CN113391256B - Electric energy meter metering fault analysis method and system of field operation terminal - Google Patents
Electric energy meter metering fault analysis method and system of field operation terminal Download PDFInfo
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Abstract
The invention belongs to the technical field of electric power metering, and provides an electric energy meter metering fault analysis method and system for a field operation terminal. The electric energy meter metering fault analysis method analyzes current data and historical data to obtain a historical data distribution function and a current data distribution function, finds out the historical data closest to the current data through the historical data distribution function, and finally judges whether the electric energy meter has faults or not through the closest historical data. Because the historical data of the electric energy meter is more and complicated, finding the data which can be used for reference and used for comparison from the historical data is a complicated process, the data is dull and vivid, and a user can more quickly sort out the closest historical data set used for comparison by the method, so that the user can intuitively feel the difference between the current data and the historical data and further quickly determine whether the electric energy meter has a metering fault or not according to the difference between the two data sets.
Description
Technical Field
The invention belongs to the technical field of electric power metering, and particularly relates to an electric energy meter metering fault analysis method and system for a field operation terminal.
Background
With the improvement of living standard and the continuous development of industry, the dependence of people on electric energy is more and more prominent, and the electric power resource is more and more important. Along with the popularization of the intelligent electric energy meter, the metering faults of the intelligent electric energy meter are more and more. The intelligent electric energy meter is a basic device for electric energy metering, the reading, checking and receiving work of a power supply enterprise is based on the metering data on the electric energy meter, and if the metering data is inaccurate, the overall benefit of the power supply enterprise is affected. Therefore, the operation and maintenance manager needs to pay attention to the reasons of the metering faults of the intelligent electric energy meter and try to eliminate the faults. The stability of maintaining the distribution network is urgent, and the normal life and the industrial development of people can be guaranteed only if the distribution network is stable.
In practical application, the factors causing the electric energy meter fault include various factors, such as power grid environment factors, human factors, detection equipment, appearance structure of the intelligent electric energy meter, software and hardware setting, false operation of a built-in relay, unstable switch, insensitive contact and the like, and the shortage of any link can cause the occurrence of the metering error of the electric energy meter.
In the conventional technology, the electric energy meter faults are checked, and firstly, the electric energy meter faults are definitely judged, namely, the electric energy meters with faults are selected from a plurality of electric energy meters. However, metering faults are often concealed, so that the electric energy meter faults are caused by a plurality of factors, clear judgment cannot be made only from the appearance, disassembly and inspection obviously delay production, the operation efficiency is low, and the method is not suitable for the situation that the metering faults of the electric energy meter are checked one by one due to abnormal line loss in one distribution area.
Disclosure of Invention
In view of this, the invention provides an electric energy meter metering fault analysis method and system for a field operation terminal, which can rapidly sort out electric energy meters with metering faults from a plurality of electric energy meters on the premise of not being disassembled and inspected.
The first aspect of the embodiments of the present invention provides a method for analyzing a metering fault of an electric energy meter, including:
determining a fault time point of the electric energy meter in the transformer area when a metering fault occurs according to the line loss information of the transformer area;
acquiring electric energy meter identification information and electric energy meter precision information of a first electric energy meter, wherein the first electric energy meter is any one electric energy meter in the distribution area;
acquiring a current data set and historical data of the first electric energy meter according to the fault time point and the identification information of the electric energy meter;
grouping the historical data by taking time as a unit to obtain a plurality of historical data sets;
selecting a target historical data set which is closest to the current data set from the plurality of historical data sets;
and judging whether the first electric energy meter has a metering fault or not according to the deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter.
With reference to the first aspect, in some embodiments, the selecting, from the plurality of historical data sets, a target historical data set that is closest to the current data set includes:
generating a plurality of historical data distribution functions according to the plurality of historical data sets, wherein each historical data distribution function corresponds to one historical data set;
generating a current data distribution function according to the current data set;
and determining the historical data set corresponding to the historical data distribution function closest to the current data probability distribution function as the target historical data set.
