CN114154864A - Quality assessment method and processor for electric microclimate monitoring data - Google Patents

Quality assessment method and processor for electric microclimate monitoring data Download PDF

Info

Publication number
CN114154864A
CN114154864A CN202111471584.7A CN202111471584A CN114154864A CN 114154864 A CN114154864 A CN 114154864A CN 202111471584 A CN202111471584 A CN 202111471584A CN 114154864 A CN114154864 A CN 114154864A
Authority
CN
China
Prior art keywords
index
data
rate
calculating
accuracy
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111471584.7A
Other languages
Chinese (zh)
Inventor
皮新宇
冯文卿
欧阳亿
何立夫
罗晶
曹启明
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, State Grid Hunan Electric Power Co Ltd, Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202111471584.7A priority Critical patent/CN114154864A/en
Publication of CN114154864A publication Critical patent/CN114154864A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management

Abstract

The embodiment of the invention provides a quality evaluation method, a processor and a storage medium for electric microclimate monitoring data. The method comprises the following steps: establishing a hierarchical structure model; extracting the bottommost index parameters in the microclimate monitoring data; calculating a basic statistical index of a first layer in the hierarchical structure model; calculating an evaluation dimension index KPI; constructing a fuzzy complementary judgment matrix meeting consistency check; calculating the weight of the fuzzy complementary judgment matrix; and calculating the overall data quality score. The method is strong in universality, can comprehensively evaluate various indexes of data quality, and has important significance and value for various applications such as data mining based on the electric microclimate monitoring data.

