CN112801817A - Electric energy quality data center construction method and system - Google Patents
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Abstract
The invention discloses a construction method and a system of a power quality data center, comprising the following steps: splitting the data of the system into ledger data and production data according to the attribute characteristics; and judging whether the standing book data in each system are the same according to the Bhattacharyya distance and the DTW algorithm so as to integrate the standing book data and mount the production data in the integrated standing book data. The invention starts from the characteristics of a power grid system, provides a fusion algorithm which is designed according to the fusion framework and the ledger data characteristics of the industry according to the power grid commonality, improves the matching correctness of single words, can solve the realization of the fusion of ledgers with different lengths, and improves the fusion efficiency of ledger data.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a method and a system for constructing a power quality data center, a computer terminal device and a readable storage medium.
Background
In recent years, with the rapid development of high-voltage direct-current transmission technology, distributed micro-grid and other technologies, the form of a power grid is changed greatly, the power quality mechanism of the power grid caused by the new technologies is more complex, and the new technologies extend to ultra-high voltage and distribution networks, so that the problem of power quality exists in each link of a modern power system.
In contrast, grid companies have installed very few power quality monitoring devices, and cover primarily 10kV and above buses. With the development of the power grid, the power grid production system constructed by devices such as the synchronous phasor measurement unit containing the power quality data enables the acquisition of the power quality data covering the whole power grid.
In view of the complexity and the immediacy of the power quality data, how to reasonably design and establish a power quality data center is of great importance to the whole monitoring system. Because the power quality industry is lack of uniform data formats and specifications all the time, a plurality of power companies and enterprises can produce monitoring equipment, the monitoring equipment and analysis tools produced by various manufacturers have characteristics, the monitoring data emphasis is different, and the data formats are more diverse and incompatible. This is very disadvantageous for information sharing and application integration between applications within the utility company and between utility companies.
Disclosure of Invention
The invention aims to provide a method for constructing an electric energy quality data center, which is characterized in that a fusion algorithm which is designed according to the fusion framework and the ledger data characteristics of the industry is provided according to the power grid commonality, so that the matching correctness of single words is improved, the realization of the ledger fusion with unequal lengths can be solved, and the fusion efficiency of ledger data is improved.
In order to achieve the above object, an embodiment of the present invention provides a method for constructing an electric energy quality data center, including:
splitting the data of the system into ledger data and production data according to the attribute characteristics;
judging whether the standing book data in each system are the same according to the Bhattacharyya distance and a DTW algorithm so as to integrate the standing book data;
and mounting the production data in the integrated standing book data.
In one embodiment, the system comprises a production management system, a scheduling automation system, a distribution network automation system, a metering automation system, a marketing system, a GIS system, a voltage system, and a power quality system.
In a certain embodiment, before determining whether the standing book data of each system are the same according to the Bhattacharyya distance and the DTW algorithm, the method further includes performing subset division on the standing book data according to a management unit.
In a certain embodiment, the determining whether the ledger data of each system is the same according to the Bhattacharyya distance and the DTW algorithm includes:
obtaining a single-word Bhattacharyya coefficient in the standing book data according to the Bhattacharyya distance;
according to the DTW algorithm, sequentially accumulating coefficients of all the points Bhattacharyya passing through, and traversing the standing book data Q in the standing book dataaAnd standing book data CgChinese characters can obtain the cumulative distance gamma (a, g), a represents the standing book QaThe number of words of the name of the middle standing book, g represents the Q of the standing bookgThe number of words of the middle standing account name; the calculation formula is as follows:
γ(a,g)＝B(qa,cg)+max{γ(a-1,g-1),γ(a-1,g),γ(a,g-1)}
wherein, B (q)a,cg) Represents the ledger data QaChinese character qaAnd the ledger data CgChinese character cgThe Bhattacharyya coefficient of (a);
and judging whether the two standing book data are the same or not, wherein the judgment formula is as follows:
wherein r is the effective matching times of gamma (a, g), and tau is a preset threshold.
