Carbon emission reasoning method and device for non-standard part of fabricated concrete building

Abstract

The invention discloses a carbon emission reasoning method and device for a non-standard component of a fabricated concrete building, and the method comprises the steps: building a carbon emission case ontology knowledge base of various produced fabricated concrete building components and components based on the carbon emission data of various produced fabricated concrete building components and components; based on the attributes of the cases, determining the case similarity between the unproduced non-standardized components and component parts and all the cases; carbon emission reasoning values of the unproduced non-standardized components and component parts are determined based on the case similarity between the unproduced non-standardized components and component parts and all the cases. And through case similarity calculation with all cases of the carbon emission case ontology knowledge base, reasoning of carbon emission of unproduced non-standardized components and components by using existing data is realized.

Description

Technical Field

[0001] The present application relates to the field of carbon emission calculation, and particularly to a carbon emission inference method and device for non-standard components of prefabricated concrete buildings. Background Art

[0002] Accurate building carbon emission data is the core basis for formulating carbon emission reduction measures. However, existing building carbon emission factors are only applicable to carbon emission calculations under traditional construction methods. Existing building carbon emission calculation methods include the top-down method and the bottom-up method. The top-down method first estimates the carbon emissions of the overall building and then performs downscaling analysis in terms of time and space. The bottom-up method considers technical details such as material consumption, mechanical equipment use, building performance, and operating characteristics of end equipment, and predicts and simulates building carbon emissions at the regional, district, and even national scales based on the carbon emissions of representative typical buildings. The bottom-up method includes the emission factor method, the mass balance method, and the measurement method. Among them, the emission factor method is the building carbon emission calculation method with the widest application scope and the most common application. The basic carbon emission calculation equation provided by the United Nations Intergovernmental Panel on Climate Change (IPCC) is as follows: Carbon emission = activity data × emission factor. For the calculation of building carbon emissions under traditional on-site wet operations, existing building carbon emission calculation methods divide the entire life cycle of a building into the building material production and transportation stage, the construction and demolition stage, and the operation stage. The building construction stage includes the building material production and transportation stage and the construction stage.

[0003] ① Carbon emission calculation in the building material production and transportation stage

[0004] The carbon emission calculation formula for the building material production stage is

[0005]

[0006] In the formula, C sc —— Carbon emission in the building material production stage (kg CO 2 e);

[0007] M i —— Consumption of the i-th main building material;

[0008] F i —— Carbon emission factor of the i-th main building material (kg CO 2 e / unit building material quantity).

[0009] The carbon emission calculation formula for the building material transportation stage is

[0010]

[0011] In the formula, C ys —— Carbon emission in the building material transportation stage (kg CO 2 e);

[0012] M i —— The consumption of the i-th major building material (t);

[0013] D i —— The average transportation distance of the i-th building material (km);

[0014] T i —— Under the transportation mode of the i-th building material, the carbon emission factor per unit weight transportation distance [kg CO 2 e / (t·km)].

[0015] ② Carbon emission calculation in the construction stage

[0016]

[0017] Wherein, C JZ —— Carbon emissions in the construction stage (KgCO 2 / m 2 );

[0018] E jz·i —— The total consumption of the i-th energy in the construction stage (kWh or kg);

[0019] EF i —— The carbon emission factor of the i-th type of energy (kgCO 2 / kWh or kgCO 2 / kg).

[0020] With the continuous popularization of precast concrete buildings, there are large errors in calculating the carbon emissions of concrete components and parts using existing building carbon emission factors. According to the requirements of the General Code for Building Energy Efficiency and Renewable Energy Utilization, new, expanded, and renovated buildings, as well as energy efficiency renovation buildings of existing buildings, shall submit carbon emission reports in the feasibility study report, construction plan, and preliminary design documents. However, since the non-standard components and parts of concrete have not been produced in the building design stage, and their specifications, material consumption, production processes, etc. are relatively complex, it is difficult to directly obtain data through research, which brings great technical difficulties to the carbon emission calculation of precast concrete buildings in the feasibility study and design stages. Summary of the Invention

[0021] To solve the above problems, the present application proposes a carbon emission inference method and device for non-standard components of precast concrete buildings, wherein the method includes:

[0022] Based on the carbon emission data of various precast concrete building components and parts that have been produced, establish an ontology knowledge base for the carbon emissions of various precast concrete building components and parts that have been produced; based on the attributes of the cases, determine the case similarity between the non-standard components and parts that have not been produced and the cases; based on the case similarity between the non-standard components and parts that have not been produced and the cases, determine the carbon emission inference value of the non-standard components and parts that have not been produced.