With reference to the first aspect, in some embodiments, the generating a historical data distribution function from the historical data set includes:
carrying out normalization processing on the historical data set to obtain a normalized historical data set;
calculating a mean and variance of the normalized historical data set;
calculating a historical data distribution function according to the average value and the variance of the normalized historical data set;
generating a current data probability distribution function from the current data set includes:
carrying out normalization processing on the current data set to obtain a normalized current data set;
calculating the mean and variance of the normalized current data set;
and calculating a current data distribution function according to the mean value and the variance of the normalized current data set.
With reference to the first aspect, in some embodiments, the determining that the historical data set corresponding to the historical data distribution function closest to the current data probability distribution function is the target historical data set includes:
generating a random number in the range of 0-1;
calculating a random number historical probability value corresponding to the random number and the historical data distribution function;
calculating the current probability value of the random number corresponding to the random number and the current data distribution function;
calculating the Euclidean distance between the historical probability value of the random number and the current probability value of the random number;
and taking the historical data set corresponding to the minimum value of the Euclidean distance as the target historical data set.
With reference to the first aspect, in some embodiments, the determining, according to the deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter, whether a metering fault exists in the electric energy meter includes:
calculating a first average of the target historical data set;
calculating a second average of the current data set;
calculating a deviation value of the second average value and the first average value;
and if the deviation value exceeds the accuracy information of the electric energy meter, judging that the first electric energy meter has a metering fault.
With reference to the first aspect, in some embodiments, before the determining whether the metering failure exists in the electric energy meter according to the deviation between the target historical data set and the current data set and the electric energy meter precision information, the method further includes:
drawing a plurality of historical data curves according to the plurality of historical data distribution functions, wherein each historical data curve corresponds to one historical data distribution function;
and drawing a current data curve according to the current data distribution function.
A second aspect of an embodiment of the present invention provides an electric energy meter metering fault analysis system for a field operation terminal, including:
an information obtaining module, configured to obtain fault analysis basic information, where the fault analysis basic information includes: the method comprises the steps of measuring a fault time point, electric energy meter identification information and electric energy meter precision information;
the data acquisition module is used for acquiring a current data set and historical data of the first electric energy meter according to the fault time point and the identification information of the electric energy meter;
the data grouping module is used for grouping the historical data by taking time as a unit to obtain a plurality of historical data sets;
the data screening module is used for selecting a target historical data set which is closest to the current data set from the plurality of historical data sets;
and the fault judging module is used for judging whether the first electric energy meter has a metering fault according to the deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter.
In combination with the second aspect, in some embodiments, further comprising:
the curve drawing module is used for drawing a plurality of historical data curves according to the plurality of historical data distribution functions, and each historical data curve corresponds to one historical data distribution function; and drawing a current data curve according to the current data distribution function.
A third aspect of an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for analyzing a metering failure of an electric energy meter according to any one of the first aspect when executing the computer program.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the electric energy meter metering failure analysis method according to any one of the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses an electric energy meter metering fault analysis method of a field operation terminal, which judges whether a fault exists in an electric energy meter by comparing current data with historical data; the operation terminal can analyze the historical data to find out the closest historical data, a user can more visually feel the difference between the current data and the historical data, and further determine whether a fault exists according to the difference between the two data sets.
According to the electric energy meter metering fault analysis method of the field operation terminal, disclosed by the embodiment of the invention, the power utilization data distribution function is generated by analyzing a plurality of historical data sets and the current data set, and then the closest historical data set is determined through the distribution function.
According to the electric energy meter metering fault analysis method of the field operation terminal, before the distribution function is generated, normalization processing is firstly carried out on the data set, the distribution function is generated through the data set after the normalization processing, and the distribution function is generated through the data after the normalization processing, so that convenience is brought to subsequent similarity judgment.
