Method for distributed data management and computing device

Abstract

The invention provides a method for distributed data management and computing equipment. The method comprises the steps that independent single nodes are configured in an energy storage cloud system, a database cluster is divided into at least one single node configuration, and any single node independently operates an instance and stores different types of data; creating a configuration table, wherein the configuration table is used for managing the mapping relationship between the stored single node and the table name of each data object; and writing the mapping relation into a relation library according to the configuration table for providing data relation information during subsequent data management operation, thereby realizing load balancing by specifying a storage node according to a load condition. According to the technical scheme, the flexibility of data fragmentation can be improved, the resource utilization rate is optimized, management is simplified, and the data writing efficiency and query efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage, and particularly to a method for distributed data management and a computing device. Background Art

[0002] With the rapid development of energy storage technology, large-scale energy storage systems play an increasingly important role in scenarios such as power grid stability, renewable energy grid connection, demand response, and power peak shaving. These energy storage systems generate a large amount of real-time operation data, such as battery voltage, current, temperature, charge and discharge status, energy storage capacity, etc. In order to effectively manage this data and improve the performance and lifespan of energy storage systems, efficient data acquisition, storage, analysis, and remote monitoring are crucial.

[0003] Currently, the energy storage cloud system uses a Cassandra cluster as the data storage backend to manage and store a large amount of time-series data through the cluster. The cluster architecture has high scalability, but there are problems such as low write and query efficiency, insufficient flexibility in data sharding, resource waste when the data volume is small, and high management complexity.

[0004] Therefore, a technical solution is needed that can improve the flexibility of data sharding, optimize resource utilization, simplify management, and improve data write and query efficiency. Summary of the Invention

[0005] The present invention aims to provide a method for distributed data management and a computing device that can improve the flexibility of data sharding, optimize resource utilization, simplify management, and improve data write and query efficiency.

[0006] According to one aspect of the present invention, a method for distributed data management is provided. The method includes:

[0007] Configure independent single nodes in the energy storage cloud system, divide the database cluster into at least one single-node configuration, and any one of the single nodes runs an independent instance to store different types of data;

[0008] Create a configuration table, which is used to manage the mapping relationship between the single node storing each data object and the table name;

[0009] Write the mapping relationship into the relational database according to the configuration table to provide data relationship information during subsequent data management operations, so as to achieve load balancing by specifying storage nodes according to the load situation.

[0010] According to some embodiments, the database cluster is a Cassandra cluster.

[0011] According to some embodiments, the configuration table includes:

[0012] The data identifier, the Internet of Things device number, is a keyword that uniquely identifies each data type;

[0013] The node address indicates the target node where the data is stored;

[0014] The table name is the primary key that uniquely identifies the table name where the data is stored;

[0015] The timestamp records the creation or update time of the data.

[0016] According to some embodiments, when the system is initialized, the data in the configuration table is loaded into the cache of the memory library;

[0017] When the data in the configuration table is updated, the system automatically detects the change in the configuration table and reloads the cache of the memory library.

[0018] According to some embodiments, the data management operations include: data storage, data reading, and data writing.

[0019] According to some embodiments, the data reading includes:

[0020] The management server responds to the data reading instruction issued by the user terminal;

[0021] According to the configuration table, find the table name and storage node information corresponding to the data to be read in the cache of the memory library;

[0022] The management server determines the target storage server node according to the storage node information corresponding to the data to be read;

[0023] Read the data to be read from the target storage server node according to the table name corresponding to the data to be read.

[0024] According to some embodiments, the data writing includes:

[0025] The Internet of Things device sends the data to be written to the management server;

[0026] The management server finds the table name and storage node information corresponding to the data to be written from the cache of the memory library through the configuration table;

[0027] Write the data to be written to the corresponding target storage server node.

[0028] According to some embodiments, during the process of data reading and data writing, if the relevant table name and storage node information are not found in the cache of the memory library, the management server then accesses the relational database, obtains the relevant table name and storage node information from the relational database, and updates the cache.

[0029] According to another aspect of the present invention, there is provided a computer program product including a computer program which, when executed by a processor, implements the method described in any one of the above.

[0030] According to another aspect of the present invention, there is provided a computing device including:

[0031] a processor; and

[0032] a memory storing a computer program which, when executed by the processor, implements the method described in any one of the above.

