A convergent management method and system for power generation field mass time series data management

Abstract

This invention provides a converged management method and system for managing massive time-series data in the power generation field. The method includes: constructing unit real-time databases and setting up a management center; each unit real-time database is responsible for managing a subset of time-series data; registering each unit real-time database with the management center; monitoring the status of each unit real-time database; allocating data ranges for each unit real-time database and locating data for external data access; the converged time-series data management system responds to data requests from client applications via API; the management center provides the location information of the corresponding unit real-time database and the response information for the data request; and a virtual set of measurement points provides time-series data services externally. This invention can converge multiple independent unit real-time databases into a high-capacity, high-performance time-series data management system, meeting the needs of large-scale power generation production data aggregation and management at the group level.

Description

Technical Field

[0001] This invention relates to the technical field of power generation production data management, and more specifically, to a converged management method and system for managing massive time-series data in the power generation field. Background Technology

[0002] With the development of digitalization and intelligentization in the power generation industry, the vast amount of time-series data generated during power generation is playing an increasingly important role as a valuable resource in enterprise production and operation. This data requires long-term storage and rapid retrieval, serving as the foundation for production data analysis, data mining, optimization control, and optimized management. Especially for large power generation or energy groups possessing massive amounts of data, equipment, and personnel resources, utilizing the latest information technology and methods for large-scale, in-depth production data collection and unified management at the group level is an inevitable choice for these enterprises to improve competitiveness and achieve cost reduction and efficiency improvement.

[0003] Existing time-series data management typically employs real-time historical database systems. This type of technology is generally suitable for the collection, storage, analysis, and dissemination of real-time / historical data at the plant / regional / branch level. However, when conducting large-scale, in-depth data collection at the group level and achieving data centralization, existing technologies fall short in terms of data scale, system capacity, read / write performance, and concurrency, given the demands for managing massive amounts of data, soaring data volumes, and higher demands for big data analysis.

[0004] Prior art document 1 provides a data processing method, apparatus, system, and computer-readable storage medium. The system includes: a data node cluster, a management node, and multiple agent nodes. The data node cluster includes multiple storage nodes, each storage node includes multiple storage shards, and each storage shard stores at least one type of time-series data, each type of time-series data corresponding to a data object. The management node is used to obtain at least one aggregation task and send a target aggregation task to at least one target agent node. The target aggregation task carries a target data object. The target agent node is used to send an aggregation request to a target storage node according to the target aggregation task. The aggregation request carries the target data object. The target storage node is used to obtain the target time-series data corresponding to the target data object in parallel from the target storage shard of the target storage node according to the aggregation request, and aggregate the target time-series data to obtain aggregated time-series data. This application helps improve data processing efficiency.

[0005] The prior art document 1 provides a method for parallel aggregation of time-series data, which is used to acquire time-series data from target storage nodes in parallel and aggregate it in parallel, thereby improving the efficiency of single-threaded aggregation of time-series data on Elasticsearch server cluster storage nodes. However, it does not address issues such as how the time-series data on the target storage nodes themselves is sharded or how the server cluster stores it. This method focuses primarily on acquiring and aggregating data from storage nodes in parallel, and belongs to the field of data processing. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an aggregated management method for managing massive time-series data in the power generation field. This method can aggregate multiple real-time databases into a high-capacity, high-performance time-series data management system, thus solving the problem that existing technologies cannot meet the requirements in terms of data scale, system capacity, read / write performance, and concurrency when facing the need for aggregated management of massive data at the group level.

[0007] The present invention adopts the following technical solution.

[0008] A converged management method for managing massive time-series data in the power generation field includes the following steps:

[0009] Step 1: Construct several independent unit real-time databases and set up a management center. Each unit real-time database is responsible for the time series data management of a subset, and registers each unit real-time database at the management center.