Description

Quality assessment method and processor for electric microclimate monitoring data
Technical Field
The invention relates to a quality evaluation method, a processor and a machine readable storage medium for electric microclimate monitoring data.
Background
The electric microclimate monitoring data can reflect the operation environment of the power grid equipment in real time, and has important significance for large-scale new energy grid connection and natural disaster prevention and control of the power grid. With the continuous expansion of the scale of the power grid, the electric microclimate monitoring data are increasingly abundant, but at the same time, more and more power transmission lines need to pass through regions with complex environmental conditions such as terrain, climate and the like. The monitoring devices in these areas are significantly affected by the environment in terms of observation and transmission, resulting in impaired data quality. For massive electric microclimate monitoring data, the variation of data quality is an important challenge to be faced, the quality of the data directly affects the result of data mining analysis, and poor quality of the data can bring damage which is difficult to estimate to system level decision. Therefore, in order to efficiently realize the value mining of the mass data of the electric microclimate detection system, the comprehensive evaluation of the data quality is urgently needed.
Disclosure of Invention
The invention aims to solve the problems that the quality of the existing electric microclimate monitoring data is uneven and the subsequent data mining analysis is seriously influenced, and provides a quality evaluation method of the electric microclimate monitoring data, which has strong universality, can comprehensively evaluate various indexes of the data quality and has important significance and value for various applications such as data mining based on the electric microclimate monitoring data and the like by constructing a quality evaluation system, establishing data specifications and determining evaluation indexes.
In order to achieve the above object, a first aspect of the present invention provides a quality assessment method for electric microclimate monitoring data, including:
establishing a hierarchical structure model;
extracting the bottommost index parameters in the microclimate monitoring data;
calculating a basic statistical index of a first layer in the hierarchical structure model;
calculating an evaluation dimension index KPI;
constructing a fuzzy complementary judgment matrix meeting consistency check;
calculating the weight of the fuzzy complementary judgment matrix;
and calculating the overall data quality score.
In an embodiment of the present invention, the establishing the hierarchical structure model includes:
evaluating the accuracy of the data by adopting the accuracy of the key field value;
evaluating the integrity of the data by adopting the integrity rate of the number of the processed files;
evaluating the consistency of the data by adopting the data association rate and the data coverage rate;
evaluating the timeliness of the data by adopting the file processing timeliness rate and the file processing average time delay;
establishing the hierarchical structure model according to the accuracy, the completeness, the consistency and the timeliness;
the hierarchical structure model sequentially comprises a good rate index level, an evaluation dimension index level and a basic statistical index level from top to bottom, wherein the good rate index level comprises a total good rate index (data set), the evaluation dimension index level comprises an accuracy index, an integrity index, a consistency index and a timeliness index, and the basic statistical index level comprises a key field value accuracy rate, a processed file number integrity rate, a data association rate, a data coverage rate, a file processing timeliness rate and a file processing delay rate.
In an embodiment of the present invention, the extracting of the lowest index parameter in the microclimate monitoring data includes:
aiming at the accuracy, extracting the total record number and the key field value compliance record number;
aiming at the completeness, extracting the total number of actual files, the total number of processed files, the duplication removing number of the time sequence and the total category number of the time sequence;
extracting the number of associated records, the de-duplication number of fields or field combinations and the total number of types of the fields or field combinations aiming at consistency; and
and aiming at timeliness, extracting the file processing time delay, the sum of the file processing time delay and the file processing hours.
In an embodiment of the present invention, the calculating the basic statistical indicator of the first layer in the hierarchical structure model includes:
calculating a first layer basic statistical index according to formula (1):
Figure BDA0003392725750000031
Figure BDA0003392725750000032
Figure BDA0003392725750000033
Figure BDA0003392725750000034
Figure BDA0003392725750000035
Figure BDA0003392725750000036
wherein, A _1001 represents the total number of records, A _1002 represents the number of key field value compliance records, A _2001 represents the total number of actual files, A _2002 represents the total number of processed files, A _3001 represents the number of associated records, A _3002 represents the number of fields or field combination duplication removal, A _3003 represents the total number of fields or field combination duplication removal, A _4002 represents the sum of file processing delays, A _4003 represents the file processing and hours;
b _1001 represents the accuracy of the key field value, C _1001 represents the number integrity rate of processed files, D _0001 represents the data association rate, D _0002 represents the data coverage rate, E _0001 represents the file processing timeliness rate, and E _0002 represents the file processing delay rate.
