CN114037164A - Prediction method and device for multi-dimensional data quality of power distribution network - Google Patents

Prediction method and device for multi-dimensional data quality of power distribution network Download PDF

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CN114037164A
CN114037164A CN202111341375.0A CN202111341375A CN114037164A CN 114037164 A CN114037164 A CN 114037164A CN 202111341375 A CN202111341375 A CN 202111341375A CN 114037164 A CN114037164 A CN 114037164A
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董世文
朱立鹏
周爱华
许梦晗
蒋静
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Global Energy Interconnection Research Institute
Information and Telecommunication Branch of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses a method and a device for predicting the quality of multidimensional data of a power distribution network, wherein the method comprises the following steps: determining a power distribution network data set of the data quality of the power distribution network to be predicted; determining multi-dimensional data prediction indexes of data quantity under each type, and calculating comprehensive weight values of the multi-dimensional data prediction indexes; calculating the total score of each dimensional data prediction index according to each dimensional data prediction index and the corresponding comprehensive weight value thereof; and determining the data quality grade of each dimensional data prediction index according to the total score of each dimensional data prediction index. The comprehensiveness, reasonability and effectiveness of the data quality prediction of the power distribution network are guaranteed by comprehensively predicting the normative index, the accuracy index, the integrity index, the consistency index, the effectiveness index, the uniqueness index, the real-time index and the accessibility index of the power distribution network data set, the prediction mode is simple, and power grid operation personnel can be helped to know the data quality condition of the intelligent power distribution network more truly.

Description

Prediction method and device for multi-dimensional data quality of power distribution network
Technical Field
The invention relates to the technical field of power data analysis, in particular to a method and a device for predicting the quality of multidimensional data of a power distribution network.
Background
The power distribution network is one of key links of the smart power grid, is a part of the whole power system directly connected with dispersed users, along with the advancing of the smart power grid, the informatization and the intellectualization of the power system are continuously developed, the operation of the smart power grid generates abundant data which mainly comprises power distribution network scheduling data, marketing data, electrical information data, asset management data and the like, and relates to various service types such as voltage, current, active power, reactive power, harmonic wave and the like, the data has the characteristics of quantization, polymorphism, isomerization and high latitude, but the measured data has certain conditions such as redundancy, deficiency, abnormity and the like, the effectiveness and the accuracy of the management and the application of the smart power distribution network are influenced, and further the safety and the reliability of the system where the whole smart power distribution network is located are influenced, however, in the data quality prediction of the current power distribution network, as the predicted data objects are single, the prediction mode is simple, so that the data quality condition of the intelligent power distribution network cannot be comprehensively, systematically and truly known.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the problem that the data quality of the power distribution network in the prior art is predicted, and the data quality condition of the intelligent power distribution network cannot be comprehensively, systematically and truly known due to the fact that the predicted data object is single and the prediction mode is simple, so that the method and the device for predicting the multidimensional data quality of the power distribution network are provided.
According to a first aspect, an embodiment of the present invention provides a method for predicting quality of multidimensional data of a power distribution network, including the following steps:
determining a power distribution network data set of the data quality of the power distribution network to be predicted;
determining multidimensional data prediction indexes of data quantity under each type, wherein the multidimensional data prediction indexes respectively comprise: normative index, accuracy index, integrity index, consistency index, effectiveness index, uniqueness index, real-time index and accessibility index;
calculating the comprehensive weight value of each dimension data prediction index;
calculating the total score of each dimensional data prediction index according to each dimensional data prediction index and the corresponding comprehensive weight value thereof;
and determining the data quality grade of each dimensional data prediction index according to the total score of each dimensional data prediction index.
In one embodiment, the step of determining the power distribution network data set of which the data quality is to be predicted comprises:
the method comprises the steps of collecting distribution network scheduling data, distribution network marketing data, distribution network electric information data and distribution network asset management data of the distribution network data quality to be predicted;
and counting the distribution network scheduling data, the distribution network marketing data, the distribution network electric information data and the distribution network asset management data in a data table to form the distribution network data set.
In one embodiment, the number of data recorded in the power distribution network data set is determined by the number of rows and columns in the data table.
