Carbon emission prediction method and system based on artificial intelligence

Abstract

The invention provides a carbon emission prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: integrating sensor data collection, data synchronization, data cleaning and feature extraction methods, and obtaining first target flow data information; building a real-time data processing platform and performing data formatting to obtain second target stream data information; and based on the second target flow data information, integrating deep learning model selection, transfer learning application, a model training strategy and a model compression technology, and obtaining a plurality of target models through model training and optimization so as to carry out carbon emission prediction in the steel production process. According to the method, deep learning model selection, transfer learning application, a model training strategy and a model compression technology are integrated, the prediction precision of carbon emission in each stage in the steel production process is improved, and the applicability and accuracy of the model in different production environments and technological processes are improved through modular design and the transfer learning technology.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and specifically to a carbon emission prediction method and system based on artificial intelligence. Background Art

[0002] In the steel production process, energy consumption and carbon emissions mainly come from key links such as ironmaking, steelmaking, and steel rolling; these links involve a large amount of energy use, including electricity, coal, natural gas, etc., among which electricity consumption is particularly critical because it is directly related to carbon emissions. The purpose of this invention is to develop a carbon emission prediction technology based on artificial intelligence, which can predict the carbon emissions of each production link by analyzing the electricity consumption data of the equipment, and provide decision support for enterprises.

[0003] In the related technology, in the process of predicting carbon emissions for steel enterprises, traditional machine learning algorithms and deep learning algorithms are mainly relied on. Specifically, on the one hand, the carbon emission prediction method based on machine learning usually uses algorithms such as support vector machine SVM, random forest or gradient boosting machine GBM, combined with historical energy consumption data and carbon emission data, to establish a prediction model. On the other hand, deep learning algorithms, such as convolutional neural network CNN or recurrent neural network RNN, can be used to predict carbon emissions. Deep learning algorithms can handle more input variables and capture nonlinear relationships between variables.

[0004] However, the limitation of the carbon emission prediction method based on machine learning is that it requires a large amount of labeled data and has limited processing capabilities for real-time data, making it difficult to meet the needs of real-time prediction. Deep learning algorithms have improved prediction accuracy, but they usually require a large amount of labeled data and have limited processing capabilities for real-time data, making it difficult to meet the needs of real-time prediction; they cannot effectively process real-time data streams, and the system flexibility and scalability are insufficient. Summary of the invention

[0005] In response to the shortcomings of the existing technology, the present application provides a carbon emission prediction method and system based on artificial intelligence, which solves the problem of inaccurate prediction accuracy caused by excessive limitations in carbon emission prediction of steel enterprises.

[0006] To achieve the above objectives, this application is implemented through the following technical solutions:

[0007] In the first aspect, an embodiment of the present application provides a carbon emission prediction method based on artificial intelligence, which includes: deploying multiple sensors based on the steel production link, integrating sensor data acquisition, data synchronization, data cleaning and feature extraction methods to obtain first target flow data information; building a real-time data processing platform, and formatting data based on the first target flow data information to obtain second target flow data information; based on the second target flow data information, integrating deep learning model selection, transfer learning application, model training strategy and model compression technology, and obtaining multiple target models through model training and optimization to predict carbon emissions from steel production; combining real-time prediction granularity, user interface design and report generation technology to provide hourly carbon emission prediction results for intuitive display; regularly updating the target model based on the obtained user feedback data and actual carbon emission data to improve the accuracy of carbon emission prediction.

[0008] In the second aspect, an embodiment of the present application provides a carbon emission prediction system based on artificial intelligence, which includes: a data acquisition and preprocessing module, a real-time data processing module, a model training and optimization module, a prediction output module and an iterative improvement module.

