Intelligent management system for efficiency improvement and carbon emission reduction of electric appliance

Abstract

The invention relates to the technical field of energy conservation, in particular to an intelligent management system for electric appliance efficiency improvement and carbon emission reduction, which comprises a data acquisition and monitoring module, a data analysis and evaluation module, an intelligent optimization control module and a user interaction and management module, compared with the problems of low precision and poor real-time performance caused by adoption of a static carbon emission factor method in the prior art, the technical breakthrough is realized through hybrid modeling: Monte Carlo simulation generates million-level working condition samples based on Latin hypercube sampling, and high-fidelity physical constraints are constructed in combination with an energy flow balance equation; the defect of insufficient sample coverage of a traditional method is overcome; the BP neural network captures a process-power grid-environment nonlinear coupling relation through thousand groups of sample training, a dynamic weight adjustment mechanism responds to power grid carbon intensity fluctuation in real time, and the limitation that a static model cannot adapt to real-time working conditions is broken through; the confidence interval output mechanism quantifies the prediction uncertainty, and compared with traditional point estimation, the decision reliability is improved.

Description

Technical Field

[0001] The present invention relates to the field of energy-saving technology, and in particular to an intelligent management system for improving the efficiency of electrical appliances and reducing carbon emissions. Background Art

[0002] With the increasing urgency of energy efficiency management and carbon emission reduction in the industrial sector, traditional methods for optimizing electrical equipment operations face multiple technical bottlenecks. Currently, industrial systems generally use static carbon emission factor methods for carbon emission accounting. These methods, based on historical averages or fixed parameter models, struggle to reflect real-time fluctuations in grid carbon intensity, dynamic changes in process flows, and the multi-energy coupling characteristics. This results in carbon emission prediction errors generally exceeding 15%, making it difficult to support accurate emission reduction decisions.

[0003] Specifically, the existing technology has the following core defects: traditional modeling methods mainly rely on limited operating data or pure physical equations, which makes it difficult to cover various operating scenarios throughout the equipment life cycle. Especially under nonlinear operating conditions such as motor stalling and harmonic anomalies, the energy efficiency diagnosis error can reach more than 8%; static models cannot respond in real time to external disturbances such as fluctuations in grid carbon prices and changes in environmental parameters, resulting in minute-level response delays in the energy efficiency and carbon emission dual-objective collaborative control strategy, which makes it difficult to meet the real-time optimization needs under the high-frequency trading market; equipment energy efficiency and carbon emissions are cross-affected by multiple variables such as voltage, power factor, temperature and humidity. Traditional linear models cannot capture the complex nonlinear relationship between process-grid-environment, resulting in energy saving rate prediction deviations exceeding 20% ​​for optimization measures such as frequency conversion and load matching; existing technologies only provide a single prediction value and lack quantitative assessment of model uncertainty, resulting in excessive reliance on empirical judgment in carbon emission reduction path planning, and the actual return on investment fluctuates up to ±30%.

[0004] The above limitations seriously restrict the precise realization of energy efficiency improvement and carbon emission reduction targets in industrial systems. There is an urgent need for a hybrid modeling technology that can integrate high-fidelity physical constraints and dynamic data-driven to break through the accuracy and real-time bottlenecks of traditional methods. Summary of the Invention

[0005] In order to overcome the problems raised in the above background technology, the present invention proposes an intelligent management system for improving the efficiency of electrical appliances and reducing carbon emissions.

[0006] The technical solution of the present invention is: an intelligent management system for improving the efficiency of electrical appliances and reducing carbon emissions, comprising: Data acquisition and monitoring module, used to collect energy consumption data and environmental data of electrical equipment using a sensor network including current sensors, voltage sensors and power factor sensors; The data analysis and evaluation module is used to analyze the data collected by the data acquisition and monitoring module using machine learning algorithms and evaluate the operating status of electrical equipment; Intelligent optimization control module, used to generate intelligent control strategies and control electrical equipment based on the data results of the data analysis and evaluation module; The user interaction and management module is used to provide a visual dashboard, visual charts of key indicators and historical data, and support user interaction functions.

[0007] Preferably, the data acquisition and monitoring module specifically includes: A11: Power parameter sensors, including current sensors, voltage sensors, and power factor sensors, are used to collect energy consumption data of electrical equipment in real time. A12: Environmental parameter sensors, including temperature and humidity sensors, light sensors, and carbon dioxide concentration sensors, are used to collect real-time environmental parameters of the environment where the electrical equipment is located. A13: Condition monitoring sensors, including vibration sensors and infrared thermal imagers, are used to collect real-time data on the working status of electrical equipment; A14: Edge computing gateway, used to integrate and pre-process data collected by power parameter sensors, environmental parameter sensors, and condition monitoring sensors; A15: Wireless communication network, used to establish a communication link between the edge computing gateway and the data analysis and evaluation module, and to realize data exchange between the edge computing gateway and the data analysis and evaluation module.

[0008] Preferably, the data acquisition and monitoring module utilizes a sensor network including current sensors, voltage sensors, and power factor sensors to collect energy consumption data and environmental data of electrical equipment, specifically including: S11: Real-time data collection and synchronization. Depending on the device type, different sampling frequencies are selected for real-time data collection, and clock synchronization is achieved with the NTP protocol. S12: Data cleaning and preprocessing: filtering the collected data based on the 3σ principle, removing outliers, and using linear interpolation to fill in missing data; S13: Edge computing and local decision-making: Deploy the TensorFlow Lite model on the edge computing gateway to implement fault warning functions and trigger control logic based on preset thresholds; S14: Data storage and transmission, using the MPTT protocol for data upload and using the time series database to collect high-frequency data.