[0033] According to an embodiment of the present invention, an independent single node is configured in an energy storage cloud system, a configuration table is created to manage the mapping relationship between the single node storing each data object and the table name, the mapping relationship in the configuration table is written into a relational database, and data relationship information is provided according to the configuration table when performing data management operations. By using single-node sharding to store data and referring to the configuration table to manage data mapping relationships, the present invention can improve the flexibility of data sharding, optimize resource utilization, simplify management, and enhance data writing and querying efficiency.

[0034] According to some embodiments, by creating a configuration table, the configuration information of the system can be centralized and managed. This not only simplifies the configuration process but also makes the configuration information easy to update and maintain. The independent single-node configuration allows the system to quickly adjust settings according to different requirements. When certain parameters need to be changed or new functions need to be added, only the configuration table needs to be modified without making large-scale changes to the entire system.

[0035] According to some embodiments, the existence of the configuration table enables the system to more easily adapt to possible future changes and expansions. New data relationships can be achieved by simply updating the configuration table without redesigning the database structure. Performing data management operations according to the configuration table can optimize the data processing flow, reduce redundant steps, and thus improve the efficiency of data reading and writing operations.

[0036] According to some embodiments, in the case of performing data relationship mapping based on the configuration table, all data-related operations follow consistent rules, which helps to ensure data consistency and integrity. When problems occur, the clear configuration table can help locate the problem faster. It provides a clear direction for troubleshooting and also facilitates the debugging work.

[0037] According to some embodiments, the single-node configuration can, to a certain extent, relieve the pressure on the main server. Especially in high-concurrency situations, it can independently process some requests and improve the system's response speed. The independent node can implement more strict access control policies to restrict access to sensitive configuration information and protect the system from unauthorized operations.

[0038] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the accompanying drawings required for the description of the embodiments.

[0040] Figure 1 A flowchart showing a method for distributed data management according to an exemplary embodiment.

[0041] Figure 2 A schematic diagram showing a system architecture according to an exemplary embodiment.

[0042] Figure 3 A block diagram showing a computing device according to an exemplary embodiment. DETAILED DESCRIPTION

[0043] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. Like reference numerals in the figures denote like or similar parts, and thus their repetitive description will be omitted.

[0044] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present invention. However, those skilled in the art will realize that the technical solutions of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be used. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present invention.

[0045] The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0046] The flowcharts shown in the drawings are merely exemplary illustrations and do not necessarily include all the content and operations / steps, nor do they necessarily execute in the described order. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0047] It should be understood that although terms such as first, second, and third may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Thus, the first component discussed below may be referred to as the second component without departing from the teachings of the inventive concept. As used herein, the term "and / or" includes any one of the associated listed items and all combinations of one or more of them.

[0048] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of the relevant data need to comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entrances are provided for the user to choose to authorize or refuse.

[0049] Those skilled in the art can understand that the drawings are only schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily essential for implementing the present invention, and thus cannot be used to limit the protection scope of the present invention.

[0050] In a Cassandra cluster, when data is written, a hash calculation is performed on the data to obtain a key value, and the data is stored on the corresponding node according to the key value matching relationship. Data sharding is automatically performed based on the consistent hashing algorithm, and the storage location and distribution of the data are automatically managed by the system, and users cannot flexibly control the specific storage nodes of the data. For specific data or business scenarios, it is difficult to achieve personalized data distribution and load optimization.

[0051] In the case of a small amount of data, the Cassandra cluster will cause waste of server resources and cannot fully utilize the computing and storage capabilities of a single node. The cluster requires specialized configuration and operation and maintenance, especially in terms of data replication and synchronization between multiple nodes, which increases the complexity of system management. In a multi-node environment, data queries need to be coordinated across multiple nodes, which may bring additional network latency.

[0052] Therefore, the present invention proposes a method for distributed data management, which can improve the flexibility of data sharding, optimize resource utilization, simplify management, and improve data writing efficiency and query efficiency.

[0053] Before describing the embodiments of the present invention, some terms or concepts related to the embodiments of the present invention are explained.

[0054] Cassandra cluster: Cassandra is a hybrid non-relational open-source distributed NoSQL database system used to store simple format data such as inboxes.

[0055] The exemplary embodiments of the present invention will be described below with reference to the accompanying drawings.

[0056] Figure 1 A method flowchart of distributed data management according to an exemplary embodiment is shown.

[0057] Refer to Figure 1 , in S101, an independent single node is configured in the energy storage cloud system.

