Artificial intelligence driven cement composite material life cycle optimization system and method

Abstract

The invention relates to an artificial intelligence-driven cement composite material life cycle optimization system and method applied to the technical field of building materials, and the method comprises the steps: firstly, carrying out the real-time monitoring and analysis of production process data and environment data through a machine learning model, and obtaining the influence weight of production conditions and material performances; then, combining the data to generate a plurality of initial material proportioning schemes; inputting the matching schemes into a preset life cycle analysis model for evaluation to obtain environment and performance evaluation indexes of each matching; screening and optimizing according to the evaluation indexes to obtain a target material proportioning scheme, and updating a relation state among the production process data, the environment data and the product quality data in real time; based on the relation states and the target material matching scheme, strategy updating is carried out, and an optimized production quality management execution strategy is generated. Through the method, the environmental footprint of the material can be remarkably reduced, and the application performance of the material is improved.

Description

Technical Field

[0001] The present invention relates to an optimization system and method, and in particular to an artificial intelligence-driven cement composite material life cycle optimization system and method applied in the field of building material technology. Background Art

[0002] Environmental impact and optimization of material properties have always been key challenges in the modern building materials industry, especially in the production of cement and its related composite materials. Traditional material preparation methods often rely on fixed formulations and production processes, which limits the ability to adapt to dynamic environmental conditions and changing construction needs. In addition, these methods are usually unable to effectively reduce energy consumption and carbon emissions during the production process, so new solutions are urgently needed to support sustainable development goals.

[0003] The development of artificial intelligence technology, especially the application of data analysis and machine learning, has provided new possibilities for optimizing the performance of building materials and reducing their environmental impact. However, existing technologies often fail to effectively combine the potential of artificial intelligence with the life cycle analysis of building materials, resulting in a gap between real-time data processing and practical applications. In addition, existing methods are still insufficient in dealing with complex environmental factors and precisely regulating material properties, and cannot meet the growing demand for high-performance building materials.

[0004] Therefore, we propose an artificial intelligence-driven cement composite life cycle optimization system and method. Summary of the invention

[0005] In view of the above-mentioned prior art, the technical problem to be solved by the present invention is that the prior art is still insufficient in dealing with complex environmental factors and accurately regulating material properties, and cannot meet the growing demand for high-performance building materials.

[0006] To solve the above problems, the present invention provides an artificial intelligence-driven cement composite material life cycle optimization system and method, comprising the following steps: S1. Collect real-time data on temperature, pressure, humidity and CO2 emissions from production lines using sensors; S2. Input the collected data into a deep learning-based neural network model that is pre-trained to analyze and optimize the performance of cement-based composites and their environmental impact; S3. Automatically adjust the material ratio and process parameters in the production process according to the model analysis results to optimize the mechanical properties of the product and reduce the environmental burden; S4. Implement full life cycle analysis and evaluate the environmental impact of the entire process from raw material acquisition to product disposal in accordance with international standards; S5. Based on the life cycle analysis results of step S4, further adjust and optimize production parameters and material ratios.

[0007] As a further improvement of the present application, an implementation method of optimizing material ratio by an intelligent system includes: a) Use the intelligent production quality management system to obtain labels and analyze data points of target production-related data to determine key production, quality and environmental parameters; b) Perform data point distance analysis to determine the production parameter combination that best matches the optimization criteria; c) Use a data-driven decision-making process to select the best combination of production parameters based on the predetermined environment and production goals.

[0008] As a further improvement of the present application, a production quality monitoring system based on artificial intelligence includes: Acquisition module, used to collect and transmit key data on the production line in real time; An analytics module that uses deep learning technology to analyze the collected data to predict and optimize material properties and production processes; The monitoring module adjusts the production parameters according to the analysis results to optimize the production process; Update module for updating production strategies based on life cycle analysis and current production data to continuously improve material performance and reduce environmental impact.

[0009] Through the method and system of the present invention, real-time monitoring and optimization of the production process of building materials can be achieved, thereby improving the performance of the materials and reducing the environmental impact of the production process. In addition, the implementation of the present invention helps to realize the development of the building materials industry in a greener, smarter and more efficient direction.

