A carbon emission accounting method and medium for a manufacturing system based on a meta-carbon emission block

Abstract

A carbon emission accounting method and medium for a manufacturing system based on a meta-carbon emission block, a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, comprising the following steps: 1) obtaining historical data of carbon emission characteristics of the manufacturing system and the corresponding historical processing parameters and historical operating conditions; 2) constructing a database of carbon emissions and state characteristics of sub-equipment of the manufacturing system; 3) constructing a meta-carbon emission block corresponding to each sub-equipment of the manufacturing system to form a knowledge base of meta-carbon emission blocks of sub-equipment of the manufacturing system; 4) based on the given processing parameters and operating conditions of the manufacturing system, and inputting them into the knowledge base of meta-carbon emission blocks of sub-equipment of the manufacturing system, calculate the carbon emissions of the manufacturing system under given conditions. The carbon emission accounting method for a manufacturing system based on a meta-carbon emission block proposed by the present invention has wide adaptability and strong promotion. The relevant meta-carbon emission block knowledge base can be applied to different manufacturing systems in combination with specific production scenarios, breaking through the limitations of existing methods.

Description

Technical Field

[0001] The present invention relates to the field of green and low-carbon manufacturing, and in particular to a carbon emission accounting method and medium for a manufacturing system based on a meta-carbon emission block. Background Art

[0002] With global warming and environmental deterioration, carbon emissions have received widespread attention from all countries. According to the 2022 International Energy Agency survey report, global energy-related carbon emissions reached 36.3Gt in 2021, of which more than 30% were generated by the manufacturing industry. Therefore, in the context of carbon peak and carbon neutrality, in order to promote the green development of the manufacturing industry, accurate accounting of carbon emissions in the manufacturing system is of great significance to the subsequent carbon efficiency evaluation of the manufacturing system, carbon emission reduction decision-making and low-carbon design.

[0003] At present, the carbon emission accounting for manufacturing systems mainly adopts the emission factor method, which is calculated based on the carbon emission activity data of the manufacturing system and the corresponding carbon emission factor. However, the carbon emissions obtained by this method are very rough, and the carbon emissions of each processing sub-equipment, each operating state, each processing stage, and each processing feature in the manufacturing system have not been revealed. Therefore, it is impossible to accurately locate and analyze the carbon emission hotspots in the manufacturing system, resulting in difficulty for manufacturing companies to implement precise energy-saving and emission reduction measures and low-carbon design optimization for subsequent manufacturing systems based on the calculated carbon emission results, which restricts the green and low-carbon development of the manufacturing industry. Summary of the invention

[0004] The purpose of the present invention is to provide a method and medium for accurately calculating carbon emissions from a manufacturing system to solve the problems existing in the prior art.

[0005] The technical solution adopted to achieve the purpose of the present invention is as follows: a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, comprising the following steps:

[0006] 1) Obtain historical data on carbon emission characteristics of the manufacturing system and the corresponding historical processing parameters and historical operating conditions.

[0007] 2) Based on the historical data of carbon emission characteristics of the manufacturing system, the historical operating status of the sub-equipment of the manufacturing system is identified and clustered through artificial intelligence algorithms to build a database of carbon emissions and status characteristics of the sub-equipment of the manufacturing system.

[0008] 3) Based on the carbon emission and state characteristic database of manufacturing system sub-equipment, construct the meta-carbon emission block corresponding to each sub-equipment of the manufacturing system to form a knowledge base of the meta-carbon emission block of the manufacturing system sub-equipment.

[0009] 4) Based on the given manufacturing system processing parameters and operating conditions, and inputting them into the manufacturing system sub-equipment element carbon emission block knowledge base, calculate the manufacturing system carbon emissions under the given processing parameters and operating conditions.

[0010] Furthermore, in step 1), the tools for obtaining historical data on carbon emission characteristics of the manufacturing system include smart sensors and relevant resources and management systems of the manufacturing enterprise.

[0011] The manufacturing enterprise related resources and management systems include ERP, PDM, BOM and MES.

[0012] Further, in step 2), the artificial intelligence algorithm includes decision tree, random forest, and support vector machine.

[0013] Furthermore, in step 2), the determining factors of the manufacturing system sub-equipment carbon emission and state characteristic database include the manufacturing system carbon emission sources, processing equipment and equipment operation status.

[0014] The carbon emission sources of the manufacturing system include direct carbon emission sources and indirect carbon emission sources.

[0015] The processing equipment includes main equipment and auxiliary equipment.

[0016] The equipment operation status includes a standby state, a processing preparation state and a processing state.

[0017] Further, in step 2), the manufacturing system status characteristic data includes carbon emission source data, processing equipment data, operation status data, processing stage data, processing sequence data and workpiece characteristic data of the manufacturing system.

[0018] Further, in step 3), the meta-carbon emission block is as follows:

[0019] CEB i =[SCEB i,1 ,SCEB i,2 ,…,SCEB i,s ,VCEB i,1 ,VCEB i,2 ,…,VCEB i,m ] (1)

[0021] In the formula, CEB i is the carbon emission block of the ith sub-equipment in the manufacturing system, i is the equipment number, i = 1, 2, ..., n, and n is the number of sub-equipment in the manufacturing system. SCEB i,a is the ath static meta-carbon emission block of the i-th sub-equipment in the manufacturing system, a is the ordinal number of the static meta-carbon emission block, a=1,2,…,s, s is the total number of static meta-carbon emission blocks. VCEB i,b is the bth variable carbon emission block of the i-th sub-equipment in the manufacturing system, b is the ordinal number of the variable carbon emission block, b=1, 2,…, m, and m is the total number of variable carbon emission blocks.