According to the electric energy meter metering fault analysis method of the field operation terminal, disclosed by the embodiment of the invention, the closest historical data set is obtained by comparing the Euclidean distance of each historical distribution function with the current data distribution function, the calculated amount is relatively small, the calculated amount is reduced, and the closest historical data set is determined by calculating the distance, so that the similarity is more guaranteed.
According to the electric energy meter metering fault analysis method of the field operation terminal disclosed by the embodiment of the invention, the historical data curve and the current data curve are drawn, the data are more visual, and when the historical data curve is obviously dissimilar to the current data curve, the data can be reselected, so that the wrong judgment of the electric energy meter metering fault is avoided.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the embodiments or the prior art description will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings may be obtained according to these drawings without inventive labor.
FIG. 1 is a flow chart of a method for analyzing metering faults of an electric energy meter according to an embodiment of the invention;
FIG. 2 is a functional block diagram of an electric energy meter metering fault analysis system of a field operation terminal according to an embodiment of the present invention;
fig. 3 is a functional block diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, the details are as follows: a metering fault analysis method for an electric energy meter comprises steps 101 to 107.
102, obtaining electric energy meter identification information and electric energy meter precision information of a first electric energy meter, wherein the first electric energy meter is any electric energy meter in the distribution area.
And 104, grouping the historical data by taking time as a unit to obtain a plurality of historical data sets.
For example, before troubleshooting the faulty electric energy meter, a time point of occurrence of the metering fault should be determined first, and the purpose is to temporally divide the existence of faulty metering data and normal metering data.
There may be more than one point in time of failure, as in one practice the data before the first point in time is normal metrology data and the data after the second point in time is failure metrology data, the data after the first point in time and before the second point in time being indeterminate. At this time, we should take the data before the first time point as the history data and the data after the second time point as the current data set.
In the determination of the failure time point, a common practice is that the failure time point is determined by taking the occurrence date of the abnormal line loss, that is, the data after the abnormal line loss occurs is the failure metering data, and the data before the occurrence date of the line loss in a reasonable range is the normal metering data.
The identification information of the electric energy meter is used for submitting an application to a database, and generally, the identification information of the electric energy meter is a series of serial numbers or two-dimensional codes, and the identification should be a unique identification.
And the electric energy meter precision information is used for calculating whether the metering fault exists or not in the follow-up process, if the allowable error of the electric energy meter marked as the level 1 meter is +/-1%, in the follow-up statistical comparison, if the allowable error exceeds the statistical value, the metering error of the meter is suspected to exist.
After the fault time point is determined, the data can be requested to be obtained from the database, the obtained data are divided into two types by taking the fault time point as a node, one type is normal metering data, and the other type is fault metering data.
The obtained original data is firstly grouped, and the historical data is grouped by taking the time unit of the current data set as a unit, so that a plurality of historical data sets are obtained, wherein the time unit of each data set is consistent with the unit of the current data set.
It should be noted that the number of the patents is at least two unless otherwise specified.
In one embodiment, the current data set is formed from data from a natural day.
The history data is data of a certain stage, such as data of a certain month.
When grouping, the historical data is grouped by natural days, and a data set of each natural day of the month is obtained.
In some embodiments, a plurality of historical data distribution functions are generated according to the plurality of historical data sets, and each historical data distribution function corresponds to one historical data set;
generating a current data distribution function according to the current data set;
and determining the historical data set corresponding to the historical data distribution function closest to the current data probability distribution function as the target historical data set.
Exemplarily, the historical data set is subjected to normalization processing to obtain a normalized historical data set;
calculating a mean and variance of the normalized historical data set;
calculating a historical data distribution function according to the average value and the variance of the normalized historical data set;
generating a current data probability distribution function from the current data set includes:
carrying out normalization processing on the current data set to obtain a normalized current data set;
calculating the mean and variance of the normalized current data set;
and calculating a current data distribution function according to the average value and the variance of the normalized current data set.
When the closest comparison data is found from the historical data sets, the historical data sets and the current data sets are sorted into distribution functions, the historical data distribution functions of the historical data sets are compared with the data distribution functions of the current data sets one by one, the closest historical data sets are found, and then the comparison is carried out.