[0010] Step 2: The management center monitors the status of the real-time database of each unit;

[0011] Step 3: The management center allocates data ranges for the real-time databases of each unit and locates data for external data access;

[0012] Step 4: The API of the aggregated time series data management system responds to the data requests of the client applications. The management center provides the location information of the real-time database of the corresponding unit for the data request. The corresponding unit database provides the response information for the data request. Based on the subset of measurement points managed by each unit's real-time database, a virtual set of measurement points is formed to provide time series data services to the outside world.

[0013] Preferably, in step 1:

[0014] In a converged time-series data management system, two independent real-time database units can be treated as a single entity and managed and allocated by the management center, thus establishing a high availability mechanism.

[0015] The two independent real-time database units that establish a high availability mechanism manage the same range of measurement point subsets and have redundant data backups between them.

[0016] The management center distributes the load of data access requests within the high-availability nodes based on the node load.

[0017] Preferably, step 2 further includes:

[0018] Step 2-1: The management center establishes a sub-node status table to manage the online and offline status of the real-time databases of the units distributed on each network node;

[0019] Step 2-2: When the management center starts up, it detects the real-time database status of each unit on each node and records the real-time database status of each unit to the sub-node status table.

[0020] In steps 2-3, the management center uses a timed heartbeat detection mechanism to detect the online and offline status of the real-time database of each unit and maintain the status table of the sub-nodes.

[0021] Preferably, step 3 further includes:

[0022] Step 3-1: The management center establishes a data range allocation table to manage the data range of the unit real-time databases distributed across various network nodes;

[0023] Step 3-2: When the management center starts up, it investigates the subset of measurement points and data range of the unit real-time database management on each node and records it in the data range allocation table.

[0024] Step 3-3: When new measurement point data needs to be added to the aggregated time series data management system, the management center first allocates the data through a predetermined strategy to a certain unit's real-time database, and the management center records the data in the data range allocation table.

[0025] Preferably, sub-step 3-3 further includes:

[0026] The prefix of the measurement point name is used as a data feature, which serves as the basis for defining the data range of the measurement point subsets in the real-time database management of each unit; the measurement point subsets between the real-time databases of each unit cannot have overlapping or duplicate sets.

[0027] When the data characteristics of a newly added measuring point belong to the data range of a subset of measuring points managed by an existing unit's real-time database, it is assigned to the existing unit's real-time database.

[0028] When the data characteristics of newly added measurement points do not fall within the data range of any existing unit's real-time database management measurement point subset, they are dynamically sorted according to factors such as the load of each unit's real-time database system, database capacity, and storage space, and then allocated based on the sorting order.

[0029] Preferably, the aggregation management method for managing massive time-series data in the power generation field is characterized by:

[0030] Step 4 also includes:

[0031] Step 4-1: The client application makes a time series data access request by calling the API of the aggregated time series data management system;

[0032] Step 4-2: The API of the aggregated time series data management system first accesses the management center. The management center searches according to the data characteristics and data range allocation table of the requested data, finds the corresponding unit real-time database, and returns it to the API of the aggregated time series data management system.

[0033] Step 4-3: The aggregated time-series data management system API obtains the access results through the underlying API provided by the real-time database of this unit and returns the access results to the client application.

[0034] The present invention also provides a converged management system for managing massive time-series data in the power generation field, which utilizes the aforementioned converged management method for managing massive time-series data in the power generation field, including: a management center, a unit real-time database, and a converged time-series data management system API;

[0035] The management center is used for real-time database status monitoring and management of each unit, data range allocation, and for locating access requests from external client applications.

[0036] The unit real-time database is used for time-series data management of a subset of measurement points within the assigned range;

[0037] The API of the aggregated time-series data management system is used by client applications to access the aggregated time-series data management system. The calling interface and calling specifications are compatible with the underlying unit real-time database API.

[0038] Preferably, the management center further includes a status detection module, a data allocation module, and a load allocation module;

[0039] Among them, the status detection module is used by the management center to detect the real-time database status and heartbeat of the units on each node;

[0040] The data allocation module is used by the management center to allocate data ranges between real-time databases of various units and to locate access requests from client applications.

