Electrical equipment product carbon footprint multi-source data evaluation method and related device

Abstract

The invention provides an electrical equipment product carbon footprint multi-source data evaluation method and a related device, and the method comprises the steps: determining the carbon footprint data of an electrical equipment product, carrying out the standardization of the carbon footprint data, and obtaining the standardized carbon footprint data; converting the standardized carbon footprint data to obtain a carbon footprint data feature vector; identifying the carbon footprint data feature vector to obtain a domain semantic tag of the carbon footprint data; and evaluating the domain semantic tag to obtain a carbon footprint data evaluation result. According to the invention, automatic acquisition of carbon footprint data and ILCD standardized mapping can be realized, the credibility of the data is dynamically checked in combination with the domain knowledge graph, and an efficient and reliable carbon footprint quantification whole-process support system is constructed.

Description

Technical Field

[0001] This disclosure relates to the field of carbon footprint data processing technology, and in particular to a method and related apparatus for assessing the carbon footprint of electrical equipment products from multiple sources. Background Technology

[0002] This section is intended to provide background or context for the embodiments of this disclosure as set forth in the claims. The description herein is not intended to be a prior art simply because it is included in this section.

[0003] Multi-source data on the carbon footprint of electrical equipment products refers to a heterogeneous collection of data from different channels used to calculate the carbon emissions of electrical equipment products throughout their entire life cycle. It covers key information such as energy consumption, material input, and process parameters in the processes of raw material acquisition, production and manufacturing, transportation, use, and waste disposal. By integrating multi-dimensional sources such as enterprise production data, supply chain data, industry benchmark data, and real-time monitoring data, it provides comprehensive support for accurately quantifying the carbon footprint of products.

[0004] However, the relevant technologies suffer from problems such as low efficiency of manual data collection, data silos due to lack of standards, and insufficient credibility due to lack of professional verification. Summary of the Invention

[0005] In view of this, the purpose of this disclosure is to propose a multi-source data assessment method and related device for the carbon footprint of electrical equipment products, which at least to some extent solves one of the technical problems in the related technologies.

[0006] To achieve the above objectives, a first aspect of the exemplary embodiments of this disclosure provides a method for assessing the carbon footprint of electrical equipment products using multi-source data, the method comprising:

[0007] Determine the carbon footprint data of electrical equipment products, and standardize the carbon footprint data to obtain standardized carbon footprint data;

[0008] The standardized carbon footprint data is transformed to obtain a carbon footprint data feature vector;

[0009] The feature vectors of the carbon footprint data are labeled to obtain the domain semantic labels of the carbon footprint data;

[0010] The semantic labels of the domain are evaluated to obtain the carbon footprint data evaluation results.

[0011] Based on the same inventive concept, a second aspect of the exemplary embodiments of this disclosure provides a multi-source data assessment device for the carbon footprint of electrical equipment products, characterized in that it includes:

[0012] The standardized data determination module is configured to determine the carbon footprint data of electrical equipment products, and to standardize the carbon footprint data to obtain standardized carbon footprint data.

[0013] The feature vector determination module is configured to transform the standardized carbon footprint data to obtain a carbon footprint data feature vector;

[0014] The semantic label determination module is configured to identify the feature vector of the carbon footprint data to obtain the domain semantic label of the carbon footprint data;

[0015] The evaluation result determination module is configured to evaluate the semantic labels of the domain to obtain carbon footprint data evaluation results.

[0016] Based on the same inventive concept, a third aspect of the exemplary embodiments of this disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in the first aspect.

[0017] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in the first aspect.

[0018] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of this disclosure provides a computer program product including computer program instructions that, when run on a computer, cause the computer to perform the method as described in the first aspect.

[0019] As can be seen from the above, the present disclosure provides a method and related apparatus for assessing the carbon footprint of electrical equipment products using multi-source data. The method includes:

[0020] This method involves determining the carbon footprint data of electrical equipment products, standardizing the carbon footprint data to obtain standardized carbon footprint data, transforming the standardized carbon footprint data to obtain carbon footprint data feature vectors, identifying the carbon footprint data feature vectors to obtain domain semantic tags for the carbon footprint data, and evaluating the domain semantic tags to obtain carbon footprint data evaluation results. This disclosure enables automated carbon footprint data collection and standardized mapping with ILCD (International Life Cycle Reference Data System), and combines this with dynamic verification of data credibility using a domain knowledge graph to construct an efficient and reliable full-process support system for carbon footprint quantification. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this disclosure or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A schematic diagram of an application scenario of the multi-source data assessment method for the carbon footprint of electrical equipment products provided in an exemplary embodiment of this disclosure;

[0023] Figure 2 A flowchart illustrating a method for evaluating the carbon footprint of electrical equipment products using multi-source data, provided as an exemplary embodiment of this disclosure.

