Method for monitoring and regulating energy consumption and carbon reduction in general assembly construction production link

Abstract

The application provides a kind of total assembly construction production energy consumption dynamic monitoring and energy saving and carbon reduction regulation method, comprising: S1, the data related to energy consumption in each process in industrial production cycle is collected;S2, an energy consumption prediction model is constructed, the energy consumption prediction model includes mechanical power energy consumption prediction model and heat process improvement energy consumption prediction model;S3, the data collected in step S1 is input into the energy consumption prediction model constructed in step S2, and relevant energy consumption prediction data is obtained;S4, the energy consumption prediction data is converted into carbon emissions, and the total carbon emissions are obtained based on the carbon intensity coefficient calculation of energy type;S5, the total carbon emissions are input into the multi-objective genetic algorithm to generate the Pareto optimal solution set, and the best combination of energy consumption and carbon emissions is obtained.The application organizes energy consumption cost, carbon emission intensity and production efficiency through multi-objective optimization algorithm, realizes global optimal decision under equipment cooperation and dynamic working condition, and effectively adapts to the complex demands of multi-variety and small-batch production.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic monitoring of energy consumption, and particularly to a method for dynamic monitoring of energy consumption and energy-saving and carbon-emission reduction regulation in the general assembly construction production process. Background Art

[0002] General assembly construction is the core link of complex equipment manufacturing, involving multi-process collaborative operations such as mechanical assembly, electrical integration, and system debugging, and is characterized by high energy consumption, concentrated carbon emissions, and large dynamic fluctuations. Traditional methods mostly perform static energy consumption management based on historical data, which is difficult to adapt to the dynamic requirements under the production mode of multiple varieties and small batches, and lack refined control of carbon emissions. Although the existing patent CN117193212A introduces online modeling technology, it does not combine carbon emission calculation with multi-device collaborative optimization. Therefore, there is an urgent need for an intelligent method that can achieve dynamic perception of energy consumption, accurate accounting of carbon footprint, and multi-objective collaborative regulation. Summary of the Invention

[0003] The present invention aims to at least solve the technical problems existing in the prior art, and particularly innovatively proposes a method for dynamic monitoring of energy consumption and energy-saving and carbon-emission reduction regulation in the general assembly construction production process.

[0004] To achieve the above object of the present invention, the present invention provides a method for dynamic monitoring of energy consumption and energy-saving and carbon-emission reduction regulation in the general assembly construction production process, including the following steps:

[0005] S1, collecting data related to energy consumption in each process during the industrial production cycle;

[0006] S2, constructing an energy consumption prediction model, the energy consumption prediction model including a mechanical power energy consumption prediction model and a thermal process improvement energy consumption prediction model:

[0007] S3, inputting the data collected in step S1 into the energy consumption prediction model constructed in step S2 to obtain relevant energy consumption prediction data;

[0008] S4, converting the energy consumption prediction data into carbon emissions, and calculating based on the carbon intensity coefficient of the energy type to obtain the total carbon emissions;

[0009] S5, inputting the total carbon emissions into a multi-objective genetic algorithm to generate a Pareto optimal solution set, and obtaining the best combination of energy consumption and carbon emissions.

[0010] Preferably, the expression of the mechanical power energy consumption prediction model is:

[0011] ;

[0012] Wherein, is the total mechanical power energy consumption;

[0013] This represents the total number of processes;

[0014] Let i be the number of independent devices involved in the i-th process.

[0015] This represents the overall efficiency coefficient of equipment j in process i;

[0016] The total duration of the i-th process;

[0017] This represents the base standby power of device j in process i;

[0018] This represents the dynamic power increment of device j in process i;

[0019] This represents the activation state function of device j in process i; when the device is in the processing state, When the equipment is in processing mode, or in standby / idling mode, ;

[0020] This indicates the total standby energy consumption and the total processing energy consumption.

[0021] Preferably, The calculation formula is:

[0022] ;

[0023] in, Indicates motor efficiency;

[0024] Indicates process matching efficiency;

[0025] This represents the aging degradation factor.

[0026] Preferably, It is calculated using the following formula:

[0027] ;

[0028] in Indicates total standby power consumption;

[0029] Indicates processing energy consumption;

[0030] , These represent the actual start and end times of equipment j in process i.

[0031] Preferably, the expression for the thermal process modification energy consumption prediction model is:

[0032] ;

[0033] in, This indicates the total energy consumption of the thermal modification process;

[0034] Indicates the total number of processes;

[0035] This represents the number of devices used in the i-th process step;

[0036] This represents the sensible heat energy consumption of equipment j in process i;

[0037] This represents the radiative heat loss of equipment j in process i.

[0038] Preferably, The specific calculation formula is as follows:

[0039] ;

[0040] in, This represents the specific heat capacity at constant pressure of the material processed by equipment j in process i.

[0041] This indicates the mass of material processed by equipment j in a single operation in process i;

[0042] This represents the absolute value of the target temperature rise of equipment j in process i;

[0043] This represents the extreme temperature of equipment j in process i, taken as... ;

[0044] This represents the reference temperature of equipment j in process i, taken as... .

[0045] Preferably, The specific calculation formula is as follows:

[0046] ;

[0047] in, This represents the overall emissivity of device j in process i;

[0048] It is the Stefan-Boltzmann constant;

[0049] This represents the effective radiation area of ​​equipment j in process i;

[0050] This represents the average operating temperature of equipment j in process i;

[0051] This indicates the ambient temperature of equipment j in process i;

[0052] This indicates the effective heating time of device j in process i.

[0053] Preferably, the step of converting energy consumption prediction data into carbon emissions and calculating the total carbon emissions based on the carbon intensity coefficient of the energy type is obtained through the following calculation formula:

[0054] ;

[0055] in, Indicates total carbon emissions;

[0056] This represents the mechanical power consumption at time t;

[0057] This represents the regional power grid carbon emission factor at time t;

[0058] This indicates the energy consumption change during the thermal process at time t;

[0059] It represents the calorific value of natural gas and is used to convert the energy consumption of thermal process modification into the natural gas consumption.

