A method and device for generating a carbon footprint inventory for substation projects based on knowledge graph

Abstract

The present invention discloses a method and device for generating a carbon footprint inventory for a substation project based on a knowledge graph. The method includes: collecting basic data related to the quantification of the substation project footprint; constructing a substation project ontology knowledge graph based on relevant provisions of the substation project bill of quantities calculation specification, the cradle-to-gate carbon footprint-related lifecycle processes of the substation project, and basic data; extracting knowledge of material and energy emission factors from the substation project ontology knowledge graph based on emission factor data to determine an emission factor knowledge graph; determining a seed set based on material activity level data and conceptual hierarchy data of the emission factor knowledge graph; aligning the substation project ontology knowledge graph and the emission factor knowledge graph using the seed set according to preset rules; and determining a cradle-to-gate carbon footprint inventory for the substation project based on the last-level entities of the aligned substation project ontology knowledge graph.

Description

Technical Field

[0001] The present invention relates to the field of low-carbon power technology, and more specifically, to a method and device for generating a carbon footprint inventory for a substation project based on a knowledge graph. Background Art

[0002] A carbon footprint primarily refers to the total amount of climate-change-related gases emitted through human living and consumption activities. Compared to other carbon emission studies, this one takes a lifecycle perspective, analyzing carbon emissions directly and indirectly related to a product's lifecycle or activities. Depending on the subject of study, carbon footprints can be categorized as national / regional, organizational / corporate, product / service, and individual.

[0003] Life Cycle Assessment (LCA) is a representative carbon footprint accounting method. Its scope is divided into a partial life cycle, from raw material acquisition to the product's departure from the production organization (cradle-to-gate), and a full life cycle, covering the entire life cycle (cradle-to-grave). This method consists of four basic steps: goal definition and scope definition, inventory analysis, impact assessment, and result interpretation. LCA compiles and quantifies the inputs and outputs of the product under study throughout its life cycle. Inputs include material consumption, natural resource consumption, and energy consumption, while outputs include products, by-products, waste disposal, and environmental emissions. Inventory analysis is the foundation of carbon footprint accounting. Currently, research on carbon emissions during substation construction is limited, and carbon footprint inventories are unclear. In the carbon footprint accounting of power transmission and transformation, due to the complexity of the construction links of power transformation projects, which involve planning, design, construction, handover and other processes, the types of resource consumption involved in the life cycle are many, spanning multiple industries, and have industry specificity. At the same time, the existing carbon emission factor library is imperfect and lacking, so it is difficult to compile a practical carbon footprint inventory. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method and device for generating a carbon footprint inventory for substation projects based on a knowledge graph.

[0005] According to one aspect of the present invention, a method for generating a carbon footprint inventory for a power transformation project based on a knowledge graph is provided, comprising:

[0006] Collect basic data related to the quantification of the substation project footprint, including: material activity level data, energy consumption activity level data, and material and energy emission factor data;

[0007] Based on the relevant provisions of the bill of quantities calculation specifications for substation projects, the cradle-to-gate carbon footprint life cycle process of substation projects, and basic data, a substation project ontology knowledge graph is constructed;

[0008] Extract knowledge of material and energy emission factors from the substation engineering ontology knowledge graph based on emission factor data to determine the emission factor knowledge graph;

[0009] Determine the seed set based on material activity level data and concept level data of emission factor knowledge graph;

[0010] According to the preset rules, the substation project ontology knowledge graph and the emission factor knowledge graph are aligned through the seed set. Based on the last-level entity of the aligned substation project ontology knowledge graph, the carbon footprint inventory of the substation project from cradle to gate life cycle is determined.

[0011] Optionally, collect basic data related to the quantification of the substation project footprint, including:

[0012] Collect material activity level data and primary energy consumption activity level data for the raw material phase, construction phase, and installation phase of each sub-project of the power transformation project. The material activity level data and primary energy consumption activity level data include: material data, machinery data, and energy data. Material data includes material type and corresponding material consumption quantity or weight; machinery data includes machinery type, machinery energy consumption type, and number of shifts; energy data includes the amount of fossil energy, electrical energy, and thermal energy consumed;

[0013] collecting second energy consumption activity level data during the transportation phase of the power transformation project, the second energy consumption activity level data including the type of transportation tool and its corresponding energy consumption type and transportation distance;

[0014] determining energy expenditure activity level data based on the first energy expenditure activity level data and the second energy expenditure activity level data;

[0015] Collect published emission factor data for materials and energy.