[0041] The load distribution module is used by the management center to distribute the load of data access requests within high-availability nodes.

[0042] Preferably, the unit real-time database type is a standalone real-time database type, and all units within the same aggregation system use the same real-time database type.

[0043] The present invention also provides a terminal, including a processor and a storage medium;

[0044] The storage medium is used to store instructions;

[0045] The processor is configured to operate according to the instructions to execute the steps of the converged management method for managing massive time-series data in the power generation field.

[0046] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the converged management method for managing massive time-series data in the power generation field.

[0047] The beneficial effects of this invention are that, compared with the prior art, the converged management method and system for managing massive time-series data in the power generation field provided by this invention can aggregate multiple independent unit real-time databases into a high-capacity, high-performance time-series data management system. By aggregating multiple unit real-time databases to form a unified converged time-series data management system, the system can be linearly expanded by adding nodes. It can meet the needs of large-scale power generation production data aggregation and management at the group level in terms of data scale, system capacity, read / write performance, and concurrency. The converged time-series data management system has a calling interface and specification compatible with the underlying unit real-time database APIs, requiring no modification or adaptation at the client application level, enabling seamless integration. Attached Figure Description

[0048] Figure 1 A flowchart illustrating the aggregation management method for managing massive time-series data in the power generation field provided by this invention;

[0049] Figure 2 This is a schematic diagram of the aggregated management system for managing massive time-series data in the power generation field, provided by the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0051] like Figure 1 As shown, this invention provides a converged management method for managing massive time-series data in the power generation field. Here, "massive" refers to data from tens of millions of measurement points, with each measurement point being the smallest unit in time-series data management. The method specifically includes the following steps:

[0052] Step 1: Construct several independent unit real-time databases and set up a management center. Each unit real-time database is responsible for the time series data management of a subset of measurement points and is registered in the management center. The management center manages each unit real-time database so that they can collaborate with each other.

[0053] The unit real-time database type can be VeStore, openplant, golden, pi or other common standalone real-time database types. Within the same aggregation system, all units should use the same real-time database type.

[0054] A unit-level real-time database is constructed based on the number of measurement point subsets. The number of unit-level real-time databases constructed is the same as the number of measurement point subsets. In this invention, there are at least two measurement point subsets, therefore, there are at least two unit-level real-time databases constructed. Each unit-level real-time database has complete ownership and management rights over the measurement point subsets it is responsible for, and possesses complete time-series data management functions. Existing data management functions do not need to be added or modified when forming a converged time-series data management system.

[0055] The original data management functions include caching, compression, indexing, archiving, and retrieval. This invention can support different types of real-time databases at the underlying level as unit real-time databases. While being used as a unit real-time database, this type of database itself does not need to be upgraded or modified, so its original data management functions can still be used.

[0056] Each unit's real-time database is registered at the management center, recording the network location information of that node. Here, "node" refers to the unit's real-time database.

[0057] Each unit's real-time database is responsible for a subset of measurement points. The subsets of measurement points managed by each unit's real-time database are aggregated to form a virtual set of measurement points, which provides time-series data management services based on the set of measurement points.

[0058] Furthermore, in the aggregated time-series data management system, two independent real-time database units can be treated as a high-availability unit group and managed and allocated by the management center to establish a high-availability mechanism. The measurement point subset data ranges managed by the two independent real-time database units that establish the high-availability mechanism are consistent, and there is data redundancy backup between them.

[0059] The management center distributes data access requests within the high-availability unit group based on the load of each node in the high-availability unit group. The aggregated time-series data management system is composed of several collaborative yet independent real-time unit databases.

[0060] Step 2: The management center monitors the status of the real-time database of each unit;

[0061] Specifically, step 2 also includes:

[0062] Step 2-1: The management center establishes a sub-node status table to manage the online and offline status of the real-time databases of the units distributed on each network node;

[0063] Step 2-2: When the management center starts up, it detects the real-time database status of each unit on each node and records the real-time database status of each unit to the sub-node status table.