[0060] This indicates the carbon emission factor of natural gas.

[0061] Preferably, the expression for inputting the total carbon emissions into the multi-objective genetic algorithm to generate the Pareto optimal solution set is:

[0062] ;

[0063] To take the sign of the minimum value;

[0064] The weighting coefficients representing energy consumption targets;

[0065] It represents total energy consumption, including mechanical power energy consumption and thermal process modification energy consumption;

[0066] This indicates the highest historical energy consumption;

[0067] Weighting coefficients representing carbon emission targets;

[0068] Indicates total carbon emissions;

[0069] The largest carbon emissions in history;

[0070] st is the constraint symbol;

[0071] Indicates the production cycle time;

[0072] Maximum permissible production cycle time;

[0073] Product quality indicators;

[0074] Minimum permissible product quality specifications.

[0075] Preferably, it also includes S6, dynamic feedback and compensation:

[0076] S6-1, based on real-time mechanical power Compared with twin model predictions To address the deviation, the equipment parameters are dynamically adjusted to bring the energy consumption error within the threshold, while also considering the dynamic accounting results of carbon emissions to optimize the adjustment strategy.

[0077] S6-2 aims to minimize the overall goal of no-load energy consumption and carbon emissions during the final assembly process by optimizing the sequence and timing of process activation, while simultaneously satisfying process dependencies and resource constraints.

[0078] In summary, by adopting the above technical solutions, this invention can monitor energy consumption and carbon emission data of multiple processes in real time, dynamically compensate for energy efficiency deviations caused by production fluctuations, and break through the limitations of traditional static management; it establishes a full life cycle carbon footprint tracking model to accurately link materials, processes and carbon emissions, solving the problem of lack of refined management; and it coordinates energy consumption costs, carbon emission intensity and production efficiency through multi-objective optimization algorithms to achieve global optimal decision-making under equipment collaboration and dynamic operating conditions, effectively adapting to the complex needs of multi-variety small-batch production.

[0079] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0080] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0081] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0082] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0083] This method integrates digital twin technology with IoT monitoring to construct a dynamic energy consumption-carbon emission model covering the entire assembly and construction process. By combining real-time data feedback with multi-objective optimization algorithms, it achieves precise control over both energy consumption and carbon emissions. The specific steps are as follows:

[0084] Step 1: Digital Twin Modeling and Energy Consumption Prediction

[0085] A digital twin model is built based on the final assembly process card to simulate the energy consumption characteristics of each process (such as mechanical assembly, welding, painting, and system testing) and predict the initial energy consumption value.

[0086] By employing 3D modeling technology (using SolidWorks and Unity3D to collaboratively construct a 1:1 physical mapping), the physical entities (equipment / materials / environment) of the final assembly workshop are transformed into digital twins. This integrates three types of data collected in real-time by IoT sensors: equipment operating parameters (current / voltage / speed); environmental parameters (temperature / humidity / cleanliness); and process parameters (material properties / processing accuracy requirements). A dynamic energy consumption simulation engine based on finite element analysis (FEA) is then constructed. The digital twin model interacts in real-time with the data acquisition system of step S1 via the OPC UA protocol. The sensor sampling frequency reaches 1kHz to ensure dynamic response, providing a millisecond-level time-series data foundation for the energy consumption prediction model in step S2, reducing the prediction error to ±3%.

[0087] (1) Mechanical power energy consumption:

[0088] The mechanical work consumed by the drive equipment during assembly is calculated using the following formula:

[0089]

[0090] in, Total energy consumption for mechanical power;

[0091] This represents the total number of processes;

[0092] Let i be the number of independent devices involved in the i-th process.

[0093] The total duration of the i-th process;

[0094] This represents the base standby power of device j in process i;

[0095] This represents the dynamic power increment of device j in process i;

[0096] This represents the activation state function of device j in process i; when the device is in the processing state, When the equipment is in processing mode, or in standby / idling mode, ;

[0097] This represents the overall efficiency coefficient of equipment j in process i:

[0098] ;

[0099] in, Indicates motor efficiency;

[0100] Indicates process matching efficiency (considering cooling / lubrication system losses);

[0101] Indicates the aging degradation factor;

[0102] A time-slicing algorithm (100ms / slice) is used to decompose the power composition of the equipment in each process. A state machine model is used to determine the equipment's activation status (processing / standby / idling) in real time. Combined with the equipment's factory energy efficiency curve and real-time efficiency decay coefficient (dynamically corrected by the optimization algorithm in step S5), energy consumption accumulation calculation over time is achieved. The energy consumption data of each process output by this model (granular to the equipment level) directly serves as the basic data source for carbon emission calculation in step S4. At the same time, the sensor sampling weights in step S1 are corrected through a feedback channel, improving the accuracy of process-level energy-saving potential analysis in typical scenarios.

[0103] A full lifecycle energy efficiency assessment model for the equipment is constructed by analyzing series motor efficiency (based on real-time query of the IEC 60034 standard curve), process matching efficiency (integrating data from cooling system flow sensors and lubrication pressure), and aging factor (using a Weibull distribution model to fit the equipment failure curve). The calculation results are used to correct the predicted mechanical power consumption value in step S2 (correction coefficient range 0.85-1.12), and also serve as a constraint on equipment health in the multi-objective optimization algorithm of step S5, guiding the optimization of equipment maintenance cycles. In typical scenarios, this can improve the overall efficiency of the equipment while extending the maintenance interval.

