Dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning

Abstract

The invention belongs to the field of energy efficiency optimization in industrial production, and discloses a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning, and the method comprises the steps: collecting energy consumption data and environment parameters in real time through a sensor network and Internet of Things equipment; cleaning, screening and standardizing the collected data; constructing an energy efficiency prediction model and an optimization model based on a machine learning algorithm; training the prediction model and the optimization model by using historical data, and adjusting model parameters; and the trained model is utilized to analyze and predict data acquired in real time, and production parameters are dynamically adjusted to realize optimization of energy efficiency. According to the method, key parameter changes in the production process can be accurately predicted and optimized, dynamic adjustment of production parameters is achieved, an optimal production adjustment strategy can be automatically made, scientificity and efficiency of production decision making are improved, and the method has remarkable technical and economic effects in the field of industrial production.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the technical field of energy efficiency optimization in industrial production, and in particular relates to a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning. Background Art

[0002] With the rapid development of industrial production, the problem of energy consumption is becoming increasingly serious. Traditional energy efficiency optimization methods mainly rely on manual experience and static models. These methods are powerless in the face of complex and changeable production environments, resulting in insignificant energy efficiency improvement effects. Static models cannot respond to changes in the environment and load in real time, and the dependence on manual experience makes the optimization effect have greater uncertainty. In recent years, artificial intelligence (AI) and machine learning (ML) technologies have made significant progress in various fields. Their advantages in data processing, predictive analysis, and adaptive optimization have provided new ideas for energy efficiency optimization. However, although these technologies have shown great potential in many fields, their application in industrial energy efficiency optimization still needs to be further studied and explored. Dynamic energy efficiency optimization methods based on artificial intelligence and machine learning can not only comprehensively improve the accuracy and real-time performance of data, but also use intelligent prediction and optimization technologies to dynamically adjust production parameters to achieve energy efficiency optimization.

[0003] In view of the above analysis, the technical problems that need to be solved urgently in the prior art are:

[0004] Traditional energy efficiency optimization methods are insufficient when dealing with complex and changing industrial production environments, and more efficient and intelligent energy efficiency optimization methods are needed. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning.

[0006] The present invention is implemented as follows: a dynamic energy efficiency optimization method based on artificial intelligence and machine learning, characterized in that the dynamic energy efficiency optimization method based on artificial intelligence and machine learning specifically includes:

[0007] S1: Data collection, using sensor networks and IoT devices to collect energy consumption data and environmental parameters in real time;

[0008] S2: Data preprocessing, cleaning, screening and standardization of collected data;

[0009] S3: Model construction, based on machine learning algorithms, building energy efficiency prediction models and optimization models;

[0010] S4: Model training: Use historical data to train the prediction model and optimization model and adjust the model parameters;

[0011] S5: Real-time optimization, using trained models to analyze and predict real-time collected data, dynamically adjust production parameters, and achieve optimal energy efficiency.

[0012] Furthermore, S1 utilizes sensor networks and Internet of Things (IoT) devices to collect energy consumption data and related environmental parameters in the production environment in real time, including but not limited to temperature, humidity, load, equipment status, etc.

[0013] Further, the step S2, data preprocessing, includes:

[0014] (1) Data cleaning

[0015] Use statistical methods to identify and process outliers and noise in the data, such as detecting abnormal data points by calculating the mean, standard deviation, and quartiles of the data. Specifically, the mean μ and standard deviation σ can be calculated using the following formula:

[0016]

[0017] (2) Data Standardization

[0018] The standardization method is Z-score standardization:

[0019]

[0020] Among them, x is the original data, μ is the mean of the data, and σ is the standard deviation of the data.

[0021] Furthermore, the step S3, model building, includes:

[0022] (1) The linear regression model describes the relationship between energy consumption and environmental parameters through the following formula:

[0023] y=β0+β1x1+β2x2+…+β n x n +∈

[0024] Among them, y is the predicted energy consumption, x1, x2, …, xn are characteristic variables (such as temperature, humidity, etc.), β0, β1, β2, …, βn are the coefficients of the model, and ∈ is the error term;

[0025] (2) The neural network model updates the weight θ through the gradient descent algorithm. The process can be described as:

[0026]

[0027] Among them, α is the learning rate, J(θ) is the loss function, and after completing the model training, the accuracy and stability of the model are verified by evaluation indicators such as mean square error (MSE), R square (R^2), and root mean square error (RMSE).

