Supply chain cluster-oriented energy carbon management and control method for complex product manufacturing process

Abstract

The invention relates to the technical field of energy-carbon management and control of a product manufacturing process, and discloses a supply chain cluster-oriented energy-carbon management and control method of a complex product manufacturing process, which comprises the following steps of: firstly, constructing a supply chain cluster manufacturing system model of a complex product; secondly, constructing a subsystem classification model based on two core indexes of'energy consumption ratio 'and'carbon emission fluctuation', and dividing subsystems in the supply chain cluster manufacturing system into four classes; and finally, for different types of subsystems, making differentiated energy-carbon management and control strategies, including an I-class subsystem management and control strategy based on static data, II-class and III-class subsystem management and control strategies based on static-dynamic data fusion, and an IV-class subsystem management and control strategy based on dynamic data. According to the invention, an efficient solution is provided for the multi-level and diversified energy-carbon management and control requirements of supply chain leading enterprises in the complex product manufacturing process, and the precision and operability of energy-carbon management and control are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy and carbon control in the product manufacturing process, and particularly relates to an energy and carbon control method for the manufacturing process of complex products for a supply chain cluster. Background Art

[0003] In recent years, some researchers at home and abroad have studied the energy and carbon control problems in the manufacturing process of complex products from the perspective of the entire life cycle of product manufacturing. These include: an energy consumption analysis method for the life cycle of discrete manufacturing workpieces integrating process planning; an energy and carbon control method; a model for quantifying and predicting carbon emissions in the product manufacturing process; and a method for low-carbon control of injection molding production equipment. Current research mainly focuses on the energy and carbon control strategies of a single manufacturing system during the manufacturing process of products or semi-finished products. However, such research still faces two significant problems. On the one hand, there is a lack of integrated research on the energy and carbon issues throughout the entire manufacturing process of complex products. On the other hand, existing research ignores the energy and carbon characteristics and differences in control requirements of each subsystem of the supply chain, resulting in a lack of pertinence in control means. Summary of the Invention

[0004] In view of the above deficiencies in the prior art, the present invention provides an energy and carbon control method for the manufacturing process of complex products for a supply chain cluster, to solve the problem of energy and carbon control in the manufacturing process of complex products from the perspective of the supply chain cluster.

[0005] The present invention provides an energy and carbon control method for the manufacturing process of complex products for a supply chain cluster, including:

[0006] S1. Construct a supply chain cluster manufacturing system for complex products; wherein, the energy and carbon characteristics of the supply chain cluster manufacturing system include heterogeneous subjects, heterogeneous spaces, temporal fluctuations, and diverse modes;

[0007] S2. Construct a subsystem classification model for the supply chain cluster manufacturing system: Construct two core indicators of "energy consumption ratio" and "carbon emission fluctuation", and define the thresholds of the two core indicators to divide the subsystems into four types; wherein, the four types are: type I subsystems with low energy consumption ratio and small carbon emission fluctuation; type II subsystems with relatively low energy consumption ratio and relatively large carbon emission fluctuation; type III subsystems with relatively high energy consumption ratio and small carbon emission fluctuation; type IV subsystems with high energy consumption ratio and large carbon emission fluctuation;

[0008] S3. Develop energy and carbon control strategies for the entire manufacturing process of complex products: Based on the subsystem classification results, develop differentiated control strategies, including energy and carbon control strategies for type I subsystems based on static data, energy and carbon control strategies for type II and III subsystems integrating static and dynamic data, and energy and carbon control strategies for type IV subsystems based on dynamic data.

[0009] Further, in the step S1, the subject heterogeneity means that the supply chain cluster manufacturing system is aggregated by subsystems of multiple levels and types, including component manufacturing, part processing, component assembly, product final assembly, and logistics transportation links;

[0010] The spatial heterogeneity means that the supply chain cluster manufacturing system is aggregated by subsystems distributed in different countries or regions, and the locations of its energy consumption and carbon emissions are highly discrete;

[0011] The temporal fluctuation means that the supply chain cluster manufacturing system is a cluster of manufacturing systems with multi-level processing coordination relationships. Based on this coordination relationship, the production plans and activities of each subsystem are usually carried out in different time periods;

[0012] The mode diversity means that the supply chain cluster manufacturing system aims to achieve the overall optimum of the whole process of product and its component manufacturing. Therefore, there must be differences in the technical level, equipment performance, process flow, and management mode of the integrated subsystems.

[0013] Further, in the step S2, the "energy consumption ratio" refers to the proportion of the energy consumed by each subsystem in the manufacturing and logistics processes of a unit of complex mechanical products in the total energy consumption of the supply chain cluster manufacturing system; this indicator is mainly affected by two energy-carbon characteristics, namely subject heterogeneity and mode diversity; among them, the difference in the task nature of the subsystems reflected by subject heterogeneity determines the energy consumption benchmark level of different subsystems; the difference in the energy efficiency of the subsystems reflected by the technical level and equipment performance in mode diversity directly affects their energy consumption performance; its model is shown as follows:

[0014]

[0015] In the formula, P i is the energy consumption ratio of subsystem i; B i is the benchmark energy consumption of subsystem i, which is mainly determined by the task nature of the subsystem; η i is the energy efficiency adjustment coefficient of subsystem i, which is used to reflect the impact of equipment performance and management level on energy consumption.

[0016] Further, in the step S2, the "carbon emission fluctuation" refers to the carbon emission difference of each subsystem in the manufacturing and logistics processes of a unit of complex mechanical products; this indicator is mainly affected by four energy-carbon characteristics, namely subject heterogeneity, spatial heterogeneity, temporal fluctuation, and mode diversity, and its model is shown as follows:

[0017]

[0018] Among them,

[0019]

[0020] In the formula, Vi is the comprehensive carbon emission fluctuation degree of subsystem i; V i-batch is the carbon emission fluctuation degree of subsystem i when manufacturing unit products of different batches; V i-energy is the estimated carbon emission fluctuation degree of subsystem i when manufacturing unit products of the same batch; is the average manufacturing carbon emission of subsystem i for unit products of the j-th batch; is the average manufacturing carbon emission of subsystem i for all batches of unit products; C i-j is the total carbon emission of subsystem i in the j-th batch; E i,k is the total consumption of the k-th type of energy in the manufacturing process of subsystem i; EF k is the carbon emission factor of the k-th type of energy; F i-c is the carbon emission fluctuation coefficient of subsystem i due to process fluctuations and control adjustments.

[0021] Further, in step S3, the energy-carbon control strategy for type-I subsystems based on static data regularly collects static energy-carbon data through an IIoT gateway or an ERP / MES system, uploads it to the energy-carbon control platform for product manufacturing process for standardization processing. The platform conducts multi-level comparisons with industry standards or historical benchmarks based on the energy-carbon accounting results of unit products to identify energy-carbon deviations; according to the deviation analysis results, dynamically adjusts the energy-carbon quota for the next production cycle. If the deviation exceeds the preset benchmark, the platform automatically generates a rectification notice and pushes it to the relevant subsystems;

[0022] Further, in step S3, in the energy-carbon control strategies for type-II and type-III subsystems based on the fusion of static and dynamic data, before the subsystems are connected, the platform needs to construct an inherent energy efficiency model based on the provided dynamic data samples. This model combines equipment operation parameters, manufacturing process routes, and static energy consumption data to simulate the energy-carbon performance at each stage; after the production stage ends, the subsystems upload static energy consumption data and timestamps. The platform uses the established efficiency model to generate pseudo-dynamic data, simulates the time-period energy-carbon fluctuation characteristics of equipment or production lines, and based on this, proposes suggestions for adjusting operation parameters or optimizing production plans; after the subsystems are adjusted according to the platform suggestions, they upload new data for effect verification and continuously optimize the control strategy.

[0023] Further, in step S3, the energy-carbon control strategy for type-IV subsystems based on dynamic data continuously monitors equipment operation parameters, real-time energy consumption, and carbon emission information through equipment sensors. The energy-carbon control platform for product manufacturing process immediately analyzes the data and dynamically adjusts the equipment operation state and energy distribution strategy; combining historical and real-time data, the platform predicts the energy consumption trend and equipment failure risk.

[0024] Furthermore, the energy and carbon management and control platform for the product manufacturing process includes a data acquisition and processing module, a management and control strategy generation module, and a management and control strategy evaluation module;

[0025] The data acquisition and processing module is used to acquire and process the energy and carbon data uploaded by various subsystems. The energy and carbon data includes static energy and carbon data and dynamic energy and carbon data. Among them, the static energy and carbon data is used to characterize the energy consumption, carbon emissions, manufacturing time, energy source, and carbon emission factor in the batch product manufacturing process. The dynamic energy and carbon data is used to characterize the real-time operation parameters, real-time energy consumption, and energy type and source of the equipment or production line during the product processing;

[0026] The management and control strategy generation module is used to generate energy and carbon management and control strategies for various subsystems according to the processed energy and carbon data: The strategy for type I subsystems is mainly based on energy and carbon quotas; The strategies for type II and type III subsystems are mainly based on management strategies such as optimizing production scheduling; The strategy for type IV subsystems is mainly based on real-time management and control of dynamically adjusting the equipment operation state and energy distribution;

[0027] The management and control strategy evaluation module is used to evaluate the implementation effect of the management and control strategy and provide feedback and optimization basis for subsequent strategy generation.

[0028] The beneficial effects of the present invention are as follows:

[0029] 1. Starting from the perspective of the supply chain cluster, the present invention proposes a subsystem classification method based on energy and carbon characteristics. By constructing two core indicators, "energy consumption ratio" and "carbon emission fluctuation", which characterize the core differences in the energy and carbon characteristics of subsystems, the subsystems of the supply chain cluster manufacturing system are divided into four types. This classification method can accurately depict the energy and carbon characteristics of different subsystems during the manufacturing process and provides a theoretical basis for the differentiated energy and carbon management and control strategies of subsystems.

[0030] 2. The present invention clarifies the differentiated energy and carbon management and control framework for the product manufacturing process, and accordingly designs the energy and carbon management and control strategy for type I subsystems based on static data, the energy and carbon management and control strategies for type II and type III subsystems based on the fusion of static and dynamic data, and the energy and carbon management and control strategy for type IV subsystems based on dynamic data. This differentiated strategy can flexibly respond to the energy and carbon management and control challenges in different production and logistics environments and provides an accurate, efficient, and low-cost enabling tool for realizing the energy and carbon management and control of the whole process of complex mechanical product manufacturing. Description of the Drawings

[0031] Figure 1 It is a schematic flow chart of the energy and carbon management and control method for the complex product manufacturing process facing the supply chain cluster of the present invention.

[0032] Figure 2 It is a schematic structural model diagram of the supply chain cluster manufacturing system constructed by the present invention.

[0033] Figure 3Schematic diagram of the energy and carbon control platform for product manufacturing process constructed for the present invention.

[0034] Figure 4 Schematic diagram of the work flow of the energy and carbon control platform for product manufacturing process constructed for the present invention.

[0035] The realization, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0036] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0037] As Figure 1 shown, the present invention provides an energy and carbon control method for the manufacturing process of complex products for supply chain clusters, including:

[0038] S1. Construct a manufacturing system model for the supply chain cluster of complex products: Based on the manufacturing system theory, with complex products as the core and the supply chain as the main line, gather the manufacturing and logistics systems of multiple enterprises; wherein, the energy and carbon characteristics of the supply chain cluster manufacturing system include heterogeneous entities, heterogeneous spaces, temporal fluctuations, and diverse modes.

[0039] The supply chain cluster manufacturing system is a new perspective proposed based on the manufacturing system theory to globally consider the carbon emission accounting and global control requirements of the manufacturing process of complex products. It is a distributed cluster manufacturing system formed by the interaction and aggregation of the manufacturing systems and logistics systems of multiple enterprises with products as the core and the supply chain as the main line.

[0040] The supply chain cluster manufacturing system is a new perspective proposed based on the manufacturing system theory to globally consider all relevant manufacturing systems and logistics systems in the whole process of manufacturing complex products and their components. It is a distributed cluster manufacturing system formed by the interaction and aggregation of the manufacturing systems and logistics systems of multiple enterprises with products as the core and the supply chain as the main line. Its system architecture schematic diagram is as Figure 2 shown.

[0041] The energy and carbon characteristics of the supply chain cluster manufacturing system include: heterogeneous entities, heterogeneous spaces, temporal fluctuations, and diverse modes.

[0042] (1) Heterogeneous entities

[0043] The heterogeneous entities refer to that the supply chain cluster manufacturing system is aggregated by multiple levels and types of subsystems, including component manufacturing, part processing, component assembly, product final assembly, and logistics transportation, etc. The subsystems involved have significant differences in energy consumption and carbon emissions due to undertaking different manufacturing or logistics tasks.

[0044] (2) Spatial heterogeneity

[0045] The so-called spatial heterogeneity means that the energy consumption and carbon emissions of the supply chain cluster manufacturing system not only occur in different spatial regions, but also, due to regional differences, the carbon emission levels are significantly different; that is, the supply chain cluster manufacturing system is formed by aggregating subsystems distributed in different countries or regions, and the locations where its energy consumption and carbon emissions occur are highly discrete. Given the differences in energy supply conditions, energy markets, and carbon emission policies in various regions, even for subsystems producing the same type of parts, their carbon emission levels may vary significantly in different regions.

[0046] (3) Temporal fluctuation

[0047] The so-called temporal fluctuation means that the energy consumption and carbon emissions of the supply chain cluster manufacturing system not only occur in different time domains, but also, due to temporal differences, the carbon emission levels are different; that is, the supply chain cluster manufacturing system is a cluster of manufacturing systems with multi-level processing collaboration relationships. Based on this collaboration relationship, the production plans and activities of each subsystem are usually carried out in different time periods. Given the impacts of factors such as energy market fluctuations, seasonal changes, and carbon emission policy adjustments on the dynamics of carbon emission factors, the carbon emission levels of subsystems for producing a unit of parts will also vary at different times.

[0048] (4) Mode diversity

[0049] The so-called mode diversity means that there are significant diversities and differences among the subsystems of the supply chain cluster manufacturing system in terms of energy types, energy sources, energy utilization methods, and energy efficiency. That is, the supply chain cluster manufacturing system aims to achieve the overall optimization of the entire process of product and its parts manufacturing. Therefore, there must be differences among the integrated subsystems in terms of technical level, equipment performance, process flow, and management mode. These differences will not only lead to the diversification of energy types and sources used by the subsystems, but also result in significant differences in the enabling methods and energy efficiency of the subsystems.

[0050] S2. Construct a subsystem classification model for the supply chain cluster manufacturing system: Construct two core indicators, namely "energy consumption ratio" and "carbon emission fluctuation", and define the thresholds of the two core indicators to divide the subsystems into four types.

[0051] The energy-carbon characteristics of the supply chain cluster manufacturing system are multi-dimensional and complex, but they mostly act on the energy consumption and carbon emission performances of the subsystems. Therefore, construct two core indicators, namely "energy consumption ratio" and "carbon emission fluctuation", to characterize the differences in the energy-carbon characteristics of the subsystems.

[0052] By defining the thresholds of two core indicators, the subsystems can be divided into four types: type I subsystems with low energy consumption ratio and small carbon emission fluctuations; type II subsystems with relatively low energy consumption ratio and large carbon emission fluctuations; type III subsystems with relatively high energy consumption ratio and small carbon emission fluctuations; type IV subsystems with high energy consumption ratio and large carbon emission fluctuations. Considering the differences in management objectives and operating environments of different leading enterprises in the supply chain, the thresholds of the two core indicators have a certain degree of flexibility. Enterprises can combine historical data, energy efficiency targets, and carbon emission tolerance to initially classify a small number of subsystems with obvious characteristics to form a labeled dataset; subsequently, through the graph semi-supervised learning method, a large number of unclassified subsystems can be classified as a whole.

[0053] The "energy consumption ratio" refers to the proportion of the energy consumed by each subsystem in the manufacturing and logistics processes of unit complex mechanical products in the total energy consumption of the supply chain cluster manufacturing system, reflecting its importance in the energy consumption structure of the supply chain cluster manufacturing system. This indicator is mainly affected by two types of energy-carbon characteristics: heterogeneous subjects and diverse modes; among them, the differences in the task nature of subsystems reflected by heterogeneous subjects determine the benchmark energy consumption levels of different subsystems; the energy efficiency differences of subsystems reflected by the technical level and equipment performance in diverse modes directly affect their energy consumption performance; its model is shown as the following formula:

[0054]

[0055] In the formula, P i is the energy consumption ratio of subsystem i; B i is the benchmark energy consumption of subsystem i, mainly determined by the task nature of the subsystem; η i is the energy efficiency adjustment coefficient of subsystem i, used to reflect the impact of equipment performance and management level on energy consumption.

[0056] The "carbon emission fluctuation" refers to the carbon emission difference of each subsystem in the manufacturing and logistics processes of unit complex mechanical products, reflecting its uncertainty in the energy-carbon control of the supply chain cluster manufacturing system. This indicator is mainly affected by four types of energy-carbon characteristics: diverse heterogeneous subjects, heterogeneous location spaces, temporal fluctuations, and multi-dimensional mode differences, and its model is shown as the following formula:

[0057]

[0058] Among them,

[0059]

[0060]

[0061] In the formula, V i is the comprehensive carbon emission fluctuation degree of subsystem i; V i-batchis the carbon emission fluctuation degree of subsystem i when manufacturing different batches of unit products; V i-energy is the estimated carbon emission fluctuation degree of subsystem i when manufacturing the same batch of unit products; is the average manufacturing carbon emission of subsystem i for the unit products of the j-th batch; is the average manufacturing carbon emission of subsystem i for all batches of unit products; C i-j is the total carbon emission of subsystem i for the j-th batch; E i,k is the total consumption of the k-th type of energy in the manufacturing process of subsystem i; EF k is the carbon emission factor of the k-th type of energy; F i-c is the carbon emission fluctuation coefficient of subsystem i due to process fluctuations and control adjustments.

[0062] S3. Develop energy-carbon control strategies for the entire process of complex product manufacturing: Based on the subsystem classification results, for the energy consumption ratio and carbon emission fluctuation characteristics of different subsystems, develop differentiated control strategies, including energy-carbon control strategies for Class I subsystems based on static data, energy-carbon control strategies for Class II and III subsystems based on the integration of static and dynamic data, and energy-carbon control strategies for Class IV subsystems based on dynamic data.

[0063] (1) Energy-carbon control strategies for Class I subsystems based on static data

[0064] Class I subsystems adopt a control strategy generation scheme based on static data. The tasks and energy structures of Class I subsystems are relatively simple, with a low energy consumption ratio and small carbon emission fluctuations. Most of them already have clear energy-carbon benchmarks or industry standards. This type of subsystem regularly collects static energy-carbon data through the IIoT gateway or ERP / MES system, uploads it to the energy-carbon control platform for product manufacturing process for standardization processing. The platform conducts multi-level comparisons between the energy-carbon accounting results of unit products and industry standards or historical benchmarks to identify energy-carbon deviations. According to the deviation analysis results, dynamically adjust the energy-carbon quota for the next production cycle. If the deviation exceeds the preset benchmark, the platform automatically generates a rectification notice and pushes it to the relevant subsystems. Subsequently, verify the control effect through data monitoring. If the target is not achieved multiple times, the platform will further evaluate the feasibility of system upgrade or equipment replacement.

[0065] (2) Energy-carbon control strategies for Class II and III subsystems based on the integration of static and dynamic data

[0066] Class II and Class III subsystems adopt a control strategy generation scheme based on static-dynamic data fusion. The energy consumption ratio of Class II subsystems is moderate, but their carbon emissions fluctuate greatly. The superposition of fluctuations in multiple Class II subsystems will have a significant impact on the overall energy-carbon performance. Relatively speaking, the energy consumption ratio of Class III subsystems is high. Although their fluctuation frequency is low, once fluctuations occur, they will have an impact on the overall energy-carbon performance of the product. Therefore, it is difficult to achieve effective control for these two types of subsystems relying solely on static data. In addition, considering that these two types of subsystems usually serve multiple supply chains and their data needs to interact with multiple external control platforms, the high-frequency transmission of dynamic data may bring risks such as information security. Therefore, the energy-carbon control platform in the product manufacturing process needs to adopt a static-dynamic data fusion control method. Before the subsystems are connected, the platform needs to construct an inherent energy efficiency model based on the provided dynamic data samples. This model combines equipment operation parameters, manufacturing process routes, and static energy consumption data to simulate the energy-carbon performance at each stage. After the production stage ends, the subsystems upload static energy consumption data and timestamps. The platform uses the established inherent energy efficiency model to generate pseudo-dynamic data, simulating the time-period energy-carbon fluctuation characteristics of the equipment or production line, and based on this, proposes suggestions for adjusting operation parameters or optimizing production plans. After the subsystems are adjusted according to the platform's suggestions, they upload new data for effect verification and continuously optimize the control strategy.

[0067] (3) Energy-carbon control strategy for Class IV subsystems based on dynamic data

[0068] Class IV subsystems adopt a control strategy generation scheme based on dynamic data. Class IV subsystems have complex tasks, high energy consumption ratios, and large carbon emission fluctuations, and need to rely on real-time data collection and high-frequency data exchange to achieve comprehensive control. By continuously monitoring equipment operation parameters, real-time energy consumption, and carbon emission information through equipment sensors, the energy-carbon control platform in the product manufacturing process can immediately analyze the data and dynamically adjust the equipment operation status and energy distribution strategy. Combining historical and real-time data, the platform can predict energy consumption trends and equipment failure risks, arrange maintenance in advance, avoid production interruptions caused by high energy consumption or equipment failures, and ensure the continuous and efficient operation of the system.

[0069] In one embodiment, as Figure 3 shown, the energy-carbon control platform in the product manufacturing process includes a data acquisition and processing module, a control strategy generation module, and a control strategy evaluation module;

[0070] The data acquisition and processing module is used to acquire and process the energy-carbon data uploaded by various subsystems. The energy-carbon data includes static energy-carbon data and dynamic energy-carbon data; among them, the static energy-carbon data is used to characterize the energy consumption, carbon emissions, manufacturing time, energy source, and carbon emission factor in the manufacturing process of batch products; the dynamic energy-carbon data is used to characterize the real-time operation parameters, real-time energy consumption, and energy types and sources of the equipment or production line during the processing of the product.

[0071] The energy-carbon control strategy generation module is used to generate energy-carbon control strategies for various subsystems based on the processed energy-carbon data: the strategies for Class I subsystems mainly focus on energy-carbon quotas; the strategies for Class II and Class III subsystems mainly focus on management strategies such as optimizing production scheduling; the strategies for Class IV subsystems mainly focus on real-time control of dynamically adjusting the operating state of equipment and energy distribution.

[0072] The control strategy evaluation module is used to evaluate the implementation effect of the control strategy and provide feedback and optimization basis for subsequent strategy generation.

[0073] As Figure 4 shown below, taking three key subsystems in a certain automotive supply chain cluster manufacturing system as examples, the method of the present invention will be further described. These three enterprises are respectively responsible for battery pack assembly, front engine compartment component die-casting and vehicle final assembly, covering the whole process from the manufacture of key components to the final assembly of complex products.

[0074] Step S1: Construct a supply chain cluster manufacturing system model for complex products.

[0075] The supply chain cluster manufacturing system is a new perspective proposed based on the manufacturing system theory to globally consider all relevant manufacturing systems and logistics systems in the whole process of manufacturing complex products and their components. This system takes the product as the core and the supply chain as the main line, and is a distributed cluster manufacturing system formed by the interaction of manufacturing systems and logistics systems of multiple enterprises.

[0076] The energy-carbon characteristics of the supply chain cluster manufacturing system include: heterogeneous subjects, heterogeneous spaces, temporal fluctuations, and diverse patterns.

[0077] The heterogeneous subjects mean that the supply chain cluster manufacturing system is aggregated by multiple levels and types of subsystems, including three links: battery pack assembly, front engine compartment component die-casting and vehicle final assembly, with significant differences in energy consumption and carbon emissions.

[0078] The heterogeneous spaces mean that the supply chain cluster manufacturing system is composed of subsystems distributed in different regions, and the locations of its energy consumption and carbon emissions are highly dispersed. Due to the differences in energy supply conditions and carbon emission policies in different regions, there are differences in the carbon emission factors of power grids in different regions, which thus affect the energy-carbon performance of the three types of subsystems. Among them, the carbon emission factors of the power grids for battery pack assembly, front engine compartment component die-casting and vehicle final assembly are 0.5721 kgCO2 / kWh, 0.4743 kgCO2 / kWh and 0.4743 kgCO2 / kWh respectively.

[0079] The described timing fluctuation means that the supply chain cluster manufacturing system is a manufacturing system cluster with multi-level processing collaboration relationships. Based on this collaboration relationship, the production plans and activities of each subsystem are usually carried out in different time periods. Given the impacts of factors such as energy market fluctuations, seasonal changes, and carbon emission policy adjustments on the dynamic changes of carbon emission factors, the carbon emission levels of the subsystems for producing unit parts will also vary at different times.

[0080] The described mode diversity means that the supply chain cluster manufacturing system aims to achieve the overall optimization of the entire process of product and its parts manufacturing. Therefore, the integrated subsystems have differences in technical level, equipment performance, process flow, and management mode, etc.; the types and sources of energy it uses are diverse, which will also lead to significant differences in the enabling methods and energy efficiency of the subsystems. Among them, the energy supply method of the battery pack assembly subsystem is direct power supply from the power grid; the energy supply methods of the front engine compartment component die-casting subsystem are direct power supply from the power grid, energy storage system (efficiency 85%, carbon emission factor about 0.558 kgCO2 / kWh), trigeneration system (efficiency 80%, carbon emission factor about 0.2525 kgCO2 / kWh), and photovoltaic (carbon emission factor is 0 kgCO2 / kWh); the energy supply methods of the vehicle final assembly subsystem are direct power supply system from the power grid, energy storage system (efficiency 85%, carbon emission factor about 0.558 kgCO2 / kWh), and photovoltaic (carbon emission factor is 0 kgCO2 / kWh).

[0081] Step S2: Construct a subsystem classification model for the supply chain cluster manufacturing system.

[0082] The energy-carbon characteristics of the supply chain cluster manufacturing system are multi-dimensional and complex, but they mostly act on the energy consumption and carbon emission performances of the subsystems. Therefore, two core indicators, namely "energy consumption ratio" and "carbon emission fluctuation", are constructed to characterize the differences in the energy-carbon characteristics of the subsystems.

[0083] The described energy consumption ratio of the subsystem refers to the proportion of the energy consumed by each subsystem in the manufacturing and logistics processes of a unit complex mechanical product to the total energy consumption of the supply chain cluster manufacturing system, reflecting its importance in the energy consumption structure of the supply chain cluster manufacturing system. This indicator is mainly affected by two types of energy-carbon characteristics: subject heterogeneity and mode diversity. Among them, the difference in the task nature of the subsystems reflected by subject heterogeneity determines the benchmark energy consumption level of different subsystems; the energy efficiency differences of the subsystems reflected by technical level and equipment performance, etc. in mode diversity directly affect their energy consumption performances. Its model is shown as follows:

[0084]

[0085] In the formula, P i is the energy consumption ratio of subsystem i; B i is the benchmark energy consumption of subsystem i, mainly determined by the task nature of the subsystem; ηi is the energy efficiency adjustment coefficient of subsystem i, which is used to reflect the impact of equipment performance, management level, etc. on energy consumption.

[0086] The carbon emission fluctuation degree of the described subsystem refers to the carbon emission difference of each subsystem in the manufacturing and logistics processes of unit complex mechanical products, reflecting its uncertainty in the energy and carbon control of the supply chain cluster manufacturing system. This indicator is mainly affected by four types of energy and carbon characteristics: multi-agent heterogeneous, location spatial heterogeneity, time sequence fluctuation, and mode multi-dimensional difference. Its model is shown as follows:

[0087]

[0088] Among them,

[0089]

[0090] In the formula, V i is the comprehensive carbon emission fluctuation degree of subsystem i; V i-batch is the carbon emission fluctuation degree of subsystem i when manufacturing different batches of unit products; V i-energy is the estimated carbon emission fluctuation degree of subsystem i when manufacturing the same batch of unit products; is the average manufacturing carbon emission of subsystem i for unit products in the jth batch; is the average manufacturing carbon emission of subsystem i for all batches of unit products; C i-j is the total carbon emission of subsystem i in the jth batch; E i,k is the total consumption of the kth type of energy by subsystem i during the manufacturing process; EF k is the carbon emission factor of the kth type of energy; F i-c is the carbon emission fluctuation coefficient of subsystem i due to process fluctuations and control adjustments.

[0091] Table 1 Subsystem order-related information

[0092]

[0093] Based on the data in Table 1, the core index values of the three subsystems are as follows:

[0094] (1) The average energy consumption per unit product of the battery pack assembly subsystem is about 27.00 kWh, accounting for about 6.54% of the energy consumption in the three systems; the average carbon emission per unit product is about 15.45 kg, and the carbon emission fluctuation degree is about 2.91%.

[0095] (2) The average energy consumption per unit product of the front engine compartment component die-casting subsystem is about 86.26 kWh, accounting for about 20.9% of the energy consumption in the three systems; the average carbon emission per unit product is 33.67 kg, and the carbon emission fluctuation degree is 29.02%.

[0096] (3) The average energy consumption per unit product of the vehicle final assembly subsystem is approximately kWh, and the energy consumption ratio in the three subsystems is approximately 72.56%; the average carbon emission per unit product is 123.05 kg, and the carbon emission fluctuation is 23.7%.

[0097] In view of the significant characteristics of the three types of subsystems, the battery pack assembly subsystem can be classified as a type I subsystem, the front engine compartment component die-casting subsystem can be classified as a type II subsystem, and the vehicle final assembly subsystem can be classified as a type IV subsystem. These clearly classified subsystems can form a labeled dataset, providing basic support for the classification of other subsequent subsystems.

[0098] Step S3: Develop an energy-carbon control strategy for the entire process of complex product manufacturing.

[0099] Construct an energy-carbon control platform for the product manufacturing process, and its structure is as Figure 3 shown. It can be seen from Figure 3 that this platform includes a data acquisition and processing module, a control strategy generation module, and a control strategy evaluation module. Among them, the data acquisition and processing module is used to acquire and process the energy-carbon data uploaded by various subsystems, and the energy-carbon data includes static energy-carbon data and dynamic energy-carbon data; the control strategy generation module generates energy-carbon control strategies for various subsystems according to the processed energy-carbon data; the control strategy evaluation module is used to evaluate the implementation effect of the control strategy and provide feedback and optimization basis for subsequent strategy generation.

[0100] The energy-carbon control of the energy-carbon control platform for the product manufacturing process in the supply chain cluster manufacturing system can formulate differentiated control strategies for different subsystems, such as Figure 4 shown.

[0101] The battery pack assembly subsystem adopts an energy-carbon control strategy for type I subsystems based on static data. By analyzing historical order data, it is determined that the energy consumption limit per unit product of this subsystem is 27.5 kWh, and the carbon emission limit is 14 kg. On this basis, the energy-carbon control platform for the product manufacturing process can compare the actual energy consumption and carbon emissions of each batch of orders, provide timely feedback and make benchmark corrections to ensure that the energy consumption and carbon emissions of assembling the battery pack in this subsystem always remain within a reasonable range.

[0102] The die-casting subsystem of the front engine compartment components adopts the energy and carbon management and control strategies for Class II and Class III subsystems with static-dynamic data fusion. Based on the inherent energy efficiency model of this subsystem constructed on the energy and carbon management and control platform for the product manufacturing process, by analyzing the static energy consumption data and timestamp information uploaded by the system for each shift, it is found that the carbon emission fluctuation of this subsystem under the same batch of orders is approximately 24.9%. The main reasons are the changes in the production rhythm and the switching of the energy supply mode, especially the differences in the energy supply paradigms and production tasks between the day shift and the night shift, which affect the energy and carbon performance of the system. Therefore, the platform puts forward the following management and control strategy suggestions for this system: First, optimize the production scheduling, model the energy efficiency characteristics of different production tasks using historical data, accurately predict the production load requirements for the day shift and the night shift, and optimize the production plan according to the prediction results; Second, use the real-time energy monitoring system to dynamically adjust the energy usage strategy and reduce the dependence on high-carbon energy; Finally, based on the real-time energy efficiency data, adjust the production process, such as adjusting the equipment temperature and pressure, to ensure that the equipment operates within the optimal energy efficiency range and reduce unnecessary energy consumption and carbon emissions.

[0103] The general assembly subsystem of the automobile adopts the energy and carbon management and control strategy for Class IV subsystems based on dynamic data. Based on the real-time collection and analysis of the dynamic data of each link, it is found that the main reasons for the energy consumption and carbon emission fluctuations are equipment failures and frequent changes in the production plan, which lead to insufficient supply of parts and then line stops. However, during the line stop period, high-power equipment such as ovens, refrigeration, and heating cannot be shut down. This phenomenon is particularly obvious during night production, and these downtime directly lead to significant fluctuations in the energy consumption and carbon emissions per unit product. Therefore, the platform puts forward the following management and control strategy suggestions for this system: On the one hand, introduce advanced predictive maintenance technologies, combine equipment operation status monitoring and big data analysis to identify potential risks of equipment failures in advance, so as to carry out timely repairs and reduce the occurrence of sudden line stop events; On the other hand, intelligently adjust the power of high-power equipment during the line stop period, especially ovens, refrigeration, and heating equipment.

[0104] In summary, the present invention proposes an energy and carbon management and control method for the complex product manufacturing process facing the supply chain cluster. A supply chain cluster manufacturing system model for complex products is constructed; on this basis, a subsystem classification model based on two core indicators of "energy consumption ratio" and "carbon emission fluctuation" is constructed, and the subsystems in the supply chain cluster manufacturing system are divided into four categories; finally, for different types of subsystems, differentiated energy and carbon management and control strategies are formulated, including the management and control strategy for Class I subsystems based on static data, the management and control strategies for Class II and Class III subsystems with static-dynamic data fusion, and the management and control strategy for Class IV subsystems based on dynamic data. The present invention provides an efficient solution for the multi-level and diverse energy and carbon management and control requirements faced by the supply chain leading enterprises in the complex product manufacturing process, and significantly improves the accuracy and operability of energy and carbon management and control.

[0105] It should be noted that, in this document, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article or method comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, apparatus, article or method. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, apparatus, article or method comprising such element.

[0106] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A carbon management method for complex product manufacturing processes for supply chain clusters, characterized in that: include: S1. Construct a supply chain cluster manufacturing system for complex products; wherein the energy and carbon characteristics of the supply chain cluster manufacturing system include subject heterogeneity, spatial heterogeneity, temporal fluctuation, and mode diversity; S2. Construct a subsystem classification model for the supply chain cluster manufacturing system: construct two core indicators, "energy consumption ratio" and "carbon emission fluctuation", and define the thresholds of the two core indicators to divide the subsystems into four types; wherein the four types are: Class I subsystems with low energy consumption ratio and small carbon emission fluctuation; Class II subsystems with low energy consumption ratio and large carbon emission fluctuation; Class III subsystems with high energy consumption ratio and small carbon emission fluctuation; Class IV subsystems with high energy consumption ratio and large carbon emission fluctuation; S3. Formulate energy and carbon management strategies for the entire manufacturing process of complex products: Based on the subsystem classification results, formulate differentiated management and control strategies, including energy and carbon management strategies for Class I subsystems based on static data, energy and carbon management strategies for Class II and Class III subsystems based on static-dynamic data fusion, and energy and carbon management strategies for Class IV subsystems based on dynamic data.

2. The carbon management method for complex product manufacturing process for supply chain clusters according to claim 1 is characterized in that: In step S1, the subject heterogeneity means that the supply chain cluster manufacturing system is aggregated by subsystems of multiple levels and types, including component manufacturing, parts processing, component assembly, product assembly and logistics transportation; The spatial heterogeneity means that the supply chain cluster manufacturing system is composed of subsystems distributed in different countries or regions, and the locations of its energy consumption and carbon emissions are highly discrete; The timing fluctuation means that the supply chain cluster manufacturing system is a manufacturing system cluster with a multi-level processing coordination relationship. Based on the coordination relationship, the production plans and activities of each subsystem are usually carried out in different time periods; The model diversity refers to the fact that the supply chain cluster manufacturing system aims to achieve overall optimization of the entire process of product and component manufacturing, and the integrated subsystems are bound to have differences in technical level, equipment performance, process flow and management model.

3. The carbon management method for complex product manufacturing process for supply chain clusters according to claim 2 is characterized in that: In step S2, the "energy consumption ratio" refers to the ratio of the energy consumed by each subsystem in the manufacturing and logistics process of a unit of complex mechanical products to the total energy consumption of the supply chain cluster manufacturing system; this indicator is mainly affected by two types of energy-carbon characteristics: subject heterogeneity and mode diversity; among them, the differences in the nature of subsystem tasks reflected by subject heterogeneity determine the energy consumption baseline level of different subsystems; the differences in subsystem energy efficiency reflected by technical level and equipment performance in mode diversity directly affect its energy consumption performance; its model is shown in the following formula: Where P i is the energy consumption proportion of subsystem i; B i is the baseline energy consumption of subsystem i, which is mainly determined by the nature of the subsystem task; η i is the energy efficiency adjustment coefficient of subsystem i, which is used to reflect the impact of equipment performance and management level on energy consumption.

4. The carbon management method for complex product manufacturing process for supply chain clusters according to claim 3 is characterized in that: In step S2, the "carbon emission fluctuation" refers to the difference in carbon emission of each subsystem in the manufacturing and logistics process of a unit of complex mechanical products; this indicator is mainly affected by the four types of energy-carbon characteristics: subject heterogeneity, spatial heterogeneity, temporal fluctuation, and mode multivariate. Its model is shown in the following formula: in, Where V i is the comprehensive carbon emission fluctuation degree of subsystem i; V i-batch V is the carbon emission fluctuation degree of subsystem i when manufacturing different batches of unit products; i-energy is the estimated carbon emission fluctuation degree of subsystem i when manufacturing the same batch of unit products; is the average manufacturing carbon emissions per unit product of subsystem i in the jth batch; is the average manufacturing carbon emissions per unit product of subsystem i in all batches; C i-j is the total carbon emissions of subsystem i in the jth batch; E i,k EF is the total consumption of the kth type of energy in the manufacturing process of subsystem i; k is the carbon emission factor of the kth energy type; F i-c is the carbon emission fluctuation coefficient of subsystem i due to process fluctuations and control adjustments.

5. The carbon management method for complex product manufacturing process for supply chain clusters according to claim 4 is characterized in that: In step S3, the Class I subsystem energy carbon management strategy based on static data regularly collects static energy carbon data through the IIoT gateway or ERP / MES system, uploads it to the product manufacturing process energy carbon management platform for standardized processing, and the platform conducts multi-level comparisons with industry standards or historical benchmarks based on the unit product energy carbon accounting results to identify energy carbon deviations; according to the deviation analysis results, the energy carbon limit of the next production cycle is dynamically adjusted. If the deviation exceeds the preset benchmark, the platform automatically generates a rectification notice and pushes it to the relevant subsystems.

6. The carbon management method for complex product manufacturing process for supply chain clusters according to claim 5 is characterized in that: In the step S3, in the energy carbon management strategy of the Class II and Class III subsystems based on static-dynamic data fusion, the platform needs to build an inherent energy efficiency model based on the provided dynamic data samples before the subsystem is connected. The model combines the equipment operating parameters, manufacturing process route and static energy consumption data to simulate the energy carbon performance of each stage; after the production stage, the subsystem uploads the static energy consumption data and timestamp, and the platform uses the established efficiency model to generate pseudo-dynamic data to simulate the energy carbon fluctuation characteristics of the equipment or production line during the period, and based on this, proposes suggestions for adjusting the operating parameters or optimizing the production plan; After the subsystem is adjusted according to the platform's suggestions, it uploads new data to verify the effect and continuously optimizes the management and control strategy.

7. The carbon management method for complex product manufacturing process for supply chain clusters according to claim 5 is characterized in that: In step S3, the energy carbon management strategy of the Class IV subsystem based on dynamic data continuously monitors equipment operating parameters, real-time energy consumption and carbon emission information through equipment sensors. The energy carbon management platform of the product manufacturing process analyzes the data in real time and dynamically adjusts the equipment operating status and energy allocation strategy; combining historical and real-time data, the platform predicts energy consumption trends and equipment failure risks.

8. The carbon management method for complex product manufacturing process for supply chain clusters according to claim 7 is characterized in that: The product manufacturing process energy carbon management and control platform includes a data acquisition and processing module, a management and control strategy generation module and a management and control strategy evaluation module; The data acquisition and processing module is used to acquire and process the energy-carbon data uploaded by various subsystems, and the energy-carbon data includes static energy-carbon data and dynamic energy-carbon data; wherein, the static energy-carbon data is used to characterize the energy consumption, carbon emissions, manufacturing time, energy sources and carbon emission factors in the batch product manufacturing process; the dynamic energy-carbon data is used to characterize the real-time operating parameters, real-time energy consumption and energy types and sources of the equipment or production line in the product processing process; The control strategy generation module is used to generate energy carbon control strategies for various subsystems based on the processed energy carbon data: the strategies for Class I subsystems are mainly based on energy carbon limits; the strategies for Class II and Class III subsystems are mainly based on management strategies such as optimizing production scheduling; the strategies for Class IV subsystems are mainly based on real-time control of dynamic adjustment of equipment operating status and energy distribution; The management and control strategy evaluation module is used to evaluate the implementation effect of the management and control strategy and provide feedback and optimization basis for subsequent strategy generation.

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