CN114638728B - Big data-based real-time operation monitoring method for power service center - Google Patents
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Abstract
The invention discloses a real-time monitoring method for operation of an electric power service center based on big data, which is characterized in that a single data configuration construction and a data presentation paradigm construction are carried out on a quantitative database corresponding to a power grid operation data monitoring target based on a vector database format of electric power operation big data application guide, then the single quantitative data is subjected to data accompanying analysis corresponding to a single data processing process, and the acquisition and presentation of the electric power operation big data result facing a monitoring target are realized by a simplified data paradigm and a reduced data processing amount.
Description
Technical Field
The invention relates to the technical field of electric power big data and application processing thereof, in particular to a real-time operation monitoring method for an electric power service center based on big data.
Background
The power grid system is built by three sets and five sets, and provincial unification operation is gradually realized in the field of user-oriented power service centers at present. The provincial power operation and service center covers 95598 of the provincial network, electronic seats, work order processing, power business halls, complaint early warning and other subsystems and submodules, and the related data amount is huge.
At present, although a real-time monitoring construction target is provided for a provincial power grid operation and service center, and a plurality of joint research and development projects are carried out, the prior project research and development mainly aims at a single detection target, a single problem is solved through the construction of a system function tree-shaped atlas and the layered setting of a software system, and the final results and the presentation forms of all research and development projects are single software systems. In particular, different development projects are usually performed by combining different third party units with a power grid unit, so that the developed software systems often have distinct data architectures. The technology is inferior in that: firstly, the work in the power system is repeated, the monitoring is carried out aiming at the same power event, and different data input is often required to be provided for different software systems; secondly, it is not beneficial to perform the interaction between the related multiple monitoring operations.
In summary, for huge data accumulation of the provincial power grid, a standardized database configuration convenient for data interaction among different single operation monitoring needs to be constructed urgently.
Disclosure of Invention
The invention aims to provide a big data-based real-time monitoring method for operation of an electric power service center.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A real-time monitoring method for operation of an electric power service center based on big data is characterized in that a quantized database corresponding to a monitoring target of electric power operation data is subjected to simplified data configuration construction and data presentation paradigm construction based on a vector database format of electric power operation big data application guide, and then the simplified quantized data is subjected to data accompanying analysis corresponding to a simplified data processing process, so that the acquisition and presentation of data results of the electric power operation big data facing the monitoring target are realized through a simplified data paradigm and simplified data processing amount.
As a preferred technical solution of the present invention, the grid operation data monitoring object includes: a single monitoring target and/or a combined single monitoring target and/or a long-term combined monitoring target; the data configuration is set as:
(1) for any single monitoring target, whether the internal data form is single data or mutually independent multiple data, the single monitoring target is uniformly set to be a non-differential vector data format, the dimensionality of the vector data format is determined by the maximum data dimensionality in all the single monitoring target internal data, and the dimensionality consistency of original data lower than the set maximum data dimensionality is completed through zero filling;
(2) for a joint monitoring target, performing first orthogonal vector data dimension expansion on the basis of the data configuration in the step (1), and obtaining a data array pointing to a second dimension on the basis of the vector data obtained in the step (1); the maximum dimension number of the second dimension is the maximum single monitoring number contained in the joint monitoring;
(3) for a long-term monitoring target, performing second orthotropic vector data dimension expansion on the basis of the data configuration in the step (1), and obtaining a data array pointing to a third dimension on the basis of the vector data obtained in the step (1); the maximum dimension of the third dimension is jointly determined by the long-term monitoring duration and the single monitoring interval duration; and the third dimension and the second dimension are also set to be in an orthogonal relation.
As a preferred technical solution of the present invention, a general form of the power operation big data application oriented vector database format is set as follows: the data configuration of the power grid operation data monitoring target is set to have three dimensional directions, the data configuration is integrally presented as a third-order tensor data configuration, the third-order tensor is a covariate or inverse third-order tensor according to the selection mode of a data unit and obeys the Einstein convention paradigm of tensor transformation, in tensor data, data vectors of any single monitoring target point to the same dimensional direction, on the basis, the tensor is constructed along the data vectors of any single monitoring target to form an application data vector, and the data vector is used as the basis of subsequent data configuration and data feature extraction.
