Enterprise energy data management method and system based on multi-dimensional dynamic hierarchical model

Abstract

The invention discloses an enterprise energy data management method and system based on a multi-dimensional dynamic hierarchical model, and relates to the technical field of enterprise energy management. The method comprises the following steps: firstly, through definition and configuration of a multi-dimensional hierarchical system, constructing an extensible hierarchical structure framework; secondly, dynamically associating hierarchical nodes with a data source, and binding a flexible calculation rule; then, on the basis of the associated data and the calculation rule, a cross-dimension hierarchical association network is constructed, and cross analysis of multi-dimension data is achieved; and finally, dynamically configuring the data granularity by using the associated network according to actual management requirements, and completing accurate calculation and optimal management of energy consumption. According to the method, through the multi-dimensional dynamic hierarchical model, the flexibility and analysis precision of enterprise energy data management are remarkably improved, and efficient support is provided for energy optimization decision making.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise energy management, and particularly relates to an enterprise energy data management method and system based on a multi-dimensional dynamic hierarchical model. BACKGROUND

[0002] With the acceleration of industrial digital transformation, large enterprises have increasingly highlighted the demand for fine control of energy data, especially in the multi-factory, cross-department, and multi-energy type (electricity, water, gas, heat, etc.) scenarios. The existing energy management methods face four core problems: 1. Fixed hierarchical structure, unable to adapt to dynamic adjustment of enterprise organization (such as addition of factory, merger of departments), leading to high cost of system reconstruction; 2. Energy data is isolated and classified, and data of types such as electricity, water, and gas are stored independently, making it difficult to realize cross-energy type correlation analysis (such as comprehensive energy consumption calculation per unit product); 3. Coarse data granularity, only supporting workshop-level or production line-level statistics, unable to be refined to single device or process level, leading to difficulty in positioning when energy consumption is abnormal; 4. Lack of multi-perspective adaptation capability, unable to flexibly adjust classification dimensions according to different user roles (such as company management, workshop director, and device maintenance personnel), leading to low management efficiency.

[0003] Currently, enterprise energy classification mainly relies on two types of technical solutions: fixed administrative hierarchy division and independent energy type classification. The fixed administrative hierarchy division divides energy data according to administrative hierarchies such as "company-factory-workshop", and stores the hierarchical structure in a relational database (such as MySQL). This type of solution realizes energy consumption aggregation level by level through a preset tree structure, but the hierarchical relationship needs to be hard-coded, and the system interface needs to be redeveloped when changes occur. The independent energy type classification stores energy data such as electricity, water, and gas in independent databases (such as time series database InfluxDB), and realizes classification query through energy type tags. This type of solution supports fine-grained analysis of a single energy type, such as tracking device electricity consumption trends through smart meters.

[0004] The two types of technical solutions mentioned above have significant shortcomings: (1) Poor adaptability, the fixed administrative hierarchy division cannot dynamically respond to enterprise structure adjustment, for example, adding a workshop requires modifying the database and calculation logic, which takes a long time on average; (2) Lack of cross-dimension analysis, independent energy type classification leads to high error rate in comprehensive energy efficiency calculation which needs to manually associate multiple system data; (3) Insufficient granularity, existing methods mostly stop at workshop-level statistics, unable to identify specific device abnormalities; (4) Weak role adaptability, a single classification mode is difficult to meet the differentiated needs of the strategic layer (company-level KPI), the executive layer (workshop energy efficiency), and the operational layer (device maintenance), leading to increased management costs.

[0005] The existing enterprise energy management technology is difficult to support the fine management and control demand under the digital transformation due to the problems of hierarchical rigidity, data island, and coarse granularity. A kind of energy management method supporting multi-level dynamic classification, cross-energy correlation analysis, fine-grained monitoring and role adaptation is needed to realize the whole-link energy efficiency optimization from macro-strategy to micro-device. SUMMARY

[0006] The present application provides an enterprise energy data management method and system based on a multi-dimensional dynamic hierarchical model, which can systematically solve the above technical bottlenecks through dynamic configuration, correlation engine and granularity adjustment.