[0009] Specifically, the data acquisition and preprocessing module is used to deploy multiple sensors based on the steel production process, integrate sensor data acquisition, data synchronization, data cleaning and feature extraction methods, and obtain the first target flow data information; the real-time data processing module is used to build a real-time data processing platform, and format the data based on the first target flow data information to obtain the second target flow data information; the model training and optimization module is used to integrate deep learning model selection, transfer learning application, model training strategy and model compression technology based on the second target flow data information, and obtain multiple target models through model training and optimization to predict carbon emissions from steel production; the prediction output module is used to combine real-time prediction granularity, user interface design and report generation technology to provide hourly carbon emission prediction results for intuitive display; the iterative improvement module is used to regularly update the target model based on the obtained user feedback data and actual carbon emission data to improve the accuracy of carbon emission prediction.

[0010] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the artificial intelligence-based carbon emission prediction method in the first aspect described above is implemented.

[0011] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a program or instruction. When the program or instruction is executed by a processor, the artificial intelligence-based carbon emission prediction method in the first aspect is implemented.

[0012] This application provides a carbon emission prediction method and system based on artificial intelligence. Compared with the existing technology, it has the following beneficial effects:

[0013] This application deploys multiple sensors to collect data and process data based on the steel production process to obtain the first target flow data information, and builds a real-time data processing platform to meet the steel companies' needs for timely response, thereby enhancing the ability to predict production changes in a timely manner. This application integrates deep learning model selection, transfer learning applications, model training strategies, and model compression technologies to improve the prediction accuracy of carbon emissions at various stages in the steel production process, reduce errors caused by data dependence and insufficient model generalization capabilities, and improve the applicability and accuracy of the model in different production environments and process flows through modular design and transfer learning technology, so that the model is not limited to specific production conditions. This application also incorporates real-time prediction granularity, user interface design, and report generation technology, which can intuitively display carbon emission prediction results; it can also regularly update the target model based on user feedback data and actual carbon emission data, support online learning, and enable the model to be dynamically updated based on the latest data. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a flow chart of a carbon emission prediction method based on artificial intelligence provided in an embodiment of the present application;

[0016] Figure 2 yes Figure 1 An exemplary flow chart of S130;

[0017] Figure 3 It is a structural schematic diagram of a carbon emission prediction system based on artificial intelligence provided in an embodiment of the present application;

[0018] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0021] The embodiments of the present application provide a carbon emission prediction method and system based on artificial intelligence, thereby solving the problem of inaccurate prediction accuracy caused by excessive limitations in carbon emission prediction for steel enterprises.

[0022] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0023] Under the background of global climate change and sustainable development, the carbon emission problem of steel production industry as an energy-intensive industry has attracted wide attention. The technology involved in this invention is a prediction method of carbon emission of steel enterprises, which aims to help enterprises optimize production processes, reduce environmental impact and comply with increasingly stringent environmental regulations by accurately predicting and managing carbon emissions.

[0024] In the steel production process, energy consumption and carbon emissions mainly come from key links such as ironmaking, steelmaking, and steel rolling; these links involve a large amount of energy use, including electricity, coal, natural gas, etc., among which electricity consumption is particularly critical because it is directly related to carbon emissions. The purpose of this invention is to develop a carbon emission prediction technology based on artificial intelligence, which can predict the carbon emissions of each production link by analyzing the electricity consumption data of the equipment, and provide decision support for enterprises.

[0025] In the related technology, in the process of predicting carbon emissions for steel enterprises, traditional machine learning algorithms and deep learning algorithms are mainly relied on. Specifically, on the one hand, the carbon emission prediction method based on machine learning usually uses algorithms such as support vector machine SVM, random forest or gradient boosting machine GBM, combined with historical energy consumption data and carbon emission data, to establish a prediction model. On the other hand, deep learning algorithms, such as convolutional neural network CNN or recurrent neural network RNN, can be used to predict carbon emissions. Deep learning algorithms can handle more input variables and capture nonlinear relationships between variables.

[0026] However, the limitation of the carbon emission prediction method based on machine learning is that it requires a large amount of labeled data and has limited processing capabilities for real-time data, making it difficult to meet the needs of real-time prediction. Deep learning algorithms have improved prediction accuracy, but they usually require a large amount of labeled data and have limited processing capabilities for real-time data, making it difficult to meet the needs of real-time prediction; they cannot effectively process real-time data streams, and the system flexibility and scalability are insufficient.

