Data processing method and equipment of energy storage system, medium and data processing system

Abstract

The invention discloses a data processing method and device of an energy storage system, a medium and a data processing system.The data processing method of the energy storage system comprises the steps that request parameters of a target energy storage system are obtained; a target database and connection information of the target database are determined according to the request parameter, collected data of the target energy storage system are stored in multiple different types of databases according to data types, and the target database is one of the multiple different types of databases; and obtaining data of the request parameter corresponding to the target energy storage system according to the target database and the connection information. According to the method, various types of databases can be flexibly accessed, data access and processing of the energy storage system under the multivariate database can be realized, data support can be provided for richer technical routes, the flexibility is high, the expansibility is high, and the system performance can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage, and in particular to a data processing method of an energy storage system, an electronic device, a computer readable storage medium and a data processing system. BACKGROUND

[0002] With the rapid development of energy storage technology, the demand for scale expansion, diverse application scenarios, rich technical routes and economy of energy storage systems is increasing. The traditional energy storage system has technical limitations, poor flexibility, limited expansion and possible low performance problems, and will not be able to meet the needs of the development of energy storage systems, and needs to be improved. SUMMARY

[0003] The present application aims to at least solve one of the technical problems in the prior art. To this end, a first object of the present application is to provide a data processing method of an energy storage system, which can flexibly access multiple types of databases, can realize data access and processing of the energy storage system based on multiple databases, can provide data support for more diverse technical routes, has high flexibility, high expansibility and can improve system performance.

[0004] A second object of the present application is to provide an electronic device.

[0005] A third object of the present application is to provide a computer readable storage medium.

[0006] A fourth object of the present application is to provide a data processing system.

[0007] In order to achieve the above-mentioned objects, the data processing method of the energy storage system according to the first aspect of the present application comprises: obtaining a request parameter of a target energy storage system; determining a target database and connection information of the target database according to the request parameter, wherein the collected data of the target energy storage system is stored in multiple different types of databases according to data types, and the target database is one of the multiple different types of databases; obtaining data corresponding to the request parameter of the target energy storage system according to the target database and the connection information.

[0008] The data processing method of the energy storage system according to the embodiment of the present application is based on a multi-element database, determines a target database from a plurality of different types of databases according to a request parameter of a target energy storage system, and determines connection information of the target database, and then accurately obtains data corresponding to the request parameter from the target database, so as to realize data access and processing of the energy storage system under the multi-element database. The method is based on the collaborative deployment of a plurality of types of databases, breaks the limitation of a single database structure, and enables different types of collected data to be stored and flexibly retrieved on demand. A plurality of different types of databases can provide data support for more technical routes, support for switching databases for more flexible application scenarios, and support for a plurality of databases for large-scale data or high-concurrency access. Therefore, the method of the present application has good flexibility, expansibility and high-performance processing capability, and can meet the data processing needs of the energy storage system under diversified technical routes and complex application scenarios.

[0009] In some embodiments, the request parameter includes a requested data type and a plant identification parameter of a plant station where the target energy storage system is located; determining the target database and the connection information of the target database according to the request parameter includes: determining a target database type according to the data type, and determining a target plant station according to the plant identification parameter; determining a database corresponding to the target database type of the target plant station as the target database, and obtaining the connection information of the target database.

[0010] In some embodiments, the configuration information of the database where the collected data of the target energy storage system is located is stored in a configuration file; determining the target database type according to the data type includes: consulting the configuration file according to the data type to determine the target database type.

[0011] In some embodiments, the connection information of the database where the collected data of the target energy storage system is located is stored in a configuration file; obtaining the connection information of the target database includes: consulting the configuration file according to the plant identification parameter and the target database to obtain the connection information of the target database.

[0012] In some embodiments, the data type includes a structured parameter of a plant station where the target energy storage system is located; determining the target database type according to the data type includes: when the data type is the structured data, the target database type is a relational database corresponding to the plant station where the target energy storage system is located.

[0013] In some embodiments, the data type includes data that needs real-time monitoring and operation and maintenance or data that needs real-time analysis and statistics of the target energy storage system; and determining the target database type according to the data type includes: when the data type is data that needs real-time monitoring and operation and maintenance or data that needs real-time analysis and statistics of the target energy storage system, the target database type is a real-time database corresponding to a station of the target energy storage system.

[0014] In some embodiments, the data type includes hotspot data; and determining the target database type according to the data type includes: when the data type is the hotspot data, the target database type is a memory database.

[0015] In some embodiments, obtaining the data corresponding to the request parameter of the target energy storage system according to the target database and the connection information includes: determining a data access model corresponding to the target database; and accessing the target database according to the data access model and the connection information to obtain the data corresponding to the request parameter of the target energy storage system.

[0016] In some embodiments, determining the data access model corresponding to the target database includes at least one of the following: when a target database type corresponding to the target database is a relational database, the data access model is a Java Database Connectivity application programming interface; when the target database type is a real-time database, the data access model uses a driver or a special software development kit provided by a manufacturer of the real-time database; and when the target database type is a memory database, the data access model uses a native memory access interface of the memory database.

[0017] In some embodiments, the data processing method further includes: after obtaining the data corresponding to the request parameter of the target energy storage system according to the target database and the connection information, integrating the data obtained from different databases, converting the data into a unified standard format, and sending the data to a display end to display the data on the display end.

[0018] In some embodiments, the data processing method further includes at least one of the following: processing data transmission of a time-consuming interface in a multithreading manner according to an asynchronous processing mechanism; performing exception processing when processing and transmitting data, and sending exception alarm information to the display end; and performing security authentication on each data access interface.

