A machine tool-operator system comprehensive carbon emission modeling method for human-oriented intelligent manufacturing

Abstract

The application provides a machine tool-operator system comprehensive carbon emission modeling method for human-oriented intelligent manufacturing, relates to the technical field of numerical control machine tool carbon emission prediction, and first introduces the machine tool-operator system perspective to model the carbon emission of the system level, reveals the influence mechanism of operator behavior on the carbon emission of the manufacturing process, and expands the modeling boundary of the carbon evaluation of the manufacturing system; by analyzing the carbon emission characteristics of the machine tool activity and the operator activity, the carbon emission models of the machine tool activity and the operator activity are respectively established, and the two are further integrated to build a system-level carbon emission analysis framework, and the dynamic interaction relationship between the machine tool and the operator and the influence mechanism of the dynamic interaction relationship on the system carbon emission level are revealed; compared with the traditional modeling method, the application more comprehensively covers the human-machine interaction carbon source in the manufacturing system, enriches the theoretical system of carbon emission modeling and energy efficiency optimization, and provides a new research idea and practical path for the green low-carbon transformation of the manufacturing industry and the deep integration of human-oriented intelligent manufacturing.

Description

A Comprehensive Carbon Emission Modeling Method for Machine Tool-Operator Systems in Human-Centered Intelligent Manufacturing Technical Field

[0001] This invention relates to the field of carbon emission prediction technology for CNC machine tools, and in particular to a comprehensive carbon emission modeling method for machine tool-operator systems oriented towards human-centered intelligent manufacturing. Background Technology

[0002] With the rapid increase in global carbon dioxide emissions, a series of environmental problems, such as global warming and rising sea levels, have become global issues. According to research by the International Energy Agency (IEA), nearly one-third of global energy consumption and 36% of carbon emissions come from manufacturing. With the formation of "dual carbon" targets, carbon emissions from manufacturing are gradually becoming a core issue of concern for both academia and industry. CNC machine tools, often referred to as "industrial mother machines" in industrial production, are characterized by high energy consumption, high carbon emissions, and low energy efficiency, with an overall energy efficiency of less than 30%, resulting in substantial energy consumption and indirect carbon dioxide emissions related to electricity consumption. Therefore, accurate assessment of carbon emissions throughout the entire machine tool processing system is fundamental to addressing climate change and achieving global carbon neutrality.

[0003] In recent years, with the introduction of the Industry 5.0 concept, the industrial and academic communities have paid widespread attention to and discussed the human-centered approach in manufacturing. Against this backdrop, "human-centered intelligent manufacturing" has gradually become an important development direction and technological path in the transformation and upgrading of the manufacturing industry. Researchers, adhering to the "human-centered" concept, have proposed a human-cyber-physical systems (HCPS) technical framework, systematically exploring the technological system and future challenges of next-generation intelligent manufacturing oriented towards HCPS. In HCPS, the subjective initiative, flexibility, and experience-driven characteristics of operators play an irreplaceable role in ensuring the efficient and stable operation of the manufacturing system. However, current HCPS research neglects the potential impact of operators' operational behavior, decision-making habits, and work rhythm on the energy consumption characteristics and carbon emission behavior of machine tool systems. For example, controlling the processing rhythm, adjusting the processing sequence, and handling standby states can all lead to different energy consumption characteristics and carbon emission behaviors of machine tools at different times. Furthermore, some high-intensity operational activities can also cause significant physiological loads on operators, leading to fatigue accumulation and safety risks. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a comprehensive carbon emission modeling method for machine tool-operator systems in human-centered intelligent manufacturing, comprising:

[0005] S1. Modeling of carbon emissions from machine tool activities.

[0006] Typical machine tool activities during the machining process of CNC machine tools are determined, and the characteristics of machine tool activities are analyzed to obtain various machine tool dynamic elements. The execution state of the machine tool dynamic elements corresponding to the machine tool activities is logically represented to obtain the machine tool activity state vector, wherein the machine tool dynamic element is represented as logic 1 when it is in the execution state and as logic 0 when it is in the non-execution state.

[0007] Construct power and time models for each machine tool dynamic element.

[0008] A carbon emission model for machine tool activities is constructed based on the power model, time model, and machine tool activity state vector of each machine tool element.

[0009] S2, Modeling carbon emissions from operator activities.

[0010] Identify typical operator activities during CNC machine tool machining.

[0011] Based on the principles of human energy metabolism, a formula for calculating the carbon emissions of operator activities is derived.

[0012] Analyze the carbon emission patterns of operator activities and construct an operator activity carbon emission model based on the calculation formula for operator activity carbon emissions.

[0013] S3, Integrated carbon emission modeling of machine tool-operator system.

[0014] The machining process of the machine tool-operator system is divided into n stages. The machine tool activities and operator activities of each stage are determined. Based on the carbon emission models corresponding to the machine tool activities and operator activities of each stage, a carbon emission model for each stage and a comprehensive carbon emission model for the machine tool-operator system are constructed.

[0015] Optionally, the typical machine tool activities include standby MA1, spindle acceleration MA2, no-load MA3 and material cutting MA4, spindle deceleration MA5 and auxiliary activities MA6, and the machine tool dynamic elements include standby operation, machine tool lighting, cutting fluid spraying, automatic chip removal, tool selection, automatic tool change, X / Y / Z axis feed, spindle rotation and material cutting.

[0016] Optionally, constructing the power model for each machine tool kinetic element includes:

[0017] The power models for standby operation, machine tool lighting, cutting fluid spraying, automatic chip removal, tool selection, and automatic tool change are constructed as follows:

[0018] .

[0019] in, Let N be the power measurement value of the i-th type of machine tool dynamic element j-th time, and N be the number of power measurement values.

[0020] X / Y / Z axis feed includes X / Y / Z axis cutting feed and X / Y / Z axis rapid traverse. Power models for X / Y / Z axis cutting feed and X / Y / Z axis rapid traverse are constructed as follows:

[0021] .

[0022] .

[0023] Among them, P xf For X / Y / Z axis cutting feed power, The coefficient of the quadratic term in the function formula is... Let be the coefficient of the linear term in the function formula, and and It was obtained through linear fitting of experimental data. P is the cutting feed rate. rfd For X / Y / Z axis rapid feed power, This is a constant value related to the rapid feed rate.

[0024] The spindle rotation power model is constructed as follows:

[0025] .

[0026] in, The coefficients of the function formula, Let be the constant term of the function formula, and and n is the spindle speed, obtained through linear fitting of experimental data.

[0027] The material cutting power model is constructed as follows:

[0028] .

[0029] in, These are the coefficients of the material cutting power model. The index of the spindle speed. This is an index of the feed rate. This is an index representing the depth of cut. is an index of the cutting width, and , , , , The values ​​were obtained through linear fitting of experimental data, where n is the spindle speed, f is the feed rate, and a is the feed rate. p For cutting depth, a e This represents the cutting width.