As a preferred technical solution of the present invention, when performing data accompanying analysis corresponding to the simplified data processing process on the quantized data after being singulated, the data processing range is a local database range corresponding to a single data monitoring target, or a global database range.
As a preferred technical solution of the present invention, the process of constructing the unified data configuration and the data presentation paradigm comprises:
A. and (3) quantitative index construction: the vector database of the electric power operation big data application guide and the included application data vector thereof contain global parameters of the operation monitoring of the power grid application end, are an unquantizable data paradigm, and are quantitatively constructed by adopting the following data paradigm: firstly, constructing a two-dimensional diagonal data array N, wherein the data order is N, and the configuration is a transposition symmetry type; further, setting the components of the application data vector to the transposition symmetry axis of the data array N according to any sequence; obtaining a vector group with a base attribute in an n-dimensional data space, wherein the vector group surrounds and forms an n-dimensional manifold with a standard volume and a standard perimeter; based on the obtained n-dimensional manifold, two groups of independent application data vector quantization indexes are constructed by adopting the following two paradigms: (1) for the resulting n-dimensional manifold, its two-dimensional parameters are calculated as follows: converting the data array isomorphism corresponding to the wrapping vector group of the n-dimensional manifold into a determinant with zero diagonal inverse number, wherein the value of the determinant is used as a two-dimensional parameter value or a volume value of the n-dimensional manifold to form a first-stage quantization index; (2) for the resulting n-dimensional manifold, its one-dimensional parameters are calculated as follows: converting the data array isomorphism corresponding to the wrapping vector group of the n-dimensional manifold into a determinant with zero diagonal inverse number, wherein the principal element sum value of the determinant is used as a one-dimensional parameter value or a length value of the n-dimensional manifold to form a second-stage quantization index; and the obtained two-dimensional parameter value or volume value is used as a leading index of power grid operation monitoring; on the premise of constructing a second-stage quantitative index, taking the obtained one-dimensional parameter value or length value as an auxiliary index for monitoring the operation of the power grid;
B. the simplification construction of the quantization index comprises the following steps: (1) firstly, a data value of a fixed interval is designated based on the specific requirement of any single monitoring target of power grid operation monitoring and is recorded as k; (2) further carrying out dimensionality extension on k, constructing a space line segment with the length corresponding to the absolute value of k, and particularly setting the space line segment into the n-dimensional space manifold constructed in the step A; (3) at this time, performing origin processing on the n-dimensional space manifold constructed in the step a, that is, placing the starting points of the wrapping vector groups of the n-dimensional manifold at the same space point, and recording the same space point as a space origin; (4) placing one end of the space line segment constructed in the step (2) on the space origin of the step (3); (5) constructing main values and parameters of the accompanying analysis: the value of k itself constitutes a main value of the accompanying analysis, and in the step (4), the k vector has n which is a complete degree of freedom in the space, the degree of freedom corresponds to a parameter of the accompanying analysis, one end of the k vector is fixed on a constructed space origin in the step (4), the complete degree of freedom is a rotational degree of freedom, the parameter of the accompanying analysis corresponds to a rotational angle value a, and the value range of a is 0-2 pi; (6) and (3) performing dimension reduction processing on the multidimensional metadata based on the principal value and the parameter value, based on the data process of the steps (1) - (5), performing dimension reduction processing on the multidimensional metadata through the principal value and the parameter value, wherein the dimension of the data after dimension reduction is two dimensions, namely the absolute value of the principal value k and the parameter value angle a, and since k is a constant value selected in advance, especially a rational constant or a natural constant, the space of the whole data dimension is reduced to one dimension, thereby completing the simplification construction of quantization indexes in the adjoint analysis.