[0064] In steps 2-3, the management center uses a timed heartbeat detection mechanism to detect the online and offline status of the real-time database of each unit and maintain the status table of the sub-nodes.

[0065] Step 3: The management center allocates data ranges for the real-time databases of each unit and locates data for external data access.

[0066] Specifically, step 3 also includes:

[0067] Step 3-1: The management center establishes a data range allocation table to manage the data range of the unit real-time databases distributed across various network nodes;

[0068] Step 3-2: When the management center starts up, it investigates the subset of measurement points and data range of the unit real-time database management on each node and records it in the data range allocation table.

[0069] Step 3-3: When new measurement point data needs to be added to the aggregated time series data management system, the management center first allocates the data through a predetermined strategy to a certain unit's real-time database, and the management center records the data in the data range allocation table.

[0070] Furthermore, when incorporating newly added measurement point data into management, the allocation through the management center using predetermined strategies also includes:

[0071] The prefix of the measurement point name is used as a data feature, which serves as the basis for defining the data range of the measurement point subsets in the real-time database management of each unit; the measurement point subsets between the real-time databases of each unit cannot have overlapping or duplicate sets.

[0072] When the data characteristics of a newly added measuring point belong to the data range of a subset of measuring points managed by an existing unit's real-time database, it is assigned to the existing unit's real-time database.

[0073] When the data characteristics of newly added measurement points do not fall within the data range of any existing unit's real-time database management measurement point subset, they are dynamically sorted according to factors such as the load of each unit's real-time database system, database capacity, and storage space, and then allocated based on the sorting order.

[0074] Step 4: The API of the aggregated time series data management system responds to the data requests of client applications. The management center provides the location information of the real-time database node of the unit corresponding to the data request. The unit database of the corresponding node provides the specific response information of the data request. Based on the subset of measurement points managed by each unit's real-time database, a virtual set of measurement points is formed to provide time series data services to the outside world.

[0075] Specifically, when a data access request occurs, the API of the aggregated time-series data management system first interacts with the management center to obtain the real-time database of the unit corresponding to the accessed data, and then calls the underlying API interface provided by the real-time database of that unit to obtain the access result. Step 4 also includes:

[0076] Step 4-1: The client application requests data through the API of the aggregated time-series data management system;

[0077] Step 4-2: The API of the aggregated time series data management system first accesses the management center. The management center searches according to the data characteristics and data range allocation table of the requested data, finds the corresponding unit real-time database, and returns it to the API of the aggregated time series data management system.

[0078] Step 4-3: The aggregated time-series data management system API obtains the access results through the underlying API provided by the unit's real-time database. The access results are then returned to the client application.

[0079] If the access result includes multiple data sets, they need to be assembled first, and then the assembled access result is returned to the client application.

[0080] like Figure 2 As shown, the present invention also provides a converged management system for managing massive time-series data in the power generation field. The above-mentioned converged management method for managing massive time-series data in the power generation field can be implemented based on this system. The system specifically includes: a management center 410, a unit real-time database 420, and a converged time-series data management system API 430.

[0081] Among them, the management center 410 is used for real-time database status monitoring and management of each unit, data range allocation; and for locating access requests from external client applications.

[0082] Furthermore, the management center 410 also includes a status detection module 440, a data distribution module 450, and a load distribution module 460;

[0083] The status detection module 440 is used by the management center to detect the real-time database status and heartbeat of the units on each node;

[0084] The data allocation module 450 is used by the management center to allocate data ranges between real-time databases of each unit and to locate access requests from client applications.

[0085] The load distribution module 460 is used by the management center to distribute the load of data access requests within high-availability nodes.

[0086] The unit real-time database 420 is used for time-series data management of a subset of measurement points within the assigned range;

[0087] Specifically, the unit real-time database type includes, but is not limited to, any of the following: VeStore, openplant, golden, pi, or other common standalone real-time database types. Within the same aggregation system, all units should use the same real-time database type.