[0007] The present application is implemented by the following technical solutions: In one aspect of the present application, an enterprise energy data management method based on a multi-dimensional dynamic hierarchical model is provided, which comprises the following steps: S1: multi-dimensional hierarchical system definition and configuration; S2: based on the configured multi-dimensional hierarchical system, data source correlation and calculation rule binding of hierarchical nodes are performed; S3: based on the multi-dimensional hierarchical system with associated data and calculation rules, a cross-dimensional hierarchical correlation network is constructed; S4: according to the management requirements, the multi-dimensional hierarchical system with completed cross-dimensional hierarchical correlation network is used to configure data granularity and calculate and manage energy consumption.

[0008] Further, in S1, the multi-dimensional hierarchical system definition and configuration comprises: S101: self-defining dimensions and hierarchical structure of dimensions according to a preset basic dimension template to obtain a multi-dimensional hierarchical system; S102: configuring attributes for each hierarchical node and generating a unique identifier based on the obtained multi-dimensional hierarchical system to complete the architecture and node information configuration of the multi-dimensional hierarchical system; S103: persistently storing the configured multi-dimensional hierarchical system to a relational database.

[0009] Further, in S101, the preset basic dimension template comprises organization dimension, energy dimension and production dimension template; and the self-defining dimensions and hierarchical structure of dimensions according to the preset basic dimension template comprises expanding on the basis of the template and creating a new dimension.

[0010] Further, in S2, the data source correlation and calculation rule binding of hierarchical nodes comprises: S201: directly associating physical meters with hierarchical nodes; S202: configuring energy consumption calculation rules for virtual devices; S203: Store the mapping relationship between node ID and data source / computing rule.

[0011] Further, in S201, the direct association of physical meters with hierarchical nodes is to select the physical meters corresponding to the production line that need to be associated with data and bind them through a pre-set data interface. The physical meters collect real-time data and transmit them to the hierarchical nodes through the data interface. The physical meters include electricity meters and water meters. The data interface includes OPC UA, Modbus TCP, and API calls.

[0012] Further, in S202, the configuration of energy consumption computing rules for virtual devices is based on a computing rule configuration interface and is completed by associating different hierarchical dimension hierarchical nodes through a hierarchical mapping algorithm, including: for physical meters that cannot be directly metered in the association with hierarchical nodes, a hierarchical mapping algorithm, i.e., a virtual computing rule, is configured through a rule configuration interface, and the calculation method is selected as a virtual meter in its node attribute. The virtual computing rule is configured using an energy consumption computing algorithm, including one or more of proportional allocation method, power integration method, and machine learning prediction method.

[0013] Further, in S3, the construction of a cross-dimension hierarchical association network includes: S301: Automatically associate different dimension nodes based on pre-set spatial rules and logical rules; Further, the spatial rule association is to automatically establish an association between nodes that belong to the same spatial range in business logic. The logical rule association is to automatically establish an association between nodes whose IDs have a containing relationship in coding rules.

[0014] S302: Manually associate based on requirements, supporting manual dragging to establish an association relationship; S303: Store high-frequency association relationships in the automatic association and the manual association into a cache to improve query efficiency.

[0015] Further, in S4, the configuration of data granularity and energy consumption computing includes: S401: Support hierarchical energy consumption aggregation calculation from bottom to top; Further, the hierarchical energy consumption aggregation calculation from bottom to top includes querying all subordinate nodes to the data storage module, obtaining the energy consumption data of these subordinate nodes from the time series database, and performing data aggregation from bottom to top.

[0016] S402: Integrate the energy consumption data and production data of the corresponding hierarchy to calculate the energy efficiency KPI of the corresponding hierarchy; S403: Provide a data drilling function to display analysis results of different fine granularities.

[0017] Furthermore, the enterprise energy data management method based on a multi-dimensional dynamic hierarchical model, as described above, also includes the dynamic updating and maintenance of enterprise energy data management, specifically including: Step A: Add / modify hierarchy nodes through the graphical interface; Step B: Automatically assign a unique ID to the new node and update the association relationship; Step C: Real-time detection of changes and triggering of the recalculation mechanism.