In one embodiment, the determining the multidimensional data prediction index for the data quantity under each type includes: the steps of the normative index, the accuracy index, the integrity index, the consistency index, the effectiveness index, the uniqueness index, the real-time index and the accessibility index are determined by the following formulas:
Q1＝C1/N*100％；
Q2＝C2/N*100％；
Q3＝C3/N*100％；
Q4＝C4/N*100％；
Q5＝C5/N*100％；
Q6＝C6/N*100％；
Q7＝C7/N*100％；
Q8＝C8/N*100％；
wherein Q is1Is a normative index, Q2As an indicator of accuracy, Q3As an integrity indicator, Q4As an index of consistency, Q5As an indication of effectiveness, Q6As a uniqueness index, Q7As an indicator of real-time performance, Q8As an index of accessibility, C1Amount of data to meet the normative index rules, C2Amount of data to meet accuracy index rules, C3To meet the data volume of the integrity index rule, C4Amount of data to comply with the rule of the consistency index, C5Amount of data to comply with the validity index rules, C6Amount of data to comply with uniqueness index rules, C7Amount of data to meet real-time criteria, C8And N is the total number of the data sets of the power distribution network to meet the data volume of the reachability index rule.
In one embodiment, the calculating the comprehensive weight value of each dimensional data predictor is performed by the following formula:
Figure BDA0003352193000000031
Figure BDA0003352193000000032
Figure BDA0003352193000000033
Figure BDA0003352193000000041
Figure BDA0003352193000000042
Figure BDA0003352193000000043
Figure BDA0003352193000000044
Figure BDA0003352193000000045
wherein, W1Is a comprehensive weight value corresponding to the normative index, Wa1A weight value W corresponding to the degree of importance of the normative indexb1The standard weight value is corresponding to the standard index; w2Is a comprehensive weight value corresponding to the accuracy index, Wa2A weight value W corresponding to the importance of the accuracy indexb2The standard weight value is corresponding to the accuracy index; w3Is the comprehensive weight value corresponding to the integrity index, Wa3A weight value, W, corresponding to the importance of the integrity indicatorb3The standard weight value is corresponding to the integrity index; w4Is the comprehensive weight value corresponding to the consistency index, Wa4A weight value, W, corresponding to the importance of the consistency indicatorb4Standard weight for consistency indexA value; w5Is a comprehensive weight value corresponding to the effectiveness index, Wa5A weight value, W, corresponding to the significance of the effectiveness indexb5The standard weight value is corresponding to the effectiveness index; w6Is a comprehensive weight value corresponding to the uniqueness index, Wa6Is a weight value corresponding to the importance degree of the uniqueness index, Wb6The standard weight value is corresponding to the uniqueness index; w7Is a comprehensive weight value, W, corresponding to the real-time indexa7A weight value, W, corresponding to the importance of the real-time indicatorb7The standard weight value is corresponding to the real-time index; w8For the combined weight value, W, corresponding to the reachability indexa8For weight values, W, corresponding to the degree of importance of the reachability indexb8And the standard weight value is corresponding to the accessibility index.
In one embodiment, calculating the total score of each dimensional data predictor according to each dimensional data predictor and its corresponding comprehensive weight value is performed by the following formula:
Figure BDA0003352193000000046
Figure BDA0003352193000000047
Figure BDA0003352193000000051
Figure BDA0003352193000000052
Figure BDA0003352193000000053
Figure BDA0003352193000000054
Figure BDA0003352193000000055
Figure BDA0003352193000000056
wherein M is1Is a total score, Q, corresponding to the normative index1Is a normative index, W1The comprehensive weight value is corresponding to the standard index; m2The total score, Q, corresponding to the accuracy index2As an indicator of accuracy, W2The comprehensive weight value is corresponding to the accuracy index; m3Is the total score, Q, corresponding to the integrity indicator3As an integrity indicator, W3The comprehensive weight value is corresponding to the integrity index; m4Is the total score, Q, corresponding to the consistency index4As an index of consistency, W4The comprehensive weight value corresponding to the consistency index; m5Is a total score, Q, corresponding to the effectiveness index5As an indication of effectiveness, W5The comprehensive weight value is corresponding to the validity index; m6Total score, Q, corresponding to the uniqueness index6As a uniqueness index, W6The comprehensive weight value is corresponding to the uniqueness index; m7Is the total score, Q, corresponding to the real-time index7As an indicator of real-time performance, W7The real-time index is a comprehensive weighted value corresponding to the real-time index; m8For total score, Q, corresponding to reachability index8As an index of accessibility, W8And the comprehensive weight value is corresponding to the accessibility index.
