A method for generating intelligent reporting tools based on comprehensive energy modeling

Abstract

The invention discloses a method for generating an intelligent reporting tool based on comprehensive energy modeling, including the following contents: S1, building an energy equipment modeling tree based on a digital twin modeling standard; S2, selecting a number of models, energy consumption attributes, and time components to bind to cells in a worksheet, and setting the arrangement to obtain an original table; S3, retrieving all cross cells in the original table, and if the cross cell is the intersection of an energy attribute and a model, it is set as a data type unit; S4, obtaining a list of instances under all models, and simultaneously retrieving all cross cells of the data type in the original table, matching the data in the instance list, and calculating the number of cells that all models and time components need to expand; S5, according to the combined relationship between each instance and the energy consumption attribute, retrieving and filling cross blank cells, that is, generating a report. This solves the problem that the existing traditional reporting tools are low in flexibility and ease of use, and are difficult to meet rapidly changing business needs.

Description

[Technical field]

[0001] The present invention belongs to the technical field of integrated energy digitization, and in particular relates to a method for generating an intelligent reporting tool based on integrated energy modeling. [Background technology]

[0002] Current reporting tools mainly rely on standardized database structures and fixed field configurations to generate reports. The process usually includes the following steps: Data extraction - extracting the data required for the report from the database, usually obtaining the target fields and records through SQL queries to meet specific report requirements; Data processing - applying the ETL (extraction, transformation, loading) process to perform necessary processing and cleaning operations on the extracted data. Usually including data filtering, sorting and aggregation to meet specific analysis goals; Template design - users set the location, style and format of the fields based on predefined templates to make the report meet the required format specifications; Data rendering - embedding the processed data into the template through the front-end framework (such as HTML / CSS) to complete the visual presentation of the data and generate tables or other forms of expression; Output generation - finally output the report to PDF, Excel or other formats for easy sharing and display.

[0003] Although current reporting technology has been widely used, its limitations are obvious. Traditional reporting tools lack flexible support for industry-specific needs. For complex industries such as energy and manufacturing, data structures and analysis dimensions are diverse, and requirements change frequently, making it difficult for traditional tools to adapt quickly. For example, it is difficult for users to dynamically adjust the structure or analysis dimensions of reports, and it is impossible to seamlessly integrate data and analysis requirements of different energy equipment in the same tool. Industry-specific analysis requirements can usually only be achieved through time-consuming custom development, which requires high professional and technical skills of personnel, resulting in low flexibility and ease of use of reporting tools, making it difficult to meet rapidly changing business needs.

[0004] In summary, in traditional reporting tools, users are usually unable to quickly adjust the report structure and analysis dimensions according to specific needs, resulting in an inability to respond to business changes and decision-making needs in a timely manner. These tools often do not support industry-specific analysis needs, forcing energy managers to rely on developers for customization, which in turn reduces work efficiency. In addition, traditional reporting tools usually require complex configuration and expertise, and non-technical personnel face a high learning curve when using them, further limiting the universal applicability of the tools. This limitation prevents companies from making full use of existing resources during data analysis, affecting the real-time and accuracy of decision-making. [Summary of the invention]

[0005] The purpose of the present invention is to provide a method for generating an intelligent reporting tool based on comprehensive energy modeling to solve the problem that the existing traditional reporting tools have low flexibility and ease of use and are difficult to meet rapidly changing business needs.

[0006] The present invention adopts the following technical solution: a method for generating an intelligent reporting tool based on comprehensive energy modeling, comprising the following contents:

[0007] S1. Construct an energy equipment modeling tree based on the digital twin modeling standard, wherein the energy equipment modeling tree includes multiple models and energy consumption attributes, and each model includes multiple instances;

[0008] The examples are various types of energy equipment; the models are abstract models of equipment in different energy systems to construct hierarchical structures of different equipment; the energy consumption attributes are real-time collected data of each energy equipment;

[0009] S2. freely select several models, energy consumption attributes and time components to bind to cells of the worksheet, and set the arrangement mode for the models and energy consumption attributes to obtain the original table;

[0010] The arrangement includes horizontal arrangement and vertical arrangement along the worksheet; after setting, a plurality of cross cells are obtained in the worksheet, and each cross cell is used to display the energy consumption attribute data of the corresponding instance;

[0011] S3, searching all intersection cells in the original table, and if the intersection cell is an intersection of an energy attribute and a model, setting the intersection cell to a data type cell;

[0012] S4, obtaining the instance list of all models selected in step S2, and searching for the cross cells of all data types in the original table, matching the data in the instance list, and calculating the number of cells that need to be expanded for all models and time components, and then expanding;

[0013] S5. According to the combined relationship between each instance and the energy consumption attribute, the intersecting blank cells are retrieved as cells to be filled with data; the real-time collected data of the energy equipment corresponding to each instance is obtained, and the data is processed and filled into the intersecting blank cells, that is, the report is generated.