In operation, in the first step, each of the historical data sets and the current data set is normalized to obtain a normalized current data set, which in one embodiment is performed according to the following equation:
in the formula:
in order to obtain the normalized data, the data is processed,
for data in a certain set of historical data,
is the minimum value in the historical data set,
for the maximum value in the historical data set, 0<i is less than or equal to n, and n is the total amount of data in the historical data set.
After normalization by the formula, all data of the data set are distributed in an interval of 0-1; and sequentially carrying out normalization processing on each historical data set and the current data set according to the mode.
An average is then calculated from each normalized data set
And variance
And finally according to the average value
And variance
Calculating to obtain distribution function
。
In some embodiments, the determining that the historical data set corresponding to the historical data distribution function closest to the current data probability distribution function is the target historical data set includes:
random numbers ranging from 0 to 1 are generated.
And calculating the historical probability value of the random number corresponding to the random number and the historical data distribution function.
And calculating the current probability value of the random number corresponding to the random number and the current data distribution function.
And calculating the Euclidean distance between the random number historical probability value and the random number current probability value.
And taking the historical data set corresponding to the minimum value of the Euclidean distance as the target historical data set.
More specifically, a random number is randomly generated within the interval 0-1
J is more than 0 and less than or equal to m, the number of m is the number of random numbers, will
Substituting into historical data distribution letter one by one
Obtaining historical probability value of random number
K is more than 0 and less than or equal to p, and p is the total number of the data sets and is substituted into the current data distribution function
Obtaining the current probability value of the random number
。
Then, the euclidean distance between the random number historical probability value and the random number current probability value is:
further, the method can be obtained as follows:
And 107, judging whether the first electric energy meter has a metering fault or not according to the deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter.
For example, the determining whether the electric energy meter has a metering fault according to the deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter includes:
a first average of the target historical data set is calculated.
A second average of the current data set is calculated.
Calculating a deviation value of the second average value from the first average value.
And if the deviation value exceeds the accuracy information of the electric energy meter, judging that the first electric energy meter has a metering fault.
More specifically, a first average value is calculated for a target historical data set
Calculating the current data set to obtain a second average value
The deviation value δ of both is measured:
and if delta exceeds the precision information of the electric energy meter, judging that the electric energy meter has a metering fault.
drawing a plurality of historical data curves according to the plurality of historical data distribution functions, wherein each historical data curve corresponds to one historical data distribution function;
and drawing a current data curve according to the current data distribution function.
More specifically, curves are respectively drawn according to the historical data distribution function and the current data distribution function, and whether the historical data distribution function and the current data distribution function have similarity or not can be intuitively sensed from the curves.
The invention discloses an electric energy meter metering fault analysis method of a field operation terminal, which judges whether a fault exists in an electric energy meter by comparing current data with historical data; the operation terminal can analyze the historical data to find out the closest historical data, a user can more visually feel the difference between the current data and the historical data, and further determine whether a fault exists according to the difference between the two data sets.
The electric energy meter metering fault analysis method of the field operation terminal disclosed by the embodiment of the invention is characterized in that an electricity utilization data distribution function is generated by analyzing a plurality of historical data sets and a current data set, and then the closest historical data set is determined through the distribution function.
According to the electric energy meter metering fault analysis method of the field operation terminal, before the distribution function is generated, the data set is firstly subjected to normalization processing, the distribution function is generated through the data set subjected to the normalization processing, and the distribution function is generated through the data subjected to the normalization processing, so that convenience is brought to subsequent similarity judgment.
According to the electric energy meter metering fault analysis method of the field operation terminal, disclosed by the embodiment of the invention, the closest historical data set is obtained by comparing the Euclidean distance between each historical distribution function and the current data distribution function, the calculated amount is relatively small, the calculated amount is reduced, and the closest historical data set is determined by calculating the distance, so that the similarity is more guaranteed.