The embodiment of the invention also provides a construction system of the electric energy quality data center, which is applied to the construction method of the electric energy quality data center in any embodiment. The method comprises the following steps:
the system data splitting module is used for splitting the system data into ledger data and production data according to the attribute characteristics;
the standing book data integration module is used for judging whether the standing book data of each system are the same according to the Bhattacharyya distance and the DTW algorithm so as to integrate the standing book data;
and the production data mounting module is used for mounting the production data in the integrated standing book data.
In one embodiment, the system further comprises an account data subset dividing module, and the account data subset dividing module is used for performing subset division on the account data according to a management unit.
In one embodiment, the ledger data integration module includes:
the standing book data single-character similarity calculation unit is used for calculating a single-character Bhattacharyya coefficient in the standing book data according to the Bhattacharyya distance;
the standing book data accumulation distance calculation unit is used for sequentially accumulating coefficients of all the points Bhattacharyya passing through according to a DTW algorithm and traversing the standing book data Q in the standing book dataaAnd standing book data CgChinese characters can obtain the cumulative distance gamma (a, g), a represents the standing book QaThe number of words of the name of the middle standing book, g represents the Q of the standing bookgThe number of words of the middle standing account name; the calculation formula is as follows:
γ(a,g)＝B(qa,cg)+max{γ(a-1,g-1),γ(a-1,g),γ(a,g-1)}
wherein, B (q)a,cg) Represents the ledger data QaChinese character qaAnd the ledger data CgChinese character cgThe Bhattacharyya coefficient of (a);
the standing book data identity judging unit is used for judging whether the two standing book data are the same or not, and the judging formula is as follows:
wherein r is the effective matching times of gamma (a, g), and tau is a preset threshold.
The embodiment of the invention also provides computer terminal equipment which comprises one or more processors and a memory. A memory coupled to the processor for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a method of building a power quality data center as in any of the embodiments described above.
The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for constructing the power quality data center according to any of the above embodiments.
According to the electric energy quality data center construction method and the electric energy quality data center construction system, based on the characteristics of a power grid system, a fusion algorithm which is designed according to the fusion framework and the ledger data characteristics of the industry is provided according to the power grid commonalities, so that the matching correctness of single words is improved, the realization of ledger fusion with unequal lengths can be solved, and the ledger data fusion efficiency is improved.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for constructing a power quality data center according to an embodiment of the present invention;
fig. 2 is a frame diagram of a power quality data center construction method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating subset partitioning in a power quality data center construction method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a result of a full-spelling probability calculation in a power quality data center construction method according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, an embodiment of the present invention provides a method for constructing a power quality data center, including:
s10, splitting the data of the system into ledger data and production data according to the attribute characteristics;
s20, judging whether the standing book data in each system are the same according to the Bhattacharyya distance and the DTW algorithm so as to integrate the standing book data;
and S30, mounting the production data in the integrated ledger data.
Referring to fig. 2, in the present embodiment, system data (a production management system, a scheduling automation system, a distribution network automation system, a metering automation system, a marketing system, a GIS system, a voltage system, and an electric energy quality system) is divided into ledger data and production data, where the ledger data is archive information representing an electric power object and is composed of a plurality of sub-attributes, and each attribute value is generally fixed and unchanged, for example, the ledger data of a transformer generally includes a transformer name, a model, a capacity, an id allocated to a system where the transformer is located, and the production data is variable, and the production data represents dynamic operation data of the electric power object and is generally related to time, for example: the transformer operation data comprises voltage, current, work, reactive power and the like of each time sequence, so that the production data is stored by virtue of the ledger data.
Finding out the commonalities of the standing book data of each system is the key to realize data integration. And (3) solving the similarity of the Chinese character full spelling of the account data by utilizing the Bhattacharyya distance, solving the account similarity by utilizing improved DTW (dynamic Time warping) dynamic Time normalization, and then judging the identity. And finally, mounting the production data in the integrated ledger data to complete the construction of the whole power quality data center. A fusion framework conforming to the industry is provided according to the power grid commonality, and the fusion algorithm designed according to the standing book data characteristics improves the matching correctness of single characters.
In one embodiment, the system comprises a production management system, a scheduling automation system, a distribution network automation system, a metering automation system, a marketing system, a GIS system, a voltage system, and a power quality system.