[0030] Optionally, the construction of the time model for each machine tool motion element includes:

[0031] A time model is constructed for standby operation, machine tool lighting, cutting fluid spraying, automatic chip removal, and spindle rotation, as detailed below:

[0032] .

[0033] in, Let i be the end time of the execution of the i-th type of machine tool motion element. t is the start time of the execution of the i-th type of machine tool motion element.

[0034] The tool selection and automatic tool change time models are constructed as follows:

[0035] .

[0036] Among them, t tc For automatic tool change time, A represents the number of tool positions rotated by the machine tool turret. tc B is the constant term in the function formula. tc A represents the coefficient of the function formula. tc and B tc It was obtained by linear fitting of experimental data.

[0037] The X / Y / Z axis cutting feed time model and the X / Y / Z axis rapid traverse time model are constructed as follows:

[0038] .

[0039] .

[0040] Among them, L xf v is the cutting feed distance. xf L is the cutting feed rate. rfd For rapid feed distance, v rfd For rapid feed rate.

[0041] The material cutting time model is constructed as follows:

[0042] .

[0043] .

[0044] Among them, V remove The total volume of material removed is denoted by , MRR by , n by , and f by , where a is the total volume of material removed. p For cutting depth, a e This represents the cutting width.

[0045] Optionally, the step of constructing a machine tool activity carbon emission model based on the power model, time model, and machine tool activity state vector of each machine tool element includes:

[0046] .

[0047] .

[0048] in, For the carbon emission model of the i-th type of machine tool activity, Indicates the carbon emission factor of electricity. This is the transpose of the machine tool dynamic element power matrix. The matrix formed by the i-th column of the machine tool motion element time matrix. The power of the machine tool's dynamic element m, Let i be the time of the activity of the i-th type of machine tool. The machine tool motion element corresponding to the i-th type of machine tool activity The logical representation of .

[0049] Optionally, the typical operator activities include standing-waiting, standing-working, sitting-waiting, sitting-working, walking-unloaded, walking-loaded, running-unloaded, running-loaded, loading and unloading workpieces.

[0050] Optionally, the step of deriving the formula for calculating the carbon emissions of operator activities based on the principles of human energy metabolism includes:

[0051] .

[0052] .

[0053] .

[0054] .

[0055] .

[0056] .

[0057] in, Energy expenditure rate per unit body weight of the operator. This refers to the operator's oxygen intake per unit body weight. The operator's activity intensity MET value, The carbon dioxide production rate per unit body weight of the operator. Carbon emission rate per unit body weight of the operator. For carbon dioxide density, BW represents the operator's activity time and the operator's weight.

[0058] Optionally, the analysis of the carbon emission patterns of operator activities, and the construction of an operator activity carbon emission model based on the operator activity carbon emission calculation formula, includes:

[0059] By analyzing the carbon emission patterns of standing-waiting, standing-working, sitting-waiting, sitting-working, loading workpieces, and unloading workpieces, it was determined that the carbon emission rates of these activities are basically stable. The MET values ​​of activity intensity for these activities were obtained by consulting literature, and the carbon emission rate per unit body weight of operators was calculated. These rates were then substituted into the operator activity carbon emission calculation formula to construct carbon emission models for each activity, as detailed below:

[0060] .

[0061] .

[0062] .

[0063] .

[0064] .

[0065] .

[0066] Among them, C StS C StO C SiS C SiO C LW C UW These are the standing-waiting carbon emission model, the standing-working carbon emission model, the sitting-waiting carbon emission model, the sitting-working carbon emission model, the loading carbon emission model, and the unloading carbon emission model, respectively. StS t StO t SiS t SiO t LW t UWThe values ​​represent the time spent standing and waiting, standing and working, sitting and waiting, sitting and working, loading and unloading workpieces, and unloading workpieces, respectively. 0.5362 represents the carbon emission rate per unit weight of the operator during standing and waiting and sitting and waiting, 0.7424 represents the carbon emission rate per unit weight of the operator during standing and working, 0.6187 represents the carbon emission rate per unit weight of the operator during sitting and working, and 1.2373 represents the carbon emission rate per unit weight of the operator during loading and unloading workpieces.

[0067] By analyzing the carbon emission patterns of walking-idle operation, it was determined that the carbon emission rate of walking-idle operation is non-linearly related to the operator's walking speed. By consulting literature, the operator's walking speed and carbon emission rate per unit weight were obtained. A polynomial fitting was performed on the operator's walking speed and carbon emission rate per unit weight to obtain a model of the operator's carbon emission rate per unit weight during walking-idle operation. This model was then substituted into the formula for calculating the operator's activity carbon emissions to construct a carbon emission model for walking-idle operation, as detailed below:

[0068] .

[0069] .

[0070] Among them, v w ξ represents the operator's walking speed. WN For the carbon emission rate per unit body weight of a walking-unloaded operator, C WN For the walking-no-load carbon emission model, t WN The time between travel and idle.

[0071] By analyzing the carbon emission patterns of running-idle operations, it was determined that the carbon emission rate of running-idle operations is non-linearly correlated with the operator's running speed. By consulting literature, the operator's running speed and carbon emission rate per unit body weight were obtained. A polynomial fitting was performed on the operator's running speed and carbon emission rate per unit body weight to obtain a model of the operator's carbon emission rate per unit body weight during running-idle operations. This model was then substituted into the formula for calculating the operator's activity carbon emissions to construct a carbon emission model for running-idle operations, as detailed below:

[0072] .

[0073] .

[0074] Among them, v r ξ represents the operator's running speed. RN For the running-idle operator's carbon emission rate per unit body weight model, C RN For the running-no-load carbon emission model, t RN The time between running and idle.

[0075] To obtain the operator's load and carbon emission rate per unit weight at the same preset speed, a polynomial fitting was performed on the operator's load and carbon emission rate per unit weight to obtain the operator's carbon emission rate per unit weight under load conditions. This model was then substituted into the operator's activity carbon emission calculation formula to construct a carbon emission model under load conditions. Finally, this carbon emission model was linearly superimposed with the walking-empty carbon emission model and the running-empty carbon emission model to construct the walking-loaded carbon emission model and the running-loaded carbon emission model, respectively, as detailed below:

[0076] .

[0077] .

[0078] .

[0079] .

[0080] Among them, FW represents the operator's load, ξ FW For the carbon emission rate per unit body weight of an operator under load, C FW For the carbon emission model under loaded conditions, C WW For the walking-carrying carbon emission model, C RW For the carbon emission model of running-weighted carrying, t FW t is the time the operator bears the load. WW t represents the time spent walking while carrying a load. RW The time spent running with added weight.

[0081] Optionally, the step of constructing a carbon emission model for each stage and a comprehensive carbon emission model for the machine tool-operator system based on the carbon emission models corresponding to machine tool activities and operator activities at each stage includes:

[0082] .

[0083] .

[0084] .

[0085] .

[0086] .