[0023] In one example, before establishing the ontology knowledge base for the carbon emissions of various precast concrete building components and parts based on the carbon emission data of various precast concrete building components and parts that have been produced, the method further includes: determining the building material consumption, building material transportation distance, energy consumption, and water consumption of the various precast concrete building components and parts that have been produced; and determining the carbon emission data of the various precast concrete building components and parts that have been produced through the following formula: where C gb is the carbon emission data of various precast concrete building components and parts that have been produced; j is the j-th type of energy consumed; m is the total number of energy types consumed; E j is the total consumption of the j-th type of energy; EF j is the carbon emission factor of the j-th type of energy; W is the water consumption; WF is the carbon emission factor of water production; M i is the consumption of the i-th type of building material; F i is the carbon emission factor of the production of the i-th type of building material; D i is the average transportation distance of the i-th type of building material; T i is the carbon emission factor per unit weight transportation distance under the transportation mode of the i-th type of building material.

[0024] In one example, the case attributes include at least one of specification, material consumption, material transportation, production environment, process type, energy consumption, and water consumption.

[0025] In one example, the determining the case similarity between the non-standard components and parts that have not been produced and the cases based on the attributes of the cases specifically includes: selecting any attribute as the target attribute among all the attributes of the target case; determining the attribute similarity between the non-standard components and parts that have not been produced and the target case based on the target attribute; traversing all the attributes of the target case, and determining the attribute similarity between each of the all attributes of the target case and the corresponding attributes of the non-standard components and parts that have not been produced; and determining the case similarity between the non-standard components and parts that have not been produced and all the cases based on the attribute similarity between each of the all attributes of the target case and the corresponding attributes of the non-standard components and parts that have not been produced.

[0026] In one example, determining the attribute similarity between the unproduced non-standard components and parts and the target case based on the target attribute specifically includes: when the target attribute is a measurable data attribute, determining the similarity between the unproduced non-standard components and parts and the target case based on the target attribute through the following formula: where p s represents the target attribute of the case, and p 0 represents the data attribute of the unproduced non-standard components and parts; V 0 is the value of p 0 , and V s is the value of p s ; MaxV c and MinV c are the maximum and minimum values of the corresponding attribute in the case.

[0027] In one example, determining the attribute similarity between the unproduced non-standard components and parts and the target case based on the target attribute specifically includes: when the target attribute is an immeasurable object attribute, determining the nearest common parent node of the target attribute of the target case and the corresponding object attribute of the unproduced non-standard components and parts; determining the first distance from the common parent node to the ontology root node; determining the second distance from the target object attribute of the unproduced non-standard components and parts to the ontology root node; determining the third distance from the target object attribute of the target case to the ontology root node; and determining the similarity between the unproduced non-standard components and parts and the target case based on the target attribute through the following formula: where Lcn(q 0 , q s ) is the nearest common parent node of the attribute q 0 of the unproduced non-standard components and parts and the target attribute q s of the target case; dist[Lcn(q 0 , q s ), g] is the first distance from the nearest common parent node of q 0 and q s to the ontology root node g; dist[q 0 , g] is the second distance from the attribute q 0 to the ontology root node g; dist[q s , g] is the third distance from the attribute q s to the ontology root node g; and λ is an adjustment factor greater than or equal to 1.

[0028] In one example, the distance between the first object attribute node and the second object attribute node is determined through the following formula:

[0029] dist(a 1 ,a k ) = dist(a 1 ,a 2 ) + dist(a 2 ,a 3 ) + … + dist(a k-2 ,a k-1 ) + dist(a k-1 ,a k ) where the nodes of the k edges connecting the first object attribute node a 1 and the second object attribute node a k are respectively a 1 ,a 2 ,a 3 ,…,a k-2 ,a k-1 ,a k ,dist(a 1 ,a 2 ) is the distance between the first object attribute node a 1 and the object attribute node a 2 connected by only one edge, dist(a 1 ,a 2 ) = λ Max(Depth(g))-Depth(a2) , where Max(Depth(g)) is the maximum level number of the object attributes of the target case; Depth(a 2 ) is the level number from the root node to the object attribute node a 2 .

[0030] In an example, determining the case similarity between the unproduced non-standard components and parts and all cases based on the attribute similarity of all attributes of all cases specifically includes: determining the number of all cases in the case ontology and the number of attributes corresponding to each case; determining the attribute similarity between the target non-standard components and parts and each case based on all attributes; determining the attribute weight values corresponding to different case attributes; based on the attribute weight values, the attribute similarity between the unproduced non-standard components and parts and each case based on all case attributes, the number of cases in the case ontology, and the number of attributes corresponding to each case, determining the case similarity between the unproduced non-standard components and parts and all cases.