[0016] Optionally, based on the relevant provisions of the bill of quantities calculation specification for substation projects, the cradle-to-gate carbon footprint-related life cycle process of substation projects, and basic data, a substation project ontology knowledge graph is constructed, including:

[0017] According to the life cycle process of the substation project from cradle to gate carbon footprint, the attribute entities of the substation project ontology are constructed, where the attribute entities include: design stage, construction stage and main equipment;

[0018] Divide according to the energy efficiency consumption characteristics and build the ontology library of the attribute entity design stage;

[0019] According to the calculation specification of the bill of quantities of substation engineering, the sub-items are taken as entities of the construction phase, and combined with the material activity level data and energy consumption activity level data, the attribute entity construction phase ontology library is constructed;

[0020] Build a main equipment ontology library based on the material and component lists of the main equipment used in the substation project;

[0021] According to the attribute entity design stage ontology library, the attribute entity construction stage ontology library and the attribute entity main equipment ontology library, a substation engineering ontology knowledge graph is constructed.

[0022] Optionally, knowledge of material and energy emission factors is extracted from the substation engineering ontology knowledge graph based on the emission factor data to determine the emission factor knowledge graph, including:

[0023] The preset emission factor structure matrix is ​​used to extract knowledge of material and energy emission factors from the substation engineering ontology knowledge graph, and an emission factor knowledge graph is formed based on the preset emission factor structure matrix.

[0024] Optionally, the preset emission factor structure matrix includes:

[0025] Concept Hierarchy Matrix:

[0026]

[0027] Where: upperlevel_ n represents the concept entity of the previous level, low level_ n represents the next level of conceptual entities, and KTs represents all the knowledge triples identified;

[0028] Emission factor discrimination matrix:

[0029]

[0030] Where: n represents the entity in the hierarchical matrix, Value represents the value of the emission factor of entity n;

[0031] Value matrix:

[0032]

[0033] Property Matrix:

[0034]

[0035] Where: attribute is the attribute of the emission factor.

[0036] Optionally, the preset rules are:

[0037] When the next-level concept entity of a certain concept level does not have an emission factor value, the concept entity of the previous level is used. If there is still no emission factor, the concept entity of the previous level is searched again until a concept entity with an emission factor is found;

[0038] When there are multiple emission factors for the same conceptual entity, the emission factors are determined according to the priority of data source > geographical scope > time, where the data source is prioritized based on credibility, the geographical scope is prioritized based on localization, and the time is prioritized based on the latest release.

[0039] When both the upper and lower levels of a conceptual entity have emission factors, the emission factor of the lower level conceptual entity is preferred.

[0040] According to another aspect of the present invention, a device for generating a carbon footprint inventory for a power transformation project based on a knowledge graph is provided, comprising:

[0041] The collection module is used to collect basic data related to the quantification of the substation project footprint, including material activity level data, energy consumption activity level data, and material and energy emission factor data;

[0042] A construction module is used to build a knowledge graph of the substation project ontology based on the relevant provisions of the bill of quantities calculation specifications for substation projects, the life cycle process of substation projects from cradle to gate carbon footprint, and basic data;

[0043] The first determination module is used to extract knowledge of material and energy emission factors from the substation engineering ontology knowledge graph based on emission factor data and determine the emission factor knowledge graph;

[0044] The second determination module is used to determine the seed set based on the material activity level data and the concept level data of the emission factor knowledge graph;

[0045] The third determination module is used to align the substation project ontology knowledge graph and the emission factor knowledge graph through the seed set according to preset rules, and determine the carbon footprint inventory of the substation project from the cradle to the gate life cycle stage based on the last-level entity of the aligned substation project ontology knowledge graph.

[0046] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method according to any one of the above aspects of the present invention.

[0047] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor for reading the executable instructions from the memory and executing the instructions to implement the method described in any one of the above aspects of the present invention.

[0048] Therefore, this application extracts knowledge and constructs knowledge graphs for the material entities and mechanical entities and relationships of the sub-items of the power transmission and transformation project, as well as the existing emission factors, and aligns entities based on factors such as the concept hierarchy seed set, whether the entity has an emission factor, and the attribute priority of the emission factor. Finally, the last-level entity after the entity alignment is the carbon footprint list of the power transmission and transformation project. The carbon footprint list provided by this application ensures that all entities in the list have valid emission factors, thereby ensuring the availability and operability of the list for carbon footprint accounting. The materials and machinery in the list can be reversely traced based on the knowledge graph relationship to obtain their life cycle stages and sub-items, thereby providing favorable support for later carbon emission reduction. With the improvement of the input emission factor data and the enrichment of engineering data, this method has the advantage of rapid updating and improvement of the carbon footprint list. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0050] Figure 1 This is a flow chart of a method for generating a carbon footprint inventory for a substation project based on a knowledge graph, provided by an exemplary embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the division of a power transformation project provided by an exemplary embodiment of the present invention;