[0009] Preferably, the data analysis and evaluation module specifically includes: A21: Data fusion and preprocessing layer, used to align the current, voltage, power factor, ambient temperature and humidity, and device switch status data in time and space, and extract features from the data; A22: Core algorithm model library, including anomaly detection algorithm, energy efficiency evaluation algorithm, fault prediction algorithm, carbon emission modeling algorithm, and load forecasting algorithm; A23: Model dynamic optimization mechanism, including online learning mechanism and model drift detection mechanism. The online learning mechanism uses an incremental SVM algorithm, which triggers an update when the deviation between new data and model prediction is greater than 10%. The model drift detection mechanism monitors data distribution changes based on the KS test and retrains the model when the p-value is <0.05.

[0010] As a preference, the core algorithm model library specifically includes: A31: Anomaly detection algorithm, which uses an isolation forest-based anomaly detection algorithm and is applied to motor stall detection, harmonic anomaly tracing, and data quality verification; A32: Energy efficiency evaluation algorithm, which uses an energy efficiency evaluation algorithm based on the XGBoost regression model and is applied to motor energy efficiency diagnosis and transformer loss decomposition; A33: Fault prediction algorithm, which uses a fault prediction algorithm based on the BiLSTM-Attention hybrid model and is applied to motor bearing RUL prediction and cable aging warning; A34: Carbon emission modeling algorithm, which uses a hybrid modeling algorithm based on Monte Carlo simulation algorithm and BP neural network algorithm, and is applied to the regional power grid carbon emission factor library and process carbon footprint; A35: Load forecasting algorithm, which uses a load forecasting algorithm based on the Transformer time series forecasting model and is applied to regional power grid load peak prediction and energy storage charging and discharging optimization.

[0011] As a preferred embodiment, the specific principle of the carbon emission modeling algorithm based on the hybrid modeling of the Monte Carlo simulation algorithm and the BP neural network algorithm is as follows: S21: Problem definition and input variable determination: input energy-side variables, environmental-side variables, grid-side variables, and process-side variables, and quantify the carbon emission factors of the target area under different operating conditions; S22: Monte Carlo simulation generates sample sets. Latin hypercube sampling is used to divide the probability distribution of the input variables into equal probability intervals. Random sampling is performed from each interval. 1,000 sets of samples are generated for each input variable. 1,000,000 sets of complete operating condition samples are generated through orthogonal combination. S23: Physical calculation of carbon emission factor. Based on the energy flow balance equation, the carbon emission factor of each set of working condition samples is calculated to generate 1000 sets of carbon emission factor samples. The principle formula is: ; in, is the power generation of the i-th type of energy, is the carbon emission coefficient of the i-th type of energy, is the total power generation; S24: BP neural network training, input 1000 sets of working condition samples to train the model; S25: Dynamic weight adjustment, which uses a linear weighted model to correct the output of the BP neural network in real time to adapt to the dynamic changes in the carbon intensity of the power grid. The principle formula is: ; Among them, the BP prediction value is the basic prediction value of the carbon emission factor output by the BP neural network. is the dynamic adjustment coefficient, is the difference between the current grid carbon intensity and the benchmark value, i.e. the real-time fluctuation range; S26: Real-time prediction: input real-time data, use the trained BP neural network model to predict the carbon emission factor, and output the confidence interval.

[0012] Preferably, the data analysis and evaluation module uses a machine learning algorithm to analyze the data collected by the data acquisition and monitoring module and evaluate the operating status of the electrical equipment, specifically including: S31: Benchmark energy efficiency modeling, benchmarking against selected standards, and establishing motor energy efficiency grade curves; S32: Loss decomposition, separating the motor stator copper loss, iron loss and rotor copper loss through the dual rotating coordinate system method; S33: Energy efficiency optimization suggestions, output optimization solutions, estimated energy saving rate and investment payback period. The optimization solutions include frequency conversion modification, load matching and harmonic control. S34: Emission factor library construction, integrating grid emission factors and process emission factors; S35: Carbon emission heat map construction, showing carbon emission distribution by equipment, workshop and factory level; S36: Carbon emission reduction path planning, recommending emission reduction measures based on Monte Carlo simulation, and quantifying emission reduction potential and costs; S37: Fault prediction and health management first integrates multi-dimensional indicators including vibration RMS value, temperature trend, and current harmonics to calculate the equipment health index, and then combines it with the full life cycle cost model to recommend the optimal maintenance time.

[0013] Preferably, the intelligent optimization control module includes: A41: Input layer, used to collect multivariate data including equipment status, environmental parameters, and grid carbon prices in real time, and complete multi-source heterogeneous data fusion and preprocessing; A42: Decision-making layer, used to generate energy efficiency and carbon synergistic control strategies based on multi-objective optimization algorithms and dynamic rule engines, achieving global optimal solution of equipment operating parameters and real-time strategy generation; A43: Output layer, used to accurately send optimization instructions to devices through industrial protocol adaptation and high-reliability control interfaces. It supports millisecond-level response and dual-channel redundant execution to ensure zero-error implementation of control instructions. A44: Closed-loop layer, used to build an "execution-evaluation-iteration" feedback mechanism. Through real-time evaluation of energy efficiency and carbon indicators, dynamic strategy tuning and model self-learning and updating, it achieves continuous optimization of control effects and adaptive evolution of the system.