[0010] As a further improvement of the present application, an intelligent algorithm implementation method for optimizing material production parameters includes: a) Collect key data about the production line in real time through an integrated sensor network; b) Apply deep learning techniques, such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), to process and analyze data collected from production lines in real time; c) Use machine learning models to automatically adjust material ratios and production processes based on analysis results to ensure that materials can achieve optimal performance under various environmental conditions; d) Use reinforcement learning algorithms to dynamically adjust production strategies based on environmental feedback and production results to optimize the energy efficiency and material quality of the entire production process.

[0011] As a further improvement of the present application, a dynamic material ratio adjustment method based on real-time data analysis specifically includes: a) Set up multiple data collection points to monitor key production parameters such as temperature, pressure and chemical composition in real time; b) Use data analysis modules to predict the relationship between material properties and these parameters and determine the optimal production conditions; c) Automatically adjust the material ratio and processing parameters on the production line to adapt to the environmental and material changes monitored in real time.

[0012] As a further improvement of this application, a complete life cycle management system that uses artificial intelligence technology to minimize the environmental impact of the entire process from raw material procurement to product disposal, including: a) Apply predictive models during the raw material procurement stage to optimize material selection and supply chain management, reducing logistics costs and environmental impact; b) Implement real-time environmental impact monitoring and control systems during the production phase to ensure that all production activities meet environmental protection standards; c) During the product use and disposal stages, optimize recycling and waste disposal processes through data analysis to maximize resource reuse.

[0013] As a further improvement of the present application, a method for integrating environmental monitoring and resource management specifically includes: a) Use advanced sensors and IoT technologies to track energy consumption and emission levels in the production process in real time; b) Apply machine learning algorithms to analyze environmental data and automatically adjust production processes to respond to environmental protection requirements; c) Implement an integrated resource management strategy to optimize the use of raw materials and reduce production waste.

[0014] As a further improvement of the present application, a method for implementing predictive maintenance and optimization algorithms is provided to improve production efficiency and reduce equipment failure rates, specifically comprising: a) Use monitoring systems to collect equipment operation data, such as operating hours, load cycles, and maintenance history; b) Apply support vector machine (SVM) and decision tree algorithms to analyze data and predict potential equipment failures and maintenance needs; c) Adjust equipment operating parameters or schedule maintenance in advance based on analysis results to prevent production interruptions.

[0015] As a further improvement of the present application, a deep learning-based quality control system for real-time detection and correction of production defects specifically includes: a) Install high-resolution cameras on the production line to capture image data of the production process in real time; b) Analyze images through convolutional neural networks (CNN) to identify material defects and process errors; c) Automatically adjust production machine settings or alert operators to intervene to ensure product quality.

[0016] As a further improvement of this application, an artificial intelligence-based environmental monitoring and management system, which optimizes the production environment by integrating environmental data collection and analysis to ensure compliance with the most stringent environmental standards, includes the following specific steps: a) Collect environmental data on air quality, noise level, temperature and humidity in real time; b) Use machine learning models to analyze the collected data and predict the impact of environmental conditions on production; c) Automatically adjust production environment settings, such as air filtration systems and temperature and humidity control, to maintain optimal production conditions.

[0017] As a further improvement of the present application, a method for optimizing production strategies using deep reinforcement learning (DRL) is provided, which can automatically learn and adapt to changes in the production environment, specifically including: a) Deploy multiple sensor networks and data collection points to fully monitor the production process; b) Using deep reinforcement learning algorithms to adjust production strategies based on real-time feedback to maximize production efficiency and material performance; c) Continuously update the learning model to adapt to new production conditions and technological advances.

[0018] As a further improvement to this application, a comprehensive life cycle assessment system aims to optimize the environmental impact of building materials through artificial intelligence, including: a) Apply life cycle assessment (LCA) technology to conduct environmental impact assessment from raw material procurement to product disposal; b) Use artificial intelligence algorithms, especially machine learning and data mining techniques, to analyse and optimise the environmental impact at every stage; c) Adjust production and recycling processes based on the evaluation results to ensure that the environmental friendliness of materials and production processes is maximized.