[0052] Figure 3 It is a schematic diagram of a knowledge graph of entity libraries and relationships in the design phase provided by an exemplary embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram of the division of the construction phase provided by an exemplary embodiment of the present invention;

[0054] Figure 5 It is a schematic diagram of the ontology division of the main production process provided by an exemplary embodiment of the present invention;

[0055] Figure 6 This is a schematic diagram of the physical division of materials and energy in a main control communication building provided by an exemplary embodiment of the present invention;

[0056] Figure 7 This is an example diagram of the division of some mechanical equipment entities and their energy entities during the construction and equipment installation phase provided by an exemplary embodiment of the present invention;

[0057] Figure 8 This is an example diagram of the structure of an emission factor knowledge extraction graph provided by an exemplary embodiment of the present invention;

[0058] Figure 9 It is a schematic structural diagram of a device for generating a carbon footprint inventory for a substation project based on a knowledge graph according to an exemplary embodiment of the present invention;

[0059] Figure 10 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0060] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0061] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0062] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0063] It should also be understood that, in the embodiments of the present invention, “a plurality of” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0064] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0065] In addition, the term "and / or" in this invention merely describes an association relationship between related objects, indicating that three possible relationships exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the character " / " in this invention generally indicates that the related objects are in an "or" relationship.

[0066] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0067] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0068] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0069] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0070] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0071] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate in conjunction with numerous other general-purpose or specialized computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with terminal devices, computer systems, servers, and other electronic devices include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above.

[0072] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system-executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media, including storage devices.

[0073] Exemplary Methods

[0074] Figure 1 This is a flow chart of a method for generating a carbon footprint inventory for a substation project based on a knowledge graph, provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the method 100 for generating a carbon footprint inventory for a substation project based on a knowledge graph includes the following steps:

[0075] Step 101: Collect basic data related to the quantification of the power transformation project footprint, wherein the basic data includes: material activity level data, energy consumption activity level data, and material and energy emission factor data;

[0076] Step 102: Construct a substation project ontology knowledge graph based on the relevant provisions of the substation project bill of quantities calculation specification, the cradle-to-gate carbon footprint-related life cycle process of the substation project, and basic data;

[0077] Step 103: Extract knowledge of material and energy emission factors from the substation engineering ontology knowledge graph based on the emission factor data to determine the emission factor knowledge graph;

[0078] Step 104: determining a seed set based on the material activity level data and the concept level data of the emission factor knowledge graph;

[0079] Step 105: Align the substation project ontology knowledge graph and the emission factor knowledge graph through the seed set according to the preset rules, and determine the carbon footprint inventory of the substation project from the cradle to the gate life cycle stage based on the last layer entity of the aligned substation project ontology knowledge graph.

[0080] Specifically, this application takes the life cycle process of substation engineering from cradle to gate as the object, and proposes a method for generating a carbon footprint inventory of substation engineering based on knowledge graph. This method extracts knowledge and constructs knowledge graphs for the material entities and mechanical entities and relationships of the substation engineering sub-items, and existing emission factors, and aligns entities based on factors such as the concept hierarchy seed set, whether the entity has an emission factor, and the attribute priority of the emission factor. Finally, the last-level entity after the entity alignment is the carbon footprint inventory of the substation engineering. The carbon footprint inventory provided by this method ensures that all entities in the inventory have valid emission factors, and the materials and machinery in the inventory can be reversely traced according to the knowledge graph relationship to obtain their life cycle stages and sub-items, thereby providing favorable support for later emission reduction. At the same time, with the improvement of the input emission factor data and the enrichment of engineering data, this method has the advantage of rapid updating and improvement of the carbon footprint inventory.

[0081] This application takes the life cycle of substation projects from cradle to gate as the object, and proposes a method for generating a carbon footprint inventory of substation projects based on knowledge graphs. This method extracts knowledge and constructs knowledge graphs for the material entities and mechanical entities and their relationships of the sub-items of the carbon footprint project, as well as existing emission factors. Entity alignment is performed based on factors such as the concept hierarchy seed set, whether the entity has an emission factor, and the attribute priority of the emission factor. Finally, the last-level entity after entity alignment is the carbon footprint inventory of the substation project. The specific steps are as follows:

[0082] (1) Collect data on material activity levels, energy consumption activity levels, and emission factors involved in the quantification of the carbon footprint of substation projects.