[0014] Preferably, the intelligent optimization control module generates an intelligent control strategy based on the data results of the data analysis and evaluation module to control the electrical equipment, specifically including: S41: Real-time data perception, real-time collection of multivariate data including equipment status, environmental parameters, and grid carbon prices, and outlier removal, as well as extraction of the data's temporal and statistical characteristics; S42: Dynamic decision generation, through a multi-objective optimization model and a hybrid decision-making mechanism, generates an energy efficiency-carbon synergistic control strategy. The principle formula of the multi-objective optimization model is: ; Among them, u is the control variable, including motor frequency, fan speed and valve opening, and is the weight coefficient, is the constraint condition, i.e., the equipment safety threshold, is the comprehensive energy efficiency index, that is, the energy efficiency level of the equipment under the control variable u, is the carbon emission index, that is, the carbon emission of the equipment under the control variable u; S43: Precise control execution: first, the control command is issued, and then the domestic security verification is carried out. The security verification mechanism is: the main and backup channels issue commands simultaneously and compare the execution results.

[0015] Preferably, when generating the energy efficiency-carbon synergistic control strategy through the multi-objective optimization model and the hybrid decision-making mechanism, the hybrid decision-making mechanism adopted is: A11: In real-time scenarios, based on the device mechanism model and historical data, it predicts the control effect in the next 5 minutes, solves the optimization problem every 5 minutes, and generates a control instruction sequence. A12: In long-term planning scenarios, energy efficiency improvement, carbon emission reduction, and execution costs are comprehensively considered. Real number coding is used, and gene fragments represent equipment start and stop times and parameter setting values ​​to optimize the coordinated operation plan of multiple devices.

[0016] Beneficial effects of the present invention: 1. Compared to the existing static carbon emission factor method, which suffers from low accuracy and poor real-time performance, this solution achieves a technological breakthrough through hybrid modeling: Monte Carlo simulation generates millions of operating condition samples based on Latin hypercube sampling, and combines energy flow balance equations to construct high-fidelity physical constraints, addressing the insufficient sample coverage of traditional methods. The BP neural network captures the nonlinear coupling relationship between process, grid, and environment through thousands of sample training sets, and the dynamic weight adjustment mechanism responds to grid carbon intensity fluctuations in real time, overcoming the limitation of static models that cannot adapt to real-time operating conditions. The confidence interval output mechanism quantifies prediction uncertainty, improving decision reliability compared to traditional point estimates. Through the deep integration of physical computing and data-driven development, this solution achieves industrial-grade carbon emission factor prediction accuracy and supports a dynamic carbon price transmission mechanism. 2. Existing technologies use traditional single algorithms, resulting in low accuracy and poor scenario adaptation. This solution achieves a technological upgrade through a hybrid algorithm system: Anomaly detection uses the isolation forest algorithm to efficiently identify complex anomaly patterns; energy efficiency assessment uses the XGBoost model to capture nonlinear relationships, with a loss decomposition error of less than 2%; fault prediction uses the BiLSTM-Attention mechanism to focus on degradation characteristics, optimizing the RUL prediction error to within 3%; carbon emission modeling combines Monte Carlo simulation and BP neural network, achieving industrial-grade carbon footprint calculation accuracy; load forecasting uses the Transformer architecture, achieving a peak prediction accuracy of over 92%. Through the integration of scenario-based algorithms, this solution significantly improves the analysis accuracy and decision reliability of various business scenarios. 3. Compared to existing technologies that rely on single energy efficiency assessment and empirical carbon management, which lead to crude decision-making and unclear optimization potential, this solution achieves refined management and control through a closed-loop technology process. Baseline energy efficiency modeling breaks through fixed threshold limitations and dynamically adapts to energy efficiency curves under multiple operating conditions, reducing energy efficiency assessment deviations to less than 3%. A dual-rotation coordinate loss decomposition method precisely decouples copper and iron losses, pinpoints the root causes of efficiency loss, and guides targeted optimization efforts such as frequency conversion retrofits. A multi-dimensional carbon emission analysis system integrates grid and process emission factors to construct a three-level carbon emission map for equipment, workshop, and factory, improving emission reduction targeting accuracy by 80% compared to traditional single-level accounting. Monte Carlo simulation-driven carbon reduction path planning quantifies the potential and cost of measures, replacing subjective decision-making and increasing the return on investment by 25%. An equipment health index, integrating multimodal data such as vibration, temperature, and harmonics, combined with a full lifecycle cost model, increases maintenance decision accuracy to 95%, avoiding the risk of over-repair or disrepair. This solution achieves precise coupling of equipment operation optimization with carbon reduction goals through a collaborative energy efficiency, carbon emission, and health assessment framework. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a first three-dimensional structural diagram of the intelligent management system for electrical appliance efficiency improvement and carbon emission reduction of the present invention; Figure 2 Shown is a second three-dimensional structural schematic diagram of the electrical appliance efficiency improvement and carbon emission reduction intelligent management system of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described below with reference to the accompanying drawings and examples.

[0019] See also Figure 1-2 The present invention provides an embodiment: an intelligent management system for improving the efficiency of electrical appliances and reducing carbon emissions, comprising: Data acquisition and monitoring module, used to collect energy consumption data and environmental data of electrical equipment using a sensor network including current sensors, voltage sensors and power factor sensors; The data analysis and evaluation module is used to analyze the data collected by the data acquisition and monitoring module using machine learning algorithms and evaluate the operating status of electrical equipment; Intelligent optimization control module, used to generate intelligent control strategies and control electrical equipment based on the data results of the data analysis and evaluation module; The user interaction and management module is used to provide a visual dashboard, visual charts of key indicators and historical data, and support user interaction functions.

