Dynamic energy consumption statistical method based on labels

Abstract

The invention provides a dynamic energy consumption statistical method based on labels, and relates to the field of industrial internet, internet of things and energy management systems. According to the method, dynamic binding of a physical instrument and label values under multi-label classification is achieved by constructing a hierarchical label system of two or more stages, and a virtual instrument associated target label can be created when no physical instrument exists; a virtual instrument calculation framework is configured, an interface setting energy consumption calculation formula is supported, and rules can be dynamically updated through RESTAPI; a hierarchical perception aggregation algorithm is adopted, and cross-hierarchical and multi-instrument energy consumption summarization is automatically completed. The method solves the problems of statistical dimension solidification, insufficient calculation flexibility and high expansion cost in the prior art, has the characteristics of flexible configuration, strong expansibility and cost optimization, and can accurately adapt to diversified and refined energy consumption statistical requirements of factories, parks, buildings and other scenes.

Description

Technical Field

[0001] This invention relates to the field of industrial internet technology, specifically to a tag-based dynamic energy consumption statistics method. Background Technology

[0002] With the rapid iteration of Industrial Internet and Internet of Things (IoT) technologies and the deep development of intelligent manufacturing, factories, industrial parks, buildings, and other scenarios are increasingly emphasizing energy management, and the demand for energy consumption monitoring is gradually evolving towards diversification and refinement. Traditional single-dimensional energy consumption statistics can no longer meet the needs of modern energy management. The industry urgently needs solutions that can conduct energy consumption statistics from multiple business perspectives, including space, equipment, processes, and applications, to achieve accurate tracking and efficient control of energy consumption, providing data support for energy conservation optimization and cost control. Currently, energy consumption statistics-related technologies are widely used in industrial production, industrial park operation, building management, and other fields, becoming one of the core directions for the integration of IoT and energy management systems.

[0003] Existing energy consumption statistics methods still have significant limitations and struggle to adapt to diverse and refined statistical needs. Traditional solutions largely rely on direct data collection from physical instruments, requiring statistical dimensions to be pre-defined through hard coding. They can only support fixed-dimensional statistics such as equipment, region, and production line, failing to dynamically respond to new classification requirements such as processes and applications. Furthermore, the energy consumption calculation logic requires developers to implement it in code, lacking configurable adjustment capabilities, necessitating system modifications when business logic changes. In addition, adding new statistical dimensions often requires deploying additional physical instruments or large-scale modifications to existing systems, leading to response delays, resource waste, and high expansion costs. Current software-based data aggregation solutions, lacking a unified dynamic modeling mechanism, struggle to balance statistical granularity and real-time performance, failing to provide users with flexible and efficient energy consumption statistics support. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a tag-based dynamic energy consumption statistics method, which solves the problem that existing energy consumption statistics methods still have significant limitations and are difficult to adapt to diversified and refined statistical needs.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a tag-based dynamic energy consumption statistics method, characterized by comprising the following steps: S1. Construct a hierarchical tag system. The tag system adopts a two-level or higher structure. The first level is the tag category, and the second level and above are the tag values ​​and support tree expansion. S2. Establish a dynamic association mechanism between instruments and tags. Physical instruments can be bound to tag values ​​under multiple tag categories. When there is no corresponding physical instrument, a virtual instrument is created and associated with the target tag. S3. Configure the virtual meter calculation framework and set the energy consumption calculation formula for the virtual meter. The formula is parsed using Java Expression Language (JEXL) and supports arithmetic operations, logical judgments, custom functions, etc. It can be adapted to scenarios such as "switching to a backup meter when there is no manual meter reading". The formula engine calculates the energy consumption data of the corresponding statistical dimensions. S4. Employs a hierarchical aggregation algorithm to automatically identify the parent-child hierarchical relationship of tags to achieve cross-level energy consumption aggregation, and automatically completes energy consumption aggregation for multiple meters associated with a tag.

[0006] Preferably, the label classification includes one or more of the following: region, equipment type, production task, and purpose.

[0007] Preferably, the tree expansion of the tag values ​​includes a hierarchical progression in the form of "park-factory-floor" or "process section-equipment".

[0008] Preferably, the physical instrument can be associated with multiple tag values ​​under different tag categories at the same time, such as simultaneously binding "Equipment type = air compressor" and "Energy efficiency level = Level 2".

[0009] Preferably, the calculation formula of the virtual instrument is implemented through interface configuration and parsed using Java Expression Language (JEXL). It consists of energy consumption data from one or more physical instruments combined with arithmetic operations, logical judgments, and user-defined functions.