[0083] Collect data on material activity and energy consumption during the raw materials, construction, and installation phases of substation projects, including material, machinery, and energy data. Material data includes material type and corresponding material consumption quantity or weight, machinery data includes machinery type, and energy data includes energy consumption type and number of machine shifts.

[0084] Collect data on energy consumption activity levels during the transportation phase, including the type of transportation vehicle and its corresponding energy consumption type and transportation distance.

[0085] Collect emission factor data of materials and energy that have been published internationally and domestically. The data must include relevant information such as the release time, release country, release unit, and data source.

[0086] (2) Based on the relevant provisions of the calculation specifications for the bill of quantities of substation projects and the relevant life cycle processes of the carbon footprint of substation projects from cradle to gate, combined with the material activity level data and energy consumption activity level data collected from substation projects, a knowledge graph of substation projects is constructed.

[0087] 1) From the perspective of the cradle-to-gate carbon footprint life cycle, the substation engineering ontology entity is divided into three attribute entities: design phase, construction phase, and main equipment. The ontology entity and the attribute entity are in a containment relationship, such as Figure 2 shown.

[0088] 2) Attribute entity design phase Ontology library is divided into office and meeting, and field survey according to energy consumption characteristics. The energy consumption entities of office and meeting include electricity, heat, and natural gas; field survey includes transportation energy consumption and equipment energy consumption. Then, according to transportation mode and equipment type, the energy consumption entities that can be obtained include electricity and fuel. The knowledge map of the entity library and relationship in the design phase is as follows: Figure 3 shown.

[0089] 3) Attribute entity construction phase ontology library takes sub-items as entities according to the calculation specification of substation project quantity list, and combines the material activity level data and energy consumption activity level data involved in the carbon footprint quantification of typical substation projects collected in step (1), and takes the specific materials and mechanical energy consumed by specific sub-items as material and energy entities.

[0090] Example: For example, the construction phase ontology mainly includes five attribute ontologies: main production engineering, auxiliary production engineering, single project related to the site, temporary facilities, construction and equipment installation phase. The relationship is an attribute relationship, such as Figure 4 As shown in the figure. The main production engineering ontology is divided as follows Figure 5As shown, all of these are inclusion relationships. The division of attribute entities for the remaining auxiliary production projects, site-related individual projects, and temporary facilities can be handled similarly, all based on the calculation specifications for the bill of quantities for substation projects. The construction, building, and equipment installation phase primarily divides mechanical entities from the construction phase. While entities in this phase are involved in the main production projects, auxiliary production projects, site-related individual projects, and temporary facilities, the specific entity types and quantities used differ. However, from the perspective of carbon footprint calculation methods and calculated entity volume, uniformly extracting them as inventory entities for the construction, building, and equipment installation phase can improve inventory compilation efficiency and ease of use.

[0091] Take the main control and communication building of the main production project as an example to illustrate its material and energy entity division, such as Figure 6 As shown, within the cradle-to-gate accounting scope, the lifecycle phases of the main control and communications building include the raw material production phase, the raw material transportation phase, and the construction and equipment installation phase. However, because the construction and equipment installation phases are already included in the overall construction phase, only the raw material production and raw material transportation phases are included in the main control and communications building. The raw material production phase divides materials into three categories: raw materials, semi-finished products, and finished products. Raw materials refer to basic materials such as steel, steel pipes, cast iron, and nonferrous metals. Semi-finished products refer to mixed materials or components that are a mixture of two or more raw materials or require further processing before use. Finished products primarily include doors and windows, bathroom fixtures, and firefighting equipment.

[0092] According to the machinery and equipment types and their fuel power types in the "Rules for Compiling Construction Engineering Machinery Unit Costs", the machinery and equipment entities and energy entities in the construction and equipment installation phase are divided. Because the fuel power types of the same type of machinery and equipment with different performance specifications may be different, the machinery and equipment entities must include specific performance specifications. Examples of the division of some machinery and equipment entities and their energy entities in the construction and equipment installation phase are as follows: Figure 7 shown.

[0093] Based on the collected international and domestic published emission factor data for materials and energy, knowledge extraction of material and energy emission factors is performed with the material and energy entities in step (2) as the objects. The knowledge extraction of emission factors needs to reflect the concept hierarchy relationship, emission factor values, and emission factor attributes. The relationship between entities is extracted using four structural matrices: the concept hierarchy matrix (UHLM), the emission factor discrimination matrix (IFM), the value matrix (VM), and the attribute matrix (VHAM). Based on these four matrices, a graphical structural relationship of emission factors is formed.