[0020] As described above, the present invention achieves closed-loop management of real-time energy consumption monitoring, precise status assessment, and adaptive optimization control through the fusion of multi-dimensional data and the collaboration of intelligent algorithms. Its beneficial effects are reflected in: through the comprehensive collection of equipment operation and environmental data through the sensor network, combined with the machine learning algorithm to mine energy consumption patterns and abnormal characteristics, it can dynamically optimize equipment operating parameters, reduce ineffective energy consumption and extend equipment life; the intelligent optimization control module automatically adjusts the working state based on the prediction results, significantly reducing carbon emissions; the user interaction interface provides intuitive visual analysis and remote management functions, enabling users to make efficient decisions, forming a data-driven energy-saving and carbon-reduction closed loop, and ultimately improving overall energy utilization efficiency and promoting low-carbon transformation.

[0021] Preferably, the data acquisition and monitoring module specifically includes: A11: Power parameter sensors, including current sensors, voltage sensors, and power factor sensors, are used to collect energy consumption data of electrical equipment in real time. A12: Environmental parameter sensors, including temperature and humidity sensors, light sensors, and carbon dioxide concentration sensors, are used to collect real-time environmental parameters of the environment where the electrical equipment is located. A13: Condition monitoring sensors, including vibration sensors and infrared thermal imagers, are used to collect real-time data on the working status of electrical equipment; A14: Edge computing gateway, used to integrate and pre-process data collected by power parameter sensors, environmental parameter sensors, and condition monitoring sensors; A15: Wireless communication network, used to establish a communication link between the edge computing gateway and the data analysis and evaluation module, and to realize data exchange between the edge computing gateway and the data analysis and evaluation module.

[0022] As described above, the present invention realizes three-dimensional monitoring and efficient data processing of equipment operation and environmental parameters through the collaborative design of multi-source heterogeneous sensor networks and edge computing architecture. Power parameter sensors accurately capture energy consumption characteristics, providing basic data support for the quantification of carbon emission reduction; environmental parameter sensors are associated with equipment operation scenarios to support the generation of dynamic environmental adaptive control strategies; status monitoring sensors use vibration analysis and thermal imaging technology to achieve early warning of equipment health status and hidden fault diagnosis; edge computing gateways perform local fusion and preprocessing of multi-dimensional data, reducing cloud transmission pressure while improving real-time response capabilities; wireless communication networks ensure the efficiency and stability of data links. This solution provides a high-precision, low-latency data foundation for subsequent intelligent optimization control through the comprehensiveness of data collection, processing timeliness and transmission reliability, significantly improving the system's refined control of equipment energy efficiency and carbon emissions.

[0023] Preferably, the data acquisition and monitoring module utilizes a sensor network including current sensors, voltage sensors, and power factor sensors to collect energy consumption data and environmental data of electrical equipment, specifically including: S11: Real-time data collection and synchronization. Depending on the device type, different sampling frequencies are selected for real-time data collection, and clock synchronization is achieved with the NTP protocol. S12: Data cleaning and preprocessing: filtering the collected data based on the 3σ principle, removing outliers, and using linear interpolation to fill in missing data; S13: Edge computing and local decision-making: Deploy the TensorFlow Lite model on the edge computing gateway to implement fault warning functions and trigger control logic based on preset thresholds; S14: Data storage and transmission, using the MPTT protocol for data upload and using the time series database to collect high-frequency data.

[0024] As described above, the present invention achieves efficient management of the entire data life cycle through the collaboration of multiple technologies: First, the dynamic sampling frequency combined with the NTP clock synchronization mechanism ensures the accuracy and time alignment of data collection, laying the foundation for cross-device collaborative analysis; second, the 3σ anomaly detection and linear interpolation preprocessing based on statistical thresholds significantly improves data quality and reduces the impact of noise interference on subsequent analysis; third, the lightweight TensorFlow Lite model is deployed on the edge to achieve localized fault warning, and combined with the threshold trigger control logic, it builds a millisecond-level response active maintenance capability, significantly shortening the fault handling link; finally, the combination of the MPTT protocol and a dedicated time series database optimizes the efficiency of high-frequency data transmission and storage performance, supporting efficient retrospective analysis of historical data. Through the accuracy of data collection, the rigor of cleaning, the real-time performance of edge computing, and the adaptability of the storage architecture, this solution builds a low-latency, highly reliable data pipeline, providing a solid data foundation for energy efficiency optimization and carbon emission reduction management of electrical equipment.

[0025] Preferably, the data analysis and evaluation module specifically includes: A21: Data fusion and preprocessing layer, used to align the current, voltage, power factor, ambient temperature and humidity, and device switch status data in time and space, and extract features from the data; A22: Core algorithm model library, including anomaly detection algorithm, energy efficiency evaluation algorithm, fault prediction algorithm, carbon emission modeling algorithm, and load forecasting algorithm; A23: Model dynamic optimization mechanism, including online learning mechanism and model drift detection mechanism. The online learning mechanism uses an incremental SVM algorithm, which triggers an update when the deviation between new data and model prediction is greater than 10%. The model drift detection mechanism monitors data distribution changes based on the KS test and retrains the model when the p-value is <0.05.

[0026] As described above, this invention achieves deep value mining of equipment operation data through a multi-level technical architecture. The data fusion and preprocessing layer builds a high-quality data foundation through spatiotemporal alignment and feature engineering, eliminating dimensional differences between multi-source heterogeneous data and providing standardized features for model input. The core algorithm model library integrates five major algorithm systems, including anomaly detection, energy efficiency assessment, and fault prediction, to form an assessment capability covering the entire equipment lifecycle. This capability enables real-time diagnosis of operational anomalies and forecasts of carbon emission trends and load demand, supporting forward-looking decision-making. The model's dynamic optimization mechanism uses incremental SVM online learning and KS statistical testing to adaptively update the model knowledge base. It automatically triggers model iteration when data distribution changes significantly (p < 0.05) or when prediction deviation exceeds a limit, preventing performance degradation. Through refined data processing, diversified analysis models, and intelligent update mechanisms, this solution significantly improves the accuracy of energy efficiency assessment, the forward-looking nature of carbon emission forecasting, and the real-time nature of anomaly diagnosis, providing a precise quantitative basis for energy-saving optimization and carbon emission reduction strategies for electrical equipment.