In a certain embodiment, before determining whether the standing book data of each system are the same according to the Bhattacharyya distance and the DTW algorithm, the method further includes performing subset division on the standing book data according to a management unit.
Referring to fig. 3, in the present embodiment, in the standing book integration implementation module, word set division is performed according to a management unit, so that standing book repetition is reduced and fusion efficiency is improved. Ledger names are the names of power system objects, but may be repeated from system to system, such as: the Zhongshan power supply bureau and the Zhuhai power supply bureau of the voltage monitoring system both find that the standing account name is the distribution transformer name of the private transformer of the local public security bureau. Therefore, by means of the management relation of the power grid company, the management unit to which the standing book name belongs is judged from top to bottom until the unit directly responsible for the standing book is found. Taking the integrated provincial system as an example, the next level is a power supply bureau, the next level of the power supply bureau is a sub-county bureau, and the next level of the sub-county bureau is a power supply station or a transformer substation, which are sequentially searched downwards. The processing is a classification process, and the multi-system ledger integration efficiency can be improved.
In a certain embodiment, the determining whether the ledger data of each system is the same according to the Bhattacharyya distance and the DTW algorithm includes:
obtaining a single-word Bhattacharyya coefficient in the standing book data according to the Bhattacharyya distance;
according to the DTW algorithm, sequentially accumulating coefficients of all the points Bhattacharyya passing through, and traversing the standing book data Q in the standing book dataaAnd standing book data CgChinese characters can obtain the cumulative distance gamma (a, g), a represents the standing book QaThe number of words of the name of the middle standing book, g represents the Q of the standing bookgThe number of words of the middle standing account name; the calculation formula is as follows:
γ(a,g)＝B(qa,cg)+max{γ(a-1,g-1),γ(a-1,g),γ(a,g-1)}
wherein, B (q)a,cg) Represents the ledger data QaChinese character qaAnd the ledger data CgChinese character cgThe Bhattacharyya coefficient of (a);
and judging whether the two standing book data are the same or not, wherein the judgment formula is as follows:
wherein r is the effective matching times of gamma (a, g), and tau is a preset threshold.
In this embodiment, the similarity of the full spelling of the Chinese characters is obtained according to the distance Bhattacharyya. The Bhattacharyya distance is used to measure the similarity of two discrete or continuous probabilities, which is defined as: in the same domain X, the babbitt distance of two discrete probability distributions p and q is defined as follows:
DB(p,q)＝-ln(BC(p,q)) (1)
the Chinese character full spelling is converted into a probability histogram, the similarity of any two Chinese characters is obtained by using the formula (1) and the formula (2), the higher the similarity is, the closer BC is to 1, and otherwise, the closer BC is to 0.
Firstly, probability conversion is carried out on Chinese characters, and the process is as follows: for any purposeChinese character y, its complete spelling is sequence H, H ═ H1,h2..hr.,ht]，hrIs the r-th letter of the full spelling of y, and t represents the full spelling length. Numbering according to the alphabet in sequence, and taking the numbering as a horizontal coordinate of the histogram; counting the total number a of phonetic letters of the Chinese character y and the number n of each letterrThe ratio P (h) of each letter is calculated according to the formula (3)i) And as the value of the histogram.
And then DTW (dynamic Time warping) dynamic Time integration is carried out to obtain the standing book similarity. The lengths of the names of the same objects in the systems are mostly different, and on the other hand, the standing book names of the power systems are named according to the power supply relation and can be considered to have time sequence, so that the DTW is suitable for solving the similarity of the standing book names.