[0094] 1) Conceptual Hierarchy Matrix (Upper-level n, has, lower-level n; UHLM)

[0095]

[0096] Where: upperlevel_ n Represents the conceptual entity of the previous level, low level_ n represents the next-level conceptual entity, and KTs represents all identified knowledge triples. This matrix reflects the conceptual hierarchical relationships between emission factor entities, which are one-to-many entity relationships. For example, steel includes rails, sections, steel plates, and wire ropes, identifying four groups of triples. Sections include ordinary sections and galvanized sections. Ordinary sections include flat steel, channel steel, equilateral angle steel, square steel, steel grating, steel wire, oil grating, I-beams, steel bars, steel sections, sections, and round steel. Galvanized sections include galvanized flat steel, galvanized hot-rolled round wire rod, and galvanized round steel.

[0097] 2) Emission factor discriminant matrix (n, ifhas, Value)

[0098]

[0099] In the formula, n represents the entity in the hierarchical matrix, and Value represents the value of the emission factor of entity n. Since the values ​​of the emission factor are different under different attributes such as release time and accounting scope, the matrix is ​​also a one-to-many entity relationship.

[0100] 3) Value Matrix (n, is, Value)

[0101]

[0102] In the formula, n represents the entity in the hierarchical matrix, and Value represents the value of the emission factor of entity n. Since the values ​​of the emission factor are different under different attributes such as release time and accounting scope, the matrix is ​​also a one-to-many entity relationship.

[0103] 4) Attribute Matrix (Value, has, attribute)

[0104]

[0105] Where: Value represents the value of the entity's emission factor, and attribute is the attribute of the factor, including: S-attribute (accounting scope), G-attribute (geographical scope), D-attribute (data source), and T-attribute (time).

[0106] Example: Take entities n1, n2...n5 as examples to illustrate the representation of these four matrices and the graph structure they form, as shown in Table 1, Table 2, Table 3, Table 4 and Figure 8 As shown, where YValue is the value of the emission factor

[0107] Table 1 UHLM matrix of matrix triples

[0108]

[0109] Table 2 IFM matrix of matrix triples

[0110]

[0111] Table 3 VM matrix of matrix triples

[0112]

[0113] Table 4 VHAM matrix of matrix triples

[0114]

[0115] (4) Generate a seed set based on the conceptual hierarchy data obtained from the material names, machinery names and emission factor data collected from the sub-project data of the substation project. The seed set reflects the conceptual hierarchy of materials and machinery.

[0116] Note: Seed Set: A seed set is a collection of equivalent entity pairs between two knowledge graphs. These entity pairs have been manually or automatically annotated and are considered highly reliable and accurate. These entity pairs are used to guide the alignment of other entities in the knowledge graph. The seed set serves as training data for the entity alignment task.

[0117] (5) Based on the seed set, the knowledge graphs formed in steps (2) and (3) are aligned to entities. In the entity alignment, it is necessary to consider whether the entity has an emission factor and the attribute priority of the emission factor.

[0118] In entity alignment, since this application focuses on the cradle-to-gate lifecycle of substation projects, the emission factor accounting scope attribute should only be cradle-to-gate. If the next-level concept entity in a certain concept hierarchy does not have an emission factor value, the previous-level concept entity is used. If no emission factor exists, the previous-level concept entity is searched again until a concept entity with an emission factor is found.

[0119] When multiple emission factors exist for the same conceptual entity, the emission factor is determined based on the priority of data source > geographic scope > time. Data source is prioritized by credibility, geographic scope is prioritized by localization, and time is prioritized by the latest release.

[0120] When both the upper and lower levels of a conceptual entity have emission factors, the emission factor of the lower level conceptual entity is preferred.

[0121] (6) After completing entity alignment, the final entity is the carbon footprint inventory for the substation project from cradle to gate lifecycle, including the bill of materials and machinery bill. The lifecycle stages and sub-projects to which the bill of materials and machinery bill belong can be traced back based on the knowledge graph relationship.

[0122] Therefore, this application extracts knowledge and constructs knowledge graphs for the material entities and mechanical entities and relationships of the sub-items of the power transmission and transformation project, as well as the existing emission factors, and aligns entities based on factors such as the concept hierarchy seed set, whether the entity has an emission factor, and the attribute priority of the emission factor. Finally, the last-level entity after the entity alignment is the carbon footprint list of the power transmission and transformation project. The carbon footprint list provided by this application ensures that all entities in the list have valid emission factors, thereby ensuring the availability and operability of the list for carbon footprint accounting. The materials and machinery in the list can be reversely traced based on the knowledge graph relationship to obtain their life cycle stages and sub-items, thereby providing favorable support for later carbon emission reduction. With the improvement of the input emission factor data and the enrichment of engineering data, this method has the advantage of rapid updating and improvement of the carbon footprint list.