As a preferred technical solution of the present invention, in the step B- (1), the value range of k is a real number domain, especially a rational number domain or a natural number domain.
As a preferred embodiment of the present invention, in the step B- (4), the other end of the line segment k is provided with a spatial directional characteristic to derive a sagittal line segment; on the other hand, the generated vector line segment is given n-dimensional degrees of freedom within the entire n-dimensional manifold.
As a preferred technical scheme of the invention, the data accompanying analysis is applied to the constructed quantitative index to extract the characteristics of the power grid operation monitoring data; the quantization index includes: a first-stage quantization index or a leading index formed by two-dimensional parameters or volume values of the n-dimensional manifold, and a second-stage quantization index or an auxiliary index formed by one-dimensional parameters or length values of the n-dimensional manifold; the application guidance of the leading indexes and the auxiliary indexes is as follows: when data processing of power grid operation monitoring is carried out, first-round data analysis and feature extraction are carried out by using leading indexes to obtain a first-class classification scheme or a first-class identification scheme, and then secondary distinguishing is carried out on the same-class data in the obtained classification scheme based on auxiliary indexes; the data extracted by the features point to the regional extreme value and/or the global maximum value of the primary leading index and/or the secondary auxiliary index; the data process of the companion analysis comprises: and taking the constructed k vector as a univariate configuration of a primary leading index and/or a secondary auxiliary index, keeping a main value and an original point of the k vector unchanged, performing n-dimensional space traversal on a parameter a of the k vector, acquiring a regional extreme value of the primary leading index and/or the secondary auxiliary index along with continuous change of the primary leading index and/or the secondary auxiliary index in the space traversal process, and acquiring a global maximum value based on comparison of the regional extreme values to finish the feature extraction of the power grid operation monitoring data.
As a preferred technical scheme of the invention, a constructed k vector is kept unchanged in a main value and an origin, a parameter a of the k vector is subjected to n-dimensional space traversal, the continuous change of a primary leading index and/or a secondary auxiliary index is accompanied in the space traversal process, then a regional extreme value of the primary leading index and/or the secondary auxiliary index is obtained on the basis of a direction parameter function of the parameter a to n coordinate dimensions, a global maximum value is obtained on the basis of comparison of the regional extreme values, and the extraction of the characteristics of the power grid operation monitoring data is completed.
As a preferred technical solution of the present invention, the direction parametric function of the parameter a for n coordinate dimensions is a cosine value of a direction angle of a for n coordinate dimensions; the direction parameter function fc of the parameter a for n coordinate dimensions satisfies the following relation:
。
the beneficial effect that adopts above-mentioned technical scheme to bring lies in:
the first core technical advantage of the invention is as follows: an application type vector database is constructed based on the application guidance of electric power operation big data, power grid operation data monitoring targets (including single monitoring targets, combined single monitoring targets, long-term single monitoring targets and long-term combined monitoring targets) are subjected to unified data configuration, the data configuration of the power grid operation data monitoring targets with three dimensional directions is obtained, the whole data configuration is presented as a three-order tensor data configuration, the three-order tensor is covariant or inverse three-order tensor according to the selection mode of a data unit and obeys the Einstein convention paradigm of tensor transformation, in tensor data, the data vector of any single monitoring target points to the same dimensional direction, and based on the situation, the tensor is constructed along the data vector of any single monitoring target to form an application data vector, and the basis of subsequent data configuration and data feature extraction is obtained.
The second core technical advantage of the invention is that: in order to simplify the data process of the quantitative index, the invention obviously realizes the dimension reduction processing of the multidimensional multi-element data space for the adjoint analysis, so that the multi-parameter value of the multidimensional space is converted into two parameter values of length k and angle a, the dimension reduction processing is carried out on the multidimensional multi-element data through the main value and the parameter value, the dimension of the data after dimension reduction is two dimensions, namely the absolute value of the main value k and the parameter value angle a, and the space of the whole data dimension is reduced into one dimension because k is a constant value selected in advance, especially a rational constant or a natural constant, thereby completing the single variable configuration of the quantitative index in the adjoint analysis.