[0087] in, For the first Phase-specific carbon emission models For the first Phase operator activity carbon emission model, For the first A phased model of carbon emissions from machine tool activities. For the first Phase 1 A carbon emission model for machine tool dynamics. For the first Phase 1 The power of various machine tool elements, For the first Phase 1 The duration of the machine tool motion element. For the first The carbon emission model for the k-th type of operator activity in stage k. For the first A model for the carbon emission rate per unit body weight of operator for the kth type of operator activity in stage k. For the first Phase 1 The duration of the operator's activity, A comprehensive carbon emission model for the machine tool-operator system.

[0088] By adopting the above technical solution, the present invention has at least the following beneficial effects:

[0089] (1) This invention analyzes the carbon emission characteristics of machine tool activities and operator activities, establishes carbon emission models for machine tool activities and operator activities respectively, and further integrates the two to construct a system-level carbon emission analysis framework, revealing the dynamic interaction relationship between machine tools and operators and its influence mechanism on the system's carbon emission level.

[0090] (2) Compared with traditional modeling methods, this invention covers more comprehensively the carbon sources of human-machine interaction in the manufacturing system, enriches the theoretical system of carbon emission modeling and energy efficiency optimization, and provides new research ideas and practical paths for the green and low-carbon transformation of the manufacturing industry and the deep integration of human-centered intelligent manufacturing.

[0091] (3) This invention introduces the machine tool-operator system perspective for the first time to model carbon emissions at the system level, revealing the mechanism by which operator behavior affects carbon emissions in the manufacturing process and expanding the modeling boundary of carbon assessment of manufacturing systems. Attached Figure Description

[0092] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0093] Figure 1 is a diagram of the integrated carbon emission modeling method for machine tool-operator systems.

[0094] Figure 2 is a logical representation of the execution state of machine tool dynamic elements corresponding to machine tool activities.

[0095] Figure 3 shows the basic information of the machine tool-operator test system: (a) the basic information of the workpiece after processing, (b) the machine tool processing path, and (c) the machine tool processing code.

[0096] Figure 4 is a schematic diagram of the carbon emissions from machine tool activities, operator activities, and overall carbon emissions at each stage of the machine tool-operator system.

[0097] Figure 5 shows the proportion of carbon emissions from machine tool activities and operator activities in the machine tool-operator system.

[0098] Figure 6 shows the targeted low-carbon optimization strategies proposed for the first and sixth stages. (a) is the optimization strategy targeting machine tool carbon emissions, (b) is the optimization strategy targeting operator carbon emissions, and (c) is the optimization strategy targeting machine tool-operator system carbon emissions.

[0099] Figure 7 compares the traditional low-carbon optimization strategy with the recommended low-carbon optimization strategy in terms of carbon emissions from the machine tool-operator system, machine tool carbon emissions, and operator carbon emissions. Detailed Implementation

[0100] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0101] With the deepening of the concept of green manufacturing, energy consumption modeling and carbon emission estimation in the manufacturing process have gradually become research hotspots in the fields of intelligent and sustainable manufacturing. Among them, CNC machine tools, as key energy-consuming carriers in the manufacturing process, have made energy consumption and carbon emission modeling during their operation a core direction. Currently, most research methods focus on the equipment level, analyzing the energy consumption behavior of machine tools at various operating stages through theoretical calculations and empirical modeling, and further estimating their carbon emission levels.

[0102] Significant progress has been made in machine tool energy consumption modeling, but accurate modeling of the entire process's carbon emissions remains challenging due to the multi-source, dynamic, and condition-dependent nature of machine tool processing carbon emissions. Carbon emissions are not only affected by the machine tool's operating status but are also closely related to processing parameters, cutting methods, material properties, and tool wear, exhibiting significant nonlinearity and phased fluctuations. Existing technologies provide a theoretical foundation for parameter modeling and life cycle assessment, but they do not pay enough attention to the role of operator behavior in switching operating states, energy consumption during non-processing periods, and rhythm control, and still lack carbon emission modeling methods oriented towards "human-machine collaboration."

[0103] As a crucial component of the machine tool-operator (MOS) system, the operator plays a vital role in intelligent manufacturing research. The operator's physical activity significantly impacts overall energy consumption and carbon emissions. Different tasks correspond to varying activity intensities and durations, leading to significant differences in the operator's basal metabolic rate (BMR) and metabolic equivalent of task (MET), thus triggering varying levels of carbon emissions. Using operator activity carbon emissions as a metric helps identify excessive energy consumption and fatigue risks caused by high-intensity activities, thereby improving energy efficiency and operational efficiency. To better explore the characteristics of operator activity carbon emissions, this invention initially establishes a machine tool-operator system energy consumption model, providing theoretical and experimental support for subsequent "human-machine collaboration" carbon emission modeling.

[0104] Currently, carbon emissions from manufacturing systems are mainly concentrated in the equipment processing process. Research on operators mainly focuses on their energy consumption, neglecting the impact of carbon emissions caused by human activities on the manufacturing system. Therefore, this invention proposes a novel carbon emission prediction model for CNC machine tool-operator system (MOS).

[0105] As shown in Figure 1, this disclosure provides a comprehensive carbon emission modeling method for machine tool-operator systems in human-centered intelligent manufacturing, including:

[0106] S1. Modeling of carbon emissions from machine tool activities.

[0107] 1. Classification of machine tool activities.

[0108] Typical machine tool activities during CNC machine tool machining include startup, standby, spindle acceleration, no-load, material cutting, spindle deceleration, and auxiliary activities. Auxiliary activities refer to machine tool activities other than the primary machining activities. In workshop production, once a machine tool is started, it will not stop unless there are abnormal circumstances; these are not considered part of the workshop machining tasks. Therefore, we mainly consider six machine tool activities (MA): standby (MA1), spindle acceleration (MA2), no-load (MA3), material cutting (MA4), spindle deceleration (MA5), and auxiliary activities (MA6). Based on the analysis of MA, we introduce the concept of machine tool dynamic elements (MTTs) and summarize 14 dynamic elements according to the characteristics of machine tool activities, as shown in Table 1.

[0109] Table 1 Types and symbols of machine tool dynamic elements

[0110]

[0111] Further use of state vectors To characterize MA, as shown in Figure 2, the state vector Composed of a set of execution state logic representations of MTTs, it can be represented as:

[0112] .

[0113] .

[0114] in, M is the logical representation of the execution state of machine tool element j, and M is the number of machine tool element types.

[0115] 2. Machine tool motion element modeling.

[0116] (1) Machine tool dynamic power model.

[0117] The power of machine tool dynamic elements such as standby operation, machine tool lighting, cutting fluid spraying, automatic chip removal, tool selection, and automatic tool changing remains basically constant under normal operating conditions. It is not significantly affected by factors such as changes in machining allowance or minor fluctuations in cutting force during machining, and is only related to the machine tool's own performance parameters. A power model is constructed for these machine tool dynamic elements as follows:

[0118] .