Referring to FIG. 4, for any two ledger names QaAnd CgThe subscript indicates the number of Chinese characters, and a and g may be different. By finding the cumulative distance γ: starting from (0,0), search is performed to find Q using the Bhattacharyya distanceaAnd CgSimilarity of two Chinese characters, e.g. QaContains the Chinese character "Tang", and Cg contains the Chinese character "box", and the total spelling probability is respectively obtained by using a formula (3). Calculating Bhattacharyya coefficients of the Tang and the frame to be 0.67 through the formula (2), sequentially accumulating the Bhattacharyya coefficients passing through all points, obtaining an accumulated distance gamma after reaching the end points (a, g), solving the formula to be the formula (4),
equation (4) is a variation of the existing DTW, modified by: the Bhattacharyya coefficient B () is used for replacing a common Euclidean distance, the maximum value is obtained by the searching process, the traditional minimum value is not solved, the maximum value is determined by the Bhattacharyya coefficient characteristic, and the calculation formula is as follows:
γ(i,j)＝B(qi,cj)+max{γ(i-1,j-1),γ(i-1,j),γ(i,j-1)} (4)
qirepresents QaThe ith Chinese character of (1), cjIs represented by CgThe jth Chinese character of (1), and B (q)i,cj) ThenRepresenting a computational Chinese character qiAnd cjThe Bhattacharyya coefficient of (a),
finally, when the weight end point (a, g) is reached, the cumulative distance γ (a, g) is obtained, and the formula is as follows:
γ(a,g)＝B(qa,cg)+max{γ(a-1,g-1),γ(a-1,g),γ(a,g-1)}
wherein, B (q)a,cg) Represents the ledger data QaChinese character qaAnd the ledger data CgChinese character cgThe Bhattacharyya coefficient of (a);
and finally, judging whether the two standing book data are the same through a formula (5), wherein the formula is as follows
r is the effective matching times of gamma (i, j), tau is a preset threshold, when the formula (5) is satisfied, the two standing book data are judged to be the same, and if the formula (5) is not satisfied, the two standing book data are judged to be different.
The embodiment of the invention also provides a construction system of the electric energy quality data center, which is applied to the construction method of the electric energy quality data center in any embodiment. The method comprises the following steps:
the system data splitting module is used for splitting the system data into ledger data and production data according to the attribute characteristics;
the standing book data integration module is used for judging whether the standing book data of each system are the same according to the Bhattacharyya distance and the DTW algorithm so as to integrate the standing book data;
and the production data mounting module is used for mounting the production data in the integrated standing book data.
In one embodiment, the system further comprises an account data subset dividing module, and the account data subset dividing module is used for performing subset division on the account data according to a management unit.
In one embodiment, the ledger data integration module includes:
the standing book data single-character similarity calculation unit is used for calculating a single-character Bhattacharyya coefficient in the standing book data according to the Bhattacharyya distance;
the standing book data accumulation distance calculation unit is used for sequentially accumulating coefficients of all the points Bhattacharyya passing through according to a DTW algorithm and traversing the standing book data Q in the standing book dataaAnd standing book data CgChinese characters can obtain the cumulative distance gamma (a, g), a represents the standing book QaThe number of words of the name of the middle standing book, g represents the Q of the standing bookgThe number of words of the middle standing account name; the calculation formula is as follows:
γ(a,g)＝B(qa,cg)+max{γ(a-1,g-1),γ(a-1,g),γ(a,g-1)}
wherein, B (q)a,cg) Represents the ledger data QaChinese character qaAnd the ledger data CgChinese character cgThe Bhattacharyya coefficient of (a);
the standing book data identity judging unit is used for judging whether the two standing book data are the same or not, and the judging formula is as follows:
wherein r is the effective matching times of gamma (a, g), and tau is a preset threshold.
For specific limitations of the power quality data center construction system, reference may be made to the above limitations on the power quality data center construction method, which will not be described herein again. The modules in the construction system of the power quality data center can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Referring to fig. 5, an embodiment of the invention provides a computer terminal device, which includes one or more processors and a memory. The memory is coupled to the processor and configured to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method of building a power quality data center as in any of the embodiments described above.
The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the construction method of the power quality data center. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is configured to perform the above-mentioned method for constructing the power quality data center, and achieve the technical effects consistent with the above-mentioned method.
In another exemplary embodiment, there is also provided a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the method of constructing a power quality data center in any of the above embodiments. For example, the computer readable storage medium may be the above-mentioned memory including program instructions, which are executable by the processor of the computer terminal device to perform the above-mentioned method for constructing the power quality data center, and achieve the technical effects consistent with the above-mentioned method.