[0019] To achieve the above object, the electronic device of the second aspect of the present application comprises: at least one processor; a memory in communication connection with the at least one processor; the memory stores a computer program executable by the at least one processor, and the at least one processor implements the data processing method of the energy storage system as described in the above embodiments when executing the computer program.

[0020] According to the electronic device of the present application, by implementing the data processing method of the energy storage system of the above embodiments, based on the multi-element database, the target database is determined from a plurality of different types of databases according to the request parameters of the target energy storage system, and the connection information of the target database is determined, and then the data corresponding to the request parameters is accurately obtained from the target database, so that the data access and processing of the energy storage system under the multi-element database are realized. The method is based on the collaborative deployment of a plurality of types of databases, breaks the limitation of the single database structure, and enables different types of collected data to be stored and flexibly retrieved on demand. A plurality of different types of databases can provide data support for more technical routes, support for switching databases for more flexible application scenarios, and support for a plurality of databases for large-scale data or high-concurrency access. Therefore, the method of the present application has good flexibility, expansibility and high-performance processing capability, and can meet the data processing needs of the energy storage system under diversified technical routes and complex application scenarios.

[0021] In some embodiments, the electronic device further comprises a display end connected with the at least one processor, for data interaction with the user.

[0022] To achieve the above object, the electronic device of the second aspect of the present application comprises: at least one processor; a memory in communication connection with the at least one processor; the memory stores a computer program executable by the at least one processor, and the at least one processor implements the data processing method of the energy storage system as described in the above embodiments when executing the computer program.

[0023] The computer readable storage medium according to the embodiment of the present application, by executing the data processing method of the energy storage system described in the above embodiment, determines a target database from a plurality of different types of databases based on a plurality of databases according to a request parameter of a target energy storage system, and determines connection information of the target database, and then accurately obtains data corresponding to the request parameter from the target database, so that data access and processing of the energy storage system under the plurality of databases are realized. The method is based on the collaborative deployment of a plurality of types of databases, breaks the limitation of a single database structure, and enables different types of collected data to be stored and flexibly retrieved on demand. A plurality of different types of databases can provide data support for more technical routes, support for switching databases for more flexible application scenarios, and support for a plurality of databases for large-scale data or high-concurrency access. Therefore, the method of the present application has good flexibility, scalability and high-performance processing capability, and can meet the data processing needs of the energy storage system under diversified technical routes and complex application scenarios.

[0024] To achieve the above-mentioned purpose, the data processing system of the fourth aspect of the present application comprises: an electronic device as described in the above embodiment; a plurality of collection devices arranged in at least one station, the station comprising at least one energy storage system, the collection device being used to obtain collected data of the energy storage system and send the collected data to the electronic device.

[0025] The data processing system according to the embodiment of the present application determines a target database from a plurality of different types of databases based on a plurality of databases according to a request parameter of a target energy storage system, and determines connection information of the target database, and then accurately obtains data corresponding to the request parameter from the target database, so that data access and processing of the energy storage system under the plurality of databases are realized. The method is based on the collaborative deployment of a plurality of types of databases, breaks the limitation of a single database structure, and enables different types of collected data to be stored and flexibly retrieved on demand. A plurality of different types of databases can provide data support for more technical routes, support for switching databases for more flexible application scenarios, and support for a plurality of databases for large-scale data or high-concurrency access. Therefore, the method of the present application has good flexibility, scalability and high-performance processing capability, and can meet the data processing needs of the energy storage system under diversified technical routes and complex application scenarios.

[0026] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which: Figure 1 This is a schematic diagram of the architecture of an energy storage system according to an embodiment of the present invention; Figure 2 This is a flowchart of a data processing method for an energy storage system according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the workflow of a data presentation layer according to an embodiment of the present invention; Figure 4 This is a structural block diagram of a data processing system according to an embodiment of the present invention.

[0028] Figure label: Data processing system 10; Electronic device 1; data acquisition device 2. Detailed Implementation

[0029] The embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. The embodiments of the present invention are described in detail below.

[0030] The following is for reference. Figures 1-2 A data processing method for an energy storage system according to an embodiment of the present invention is described.

[0031] The data processing method for energy storage systems provided by this invention can be applied to, for example... Figure 1 The energy storage system architecture shown is divided into a plant layer, a data service layer, and a data display layer.

[0032] The data processing method of the energy storage system in this embodiment of the invention can be used in the field of energy storage, photovoltaic field or other applicable fields, and is not specifically limited here.

[0033] Taking energy storage systems as an example, such as Figure 1 As shown, the plant layer includes multiple different types of plants, such as general industrial and commercial plant 1, general industrial and commercial plant 2, general industrial and commercial plant 3, large energy storage plant 1, large energy storage plant 2, etc. Each plant includes at least one energy storage system. The energy storage system is used to store and schedule the energy of each production module within the plant. Here, a production module can refer to the equipment or system that generates energy within the plant. Multiple data sampling points can be deployed for each energy storage system to collect data from each energy storage system.

[0034] The data of the energy storage system of each station collected can be sent to the cloud through a communication network, and the cloud is deployed with a data service layer and a data display layer. In the embodiment of the present application, the cloud can be configured with a multi-element database, and the databases corresponding to different stations can be different. For example, the general industrial and commercial station 1 can be configured with PostgreSQL, Redis and InfluxDB databases, the general industrial and commercial station 3 can be configured with MySQL, Redis and InfluxDB databases, or the corresponding database can be configured according to the specific needs of the station, which is not limited here.