[0058] According to some embodiments, the Cassandra cluster is divided into at least one single node configuration; any one of the single nodes runs an independent instance and stores different types of data.

[0059] According to some embodiments, based on the efficient data writing and reading method of a single node distributed architecture, the original Cassandra cluster is transformed into multiple independent single node configurations, and combined with the management method of the configuration table, the directional writing and reading of data are realized. Each single node instance independently processes specific types of data, avoiding the overhead caused by multi-node data replication and consistency verification in traditional clusters.

[0060] The present invention improves the efficiency of data writing and reading by reducing cross-node synchronization and coordination operations, and is particularly suitable for Internet of Things scenarios that require quick response. By transforming the Cassandra cluster into a distributed architecture of multiple single node instances, more efficient resource utilization is achieved. Each single node runs independently, avoiding the problem of multi-node resource waste in traditional clusters and reducing the hardware and operation and maintenance costs of the Internet of Things platform. The management of data storage nodes is simplified. Through the centralized management of the configuration table, the complexity of multi-node coordination and load balancing is reduced, making the system more adaptable to dynamically changing business requirements.

[0061] According to some embodiments, the energy storage cloud system is an integrated management platform based on cloud computing technology, which is used for remote monitoring, management, and optimization of large-scale distributed energy storage systems. It connects energy storage devices to the cloud platform to realize functions such as data collection, storage, analysis, and remote control. The energy storage cloud system usually integrates Internet of Things, cloud computing, big data analysis, and artificial intelligence technologies, and is an important part of modern energy management and smart grids.

[0062] The energy storage cloud system can integrate multiple geographically dispersed energy storage systems onto a unified platform for management, reducing operation and maintenance costs and improving management efficiency. Through in-depth analysis of the operation data of the energy storage system, the energy storage cloud system can help optimize the charge and discharge strategies of the energy storage devices, extend the service life of the devices, and improve the overall energy efficiency. It can also monitor the device status in real time and, through an intelligent alarm mechanism, notify the operation and maintenance personnel in a timely manner when a device fails or its performance is abnormal, reducing the downtime caused by failures. As the scale of the energy storage system expands, the cloud platform can easily increase its data processing and storage capabilities to accommodate more device access and data traffic.

[0063] In S103, a configuration table is created, which is used to manage the mapping relationship between the storage single nodes and table names of each data object.

[0064] According to some embodiments, the configuration table includes: a data identifier, an Internet of Things device number, which is a keyword uniquely identifying each data type; a node address, indicating the target node where the data is stored; a table name, which is the primary key uniquely identifying the table name storing the data; and a timestamp, recording the creation or update time of the data.

[0065] During system initialization, the data in the configuration table is loaded into the cache of the memory library; when the data in the configuration table is updated, the system automatically detects the change in the configuration table and reloads the cache of the memory library.

[0066] According to some embodiments, the configuration table design includes: data_key, that is, the data identifier, which is a keyword uniquely identifying each data type key, and this is the device id; node_address, the node address, indicating the target node where the data is stored; table_name, the table name, which is the id of the table name storing the data and serves as the primary key, uniquely identifying it; and timestamp, the time stamp, recording the creation or update time of the data.

[0067] For an example of the core content of the configuration table, see Table 1. Among them, id is the primary key, uniquely identifying each configuration record, and the device id is used to identify the device associated with the Cassandra node. The system creates the devices of each station in advance. The energy storage devices are fixed. If new devices are added later, the configuration must be updated first, otherwise the newly added devices cannot be queried. Once a device is created, its unique identifier remains unchanged, and subsequent queries will query the configuration table based on the device's unique identifier.

[0068] Table 1

[0069] Device ID Device 01 ID 1 Node IP 192.168.0.101 Timestamp XX Device ID Device 01 ID 2 Node IP 192.168.0.101 Timestamp XX Device ID Device 02 ID 3 Node IP 192.168.0.102 Timestamp XX Device ID Device 03 ID 4 Node IP 192.168.0.103 Timestamp XX

[0070] By introducing a configuration table, users can flexibly select the storage nodes and table names of data according to business requirements, rather than relying on the traditional Cassandra consistent hashing for automatic sharding. This method allows users to customize the storage location of data, thereby achieving high controllability of data sharding and business customization. This strategy improves the flexibility of data sharding and storage management, enabling the system to optimize the data storage path according to actual needs and enhancing the overall performance.