[0024] Figure 3 A schematic diagram of a multi-source data assessment method apparatus for the carbon footprint of electrical equipment products provided as an exemplary embodiment of this disclosure;

[0025] Figure 4 A schematic diagram of the hardware structure of an electronic device provided for an exemplary embodiment of this disclosure. Detailed Implementation

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of this application, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this application in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0027] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this application's technical solution, based on the prompt message.

[0028] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0029] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0030] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0031] To make the objectives, technical solutions, and advantages of this disclosure clearer, the principles and spirit of this disclosure will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement this disclosure, and are not intended to limit the scope of this disclosure in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0032] In this article, it is important to understand that any number of elements in the accompanying figures is for illustrative purposes and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0033] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar words used in the embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "a" or "an" preceding an element does not exclude the existence of multiple such elements.

[0034] The principles and spirit of this disclosure will be explained in detail below with reference to several representative embodiments.

[0035] As described in the background section, related technologies suffer from problems such as low efficiency in manual data collection, data silos due to a lack of data standards, and insufficient credibility due to a lack of professional verification. Specifically, existing technologies mainly rely on manual data entry, which suffers from low automation, low efficiency, and susceptibility to errors. This makes it difficult to meet the requirements of carbon footprint accounting for real-time data, accuracy, and continuity. Furthermore, the lack of unified data standards in existing technologies leads to inconsistencies in the format, units, and system boundaries of carbon footprint data from different sources (such as suppliers and production systems), forming "data silos" that are difficult to recognize and integrate, thus failing to provide a consistent and comparable data foundation for quantitative analysis.

[0036] Furthermore, existing data fusion methods lack the embedding of professional knowledge in the field of electrical equipment, and cannot automatically identify and verify logical errors in the data that violate physical laws and industry common sense, resulting in low credibility of fused data, which may mislead subsequent process optimization analysis and carbon footprint assessment.

[0037] To address the aforementioned issues, this disclosure provides a method and related apparatus for assessing the carbon footprint of electrical equipment products using multi-source data. The method includes:

[0038] This invention discloses a method for automating, real-time, and highly accurate carbon footprint data collection. By identifying the carbon footprint data of electrical equipment products, standardizing the carbon footprint data, and transforming the standardized carbon footprint data to obtain carbon footprint data feature vectors, and then identifying the carbon footprint data feature vectors to obtain domain semantic labels, the method evaluates the domain semantic labels to obtain carbon footprint data evaluation results. Compared to the outdated methods relying on manual entry and report aggregation, this invention overcomes the data access challenges of multi-source heterogeneous equipment in manufacturing sites through intelligent IoT agents and protocol parsing libraries. It achieves unmanned, real-time, and automatic collection of carbon footprint data from systems such as sensors, MES, and ERP. This completely avoids the inefficiency, delays, and errors caused by manual operation, significantly improving the efficiency and accuracy of data collection and providing a continuous and reliable data foundation for dynamic quantification. Furthermore, this invention overcomes the industry challenge of inconsistent standards and ineffective integration of multi-source carbon footprint data.

[0039] Unlike existing technologies that suffer from "data silos" due to inconsistent data formats, units, and system boundaries, this disclosure adopts the ILCD international standard as a unified "data intermediate language." By constructing a core data model and a semantic mapping rule engine, heterogeneous data is automatically converted into standardized datasets with consistent semantics and formats. This enables seamless integration and comparison of data from different stages such as suppliers, production, and transportation, fundamentally ensuring the consistency and comparability of carbon footprint accounting results.