[0027] Specifically, the related technology has the following defects:

[0028] (1) High data dependence: Many existing carbon emission prediction models, especially those based on traditional machine learning, are highly dependent on large amounts of labeled data. In the field of steel production, it is often impractical to obtain sufficient labeled data because the collection and labeling of such data is time-consuming and expensive.

[0029] (2) Insufficient real-time processing capabilities: Existing models are generally unable to effectively process real-time data streams. In the steel production process, it is crucial to be able to respond to production changes instantly and predict carbon emissions, but existing technologies often cannot meet this need.

[0030] (3) Limited generalization ability: Many models perform well under specific conditions, but their prediction accuracy drops significantly when applied to different production environments or process flows. This limits the practicality and scalability of the models.

[0031] (4) Difficulty in model updating: Traditional models need to be retrained when faced with changes in production conditions or shifts in data distribution. This is not only time-consuming but may also require additional labeled data, which is not feasible in actual operations.

[0032] (5) Lack of modular design: Existing forecasting systems are often not modularly designed according to the specific processes of steel production, resulting in insufficient system flexibility and scalability.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] The following first introduces a carbon emission prediction method based on artificial intelligence provided in an embodiment of the present application.

[0035] The present application provides a flow chart of a carbon emission prediction method based on artificial intelligence, such as Figure 1 As shown, the artificial intelligence-based carbon emission prediction method may include the following steps S110-S150.

[0036] S110, deploy multiple sensors based on the steel production process, integrate sensor data acquisition, data synchronization, data cleaning and feature extraction methods, and obtain first target flow data information;

[0037] S120, building a real-time data processing platform, and performing data formatting based on the first target stream data information to obtain second target stream data information;

[0038] S130, based on the second target stream data information, integrating deep learning model selection, transfer learning application, model training strategy and model compression technology, and obtaining multiple target models through model training and optimization to predict carbon emissions from steel production;

[0039] S140, combining real-time forecast granularity, user interface design and report generation technology to provide hourly carbon emission forecast results for intuitive display;

[0040] S150. Regularly update the target model according to the acquired user feedback data and actual carbon emission data to improve the accuracy of carbon emission prediction.

[0041] The above is a specific implementation method of a carbon emission prediction method based on artificial intelligence provided by an embodiment of the present application. It can be understood that the present application deploys multiple sensors based on the steel production link to collect data and perform data processing to obtain the first target flow data information, and builds a real-time data processing platform to meet the needs of steel companies for timely response, thereby enhancing the ability to predict production changes in a timely manner. The present application integrates deep learning model selection, transfer learning applications, model training strategies, and model compression technologies to improve the prediction accuracy of carbon emissions at various stages in the steel production process, reduce errors caused by data dependence and insufficient model generalization capabilities, and improves the applicability and accuracy of the model in different production environments and process flows through modular design and transfer learning technology, so that the model is not limited to specific production conditions.

[0042] Furthermore, this application reduces the dependence on large amounts of labeled data through unsupervised and self-supervised learning, and uses unlabeled data to improve the learning ability of the model; it uses stream data processing technology to achieve efficient processing of real-time data streams to meet the needs of real-time prediction.

[0043] In addition, this application also incorporates real-time prediction granularity, user interface design, and report generation technology, which can intuitively display carbon emission prediction results; it can also regularly update the target model based on user feedback data and actual carbon emission data, support online learning, and enable the model to be dynamically updated based on the latest data. This application overcomes the shortcomings of existing technologies in terms of data processing capabilities, prediction accuracy, real-time performance, and generalization capabilities.

