Energy consumption data processing method and device
By constructing a hierarchical data model and an interactive analysis platform, the problems of cumbersome data processing and biased comparison results in the factory energy consumption management system have been solved, achieving efficient and real-time energy consumption data management and analysis.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-27
- Publication Date
- 2026-03-10
AI Technical Summary
In existing factory energy management systems, the simple stacking integration of BI analysis platforms and SCADA platforms leads to cumbersome data processing, large deviations in comparison results, and difficulty in meeting the needs of real-time and flexible management. Furthermore, the lack of structured and hierarchical processing makes it impossible to achieve multi-dimensional energy consumption data comparison and predictive analysis.
By constructing a hierarchical data model, metadata processing is applied to energy consumption data. Structured data is built using the object-oriented functionality of SCADA software, and data analysis is performed through the H2Server and Tableau platforms. This enables unified data storage and visualization, supporting multi-dimensional interactive analysis.
It simplifies the creation process of data association models, reduces human error, improves data processing efficiency, realizes real-time and flexible energy consumption management, supports multi-dimensional energy consumption analysis and prediction, and improves management efficiency and accuracy.
Smart Images
Figure CN121636593A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present invention relate to the field of factory energy consumption management and analysis, and particularly to a method and apparatus for processing energy consumption data. Background Technology
[0002] Factory energy management is undergoing a profound digital transformation globally, with solutions combining BI reporting and SCADA becoming a focal point of industry attention. From an international perspective, developed countries in Europe and America started their research and development and implementation of energy management platforms earlier, and have established relatively mature technological systems and market structures.
[0003] However, existing technical solutions are mostly simple, piecemeal integrations of BI analytics platforms and SCADA platforms. Data is collected and stored in a database through platforms like SCADA, and then the BI platform extracts data from the database, providing only basic report display and data analysis functions. If enterprises need to achieve multi-dimensional energy consumption data comparison, on the one hand, they need to manually create multiple data association models in the BI platform, repeatedly filtering fields and adjusting dimensions—a cumbersome and time-consuming process. On the other hand, human error can easily lead to biased comparison results, making it difficult to meet the needs of real-time and flexible management. Summary of the Invention
[0004] The purpose of this invention is to provide at least one method and apparatus for processing energy consumption data, which can at least solve the problems of cumbersome manual creation of multiple data association models and large deviations in comparison results, and can at least improve efficiency and reduce the deviations in comparison results.
[0005] To address the aforementioned technical problems, at least one embodiment of this application provides a method for processing energy consumption data, comprising: The collected energy consumption data is processed by adding metadata based on the hierarchical data model to obtain structured data, which is then written into the database. The structured data is extracted from the database and analyzed to obtain visualized information.
[0006] At least one embodiment of this application also provides an energy consumption data processing apparatus, comprising: The data acquisition module is used to perform metadata appending processing on the collected energy consumption data according to the hierarchical data model, obtain structured data, and write it into the database. The data analysis module is used to extract the structured data from the database for analysis and to obtain visualized information.
[0007] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described energy consumption data processing method.
[0008] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for processing energy consumption data.
[0009] The energy consumption data processing method provided in this application involves collecting energy consumption data, such as electricity meter data, from a factory using industrial control software like SCADA software. A hierarchical data model is constructed using the object-oriented functionality of the industrial control software. Metadata tags are then appended to the energy consumption data based on this hierarchical data model, resulting in structured data with hierarchical relationships. A server with data structure preservation features, such as H2Server, is used to transmit the structured data to a database. This ensures that the energy consumption data stored in the database inherits the hierarchical relationships constructed in the industrial control software, laying a structural foundation for subsequent visualization analysis. This approach reduces the manual work required to create multiple data association models, filter fields, and adjust dimensions on the visualization analysis platform, simplifying manual work and improving efficiency. Furthermore, it reduces deviations in comparison results caused by human error, thus meeting the needs of real-time and flexible management. Attached Figure Description
[0010] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.
[0011] Figure 1 This is a flowchart of an energy consumption data processing method provided in one embodiment of this application; Figure 2 This is a schematic diagram of the platform core architecture and data flow provided in one embodiment of this application; Figure 3 This is a schematic diagram illustrating the reverse integration and optimization of a SCADA system with the Tableau platform provided in one embodiment of this application; Figure 4 This is a schematic diagram of the structure of an energy consumption data processing device provided in one embodiment of this application. Detailed Implementation
[0012] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.