[0040] Finally, this disclosure ensures the authenticity, reliability, and consistency with common sense in the field of carbon footprint data, thereby enhancing the credibility of the quantification results. Addressing the issue that general fusion methods cannot identify logical errors in the data, this disclosure employs an adaptive credibility verification mechanism based on a knowledge graph in the electrical equipment field. This mechanism automatically verifies whether the data violates the physical laws governing materials and processes, and performs quantifiable credibility scoring and precise source tracing for abnormal data. This not only effectively intercepts erroneous data but also ensures the authenticity and reliability of the data input into the quantification model, making subsequent carbon footprint analysis, process optimization, and low-carbon decision-making more accurate and effective.

[0041] After introducing the basic principles of this disclosure, various non-limiting embodiments of this disclosure will be described in detail below.

[0042] refer to Figure 1 This is a schematic diagram of an application scenario of the multi-source data assessment method for the carbon footprint of electrical equipment products provided in the exemplary embodiments of this disclosure.

[0043] This application scenario includes a terminal device 101 and a server 102. The terminal device 101 and the server 102 can be connected via a wired or wireless communication network to achieve data interaction.

[0044] Terminal device 101 may be an electronic device located close to the user side, possessing data transmission and multimedia input / output functions, including but not limited to desktop computers, mobile phones, portable computers, tablet computers, media players, smart wearable devices, personal digital assistants (PDAs), or other electronic devices capable of performing the aforementioned functions. This electronic device may include a processor and a display screen with touch input functionality. The display screen is used to present a graphical user interface (GUI), which can display an application interface. The processor is used to process application data, generate the GUI, and control the display of the GUI on the screen.

[0045] Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0046] In some exemplary embodiments, the multi-source data assessment method for the carbon footprint of electrical equipment products can be run on terminal device 101 or server 102.

[0047] When the multi-source data assessment method for the carbon footprint of electrical equipment products is run on server 102, server 102 is used to provide multi-source data assessment services for the carbon footprint of electrical equipment products to users of terminal device 101.

[0048] Server 102 determines the carbon footprint data of electrical equipment products, and server 102 standardizes the carbon footprint data to obtain standardized carbon footprint data.

[0049] Server 102 transforms the standardized carbon footprint data to obtain a carbon footprint data feature vector;

[0050] Server 102 identifies the feature vector of the carbon footprint data to obtain the domain semantic label of the carbon footprint data;

[0051] After evaluating the domain semantic tags and obtaining the carbon footprint data evaluation result, the server 102 transmits the carbon footprint data evaluation result to the terminal device 101.

[0052] It should be noted that the above application scenarios are shown only to facilitate understanding of the spirit and principles of this disclosure, and the implementation of this disclosure is not limited in any way. On the contrary, the implementation of this disclosure can be applied to any applicable scenario.

[0053] refer to Figure 2 A method for assessing the carbon footprint of electrical equipment products using multi-source data, the method comprising the following steps:

[0054] Step S210: Determine the carbon footprint data of electrical equipment products, and standardize the carbon footprint data to obtain standardized carbon footprint data.

[0055] In this step, by connecting with the supplier's MES / ERP, energy management, transportation scheduling and other systems, real-time raw carbon emission data of electrical equipment products in each stage of raw materials, production, transportation, use and dismantling are automatically collected on a "T+1" basis. Based on the ILCD international standard, unit conversion, format unification and semantic mapping are performed on multi-source heterogeneous data to completely eliminate the delay and error caused by manual reporting.

[0056] In some exemplary embodiments, the carbon footprint data includes: carbon footprint data of raw material acquisition, carbon footprint data of product manufacturing, carbon footprint data of product transportation, carbon footprint data of product use, and carbon footprint data of product recycling.

[0057] In practice, the method for determining the carbon footprint data of electrical equipment products is as follows:

[0058] The methods for determining the carbon footprint data of raw materials include the following steps:

[0059] By conducting on-site supervision, the relationship between the component names in the supplier's MES / ERP system's BOM list group and the component names in the model group was clarified.

[0060] By conducting on-site supervision, the weight of components in the BOM list is carried out. Some components are weighed in units of weight, while other components that are not weighed in units of weight can be weighed according to the design drawings or by actual weighing.

[0061] Expand the business interfaces between the supplier's MES / ERP system and EIP system. After the BOM list data is maintained in the supplier's MES / ERP system, it is actively pushed to the EIP system through the data interface. The carbon footprint multi-source data fusion system for electrical equipment products relies on the State Grid data platform and obtains existing BOM list information through the EIP system in a "T+1" manner, thereby obtaining carbon footprint data of raw material acquisition.