[0014] Furthermore, in S4, the specific method for calculating the number of cells that need to be expanded for all models and time components is:

[0015] Calculate the number of instance cells that need to be expanded for each model one by one, calculate the number of cells for model expansion through extendedCache (cache variable of model extension information), bind the instance name and dataCode (unique code), and the expansion is completed.

[0016] Furthermore, in S4, the multi-dimensional data expansion algorithm is used for expansion, specifically:

[0017] Pn=Pn-1+En-1+Bn,

[0018] Where Pn is the starting position of the nth model extension, Pn-1 is the starting position of the previous model extension, En-1 is the number of cells extended by the previous model, and Bn is the number of blank cells between the current model and the previous model;

[0019] Ci={Di,j|j=1,2,…,N},

[0020] Among them, Ci is the attribute set of the i-th instance, Di,j represents the j-th attribute under the i-th instance; N is the total number of attributes under the instance.

[0021] Furthermore, after the report is generated, the user creates a new visualization panel on the server side through the page and adds multiple report template cards; based on the data in the report template card, a DSL file is generated as the configuration data for downloading the report, which is mixed and packaged in the platform project; after downloading, the DSL file in the root directory is obtained, and then the data is saved in the local cache storage. When loading, the template card data is filled one by one according to the configuration file to complete the data rendering of the overall panel on the platform side; that is, the server can send the report by clicking, and the platform side can receive the report preview interface in real time.

[0022] The beneficial effects of the present invention are as follows: the present invention focuses on the integrated energy field facing the user side, such as electricity, refrigeration, heating, air compression, gas, hydrogen and other multi-energy systems and equipment, and is an intelligent reporting tool based on integrated energy modeling. It realizes the seamless integration of report design and digital twin modeling standards, provides multi-dimensional report analysis functions, multiple export methods and custom layouts, improves the data processing and display efficiency of reports, ensures the user-friendliness and ease of use of reports, realizes personalized customization and good scalability of reports, and supports cloud-edge integration and cross-platform data interaction. It solves the problem that the existing traditional reporting tools have low flexibility and ease of use, and are difficult to meet rapidly changing business needs. [Specific implementation method]

[0023] The present invention is described in detail below based on specific implementation modes.

[0024] The present invention provides a method for generating an intelligent reporting tool based on comprehensive energy modeling, comprising the following contents:

[0025] S1. Construct an energy equipment modeling tree based on the digital twin modeling standard, wherein the energy equipment modeling tree includes multiple models and energy consumption attributes, and each model includes multiple instances;

[0026] The examples are various types of energy equipment such as heat, water, cold, electricity, gas, hydrogen, etc.; the model is to abstract the models of equipment in different energy systems and construct a hierarchical structure of different equipment; the energy consumption attributes are real-time collected data of each energy equipment;

[0027] The digital twin device model has built a unified comprehensive energy modeling standard for multiple types of energy equipment models such as heat, water, cooling, electricity, gas, and hydrogen in business scenarios such as enterprises, parks, hospitals, factories, and heating. Different from the CIM standard of electricity IEC61970, it solves the problem of standardization of multi-source heterogeneous data based on the underlying access of the Internet of Things in comprehensive energy scenarios. At the same time, a digital twin model configuration tool (with a built-in basic model library for digital twin modeling of comprehensive energy systems) has been developed according to the standard.

[0028] S2. freely select several models, energy consumption attributes and time components to bind to cells of the worksheet, and set the arrangement mode for the models and energy consumption attributes to obtain the original table;

[0029] The arrangement includes horizontal arrangement and vertical arrangement along the worksheet; after setting, a plurality of cross cells are obtained in the worksheet, and each cross cell is used to display the energy consumption attribute data of the corresponding instance;

[0030] S3. Retrieve all intersection cells in the original table. If the intersection cell is the intersection of an energy attribute and a model, set the intersection cell to a data type cell; and disable editing and centering. If the intersection cell is the intersection of a model and a model, or the intersection of an energy attribute and an energy attribute, set it to a blank cell. If the data in the original table changes or is updated, a custom rendering update will be triggered to ensure real-time reflection of the data.