[0013] To facilitate understanding of the embodiments of this application, relevant content on factory energy consumption management will be introduced first.
[0014] Factory energy management is undergoing a profound digital transformation globally, with solutions combining BI reporting and SCADA becoming a focal point of industry attention. From an international perspective, developed countries in Europe and America started their research and development and implementation of energy management platforms earlier, and have established relatively mature technological systems and market structures.
[0015] Domestic and international enterprises are actively integrating SCADA systems with BI tools, leveraging SCADA's real-time data acquisition capabilities and BI tools' advanced visualization functions to build comprehensive energy consumption management platforms. The SCADA system collects real-time data from various sensors, smart meters, and control devices in the factory. This data is transmitted to the SCADA platform via standard protocols such as OPCUA and Modbus, and then stored in a database on the SCADA platform. This database data is then used by BI tools for in-depth analysis and visualization. This technological combination overcomes the data silo problem in traditional energy management, providing a more comprehensive and accurate view of energy consumption. However, existing technical solutions are mostly simple, stacked integrations of BI analysis platforms and SCADA platforms. Data is collected and stored in a database through SCADA platforms, and then the BI platform extracts data from the database, only providing basic report display and data analysis functions. If enterprises need to achieve multi-dimensional energy consumption data comparisons, such as energy consumption comparisons across workshops during the same period, energy consumption comparisons of the same equipment under different production loads, and comprehensive comparisons of the combined proportions of multiple energy types (electricity / water / steam), multiple data association models need to be manually created in the BI platform, repeatedly filtering fields and adjusting dimensions. This is not only cumbersome and time-consuming, but also prone to errors due to human intervention, making it difficult to meet the needs of real-time and flexible management.
[0016] To achieve predictive energy consumption analysis, such as forecasting total energy consumption for the next day / month based on historical energy consumption data and production plans, predicting the energy consumption growth trend of a single piece of equipment based on equipment aging curves, and predicting peak energy demand in different quarters based on seasonal fluctuations, existing solutions have significant shortcomings. Due to the lack of deep integration between SCADA and BI platforms at the data logic level, BI tools cannot obtain dynamic data such as equipment operating status and production load changes from the SCADA system in real time. They can only rely on static historical database data for prediction, resulting in a significant reduction in the timeliness and accuracy of prediction models. This makes it difficult for enterprises to formulate energy optimization strategies in advance, such as adjusting energy procurement plans based on predicted peak values and identifying potential hazards in high-energy-consuming equipment. Ultimately, it fails to fully leverage the supporting value of energy consumption data for green production decisions.
[0017] Current industry solutions are mostly simple, piecemeal integrations of BI and SCADA platforms. Data is collected and stored in a database through SCADA platforms, and then the BI platform extracts data from the database. This approach only provides basic report display and data analysis functions, and has the following key limitations: 1. Energy consumption data lacks structured and hierarchical processing, resulting in weak data value mining capabilities. (1) Objectless packaging design: The energy consumption data collected by SCADA is mostly "isolated data points" (such as real-time power data of a single sensor), which are not associated with the physical hierarchy of "factory-workshop-production line-equipment", and are not attached with key metadata such as equipment model, energy consumption type, and rated power, resulting in the lack of complete "contextual information" and the inability to directly trace the source of energy consumption. (2) No digital mirror support system: When the data is stored in the database, the hierarchical relationship of the physical factory is not preserved, and it is impossible to build a "digital mapping" that corresponds one-to-one with the actual production scenario. Subsequent BI platform analysis requires manual repeated association of data dimensions, which is not only inefficient, but also prone to data matching errors, making it difficult to achieve refined energy consumption anomaly location at the "workshop-production line-equipment" level. 2. The interactive analysis value of BI platforms is limited.