[0119] in, Let N be the power measurement value of the i-th type of machine tool dynamic element j-th time, and N be the number of power measurement values.

[0120] Feed motion refers to the linear movement of the feed axis along a specific direction (usually axial), enabling the tool to cut the workpiece according to a predetermined trajectory and depth. There are two types of movement: 1) X / Y / Z axis cutting feed, which is the process where the feed axis maintains a certain feed speed and cuts along a specified direction; 2) X / Y / Z axis rapid traverse feed, which is the process where the feed axis moves rapidly to a specified position at a high feed speed. The X / Y / Z axis cutting feed power P... xf Primarily the power used to overcome friction, the X / Y / Z axis cutting feed power P xf This can be expressed as the X / Y / Z axis feed rate. Quadratic function, X / Y / Z axis rapid feed power Depends on rapid feed rate ,when When determined, rapid feed power It is a constant value, as follows:

[0121] .

[0122] .

[0123] Among them, P xf For X / Y / Z axis cutting feed power, The coefficient of the quadratic term in the function formula is... Let be the coefficient of the linear term in the function formula, and and It was obtained through linear fitting of experimental data. P is the cutting feed rate. rfd For X / Y / Z axis rapid feed power, This is a constant value related to the rapid feed rate.

[0124] Spindle rotation refers to the process by which the machine tool spindle module rotates to drive the relative movement of the cutting tool or workpiece, thereby achieving material removal and shape processing. Power P src It can be written as a linear function related to the spindle speed n, as follows:

[0125] .

[0126] in, The coefficients of the function formula, Let be the constant term of the function formula, and and n is the spindle speed, obtained through linear fitting of experimental data.

[0127] Material cutting refers to the machining process in which excess material on a workpiece is separated from the base material in the form of chips through the relative motion between the cutting tool and the workpiece, thereby achieving the desired shape, size, and surface quality of the part. Material cutting (milling) power... Mainly related to spindle speed Feed rate Milling depth Milling width Related, can be expressed as with , , , The relevant exponential functions are as follows:

[0128] .

[0129] in, These are the coefficients of the material cutting power model. The index of the spindle speed. This is an index of the feed rate. This is an index representing the depth of cut. is an index of the cutting width, and , , , , The values ​​were obtained through linear fitting of experimental data, where n is the spindle speed, f is the feed rate, and a is the feed rate. p For cutting depth, a e This represents the cutting width.

[0130] (2) Machine tool motion element time model.

[0131] The duration of machine tool dynamic elements such as standby operation, machine tool lighting, cutting fluid spraying, automatic chip removal, and spindle rotation is related to the machine tool model, process equipment, and the skill level of the on-site operator. The model is relatively simple, as detailed below:

[0132] .

[0133] in, Let i be the end time of the execution of the i-th type of machine tool motion element. t is the start time of the execution of the i-th type of machine tool motion element.

[0134] The tool selection and automatic tool change time models are constructed as follows:

[0135] .

[0136] Among them, t tc For automatic tool change time, A represents the number of tool positions rotated by the machine tool turret. tcB is the constant term in the function formula. tc A represents the coefficient of the function formula. tc and B tc It was obtained by linear fitting of experimental data.

[0137] The X / Y / Z axis cutting feed time is determined by the X / Y / Z axis cutting feed distance L. xf and cutting feed rate v xf It is determined that the rapid traverse time of the X / Y / Z axes is determined by the rapid traverse distance L. rfd and rapid feed rate v rfd It has been decided to construct X / Y / Z axis cutting feed time models and X / Y / Z axis rapid traverse time models, as detailed below:

[0138] .

[0139] .

[0140] Among them, L xf v is the cutting feed distance. xf L is the cutting feed rate. rfd For rapid feed distance, v rfd For rapid feed rate.

[0141] Based on the total volume removed from the material and material removal rate The material cutting time model is constructed as follows:

[0142] .

[0143] .

[0144] Among them, V remove The total volume of material removed is denoted by , MRR by , n by , and f by , where a is the total volume of material removed. p For cutting depth, a e This represents the cutting width.

[0145] 3. Machine tool activity carbon emission model.

[0146] A carbon emission model for machine tool activities is constructed by combining the power model, time model, and machine tool activity state vector for each machine tool dynamic element, including:

[0147] .

[0148] .

[0149] in, For the carbon emission model of the i-th type of machine tool activity, Indicates the carbon emission factor of electricity. This is the transpose of the machine tool dynamic element power matrix. The matrix formed by the i-th column of the machine tool motion element time matrix. The power of the machine tool's dynamic element m, Let i be the time of the activity of the i-th type of machine tool. The machine tool motion element corresponding to the i-th type of machine tool activity The logical representation of .

[0150] S2, Modeling carbon emissions from operator activities.

[0151] 1. Operator activity classification and carbon emission calculation principles.

[0152] Operators perform various activities during machining processes in MOS systems, consuming energy and generating carbon emissions. Operator activities are a collection of human activities undertaken in a machining environment to achieve production goals. By analyzing the behavioral characteristics and activity intensity of operators in MOS systems, this study conducts in-depth research and summarization of operator activity types, identifying 10 typical operator activities, including standing-waiting, standing-working, sitting-waiting, sitting-working, walking-idle, walking-loaded, running-idle, running-loaded, loading workpieces, and unloading workpieces.

[0153] In industrial environments, operators continuously engage in metabolic activities to maintain their various physiological functions and motor behaviors, which involves a certain amount of energy consumption, usually expressed as Total Energy Expenditure (TEE). This comprehensively reflects the metabolic energy required by an individual to complete various activities within a specific time period, and can be further expressed as the energy expenditure rate. With time The product of these factors. The human body's energy supply primarily relies on aerobic metabolism, which involves breaking down organic matter such as carbohydrates and fats into energy through the intake of oxygen (O2), while simultaneously expelling metabolic waste products—carbon dioxide (CO2) and water. Therefore, the energy consumption rate of the human body during activity... It can be achieved by monitoring oxygen intake rate and carbon dioxide production rate Estimating energy expenditure through indirect measurements is a method called indirect calorimetry. This involves estimating the operator's energy expenditure rate and oxygen intake rate. The activity intensity represented by 1 MET can be calculated from the metabolic equivalent (MET). 1 MET represents an activity intensity where 3.5 ml of oxygen is consumed per kilogram of body weight per minute, or 1.05 kilocalories of energy are consumed per kilogram of body weight per hour. Weir JB combined the metabolic characteristics of the human body with respiratory entropy (RQ) to derive the human body's energy expenditure. With oxygen consumption and carbon dioxide production The relationship between them.

[0154] The formula for calculating carbon emissions from operator activities is derived, including:

[0155] .

[0156] .

[0157] .

[0158] .

[0159] .

[0160] .

[0161] in, Energy expenditure rate per unit body weight of the operator. This refers to the operator's oxygen intake per unit body weight. The operator's activity intensity MET value, The carbon dioxide production rate per unit body weight of the operator. Carbon emission rate per unit body weight of the operator. For carbon dioxide density, BW represents the operator's activity time and the operator's weight.