[0044] In some embodiments, the aforementioned deployment of multiple sensors based on the steel production process, integrating sensor data acquisition, data synchronization, data cleaning and feature extraction methods, and obtaining the first target flow data information, that is, the aforementioned S110 may specifically include the following steps:

[0045] S210. Deploy multiple sensors based on key equipment and process flows of steel production; the process flows include: raw material preparation stage, coking stage, blast furnace ironmaking stage, steelmaking stage, continuous casting stage, hot rolling stage, cold rolling stage, post-processing stage, energy management stage, waste treatment and recycling stage;

[0046] S220, collecting data in real time through multiple sensors, synchronizing timestamps of data from different sensors and devices, and obtaining initial flow data information, the initial flow data information including temperature data, pressure data, flow data, and energy consumption data;

[0047] S230, introduce unsupervised learning algorithms and use automated scripts to identify and correct missing values ​​and outliers in the initial stream data information and remove noise;

[0048] S240: Extract key features related to carbon emissions from the data to obtain first target flow data information.

[0049] In the embodiment of the present application, it is understood that after obtaining the initial flow data information, an unsupervised learning algorithm, such as K-means clustering, can be introduced to automatically identify and correct outliers and enhance the robustness of the data. Statistical analysis and domain knowledge can be applied in the process of extracting key features related to carbon emissions.

[0050] In some embodiments, the aforementioned real-time data processing platform is built, and data is formatted based on the first target stream data information to obtain the second target stream data information, that is, the aforementioned S120 may specifically include the following steps:

[0051] S310, building a real-time data processing platform using a preset stream processing framework; the stream processing framework includes at least one of Apache Kafka and Apache Storm;

[0052] S320, applying a sliding window time window technology, processing the first target stream data information, and performing normalization processing and encoding conversion to complete data format conversion, thereby obtaining the second target stream data information.

[0053] In the embodiments of the present application, it can be understood that the present application can use stream processing frameworks such as Apache Kafka and Apache Storm to build a real-time data processing platform to process high-speed data streams; and apply time window technologies such as sliding windows to process streaming data to provide the fixed time interval data required by the model, and through data format conversion, the data can be converted into an input format acceptable to the model.

[0054] In some embodiments, Figure 2 As shown, based on the second target stream data information, the aforementioned deep learning model selection, transfer learning application, model training strategy and model compression technology are integrated to obtain multiple target models through model training and optimization to predict carbon emissions from steel production, that is, the aforementioned S130 may specifically include the following steps:

[0055] S410, selecting a suitable deep learning model architecture to construct multiple pre-trained models, wherein the multiple pre-trained models correspond one-to-one to each stage in the process flow;

[0056] S420, defining an input and output interface for each pre-trained model to determine the compatibility and data flow between the pre-trained models;

[0057] S430, migrating each pre-trained model to the carbon emission prediction task at each stage in the process flow, and fine-tuning using the data corresponding to each stage in the second target flow data information;

[0058] S440, using a small batch gradient descent optimization algorithm to train the model and adjust hyperparameters, applying model pruning and quantization techniques to reduce the model size to increase the inference speed, and obtaining multiple target models;

[0059] S450, adopting an online learning mechanism to update the parameters of the target model in real time according to the new data collected by the sensor.

[0060] In the embodiments of the present application, it can be understood that the present application is based on a deep learning algorithm to improve the prediction accuracy of carbon emissions at various stages in the steel production process and reduce errors caused by data dependence and insufficient model generalization ability.

[0061] It should be noted that this application conducts modular design for the production process of steel enterprises, decomposes the production process into independent processes, customizes the pre-trained model for each independent process, and enhances the generalization ability and adaptability of the system. In the model training strategy, this application uses optimization algorithms such as small batch gradient descent to train the model, and applies model compression technologies such as model pruning and quantization to reduce the model size and improve the inference speed.

[0062] It should be emphasized that this application improves the applicability and accuracy of the model in different production environments and process flows through modular design and transfer learning technology, so that the model is not limited to specific production conditions. By using transfer learning technology, the model pre-trained on a large-scale general data set is migrated to the carbon emission prediction task of a specific module, reducing the reliance on a large amount of labeled data.

[0063] In addition, this application introduces an online learning mechanism that enables the model to update parameters in real time based on newly arrived data, using algorithms such as online gradient descent. The prediction performance of the monitoring model is also continuously monitored, such as accuracy, recall, etc., and the learning rate and model parameters are dynamically adjusted based on performance feedback. This application supports online learning and model fine-tuning, so that when production conditions change or data distribution shifts, it can quickly adapt without retraining the entire model.