[0123] Exemplary devices

[0124] Figure 9 This is a schematic diagram of the structure of a device for generating a carbon footprint inventory for a substation project based on a knowledge graph, provided by an exemplary embodiment of the present invention. Figure 9 As shown, the apparatus 900 includes:

[0125] The collection module 910 is used to collect basic data related to the quantification of the power substation project footprint, wherein the basic data includes: material activity level data, energy consumption activity level data, and material and energy emission factor data;

[0126] Construction module 920 is used to construct a substation project ontology knowledge graph based on the relevant provisions of the bill of quantities calculation specification for substation projects, the relevant life cycle process of the substation project from cradle to gate carbon footprint, and basic data;

[0127] The first determination module 930 is configured to extract knowledge of material and energy emission factors from the substation engineering ontology knowledge graph based on the emission factor data and determine the emission factor knowledge graph;

[0128] The second determination module 940 is used to determine a seed set based on the material activity level data and the concept level data of the emission factor knowledge graph;

[0129] The third determination module 950 is used to align the substation project ontology knowledge graph and the emission factor knowledge graph through a seed set according to preset rules, and determine the carbon footprint inventory of the substation project from the cradle to the gate life cycle stage based on the last layer entity of the aligned substation project ontology knowledge graph.

[0130] Optionally, the collection module 910 includes:

[0131] The first collection submodule is used to collect material activity level data and first energy consumption activity level data during the raw material stage, construction stage, and installation stage of the sub-projects of the power transformation project. The material activity level data and the first energy consumption activity level data include: material data, machinery data, and energy data. The material data includes material type and corresponding material consumption quantity or weight; the machinery data includes machinery type, machinery energy consumption type, and number of shifts; the energy data includes the amount of fossil energy, electrical energy, and thermal energy consumed;

[0132] A second collecting submodule is used to collect second energy consumption activity level data during the transportation phase of the power transformation project, where the second energy consumption activity level data includes a type of transportation tool and its corresponding energy consumption type and a transportation distance;

[0133] a determination submodule, configured to determine energy consumption activity level data based on the first energy consumption activity level data and the second energy consumption activity level data;

[0134] The third collection submodule is used to collect published emission factor data of materials and energy.

[0135] Optionally, building block 920 includes:

[0136] The first construction submodule is used to construct attribute entities of the ontology entity of the substation project based on the carbon footprint-related life cycle process from cradle to gate of the substation project, wherein the attribute entities include: design stage, construction stage and main equipment;

[0137] The second construction submodule is used to divide according to the energy efficiency consumption characteristics and build the ontology library of the attribute entity design stage;

[0138] The third construction submodule is used to take the sub-items as entities of the construction phase according to the calculation specifications of the bill of quantities of the substation project, and to build the attribute entity construction phase ontology library in combination with the material activity level data and the energy consumption activity level data;

[0139] The fourth construction submodule is used to construct a main equipment ontology library of attribute entities based on the list of materials and components of the main equipment used in the power transformation project;

[0140] The fifth construction submodule is used to construct a substation engineering ontology knowledge graph based on the attribute entity design stage ontology library, the attribute entity construction stage ontology library and the attribute entity main equipment ontology library.

[0141] Optionally, the first determining module 930 includes:

[0142] A submodule is formed to extract knowledge of material and energy emission factors from the substation engineering ontology knowledge graph using a preset emission factor structure matrix, and to form an emission factor knowledge graph based on the preset emission factor structure matrix.

[0143] Optionally, knowledge of material and energy emission factors is extracted from the substation engineering ontology knowledge graph based on the emission factor data to determine the emission factor knowledge graph, including:

[0144] The preset emission factor structure matrix is ​​used to extract knowledge of material and energy emission factors from the substation engineering ontology knowledge graph, and an emission factor knowledge graph is formed based on the preset emission factor structure matrix.

[0145] Optionally, the preset emission factor structure matrix includes:

[0146] Concept Hierarchy Matrix:

[0147]

[0148] Where: upperlevel_ n Represents the conceptual entity of the previous level, low level_ n represents the next level of conceptual entities, and KTs represents all the knowledge triples identified;

[0149] Emission factor discrimination matrix:

[0150]

[0151] Where: n represents the entity in the hierarchical matrix, Value represents the value of the emission factor of entity n;

[0152] Value matrix:

[0153]

[0154] Property Matrix:

[0155]

[0156] Where: attribute is the attribute of the emission factor.