[0031] In one example, determining the carbon emission inference value of the unproduced non-standard components and parts based on the case similarity between the unproduced non-standard components and parts and the cases specifically includes: according to the case similarity between the unproduced non-standard components and parts and the cases, determining the target case with the highest case similarity to the unproduced non-standard components and parts among all cases; determining the average carbon emission of the produced components and parts of the same type per unit volume and the target carbon emission of the target case per unit volume; and determining the carbon emission inference value of the unproduced non-standard components and parts based on the volume of the unproduced non-standard components and parts, the average carbon emission, and the target carbon emission.

[0032] This application also provides a carbon emission inference device for non-standard components of precast concrete buildings, including: a knowledge base establishment module for establishing a carbon emission case ontology knowledge base of various produced precast concrete building components and parts based on the carbon emission data of various produced precast concrete building components and parts; a similarity determination module for determining the case similarity between the unproduced non-standard components and parts and the cases based on the attributes of the cases; and a carbon emission inference module for determining the carbon emission inference value of the unproduced non-standard components and parts based on the case similarity between the unproduced non-standard components and parts and the cases.

[0033] The method proposed in this application can bring the following beneficial effects: calculating the carbon emissions of the produced precast concrete building components and parts based on the energy consumption data of the produced precast concrete building components and parts, and then establishing a carbon emission case ontology knowledge base for the components and parts. When encountering non-standard components and parts that have not been produced and for which it is difficult to directly obtain carbon emission data through research, by calculating the case similarity with the cases in the ontology library, it is possible to infer the carbon emissions of the unproduced non-standard components and parts using the existing data. This can alleviate the contradiction between the existing building carbon emission factors being only applicable to traditional construction and the increasing popularity of precast concrete buildings, and bring great convenience to the carbon emission calculation of precast concrete buildings in the feasibility study and design stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of this application and constitute a part of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0035] Figure 1 is a structural schematic diagram of a carbon emission inference method for non-standard components of a precast concrete building in an embodiment of this application;

[0036] Figure 2Schematic diagram of the carbon emissions ontology of a prefabricated concrete building component and parts in an embodiment of the present application;

[0037] Figure 3 Schematic diagram of the object attributes of the first case object and the predicted case object attributes in an embodiment of the present application;

[0038] Figure 4 Schematic diagram of the object attributes of the second case object and the predicted case object attributes in an embodiment of the present application;

[0039] Figure 5 Schematic diagram of the object attributes of a numerical example and the predicted case object attributes in an embodiment of the present application;

[0040] Figure 6 Schematic diagram of the structure of a carbon emissions reasoning device for non-standard components of prefabricated concrete buildings in an embodiment of the present application;

[0041] Figure 7 Schematic diagram of the structure of a carbon emissions reasoning device for non-standard components of prefabricated concrete buildings in an embodiment of the present application. Detailed implementation manners

[0042] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0043] The following will detail the technical solutions provided by each embodiment of the present application in conjunction with the drawings.

[0044] Figure 1 Flow chart of a carbon emissions reasoning method for non-standard components of prefabricated concrete buildings provided by one or more embodiments of this specification. Here, non-standard components refer to non-standardized components and parts. This method can be applied to the carbon emissions calculation of different types of non-standardized components and parts of prefabricated concrete buildings. This process can be executed by computing devices in the corresponding field, and some input parameters or intermediate results in the process allow manual intervention and adjustment to help improve accuracy.

[0045] The implementation of the analysis method involved in the embodiments of the present application can be a terminal device or a server, and the present application does not make special restrictions on this. For the convenience of understanding and description, the following embodiments will be described in detail using a server as an example.

[0046] It should be noted that the server can be a single device or a system composed of multiple devices, that is, a distributed server. This application does not make specific limitations on this.

[0047] As Figure 1 shown, an embodiment of this application provides a carbon emission inference method for non-standard components of prefabricated concrete buildings, including:

[0048] S101: Based on the carbon emission data of various prefabricated concrete building components and parts that have been produced, establish a carbon emission case ontology knowledge base for various prefabricated concrete building components and parts that have been produced.

[0049] First of all, although the carbon emissions of prefabricated concrete building components and parts that have not been produced cannot be directly calculated, the carbon emission data of various prefabricated concrete building components and parts that have been produced can be calculated. After the calculation is completed, a carbon emission case ontology knowledge base is established according to the carbon emission data of different prefabricated concrete building components and parts. Among them, each case corresponds to a prefabricated concrete building component and part. Here, the case ontology refers to a case library with a large number of cases, that is, a collection of cases.

