Method and system for constructing spatio-temporal data storage graph model

Abstract

The invention discloses a spatio-temporal data storage graph model construction method and system, and the method comprises the steps: obtaining a power system structure and equipment relation data; based on the structure and equipment relation data, constructing a graph database for forming a preliminary graph model; introducing historical measurement storage nodes into the graph database, and associating the historical measurement storage nodes with equipment nodes and topological nodes in the graph model; and constructing state estimation result storage nodes in the graph database, and associating the state estimation result storage nodes with equipment nodes and topological nodes in the graph model to obtain a final graph model. According to the method, the flexibility and the expandability of the graph database are utilized, the storage and the query of the time series data are realized by adding the historical data storage nodes, and the historical data storage nodes are associated with the graph nodes, so that the time-space characteristics of the power system can be comprehensively and accurately represented, and the storage and the query of the historical data are realized; a more comprehensive and accurate power system diagram model is constructed, and richer information is provided for system operation state analysis, planning and optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power big data, and in particular to a method and system for constructing a spatiotemporal data storage graph model. Background Art

[0002] The power system is a dynamic system whose operating state changes over time. Timing information is very important for understanding the state of the system at different points in time and the operating trend. For example, factors such as power load, power generation, and switch status will change over time. In addition, the power system involves a wide geographical area, including substations, transmission lines, power plants and other equipment distributed in different locations. Spatial information is the key to understanding the structure of the power system, the relationship between equipment, and the geographical location.

[0003] However, the current power system graph model is usually constructed using relational databases or external storage methods. These methods are used to store spatial data and time series data respectively, but they cannot effectively combine spatiotemporal information, limiting the comprehensive understanding and prediction of the power system operation status. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for constructing a spatiotemporal data storage graph model to solve the problem that the currently commonly used relational database or external storage method cannot effectively combine spatiotemporal information, limiting the comprehensive understanding and prediction of the operating status of the power system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for constructing a spatiotemporal data storage graph model, comprising:

[0008] Obtain power system structure and equipment relationship data;

[0009] Based on the structure and device relationship data, a graph database is constructed to form a preliminary graph model;

[0010] Introducing historical measurement storage nodes into the graph database and associating them with device nodes and topology nodes in the graph model for synchronous storage of time series data query analysis and time series state estimation results;

[0011] A state estimation result storage node is constructed in the graph database and associated with the device nodes and topology nodes in the graph model, so as to associate the estimation result with the corresponding device and topology elements and store them to obtain the final graph model.

[0012] As a preferred solution of the method for constructing a spatiotemporal data storage graph model of the present invention, wherein: based on the structure and device relationship data, a graph database is constructed, including:

[0013] Each device in the system is represented by a graph node;

[0014] The relationship between each device is represented by graph edges;

[0015] The graph nodes are connected via first graph edges according to the relationship between the devices.

[0016] As a preferred solution of the method for constructing a spatiotemporal data storage graph model described in the present invention, a historical measurement storage node is introduced into the graph database and associated with the device node and the topology node in the graph model, including:

[0017] By creating a second graph edge between the historical measurement storage node and the device node to establish an association, it is used to realize the query and analysis of the time series data;

[0018] A third graph edge is created between the historical measurement storage node and the topology node to establish an association, so as to realize synchronous storage of the timing state estimation result.

[0019] As a preferred solution of the method for constructing a spatiotemporal data storage graph model described in the present invention, it also includes setting attributes inside the historical measurement storage node to store time series data of different data sampling periods.

[0020] As a preferred solution of the method for constructing a spatiotemporal data storage graph model described in the present invention, it also includes storing the switch status data of the connected device and the timing measurement of the running device into a historical measurement storage node, represented by a timestamp and a measurement value.

[0021] As a preferred solution of the method for constructing a spatiotemporal data storage graph model of the present invention, wherein: constructing a state estimation result storage node in the graph database and associating it with the device node and the topology node in the graph model includes:

[0022] Define and determine the data structure of state estimation results;

[0023] Construct a state estimation result storage node based on the data structure;

[0024] The association is established by creating a fourth graph edge between the state estimation result storage node and the device node and the topology node.

[0025] As a preferred solution of the method for constructing a spatiotemporal data storage graph model described in the present invention, it also includes that the fourth graph edge attribute is provided with relevant estimation result information.