[0062] In practice, the methods for determining the carbon footprint data of product manufacturing are as follows:

[0063] Currently, suppliers with a high level of information technology use energy management systems to monitor the real-time power consumption of major production equipment. However, monitoring of other energy sources such as coal, oil, and gas at the individual equipment level is not yet possible. Furthermore, the carbon emissions from other energy consumption account for a small proportion of the product manufacturing process, less than 10%. It is recommended that the consumption of other energy sources such as coal, oil, and gas be excluded from the carbon emission data collection scope for the time being, and that only the power consumption data of individual electrical equipment products be considered.

[0064] The supplier's energy management system records the real-time power consumption of each device and expands business interface information. The multi-source data fusion system for the carbon footprint of electrical equipment products relies on the data platform and obtains power consumption information in a "T+1" manner through the EIP system.

[0065] The supplier's MES / ERP system records the production schedule of a single transformer in each production process, including specific production time, production equipment, production duration, and other information. It also expands business interface information. The multi-source data fusion system for the carbon footprint of electrical equipment products relies on the data platform and obtains the production schedule in a "T+1" manner through the EIP system.

[0066] By conducting on-site supervision, the accuracy of the supplier's production schedule for each electrical equipment product in each production process is verified, and the authenticity of the data is verified through random inspections, ultimately enabling the acquisition of carbon footprint data for product manufacturing.

[0067] In practice, the method for determining the carbon footprint data of product transportation is as follows:

[0068] Based on the existing material codes and actual transportation mileage in the ELP system, the system adds technical specification IDs and transportation vehicle type information, and distinguishes between the actual mileage of road and rail transportation. The multi-source data fusion system for carbon footprint of electrical equipment products relies on the data platform and obtains actual transportation mileage and transportation vehicle type information in a "T+1" manner through the ELP system.

[0069] Based on the existing material codes and technical specification IDs in the EIP system, product weight information is added. The multi-source data fusion system for the carbon footprint of electrical equipment products relies on the data platform to obtain product weight information through the EIP system in a "T+1" manner, thereby obtaining carbon footprint data of product transportation.

[0070] In practice, the methods for determining the carbon footprint data of a product are as follows:

[0071] Testing and Installation Phase: Currently, the e-Infrastructure system does not collect information on testing and installation equipment or energy consumption; data cannot be automatically collected at this time. It is recommended that headquarters collaborate with the Infrastructure Department to develop a carbon emission data collection plan for the testing and installation phase.

[0072] Operational phase: First, the multi-source data fusion system for the carbon footprint of electrical equipment products obtains the physical ID and equipment ID information of the transformer from the PMS3.0 system; second, the multi-source data fusion system for the carbon footprint of electrical equipment products obtains the transformer equipment ID, real-time input power and output power information of the equipment from the real-time measurement center, thereby obtaining the carbon footprint data of the product.

[0073] Currently, during the monitoring and maintenance phase, the PMS3.0 system is not collecting information on monitoring and maintenance equipment or energy consumption; data cannot be automatically collected at this time. It is recommended that headquarters collaborate with the Equipment Department to develop a carbon emission data collection plan for the monitoring and maintenance phase.

[0074] In practice, the method for determining the carbon footprint data of product recycling is as follows:

[0075] Transportation and handling phase: Given that the ELP system has not yet included the management of decommissioned materials transportation for municipal and prefecture-level material departments, it is recommended that the transportation of decommissioned materials not be included in the scope of carbon emission data collection for the time being.

[0076] Dismantling: Currently, the dismantling center does not monitor individual devices for energy sources such as water and gas. Furthermore, the carbon emissions from water and gas consumption account for a small proportion of the dismantling and post-dismantling disposal phases, less than 10%. It is recommended that water and gas consumption not be included in the carbon emission data collection scope for the time being, and that only the power consumption data of individual electrical equipment products be considered. Firstly, power consumption data collection devices should be installed on each piece of equipment in each dismantling process at the dismantling center to record the real-time operating time and power of each device. The green center should store the real-time power information of each piece of equipment. Secondly, the green center should record the production schedule for individual transformers in each dismantling process, including specific dismantling time, production equipment, and dismantling duration.