[0064] In one example, the aforementioned multiple pre-trained models include:

[0065] A raw material preparation model, based on a convolutional neural network (CNN) configuration, the raw material preparation model is used to process image data, the image data including at least one of an ore image and a coke image;

[0066] Coking model, based on the long short-term memory network LSTM or gated recurrent unit GRU setting, the coking model is used to process time series data and predict the temperature and energy consumption changes in the coking process;

[0067] Blast furnace ironmaking model, based on deep belief network DBN or variational autoencoder VAE setting, blast furnace ironmaking model is used to capture the complex physical and chemical reactions inside the blast furnace;

[0068] Steelmaking model, based on RNN or Transformer model setting, the steelmaking model is used to process complex time series data in the steelmaking process;

[0069] The continuous casting model is based on the convolutional LSTM network setting, and combines the spatial feature extraction capability of the convolutional neural network CNN and the time series analysis capability of the long short-term memory network LSTM. The continuous casting model is used to predict carbon emissions during the continuous casting process;

[0070] The hot rolling model is based on a one-dimensional convolutional neural network setup and is used to analyze stress and strain data during the hot rolling process;

[0071] The cold rolling model is a hybrid model that combines the convolutional neural network (CNN) and the long short-term memory (LSTM) network. The cold rolling model is used to process multidimensional data in the cold rolling process, including pressure, speed, and temperature.

[0072] The post-processing model is a fully connected deep neural network (DNN) and is used to learn complex nonlinear relationships in the post-processing process;

[0073] The energy management model is an attention-enhanced Transformer model that analyzes and predicts energy consumption and carbon emissions across modules; and

[0074] The waste treatment and recycling model is based on a graph neural network (GNN) setting and is used to simulate the flows and transformations in waste treatment and recycling networks.

[0075] It should be noted that this application uses modular design and customized deep learning models to accurately predict carbon emissions at each stage of the steel production process, thereby providing steel companies with more effective carbon emission management tools. In view of the complexity of steel production, this application proposes a modular solution so that carbon emissions can be predicted in a targeted manner at each production stage, improving the flexibility and scalability of the system.

[0076] In some embodiments, the aforementioned combined real-time prediction granularity, user interface design and report generation technology provide hourly carbon emission prediction results for intuitive display, that is, the aforementioned S140 may specifically include the following steps:

[0077] S510, configuring the target model to achieve hourly carbon emission prediction;

[0078] S520. Develop an intuitive user interface to display hourly carbon emission forecast data in real time and present it visually through charts and dashboards;

[0079] S530, automatically generate hourly carbon emission reports, including forecast results, actual emissions, energy consumption data, support corporate decision-making and environmental responsibility reports.

[0080] In some embodiments, the target model is regularly updated according to the acquired user feedback data and actual carbon emission data to improve the accuracy of carbon emission prediction, that is, the aforementioned S150 may specifically include the following steps:

[0081] S610, collecting user feedback data and actual carbon emission data, and establishing a feedback mechanism for evaluating the performance of the target model;

[0082] S620. Based on the feedback and performance evaluation results, the target model is regularly updated to adapt to changes in the production process.

[0083] In some embodiments, the present application provides a carbon emission prediction system 700 based on artificial intelligence, such as Figure 3 As shown, the carbon emission prediction system 700 may include the following modules:

[0084] The data acquisition and preprocessing module 710 is used to deploy multiple sensors based on the steel production process, integrate sensor data acquisition, data synchronization, data cleaning and feature extraction methods, and obtain the first target flow data information;

[0085] The real-time data processing module 720 is used to build a real-time data processing platform and perform data formatting based on the first target stream data information to obtain the second target stream data information;

[0086] A model training and optimization module 730 is used to integrate deep learning model selection, transfer learning application, model training strategy and model compression technology based on the second target stream data information, and obtain multiple target models through model training and optimization to predict carbon emissions from steel production;

[0087] A forecast output module 740 for combining real-time forecast granularity, user interface design, and report generation technology to provide hourly carbon emission forecast results for intuitive display;

[0088] The iterative improvement module 750 is used to regularly update the target model according to the acquired user feedback data and actual carbon emission data to improve the accuracy of carbon emission prediction.