[0019] As a further improvement of the present application, a method for optimizing resource recovery and waste management through an intelligent feedback system comprises the following specific steps: a) Install IoT devices in the production process to monitor and analyze waste output and recycling in real time; b) Use deep learning models to classify and predict waste types and quantities, and optimize waste treatment and recycling paths; c) Automatically adjust the recycling process to improve resource utilization and waste recovery efficiency based on real-time data and model predictions.

[0020] As a further improvement of the present application, a highly integrated intelligent building material production system is provided, which utilizes artificial intelligence technology to comprehensively improve the quality and production efficiency of building materials, specifically including: a) Integrate multiple intelligent sensors and data analysis platforms to monitor key parameters in the production process in real time; b) Apply predictive and adaptive control algorithms to automatically adjust production parameters in response to production variables and market demands; c) Implement comprehensive performance testing and quality assurance procedures to ensure that each batch of materials meets high standards.

[0021] As a further improvement of the present application, an artificial intelligence-based environmental monitoring and control system is used to optimize environmental parameters in the production process of building materials in real time, specifically including: a) Use environmental monitoring equipment, such as temperature and humidity sensors and gas analyzers, to collect environmental data inside and outside the factory; b) Apply machine learning algorithms to conduct in-depth analysis of environmental data and predict its potential impact on production processes; c) Automatically adjust production equipment and environmental control systems to optimize production conditions and reduce energy consumption and emissions.

[0022] As a further improvement of the present application, a method for production optimization and environmental monitoring using data analysis is provided, which integrates advanced analytical tools and automation technology to achieve precise control of the production process and minimize environmental impact, specifically including: a) Implement advanced data collection systems to collect comprehensive data on raw material use, energy consumption, waste generation and emission levels; b) Apply statistical analysis and machine learning algorithms to conduct comprehensive analysis of the collected data to identify opportunities for efficiency improvements and cost savings; c) Automatically adjust production strategies and processes based on analysis results to optimize resource usage and reduce environmental pollution.

[0023] As a further improvement of the present application, an automated production monitoring system based on a deep learning algorithm is used to detect and adjust operations on a production line in real time to ensure product quality and production efficiency. The specific steps include: a) Deploy multiple smart cameras and sensors on the production line to monitor production activities and environmental conditions in real time; b) Use convolutional neural networks (CNNs) to analyze monitoring data in real time and automatically identify potential production problems and environmental risks; c) Adjust the operation of production equipment through predictive maintenance algorithms to prevent failures and reduce downtime, thereby improving production continuity and efficiency.

[0024] As a further improvement of this application, a comprehensive quality control and environmental management framework is designed to comprehensively improve the performance of building materials and the environmental friendliness of the production process through intelligent technology, including: a) Develop a comprehensive management platform that integrates real-time data monitoring, performance evaluation, environmental impact assessment and quality control; b) Adopt AI-driven decision support systems to optimize production decisions and operations based on real-time data and historical performance indicators; c) Implement an automatic feedback mechanism to continuously adjust production parameters and environmental control measures based on product quality test results and environmental monitoring data.

[0025] As a further improvement of the present application, a method for optimizing building material production and environmental management using a hybrid intelligent algorithm specifically includes: a) Integrate linear regression, neural network and reinforcement learning algorithms to adapt to various production and environmental conditions; b) Leverage the advantages of these algorithms to improve forecast accuracy and operational flexibility, ensuring optimal production and environmental performance in different production scenarios; c) Design a user-friendly control interface that allows operators to easily adjust algorithm settings and monitor system performance.

[0026] In summary, the optimization method of the present invention can achieve precise control and optimization of the entire life cycle of building materials from raw material procurement to product disposal. This method should be able to analyze and respond to changes in the production process in real time, and dynamically adjust material ratios and production parameters to optimize material performance and reduce environmental burden. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a system structure block diagram of the artificial intelligence-driven cement composite material life cycle optimization system of the present invention; Figure 2 A workflow diagram of the artificial intelligence-driven cement composite material life cycle optimization system of the present invention; Figure 3 Schematic diagram of the artificial intelligence model optimization process in the present invention. DETAILED DESCRIPTION

[0028] This embodiment provides an artificial intelligence-driven cement composite material life cycle optimization system and method. In order to make the purpose, technical solution and effect of this application clearer, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0029] Those skilled in the art will appreciate that, unless otherwise specified, singular forms such as "a", "an", "the" and the like used herein also include plural forms. It should be understood that the terms "include" or "comprise" used in this application refer to the inclusion of the features, integers, steps, operations, elements, parts or combinations thereof, but do not exclude the existence or addition of one or more other features, integers, steps, operations, elements, parts or combinations thereof.