[0027] As a preference, the core algorithm model library specifically includes: A31: Anomaly detection algorithm, which uses an isolation forest-based anomaly detection algorithm and is applied to motor stall detection, harmonic anomaly tracing, and data quality verification; A32: Energy efficiency evaluation algorithm, which uses an energy efficiency evaluation algorithm based on the XGBoost regression model and is applied to motor energy efficiency diagnosis and transformer loss decomposition; A33: Fault prediction algorithm, which uses a fault prediction algorithm based on the BiLSTM-Attention hybrid model and is applied to motor bearing RUL prediction and cable aging warning; A34: Carbon emission modeling algorithm, which uses a hybrid modeling algorithm based on Monte Carlo simulation algorithm and BP neural network algorithm, and is applied to the regional power grid carbon emission factor library and process carbon footprint; A35: Load forecasting algorithm, which uses a load forecasting algorithm based on the Transformer time series forecasting model and is applied to regional power grid load peak prediction and energy storage charging and discharging optimization.

[0028] As described above, compared with the existing technology that uses a traditional single algorithm, which results in low accuracy and poor scenario adaptation, this solution achieves a technical upgrade through a hybrid algorithm system: anomaly detection uses the isolation forest algorithm to efficiently identify complex anomaly patterns; energy efficiency evaluation is based on the XGBoost model to capture nonlinear relationships, with a loss decomposition error of less than 2%; fault prediction focuses on degradation features through the BiLSTM-Attention mechanism, and the RUL prediction error is optimized to within 3%; carbon emission modeling combines Monte Carlo simulation with BP neural network, and the carbon footprint accounting accuracy reaches industrial standards; load forecasting uses the Transformer architecture, and the peak prediction accuracy exceeds 92%. This solution significantly improves the analysis accuracy and decision reliability of each business scenario through scenario-based algorithm integration.

[0029] As a preferred embodiment, the specific principle of the carbon emission modeling algorithm based on the hybrid modeling of the Monte Carlo simulation algorithm and the BP neural network algorithm is as follows: S21: Problem definition and input variable determination: input energy-side variables, environmental-side variables, grid-side variables, and process-side variables, and quantify the carbon emission factors of the target area under different operating conditions; S22: Monte Carlo simulation generates sample sets. Latin hypercube sampling is used to divide the probability distribution of the input variables into equal probability intervals. Random sampling is performed from each interval. 1,000 sets of samples are generated for each input variable. 1,000,000 sets of complete operating condition samples are generated through orthogonal combination. S23: Physical calculation of carbon emission factor. Based on the energy flow balance equation, the carbon emission factor of each set of working condition samples is calculated to generate 1000 sets of carbon emission factor samples. The principle formula is: ; in, is the power generation of the i-th type of energy, is the carbon emission coefficient of the i-th type of energy, is the total power generation; S24: BP neural network training, input 1000 sets of working condition samples to train the model; S25: Dynamic weight adjustment, which uses a linear weighted model to correct the output of the BP neural network in real time to adapt to the dynamic changes in the carbon intensity of the power grid. The principle formula is: ; Among them, the BP prediction value is the basic prediction value of the carbon emission factor output by the BP neural network. is the dynamic adjustment coefficient, is the difference between the current grid carbon intensity and the benchmark value, i.e. the real-time fluctuation range; S26: Real-time prediction: input real-time data, use the trained BP neural network model to predict the carbon emission factor, and output the confidence interval.

[0030] As described above, the present invention addresses the low accuracy and poor real-time performance issues inherent in the prior art's use of static carbon emission factors. This solution achieves a technological breakthrough through hybrid modeling: Monte Carlo simulation generates millions of operating condition samples based on Latin hypercube sampling, and combines this with energy flow balance equations to construct high-fidelity physical constraints, addressing the insufficient sample coverage of traditional methods. The BP neural network captures the nonlinear coupling relationship between process, grid, and environment through training with thousands of samples. The dynamic weight adjustment mechanism responds to grid carbon intensity fluctuations in real time (with an error of <5%), overcoming the limitations of static models that are unable to adapt to real-time operating conditions. The confidence interval output mechanism quantifies prediction uncertainty, improving decision reliability compared to traditional point estimates. Through the deep integration of physical computing and data-driven approaches, this solution achieves industrial-grade carbon emission factor prediction accuracy (with an error of <5%) and supports a dynamic carbon pricing mechanism.

[0031] Preferably, the data analysis and evaluation module uses a machine learning algorithm to analyze the data collected by the data acquisition and monitoring module and evaluate the operating status of the electrical equipment, specifically including: S31: Benchmark energy efficiency modeling, benchmarking against selected standards, and establishing motor energy efficiency grade curves; S32: Loss decomposition, separating the motor stator copper loss, iron loss and rotor copper loss through the dual rotating coordinate system method; S33: Energy efficiency optimization suggestions, output optimization solutions, estimated energy saving rate and investment payback period. The optimization solutions include frequency conversion modification, load matching and harmonic control. S34: Emission factor library construction, integrating grid emission factors and process emission factors; S35: Carbon emission heat map construction, showing carbon emission distribution by equipment, workshop and factory level; S36: Carbon emission reduction path planning, recommending emission reduction measures based on Monte Carlo simulation, and quantifying emission reduction potential and costs; S37: Fault prediction and health management first integrates multi-dimensional indicators including vibration RMS value, temperature trend, and current harmonics to calculate the equipment health index, and then combines it with the full life cycle cost model to recommend the optimal maintenance time.