[0077] Post-dismantling disposal phase: Currently, the dismantling center has not obtained energy consumption data from solid waste recycling stations and waste-to-energy plants. It is recommended that non-recyclable components not be included in the carbon emission data collection scope for the time being. Regarding recyclable weight data, it is recommended that the recyclable weight of each group of components of a single transformer be recorded at the green center to ultimately obtain the carbon footprint data of product recycling.

[0078] In some exemplary embodiments, the standardization of the carbon footprint data to obtain standardized carbon footprint data includes:

[0079] The carbon footprint data is cleaned to obtain purified carbon footprint data;

[0080] The carbon footprint clean data is converted to obtain unified carbon footprint data;

[0081] A unified data framework was constructed based on international lifecycle data standards.

[0082] The carbon footprint unified data is mapped to the data unification framework to obtain the standardized carbon footprint data.

[0083] In practice, the carbon footprint data is cleaned to obtain purified carbon footprint data in the following way:

[0084] The system receives and performs preliminary processing of raw carbon footprint data in various formats from different stages of the entire lifecycle of electrical equipment products. A unified data input interface is established to receive various data sources, clean missing and outlier values, and ultimately obtain clean carbon footprint data for electrical equipment products.

[0085] In practice, the carbon footprint clean data is converted to obtain unified carbon footprint data in the following way:

[0086] The carbon footprint clean data is converted to a unified unit and initially classified and labeled according to life cycle stage and source to obtain unified carbon footprint data, laying the foundation for subsequent standardization and integration.

[0087] In practice, a unified data framework is constructed based on international lifecycle data standards.

[0088] Within the system, a unified data structure and semantic framework (i.e., a unified data framework) conforming to the ILCD (International Life Cycle Reference Data System) international standard is established. Based on the core concepts of the ILCD standard (such as Process, Flow, FlowProperty, UnitGroup, and LCIAMethod), corresponding data models are defined and constructed in the system database or structure, and necessary metadata attributes (such as substance name, CAS number, environmental medium, and characterization factor references) are pre-defined or managed, providing a standardized "skeleton" and "dictionary" for data fusion.

[0089] In specific implementation, the standardized carbon footprint data is obtained by mapping the unified carbon footprint data to the unified data framework as follows:

[0090] This method accurately transforms and maps carbon footprint data to corresponding entities and attributes within a unified data framework, resolving the issue of inconsistent standards. Utilizing a predefined mapping rule library (containing correspondences from common non-ILCD standards to ILCD standards, and standard ILCD naming for specific materials / energy / processes in electrical equipment), a rule engine combining keywords, semantics, or mapping tables identifies and transforms raw data items (such as "electricity consumption of XX process") into specific ILCD processes and input / output flows. Characterization factors are applied to calculate CO2 equivalents, and uncertainties in allocation rules and data transmission are addressed, achieving standardized data semantics and format, thereby obtaining standardized carbon footprint data.

[0091] Step S220: Convert the standardized carbon footprint data to obtain a carbon footprint data feature vector.

[0092] In some exemplary embodiments, the standardized carbon footprint data is transformed to obtain a carbon footprint data feature vector, including:

[0093] Data association is performed on the standardized carbon footprint data to obtain carbon footprint data association information;

[0094] The carbon footprint data association information is integrated at the boundary to obtain a carbon footprint fusion dataset;

[0095] The carbon footprint fusion dataset is stored to obtain a carbon footprint database;

[0096] The carbon footprint database is format-converted to obtain the carbon footprint data feature vector.

[0097] In specific implementation, the standardized carbon footprint data is correlated to obtain carbon footprint data correlation information; the carbon footprint data correlation information is then integrated at the boundaries to obtain a carbon footprint fusion dataset.

[0098] By associating data, standardized carbon footprint data is constructed into a network of input-output relationships between processes. Then, boundary integration clarifies the system boundaries and coverage, forming a carbon footprint fusion dataset. At the same time, based on the product lifecycle process (bill of materials, process route, logistics information), the data of each unit process are connected into a logically self-consistent system model, and upstream background data, processing and recycling streams and other special scenarios are linked. Ultimately, the fusion dataset of data association and boundary integration and the lifecycle system model are unified.

[0099] In specific implementation, the carbon footprint fusion dataset is stored to obtain the carbon footprint database in the following way:

[0100] The complete and standardized carbon footprint fusion dataset, after fusion, mapping, and association, is persistently stored and ensures its compliance with the ILCD specification, thus obtaining a carbon footprint database. A carbon footprint database structure conforming to the ILCD data model is designed or adopted, storing ILCD process datasets, streaming data, and impact assessment method information containing complete metadata, input / output stream lists, allocation rules, and uncertainty information. It supports exporting in the ILCD standard format and provides version management and data traceability functions.