[0157] Optionally, the preset rules are:

[0158] When the next-level concept entity of a certain concept level does not have an emission factor value, the concept entity of the previous level is used. If there is still no emission factor, the concept entity of the previous level is searched again until a concept entity with an emission factor is found;

[0159] When there are multiple emission factors for the same conceptual entity, the emission factors are determined according to the priority of data source > geographical scope > time, where the data source is prioritized based on credibility, the geographical scope is prioritized based on localization, and the time is prioritized based on the latest release.

[0160] When both the upper and lower levels of a conceptual entity have emission factors, the emission factor of the lower level conceptual entity is preferred.

[0161] Exemplary electronic devices

[0162] Figure 10 This is the structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 10 As shown, the electronic device 100 includes one or more processors 101 and a memory 102 .

[0163] The processor 101 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0164] The memory 102 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 101 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may further include: an input device 103 and an output device 104, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0165] In addition, the input device 103 may also include, for example, a keyboard, a mouse, and the like.

[0166] The output device 104 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0167] Of course, to simplify, Figure 10 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0168] Exemplary computer program products and computer-readable storage media

[0169] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to perform the steps of the method according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0170] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0171] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0172] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0173] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in the present invention are merely illustrative and non-limiting, and should not be construed as necessarily possessed by each embodiment of the present invention. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, and are not intended to be limiting. These details do not necessarily limit the present invention to being implemented using these specific details.

[0174] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. For system embodiments, since they largely correspond to method embodiments, their description is relatively simple. For relevant parts, references to the description of the method embodiments are sufficient.

[0175] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0176] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above, unless otherwise specified. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers recording media that store programs for executing the method according to the present invention.

[0177] It should also be noted that, in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in this field to make or use the present invention. Various modifications to these aspects will be very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but according to the widest scope consistent with the principles disclosed here and novel features.

[0178] The above description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for generating a carbon footprint inventory for a power transformation project based on a knowledge graph, characterized in that: include: Collect basic data related to the quantification of the substation project footprint, including: material activity level data, energy consumption activity level data, and material and energy emission factor data; Constructing a substation project ontology knowledge graph based on the relevant provisions of the bill of quantities calculation specification for the substation project, the cradle-to-gate carbon footprint-related life cycle process of the substation project, and the basic data; Extracting knowledge of material and energy emission factors from the substation engineering ontology knowledge graph based on the emission factor data to determine an emission factor knowledge graph; Determining a seed set based on the material activity level data and the concept level data of the emission factor knowledge graph; Aligning the substation project ontology knowledge graph and the emission factor knowledge graph using the seed set according to a preset rule, and determining a carbon footprint inventory of the substation project from cradle to gate life cycle based on the last-layer entity of the aligned substation project ontology knowledge graph; Based on the relevant provisions of the bill of quantities calculation specification for the substation project, the cradle-to-gate carbon footprint life cycle of the substation project, and the basic data, a substation project ontology knowledge graph is constructed, including: Constructing attribute entities of the ontology entity of the substation project based on the cradle-to-gate carbon footprint-related life cycle process of the substation project, wherein the attribute entities include: design stage, construction stage, and main equipment; Divide according to the energy efficiency consumption characteristics and build the ontology library of the attribute entity design stage; According to the calculation specification of the bill of quantities of the substation project, the sub-items are taken as entities of the construction phase, and the material activity level data and the energy consumption activity level data are combined to construct an attribute entity construction phase ontology library; Build a main equipment ontology library based on the material and component lists of the main equipment used in the substation project; The substation engineering ontology knowledge graph is constructed based on the attribute entity design phase ontology library, the attribute entity construction phase ontology library, and the attribute entity main equipment ontology library.

2. The method according to claim 1, characterized in that Collect basic data related to the quantification of the substation project footprint, including: Collecting the material activity level data and first energy consumption activity level data during the raw material stage, construction stage, and installation stage of the sub-projects of the substation project, wherein the material activity level data and the first energy consumption activity level data include: material data, machinery data, and energy data, wherein the material data includes the material type and the corresponding material consumption quantity or weight, and the machinery data includes the machinery type, the energy consumption type of the machinery, and the number of shifts; the energy data includes the amount of fossil energy, electrical energy, and thermal energy consumed; collecting second energy consumption activity level data during the transportation phase of the power transformation project, wherein the second energy consumption activity level data includes a type of transportation tool and its corresponding energy consumption type and a transportation distance; determining the energy consumption activity level data based on the first energy consumption activity level data and the second energy consumption activity level data; Collect published data on emission factors for materials and energy.