[0018] In another aspect, based on the same inventive concept as the above-described enterprise energy data management method based on a multidimensional dynamic hierarchical model, the present invention also provides an enterprise energy data management system based on a multidimensional dynamic hierarchical model for executing the steps of the method; the system includes a dimension configuration module, a data association module, a hierarchical association module, a granularity management module, a dynamic update module, and a data storage module, wherein: The dimension configuration module is used to preset and customize multiple classification dimensions and their hierarchical structures; The data association module is used to obtain real data sources and bind nodes in the hierarchical structure with real data sources and virtual computing rules to provide a basis for data calculation. The hierarchical association module is used to establish the association relationship between nodes of different dimensions through a mapping algorithm, forming a multi-dimensional cross-hierarchical network; The granularity management module is used to flexibly configure the granularity of data statistics according to needs, and automatically aggregate and decompose energy consumption data based on the calculation rules bound in the data association module to generate energy consumption indicators at each level. The dynamic update module is used to add, delete, and modify hierarchical nodes and relationships when the enterprise undergoes changes in organizational structure and production process, so as to achieve smooth updates of the classification system. The data storage module is used to store structured hierarchical and relational data, as well as real-time energy consumption data.

[0019] Furthermore, the data storage module specifically employs a relational database to store structured hierarchical and relational data, and a time-series database to store high-frequency energy consumption time-series data.

[0020] Compared with the prior art, the present invention has the following advantages: 1. It has strong adaptability, supports dynamic expansion and adjustment of classification dimensions and levels, perfectly adapts to frequent changes in enterprise organizational structure and business processes, and greatly reduces system maintenance costs and secondary development needs. 2. It has strong comprehensive analysis capabilities. Through multi-dimensional cross-correlation, it naturally integrates various energy data such as electricity, water, and gas, providing a solid data foundation for calculating comprehensive energy consumption and conducting in-depth energy efficiency analysis. 3. Precise and efficient anomaly location: It can drill down energy consumption data to the equipment and process level, realize the rapid and accurate location of energy consumption anomalies, provide direct and reliable data support for energy-saving optimization measures, and improve energy management efficiency; 4. Flexible perspective and low management cost: One system can meet the perspective needs of different management roles, eliminating the need to build multiple systems for different departments and reducing the total cost of ownership.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the enterprise energy data management method based on a multi-dimensional dynamic hierarchical model in Embodiment 1 of the present invention.

[0024] Figure 2 This is an architecture diagram of an enterprise energy data management system based on a multi-dimensional dynamic hierarchical model in Embodiment 2 of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1: In one embodiment, please refer to Figure 1 This paper provides a method for enterprise energy data management based on a multi-dimensional dynamic hierarchical model, the method comprising the following steps: Step S1: Define and configure the multi-dimensional hierarchical system; Furthermore, in step S1, the definition and configuration of the multi-dimensional hierarchical system includes: Step S101: Customize dimensions and hierarchical structures based on the preset basic dimension template to obtain a multi-dimensional hierarchical system; Furthermore, the preset basic dimension templates include organizational dimension, energy dimension, and production dimension templates; the custom dimension and dimension hierarchy based on the preset basic dimension templates include extending the templates and creating entirely new dimensions.

[0027] In this embodiment, we take the custom 'organization dimension' as an example to illustrate dimension expansion. Under the 'organization dimension', we can customize the hierarchy, including 'Group -> Plant A -> Production Workshop -> Production Line -> Main Energy-Consuming Equipment'. To create a completely new dimension, we can customize it, such as the custom dimension: 'Energy Consumption Attribute Dimension'; hierarchy: 'Entire Plant -> Production Energy Consumption -> Auxiliary Production Energy Consumption -> Office Energy Consumption -> Public Facility Energy Consumption'.

[0028] Step S102: Based on the obtained multi-dimensional hierarchical system, configure attributes for each level node and generate a unique identifier to complete the architecture and node information configuration of the multi-dimensional hierarchical system; Furthermore, in this embodiment, configuring attributes for each level node involves configuring corresponding attributes according to the different needs of each level node. For example, under the 'organization dimension', the attributes included in the custom level 'Factory Area A' node are ID, name, person in charge, and geographical location; the attributes included in the 'Main Energy-Consuming Equipment' node are equipment ID, equipment model, rated power, and the production line to which it belongs; the generation of unique identifiers is a globally unique identifier (UUID) assigned to each newly created node by the ID generator, ensuring that it is uniquely identified and retrieved during the management process.