[0104] This indicates the total standby energy consumption and the total processing energy consumption;

[0105]

[0106] in Indicates total standby power consumption;

[0107] Indicates processing energy consumption;

[0108] , These represent the actual start and end times of equipment j in process i;

[0109] (2) Thermal process modification of energy consumption

[0110] The effective energy consumption of heating processes such as welding and coating drying is determined using an improved enthalpy change model:

[0111] Based on the thermodynamic enthalpy change theory (ΔH=mcΔT), a temperature-energy consumption dynamic response model is established by integrating sensible heat transfer data collected by an infrared temperature sensor with a radiation heat loss calculation model (using the Monte Carlo method to simulate the thermal radiation path). This model interacts in real time with the infrared thermal imager (sampling frequency 25Hz) in the IoT system of step S1, improving accuracy by 15% compared to traditional thermal balance models. It can capture dynamic characteristics such as welding arc heat loss (error ≤ ±5%) and temperature field distribution in the coating drying chamber (spatial resolution 0.1℃) in real time, providing specific thermal process correction parameters for energy consumption prediction in step S3.

[0112]

[0113] in, This indicates the total energy consumption of the thermal modification process;

[0114] Indicates the total number of processes;

[0115] This represents the number of devices used in the i-th process step;

[0116] This represents the sensible heat energy consumption of equipment j in process i; The specific calculation formula is as follows:

[0117] ,

[0118] in, This represents the specific heat capacity at constant pressure of the material processed by equipment j in process i (unit: J / (kg·K)).

[0119] This indicates the mass (in kg) of material processed by equipment j in a single operation i.

[0120] This represents the absolute value of the target temperature rise of equipment j in process i;

[0121] This represents the extreme temperature of equipment j in process i, taken as... ;

[0122] This represents the reference temperature of equipment j in process i, taken as... ;

[0123] This represents the radiative heat loss of equipment j in process i; The specific calculation formula is as follows:

[0124]

[0125] in, This represents the overall emissivity of device j in process i; this parameter is affected by the surface oxide layer, such as the emissivity of aluminum alloy decreasing after heat treatment.

[0126] is the Stefan-Boltzmann constant, which is the ratio of the total energy radiated by a blackbody per unit surface area per unit time into the hemispherical space to the fourth power of the thermodynamic temperature. .

[0127] This represents the effective radiation area of ​​equipment j in process i;

[0128] This represents the average operating temperature of equipment j in process i;

[0129] This indicates the ambient temperature of equipment j in process i;

[0130] This indicates the effective heating time of equipment j in process i; it is related to the process curve (such as annealing holding time).

[0131] The theoretical sensible heat demand is calculated by multiplying the material specific heat capacity database (integrating parameters of 1000+ industrial materials) with real-time mass data from weighing sensors, combined with the target temperature rise and temperature threshold correction coefficient fed back by an infrared thermometer (dynamically adjusted based on PID closed-loop control). The output value serves as the core parameter of the energy consumption model for the thermal process modification in step S2, and is also transmitted to the optimization algorithm in step S5 via an OPC server. This provides a theoretical benchmark value for adjusting the power of the heating equipment, avoiding energy waste caused by overheating. In aluminum alloy welding scenarios, this reduces heat loss and minimizes the calculation deviation of carbon emissions in step S4.

[0132] Based on Stefan-Boltzmann law (P=εσA(T)) 4 -T0 4 The overall emissivity (ε) is calculated by real-time acquisition of the oxide layer thickness on the equipment surface using an infrared spectrometer. Combined with the effective radiation area (A) and temperature field distribution (T is the equipment temperature, T0 is the ambient temperature) obtained by a thermal imager, the finite difference method is used to calculate the radiative heat loss at different process stages. The calculation results, on the one hand, correct the radiative heat loss parameters of the thermal process performance improvement model in step S2 (correction coefficient 0.92-1.08), and on the other hand, are transmitted to the carbon emission calculation model in step S4 via a data bus. This provides a quantitative basis for optimizing the insulation layer of the heating equipment and adjusting process parameters, reducing radiative heat loss in the coating and drying process, and simultaneously lowering the carbon emission calculation error in step S4.

[0133] Step 2: Dynamic Calculation of Carbon Emissions

[0134] Energy consumption data is converted into carbon emissions, and calculated based on the carbon intensity coefficient of energy type (electricity, gas, etc.):

[0135]

[0136] in, This represents the total carbon emissions (unit: kilograms of carbon dioxide equivalent, kgCO2e).

[0137] ∑ is the summation symbol, representing the accumulation of carbon emissions at different times or for different energy types.

[0138] This represents the mechanical power consumption at time t (unit: kilowatt-hour, kWh), assuming it all comes from electricity consumption.

[0139] The carbon emission factor of the regional power grid at time t is expressed in kgCO2e / kWh.

[0140] The energy consumption of thermal process at time t is expressed in joules (J).

[0141] The calorific value of natural gas is expressed in units of J / m³. 3 This is used to convert the energy consumption of thermal process modification into natural gas consumption.

[0142] Carbon emission factor of natural gas (unit: kgCO2e / m³) 3 ).

[0143] Based on Life Cycle Assessment (LCA) theory (ISO 14040 standard framework), an "energy consumption-carbon emission" mapping model is constructed. It connects to the regional power grid dispatch center via API to obtain real-time carbon intensity coefficients (updated every 15 minutes). This model integrates the mechanical power energy consumption (electricity) and thermal process modification energy consumption (gas gas) classification data output from step S2 to establish a spatiotemporally coupled dynamic accounting engine. It interacts in real-time with the IoT system (collection frequency 1Hz) from step S1, separating energy consumption data by energy type (electricity / gas gas) and multiplying each by the corresponding carbon intensity coefficient (electricity data from the regional power grid, gas gas data using GB / T 32151.2-2015 benchmark values). This achieves minute-level dynamic carbon emission measurement, improving accuracy compared to traditional batch accounting and providing second-level carbon emission data input for the multi-objective optimization in step S5.