[0162] 2. Modeling carbon emissions from operator activities.

[0163] (1) Carbon emission modeling for standing-waiting, standing-working, loading and unloading workpieces.

[0164] There is no positional movement during the standing-waiting, standing-working, loading and unloading of workpieces, and the carbon emission rate is basically stable once these activities are in normal execution. The MET values ​​of the activity intensity of standing-waiting, standing-working, loading and unloading of workpieces were obtained by consulting literature, and the carbon emission rate per unit weight of the operator in standing-waiting, standing-working, loading and unloading of workpieces was calculated, as shown in Table 2.

[0165] Table 2. Activity intensity MET values ​​and operator carbon emission rate per unit body weight for standing-waiting, standing-working, sitting-waiting, sitting-working, loading and unloading workpieces.

[0166]

[0167] By substituting the operator's carbon emission rate per unit body weight for standing-waiting, standing-working, sitting-waiting, sitting-working, loading workpieces, and unloading workpieces into the operator activity carbon emission calculation formula, a carbon emission model for standing-waiting, standing-working, sitting-waiting, sitting-working, loading workpieces, and unloading workpieces is constructed, as follows:

[0168] .

[0169] .

[0170] .

[0171] .

[0172] .

[0173] .

[0174] Among them, C StS C StO C SiS C SiO C LW C UW These are the standing-waiting carbon emission model, the standing-working carbon emission model, the sitting-waiting carbon emission model, the sitting-working carbon emission model, the loading carbon emission model, and the unloading carbon emission model, respectively. StS t StO t SiS t SiO t LW t UW These are the time spent standing and waiting, standing and working, sitting and waiting, sitting and working, loading workpiece, and unloading workpiece.

[0175] (2) Modeling of carbon emissions during walking-in-the-empty journey.

[0176] Operator walking-no-load activities refer to non-productive displacement behaviors performed by operators between machine tools and upstream / downstream workstations and machine tools during machining processes to ensure process continuity. The carbon emission intensity of this activity has a non-linear relationship with operator walking speed: different walking speeds correspond to different levels of exercise metabolism, which in turn affect the oxygen consumption rate and carbon emission equivalent per unit time. The operator walking speed and carbon emission rate per unit body weight were obtained by consulting literature, as shown in Table 3.

[0177] Table 3. Walking-unloaded operator walking speed and carbon emission rate per unit body weight

[0178]

[0179] To establish the relationship between operator walking speed and carbon emission rate per unit weight, a polynomial fitting was performed on the operator walking speed and carbon emission rate per unit weight in Table 3 to obtain a walking-idle operator carbon emission rate per unit weight model. This walking-idle operator carbon emission rate per unit weight model was then substituted into the operator activity carbon emission calculation formula to construct a walking-idle carbon emission model, as follows:

[0180] .

[0181] .

[0182] Among them, v w ξ represents the operator's walking speed. WN For the carbon emission rate per unit body weight of a walking-unloaded operator, C WN For the walking-no-load carbon emission model, t WN The time between travel and idle.

[0183] (3) Running-no-load carbon emission modeling.

[0184] Operator jogging-no-load activity refers to the rapid, non-productive movement between machine tools and upstream / downstream workstations, as well as between different machine tools, undertaken by operators during machining processes when faced with urgent order demands, sudden equipment malfunctions requiring rapid response, or to significantly shorten process transition times, in order to ensure the efficiency and continuity of the entire production chain. Compared to walking-no-load activity, jogging-no-load activity is characterized by higher speed and more concentrated and intense energy consumption. Table 4 shows the operator's running speed and carbon emission rate per unit body weight obtained from literature reviews during jogging-no-load activity.

[0185] Table 4. Running - Running speed and carbon emission rate per unit body weight for unloaded operators.

[0186]

[0187] To establish the relationship between operator running speed and carbon emission rate per unit body weight, a polynomial fitting was performed on the operator running speed and carbon emission rate per unit body weight in Table 4 to obtain a running-idle operator carbon emission rate per unit body weight model. This running-idle operator carbon emission rate model was then substituted into the operator activity carbon emission calculation formula to construct a running-idle carbon emission model, as detailed below:

[0188] .

[0189] .

[0190] Among them, vr ξ represents the operator's running speed. RN For the running-idle operator's carbon emission rate per unit body weight model, C RN For the running-no-load carbon emission model, t RN The time between running and idle.

[0191] (4) Modeling carbon emissions from walking-carrying and running-carrying.

[0192] The operator's load encompasses various forms and weights of objects. This might include a complete set of specialized tools carried for machine tool maintenance, which are often diverse and heavy; or raw materials and semi-finished products moved to meet production needs. As the load increases, the operator must overcome greater gravity and inertia during movement, requiring more effort from muscles to maintain balance and perform the movement. This not only significantly increases the difficulty and fatigue of the exercise but also markedly alters the body's metabolic rate and energy expenditure mechanisms, thus having a significant impact on carbon emissions. Table 5 shows the load weight, energy consumption, and carbon emission rate per unit body weight of operators walking at 4.5 km / h under no-load and four different load conditions.

[0193] Table 5 Carbon emission rate per unit body weight under different load conditions

[0194]

[0195] To establish the relationship between different load conditions of operators and carbon emission rate per unit body weight, a polynomial fitting was performed on the different load conditions and carbon emission rate per unit body weight in Table 5 to obtain the carbon emission rate per unit body weight model of operators under load conditions. This model was then substituted into the operator activity carbon emission calculation formula to construct a carbon emission model under load conditions. This model was then linearly superimposed with the walking-empty carbon emission model and the running-empty carbon emission model to construct the walking-loaded carbon emission model and the running-loaded carbon emission model, respectively, as detailed below:

[0196] .

[0197] .

[0198] .

[0199] .

[0200] Among them, FW represents the operator's load, ξ FW For the carbon emission rate per unit body weight of an operator under load, C FWFor the carbon emission model under loaded conditions, C WW For the walking-carrying carbon emission model, C RW For the carbon emission model of running-weighted carrying, t FW t is the time the operator bears the load. WW t represents the time spent walking while carrying a load. RW The time spent running with added weight.

[0201] S3, Integrated carbon emission modeling of machine tool-operator system.

[0202] Starting from the coordinated sequence of "machine tool activities – human activities," the entire machining process is divided into a series of basic activity units with clear temporal and functional divisions. Each unit includes specific machining tasks, required energy consumption characteristics, and carbon emission sources.

[0203] Based on the sequential dependency between human activities and machine tool activities, the typical machining process of a MOS system is further divided into n stages. The carbon emissions of each stage include both operator activity carbon emissions and machine tool activity carbon emissions corresponding to that stage. Machine tool activities consist of multiple machine tool dynamic elements, and operator activities can be further divided into various sub-activities. A comprehensive carbon emission model for the machine tool-operator system is constructed as follows:

[0204] .