[0028] Further, the step S4, model training, includes:

[0029] (1) A linear regression model is used to predict energy consumption. The model adjusts the parameter θ by minimizing the loss function J(θ), which is defined as the average of the squared errors between the predicted values ​​and the actual values ​​in all training samples:

[0030]

[0031] Among them, h θ (x (i) )=θ T x (i) is the model prediction value, θ is the parameter vector, x (i) is the feature vector of the i-th training sample, y (i) is the actual label corresponding to it, m is the number of training samples, and the gradient descent method is used to minimize the loss function, where α is the learning rate, which controls the magnitude of parameter update at each iteration;

[0032] (2) Normal equation method:

[0033] θ=(X T X) -1 X T y

[0034] This method is suitable for situations where the sample size is not large and can directly calculate the optimal parameter θ;

[0035] (3) In neural network training, the back-propagation algorithm is used to calculate the gradient of the loss function with respect to each parameter, and then update the parameters by gradient descent or its variants. For the weight parameter θij between the i-th layer and the j-th layer, the update rule is:

[0036]

[0037] Further, the step S5 includes:

[0038] (1) Based on the latest real-time data, use the trained model to predict energy efficiency:

[0039]

[0040] Among them, θ is the model parameter, x is the feature vector collected in real time, and b is the bias term;

[0041] (2) Compare and analyze the real-time prediction results with the set energy efficiency optimization goals, using optimization algorithms. The optimization goals include minimizing energy consumption, maximizing production efficiency, or considering multiple indicators at the same time. The core of the optimization algorithm is to define appropriate objective functions and constraints:

[0042] min x f(x)subject to g i (x)≤0,i=1,...,m

[0043] Among them, f(x) is the objective function, gi(x) is the constraint condition, and the conditions and objectives should be adjusted according to the actual situation to achieve the optimal production parameter adjustment.

[0044] Another object of the present invention is to provide a dynamic energy efficiency optimization system based on artificial intelligence and machine learning, the system specifically comprising:

[0045] Data collection module, which is used to collect energy consumption data and environmental parameters in real time through sensor networks and IoT devices, including temperature, humidity, equipment load and equipment status;

[0046] Data preprocessing module, used to clean, filter and standardize the collected raw data, including outlier detection, noise filtering and data standardization;

[0047] A model building module, used to build an energy efficiency prediction model and an optimization model based on a machine learning algorithm, wherein the model may be based on a linear regression or a neural network algorithm;

[0048] Model training module, used to train and adjust parameters of prediction models and optimization models through historical data;

[0049] The real-time optimization module is used to analyze the data collected in real time and dynamically adjust the production parameters based on the trained model to achieve optimal energy efficiency.

[0050] Furthermore, the data acquisition module collaborates with the sensor network through the Internet of Things (IoT) devices to collect energy consumption data from the production environment in real time, and collects environmental parameters such as temperature, humidity, load, equipment status, etc. to ensure the integrity and real-time nature of the data.

[0051] Furthermore, the data preprocessing module includes:

[0052] Data cleaning unit, used to identify and process abnormal data points and noisy data using statistical methods, and detect the mean and standard deviation of the data to filter out noise;

[0053] The data standardization unit is used to perform Z-score standardization on data of different dimensions to ensure the consistency and accuracy of the data in the model.

[0054] Further, the real-time optimization module includes:

[0055] Energy efficiency prediction unit, which uses trained models to perform energy efficiency prediction based on real-time data;

[0056] The optimization analysis unit compares the prediction results with the energy efficiency optimization target, and by defining the objective function and constraints, applies the optimization algorithm to dynamically adjust the production parameters to minimize energy consumption or maximize production efficiency.

[0057] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0058] First, the present invention aims to improve energy efficiency, reduce energy consumption and carbon emissions, and has significant technical and economic effects in the field of industrial production. By introducing advanced artificial intelligence and machine learning algorithms, through real-time data analysis and improved prediction accuracy, it is possible to accurately predict and optimize changes in key parameters in the production process. The present invention is not just a simple replacement for static models, but through real-time data-driven and intelligent decision support, it realizes the dynamic adjustment of production parameters. By introducing optimization algorithms such as constrained optimization and genetic algorithms, it is possible to automatically formulate the best production adjustment strategy, reduce human intervention, and improve the scientificity and efficiency of production decisions.