[0144] The mechanical power consumption (electricity, in kWh) output from step S2 is separated from the thermal process modification energy consumption (gas, in J) using the Energy Flow Decomposition (EFA) algorithm. The electricity portion is multiplied by the real-time regional power grid carbon intensity factor (kgCO2e / kWh) obtained in step S1, and the gas portion is converted by calorific value (natural gas calorific value 36 MJ / m³). 3 Multiply by the gas carbon emission factor (2.16 kg CO2e / m³) 3 The total carbon emissions for the entire process are calculated by accumulating the results using a time-weighted average method. The calculation results are stored in a blockchain-based evidence storage system (meeting carbon footprint traceability requirements) and fed back in real time to the energy consumption prediction model in step S3 to correct deviations (feedback cycle 500ms). This provides a quantitative benchmark for carbon emissions for dual-objective optimization and can accurately distinguish the carbon contribution ratio of electricity and gas in multi-energy structure scenarios (electricity typically accounts for 65%-75%), supporting the dynamic adjustment of the weight coefficients in step S5.

[0145] Step 3: Optimization of both energy consumption and carbon emissions

[0146] A multi-objective genetic algorithm (NSGA-II) is used to generate a Pareto optimal solution set, balancing energy consumption and carbon emissions.

[0147]

[0148] The minimization symbol represents the search for a solution that minimizes the objective function;

[0149] The weighting coefficients representing energy consumption targets;

[0150] Total energy consumption (unit: J) includes mechanical power energy consumption and thermal process modification energy consumption;

[0151] This indicates the highest historical energy consumption;

[0152] Weighting coefficients representing carbon emission targets;

[0153] This represents total carbon emissions (unit: kgCO2e).

[0154] The highest historical carbon emissions (unit: kgCO2e);

[0155] st is a constraint symbol, and the constraints include the upper limit of the production cycle and quality requirements.

[0156] Indicates the production cycle time;

[0157] Maximum permissible production cycle time;

[0158] Product quality indicators;

[0159] Minimum permissible product quality specifications.

[0160] A dual-objective optimization framework is constructed using the NSGA-II algorithm. The total energy consumption (mechanical + thermal processes) output in step S2 and the total carbon emissions calculated in step S4 are the optimization objectives. Population diversity is maintained through Fast Nondominated Sorting and Crowding Distance. Three types of constraints are integrated: production cycle ≤ T_max from process card data in step S1; product qualification rate ≥ 99.5% from the quality inspection system; and equipment health ≥ 0.85 from the aging factor model in step S2. During algorithm iteration, real-time IoT data (equipment load rate, material properties) from step S1 is called in real time to dynamically adjust the fitness function. The crossover probability (0.7-0.9) and mutation probability (0.01-0.05) are adaptively adjusted based on the energy consumption prediction deviation (from step S3). Under the premise of ensuring a product qualification rate (≥ 99.5%), a Pareto optimal balance between energy consumption and carbon emissions is achieved. The optimization efficiency is improved by 50% compared to traditional single-objective algorithms, and the generated solution set directly drives the dynamic control strategy in step S5.

[0161] The optimization priority of energy consumption and carbon emissions is adjusted by dynamic weighting coefficients (ω1, ω2, satisfying ω1+ω2=1). The weighting coefficients are corrected in real time by the fuzzy logic controller based on external factors (ω2 increases to 0.6-0.7 when triggered by carbon tariff policies, and ω1 increases to 0.6-0.7 when triggered by peak energy prices). The optimization function is constructed by combining the production cycle upper limit (T≤T_max) in step S1 and the equipment efficiency constraint (η≥η_min) in step S2. The energy consumption term (E / E_max) of the objective function is normalized using the predicted energy consumption data in step S2, and the carbon emission term (C / C_max) is normalized using the calculated data in step S4. The generated Pareto front (8-12 sets of optimization schemes) is displayed through a visual interface, allowing decision-makers to select control strategies based on real-time operating conditions and pass the optimal scheme parameters (such as equipment power and process sequence) to the adaptive PID controller in step S5. Under typical scenarios, the decision response time is shortened to 30 seconds.

[0162] The generated Pareto optimal solution set represents a series of optimal combinations of energy consumption and carbon emissions under different trade-offs. These solutions form a frontier, the Pareto front, where each solution represents a possible optimal solution. Decision-makers can select the most suitable solution as the optimization objective based on the company's specific needs (such as energy costs, environmental policies, etc.).

[0163] Step 4: Dynamic Feedback and Compensation Regulation

[0164] The dynamic feedback and compensation control mechanism is mainly divided into two parts: intra-process compensation and inter-process coordination.

[0165] A three-tiered closed-loop system of "real-time monitoring - deviation analysis - dynamic control" is constructed. In step S1, real-time energy consumption data is collected by IoT sensors (1kHz sampling rate). This data is then input into the digital twin model in step S2 to calculate theoretical predictions. After passing through the deviation analysis module (using Kalman filtering for noise reduction), the system drives adaptive PID control within the process (adjusting equipment power) and time-series optimization between processes (adjusting process order). The real-time monitoring module interacts with the data acquisition system in step S1 via the MQTT protocol. Deviation analysis data is fed back to step S2 to correct the energy consumption prediction model (correction period 200ms). The dynamic control command receives the Pareto optimal parameters output by the multi-objective optimization algorithm in step S5, ensuring that energy consumption fluctuations in the production process are controlled within ±5%, peak carbon emissions are reduced by 22%, and the response speed reaches the second level (≤3s).

[0166] (1) In-process compensation: Adaptive PID control

[0167] Based on real-time mechanical power Compared with twin model predictions To mitigate deviations, equipment parameters (such as motor speed and heating power) are dynamically adjusted to bring energy consumption errors within the threshold (±5%). At the same time, the dynamic accounting results of carbon emissions are considered to optimize the adjustment strategy.