[0010] Preferably, examples of the calculation formulas for the virtual meters include: Canteen virtual meter formula = Energy consumption of meter 1 × 0.8 + Energy consumption of meter 2 × 0.6 + Energy consumption of meter 3; Dormitory virtual meter formula = Energy consumption of meter 1 × 0.2 + Energy consumption of meter 2 × 0.4; Logical judgment example formula = if (meter A. manual meter reading == null) { return meter B. energy consumption × 0.8;} else { return meter A. energy consumption × 0.7;}.

[0011] Preferably, the cross-level aggregation specifically means: when aggregating the energy consumption corresponding to the upper-level label, it automatically includes the energy consumption data associated with all its lower-level labels.

[0012] Preferably, the addition, modification, and configuration of the tags are completed through the system interface without modifying the underlying code.

[0013] Preferably, the instruments include intelligent data acquisition instruments and virtual instruments, and both types of instruments are uniformly incorporated into the tag association and energy consumption statistics system.

[0014] This invention provides a tag-based dynamic energy consumption statistics method. It has the following beneficial effects: 1. This invention significantly improves the configuration flexibility and ease of operation of energy consumption statistics by combining a hierarchical tagging system with a virtual instrument calculation framework. The addition and modification of tags and the configuration of virtual instrument calculation formulas are all completed through a visual interface, without the need for developers to code or modify the underlying system code. It can quickly adapt to changes in business needs. At the same time, the REST API supports dynamic updates of calculation rules without restarting the system, solving the problems of fixed dimensions and cumbersome adjustment of calculation logic in traditional methods. This allows energy consumption statistics to flexibly respond to statistical needs from different business perspectives, reducing the operational threshold and maintenance costs.

[0015] 2. This invention possesses strong scalability and adaptability. Without requiring additional physical instrument deployment or large-scale system modifications, it enables multi-dimensional energy consumption statistics through tag expansion and virtual instrument creation. The tree-like expansion of tag values ​​supports unlimited levels of statistical granularity, and the unified statistical system of physical and virtual instruments ensures the integrity of data aggregation. It is adaptable to various scenarios such as factories, industrial parks, and buildings, efficiently achieving cross-level, multi-dimensional automatic energy consumption aggregation. This avoids resource waste while improving statistical efficiency and accuracy, providing a lightweight and efficient solution for energy management. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] Example: This embodiment of the invention provides a tag-based dynamic energy consumption statistics method, including the following steps: S1. Construct a hierarchical tag system. The tag system adopts a two-level or higher structure. The first level is the tag category, and the second level and above are the tag values, which also support tree expansion. S2. Establish a dynamic association mechanism between instruments and tags. Physical instruments can be bound to tag values ​​under multiple tag categories. When there is no corresponding physical instrument, a virtual instrument is created and associated with the target tag. S3. Configure the virtual meter calculation framework and set the energy consumption calculation formula for the virtual meter. The formula is parsed using Java Expression Language (JEXL) and supports arithmetic operations, logical judgments, custom functions, etc. It can be adapted to scenarios such as "switching to a backup meter when there is no manual meter reading". The formula engine calculates the energy consumption data of the corresponding statistical dimensions. S4. Employing a hierarchical aggregation algorithm, the system automatically identifies the parent-child hierarchical relationship of tags to achieve cross-level energy consumption aggregation, and automatically aggregates energy consumption for multiple meters associated with a tag. A hierarchical tag system with two or more levels is built according to preset rules, clearly defining the classification and value division standards to ensure the tag system covers the core dimensions of the target statistical scenario. Secondly, a standardized meter association mechanism is established. Physical meters establish mapping relationships with multiple tag values ​​under the tag category through the system configuration interface. For statistical dimensions without physical meters, virtual meter instances are first created, and then the virtual meters are bound to the target tags. Next, a visual configuration interface is designed for virtual meters, supporting user-defined energy consumption calculation formulas. These formulas are parsed by a formula engine and associated with corresponding physical meter data. Finally, a hierarchical aggregation algorithm is deployed. By parsing the hierarchical structure of the tag system and the meter association relationships, the algorithm automatically performs cross-level, multi-meter energy consumption aggregation calculations without manual intervention, ensuring the automation and efficiency of the statistical process.

[0018] The tag classification includes one or more of the following: region, equipment type, production task, and purpose. The selection of tag classification is based on common statistical needs of industrial internet, IoT, and energy management systems, covering four core categories: region, equipment type, production task, and purpose. These categories can be flexibly added or removed according to actual application scenarios. Among them, the "region" classification is used to count energy consumption by spatial dimension, adapting to the spatial division needs of scenarios such as factories, parks, and buildings; the "equipment type" classification is used to count different types of energy-consuming equipment (such as air compressors, electricity meters, heat treatment equipment, etc.); the "production task" classification is suitable for counting energy consumption by specific production work orders and production processes; and the "purpose" classification supports dividing statistical dimensions by usage scenarios such as production and living. The various tag classifications are independent of each other but can be cross-linked, providing a foundation for multi-dimensional energy consumption statistics.