In an embodiment of the present invention, the calculating an evaluation dimension indicator KPI includes:
d _0001 and D _0002 are combined, and E _0001 and E _0002 are combined, according to equation (2), to calculate an accuracy index B, an integrity index C, a consistency index D, and a timeliness index E:
B＝B_1001
C＝C_1001
Figure BDA0003392725750000041
Figure BDA0003392725750000042
in the low-level data indexes, data indexes related to temperature, humidity, rainfall, wind direction and wind speed are combined.
In the embodiment of the present invention, the constructing the fuzzy complementation judgment matrix satisfying the consistency check includes:
based on the accuracy index, the integrity index, the consistency index and the timeliness index, the following four-order fuzzy complementary judgment matrix A is constructed:
accuracy of Integrity of Consistency Timeliness
Accuracy of 0.50 0.50 0.60 0.70
Integrity of 0.50 0.50 0.60 0.70
Consistency 0.40 0.40 0.50 0.60
Timeliness 0.30 0.30 0.40 0.50
In this embodiment of the present invention, the calculating the weight of the fuzzy complementary judging matrix includes:
calculating the weight of the fuzzy complementary judgment matrix according to the formula (3):
Figure BDA0003392725750000043
wherein WiRepresents a weight, aijThe element values of the ith row and the jth column of the fuzzy judgment matrix are represented, and n represents the order of the fuzzy complementary judgment matrix.
In an embodiment of the present invention, the calculating the overall score of data quality includes:
calculating the overall data quality score according to formula (4):
s ═ W [ B; c; d; ...; z100 formula (4)
Where S denotes the overall data quality score and W ═ W (W)1，W2，...，Wn)TIs a weight vector of the fuzzy decision matrix A, wherein
Figure BDA0003392725750000051
B represents an accuracy index, C represents an integrity index, D represents a consistency index, and Z represents a total good rate index.
A second aspect of the invention provides a processor for executing a program, wherein the program is executed for executing the above-mentioned quality assessment method for electric microclimate monitoring data.
A third aspect of the invention provides a machine-readable storage medium having stored thereon instructions for causing a machine to perform the above-described quality assessment method for electric microclimate monitoring data.
Through the technical scheme, the method is strong in universality, can comprehensively evaluate various indexes of data quality, and has important significance and value for various applications such as data mining based on electric microclimate monitoring data.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 schematically illustrates a flow diagram of a method of quality assessment for electric microclimate monitoring data according to an embodiment of the invention; and
FIG. 2 schematically shows a hierarchy model diagram according to an embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
FIG. 1 schematically illustrates a flow chart of a method of quality assessment for electric microclimate monitoring data according to an embodiment of the invention. As shown in fig. 1, in the embodiment of the present invention, the method for evaluating the quality of the electric microclimate monitoring data may include the following steps.
In step S11, a hierarchical structure model is built.
Specifically, in the embodiment of the present invention, the establishing the hierarchical structure model includes:
evaluating the accuracy of the data by adopting the accuracy of the key field value;
evaluating the integrity of the data by adopting the integrity rate of the number of the processed files;
evaluating the consistency of the data by adopting the data association rate and the data coverage rate;
evaluating the timeliness of the data by adopting the file processing timeliness rate and the file processing average time delay;
establishing the hierarchical structure model according to the accuracy, the completeness, the consistency and the timeliness.
In the embodiment of the invention, the accuracy of the data is evaluated by adopting the accuracy rate of the key field value, the integrity of the data is evaluated by adopting the integrity rate of the number of processed files, the consistency of the data is evaluated by adopting the data association rate and the data coverage rate, and the timeliness of the data is evaluated by adopting the file processing timeliness rate and the file processing average time delay.
FIG. 2 schematically shows a hierarchy model diagram according to an embodiment of the invention. As shown in fig. 2, the hierarchical structure model sequentially includes, from top to bottom, a yield indicator (KQI) level (a third layer), an evaluation dimension indicator (KPI) level (a second layer), and a basic statistics indicator (Counter) level (a first layer), where the yield indicator level includes a total yield indicator Z, the evaluation dimension indicator level includes an accuracy indicator B, an integrity indicator C, a consistency indicator D, and a timeliness indicator E, and the basic statistics indicator level includes a key field value accuracy B _1001, a processed file number integrity C _2001, a data association rate D _0001, a data coverage rate D _0002, a file processing timeliness rate E _0001, and a file processing delay rate D _ 0002.
In step S12, the lowest index parameter in the microclimate monitoring data is extracted.
Specifically, in the embodiment of the present invention, the extracting the lowest-layer index parameter in the microclimate monitoring data includes:
aiming at the accuracy, extracting the total record number and the key field value compliance record number;
aiming at the completeness, extracting the total number of actual files, the total number of processed files, the duplication removing number of the time sequence and the total category number of the time sequence;