[0050] In one embodiment, calculate the carbon emission data of prefabricated concrete building components and parts that have been produced. For the prefabricated concrete building components and parts that have been completed in production, determine the average material consumption, energy consumption, and tap water consumption in the production of the components and parts. The following formula can be used to calculate the carbon emissions of prefabricated concrete building components and parts that have been produced.

[0051]

[0052] Among them, C gb is the carbon emission data of various prefabricated concrete building components and parts that have been produced; j is the jth type of energy consumed; m is the total number of energy types consumed; E j is the total consumption of the jth type of energy; EF j is the carbon emission factor of the jth type of energy, with the unit of kgCO 2 / kWh or kgCO 2 / kg; W is the water consumption, with the unit of t; WF is the carbon emission factor of water production, with the unit of kg CO 2 / t; M i is the consumption of the ith (main) building material; F i is the carbon emission factor of the production of the ith (main) building material, with the unit of kg CO 2 e / unit (main) building material quantity; D i is the average transportation distance of the ith (main) building material, with the unit of km; T iThe carbon emission factor per unit weight transportation distance under the transportation mode of the i-th (main) building material, with the unit of kg CO 2 e / (t·km).

[0053] In one embodiment, the carbon emission data of the prefabricated concrete building components and parts that have been produced can be pre-stored in the storage device of the computer device. When it is necessary to establish the carbon emission case ontology knowledge base for the prefabricated concrete building components and parts that have been produced, the computer device can select the carbon emission data of the prefabricated concrete building components and parts that have been produced from the storage device. Of course, the computer device can also obtain the carbon emission data of the prefabricated concrete building components and parts that have been produced from other external devices. For example, the carbon emission data of the prefabricated concrete building components and parts that have been produced are stored in the cloud. When it is necessary to establish the carbon emission case ontology knowledge base for the prefabricated concrete building components and parts that have been produced, the computer device can obtain the carbon emission data of the prefabricated concrete building components and parts that have been produced from the cloud. This embodiment does not limit the acquisition method of the carbon emission data of the prefabricated concrete building components and parts that have been produced.

[0054] As Figure 2 shown, when establishing the carbon emission case ontology knowledge base for the prefabricated concrete building components and parts that have been produced, a carbon emission case ontology knowledge base for the prefabricated concrete building components and parts is established from seven dimensions: specification, material consumption, material transportation, production environment, process type, energy consumption, and water consumption. These seven dimensions are called the attributes of the case.

[0055] S102: Based on the attributes of the case, determine the case similarity between the non-produced non-standard components and parts and the case.

[0056] When the case attribute is measurable, this attribute is a data attribute; when the case attribute is not measurable, this attribute is an object attribute. To determine the similarity between the non-produced non-standard components and parts and the case, different similarity comparison rules are adopted for different types of attributes.

[0057] In one embodiment, when determining the case similarity between the non-produced non-standard components and parts and the case, first, any case attribute is selected as the target attribute from all the attributes of the target case, and the attribute similarity between the non-produced non-standard components and parts and the target case based on the target attribute is determined. Then, all the attributes of the target case are traversed to determine the attribute similarity between each of the all attributes of the target case and the corresponding attributes of the non-produced non-standard components and parts. Finally, the case similarity between the non-produced non-standard components and parts and all the cases can be determined based on all the case attribute similarities.

[0058] Further, when the attribute of the case is a measurable data attribute, the similarity between the unproduced non-standard components and parts and the target case based on the target attribute can be determined by the following formula:

[0059]

[0060] Where p s represents the data attribute corresponding to the target case, and p 0 represents the data attribute of the unproduced non-standard components and parts; V 0 is the value of p 0 , and V s is the value of p s ; MaxV c and MinV c are the maximum and minimum values of the corresponding attributes in all cases.

[0061] In one embodiment, when the attribute of the case is an immeasurable object attribute, as Figure 3 shown, in terms of comparing the similarity of object attributes, the following formula is traditionally used to calculate the similarity between the case object attribute C 1 and the predicted case object attribute C 2 :

[0062]