[0018] (1) The interactive functions are seriously insufficient: the energy consumption data is presented in the form of fixed reports and static charts, similar to the "fixed monitoring screen" of traditional configuration software. Users cannot conduct independent data exploration through drag, filter, drill down and other operations (for example, drill down from the total energy consumption of the factory to the time-sharing energy consumption curve of a single device, or filter the energy consumption comparison data of multiple production lines in a specific period). (2) Lack of proactive analysis capabilities: Managers can only passively view the content of preset reports and cannot flexibly adjust the analysis dimensions according to real-time business needs, making it difficult to quickly locate the root cause of energy consumption fluctuations (such as the inability to link the intrinsic relationship between "abnormal energy consumption" and "changes in production load" and "equipment operating status" in real time). 3. The SCADA and BI platforms have not achieved functional and logical integration, resulting in a fragmented system architecture.
[0019] (1) There is a gap in the data link: SCADA and BI platform only transmit data through the database as an intermediate medium.
[0020] (2) No unified management architecture: No integrated energy management center has been built. SCADA only undertakes the function of "data collection" and the BI platform is only responsible for "data display". The two functions are separated, and users need to switch between different platforms to view real-time data and analysis reports, resulting in low operational efficiency. To address the aforementioned technical problems in factory energy management, this invention proposes a method for processing energy consumption data. The implementation details of this embodiment's energy consumption data processing method are described below. The following content is merely for ease of understanding and is not essential for implementing this solution.
[0021] Example 1: The energy consumption data processing method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 1 As shown, it includes: Step 110: Perform metadata appending processing on the collected energy consumption data according to the hierarchical data model to obtain structured data, and write it into the database.
[0022] Specifically, by using a hierarchical data model and object-oriented encapsulation design, key metadata such as equipment model, energy consumption type, and rated power are added to energy consumption data. This completes the contextual information of the energy consumption data, allowing for the tracing of energy consumption sources, establishing hierarchical relationships, and obtaining structured data. A digital mirroring support system is established. H2 Server has the characteristic of preserving structured data. When storing structured data in the database, the hierarchical relationships of the structured data can be preserved, constructing a digital mapping that corresponds one-to-one with the actual production scenario. In subsequent Tableau platform analysis, this reduces the need for manual, repetitive association of data dimensions, improves efficiency, reduces errors caused by data matching, and enables multi-level, refined energy consumption anomaly localization.
[0023] In this embodiment, SCADA software serves as the core of front-end data acquisition. It collects energy consumption-related data across the entire factory in real time through mainstream industrial protocols such as Modbus, OPC, and MQTT. This includes metering data from electricity meters (electricity consumption), water meters (water resource consumption), flow meters (steam / gas and other medium consumption), and production process data output by PLCs (programmable logic controllers).
[0024] Step 120: Extract the structured data from the database and analyze it to obtain visualized information.
[0025] Specifically, the Tableau platform extracts data from a database, allowing users to perform structured data analysis independently through drag-and-drop, filtering, and drill-down operations. For example, users can drill down from total factory energy consumption to the time-of-use energy consumption curve of a single piece of equipment, or filter energy consumption comparison data across multiple production lines for a specific time period. Management personnel can view preset report content and flexibly adjust analysis dimensions according to real-time business needs, quickly pinpointing the root causes of energy consumption fluctuations, such as establishing real-time correlations between energy consumption anomalies and changes in production load and equipment operating status.
[0026] This embodiment of the energy consumption data processing method involves collecting energy consumption data such as electricity meter data from the factory using SCADA software. A hierarchical data model is constructed using the object-oriented functionality of the SCADA software. Metadata tags are then appended to the energy consumption data based on this hierarchical model, resulting in structured data with hierarchical relationships. H2 Server, with its data structure preservation feature, transmits the structured data to the database. This allows the energy consumption data stored in SQL Server to inherit the hierarchical relationships built in the SCADA software, laying a structural foundation for subsequent Tableau platform analysis. This reduces the need for manual work in Tableau, such as creating multiple data association models, filtering fields, and adjusting dimensions, simplifying manual processes and improving efficiency. Furthermore, it reduces the deviation in comparison results caused by human error, thus meeting the demands of real-time and flexible management.
[0027] In one embodiment, the hierarchical data model is constructed according to the structure of factories, workshops, production lines, and equipment.