extracting the number of associated records, the de-duplication number of fields or field combinations and the total number of types of the fields or field combinations aiming at consistency; and
and aiming at timeliness, extracting the file processing time delay, the sum of the file processing time delay and the file processing hours.
In one example, the lowest level indicator parameter may be extracted with reference to table 1 below:
TABLE 1
Figure BDA0003392725750000071
Figure BDA0003392725750000081
In step S13, a basic statistical index of the first layer (lowest layer) in the hierarchical structure is calculated.
Specifically, the basic statistical index of the first layer (i.e., the basic statistical index Counter shown in fig. 2) may be calculated according to formula (1):
Figure BDA0003392725750000082
Figure BDA0003392725750000083
Figure BDA0003392725750000091
Figure BDA0003392725750000092
Figure BDA0003392725750000093
Figure BDA0003392725750000094
wherein, A _1001 represents the total number of records, A _1002 represents the number of key field value compliance records, A _2001 represents the total number of actual files, A _2002 represents the total number of processed files, A _3001 represents the number of associated records, A _3002 represents the number of fields or field combination duplication removal, A _3003 represents the total number of fields or field combination duplication removal, A _4002 represents the sum of file processing delays, A _4003 represents the file processing and hours;
b _1001 represents the accuracy of the key field value, C _1001 represents the number integrity rate of processed files, D _0001 represents the data association rate, D _0002 represents the data coverage rate, E _0001 represents the file processing timeliness rate, and E _0002 represents the file processing delay rate.
In step S14, an evaluation dimension index KPI is calculated.
Specifically, under the consistency index C and the timeliness index E, the consistency importance of the "data association rate" and the "data coverage rate" to the evaluation data is considered to be equal, the timeliness importance of the "file processing timeliness rate" and the "average file processing delay" to the evaluation data is considered to be equal, and the inconsistency is 0. Based on this, D _0001 and D _0002 can be combined, and E _0001 and E _0002 can be combined, according to equation (2), to calculate the accuracy index B, the integrity index C, the consistency index D, and the timeliness index E:
B＝B_1001
C＝C_1001
Figure BDA0003392725750000095
Figure BDA0003392725750000096
in addition, in the lower layer data indexes, the importance of temperature, humidity, rainfall, wind direction, and wind speed to the data quality evaluation is considered to be equivalent, and therefore, the data indexes related to temperature, humidity, rainfall, wind direction, and wind speed may be combined.
In step S15, a fuzzy complement determination matrix satisfying the consistency check is constructed.
Specifically, in the embodiment of the present invention, the constructing a fuzzy complementation judgment matrix satisfying the consistency check includes:
based on the accuracy index, the integrity index, the consistency index and the timeliness index, the following four-order fuzzy complementary judgment matrix A can be constructed:
accuracy of Integrity of Consistency Timeliness
Accuracy of 0.50 0.50 0.60 0.70
Integrity of 0.50 0.50 0.60 0.70
Consistency 0.40 0.40 0.50 0.60
Timeliness 0.30 0.30 0.40 0.50
In step S16, the weight of the fuzzy complementary determining matrix is calculated.
Specifically, in this embodiment of the present invention, the calculating the weight of the fuzzy complementary judging matrix includes:
calculating the weight of the fuzzy complementary judgment matrix according to the formula (3):
Figure BDA0003392725750000101
wherein WiRepresents a weight, aijThe element values of the ith row and the jth column of the fuzzy judgment matrix are represented, and n represents the order of the fuzzy complementary judgment matrix.
In step S17, a data quality overall score is calculated.
Specifically, in the embodiment of the present invention, the calculating the overall data quality score includes:
calculating the overall data quality score according to formula (4):
s ═ W [ B; c; d; ...; z100 formula (4)
Where S denotes the overall data quality score and W ═ W (W)1,W2,...,Wn)TIs a weight vector of the fuzzy decision matrix A, wherein
Figure BDA0003392725750000111
B represents an accuracy index, C represents an integrity index, D represents a consistency index, and Z represents a total good rate index.
The embodiment of the invention provides a processor for running a program, wherein the program is run for executing the quality evaluation method for the electric microclimate monitoring data.
In particular, the processor may be configured to:
establishing a hierarchical structure model;
extracting the bottommost index parameters in the microclimate monitoring data;
calculating a basic statistical index of a first layer in the hierarchical structure model;
calculating an evaluation dimension index KPI;
constructing a fuzzy complementary judgment matrix meeting consistency check;
calculating the weight of the fuzzy complementary judgment matrix;
and calculating the overall data quality score.
In an embodiment of the present invention, the establishing the hierarchical structure model includes:
evaluating the accuracy of the data by adopting the accuracy of the key field value;
evaluating the integrity of the data by adopting the integrity rate of the number of the processed files;
evaluating the consistency of the data by adopting the data association rate and the data coverage rate;
evaluating the timeliness of the data by adopting the file processing timeliness rate and the file processing average time delay;
establishing the hierarchical structure model according to the accuracy, the completeness, the consistency and the timeliness;