[0063] In the formula: Lcn(C 1 , C 2 ) represents the nearest common parent node of attribute C 1 and attribute C 2 ; Depth(Lcn(C 1 , C 2 )) represents the number of connected edges between the root node of the ontology structure and the nearest common parent node of attribute C 1 and attribute C 2 . Among them, Lcn(C 1 , C 2 ) and the root node are as Figure 3 shown. Now, the parent node and the root node are described as follows: In Figure 2 , the root node is the leftmost "Carbon Emission of Prefabricated Concrete Building Components and Parts". Taking "HPB335" as an example, its parent node is "Plain Round Steel Bar", and the parent node of "Plain Round Steel Bar" is "Steel Bar". The common parent node of "HRB335" and "HPB335" is Steel Bar. This method does not consider the influence of the difference in the number of connected edges from the root node to attribute C 1 and attribute C 2 on the similarity calculation. Therefore, the improvement is as follows: Assume that q represents any object attribute, q sThe object attribute representing the s-th produced component or part, q 0 The object attribute representing the unproduced non-standard component or part, then q 0 and q s The similarity calculation formula is:

[0064]

[0065] where, Lcn(q 0 , q s ) is the nearest common parent node of the case attributes q 0 and q s ; dist[Lcn(q 0 , q s ), g] is the first distance from the nearest common parent node of q 0 and q s to the ontology root node g; dist[q 0 , g] is the distance from the attribute q 0 to the ontology root node g; dist[q s , g] is the distance from the attribute q s to the ontology root node g; λ is a regulation factor greater than or equal to 1, and in the present invention, λ = 2.

[0066] where, the calculation of dist[Lcn(q 0 , q s ), g], dist[q 0 , g], dist[q s , g] refers to the following formula. As in the ontology graph shown in Figure 4 , assuming a 1 and a 2 are two attribute nodes connected by only one edge in the ontology, then the edge length between the two nodes a 1 and a 2 is defined as In the formula: Max(Depth(g)) is the maximum level number of the case attributes. For example, in Figure 2 , the maximum level number of the index system is 5 levels. Depth(a 2 ) is the level number from the root node to the attribute a 2 , that is, the index level number from the root node to the attribute a 2 .

[0067] In the ontology graph as in Figure 4 , assuming a 1 and a k are connected by k edges, and the nodes of the k edges connecting a 1 and a k are respectively a 1 , a 2 , a3 ,..., a k-2 , a k-1 , a k , then a 1 and a k 's distance can be expressed as

[0068] dist(a 1 , a k ) = dist(a 1 , a 2 ) + dist(a 2 , a 3 ) +... + dist(a k-2 , a k-1 ) + dist(a k-1 , a k ).

[0069] In one embodiment, when determining the case similarity between the unproduced non-standard components and parts and all cases, it is necessary to determine the number of all cases, as well as the number of attributes corresponding to each case, then determine the attribute similarity between the target non-standard components and parts and each attribute of each case, and then determine the weight values corresponding to the different case attributes. Finally, based on the attribute weight values, the attribute similarity between the unproduced non-standard components and parts and each attribute of each case, the number of all cases, and the number of attributes of each case, determine the case similarity between the unproduced non-standard components and parts and all cases.

[0070] Assume that there are x cases and y attributes in the case ontology, Q zs represents the s-th attribute of the z-th case, Q 0s represents the s-th attribute of the unproduced non-standard component or part. sim(Q 0s , Q zs ) represents the attribute similarity between the unproduced non-standard component or part and the z-th case in the case ontology at the s-th attribute, ω s represents the weight value of the s-th attribute, which can be determined by the expert scoring method. Then, the case similarity between the unproduced non-standard components and parts and the z-th case in the case ontology is:[[]]

[0071]

[0072] where s = 1, 2, 3,..., y; z = 1, 2, 3,..., x.

[0073] S103: Based on the case similarity between the unproduced non-standard components and parts and the cases, determine the carbon emission inference value of the unproduced non-standard components and parts.

[0074] Since non-standard components and parts have both similarities and differences compared with other components and parts of the same type, when calculating the carbon emissions of non-standard components and parts, it is necessary to consider the similarities and differences with other components.

[0075] In one embodiment, the present application determines the target case with the highest similarity to the unproduced non-standard components and parts among all cases according to the similarity between the unproduced non-standard components and parts and the cases, then determines the average carbon emissions of the produced components and parts of the same type per unit volume, and the target carbon emissions of the target case per unit volume. Finally, according to the volume, average carbon emissions, and target carbon emissions of the unproduced non-standard components and parts, the carbon emission inference value of the unproduced non-standard components and parts is determined. Specifically, it can be calculated through the following calculation formula:

[0076] CE = (μ 1 A 1 + μ 2 A 2 )Q

[0077] Wherein, CE represents the carbon emissions of the unproduced non-standard components and parts; A 1 refers to the average carbon emissions of the produced components and parts of the same type per unit volume; A 2 refers to the carbon emissions of the components and parts with the highest similarity to the unproduced non-standard components and parts per unit volume; Q refers to the volume of the unproduced non-standard components and parts; μ 1 , μ 2 are weights. Specifically, μ 2 is the highest similarity between the unproduced non-standard components and parts and the target case calculated above, and μ 1 + μ 2 = 1.

