A multi-dimensional data network load storage method and system

Abstract

A multi-dimensional data network load storage method and system, including: business classification of data obtained from a micro-synchronous phasor measurement device to construct a grid data matrix; constructing field-type data columns based on the grid data matrix and separating them by identifiers, then forming a data pool in a T-tree construction manner, and storing the data pool; wherein the data in each data pool corresponds to the T-tree node according to a relationship; based on the identifier sequence, an index tree is constructed, and a corresponding relationship is established between the index tree and the data in the data pool. By constructing a grid storage through a network matrix and applying a T-tree index to establish a fast response engine, a large amount of PMU collected phasor data is rationally decomposed to achieve rapid support for data storage and analysis, thereby solving the problem of structuring unstructured data in real-time data collection of distribution networks by micro PMUs, and achieving deep matching of business logic by grid data matrix and T-tree.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to a multi-dimensional data grid load storage method and system. Background Art

[0002] PMU power system synchronous phasor measurement device is mainly used for synchronous phasor measurement and output as well as dynamic recording. It plays a very good role in power system state estimation and dynamic monitoring, stability prediction and control, model verification, relay protection and fault location. As the management accuracy of distribution network becomes more and more refined, the data requirements for basic support are getting higher and higher. According to the application effect of PMU in power grid, micro synchronous phasor measurement device is developed to collect distribution network data, apply advanced analysis, optimize the management of distribution network, and improve the safety and reliability of distribution network.

[0003] However, when processing a large amount of micro-PMU data, the problem of resource response is caused by storage limitations, resulting in low storage efficiency and low analysis accuracy and reliability in the response demand information for business and data during distribution network analysis. Summary of the invention

[0004] In order to solve the problem of resource response caused by storage limitation in the prior art, the present invention provides a multi-dimensional data network load matrix mode storage and T-tree indexing method and system.

[0005] The technical solution provided by the present invention is:

[0006] A multi-dimensional data network load storage method and system, comprising:

[0007] The data obtained from the micro-synchronous phasor measurement device are classified into business categories to construct a gridded data matrix;

[0008] Constructing field-type data columns based on the gridded data matrix and separating them by identifiers, then forming a data pool in a T-tree construction manner, and storing the data pool; wherein the data in each data pool corresponds to the T-tree nodes in a relationship;

[0009] Based on the identifier sequence, an index tree is constructed, and a correspondence is established between the index tree and the data in the data pool.

[0010] Preferably, constructing field-type data columns based on the gridded data matrix and separating them by identifiers, and then forming a data pool in a T-tree construction manner includes:

[0011] In the memory, field-type data columns are constructed based on the gridded data matrix, and separated by database-recognizable identifiers to obtain identification data columns;

[0012] Store all identification data in an in-memory database;

[0013] In the memory database, the data sequence unit is used to establish left and right child nodes according to a T tree, the left child node stores the business type in the current identification data column, and the right child node stores the data parameter of the current identification data column, and the left child node and the right child node form a T tree network structure;

[0014] Building a data pool based on the T-tree network structure;

[0015] The database-identifiable identifiers include: distribution network topology and distribution network basic services.

[0016] Preferably, storing the data pools includes: storing all data pools in a unified manner to form a comprehensive data warehouse.

[0017] Preferably, constructing an index tree based on the identifier sequence and establishing a correspondence between the index tree and the data in the data pool includes:

[0018] Establishing a business logic relationship between the index tree and the data pool based on the identifier of the identifier sequence;

[0019] Establishing a corresponding set based on the logical relationship;

[0020] The identifier, the network data corresponding to the identifier, the service corresponding to the network data, and the logical relationship are associated.

[0021] Preferably, the data acquired from the micro-synchronous phasor measurement device is classified into service categories to construct a gridded data matrix, including:

[0022] Construction of accurate business classification model based on data obtained from micro-synchrophasor measurement device;

[0023] Based on the precise business classification model, the data is classified and processed according to business types, and data cells of each business type are constructed;

[0024] Storing the classified data in the corresponding data cells;

[0025] Based on the data cells, a gridded data matrix is ​​constructed.

[0026] Preferably, the construction of an accurate service classification model based on data acquired by the micro-synchronous phasor measurement device includes:

[0027] The data obtained from the micro-synchronous phasor measurement device is clustered to obtain the center location information of the data of various services;

[0028] Repeat the above steps for a set number of times to obtain a collection of energy center location information for each type of business;

[0029] Based on the condensation center position information set, the convergence position information of the condensation center position information of each type of business is obtained to complete the construction of the precise business classification model.