[0101] In specific implementation, the carbon footprint database is converted to obtain the carbon footprint data feature vector in the following way:

[0102] By converting the standardized data in the carbon footprint database into structured feature vectors through format conversion, and building a standardized API access layer, the dynamic quantification calculation module can directly retrieve semantically clear and formatted carbon footprint data based on ILCD concepts (such as process name, life cycle stage, timestamp, etc.), thereby eliminating the complexity of heterogeneous data processing and providing efficient and consistent data access support for downstream computing.

[0103] Step S230: Identify the feature vector of the carbon footprint data to obtain the domain semantic label of the carbon footprint data.

[0104] In some exemplary embodiments, the carbon footprint data feature vector is identified to obtain a domain semantic label for the carbon footprint data, including:

[0105] Knowledge extraction is performed on the electrical equipment products to obtain a domain knowledge graph;

[0106] Semantic matching is performed between the carbon footprint data feature vector and the domain knowledge graph to obtain the domain semantic labels.

[0107] In specific implementation, the following method is used to extract knowledge from the electrical equipment products to obtain a domain knowledge graph:

[0108] The implicit knowledge and physical laws of the electrical equipment industry are transformed into an explicit, structured rule base that can be recognized and processed by computers. This is achieved by extracting key entities, attributes, and their interrelationships from international standards, industry manuals, technical papers, and expert experience. A domain knowledge graph depicting the complex relationships between "materials-processes-performance-emissions" is then constructed using a graph database. The logic and constraints in this graph are further transformed into executable verification rules. These rules include defining energy consumption ranges for specific material processes, establishing mathematical correlation functions between different parameters (such as the negative correlation between energy consumption and iron loss in silicon steel rolling), and determining the logical consistency between component weight and total weight. This lays a solid foundation for subsequent automated verification.

[0109] In specific implementation, the method for obtaining the domain semantic labels by semantically matching the carbon footprint data feature vector with the domain knowledge graph is as follows:

[0110] The carbon footprint data feature vectors, fused according to the ILCD standard, are precisely associated with predefined domain entities and relationships in the knowledge graph. This is achieved by leveraging the unified semantic tags inherent in the ILCD data model and using an automated semantic matching algorithm to accurately map carbon footprint data feature vectors to corresponding nodes in the knowledge graph. This imbues each feature vector with a profound domain semantic label, transforming it from an isolated numerical value into a knowledge object rich in contextual relationships.

[0111] Step S240: Evaluate the semantic tags of the domain to obtain carbon footprint data evaluation results.

[0112] In some exemplary embodiments, the evaluation of the domain semantic labels to obtain carbon footprint data evaluation results includes:

[0113] The credibility score of the carbon footprint data is obtained by performing credibility quantification on the semantic tags of the domain.

[0114] The credibility score is evaluated using a threshold to obtain the carbon footprint data evaluation result.

[0115] In specific implementation, the credibility score of the carbon footprint data is obtained by performing credibility quantification on the domain semantic tags as follows:

[0116] A comprehensive and automated credibility review is performed on the mapped fused data. Specifically, the rule engine dynamically triggers all relevant verification rules in the knowledge graph based on domain semantic tags, performing multi-level verification from single data points to multiple data associations: including checking whether a single process parameter is within a reasonable value range, and verifying whether the relationship between multiple data points violates physical laws (e.g., verifying whether the "rolling energy consumption" and "iron loss value" data of the same batch of silicon steel meet the preset negative correlation constraint); once a data value is detected to conflict with the rule assertion, the engine immediately generates a detailed "credibility event" record, accurately describing the anomaly type, the violated rule, and the relevant data items.

[0117] Finally, a quantifiable confidence score (e.g., 0-100) is calculated for each data item, and deductions are made based on the level and number of rule-triggered alerts, thus intuitively reflecting its reliability.

