CN112348695A - Electric power data quality evaluation model construction method based on analytic hierarchy process - Google Patents
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Abstract
The invention discloses a method for constructing a power data quality evaluation model based on an analytic hierarchy process, which comprises the following steps: constructing a quality evaluation model of the electric power data in the metering automation system based on an analytic hierarchy process; acquiring power data in a metering system, and determining a corresponding data quality evaluation object; selecting and accurately defining evaluation dimensions according to the requirements of data quality evaluation; designing evaluation rules of different dimensionality indexes by combining different calculation rules of the measurement data object business; combining the measurement business practice, fully utilizing expert experience, constructing a judgment matrix to determine the weight of each evaluation index, and giving each evaluation index a desired value; and running the configured evaluation rule for evaluation, and calculating the qualified percentage to obtain the data quality score. The method can accurately and effectively evaluate the mass data in the measurement automation system, timely find the hidden danger of the quality reduction of the electric power data, and improve the management level of a power grid company on the quality of the electric power data.
Description
Technical Field
The invention relates to a method for constructing a power data quality evaluation model based on an analytic hierarchy process, and belongs to the technical field of power data management.
Background
With the rapid development of new-generation information technologies such as big data, cloud computing, internet of things and the like, the high fusion of the human, machine and thing ternary world causes the explosive growth of data scale and the high complexity of data modes. In recent years, studies on data quality management have been paid attention by scholars at home and abroad, and a large amount of studies have been conducted, mainly focusing on the fields of government management, health care, education management, electric power and the like. However, due to the diversity, complexity, multi-source and the like of the existing electric power data, a lot of electric power data quality problems exist, and the provided electric power data cannot be practically applied or has poor application effect, so that a set of electric power data quality evaluation model is urgently needed to be constructed for accurately evaluating the electric power data quality.
Disclosure of Invention
In view of this, a first aspect of the present invention is to provide a method for constructing a power data quality evaluation model based on an analytic hierarchy process, which can accurately and effectively evaluate mass data in a metering automation system, timely discover hidden dangers of power data quality degradation, and improve a management level of a power grid company on power data quality.
The purpose of the first aspect of the invention is realized by the following technical scheme:
a power data quality evaluation model construction method based on an analytic hierarchy process comprises the following steps:
s1: constructing a quality evaluation model of the electric power data in the metering automation system based on an analytic hierarchy process;
s2: acquiring power data in a metering system, and determining a corresponding data quality evaluation object according to business practice;
s3: selecting and accurately defining evaluation dimensions according to the requirements of data quality evaluation;
s4: designing evaluation rules of different dimensionality indexes by combining different calculation rules of the measurement data object business;
s5: combining the measurement business practice, fully utilizing expert experience, constructing a judgment matrix to determine the weight of each evaluation index, and giving each evaluation index a desired value;
s6: and running the configured evaluation rule for evaluation, and calculating the qualified percentage to obtain the data quality score.
Further, the step S1 specifically includes:
constructing a power data quality evaluation model based on an analytic hierarchy process, wherein the model is a six-tuple as follows:
M＝<D,I,R,W,E,S>
in the formula: d is a statistical data object to be evaluated; i is an index set needing to be evaluated on the statistical data D; r is a rule set corresponding to the evaluation index; w is the weight given to the rule; e is the expected value given for rule R; and S is a final evaluation score result of the data object for data quality evaluation based on the evaluation rule.
Further, the step S2 specifically includes:
according to business practice, determining corresponding data quality evaluation objects, wherein the evaluation objects can be data items or data sets, and dividing each data object into data object subclasses by subdividing and combing the data objects.
Further, the step S3 specifically includes:
the data quality evaluation dimension mainly comprises 6 dimension indexes of completeness, uniqueness, effectiveness, accuracy, consistency and timeliness, and in different data object types, the same quality dimension has different specific meanings and contents, and the evaluation dimension is selected and accurately defined according to actual conditions.