In one embodiment, determining the data quality level of each of the dimensional data predictors from the overall score of the each of the dimensional data predictors is performed by:
when the total score of each dimensional data prediction index belongs to a first range of numerical values, the total score of each dimensional data prediction index is a first type grade;
when the total score of each dimensional data prediction index belongs to a second range of numerical values, the total score of each dimensional data prediction index is a second type grade;
and when the total score of each dimensional data prediction index belongs to a third range of numerical values, the total score of each dimensional data prediction index is a third type grade.
According to a second aspect, an embodiment of the present invention provides an apparatus for predicting quality of multidimensional data of a power distribution network, including the following modules:
the power distribution network data set determining module is used for determining a power distribution network data set of the power distribution network data quality to be predicted;
the multi-dimensional data prediction index module is used for determining multi-dimensional data prediction indexes of data quantity under each type, and the multi-dimensional data prediction indexes respectively comprise: normative index, accuracy index, integrity index, consistency index, effectiveness index, uniqueness index, real-time index and accessibility index;
the comprehensive weight value calculation module is used for calculating the comprehensive weight value of each dimensional data prediction index;
the total score calculation module is used for calculating the total score of each dimensional data prediction index according to the weight value of each dimensional data prediction index;
and the data quality grade determining module is used for determining the data quality grade of each dimensional data prediction index according to the total score of each dimensional data prediction index.
According to a third aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause the computer to execute the method for predicting the multidimensional data quality of the power distribution network described in the first aspect or any implementation manner of the first aspect.
According to a fourth aspect, an embodiment of the present invention provides an electronic device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing therein computer instructions, and the processor executing the computer instructions to perform the method for predicting the multidimensional data quality of the power distribution network according to the first aspect or any embodiment of the first aspect.
The technical scheme of the invention has the following advantages:
the invention discloses a method and a device for predicting the quality of multidimensional data of a power distribution network, wherein the method comprises the following steps: determining a power distribution network data set of the data quality of the power distribution network to be predicted; determining multidimensional data prediction indexes of data quantity under each type, wherein the multidimensional data prediction indexes respectively comprise: normative index, accuracy index, integrity index, consistency index, effectiveness index, uniqueness index, real-time index and accessibility index; calculating the comprehensive weight value of each dimension data prediction index; calculating the total score of each dimensional data prediction index according to each dimensional data prediction index and the corresponding comprehensive weight value thereof; and determining the data quality grade of each dimensional data prediction index according to the total score of each dimensional data prediction index. The comprehensiveness, reasonability and effectiveness of the data quality prediction of the power distribution network are guaranteed by comprehensively predicting the normative index, the accuracy index, the integrity index, the consistency index, the effectiveness index, the uniqueness index, the real-time index and the accessibility index of the power distribution network data set, the prediction mode is simple, power grid operation personnel can be helped to know the data quality condition of the intelligent power distribution network more truly, and further more powerful support is provided for operation management and correct decision of the intelligent power distribution network.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for predicting the quality of multidimensional data of a power distribution network in an embodiment of the present invention;
fig. 2 is a block diagram of a prediction apparatus for multidimensional data quality of a power distribution network according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device in an embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the technical field of electric power data analysis, the data quality of the current power distribution network is predicted, and the predicted data object is single, and the prediction mode is simple, so that the data quality condition of the intelligent power distribution network cannot be comprehensively, systematically and truly known.
In view of this, an embodiment of the present invention provides a method for predicting quality of multidimensional data of a power distribution network, as shown in fig. 1, including the following steps:
step S11: and determining a power distribution network data set of the data quality of the power distribution network to be predicted.
In one embodiment, the step of determining the power distribution network data set to be predicted in the step S11 includes:
the first step is as follows: the method comprises the steps of collecting distribution network scheduling data, distribution network marketing data, distribution network electricity information data and distribution network asset management data of the distribution network data quality to be predicted.
The power distribution network scheduling data refers to data information used for transmitting power distribution network automation information, scheduling command instructions, relay protection and safety automatic control; the distribution network marketing data refers to daily electricity consumption charging data information used for analyzing and managing users; the power distribution network electrical information data refers to electrical data information such as voltage, current, active power, reactive power and the like; distribution grid asset management data refers to data information used to manage distribution grid asset economic benefits.