[0205] .

[0206] .

[0207] .

[0208] .

[0209] in, For the first Phase-specific carbon emission models For the first Phase operator activity carbon emission model, For the first A phased model of carbon emissions from machine tool activities. For the first Phase 1 A carbon emission model for machine tool dynamics. For the first Phase 1 The power of various machine tool elements, For the first Phase 1 The duration of the machine tool motion element. For the first The carbon emission model for the k-th type of operator activity in stage k. For the first A model for the carbon emission rate per unit body weight of operator for the kth type of operator activity in stage k. For the first Phase 1 The duration of the operator's activity, A comprehensive carbon emission model for the machine tool-operator system.

[0210] To verify the feasibility and effectiveness of the carbon emission calculation method for the MOS system described above, a MOS system case study is established for modeling and analysis.

[0211] (1) Machine tool activity carbon emission model and operator activity carbon emission model.

[0212] Planar milling experiments were conducted using an XHK-714F vertical machining center and a W400F-FS cutting tool. The technical parameters of the XHK-714F vertical machining center and the W400F-FS cutting tool are shown in Table 6. Furthermore, our research team, combining the previously developed power consumption data acquisition system, has obtained the power and time models of the machine tool's dynamic elements, as follows.

[0213] The power acquisition method for constant power machine tool dynamic elements such as standby operation, cutting fluid spraying, X-axis rapid feed, Y-axis rapid feed, Z-axis upward rapid feed and Z-axis downward rapid feed is to perform various operations sequentially, record 50 sets of data, calculate the average value, and the power of constant power machine tool dynamic elements is shown in Table 6.

[0214] Table 6. Dynamic Power of XHK-714F Vertical Machining Center (Constant Power)

[0215]

[0216] The variable power machine tool dynamics, such as spindle rotation, X / Y / Z axis feed, and material cutting, are mainly determined by continuously changing experimental parameters and synchronously measuring them using a power-energy consumption data acquisition platform, thereby deriving the spindle rotation power. X / Y / Z axis feed power and material cutting power As shown in Table 7.

[0217] Table 7. Dynamic Power of XHK-714F Vertical Machining Center with Variable Power

[0218]

[0219] Based on the above data, a carbon emission model for machine tool activities is constructed. The carbon emission model for operator activities has already been obtained and will not be repeated here.

[0220] (2) Carbon emission calculation.

[0221] Continuing with the XHK-714F vertical machining center described earlier, as shown in Figure 3, the original workpiece is a cuboid measuring 105mm × 52mm × 30mm. The operator (weighing 75kg) carries the workpiece from the workpiece storage area 5.0 meters away from the machine tool and retrieves the workpiece (weighing 1.8kg) at a certain speed. Move to the machine tool and select the appropriate machining parameters ( , , , The original workpiece is processed by planar milling into a stepped part as shown in Figure 3(a). The workpiece is then removed and returned to the workpiece storage area.

[0222] The processing is divided into six stages according to the processing time sequence. Machine tool activities include standby activities, no-load activities, and cutting activities, while operator activities include walking-loading, loading workpieces, standing-working, standing-waiting, unloading workpieces, and walking-no-load activities. Carbon emission models are performed for the machine tool activities and operator activities in each stage.

[0223] The overall carbon emissions of a MOS system can be calculated as follows: Figure 4 shows the carbon emissions from machine tool activities, operator activities, and the cumulative total carbon emissions of the MOS system at each stage.

[0224] (3) Results analysis.

[0225] Observing Figure 4, we find that the fourth stage generates the most carbon emissions. Analyzing the above carbon emission calculation results, we draw the following conclusions:

[0226] 1) Operator activity carbon emissions account for a significant proportion of carbon emissions in MOS systems.

[0227] In the carbon emission composition of the MOS system, operator activity carbon emissions occupy an important and undeniable position, becoming a key influencing factor in the overall carbon footprint assessment of the MOS system. As shown in Figure 5, in processing stages 1, 2, 3, 5, and 6, the proportion of operator activity carbon emissions in the total carbon emissions of the system reaches 29.5%, 29.5%, 20.6%, 29.5%, and 32.0%, respectively, indicating that operator behavior has a high carbon emission proportion in multiple processing stages, fully demonstrating its significant impact on system energy consumption and environmental load. In contrast, in processing stage 4, since the operator is in a static state, the carbon emissions are significantly reduced. However, the main processing tasks in this stage are completed by the machine tool, including high-power dynamic elements such as spindle rotation, XYZ axis feed, and material cutting, resulting in a significant increase in machine tool activity carbon emissions. Further analysis from a holistic perspective reveals that operator activities in the MOS system account for 3.649g of total carbon emissions, representing 15.68% of the system's total carbon emissions. Machine tool activities, on the other hand, contribute 19.626g, or 84.32%. Although the latter remains the dominant source, operator-contributed carbon emissions still constitute a significant proportion of the system's total emissions and cannot be ignored. These results highlight the lack of a human factor dimension in traditional manufacturing system carbon footprint analysis, emphasizing the need to incorporate operator behavior into a systematic consideration when constructing high-precision, full-lifecycle carbon emission assessment models to improve model accuracy and the scientific basis of decision-making. This finding provides a new research perspective and methodological path for promoting the low-carbon transformation of manufacturing systems and lays a theoretical foundation and data support for the formulation and implementation of subsequent human-machine collaborative emission reduction strategies, further highlighting the role of operators in green and intelligent manufacturing systems.

[0228] 2) Low-carbon optimization strategies for machine tool-operator systems that take into account operator carbon emissions.

[0229] In machine-operator system processing, carbon emission modeling of the machine tool, operator, and machine-operator system helps identify additional low-carbon optimization potential. Based on the case study, in the first phase, the operator needs to achieve a certain level of carbon emissions. w1 The speed is such that the workpiece is carried 5.0 meters to approach the machine tool in standby mode. The sixth stage requires a speed of v. w2 The machine tool is moved 5.0 meters away from the machine tool, which is still in standby mode. These two stages significantly impact the carbon emissions of the MOS system. Therefore, this study proposes targeted low-carbon optimization strategies for the first and sixth stages: a) a low-carbon optimization strategy for machine tool carbon emissions, b) a low-carbon optimization strategy for operator carbon emissions, and c) a low-carbon optimization strategy for the machine tool-operator system. As shown in Figure 6, the optimization potential of the three strategies differs.