[0071] In S105, the configuration table writes the mapping relationship into the relational database, which is used to provide data relationship information during subsequent data management operations.

[0072] According to some embodiments, the data management operations include data storage, data reading, and data writing. During the data reading and writing processes, if the relevant table name and storage node information are not found in the cache of the memory database, the management server then accesses the relational database, obtains the relevant table name and storage node information from the relational database, and updates the cache.

[0073] The management server responds to a data reading instruction issued by the user terminal; according to the configuration table, finds the table name and storage node information corresponding to the data to be read in the cache of the memory database; the management server determines the target storage server node according to the storage node information corresponding to the data to be read; reads the data to be read from the target storage server node according to the table name corresponding to the data to be written.

[0074] The Internet of Things device sends the data to be written to the management server; the management server finds the table name and storage node information corresponding to the data to be written from the cache of the memory database through the configuration table; writes the data to be written to the corresponding target storage server node.

[0075] According to some embodiments, during system initialization, the data in the configuration table is loaded into the cache. The cache is used to quickly query the storage nodes and table names of data objects, thereby accelerating data writing and querying. When the data in the configuration table is updated, such as adding or modifying the data storage path, the system will automatically detect the change in the configuration table and reload the cache to keep the information in the cache consistent with the configuration table.

[0076] According to the data type or business requirements of the Internet of Things device, the system first looks up the storage node of the target data object in the cache and writes the data to the corresponding Cassandra single-node instance. If the relevant information is not found in the cache, the system looks up the corresponding record in the configuration table and updates the cache for subsequent quick query. When writing, directly obtain the data storage location from the cache, without frequently accessing the configuration table database, avoiding cross-node data replication and consistency verification, and significantly reducing the writing latency.

[0077] Obtain node information through the configuration table, making the data sharding process more flexible and controllable. Since in a traditional cluster, data is fixed on a certain node because the hash calculation based on the device ID always yields the same result, so the data is always on a certain determined node. Therefore, according to business requirements or device types, the storage nodes and sharding strategies of data can be dynamically adjusted, thus avoiding the fixity of the traditional Cassandra consistent hashing sharding.

[0078] The design of the configuration table makes the storage path of each data object clear and controllable, not only simplifying the complexity of data management, but also making the distribution and query of data clearer. Managers can flexibly specify storage nodes according to different data load conditions, thus achieving a better load balancing effect.

[0079] Different from the possible frequent data migrations in a traditional multi-node architecture, the method based on the configuration table and cache can directly determine the final storage location of data when writing data, thus reducing the transmission of data between different nodes and improving the overall writing efficiency.

[0080] When querying, the system first looks for the single-node instance where the target data is located in the cache, and then directs to the corresponding node to read the data. If the relevant information is not found in the cache, the system obtains the storage location of the data from the configuration table and synchronously updates the cache for subsequent queries. The cache accelerates the query process of the data storage location, enabling the system to quickly locate the node where the data is located, avoiding the coordination between multiple nodes and the cross-node data aggregation process, and further reducing the query latency.

[0081] A relational database, that is, a relational database, is a data storage system based on the relational model, where data is organized into tables in the form of rows and columns. Each table contains records (rows) and fields (columns), and the tables can be associated through key values. Data is managed and operated through SQL (Structured Query Language). The data in a relational database is stored in the form of tables, suitable for highly structured data, supporting ACID (Atomicity, Consistency, Isolation, and Durability) characteristics, and applicable to scenarios with high requirements for data consistency such as finance.

[0082] A memory database, also known as an in-memory database, is a database system that stores data in the main memory (RAM) of a server. Different from traditional disk-based databases, in-memory databases store data entirely in memory, thus significantly improving the speed and performance of data access. Since data is directly read and written in memory, the data access speed is much higher than that of traditional disk databases. In-memory databases are often used in application scenarios that require low latency and high throughput, such as real-time analytics, financial transactions, caching, etc. To ensure that data is not lost in case of power failure or system crash, in-memory databases usually provide logging or periodic data snapshot functions to achieve data persistence.

[0083] According to some embodiments, querying the memory database can significantly reduce the number of accesses to the relational database, reduce I / O operations, and improve query speed. If directly querying the table, each time it is necessary to access the relational database, which may lead to database performance bottlenecks, while caching can relieve the load pressure on the relational database. In the case where the relational database is temporarily unavailable, the memory database can provide a certain degree of degraded service to ensure the availability and continuity of the system. After caching the table name and storage node information, it can be directly read from the memory database without the need to repeatedly parse and calculate data each time, saving system resources and improving the concurrent processing ability. The high throughput and low latency performance of the memory database can meet the needs of a large number of concurrent requests, while the performance of the relational database may not be ideal in high-concurrency scenarios.