[0030] Preferably, based on the precise business classification model, the data is classified according to business type and data cells of each business type are constructed, including:

[0031] Constructing a three-dimensional data cell based on the precise business classification model;

[0032] Based on the three-dimensional data cells, assigning indicator content to the X-axis, including voltage, current or power;

[0033] Based on the three-dimensional data cells, assigning a Y axis to business content, including fault location, voltage analysis or dynamic monitoring;

[0034] Based on the three-dimensional data cell, assigning a Z axis to data information content;

[0035] The data cell is jointly determined by the X-axis, the Y-axis and the Z-axis.

[0036] A multidimensional data network load storage system, comprising:

[0037] Gridded data matrix construction module: classify the data obtained from the micro-synchronous phasor measurement device and construct a gridded data matrix;

[0038] T-tree construction module: constructs field-type data columns based on the gridded data matrix and separates them by identifiers, then forms a data pool according to the T-tree construction method, and stores the data pool; wherein the data in each data pool corresponds to the T-tree node according to the relationship;

[0039] Association module: constructs an index tree based on the identifier sequence, and establishes a corresponding relationship between the index tree and the data in the data pool.

[0040] Preferably, the T-tree construction module includes:

[0041] Identification submodule: in the memory, constructing a field-type data column based on the gridded data matrix, separating the data columns by a database-recognizable identifier, and obtaining an identification data column;

[0042] Identification storage submodule: stores all identification data in the memory database;

[0043] T-tree construction submodule: in the memory database, the data sequence unit is used to establish left and right child nodes according to the T-tree, the left child node stores the business type in the current identification data column, and the right child node stores the data parameters of the current identification data column, and the left child node and the right child node form a T-tree network structure;

[0044] Data pool construction submodule: constructing a data pool based on the T-tree network structure;

[0045] The database-identifiable identifiers include: distribution network topology and distribution network basic services.

[0046] Preferably, the T-tree construction module also includes the same storage submodule: all data pools are uniformly stored to form a comprehensive data warehouse.

[0047] Preferably, the association module includes:

[0048] A business logic relationship building submodule: establishing a business logic relationship between the index tree and the data pool based on the identifier of the identifier sequence;

[0049] Correspondence set construction submodule: establishes a correspondence set based on the logical relationship;

[0050] An association submodule is configured to associate the identifier, the network data corresponding to the identifier, the service corresponding to the network data, and the logical relationship.

[0051] Preferably, the gridded data matrix building module comprises:

[0052] Precise business model construction submodule: constructs a precise business classification model based on the data obtained by the micro-synchronous phasor measurement device;

[0053] Data cell construction submodule: based on the precise business classification model, classify the data according to business type and construct data cells of each business type;

[0054] A second storage submodule: storing the classified data in the corresponding data cells;

[0055] Gridded data matrix construction submodule: constructs a gridded data matrix based on the data cells.

[0056] Compared with the prior art, the beneficial effects of the present invention are: a multi-dimensional data network load storage method and system, including: classifying the data obtained from the micro-synchronous phasor measurement device for business, and constructing a grid data matrix; constructing field-type data columns based on the grid data matrix, and separating them by identifiers, and then forming a data pool according to the T-tree construction method, and storing the data pool; wherein the data in each data pool corresponds to the T-tree node according to the relationship; based on the identifier sequence, constructing an index tree, and establishing a corresponding relationship between the index tree and the data in the data pool. The data storage method of the constructed T-tree index and the network matrix solves the problem of resource response due to storage limitations when processing a large amount of micro-PMU data, as well as the business policy information set in the T-tree. This method rationally decomposes the phasor data collected by a large number of PMUs, realizes rapid support for data storage and analysis, effectively improves the response demand information for business and data during distribution network analysis, and improves storage efficiency and analysis accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The multi-dimensional data network matrix mode storage and T-tree indexing method and system method flow of the present invention;

[0058] Figure 2 This is a flow chart of the data grid matrix mode storage and T-tree index implementation method of the present invention. DETAILED DESCRIPTION

[0059] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and examples.

