Controllable intelligent photovoltaic management platform
The controllable intelligent photovoltaic management platform, based on a distributed architecture and data mining algorithms, solves the real-time and diversity problems of traditional data processing architectures in industrial big data, achieving efficient data integration, storage, and analysis, and supporting intelligent management and optimization of photovoltaic power plants.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 华能(嘉峪关)新能源有限公司
- Filing Date
- 2024-11-22
- Publication Date
- 2026-05-22
AI Technical Summary
Traditional data processing architectures struggle to cope with the complexity, diversity, and real-time requirements of industrial big data, resulting in difficulties in data integration, low processing efficiency, high storage costs, insufficient analytical capabilities, and an inability to quickly respond to changes in the production process.
The controllable intelligent photovoltaic management platform adopts a distributed architecture, integrating data access, storage, computing, analysis and management modules. Combining distributed storage technology, data mining and machine learning algorithms, it enables access to diverse data sources, real-time processing and efficient storage, and supports customized computing tasks and data visualization.
It significantly improves the real-time processing capability and rapid response speed of industrial big data, increases data processing throughput, reduces system failure risk, ensures data security and accuracy, supports intelligent management of photovoltaic power plants and optimizes power generation strategies, and improves economic and environmental benefits.
Smart Images
Figure CN122072906A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of photovoltaic power plant technology, and more specifically to a controllable intelligent photovoltaic management platform. Background Technology
[0002] In today's rapidly developing Industry 4.0 era, industrial big data has become a key element in driving the transformation and upgrading of the manufacturing industry and realizing intelligent manufacturing. However, with the widespread deployment of industrial IoT devices and the explosive growth of industrial data, enterprises are facing unprecedented challenges in data processing and analysis. Traditional data processing architectures often struggle to cope with the complexity, diversity, and real-time requirements of big data, leading to a series of technical problems such as difficulties in data integration, low processing efficiency, high storage costs, and insufficient analytical capabilities.
[0003] The integration of industrial big data faces challenges such as diverse data sources, complex protocols, and inconsistent data formats. Traditional data integration methods often require developing customized interfaces for different data sources, which is not only time-consuming and costly but also makes it difficult to guarantee the real-time performance and accuracy of the data. At the same time, the diversity of industrial data also poses a significant challenge to data storage. The coexistence of structured, semi-structured, and unstructured data makes traditional relational databases inadequate for storing large-scale data. Furthermore, industrial big data has extremely high real-time requirements, needing to respond quickly to various changes in the production process, a requirement that traditional batch processing models clearly cannot meet. Summary of the Invention
[0004] To address the problem that existing technologies for industrial big data lack real-time performance and cannot quickly respond to various changes in the production process, this invention provides a controllable intelligent photovoltaic management platform. It adopts a distributed architecture and is capable of data integration, real-time data processing, big data storage and management, data visualization and analysis, and rapid response to various changes in the production process.
[0005] To achieve the above objectives, the present invention provides the following technical solution.
[0006] This invention provides a controllable intelligent photovoltaic management platform, including a data access module, a data storage module, a data calculation module, a data mining and analysis module, a data visualization module, and a data management module. The data access module provides data input for the entire platform. The data storage module receives and stores data sent by the data access module. The data calculation module performs offline calculations, task development, and task scheduling based on the data in the data storage module to generate data calculation results. The data mining and analysis module analyzes and mines the data from the data calculation module to obtain analysis and mining results. The data visualization module displays the analysis and mining results. The data management module provides data directory management, data governance, data sharing, and permission setting functions.
[0007] As a further improvement of the present invention, it also includes a service catalog module and a platform operation management module; the service catalog module is used to display information of the data access module, data storage module, data calculation module, data mining and analysis module, data visualization module and data management module; the platform operation management module is used for the data access module, data storage module, data calculation module, data mining and analysis module, data visualization module and data management module to interface with external systems.
[0008] As a further improvement of the present invention, the analysis and mining module includes a data modeling submodule, a pre-aggregation modeling submodule, and an operator library; the data modeling submodule is used for interactive modeling and automatic modeling; the pre-aggregation modeling submodule is used for pre-aggregation data sources, pre-aggregation analysis, and pre-aggregation management; and the operator library is used for data processing, machine learning, model evaluation, and custom operators.
[0009] As a further improvement of the present invention, the data access module is used to provide data input for the entire platform, including: the data access module collects source configuration data, target configuration data, and conversion tool data, performs data ETL processing and offline access management, and provides the obtained data to the data storage module.