[0035] The data service layer is used to set different data access models for different types of databases to realize efficient and flexible data access and management. For example, as shown in Figure 1 The data service layer can include PostgreSQL service, MySQL service, InfluxDB service, NoSQL service, etc. Through these services, data reading and processing functions can be realized.

[0036] The data display layer can be a cloud display screen for displaying various data of the energy storage system. The data display layer obtains data from the data service layer and displays it intuitively. Specifically, as shown in Figure 1 The cloud display screen can display real-time running data of each station, alarm information display, station running report, station architecture topology display, historical data display, etc. The real-time running data of each station can include SOC (State of charge), SOH (State of health), temperature, voltage, etc. The station running report can be displayed from the day, month, year, etc. The station architecture topology display can include feeder, PCS (Power Conversion System), BMS (Battery Management System), battery stack cluster package distribution, etc.

[0037] Figure 2 The flow chart of the data processing method of the energy storage system according to an embodiment of the present application is shown in Figure 2 As shown in

[0038] Step S1, obtaining the request parameters of the target energy storage system.

[0039] The request parameters can be understood as parameters related to the data requested by the user to obtain or display.

[0040] Specifically, the user can manually input or select the required target energy storage system and the corresponding data, and the data display layer constructs the corresponding request parameters according to the user input data, and then transmits the request parameters to the data service layer through the data access interface provided by the data service layer, so that the data service layer can obtain the request parameters of the target energy storage system.

[0041] In step S2, the target database and the connection information of the target database are determined according to the request parameters, wherein the collected data of the target energy storage system is stored in a plurality of different types of databases according to the data type, and the target database is one of the plurality of different types of databases.

[0042] The target database is a database that stores the required data corresponding to the request parameters. The connection information of the target database can be understood as the connection details of the target database, such as database address, port, username and password, etc., and the connection information is used to access the target database.

[0043] Specifically, after obtaining the request parameters, the data service layer can determine the target database from a plurality of different types of databases and obtain the connection information of the target database by analyzing the request parameters.

[0044] The energy storage system of the embodiment of the application integrates a plurality of different types of databases, such as diversified databases including relational databases, real-time databases and memory databases, thereby providing solid data support for the energy storage system, and further meeting the needs of the energy storage system for complex and diversified data storage and management.

[0045] In addition, in addition to the above-mentioned three types of databases, the plurality of different types of databases can also include other types of databases, such as file-type databases.

[0046] In step S3, the data corresponding to the request parameters of the target energy storage system is obtained according to the target database and the connection information.

[0047] Specifically, after determining the target database and the connection information, the data service layer can connect to the target database according to the connection information, and further perform data query, processing and other operations to obtain the data corresponding to the request parameters.

[0048] Traditional energy storage systems mostly use a single database, which has technical limitations, poor flexibility, limited expansion, and possible high cost and performance problems.

[0049] However, the data processing method of the energy storage system of the application based on the multi-database can meet the needs of the energy storage system for scale expansion, diverse application scenarios, rich technical routes and economic improvement.

[0050] For example, in terms of technical limitations, traditional energy storage systems cannot fully utilize the technical advantages of different types of databases. Each database system has its unique design and optimization, suitable for specific application scenarios. For example, relational databases perform well in handling structured data, while NoSQL databases are more suitable for handling unstructured or semi-structured data. If the energy storage system only supports a single database, the system may not perform optimally in certain specific application scenarios, or even cause limited functionality.

[0051] For another example, in terms of flexibility, as the business develops and changes, enterprises may need to quickly adjust the database according to actual needs. If the system only supports a single database, the enterprise may be greatly limited in the face of such changes. The data processing method of the energy storage system of the present application is based on multiple databases, which can provide support for quickly adjusting the database, and therefore has higher flexibility.

[0052] For another example, in terms of scalability, when dealing with large-scale data or high-concurrency access, a single database system may encounter performance bottlenecks. The present application supports multiple databases, which can share the load by introducing other types of databases, improving the scalability and performance of the system.

[0053] For another example, in terms of performance, certain databases may have higher performance advantages in specific scenarios. If the system does not support multiple databases, the enterprise may have to choose a suboptimal database, resulting in performance problems. The present application is based on multiple databases, and can flexibly switch to higher-performance database support for different scenarios, with superior performance.

[0054] The data processing method of the energy storage system according to the embodiment of the present application is based on multiple databases, determines a target database from multiple different types of databases according to the request parameters of the target energy storage system, and determines the connection information of the target database, and then accurately obtains the data corresponding to the request parameters from the target database, thereby realizing data access and processing of the energy storage system under multiple databases. This method is based on the collaborative deployment of multiple types of databases, breaking the limitations of single database structure, so that different types of collected data can be stored and flexibly retrieved on demand. Multiple different types of databases can provide data support for more technical routes, support for more flexible application scenario switching databases, and multiple database support for large-scale data or high-concurrency access. Therefore, the method of the present application has good flexibility, scalability and high-performance processing capability, and can meet the data processing needs of the energy storage system in diversified technical routes and complex application scenarios.

[0055] In some embodiments, the request parameter comprises a data type of the request and a station identification parameter of a station where the target energy storage system is located. The target database and the connection information of the target database are determined according to the request parameter, comprising: determining a target database type according to the data type, determining a target station according to the station identification parameter, determining a database corresponding to the target database type in the target station as the target database, and obtaining the connection information of the target database.