[0029] Step S103: Persistently store the configured multi-dimensional hierarchical system to a relational database.

[0030] Step S2: Based on the configured multi-dimensional hierarchical system, associate the data sources of the hierarchical nodes and bind the calculation rules; Furthermore, in step S2, the process of associating the data source with the hierarchical nodes and binding the calculation rules includes: Step S201: Directly associate physical meters with hierarchical nodes; Furthermore, the direct association of physical meters with hierarchical nodes involves selecting the physical meters corresponding to the production line and binding them to the hierarchical nodes that need to be associated with data through a pre-set data interface. The physical meters collect real-time data and transmit it to the hierarchical nodes using the data interface.

[0031] Furthermore, the physical meters include electricity meters and water meters; the data interfaces include OPC UA, Modbus TCP, and API calls.

[0032] Step S202: Configure energy consumption calculation rules for virtual devices; Furthermore, configuring energy consumption calculation rules for virtual devices is based on a calculation rule configuration interface. Through a hierarchical mapping algorithm, different hierarchical nodes are associated. This includes configuring a hierarchical mapping algorithm, i.e., a virtual calculation rule, for physical meters that cannot be directly measured in association with hierarchical nodes through the rule configuration interface, and selecting virtual meter as the calculation method in its node attributes. The virtual calculation rule is configured using an energy consumption calculation algorithm, including one or more of the following: proportional allocation method, power integration method, and machine learning prediction method.

[0033] Furthermore, the energy consumption of the equipment is calculated using the proportional allocation method. The power integration method is used to calculate the equipment energy consumption. The machine learning prediction method calculates the equipment energy consumption as follows: Equipment Energy Consumption = AI Model (Production Load, Ambient Temperature).

[0034] Step S203: Mapping relationship between storage node ID and data source / computation rule.

[0035] Step S3: Construct a cross-dimensional hierarchical association network based on the multi-dimensional hierarchical system of completed associated data and calculation rules; Furthermore, in step S3, constructing the cross-dimensional hierarchical network includes: Step S301: Automatically associate nodes of different dimensions based on preset spatial and logical rules; Furthermore, the spatial rule association automatically establishes an association between nodes that belong to the same spatial range in terms of business logic; the logical rule association automatically establishes an association between IDs of nodes that have an inclusion relationship in the encoding rules.

[0036] Step S302: Manually associate relationships based on requirements, supporting manual drag-and-drop to establish associations; Step S303: Store high-frequency associations from automatic and manual associations into a cache to improve query efficiency.

[0037] Furthermore, in this embodiment, for the high-frequency association relationships mentioned in step S303, an example is shown in Table 1 below, forming a complete association relationship network and persistently storing it in the relationship database.

[0038] Table 1: High-frequency association data table.

[0039]

[0040] Step S4: Based on management needs, utilize the multi-dimensional hierarchical system that completes the cross-dimensional hierarchical relationship network to configure data granularity and calculate energy consumption; Furthermore, the configuration of data granularity and energy consumption calculation include: Step S401: Support bottom-up hierarchical energy consumption aggregation calculation; Furthermore, the bottom-up hierarchical energy consumption aggregation calculation includes querying all lower-level nodes in the data storage module, obtaining the energy consumption data of these lower-level nodes from the time-series database, and the algorithm performing bottom-up data aggregation.

[0041] Step S402: Integrate the energy consumption data and production data of the corresponding level, and calculate the energy efficiency KPI of the corresponding level; Step S403: Provide data drill-down functionality to display analysis results at different levels of granularity.

[0042] Furthermore, in this embodiment, the enterprise energy data management method based on a multi-dimensional dynamic hierarchical model also includes dynamic updating and maintenance of enterprise energy data management, specifically including: Step A: Add / modify hierarchy nodes through the graphical interface; Step B: Automatically assign a unique ID to the new node and update the association relationship; Step C: Real-time detection of changes and triggering of the recalculation mechanism.