[0010] As a further improvement of the present invention, the data storage module includes a structured storage submodule and an unstructured storage submodule; the structured storage submodule includes a relational database and Hive; the unstructured storage submodule includes HBase, HDFS, object storage, Redis, graph database and message queue.
[0011] As a further improvement of the present invention, the data visualization module is used to display the analysis and mining results, including: the data visualization module is used for data docking, visualization display, data processing, sharing and sending, data analysis, multi-screen application, management and maintenance.
[0012] As a further improvement of the present invention, the data directory management is used for technical directories and business directories.
[0013] As a further improvement of the present invention, the data governance is used for metadata management, data quality management, and data standard management.
[0014] As a further improvement of the present invention, the data sharing is used for API management and external data sharing.
[0015] As a further improvement of the present invention, the permission configuration is used for data directory permission assignment and platform function permission management.
[0016] Compared with the prior art, the present invention has the following beneficial effects:
[0017] The controllable intelligent photovoltaic management platform of this invention significantly improves the real-time processing capability of industrial big data and the rapid response speed to various changes in the production process by integrating a series of highly collaborative functional modules, namely, a data access module, a data storage module, a data computing module, a data mining and analysis module, a data visualization module, and a data management module. Firstly, through the design of the data access module, this invention achieves broad access to diverse data sources, including but not limited to real-time monitoring data of photovoltaic power plants, equipment operation logs, environmental parameters, and market electricity price information, providing a rich and comprehensive data foundation for the platform. The data storage module adopts advanced distributed storage technology, effectively solving the performance bottleneck problem faced by traditional databases when processing large-scale, high-frequency data. Through data sharding, load balancing, and other technologies, this module achieves efficient data storage and fast access, while ensuring data security and reliability. This distributed architecture not only improves the throughput of data processing but also reduces the risk of single points of failure in the system, providing strong support for the stable operation of the platform. The data computing module, as the core of the platform, integrates powerful functions such as offline computing, task development, and task scheduling. It can perform deep processing on the massive amounts of data in the storage module, such as data cleaning, transformation, and aggregation, to generate data computing results with business value. Furthermore, this module supports user-defined computing tasks, flexibly addressing the needs of different business scenarios. Through a task scheduling mechanism, the system can automatically allocate computing resources, optimize task execution efficiency, and ensure the timeliness and accuracy of data processing results. The data mining and analysis module, based on advanced data mining and machine learning algorithms, performs in-depth mining on the data output by the computing module, revealing the inherent connections and potential patterns between data. This module can detect abnormal patterns in the operation of photovoltaic power plants, predict equipment failures, and optimize power generation strategies, providing a scientific basis for the intelligent management of photovoltaic power plants. Through continuous learning and optimization, the accuracy of the analysis model will continuously improve, bringing higher economic and environmental benefits to photovoltaic power plants. Finally, the data management module provides the platform with comprehensive data governance services, including data catalog management, data quality monitoring, data sharing, and permission settings. Through this module, users can easily manage all data resources on the platform, ensuring data compliance, consistency, and security. At the same time, the data sharing function promotes data flow and cooperation between different departments and systems, breaking down information silos and improving overall operational efficiency. Attached Figure Description
[0018] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. In the drawings:
[0019] Figure 1 This is a schematic diagram of the structure of a controllable intelligent photovoltaic management platform according to the present invention. Detailed Implementation
[0020] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.
[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0022] To address the problem of low real-time performance of existing industrial big data technologies, which hinder rapid response to various changes in the production process, this invention provides a controllable intelligent photovoltaic management platform, such as... Figure 1 As shown, the platform includes a data management module, a service catalog module, a platform operation management module, a data access module, a data storage module, a data computing module, an analysis and mining module, and a data visualization module.
[0023] The data visualization module is used for data integration, visualization, data processing, sharing and distribution, data analysis, multi-screen applications, and management and maintenance. It supports data connection, visualization, data processing, sharing and publishing, data analysis, multi-screen application adaptation, and management and maintenance operations. The module presents the results of data mining and analysis in an intuitive and easy-to-understand way, such as charts, dashboards, and reports, allowing users to quickly understand the business implications behind the data. This module not only supports customized visualization designs but also features interactive query functions, allowing users to explore data details in depth through simple operations, improving decision-making efficiency and accuracy.