[0168] A Mamdani-type fuzzy logic controller (inputs are energy consumption deviation e(t) and carbon emission deviation ΔC(t) = C_real - C_target, output is PID coefficient correction) is used to establish a two-dimensional fuzzy rule base (5×5 rule matrix). The deviation between the dynamic carbon emission calculation result (C_real) in step S4 and the target value (C_target comes from the optimization scheme in step S5) is used as a feedback variable to correct the proportional (Kp), integral (Ki), and derivative (Kd) coefficients in real time. The carbon emission weight coefficients in the fuzzy logic rule base are dynamically updated by the optimization algorithm in step S5 (the weight of ΔC(t) is increased to 0.6 when the carbon price is high). Compared with traditional PID control, the overshoot is reduced by 25%, the steady-state error convergence time is shortened to 15 seconds, and the energy saving rate in motor-driven equipment reaches 8%-10%, while reducing the carbon emission calculation deviation in step S4 to ±2%.

[0169] (1.1) Deviation Calculation

[0170] Calculate the deviation between real-time power and predicted power:

[0171]

[0172] In step S1, the power sensor of the IoT system (accuracy ±0.1kW) collects P_real in real time (sampling frequency 1kHz), and calculates the difference with P_pred output from the digital twin model in step S2 (update frequency 500ms). A moving average filter (window size 5 sampling points) is used to eliminate high-frequency noise, quantifying the deviation between actual energy consumption and theoretical values. The deviation signal e(t) serves as the core input parameter for the adaptive PID control in this step (polarity determines the power adjustment direction: positive deviation reduces power, negative deviation increases power). Furthermore, it corrects the efficiency coefficient of the energy consumption prediction model in step S2 through a feedback channel (correction formula: η_new=η_old×(1-0.1×|e(t)| / P_pred)). In typical scenarios, the model prediction accuracy is improved by 18%.

[0173] (1.2) Adaptive PID output

[0174] Calculate the control quantity based on the deviation. u ( t (Using time-varying gain coefficient) , , :

[0175]

[0176] The weights of the proportional, integral, and derivative components are dynamically adjusted using time-varying gain coefficients (Kp(t), Ki(t), Kd(t)). These gain coefficients are corrected in real-time by the fuzzy logic controller based on dual deviations (energy consumption deviation e(t) from the comparison between steps S1 and S2, and carbon emission deviation ΔC(t) from the calculation result of step S4). The correction formula is Kp(t) = α × γ × δ (α is the base coefficient, γ is the energy consumption error factor, and δ is the carbon emission factor). The calculation of the control quantity u(t) integrates the weight coefficients (ω1, ω2) output from the multi-objective optimization algorithm in step S5. When the carbon emission weight ω2 > 0.5, Kd(t) is automatically increased to accelerate the carbon emission response. The generated control quantity guides equipment power adjustment, causing the energy consumption deviation to converge to the ±5% threshold within 15 seconds. In typical scenarios, the dynamic response speed is improved by 30%, while providing accurate input for equipment parameter correction in step S106.

[0177] in, The gain coefficient is a time-varying value, dynamically adjusted using fuzzy logic, with the adjustment rules taking into account the dynamic calculation results of carbon emissions. Feedback.

[0178] (1.3) Adjustment rules for time-varying gain coefficient

[0179] The gain coefficient is dynamically adjusted using fuzzy logic, taking into account the dynamic carbon emission accounting results. Feedback:

[0180]

[0181] Adjust the error factor ( Based on the energy consumption prediction deviation e(t) in step S2, the PI algorithm calculates γ = 1 + 0.2 × |e(t)| / P_pred) and carbon emission adjustment factors (δ, ζ, ι, calculated based on the carbon emission deviation ΔC(t) in step S4: δ = 1 + 0.3 × max(ΔC(t) / C_target, 0)). These are coupled through a fuzzy logic rule base (containing 25 if-then rules). When the actual carbon emission exceeds the target value (C_target comes from the optimized solution set in step S5), the weights of the carbon emission factors are automatically increased (δ, ζ, ι with an upper limit of 1.5). Output function: To achieve a coordinated response of the PID controller to the dual targets of "energy consumption-carbon emission," the carbon emission adjustment sensitivity can be improved by 40% during periods of high carbon price (>80 yuan / ton). Simultaneously, the adjustment results are fed back to the optimization algorithm in step S5 via the data bus to update the Pareto optimal solution set.

[0182] in, α , β , i All are basic gain coefficients;

[0183] All are error-related adjustment factors;

[0184] d , g , ι All are adjustment factors related to carbon emissions;

[0185] For at any time t The carbon emission target value.

[0186] (1.4) Equipment parameter correction

[0187] According to the control quantity u ( t Adjust the equipment power:

[0188] For example: The predicted power of a certain welding equipment is =50kW, real-time monitoring =54kW, deviation =+4kW. If =0.8, then the corrected power is: =50 + 0.8 × 4 = 53.2 kW.

[0189] Based on the linear superposition model of the control quantity u(t) output in step S104 and the predicted power P_pred from the twin model in step S2 (P_corr=P_pred+K×u(t), where K is the proportional coefficient dynamically tuned by the optimization algorithm in step S5), a dynamic mapping relationship of "deviation-correction" is established (mapping accuracy reaches 0.01kW). The corrected power P_corr is fed back to the IoT system in step S1 in real time to adjust the equipment output, and at the same time, the actual power data P_real is sent back to step S4 to update the carbon emission calculation result, so that the deviation between the actual power of the equipment and the predicted value of the twin model is controlled within ±2%, the energy consumption fluctuation of a single process is reduced by 18%, the power adjustment accuracy of a typical welding process reaches 0.1kW, and the real-time performance of carbon emission calculation is improved to the second level.