[0078] The following uses a simple calculation example to illustrate the carbon emission inference method for non-standard components of precast concrete buildings provided by the present application:

[0079] Beam K 1 , K 3 are the ontology cases, and beam K 2 is the prediction case. The details of beam K 1 , K 2 , K 3 are shown in Table 1. Among them, the concrete consumption is determined according to the "Guide for the Dimensions of Main Components of Prefabricated Concrete Structures for Residential Buildings", and the carbon emissions are determined according to the concrete consumption, process type, and "Standard for Calculating Building Carbon Emissions" GB / T 51366-2019. Beam K 1 , K 2 , K 3Details are shown in the following table:

[0080] Table 1 Beam K 1 , K 2 , K 3 Details

[0081]

[0082] Taking only the indicators "specification" and "process type" as examples (the ontology indicator system is as Figure 5 shown), the similarities between Beam K 2 and Beam K 1 , K 3 are solved respectively, and on this basis, the carbon emissions of Beam K 2 are inferred. By comparing the inference results of the method of the present invention and the inference results of the existing method, the superiority of the present invention is verified.

[0083] (1) Similarity comparison

[0084] In this example, "specification" is a data attribute and "process type" is an object attribute. Therefore, the data attribute similarity comparison and the object attribute similarity calculation are carried out respectively.

[0085] 1) Data attribute similarity calculation

[0086] In calculating the similarity of "specification", the similarities of Beam K 2 and Beam K 1 in terms of length, height, and width are respectively:

[0087]

[0088] Beam K 2 and Beam K 3 in terms of length, height, and width are respectively:

[0089]

[0090] 2) Object attribute similarity calculation

[0091] In calculating the similarity of "process type", when using the traditional method, the denominator of the formula takes the maximum value of the number of levels from the root node of the ontology structure to the two compared object attributes (Max(Depth(C)), Max(Depth(C'))), without considering the influence of the difference in the number of levels from the root node to the two compared attributes on the similarity calculation.

[0092] When using the traditional calculation method, the process type similarity between Beam K 2 and Beam K 1 is:

[0093]

[0094] Beam K 2 and Beam K 3 The similarity of process types is:

[0095]

[0096] When calculated using the improved similarity comparison method, Beam K 2 and Beam K 1 The similarity of process types is:

[0097]

[0098] Beam K 2 and Beam K 3 The similarity of process types is:

[0099]

[0100] By comparing the traditional calculation method and the improved calculation method, it can be seen that when the number of levels from the root node to the two comparison attributes is different, the similarity obtained by the traditional calculation method is on the high side, and the calculation result of the improved calculation method is more reasonable.

[0101] 3) Similarity calculation

[0102] Beam K 2 and Beam K 1 、K 3 The similarities in different attributes are shown in the following table. The weights of different attributes are all taken as 0.25.

[0103] Table Beam K 2 and Beam K 1 、K 3 The similarities in different attributes

[0104] Attribute Attribute weight <![CDATA[K 1 > <![CDATA[K 3 > Length 0.25 0.6875 0.3125 Width 0.25 1 0 Height 0.25 0.5 0.5 Process type 0.25 0.23 1

[0105] Therefore, the similarities of Beam K 2 and Beam K 1 、K 3 are respectively: sim(K 1 ) = 0.25×(0.6875 + 1 + 0.5 + 0.23) = 0.60; sim(K 3 ) = 0.25×(0.3125 + 0 + 0.5 + 1) = 0.45. From this, it can be obtained that the similarity between Beam K 2 and Beam K 3 is the highest.

[0106] (2) Carbon emission inference

[0107] The traditional method directly takes the carbon emission of Beam K 3 as the carbon emission of Beam K2 The carbon emissions of, i.e., beam K 2 are 155.465 kg. In this example, the carbon emissions per unit volume of beam K 1 and beam K 3 are as follows:

[0108] The carbon emissions per unit volume of beam K 3 are as follows: In the present invention, the carbon emissions of beam K deduced by formula are: 2 CE = [0.6×294.94+(1 - 0.6)×295.05]×(5.52×0.3×0.4)=195.40 kgCO 2 e. Compared with the actual carbon emissions of 195.29 kgCO 2 e of beam K 2 e, the deduced result of 195.40 kgCO 2 e of the present invention is much more accurate than the deduced result of 155.465 kgCO 2 e obtained by the traditional method.