[0024] The analysis and mining module comprises a data modeling submodule, a pre-aggregation modeling submodule, and an operator library. The analysis and mining module covers three sub-components: data modeling, pre-aggregation modeling, and an algorithm library. These are responsible for interactive and automated modeling, pre-aggregation data source management, analysis, and management, respectively, and providing algorithms for data processing, machine learning, model evaluation, and user-defined operations. The mining and analysis module, based on advanced data mining and machine learning algorithms, performs in-depth mining on the data output by the computation module, revealing the inherent relationships and potential patterns between the data. This module can discover abnormal patterns in the operation of photovoltaic power plants, predict equipment failures, and optimize power generation strategies, providing a scientific basis for the intelligent management of photovoltaic power plants. Through continuous learning and optimization, the accuracy of the analysis model will continuously improve, bringing higher economic and environmental benefits to photovoltaic power plants.
[0025] The data modeling submodule is used for interactive modeling and automatic modeling;
[0026] The pre-aggregation modeling submodule is used for pre-aggregation data sources, pre-aggregation analysis, and pre-aggregation management;
[0027] The operator library is used for data processing, machine learning, model evaluation, and custom operators;
[0028] The data computing module is used for offline computing, task development, and task scheduling. As the core of the platform, it integrates powerful functions such as offline computing, task development, and task scheduling. It can perform in-depth processing on massive amounts of data in the storage module, such as data cleaning, transformation, and aggregation, to generate data computing results with business value. Furthermore, this module supports user-defined computing tasks, flexibly addressing the needs of different business scenarios. Through a task scheduling mechanism, the system can automatically allocate computing resources, optimize task execution efficiency, and ensure the timeliness and accuracy of data processing results.
[0029] The data storage module is divided into structured storage and unstructured storage sub-modules. The structured storage sub-module includes relational databases and Hive, while the unstructured storage sub-module includes HBase, HDFS, object storage, Redis, graph databases, and message queues. The data storage module employs advanced distributed storage technology, effectively solving the performance bottlenecks faced by traditional databases when processing large-scale, high-frequency data. Through data sharding, load balancing, and other techniques, this module achieves efficient data storage and fast access while ensuring data security and reliability. This distributed architecture not only improves data processing throughput but also reduces the risk of single points of failure, providing strong support for the platform's stable operation.
[0030] The data access module is used for source configuration, target configuration, data conversion tools, data ETL processing, and offline access management.
[0031] This invention, through the design of a data access module, enables the broad access of diverse data sources, including but not limited to real-time monitoring data from photovoltaic power plants, equipment operation logs, environmental parameters, and market electricity price information, providing a rich and comprehensive data foundation for the platform. This module not only supports the parsing and conversion of various data formats but also possesses high-concurrency processing capabilities, ensuring the real-time nature and accuracy of data at the source, thus laying a solid foundation for subsequent data processing and analysis.
[0032] The data management module is used for data catalog management, data governance, data sharing, and permission configuration. It provides functions for data catalog management, data governance, data sharing, and permission settings.
[0033] Data catalog management is used for technical catalogs and business catalogs; data governance is used for metadata management, data quality management, and data standard management; data sharing is used for API management and external data sharing; and permission configuration is used for data catalog authorization and platform function permission management.
[0034] The service catalog module displays information from all modules on the platform. Platform operations management is used for control based on the information from each module. Platform operations management provides convenience for platform administrators, including integrating the application marketplace module, comprehensive portal module, operations management module, and security center module.
[0035] The application marketplace module provides unified publishing and management capabilities for applications. Within the application marketplace, users can perform operations such as delisting and adding to favorites, deploying applications, and subscribing to applications.
[0036] The integrated portal module provides a unified entry point for all components of the platform. It serves as a platform to showcase the capabilities of platform components and can also extract data into the portal for real-time presentation of summary information.
[0037] The Operations Center module allows platform operations administrators to perform operational tasks such as work order approval, tenant management, and tool management. Individual users can also initiate work orders and apply for platform resources in the Operations Center.
[0038] The Security Center module provides access control for platform components and data, and also enables operations such as log auditing and platform backup.
[0039] The data access module collects various types of data from edge devices to provide data input for the entire platform, including protocol data such as MQTT / RS485 / ModBus / Http. The collected data can be used for edge computing at the edge segment, and also sent to the cloud for the platform to process in real time or offline.
[0040] Edge device management provides integrated management functions for edge devices, including receiving access layer data for production monitoring; it also provides edge device management, smart gateway management, and edge computing management functions.