The third core technology of the invention has the following advantages: the method constructs a simple and feasible data process accompanied with analysis, enables the parameter a to carry out n-dimensional space traversal on the basis of the constructed k vector, obtains the regional extreme value of the primary leading index and/or the secondary auxiliary index along with the continuous change of the primary leading index and/or the secondary auxiliary index in the space traversal process, and obtains the global maximum value on the basis of the comparison of the regional extreme values, thereby completing the characteristic extraction of the power grid operation monitoring data through a single variable data process.
Detailed Description
The following examples illustrate the invention in detail. In the following description of embodiments, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated 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.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to monitoring ". Similarly, the phrase "if it is determined" or "if [ a described condition or event ] is monitored" may be interpreted in accordance with the context to mean "upon determining" or "in response to determining" or "upon monitoring [ a described condition or event ]" or "in response to monitoring [ a described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
Example 1 construction of standardized data configuration of monitoring data
The overall classification of the monitoring target of the power grid operation data comprises single-item single-time monitoring, combined single-time monitoring, long-term single-item monitoring, long-term combined monitoring and the like. Based on the data structure, a standardized data structure is constructed so as to facilitate the interaction and exchange of basic monitoring data.
Specifically, the data configuration is set as: (1) for any single monitoring target, whether the internal data form is single data or mutually independent multiple data, the single monitoring target is uniformly set to be a non-differential vector data format, the dimensionality of the vector data format is determined by the maximum data dimensionality in all the single monitoring target internal data, and the dimensionality consistency of original data lower than the set maximum data dimensionality is completed through zero filling; (2) for a joint monitoring target, performing first orthogonal vector data dimension expansion on the basis of the data configuration in the step (1), and obtaining a data array pointing to a second dimension on the basis of the vector data obtained in the step (1); the maximum dimension number of the second dimension is the maximum single monitoring number contained in the joint monitoring; (3) for a long-term monitoring target, performing second orthotropic vector data dimension expansion on the basis of the data configuration in the step (1), and obtaining a data array pointing to a third dimension on the basis of the vector data obtained in the step (1); the maximum dimension of the third dimension is jointly determined by the long-term monitoring duration and the single monitoring interval duration; and the third dimension and the second dimension are also set to be in an orthogonal relation.
Embodiment 2 monitoring database overall architecture as data processing basis
Based on the data configuration paradigm of example 1, the general form of the power operations big data application oriented vectorial database format is: the data configuration of the power grid operation data monitoring target is set to have three dimensional directions, the data configuration is integrally presented as a third-order tensor data configuration, the third-order tensor data monitoring target is a covariate third-order tensor or an inverse third-order tensor according to a selection mode of a data unit and obeys an Einstein convention paradigm of tensor transformation, in tensor data, data vectors of any single monitoring target point to the same dimensional direction, on the basis, the tensor data vectors along the data vectors of any single monitoring target are constructed to form an application data vector, and the data vectors are used as a basis for subsequent data configuration and data feature extraction. And during subsequent data processing, when data accompanying analysis corresponding to the simplified data processing process is carried out on the simplified quantized data, the data processing range is a local database range corresponding to a single data monitoring target or a global database range.
Example 3 quantization construction of high order data
The vectorial data, especially the tensor data, although implement the normalization and unification of the data, bring extra burden to the data processing of the system, and sometimes even far exceed the computing power of the system. It is therefore necessary and important to perform quadratic construction of quantization indexes for tensor data.
The vector database of the electric power operation big data application guide and the included application data vector thereof contain global parameters of the operation monitoring of the power grid application end, are an unquantizable data paradigm, and are quantitatively constructed by adopting the following data paradigm: firstly, a two-dimensional diagonal data array N is constructed, the data order of the two-dimensional diagonal data array N is N, and the configuration of the two-dimensional diagonal data array N is a transposition symmetric type; further, setting the components of the application data vector to the transposition symmetry axis of the data array N according to any sequence; obtaining a vector group with a base attribute in an n-dimensional data space, wherein the vector group surrounds and forms an n-dimensional manifold with a standard volume and a standard perimeter; based on the resulting n-dimensional manifold.