[0056] The data type can include, but is not limited to, power, voltage, current, etc. The station identification parameter of the station where the target energy storage system is located can be a station code or a unique identifier.

[0057] Specifically, since the collected data of the target energy storage system is stored in multiple databases of different types according to the data type, the data service layer can determine the database type storing the data corresponding to the request parameter according to the data type, and further determine the target database according to the database type. In addition, since the station layer includes multiple stations, each station includes at least one energy storage system, and different stations are respectively deployed with appropriate databases according to their unique attributes, the specific station to which the request is directed, i.e., the target station, can be determined according to the station identification parameter (such as the station code). Further, in the database deployed in the station, the database corresponding to the target database type in the target station is determined as the target database. Once the target database is identified, the system obtains the connection information of the database from a unified configuration file or a service registry center, including the database address, port, username, and password, etc., as the basis for subsequent data access.

[0058] In some embodiments, the configuration information of the database where the collected data of the target energy storage system is located is stored in a configuration file, which means that the configuration information about the database in the system is not hard-coded in the system program, but is stored and managed in the form of a configuration file. Such a configuration method has high flexibility and maintainability, so that when adding or deleting database types or station instances later, the program code does not need to be frequently modified, and only the configuration file needs to be updated, which is conducive to later maintenance and multi-environment deployment (such as test and production environments).

[0059] In some embodiments, determining the target database type according to the data type comprises: consulting the configuration file according to the data type to determine the target database type. This process emphasizes a dynamic database type inference mechanism based on the configuration file, which avoids hard-coded matching logic, thereby improving the configurability and expandability of the system.

[0060] In some embodiments, the connection information of the database in which the collected data of the target energy storage system is stored is stored in a configuration file. This means that the connection information of the database, including but not limited to the database address, port number, username, password, connection pool parameters, etc., is not hard-coded in the system, but is centrally managed. In this way, without modifying the program code, only adjusting the configuration file can cope with changes in connection information.

[0061] In some embodiments, obtaining the connection information of the target database includes: according to the station identification parameter and the target database, consulting a configuration file to obtain the connection information of the target database.

[0062] In some embodiments, the data source can be the source or origin of the data, and the data source can include other data information in addition to the database. Specifically, the energy storage system manages the data source efficiently by using a database connection pool, which can include HikariCP (Hikari Connection Pool), DBCP (DataBase Connection Pool), etc. The database connection pool is responsible for allocating, managing and releasing database connections, allowing an application to repeatedly use an existing database connection without the need to establish a new connection. The connection pool mechanism can significantly improve the efficiency and response speed of database access by reducing the frequent creation and destruction of database connections.

[0063] Secondly, the data service layer centrally maintains the connection parameters of the heterogeneous data source in a unified configuration file to realize the decoupling and flexible configuration of the data access layer. The connection parameters of the heterogeneous data source can include database type, connection string, authentication credentials, timeout threshold, etc. The connection parameters of the data source are the key information necessary for configuring the database connection, which is used to determine the way the application establishes a connection with the database and interacts with the data. Specifically, the connection information of the data source can be determined according to the station identification parameter, and then the corresponding database connection pool can be determined.

[0064] Specifically, after obtaining the setting parameter, the data service layer can first parse the data type and the station identification parameter according to the setting parameter, and then consult the configuration file according to the parsed data type to determine the database type to be used, which can include a relational database, a real-time database, an in-memory database, etc. At the same time, the data service layer can also retrieve the connection information of the target database such as the database address, port, username and password from the configuration file according to the station identification parameter. Specifically, the data access model can correspond to multiple connection pools, and the corresponding connection pool can be determined according to the station identification parameter. In addition, the data access module can be determined according to the database type, and then the target database can be accessed based on the determined data access model and the corresponding connection pool.

[0065] Based on the above, only the configuration file needs to be modified to add, delete or adjust the database configuration, without changing the code, greatly improving the flexibility and scalability of the energy storage system.

[0066] In some embodiments, the data type includes a structured parameter of a station where the target energy storage system is located; determining the target database type according to the data type includes: when the data type is structured data, the target database type is a relational database corresponding to the station where the target energy storage system is located.

[0067] The relational database can include PostgreSQL, MySQL, SQLite, etc.

[0068] Specifically, if the data type of the collected data of the target energy storage system is a structured parameter, such as a station model or other data with a fixed structure, the target energy storage system uses a relational database for storage. Therefore, when determining the target database type according to the data type, if the data type is structured data, the target database type is a relational database corresponding to the station where the target energy storage system is located.

[0069] For example, the specific implementation process of the target energy storage system using a relational database to store structured data can include steps S4-S7, as follows.

[0070] Step S4: Considering factors such as business requirements, data size, concurrent access volume, etc., select a relational database such as MySQL, PostgreSQL, SQLite, Oracle, etc.

[0071] Step S5: Create a user account for the database system and strictly assign corresponding access permissions to ensure data security.

[0072] Step S6: Use SQL scripts, ETL (Extract, Transform, Load) tools or other efficient data import methods to import the collected data of the target energy storage system into the created database.

[0073] Step S7: After the data import is completed, comprehensively detect the running status of the database, for example, indexes can also be created according to actual query performance and storage requirements to further improve the performance and response speed of the database.

[0074] The above steps can ensure the organization and queryability of the collected data of the target energy storage system.