[0043] Furthermore, in this embodiment, taking the addition of a welding machine (equipment A1-1-03) to a company, triggering the dynamic update and maintenance of the company's energy data management, as an example, we will provide further analysis: First, the conditions for updating and maintaining the equipment are triggered when the company adds a welding machine (equipment A1-1-03), thus triggering a change. Secondly, by calling the dynamic update module, the administrator can right-click on the "Production Line A1-1" node under the "Organization Dimension" tree on the interface and select "Add Equipment" to fill in the new equipment information; Next, node operations are performed. The node operation interface receives instructions, the ID generator assigns a unique ID to the new device, and adds a new node record to the database. Then, the association is adjusted, and the administrator then binds the new device to the corresponding power circuit (data association) and production process (hierarchical association); Finally, the change is executed and recalculated. Once the change is detected, the change monitor automatically notifies the granularity management module. This module will then automatically include the new device in the energy consumption statistics model in the next calculation cycle, without requiring manual intervention.

[0044] As can be seen from the description in Example 1, the technical effects achieved by the present invention include: (1) strong adaptability, supporting dynamic expansion and adjustment of classification dimensions and levels, perfectly adapting to frequent changes in enterprise organizational structure and business processes, and significantly reducing system maintenance costs and secondary development needs. (2) powerful comprehensive analysis capabilities, through multi-dimensional cross-correlation, naturally integrating various energy data such as electricity, water, and gas, providing a solid data foundation for calculating comprehensive energy consumption and conducting in-depth energy efficiency analysis. (3) accurate and efficient anomaly location, capable of drilling down energy consumption data to the equipment level and process level, realizing rapid and accurate location of energy consumption anomalies, providing direct and reliable data support for energy-saving optimization measures, and improving energy management efficiency. (4) flexible perspective and low management cost, one system can meet the perspective needs of different management roles, without the need to build multiple systems for different departments, reducing the total cost of ownership.

[0045] Example 2: In one embodiment, such as Figure 2 As shown, an enterprise energy data management system based on a multi-dimensional dynamic hierarchical model is also provided. The system includes a dimension configuration module, a data association module, a hierarchical association module, a granularity management module, a dynamic update module, and a data storage module, wherein: The dimension configuration module is used to preset and customize multiple classification dimensions and their hierarchical structures; The data association module is used to obtain real data sources and bind nodes in the hierarchical structure with real data sources and virtual computing rules to provide a basis for data calculation. The hierarchical association module is used to establish the association relationship between nodes of different dimensions through a mapping algorithm, forming a multi-dimensional cross-hierarchical network; The granularity management module is used to flexibly configure the granularity of data statistics according to needs, and automatically aggregate and decompose energy consumption data based on the calculation rules bound in the data association module to generate energy consumption indicators at each level. The dynamic update module is used to add, delete, and modify hierarchical nodes and relationships when the enterprise undergoes changes in organizational structure and production process, so as to achieve smooth updates of the classification system. The data storage module is used to store structured hierarchical and relational data, as well as real-time energy consumption data.

[0046] Furthermore, the data storage module specifically employs a relational database to store structured hierarchical and relational data, and a time-series database to store high-frequency energy consumption time-series data.

[0047] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for enterprise energy data management based on a multi-dimensional dynamic hierarchical model, characterized in that, The method includes: Multi-dimensional hierarchical system definition and configuration; Based on the configured multi-dimensional hierarchical system, data source association and calculation rule binding are performed for hierarchical nodes; Based on a multi-dimensional hierarchical system that completes the association of data and calculation rules, a cross-dimensional hierarchical association network is constructed; Based on management needs, a multi-dimensional hierarchical system that completes cross-dimensional hierarchical association networks is used to calculate and manage data granularity configuration and energy consumption.