[0088] The Aggregated Time Series Data Management System API 430 is used for client applications to access the Aggregated Time Series Data Management System. The calling interface and calling specifications are compatible with the underlying unit real-time database API.

[0089] To verify the practical application of the present invention, the aggregation management method for managing massive time-series data in the power generation field proposed in this invention is illustrated with the following examples:

[0090] As shown in Table 1 below, Table 1 is a sample table of the sub-node status table. The online or offline status of the unit real-time database distributed on each network node can be obtained through the sub-node status table.

[0091] Table 1: Example Table of Child Node States

[0092]

[0093]

[0094] As shown in Table 1, nodes N1, N3, and N4 are online, while node N2 is offline. IP and Port represent the IP address and port number of the machine where the database of each unit is located, and State indicates whether the node is online or offline.

[0095] As shown in Table 2 below, Table 2 is a sample table for data range allocation:

[0096] Table 2: Sample Table of Data Range Allocation

[0097] Node Table N1 T1~T7 N2 T8~T14 N3 T15 N4 T15

[0098] As shown in Table 3 below, Table 3 is a sample table omitting the measurement point categories after T7 to T15:

[0099] Table 3: Sample Table of Measurement Point Data Feature Classification

[0100] Table features T1 A T2 B T3 C T4 D T5 E T6 F … …

[0101] Based on Table 3 and the measurement points that each node is responsible for, the measurement points in Table 3 are assigned to each node.

[0102] Assume that the real-time database of node N1 is responsible for measurement points whose prefixes start with A to G; and the real-time database of node N2 is responsible for measurement points whose prefixes start with H to N.

[0103] As can be seen from Tables 2 and 3: For example, if a new measurement point is added with the name "Axxx", the management center will determine that the point belongs to a subset of existing measurement points of node N1, and then include the point in the management of node N1.

[0104] For example, if a new measurement point is added and named "Pxxx", the management center determines, based on a predetermined strategy, that this point does not belong to any subset of existing node measurement points, and then assigns it according to the predetermined strategy.

[0105] The data is dynamically sorted according to factors such as the real-time database system load, database capacity, and storage space of each unit. The data is then allocated based on the sorting order, with node N3 being the first in the sorting. This node is then assigned to node N3 for management and recorded in the data range allocation table.

[0106] When a data access request occurs, such as accessing the data of measurement point "Axxx", the management center searches according to the data characteristics and data range allocation table of the requested data, finds the corresponding unit real-time database N1, and returns it to the system API. Then the system API obtains the access result through the underlying API provided by the N1 real-time database.

[0107] The beneficial effects of this invention are that, compared with the prior art, this invention aggregates multiple independent real-time database units into a large-capacity, high-performance time-series data management system. The system scale can be linearly expanded, and it can meet the needs of group-level massive-scale power generation production data aggregation and management in terms of measurement point scale, data capacity, read and write performance, and concurrency. No modification or adaptation is required at the client application level, achieving seamless integration.

[0108] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0109] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0110] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0111] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0112] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0113] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0114] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A convergent management method for massive time series data management in the field of power generation, characterized in that, Includes the following steps: Step 1: Construct several independent unit real-time databases and set up a management center. Each unit real-time database is responsible for the time series data management of a subset, and registers each unit real-time database at the management center. Step 2: The management center monitors the status of the real-time database of each unit; Step 3: The management center allocates data ranges for the real-time databases of each unit and locates data for external data access; Step 3 also includes: Step 3-1: The management center establishes a data range allocation table to manage the data range of the unit real-time databases distributed across various network nodes. Step 3-2: When the management center starts up, it investigates the subset of measurement points and data range of the unit real-time database management on each node and records it in the data range allocation table. Step 3-3: When new measurement point data needs to be added to the management system in the aggregated time series data management system, the management center first allocates the data through a predetermined strategy and assigns it to a certain unit's real-time database. The management center records this data in the data range allocation table. Step 3-3 further includes: The prefix of the measurement point name is used as a data feature, serving as the basis for defining the data range of the measurement point subsets in the real-time database management of each unit; the measurement point subsets between the real-time databases of each unit cannot have overlapping or duplicate sets. When the data characteristics of a newly added measuring point belong to the data range of a subset of measuring points managed by an existing real-time database of a certain unit, it is assigned to the existing real-time database of the unit. When the data characteristics of newly added measurement points do not fall within the data range of any existing unit's real-time database management measurement point subset, they are dynamically sorted according to the factors of each unit's real-time database system load, database capacity, and storage space, and then allocated based on the sorting order. Step 4: The API of the aggregated time series data management system responds to the data requests of the client applications. The management center provides the location information of the real-time database of the corresponding unit for the data request. The corresponding unit database provides the response information for the data request. Based on the subset of measurement points managed by each unit's real-time database, a virtual set of measurement points is formed to provide time series data services to the outside world.