[0230] Specifically, for a) a low-carbon optimization strategy targeting machine tool carbon emissions, the operator's moving speed (load travel speed v) w1 and unloaded walking speed vw2 The speed can be increased within permissible limits to reduce machine tool downtime, thereby minimizing machine tool carbon emissions (as shown in Figure 6(a)). Under this strategy, the optimal moving speed for both the first stage (walking-loaded) and the sixth stage (walking-unloaded) is 3.0 m / s, achieving a minimum operator carbon emission of 0.206 g. For b) the low-carbon optimization strategy targeting operator carbon emissions, the operator carbon emission rate increases with increasing moving speed. However, since the distance remains constant at 5.0 m, the required time decreases, resulting in a trend of first decreasing and then increasing operator carbon emissions (as shown in Figure 6(b)). Under this strategy, the optimal walking speed v for the first stage with load is... w1 The optimal walking speed v in the sixth stage without load is 1.34 m / s. w2 The optimal travel speed is 1.32 m / s, corresponding to a minimum operator carbon emission of 0.249 g. For the low-carbon optimization strategy shown in Figure 6(c) targeting machine tool-operator system carbon emissions, the optimal travel speed for both the first stage (walking with load) and the sixth stage (walking without load) is 2.66 m / s, with a minimum system carbon emission of 0.557 g. Although strategy a) achieves the minimum machine tool carbon emission of 0.206 g, its operator carbon emission of 0.562 g is not optimal. Similarly, although strategy b) achieves the minimum operator carbon emission of 0.249 g, its system carbon emission of 0.713 g is also not optimal. Therefore, only strategy c) achieves the optimal machine tool-operator system. This represents an improvement of 0.9% compared to strategy a) and 21.9% compared to strategy b). Table 8 details the three optimal optimization strategies.

[0231] Table 8. Carbon emission results for the three low-carbon optimization strategies in the first and sixth stages.

[0232]

[0233] Based on the above analysis, without considering the operator, traditional low-carbon strategies include increasing operator walking speed and reducing machine tool idle time to reduce carbon emissions. The operator's movement speed ranges from 0.2 m / s to 3.0 m / s. Therefore, in strategy a), to achieve low-carbon optimization, the operator's walking-load speed is increased from 0.94 m / s to 3.0 m / s, and the walking-idle speed is increased from 1.19 m / s to 3.0 m / s. The machine tool's carbon emissions in the first and sixth stages also decrease from 0.586 g to 0.206 g, a reduction of 64.85%. This seems to achieve the energy-saving target. However, due to the increased operator walking speed in the first and sixth stages, the carbon emissions increase from 0.259 g to 0.356 g, an increase of 37.45%. Therefore, the machine tool-operator system emissions decrease from 0.845 g to 0.562 g, a reduction of 33.49%. Considering both machine tool carbon emissions and operator carbon emissions, as the machine tool travel speed increases, machine tool carbon emissions show a decreasing trend, while operator carbon emissions initially increase and then decrease as operator travel speed increases. Therefore, to minimize operator carbon emissions: Operator travel speed - load speed v w1 The travel-no-load speed v increased from 0.94 m / s to 2.66 m / s. w2 The speed increased from 1.19 m / s to 2.66 m / s. Machine tool carbon emissions decreased from 0.586 g to 0.232 g, a reduction of 60.41%; operator carbon emissions increased from 0.259 g to 0.325 g, an increase of 25.48%. The overall carbon emissions of the machine tool-operator system decreased from 0.845 g to 0.557 g, a reduction of 34.08%, as shown in Figure 7. Comprehensive analysis indicates that the low-carbon optimization strategy for the machine tool-operator system can achieve better low-carbon optimization results.

[0234] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A comprehensive carbon emission modeling method for machine tool-operator systems oriented towards human-centered intelligent manufacturing, characterized in that, include: S1. Modeling of carbon emissions from machine tool activities; Typical machine tool activities during CNC machine tool processing are identified, and the characteristics of these activities are analyzed to obtain various machine tool dynamic elements. A machine tool activity state vector is obtained by logically representing the execution state of these dynamic elements, where a dynamic element in an executing state is represented by logic 1, and in a non-executing state by logic 0. Power and time models for each type of machine tool dynamic element are constructed. Based on the power, time, and machine tool activity state vectors for each dynamic element, a carbon emission model for machine tool activities is constructed. S2. Operator activity carbon emission modeling: Typical operator activities during CNC machine tool processing are identified; a formula for calculating operator activity carbon emissions is derived based on the principles of human energy metabolism; the carbon emission patterns of operator activities are analyzed, and... The carbon emission model for operator activities is constructed using the formula for calculating carbon emissions from combined operator activities; S3, integrated carbon emission modeling of the machine tool-operator system; the machining process of the machine tool-operator system is divided into n stages, and the machine tool activities and operator activities of each stage are determined. Based on the carbon emission models corresponding to the machine tool activities and operator activities of each stage, a carbon emission model for each stage and an integrated carbon emission model for the machine tool-operator system are constructed. Typical operator activities include standing-waiting, standing-working, sitting-waiting, sitting-working, walking-unloaded, walking-loaded, running-unloaded, running-loaded, loading workpieces, and unloading workpieces. The formula for calculating carbon emissions from operator activities, derived based on the principles of human energy metabolism, includes: ； ； ； ； ； ;in, Energy expenditure rate per unit body weight of the operator. This refers to the operator's oxygen intake per unit body weight. The operator's activity intensity MET value, The carbon dioxide production rate per unit body weight of the operator. Carbon emission rate per unit body weight of the operator. For carbon dioxide density, Here, BW represents the operator's activity time and BW represents the operator's weight. The analysis of the carbon emission patterns of operator activities, combined with the calculation formula for operator activity carbon emissions, constructs an operator activity carbon emission model. This includes: determining that the carbon emission rates for standing-waiting, standing-working, sitting-waiting, sitting-working, loading workpieces, and unloading workpieces are basically stable by analyzing the carbon emission patterns of these activities; obtaining the MET values ​​of activity intensity for standing-waiting, standing-working, sitting-waiting, sitting-working, loading workpieces, and unloading workpieces by consulting literature, and calculating the operator's carbon emission rate per unit weight for these activities; and substituting this operator's carbon emission rate per unit weight into the operator activity carbon emission calculation formula to construct a carbon emission model for standing-waiting, standing-working, sitting-waiting, sitting-working, loading workpieces, and unloading workpieces, as detailed below: ； ； ； ； ； Among them, C StS C StO C SiS C SiO C LW C UW These are the standing-waiting carbon emission model, the standing-working carbon emission model, the sitting-waiting carbon emission model, the sitting-working carbon emission model, the loading carbon emission model, and the unloading carbon emission model, respectively. StS t StO t SiS t SiO t LW t UW The timeframes are: standing-waiting time, standing-working time, sitting-waiting time, sitting-working time, loading workpiece time, and unloading workpiece time. 0.5362 represents the carbon emission rate per unit weight of the operator during standing-waiting and sitting-waiting periods; 0.7424 represents the carbon emission rate per unit weight of the operator during standing-working periods; 0.6187 represents the carbon emission rate per unit weight of the operator during sitting-working periods; and 1.2373 represents the carbon emission rate per unit weight of the operator during loading and unloading workpieces. Analysis of the carbon emission patterns during walking-idle conditions revealed a non-linear correlation between the carbon emission rate during walking-idle conditions and the operator's walking speed. By consulting literature, the operator's walking speed and carbon emission rate per unit weight during walking-idle conditions were obtained. A polynomial fitting model of the carbon emission rate per unit weight of the operator during walking-idle conditions was obtained. This model was then substituted into the operator activity carbon emission calculation formula to construct a carbon emission model for walking-idle conditions, as detailed below: ； ; where v w ξ represents the operator's walking speed. WN For the carbon emission rate per unit body weight of a walking-unloaded operator, C WN For the walking-no-load carbon emission model, t WN The time spent walking-idle was used as the starting point. Analysis of the carbon emission patterns during the running-idle period revealed a non-linear correlation between the running-idle carbon emission rate and the operator's running speed. Literature review was conducted to obtain the operator's running speed and carbon emission rate per unit body weight during the running-idle period. A polynomial fitting was performed on the operator's running speed and carbon emission rate per unit body weight to obtain a model of the operator's carbon emission rate per unit body weight during the running-idle period. This model was then substituted into the operator's activity carbon emission calculation formula to construct a carbon emission model for the running-idle period, as detailed below: ； ; where v r ξ represents the operator's running speed. RN For the running-idle operator's carbon emission rate per unit body weight model, C RN For the running-no-load carbon emission model, t RN The time between running and idle is used as an example. The operator's load and carbon emission rate per unit weight are obtained at the same preset speed. A polynomial fitting is performed on the operator's load and carbon emission rate per unit weight to obtain a carbon emission rate model per unit weight under load. This model is then substituted into the operator's activity carbon emission calculation formula to construct a carbon emission model under load. Finally, the carbon emission model under load is linearly superimposed with the walking-idle carbon emission model and the running-idle carbon emission model to construct the walking-loaded carbon emission model and the running-loaded carbon emission model, respectively. The details are as follows: ； ； ； ; where FW represents the operator's load, ξ FW For the carbon emission rate per unit body weight of an operator under load, C FW For the carbon emission model under loaded conditions, C WW For the walking-carrying carbon emission model, C RW For the carbon emission model of running-weighted carrying, t FW t is the time the operator bears the load. WW t represents the time spent walking while carrying a load. RW The time spent running with added weight.