[0059] Second, the dynamic energy efficiency optimization method based on artificial intelligence and machine learning proposed in the present invention effectively solves multiple technical problems in the prior art in industrial applications and has achieved significant technical progress, which is mainly reflected in the following aspects:

[0060] 1. Improve the accuracy and real-time performance of energy efficiency optimization: Traditional energy efficiency optimization methods rely on fixed rules or historical experience, which are difficult to adapt to complex and dynamically changing production environments. The present invention can collect and analyze energy consumption data and environmental parameters in real time by introducing machine learning algorithms (such as linear regression and neural networks), automatically predict energy efficiency changes, and dynamically adjust production parameters. Its high-precision data processing and prediction capabilities significantly improve the accuracy and real-time performance of energy efficiency optimization, ensuring the optimization of energy efficiency in complex production environments.

[0061] 2. Enhance the flexibility of data processing and model training: In the prior art, data noise and outliers often lead to poor energy efficiency optimization results. The present invention effectively solves this problem through data preprocessing steps (such as data cleaning and standardization), and combines methods such as Z-score standardization to further improve data consistency. At the same time, multiple models such as linear regression and neural networks and their corresponding optimization algorithms (such as gradient descent, normal equation method, etc.) are used to ensure that model training can adapt to data sets of different sizes, and the optimization effect is significantly improved.

[0062] 3. Realize multi-objective energy efficiency optimization and adaptive adjustment: The present invention constructs a composite energy efficiency optimization target, not only taking into account the minimization of energy consumption, but also combining multiple indicators such as production efficiency and equipment operation status for comprehensive analysis and optimization. Real-time monitoring and prediction of energy consumption changes, combined with optimization algorithms to dynamically adjust parameters in the production process, so that energy efficiency optimization targets can be adaptively adjusted to meet the needs of different scenarios. This multi-objective optimization mechanism greatly improves the flexibility and applicability of energy efficiency optimization.

[0063] 4. Significantly improve the level of industrial automation: Through IoT devices and sensor networks, real-time collection of energy consumption and environmental data in the production environment, combined with machine learning models to automatically analyze and adjust production parameters, the present invention realizes full process automation from data collection to optimized execution, reduces manual intervention, and optimizes production efficiency and energy efficiency. Compared with traditional manual adjustment and experience-based decision-making, the present invention greatly improves the level of industrial automation and intelligence, and promotes energy conservation, emission reduction and green production.

[0064] In summary, the present invention has made significant technological progress in improving the accuracy, real-time performance and flexibility of energy efficiency optimization through intelligent data processing and optimization algorithms, and has provided a more efficient and intelligent solution for energy efficiency management in complex production environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a flow chart of a dynamic energy efficiency optimization method based on artificial intelligence and machine learning provided by an embodiment of the present invention;

[0066] Figure 2 is a real-time optimization flow chart provided by an embodiment of the present invention;

[0067] Figure 3 It is a module diagram of a dynamic energy efficiency optimization system based on artificial intelligence and machine learning provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0069] Example 1: Energy efficiency optimization in intelligent manufacturing

[0070] In the field of intelligent manufacturing, this dynamic energy efficiency optimization method based on artificial intelligence and machine learning can be used to optimize the energy use of production lines. The equipment and machines in the factory usually consume a lot of energy. The energy efficiency optimization method continuously analyzes the energy consumption trend by monitoring the energy consumption data of the equipment in real time (such as power consumption, temperature, pressure, etc.) and combining it with environmental parameters (such as workshop temperature, humidity, air quality, etc.).

[0071] Specific implementation steps:

[0072] 1. Data collection: The sensor network collects energy consumption data of each machine and equipment, as well as parameters such as temperature and humidity of the workshop environment in real time.

[0073] 2. Data preprocessing: The system automatically cleans up noise data and abnormal data, and screens out key features related to energy consumption for standardized processing.

[0074] 3. Model construction and training: The prediction model based on machine learning is used to predict the energy consumption trend under different production conditions, and the optimization model proposes how to reduce energy consumption by adjusting the production line speed, machine power, etc.

[0075] 4. Real-time optimization: The system automatically adjusts production parameters based on real-time data, such as reducing machine power or adjusting operating hours during off-peak hours to reduce energy consumption and improve production efficiency.