According to the electric energy meter metering fault analysis method of the field operation terminal disclosed by the embodiment of the invention, the historical data curve and the current data curve are drawn, the data are more visual, and when the historical data curve is obviously dissimilar to the current data curve, the data can be reselected, so that the wrong judgment of the electric energy meter metering fault is avoided.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The following is an embodiment of an electric energy meter metering fault analysis system of a field operation terminal, and for details which are not described in detail therein, reference may be made to the corresponding method embodiment described above.
Fig. 2 shows a functional block diagram of the electric energy meter metering failure analysis system 2 of the field operation terminal according to the embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown.
Referring to fig. 2, the electric energy meter metering fault analysis system 2 of the field operation terminal may include an information obtaining module 21, a data obtaining module 22, a data grouping module 23, a data screening module 24, and a fault determining module 26.
An information obtaining module 21, configured to obtain fault analysis basic information, where the fault analysis basic information includes: the method comprises the steps of measuring the time point of the occurrence of the metering fault, the identification information of the electric energy meter and the precision information of the electric energy meter.
And the data acquisition module 22 is configured to acquire a current data set and historical data of the first electric energy meter according to the fault time point and the electric energy meter identification information.
And the data grouping module 23 is configured to group the historical data in units of time to obtain a plurality of historical data sets.
And a data screening module 24, configured to select a target historical data set closest to the current data set from the multiple historical data sets.
And a fault determination module 26, configured to determine whether a metering fault exists in the first electric energy meter according to a deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter.
In some embodiments, the electric energy meter metering failure analysis system 2 of the field operation terminal may further include a curve drawing module 25.
A curve drawing module 25, configured to draw a plurality of historical data curves according to the plurality of historical data distribution functions, where each historical data curve corresponds to one historical data distribution function; and drawing a current data curve according to the current data distribution function.
Fig. 3 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 3, the terminal 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The steps in the various method embodiments described above are implemented when the computer program 42 is executed by the processor 40. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the device embodiments, such as the functions of the units 21 to 24 and 26 shown in fig. 2.
Illustratively, the computer program 42 may be partitioned into one or more modules/units, which are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 42 in the terminal 4.
The terminal 4 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 3 is merely an example of a terminal 4 and is not intended to be limiting of terminal 4, and may include more or fewer components than those shown, or some components in combination, or different components, e.g., the terminal may also include input-output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal 4, such as a hard disk or a memory of the terminal 4. The memory 41 may also be an external storage device of the terminal 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the terminal 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the apparatus may be divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present application. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated module/unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (7)
1. A method for analyzing metering faults of an electric energy meter is characterized by comprising the following steps:
determining a fault time point of the electric energy meter in the transformer area when a metering fault occurs according to the line loss information of the transformer area; acquiring electric energy meter identification information and electric energy meter precision information of a first electric energy meter, wherein the first electric energy meter is any electric energy meter in the distribution area;
acquiring a current data set and historical data of the first electric energy meter according to the fault time point and the electric energy meter identification information;
grouping the historical data by taking time as a unit to obtain a plurality of historical data sets;
selecting a target historical data set which is closest to the current data set from the plurality of historical data sets; judging whether the first electric energy meter has a metering fault according to the deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter;
wherein the selecting a target historical data set that is closest to the current data set from the plurality of historical data sets comprises:
generating a plurality of historical data distribution functions according to the plurality of historical data sets, wherein each historical data distribution function corresponds to one historical data set;
generating a current data distribution function according to the current data set;
determining a historical data set corresponding to the historical data distribution function closest to the current data probability distribution function as the target historical data set;
wherein the generating a historical data distribution function from the historical data set comprises:
carrying out normalization processing on the historical data set to obtain a normalized historical data set;
calculating the mean and variance of the normalized historical data set;
calculating a historical data distribution function according to the average value and the variance of the normalized historical data set;
generating a current data probability distribution function from the current data set includes:
carrying out normalization processing on the current data set to obtain a normalized current data set;
calculating the mean and variance of the normalized current data set;
calculating a current data distribution function according to the average value and the variance of the normalized current data set;
wherein the determining that the historical data set corresponding to the historical data distribution function closest to the current data probability distribution function is the target historical data set comprises:
generating a random number in the range of 0-1;
calculating the historical probability value of the random number corresponding to the random number and the historical data distribution function;
calculating the current probability value of the random number corresponding to the random number and the current data distribution function;
calculating the Euclidean distance between the random number historical probability value and the random number current probability value;
and taking the historical data set corresponding to the minimum value of the Euclidean distance as the target historical data set.