[0060] Embodiment 1:

[0061] The present invention provides a multi-dimensional data network load storage method and system, such as Figure 1 As shown:

[0062] S1: Classify the data obtained from the micro-synchronous phasor measurement device and build a grid data matrix:

[0063] According to the amount of data collected by the micro PMU device, the business logic is initialized and classified. Since the micro PMU device collects a large amount of data points, reaching the second-level data, the data throughput reception reaches the collection of thousands of data in 1 second. Therefore, on the basis of the collection of business classification, a simple business classification model is set, and all the collected indicator data are clustered through cluster analysis. Through autonomous training, the remaining records are clustered to the condensation point according to the principle of proximity, and the center position of each initial classification is calculated. The calculated center position is used for re-clustering, and the cycle is repeated until the condensation point position converges, so as to locate the business indicators more accurately. After clustering the indicator data, the decision tree method is applied for prediction. The decision tree is a prediction model; it represents a mapping relationship between object attributes and object values. Each node in the tree represents an object, and each fork path represents a possible attribute value, and each leaf node corresponds to the value of the object represented by the path from the root node to the leaf node. Therefore, the above learning and training method is used for classification and matching to establish an accurate business data classification model.

[0064] The data collected by the micro-synchronous phasor device is processed in a grid manner. Due to the large amount of data, the X-axis represents indicators such as voltage, current, and power, the Y-axis represents business fault location, voltage analysis, dynamic monitoring, etc., and the Z-axis represents data information. Through a single data cell, an overall grid data matrix is ​​constructed. The data matrix initially locates and stores the data in a multi-dimensional manner. The multi-source data as a whole constitutes the overall matrix of the business, realizing matrix-based dedicated storage.

[0065] S2: constructing field-type data columns based on the gridded data matrix and separating them by identifiers, and then forming a data pool in a T-tree construction manner, and storing the data pool; wherein the data in each data pool corresponds to the T-tree node according to the relationship:

[0066] A field-type data column is constructed from the uniformly formed grid matrix data, which is separated by a database-recognizable identifier, and the data constructed by the grid identification is formed into an identification data column, which is pushed to a memory database that redesigns the architecture in memory based on all the data, builds data cache and fast algorithms, and establishes left and right child nodes for the data sequence units according to a T-tree in the memory database. The identification data column storing data in the left child node that is smaller than the minimum data in the current node corresponds to the field-type data column storing data larger than the node data in the right child node, forming a T-tree network structure. Because the dynamic increment of data collection points will increase (the dynamic increase of collection points is generated by the heartbeat of the collection device), the purpose of this is to enable the storage size of each data pool to be dynamically allocated.

[0067] A K-dimensional array is used to represent the data grid directory composed of sequences constructed by the T-tree. The key points of the data collected by these phasor devices are sorted in different sequences to construct a data resource collection, which is composed of data pool elements composed according to business logic, and multiple data pools are unified into a comprehensive data warehouse.

[0068] S3: Based on the identifier sequence, construct an index tree, and establish a correspondence between the index tree and the data in the data pool:

[0069] In the data network structure composed of T trees, the identifier sequence constructed by the memory database is built into an index tree. Since the initial state of the data is related to the business, the business indicator identifier is designed according to the data meaning, and the business logic relationship is established through the indicator identifier, including distribution network topology, distribution network basic business, etc., and these identifiers are used to establish a network corresponding set. By associating the identifier, data, business, and logic one by one through a peer relationship, the index tree constructed by the T tree is matched one by one, and the data in the index tree is assigned to the business.

[0070] The data model constructed in the first step forms a multi-dimensional data grid matrix with the data pool to solve the problem of the relationship dimension between data. The data business indicators in the grid matrix correspond to the T-tree, and the T-tree index is associated with the indicator respectively. This is done to solve the problem of non-single index in the memory library and solve the multiple resource occupation caused by multi-source data storage caused by the non-single indicator structure of the business and the intersection of business indicators (business is associated with indicators, but indicators will be related to multiple businesses. At present, indicators in the business will form multi-channel storage, that is, handover indicators will be stored in multiple data storage).

[0071] Embodiment 2: Based on the same inventive concept, the present invention also provides a multi-dimensional data network load storage system, including:

[0072] Gridded data matrix construction module: classify the data obtained from the micro-synchronous phasor measurement device and construct a gridded data matrix;

[0073] T-tree construction module: constructs field-type data columns based on the gridded data matrix and separates them by identifiers, then forms a data pool according to the T-tree construction method, and stores the data pool; wherein the data in each data pool corresponds to the T-tree node according to the relationship;

[0074] Association module: constructs an index tree based on the identifier sequence, and establishes a corresponding relationship between the index tree and the data in the data pool.

[0075] The T-tree construction module comprises:

[0076] Identification submodule: in the memory, constructing a field-type data column based on the gridded data matrix, separating the data columns by a database-recognizable identifier, and obtaining an identification data column;

[0077] Identification storage submodule: stores all identification data in the memory database;

[0078] T-tree construction submodule: in the memory database, the data sequence unit is used to establish left and right child nodes according to the T-tree, the left child node stores the business type in the current identification data column, and the right child node stores the data parameters of the current identification data column, and the left child node and the right child node form a T-tree network structure;

[0079] Data pool construction submodule: constructing a data pool based on the T-tree network structure;

[0080] The database-identifiable identifiers include: distribution network topology and distribution network basic services.