the hierarchical structure model sequentially comprises a good rate index level, an evaluation dimension index level and a basic statistical index level from top to bottom, wherein the good rate index level comprises a total good rate index (data set), the evaluation dimension index level comprises an accuracy index, an integrity index, a consistency index and a timeliness index, and the basic statistical index level comprises a key field value accuracy rate, a processed file number integrity rate, a data association rate, a data coverage rate, a file processing timeliness rate and a file processing delay rate.
In an embodiment of the present invention, the extracting of the lowest index parameter in the microclimate monitoring data includes:
aiming at the accuracy, extracting the total record number and the key field value compliance record number;
aiming at the completeness, extracting the total number of actual files, the total number of processed files, the duplication removing number of the time sequence and the total category number of the time sequence;
extracting the number of associated records, the de-duplication number of fields or field combinations and the total number of types of the fields or field combinations aiming at consistency; and
and aiming at timeliness, extracting the file processing time delay, the sum of the file processing time delay and the file processing hours.
In an embodiment of the present invention, the calculating the basic statistical indicator of the first layer in the hierarchical structure model includes:
calculating a first layer basic statistical index according to formula (1):
Figure BDA0003392725750000121
Figure BDA0003392725750000122
Figure BDA0003392725750000123
Figure BDA0003392725750000124
Figure BDA0003392725750000125
Figure BDA0003392725750000126
wherein, A _1001 represents the total number of records, A _1002 represents the number of key field value compliance records, A _2001 represents the total number of actual files, A _2002 represents the total number of processed files, A _3001 represents the number of associated records, A _3002 represents the number of fields or field combination duplication removal, A _3003 represents the total number of fields or field combination duplication removal, A _4002 represents the sum of file processing delays, A _4003 represents the file processing and hours;
b _1001 represents the accuracy of the key field value, C _1001 represents the number integrity rate of processed files, D _0001 represents the data association rate, D _0002 represents the data coverage rate, E _0001 represents the file processing timeliness rate, and E _0002 represents the file processing delay rate.
In an embodiment of the present invention, the calculating an evaluation dimension indicator KPI includes:
d _0001 and D _0002 are combined, and E _0001 and E _0002 are combined, according to equation (2), to calculate an accuracy index B, an integrity index C, a consistency index D, and a timeliness index E:
B＝B_1001
C＝C_1001
Figure BDA0003392725750000131
Figure BDA0003392725750000132
in the low-level data indexes, data indexes related to temperature, humidity, rainfall, wind direction and wind speed are combined.
In the embodiment of the present invention, the constructing the fuzzy complementation judgment matrix satisfying the consistency check includes:
based on the accuracy index, the integrity index, the consistency index and the timeliness index, the following four-order fuzzy complementary judgment matrix A is constructed:
_ accuracy of Integrity of Consistency Timeliness
Accuracy of 0.50 0.50 0.60 0.70
Integrity of 0.50 0.50 0.60 0.70
Consistency 0.40 0.40 0.50 0.60
Timeliness 0.30 0.30 0.40 0.50
In this embodiment of the present invention, the calculating the weight of the fuzzy complementary judging matrix includes:
calculating the weight of the fuzzy complementary judgment matrix according to the formula (3):
Figure BDA0003392725750000141
wherein WiRepresents a weight, aijThe element values of the ith row and the jth column of the fuzzy judgment matrix are represented, and n represents the order of the fuzzy complementary judgment matrix.
In an embodiment of the present invention, the calculating the overall score of data quality includes:
calculating the overall data quality score according to formula (4):
s ═ W [ B; c; d; ...; z100 formula (4)
Wherein S represents the overall score of data quality，W＝(W1，W2，...，Wn)TIs a weight vector of the fuzzy decision matrix A, wherein
Figure BDA0003392725750000142
B represents an accuracy index, C represents an integrity index, D represents a consistency index, and Z represents a total good rate index.
The embodiment of the invention provides a machine-readable storage medium, wherein the machine-readable storage medium is stored with instructions, and the instructions are used for enabling a machine to execute the quality evaluation method for the electric microclimate monitoring data.
Through the technical scheme, the quality evaluation method for the electric microclimate monitoring data is strong in universality, can comprehensively evaluate various indexes of data quality, and has important significance and value for various applications such as data mining based on the electric microclimate monitoring data.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A quality assessment method for electric microclimate monitoring data is characterized by comprising the following steps:
establishing a hierarchical structure model;
extracting the bottommost index parameters in the microclimate monitoring data;
calculating a basic statistical index of a first layer in the hierarchical structure model;
calculating an evaluation dimension index KPI;
constructing a fuzzy complementary judgment matrix meeting consistency check;
calculating the weight of the fuzzy complementary judgment matrix;
and calculating the overall data quality score.
2. The quality assessment method according to claim 1, wherein said establishing a hierarchical structure model comprises:
evaluating the accuracy of the data by adopting the accuracy of the key field value;
evaluating the integrity of the data by adopting the integrity rate of the number of the processed files;