[0028] In this embodiment, relying on the object-oriented function of SCADA software, a hierarchical data model is constructed according to the actual physical structure of the factory (factory → workshop → production line → equipment). Metadata tags such as equipment attributes (e.g., equipment model, rated power) and energy consumption type (e.g., power electricity, lighting electricity) are automatically added to each collected data point, realizing the structured organization of data from the source.
[0029] In one embodiment, the step of extracting the structured data from the database for analysis to obtain visualized information includes: The structured data is extracted from the database and designed using drag-and-drop development functionality to obtain an interactive visual dashboard, the visual information of which includes the interactive visual dashboard.
[0030] In this embodiment, the structured data is extracted from the database through the Tableau platform. Using Tableau's drag-and-drop development function, a series of interactive visualization dashboards are designed to support multi-dimensional data filtering (such as by time, region, and equipment type), hierarchical drill-down (such as drill-down from the total energy consumption of the factory to the time-sharing energy consumption of a single device), and indicator linkage (such as clicking on an energy consumption anomaly area to synchronously display the operating status of the corresponding equipment), thereby achieving intuitive and efficient energy consumption analysis.
[0031] In one embodiment, the method further includes: A browser plugin is embedded into the industrial control software, and the access link for the visualized information is loaded through the browser plugin.
[0032] In this embodiment, to further improve ease of operation and management efficiency, a browser plugin is embedded into industrial control software such as SCADA software. This plugin loads access links to Tableau platform reports, achieving reverse integration of the Tableau platform (an energy management platform) into the SCADA system interface. By embedding the browser plugin into the SCADA software and directly loading web access links to Tableau reports, the real-time monitoring interface of the SCADA software and the energy consumption analysis reports of the Tableau platform can be displayed simultaneously on the same screen. Users can view real-time production data and energy consumption analysis results without switching software. In one embodiment, the method further includes: The operation window of the industrial control software is divided into several display areas, and each display area is used to display at least one of the visual information, monitoring screen and management interface.
[0033] In this embodiment, the single operation window of industrial control software such as SCADA software is divided into several independent display areas, for example, four. These areas can simultaneously load process monitoring screens (such as real-time equipment operating status), visual information, video monitoring screens (such as real-time workshop footage), and other management modules (such as energy consumption warning notifications). This enables parallel processing of multi-dimensional information, significantly improving the decision-making efficiency and operational convenience for management personnel. Visual information, for example, is a Tableau energy consumption analysis report, which is an interactive visual dashboard designed using Tableau's drag-and-drop development functionality.
[0034] In one embodiment, the step of performing metadata appending processing on the collected energy consumption data according to the hierarchical data model to obtain structured data includes: The collected energy consumption data is cleaned and / or edge computing is performed to obtain preprocessed energy consumption data; The preprocessed energy consumption data is subjected to metadata tag appending processing based on the hierarchical data model to obtain the structured data.
[0035] In this embodiment, the raw data / energy consumption data collected is cleaned and edge-computed using the data processing module built into the SCADA software to ensure the quality of the energy consumption data. Data cleaning includes removing outliers and filling in missing values. Edge computing, for example, calculates the instantaneous energy consumption of a single device in real time.
[0036] In one embodiment, the method further includes: Map the hierarchical tags in the database to semantic layer business metrics.
[0037] In this embodiment, a connection to the SQL Server database is directly established within the Tableau platform. Its data modeling capabilities are used to map hierarchical tags in the database, such as the field "Area=A", to semantic layers that better fit the business scenario. For example, Area=A is mapped to production line A, reducing the operational threshold for business personnel. Based on this semantic layer, a core business indicator system is constructed, including unit product energy consumption, regional energy consumption (total energy consumption at the workshop / production line level), real-time efficiency (equipment energy utilization rate), and energy consumption correlation between "on / off status" and "equipment runtime."
[0038] The technical effects of this embodiment are as follows: (1) Solve the problem of data dispersion and silos: Utilize the hierarchical label function (such as factory-workshop-production line-equipment) to uniformly model and store multi-source heterogeneous energy consumption data according to business logic, fundamentally break down data barriers, and lay a high-quality data foundation for global analysis.