[0118] In practice, the carbon footprint data evaluation result is obtained by performing a threshold assessment on the credibility score as follows:

[0119] The system categorizes anomalies (e.g., fatal errors, warnings, alerts). For example, an alert is generated when the credibility score is less than 80; a warning is generated when the credibility score is less than 60; and a fatal error alert is generated when the credibility score is less than 40, triggering a source tracing mechanism. By traversing the relationship chains in the knowledge graph, the upstream source of the abnormal data is accurately located (e.g., an abnormal iron loss value can be traced back to the raw material data of a specific supplier's batch or its corresponding rolling energy consumption anomaly), and the entire impact chain is presented in a visual manner, greatly improving the efficiency and accuracy of data quality problem investigation.

[0120] In this step, a continuously improving closed-loop system can also be built, enabling the verification mechanism to self-evolve and optimize. Specifically, this involves establishing a human feedback interface that allows domain experts to review and confirm system alerts. "False alarms" rejected by experts can be used to adjust the sensitivity or threshold of corresponding rules. Simultaneously, the system continuously analyzes historical verification data and expert feedback using machine learning algorithms (such as association rule mining), proactively discovering potential new patterns or anomalies not yet included in the knowledge base. These are then provided to administrators as new rule suggestions, thereby continuously enriching and optimizing the knowledge graph, making the entire verification system increasingly intelligent and accurate over time.

[0121] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0122] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] Based on the same inventive concept, and corresponding to any of the above embodiments, this disclosure also provides a multi-source data assessment device for the carbon footprint of electrical equipment products.

[0124] refer to Figure 3 The multi-source data assessment device for the carbon footprint of electrical equipment products includes:

[0125] The standardized data determination module 310 is configured to determine the carbon footprint data of electrical equipment products, and to standardize the carbon footprint data to obtain standardized carbon footprint data.

[0126] The feature vector determination module 320 is configured to transform the standardized carbon footprint data to obtain a carbon footprint data feature vector;

[0127] The semantic label determination module 330 is configured to identify the feature vector of the carbon footprint data to obtain the domain semantic label of the carbon footprint data;

[0128] The evaluation result determination module 340 is configured to evaluate the semantic labels of the domain to obtain carbon footprint data evaluation results.

[0129] In this exemplary embodiment, the standardized data determination module 310 is specifically configured as follows:

[0130] The carbon footprint data of electrical equipment products is determined, and the carbon footprint data is cleaned to obtain carbon footprint clean data. The carbon footprint clean data is then converted to obtain unified carbon footprint data. A unified data framework is constructed based on international life cycle data standards. The unified carbon footprint data is mapped to the unified data framework to obtain standardized carbon footprint data. The carbon footprint data includes: carbon footprint data of raw material acquisition, carbon footprint data of product manufacturing, carbon footprint data of product transportation, carbon footprint data of product use, and carbon footprint data of product recycling.

[0131] In this exemplary embodiment, the feature vector determination module 320 is specifically configured as follows:

[0132] The standardized carbon footprint data is correlated to obtain carbon footprint data correlation information; the carbon footprint data correlation information is integrated at the boundary to obtain a carbon footprint fusion dataset; the carbon footprint fusion dataset is stored to obtain a carbon footprint database; the carbon footprint database is converted to a new format to obtain the carbon footprint data feature vector.

[0133] In this exemplary embodiment, the semantic tag determination module 330 is specifically configured as follows:

[0134] Knowledge is extracted from the electrical equipment products to obtain a domain knowledge graph; semantic matching is performed between the carbon footprint data feature vector and the domain knowledge graph to obtain the domain semantic tags.

[0135] In this exemplary embodiment, the evaluation result determination module 340 is specifically configured as follows:

[0136] The domain semantic tags are quantified to obtain a credibility score for the carbon footprint data; the credibility score is then evaluated using a threshold to obtain an evaluation result for the carbon footprint data.

[0137] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, in implementing this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0138] The apparatus described above is used to implement the multi-source data assessment method for the carbon footprint of electrical equipment products in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0139] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the multi-source data assessment method for the carbon footprint of electrical equipment products described in any of the above embodiments.

[0140] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0141] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0142] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0143] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0144] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0145] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0146] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0147] The electronic devices described above are used to implement the multi-source data assessment method for the carbon footprint of electrical equipment products in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0148] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the multi-source data assessment method for the carbon footprint of electrical equipment products as described in any of the above embodiments.