[0022] The formed manufacturing system sub-equipment carbon emission block knowledge base CEB is as follows:

[0023]

[0024] Furthermore, the static carbon emission block SCEB i,a As shown below:

[0025] SCEB i,a =ΔQ i,a ×ΔT i,a ×CF j (3)

[0026] Where ΔQ i,a It is the number of unit carbon emission elements corresponding to the ath static carbon emission state of the i-th sub-equipment in the manufacturing system. ΔT i,a It is the unit time corresponding to the static carbon emission state. j is the carbon emission coefficient of the jth carbon emission factor, and j is the carbon emission factor.

[0027] The variable carbon emission block VCEB i,b As shown below:

[0028] VCEB i,b =Δq i,b ×Δt i,b ×CF j (4)

[0029] In the formula, Δq i,b Δt is the number of unit carbon emission elements related to processing parameters under the bth variable carbon emission state of the i-th sub-equipment of the manufacturing system. i,b is the unit time under the corresponding variable carbon emission state. CF j is the carbon emission coefficient of the jth carbon emission factor, and j is the carbon emission factor.

[0030] Furthermore, the carbon emission elements include energy, materials and waste.

[0031] Further, in step 4), based on the given manufacturing system processing parameters and operating conditions, and inputting them into the manufacturing system sub-equipment element carbon emission block knowledge base, the steps of calculating the manufacturing system carbon emissions under the given processing parameters and operating conditions include:

[0032] 4.1) Based on the processing parameters and operating conditions of the given manufacturing system, establish the characteristic time parameter matrix of the manufacturing system sub-equipment.

[0033] Among them, the characteristic time parameter matrix T of the i-th sub-equipment of the manufacturing system i As shown below:

[0034]

[0035] Where, T i,c is the characteristic time parameter of the cth carbon emission block of the ith sub-equipment in the manufacturing system, i is the sub-equipment ordinal number, i = 1, 2, ..., n, n is the number of sub-equipment in the manufacturing system. c is the ordinal number of the carbon emission block, c = 1, 2, ..., m 0 , m 0 is the total number of carbon emission blocks.

[0036] The characteristic time parameter set T of the manufacturing system sub-equipment is as follows:

[0037]

[0038] 4.2) Input the characteristic time parameter matrix of the manufacturing system sub-equipment into the manufacturing system sub-equipment meta-carbon emission block knowledge base to calculate the carbon emissions of the manufacturing system under given processing parameters and operating conditions.

[0039] Among them, the carbon emission matrix CE of the manufacturing system sub-equipment is as follows:

[0040]

[0041] Carbon emission CE of the i-th sub-equipment in the manufacturing system i As shown below:

[0042]

[0043] Total carbon emissions of the manufacturing system total As shown below:

[0044]

[0045] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0046] The technical effect of the present invention is undoubted, and the beneficial effects of the present invention are as follows:

[0047] 1) In view of the characteristics of changeable equipment operating conditions, multi-source timing and dynamic coupling of carbon emissions in manufacturing systems, the present invention innovatively proposes a meta-carbon emission block model based on the characteristics of multiple carbon emission sources, multiple processing equipment, multiple operating states, multiple processing stages, multiple processing sequences, and multiple workpiece characteristics of the manufacturing system, and invents a manufacturing system carbon emission accounting method based on the meta-carbon emission block, which overcomes the shortcomings of the prior art in carbon emission accounting for manufacturing systems, such as rough carbon emission data and difficulty in locating carbon emission hotspots.

[0048] 2) The carbon emission accounting method for manufacturing systems based on meta-carbon emission blocks proposed in the present invention has wide adaptability and strong scalability. The relevant meta-carbon emission block knowledge base can be applied to different manufacturing systems in combination with specific production scenarios, breaking through the limitations of existing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the carbon emission accounting method of the manufacturing system based on the meta-carbon emission block of the present invention;

[0050] Figure 2 It is a schematic diagram of characteristic attributes of sub-equipment of the manufacturing system of the present invention;

[0051] Figure 3 This is a schematic diagram of the carbon emission block model of the manufacturing system sub-equipment of the present invention;

[0052] Figure 4 This is a schematic diagram of constructing a knowledge base of a carbon emission block of a manufacturing system according to the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described below in conjunction with the embodiments, but it should not be understood that the above subject matter of the present invention is limited to the following embodiments. Without departing from the above technical ideas of the present invention, various substitutions and changes are made according to the common technical knowledge and customary means in the art, which should all be included in the protection scope of the present invention.

[0054] Embodiment 1:

[0055] See also Figures 1 to 4 , a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, comprising the following steps:

[0056] 1) Obtain historical data on carbon emission characteristics of the manufacturing system and the corresponding historical processing parameters and historical operating conditions.

[0057] 2) Based on the historical data of carbon emission characteristics of the manufacturing system, the historical operating status of the sub-equipment of the manufacturing system is identified and clustered through artificial intelligence algorithms to build a database of carbon emissions and status characteristics of the sub-equipment of the manufacturing system.