[0190] (2) Inter-process collaboration: timing optimization model

[0191] The timing optimization model minimizes the combined goal of idle energy consumption and carbon emissions during the final assembly process by optimizing the sequence and timing of process activation, while simultaneously satisfying process dependencies and resource constraints. This constitutes the objective function:

[0192]

[0193] in, These are the weighting coefficients for energy consumption and carbon emissions;

[0194] Indicate process i The no-load energy consumption;

[0195] Indicates total carbon emissions;

[0196] , indicating process i Parameters indicating whether to enable or disable.

[0197] Using 0-1 integer programming theory, based on the process activation status ( (1 indicates enabled, 0 indicates disabled) are used as decision variables, and weighted by coefficients. (Dynamically output by the multi-objective optimization algorithm in step S5, ω∈[0,1]) Balance idle energy consumption (from the standby data of the device monitored by the Internet of Things in step S1) and carbon emission target (from the dynamic calculation result in step S4). The constraints of the decision variables are integrated with the process energy consumption matrix output by the energy consumption prediction model in step S2. The integer programming problem is solved by the branch and bound method. The process sequence is optimized to reduce the idle waiting time of the final assembly process by 45 minutes / batch and reduce carbon emissions in the multi-device collaborative scenario. At the same time, the optimized process sequence is fed back to step S5 to update the Pareto optimal solution set.

[0198] Based on the process dependency matrix generated by the digital twin model in step S2, a genetic algorithm (GA) is used to traverse all possible process combinations (population size 200, 50 generations). Combined with the carbon emission accounting data in step S4, non-dominated solutions that meet resource constraints (equipment load rate ≤ 90% from step S1) are selected. The smaller the objective function value, the better the overall optimization effect. The generated non-dominated solutions serve as the input subset of the multi-objective optimization algorithm in step S5. After secondary optimization by the NSGA-II algorithm, the final decision scheme is formed, providing an executable process sequencing scheme for production scheduling. This allows decision-makers to flexibly choose between "fast delivery" (high energy consumption, ω1=0.7) and "low-carbon production" (low energy consumption, ω2=0.7). In typical scenarios, the scheduling scheme generation time is shortened to 2 minutes.

[0199] The constraints on the above objective function are:

[0200] (a) Process timing constraints:

[0201]

[0202] in, Indicate process i Equipment preparation time.

[0203] This indicates the maximum allowable production cycle time.

[0204] The processing time of each step (real-time PLC data from the IoT system in step S1, with an accuracy of ±1 second) and the equipment preparation time (simulation results of equipment switching based on the digital twin model in step S2) are summed up, and a linear programming (LP) model is used to ensure that the total cycle time does not exceed the upper limit. (Dynamically set by the multi-objective optimization algorithm in step S5 based on the order delivery cycle). The calculation results of the timing constraints are fed back to the optimization algorithm in step S5 in real time. When the predicted total cycle exceeds the limit, the process splitting or parallel processing strategy is automatically triggered to prevent equipment conflicts caused by process overlap, ensure the stability of the production rhythm, and control the cycle fluctuation of a typical production line within ±3%. At the same time, it provides cycle constraint parameters for the timing optimization model in step S110.

[0205] (b) Energy consumption upper limit constraint:

[0206]

[0207] in, Indicate process i Total energy consumption (including mechanical power energy consumption and thermal process modification energy consumption).

[0208] This indicates the maximum permissible total energy consumption.

[0209] The total energy consumption of each process (mechanical power energy consumption + thermal process modification energy consumption, from the output of the energy consumption prediction model of step S2, at the granularity of each process) is summed and then compared with a threshold. (Dynamically generated by the multi-objective optimization algorithm in step S5 based on the carbon quota policy) In comparison, a logistic regression (LR) model is used to predict energy consumption trends 0.5 steps in advance, forcibly intercepting processes that exceed energy consumption limits. The interception signal is sent in real time to the IoT system in step S1 via the OPC UA protocol, triggering the equipment to reduce power (refer to the PID control parameters in step S105) to avoid total energy consumption exceeding the limit due to a surge in energy consumption in local processes. Energy consumption anomalies in the motor assembly process can be warned in advance, and the interception record is fed back to step S4 to correct the carbon emission accounting model.

[0210] (c) Carbon emission cap constraints

[0211]

[0212] in, This indicates the maximum permissible carbon emissions.

[0213] The real-time carbon emissions dynamically calculated in step S4 (sampling frequency 1Hz) are compared with the threshold. (Based on the process-level thresholds of the enterprise's annual carbon quota decomposition, updated monthly by the optimization algorithm in step S5) Dynamic comparison is performed. When the threshold is exceeded for three consecutive sampling periods, inter-process collaborative optimization (adjusting the process sequence or equipment load allocation) is triggered through the time-series optimization model in step S110. The collaborative optimization instruction calls the scheme with the highest carbon emission weight (ω2=0.7) in the Pareto optimal solution set of step S5 to ensure that the production process complies with the carbon quota policy requirements. At the same time, the optimization results are sent back to step S2 to update the carbon emission prediction parameters of the digital twin model.