Further, the step S4 specifically includes:
and designing evaluation rules of different dimensionality indexes according to different metering data object service calculation rules. And referring to the standard definition of the evaluation dimension, defining the specific description and judgment basis of each rule, and describing different dimension rules under each fine data object according to the definition of the index dimension.
Further, the step S5 specifically includes:
and combining the measurement business reality, fully using expert experience, and using an analytic hierarchy process to construct a judgment matrix to perform weight setting on each dimension index of the data quality.
Constructing a judgment matrix by adopting a 1-9 scale method;
obtaining the weight of each evaluation index through data normalization calculation;
and checking the consistency of the judgment matrix.
Further, the step S6 specifically includes:
and running the configured evaluation rule, calculating the qualified percentage of each evaluation index, and calculating the data quality score by combining the weight corresponding to the index.
It is an object of a second aspect of the invention to provide a computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described above when executing the computer program.
It is an object of a third aspect of the invention to provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method as previously described.
The invention has the beneficial effects that: the method is based on the characteristics of the power grid measurement automation system and the data objects, corresponding evaluation rules are specified for different objects from actual business, a power data quality evaluation model based on an analytic hierarchy process for the power grid measurement automation system is established, mass data in the measurement automation system can be accurately and effectively evaluated, the hidden danger of power data quality reduction is found in time, and the management level of a power grid company on the power data quality is improved.
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In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
fig. 1 is a flowchart of an embodiment of a method for constructing a power data quality evaluation model based on an analytic hierarchy process according to the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an embodiment of a method for constructing a power data quality evaluation model based on an analytic hierarchy process according to the present invention is shown. The power data quality evaluation method comprises the following specific processes:
step 1: and constructing a quality evaluation model of the electric power data in the metering automation system based on an analytic hierarchy process.
Constructing a power data quality evaluation model based on an analytic hierarchy process, wherein the model is a six-tuple as follows:
M＝<D,I,R,W,E,S>
in the formula: d is a statistical data object to be evaluated; i is an index set needing to be evaluated on the statistical data D; r is a rule set corresponding to the evaluation index; w is the weight given to the rule; e is the expected value given for rule R; and S is a final evaluation score result of the data object for data quality evaluation based on the evaluation rule.
The data sets may have different quality assessment requirements depending on the application, so one data set may correspond to a plurality of quality assessment models. In a data quality evaluation model, one data set may correspond to a plurality of evaluation indexes, and one evaluation index may correspond to a plurality of rules.
Step 2: and acquiring power data in the metering system, and determining a corresponding data quality evaluation object according to the business reality.
According to business practice, determining corresponding data quality evaluation objects, wherein the evaluation objects can be data items or data sets, and dividing each data object into data object subclasses by subdividing and combing the data objects. If the platform area is taken as an evaluation object, the data objects such as the table code, the electric quantity, the line loss rate, the coverage rate and the like are subdivided and sorted.
And step 3: and selecting and accurately defining the evaluation dimension according to the requirement of data quality evaluation.
The data quality evaluation dimension mainly comprises 6 dimension indexes of completeness, uniqueness, effectiveness, accuracy, consistency and timeliness, and in different data object types, the same quality dimension has different specific meanings and contents, and the evaluation dimension is selected and accurately defined according to actual conditions.
Integrity is the description of whether a missing record or a missing field exists in the data. Data integrity refers to whether the data is sufficient, whether there are missing records and fields, and the required data is present. Defined as missing data records, missing data fields.
Uniqueness is the record describing whether there is duplication of data. Data uniqueness refers to whether there is duplication in the data record. Defined as data record repetition.
Validity is to describe whether the data meets a defined condition or is within a certain threshold range. Data validity refers to the extent to which a predefined condition or range of threshold values is satisfied. Including both format validity and value validity, the data for any field should conform to the particular data format and value range. Defined as format validity, numerical validity (e.g., various rates between 0-1, positive power generation, etc.).