[0058] 3) Based on the carbon emission and state characteristic database of manufacturing system sub-equipment, construct the meta-carbon emission block corresponding to each sub-equipment of the manufacturing system to form a knowledge base of the meta-carbon emission block of the manufacturing system sub-equipment.

[0059] 4) Based on the given manufacturing system processing parameters and operating conditions, and inputting them into the manufacturing system sub-equipment element carbon emission block knowledge base, calculate the manufacturing system carbon emissions under the given processing parameters and operating conditions.

[0060] In step 1), the tools for obtaining carbon emission characteristic data of the manufacturing system include intelligent sensors and relevant resources and management systems of the manufacturing enterprise.

[0061] The manufacturing enterprise-related resources and management systems include Enterprise Resource Planning (ERP), Product Data Management (PDM), Bill of Material (BOM) and Manufacturing Execution System (MES).

[0062] In step 2), the artificial intelligence algorithm includes decision tree, random forest, and support vector machine.

[0063] In step 2), the determining factors of the manufacturing system sub-equipment carbon emission and status characteristic database include the manufacturing system carbon emission sources, processing equipment and equipment operation status.

[0064] The carbon emission sources of the manufacturing system include direct carbon emission sources and indirect carbon emission sources.

[0065] The processing equipment includes main equipment and auxiliary equipment.

[0066] The equipment operation status includes a standby state, a processing preparation state and a processing state.

[0067] In step 2), the manufacturing system status characteristic data includes carbon emission source data, processing equipment data, operation status data, processing stage data, processing sequence data and workpiece characteristic data of the manufacturing system.

[0068] In step 3), the meta-carbon emission block is used as the basic characteristic unit of the manufacturing system sub-equipment and the basic unit of carbon emission accounting of the manufacturing system. It includes two parts: the static meta-carbon emission block SCEB and the variable meta-carbon emission block VCEB. The meta-carbon emission block is as follows:

[0069] CEB i =[SCEB i,1 ,SCEB i,2 ,...,SCEB i,s ,VCEB i,1 ,VCEB i,2 ,…,VCEB i,m ] (1)

[0071] In the formula, CEB i is the carbon emission block of the ith sub-equipment in the manufacturing system, i is the equipment number, i = 1, 2, ..., n, and n is the number of sub-equipment in the manufacturing system. SCEBi,a is the ath static meta-carbon emission block of the i-th sub-equipment in the manufacturing system, a is the ordinal number of the static meta-carbon emission block, a=1,2,…,s, s is the total number of static meta-carbon emission blocks. VCEB i,b is the bth variable carbon emission block of the i-th sub-equipment in the manufacturing system, b is the ordinal number of the variable carbon emission block, b=1, 2,…, m, and m is the total number of variable carbon emission blocks.

[0072] The formed manufacturing system sub-equipment carbon emission block knowledge base CEB is as follows:

[0073]

[0074] The static carbon emission block is related to the inherent properties of the corresponding equipment, which is determined during the design and manufacturing stage of the equipment and exhibits corresponding properties during the service stage of the equipment. i,a As shown below:

[0075] SCEB i,a =ΔQ i,a ×ΔT i,a ×CF j (3)

[0076] Where ΔQ i,a It is the number of unit carbon emission elements corresponding to the ath static carbon emission state of the i-th sub-equipment in the manufacturing system. ΔT i,a It is the unit time corresponding to the static carbon emission state. j is the carbon emission coefficient of the jth carbon emission factor, and j is the carbon emission factor.

[0077] The variable carbon emission block is related to the processing parameters, and its carbon emission behavior is unique for a given set of processing parameters. i,b As shown below:

[0078] VCEB i,b =Δq i,b ×Δt i,b ×CF j (4)

[0079] In the formula, Δq i,b Δt is the number of unit carbon emission elements related to processing parameters under the bth variable carbon emission state of the i-th sub-equipment of the manufacturing system. i,b is the unit time under the corresponding variable carbon emission state. CF j is the carbon emission coefficient of the jth carbon emission factor, and j is the carbon emission factor.

[0080] The carbon emission elements include energy, materials and waste.

[0081] In step 4), for given process parameters, artificial intelligence algorithms can be used to decompose the manufacturing system characteristics, and based on the constructed sub-equipment static meta-carbon emission block and variable meta-carbon emission block knowledge base, the carbon emission characteristics of the manufacturing system can be intelligently matched to achieve accurate carbon emission accounting of the manufacturing system. Based on the given manufacturing system processing parameters and operating conditions, and input into the manufacturing system sub-equipment meta-carbon emission block knowledge base, the steps of calculating the manufacturing system carbon emissions under given processing parameters and operating conditions include:

[0082] 4.1) Based on the processing parameters and operating conditions of the given manufacturing system, establish the characteristic time parameter matrix of the manufacturing system sub-equipment.

[0083] Among them, the characteristic time parameter matrix T of the i-th sub-equipment of the manufacturing system i As shown below:

[0084]

[0085] Where, T i,c is the characteristic time parameter of the cth carbon emission block of the ith sub-equipment in the manufacturing system, i is the sub-equipment ordinal number, i = 1, 2, ..., n, n is the number of sub-equipment in the manufacturing system. c is the ordinal number of the carbon emission block, c = 1, 2, ..., m 0 , m 0 is the total number of carbon emission blocks.

[0086] The characteristic time parameter set T of the manufacturing system sub-equipment is as follows:

[0087]

[0088] 4.2) Input the characteristic time parameter matrix of the manufacturing system sub-equipment into the manufacturing system sub-equipment meta-carbon emission block knowledge base to calculate the carbon emissions of the manufacturing system under given processing parameters and operating conditions.

[0089] Among them, the carbon emission matrix CE of the manufacturing system sub-equipment is as follows:

[0090]

[0091] Carbon emission CE of the i-th sub-equipment in the manufacturing system i As shown below:

[0092]

[0093] Total carbon emissions of the manufacturing system total As shown below:

[0094]

[0095] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0096] Embodiment 2:

[0097] See also Figures 1 to 4 , a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, comprising the following steps:

[0098] 1) Obtain historical data on carbon emission characteristics of the manufacturing system and the corresponding historical processing parameters and historical operating conditions.

[0099] 2) Based on the historical data of carbon emission characteristics of the manufacturing system, the historical operating status of the sub-equipment of the manufacturing system is identified and clustered through artificial intelligence algorithms to build a database of carbon emissions and status characteristics of the sub-equipment of the manufacturing system.

[0100] 3) Based on the carbon emission and state characteristic database of manufacturing system sub-equipment, construct the meta-carbon emission block corresponding to each sub-equipment of the manufacturing system to form a knowledge base of the meta-carbon emission block of the manufacturing system sub-equipment.

[0101] 4) Based on the given manufacturing system processing parameters and operating conditions, and inputting them into the manufacturing system sub-equipment element carbon emission block knowledge base, calculate the manufacturing system carbon emissions under the given processing parameters and operating conditions.

[0102] Embodiment 3:

[0103] A method for calculating carbon emissions of a manufacturing system based on a meta-carbon emission block, the main contents of which are shown in Example 2, wherein in step 1), the tool for obtaining carbon emission characteristic data of the manufacturing system includes intelligent sensors and relevant resources and management systems of the manufacturing enterprise.

[0104] The manufacturing enterprise-related resources and management systems include Enterprise Resource Planning (ERP), Product Data Management (PDM), Bill of Material (BOM) and Manufacturing Execution System (MES).

[0105] Embodiment 4:

[0106] A method for calculating carbon emissions from a manufacturing system based on a meta-carbon emission block, the main contents of which are shown in Example 2, wherein in step 2), the artificial intelligence algorithm includes a decision tree, a random forest, and a support vector machine.

[0107] Embodiment 5:

[0108] A method for calculating carbon emissions of a manufacturing system based on a meta-carbon emission block, the main contents of which are shown in Example 2, wherein in step 2), the determining factors of the manufacturing system sub-equipment carbon emissions and state characteristic database include the manufacturing system carbon emission sources, processing equipment and equipment operating status.

[0109] The carbon emission sources of the manufacturing system include direct carbon emission sources and indirect carbon emission sources.

[0110] The processing equipment includes main equipment and auxiliary equipment.

[0111] The equipment operation status includes a standby state, a processing preparation state and a processing state.

[0112] Embodiment 6:

[0113] A method for calculating carbon emissions of a manufacturing system based on a meta-carbon emission block, the main contents of which are shown in Example 2, wherein in step 2), the manufacturing system status characteristic data includes the carbon emission source data, processing equipment data, operating status data, processing stage data, processing sequence data and workpiece characteristic data of the manufacturing system.

[0114] Embodiment 7:

[0115] A method for calculating carbon emissions of a manufacturing system based on a meta-carbon emission block, the main contents of which are shown in Example 2, wherein in step 3), the meta-carbon emission block is used as a basic characteristic unit of a manufacturing system sub-equipment and is also a basic unit for calculating carbon emissions of a manufacturing system, and comprises a static meta-carbon emission block SCEB and a variable meta-carbon emission block VCEB, and the meta-carbon emission block is shown as follows:

[0116] CEB i =[SCEB i,1 ,SCEB i,2 ,…,SCEB i,s ,VCEB i,1 ,VCEB i,2 ,…,VCEB i,m ] (1)

[0118] In the formula, CEB i is the carbon emission block of the ith sub-equipment in the manufacturing system, i is the equipment number, i = 1, 2, ..., n, and n is the number of sub-equipment in the manufacturing system. SCEB i,a is the ath static meta-carbon emission block of the i-th sub-equipment in the manufacturing system, a is the ordinal number of the static meta-carbon emission block, a=1,2,…,s, s is the total number of static meta-carbon emission blocks. VCEB i,b is the bth variable carbon emission block of the i-th sub-equipment in the manufacturing system, b is the ordinal number of the variable carbon emission block, b=1, 2,…, m, and m is the total number of variable carbon emission blocks.

[0119] The formed manufacturing system sub-equipment carbon emission block knowledge base CEB is as follows:

[0120]

[0121] Embodiment 8:

[0122] A method for calculating carbon emissions of a manufacturing system based on a static carbon emission block, the main contents of which are shown in Example 7, wherein the static static carbon emission block is related to the inherent properties of the corresponding equipment, which is determined during the design and manufacturing stage of the equipment and exhibits corresponding properties during the service stage of the equipment. The static static carbon emission block SCEB i,a As shown below:

[0123] SCEB i,a =ΔQ i,a ×ΔT i,a ×CF j (3)

[0124] Where ΔQ i,a It is the number of unit carbon emission elements corresponding to the ath static carbon emission state of the i-th sub-equipment in the manufacturing system. ΔT i,a It is the unit time corresponding to the static carbon emission state. j is the carbon emission coefficient of the jth carbon emission factor, and j is the carbon emission factor.

[0125] The variable carbon emission block is related to the processing parameters, and its carbon emission behavior is unique for a given set of processing parameters. i,b As shown below:

[0126] VCEB i,b =Δq i,b ×Δt i,b ×CF j (4)

[0127] In the formula, Δq i,b is the number of unit carbon emission elements related to processing parameters under the bth variable carbon emission state of the i-th sub-equipment of the manufacturing system, Δt i,b is the unit time under the corresponding variable carbon emission state. CF j is the carbon emission coefficient of the jth carbon emission factor, and j is the carbon emission factor.

[0128] Embodiment 9:

[0129] A method for calculating carbon emissions from a manufacturing system based on a meta-carbon emission block, the main contents of which are shown in Example 8, wherein the carbon emission elements include energy, materials and waste.

[0130] Embodiment 10:

[0131] A method for calculating carbon emissions of a manufacturing system based on a meta-carbon emission block, the main contents of which are shown in Example 2, wherein, in step 4), for given process parameters, an artificial intelligence algorithm can be used to decompose the characteristics of the manufacturing system, and based on the constructed sub-equipment static meta-carbon emission block and variable meta-carbon emission block knowledge base, the carbon emission characteristics of the manufacturing system are intelligently matched to achieve accurate carbon emission accounting of the manufacturing system. Based on the given manufacturing system processing parameters and operating conditions, and input into the manufacturing system sub-equipment meta-carbon emission block knowledge base, the steps of calculating the carbon emissions of the manufacturing system under given processing parameters and operating conditions include:

[0132] 4.1) Based on the given processing parameters and operating conditions of the manufacturing system, establish the characteristic time parameter matrix of the manufacturing system sub-equipment.

[0133] Among them, the characteristic time parameter matrix T of the i-th sub-equipment of the manufacturing system i As shown below:

[0134]

[0135] Where, T i,c is the characteristic time parameter of the cth carbon emission block of the ith sub-equipment in the manufacturing system, i is the sub-equipment ordinal number, i = 1, 2, ..., n, n is the number of sub-equipment in the manufacturing system. c is the ordinal number of the carbon emission block, c = 1, 2, ..., m 0 , m 0 is the total number of carbon emission blocks.

[0136] The characteristic time parameter set T of the manufacturing system sub-equipment is as follows:

[0137]

[0138] 4.2) Input the characteristic time parameter matrix of the manufacturing system sub-equipment into the manufacturing system sub-equipment meta-carbon emission block knowledge base to calculate the carbon emissions of the manufacturing system under given processing parameters and operating conditions.

[0139] Among them, the carbon emission matrix CE of the manufacturing system sub-equipment is as follows:

[0140]

[0141] Carbon emission CE of the i-th sub-equipment in the manufacturing system i As shown below:

[0142]

[0143] Total carbon emissions of the manufacturing system total As shown below:

[0144]

[0145] Embodiment 11:

[0146] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of Embodiments 2 to 10.

[0147] Embodiment 12:

[0148] See also Figures 1 to 4 , a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, comprising the following steps:

[0149] 1) Use intelligent sensors and relevant resource and management systems of manufacturing enterprises (such as ERP, PDM, BOM and MES) to obtain carbon emission characteristic data of manufacturing systems. Based on the obtained carbon emission characteristic data, use artificial intelligence algorithms (such as decision trees, random forests, support vector machines) to identify and cluster the operating status of sub-equipment of manufacturing systems, and build a database of carbon emission and status characteristics of sub-equipment of manufacturing systems;

[0150] 2) Based on the above-mentioned manufacturing system sub-equipment carbon emission and state characteristic database, construct the meta-carbon emission block corresponding to each sub-equipment of the manufacturing system to form a manufacturing system sub-equipment meta-carbon emission block knowledge base;

[0151] 3) On this basis, according to the subsequent given processing parameters and operating conditions of the manufacturing system, and based on the constructed meta-carbon emission block knowledge base, accurate calculation of carbon emissions of the manufacturing system under given process conditions can be carried out.

[0152] In the step 1), the carbon emission and status characteristic database of the manufacturing system sub-equipment is determined by the carbon emission sources of the manufacturing system (direct carbon emissions and indirect carbon emissions), processing equipment (main equipment and auxiliary equipment) and equipment operating status (standby, processing preparation and processing).

[0153] In the step 1), the manufacturing system status characteristic data includes six types of characteristic data, namely, multiple carbon emission sources, multiple processing equipment, multiple operating states, multiple processing stages, multiple processing sequences, and multiple workpiece characteristics of the manufacturing system.

[0154] In step 2), the carbon emission block is used as the basic characteristic unit of the manufacturing system sub-equipment and is also the basic unit for carbon emission accounting of the manufacturing system. It includes two parts: the static carbon emission block SCEB and the variable carbon emission block VCEB. The carbon emission block CEB of the manufacturing system sub-equipment i is i It can be expressed as:

[0155] CEB i =[SCEB i,1 ,SCEB i,2,…,SCEB i,s ,VCEB i,1 ,VCEB i,2 ,...,VCEB i,m ] (1)

[0157] The manufacturing system sub-equipment carbon emission block knowledge base CEB can be expressed as:

[0158]

[0159] The static meta-carbon emission block is related to the inherent properties of the corresponding equipment, which is determined during the design and manufacturing stage of the equipment and exhibits the corresponding properties during the service stage of the equipment. i,a It can be expressed as:

[0160] SCEB i,a =ΔQ i,a ×ΔT i,a ×CF j (3)

[0161] Among them, ΔQ i,a is the number of unit carbon emission elements (such as energy, materials and waste) corresponding to the a-th static carbon emission state of sub-equipment i; ΔT i,a is the unit time corresponding to the static carbon emission state; CF j is the carbon emission coefficient of the jth carbon emission factor.

[0162] The variable carbon emission block is related to the processing parameters, and its carbon emission behavior is unique for a given set of processing parameters. i,b It can be expressed as:

[0163] VCEB i,b =Δq i,b ×Δt i,b ×CF j (4)

[0164] Among them, Δq i,b is the number of unit carbon emission elements related to the processing parameters in the bth variable carbon emission state of sub-equipment i, Δt i,b is the unit time under the corresponding variable carbon emission state.

[0165] In the step 3), for given process parameters, the artificial intelligence algorithm can be used to decompose the manufacturing system characteristics, and based on the constructed sub-equipment static meta-carbon emission block and variable meta-carbon emission block knowledge base, the carbon emission characteristics of the manufacturing system can be intelligently matched to achieve accurate carbon emission accounting of the manufacturing system.

[0166] For a given process condition of a manufacturing system, the characteristic time parameter matrix T of its sub-equipment i is i It can be expressed as:

[0167]

[0168] The characteristic time parameter set T of the manufacturing system sub-equipment can be expressed as:

[0169]

[0170] Under given process parameters, the carbon emission matrix CE of the manufacturing system sub-equipment can be expressed as:

[0171]

[0172] Carbon emissions CE of manufacturing system sub-equipment i i It can be expressed as:

[0173]

[0174] Total carbon emissions of the manufacturing system total As shown below:

[0175]

[0176] Embodiment 13:

[0177] See also Figures 1 to 4 , a carbon emission accounting method and medium for a manufacturing system based on a meta-carbon emission block, the contents are as follows:

[0178] 1) Figure 1 This is a schematic diagram of a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, which mainly includes three parts: ① Construction of a carbon emission and state feature database for sub-equipment of a manufacturing system, ② Construction of a knowledge base for meta-carbon emission blocks for sub-equipment of a manufacturing system, and ③ Carbon emission accounting for a manufacturing system based on a meta-carbon emission block. In this method, intelligent sensors and relevant resources and management systems of manufacturing enterprises are used to obtain the required carbon emission feature data. Based on the obtained carbon emission feature data, intelligent algorithms such as decision trees, random forests, and support vector machines are used to identify and cluster the operating status of sub-equipment of the manufacturing system, and a carbon emission and state feature database for sub-equipment of the manufacturing system is constructed; Step ② constructs the meta-carbon emission blocks corresponding to each sub-equipment of the manufacturing system based on the sub-equipment state feature database to form a knowledge base for meta-carbon emission blocks for sub-equipment of the manufacturing system; Step ③ intelligently matches the carbon emission features of the manufacturing system based on the constructed knowledge base of static meta-carbon emission blocks and variable meta-carbon emission blocks for sub-equipment, and realizes accurate carbon emission accounting for the manufacturing system.

[0179] 2) Figure 2This is a schematic diagram of the characteristic attributes of sub-equipment in a manufacturing system. Common manufacturing systems contain many devices (such as main equipment and auxiliary equipment). At different operating stages of the manufacturing system, each device has different operating states (such as standby state, processing preparation state, and processing state). At the same time, for different processing characteristics of the workpiece, the corresponding process parameters in the processing stage are different. Therefore, the carbon emissions of the manufacturing system during operation have the characteristics of multi-source and dynamic coupling.

[0180] 3) Figure 3 This is a schematic diagram of the meta-carbon emission block model of the sub-equipment of the manufacturing system. According to the database of carbon emissions and state characteristics of the manufacturing system, the carbon emission sources (direct carbon emissions and indirect carbon emissions), processing equipment (main equipment and auxiliary equipment) and equipment operating status (standby, processing preparation and processing) of the manufacturing system are analyzed. Based on the concept of the meta-model, the meta-carbon emission block is defined from the quantity dimension, carbon emission coefficient dimension and time dimension. At the same time, according to the determinants of carbon emissions of the equipment in the manufacturing system under different operating conditions, its meta-carbon emissions can be divided into static meta-carbon emission blocks and variable meta-carbon emission blocks. Among them, the static meta-carbon emission block is related to the inherent properties of the equipment, and the variable meta-carbon emission block is related to the process parameters (such as processing parameters, workpiece materials, tool parameters and process conditions).