[0076] Through real-time monitoring and dynamic adjustments, factories can significantly reduce energy costs, optimize production efficiency, and reduce equipment loss and maintenance costs caused by excessive energy use.

[0077] Example 2: Energy management in smart buildings

[0078] In the field of smart buildings, this method can be applied to energy efficiency management in commercial buildings, residential areas or industrial parks. Sensors are installed on various devices in the building (such as air conditioners, lighting systems, elevators, etc.) to collect data such as power consumption, temperature, humidity, and personnel flow in real time. Based on this data, the system can predict future energy consumption needs and optimize the operation strategy of the equipment to maximize energy savings.

[0079] Specific implementation steps:

[0080] 1. Data collection: Through the intelligent sensor network, real-time monitoring of environmental data such as power consumption, temperature, and light in the building, especially the power consumption of air conditioning and lighting systems.

[0081] 2. Data preprocessing: Clean and screen the collected data, standardize environmental parameters in different units such as temperature and humidity, and ensure data quality.

[0082] 3. Model construction and training: Based on the historical energy consumption data of the building, an energy efficiency prediction model is constructed, and an optimization model is constructed based on different environmental conditions to control the start and stop timing and power size of the equipment.

[0083] 4. Real-time optimization: The system dynamically adjusts the building's air conditioning and lighting systems based on real-time data. For example, the air conditioning power is increased during crowded hours and reduced during less crowded hours to minimize energy consumption while maintaining comfort.

[0084] By implementing this method, smart buildings can save a lot of energy, improve operational efficiency, and reduce energy waste, especially in the optimization and regulation of high-energy-consuming systems such as air conditioning and lighting.

[0085] like Figure 1 As shown, an embodiment of the present invention provides a dynamic energy efficiency optimization method based on artificial intelligence and machine learning, and the method specifically includes:

[0086] S1: Data collection, using sensor networks and IoT devices to collect energy consumption data and environmental parameters in real time;

[0087] S2: Data preprocessing, cleaning, screening and standardization of collected data;

[0088] S3: Model construction, based on machine learning algorithms, building energy efficiency prediction models and optimization models;

[0089] S4: Model training: Use historical data to train the prediction model and optimization model and adjust the model parameters;

[0090] S5: Real-time optimization, using trained models to analyze and predict real-time collected data, dynamically adjust production parameters, and achieve optimal energy efficiency.

[0091] The dynamic energy efficiency optimization method in the embodiment of the present invention first uses S1: data collection to monitor energy consumption data and environmental parameters in real time using sensor networks and Internet of Things devices. These sensors are arranged on production equipment, building environments and other related energy consumption nodes, and can accurately record energy consumption information as well as environmental variables such as temperature, humidity, and equipment working status. The accurate collection of these data provides a basis for subsequent analysis and optimization.

[0092] Next, we enter S2: data preprocessing stage, where the collected raw data is cleaned, screened, and standardized. Since the data collected by the sensor may be incomplete or inaccurate, such as noise and missing values, data cleaning will remove invalid or erroneous data. Data screening ensures that only important features related to energy efficiency are retained. Through standardization, data of different dimensions are uniformly converted into a standard format that is easy for the model to process, thereby providing high-quality data input for subsequent machine learning models.

[0093] S3: In the model building phase, energy efficiency prediction models and optimization models are built based on machine learning algorithms. The energy efficiency prediction model is used to predict future energy consumption trends and environmental changes, while the optimization model makes corresponding adjustment suggestions based on these prediction results. Common machine learning algorithms such as linear regression, decision trees, and neural networks are used to build these models. The construction of the model not only needs to consider the correlation of historical data, but also the current energy consumption status and environmental factors.

[0094] Finally, we enter S4: model training and S5: real-time optimization. Through historical data, the model is continuously trained and parameters are adjusted, so that the accuracy of energy efficiency prediction and optimization is gradually improved. When the model training is completed, the system predicts and analyzes the real-time collected data, and dynamically adjusts the operating parameters of the production equipment, such as adjusting the power and temperature control system settings, to ensure that the optimal energy efficiency state is achieved in the current environment. This optimization process is carried out in real time and is continuously updated and adjusted as the environment changes, thereby achieving continuous energy efficiency improvement.