[0026] In a second aspect, the present invention provides a spatiotemporal data storage graph model construction system, comprising:

[0027] An acquisition module, used to acquire power system structure and equipment relationship data;

[0028] A first construction module, for constructing a graph database based on the structure and device relationship data, for forming a preliminary graph model;

[0029] The second construction module is used to introduce historical measurement storage nodes into the graph database and associate them with device nodes and topology nodes in the graph model, so as to perform query analysis on time series data and synchronize storage of time series state estimation results;

[0030] The third construction module is used to construct a state estimation result storage node in the graph database and associate it with the device nodes and topology nodes in the graph model, so as to associate the estimation results with the corresponding devices and topology elements and store them to obtain the final graph model.

[0031] In a third aspect, the present invention provides a computing device, comprising:

[0032] Memory and processor;

[0033] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for constructing a spatiotemporal data storage graph model are implemented.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for constructing a spatiotemporal data storage graph model.

[0035] Compared with the prior art, the present invention has the following beneficial effects: the present invention utilizes the flexibility and scalability of the graph database, adds historical data storage nodes to realize the storage and query of time series data, and associates them with graph nodes, which can comprehensively and accurately represent the spatiotemporal characteristics of the power system. By associating time series data with device nodes, the storage and query of historical data are realized to build a more comprehensive and accurate power system graph model, providing richer information for system operation status analysis, planning and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0037] Figure 1 A schematic diagram of the overall process logic of a method for constructing a spatiotemporal data storage graph model according to an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of a physical model of a power grid in a method for constructing a spatiotemporal data storage graph model according to an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of a power spatiotemporal data storage model based on a graph database in a method for constructing a spatiotemporal data storage graph model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] Example 1

[0042] Reference Figure 1-Figure 3 , as an embodiment of the present invention, provides a method for constructing a spatiotemporal data storage graph model, comprising:

[0043] S100: Acquire power system structure and equipment relationship data;

[0044] S200: Building a graph database based on the structure and device relationship data to form a preliminary graph model;

[0045] S300: Introduce historical measurement storage nodes into the graph database and associate them with device nodes and topology nodes in the graph model for synchronous storage of time series data query analysis and time series state estimation results;

[0046] S400: constructing a state estimation result storage node in the graph database and associating it with the device nodes and topology nodes in the graph model, so as to associate the estimation result with the corresponding device and topology elements and store them, thereby obtaining a final graph model.

[0047] It should be noted that the operation of the power system is not only affected by time but also by spatial location. The graph model in the above steps is represented and stored using a graph database, which helps to understand the interaction between devices in the power system through spatiotemporal correlation information.

[0048] For example, an increase in load in a certain area may cause a voltage drop in that area. By combining temporal and spatial data, the operating characteristics of the power system can be analyzed more comprehensively, improving the understanding and prediction of the system status.

[0049] In an embodiment of the present application, the power system structure and equipment relationship data in the above step S100 may include: equipment information: equipment identifier (ID), equipment type (generator, transformer, line, circuit breaker, load, etc.), equipment parameters (rated voltage, capacity, resistance, reactance, etc.); connection information: connection relationship (which device is connected to which device), connection point (bus, node, etc.); operating status: switch status (on / off), circuit breaker status and relay status, etc.

[0050] In the embodiment of the present application, the above step S100 can obtain the power system structure and equipment relationship data in the following three ways:

[0051] In the implementation mode of the present application, the third-party software system interface input method. When the required data source comes from a third-party software system, it is obtained through a database access interface, a communication protocol method, an API interface method, etc. The module will automatically identify and interpret the data format according to the acquired data, and perform data verification, and then automatically load it into the data model module.

[0052] In one feasible method, manual input is adopted. When the format of the required data source cannot be automatically recognized by the software, the data is input item by item through the manual interface or input interface, and the module will automatically load the input data into the data model module after verification.

[0053] In another feasible method, data file import method: when the required data source is in a structured file format, such as Excel format, Word format, etc., the file is imported through the file loading interface of the software, and the module will automatically identify the file type, interpret the data format, and perform data verification, and then automatically load it into the data model module.

[0054] It should be noted that the specific data content and type requirements for data access will be defined by the graph model creation module. This step is used to build a comprehensive and accurate graph model of the power system to provide richer power system operation status information.

[0055] In the embodiment of the present application, in the above step S200, a graph database is constructed based on the structure and device relationship data, including the following steps A1-A3:

[0056] A1: Each device in the system is represented by a graph node;

[0057] A2: The relationship between each device is represented by graph edges;

[0058] A3: Connect the graph nodes through the first graph edge according to the relationship between the devices.