Further, considering different application guidance of different single monitoring operations, two groups of independent application data vector quantization indexes are constructed and obtained by adopting the following two normal forms:
(1) for the resulting n-dimensional manifold, its two-dimensional parameters are calculated as follows: converting the data array isomorphism corresponding to the wrapping vector group of the n-dimensional manifold into a determinant with zero diagonal inverse number, wherein the value of the determinant is used as a two-dimensional parameter value or a volume value of the n-dimensional manifold to form a first-stage quantization index;
(2) for the resulting n-dimensional manifold, its one-dimensional parameters are calculated as follows: converting the data array homotype corresponding to the wrapping vector group of the n-dimensional manifold into a determinant with zero diagonal inverse number, wherein the principal element sum value of the determinant is used as a one-dimensional parameter value or a length value of the n-dimensional manifold to form a second-stage quantization index; and the obtained two-dimensional parameter value or volume value is used as a leading index of power grid operation monitoring; and on the premise of constructing a second-stage quantization index, taking the obtained one-dimensional parameter value or length value as an auxiliary index for monitoring the operation of the power grid.
Example 4 singulation construction of quantization indices
Corresponding to the quantization of the index, usually a certain single-line monitoring operation is usually directed to a specific data source, so that the simplification construction of the quantization index is carried out, and the data processing calculation amount of the system can be further greatly reduced.
We propose a simple and feasible method for constructing a single data structure, which has the following advantages: (1) the method has good harmony with the configuration models of the basic databases in the embodiments 1 and 2; (2) directly associated with subsequent companion analysis. The specific method comprises the following steps:
(1) firstly, a data value of a fixed interval is designated based on the specific requirement of any single monitoring target of power grid operation monitoring and is recorded as k; the value range of k is a real number domain, especially a rational number domain or a natural number domain;
(2) further performing dimension extension on k, and constructing a space line segment with a length corresponding to the absolute value of k, and particularly, setting the space line segment into the n-dimensional space manifold constructed in the foregoing embodiment;
(3) at this time, the n-dimensional space manifold constructed in the foregoing embodiment is subjected to origin processing, that is, the starting points of the wrapping vector groups of the n-dimensional manifold are located at the same space point, and the same space point is referred to as a space origin;
(4) placing one end of the space line segment constructed in the step (2) on the space origin of the step (3);
(5) constructing main values and parameters of the accompanying analysis: the value of k itself forms a main value of the accompanying analysis, in the step (4), the k vector has n which is the complete freedom degree in the space, the freedom degree corresponds to a parameter value of the accompanying analysis, one end of the k vector is fixed on the constructed space origin in the step (4), the complete freedom degree is the rotation freedom degree, the parameter value of the accompanying analysis corresponds to a rotation angle value a, and the value range of a is 0-2 pi; for the other end of the line segment k, on one hand, the spatial directional characteristic is given, so that the line segment k is derived as a vector line segment; on the other hand, the generated vector line segment is endowed with n-dimensional freedom degree in the whole n-dimensional manifold;
(6) and (3) performing dimension reduction processing on the multidimensional metadata based on the principal value and the parameter value, based on the data process of the steps (1) - (5), performing dimension reduction processing on the multidimensional metadata through the principal value and the parameter value, wherein the dimension of the data after dimension reduction is two dimensions, namely the absolute value of the principal value k and the parameter value angle a, and since k is a constant value selected in advance, especially a rational constant or a natural constant, the space of the whole data dimension is reduced to one dimension, thereby completing the simplification construction of quantization indexes in the adjoint analysis.
Example 5 data processing procedure for arbitrary single monitoring event
The present invention proposes a data processing method, i.e., a data accompanying analysis method, which is highly adaptive to the data configuration method in the foregoing embodiment. One of the core implications of the above-described companion analysis lies in the extreme and most significant values of the data, since the most common and most important forms of data tend to be reflected in abnormal deviations and fluctuations of the data due to event monitoring at the electrical service center.