[0214] Example 1: Application of Algorithm for Energy Consumption Optimization in Automobile Chassis Assembly Production Line

[0215] 1. Basic parameter settings:

[0216] Number of processes =5 (Welding, Painting, Mechanical Assembly, Inspection, Packaging);

[0217] Electricity carbon intensity factor =0.58kgCO2e / kWh, fuel gas carbon intensity factor =2.1kgCO2e / m³ 3 ;

[0218] Maximum production cycle =240 minutes, maximum total energy consumption =8000kWh;

[0219] 2. Digital twin modeling and energy consumption prediction:

[0220] Mechanical power energy consumption: Welding robots =15kW (standby) + 30kW (dynamic loading), overall efficiency coefficient =0.82, predicted energy consumption =4200 kWh;

[0221] Thermal process energy consumption: Specific heat capacity of materials in the coating drying chamber =1200J / (kg·K), mass =500kg, target temperature rise =180K, predicted heat energy consumption = 1.2 × 10 8 J;

[0222] 3. Carbon emission calculation:

[0223] Carbon emissions from mechanical power = 4200 kWh × 0.58 kg CO2e / kWh = 2436 kg CO2e;

[0224] Carbon emissions from thermal processes = (1.2 × 10⁻⁶) 8 J / 3.6×10 6 J / kWh) × 2.1kgCO2e / kWh = 70kgCO2e, total carbon emissions =2506kgCO2e;

[0225] 4. Dual-objective optimization:

[0226] Set weighting coefficients =0.4 (energy consumption) =0.6 (carbon emissions), the NSGA-II algorithm generates the Pareto optimal solution: energy consumption 7200kWh, carbon emissions 2100kgCO2e;

[0227] 5. Dynamic control effect:

[0228] In-process compensation: The real-time power of the welding equipment is 54kW, the predicted power is 50kW, the deviation is e(t)=4kW, the PID control quantity is u(t)=3.2kW, the corrected power is 53.2kW, and the energy consumption of a single process is reduced.

[0229] Inter-process collaboration: The optimized process sequence is "mechanical assembly → welding → painting → inspection → packaging", which reduces idle energy consumption by 280kWh and shortens the production cycle by 22 minutes;

[0230] 6. Implementation results: Total energy consumption was reduced by 10%, carbon emissions were reduced by 16%, and production efficiency was increased by 9%, meeting the requirements of GB / T23331 energy management system.

[0231] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for dynamic monitoring and energy conservation / carbon reduction control in the final assembly and construction production process, which integrates digital twin technology and IoT monitoring to construct a dynamic energy consumption-carbon emission model for the entire final assembly and construction process. By combining real-time data feedback and multi-objective optimization algorithms, it achieves precise control of both energy consumption and carbon emissions. Its features include: Includes the following steps: S1 collects data related to energy consumption in each process of the industrial production cycle; S2, Construct an energy consumption prediction model, which includes a mechanical power energy consumption prediction model and a thermal process modification energy consumption prediction model, specifically: A digital twin model is built based on the final assembly process card to simulate the energy consumption characteristics of each process and predict the initial energy consumption value; the physical entity of the final assembly workshop is transformed into a digital twin through 3D modeling technology, and three types of data collected in real time by IoT sensors are integrated, including equipment operating parameters, environmental parameters and process parameters; a dynamic energy consumption simulation engine based on finite element analysis is built. The digital twin model interacts in real time with the data acquisition system in step S1 via the OPC UA protocol. The sensor sampling frequency reaches 1kHz to ensure dynamic response, providing a millisecond-level time-series data foundation for the energy consumption prediction model in step S2. The mechanical power energy consumption prediction model is used to calculate the mechanical power consumption of the driving equipment during the assembly process. It uses a time slicing algorithm to decompose the power composition of the process equipment, uses a state machine model to judge the activation state of the equipment in real time, and combines the equipment's factory energy efficiency curve and real-time efficiency decay coefficient to realize the cumulative calculation of energy consumption in the time dimension. The energy consumption data of each process output by the model is directly used as the basic data source for carbon emission calculation in step S4. At the same time, the sensor sampling weights in step S1 are corrected through the feedback channel, which improves the accuracy of process-level energy-saving potential analysis in typical scenarios. By considering the efficiency of the series motor, the process matching efficiency, and the aging factor, a full life cycle energy efficiency assessment model for the equipment is constructed. The calculation results are used to correct the predicted mechanical power energy consumption value in step S2, and also serve as the equipment health constraint condition for the multi-objective optimization algorithm in step S5, guiding the optimization of equipment maintenance cycle. In typical scenarios, this can improve the overall efficiency of the equipment and extend the maintenance interval. The energy consumption prediction model for the thermal process modification adopts an improved enthalpy change model to calculate the effective energy consumption of the welding and coating drying heating stages: based on the thermodynamic enthalpy change theory, it integrates sensible heat transfer data collected by infrared temperature sensors with a radiation heat loss calculation model to establish a temperature-energy consumption dynamic response model; it interacts in real time with the infrared thermal imager in the IoT system of step S1 to capture the dynamic characteristics of welding arc heat loss and temperature field distribution in the coating drying chamber, providing thermal process-specific correction parameters for energy consumption prediction in step S3; the theoretical sensible heat demand is calculated by multiplying the material specific heat capacity database and the real-time mass data from the weighing sensor, combined with the target temperature rise and temperature threshold correction coefficient fed back by the infrared thermometer: output value one side The surface layer serves as the core parameter of the thermal process performance improvement model in step S2. On the other hand, it is transmitted to the optimization algorithm in step S5 via the OPC server, providing a theoretical benchmark value for the power adjustment of the heating equipment. Based on the Stefan-Boltzmann law, the comprehensive emissivity is calculated by real-time acquisition of the oxide layer thickness on the equipment surface using an infrared spectrometer. Combined with the effective radiation area and temperature field distribution obtained by the thermal imager, the finite difference method is used to calculate the radiation heat loss at different process stages. The calculation results correct the radiation heat loss parameters of the thermal process performance improvement model in step S2 on the one hand, and are transmitted to the carbon emission calculation model in step S4 via the data bus on the other hand, providing a quantitative basis for the optimization of the insulation layer and adjustment of process parameters of the heating equipment. S3, input the data collected in step S1 into the energy consumption prediction model constructed in step S2 to obtain relevant energy consumption prediction data; S4 converts energy consumption forecast data into carbon emissions and calculates the total carbon emissions based on the carbon intensity coefficient of the energy type. S5, input the total carbon emissions into a multi-objective genetic algorithm to generate a Pareto optimal solution set, obtaining the optimal combination of energy consumption and carbon emissions. The expression for inputting the total carbon emissions into the multi-objective genetic algorithm to generate the Pareto optimal solution set is: ； To take the sign of the minimum value; The weighting coefficients representing energy consumption targets; Indicates total energy consumption; This indicates the highest historical energy consumption; Weighting coefficients representing carbon emission targets; Indicates total carbon emissions; The largest carbon emissions in history; st is the constraint symbol; Indicates the production cycle time; Maximum permissible production cycle time; Product quality indicators; Minimum permissible product quality specifications; A dual-objective optimization framework is constructed using the NSGA-II algorithm, with the total energy consumption output in step S2 and the total carbon emissions calculated in step S4 as optimization objectives. Population diversity is maintained through fast non-dominated sorting and crowding distance, and three types of constraints are integrated: production cycle ≤ T_max from process card data in step S1, product qualification rate ≥ 99.5% from the quality inspection system, and equipment health ≥ 0.85 from the aging factor model in step S2. During the algorithm iteration process, real-time IoT data from step S1 is called in real time: the fitness function is dynamically adjusted based on equipment load rate and material properties, and the crossover probability and mutation probability are adaptively adjusted based on the energy consumption prediction deviation in step S3. Under the premise of ensuring product qualification rate, Pareto optimal balance between energy consumption and carbon emissions is achieved, and the generated solution set directly drives the dynamic control strategy in step S5. The optimization priority of energy consumption and carbon emissions is adjusted by dynamic weighting coefficients. The weighting coefficients are corrected in real time by the fuzzy logic controller based on external factors. The optimization function is constructed by combining the production cycle upper limit of step S1 (T≤T_max) and the equipment efficiency constraint of step S2 (η≥η_min). The energy consumption item of the objective function is normalized by the predicted energy consumption data of step S2, and the carbon emission item is normalized by the accounting data of step S4. The generated Pareto front is displayed through a visualization interface, which supports decision-makers to select control strategies according to real-time operating conditions and transmit the optimal scheme parameters to the adaptive PID controller in step S5.