[0075] In some embodiments, the data type includes data that needs to be monitored and maintained in real time or needs to be analyzed in real time by the target energy storage system; and the target database type is determined according to the data type, including: when the data type is data that needs to be monitored and maintained in real time or needs to be analyzed in real time by the target energy storage system, the target database type is a real-time database corresponding to a station where the target energy storage system is located.

[0076] The real-time database can include InfluxDB, etc. InfluxDB is designed for high-frequency and high-throughput time series data, which perfectly meets the real-time collection, monitoring and analysis requirements of data such as battery charging and discharging power, SOC, SOH, temperature, voltage and alarm information.

[0077] Because InfluxDB has the functions of second-level data collection and transmission, powerful time series data processing and real-time alarm information processing. Specifically, InfluxDB supports high-speed data collection and transmission, ensuring that real-time operation and maintenance data are quickly responded and processed. Secondly, InfluxDB has built-in time series data processing functions, which simplify the aggregation, analysis and visualization process of a large amount of time series data. In the station operation and maintenance scenario, InfluxDB easily meets the needs of operation and maintenance report generation, device running status and performance index statistical analysis through rich query statements and aggregation functions such as calculating average, maximum, minimum and sum, etc., which improves the efficiency and accuracy of data analysis. In addition, InfluxDB can detect data anomalies in real time and trigger an alarm mechanism when device parameters exceed the preset threshold, notifying the operation and maintenance personnel to handle potential problems in time and ensure the stable operation of the system. Therefore, for data that needs to be monitored and maintained in real time or needs to be analyzed in real time, the target energy storage system uses a real-time database for storage.

[0078] Specifically, when the target database type is determined according to the data type, if the data type is data that needs to be monitored and maintained in real time or needs to be analyzed in real time by the target energy storage system, the target database type is a real-time database corresponding to a station where the target energy storage system is located.

[0079] In some embodiments, the data type includes hot data; and the target database type is determined according to the data type, including: when the data type is hot data, the target database type is a memory database.

[0080] The hot data can be data that is accessed more frequently by the target energy storage system. The memory database can include Redis, etc.

[0081] Since hotspot data needs to be accessed frequently, and the data of the in-memory database is directly stored in the RAM (random access memory), the in-memory database has the advantages of fast read-write speed, strong real-time performance, low delay, and high throughput, etc. Therefore, the target energy storage system can use the in-memory database to store hotspot data.

[0082] Specifically, when the target database type is determined according to the data type, if the data type is hotspot data, the target database is an in-memory database.

[0083] Exemplarily, the specific implementation process of the target energy storage system using the in-memory database to store hotspot data can include steps S8-S10, which are as follows.

[0084] In step S8, the frequently accessed data is serialized according to the needs, so as to be stored in Redis in a suitable format. With the help of the Redis client tool library, the batch and efficient import of data is realized, and it is ensured that the data is quickly and accurately loaded into Redis.

[0085] In step S9, according to the data storage requirements of the target energy storage system, the persistence mechanism of Redis is configured, such as RDB (Redis Database Backup) snapshot, AOF (Append Only File) log, etc., so as to ensure that the data can be recovered when the system restarts or fails, and to guarantee the reliability and integrity of the data.

[0086] In step S10, after the data persistence configuration is implemented, the performance indicators of Redis are continuously monitored, such as memory usage, hit rate, response time, etc., and corresponding optimization adjustment is made according to the monitoring results, so as to ensure that Redis always maintains the best performance state.

[0087] Based on the above steps, it can be ensured that the hotspot data can be efficiently and safely stored in Redis, and the advantages of Redis as an in-memory database can be fully utilized, providing excellent performance support for the target energy storage system.

[0088] Based on the above, the application can flexibly customize the database configuration scheme according to the unique needs of different stations, fully exert the unique advantages of each type of database, and ensure that each data source is accurately connected with the database of a specific station. Specifically, the target energy storage system can select a relational database to store the model data of the station with strong structured storage requirements, to ensure the organization and queryability of the data; at the same time, the target energy storage system can use a real-time database to store telemetry data, remote signaling data and the like with high real-time requirements, to fully utilize its efficient time series data processing capability; the target energy storage system can also select a memory database to store hot data, to improve the response speed of the target energy storage system. The architecture not only can realize physical isolation of data, effectively avoiding data confusion and conflict, but also can guarantee the independence and consistency of data, thereby ensuring data security.

[0089] In addition, since the real-time database has high-performance write and query functions, excellent data compression and storage capabilities, and the memory database has high read-write performance, supports more data types and has strong transaction processing capabilities, the real-time database and the memory database can be used in combination in the target energy storage system, to effectively improve the density and efficiency of data storage and the response speed of data access, thereby ensuring the stable operation and efficient management of the target energy storage system.

[0090] In some embodiments, obtaining the data of the target energy storage system corresponding to the request parameter according to the target database and the connection information includes: determining a data access model corresponding to the target database; and accessing the target database according to the data access model and the connection information, to obtain the data of the target energy storage system corresponding to the request parameter.

[0091] The data access model can be understood as an architecture and mechanism for describing and managing data access methods. According to different application scenarios and technical requirements, the data access model can be divided into various types, including connection-oriented models, disconnected models, data access control models, hypergraph-based models, attribute-based access control models, and data sharing-based models, etc.

[0092] Specifically, in the data service layer, for different types of databases, independent data access models can be respectively set, and all core logics for interacting with the target database are encapsulated in the data access model, including connection management, query execution, result processing, etc. Therefore, when obtaining the data corresponding to the request parameter according to the target database and the connection information, the data access model corresponding to the target database needs to be determined. Further, when querying data, the target database can be accessed according to the data access model and the connection information, which can effectively ensure the convenience and efficiency of data operation.