[0095] S1, using sensor networks and Internet of Things (IoT) devices, collects energy consumption data and related environmental parameters in the production environment in real time, including but not limited to temperature, humidity, load, equipment status, etc.

[0096] The step S2, data preprocessing, includes:

[0097] (1) Data cleaning

[0098] The data cleaning phase uses statistical methods to identify and process outliers and noise in the data, such as detecting abnormal data points by calculating the mean, standard deviation, and quartiles of the data. Specifically, the mean μ and standard deviation σ can be calculated using the following formula:

[0099]

[0100] Data cleaning can not only eliminate outliers, but also handle missing values ​​in the data to ensure the integrity and consistency of the data set.

[0101] (2) Data Standardization

[0102] For industrial energy efficiency optimization, key features such as temperature, humidity, and load are selected for further analysis and modeling. Through the data standardization process, the numerical ranges of different features are unified into similar intervals to eliminate the impact of dimensions. The standardization method is Z-score standardization:

[0103]

[0104] Among them, x is the original data, μ is the mean of the data, and σ is the standard deviation of the data.

[0105] The S3 model building steps include:

[0106] (1) The linear regression model describes the relationship between energy consumption and environmental parameters through the following formula:

[0107] y=β0+β1x1+β2x2+...+β n x n +∈

[0108] Among them, y is the predicted energy consumption, x1, x2, …, xn are characteristic variables (such as temperature, humidity, etc.), β0, β1, β2, …, βn are the coefficients of the model, and ∈ is the error term. In the model training and optimization stage, the training data set is used to adjust the model parameters to minimize the prediction error.

[0109] (2) The neural network model updates the weight θ through the gradient descent algorithm. The process can be described as:

[0110]

[0111] Among them, α is the learning rate and J(θ) is the loss function, which is used to measure the difference between the predicted value and the true value. After the model training is completed, the accuracy and stability of the model are verified by evaluation indicators such as mean square error (MSE), R square (R^2), and root mean square error (RMSE). Finally, understanding the interpretability and explainability of the model can help engineers and decision makers deeply analyze how the model makes predictions and optimizes decisions based on input features.

[0112] The step S4, model training, includes:

[0113] (1) A linear regression model is used to predict energy consumption. The model adjusts the parameter θ by minimizing the loss function J(θ), which is defined as the average of the squared errors between the predicted values ​​and the actual values ​​in all training samples:

[0114]

[0115] Among them, h θ (x (i) )=θ T x(i) is the model prediction value, θ is the parameter vector, x (i) is the feature vector of the i-th training sample, y (i) is the actual label corresponding to it, and m is the number of training samples. Gradient descent is used to minimize the loss function, where α is the learning rate, which controls the magnitude of parameter update at each iteration.

[0116] (2) Normal equation method:

[0117] θ=(X T X) -1 X T y

[0118] This method is suitable for situations where the sample size is not large, and can directly calculate the optimal parameter θ.

[0119] (3) Neural network models perform well in processing complex nonlinear relationships. Neural networks learn and represent complex patterns through multiple layers of neurons and activation functions. In neural network training, the back propagation algorithm is used to calculate the gradient of the loss function for each parameter, and then update the parameters through gradient descent or its variants. For example, for the weight parameter θij between the i-th layer and the j-th layer, the update rule is:

[0120]

[0121] This can be done using either batch gradient descent, where all training examples are used to compute the gradient, or stochastic gradient descent, where only one random example is used at a time.

[0122] like Figure 2 As shown, the step S5 includes:

[0123] (1) Based on the latest real-time data, use the trained model to predict energy efficiency:

[0124]

[0125] Among them, θ is the model parameter, x is the feature vector collected in real time, and b is the bias term. For complex models such as neural networks, the prediction process will be more complicated, but the basic principle is similar, and the predicted value is obtained through forward propagation.

[0126] (2) Compare and analyze the real-time prediction results with the set energy efficiency optimization goals, using optimization algorithms. The optimization goals include minimizing energy consumption, maximizing production efficiency, or considering multiple indicators at the same time. The core of the optimization algorithm is to define appropriate objective functions and constraints:

[0127] min x f(x)subject to gi (x)≤0,i=1,...,m

[0128] Among them, f(x) is the objective function, gi(x) is the constraint condition, and the conditions and objectives should be adjusted according to the actual situation to achieve the optimal production parameter adjustment.