[0030] It should be understood that although the terms "connect" or "connected to" may be used to indicate a physical or logical relationship between two elements, these terms should also be understood to include possible intermediate components between the two elements if it is appropriate in the text of the described technical solution. In addition, the terms "set", "send", "respond", "determine", "judge", "implement" and other operations may also be automatic or manual operations.

[0031] After research, it was found that the existing technology lacks effective real-time monitoring and quality control in the production process of cement-based materials, and cannot respond to changes in material ratios and production processes in a timely manner, resulting in difficulty in achieving optimal material performance and environmental friendliness at the same time. In particular, in the production of modern building materials, the requirements for environmental protection and resource utilization efficiency are becoming increasingly higher, and traditional production technologies can no longer meet these requirements.

[0032] To this end, the present invention adopts a system that integrates artificial intelligence technology, uses deep learning and data analysis technology to analyze and process the real-time data of the production line, and automatically adjusts the material ratio and production parameters to achieve dual optimization of material performance and environmental impact. The system includes a data collection module, a data processing module, a decision execution module, and a feedback adjustment module.

[0033] In the data collection module, the system collects data about the production environment and material status in real time through various sensors installed on the production line (such as temperature sensors, humidity sensors, pressure sensors, and CO2 emission monitors).

[0034] In the data processing module, the collected data is transmitted to a central processing unit, where a deep learning network analyzes the data, identifies key influencing factors in the production process, and predicts changing trends in material properties and environmental impacts.

[0035] In the decision-making execution module, based on the analysis results of the data processing module, the system automatically adjusts the material ratio and process parameters, such as changing the addition ratio of slag and fly ash, adjusting the mixing time and curing conditions, etc., to optimize the mechanical properties of the product and reduce the environmental burden.

[0036] In the feedback adjustment module, the system evaluates the implementation effect of previous decisions based on the newly collected data in the production process. If it is found that the actual effect deviates from the expected goal, the system will further adjust the decision to form a closed-loop control system with continuous learning and optimization.

[0037] Through the above steps, the present invention can achieve real-time monitoring and dynamic optimization of the life cycle of cement-based materials, significantly improve the performance and production efficiency of the materials, and reduce energy consumption and environmental pollution in the production process.

[0038] The application content is further explained below by describing an embodiment in conjunction with the accompanying drawings.

[0039] This embodiment provides an artificial intelligence driven cement composite material life cycle optimization system, such as Figure 1 As shown, the system includes: S101, data collection module: collects data about the production environment and material status in real time through sensors installed on the production line. These sensors include temperature sensors, humidity sensors, pressure sensors, and CO2 emission monitors, etc.; S102, data transmission module: transmitting the collected data to a central processing unit (CPU), and the data is encrypted during the transmission process to ensure information security; S103, Central Processing Unit (CPU): The central processing unit performs preliminary analysis on the collected data and selects key data for further processing; S104, Optimization Decision Module: The deep learning network in the central processing unit analyzes the screened data, identifies the key influencing factors in the production process, and predicts the changing trends of material properties and environmental impacts; S105, mixer and feedback system: automatically adjust material ratio and production parameters according to the analysis results of the optimization decision module, for example, adjust the addition ratio of slag and fly ash, change the mixing time and curing conditions, etc.; S106, monitoring communication system: The monitoring system updates the production status in real time and feeds back the adjusted results to the optimization decision module, forming a closed-loop control system with continuous learning and optimization; S107, Safety and Environmental Protection: The system continuously monitors the emissions and resource utilization of the production line to ensure that the production process meets environmental standards while improving resource utilization efficiency.

[0040] By implementing the above steps, the present invention can effectively realize real-time monitoring and dynamic optimization of the life cycle of cement-based materials, significantly improve the performance and production efficiency of materials, and reduce energy consumption and environmental pollution in the production process. In addition, the implementation of the system helps to enhance the market competitiveness of cement production enterprises and bring greater economic benefits and social value to enterprises.