[0109] Based on the energy consumption data of the prefabricated concrete building components and parts that have been produced, this application calculates the carbon emissions of the components and parts, and then establishes a carbon emission case for the components and parts. When encountering non-standard components and parts that have not been produced and whose carbon emission data are difficult to directly obtain through investigation, the carbon emissions of the non-standard components and parts that have not been produced are deduced by calculating the similarity with the cases in the ontology library. This alleviates the contradiction that the existing building carbon emission factors are only applicable to traditional construction and the increasing popularity of prefabricated concrete buildings, and brings great convenience to the carbon emission calculation of prefabricated concrete buildings in the feasibility study and design stage.

[0110] As Figure 6 shown, the embodiment of this application also provides a carbon emission inference device for non-standard components of prefabricated concrete buildings, including:

[0111] A knowledge base establishment module 601, which establishes an ontology knowledge base of carbon emission cases for various prefabricated concrete building components and parts that have been produced based on the carbon emission data of various prefabricated concrete building components and parts that have been produced.

[0112] A similarity determination module 602, which determines the case similarity between the non-standard components and parts that have not been produced and the cases based on the attributes of the cases.

[0113] A carbon emission inference module 603, which determines the carbon emission inference value of the non-standard components and parts that have not been produced based on the case similarity between the non-standard components and parts that have not been produced and the cases.

[0114] As Figure 7 shown, an embodiment of the present application further provides a carbon emission inference device for non-standard components of prefabricated concrete buildings, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0115] Based on the carbon emission data of various prefabricated concrete building components and parts that have been produced, establish a carbon emission case ontology knowledge base for various prefabricated concrete building components and parts that have been produced; determine the case similarity between non-standard components and parts that have not been produced and the cases based on the attributes of the cases; and determine the carbon emission inference value of the non-standard components and parts that have not been produced based on the case similarity between the non-standard components and parts that have not been produced and the cases.

[0116] An embodiment of the present application further provides a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set to:

[0117] Based on the carbon emission data of various prefabricated concrete building components and parts that have been produced, establish a carbon emission case ontology knowledge base for various prefabricated concrete building components and parts that have been produced; determine the case similarity between non-standard components and parts that have not been produced and the cases based on the attributes of the cases; and determine the carbon emission inference value of the non-standard components and parts that have not been produced based on the case similarity between the non-standard components and parts that have not been produced and the cases.

[0118] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0119] The device and medium provided by the embodiment of the present application correspond one-to-one with the method. Therefore, the device and medium also have beneficial technical effects similar to those of the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be elaborated here.

[0120] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0121] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0122] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0124] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0125] The memory may include non-permanent memory in the computer-readable medium, random access memory (RAM), and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0126] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The 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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0127] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising the element.

[0128] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A carbon emission reasoning method for non-standard parts of prefabricated concrete buildings, characterized in that: include: Based on the carbon emission data of various types of prefabricated concrete building components and parts that have been produced, establish a knowledge base of carbon emission cases of various types of prefabricated concrete building components and parts that have been produced; Based on the attributes of the cases, determining the case similarity between the unproduced non-standardized components and parts and the cases; Based on the case similarity between the unproduced non-standardized components and parts and the case, the carbon emission inference value of the unproduced non-standardized components and parts is determined.

2. The method according to claim 1, characterized in that Before establishing a knowledge base of carbon emission cases of various types of prefabricated concrete building components and parts produced based on the carbon emission data of various types of prefabricated concrete building components and parts produced, the method further includes: Determine the building material consumption, building material transportation distance, energy consumption and water consumption of each type of prefabricated concrete building components and parts produced; The carbon emission data of various types of prefabricated concrete building components and parts produced are determined by the following formula: Among them, C gb is the carbon emission data of various types of prefabricated concrete building components and parts produced; j is the jth type of energy consumed; m is the total number of energy types consumed; E j is the total consumption of the jth energy; EF j is the carbon emission factor of the jth energy source; W is water consumption; WF is the carbon emission factor of water production; M i is the consumption of the i-th building material; F i is the carbon emission factor of the production of the i-th building material; D i is the average transportation distance of the i-th building material; T i is the carbon emission factor per unit weight and transportation distance under the transportation mode of the i-th building material.

3. The method according to claim 1, characterized in that The case attributes include at least one of specifications, material consumption, material transportation, production environment, process type, energy consumption, and water consumption.