[0129] like Figure 3 As shown, an embodiment of the present invention provides a dynamic energy efficiency optimization system based on artificial intelligence and machine learning, which specifically includes:

[0130] Input module, used to collect energy consumption data and environmental parameters;

[0131] The preprocessing module is used to clean, filter and standardize the collected data, remove abnormal data and noise, and ensure data quality;

[0132] The analysis and prediction module uses the trained model to analyze and predict the data collected in real time, and dynamically adjusts the production parameters to achieve optimal energy efficiency.

[0133] The working principle of the dynamic energy efficiency optimization system based on artificial intelligence and machine learning provided by the embodiment of the present invention is divided into four main parts: data acquisition, preprocessing, analysis and prediction, and dynamic optimization control.

[0134] 1. Data Collection

[0135] First, the system collects energy consumption data and environmental parameters from sensor networks and Internet of Things (IoT) devices in real time through input modules. Sensors are distributed in different production equipment and systems to capture parameters such as power consumption, temperature, humidity, and equipment status. These data provide basic input for subsequent energy efficiency analysis and optimization. Through IoT devices, data can be automatically uploaded to the central server to achieve seamless data collection and storage.

[0136] 2. Data Preprocessing

[0137] After the preprocessing module receives the data from the input module, it first performs data cleaning and standardization. Specifically, the system uses statistical analysis methods (such as mean, standard deviation, quartiles, etc.) to identify outliers or noise in the data. For data that does not meet the standards, the system will filter or correct it to ensure data quality. Next, the data will be standardized through the Z-score method to unify the energy consumption and environmental data of different units to adapt them to the input requirements of the machine learning model.

[0138] 3. Analysis and prediction

[0139] After data preprocessing, the system passes the cleaned and standardized data to the analysis and prediction module. This module performs real-time analysis and prediction of current energy consumption data based on trained machine learning models (such as linear regression, neural networks, etc.). Through the model, the system can predict future energy consumption trends and environmental changes. Combining historical data with real-time data, the model dynamically adjusts system parameters and evaluates potential opportunities for energy efficiency optimization. The model's high-precision prediction helps the system detect energy consumption fluctuations in advance and ensure that the energy efficiency of the production system remains at the optimal state.

[0140] 4. Dynamic optimization control

[0141] After obtaining the energy efficiency prediction results, the system optimizes energy efficiency by dynamically adjusting the operating parameters of the production equipment. The results of the analysis and prediction module are fed back to the production system, and the control module automatically adjusts the working mode of the equipment according to the optimization goals (such as reducing energy consumption or improving production efficiency). For example, the air-conditioning system adjusts the cooling capacity according to the changes in ambient temperature, and the production equipment adjusts the workload according to the predicted peak energy consumption. This adaptive adjustment mechanism ensures that the system continuously optimizes energy efficiency in different environments and working conditions, reduces energy waste, and improves production efficiency.

[0142] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0143] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A dynamic energy efficiency optimization method based on artificial intelligence and machine learning, characterized in that: The method specifically includes: S1: Data collection, using sensor networks and IoT devices to collect energy consumption data and environmental parameters in real time; S2: Data preprocessing, cleaning, screening and standardization of collected data; S3: Model construction, based on machine learning algorithms, building energy efficiency prediction models and optimization models; S4: Model training: Use historical data to train the prediction model and optimization model and adjust the model parameters; S5: Real-time optimization, using trained models to analyze and predict real-time collected data, dynamically adjust production parameters, and achieve optimal energy efficiency.

2. The dynamic energy efficiency optimization method based on artificial intelligence and machine learning as claimed in claim 1, characterized in that: S1 utilizes sensor networks and Internet of Things (IoT) devices to collect energy consumption data and related environmental parameters in the production environment in real time, including but not limited to temperature, humidity, load, and equipment status.

3. The dynamic energy efficiency optimization method based on artificial intelligence and machine learning as claimed in claim 1, characterized in that: The step S2, data preprocessing, includes: (1) Data cleaning Use statistical methods to identify and process outliers and noise in the data, such as detecting abnormal data points by calculating the mean, standard deviation, and quartiles of the data. Specifically, the mean μ and standard deviation σ can be calculated using the following formula: (2) Data Standardization The standardization method is Z-score standardization: Among them, x is the original data, μ is the mean of the data, and σ is the standard deviation of the data.