[0041] In order to describe this embodiment in more detail, the key modules and data processing flow of the system are further explained below through algorithms and formulas.

[0042] In the data collection module, a variety of sensor combinations are used to monitor environmental and material parameters. For example, the data collected by the temperature sensor is preliminarily processed using the following formula:

[0043] in, It is The temperature value of the data point, is the total number of data sampling points, is the average temperature. This step helps smooth out random fluctuations in real-time data and provide a more stable input for subsequent processing.

[0044] The optimization decision module contains a neural network-based learning model that is trained to predict material properties and environmental impacts. The neural network is optimized using the following loss function:

[0045] in, are input features, is the target output, is a neural network model, are model parameters, is the regularization parameter, is the number of samples in the batch. This loss function not only takes into account the prediction error, but also prevents the model from overfitting through the regularization term.

[0046] The dispatcher automatically adjusts production parameters according to the output of the optimization decision module. Parameter adjustment is based on the following dynamic adjustment formula:

[0047] are the original production parameters, is the parameter change recommended by the optimization decision module, is the learning rate, which controls the speed of parameter update.

[0048] The monitoring system ensures that the data of all modules is updated and synchronized in real time. The following time synchronization algorithms are used to ensure data consistency:

[0049] is the synchronization time, is the local time, is the sending time, is the receiving time. This algorithm helps to reduce the time error caused by network delay and improve the overall response speed of the system.

[0050] Through these detailed algorithms and mathematical formulas, the system can process data and make decisions more accurately, thereby effectively improving the production efficiency and environmental sustainability of cement-based materials.

[0051] Model evaluation and validation module details: To ensure the accuracy and practicality of the prediction model, this system uses a cross-validation method for model evaluation. The method uses k-fold cross-validation, which divides the data set into k smaller sets. The following is a mathematical description of the process:

[0052] in, It is The mean square error on the data set is is a model parameter. This method can effectively evaluate the performance of the model on unseen data, thereby ensuring the generalization ability of the model in practical applications.

[0053] In addition, the real-time update and verification of the model is based on an online learning mechanism, which uses the latest data to continuously adjust the model parameters to adapt to changes in production conditions. The update formula is as follows:

[0054] in, and are the model parameters before and after the update, is the learning rate, is the loss function, and They are the most recently collected feature data and target data. This dynamic update strategy helps the model continuously adapt to the new production environment.

[0055] Continuous improvement and self-optimization module: This system is designed with a self-optimization mechanism that enables it to automatically adjust the processing flow and parameters based on production data and environmental feedback. This process is achieved through a complex feedback control algorithm, the basic idea is to minimize production costs while maximizing product quality. The specific implementation formula is:

[0056] in, Represents production parameters, is with parameter The associated cost function, is the quality loss function, is a coefficient that weighs the importance of both.

[0057] To achieve this goal, the system integrates inputs from multiple sensors and actuators to adjust the production process through real-time data-driven decision trees. These decision trees are dynamically adjusted based on current production efficiency and environmental data to ensure that every decision in the production process is made under the current optimal conditions.

[0058] System maintenance and upgrade strategy: System maintenance is a key link to ensure long-term stable operation, including regular hardware inspections and software updates. To this end, this system introduces a cloud-based monitoring service that can monitor system status in real time and automatically deploy updates to cope with potential technological evolution and market demand changes. The automatic update function achieved through the cloud service can quickly deploy the latest algorithms and security patches to the system to keep the system up to date and at its highest performance.

[0059] This strategy uses the following formula for risk assessment and resource optimization:

[0060] in, Is the system status The total risk score under It is The weight of each risk factor, Is the risk factor in the state This approach helps prioritize issues that may have a significant impact on production and ensure the efficient and stable operation of the production system.

[0061] The integration and coordinated operation of the above modules ensure the optimal performance and lowest cost of the production system in various production environments, while maintaining high standards of product quality and environmental sustainability.