[0059] For example, Figure 2 As shown, a graph database is used to represent and store the structure of the power system and the relationship between the devices, where circles represent graph nodes of each device (such as generators, substations, circuit breakers, etc.), and connections represent graph edges of the relationship between each device.

[0060] In another embodiment, the graph database based on the structure and device relationship data in the above step S200 can also be constructed by hierarchical modeling, that is, the graph model is constructed according to the hierarchical structure of the power system, for example, the substation is taken as a node, and then each device is further subdivided as a sub-node. In this way, the relationship between devices at different levels can be more clearly displayed.

[0061] In another embodiment, the graph database constructed based on the structure and device relationship data in the above step S200 can also be modeled through a multi-layer network, that is, a multi-layer graph model is constructed, in which each layer represents a specific aspect of the power system (such as the physical connection layer, the control layer, the information flow layer, etc.). Each layer can have different devices and relationships, and there can also be edges between layers to represent cross-layer influences.

[0062] In the embodiment of the present application, in the above step S300, the historical measurement storage node is introduced into the graph database and associated with the device node and the topology node in the graph model, including the following steps B1-B2:

[0063] B1: Create a second graph edge between the historical measurement storage node and the device node to establish an association, which is used to query and analyze the time series data;

[0064] B2: A third graph edge is created between the historical measurement storage node and the topology node to establish an association, which is used to achieve synchronous storage of the timing state estimation results.

[0065] It should be noted that storing time series information is helpful for historical data analysis, trend prediction and real-time operation status monitoring. Among them, topological nodes are used to represent the network structure of the power system, and storing spatial information is the key to understanding the power system structure, the relationship between equipment and geographical location.

[0066] In the embodiment of the present application, the above step S300 also includes setting properties inside the historical measurement storage node to store time series data of different data sampling periods.

[0067] In one implementation, the sampling period may be annual, monthly, daily, hourly, or every 15 minutes.

[0068] In the embodiment of the present application, the above step S300 also includes storing the switch state data of the connected device and the timing measurement of the running device into the historical measurement storage node, which is represented by a timestamp and a measurement value.

[0069] In one implementation, the switch state data may be binary data or data in other forms such as polymorphic data or character strings.

[0070] Specifically, the timestamp and the measurement value can be stored in the form of a [Timestamp,Value] array, where Timestamp represents the timestamp and Value represents the measurement value at the corresponding time.

[0071] In the embodiment of the present application, in the above step S400, a state estimation result storage node is constructed in the graph database and associated with the device node and the topology node in the graph model, including the following steps C1-C3:

[0072] C1: Define and determine the data structure of the state estimation results;

[0073] In one embodiment, the data structure may include status information of device nodes, such as voltage, current, power, etc., and information related to the topology structure, such as connection relationships of nodes, load conditions of lines, etc., so as to be stored corresponding to the topology structure.

[0074] C2: Constructs a state estimation result storage node based on the data structure;

[0075] It should be noted that the state estimation result storage node is specifically used to store the state estimation results, and thus associate the estimation results with the corresponding devices and topology elements.

[0076] C3: Establish association by creating a fourth graph edge between the state estimation result storage node and the device node and the topology node.

[0077] In the embodiment of the present application, the above step S400 also includes that the fourth graph edge attribute is set with relevant estimation result information.

[0078] For example, Figure 3 As shown, graph data including the introduction of historical measurement storage nodes and state estimation result storage nodes associated with device nodes and topology nodes is displayed.

[0079] In an implementable manner, the attributes of the fourth graph edge may include information such as a timestamp of the estimation result and an estimated state value.

[0080] It should be noted that an edge can represent the relationship between a device node and its corresponding topological node, so that the estimation result is stored corresponding to the topological structure.

[0081] It should also be noted that the above scheme can fully and accurately represent the spatiotemporal characteristics of the power system. This method realizes the storage and query of historical data by associating time series data with device nodes. At the same time, this method can also associate time series state estimation results with topological nodes to achieve comprehensive analysis and prediction of the operating status of the power system, providing richer information for system operating status analysis, planning and optimization.

