analyzed aiming at different submodels, and the obtained abnormal analysis result is more comprehensive, so that the finally obtained state information is more accurate.
Example 3
Referring to fig. 2, a third embodiment of the present invention is a method for determining a station area anomaly according to any embodiment of the present invention, which includes functional modules corresponding to an execution method, and the functional modules are a station area data acquisition module, a type determination module, an anomaly determination module, and a state information determination module, respectively; wherein:
the distribution area data acquisition module is used for acquiring distribution area data of each distribution area aiming at each distribution area, wherein the distribution area data comprise total power consumption of the distribution area, sub power consumption of each power consumer in the distribution area, distribution area archive data and electric energy meter archive data; the type determining module is used for processing the station area data of each station area based on the error analysis model to obtain a data analysis result; the abnormality determination module is used for determining whether the station area corresponding to the data analysis result is abnormal or not based on each data analysis result; if yes, judging the abnormal analysis result of the transformer area; and the state information determining module is used for generating state information corresponding to the distribution room based on the abnormity analysis result.
Further, the model judgment module is used for judging whether the station area is an enhanced station area or not based on a category judgment rule aiming at the station area data of each station area; if so, modifying the station area data based on an enhanced model corresponding to the enhanced station area to obtain modified data; the type determining module is also used for processing the correction data based on the error analysis model; wherein, the enhancement mode platform district includes: at least one of a large-scale platform area, a newly-built platform area, a light-load platform area and a multi-user platform area.
Further, the error analysis model includes: the first analysis submodel is used for processing the total power consumption of the transformer area, each sub power consumption and the electric quantity metering period to obtain a relative error result corresponding to the electric energy meter; the second analysis submodel is used for processing the electric energy meter archive data to obtain a user variation relation result; the third analysis submodel is used for processing the clock display time corresponding to the sub power consumption of each power consumer to obtain a clock abnormal result; the fourth analysis submodel is used for processing the sub electricity consumption of each electricity consumer to obtain a suspected electricity stealing result; and the type determining module is also used for processing the platform area data based on each analysis submodel in the error analysis model to obtain data analysis submodules corresponding to each analysis submodel.
Further, the anomaly determination module is further configured to determine an anomaly sub-result corresponding to each anomaly type according to the anomaly type corresponding to each data analysis sub-result one to one, where the anomaly analysis result includes at least one anomaly sub-result.
Further, the abnormality determination module is further configured to determine that the abnormal sub-result is out-of-tolerance abnormality if a relative error calculated based on the total power consumption and/or the sub-power consumption is greater than an error threshold; if the clock of at least one electric energy meter is abnormal, judging that the abnormal sub-result is clock abnormality; if the total electricity consumption of the current distribution area is less than the sum of the sub electricity consumptions of all the users, judging that the abnormal sub result is abnormal in the user-to-user relationship; if at least one sub-electricity consumption quantity in the current transformer area is smaller than the preset electricity quantity value, judging that the abnormal sub-result is suspected electricity stealing abnormality; the electric energy meter displays the electric indication value, and the electric indication value is used for determining the electronic electricity consumption.
Further, the abnormal transformer area analysis module is used for inputting transformer area data into the abnormal transformer area analysis model aiming at each transformer area data, and judging whether the transformer area corresponding to the transformer area data is abnormal or not based on the abnormal transformer area analysis model; the abnormal transformer area analysis model comprises at least one of a transformer area general table abnormality analysis submodel, a transformer area acquisition rate abnormality analysis submodel, a transformer area overload analysis submodel, a transformer area light load analysis submodel and an edge transformer area analysis submodel; if so, judging an initial abnormal result of the transformer area; the state information determination module 440 includes: generating state information corresponding to the distribution room based on the initial abnormality result and the abnormality analysis result.
Further, the early warning information generation module is used for monitoring the total electric energy meter and the sub-electric energy meters in each distribution area and judging whether the total electric energy meter and/or the sub-electric energy meters are abnormal or not; if yes, generating early warning information; and the state information determining module is also used for generating state information corresponding to the transformer area based on the early warning information and the abnormal analysis result.