[0093] In addition, when new database support is added, only the new data access model needs to be developed and integrated, without the need for large-scale adjustments to the existing code, making it easy to respond to changes in future data storage requirements. At the same time, the encapsulated data access model can be reused in multiple projects, avoiding the need to repeatedly write database access code, thereby speeding up the development process of new projects.

[0094] In some embodiments, determining the data access model corresponding to the target database includes at least one of the following: when the target database type corresponding to the target database is a relational database, the data access model uses a Java Database Connectivity application programming interface; when the target database type is a real-time database, the data access model uses a driver or a special software development kit provided by the real-time database vendor; and when the target database type is an in-memory database, the data access model uses a native memory access interface of the in-memory database.

[0095] When the target database type corresponding to the target database is a relational database, JDBC (Java Database Connectivity) can be used as the data access model, and JDBC API (Application Programming Interface) can be used for database operations and SQL connections. JDBC is a standard API (Application Program Interface) in Java for interacting with databases, providing a unified way for Java applications to connect and operate various relational databases. The core goal of JDBC is to achieve the function of "write once, run anywhere", that is, to access different databases through pure Java code without the need to recompile or modify the code.

[0096] In some embodiments, when the target database type is a real-time database, a driver or a special software development kit such as the InfluxClient library provided by the real-time database vendor can be used as the data access model to establish a connection and execute queries. The InfluxClient library is a client library for interacting with InfluxDB databases, supporting multiple programming languages including Python,.NET, Rust, C#, PHP, Java, etc. The InfluxClient library provides functions for writing, querying, and managing InfluxDB data, suitable for different application scenarios and technology stacks.

[0097] In some embodiments, when the target database type is an in-memory database, the data access model adopts the native memory access interface of the in-memory database for data access operations. The in-memory database can include Jedis, Lettuce, Redisson, etc. Redis is a high-performance in-memory database widely used in scenarios such as caching, message queuing, session management, and real-time data processing. Jedis is one of the official Java clients recommended by Redis, providing a rich set of synchronous operation APIs to facilitate quick implementation of read-write operations on Redis data structures such as strings, lists, hashes, sets, etc. Lettuce is also a popular Redis Java client that supports asynchronous, reactive, and thread-safe operations, making it suitable for high-concurrency scenarios. Redisson is a more feature-rich Redis client that encapsulates advanced features such as distributed locks, queues, Bloom filters, and Map / Set, in addition to basic data access functions, making it suitable for enterprise-level distributed systems.

[0098] In some embodiments, in the energy storage system, the types of collected data include battery voltage, current, state of charge (SOC), charging and discharging status, etc. These data can be stored in different types of databases such as InfluxDB, Redis, and relational databases according to real-time or storage strategies. Upon receiving an access request for a certain type of data in the energy storage system, the system can accurately locate the required database and its connection information based on the data type and plant identification parameters carried in the request parameters, thereby efficiently obtaining the required data. In a photovoltaic system, the same data processing method for the energy storage system provided by the present application is applicable, ensuring that various types of data such as irradiance, module temperature, voltage, etc. can be efficiently accessed and managed, demonstrating the broad applicability and system adaptation capability of the method.

[0099] In some embodiments, if the energy storage system only supports a single database, the system's functionality is limited in certain specific application scenarios, and it cannot perform at its best. Compared to the single-database system in the prior art, which cannot fully utilize the technical advantages of different types of databases, the present application deploys appropriate databases according to the unique attributes of each plant, stores different types of energy storage data in different types of databases, and each type of database has its unique design and optimization, making it suitable for specific application scenarios. For example, relational databases are suitable for processing structured data, while NoSQL databases are more suitable for processing unstructured or semi-structured data.

[0100] Exemplarily, if the data type of the collected data is structured data, since a relational database has an advantage in processing structured data, the collected data is stored in the relational database, so that the organization and queryability of the collected data can be ensured; if the data type of the collected data is data that needs to be monitored and operated in real time or needs to be analyzed and counted in real time, such as telemetry data and remote signaling data with high real-time requirements, the data can be stored in a real-time database to fully utilize the efficient time series data processing capability of the real-time database; if the data type of the collected data is hot data, since an in-memory database has good read-write performance, the data can be stored in the in-memory database to greatly improve the response speed of the energy storage system.

[0101] In some embodiments, the data processing method of the energy storage system further includes: after obtaining the data of the requested parameters of the target energy storage system according to the target database and the connection information, integrating the data obtained from different databases, and converting the data into a unified standard format, the data is sent to the display end to display the data through the display end.

[0102] Specifically, a set of standardized data access interfaces can be defined in the data service layer, which is used to provide a general way for the upper layer application to call data in different databases, that is, for different types of databases with different data formats, the data obtained from different databases can be integrated through the data access interface, and then sent to the display end in a unified data format, so as to realize the unified display data format of the display end. Each data access model is implemented in accordance with the interface specification, thereby ensuring seamless interconversion and high compatibility between modules to improve the flexibility and scalability of the energy storage system.

[0103] Specifically, the data access interface is a standardized method for interaction between application programs and databases or other data sources. The data access interface provides an abstraction layer, so that the application program can be independent of the underlying data storage technology, thereby improving the maintainability and scalability of the code. The main functions of the data access interface include data conversion, security assurance and interoperability. Data conversion is to convert the request of the application program into the format of the underlying database or data source, for example, the Microsoft OLE DB provider converts COM OLE DB commands and data types into DRDA protocol code bits and data formats. The data access interface can also provide identity verification and data encryption functions to ensure the security of data during transmission and storage. The data access interface supports cross-platform data access, such as defining and testing database connections through OLE DB data link files. Based on the above, through the unified data access interface, the data service layer can efficiently retrieve the required data from the target data source.