[0062] In a specific embodiment, the process of executing step S102 may specifically include the following steps: S201, real-time monitoring and recording of carbon dots added during the production process and key environmental parameters (temperature, pressure, humidity and CO2 emissions), this step is carried out using a high-precision sensor network, which is deployed at key production nodes to ensure real-time acquisition of accurate data. These data will directly affect subsequent data processing and decision-making;

[0063] S202, curve fitting is performed on the collected real-time data to form a carbon dot addition amount change curve, a CO2 emission change curve and other environmental parameter change curves. The curve fitting algorithms used in this process include polynomial fitting, exponential fitting or advanced neural network models, which can effectively extract clear trends and patterns from noise data, providing a solid foundation for further analysis. Polynomial fitting is used to predict and simulate the relationship between carbon dot addition amount and production efficiency. The specific algorithm is as follows:

[0064] in, is the amount of carbon dots added, is production efficiency, are model parameters;

[0065] The least squares method is used to determine the polynomial coefficients. This method finds the best fitting curve by minimizing the sum of squared errors:

[0066] in, is the number of data points, and is the observed value;

[0067] Exponential fitting is used to simulate the relationship between CO2 emissions and time:

[0068] in, is the CO2 emissions, It's time. and are the fitting parameters;

[0069] Nonlinear least squares method For nonlinear models such as exponential fitting, nonlinear least squares method is usually used for parameter estimation:

[0070] S203, extract key features from the curve, and calculate the correlation coefficient between the amount of carbon dots added and CO2 emissions, as well as between other environmental parameters and product quality. In this step, features such as the slope, peak, mean and variance of the curve are extracted and used to calculate the correlation coefficient. This analysis not only helps understand the dependencies between different parameters, but also guides how to adjust production parameters to optimize the production process and product quality;

[0071] Pearson Correlation Coefficient The correlation coefficient is used to evaluate the linear relationship between carbon dot addition and CO2 emissions:

[0072] in, and are the observed values ​​of the two variables, and is the corresponding mean;

[0073] S204. Evaluate the weight of carbon dot addition on production efficiency and environmental impact based on the correlation coefficient, and further adjust the production strategy to optimize performance and reduce environmental burden. Based on the results of the correlation coefficient, the production team can adjust the addition ratio of carbon dots or change production conditions such as temperature and pressure to achieve enhanced product performance while reducing CO2 emissions and other adverse environmental impacts.

[0074] Machine Learning Algorithms For complex production data analysis, machine learning algorithms can be used to extract features and make predictions: Neural network fitting,Neural networks, especially deep neural networks (DNNs), are suitable for simulating complex nonlinear relationships.,The network learns the relationship between input (such as time, carbon dot addition) and output (such as production efficiency, environmental impact) through multiple layers of processing units.

[0075] The network weights are adjusted using the back-propagation algorithm and gradient descent to minimize the difference between the predicted and actual outputs. This usually involves defining a loss function, such as the mean squared error (MSE):

[0076] in, is the actual output, It is a network prediction.

[0077] In addition, data from the production process is not limited to real-time monitoring, but also includes predictive analysis using advanced deep learning networks. These networks can predict future production trends and potential problem points by analyzing long-term accumulated data, thereby achieving more proactive production management and control. For example, by analyzing historical data, AI models can predict the specific effects of carbon dot addition on product quality and environmental impact under specific environmental conditions, thereby presetting the optimal addition amount and production parameters before production begins.

[0078] In addition, the integrated AI system can also implement adaptive learning. As time goes by and data accumulates, the system's prediction accuracy and operational efficiency will continue to improve. This adaptive mechanism enables the production process to flexibly respond to changes in the market and environment and achieve long-term sustainable production.

[0079] Through this integrated technical solution, enterprises can not only improve the transparency and control of the production process, but also significantly improve product quality and market competitiveness, while responding to global requirements for environmental sustainability and achieving a win-win situation for economic benefits and environmental protection. The implementation of this approach has brought innovative solutions to the building materials industry and promoted the transformation of traditional building materials to a greener and smarter one.