[0039] (2) Solving the problems of superficial analysis and low efficiency: Tableau can directly perform multi-dimensional and visualized deep drill-down analysis based on the pre-built hierarchical data model without complicated data preparation and secondary modeling, quickly locate the root cause of energy waste, and realize the transformation from post-event statistics to in-event insight and pre-event prediction.
[0040] (3) Solve the problems of system rigidity and high operation and maintenance costs: Based on standardized hierarchical data structure and modular design, the system functions are easy to expand and flexibly adjust, and can respond quickly to business changes, significantly reducing the long-term operation and maintenance and iteration costs of the system.
[0041] (4) Solve the complex problems of compliance management: Relying on a standardized hierarchical data model, business data is automatically linked and integrated, and various energy and compliance reports that meet the requirements of internal and external audits are generated with one click, which greatly reduces management complexity and labor costs.
[0042] (5) Solving the problem of personalized and efficient report development: Based on Tableau and hierarchical data model, a set of reusable BI report templates is encapsulated. Users can quickly generate standardized analysis reports that meet the requirements of different levels (such as group, factory, workshop) and different dimensions through simple filtering and selection, which greatly improves development efficiency and data analysis consistency while meeting personalized needs.
[0043] In this embodiment, object-oriented and hierarchical modeling is used. Relying on the object-oriented modeling capabilities of the Macro-Group SCADA platform, energy consumption data points are logically organized and encapsulated according to the physical hierarchy of "factory-workshop-production line-equipment-sensor" during the data acquisition stage. Metadata such as equipment model, energy consumption type, and rated power are added simultaneously to ensure the structure and relevance of the data from the source.
[0044] In this embodiment, a "digital mirror" is constructed: all real-time data is stored in the database through the H2 server in the macro-collection SCADA system. The data comes with rich contextual information such as hierarchical tags and metadata, laying a solid foundation for subsequent efficient data analysis and visualization.
[0045] This embodiment utilizes an immersive interactive experience: leveraging Tableau's interactive analytics capabilities, a dynamic energy consumption dashboard with multi-dimensional linkage and deep drill-down support is designed, offering an interactive experience far exceeding the fixed monitoring screens of traditional configuration software. Administrators can independently explore data and quickly pinpoint energy consumption anomalies through simple operations such as dragging and filtering.
[0046] This embodiment demonstrates through an integrated platform: by deploying Tableau on a cloud platform, the Hongji SCADA platform supports integrating the web address into a single UI interface, enabling real-time data viewing and in-depth analysis report viewing on the same platform, greatly improving operational convenience and management efficiency.
[0047] Example 2: The energy consumption data processing method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. For the specific process, please refer to [link / reference needed]. Figure 2 ,include: Step 210: Multi-source energy consumption data acquisition and structured modeling.
[0048] In this embodiment, SCADA software serves as the core of front-end data acquisition. It collects real-time energy consumption data across the entire factory using mainstream industrial protocols such as Modbus, OPC, and MQTT. This includes metering data from electricity meters (electricity consumption), water meters (water resource consumption), flow meters (steam / gas / other medium consumption), and production process data output by PLCs (Programmable Logic Controllers). Leveraging the object-oriented functionality of the SCADA software, a hierarchical data model is constructed according to the factory's actual physical structure (factory → workshop → production line → equipment). Metadata tags such as equipment attributes (e.g., equipment model, rated power) and energy consumption type (e.g., power supply, lighting supply) are automatically added to each collected data point, achieving structured data organization from the source.
[0049] Step 220: Data preprocessing and efficient transmission and storage.
[0050] In this embodiment, the data processing module built into the SCADA software performs preliminary cleaning (removing outliers and filling in missing values) and edge computing (such as real-time calculation of instantaneous energy consumption of a single device) on the collected raw data to ensure data quality. The processed structured data is transmitted at high speed via H2 Server (a lightweight in-memory database server) and finally written to an SQL Server relational database. Leveraging H2 Server's data structure preservation features, the energy consumption data stored in SQL Server can fully inherit the "factory-workshop-production line-equipment" hierarchical relationship built in the SCADA software, laying a structural foundation for subsequent analysis.
[0051] Step 230: Business modeling and interactive visualization analysis.