[0031] S4, obtaining the instance list of all models selected in step S2, and searching for the cross cells of all data types in the original table, matching the data in the instance list, and calculating the number of cells that need to be expanded for all models and time components, and then expanding;

[0032] S5, data rendering: according to the combined relationship between each instance and the energy consumption attribute, the intersecting blank cells are retrieved as the cells to be filled with data; the real-time collected data of the energy equipment corresponding to each instance is obtained, and the data is processed and filled into the intersecting blank cells, that is, the report is generated.

[0033] In some embodiments, the specific method for calculating the number of cells that need to be expanded for all models and time components in S4 is:

[0034] Calculate the number of instance cells that need to be expanded for each model one by one, calculate the number of cells for model expansion through extendedCache (cache variable of model extension information), bind the instance name and dataCode (unique code), and the expansion is completed.

[0035] In some embodiments, the extension in S4 uses a multi-dimensional data expansion algorithm, specifically: the algorithm supports complex data structures and display requirements.

[0036] Pn=Pn-1+En-1+Bn,

[0037] Among them, Pn is the starting position of the nth model expansion, Pn-1 is the starting position of the previous model expansion, En-1 is the number of cells expanded by the previous model, and Bn is the number of blank cells between the current model and the previous model; this formula ensures that during the expansion process, the starting position of each model can be dynamically calculated based on the previous model.

[0038] Ci={Di,j|j=1,2,…,N},

[0039] Where Ci is the attribute set of the i-th instance, Di,j represents the j-th attribute under the i-th instance, and N is the total number of attributes under the instance. This formula ensures that the attributes of all instances can be correctly bound to the expanded cells.

[0040] In some embodiments, after the report is generated, the user creates a new visualization panel on the server side through the page and adds multiple report template cards; based on the data in the report template card, a DSL file is generated as the configuration data for downloading the report, and is mixed into and packaged in the platform-side project; after downloading, the DSL file in the root directory is obtained, and then the data is saved in the local cache storage. When loading, the template card data is filled in one by one according to the configuration file to complete the data rendering of the overall panel on the platform side; that is, the server side can send the report by clicking, and the platform side can receive the report preview interface in real time.

[0041] Example

[0042] 1. User interface and report design: When a user enters the user interface of an intelligent report tool based on comprehensive energy modeling, the right side is a data binding module, which specifically builds an energy equipment modeling tree based on the digital twin modeling standard, so that users can intuitively select and configure the required energy equipment. The basic table canvas in the center of the user interface allows users to quickly initialize the row and column settings of the table through the upper toolbar, and flexibly adjust the layout style to meet specific analysis needs.

[0043] 2. Node data acquisition: Users complete data binding through the energy equipment modeling tree on the right. The system provides a name and data code (dataCode) for each cell. When the user selects a draggable energy equipment model or energy consumption attribute data, the cell information will be cached in the drag data cache (dataTransfer) to achieve temporary storage and transfer of data. During the dragging process, the cell will be bound to listen to the drag event to ensure that the table content can be updated in real time when the node is placed.

[0044] 3. Node data binding: After obtaining the cell data, bind the node type, name and dataCode to the current cell; if the cell comes from the energy equipment modeling tree or time component, bind the arrangement mode to the current cell, and set the mouse click listening event to display the pop-up window for selecting the arrangement mode. The arrangement mode set by the user will be stored in the cell data, and the cell data will be bound to obtain the original table.

[0045] 4. Custom rendering preparation: After the cell data is bound, start the custom rendering preparation. According to the arrangement of the cell data, traverse each cell and bind them to the type and dataCode of the current cell node.

[0046] 5. Custom rendering work: retrieve all the intersection cells in the original table. If the intersection cell is the intersection of energy attribute and model, set the intersection cell to data type cell; and disable editing and centering. If the intersection cell is the intersection of model and model, or the intersection of energy attribute and energy attribute, set it to a blank cell. If the data in the original table changes or is updated, it will trigger the custom rendering update to ensure real-time reflection of the data.