2. The enterprise energy data management method based on a multi-dimensional dynamic hierarchical model as described in claim 1, characterized in that, The definition and configuration of the multi-dimensional hierarchical system also include: Based on the preset basic dimension template, customize dimensions and the hierarchical structure of dimensions to obtain a multi-dimensional hierarchical system; Based on the obtained multi-dimensional hierarchical system, configure attributes for each level node and generate a unique identifier to complete the architecture and node information configuration of the multi-dimensional hierarchical system; The configured multi-dimensional hierarchical system is persistently stored in a relational database.

3. The enterprise energy data management method based on a multi-dimensional dynamic hierarchical model as described in claim 2, characterized in that: The preset basic dimension templates include organizational dimension, energy dimension, and production dimension templates; The process of customizing dimensions and their hierarchical structure based on a preset basic dimension template includes extending the template and creating entirely new dimensions.

4. The enterprise energy data management method based on a multi-dimensional dynamic hierarchical model as described in claim 1, characterized in that, The process of associating data sources and binding calculation rules for hierarchical nodes also includes: Directly associate physical meters with hierarchical nodes; Configure energy consumption calculation rules for virtual devices; Mapping relationship between storage node ID and data source / computation rule.

5. The enterprise energy data management method based on a multi-dimensional dynamic hierarchical model as described in claim 4, characterized in that: The step of directly associating physical meters with hierarchical nodes involves selecting the physical meters corresponding to the production line and binding them to the hierarchical nodes that need to be associated with data through a pre-set data interface. The physical meters collect real-time data and transmit it to the hierarchical nodes using the data interface. The physical meters include electricity meters and water meters; The data interfaces include OPC UA, Modbus TCP, and API calls.

6. The enterprise energy data management method based on a multi-dimensional dynamic hierarchical model as described in claim 4, characterized in that: The configuration of energy consumption calculation rules for virtual devices is based on the calculation rule configuration interface. Through a hierarchical mapping algorithm, different hierarchical nodes are associated. This includes configuring a hierarchical mapping algorithm for physical meters that cannot be directly measured in association with hierarchical nodes, and selecting virtual meter as the calculation method in their node attributes.

7. The enterprise energy data management method based on a multi-dimensional dynamic hierarchical model as described in claim 1, characterized in that, The construction of the cross-dimensional hierarchical network also includes: Automatically associates nodes of different dimensions based on preset spatial and logical rules; Manual association based on requirements; supports manual drag-and-drop to establish association relationships. Storing high-frequency relationships from both automatic and manual associations in a cache improves query efficiency.

8. The enterprise energy data management method based on a multi-dimensional dynamic hierarchical model as described in claim 1, characterized in that, The configuration of data granularity and energy consumption calculation also include: Supports bottom-up hierarchical energy consumption aggregation calculation; Integrate energy consumption data and production data at the corresponding levels to calculate the corresponding energy efficiency KPIs; Provides data drill-down functionality to display analysis results at different levels of granularity.

9. A method for enterprise energy data management based on a multi-dimensional dynamic hierarchical model as described in any one of claims 1 to 8, characterized in that, The method also includes dynamic updating and maintenance of enterprise energy data management, specifically including: Add / modify hierarchy nodes through the graphical interface; Automatically assign a unique ID to new nodes and update the association relationships; Real-time detection of changes and triggering of recalculation mechanism.

10. A system for implementing the method according to any one of claims 1 to 9, characterized in that, The system includes a dimension configuration module, a data association module, a hierarchical association module, a granularity management module, a dynamic update module, and a data storage module, wherein: The dimension configuration module is used to preset and customize multiple classification dimensions and their hierarchical structures; The data association module is used to obtain real data sources and bind nodes in the hierarchical structure with real data sources and virtual computing rules to provide a basis for data calculation. The hierarchical association module is used to establish the association relationship between nodes of different dimensions through a mapping algorithm, forming a multi-dimensional cross-hierarchical network; The granularity management module is used to flexibly configure the granularity of data statistics according to needs, and automatically aggregate and decompose energy consumption data based on the calculation rules bound in the data association module to generate energy consumption indicators at each level. The dynamic update module is used to add, delete, and modify hierarchical nodes and relationships when the enterprise undergoes changes in organizational structure and production process, so as to achieve smooth updates of the classification system. The data storage module is used to store structured hierarchical and relational data, as well as real-time energy consumption data.

Images