4. The method according to claim 3, characterized in that The case-based attribute determination of the case similarity between the unproduced non-standardized components and parts and the case specifically includes: Among all the attributes of the target case, select any attribute as the target attribute; Determining the attribute similarity between the unproduced non-standardized components and parts and the target case based on the target attribute; Traversing all attributes of the target case, and determining attribute similarities between all attributes of the target case and corresponding attributes of the unproduced non-standardized components and parts; Based on the attribute similarities between all attributes of the target case and the corresponding attributes of the unproduced non-standardized components and parts, the case similarities between the unproduced non-standardized components and parts and all cases are determined.

5. The method according to claim 4, characterized in that The determining of the attribute similarity between the unproduced non-standardized components and parts and the target case based on the target attribute specifically includes: When the target attribute is a measurable data attribute, the similarity between the unproduced non-standardized components and parts and the target case based on the target attribute is determined by the following formula: Among them, p s represents the target attribute of the case, p0 represents the data attribute of the non-standardized components and parts that have not been produced; V0 is the value of p0, V s For p s The value of MaxV c 、MinV c are the maximum and minimum values ​​of the corresponding attributes in the case.

6. The method according to claim 4, characterized in that The determining of the attribute similarity between the unproduced non-standardized components and parts and the target case based on the target attribute specifically includes: When the target attribute is an immeasurable object attribute, determining the nearest common parent node of the target attribute of the target case and the corresponding object attribute of the unproduced non-standardized components and parts; Determine a first distance from the common parent node to the root node of the ontology; Determine a second distance from the target object attribute of the unproduced non-standardized components and parts to the ontology root node; Determine a third distance from the target object attribute of the target case to the ontology root node; The similarity between the unproduced non-standardized components and parts and the target case based on the target attribute is determined by the following formula: Among them, Lcn(q0,q s ) is the attribute q0 of the non-standardized components and parts that have not been produced and the target attribute q s The nearest common parent node; dist[Lcn(q0,q s ), g] is q0 and q s The first distance from the nearest common parent node to the ontology root node g; dist[q0, g] is the second distance from attribute q0 to the ontology root node g; dist[q s , g] is the attribute q s The third distance to the root node g of the ontology; λ is an adjustment factor greater than or equal to 1.

7. The method according to claim 6, characterized in that The distance between the first object attribute node and the second object attribute node is determined by the following formula: dist(a1,a k )=dist(a1,a2)+dist(a2,a3)+…+dist(a k-2 ,a k-1 )+dist(a k-1 ,a k ) Wherein, the first object attribute node a1 and the second object attribute node a1 are connected. k The nodes of the k edges are a1, a2, a3, ..., a k-2 、a k-1 、a k , dist(a1, a2) is the distance between the first object attribute node a1 and the object attribute node a2 connected by only one edge, dist(a1, a2) = λ Max(Depth(g))-Depth(a2) , where Max(Depth(g)) is the maximum number of levels of the object attribute of the target case; Depth(a2) is the number of levels from the root node to the object attribute node a2.

8. The method according to claim 4, characterized in that The determining of the case similarity between the unproduced non-standardized components and parts and all cases based on the attribute similarity of all attributes of all cases specifically includes: Determine the number of all cases in the case ontology and the number of attributes corresponding to each case; Determine the attribute similarity between the target non-standardized components and parts and each case based on all attributes; Determine the attribute weight values ​​corresponding to different case attributes; Based on the attribute weight value, the case similarity between the unproduced non-standardized components and parts and all cases is determined based on the attribute similarity of all case attributes, the number of cases in the case body and the number of attributes corresponding to each case.

9. The method according to claim 1, characterized in that: The determining of the carbon emission inference value of the unproduced non-standardized components and parts based on the similarity between the unproduced non-standardized components and parts and the case specifically includes: According to the case similarity between the unproduced non-standardized components and parts and the case, determine the target case with the highest similarity to the unproduced non-standardized components and parts among all the cases; Determine the average carbon emissions per unit volume of components and parts of the same type that have been produced, and the target carbon emissions per unit volume of the target case; The carbon emission inference value of the unproduced non-standardized components and parts is determined according to the volume of the unproduced non-standardized components and parts, the average carbon emission and the target carbon emission.

10. A carbon emission inference device for non-standard parts of prefabricated concrete buildings, characterized in that: include: The knowledge base establishment module establishes a knowledge base of carbon emission cases of various types of prefabricated concrete building components and parts produced based on the carbon emission data of various types of prefabricated concrete building components and parts produced; A similarity determination module, based on the attributes of the case, determines the case similarity between the unproduced non-standardized components and parts and the case; The carbon emission reasoning module determines the carbon emission reasoning value of the unproduced non-standardized components and parts based on the case similarity between the unproduced non-standardized components and parts and the case.

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