[0047] 6. Preview preparation: In the preview preparation stage, set the three elements of the preview: project, time, and interval. Obtain the corresponding energy equipment modeling system instance information through project retrieval, dynamically calculate the time list within the configured time range, and store the processed data in storage.

[0048] 7. Preview starts: traverse from row to column to obtain the original table configuration, initialize the custom style, merge value, and arrangement of the cell, calculate the number of blank cells under the original table definition layout, store the index position relationship to indexRelationData, and enter the dynamic expansion preparation stage of the table.

[0049] 8. Preparation for dynamic table expansion: Retrieve cells in the original table one by one, match instance list data in storage, and create a cache variable extendedCache of model extension information to store the row and column positions of the original cells in the model and the row and column positions after expansion, as well as the length of the expanded and merged rows and columns.

[0050] 9. Dynamic expansion of the table: Calculate the model extension instance cells one by one, calculate the number of cells extended by the model through extendedCache, process the merged instance cells, and bind the instance name and dataCode, and the expansion is completed.

[0051] 10. Data rendering: After data rendering starts, the custom renderer identifies and obtains data based on the binding conditions of energy device instances and attributes, and finally updates the rendering of the table view, that is, report generation.

[0052] 11. After the report is generated, the user creates a new visualization panel on the server side through the page and adds multiple report template cards; based on the data in the report template card, a DSL file is generated as the configuration data for downloading the report, which is mixed and packaged in the platform project; after downloading, the DSL file in the root directory is obtained, and then the data is saved in the local cache storage. When loading, the template card data is filled one by one according to the configuration file to complete the data rendering of the overall panel on the platform side; that is, the server can send the report by clicking, and the platform side can receive the report preview interface in real time.

[0053] The present invention emphasizes the concept of cloud-edge integration. The server can send a report by clicking on it, and the platform can receive the report preview interface in real time. This design realizes the effective combination of cloud storage and edge computing of data, ensuring the efficient flow and real-time interaction of data locally and in the cloud. At the same time, the cloud-edge collaborative mechanism ensures seamless data connection between different devices and systems, supports multi-terminal access, and improves the user's operational flexibility.

[0054] 12. Legend generation: The present invention supports legend generation based on ECharts. Users can quickly generate visual charts such as bar charts, line charts, and pie charts by dragging the comprehensive energy consumption modeling tree. The rendering engine integrated in ECharts provides a variety of chart types. Users can view data changes in real time through simple dragging and configuration, thereby improving the interactivity and readability of the report.

[0055] The present invention focuses on the integrated energy field on the user side (multi-energy systems and equipment such as electricity, refrigeration, heating, air compression, gas, hydrogen, etc.), and is an intelligent reporting tool based on integrated energy modeling. It realizes the seamless integration of the report designer and the digital twin modeling standard (a unified integrated energy modeling standard is built for multiple types of energy equipment models such as heat, water, cold, electricity, gas, hydrogen, etc. in business scenarios such as enterprises, parks, hospitals, factories, and heating), provides multi-dimensional report analysis functions, multiple export methods and custom layouts, improves the data processing and display efficiency of reports, ensures the user-friendliness and ease of use of reports, realizes personalized customization and good scalability of reports, and supports cloud-edge integration and cross-platform data interaction. By achieving these goals, users will be provided with more powerful and convenient data analysis tools to promote data-driven decision-making and business optimization of enterprises.

[0056] The present invention adopts the digital twin modeling standard to virtually model energy equipment and systems. This modeling method can accurately reflect the real-time status of equipment and systems, and provide real-time and dynamic data support for reports. Ensure that report design can automatically adapt to industry-specific analysis needs. The application of digital twin modeling can accurately map the operating conditions of equipment and systems to reports, ensure the accuracy of report data, and reduce human errors. When business needs change (such as adding new energy equipment or adjusting energy configuration), the digital twin model can be quickly adjusted to ensure that the report can accurately reflect the changes.