Applying data accompanying analysis to the constructed quantitative indexes to extract the characteristics of the power grid operation monitoring data; the quantization index includes: a first-stage quantization index or a leading index formed by two-dimensional parameters or volume values of the n-dimensional manifold, and a second-stage quantization index or an auxiliary index formed by one-dimensional parameters or length values of the n-dimensional manifold; the application guidance of the main index and the auxiliary index is as follows: when data processing of power grid operation monitoring is carried out, a leading index is used for carrying out first-round data analysis and feature extraction to obtain a first-grade classification scheme or a first-grade identification scheme, and then secondary distinguishing is carried out on the same type data in the obtained classification scheme based on an auxiliary index; the data extracted by the features point to be the regional extreme value and/or the global maximum value of the primary leading index and/or the secondary auxiliary index; the data process accompanying the analysis includes: for constructedAnd the k vector is used as a univariate configuration of the primary leading index and/or the secondary auxiliary index, a main value and an origin point of the k vector are kept unchanged, the parameter a is subjected to n-dimensional space traversal, a regional extreme value of the primary leading index and/or the secondary auxiliary index is obtained along with continuous change of the primary leading index and/or the secondary auxiliary index in the space traversal process, a global maximum value is obtained based on comparison of the regional extreme values, and the extraction of the power grid operation monitoring data feature is completed. Here, the most common data process is: keeping the principal value and the origin of the constructed k vector unchanged, and enabling the parameter a to carry out n-dimensional space traversal, wherein the continuous change of a primary leading index and/or a secondary auxiliary index is accompanied in the space traversal process, which means that the direction parameter function of the parameter a to n coordinate dimensions is the cosine value of the direction angle of a to n coordinate dimensions; the direction parameter function fc of the parameter a for n coordinate dimensions satisfies the following relationship:
(ii) a Further, regional extreme values of the primary leading indexes and/or the secondary auxiliary indexes are obtained based on the direction parametric functions of the parameter a to the n coordinate dimensions, global maximum values are obtained based on comparison of the regional extreme values, and feature extraction of the power grid operation monitoring data is completed.
In summary, it can be seen from the above embodiments that, based on the electric power operation big data application oriented vector database format, the present invention performs the simplified data configuration construction and the data presentation paradigm construction on the quantization database corresponding to the electric power operation data monitoring object, and then performs the data accompanying analysis corresponding to the simplified data processing process on the simplified quantization data, so as to achieve the data result acquisition and presentation facing the electric power operation monitoring object with the simplified data paradigm and the simplified data processing amount.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
In various embodiments, the hardware implementation of the technology may directly employ existing intelligent devices, including but not limited to industrial personal computers, PCs, smart phones, handheld stand-alone machines, floor stand-alone machines, and the like. The input device preferably adopts a screen keyboard, the data storage and calculation module adopts the existing memory, calculator and controller, the internal communication module adopts the existing communication port and protocol, and the remote communication adopts the existing gprs network, the web and the like.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules, so as to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Each functional unit in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program instructing related hardware, and the computer program may be stored in a computer readable storage medium, and when executed by a processor, the computer program may implement the steps of the above-described embodiments of the method. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (7)
1. A big data-based real-time monitoring method for operation of an electric power service center is characterized by comprising the following steps: the method is based on a vector database format guided by power operation big data application, a quantized database corresponding to a power grid operation data monitoring object is subjected to unified data configuration construction and data presentation normal form construction, then data accompanying analysis corresponding to a unified data processing process is carried out on the unified quantized data, and data result acquisition and presentation facing to the power operation monitoring object are realized through a simplified data normal form and simplified data processing amount;
the power grid operation data monitoring target comprises: a single monitoring target and/or a combined single monitoring target and/or a long-term combined monitoring target; the data configuration is set as:
(1) for any single monitoring target, whether the internal data form is single data or mutually independent multiple data, the single monitoring target is uniformly set to be an undifferentiated vector data format, the dimensionality of the vector data format is determined by the maximum data dimensionality in all the internal data of the single monitoring target, and the dimensionality consistency of original data lower than the set maximum data dimensionality is completed through zero filling;
(2) for a joint monitoring target, performing first orthogonal vector data dimension expansion on the basis of the data configuration in the step (1), and obtaining a data array pointing to a second dimension on the basis of the vector data obtained in the step (1); the maximum dimension number of the second dimension is the maximum single monitoring number contained in the joint monitoring;
(3) for a long-term monitoring target, performing second orthotropic vector data dimension expansion on the basis of the data configuration in the step (1), and obtaining a data array pointing to a third dimension on the basis of the vector data obtained in the step (1); the maximum dimension of the third dimension is determined by the long-term monitoring duration in a single monitoring interval; the third dimension and the second dimension are also set to be in an orthogonal relation;
the vector database format of the power operation big data application guide is set as follows: the data configuration of the power grid operation data monitoring target is set to have three dimensional directions, the data configuration is integrally presented as a third-order tensor data configuration, the third-order tensor data monitoring target is a covariate third-order tensor or an inverse third-order tensor according to a selection mode of a data unit and obeys an Einstein convention paradigm of tensor transformation, in tensor data, data vectors of any single monitoring target point to the same dimensional direction, on the basis, the tensor data vectors along the data vectors of any single monitoring target are constructed to form an application data vector, and the data vectors are used as a basis for subsequent data configuration and data feature extraction.
2. The big data-based power service center operation real-time monitoring method according to claim 1, wherein the method comprises the following steps: when the data accompanying analysis corresponding to the simplified data processing process is carried out on the quantized data after the simplification, the data processing range is a local database range corresponding to a single data monitoring target or a global database range.
3. The big data-based power service center operation real-time monitoring method according to claim 1, wherein the method comprises the following steps: the simplified data configuration construction and data presentation paradigm construction process comprises:
A. and (3) quantitative index construction: the vector database of the electric power operation big data application guide and the included application data vector thereof contain global parameters of the operation monitoring of the power grid application end, are an unquantizable data paradigm, and are quantitatively constructed by adopting the following data paradigm: firstly, constructing a two-dimensional diagonal data array N, wherein the data order is N, and the configuration is a transposition symmetry type; further, setting the components of the application data vector to a transposition symmetry axis of the data array N according to any sequence; obtaining a vector group with a base attribute in an n-dimensional data space, wherein the vector group surrounds and forms an n-dimensional manifold with a standard volume and a standard perimeter; based on the obtained n-dimensional manifold, two groups of independent application data vector quantization indexes are constructed by adopting the following two paradigms: (1) for the resulting n-dimensional manifold, its two-dimensional parameters are calculated as follows: converting the data array isomorphism corresponding to the wrapping vector group of the n-dimensional manifold into a determinant with zero diagonal inverse number, wherein the value of the determinant is used as a two-dimensional parameter value or a volume value of the n-dimensional manifold to form a first-stage quantization index; (2) for the resulting n-dimensional manifold, its one-dimensional parameters are calculated as follows: converting the data array isomorphism corresponding to the wrapping vector group of the n-dimensional manifold into a determinant with zero diagonal inverse number, wherein the principal element sum value of the determinant is used as a one-dimensional parameter value or a length value of the n-dimensional manifold to form a second-stage quantization index; and the obtained two-dimensional parameter value or volume value is used as a leading index of power grid operation monitoring; on the premise of constructing a second-stage quantization index, taking the obtained one-dimensional parameter value or length value as an auxiliary index for monitoring the operation of the power grid;