Accuracy is information describing whether there is an anomaly or error in the data. The data accuracy refers to whether the data is consistent with the characteristics of objective entities described by the data, and the degree of the data really reflecting actual information is measured. The precision of the data attribute value, whether the value range interval and the specification requirement are met (a few bits after decimal point reservation, data in the [ A, B ] interval, etc.) are defined.
Consistency is the description of data to data that satisfies a same condition or state under a particular condition. Data consistency refers to whether the same data stored in different systems have differences or contradictorily, and measures the degree of data acquisition through different ways. Defined as whether the detail is consistent with the summary data, consistent with the records in the metering and marketing system.
Timeliness is the degree to which the temporal characteristics of the assessment data satisfy the application study. The data timeliness refers to the property that information has value to decision only in a certain time period, and measures the degree that data generation and circulation meet the requirements of management and use timeliness. Defined as whether the calculation period is larger than the acquisition time difference, etc.
And 4, step 4: and designing evaluation rules of different dimensionality indexes by combining different calculation rules of the measurement data object business.
And designing evaluation rules of different dimensionality indexes according to different metering data object service calculation rules. And referring to the standard definition of the evaluation dimension, defining the specific description and judgment basis of each rule, and describing different dimension rules under each fine data object according to the definition of the index dimension.
According to the measurement data quality evaluation dimension definition reference, and by combining the data quality actual condition of the data object, respectively selecting appropriate evaluation dimension indexes from 6 dimensions of completeness, uniqueness, effectiveness, accuracy, consistency and timeliness, and defining and describing the evaluation rules of each dimension index to form a measurement data quality evaluation rule base.
And 5: and combining the measurement business reality, fully utilizing expert experience, constructing a judgment matrix to determine the weight of each evaluation index, and giving each evaluation index an expected value.
And combining the measurement business reality, fully using expert experience, and using an analytic hierarchy process to construct a judgment matrix to perform weight setting on each dimension index of the data quality.
An Analytic Hierarchy Process (AHP) is a simple, flexible and practical multi-criterion decision-making method for quantitative analysis of qualitative problems, and its main contents are the construction of a judgment matrix and its consistency check.
The method comprises the following steps of carrying out expert scoring on two aspects of actual perception degree and theoretical thought importance degree of the quality of measured data, forming a judgment matrix by a 1-9 geometric scaling method according to the scoring, forming a judgment matrix scale and meaning as shown in a table, and forming a judgment matrix A according to the table, and then carrying out an analytic hierarchy process to solve the consistency test process as follows:
judging matrix scale and meaning:
determine the matrix A as
And calculating the characteristic vector and the maximum characteristic value, and obtaining the weight of each evaluation index through data normalization calculation.
Solving the eigenvector of the judgment matrix A by adopting a square root methodApproximate solution and maximum eigenvalue, the solution process firstly calculates the n-th root of the product of each row of elements of the judgment matrix
And carrying out normalization processing to obtain the weight of each index. Calculating the quotient of the square root and the square root sum of the product of each row for n times to form a feature vector Wi＝(w1,w2,…,wn)T，
Calculating the maximum eigenvalue lambda of the judgment matrix AmaxWhere A is a decision matrix, (AW) i represents the ith element of the vector AW,
and checking the consistency of the judgment matrix. The formula for calculating the consistency check index CR is:
wherein, RI is a random consistency index and can be obtained by looking up a table. If CR is<0.1, then A passes consistency check; if not, the original judgment matrix is required to be corrected until the consistency check is passed, and at the moment, the approximate solution of the feature vector is the weight coefficient of each element.
Step 6: and running the configured evaluation rule for evaluation, and calculating the qualified percentage to obtain the data quality score.
And running the configured evaluation rule, calculating the qualified percentage of each evaluation index, and calculating the data quality score by combining the weight corresponding to the index.