[0057] The present invention adopts dynamic cell expansion to solve the problem of insufficient scalability of existing tools. Existing reporting tools usually adopt a fixed cell structure in terms of table layout, which lacks flexibility. Once it is necessary to add a new dimension to the table or expand the existing data set, developers are usually required to manually modify the template or configure the data source. This method is not only inefficient, but also difficult to respond quickly to changes in business needs. The dynamic cell expansion technology is based on the automatic expansion of the model, and the table cell layout can be dynamically adjusted when the data source changes. This means that when a new model corresponding instance is added or the analysis model is modified, the report can automatically adapt without manual intervention. Users can dynamically adjust the report structure according to the digital twin modeling tree to meet changes in business needs without relying on developers. This flexibility is extremely important in the industry, especially in rapidly changing business scenarios such as integrated energy management. Dynamic expansion reduces the need for manual configuration and code modification, significantly improves the efficiency of report design and adjustment, and reduces development and maintenance costs.

[0058] The present invention is adaptable to a variety of business scenarios. In the field of integrated energy, energy management needs to consider data of multiple dimensions and levels, including equipment layer, system layer, regional layer, etc., and the relationship between different dimensions is intricate. Existing reporting tools are difficult to cope with such complex and changeable needs, and usually can only display a specific dimension or data set. By combining digital twin modeling, the layout of report cells can be automatically adjusted to adapt to data analysis needs of different levels and dimensions. Support industry-specific complex needs: Whether it is multi-energy systems such as electricity, refrigeration, heating, or different levels of analysis needs, dynamic expansion technology can automatically match to ensure that the report covers all important dimensions and data points.

Claims

1. A method for generating an intelligent reporting tool based on comprehensive energy modeling, characterized in that: Includes the following: S1. Construct an energy equipment modeling tree based on the digital twin modeling standard, wherein the energy equipment modeling tree includes multiple models and energy consumption attributes, and each model includes multiple instances; The examples are various types of energy equipment; the models are abstract models of equipment in different energy systems to construct hierarchical structures of different equipment; the energy consumption attributes are real-time collected data of each energy equipment; S2. freely select several models, energy consumption attributes and time components to bind to cells of the worksheet, and set the arrangement mode for the models and energy consumption attributes to obtain the original table; The arrangement includes horizontal arrangement and vertical arrangement along the worksheet; after setting, a plurality of cross cells are obtained in the worksheet, and each cross cell is used to display the energy consumption attribute data of the corresponding instance; S3, searching all intersection cells in the original table, and if the intersection cell is an intersection of an energy attribute and a model, setting the intersection cell to a data type cell; S4, obtaining the instance list of all models selected in step S2, and searching for the cross cells of all data types in the original table, matching the data in the instance list, and calculating the number of cells that need to be expanded for all models and time components, and then expanding; S5. According to the combined relationship between each instance and the energy consumption attribute, the intersecting blank cells are retrieved as cells to be filled with data; the real-time collected data of the energy equipment corresponding to each instance is obtained, and the data is processed and filled into the intersecting blank cells, that is, the report is generated.

2. The method for generating an intelligent reporting tool based on comprehensive energy modeling according to claim 1, characterized in that: In S4, the specific method for calculating the number of cells that need to be expanded for all models and time components is: Calculate the number of instance cells that need to be expanded for each model one by one, calculate the number of cells for model expansion through extendedCache (cache variable of model extension information), bind the instance name and dataCode (unique code), and the expansion is completed.

3. The method for generating an intelligent reporting tool based on comprehensive energy modeling according to claim 1 or 2, characterized in that: In S4, the expansion uses a multi-dimensional data expansion algorithm, specifically: Pn=Pn-1+En-1+Bn, Where Pn is the starting position of the nth model extension, Pn-1 is the starting position of the previous model extension, En-1 is the number of cells extended by the previous model, and Bn is the number of blank cells between the current model and the previous model; Ci={Di,j|j=1,2,…,N}, Among them, Ci is the attribute set of the i-th instance, Di,j represents the j-th attribute under the i-th instance; N is the total number of attributes under the instance.

4. The method for generating an intelligent reporting tool based on comprehensive energy modeling according to claim 1 or 2, characterized in that: After the report is generated, the user creates a new visualization panel on the server side through the page and adds multiple report template cards; Based on the data in the report template card, a DSL file is generated as the configuration data for downloading the report, and mixed into the platform-side project. After downloading, the DSL file in the root directory is obtained, and then the data is saved in the local cache storage. When loading, the template card data is filled one by one according to the configuration file to complete the data rendering of the overall panel on the platform side. That is, the server can send the report by clicking, and the platform side can receive the report preview interface in real time.