[0089] According to an embodiment of the present application, any multiple modules among the data acquisition and preprocessing module 710, the real-time data processing module 720, the model training and optimization module 730, the prediction output module 740 and the iterative improvement module 750 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0090] In some embodiments, the data collection and preprocessing module 710 may be specifically used for:

[0091] Deploy multiple sensors based on key equipment and process flows of steel production; the process flows include: raw material preparation, coking, blast furnace ironmaking, steelmaking, continuous casting, hot rolling, cold rolling, post-processing, energy management, waste treatment and recycling;

[0092] Collect data in real time through multiple sensors, synchronize the timestamps of data from different sensors and devices, and obtain initial flow data information, which includes temperature data, pressure data, flow data, and energy consumption data;

[0093] Introducing unsupervised learning algorithms and using automated scripts to identify and correct missing values ​​and outliers in the initial streaming data information and remove noise;

[0094] Key features related to carbon emissions are extracted from the data to obtain first target flow data information.

[0095] In some embodiments, the real-time data processing module 720 may be specifically used for:

[0096] Use the preset stream processing framework to build a real-time data processing platform; the stream processing framework includes at least one of Apache Kafka and Apache Storm;

[0097] The sliding window time window technology is applied to process the first target stream data information, and normalization processing and encoding conversion are performed to complete data format conversion to obtain the second target stream data information.

[0098] In some embodiments, the model training and optimization module 730 may be specifically used to:

[0099] Select appropriate deep learning model architecture to build multiple pre-trained models, where the multiple pre-trained models correspond to each stage in the process flow;

[0100] Define input and output interfaces for each pre-trained model to ensure compatibility and data flow between pre-trained models;

[0101] Migrate each pre-trained model to the carbon emission prediction task at each stage in the process flow, and fine-tune it using the data corresponding to each stage in the second target flow data information;

[0102] A mini-batch gradient descent optimization algorithm is used to train the model and adjust hyperparameters. Model pruning and quantization techniques are applied to reduce the model size to increase the inference speed, and multiple target models are obtained.

[0103] An online learning mechanism is used to update the parameters of the target model in real time according to the new data collected by the sensors.

[0104] In some embodiments, the prediction output module 740 may be specifically used to:

[0105] Configure the target model to achieve hourly carbon emission forecasts;

[0106] Develop an intuitive user interface to display hourly carbon emission forecast data in real time and present it visually through charts and dashboards;

[0107] Automatically generate hourly carbon emission reports, including forecast results, actual emissions, energy consumption data, support corporate decision-making and environmental responsibility reports.

[0108] In some embodiments, the iterative improvement module 750 may be specifically used to:

[0109] Collect user feedback data and actual carbon emission data, and establish a feedback mechanism to evaluate the performance of the target model;

[0110] Based on feedback and performance evaluation results, the target model is updated regularly to adapt to changes in the production process.

[0111] Figure 3 Each module in the system shown has the function of implementing each step in the aforementioned artificial intelligence-based carbon emission prediction method and can achieve its corresponding technical effect. For the sake of concise description, it will not be repeated here.

[0112] In some embodiments, the present application provides an electronic device, the structure diagram of the electronic device is as follows Figure 4 shown.

[0113] The electronic device may include a processor 810 and a memory 820 storing computer program instructions.

[0114] Specifically, the processor 810 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0115] The memory 820 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 820 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 820 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 820 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 820 is a non-volatile solid-state memory.

[0116] The memory 820 may include a read-only memory (ROM), a random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, typically, the memory 820 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any one of the artificial intelligence-based carbon emission prediction methods in the above-mentioned embodiments.

[0117] The processor 810 implements any one of the artificial intelligence-based carbon emission prediction methods in the above embodiments by reading and executing computer program instructions stored in the memory 820 .

[0118] In one example, the electronic device may further include a communication interface 830 and a bus 800. Figure 4 As shown, the processor 810, the memory 820, and the communication interface 830 are connected via a bus 800 and communicate with each other.