[0019] The tree-like expansion of tag values ​​includes hierarchical progression based on either "park-factory-floor" or "process section-equipment". This expansion utilizes a parent-child node mapping mechanism, establishing a clear hierarchical relationship between parent and child tag values, supporting unlimited levels of subdivision. The expansion format can be flexibly set according to actual statistical granularity requirements. The "park-factory-floor" expansion is suitable for hierarchical subdivision of spatial dimensions, meeting energy consumption statistics needs from the entire park to a specific floor. The "process section-equipment" expansion adapts to production scenarios, supporting energy consumption tracking from the entire process section to a single piece of equipment. The tree structure of tag values ​​is stored and managed through hierarchical fields in the system database, ensuring the accuracy and identifiability of hierarchical relationships.

[0020] Physical instruments can be associated with multiple tag values ​​under different tag categories simultaneously. For example, they can be bound to both "Equipment Type = Air Compressor" and "Energy Efficiency Rating = Level 2". Binding physical instruments to multiple tag values ​​is achieved through the system configuration module. Users can select multiple tag values ​​under different tag categories for a single physical instrument in the instrument management interface to complete the binding operation. During the binding process, the system automatically records the association between the instrument and the tag values ​​and stores them in the database, supporting subsequent multi-dimensional cross-queries. For example, an air compressor can be bound to multiple tag values ​​such as "Equipment Type = Air Compressor", "Area = Plant 1", "Energy Efficiency Rating = Level 2", and "Purpose = Production". This allows the energy consumption data of the air compressor to be queried and statistically analyzed through any associated tag, fulfilling the need for multi-dimensional classification and statistical analysis of a single device.

[0021] The calculation formulas for virtual meters are configured through a user interface and parsed using Java Expression Language (JEXL). They consist of energy consumption data from one or more physical meters combined with arithmetic operations, logical judgments, and custom functions. The configuration of these formulas is done through a visual interface, providing modules for selecting physical meters, inputting operators, setting logical conditions, and calling custom functions. Users can build formulas without coding. The calculation formulas are based on real-time energy consumption data from physical meters, incorporating actual energy allocation rules, equipment operating ratios, and data validity checks to set the calculation logic. Scenarios such as "if meter A is not manually read, then use meter B for calculation" are supported. The formulas are formed through a combination of arithmetic operations, logical judgments, and custom functions. The system has a built-in formula verification mechanism that automatically verifies the validity of physical meter associations and the rationality of the calculation logic after a user submits a formula, ensuring that the formula can be parsed and executed correctly. It also supports temporary saving of formulas and historical version rollback for easy subsequent adjustments and optimizations. Examples of calculation formulas for virtual meters include: Cafeteria virtual meter formula = Meter 1 energy consumption × 0.8 + Meter 2 energy consumption × 0.6 + Meter 3 energy consumption; Dormitory virtual meter formula = Meter 1 energy consumption × 0.2 + Meter 2 energy consumption × 0.4; Logical judgment example formula = if (Meter A. Manual meter reading == null) { return Meter B. Energy consumption × 0.8;} else { return Meter A. Energy consumption × 0.7;}. The calculation formula examples for virtual meters strictly follow the combined logic of 'physical meter energy consumption data + multi-dimensional calculation logic', where the coefficients are set based on the energy consumption allocation ratio of the actual scenario, and the logical judgments are set based on business needs such as data validity verification and backup meter switching. Among them, the canteen virtual meter formula integrates the energy consumption data of meter 1, meter 2, and meter 3 and assigns different coefficients to achieve accurate calculation of canteen-specific energy consumption. The coefficients 0.8 and 0.6 correspond to the canteen's share of energy consumption in each meter. The dormitory virtual meter formula is based on the same meter data and sets the coefficients 0.2 and 0.4 according to the dormitory's energy consumption sharing ratio. The two types of formulas are adapted to the energy consumption statistics needs of different scenarios without dedicated meters, fully demonstrating the flexibility and practicality of formula configuration.

[0022] Cross-level aggregation specifically involves aggregating energy consumption data for higher-level tags, automatically including energy consumption data associated with all lower-level tags. The implementation process for updating virtual meter calculation rules via REST API is as follows: Users submit a calculation rule update request to the system through the API interface. The request includes parameters such as the virtual meter identifier, the new calculation formula, and the effective time. After receiving the request, the system verifies the request's legality and parameter completeness through an interface verification mechanism. Upon successful verification, the system automatically replaces the original calculation rules corresponding to the virtual meter, and the new rules take effect immediately without requiring a system service restart. This update method supports batch operations and scheduled activation. Users can automate the management of calculation rules by calling the API through scripts or third-party systems, ensuring the efficiency and real-time nature of business logic adjustments.