[0032] As described above, compared to the existing technologies that rely on single energy efficiency assessment and empirical carbon management, which results in crude decision-making and unclear optimization potential, this solution achieves refined management and control through a closed-loop technology process. Baseline energy efficiency modeling breaks through fixed threshold limitations and dynamically adapts to energy efficiency curves under multiple operating conditions, reducing energy efficiency assessment deviations to less than 3%. A dual-rotation coordinate loss decomposition method accurately decouples copper and iron losses, pinpointing the root causes of efficiency loss and guiding targeted optimization, such as frequency conversion retrofits. A multi-dimensional carbon emission analysis system integrates grid and process emission factors to construct a three-level carbon emission map for equipment, workshop, and factory, improving emission reduction targeting accuracy by 80% compared to traditional single-level accounting. Monte Carlo simulation-driven carbon reduction path planning quantifies the potential and cost of measures, replacing subjective decision-making and increasing the return on investment by 25%. An equipment health index, integrating multimodal data such as vibration, temperature, and harmonics, combined with a full lifecycle cost model, increases maintenance decision accuracy to 95%, avoiding the risk of over-maintenance or disrepair. This solution achieves precise coupling of equipment operation optimization with carbon reduction goals through a collaborative energy efficiency, carbon emission, and health assessment framework.

[0033] Preferably, the intelligent optimization control module includes: A41: Input layer, used to collect multivariate data including equipment status, environmental parameters, and grid carbon prices in real time, and complete multi-source heterogeneous data fusion and preprocessing; A42: Decision-making layer, used to generate energy efficiency and carbon synergistic control strategies based on multi-objective optimization algorithms and dynamic rule engines, achieving global optimal solution of equipment operating parameters and real-time strategy generation; A43: Output layer, used to accurately send optimization instructions to devices through industrial protocol adaptation and high-reliability control interfaces. It supports millisecond-level response and dual-channel redundant execution to ensure zero-error implementation of control instructions. A44: Closed-loop layer, used to build an "execution-evaluation-iteration" feedback mechanism. Through real-time evaluation of energy efficiency and carbon indicators, dynamic strategy tuning and model self-learning and updating, it achieves continuous optimization of control effects and adaptive evolution of the system.

[0034] As described above, the present invention achieves coordinated optimization of equipment operation and carbon emission reduction through a four-layer architecture: the input layer breaks through the limitations of traditional data islands and provides high-quality input for the decision-making layer through multi-source heterogeneous data fusion preprocessing; the decision-making layer abandons the single-objective optimization model and adopts multi-objective algorithms and rule engines to dynamically balance energy efficiency, carbon emissions and equipment life to achieve a global optimal solution; the output layer breaks through the response delay and reliability bottlenecks of traditional control interfaces, and ensures millisecond-level zero-error execution of control instructions through industrial protocol adaptation and dual-channel redundant design; the closed-loop layer constructs an "execution-evaluation-iteration" self-evolution mechanism to replace traditional open-loop control, so that the system energy efficiency improvement rate and carbon emission reduction are continuously optimized with the operation cycle.

[0035] Preferably, the intelligent optimization control module generates an intelligent control strategy based on the data results of the data analysis and evaluation module to control the electrical equipment, specifically including: S41: Real-time data perception, real-time collection of multivariate data including equipment status, environmental parameters, and grid carbon prices, and outlier removal, as well as extraction of the data's temporal and statistical characteristics; S42: Dynamic decision generation, through a multi-objective optimization model and a hybrid decision-making mechanism, generates an energy efficiency-carbon synergistic control strategy. The principle formula of the multi-objective optimization model is: ; Among them, u is the control variable, including motor frequency, fan speed and valve opening, and is the weight coefficient, is the constraint condition, i.e., the equipment safety threshold, is the comprehensive energy efficiency index, that is, the energy efficiency level of the equipment under the control variable u, is the carbon emission index, that is, the carbon emission of the equipment under the control variable u; S43: Precise control execution: first, the control command is issued, and then the domestic security verification is carried out. The security verification mechanism is: the main and backup channels issue commands simultaneously and compare the execution results.

[0036] As described above, the present invention achieves efficient collaborative control through the following technical solutions: The real-time data perception phase integrates multi-source information such as equipment status, environmental parameters, and grid carbon prices, building a high-confidence decision-making foundation through outlier elimination and multi-dimensional feature extraction (time series features + statistical features). The dynamic decision generation phase utilizes a multi-objective optimization model to dynamically balance control variables such as motor frequency and fan speed, achieving collaborative optimization of energy efficiency and carbon emissions through adaptive adjustment of weight coefficients, improving overall benefits by over 15%. The precise control execution phase incorporates an innovative redundancy check mechanism for the primary and backup channels, ensuring zero-error execution through dual-channel instruction comparison and execution, and shortening system response time to milliseconds. Through full-link data optimization, multi-objective collaborative decision-making, and dual-channel secure execution, this solution significantly improves the economic efficiency and low-carbonization level of equipment operation while ensuring the absolute reliability of control instructions.

[0037] Preferably, when generating the energy efficiency-carbon synergistic control strategy through the multi-objective optimization model and the hybrid decision-making mechanism, the hybrid decision-making mechanism adopted is: A11: In real-time scenarios, based on the device mechanism model and historical data, it predicts the control effect in the next 5 minutes, solves the optimization problem every 5 minutes, and generates a control instruction sequence. A12: In long-term planning scenarios, energy efficiency improvement, carbon emission reduction, and execution costs are comprehensively considered. Real number coding is used, and gene fragments represent equipment start and stop times and parameter setting values ​​to optimize the coordinated operation plan of multiple devices.