The second step is that: and counting the distribution network scheduling data, the distribution network marketing data, the distribution network electric information data and the distribution network asset management data in a data table to form a distribution network data set.
For example: the data table for counting the data set of the power distribution network is an Excel table, and the table is composed of rows and columns. Specifically, the number of data recorded in the power distribution network data set is determined by the number of rows and columns in the data table. For example: the total number of data recorded in the power distribution network data set is N, wherein N is m × N, m is the number of data lines, and N is the number of data columns.
Step S12: determining multidimensional data prediction indexes of data quantity under each type, wherein the multidimensional data prediction indexes respectively comprise: normative index, accuracy index, integrity index, consistency index, effectiveness index, uniqueness index, real-time index and accessibility index.
Wherein, the normative index refers to the degree that the expression of the data content accords with relevant standards and standards at home and abroad, and the quantization rule is whether the data accords with the standards or not; the accuracy index refers to the degree of conformity between the data and the objective entity characteristics, and the quantization rule is whether the data value is accurate or not; the integrity index refers to the integrity degree of the description information, the quantization rule is whether the data is non-empty, whether the domain is complete, whether the citation is complete, the non-empty requirement data cannot be recorded as empty, the column dereferencing of a certain column in the domain complete requirement table needs to be in the legal dereferencing range of the column, and the citation integrity requires the completeness of the citation between related columns of different tables in a relational database; the consistency index refers to the degree that the data structure, the element attributes and the mutual relation among the data structure and the element attributes accord with the logic rule, the quantization rule refers to whether the concept is consistent or not, whether the value range is consistent or not, whether the format is consistent or not and whether the topology is consistent or not, the concept consistency index data accords with the concept mode rule, the value range consistency refers to that the data value is in the value range, the format consistency index data is stored to be consistent with the physical structure of the data, and the topology consistency index data has correct topological relation; the validity index refers to the validity degree of data meeting the service requirement; the uniqueness index refers to the determination degree of data recorded in the table, the quantization rule is whether the data is unique in entity and unique in primary key, the entity only requires that each row in one table is unique, and the primary key only requires that each row of data in one table is unique in primary key; the real-time index refers to the satisfaction degree of the time characteristic of the data to the application, and the quantification rule is whether the data is invalid or not; the reachability index refers to the satisfaction degree of the data volume of the data to the application, and the quantification rule is that the ratio of the total data volume to the total volume required by the application can be acquired.
In one embodiment, the step S12 determines the multi-dimensional data prediction indexes of the data amount for each type, and each of the multi-dimensional data prediction indexes includes: the steps of the normative index, the accuracy index, the integrity index, the consistency index, the effectiveness index, the uniqueness index, the real-time index and the accessibility index are determined by the following formulas:
Q1＝C1/N*100％； (1)
Q2＝C2/N*100％； (2)
Q3＝C3/N*100％； (3)
Q4＝C4/N*100％； (4)
Q5＝C5/N*100％； (5)
Q6＝C6/N*100％； (6)
Q7＝C7/N*100％； (7)
Q8＝C8/N*100％； (8)
wherein Q is1Is a normative index, Q2As an indicator of accuracy, Q3As an integrity indicator, Q4As an index of consistency, Q5As an indication of effectiveness, Q6As a uniqueness index, Q7As an indicator of real-time performance, Q8As an index of accessibility, C1Amount of data to meet the normative index rules, C2Amount of data to meet accuracy index rules, C3To meet the data volume of the integrity index rule, C4Amount of data to comply with the rule of the consistency index, C5Amount of data to comply with the validity index rules, C6Amount of data to comply with uniqueness index rules, C7Amount of data to meet real-time criteria, C8And N is the total number of the data sets of the power distribution network to meet the data volume of the reachability index rule.
Therefore, the data prediction indexes of the eight different dimensions are respectively determined according to the respective data volume and the total number of the power distribution network data sets.
Step S13: and calculating the comprehensive weight value of each dimension data prediction index.