[0041] The general-purpose PaaS platform is deeply integrated and customized based on open-source technologies such as Kubernetes, Docker, and Jenkins, providing excellent PaaS layer support for upper-layer applications or components. It offers CI / CD continuous integration and continuous delivery for R&D, testing, and operations personnel; various middleware resources for developers; image repository management for business development; and comprehensive cluster management and platform management functions for platform operations and management personnel.
[0042] Visual report development platforms can process, combine, and package data from industrial big data platforms according to different business needs, ultimately generating various sub-business analysis modules. After configuring the data through drag-and-drop, it can be viewed anytime, anywhere on PCs and mobile devices. Furthermore, the system can generate periodic reports, which can be sent by the system administrator to various business personnel, significantly saving human resources for the data service department.
[0043] The industrial big data platform is customized and optimized based on open-source Hadoop components, providing upper-layer data applications with functions such as data access, data storage, big data management, and big data modeling and analysis.
[0044] Big Data Access is responsible for connecting real-time / offline data sources to the big data platform, and performing ELT processing and collector management during the connection process.
[0045] Big data storage uses HDFS, Swift, and HBase for unstructured data storage; and Hive, relational databases, and Cassandra for structured data storage.
[0046] Big data management provides offline and real-time computing engines, a one-stop big data development and scheduling tool, big data governance functions such as data catalog management, data governance, and data sharing, and overall platform operation and maintenance monitoring, tenant management, and security management.
[0047] The big data modeling tool provides a multi-dimensional analysis engine and visual modeling tools for designing cube models, supporting dimensions, metrics, hierarchical levels, and attributes, as well as star and snowflake schemas. It also offers a data mining toolkit, enabling online development of machine learning and artificial intelligence models through canvas-based configuration.
[0048] The service governance platform serves as a unified entry point for managing various microservices within an enterprise. It provides service governance capabilities including microservice management, service cataloging, and microservice governance. Developers and operations personnel can use the platform to implement a range of management measures for various microservices, such as adding / dropping them online, scaling them up / down, rate limiting, and monitoring.
[0049] Through the data access layer, it can support data collection of multiple protocols (such as MQTT, RS485, ModBus, Http, etc.) and realize data interaction between edge devices and the cloud.
[0050] It supports structured (such as relational databases and Hive) and unstructured (such as HBase, HDFS, object storage, Redis, graph databases, and message queues) data storage to meet the storage needs of different data types.
[0051] Edge computing capabilities allow for initial processing at the source of data generation, reducing data transmission volume and increasing processing speed.
[0052] The cloud supports real-time and offline data processing, enabling instant data analysis and historical data analysis through big data access, storage, and management functions.
[0053] It provides a one-stop big data development and scheduling tool to simplify the development process of big data applications.
[0054] Data catalog management, data governance (including metadata management, data quality management, and data standards management), and data sharing functions ensure data accuracy, consistency, and security. Data visualization capabilities make data analysis and result presentation more intuitive and understandable. The visualization report development platform supports drag-and-drop configuration, enabling the rapid generation of reports and periodic reports that meet diverse business needs, improving the efficiency of data services. The service governance platform serves as a unified entry point for microservices, providing microservice management, service cataloging, and microservice governance functions, simplifying the deployment, monitoring, and management of microservices.
[0055] Support for microservice deployment / deployment, scaling up / down, and rate limiting enhances system flexibility and stability. The platform operations management provides administrators with a user-friendly interface, integrating four modules: application marketplace, comprehensive portal, operations management, and security center, improving operational efficiency and security. The security center offers access control and log auditing to ensure platform and data security. The software supports operation on domestically produced processors, servers, and operating systems, meeting the needs of domestic substitution and enhancing system autonomy and controllability.
[0056] In summary, this invention's platform achieves efficient management of industrial big data by optimizing data access, storage, and processing workflows. In particular, the introduction of edge computing capabilities allows for preliminary processing at the source of data generation, significantly reducing data transmission volume and improving processing speed. The cloud supports real-time and offline data processing, ensuring immediate and historical data analysis, thereby enabling rapid response to various changes in the production process. Secondly, the platform provides a one-stop big data development and scheduling tool, simplifying the development process of big data applications and ensuring data accuracy. Through data catalog management, data governance, and data sharing functions, the platform can comprehensively monitor and manage the data lifecycle, effectively preventing data errors and redundancy, and improving data consistency and reliability.