[0119] The communication interface 830 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0120] Bus 800 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 800 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0121] In addition, in combination with the carbon emission prediction method based on artificial intelligence in the above embodiments, the embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the carbon emission prediction methods based on artificial intelligence in the above embodiments is implemented.

[0122] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0123] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0124] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0125] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0126] In summary, compared with the prior art, this application has the following beneficial effects:

[0127] 1. This application integrates deep learning model selection, transfer learning application, model training strategy and model compression technology to improve the prediction accuracy of carbon emissions at various stages in the steel production process, reduce errors caused by data dependence and insufficient model generalization ability, and improve the applicability and accuracy of the model in different production environments and process flows through modular design and transfer learning technology, so that the model is not limited to specific production conditions.

[0128] 2. This application combines real-time prediction granularity, user interface design and report generation technology, which can intuitively display carbon emission prediction results; it can also regularly update the target model based on user feedback data and actual carbon emission data, support online learning, and enable the model to be dynamically updated according to the latest data.

[0129] 3. This application can process data streams in real time and quickly output prediction results; it uses transfer learning and unsupervised learning techniques to reduce the need for large amounts of labeled data. This application uses deep learning technology to customize models for each production process, which can more accurately capture the complex patterns of carbon emissions and improve prediction accuracy.

[0130] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A carbon emission prediction method based on artificial intelligence, characterized in that: include: Deploy multiple sensors in the steel production process, integrate sensor data collection, data synchronization, data cleaning and feature extraction methods to obtain the first target flow data information; Building a real-time data processing platform, and performing data formatting based on the first target flow data information to obtain second target flow data information; Based on the second target stream data information, deep learning model selection, transfer learning application, model training strategy and model compression technology are integrated to obtain multiple target models through model training and optimization to predict carbon emissions from steel production; Combine real-time forecast granularity, user interface design and report generation technology to provide hourly carbon emission forecast results for intuitive display; The target model is regularly updated according to the acquired user feedback data and actual carbon emission data to improve the accuracy of carbon emission prediction.

2. The carbon emission prediction method based on artificial intelligence according to claim 1, characterized in that: The method of deploying multiple sensors based on the steel production process, integrating sensor data acquisition, data synchronization, data cleaning and feature extraction methods, and obtaining the first target flow data information includes: Deploy multiple sensors based on key equipment and process flows of steel production; the process flows include: raw material preparation stage, coking stage, blast furnace ironmaking stage, steelmaking stage, continuous casting stage, hot rolling stage, cold rolling stage, post-processing stage, energy management stage, waste treatment and recycling stage; Collect data in real time through the multiple sensors, synchronize the timestamps of the data from different sensors and devices, and obtain initial flow data information, wherein the initial flow data information includes temperature data, pressure data, flow data and energy consumption data; Introducing unsupervised learning algorithms and using automated scripts to identify and correct missing values ​​and outliers in the initial stream data information and remove noise; Key features related to carbon emissions are extracted from the data to obtain first target flow data information.

3. The carbon emission prediction method based on artificial intelligence according to claim 2, characterized in that: The step of building a real-time data processing platform and formatting the data based on the first target stream data information to obtain the second target stream data information includes: A real-time data processing platform is built using a preset stream processing framework; the stream processing framework includes at least one of Apache Kafka and Apache Storm; The sliding window time window technology is applied to process the first target stream data information, and normalize and encode the information to complete data format conversion to obtain the second target stream data information.

4. The carbon emission prediction method based on artificial intelligence according to claim 2, characterized in that: Based on the second target stream data information, the deep learning model selection, transfer learning application, model training strategy and model compression technology are integrated to obtain multiple target models through model training and optimization to predict carbon emissions from steel production, including: Selecting a suitable deep learning model architecture to construct a plurality of pre-trained models, wherein the plurality of pre-trained models correspond one-to-one to each stage in the process flow; Defining input and output interfaces for each of the pre-trained models to determine compatibility and data flow between the pre-trained models; Migrating each of the pre-trained models to the carbon emission prediction tasks at each stage in the process flow, and fine-tuning using the data corresponding to each stage in the second target flow data information; A mini-batch gradient descent optimization algorithm is used to train the model and adjust hyperparameters. Model pruning and quantization techniques are applied to reduce the model size to increase the inference speed, and multiple target models are obtained. An online learning mechanism is adopted to update the parameters of the target model in real time according to new data collected by sensors.