In one embodiment, the step S13 of calculating the comprehensive weight value of each dimensional data predictor is performed by the following formula:
Figure BDA0003352193000000121
Figure BDA0003352193000000122
Figure BDA0003352193000000123
Figure BDA0003352193000000124
Figure BDA0003352193000000125
Figure BDA0003352193000000126
Figure BDA0003352193000000127
Figure BDA0003352193000000128
wherein, W1Is a comprehensive weight value corresponding to the normative index, Wa1A weight value W corresponding to the degree of importance of the normative indexb1The standard weight value is corresponding to the standard index; w2Is a comprehensive weight value corresponding to the accuracy index, Wa2A weight value W corresponding to the importance of the accuracy indexb2The standard weight value is corresponding to the accuracy index; w3Is the comprehensive weight value corresponding to the integrity index, Wa3A weight value, W, corresponding to the importance of the integrity indicatorb3The standard weight value is corresponding to the integrity index; w4Is the comprehensive weight value corresponding to the consistency index, Wa4A weight value, W, corresponding to the importance of the consistency indicatorb4The standard weight value is corresponding to the consistency index;W5is a comprehensive weight value corresponding to the effectiveness index, Wa5A weight value, W, corresponding to the significance of the effectiveness indexb5The standard weight value is corresponding to the effectiveness index; w6Is a comprehensive weight value corresponding to the uniqueness index, Wa6Is a weight value corresponding to the importance degree of the uniqueness index, Wb6The standard weight value is corresponding to the uniqueness index; w7Is a comprehensive weight value, W, corresponding to the real-time indexa7A weight value, W, corresponding to the importance of the real-time indicatorb7The standard weight value is corresponding to the real-time index; w8For the combined weight value, W, corresponding to the reachability indexa8For weight values, W, corresponding to the degree of importance of the reachability indexb8And the standard weight value is corresponding to the accessibility index.
For the data predictors of the eight different dimensions, the relative importance of each predictor is ranked as: the method comprises the following steps of normalization index, accuracy index, integrity index, consistency index, effectiveness index, uniqueness index, real-time index and accessibility index.
Step S14: and calculating the total score of each dimensional data prediction index according to each dimensional data prediction index and the corresponding comprehensive weight value thereof.
In one embodiment, the step S14 is executed by calculating the total score of each of the dimensional data predictors according to each of the dimensional data predictors and the corresponding integrated weight value thereof by:
Figure BDA0003352193000000131
Figure BDA0003352193000000132
Figure BDA0003352193000000133
Figure BDA0003352193000000134
Figure BDA0003352193000000135
Figure BDA0003352193000000136
Figure BDA0003352193000000137
Figure BDA0003352193000000138
wherein M is1Is a total score, Q, corresponding to the normative index1Is a normative index, W1The comprehensive weight value is corresponding to the standard index; m2The total score, Q, corresponding to the accuracy index2As an indicator of accuracy, W2The comprehensive weight value is corresponding to the accuracy index; m3Is the total score, Q, corresponding to the integrity indicator3As an integrity indicator, W3The comprehensive weight value is corresponding to the integrity index; m4Is the total score, Q, corresponding to the consistency index4As an index of consistency, W4The comprehensive weight value corresponding to the consistency index; m5Is a total score, Q, corresponding to the effectiveness index5As an indication of effectiveness, W5The comprehensive weight value is corresponding to the validity index; m6Total score, Q, corresponding to the uniqueness index6As a uniqueness index, W6The comprehensive weight value is corresponding to the uniqueness index; m7Is the total score, Q, corresponding to the real-time index7As an indicator of real-time performance, W7The real-time index is a comprehensive weighted value corresponding to the real-time index; m8For total score, Q, corresponding to reachability index8As an index of accessibility, W8For corresponding integration of reachability indicatorsAnd (4) weighting values.
Step S15: and determining the data quality grade of each dimensional data prediction index according to the total score of each dimensional data prediction index.
In one embodiment, the step S15 above determining the data quality grade of each of the dimensional data predictors according to the total score of each of the dimensional data predictors is performed by:
the first step is as follows: when the total score of each dimension data prediction index belongs to a first range of values, the total score of each dimension data prediction index is a first type grade.
For example: the first range of values is (0, 0.6)]The first type class is class III. For example: standard index M10.5, the value falls within the first range of values.
The second step is that: when the total score of each dimension data prediction index belongs to a second range of values, the total score of each dimension data prediction index is a second type level.
For example: the second range of values is (0.6, 0.8)]And the second type level is level II. For example: effectiveness index M50.7, the value falls within the second range of values.
The third step: and when the total score of each dimension data prediction index belongs to a third range of values, the total score of each dimension data prediction index is a third type level.