[0104] Further, the data service layer encapsulates the retrieved data corresponding to the request parameter in a standardized manner, for example, in JSON, XML or other format, and returns the encapsulated data to the data display layer, which parses and formats the received data and presents the data to the user in the form of charts, tables, reports and the like through the display screen.

[0105] Referring to Figure 3 as shown, Figure 3 is a flowchart of the workflow of the data display layer according to an embodiment of the present application, which includes steps S11-S16.

[0106] Step S11, start.

[0107] Step S12, the data display layer issues a request parameter to the data service layer.

[0108] Step S13, the data service layer parses the request parameter, obtains the database type, connection information and module positioning, and performs data source switching, data acquisition and the like.

[0109] Step S14, the data service layer encapsulates the obtained data into a uniform format and returns it to the data display layer.

[0110] Step S15, the data display layer parses and formats the received data, and finally presents the data to the user in the form of charts, tables, reports and the like.

[0111] Step S16, end.

[0112] Based on the above, the efficiency, stability and security of the interaction between the data display layer and the data service layer can be ensured.

[0113] In some embodiments, the data processing method of the energy storage system further includes at least one of the following: processing data transmission of time-consuming interfaces in a multithreading manner according to an asynchronous processing mechanism; performing exception handling during data processing and transmission, and sending exception alarm information to the display end; and performing security authentication on each data access interface.

[0114] The time-consuming interface refers to an interface with a long response time, which can refer to the total time required from the initiation of a request from the client to the completion of the server response exceeding a normal threshold. The response time of the interface is usually measured in milliseconds.

[0115] Specifically, the data service layer can also introduce an asynchronous processing mechanism to process the data transmission of time-consuming interfaces in a multithreading manner, that is, multiple data can be processed at the same time, significantly improving the efficiency of data processing. At the same time, the data service layer is also equipped with error handling mechanisms such as retry, service degradation and error prompt. When an exception occurs during data processing and transmission, the display end can send an exception alarm message to the display end to deal with possible errors in data transmission, thereby ensuring the stability of the energy storage system. In addition, the data service layer can also perform security authentication on each data access interface, for example, a token authentication mechanism can be set up to ensure the safe reading of data, thereby ensuring the security of data during transmission and storage.

[0116] In some embodiments, the second aspect of the present application also provides an electronic device. The electronic device comprises at least one processor and a memory connected to the at least one processor in communication.

[0117] The memory stores a computer program executable by the at least one processor, and the at least one processor executes the computer program to implement the data processing method of the energy storage system of the above embodiments.

[0118] The electronic device can be a cloud server or other data processing device.

[0119] According to the electronic device of the embodiments of the present application, by implementing the data processing method of the energy storage system of the above embodiments, based on the multi-element database, the target database is determined from a plurality of different types of databases according to the request parameters of the target energy storage system, and the connection information of the target database is determined, and then the data corresponding to the request parameters is accurately obtained from the target database, thereby realizing the data access and processing of the energy storage system under the multi-element database. This method is based on the collaborative deployment of multiple types of databases, breaking the limitations of single database structure, so that different types of collected data can be stored and flexibly retrieved on demand. Multiple different types of databases can provide data support for more technical routes, support for more flexible application scenario switching databases, and multiple database support for large-scale data or high concurrency access. Therefore, the method of the present application has good flexibility, scalability and high performance processing capability, and can meet the data processing needs of the energy storage system in diversified technical routes and complex application scenarios.

[0120] In some embodiments, the electronic device further comprises a display end. The display end is connected to the at least one processor and is used for data interaction with the user.

[0121] The third aspect of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the data processing method of the energy storage system of the above embodiments.

[0122] According to the computer readable storage medium of the embodiment of the present application, by executing the data processing method of the energy storage system of the above embodiment, based on the multi-element database, the target database is determined from a plurality of different types of databases according to the request parameter of the target energy storage system, and the connection information of the target database is determined, and then the data corresponding to the request parameter is accurately obtained from the target database, so that the data access and processing of the energy storage system under the multi-element database are realized. The method is based on the collaborative deployment of multiple types of databases, breaks the limitation of single database structure, and enables different types of collected data to be stored and flexibly retrieved on demand. Multiple different types of databases can provide data support for more technical routes, support for more flexible application scenario switching databases, and multiple database support for large-scale data or high concurrency access. Therefore, the method of the present application has good flexibility, scalability and high performance processing capability, and can meet the data processing needs of the energy storage system under diversified technical routes and complex application scenarios.

[0123] The fourth aspect embodiment of the present application also provides a data processing system, as shown in Figure 4 The data processing system 10 includes the electronic device 1 and the plurality of collection devices 2 of the above embodiment.

[0124] Among them, the plurality of collection devices are arranged in at least one station, and the station includes at least one energy storage system, and the collection device is used to obtain the collected data of the energy storage system and send it to the electronic device.