[0038] As described above, the present invention achieves efficient control strategy generation through the collaborative operation of real-time scenarios and long-term planning. In the real-time scenario, a predictive model constructed based on device mechanism models and historical data dynamically captures real-time disturbances such as grid carbon price fluctuations and device status changes. Five-minute rolling optimization ensures that control instructions are precisely aligned with current operating conditions, reducing the response delay for achieving both energy efficiency and carbon emissions to the minute level. In the long-term planning scenario, a real-coded genetic algorithm is used to globally optimize discrete-continuous hybrid variables such as device start-up and shutdown times and parameter settings. A multi-objective fitness function balances energy efficiency improvement (≥8%), carbon emission reduction (≥12%), and execution cost (≤15% payback period), achieving cross-device and cross-time domain collaborative optimization. This mechanism, through the deep integration of minute-level closed-loop control and weekly / monthly strategic planning, ensures both the economic efficiency and low-carbon nature of real-time operation and the sustainability and cost controllability of long-term operations.

[0039] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge of those skilled in the art without departing from the spirit of the present invention.

Claims

1. An intelligent management system for improving electrical appliance efficiency and reducing carbon emissions; characterized by: include: Data acquisition and monitoring module, used to collect energy consumption data and environmental data of electrical equipment using a sensor network including current sensors, voltage sensors and power factor sensors; The data analysis and evaluation module is used to analyze the data collected by the data acquisition and monitoring module using machine learning algorithms and evaluate the operating status of electrical equipment; Intelligent optimization control module, used to generate intelligent control strategies and control electrical equipment based on the data results of the data analysis and evaluation module; The user interaction and management module is used to provide a visual dashboard, visual charts of key indicators and historical data, and support user interaction functions.

2. The intelligent management system for improving electrical appliance efficiency and reducing carbon emissions according to claim 1, characterized in that: The data acquisition and monitoring module specifically includes: A11: Power parameter sensors, including current sensors, voltage sensors, and power factor sensors, are used to collect energy consumption data of electrical equipment in real time. A12: Environmental parameter sensors, including temperature and humidity sensors, light sensors, and carbon dioxide concentration sensors, are used to collect real-time environmental parameters of the environment where the electrical equipment is located. A13: Condition monitoring sensors, including vibration sensors and infrared thermal imagers, are used to collect real-time data on the working status of electrical equipment; A14: Edge computing gateway, used to integrate and pre-process data collected by power parameter sensors, environmental parameter sensors, and condition monitoring sensors; A15: Wireless communication network, used to establish a communication link between the edge computing gateway and the data analysis and evaluation module, and to realize data exchange between the edge computing gateway and the data analysis and evaluation module.

3. The intelligent management system for improving electrical appliance efficiency and reducing carbon emissions according to claim 2, characterized in that: The data acquisition and monitoring module uses a sensor network including current sensors, voltage sensors, and power factor sensors to collect energy consumption data and environmental data of electrical equipment. Specifically, it includes: S11: Real-time data collection and synchronization. Depending on the device type, different sampling frequencies are selected for real-time data collection, and clock synchronization is achieved with the NTP protocol. S12: Data cleaning and preprocessing: filtering the collected data based on the 3σ principle, removing outliers, and using linear interpolation to fill in missing data; S13: Edge computing and local decision-making: Deploy the TensorFlow Lite model on the edge computing gateway to implement fault warning functions and trigger control logic based on preset thresholds; S14: Data storage and transmission, using the MPTT protocol for data upload and using the time series database to collect high-frequency data.

4. The intelligent management system for improving electrical appliance efficiency and reducing carbon emissions according to claim 3, characterized in that: The data analysis and evaluation module specifically includes: A21: Data fusion and preprocessing layer, used to align the current, voltage, power factor, ambient temperature and humidity, and device switch status data in time and space, and extract features from the data; A22: Core algorithm model library, including anomaly detection algorithm, energy efficiency evaluation algorithm, fault prediction algorithm, carbon emission modeling algorithm, and load forecasting algorithm; A23: Model dynamic optimization mechanism, including online learning mechanism and model drift detection mechanism. The online learning mechanism uses an incremental SVM algorithm, which triggers an update when the deviation between new data and model prediction is greater than 10%. The model drift detection mechanism monitors data distribution changes based on the KS test and retrains the model when the p-value is <0.

05.

5. The intelligent management system for improving electrical appliance efficiency and reducing carbon emissions according to claim 4, characterized in that: The core algorithm model library specifically includes: A31: Anomaly detection algorithm, which uses an isolation forest-based anomaly detection algorithm and is applied to motor stall detection, harmonic anomaly tracing, and data quality verification; A32: Energy efficiency evaluation algorithm, which uses an energy efficiency evaluation algorithm based on the XGBoost regression model and is applied to motor energy efficiency diagnosis and transformer loss decomposition; A33: Fault prediction algorithm, which uses a fault prediction algorithm based on the BiLSTM-Attention hybrid model and is applied to motor bearing RUL prediction and cable aging warning; A34: Carbon emission modeling algorithm, which uses a hybrid modeling algorithm based on Monte Carlo simulation algorithm and BP neural network algorithm, and is applied to the regional power grid carbon emission factor library and process carbon footprint; A35: Load forecasting algorithm, which uses a load forecasting algorithm based on the Transformer time series forecasting model and is applied to regional power grid load peak prediction and energy storage charging and discharging optimization.