evaluating the consistency of the data by adopting the data association rate and the data coverage rate;
evaluating the timeliness of the data by adopting the file processing timeliness rate and the file processing average time delay;
establishing the hierarchical structure model according to the accuracy, the completeness, the consistency and the timeliness;
the hierarchical structure model sequentially comprises a good rate index level, an evaluation dimension index level and a basic statistical index level from top to bottom, wherein the good rate index level comprises a total good rate index, the evaluation dimension index level comprises an accuracy index, an integrity index, a consistency index and a timeliness index, and the basic statistical index level comprises a key field value accuracy rate, a processed file number integrity rate, a data association rate, a data coverage rate, a file processing timeliness rate and a file processing delay rate.
3. The quality assessment method according to claim 2, wherein said extracting the lowest level indicator parameter in the micrometeorological monitoring data comprises:
aiming at the accuracy, extracting the total record number and the key field value compliance record number;
aiming at the completeness, extracting the total number of actual files, the total number of processed files, the duplication removing number of the time sequence and the total category number of the time sequence;
extracting the number of associated records, the de-duplication number of fields or field combinations and the total number of types of the fields or field combinations aiming at consistency; and
and aiming at timeliness, extracting the file processing time delay, the sum of the file processing time delay and the file processing hours.
4. The quality assessment method according to claim 3, wherein said calculating the basic statistical indicator of the first layer in the hierarchical model comprises:
calculating a basic statistical index of the first layer according to formula (1):
Figure FDA0003392725740000021
Figure FDA0003392725740000022
Figure FDA0003392725740000023
Figure FDA0003392725740000024
Figure FDA0003392725740000025
Figure FDA0003392725740000026
wherein, A _1001 represents the total number of records, A _1002 represents the number of key field value compliance records, A _2001 represents the total number of actual files, A _2002 represents the total number of processed files, A _3001 represents the number of associated records, A _3002 represents the number of fields or field combination duplication removal, A _3003 represents the total number of fields or field combination duplication removal, A _4002 represents the sum of file processing delays, A _4003 represents the file processing and hours;
b _1001 represents the accuracy of the key field value, C _1001 represents the number integrity rate of processed files, D _0001 represents the data association rate, D _0002 represents the data coverage rate, E _0001 represents the file processing timeliness rate, and E _0002 represents the file processing delay rate.
5. The quality assessment method according to claim 4, wherein said calculating an assessment dimension indicator KPI comprises:
d _0001 and D _0002 are combined, and E _0001 and E _0002 are combined, according to equation (2), to calculate an accuracy index B, an integrity index C, a consistency index D, and a timeliness index E:
B＝B_1001
C＝C_1001
Figure FDA0003392725740000031
Figure FDA0003392725740000032
in the low-level data indexes, data indexes related to temperature, humidity, rainfall, wind direction and wind speed are combined.
6. The quality evaluation method according to claim 5, wherein the constructing of the fuzzy complementation judgment matrix satisfying the consistency check comprises:
based on the accuracy index, the integrity index, the consistency index and the timeliness index, the following four-order fuzzy complementary judgment matrix A is constructed:
accuracy of Integrity of Consistency Timeliness Accuracy of 0.50 0.50 0.60 0.70 Integrity of 0.50 0.50 0.60 0.70 Consistency 0.40 0.40 0.50 0.60 Timeliness 0.30 0.30 0.40 0.50
7. The quality assessment method according to claim 6, wherein said calculating the weight of the fuzzy complementary judging matrix comprises:
calculating the weight of the fuzzy complementary judgment matrix according to the formula (3):
Figure FDA0003392725740000041
wherein WiRepresents a weight, aijThe element values of the ith row and the jth column of the fuzzy judgment matrix are represented, and n represents the order of the fuzzy complementary judgment matrix.
8. The quality assessment method of claim 7, wherein said calculating a data quality overall score comprises:
calculating the overall data quality score according to formula (4):
s ═ W [ B; c; d; ...; z100 formula (4)
Where S denotes the overall data quality score and W ═ W (W)1,W2,...,Wn)TIs a weight vector of the fuzzy decision matrix A, wherein
Figure FDA0003392725740000042
B represents an accuracy index, C represents an integrity index, D represents a consistency index, and Z represents a total good rate index.
9. A processor characterized by being configured to run a program, wherein the program is configured to execute the method for quality assessment of electrical microclimate monitoring data according to any one of claims 1-8.
10. A machine-readable storage medium having instructions stored thereon for causing a machine to perform the method for quality assessment of electrical microclimate monitoring data according to any one of claims 1-8.
CN202111471584.7A 2021-12-06 2021-12-06 Quality assessment method and processor for electric microclimate monitoring data Pending CN114154864A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111471584.7A CN114154864A (en) 2021-12-06 2021-12-06 Quality assessment method and processor for electric microclimate monitoring data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111471584.7A CN114154864A (en) 2021-12-06 2021-12-06 Quality assessment method and processor for electric microclimate monitoring data