[0023] Adding, modifying, and configuring tags are all done through the system interface without modifying the underlying code. All tag addition, modification, and deletion operations are performed through the system's visual management interface, which provides functions such as category management, tag value maintenance, and hierarchy adjustment. When adding a tag, the user enters the tag category name, tag value content, and hierarchy relationship; the system automatically completes the data storage. When modifying a tag, the user can adjust the tag name, hierarchy affiliation, and other information, while simultaneously updating the tag mapping relationship of associated instruments. When deleting a tag, the system first checks if the tag has an associated instrument; if so, it prompts the user to unassociate it before performing the deletion operation to avoid data association anomalies. All operations are implemented by calling the system's preset interfaces, requiring no modification to the underlying code, thus reducing the operational threshold and system risk.

[0024] The instruments include intelligent data acquisition instruments and virtual instruments. Both types of instruments are uniformly incorporated into the tag association and energy consumption statistics system. Intelligent data acquisition instruments (physical instruments) and virtual instruments are uniformly incorporated into this system, and both types of instruments follow the same tag binding rules and data statistics standards. The system assigns a unique identifier to each instrument to distinguish instrument types. Simultaneously, a unified data structure is used in the database to store basic instrument information, tag association relationships, and energy consumption data. During the statistical process, the system does not need to distinguish instrument types; it only collects the energy consumption data of the corresponding instrument based on the tag association relationship (physical instruments directly collect real-time data, while virtual instruments generate data through formula calculations). This ensures that the energy consumption data of both types of instruments can be seamlessly integrated and summarized, achieving the uniformity and integrity of the statistical system.

[0025] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A tag-based dynamic energy consumption statistics method, characterized in that, Includes the following steps: S1. Construct a hierarchical tag system. The tag system adopts a two-level or higher structure. The first level is the tag category, and the second level and above are the tag values ​​and support tree expansion. S2. Establish a dynamic association mechanism between instruments and tags. Physical instruments can be bound to tag values ​​under multiple tag categories. When there is no corresponding physical instrument, a virtual instrument is created and associated with the target tag. S3. Configure the virtual meter calculation framework and set the energy consumption calculation formula for the virtual meter. The formula is parsed using Java Expression Language (JEXL) and supports arithmetic operations, logical judgments, and custom functions. It can be adapted to scenarios such as "switching to a backup meter when there is no manual meter reading" and calculates the energy consumption data of the corresponding statistical dimensions through the formula engine. S4. Employs a hierarchical aggregation algorithm to automatically identify the parent-child hierarchical relationship of tags to achieve cross-level energy consumption aggregation, and automatically completes energy consumption aggregation for multiple meters associated with a tag.

2. The tag-based dynamic energy consumption statistics method according to claim 1, characterized in that: The label classification includes one or more of the following: region, equipment type, production task, and purpose.

3. The tag-based dynamic energy consumption statistics method according to claim 1, characterized in that: The tree expansion of the tag values ​​includes a hierarchical progression in the form of "park-factory-floor" or "process section-equipment".

4. The tag-based dynamic energy consumption statistics method according to claim 1, characterized in that: The physical instrument can be associated with multiple tag values ​​under different tag categories at the same time, such as binding "Equipment type = air compressor" and "Energy efficiency level = Level 2" at the same time.

5. The tag-based dynamic energy consumption statistics method according to claim 1, characterized in that: The calculation formula of the virtual instrument is implemented through interface configuration and parsed using Java Expression Language (JEXL). It consists of energy consumption data from one or more physical instruments combined with arithmetic operations, logical judgments, and user-defined functions.

6. The tag-based dynamic energy consumption statistics method according to claim 1, characterized in that: Examples of calculation formulas for the virtual meters include: Cafeteria virtual meter formula = Meter 1 energy consumption × 0.8 + Meter 2 energy consumption × 0.6 + Meter 3 energy consumption; Dormitory virtual meter formula = Meter 1 energy consumption × 0.2 + Meter 2 energy consumption × 0.4; Logical judgment example formula = if (Meter A. Manual meter reading == null) { return Meter B. Energy consumption × 0.8;} else { return Meter A. Energy consumption × 0.7;}.

7. The tag-based dynamic energy consumption statistics method according to claim 1, characterized in that: The cross-level aggregation specifically means that when aggregating the energy consumption corresponding to a higher-level label, the energy consumption data associated with all its lower-level labels is automatically included.

8. The tag-based dynamic energy consumption statistics method according to claim 1, characterized in that: The addition, modification, and configuration of the tags are completed through the system interface without modifying the underlying code.

9. The tag-based dynamic energy consumption statistics method according to claim 1, characterized in that: The instruments include intelligent data acquisition instruments and virtual instruments, and both types of instruments are uniformly incorporated into the tag association and energy consumption statistics system.