[0052] In this embodiment, a connection to the SQL Server database is directly established in the Tableau platform. Its data modeling function is used to map the hierarchical tags in the database (such as the field "Area=A") to a semantic layer that is more in line with the business scenario (such as "Area=A→A production line"), thereby reducing the operational threshold for business personnel. The core business indicator system is built based on the semantic layer, including "unit product energy consumption", "regional energy consumption" (total energy consumption at the workshop / production line level), "real-time efficiency" (equipment energy utilization rate), and "on / off status" (energy consumption correlation during equipment operation). By leveraging Tableau's drag-and-drop development capabilities, a series of interactive visual dashboards can be designed, supporting multi-dimensional data filtering (such as by time, region, and equipment type), hierarchical drill-down (such as drilling down from the total energy consumption of the factory to the time-sharing energy consumption of a single device), and indicator linkage (such as clicking on an energy consumption anomaly area to synchronously display the operating status of the corresponding equipment), thereby achieving intuitive and efficient energy consumption analysis.
[0053] Step 240: SCADA and Tableau reverse integration optimization.
[0054] See Figure 3 In this embodiment, to further improve ease of operation and management efficiency, this solution innovatively achieves reverse integration of the Tableau energy management platform into the SCADA interface, as follows: (1) Embed a browser plugin in the SCADA software and load the web access link of Tableau report directly through the plugin to realize the simultaneous display of "SCADA real-time monitoring interface" and "Tableau energy consumption analysis report". Users can view real-time production data and energy consumption analysis results simultaneously without switching software. (2) At the same time, the single operation window of the SCADA software is divided into 4 independent display areas, which can simultaneously load process monitoring screens (such as real-time equipment operation status), Tableau energy consumption analysis reports, video monitoring screens (such as real-time images of the workshop site) and other management modules (such as energy consumption early warning notifications), realizing parallel processing of multi-dimensional information and greatly improving the decision-making efficiency and operation convenience of management personnel.
[0055] In this embodiment, the core architecture consists of four major components: SCADA software, H2 server, SQL Server database, and Tableau visualization analysis platform. Each module works together to achieve end-to-end management of "data acquisition-processing-storage-analysis-visualization".
[0056] This embodiment proposes a factory energy consumption intelligent management and analysis system based on the deep integration of the Tableau advanced visualization analysis platform and an industrial-grade SCADA system. The core objective is to address the fundamental pain points of manufacturing enterprises—"invisible, unclear, and uncontrollable" energy consumption data—and promote the transformation of energy use towards transparent, refined, and intelligent management. By deeply integrating the real-time data acquisition and control capabilities of the SCADA system with Tableau's efficient data analysis and visualization capabilities, an energy management center integrating "real-time monitoring, multi-dimensional analysis, predictive early warning, and optimized decision-making" is constructed. Ultimately, this achieves a significant reduction in overall energy consumption and an improvement in energy utilization efficiency, providing core technological support for the implementation of enterprises' green and low-carbon strategies.
[0057] In this embodiment, object-oriented and hierarchical modeling is performed. Relying on the object-oriented modeling capabilities of the MacroGroup SCADA platform, energy consumption data points are logically organized and encapsulated according to the physical hierarchy of "factory-workshop-production line-equipment-sensor" during the data acquisition stage. Metadata such as equipment model, energy consumption type, and rated power are added simultaneously to ensure the structure and relevance of the data from the source.
[0058] In this embodiment, a "digital mirror" is constructed: the hierarchical data structure described above is fully preserved when stored in a real-time / historical database, forming a "lightweight digital mirror (DigitalTwinLite)" that corresponds one-to-one with the physical factory. The data comes with rich contextual information such as hierarchical tags and metadata, laying a solid foundation for subsequent efficient data analysis and visualization.
[0059] This embodiment utilizes an immersive interactive experience: fully leveraging Tableau's interactive analytics advantages, a dynamic energy consumption dashboard with multi-dimensional linkage and deep drill-down support is designed, providing an interactive experience far exceeding the fixed monitoring screens of traditional configuration software. Administrators can independently explore data and quickly locate anomalies in energy consumption through simple operations such as dragging and filtering.
[0060] This embodiment demonstrates an integrated platform: the Hongji SCADA platform supports the embedding of the already integrated Tableau functionality, achieving a unified platform display of "real-time data monitoring + data analysis reports." Users do not need to switch between multiple software programs to complete real-time data viewing and in-depth analysis on a single interface, significantly improving operational convenience and management efficiency.