2. The integrated carbon emission modeling method for machine tool-operator systems oriented towards human-centered intelligent manufacturing according to claim 1, characterized in that, The typical machine tool activities include standby MA1, spindle acceleration MA2, no-load MA3 and material cutting MA4, spindle deceleration MA5 and auxiliary activities MA6. The machine tool dynamic elements include standby operation, machine tool lighting, cutting fluid spraying, automatic chip removal, tool selection, automatic tool change, X / Y / Z axis feed, spindle rotation and material cutting.

3. The integrated carbon emission modeling method for machine tool-operator systems oriented towards human-centered intelligent manufacturing according to claim 2, characterized in that, The construction of power models for each machine tool dynamic element includes: constructing power models for standby operation, machine tool lighting, cutting fluid spraying, automatic chip removal, tool selection, and automatic tool change, as detailed below: ;in, Let be the power measurement value of the j-th power element of the i-th machine tool, and N be the number of power measurement values; X / Y / Z axis feed includes X / Y / Z axis cutting feed and X / Y / Z axis rapid traverse. The power models for X / Y / Z axis cutting feed and X / Y / Z axis rapid traverse are constructed as follows: ； Among them, P xf For X / Y / Z axis cutting feed power, The coefficient of the quadratic term in the function formula is... Let be the coefficient of the linear term in the function formula, and and It was obtained through linear fitting of experimental data. P is the cutting feed rate. rfd For X / Y / Z axis rapid feed power, To establish a constant related to the rapid traverse speed, a spindle rotation power model is constructed as follows: ;in, The coefficients of the function formula, Let be the constant term of the function formula, and and n is the spindle speed, obtained through linear fitting of experimental data; a material cutting power model is constructed as follows: ;in, These are the coefficients of the material cutting power model. The index of the spindle speed. This is an index of the feed rate. This is an index representing the depth of cut. is an index of the cutting width, and 、 、 、 、 The values ​​were obtained through linear fitting of experimental data, where n is the spindle speed, f is the feed rate, and a is the feed rate. p For cutting depth, a e This represents the cutting width.

4. The integrated carbon emission modeling method for machine tool-operator systems oriented towards human-centered intelligent manufacturing according to claim 3, characterized in that, The construction of the time model for each machine tool dynamic element includes: constructing time models for standby operation, machine tool lighting, cutting fluid spraying, automatic chip removal, and spindle rotation, as detailed below: ;in, Let i be the end time of the execution of the i-th type of machine tool motion element. Let be the start time of the i-th type of machine tool motion element execution; construct the tool selection and automatic tool change time model as follows: ; where t tc For automatic tool change time, A represents the number of tool positions rotated by the machine tool turret. tc B is the constant term in the function formula. tc A represents the coefficient of the function formula. tc and B tc The X / Y / Z axis cutting feed time model and the X / Y / Z axis rapid traverse time model were obtained through linear fitting of experimental data, as follows: ； ; among which, L xf v is the cutting feed distance. xf L is the cutting feed rate. rfd For rapid feed distance, v rfd To achieve a rapid feed rate, a material cutting time model is constructed as follows: ； Among them, V remove The total volume of material removed is denoted by , MRR by , n by , and f by , where a is the total volume of material removed. p For cutting depth, a e This represents the cutting width.

5. The integrated carbon emission modeling method for machine tool-operator systems in human-centered intelligent manufacturing according to claim 4, characterized in that, The construction of a machine tool activity carbon emission model based on the power model, time model, and machine tool activity state vector for each machine tool element includes: ； ;in, For the carbon emission model of the i-th type of machine tool activity, Indicates the carbon emission factor of electricity. This is the transpose of the machine tool dynamic element power matrix. The matrix formed by the i-th column of the machine tool motion element time matrix. The power of the machine tool's dynamic element m, Let i be the time of the activity of the i-th type of machine tool. The machine tool motion element corresponding to the i-th type of machine tool activity The logical representation of .

6. The integrated carbon emission modeling method for machine tool-operator systems in human-centered intelligent manufacturing according to claim 5, characterized in that, The construction of a carbon emission model for each stage and a comprehensive carbon emission model for the machine tool-operator system, based on the carbon emission models corresponding to machine tool activities and operator activities at each stage, includes: ； ； ； ； ;in, For the first Phase-specific carbon emission models For the first Phase operator activity carbon emission model, For the first A phased model of carbon emissions from machine tool activities. For the first Phase 1 A carbon emission model for machine tool dynamics. For the first Phase 1 The power of various machine tool elements, For the first Phase 1 The duration of the machine tool motion element. For the first The carbon emission model for the k-th type of operator activity in stage k. For the first A model for the carbon emission rate per unit body weight of operator for the kth type of operator activity in stage k. For the first Phase 1 The duration of the operator's activity, A comprehensive carbon emission model for the machine tool-operator system.

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