For example: the third range of values is (0.8, 1)]Then the third type level is level I. For example: real-time index M70.9, the value falls within the third range of values.
According to the prediction method for the quality of the multidimensional data of the power distribution network, disclosed by the embodiment of the invention, the normative index, the accuracy index, the integrity index, the consistency index, the validity index, the uniqueness index, the real-time index and the accessibility index of the data set of the power distribution network are comprehensively predicted, so that the comprehensiveness, the reasonability and the validity of the data quality prediction of the power distribution network are ensured, the prediction mode is simpler, power grid operators can be helped to know the data quality condition of the intelligent power distribution network more truly, and further, more powerful support is provided for the operation management and the correct decision of the intelligent power distribution network.
Based on the same concept, an embodiment of the present invention further provides a prediction apparatus for multidimensional data quality of a power distribution network, as shown in fig. 2, including the following modules:
and the power distribution network data set determining module 21 is configured to determine a power distribution network data set of the power distribution network data quality to be predicted.
A multi-dimensional data prediction index module 22, configured to determine multi-dimensional data prediction indexes of data quantities under various types, where the multi-dimensional data prediction indexes respectively include: normative index, accuracy index, integrity index, consistency index, effectiveness index, uniqueness index, real-time index and accessibility index.
And the comprehensive weight value calculating module 23 is configured to calculate a comprehensive weight value of each dimensional data prediction index.
And the total score calculation module 24 is configured to calculate a total score of each of the dimensional data prediction indexes according to the weight value of each of the dimensional data prediction indexes.
And the data quality grade determining module 25 is configured to determine the data quality grade of each dimensional data prediction index according to the total score of each dimensional data prediction index.
In one embodiment, the power distribution network data set determination module 21 includes:
and the data acquisition submodule is used for acquiring distribution network scheduling data, distribution network marketing data, distribution network electrical information data and distribution network asset management data of the data quality of the distribution network to be predicted.
And the data statistics submodule is used for carrying out statistics on the distribution network scheduling data, the distribution network marketing data, the distribution network electrical information data and the distribution network asset management data to form a distribution network data set in the data table.
In one embodiment, the number of data recorded in the power distribution network data set is determined by the number of rows and columns in the data table.
In one embodiment, the multidimensional data prediction index module 22 determines multidimensional data prediction indexes of data quantities under each type, and the multidimensional data prediction indexes respectively include: the normative index, the accuracy index, the integrity index, the consistency index, the validity index, the uniqueness index, the real-time index, and the reachability index are performed by the above equations (1) to (8).
In one embodiment, the calculation of the comprehensive weight value of each dimensional data predictor by the comprehensive weight value calculation module 23 is performed by performing (9) to (16) through the above equations.
In one embodiment, calculating the total score for each dimensional data predictor based on each dimensional data predictor and its corresponding composite weight value is performed by equations (17) - (24) above.
In one embodiment, data quality level determination module 25 includes:
and the first type level determining submodule is used for determining the total score of each dimension data prediction index as a first type level when the total score of each dimension data prediction index belongs to a first range of numerical values.
And the second type level determining submodule is used for determining the total score of each dimension data prediction index as a second type level when the total score of each dimension data prediction index belongs to a second range of numerical values.
And a third type level determination submodule for determining the total score of each of the dimensional data predictors as a third type level when the total score of each of the dimensional data predictors belongs to a third range of values.
The prediction device for the multidimensional data quality of the power distribution network in the embodiment of the invention comprehensively predicts the normative index, the accuracy index, the integrity index, the consistency index, the validity index, the uniqueness index, the real-time index and the accessibility index of the data set of the power distribution network, thereby ensuring the comprehensiveness, the rationality and the validity of the data quality prediction of the power distribution network, ensuring that power grid operation personnel can more truly know the data quality condition of the intelligent power distribution network, and further providing more powerful support for the operation management and the correct decision of the intelligent power distribution network.
An embodiment of the present invention further provides an electronic device, as shown in fig. 3, the electronic device may include a processor 31 and a memory 32, where the processor 31 and the memory 32 may be connected by a bus or in another manner, and fig. 3 takes the connection by the bus as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 32, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 31 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 32, namely, implementing the prediction method of the multidimensional data quality of the power distribution network in the above method embodiment.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 31, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the power grid, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 32 and when executed by the processor 31 perform a method for predicting the quality of multidimensional data of an electrical distribution network as in the embodiment shown in the figures.