According to the technical scheme, the method and the device for analyzing the power distribution area data obtain the power distribution area data of each power distribution area, process the power distribution area data on the basis of the error analysis model aiming at the power distribution area data of each power distribution area to obtain a data analysis result, judge whether the power distribution area corresponding to the data analysis result is abnormal or not on the basis of the data analysis result, judge the abnormal analysis result of the power distribution area if the power distribution area corresponding to the data analysis result is abnormal, and generate the state information of the power distribution area on the basis of the abnormal analysis result. According to the technical scheme of the embodiment of the invention, the generation of the state information of the transformer area based on the transformer area data is realized, the accuracy of the generation of the state information is improved, and convenience is provided for the overhaul of working personnel.
It should be noted that the modules and sub-modules are divided according to functional logic, but not limited to the above division, as long as the corresponding functions can be implemented; in addition, specific names of the functional modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the present invention.
Example 4
Referring to fig. 2, a fourth embodiment of the present invention, the method and system of the present invention operate on an electronic device that includes one or more processors and a memory device for storing one or more programs.
The electronic device is embodied in the form of a general purpose computing device, and components of the electronic device may include, but are not limited to: one or more processors or processing units, a system memory, and a bus connecting the various system components (including the system memory and the processing units).
A bus represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic devices typically include a variety of computer system readable media. Such media may be any available media that is accessible by the electronic device and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 504 and/or cache memory, and the electronic device may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system may be used to read from and write to non-removable, nonvolatile magnetic media (commonly referred to as "hard disk drives"). A magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus by one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility having a set (at least one) of program modules may be stored, for example, in the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination may comprise an implementation of a network environment. The program modules generally perform the functions and/or methodologies of the described embodiments of the invention.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), one or more devices that enable a user to interact with the electronic device, and/or any devices (e.g., network card, modem, etc.) that enable the electronic device to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface. Further, the electronic device may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via a network adapter, and other hardware and/or software modules may be used in conjunction with the electronic device 50, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit executes various functional applications and data processing by running the program stored in the system memory, for example, the method for determining the abnormal area provided by the embodiment of the present invention is implemented, and the method includes:
acquiring station area data of each station area aiming at each station area, wherein the station area data comprises total power consumption of the station area, sub power consumption of each power consumer in the station area, station area archive data and electric energy meter archive data; processing the platform area data based on the error analysis model aiming at the platform area data of each platform area to obtain a data analysis result; judging whether the area corresponding to the data analysis result is abnormal or not based on each data analysis result; if yes, judging the abnormal analysis result of the transformer area; status information corresponding to the station area is generated based on the abnormality analysis result.
Computer storage media for embodiments of the present invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (5)
1. A method for determining an abnormality of a distribution room is characterized in that: comprises the following steps of (a) preparing a solution,
acquiring initial station area data of a target station area, and performing format conversion and cleaning pretreatment on the initial station area data, wherein the initial station area data comprises total station area power consumption, sub-power consumption of each power consumer in the station area, station area archive data and electric energy meter archive data;
judging and correcting the preprocessed transformer area data, and processing the transformer area data by using an error analysis-based model to obtain a data analysis result;
judging whether the target platform area is abnormal or not based on the data analysis result;
when the target station area is determined to be abnormal, generating an abnormal analysis result of the target station area, and generating state information of the target station area according to the abnormal analysis result;
in the judgment and correction of the station area data, judging whether the target station area is an enhanced station area or not by adopting a category-based judgment rule for the station area data, and when the target station area is determined to be the enhanced station area, correcting the data of the enhanced station area by adopting an enhanced model; the error analysis model comprises a plurality of analysis submodels for processing the data of the target platform area to obtain corresponding data analysis sub-results:
the first analysis submodel is used for processing the total power consumption of the transformer area, each sub power consumption and the electric quantity metering period to obtain a relative error result corresponding to the electric energy meter;
the second analysis submodel is used for processing the electric energy meter archive data to obtain a user-variable relation result;
the third analysis submodel is used for processing the clock display time corresponding to the sub power consumption of each power consumer to obtain a clock abnormal result;
the fourth analysis submodel is used for processing the sub electricity consumption of each electricity consumer to obtain a suspected electricity stealing result;
judging abnormal sub-results corresponding to various abnormal types according to abnormal types corresponding to various data analysis sub-results one by one, wherein the abnormal analysis result of the target platform area comprises at least one abnormal sub-result;
the step of determining the abnormal sub-result is as follows:
if the relative error calculated based on the total electricity consumption and/or the sub electricity consumption is larger than the error threshold, judging that the abnormal sub-result is out-of-tolerance abnormal;
if the clock of at least one electric energy meter is abnormal, judging that the abnormal sub-result is abnormal, and displaying an electric indication value by the electric energy meter, wherein the electric indication value is used for determining the sub-electricity consumption;
if the total electricity consumption of the current distribution area is less than the sum of the sub electricity consumptions of all the users, judging that the abnormal sub result is abnormal in the user-to-user relationship;
if at least one sub-electricity consumption quantity in the current transformer area is smaller than the preset electricity quantity value, judging that the abnormal sub-result is suspected electricity stealing abnormality;
and monitoring the total electric energy meter and the sub electric energy meters of the target platform area, and generating early warning information when the total electric energy meter and/or the sub electric energy meters are abnormal.