2. The method for analyzing the metering failure of the electric energy meter according to claim 1, wherein the step of judging whether the electric energy meter has the metering failure according to the deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter comprises the following steps:
calculating a first average of the target historical data set;
calculating a second average of the current data set;
calculating a deviation value of the second average value and the first average value;
and if the deviation value exceeds the accuracy information of the electric energy meter, judging that the first electric energy meter has a metering fault.
3. The method for analyzing the metering failure of the electric energy meter according to claim 1, wherein before the step of judging whether the electric energy meter has the metering failure according to the deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter, the method further comprises the following steps:
drawing a plurality of historical data curves according to the plurality of historical data distribution functions, wherein each historical data curve corresponds to one historical data distribution function;
and drawing a current data curve according to the current data distribution function.
4. The utility model provides an electric energy meter measurement fault analysis system of field operation terminal which characterized in that includes:
an information obtaining module, configured to obtain fault analysis basic information, where the fault analysis basic information includes: the method comprises the steps of measuring a fault time point, electric energy meter identification information and electric energy meter precision information;
the data acquisition module is used for acquiring a current data set and historical data of the first electric energy meter according to the fault time point and the electric energy meter identification information;
the data grouping module is used for grouping the historical data by taking time as a unit to obtain a plurality of historical data sets;
the data screening module is used for selecting a target historical data set which is closest to the current data set from the plurality of historical data sets;
the fault judging module is used for judging whether the first electric energy meter has a metering fault according to the deviation between the target historical data set and the current data set and the accuracy information of the electric energy meter;
wherein the selecting a target historical data set that is closest to the current data set from the plurality of historical data sets comprises:
generating a plurality of historical data distribution functions according to the plurality of historical data sets, wherein each historical data distribution function corresponds to one historical data set;
generating a current data distribution function according to the current data set;
determining a historical data set corresponding to the historical data distribution function closest to the current data probability distribution function as the target historical data set;
wherein the generating a historical data distribution function from the historical data set comprises:
carrying out normalization processing on the historical data set to obtain a normalized historical data set;
calculating a mean and variance of the normalized historical data set;
calculating a historical data distribution function according to the average value and the variance of the normalized historical data set;
the generating a current data probability distribution function according to the current data set includes:
carrying out normalization processing on the current data set to obtain a normalized current data set;
calculating the mean and variance of the normalized current data set;
calculating a current data distribution function according to the average value and the variance of the normalized current data set;
wherein the determining that the historical data set corresponding to the historical data distribution function closest to the current data probability distribution function is the target historical data set comprises:
generating a random number in the range of 0-1;
calculating the historical probability value of the random number corresponding to the random number and the historical data distribution function;
calculating the current probability value of the random number corresponding to the random number and the current data distribution function;
calculating the Euclidean distance between the random number historical probability value and the random number current probability value;
and taking the historical data set corresponding to the minimum value of the Euclidean distance as the target historical data set.
5. The electric energy meter metering fault analysis system of the field operation terminal according to claim 4, characterized by further comprising:
the curve drawing module is used for drawing a plurality of historical data curves according to the plurality of historical data distribution functions, and each historical data curve corresponds to one historical data distribution function; and drawing a current data curve according to the current data distribution function.
6. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method of analyzing a meter fault of an electric energy meter according to any of the preceding claims 1 to 3.
7. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for analyzing a metering failure of an electric energy meter according to any one of claims 1 to 3.
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