The details of the electronic device may be understood by referring to the corresponding related descriptions and effects in the embodiments shown in the drawings, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims (10)

1. A prediction method for the quality of multidimensional data of a power distribution network is characterized by comprising the following steps:
determining a power distribution network data set of the data quality of the power distribution network to be predicted;
determining multidimensional data prediction indexes of data quantity under each type, wherein the multidimensional data prediction indexes respectively comprise: normative index, accuracy index, integrity index, consistency index, effectiveness index, uniqueness index, real-time index and accessibility index;
calculating the comprehensive weight value of each dimension data prediction index;
calculating the total score of each dimensional data prediction index according to each dimensional data prediction index and the corresponding comprehensive weight value thereof;
and determining the data quality grade of each dimensional data prediction index according to the total score of each dimensional data prediction index.
2. The method for predicting the multidimensional data quality of the power distribution network according to claim 1, wherein the step of determining the power distribution network data set of the power distribution network data quality to be predicted comprises:
the method comprises the steps of collecting distribution network scheduling data, distribution network marketing data, distribution network electric information data and distribution network asset management data of the distribution network data quality to be predicted;
and counting the distribution network scheduling data, the distribution network marketing data, the distribution network electric information data and the distribution network asset management data in a data table to form the distribution network data set.
3. The method for predicting the quality of the multidimensional data of the power distribution network according to claim 2, wherein the amount of data recorded in the data set of the power distribution network is determined by the number of rows and the number of columns in the data table.
4. The method according to claim 1, wherein the multidimensional data prediction indexes for determining the data amount in each type respectively comprise: the steps of the normative index, the accuracy index, the integrity index, the consistency index, the effectiveness index, the uniqueness index, the real-time index and the accessibility index are determined by the following formulas:
Q1＝C1/N*100％；
Q2＝C2/N*100％；
Q3＝C3/N*100％；
Q4＝C4/N*100％；
Q5＝C5/N*100％；
Q6＝C6/N*100％；
Q7＝C7/N*100％；
Q8＝C8/N*100％；
wherein Q is1Is a normative index, Q2As an indicator of accuracy, Q3As an integrity indicator, Q4As an index of consistency, Q5As an indication of effectiveness, Q6As a uniqueness index, Q7As an indicator of real-time performance, Q8As an index of accessibility, C1Amount of data to meet the normative index rules, C2Amount of data to meet accuracy index rules, C3To meet the data volume of the integrity index rule, C4Amount of data to comply with the rule of the consistency index, C5Amount of data to comply with the validity index rules, C6Amount of data to comply with uniqueness index rules, C7Amount of data to meet real-time criteria, C8And N is the total number of the data sets of the power distribution network to meet the data volume of the reachability index rule.
5. The method for predicting the multidimensional data quality of the power distribution network according to claim 1, wherein the calculating of the comprehensive weight value of each dimensional data prediction index is performed by the following formula:
Figure FDA0003352192990000021
Figure FDA0003352192990000031
Figure FDA0003352192990000032
Figure FDA0003352192990000033
Figure FDA0003352192990000034
Figure FDA0003352192990000035
Figure FDA0003352192990000036
Figure FDA0003352192990000037
wherein, W1Is a comprehensive weight value corresponding to the normative index, Wa1A weight value W corresponding to the degree of importance of the normative indexb1The standard weight value is corresponding to the standard index; w2Is a comprehensive weight value corresponding to the accuracy index, Wa2A weight value W corresponding to the importance of the accuracy indexb2The standard weight value is corresponding to the accuracy index; w3Is the comprehensive weight value corresponding to the integrity index, Wa3A weight value, W, corresponding to the importance of the integrity indicatorb3The standard weight value is corresponding to the integrity index; w4Is the comprehensive weight value corresponding to the consistency index, Wa4A weight value, W, corresponding to the importance of the consistency indicatorb4The standard weight value is corresponding to the consistency index; w5Is a comprehensive weight value corresponding to the effectiveness index, Wa5A weight value, W, corresponding to the significance of the effectiveness indexb5The standard weight value is corresponding to the effectiveness index; w6Is a comprehensive weight value corresponding to the uniqueness index, Wa6Is a weight value corresponding to the importance degree of the uniqueness index, Wb6The standard weight value is corresponding to the uniqueness index; w7Is a comprehensive weight value, W, corresponding to the real-time indexa7A weight value, W, corresponding to the importance of the real-time indicatorb7The standard weight value is corresponding to the real-time index; w8For the combined weight value, W, corresponding to the reachability indexa8Important for accessibility indicatorsWeight value corresponding to degree, Wb8And the standard weight value is corresponding to the accessibility index.