Publications (1)

Publication Number Publication Date
CN114154864A true CN114154864A (en) 2022-03-08

Family

ID=80452665

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111471584.7A Pending CN114154864A (en) 2021-12-06 2021-12-06 Quality assessment method and processor for electric microclimate monitoring data

Country Status (1)

Country Link
CN (1) CN114154864A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116610663A (en) * 2023-07-17 2023-08-18 成都岷山绿氢能源有限公司 Carbon monitoring data quality evaluation method, device, equipment and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116610663A (en) * 2023-07-17 2023-08-18 成都岷山绿氢能源有限公司 Carbon monitoring data quality evaluation method, device, equipment and storage medium

Similar Documents

Publication Publication Date Title
CN106897178B (en) Slow disk detection method and system based on extreme learning machine
CN110209560B (en) Data anomaly detection method and detection device
US7181364B2 (en) Automated detecting and reporting on field reliability of components
CN104281779A (en) Abnormal data judging and processing method and device
CN115145252B (en) Fault tree-based fault diagnosis method, system and medium for water turbine speed regulator
CN111709668A (en) Power grid equipment parameter risk identification method and device based on data mining technology
CN113449257A (en) Power distribution network line loss prediction method, control device, and storage medium
CN114154864A (en) Quality assessment method and processor for electric microclimate monitoring data
CN113705074B (en) Chemical accident risk prediction method and device
Zhang et al. Automatic identification of structural modal parameters based on density peaks clustering algorithm
CN114925905A (en) Industrial energy consumption allocation method, equipment and medium based on identification analysis
CN113742993A (en) Method, device, equipment and storage medium for predicting life loss of dry-type transformer
CN115660774B (en) Block chain-based material supply chain system credit evaluation method
CN110807014A (en) Cross validation based station data anomaly discrimination method and device
US10713232B2 (en) Efficient data processing
CN113468384B (en) Processing method, device, storage medium and processor for network information source information
CN115373339A (en) Machine tool spare part monitoring method, equipment and medium based on industrial Internet
CN114781473A (en) Method, device and equipment for predicting state of rail transit equipment and storage medium
CN109871998B (en) Power distribution network line loss rate prediction method and device based on expert sample library
CN112732773A (en) Uniqueness checking method and system for relay protection defect data
CN115981911A (en) Memory failure prediction method, electronic device and computer-readable storage medium
CN113722403A (en) Abnormal operation data clustering method and device, storage medium and processor
CN110287272A (en) A kind of configurable real-time feature extraction method, apparatus and system
CN110942234A (en) Medium-low pressure gas pipeline risk evaluation method and device
Nayak et al. Capacity value of wind power using kd tree and nearest neighbor search algorithm

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination