A carbon fluxification method based on a hierarchical time-colored Petri net model

Abstract

This application provides a carbon flow method based on a hierarchical time-colored Petri net model, belonging to the field of carbon management technology. It constructs a formally defined hierarchical time-colored Petri net basic model, and based on this basic model, obtains an application model containing enterprise, workshop, and equipment layers. In the application model, transitions are distinguished into decision-making transitions and execution transitions. A potential carbon flow calculation function is configured for decision-making transitions to calculate the potential carbon emission increment generated by the current decision-making activity and update it in the carbon account data of the flowing token. An actual carbon flow calculation function is configured for execution transitions to calculate the actual carbon emission increment generated by the current execution activity within its time delay and update it in the carbon account data of the flowing token. Based on the calculated potential and actual carbon emission increments, carbon footprint data is automatically collected from the equipment layer through the workshop layer to the enterprise layer. This enables real-time, dynamic, and cross-level automatic collection of the carbon footprint throughout the product's entire lifecycle.

Description

Technical Field

[0001] This invention belongs to the field of carbon management technology, and in particular relates to a carbon fluxification method based on a hierarchical time-colored Petri net model. Background Technology

[0002] With increasing global concern about climate change, the green and low-carbon transformation of the manufacturing industry has become an inevitable trend. Against this backdrop, accurately calculating, analyzing, and optimizing the carbon footprint throughout the entire product lifecycle is a core element for enterprises to fulfill their environmental responsibilities and enhance their competitiveness. Achieving precise carbon management primarily relies on carbon flow methods that can accurately reflect the complex dynamics of the manufacturing system.

[0003] To calculate the carbon emissions of products, existing technologies generally employ a Life Cycle Assessment (LCA) framework. This method identifies each stage of a product's life cycle, from raw material acquisition to disposal, collects resource consumption and emission data for each stage, and multiplies these data by a forward-looking static emission factor to calculate the product's overall carbon footprint. This is a top-down, inventory-based approach designed to assess the product's environmental impact from a macro-level perspective.

[0004] However, the limitations of the LCA method based on static inventory and fixed parameters become increasingly apparent when applied to discrete manufacturing systems. First, this method treats the production process as a black box, failing to reflect the dynamic evolution of carbon emissions over time. Second, the input data required by this method typically comes from upstream design and planning systems such as product lifecycle management and enterprise resource planning, lacking effective integration and modeling with real-time process data from manufacturing execution systems and energy management systems, creating a gap between planning and execution data. Finally, the carbon footprint tracing using this method often remains at the product or component level, making it difficult to drill down to specific workshops, processes, or even equipment. Summary of the Invention

[0005] This invention provides a carbon flow method based on a hierarchical time-colored Petri net model, which at least solves the problems of static, lagging, and difficult-to-trace carbon footprint accounting methods in the prior art.

[0006] This application provides a carbon fluxification method based on a hierarchical time-colored Petri net model, the method comprising: Step S1: Construct a formally defined hierarchical time-colored Petri net base model, define the base model's locations, transitions, and directed arcs, and define a composite record structure as a token flowing in the base model to carry and update the business data and carbon account data of the product instance. Step S2: Based on the basic model, an application model containing an enterprise layer, a workshop layer, and an equipment layer is obtained. In the application model, the transitions are divided into decision-making transitions and execution transitions. The decision-making transitions map upstream business activities that do not directly consume materials and energy. The execution transitions map manufacturing execution activities that actually consume resources. Step S3: In the application model, a potential carbon flow calculation function is configured for decision-type transitions. When a decision-type transition is triggered, the potential carbon emission increment generated by the current decision-making activity is calculated based on the product design parameters and planned process parameters, and updated to the carbon account data of the token that flows through it. Step S4: In the application model, configure the actual carbon flow calculation function for the execution class transition. When the execution class transition is triggered, calculate the actual carbon emission increment generated by the current execution activity within its time delay based on the energy consumption data in the manufacturing execution process, and update it to the carbon account data of the token that flows through it. Step S5: In the application model, the enterprise layer application model calls the workshop layer application model by defining subnet transitions, and the token and carbon data are returned to the enterprise layer application model after the workshop layer application model is executed. Based on the calculated potential carbon emission increment and actual carbon emission increment, the carbon footprint data is automatically collected from the equipment layer through the workshop layer to the enterprise layer.

[0007] Further, in step S1, the composite record structure is defined as a record containing the following fields: unique identifier of the product instance. Current life cycle stage Design parameter vector , planned process parameter vector Actual process parameter vector Cumulative potential carbon emissions Cumulative actual carbon emissions and timestamp .

[0008] Furthermore, the potential increase in carbon emissions includes embodied carbon emissions from materials, processing baseline carbon emissions, and auxiliary system baseline carbon emissions; The formula for calculating the implicit carbon emissions of the material is as follows:

[0009] in, The materials contain hidden carbon emissions. This refers to the net weight of the parts. To improve material utilization, Carbon emission factor of materials; The formula for calculating the carbon emissions based on the processing baseline is as follows:

[0010] in, Based on carbon emissions for processing, For the first Standard working hours for each process For the first The rated power of the equipment used in each process This represents the typical load factor of the equipment. As a carbon emission factor for electricity, This represents the total number of processes. The formula for calculating the baseline carbon emissions of the auxiliary system is as follows:

[0011] in, To support the system's baseline carbon emissions, Total operation time. For the first Rated power of each auxiliary device Its load factor, For the first Estimated consumption of auxiliary materials, Its carbon emission factor.

[0012] Furthermore, in step S4, the actual carbon flow calculation function is based on the execution class transition triggering period. The actual energy consumption within is calculated, and its expression is:

[0013] in, For actual carbon emission increments, For real-time energy consumption, These are the actual process parameters. For system status, For the time delay of change.

[0014] Furthermore, in step S4, a hierarchical strategy is used to invoke the actual carbon flow calculation function: Level: When the real-time power sequence of the device can be obtained, the expression for the actual carbon flow calculation function is:

[0015] in, for Actual carbon emission increments at the level For the device's real-time power sequence, Working hours Average grid carbon intensity within the region; Level: When the device status sequence can be obtained, the expression for the actual carbon flow calculation function is:

[0016] in, for Actual carbon emission increments at the level For the set of device states, For state The cumulative duration, For state The corresponding typical power, The average carbon intensity of the power grid; Level: When both the real-time power sequence and the device status sequence are unavailable, the expression for the actual carbon flow calculation function is:

[0017] in, for Actual carbon emission increments at the level For standard working hours, Rated power of the equipment This is the default load factor.

[0018] Furthermore, in step S5, the triggering nodes for the carbon footprint data collection include: The completion of the process at the equipment level triggers the accumulation of the actual carbon increment at the process level into the workshop level token; Once the workshop-level subnet is completed, it triggers the aggregation of the workshop's cumulative actual carbon emissions to the enterprise-level token. When a change in product delivery at the enterprise level is triggered, the final collection of actual carbon at the product level is completed.

[0019] Furthermore, in step S2, when a decision-class transition is triggered, the cumulative potential carbon emissions of the token output by the decision-class transition are updated as follows:

[0020]

[0021] in, The cumulative potential carbon emissions of tokens as outputs of decision-making changes. To input the cumulative potential carbon emissions of Token, This represents the potential increase in carbon emissions resulting from this decision-making activity.

[0022] Furthermore, in step S2, when the class transition is triggered, the cumulative actual carbon emissions of the token output by the class transition are updated as follows:

[0023] in, To calculate the cumulative actual carbon emissions of the token that performs the class transition output, This is the input of the cumulative actual carbon emissions of the Token. For this execution activity, there was a delay The actual increase in carbon emissions generated within the country.

[0024] Further, in step S5, the subnet transition is associated with a lower-level subnet mapping; when the subnet transition is triggered at the upper level, an input token is consumed and the corresponding lower-level subnet is activated, and the input token is injected into the initial place of the lower-level subnet; when the lower-level subnet reaches the termination place, the updated token has been returned to the output place corresponding to the subnet transition.

[0025] Furthermore, the basic model constructed in step S1 is formally defined as a tuple. ,in, For a finite set of hierarchical networks, For a finite set of places, For a finite set of changes, For a set of directed arcs, For color sets, For a set of variables, For color functions, For guard functions, For arc expression functions, This is the initial identifier.

[0026] As can be seen from the above technical solutions, the present invention has the following advantages: The carbon flow method based on the hierarchical time-colored Petri net model provided in this application defines a structured token that can carry carbon account data and flows in the model, realizing the dynamic, real-time evolution and visualization of carbon footprint along with business activities, and transforming the accounting from a static summation after the fact to dynamic tracking of the process.

[0027] By establishing a three-tiered model architecture of enterprise-workshop-equipment and dividing it into decision-making and execution transitions, the hierarchical and differentiated management of carbon flow impacts is achieved, enabling the calculation and attribution of potential carbon emissions in the design and planning stage and actual carbon emissions in the manufacturing and execution stage.

[0028] Through subnet transitions and event-driven mechanisms, carbon data is automatically and accurately collected and accumulated from the equipment process level to the workshop level, and then to the enterprise product level. This establishes a link between operational data and product carbon footprint, providing a real-time and reliable data foundation for carbon management, process optimization, and emission reduction decisions. Attached Figure Description

[0029] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of the carbon fluxification method based on the hierarchical time-colored Petri net model described in this invention.

[0031] Figure 2 This diagram illustrates the cross-level carbon flow aggregation and Token update of the carbon flow quantization method based on the hierarchical time-colored Petri net model described in this invention.

[0032] Figure 3 This diagram illustrates the cross-level carbon flow aggregation and Token update of an application model of an embodiment of the carbon flow quantization method based on the hierarchical time-colored Petri net model described in this invention. Detailed Implementation

[0033] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0034] This application provides a carbon flow method based on a hierarchical time-colored Petri net model, which solves the current urgent technical problem of realizing real-time, dynamic, traceable quantification and cross-level automatic aggregation of the carbon footprint of a product throughout its entire life cycle.

[0035] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0036] Figure 1 This is a flowchart illustrating a carbon fluxification method based on a hierarchical time-colored Petri net model, provided as an embodiment of this application. Figure 1 As shown in the embodiment of this application, a carbon fluxification method based on a hierarchical time-colored Petri net model is provided, which specifically includes the following steps: Step S1: Construct a formally defined hierarchical time-colored Petri net base model, define the base model's locations, transitions, and directed arcs, and define a composite record structure as a token flowing in the base model to carry and update the business data and carbon account data of the product instance. Step S2: Based on the basic model, an application model containing an enterprise layer, a workshop layer, and an equipment layer is obtained. In the application model, the transitions are divided into decision-making transitions and execution transitions. The decision-making transitions map upstream business activities that do not directly consume materials and energy. The execution transitions map manufacturing execution activities that actually consume resources. Step S3: In the application model, a potential carbon flow calculation function is configured for decision-type transitions. When a decision-type transition is triggered, the potential carbon emission increment generated by the current decision-making activity is calculated based on the product design parameters and planned process parameters, and updated to the carbon account data of the token that flows through it. Step S4: In the application model, configure the actual carbon flow calculation function for the execution class transition. When the execution class transition is triggered, calculate the actual carbon emission increment generated by the current execution activity within its time delay based on the energy consumption data in the manufacturing execution process, and update it to the carbon account data of the token that flows through it. Step S5: In the application model, the enterprise layer application model calls the workshop layer application model by defining subnet transitions, and the token and carbon data are returned to the enterprise layer application model after the workshop layer application model is executed. Based on the calculated potential carbon emission increment and actual carbon emission increment, the carbon footprint data is automatically collected from the equipment layer through the workshop layer to the enterprise layer.

[0037] It should be noted that the carbon data mentioned in step S5 specifically refers to the cumulative actual carbon emissions value carried in the returned token when the application model at the workshop level is completed and returned to the application model at the enterprise level after subnet transition.

[0038] The automatic collection of carbon footprint data implemented in step S5 results in the final value of the cumulative actual carbon emissions recorded in the token flowing through the transition when a transition representing product delivery is triggered in the enterprise-level application model.

[0039] It should be noted that subnet transitions are the triggering nodes and transmission channels for the bottom-up automatic aggregation of carbon data. Specifically: At the workshop level: When a workshop (such as a die-casting workshop) subnet completes all its internal processes (a series of execution-type transitions), the subnet transition representing the completion of the workshop's processing task is... It is triggered. At this point, the actual increase in carbon emissions generated by all processes within this subnetwork is... It has been accumulated in the tokens that flow through it.

[0040] Data aggregation: Subnet changes The completion of this execution marks the trigger point for carbon flow collection from the workshop level to the enterprise level. Subnet transition Carrying an updated carbon account ( The token (with the field already accumulated) is returned to the enterprise layer. The enterprise layer subnet in the hierarchical time-colored Petri net model receives this token, thereby enabling the aggregation of the accumulated actual carbon at the shop floor level to the product-level account.

[0041] Subnet transitions are a key logical component used to implement model layering, call lower-level subnet models, and trigger the aggregation of data (especially carbon footprint data) to the upper level after the lower-level subnet has finished running. It is an essential design to connect the enterprise, workshop, and equipment layers and enable the automatic aggregation of carbon flow data along this path.

[0042] It should be noted that in step S1, the composite record structure is defined as a record containing the following fields: unique identifier of the product instance. Current life cycle stage Design parameter vector , planned process parameter vector Actual process parameter vector Cumulative potential carbon emissions Cumulative actual carbon emissions and timestamp .

[0043] Unlike the simple structure in traditional CPNs used only to identify resource existence, the token in the hierarchical time-colored Petri net model is defined as a composite record structure. The color set is defined for business objects throughout the product lifecycle as follows:

[0044] in, Used as a unique identifier for product instances; The current stage of the life cycle ; For design decision parameter vectors (such as material, quality, tolerance); For planned process parameter vectors (such as standard man-hours, target equipment); This is a vector of actual process parameters (such as actual working hours and energy consumption), initially empty; To accumulate potential carbon emissions; For cumulative actual carbon emissions; This serves as a timestamp for time-series recording and control. The token structure, acting as an information carrier, circulates within the network, recording the evolution of product instances and their carbon footprint data.

[0045] In one exemplary embodiment, the potential carbon flow calculation function and decision class transitions are discussed. Correspondingly, it is used in the product design and process planning stage to assess the carbon emissions that will inevitably be generated in the subsequent manufacturing stage based on the determined static parameters.

[0046] The potential carbon flow calculation function maps design parameters and planned process parameters to theoretical carbon emission values. In step S3, the potential carbon flow calculation function calculates the potential carbon emission increment using the following formula:

[0047] in, This represents a potential increase in carbon emissions; The function for calculating potential carbon flows; The design parameter vector is determined by upstream activities and stored in the PLM system; The planned process parameter vector is generated by process planning activities and stored in the CAPP / ERP system. This is a resource demand vector, comprising three components: material demand, equipment power demand, and auxiliary resource demand. ,in, For material requirements, For equipment power requirements, To support resource needs; To match the resource demand vector The components correspond one-to-one with the static carbon emission factor vector.

[0048] Specifically, the potential increase in carbon emissions includes embodied carbon emissions from materials, processing baseline carbon emissions, and auxiliary system baseline carbon emissions; The formula for calculating the implicit carbon emissions of the material is as follows:

[0049] in, The materials contain hidden carbon emissions. The net weight of the part (kg) For material utilization rate ( ), The carbon emission factor of the material (kg CO2e / kg); It should be noted that the carbon emissions implied by materials are the carbon emissions that the raw materials of a product have already potentially affected during its production process. They are determined by the type of material selected for the parts, the net weight, and the material utilization rate during processing. Their calculation reflects the lock-in effect of design material selection decisions on carbon emissions.

[0050] The formula for calculating the carbon emissions based on the processing baseline is as follows:

[0051] in, Based on carbon emissions for processing, For the first Standard working time (h) for each process. For the first The rated power (kW) of the equipment used in each process. Typical load factor of the equipment According to the average of historical MES data, The carbon emission factor for electricity (kgCO2e / kWh). This represents the total number of processes. It should be noted that the processing baseline carbon emissions are a theoretical estimate of carbon emissions related to energy consumption in the manufacturing process based on standard process planning parameters, reflecting the potential impact of process route planning decisions on carbon emissions in subsequent manufacturing stages.

[0052] The formula for calculating the baseline carbon emissions of the auxiliary system is as follows:

[0053] in, To support the system's baseline carbon emissions, The total operation time is the sum of the standard working hours for each process. For the first The rated power (kW) of each auxiliary device is obtained from the device master data. Its load factor is set to 0.3-0.5. For the first Estimated consumption (L or kg) of auxiliary materials. Its carbon emission factor (kgCO2e / unit).

[0054] It should be noted that the baseline carbon emissions of auxiliary systems include the estimated carbon emissions corresponding to the consumption of auxiliary materials such as cutting fluid and lubricating oil, as well as the energy consumption of workshop auxiliary equipment such as air compressors and ventilation systems.

[0055] The mapping relationship between the parameters required for the above calculations and the enterprise information system is shown in Table 1: Table 1. Mapping Relationship between Potential Carbon Flow Calculation Parameters and Enterprise Information Systems

[0056] According to another embodiment of the present invention, in step S4, the actual carbon flow calculation function is based on the execution class transition triggering period. The actual energy consumption within is calculated, and its expression is:

[0057] in, For actual carbon emission increments, For real-time energy consumption, These are the actual process parameters. For system status, For the time delay of change.

[0058] According to embodiments of this application, the actual carbon flow calculation function and the execution class transition Binding is used to calculate actual carbon emissions during the manufacturing execution phase based on real-time data. Considering the varying completeness of enterprise data acquisition systems, a tiered calculation strategy is adopted to ensure reasonable estimates are provided under different data availability conditions.

[0059] The actual carbon flow calculation function is defined as the integral of actual energy consumption over time, reflecting the real energy consumption of physical manufacturing activities and their corresponding carbon emissions:

[0060] in, This represents the actual increase in carbon emissions from this implementation activity; Real-time power of the device; Real-time grid carbon intensity; The actual duration of the change; In discrete event simulations, a piecewise accumulation approximation is typically used:

[0061] in, The variable of actual carbon emissions is calculated based on actual energy consumption data. For the first Average actual power over a time period; For the first The length of each time period; For the first Average grid carbon intensity over a given time period; This represents the total number of time periods. This refers to the total actual carbon emissions from the operation of auxiliary equipment in the workshop (such as air compressors, ventilation systems, and cooling systems).

[0062] Considering the varying completeness of enterprise data acquisition systems, and to ensure the model can reasonably calculate carbon emissions under different scenarios, step S4 employs a tiered strategy to call the actual carbon flow calculation function: Level: When the real-time power sequence of the device can be obtained, the expression for the actual carbon flow calculation function is:

[0063] in, for Actual carbon emission increments at the level For the device's real-time power sequence, Working hours Average grid carbon intensity within the region; When the device is equipped with a smart meter and connected to an energy management system (EMS) to obtain real-time power data, it employs accurate calculations based on energy consumption integrals and real-time (or time-of-use) grid carbon intensity.

[0064] Level: When the device status sequence can be obtained, the expression for the actual carbon flow calculation function is:

[0065] in, for Actual carbon emission increments at the level This is a set of device states (e.g., running, idle, standby). For state The cumulative duration, For state The corresponding typical power, The average carbon intensity of the power grid; When real-time power cannot be obtained, but the device status sequence (such as running, no-load, standby) can be obtained through the MES / SCADA system, estimation calculation based on device status-power mapping is adopted.

[0066] Level: When both the real-time power sequence and the equipment status sequence are unavailable, the calculation reverts to a baseline based on planned process parameters and company experience coefficients. The expression for the actual carbon flow calculation function is:

[0067] in, for Actual carbon emission increments at the level For standard working hours, Rated power of the equipment The default load factor is usually taken as an empirical value of 0.4.

[0068] The mapping relationship between the parameters required for the above calculations and the enterprise information system is shown in Table 2: Table 2 Mapping Relationship between Actual Carbon Flow Calculation Parameters and Enterprise Information System

[0069] Based on the defined carbon flow mapping function, a corresponding data collection and update mechanism needs to be established to support the dynamic accumulation and traceability of the carbon footprint throughout the product's entire lifecycle. This mechanism is coupled with the hierarchical structure of the hierarchical time-colored Petri net model, using the composite token carrying the product's entire lifecycle status and carbon account as the sole data carrier. This token flows through the enterprise-workshop-equipment three-layer network along with business processes, and achieves automatic bottom-up collection and real-time updates of carbon data through an event-driven approach.

[0070] like Figure 2 As shown, the collection of carbon data follows the following hierarchical path: At the equipment level, energy consumption monitoring data is processed using the actual carbon flow calculation function. Generate incremental carbon emissions at the process level This increment performs a transition at the corresponding workshop level. Upon completion, an update operation is triggered. This amount is accumulated in the product token account that flows through this location.

[0071] At the shop floor level, the processing activities of a shop floor are typically represented by a subnet model containing multiple transitions, and a complete subnet (such as a processing production line) represents a set of consecutive processes. When the token traverses all processes within the subnet and reaches the final storage location, the carbon footprint accumulation in that shop is complete. The token tracks subnet transitions... Return to the enterprise layer.

[0072] After receiving the data from the enterprise-level model, the model calculates the total cumulative actual carbon emissions generated by a workshop subnetwork during its operating cycle. The data is aggregated into product-level carbon accounts, thus completing the convergence of carbon data from micro-processes to macro-products. The entire process is driven by business events from systems such as MES and EMS, ensuring that the evolution of carbon flows is synchronized with the actual physical processes.

[0073] Among them, the carbon flow collection trigger node from the equipment layer to the workshop layer is the process execution transition. Completed; the carbon flow aggregation trigger node from the workshop level to the enterprise level is the subnet transition. The execution ends when the token reaches the workshop level final warehouse; the trigger node for the enterprise level to complete the product-level carbon flow collection is the product delivery transition trigger, which means the token reaches the enterprise level final warehouse.

[0074] In one embodiment, in step S5, the triggering node for carbon footprint data collection includes: The completion of the process at the equipment level triggers the accumulation of the actual carbon increment at the process level into the workshop level token; Once the workshop-level subnet is completed, it triggers the aggregation of the workshop's cumulative actual carbon emissions to the enterprise-level token. When a change in product delivery at the enterprise level is triggered, the final collection of actual carbon at the product level is completed.

[0075] It should be further explained that in step S2, when the decision transition is triggered, the cumulative potential carbon emissions of the token output by the decision transition are updated as follows:

[0076]

[0077] in, The cumulative potential carbon emissions of tokens as outputs of decision-making changes. To input the cumulative potential carbon emissions of Token, This refers to the potential increase in carbon emissions resulting from this decision-making activity.

[0078] In step S2, when the class transition is triggered, the cumulative actual carbon emissions of the token output by the class transition are updated to:

[0079] in, To calculate the cumulative actual carbon emissions of the token that performs the class transition output, This is the input of the cumulative actual carbon emissions of the Token. For this execution activity, there was a delay The actual increase in carbon emissions generated within the country.

[0080] In this embodiment, the hierarchical time-colored Petri net model uses a continuous-time system, specifying each transition... Define time attributes Transition triggering follows the principle of separating enable and triggering, and is protected by guard functions. Constraints. Change In the logo The enable condition is satisfied if and only if: All input libraries contain a quantity greater than or equal to the arc weight, and the color value satisfies the input arc expression. The constrained token.

[0081] Guard function The result is true. Encoding business rules or resource status.

[0082] In the hierarchical time-colored Petri net model, transitions can be categorized into decision-type transitions based on the nature of the mapped business activities and their impact on carbon flow. With execution class changes The definitions and triggering rules for the two types of transitions are as follows: 1) Decision-related changes: Decision-making transitions primarily correspond to upstream activities such as product design and process planning, and do not directly consume materials or energy. When the input library contains a token that satisfies the arc expression requirements, and the guard function... (Generally, this is a business rule) A transition is triggered when the evaluation result is true. Decision-type transitions. ,That This indicates that its triggering can be considered instantaneous, consuming the input token and immediately generating the output token. Its operation is based on the design parameter vector carried by the token. Processing is performed through mapping functions. Generate or update process parameter vectors Simultaneously, the potential carbon flow calculation function is invoked. The potential additional carbon emissions from this decision-making process are calculated based on static parameters. and update the cumulative potential carbon emissions of the output token: .

[0083] 2) Execution-type transitions: Execution-type transitions correspond to the physical activities that actually consume resources or energy during the manufacturing execution phase. Their triggering, in addition to satisfying the input tickenzie set, must be achieved through a guard function. Verify the availability of physical resources. Perform class transitions. , This indicates that the physical activity needs to continue. Each time unit is used to express the actual duration of the activity. During the delay period, relevant resources are in a state of occupancy. The hierarchical time-colored Petri net model obtains real-time energy consumption data through an interface. .

[0084] When the delay ends, an output token is generated and resources are released, then the actual carbon calculation function is invoked. According to actual process parameters With system status calculate Actual carbon emission increment during the period And update the cumulative actual carbon output of the token:

[0085] Meanwhile, actual process parameters Recorded. A comparison of the characteristics of decision-making and executive transitions is shown in Table 3.

[0086] Table 3. Comparison of characteristics between decision-making and executive-oriented changes.

[0087] In addition, subnetting This is a key method for implementing hierarchical time-colored Petri net models. It involves expanding a transition in an upper-level network into a subnet, thereby representing the activities at different levels. Each subnet transition... With a mapping function Related, This function defines the subnet transitions. Activated specific lower-level subnets When subnets change When enabled and triggered in the upper-layer network, the input token will be consumed, and the mapping function will be used accordingly. Activate the corresponding lower-level subnet The current token is injected into the initial library of the subnet as input data for subnet operation. Subnet It runs independently according to its own transition logic and timing. When a subnet reaches its termination location, the operation ends, and the updated tokens are returned to the parent subnet and stored in the subnet transitions. The output library is located there. From the perspective of the upper-layer network, subnetting changes... The completion time is equal to the cumulative time of all activities within the subnet it activates.

[0088] It should be further explained that after defining the basic formal rules and timing mechanism of the model, its hierarchical structure needs to be constructed to manage the complexity of real-world manufacturing systems. Based on different data granularities, the model is divided into a hierarchical time-colored Petri net architecture: enterprise layer - workshop layer - equipment layer. 1) The enterprise layer uses customer orders as the main thread, modeling cross-departmental business processes from demand receipt to production planning. This layer primarily assesses the potential impact of design parameters and process schemes on carbon emissions and invokes lower-level models through subgrid transitions.

[0089] 2) The workshop level receives production instructions, schedules and models the flow of processes, resources and work-in-process, estimates or obtains actual energy consumption based on process parameters and real-time status, and calculates the actual carbon emissions at the process level. 3) The equipment layer stores the micro-state and energy consumption data of individual devices, providing a fine-grained data foundation for carbon emission calculations. In the three-layer architecture of this model, the enterprise layer corresponds to the three upstream stages of concept and scheme design, detailed engineering design, and production preparation and planning, mainly realizing the triggering of decision-related changes and the calculation of potential carbon flows; the workshop layer and equipment layer correspond to the manufacturing execution and delivery stage, mainly realizing the triggering of execution-related changes and the calculation of actual carbon flows. The two work together to achieve the coupling of the entire life cycle business flow and carbon footprint flow.

[0090] In step S5, the subnet transition is associated with a lower-level subnet mapping; when the subnet transition is triggered at the upper level, the input token is consumed and the corresponding lower-level subnet is activated, and the input token is injected into the initial place of the lower-level subnet; when the lower-level subnet reaches the termination place, the updated token has been returned to the output place corresponding to the subnet transition.

[0091] It should be noted that the lower-level subnet in step S5 refers to an independent and complete hierarchical time-colored Petri net model located at the next lower level (such as the workshop level) that is called and activated by a subnet transition in the upper-level model (such as the enterprise level).

[0092] The specific explanation is as follows: Position in the hierarchical model architecture: The hierarchical time-colored Petri net model constructs a three-level architecture: Enterprise Layer, Shop Floor Layer, and Equipment Layer. The lower-level subnets refer to the hierarchical time-colored Petri net model located at the Shop Floor Layer. For example, at the Enterprise Layer, a subnet transition representing the execution of processing tasks in the die-casting shop, and the lower-level subnets it is associated with and activated, constitute a complete hierarchical time-colored Petri net model describing the internal processes of the die-casting shop (such as loading, die-casting, unloading, and cleaning).

[0093] Function and Structure: Each lower-level subnet (workshop subnet) is itself a complete, small-scale hierarchical time-shaded Petri net model. It has its own independent: Warehouse: Indicates the state inside the workshop, such as pending processing or completed processing.

[0094] Changes: These are mainly execution-type changes, corresponding to specific physical processes within the workshop (such as die casting and CNC machining).

[0095] Initial location and final location: Define the entry and exit points of this subnet model.

[0096] The calling relationship with the upper-level model: The logic described in step S5 is precisely the process of the upper-level model calling and the lower-level model executing. When invoked: The subnet transition at the upper layer (enterprise layer) is triggered, which consumes an input token from the upper layer (this token carries product design, planning, and other data) and injects this token into the initial repository of its associated lower subnet. This is equivalent to issuing a production instruction to the lower subnet.

[0097] During execution: The activated lower-level subnet begins to operate independently. Tokens circulate within them, triggering various execution class transitions in sequence, completing the actual processing activities, and simultaneously calculating and accumulating actual carbon emissions to obtain the cumulative actual carbon emissions of the tokens output by the execution class transitions. .

[0098] Upon return: When the token completes all processes in the lower-level subnet and reaches the terminal warehouse of that subnet, it signifies the end of the workshop task. At this point, the data has been updated (e.g., The token (which includes the total emissions from the workshop) will be returned to the upper-level subnet transition and placed in its output repository. This completes a full cross-level call and data aggregation.

[0099] The lower-level subnet is the concrete embodiment of the model's hierarchical structure and is the model unit that carries the details of manufacturing execution activities. It is dynamically invoked by the changes of the upper-level subnets, responsible for handling specific production tasks, calculating the actual carbon emissions at this level, and returning the results to the upper level through the token, thereby realizing the automatic bottom-up collection of carbon data.

[0100] The basic model constructed in step S1 is formally defined as a tuple. ,in, For a finite set of hierarchical networks, For a finite set of places, For a finite set of changes, For a set of directed arcs, For color sets, For a set of variables, For color functions, For guard functions, For arc expression functions, This is the initial identifier.

[0101] It should be noted that the Hierarchical Timed Colored Petri Net-Business Flow-Carbon Footprint Flow (HTCPN-BCF) model can be formally defined as a 10-tuple:

[0102] in, It is a finite set of hierarchical networks. Each element A subnet represents a view of a business at a specific level. For a finite set of places, each place A status cache node or resource node corresponding to a type of business object (such as design task, production work order, machine tool equipment); For a finite set of changes, each change Corresponding to a business activity, Based on the nature of the business activities they map, they are categorized into decision-making changes. With execution class changes ; It is a set of directed arcs, representing the flow relationship between places and changes; Define the set of all data types and operations that a token can carry for a color set; This is a set of variables, the core variables for carbon flow calculation and network element operation, including carbon emission increments. Transition delay Equipment power Working hours All variables belong to the type of ; For the color function, specify the color type of the token in each place, that is, the finite set of places. The Token type in the mapping is to The color set defined in [the document / reference]; For guard functions, set logical conditions for triggering transitions to clarify business rule constraints; This is an arc expression function that defines the logic for data calculation and transmission when the token moves on the arc. As the initial identifier, assign an initial token and its color value to each library.

[0103] As an example, the research object of this application is a die-cast enclosure product of a certain type of wireless base station, the structure of which is as follows: Figure 2 As shown, the enclosure consists of six main modules: main cavity assembly, heat dissipation assembly, door assembly, protective assembly, auxiliary assembly, and fastener assembly. It encompasses various types of components, including die-cast aluminum alloy parts, machined parts, stamped parts, and purchased standard parts. The product's bill of materials is characterized by its multi-layered structure, diverse materials, and a high proportion of critical design components, making it a core target for carbon footprint management.

[0104] Instantiation of a hierarchical time-shaded Petri net model: Combination Figure 3 Based on the collaborative model construction method, a hierarchical time-colored Petri net model with enterprise layer, workshop layer, and equipment layer was constructed using the die-cast enclosure product of wireless base stations. (1) Enterprise Layer: Driven by customer orders, this layer encompasses decision-making activities from order receipt to product delivery. It primarily maps business activities in the first three stages of the product lifecycle (conceptual design, detailed design, and production preparation). Potential carbon flow calculations are performed through decision-type transitions, and lower-level models are invoked through subnet transitions. When a decision-type transition is triggered, it is triggered through a mapping function. The design parameters are converted into planned process parameters, and the potential carbon flow calculation function is called simultaneously. Update cumulative potential carbon emissions .

[0105] (2) Workshop Level: The production instructions at the enterprise level are decomposed into specific process executions. Subnets are constructed for the core workshops of the enclosure manufacturing (die casting workshop, machining workshop, painting workshop) and the assembly workshop. Each subnet contains process-level execution class transitions and calls the actual carbon flow calculation function. Update cumulative actual carbon emissions .

[0106] (3) Equipment layer: As the underlying data foundation, it stores parameters such as the rated power, load factor, and real-time status of each processing equipment, as well as operator resources and auxiliary material inventory, providing granular support for the actual carbon flow calculation of workshop layer changes. Modeling the operating status of a single piece of equipment, such as running, standby, no-load, fault, etc., and associating it with the equipment power, is the source of calculation parameters for the actual carbon flow.

[0107] Table 4 shows the identifiers and meanings of some core storage locations in the Enterprise, Workshop, and Equipment layers. Enterprise layer storage locations primarily store the status of product tokens at different business stages. Workshop layer storage locations are used for the input and output of process-level product tokens. Both the Enterprise and Workshop layer color types belong to ProductToken. Equipment layer storage locations store resource data for equipment, personnel, and auxiliary materials; their color types are independent of product tokens and are defined as MachineData, WorkerData, and AuxMaterialData, respectively.

[0108] Table 4 Core repository identifiers and their meanings

[0109] Based on the definition in the database, it is necessary to further clarify the set of transitions driving state transitions and their behavioral rules. Table 5 shows the identifiers of the main transitions at different levels in the instance model and their key elements. Among them, decision-making transitions correspond to upstream business activities such as design and planning, which are completed instantaneously and mainly update the potential carbon account. Subnet transitions are used to activate lower-level workshop subnets, enabling cross-level calls; execution-type transitions correspond to physical activities in the manufacturing stage, have a time delay, and collect energy consumption data upon triggering to update the actual carbon account. Equipment layer transitions are responsible for updating resource status and managing auxiliary material consumption.

[0110] Table 5. Identifiers and Key Elements of Enterprise Level Transitions

[0111] Key Changes in Carbon Quantification and Data Updates: Equipment layer → Workshop layer: When the workshop layer changes (Die casting) During execution, the equipment warehouse The die-casting machine transmits its real-time power data to the transition. After the transition is completed, the B-level calculation method is used to obtain... This value is accumulated in Token's Field. At this time, the database is... (To be die-cast) Moved to warehouse (Die casting completed), its The field has been updated from 0 to 0.70.

[0112] Accumulated processes within the workshop layer: Token enters the CNC machining subnet, workshop layer changes. (CNC machining) Accumulation after triggering (Including combined processes such as original cavity, cover plate, stamping, bending, and welding). The field was updated to 0.70 + 1.988 = 2.688. The subsequent spraying process... (Spraying) Cumulative 0.037 , The field has been updated to 2.725. Final assembly process. (Assembly) Accumulated 0.075 , The field reached 2.80. Carbon emissions from auxiliary materials (cutting fluid, cleaning fluid, etc.) are managed through the auxiliary material inventory. (Auxiliary material inventory) is linked to execution-related changes in accounting, and is accumulated synchronously to 11.0. Therefore, the final cavity token The field value is 2.80 + 11.0 = 13.80 (Overall machine level).

[0113] Workshop Level → Enterprise Level: Subnet transition occurs after all processes at the workshop level are completed. End of text, Token returned to the corporate warehouse. (Waiting for quality inspection). At this time, the enterprise level of Token... The field already includes the actual carbon emissions of the entire cavity manufacturing process.

[0114] Enterprise layer → Product-level aggregation: The enterprise layer performs subnet transitions synchronously or sequentially, i.e. (Activate the die-casting workshop subnet) (Activate the machining workshop subnet) (Activate the assembly workshop subnet) to aggregate the carbon footprint of each component into a single product token. Once all components are manufactured, the transition... When (product delivery) is triggered, the product token's This is the total actual carbon footprint of the entire machine. Meanwhile, changes... (Product design and selection) and changes (Process route and planning) Decision-making changes have already added potential carbon accumulation to The fields will be output together at the end.

[0115] Table 6 shows the carbon flow increments and aggregation results for each stage of the product's lifecycle, verifying that the model can accurately trace the carbon emission contribution of each process and achieve data aggregation from the micro (process) to the macro (whole machine).

[0116] Table 6 Summary of Carbon Flow Changes Throughout the Product Lifecycle ( )

[0117] It should be noted that the original stamping, bending, and welding processes have been incorporated into the changes. (CNC machining), carbon increments are correspondingly combined. The actual carbon flow consumed by auxiliary materials (cutting fluid 10.32, cleaning fluid 0.61, lubricating oil 0.47, etc.) totals approximately 11.00. During the manufacturing process The changes in (auxiliary material consumption) are gradually accumulated and summarized in the table for the sake of simplicity.

[0118] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0119] Any changes, modifications, substitutions, and variations made to the embodiments without departing from the principles and spirit of the present invention still fall within the protection scope of the present invention.

Claims

1. A carbon fluxification method based on a hierarchical time-colored Petri net model, characterized in that, The method includes: Step S1: Construct a formally defined hierarchical time-colored Petri net base model, define the base model's locations, transitions, and directed arcs, and define a composite record structure as a token flowing in the base model to carry and update the business data and carbon account data of the product instance. Step S2: Based on the basic model, an application model containing an enterprise layer, a workshop layer, and an equipment layer is obtained. In the application model, the transitions are divided into decision-making transitions and execution transitions. The decision-making transitions map upstream business activities that do not directly consume materials and energy. The execution transitions map manufacturing execution activities that actually consume resources. Step S3: In the application model, a potential carbon flow calculation function is configured for decision-type transitions. When a decision-type transition is triggered, the potential carbon emission increment generated by the current decision-making activity is calculated based on the product design parameters and planned process parameters, and updated to the carbon account data of the token that flows through it. Step S4: In the application model, configure the actual carbon flow calculation function for the execution class transition. When the execution class transition is triggered, calculate the actual carbon emission increment generated by the current execution activity within its time delay based on the energy consumption data in the manufacturing execution process, and update it to the carbon account data of the token that flows through it. Step S5: In the application model, the enterprise layer application model calls the workshop layer application model by defining subnet transitions, and the token and carbon data are returned to the enterprise layer application model after the workshop layer application model is executed. Based on the calculated potential carbon emission increment and actual carbon emission increment, the carbon footprint data is automatically collected from the equipment layer through the workshop layer to the enterprise layer.

2. The method according to claim 1, characterized in that, In step S1, the composite record structure is defined as a record containing the following fields: unique identifier of the product instance. Current life cycle stage Design parameter vector , planned process parameter vector Actual process parameter vector Cumulative potential carbon emissions Cumulative actual carbon emissions and timestamp .

3. The method according to claim 1, characterized in that, The potential carbon emission increments include embodied carbon emissions from materials, processing baseline carbon emissions, and auxiliary system baseline carbon emissions. The formula for calculating the implicit carbon emissions of the material is as follows: in, The materials contain hidden carbon emissions. This refers to the net weight of the parts. To improve material utilization, Carbon emission factor of materials; The formula for calculating the carbon emissions based on the processing baseline is as follows: in, Based on carbon emissions for processing, For the first Standard working hours for each process For the first The rated power of the equipment used in each process This represents the typical load factor of the equipment. As a carbon emission factor for electricity, This represents the total number of processes. The formula for calculating the baseline carbon emissions of the auxiliary system is as follows: in, To support the system's baseline carbon emissions, Total operation time. For the first Rated power of each auxiliary device Its load factor, For the first Estimated consumption of auxiliary materials, Its carbon emission factor.

4. The method according to claim 3, characterized in that, In step S4, the actual carbon flow calculation function is based on the execution class transition triggering period. The actual energy consumption within is calculated, and its expression is: in, For actual carbon emission increments, For real-time energy consumption, These are the actual process parameters. For system status, For the time delay of change.

5. The method according to claim 1, characterized in that, In step S4, the actual carbon flow calculation function is invoked using a hierarchical strategy: Level: When the real-time power sequence of the device can be obtained, the expression for the actual carbon flow calculation function is: in, for Actual carbon emission increments at the level of For the device's real-time power sequence, Working hours Average grid carbon intensity within the region; Level: When the device status sequence can be obtained, the expression for the actual carbon flow calculation function is: in, for Actual carbon emission increments at the level of For the set of device states, For state The cumulative duration, For state The corresponding typical power, The average carbon intensity of the power grid; Level: When both the real-time power sequence and the device status sequence are unavailable, the expression for the actual carbon flow calculation function is: in, for Actual carbon emission increments at the level of For standard working hours, Rated power of the equipment This is the default load factor.

6. The method according to claim 5, characterized in that, In step S5, the triggering nodes for carbon footprint data collection include: The completion of the process at the equipment level triggers the accumulation of the actual carbon increment at the process level into the workshop level token; Once the workshop-level subnet is completed, it triggers the aggregation of the workshop's cumulative actual carbon emissions to the enterprise-level token. When a change in product delivery at the enterprise level is triggered, the final collection of actual carbon at the product level is completed.

7. The method according to claim 1, characterized in that, In step S2, when a decision transition is triggered, the cumulative potential carbon emissions of the token output by the decision transition are updated as follows: in, The cumulative potential carbon emissions of tokens as outputs of decision-making changes. To input the cumulative potential carbon emissions of Token, This represents the potential increase in carbon emissions resulting from this decision-making activity.

8. The method according to claim 1, characterized in that, In step S2, when the class transition is triggered, the cumulative actual carbon emissions of the token output by the class transition are updated to: in, To calculate the cumulative actual carbon emissions of the token that performs the class transition output, This is the input of the cumulative actual carbon emissions of the Token. For this execution activity, there was a delay The actual increase in carbon emissions generated within the country.

9. The method according to claim 1, characterized in that, In step S5, the subnet transition is associated with a lower-level subnet mapping; when the subnet transition is triggered at the upper level, the input token is consumed and the corresponding lower-level subnet is activated, and the input token is injected into the initial place of the lower-level subnet; when the lower-level subnet reaches the termination place, the updated token has been returned to the output place corresponding to the subnet transition.

10. The method according to claim 1, characterized in that, The basic model constructed in step S1 is formally defined as a tuple. ,in, For a finite set of hierarchical networks, For a finite set of places, For a finite set of changes, For a set of directed arcs, For color sets, For a set of variables, For color functions, For guard functions, For arc expression functions, This is the initial identifier.

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