[0125] According to the data processing system of the embodiment of the present application, based on the multi-element database, the target database is determined from a plurality of different types of databases according to the request parameter of the target energy storage system, and the connection information of the target database is determined, and then the data corresponding to the request parameter is accurately obtained from the target database, so that the data access and processing of the energy storage system under the multi-element database are realized. The method is based on the collaborative deployment of multiple types of databases, breaks the limitation of single database structure, and enables different types of collected data to be stored and flexibly retrieved on demand. Multiple different types of databases can provide data support for more technical routes, support for more flexible application scenario switching databases, and multiple database support for large-scale data or high concurrency access. Therefore, the method of the present application has good flexibility, scalability and high performance processing capability, and can meet the data processing needs of the energy storage system under diversified technical routes and complex application scenarios.

[0126] In the description of this specification, any process or method described in the flowcharts or otherwise herein may be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), fiber optic devices, and compact disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0128] It should be understood that various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. As such, in some embodiments, the steps or methods can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), central processing units (CPUs), graphics processing units (GPUs), controllers, microcontrollers, microprocessors, other electronic units mentioned above, or some or all of the above in combination. In some embodiments, the steps or methods can be implemented in one or more computers or other processing units using software programs written in any of various languages, including C, C++, Java, Visual Basic, Python, and / or other languages.

[0129] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0130] In addition, each functional unit in each embodiment of the present application can be integrated into one processing module, or each unit can exist physically independently, or two or more units can be integrated into one module. The above integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0131] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

[0132] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the exemplary description of the above terms does not necessarily mean the same embodiment or example.

[0133] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely divergences of the principles and application of the present application and that numerous modifications, changes, substitutions, and alterations can be made thereto without departing from the spirit and scope of the present application, which is defined by the following claims and their equivalents.

Claims

1. A data processing method for an energy storage system, characterized by, The method comprises: obtaining a request parameter of a target energy storage system; determining a target database and connection information of the target database according to the request parameter, wherein the collected data of the target energy storage system is stored in a plurality of databases of different types according to data types, and the target database is one of the plurality of databases of different types; obtaining data corresponding to the request parameter of the target energy storage system from the target database and the connection information.

2. The data processing method of claim 1, wherein the request parameter comprises a requested data type and a station identification parameter of a station where the target energy storage system is located; and determining the target database and the connection information of the target database according to the request parameter comprises: determining a target database type according to the data type and a target station according to the station identification parameter; and determining a database corresponding to the target database type of the target station as the target database and obtaining the connection information of the target database.

3. The data processing method of claim 2, wherein configuration information of the database where the collected data of the target energy storage system is stored is stored in a configuration file; and determining the target database type according to the data type comprises: consulting the configuration file according to the data type to determine the target database type.

4. The data processing method of claim 3, wherein connection information of the database where the collected data of the target energy storage system is stored is stored in a configuration file; and obtaining the connection information of the target database comprises: consulting the configuration file according to the station identification parameter and the target database to obtain the connection information of the target database.

5. The data processing method of claim 2, wherein the data type comprises a structured parameter of the station where the target energy storage system is located; and determining the target database type according to the data type comprises: when the data type is the structured data, the target database type is a relational database corresponding to the station where the target energy storage system is located.

6. The data processing method of claim 2, wherein the data type comprises data that needs to be monitored and operated in real time or needs to be analyzed and counted in real time by the target energy storage system; and determining the target database type according to the data type comprises: when the data type is the data that needs to be monitored and operated in real time or needs to be analyzed and counted in real time by the target energy storage system, the target database type is a real-time database corresponding to the station where the target energy storage system is located.

7. The data processing method of claim 2, wherein the data type comprises hot data; and determining the target database type according to the data type comprises: when the data type is the hot data, the target database type is an in-memory database. Obtaining the data corresponding to the request parameter of the target energy storage system from the target database and the connection information comprises: determining a data access model corresponding to the target database; and ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 8. The data processing method of claim 1, wherein, ​ ​ Accessing the target database according to the data access model and the connection information to obtain data of the target energy storage system corresponding to the request parameter.

9. The data processing method according to claim 8, characterized in that, Determining the data access model corresponding to the target database, including at least one of the following: When the target database type corresponding to the target database is a relational database, the data access model adopts a Java Database Connectivity Application Program Interface; When the target database type is a real-time database, the data access model adopts a driver or a special software development kit provided by the real-time database vendor; When the target database type is an in-memory database, the data access model adopts a native memory access interface of the in-memory database.

10. The data processing method according to any one of claims 1 to 9, characterized in that, The data processing method further includes: After obtaining the data of the target energy storage system corresponding to the request parameter according to the target database and the connection information, the data obtained from different databases is integrated, converted into a unified standard format, and sent to a display end to display the data on the display end.

11. The data processing method according to any one of claims 1 to 9, characterized in that, The data processing method further includes at least one of the following: According to an asynchronous processing mechanism, the data transmission of time-consuming interfaces is processed in a multi-threaded manner; Abnormality processing is performed during data processing and transmission, and abnormality alarm information is sent to the display end; Security authentication is performed on each data access interface.

12. An electronic device, comprising: It includes: At least one processor; A memory connected in communication with the at least one processor; The memory stores a computer program executable by the at least one processor, and the at least one processor executes the computer program to implement the data processing method of the energy storage system according to any one of claims 1-11.

13. The electronic device of claim 12, wherein, The electronic device further includes: A display end connected with the at least one processor, used for data interaction with a user.

14. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the data processing method of the energy storage system according to any one of claims 1-11.

15. A data processing system, characterized by It includes: The electronic device according to claim 12 or 13; A plurality of acquisition devices arranged in at least one station, the station including at least one energy storage system, the acquisition device being used to obtain acquisition data of the energy storage system and send the data to the electronic device.

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