According to the electric energy quality data center construction method and the electric energy quality data center construction system, based on the characteristics of a power grid system, a fusion algorithm which is designed according to the fusion framework and the ledger data characteristics of the industry is provided according to the power grid commonalities, so that the matching correctness of single words is improved, the realization of the fusion of ledgers with different lengths can be solved, and the fusion efficiency of ledger data is improved.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (9)
1. A construction method of a power quality data center is characterized by comprising the following steps:
splitting the data of the system into ledger data and production data according to the attribute characteristics;
judging whether the standing book data in each system are the same according to the Bhattacharyya distance and a DTW algorithm so as to integrate the standing book data;
and mounting the production data in the integrated standing book data.
2. The method of constructing a power quality data center according to claim 1,
the system comprises a production management system, a scheduling automation system, a distribution network automation system, a metering automation system, a marketing system, a GIS system, a voltage system and an electric energy quality system.
3. The method for constructing the power quality data center according to claim 1, wherein before judging whether the standing book data of each system are the same according to the Bhattacharyya distance and the DTW algorithm, the method further comprises the step of performing subset division on the standing book data according to a management unit.
4. The method for constructing the power quality data center according to claim 1, wherein the step of judging whether the standing book data of each system are the same according to the Bhattacharyya distance and the DTW algorithm comprises the steps of:
obtaining a single-word Bhattacharyya coefficient in the standing book data according to the Bhattacharyya distance;
according to the DTW algorithm, sequentially accumulating coefficients of all the points Bhattacharyya passing through, and traversing the standing book data Q in the standing book dataaAnd standing book data CgChinese characters can obtain the cumulative distance gamma (a, g), a represents the standing book QaThe number of words of the name of the middle standing book, g represents the Q of the standing bookgThe number of words of the middle standing account name; the calculation formula is as follows:
γ(a,g)＝B(qa,cg)+max{γ(a-1,g-1),γ(a-1,g),γ(a,g-1)}
wherein, B (q)a,cg) Represents the ledger data QaChinese character qaAnd the ledger data CgChinese character cgThe Bhattacharyya coefficient of (a);
and judging whether the two standing book data are the same or not, wherein the judgment formula is as follows:
wherein r is the effective matching times of gamma (a, g), and tau is a preset threshold.
5. A construction system of a power quality data center is characterized by comprising:
the system data splitting module is used for splitting the system data into ledger data and production data according to the attribute characteristics;
the standing book data integration module is used for judging whether the standing book data of each system are the same according to the Bhattacharyya distance and the DTW algorithm so as to integrate the standing book data;
and the production data mounting module is used for mounting the production data in the integrated standing book data.
6. The system for constructing the power quality data center according to claim 5, further comprising a ledger data subset partitioning module, wherein the ledger data subset partitioning module is configured to perform subset partitioning on the ledger data according to a management unit.
7. The system for building a power quality data center according to claim 5, wherein the ledger data integration module comprises:
the standing book data single-character similarity calculation unit is used for calculating a single-character Bhattacharyya coefficient in the standing book data according to the Bhattacharyya distance;
the standing book data accumulation distance calculation unit is used for sequentially accumulating coefficients of all the points Bhattacharyya passing through according to a DTW algorithm and traversing the standing book data Q in the standing book dataaAnd standing book data CgChinese characters can obtain the cumulative distance gamma (a, g), a represents the standing book QaThe number of words of the name of the middle standing book, g represents the Q of the standing bookgThe number of words of the middle standing account name; the calculation formula is as follows:
γ(a,g)＝B(qa,cg)+max{γ(a-1,g-1),γ(a-1,g),γ(a,g-1)}
wherein, B (q)a,cg) Represents the ledger data QaChinese character qaAnd the ledger data CgChinese character cgThe Bhattacharyya coefficient of (a);
the standing book data identity judging unit is used for judging whether the two standing book data are the same or not, and the judging formula is as follows:
wherein r is the effective matching times of gamma (a, g), and tau is a preset threshold.
8. A computer terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of constructing a power quality data center of any of claims 1 to 4.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of building a power quality data center according to any one of claims 1 to 4.
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