3. The method according to claim 1, characterized in that Extracting knowledge of material and energy emission factors from the substation engineering ontology knowledge graph based on the emission factor data to determine an emission factor knowledge graph includes: The preset emission factor structure matrix is ​​used to extract knowledge of material and energy emission factors from the substation engineering ontology knowledge graph, and the emission factor knowledge graph is formed based on the preset emission factor structure matrix.

4. The method according to claim 3, characterized in that The preset emission factor structure matrix includes: Concept Hierarchy Matrix: Where: upperlevel_n Represents the conceptual entity of the previous level, lowlevel_n represents the next level of conceptual entities, and KTs represents all the knowledge triples identified; Emission factor discrimination matrix: Where: n represents the entity in the hierarchical matrix, Value represents the value of the emission factor of entity n; Value matrix: Property Matrix: Where: attribute is the attribute of the emission factor.

5. The method according to claim 1, wherein The preset rules are: When the next-level concept entity of a certain concept level does not have an emission factor value, the concept entity of the previous level is used. If there is still no emission factor, the concept entity of the previous level is searched again until a concept entity with an emission factor is found; When there are multiple emission factors for the same conceptual entity, the emission factors are determined according to the priority of data source > geographical scope > time. The data source is prioritized by credibility, the geographical scope is prioritized by localization, and the time is prioritized by the latest release. When both the upper and lower levels of a conceptual entity have emission factors, the emission factor of the lower level conceptual entity is preferred.

6. A device for generating a carbon footprint inventory for a power transformation project based on a knowledge graph, characterized in that: include: A collection module is used to collect basic data related to the quantification of the substation project footprint, wherein the basic data includes: material activity level data, energy consumption activity level data, and material and energy emission factor data; A construction module for constructing a knowledge graph of the substation project ontology based on the relevant provisions of the bill of quantities calculation specification of the substation project, the life cycle process of the substation project from cradle to gate carbon footprint, and the basic data; A first determination module is configured to extract knowledge of material and energy emission factors from the substation engineering ontology knowledge graph based on the emission factor data, and determine an emission factor knowledge graph; A second determination module is configured to determine a seed set based on the material activity level data and the concept level data of the emission factor knowledge graph; a third determination module, configured to align the substation project ontology knowledge graph and the emission factor knowledge graph using the seed set according to a preset rule, and determine a carbon footprint inventory of the substation project from a cradle-to-gate life cycle stage based on the last-layer entities of the aligned substation project ontology knowledge graph; Building blocks, including: The first construction submodule is configured to construct attribute entities of the ontology entity of the substation project according to the cradle-to-gate carbon footprint-related life cycle process of the substation project, wherein the attribute entities include: design stage, construction stage, and main equipment; The second construction submodule is used to divide according to the energy efficiency consumption characteristics and build the ontology library of the attribute entity design stage; The third construction submodule is used to take the sub-items as entities of the construction phase according to the calculation specification of the bill of quantities of the substation project, and to construct an attribute entity construction phase ontology library in combination with the material activity level data and the energy consumption activity level data; The fourth construction submodule is used to construct a main equipment ontology library of attribute entities based on the list of materials and components of the main equipment used in the power transformation project; The fifth construction submodule is used to construct the substation engineering ontology knowledge graph based on the attribute entity design phase ontology library, the attribute entity construction phase ontology library and the attribute entity main equipment ontology library.

7. The device according to claim 6, characterized in that Collection modules, including: A first collection submodule is configured to collect the material activity level data and first energy consumption activity level data during the raw material stage, construction stage, and installation stage of the sub-projects of the power transformation project, wherein the material activity level data and the first energy consumption activity level data include: material data, machinery data, and energy data, wherein the material data includes the material type and the corresponding material consumption quantity or weight, and the machinery data includes the machinery type, the energy consumption type of the machinery, and the number of shifts; and the energy data includes the amount of fossil energy, electrical energy, and thermal energy consumed; A second collecting submodule is configured to collect second energy consumption activity level data during the transportation phase of the power transformation project, wherein the second energy consumption activity level data includes a type of transportation tool and its corresponding energy consumption type and a transportation distance; a determination submodule, configured to determine the energy consumption activity level data based on the first energy consumption activity level data and the second energy consumption activity level data; The third collection submodule is used to collect the emission factor data of the published materials and energy.

8. The device according to claim 6, characterized in that The first determination module includes: A submodule is formed for extracting knowledge of material and energy emission factors from the substation engineering ontology knowledge graph using a preset emission factor structure matrix, and forming the emission factor knowledge graph based on the preset emission factor structure matrix.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 5.

10. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 5.

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