B. the simplification construction of the quantization index comprises the following steps: (1) firstly, a data value of a fixed interval is designated based on the specific requirement of any single monitoring target of power grid operation monitoring and is recorded as k; (2) d, further carrying out dimensionality extension on k, constructing a space line segment with the length corresponding to the absolute value of k, and setting the space line segment into the n-dimensional space manifold constructed in the step A; (3) at this time, performing origin processing on the n-dimensional space manifold constructed in the step a, that is, placing the starting points of the wrapping vector groups of the n-dimensional manifold at the same space point, and recording the same space point as a space origin; (4) placing one end of the space line segment constructed in the step (2) on the space origin of the step (3); (5) constructing main values and parameters of the accompanying analysis: the value of k itself constitutes a main value of the accompanying analysis, and in the step (4), the k vector has a complete degree of freedom inside the n-dimensional space, the degree of freedom corresponds to a parameter value of the accompanying analysis, one end of the k vector is fixed on a constructed space origin in the step (4), the complete degree of freedom is a rotational degree of freedom, the parameter value of the accompanying analysis corresponds to a rotational angle value a, and the value range of a is 0-2 pi; (6) and (3) performing dimension reduction processing on the multidimensional metadata based on the principal value and the parameter value, performing dimension reduction processing on the multidimensional metadata through the principal value and the parameter value based on the data process of the steps (1) - (5), wherein the dimension of the data after dimension reduction is two dimensions, namely the absolute value of the principal value k and the parameter value angle a, and since k is a constant value selected in advance and comprises a rational constant or a natural constant, the space of the whole data dimension is reduced to one dimension, thereby completing the simplification construction of the quantization index in the adjoint analysis.
4. The big data-based power service center operation real-time monitoring method according to claim 3, wherein the big data-based power service center operation real-time monitoring method comprises the following steps: in the step B- (1), the value range of k is a real number domain, including a rational number domain or a natural number domain.
5. The big data based electric power service center operation real-time monitoring method according to claim 3, characterized in that: in the step B- (4), the other end of the line segment k is endowed with the space directional characteristic on one hand, so that the line segment k is derived into a vector line segment; on the other hand, the generated vector line segment is given n-dimensional degrees of freedom within the entire n-dimensional manifold.
6. The big data-based power service center operation real-time monitoring method according to claim 3, wherein the big data-based power service center operation real-time monitoring method comprises the following steps: applying the data accompanying analysis to the constructed quantitative indexes to extract the characteristics of the power grid operation monitoring data;
the quantization index includes: a first-stage quantization index or a leading index formed by two-dimensional parameters or volume values of the n-dimensional manifold, and a second-stage quantization index or an auxiliary index formed by one-dimensional parameters or length values of the n-dimensional manifold; the application guidance of the main index and the auxiliary index is as follows: when data processing of power grid operation monitoring is carried out, a leading index is used for carrying out first-round data analysis and feature extraction to obtain a first-grade classification scheme or a first-grade identification scheme, and then secondary distinguishing is carried out on the same type data in the obtained classification scheme based on an auxiliary index;
the data extracted by the features points to the regional extreme value and/or the global maximum value of the primary leading index and/or the secondary auxiliary index;
the data process of the companion analysis comprises: and taking the constructed k vector as a univariate configuration of a primary leading index and/or a secondary auxiliary index, keeping a main value and an original point of the k vector unchanged, performing n-dimensional space traversal on a parameter a of the k vector, acquiring a regional extreme value of the primary leading index and/or the secondary auxiliary index along with continuous change of the primary leading index and/or the secondary auxiliary index in the space traversal process, and acquiring a global maximum value based on comparison of the regional extreme values to finish the feature extraction of the power grid operation monitoring data.
7. The big data-based power service center operation real-time monitoring method according to claim 6, wherein: and keeping the principal value and the origin of the constructed k vector unchanged, performing n-dimensional space traversal on the parameter a, accompanying with the continuous change of the primary leading index and/or the secondary auxiliary index in the space traversal process, then obtaining the regional extreme value of the primary leading index and/or the secondary auxiliary index based on the direction parameter function of the parameter a to n coordinate dimensions, and obtaining the global maximum value based on the comparison of the regional extreme values to complete the extraction of the grid operation monitoring data feature.
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