Let the rule set corresponding to the data set T be RT＝(R1,R2,…,Rn)TImpartation of RTMiddle rule RiThe weight of is WiThe expected value is Ei，RiThe result of the calculation is scored as SiThereby calculating the data quality of the data set T:
absolute quantized value of data quality of
Relative quantization value of data quality of
SA is rule set RTThe resulting result score is a weighted average that reflects the true data quality status of the data set T.
SR is the difference value of SA and the expected value, which reflects the data quality condition of the data set T relative to the expected value, if the SR sign is positive, the larger the value is, the better the data quality is than expected; if the SR sign is negative, the larger the value, indicating that the data quality is worse than expected.
It should be noted that any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and that the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, and the program may be stored in a computer readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Claims (9)
1. A method for constructing a power data quality evaluation model based on an analytic hierarchy process is characterized by comprising the following steps: the method comprises the following steps:
s1: constructing a quality evaluation model of the electric power data in the metering automation system based on an analytic hierarchy process;
s2: acquiring power data in a metering system, and determining a corresponding data quality evaluation object according to business practice;
s3: selecting and accurately defining evaluation dimensions according to the requirements of data quality evaluation;
s4: designing evaluation rules of different dimensionality indexes by combining different calculation rules of the measurement data object business;
s5: combining the measurement business practice, fully utilizing expert experience, constructing a judgment matrix to determine the weight of each evaluation index, and giving each evaluation index a desired value;
s6: and running the configured evaluation rule for evaluation, and calculating the qualified percentage to obtain the data quality score.
2. The analytic hierarchy process-based power data quality evaluation model construction method of claim 1, wherein: the step S1 specifically includes:
constructing a power data quality evaluation model based on an analytic hierarchy process, wherein the model is a six-tuple as follows:
M＝<D,I,R,W,E,S>
in the formula: d is a statistical data object to be evaluated; i is an index set needing to be evaluated on the statistical data D; r is a rule set corresponding to the evaluation index; w is the weight given to the rule; e is the expected value given for rule R; and S is a final evaluation score result of the data object for data quality evaluation based on the evaluation rule.
3. The analytic hierarchy process-based power data quality evaluation model construction method of claim 1, wherein: the step S2 specifically includes:
according to business practice, determining corresponding data quality evaluation objects, wherein the evaluation objects can be data items or data sets, and dividing each data object into data object subclasses by subdividing and combing the data objects.
4. The analytic hierarchy process-based power data quality evaluation model construction method of claim 1, wherein: the step S3 specifically includes:
the data quality evaluation dimension mainly comprises 6 dimension indexes of completeness, uniqueness, effectiveness, accuracy, consistency and timeliness, and in different data object types, the same quality dimension has different specific meanings and contents, and the evaluation dimension is selected and accurately defined according to actual conditions.
5. The analytic hierarchy process-based power data quality evaluation model construction method of claim 1, wherein: the step S4 specifically includes:
and designing evaluation rules of different dimensionality indexes according to different metering data object service calculation rules. And referring to the standard definition of the evaluation dimension, defining the specific description and judgment basis of each rule, and describing different dimension rules under each fine data object according to the definition of the index dimension.
6. The analytic hierarchy process-based power data quality evaluation model construction method of claim 1, wherein: the step S5 specifically includes:
and combining the measurement business reality, fully using expert experience, and using an analytic hierarchy process to construct a judgment matrix to perform weight setting on each dimension index of the data quality.
Constructing a judgment matrix by adopting a 1-9 scale method;
obtaining the weight of each evaluation index through data normalization calculation;
and checking the consistency of the judgment matrix.
7. The analytic hierarchy process-based power data quality evaluation model construction method of claim 1, wherein: the step S6 specifically includes:
and running the configured evaluation rule, calculating the qualified percentage of each evaluation index, and calculating the data quality score by combining the weight corresponding to the index.
8. A computer apparatus comprising a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein: the processor, when executing the computer program, implements the method of any of claims 1-7.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements the method of any one of claims 1-7.
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