6. The intelligent management system for improving electrical appliance efficiency and reducing carbon emissions according to claim 5, characterized in that: The specific principles of the carbon emission modeling algorithm based on the hybrid modeling of the Monte Carlo simulation algorithm and the BP neural network algorithm are as follows: S21: Problem definition and input variable determination: input energy-side variables, environmental-side variables, grid-side variables, and process-side variables, and quantify the carbon emission factors of the target area under different operating conditions; S22: Monte Carlo simulation generates sample sets. Latin hypercube sampling is used to divide the probability distribution of the input variables into equal probability intervals. Random sampling is performed from each interval. 1,000 sets of samples are generated for each input variable. 1,000,000 sets of complete operating condition samples are generated through orthogonal combination. S23: Physical calculation of carbon emission factor. Based on the energy flow balance equation, the carbon emission factor of each set of working condition samples is calculated to generate 1000 sets of carbon emission factor samples. The principle formula is: ； in, is the power generation of the i-th type of energy, is the carbon emission coefficient of the i-th type of energy, is the total power generation; S24: BP neural network training, input 1000 sets of working condition samples to train the model; S25: Dynamic weight adjustment, which uses a linear weighted model to correct the output of the BP neural network in real time to adapt to the dynamic changes in the carbon intensity of the power grid. The principle formula is: ； Among them, the BP prediction value is the basic prediction value of the carbon emission factor output by the BP neural network. is the dynamic adjustment coefficient, is the difference between the current grid carbon intensity and the benchmark value, i.e. the real-time fluctuation range; S26: Real-time prediction: input real-time data, use the trained BP neural network model to predict the carbon emission factor, and output the confidence interval.

7. The intelligent management system for improving electrical appliance efficiency and reducing carbon emissions according to claim 6, characterized in that: The data analysis and evaluation module uses machine learning algorithms to analyze the data collected by the data acquisition and monitoring module and evaluate the operating status of electrical equipment, specifically including: S31: Benchmark energy efficiency modeling, benchmarking against selected standards, and establishing motor energy efficiency grade curves; S32: Loss decomposition, separating the motor stator copper loss, iron loss and rotor copper loss through the dual rotating coordinate system method; S33: Energy efficiency optimization suggestions, output optimization solutions, estimated energy saving rate and investment payback period. The optimization solutions include frequency conversion modification, load matching and harmonic control. S34: Emission factor library construction, integrating grid emission factors and process emission factors; S35: Carbon emission heat map construction, showing carbon emission distribution by equipment, workshop and factory level; S36: Carbon emission reduction path planning, recommending emission reduction measures based on Monte Carlo simulation, and quantifying emission reduction potential and costs; S37: Fault prediction and health management first integrates multi-dimensional indicators including vibration RMS value, temperature trend, and current harmonics to calculate the equipment health index, and then combines it with the full life cycle cost model to recommend the optimal maintenance time.

8. The intelligent management system for improving electrical appliance efficiency and reducing carbon emissions according to claim 7, characterized in that: The intelligent optimization control module includes: A41: Input layer, used to collect multivariate data including equipment status, environmental parameters, and grid carbon prices in real time, and complete multi-source heterogeneous data fusion and preprocessing; A42: Decision-making layer, used to generate energy efficiency and carbon synergistic control strategies based on multi-objective optimization algorithms and dynamic rule engines, achieving global optimal solution of equipment operating parameters and real-time strategy generation; A43: Output layer, used to accurately send optimization instructions to devices through industrial protocol adaptation and high-reliability control interfaces. It supports millisecond-level response and dual-channel redundant execution to ensure zero-error implementation of control instructions. A44: Closed-loop layer, used to build an "execution-evaluation-iteration" feedback mechanism. Through real-time evaluation of energy efficiency and carbon indicators, dynamic strategy tuning, and model self-learning updates, it achieves continuous optimization of control effects and adaptive evolution of the system.

9. The intelligent management system for improving electrical appliance efficiency and reducing carbon emissions according to claim 8, characterized in that: The intelligent optimization control module generates intelligent control strategies based on the data results of the data analysis and evaluation module to control electrical equipment, specifically including: S41: Real-time data perception, real-time collection of multivariate data including equipment status, environmental parameters, and grid carbon prices, and outlier removal, as well as extraction of the data's temporal and statistical characteristics; S42: Dynamic decision generation, through a multi-objective optimization model and a hybrid decision-making mechanism, generates an energy efficiency-carbon synergistic control strategy. The principle formula of the multi-objective optimization model is: ； Among them, u is the control variable, including motor frequency, fan speed and valve opening, and is the weight coefficient, is the constraint condition, i.e., the equipment safety threshold, is the comprehensive energy efficiency index, that is, the energy efficiency level of the equipment under the control variable u, is the carbon emission index, that is, the carbon emission of the equipment under the control variable u; S43: Precise control execution: first, the control command is issued, and then the domestic security verification is carried out. The security verification mechanism is: the main and backup channels issue commands simultaneously and compare the execution results.

10. The intelligent management system for improving electrical appliance efficiency and reducing carbon emissions according to claim 9, characterized in that: When generating the energy efficiency-carbon synergistic control strategy through the multi-objective optimization model and hybrid decision-making mechanism, the hybrid decision-making mechanism adopted is: A11: In real-time scenarios, based on the device mechanism model and historical data, it predicts the control effect in the next 5 minutes, solves the optimization problem every 5 minutes, and generates a control instruction sequence. A12: In long-term planning scenarios, energy efficiency improvement, carbon emission reduction, and execution costs are comprehensively considered. Real number coding is used, and gene fragments represent equipment start and stop times and parameter setting values ​​to optimize the coordinated operation plan of multiple devices.

Images