2. The station area abnormality determination method according to claim 1, characterized in that: inputting the data of the target station area into an abnormal station area analysis model after the data of the target station area, judging whether the target station area is abnormal or not based on the abnormal station area analysis model, if so, generating an abnormal analysis result of the target station area when the target station area is determined to be abnormal, and generating the state information of the target station area based on the abnormal analysis result.
3. The station area abnormality determination method according to claim 2, characterized in that: the enhanced station zone comprises: at least one of a large-scale platform area, a newly-built platform area, a light-load platform area and a multi-user platform area.
4. The station area abnormality determination method according to claim 3, characterized in that: the abnormal transformer area analysis model comprises at least one of a transformer area general table abnormality analysis submodel, a transformer area acquisition rate abnormality analysis submodel, a transformer area overload analysis submodel, a transformer area light load analysis submodel and an edge transformer area analysis submodel.
5. A station area abnormality determination system is characterized in that: the method comprises the following steps:
the distribution area data acquisition module is used for acquiring distribution area data of each distribution area aiming at each distribution area, wherein the distribution area data comprises total power consumption of the distribution area, sub power consumption of each power consumer in the distribution area, distribution area archive data and electric energy meter archive data;
the type determining module is used for processing the station area data of each station area based on an error analysis model to obtain a data analysis result;
in the judgment and correction of the station area data, judging whether a target station area is an enhanced station area or not by adopting a category-based judgment rule for the station area data, and when the target station area is determined to be the enhanced station area, correcting the data of the enhanced station area by adopting an enhanced model;
the error analysis model comprises a plurality of analysis submodels for processing the data of the target platform area to obtain corresponding data analysis sub-results:
the first analysis submodel is used for processing the total power consumption of the transformer area, each sub power consumption and the electric quantity metering period to obtain a relative error result corresponding to the electric energy meter;
the second analysis submodel is used for processing the electric energy meter archive data to obtain a user variation relation result;
the third analysis submodel is used for processing the clock display time corresponding to the sub power consumption of each power consumer to obtain a clock abnormal result;
the fourth analysis submodel is used for processing the sub electricity consumption of each electricity consumer to obtain a suspected electricity stealing result;
the abnormality determination module is used for determining whether the station area corresponding to each data analysis result is abnormal or not based on each data analysis result; if yes, judging an abnormal analysis result of the distribution room;
judging abnormal sub-results corresponding to various abnormal types according to abnormal types corresponding to various data analysis sub-results one by one, wherein the abnormal analysis result of the target platform area comprises at least one abnormal sub-result;
the step of determining the abnormal sub-result is as follows:
if the relative error calculated based on the total power consumption and/or the electronic power consumption is larger than the error threshold, judging that the abnormal sub-result is out-of-tolerance abnormal;
if the clock of at least one electric energy meter is abnormal, judging that the abnormal sub-result is abnormal, and displaying an electric indication value by the electric energy meter, wherein the electric indication value is used for determining the sub-electricity consumption;
if the total electricity consumption of the current distribution area is less than the sum of the sub electricity consumptions of all the users, judging that the abnormal sub result is abnormal in the user-to-user relationship;
if at least one sub-electricity consumption quantity in the current transformer area is smaller than the preset electricity quantity value, judging that the abnormal sub-result is suspected electricity stealing abnormality;
monitoring a total electric energy meter and a sub electric energy meter of the target platform area, and generating early warning information when the total electric energy meter and/or the sub electric energy meter are abnormal;
and the state information determining module is used for generating state information corresponding to the distribution area based on the abnormal analysis result.
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