[0081] The T-tree construction module also includes a same storage submodule: all data pools are uniformly stored to form a comprehensive data warehouse.

[0082] The association module comprises:

[0083] A business logic relationship building submodule: establishing a business logic relationship between the index tree and the data pool based on the identifier of the identifier sequence;

[0084] Correspondence set construction submodule: establishes a correspondence set based on the logical relationship;

[0085] An association submodule is configured to associate the identifier, the network data corresponding to the identifier, the service corresponding to the network data, and the logical relationship.

[0086] The gridded data matrix building module comprises:

[0087] Precise business model construction submodule: constructs a precise business classification model based on the data obtained by the micro-synchronous phasor measurement device;

[0088] Data cell construction submodule: based on the precise business classification model, classify the data according to business type and construct data cells of each business type;

[0089] A second storage submodule: storing the classified data in the corresponding data cells;

[0090] Gridded data matrix construction submodule: constructs a gridded data matrix based on the data cells.

[0091] The precise business model construction submodule includes:

[0092] Central location information acquisition unit: The data acquired from the micro-synchronous phasor measurement device is clustered and analyzed to obtain the condensed central location information of the data of various services;

[0093] Center location set acquisition unit: repeat the above steps for a set number of times to obtain the center location information set of each type of business;

[0094] The precise service classification model construction unit obtains the convergence position information of the condensation center position information of each type of service based on the condensation center position information set, and completes the construction of the precise service classification model.

[0095] The data cell construction submodule includes:

[0096] A three-dimensional data cell construction unit: constructing a three-dimensional data cell based on the precise business classification model;

[0097] X-axis construction unit: based on the three-dimensional data cells, assigning indicator content to the X-axis, including voltage, current or power;

[0098] Y-axis construction unit: based on the three-dimensional data cells, assigning the Y-axis to business content, including fault location, voltage analysis or dynamic monitoring;

[0099] Z-axis construction unit: based on the three-dimensional data cell, assigning the Z-axis to the data information content;

[0100] The data cell is jointly determined by the X-axis, the Y-axis and the Z-axis.

[0101] Embodiment 3:

[0102] According to a multidimensional data load storage method provided by the present invention, steps of constructing a multidimensional data load and storing it in a T-tree index are as follows:

[0103] Build a data grid, establish criteria based on business classification identification key points and data universality, create corresponding indexes in the data grid, construct a three-dimensional matrix based on business, data, and indicators, construct the grid matrix, and establish a T-tree model index primary key based on the business and the matrix.

[0104] A memory library is established, and the data points collected by the micro PMU are entered into the memory library. A memory sequence in the form of data is performed in the memory database, and the sequence changes dynamically according to the data.

[0105] The mining algorithm model is introduced to perform data mining calculations on the data collected by the micro-PMU, self-identify the data, and establish index dependency values ​​for the classified data header files to correspond to the primary key of the T-tree model index.

[0106] The memory data sequence is matched with the T-tree model, the grid matrix is ​​deeply matched with the T-tree data to achieve integration, and the three factors of the T-tree are combined to build a T-data directory.

[0107] The core indicator application data of the T-tree directory is clustered for in-depth analysis to form a T-tree business node classification containing business data such as voltage, current, and power.

[0108] The T-tree is associated with the grid matrix business to analyze and resolve the dimension problem of the relationship between data. The data business indicators in the grid matrix are matched with the T-tree to form a real-time and rapid matching and analysis of the grid matrix data and business.

[0109] Obviously, the described embodiments are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A multidimensional data network load storage method, characterized in that: include: The data obtained from the micro-synchronous phasor measurement device are classified into business categories to construct a gridded data matrix; Constructing field-type data columns based on the gridded data matrix and separating them by identifiers, then forming a data pool in a T-tree construction manner, and storing the data pool; wherein the data in each data pool corresponds to the T-tree nodes in a relationship; Based on the identifier sequence, an index tree is constructed, and a correspondence is established between the index tree and the data in the data pool; The step of constructing a field-type data column based on the gridded data matrix and separating the data columns by identifiers, and then forming a data pool in a T-tree construction manner includes: In the memory, field-type data columns are constructed based on the gridded data matrix, and separated by database-recognizable identifiers to obtain identification data columns; Store all identification data in an in-memory database; In the memory database, the data sequence unit is used to establish left and right child nodes according to the T tree, the left child node stores the business type in the current identification data column, and the right child node stores the data parameter of the current identification data column, and the left child node and the right child node form a T tree network structure; Building a data pool based on the T-tree network structure; The database-identifiable identifiers include: distribution network topology and distribution network basic services.