[0061] Example 3: Another embodiment of this application relates to an energy consumption data processing device. The implementation details of this energy consumption data processing device are described below. The following details are for ease of understanding and are not essential for implementing this solution. A schematic diagram of the energy consumption data processing device in this embodiment can be seen as follows: Figure 4 As shown, it includes a data acquisition module 401 and a data analysis module 403.
[0062] The data acquisition module is used to perform metadata appending processing on the collected energy consumption data according to the hierarchical data model, obtain structured data, and write it into the database. The data analysis module is used to extract the structured data from the database for analysis and to obtain visualized information.
[0063] In one embodiment, the hierarchical data model is constructed according to the structure of factories, workshops, production lines, and equipment.
[0064] In one embodiment, the data analysis module 403 is used to extract the structured data from the database, design it through drag-and-drop development, and obtain an interactive visual dashboard, wherein the visual information includes the interactive visual dashboard.
[0065] In one embodiment, the device further includes a reverse integration module for embedding a browser plugin into industrial control software and loading access links for the visualization information through the browser plugin.
[0066] In one embodiment, the reverse integration module is further configured to divide the operation window of the industrial control software into several display areas, each of which is used to display at least one of the visualization information, monitoring screen, and management interface.
[0067] In one embodiment, the data acquisition module 401 is used to clean and / or perform edge computing on the acquired energy consumption data to obtain preprocessed energy consumption data; and to perform metadata tag appending processing on the preprocessed energy consumption data according to the hierarchical data model to obtain the structured data.
[0068] In one embodiment, the apparatus further includes a tag mapping module, which is used to map hierarchical tags in the database to semantic layer business metrics.
[0069] It is worth mentioning that the energy consumption data processing device described in this embodiment can be used to execute any step of the embodiment of the above-described energy consumption data processing method. All modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units do not exist in this embodiment.
[0070] Example 4: Another embodiment of this application relates to an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the energy consumption data processing method in the above embodiments.
[0071] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.
[0072] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.
[0073] Example 5: Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.
[0074] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0075] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.
Claims
1. A method of processing energy consumption data, characterized by, The method comprises the following steps: According to the hierarchical data model, the collected energy consumption data is subjected to metadata processing to obtain structured data, which is written into a database; The structured data is extracted from the database for analysis to obtain visualized information.
2. The treatment method according to claim 1, characterized in that, The hierarchical data model is constructed according to the structure of a factory, a workshop, a production line and equipment.
3. The treatment method of claim 1, wherein, The step of extracting the structured data from the database for analysis to obtain visualized information comprises: The structured data is extracted from the database and designed through a drag-and-drop development function to obtain an interactive visualized dashboard, and the visualized information comprises the interactive visualized dashboard.
4. The treatment method of claim 1, wherein The method further comprises: A browser plug-in is embedded into industrial control software, and an access link of the visualized information is loaded through the browser plug-in.
5. The treatment method according to claim 4, characterized in that, The method further comprises: An operation window of the industrial control software is divided into a plurality of display areas, and each display area is used to display at least one of the visualized information, a monitoring screen and a management interface.
6. The treatment method of claim 1, wherein The step of collecting energy consumption data and performing metadata processing according to a hierarchical data model to obtain structured data comprises: The collected energy consumption data is subjected to cleaning and / or edge computing to obtain preprocessed energy consumption data; The preprocessed energy consumption data is subjected to metadata label processing according to the hierarchical data model to obtain the structured data.
7. The treatment method of claim 1, wherein The method further comprises: The hierarchical labels in the database are mapped to semantic layer business indicators.
8. An energy consumption data processing apparatus, characterized by comprising: The method comprises: A data collection module is configured to collect energy consumption data and perform metadata processing according to a hierarchical data model to obtain structured data, which is written into a database; A data analysis module is configured to extract the structured data from the database for analysis to obtain visualized information.
9. An electronic device, comprising: The method comprises: At least one processor; And A memory connected in communication with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the energy consumption data processing method according to any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the energy consumption data processing method according to any one of claims 1 to 7.