4. The dynamic energy efficiency optimization method based on artificial intelligence and machine learning as claimed in claim 1, characterized in that: The S3 model building steps include: (1) The linear regression model describes the relationship between energy consumption and environmental parameters through the following formula: y=β0+β1x1+β2x2+…+β n x n +∈ Among them, y is the predicted energy consumption, x1, x2, ..., xn are characteristic variables, β0, β1, β2, ..., βn are the coefficients of the model, and ∈ is the error term; (2) The neural network model updates the weight θ through the gradient descent algorithm. The process can be described as: Among them, α is the learning rate, J(θ) is the loss function, and after completing the model training, the accuracy and stability of the model are verified by evaluation indicators such as mean square error (MSE), R square (R^2), and root mean square error (RMSE).

5. The dynamic energy efficiency optimization method based on artificial intelligence and machine learning as claimed in claim 1, characterized in that: The step S4, model training, includes: (1) A linear regression model is used to predict energy consumption. The model adjusts the parameter θ by minimizing the loss function J(θ), which is defined as the average of the squared errors between the predicted values ​​and the actual values ​​in all training samples: Among them, h θ (x (i) )=θ T x (i) is the model prediction value, θ is the parameter vector, x (i) is the feature vector of the i-th training sample, y (i) is the actual label corresponding to it, m is the number of training samples, and the gradient descent method is used to minimize the loss function, where α is the learning rate, which controls the magnitude of parameter update at each iteration; (2) Normal equation method: θ=(X T X) -1 X T y This method is suitable for situations where the sample size is not large and can directly calculate the optimal parameter θ; (3) In neural network training, the back-propagation algorithm is used to calculate the gradient of the loss function with respect to each parameter, and then update the parameters by gradient descent or its variants. For the weight parameter θij between the i-th layer and the j-th layer, the update rule is:

6. The dynamic energy efficiency optimization method based on artificial intelligence and machine learning as claimed in claim 1, characterized in that: The step S5 comprises: (1) Based on the latest real-time data, use the trained model to predict energy efficiency: Among them, θ is the model parameter, x is the feature vector collected in real time, and b is the bias term; (2) Compare and analyze the real-time prediction results with the set energy efficiency optimization goals, using optimization algorithms. The optimization goals include minimizing energy consumption, maximizing production efficiency, or considering multiple indicators at the same time. The core of the optimization algorithm is to define appropriate objective functions and constraints: min x f(x)subject to g i (x)≤0,i=1,...,m Among them, f(x) is the objective function, gi(x) is the constraint condition, and the conditions and objectives should be adjusted according to the actual situation to achieve the optimal production parameter adjustment.

7. A dynamic energy efficiency optimization system based on artificial intelligence and machine learning as described in claims 1-6, characterized in that: The system specifically includes: Data acquisition module, which is used to collect energy consumption data and environmental parameters in real time, including temperature, humidity, equipment load and equipment status, through sensor networks and IoT devices; Data preprocessing module, used to clean, filter and standardize the collected raw data, including outlier detection, noise filtering and data standardization; A model building module, used to build an energy efficiency prediction model and an optimization model based on a machine learning algorithm, wherein the model may be based on a linear regression or a neural network algorithm; Model training module, used to train and adjust parameters of prediction models and optimization models through historical data; The real-time optimization module is used to analyze the data collected in real time and dynamically adjust the production parameters based on the trained model to achieve optimal energy efficiency.

8. The system according to claim 7, characterized in that The data acquisition module collaborates with the sensor network through the Internet of Things (IoT) devices to collect energy consumption data from the production environment in real time and collect environmental parameters to ensure the integrity and real-time nature of the data.

9. The system according to claim 7, characterized in that The data preprocessing module comprises: Data cleaning unit, used to identify and process abnormal data points and noisy data using statistical methods, and detect the mean and standard deviation of the data to filter out noise; The data standardization unit is used to perform Z-score standardization on data of different dimensions to ensure the consistency and accuracy of the data in the model.

10. The system according to claim 7, characterized in that The real-time optimization module comprises: Energy efficiency prediction unit, which uses trained models to perform energy efficiency prediction based on real-time data; The optimization analysis unit compares the prediction results with the energy efficiency optimization target, and by defining the objective function and constraints, applies the optimization algorithm to dynamically adjust the production parameters to minimize energy consumption or maximize production efficiency.

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