5. The carbon emission prediction method based on artificial intelligence according to claim 4, characterized in that: The multiple pre-trained models include: A raw material preparation model, arranged based on a convolutional neural network (CNN), the raw material preparation model being used to process image data, the image data comprising at least one of an ore image and a coke image; A coking model, based on a long short-term memory network (LSTM) or a gated recurrent unit (GRU), is used to process time series data and predict changes in temperature and energy consumption during the coking process; A blast furnace ironmaking model, based on a deep belief network DBN or a variational autoencoder VAE setting, the blast furnace ironmaking model is used to capture the complex physical and chemical reactions inside the blast furnace; A steelmaking model, based on a recurrent neural network (RNN) or a Transformer model, which is used to process complex time series data in the steelmaking process; A continuous casting model, based on a convolutional LSTM network setting, and combining the spatial feature extraction capability of a convolutional neural network (CNN) and the time series analysis capability of a long short-term memory (LSTM) network, the continuous casting model is used to predict carbon emissions during the continuous casting process; The hot rolling model is based on a one-dimensional convolutional neural network setup and is used to analyze stress and strain data during the hot rolling process; The cold rolling model is a hybrid model combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network. The cold rolling model is used to process multidimensional data in the cold rolling process, including pressure, speed, and temperature. The post-processing model is a fully connected deep neural network (DNN) and is used to learn complex nonlinear relationships in the post-processing process; The energy management model is an attention-enhanced Transformer model that analyzes and predicts energy consumption and carbon emissions across modules; and The waste treatment and recycling model is based on a graph neural network (GNN) setting and is used to simulate the flows and transformations in waste treatment and recycling networks.

6. The carbon emission prediction method based on artificial intelligence according to claim 1, characterized in that: The combined real-time forecast granularity, user interface design and report generation technology provide hourly carbon emission forecast results for intuitive display, including: Configuring the target model to achieve hourly carbon emissions prediction; Develop an intuitive user interface to display hourly carbon emission forecast data in real time and present it visually through charts and dashboards; Automatically generate hourly carbon emission reports, which include forecast results, actual emissions, energy consumption data, support corporate decision-making and environmental responsibility reports.

7. The carbon emission prediction method based on artificial intelligence according to claim 1, characterized in that: The target model is regularly updated according to the acquired user feedback data and actual carbon emission data to improve the accuracy of carbon emission prediction, including: Collect user feedback data and actual carbon emission data, and establish a feedback mechanism for evaluating the performance of the target model; Based on feedback and performance evaluation results, the target model is regularly updated to adapt to changes in the production process.

8. A carbon emission prediction system based on artificial intelligence, characterized in that: include: A data acquisition and preprocessing module is used to deploy multiple sensors based on the steel production process, integrate sensor data acquisition, data synchronization, data cleaning and feature extraction methods, and obtain the first target flow data information; A real-time data processing module, used to build a real-time data processing platform and perform data formatting based on the first target stream data information to obtain second target stream data information; A model training and optimization module, which is used to integrate deep learning model selection, transfer learning application, model training strategy and model compression technology based on the second target stream data information, and obtain multiple target models through model training and optimization to predict carbon emissions from steel production; The forecast output module is used to combine real-time forecast granularity, user interface design and report generation technology to provide hourly carbon emission forecast results for intuitive display; The iterative improvement module is used to regularly update the target model according to the acquired user feedback data and actual carbon emission data to improve the accuracy of carbon emission prediction.

9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the method for predicting carbon emissions based on artificial intelligence as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the artificial intelligence-based carbon emission prediction method as described in any one of claims 1 to 7 is implemented.

Images