[0084] User terminals generally refer to devices in a computer network. These devices are the endpoints of the network and are used to interact with the system or perform specific tasks.

[0085] Compared with the automatic sharding method of traditional Cassandra consistent hashing, data sharding controllability is achieved through the configuration table. Users can customize the storage location of data according to data characteristics, business requirements, etc., so as to better optimize storage and query performance. The configuration table greatly simplifies the management of data storage nodes. By directly managing the data storage paths of each node, the coordination and synchronization overhead in a multi-node environment are reduced, and the operation and maintenance complexity is lowered. By reducing the number of cross-node data queries, the system query latency is reduced, which is especially suitable for the Internet of Things scenarios with high requirements for response speed. By reducing the dependence on the cluster, the optimization of server hardware and operation and maintenance costs is achieved. Through the management of the configuration table, the addition or reduction of data storage nodes is allowed to be more flexible, facilitating adjustment according to business requirements.

[0086] Figure 2 Shows a schematic diagram of the system architecture according to an example embodiment.

[0087] See Figure 2 , Figure 2The system architecture is shown. The present invention transforms the original Cassandra cluster into n independent single-node configurations, and each node runs a Cassandra instance independently for storing different types of data. A configuration table is introduced to manage the mapping relationship between the storage nodes and table names of each data object, and a memory library is used to cache the information of the configuration table.

[0088] The Cassandra cluster requires specialized configuration and operation and maintenance, especially in terms of data replication and synchronization among multiple nodes, which increases the complexity of system management. In a multi-node environment, data queries need to be coordinated across multiple nodes. Before storage, data will first perform a hash calculation for the token range (Token range), and then be placed on the corresponding nodes according to the Token range of the data. For example, when querying a large amount of data, data aggregation across multiple nodes is required before the data can be returned. The mechanism of the cluster itself is relatively slow in this process.

[0089] The present invention provides a method for implementing distributed Internet of Things data storage management based on a Cassandra single node. By transforming the original Cassandra cluster architecture into n independent single-node configurations, where n≥1, and introducing a configuration table to manage data storage nodes and table names, the flexibility of data sharding is improved, resource utilization is optimized, management is simplified, and data writing and query efficiency are enhanced. The original cluster is divided into multiple separate nodes, and the configuration table is used to determine which data is on which node, and the same business data is placed on one node. The existence of the configuration table solves both the load balancing of multiple nodes and the problem that the same batch of business data is on one node, resulting in a significant improvement in query speed.

[0090] Each single node in the system corresponds to a storage server, and the storage server node is used to store data; N Internet of Things devices are used to generate time-series data and write data to each storage server node; staff can initiate instructions on the user terminal, such as a modification instruction to modify the configuration table and a read instruction to read a certain data; the relational database and the memory database are two databases of the management server. The relational database of the management server stores the configuration table, and in response to the user's modification instruction for the configuration table, the relevant data of the configuration table can be modified. The memory database of the management server has a cache of the configuration table.

[0091] The following describes a data reading process. The user issues a data reading instruction for data A on the user terminal; the management server responds to the data reading instruction and finds the table name and storage node information corresponding to the data A from the cache of the memory database; the management server determines the target storage server node according to the storage node information, and then reads the data A from the target storage server node according to the table name, and feeds back the data A to the user terminal to complete the data reading process.

[0092] A data writing process is described below. When the IoT device generates data B, the IoT device sends data B to the management server. The management server finds the table name and storage node information corresponding to the data B from the cache. The data B is stored in the target storage server node to complete the data writing process.

[0093] During the data reading and data writing processes, if the relevant table name and storage node information are not found in the cache of the memory library, the management server then accesses the relational database to obtain the relevant table name and storage node information from the relational database and updates the cache.

[0094] When there is more data stored in node A and less data stored in node B, the nodes corresponding to the data of different service requirements can be changed subsequently, and the data generated subsequently is allocated according to the new configuration table.

[0095] Figure 3 A block diagram of a computing device according to an exemplary embodiment is shown.