2. The aggregation management method for managing massive time-series data in the power generation field according to claim 1, characterized in that, In step 1: In a converged time-series data management system, two independent real-time database units can be treated as a single entity and managed and allocated by the management center, thus establishing a high availability mechanism. The two independent real-time database units that establish a high availability mechanism manage the same range of measurement point subsets and have redundant data backups between them. The management center distributes the load of data access requests within the high-availability nodes based on the node load.

3. The aggregation management method for managing massive time-series data in the power generation field according to claim 1, characterized in that, Step 2 also includes: Step 2-1: The management center establishes a sub-node status table to manage the online and offline status of the real-time databases of the units distributed on each network node; Step 2-2: When the management center starts up, it detects the real-time database status of each unit on each node and records the real-time database status of each unit to the sub-node status table. In steps 2-3, the management center uses a timed heartbeat detection mechanism to detect the online and offline status of the real-time database of each unit and maintain the status table of the sub-nodes.

4. The aggregation management method for managing massive time-series data in the power generation field according to claim 1, characterized in that, Step 4 also includes: Step 4-1: The client application makes a time series data access request by calling the API of the aggregated time series data management system; Step 4-2: The API of the aggregated time series data management system first accesses the management center. The management center searches according to the data characteristics and data range allocation table of the requested data, finds the corresponding unit real-time database, and returns it to the API of the aggregated time series data management system. Step 4-3: The aggregated time-series data management system API obtains the access results through the underlying API provided by the real-time database of this unit and returns the access results to the client application.

5. A converged management system for managing massive time-series data in the power generation field, utilizing the converged management method for managing massive time-series data in the power generation field as described in any one of claims 1-4, characterized in that, include: Management center, unit real-time database and aggregated time-series data management system API; The management center is used for real-time database status monitoring and management of each unit, data range allocation, and for locating access requests from external client applications. The unit real-time database is used for time-series data management of a subset of measurement points within the assigned range; The API of the aggregated time-series data management system is used by client applications to access the aggregated time-series data management system. The calling interface and calling specifications are compatible with the underlying unit real-time database API.

6. The aggregation management system for managing massive time-series data in the power generation field according to claim 5, characterized in that, The management center also includes a status detection module, a data allocation module, and a load allocation module; Among them, the status detection module is used by the management center to detect the real-time database status and heartbeat of the units on each node; The data allocation module is used by the management center to allocate data ranges between real-time databases of various units and to locate access requests from client applications. The load distribution module is used by the management center to distribute the load of data access requests within high-availability nodes.

7. The aggregation management system for managing massive time-series data in the power generation field according to claim 5, characterized in that, The unit's real-time database type is a standalone real-time database type, and all units within the same aggregation system use the same real-time database type.

8. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the aggregation management method for managing massive time-series data in the power generation field according to any one of claims 1-4.

9. A computer readable storage medium having stored thereon a computer program, characterized in that When executed by the processor, the program implements the steps of the aggregation management method for managing massive time-series data in the power generation field as described in any one of claims 1-4.

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