[0149] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0150] The aforementioned non-transitory computer-readable storage media can be any available medium or data storage device that a computer can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0151] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the multi-source data assessment method for the carbon footprint of electrical equipment products as described in any of the embodiments in the exemplary method section above, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0152] Based on the same inventive concept, corresponding to the multi-source data assessment method for the carbon footprint of electrical equipment products described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to execute the multi-source data assessment method for the carbon footprint of electrical equipment products. Corresponding to the execution entity for each step in each embodiment of the multi-source data assessment method for the carbon footprint of electrical equipment products, the processor executing the corresponding step can belong to the corresponding execution entity.

[0153] The computer program product of the above embodiments is used to enable the computer and / or the processor to execute the multi-source data assessment method for carbon footprint of electrical equipment products as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0154] Those skilled in the art will recognize that embodiments of this disclosure can be implemented as a system, method, or computer program product. Therefore, this disclosure can be implemented as entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this disclosure can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0155] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (not exhaustive) of a computer-readable storage medium may include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0156] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0157] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0158] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0159] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine that, when executed by a computer or other programmable data processing device, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0160] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce a product comprising an instruction apparatus that implements the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0161] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable apparatus can provide a process for implementing the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0162] Furthermore, although the operations of the methods of this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Rather, the steps depicted in the flowcharts may be executed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0163] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0164] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0165] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0166] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0167] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0168] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

[0169] While the spirit and principles of this disclosure have been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; such division is merely for convenience of expression. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the appended claims is to be interpreted in the broadest sense, thereby encompassing all such modifications and equivalent structures and functions.

Claims

1. A method for assessing the carbon footprint of electrical equipment products using multi-source data, characterized in that, include: Determine the carbon footprint data of electrical equipment products, and standardize the carbon footprint data to obtain standardized carbon footprint data; The standardized carbon footprint data is transformed to obtain a carbon footprint data feature vector; The feature vectors of the carbon footprint data are labeled to obtain the domain semantic labels of the carbon footprint data; The semantic labels of the domain are evaluated to obtain the carbon footprint data evaluation results.

2. The method according to claim 1, characterized in that, The carbon footprint data includes: carbon footprint data of raw material acquisition, carbon footprint data of product manufacturing, carbon footprint data of product transportation, carbon footprint data of product use, and carbon footprint data of product recycling.

3. The method according to claim 1, characterized in that, The standardization of the carbon footprint data to obtain standardized carbon footprint data includes: The carbon footprint data is cleaned to obtain purified carbon footprint data; The carbon footprint clean data is converted to obtain unified carbon footprint data; A unified data framework is constructed based on international lifecycle data standards. The carbon footprint unified data is mapped to the data unification framework to obtain the standardized carbon footprint data.

4. The method according to claim 1, characterized in that, The process of transforming the standardized carbon footprint data to obtain a carbon footprint data feature vector includes: Data association is performed on the standardized carbon footprint data to obtain carbon footprint data association information; The carbon footprint data association information is integrated at the boundary to obtain a carbon footprint fusion dataset; The carbon footprint fusion dataset is stored to obtain a carbon footprint database; The carbon footprint database is format-converted to obtain the carbon footprint data feature vector.

5. The method according to claim 1, characterized in that, The process of identifying the feature vector of the carbon footprint data to obtain the domain semantic label of the carbon footprint data includes: Knowledge extraction is performed on the electrical equipment products to obtain a domain knowledge graph; Semantic matching is performed between the carbon footprint data feature vector and the domain knowledge graph to obtain the domain semantic labels.

6. The method according to claim 1, characterized in that, The evaluation of the semantic labels in the domain to obtain carbon footprint data evaluation results includes: The credibility score of the carbon footprint data is obtained by performing credibility quantification on the semantic tags of the domain. The credibility score is evaluated using a threshold to obtain the carbon footprint data evaluation result.

7. A multi-source data assessment device for the carbon footprint of electrical equipment products, characterized in that, include: The standardized data determination module is configured to determine the carbon footprint data of electrical equipment products, and to standardize the carbon footprint data to obtain standardized carbon footprint data. The feature vector determination module is configured to transform the standardized carbon footprint data to obtain a carbon footprint data feature vector; The semantic label determination module is configured to identify the feature vector of the carbon footprint data to obtain the domain semantic label of the carbon footprint data; The evaluation result determination module is configured to evaluate the semantic labels of the domain to obtain carbon footprint data evaluation results.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer program instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 6.

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