[0082] The above is a schematic scheme of a method for constructing a spatiotemporal data storage graph model in this embodiment. It should be noted that the technical scheme of the system for constructing the spatiotemporal data storage graph model and the technical scheme of the method for constructing the spatiotemporal data storage graph model belong to the same concept. For details not described in detail in the technical scheme of the system for constructing the spatiotemporal data storage graph model in this embodiment, please refer to the description of the technical scheme of the method for constructing the spatiotemporal data storage graph model.

[0083] This embodiment also provides a system for constructing a spatiotemporal data storage graph model, including:

[0084] An acquisition module, used to acquire power system structure and equipment relationship data;

[0085] A first building module is used to build a graph database based on structure and device relationship data to form a preliminary graph model;

[0086] The second building module is used to introduce historical measurement storage nodes into the graph database and associate them with device nodes and topology nodes in the graph model for synchronous storage of time series data query analysis and time series state estimation results;

[0087] The third construction module is used to construct a state estimation result storage node in the graph database and associate it with the device nodes and topology nodes in the graph model, so as to associate the estimation results with the corresponding devices and topology elements and store them to obtain the final graph model.

[0088] This embodiment also provides a computing device suitable for constructing a spatiotemporal data storage graph model, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method for constructing a spatiotemporal data storage graph model as proposed in the above embodiment.

[0089] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for constructing a spatiotemporal data storage graph model as proposed in the above embodiment is implemented.

[0090] The storage medium proposed in this embodiment and the method for constructing a spatiotemporal data storage graph model proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0091] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for constructing a spatiotemporal data storage graph model, characterized in that: include: Obtain power system structure and equipment relationship data; Based on the structure and device relationship data, a graph database is constructed to form a preliminary graph model; Introducing historical measurement storage nodes into the graph database and associating them with device nodes and topology nodes in the graph model for synchronous storage of time series data query analysis and time series state estimation results; A state estimation result storage node is constructed in the graph database and associated with the device nodes and topology nodes in the graph model, so as to associate the estimation result with the corresponding device and topology elements and store them to obtain the final graph model.

2. The method for constructing a spatiotemporal data storage graph model according to claim 1, characterized in that: Based on the structure and device relationship data, a graph database is constructed, including: Each device in the system is represented by a graph node; The relationship between each device is represented by graph edges; The graph nodes are connected via first graph edges according to the relationship between the devices.

3. The method for constructing a spatiotemporal data storage graph model according to claim 1 or 2, characterized in that: Introducing historical measurement storage nodes into the graph database and associating them with device nodes and topology nodes in the graph model includes: By creating a second graph edge between the historical measurement storage node and the device node to establish an association, it is used to realize the query and analysis of the time series data; A third graph edge is created between the historical measurement storage node and the topology node to establish an association, so as to realize synchronous storage of the timing state estimation result.

4. The method for constructing a spatiotemporal data storage graph model according to claim 3, characterized in that: It also includes setting properties inside the historical measurement storage node to store time series data of different data sampling periods.

5. The method for constructing a spatiotemporal data storage graph model according to claim 4, characterized in that: It also includes storing the switch status data of the connected device and the timing measurement of the running device into the historical measurement storage node, represented by a timestamp and a measurement value.

6. The method for constructing a spatiotemporal data storage graph model according to claim 1 or 5, characterized in that: Constructing a state estimation result storage node in the graph database and associating it with the device node and the topology node in the graph model, including: Define and determine the data structure of state estimation results; Construct a state estimation result storage node based on the data structure; The association is established by creating a fourth graph edge between the state estimation result storage node and the device node and the topology node.

7. The method for constructing a spatiotemporal data storage graph model according to claim 6, characterized in that: It also includes that the fourth graph edge attribute is set with relevant estimation result information.

8. A system applied to the method for constructing a spatiotemporal data storage graph model according to any one of claims 1 to 7, characterized in that: include: An acquisition module, used to acquire power system structure and equipment relationship data; A first construction module is used to construct a graph database based on the structure and device relationship data to form a preliminary graph model; The second construction module is used to introduce historical measurement storage nodes into the graph database and associate them with device nodes and topology nodes in the graph model, so as to perform query analysis on time series data and synchronize storage of time series state estimation results; The third construction module is used to construct a state estimation result storage node in the graph database and associate it with the device nodes and topology nodes in the graph model, so as to associate the estimation results with the corresponding devices and topology elements and store them to obtain the final graph model.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for constructing a spatiotemporal data storage graph model according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for constructing a spatiotemporal data storage graph model as described in any one of claims 1 to 7.

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