[0181] 4) Figure 4 The schematic diagram of the manufacturing system meta-carbon emission block knowledge base is constructed. First, the required carbon emission characteristic data is obtained by using intelligent sensors and related resources and management systems of manufacturing enterprises. Based on the obtained carbon emission characteristic data, the intelligent algorithm is used to identify and cluster the operating status of the manufacturing system sub-equipment, and a database of carbon emission and status characteristics of the manufacturing system sub-equipment is constructed. Figure 3 The concept of meta-carbon emission block is defined to decompose the carbon emissions of sub-equipment of the manufacturing system and form a knowledge base of meta-carbon emission blocks of the manufacturing system.

[0182] 5) The fiber laser welding system is a typical manufacturing system with multiple carbon emission sources, multiple processing equipment, multiple operating states, multiple processing stages and multiple processing sequences. The present invention takes the fiber laser butt welding of 6061 aluminum alloy as an example to carry out carbon emission accounting verification of the laser processing system based on the meta-carbon emission block, wherein the equipment of the fiber laser welding system includes a fiber laser, a welding robot, a chiller, a system control computer, and a shielding gas (argon) and compressed air supply equipment, wherein the shielding gas (argon) is supplied by a gas storage cylinder, and the compressed air is centrally supplied by an air compressor station. The size of the 6061 aluminum alloy plate used in the case is 2.5mm×200mm×100mm, and the length of the butt weld is 190mm.

[0183] The carbon emission intelligent monitoring system for fiber laser processing system built in the early stage (DPM-C520 intelligent meter collects power information of sub-equipment, and MF5200 flowmeter collects gas consumption information of shielding gas and compressed air) can be used to obtain the power information of the main equipment of the fiber laser welding system. Among them, the average standby power, light preparation power and average photoelectric conversion efficiency of IPG YLR-4000 fiber laser, the average state maintenance power and heat exchange average power of Riedel chiller, the average standby power of ABB IRB4400 robot and the average power of system control computer are inherent attributes of the equipment, which belong to the static meta-carbon emission block related data; while the laser light output power, robot operation power, shielding gas and compressed air flow values ​​of the fiber laser welding system are determined by the actual process parameters and belong to the variable meta-carbon emission block data. Table 1 shows the main equipment information of the fiber laser welding system, and Table 2 shows 16 sets of 6061 aluminum alloy fiber laser butt welding process parameter information.

[0184] Table 1 Main equipment information of fiber laser welding system

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[0186] Table 2 6061 aluminum alloy fiber laser butt welding process parameters

[0187] Serial number Laser output power(W) Welding speed (mm s) Defocus (mm) 1 3067 55 -2 2 3040 62 -2 3 2853 63 -2 4 2880 56 .2 5 2827 60 -1 6 3200 59 -1 7 3013 58 -1 8 3120 52 -1 9 2800 54 -1 10 3147 64 -1 11 2933 50 -1 12 2960 65 -1 13 3093 51 0 14 31 73 57 0 15 2907 53 0 16 2987 61 0

[0188] Based on the above data, according to the attached Figure 4 The process of building the meta-carbon emission block knowledge base of the manufacturing system can establish the meta-carbon emission block knowledge base of the fiber laser welding system sub-equipment. In this case, based on a large number of preliminary experiments, the flow rate of the shielding gas is set to 20 L / min, and the flow rate of the compressed air is set to 240 L / min. According to the 2022 electricity carbon emission factor data, 0.5810 tCO 2 e / MWh (i.e. 0.1614 gCO 2 e / kJ); the carbon emission coefficients of the protective gas (argon) and compressed air are 0.8844 gCO 2 e / L and 0.0983 gCO 2 e / L.

[0189] According to the steps of the manufacturing system carbon emission accounting method based on the meta-carbon emission block of the present invention, the carbon emissions of the fiber laser welding system for butt welding of 6061 aluminum alloy under the above-mentioned 16 given sets of process parameters are calculated, and compared with the carbon emission data measured and calculated by the laser processing system carbon emission intelligent monitoring system. The comparative analysis results are shown in Table 3. The average relative error between the two is about 6.73%, which verifies the feasibility of the calculation method based on the meta-carbon emission block of the present invention.

[0190] Table 3 Carbon emissions of 6061 aluminum alloy laser butt welding system based on meta-carbon emission block and intelligent monitoring system

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[0192] At the same time, the carbon emissions of each sub-equipment of the laser welding system under different process parameters are shown in Table 4. This table can be used to analyze the carbon emission proportion of each sub-equipment of the laser welding system, and locate the carbon emission hotspots of the sub-equipment of the laser welding system and its operation stage. Among them, the carbon emissions generated by the fiber laser and the chiller account for the largest proportion. In this case, the photoelectric conversion efficiency of the fiber laser is about 32%, and its energy utilization rate is low, resulting in a large power demand for the input laser, which leads to serious carbon emissions generated by power consumption; the chiller is used to maintain the stable operation of the laser welding system. Due to the low photoelectric conversion efficiency of the laser (about 68% of the energy cannot be used), the chiller needs to take away a large amount of heat energy generated by the laser but not yet used, and the required energy consumption is large. Therefore, the low-carbon and energy-saving design of the laser welding system can be carried out in the future around the improvement of the photoelectric conversion efficiency of the laser and the matching of the performance of the chiller and the laser; at the same time, according to 16 different sets of laser welding process parameters, under the premise of ensuring welding quality, by optimizing the processing parameters, it is also possible to reduce the carbon emissions of the manufacturing system.