2. The method for dynamic monitoring and energy-saving and carbon-reduction control of energy consumption in the final assembly and construction production process according to claim 1, characterized in that, The expression for the mechanical power energy consumption prediction model is as follows: ； in, Total energy consumption for mechanical power; This represents the total number of processes; Let i be the number of independent devices involved in the i-th process. This represents the overall efficiency coefficient of equipment j in process i; The total duration of the i-th process; This represents the base standby power of device j in process i; This represents the dynamic power increment of device j in process i; This represents the activation state function of device j in process i; This indicates the total standby energy consumption and the total processing energy consumption.

3. The method for dynamic monitoring and energy-saving and carbon-reduction control of energy consumption in the final assembly and construction production process according to claim 2, characterized in that, The calculation formula is: ； in, Indicates motor efficiency; Indicates process matching efficiency; This represents the aging degradation factor.

4. The method for dynamic monitoring and energy-saving and carbon-reduction control of energy consumption in the final assembly and construction production process according to claim 2, characterized in that, It is calculated using the following formula: ； in Indicates total standby power consumption; Indicates processing energy consumption; , These represent the actual start and end times of equipment j in process i.

5. The method for dynamic monitoring and energy-saving and carbon-reduction control of energy consumption in the final assembly and construction production process according to claim 1, characterized in that, The expression for the thermal process performance modification energy consumption prediction model is as follows: ； in, This indicates the total energy consumption of the thermal modification process; Indicates the total number of processes; This represents the number of devices used in the i-th process step; This represents the sensible heat energy consumption of equipment j in process i; This represents the radiative heat loss of equipment j in process i.

6. The method for dynamic monitoring and energy-saving and carbon-reduction control of energy consumption in the final assembly and construction production process according to claim 5, characterized in that, The specific calculation formula is as follows: ； in, This represents the specific heat capacity at constant pressure of the material processed by equipment j in process i. This indicates the mass of material processed by equipment j in a single operation in process i; This represents the absolute value of the target temperature rise of equipment j in process i; This indicates the extreme temperature of equipment j in process i; This represents the reference temperature of equipment j in process i.

7. The method for dynamic monitoring and energy-saving and carbon-reduction control of energy consumption in the final assembly and construction production process according to claim 5, characterized in that, The specific calculation formula is as follows: ； in, This represents the overall emissivity of device j in process i; It is the Stefan-Boltzmann constant; This represents the effective radiation area of ​​equipment j in process i; This represents the average operating temperature of equipment j in process i; This indicates the ambient temperature of equipment j in process i; This indicates the effective heating time of device j in process i.

8. The method for dynamic monitoring and energy-saving and carbon-reduction control of energy consumption in the final assembly and construction production process according to claim 1, characterized in that, The process involves converting energy consumption forecast data into carbon emissions and calculating the total carbon emissions based on the carbon intensity coefficient of the energy type. This is achieved using the following formula: ； in, Indicates total carbon emissions; This represents the mechanical power consumption at time t; This represents the regional power grid carbon emission factor at time t; This indicates the energy consumption change during the thermal process at time t; Indicates the calorific value of natural gas; This indicates the carbon emission factor of natural gas.

9. The method for dynamic monitoring and energy-saving and carbon-reduction regulation of energy consumption in the final assembly and construction production process according to claim 1, characterized in that, It also includes S6, dynamic feedback and compensation: S6-1, based on real-time mechanical power Compared with twin model predictions To address the deviation, the equipment parameters are dynamically adjusted to bring the energy consumption error within the threshold, while also considering the dynamic accounting results of carbon emissions to optimize the adjustment strategy. S6-2 aims to minimize the overall goal of no-load energy consumption and carbon emissions during the final assembly process by optimizing the sequence and timing of process activation, while simultaneously satisfying process dependencies and resource constraints.

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