[0057] The data visualization module in this invention supports functions such as data connection, visualization presentation, data processing, sharing, and publishing, making data analysis and result display more intuitive and easy to understand. The visualization report development platform supports drag-and-drop configuration, enabling rapid generation of reports and periodic reports that meet different business needs, greatly improving the efficiency of data services and the accuracy of decision-making. The service governance platform, as the unified entry point for microservices, provides microservice management, service catalogs, and microservice governance functions, simplifying the deployment, monitoring, and management of microservices. It supports management measures such as microservice online / offline, scaling up / down, and rate limiting, allowing the system to flexibly adjust resource allocation according to actual needs, improving system flexibility and stability. The platform operation management module provides a convenient operating interface for platform administrators, integrating four major modules: application market, comprehensive portal, operation management, and security center, improving platform operation and maintenance efficiency. The security center provides access control and log auditing functions to ensure platform and data security, effectively preventing data leakage and unauthorized access. The software supports running on domestic processors, domestic servers, and domestic operating system environments, meeting the needs of domestic substitution. This not only enhances the system's autonomy and controllability but also reduces dependence on external technologies, improving system security and stability.
[0058] Many embodiments and applications beyond the examples provided will be apparent to those skilled in the art upon reading the foregoing description. Therefore, the scope of this teaching should not be determined by reference to the foregoing description, but rather by reference to the foregoing claims and the full scope of their equivalents. For purposes of completeness, all articles and references, including patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the foregoing claims is not intended as a waiver of that subject matter, nor should it be construed as an indication that the applicant has not considered that subject matter as part of the disclosed inventive subject matter.
[0059] The above content provides a further detailed description of the present invention. It should not be construed that the specific embodiments of the present invention are limited to this. For those skilled in the art, several simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention as defined by the submitted claims.
Claims
1. A controllable intelligent photovoltaic management platform, characterized in that, It includes a data access module, a data storage module, a data computing module, a data mining and analysis module, a data visualization module, and a data management module; The data access module is used to provide data input for the entire platform; The data storage module is used to receive and store data sent by the data access module; The data calculation module is used to perform offline calculations, task development, and task scheduling based on the data in the data storage module, and generate data calculation results. The analysis and mining module is used to perform analysis and mining based on the data from the data calculation module to obtain analysis and mining results. The data visualization module is used to display the results of the analysis and mining. The data management module provides functions for data catalog management, data governance, data sharing, and permission settings.
2. The controllable intelligent photovoltaic management platform according to claim 1, characterized in that, It also includes a service catalog module and a platform operation management module; The service catalog module is used to display information about the data access module, data storage module, data computing module, data mining and analysis module, data visualization module, and data management module. The platform operation and management module is used to interface with external systems for the data access module, data storage module, data calculation module, data mining and analysis module, data visualization module, and data management module.
3. The controllable intelligent photovoltaic management platform according to claim 1, characterized in that, The analysis and mining module includes a data modeling submodule, a pre-aggregation modeling submodule, and an operator library; The data modeling submodule is used for interactive modeling and automatic modeling; The pre-aggregation modeling submodule is used for pre-aggregation data sources, pre-aggregation analysis, and pre-aggregation management. The operator library is used for data processing, machine learning, model evaluation, and custom operators.
4. The controllable intelligent photovoltaic management platform according to claim 1, characterized in that, The data access module is used to provide data input for the entire platform, including: The data access module collects source configuration data, target configuration data, and conversion tool data, performs data ETL processing and offline access management, and provides the obtained data to the data storage module.
5. The controllable intelligent photovoltaic management platform according to claim 1, characterized in that, The data storage module includes a structured storage submodule and an unstructured storage submodule; The structured storage submodule includes a relational database and Hive; The unstructured storage submodule includes HBase, HDFS, object storage, Redis, graph database, and message queue.
6. The controllable intelligent photovoltaic management platform according to claim 1, characterized in that, The data visualization module is used to display the results of the analysis and mining, including: The data visualization module is used for data connection, visualization display, data processing, sharing and sending, data analysis, multi-screen application, management and maintenance.
7. The controllable intelligent photovoltaic management platform according to claim 1, characterized in that, The data directory management is used for the technology directory and the business directory.
8. The controllable intelligent photovoltaic management platform according to claim 1, characterized in that, The data governance is used for metadata management, data quality management, and data standards management.
9. A controllable intelligent photovoltaic management platform according to claim 1, characterized in that, The data sharing is used for API management and external data sharing.
10. A controllable intelligent photovoltaic management platform according to claim 1, characterized in that, The permission configuration is used for data directory authorization and platform function permission management.