6. The method for predicting the multidimensional data quality of the power distribution network according to claim 1, wherein calculating the total score of each dimensional data prediction index according to each dimensional data prediction index and the corresponding comprehensive weight value thereof is performed by the following formula:
Figure FDA0003352192990000041
Figure FDA0003352192990000042
Figure FDA0003352192990000043
Figure FDA0003352192990000044
Figure FDA0003352192990000045
Figure FDA0003352192990000046
Figure FDA0003352192990000047
Figure FDA0003352192990000048
wherein M is1Is a total score, Q, corresponding to the normative index1Is a normative index, W1The comprehensive weight value is corresponding to the standard index; m2The total score, Q, corresponding to the accuracy index2As an indicator of accuracy, W2The comprehensive weight value is corresponding to the accuracy index; m3Is the total score, Q, corresponding to the integrity indicator3As an integrity indicator, W3The comprehensive weight value is corresponding to the integrity index; m4Is the total score, Q, corresponding to the consistency index4As an index of consistency, W4The comprehensive weight value corresponding to the consistency index; m5Is a total score, Q, corresponding to the effectiveness index5As an indication of effectiveness, W5The comprehensive weight value is corresponding to the validity index; m6Total score, Q, corresponding to the uniqueness index6As a uniqueness index, W6The comprehensive weight value is corresponding to the uniqueness index; m7Is the total score, Q, corresponding to the real-time index7As an indicator of real-time performance, W7The real-time index is a comprehensive weighted value corresponding to the real-time index; m8For total score, Q, corresponding to reachability index8As an index of accessibility, W8And the comprehensive weight value is corresponding to the accessibility index.
7. The method for predicting the multidimensional data quality of the power distribution network according to claim 1, wherein the step of determining the data quality grade of each dimensional data prediction index according to the total score of each dimensional data prediction index is performed by:
when the total score of each dimensional data prediction index belongs to a first range of numerical values, the total score of each dimensional data prediction index is a first type grade;
when the total score of each dimensional data prediction index belongs to a second range of numerical values, the total score of each dimensional data prediction index is a second type grade;
and when the total score of each dimensional data prediction index belongs to a third range of numerical values, the total score of each dimensional data prediction index is a third type grade.
8. The prediction device of the multidimensional data quality of the power distribution network is characterized by comprising the following modules:
the power distribution network data set determining module is used for determining a power distribution network data set of the power distribution network data quality to be predicted;
the multi-dimensional data prediction index module is used for determining multi-dimensional data prediction indexes of data quantity under each type, and the multi-dimensional data prediction indexes respectively comprise: normative index, accuracy index, integrity index, consistency index, effectiveness index, uniqueness index, real-time index and accessibility index;
the comprehensive weight value calculation module is used for calculating the comprehensive weight value of each dimensional data prediction index;
the total score calculation module is used for calculating the total score of each dimensional data prediction index according to the weight value of each dimensional data prediction index;
and the data quality grade determining module is used for determining the data quality grade of each dimensional data prediction index according to the total score of each dimensional data prediction index.
9. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of predicting the quality of multidimensional data for an electrical distribution network of any of claims 1 to 7.
10. An electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the method of predicting the quality of multidimensional data of an electrical distribution network according to any of claims 1 to 7.
CN202111341375.0A 2021-11-12 2021-11-12 Prediction method and device for multi-dimensional data quality of power distribution network Pending CN114037164A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114720764A (en) * 2022-02-23 2022-07-08 江苏森维电子有限公司 Harmonic analysis method and system based on real-time monitoring data of electric meter

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114720764A (en) * 2022-02-23 2022-07-08 江苏森维电子有限公司 Harmonic analysis method and system based on real-time monitoring data of electric meter
CN114720764B (en) * 2022-02-23 2023-02-07 江苏森维电子有限公司 Harmonic analysis method and system based on real-time monitoring data of electric meter

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