[0096] As Figure 3 shown, the computing device 30 includes a processor 12 and a memory 14. The computing device 30 may also include a bus 22, a network interface 16, and an I / O interface 18. The processor 12, the memory 14, the network interface 16, and the I / O interface 18 may communicate with each other through the bus 22.

[0097] The processor 12 may include one or more general-purpose CPUs (Central Processing Unit), microprocessors, or application-specific integrated circuits, etc., for executing relevant program instructions. According to some embodiments, the computing device 30 may further include a high-performance display adapter (GPU) 20 for accelerating the processor 12.

[0098] The memory 14 may include a machine system-readable medium in the form of volatile memory, such as random access memory (RAM), read-only memory (ROM), and / or cache memory. The memory 14 is used to store one or more programs containing instructions and data. The processor 12 can read the instructions stored in the memory 14 to execute the methods according to the embodiments of the present invention above.

[0099] The computing device 30 can also communicate with one or more networks through the network interface 16. The network interface 16 can be a wireless network interface.

[0100] The bus 22 may include an address bus, a data bus, a control bus, etc. The bus 22 provides a path for exchanging information between the components.

[0101] It should be noted that, in the specific implementation process, the computing device 30 may further include other components necessary for normal operation. In addition, those skilled in the art can understand that the above devices may also only include the components necessary to implement the solution of the embodiments of this specification, and do not necessarily include all the components shown in the figures.

[0102] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), network storage devices, cloud storage devices, or any type of medium or device suitable for storing instructions and / or data.

[0103] The embodiments of the present invention also provide a computer program product, which includes a computer program, and the computer program can be operated to enable a computer to execute some or all of the steps of any one of the methods described in the above method embodiments.

[0104] Those skilled in the art can clearly understand that the technical solution of the present invention can be realized by means of software and / or hardware. The "units" and "modules" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, and the hardware can be, for example, a field programmable gate array, an integrated circuit, etc.

[0105] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0106] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] In several embodiments provided by the present invention, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some service interfaces. The indirect couplings or communication connections of devices or units can be in electrical or other forms.

[0108] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0110] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present invention.

[0111] In the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] The above specifically shows and describes the exemplary embodiments of the present invention. It should be understood that the present invention is not limited to the detailed structures, setting methods, or implementation methods described here; on the contrary, the present invention is intended to cover various modifications and equivalent settings included within the spirit and scope of the appended claims.

Claims

1. A method for distributed data management, the method comprising: Configure an independent single node in the energy storage cloud system, divide the database cluster into at least one single node configuration, and any of the single nodes independently runs an instance to store different types of data; Create a configuration table, where the configuration table is used to manage the mapping relationship between a single node and a table name for storing each data object; The mapping relationship is written into the relationship library according to the configuration table, so as to provide data relationship information in subsequent data management operations, thereby achieving load balancing by specifying storage nodes according to load conditions.

2. The method according to claim 1, characterized in that include: The database cluster is a Cassandra cluster.

3. The method according to claim 1, characterized in that: The configuration table includes: Data identifier, a keyword that uniquely identifies each data type; Node address, indicating the target node for data storage; Table name, the primary key that uniquely identifies the table name where data is stored; Timestamp, which records the time when the data is created or updated.

4. The method according to claim 1, characterized in that Also includes: When the system is initialized, the data in the configuration table is loaded into the cache of the memory bank; When the data in the configuration table is updated, the system automatically detects the change in the configuration table and reloads the cache of the memory bank.

5. The method according to claim 1, characterized in that Data management operations include: data storage, data reading, and data writing.

6. The method according to claim 5, characterized in that The data reading includes: The management server responds to the data reading instruction issued by the user terminal; According to the configuration table, find the table name and storage node information corresponding to the data to be read in the cache of the memory bank; The management server determines a target storage server node according to the storage node information corresponding to the data to be read; The data to be read is read from the target storage server node according to the table name corresponding to the data to be read.

7. The method according to claim 5, characterized in that The data writing includes: The Internet of Things device sends the data to be written to the management server; The management server finds the table name and storage node information corresponding to the data to be written from the cache of the memory bank through the configuration table; The data to be written is written to the corresponding target storage server node.

8. The method according to claim 5, characterized in that Also includes: During data reading and writing, if the relevant table name and storage node information are not found in the cache of the memory library, the management server accesses the relationship library again, obtains the relevant table name and storage node information from the relationship library, and then updates the cache.

9. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 8 when being executed by a processor.

10. A computing device, characterized in that include: processor; as well as A memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 8 is implemented.

Images