2. A multidimensional data network storage method as claimed in claim 1, characterized in that: Storing the data pools includes: storing all data pools in a unified manner to form a comprehensive data warehouse.

3. A multidimensional data network storage method as claimed in claim 1, characterized in that: The step of constructing an index tree based on the identifier sequence and establishing a corresponding relationship between the index tree and the data in the data pool includes: Establishing a business logic relationship between the index tree and the data pool based on the identifier of the identifier sequence; Establishing a corresponding set based on the logical relationship; The identifier, the network data corresponding to the identifier, the service corresponding to the network data, and the logical relationship are associated.

4. A multidimensional data network storage method as claimed in claim 1, characterized in that: The data obtained from the micro-synchronous phasor measurement device is classified into business categories to construct a grid data matrix, including: Construction of accurate business classification model based on data obtained from micro-synchrophasor measurement device; Based on the precise business classification model, the data is classified and processed according to business types, and data cells of each business type are constructed; Storing the classified data in the corresponding data cells; Based on the data cells, a gridded data matrix is ​​constructed.

5. A multi-dimensional data network storage method as claimed in claim 4, characterized in that: The construction of an accurate business classification model based on data obtained by the micro-synchronous phasor measurement device includes: The data obtained from the micro-synchronous phasor measurement device is clustered to obtain the center location information of the data of various services; Repeat the above steps for a set number of times to obtain a collection of cohesive center location information for each type of business; Based on the condensation center position information set, the convergence position information of the condensation center position information of each type of business is obtained to complete the construction of the precise business classification model.

6. A multi-dimensional data network storage method as claimed in claim 4, characterized in that: Based on the precise business classification model, the data is classified and processed according to the business type, and data cells of each business type are constructed, including: Constructing a three-dimensional data cell based on the precise business classification model; Based on the three-dimensional data cells, assigning indicator content to the X-axis, including voltage, current or power; Based on the three-dimensional data cells, assigning a Y axis to business content, including fault location, voltage analysis or dynamic monitoring; Based on the three-dimensional data cell, assigning a Z axis to data information content; The data cell is jointly determined by the X-axis, the Y-axis and the Z-axis.

7. A multidimensional data network load storage system, characterized in that: include: Gridded data matrix construction module: classify the data obtained from the micro-synchronous phasor measurement device and construct a gridded data matrix; T-tree construction module: constructs field-type data columns based on the gridded data matrix and separates them by identifiers, then forms a data pool according to the T-tree construction method, and stores the data pool; wherein the data in each data pool corresponds to the T-tree node according to the relationship; Association module: construct an index tree based on the identifier sequence, and establish a corresponding relationship between the index tree and the data in the data pool; The T-tree construction module comprises: Identification submodule: in the memory, constructing a field-type data column based on the gridded data matrix, separating the data columns by a database-recognizable identifier, and obtaining an identification data column; Identification storage submodule: stores all identification data in the memory database; T-tree construction submodule: in the memory database, the data sequence unit is used to establish left and right child nodes according to the T-tree, the left child node stores the business type in the current identification data column, and the right child node stores the data parameters of the current identification data column, and the left child node and the right child node form a T-tree network structure; Data pool construction submodule: constructing a data pool based on the T-tree network structure; The database-identifiable identifiers include: distribution network topology and distribution network basic services.

8. A multi-dimensional data network storage system as claimed in claim 7, characterized in that: The T-tree construction module also includes a unified storage submodule: all data pools are stored uniformly to form a comprehensive data warehouse.

9. A multi-dimensional data network storage system as claimed in claim 7, characterized in that: The association module comprises: A business logic relationship building submodule: establishing a business logic relationship between the index tree and the data pool based on the identifier of the identifier sequence; Correspondence set construction submodule: establishes a correspondence set based on the logical relationship; An association submodule is configured to associate the identifier, the network data corresponding to the identifier, the service corresponding to the network data, and the logical relationship.

10. A multi-dimensional data network storage system as claimed in claim 7, characterized in that: The gridded data matrix building blocks include: Precise business model construction submodule: constructs a precise business classification model based on the data obtained by the micro-synchronous phasor measurement device; Data cell construction submodule: based on the precise business classification model, classify the data according to business type and construct data cells of each business type; A second storage submodule: storing the classified data in the corresponding data cells; Gridded data matrix construction submodule: constructs a gridded data matrix based on the data cells.

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