[0193] Table 4 Carbon emissions of each sub-equipment of the laser welding system under different process parameters

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Claims

1. A carbon emission accounting method for manufacturing systems based on meta-carbon emission blocks, It is characterized in that The following steps are involved: 1) Obtain historical data on carbon emission characteristics of the manufacturing system and the corresponding historical processing parameters and historical operating conditions; 2) Based on the historical data of carbon emission characteristics of the manufacturing system, the historical operating status of the sub-equipment of the manufacturing system is identified and clustered through artificial intelligence algorithms to build a database of carbon emission and status characteristics of the sub-equipment of the manufacturing system; 3) Based on the carbon emission and state characteristic database of manufacturing system sub-equipment, the meta-carbon emission block corresponding to each sub-equipment of the manufacturing system is constructed to form a knowledge base of meta-carbon emission blocks of manufacturing system sub-equipment; The meta-carbon emission block is shown below: CEB i =[SCEB i,1 ,SCEB i,2 ,...,SCEB i,s ,VCEB i,1 ,VCEB i,2 ,...,VCEB i,m ] (1) In the formula, CEB i is the carbon emission block of the ith sub-equipment in the manufacturing system, i is the equipment number, i = 1, 2, ..., n, n is the number of sub-equipment in the manufacturing system; SCEB i,a is the ath static meta-carbon emission block of the i-th sub-equipment in the manufacturing system, a is the ordinal number of the static meta-carbon emission block, a=1,2,…,s, s is the total number of static meta-carbon emission blocks; VCEB i,b is the bth variable carbon emission block of the i-th sub-equipment of the manufacturing system, b is the ordinal number of the variable carbon emission block, b = 1, 2, ..., m, and m is the total number of variable carbon emission blocks; The formed manufacturing system sub-equipment carbon emission block knowledge base CEB is as follows: The static meta-carbon emission block SCEB i,a As shown below: SCEB i,a =ΔQ i,a ×ΔT i,a ×CF j (3) Where ΔQ i,a is the number of unit carbon emission elements corresponding to the ath static carbon emission state of the i-th sub-equipment of the manufacturing system; ΔT i,a is the unit time corresponding to the static carbon emission state; CF j is the carbon emission coefficient of the jth carbon emission element, j is the carbon emission element; The variable carbon emission block VCEB i,b As shown below: VCEB i,b =Δq i,b ×Δt i,b ×CF j (4) In the formula, Δq i,b is the number of unit carbon emission elements related to processing parameters under the bth variable carbon emission state of the i-th sub-equipment of the manufacturing system; Δt i,b is the unit time under the corresponding variable carbon emission state; CF j is the carbon emission coefficient of the jth carbon emission element, j is the carbon emission element; 4) Based on the given manufacturing system processing parameters and operating conditions, and input into the manufacturing system sub-equipment element carbon emission block knowledge base, calculate the manufacturing system carbon emissions under the given processing parameters and operating conditions. The steps are as follows: 4.1) Based on the processing parameters and operating conditions of the given manufacturing system, establish the characteristic time parameter matrix of the sub-equipment of the manufacturing system; Among them, the characteristic time parameter matrix T of the i-th sub-equipment of the manufacturing system i As shown below: Where, T i,c is the characteristic time parameter of the cth carbon emission block of the ith sub-equipment in the manufacturing system, i is the sub-equipment ordinal number, i = 1, 2, ..., n, n is the number of sub-equipment in the manufacturing system; c is the ordinal number of the carbon emission block, c = 1, 2, ..., m 0 , m 0 is the total number of carbon emission blocks; The characteristic time parameter set T of the manufacturing system sub-equipment is as follows: 4.2) Input the characteristic time parameter matrix of the manufacturing system sub-equipment into the manufacturing system sub-equipment meta-carbon emission block knowledge base to calculate the manufacturing system carbon emissions under given processing parameters and operating conditions; Among them, the carbon emission matrix CE of the manufacturing system sub-equipment is as follows: Carbon emission CE of the i-th sub-equipment in the manufacturing system i As shown below: Total carbon emissions of the manufacturing system total As shown below:

2. According to claim 1, a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, It is characterized in that In step 1), the tools for obtaining historical data on carbon emission characteristics of the manufacturing system include smart sensors and relevant resources and management systems of the manufacturing enterprise; The manufacturing enterprise related resources and management systems include ERP, PDM, BOM and MES.

3. According to claim 1, a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, It is characterized in that In step 2), the artificial intelligence algorithm includes decision tree, random forest, and support vector machine.

4. According to claim 1, a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, It is characterized in that In step 2), the determinants of the manufacturing system sub-equipment carbon emission and state characteristic database include the manufacturing system carbon emission sources, processing equipment and equipment operation status; The carbon emission sources of the manufacturing system include direct carbon emission sources and indirect carbon emission sources; The processing equipment includes main equipment and auxiliary equipment; The equipment operation status includes a standby state, a processing preparation state and a processing state.

5. According to claim 1, a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, It is characterized in that In step 2), the manufacturing system status characteristic data includes carbon emission source data, processing equipment data, operation status data, processing stage data, processing sequence data and workpiece characteristic data of the manufacturing system.

6. According to claim 1, a carbon emission accounting method for a manufacturing system based on a meta-carbon emission block, It is characterized in that The carbon emission elements include energy, materials and waste.

7. A computer-readable storage medium, It is characterized in that A computer program is stored thereon, and when the program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

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