[0080] In a specific embodiment, the process of executing step S102 may specifically include the following steps: S201. Real-time monitoring and recording of the amount of carbon dots added and key environmental parameters (such as temperature, pressure, humidity and CO2 emissions) during the production process;

[0081] Technology application: A sensor array configured using IoT (Internet of Things) technology collects temperature, pressure, humidity and CO2 data in real time, which is transmitted to a central database via a wireless network for real-time analysis;

[0082] Example formula: Temperature monitoring: in In hours;

[0083] S202. Use advanced data processing technology to perform curve fitting and model analysis on the collected data. Applied data analysis methods: Polynomial regression is used to simulate the effect of carbon dot addition on CO2 emissions. The fitting formula is as follows: ,in Represents the amount of carbon dots added. The machine learning algorithm uses the random forest algorithm to predict the environmental impact at different addition amounts and the changes in production quality;

[0084] S203, extract key features from the fitted curve, calculate the correlation coefficient, analyze the relationship between the amount of carbon dot addition and environmental parameters, feature extraction and correlation coefficient calculation, the features include slope, peak value, variance, etc., these data provide a basis for further adjusting the production parameters, the correlation coefficient calculation formula is:

[0085] here, and represent the amount of carbon dots added and CO2 emissions, respectively;

[0086] S204. Evaluate and adjust production strategies based on correlation coefficients to optimize the balance between production efficiency and environmental protection. Implement strategies to adjust the proportion of carbon dot addition and optimize production process settings such as temperature control and pressure regulation to ensure maximum production efficiency while reducing environmental impact. For example, if it is found that increasing the amount of carbon dot addition can significantly reduce CO2 emissions but slightly reduce production efficiency, the strategy will be to find the optimal balance between the two. S205. Implement advanced data visualization technology to monitor real-time changes in production efficiency and environmental parameters; Data Visualization Tools: Real-time dashboards are used to display key performance indicators (KPIs), such as CO2 emission levels, energy consumption, and carbon point utilization efficiency. These dashboards can help operators understand the production status in real time and quickly respond to any links that need adjustment. Example visualization technology uses time series graphs to show the change of CO2 emissions over time, and scatter plots to show the relationship between carbon point addition and product quality;

[0087] S206. Develop an automated feedback system to adjust production parameters based on real-time data to continuously optimize the production process.

[0088] Automated control systems: Based on preset algorithms, such as closed-loop control systems, automatically adjust machine settings, such as temperature and pressure, to respond to changes in environmental parameters and maintain product quality standards.

[0089] Feedback control algorithm example:

[0090] in, is the control input, is the deviation between the set point and the measured value, and They are proportional, integral and derivative gains respectively.

[0091] By implementing the above steps, precise control of the amount of carbon dots added during the production process and optimized management of environmental parameters can be achieved, thereby improving product quality and significantly reducing the burden of production on the environment.

[0092] The implementation of this method not only improves production efficiency and product quality, but also effectively reduces environmental impact, achieves full compliance with environmental regulations, and also brings sustained economic benefits and market competitiveness to enterprises. Through precise data analysis and control strategies, the production process is more sustainable, transparent and efficient, becoming an important driving force for the green and intelligent transformation of the building materials industry.

[0093] In view of current practical needs, the above-mentioned implementation mode adopted in this application is not limited to the scope of protection. Various changes made within the knowledge scope of technical personnel in this field without departing from the concept of this application still fall within the scope of protection of the present invention.

Claims

1. An artificial intelligence-driven cement composite life cycle optimization method, characterized in that: The following steps are involved: S1. Collect real-time data on temperature, pressure, humidity and CO2 emissions from production lines using sensors; S2. Input the collected data into a deep learning-based neural network model that is pre-trained to analyze and optimize the performance of cement-based composites and their environmental impact; S3. Automatically adjust the material ratio and process parameters in the production process according to the model analysis results to optimize the mechanical properties of the product and reduce the environmental burden; S4. Implement full life cycle analysis and evaluate the environmental impact of the entire process from raw material acquisition to product disposal in accordance with international standards; S5. Based on the life cycle analysis results of step S4, further adjust and optimize production parameters and material ratios.

2. The artificial intelligence driven cement composite material life cycle optimization method according to claim 1, characterized in that: The type of the deep learning-based neural network model is a convolutional neural network model, which is used to process visual data from the production line and automatically detect product quality and abnormal conditions in the production process.

3. The artificial intelligence driven cement composite material life cycle optimization method according to claim 2, characterized in that: The neural network model includes an adaptive learning mechanism that can continuously improve the model's prediction accuracy and operating efficiency over time and with the accumulation of data.

4. The artificial intelligence driven cement composite material life cycle optimization method according to claim 3, characterized in that: The adaptive learning mechanism includes using a reinforcement learning algorithm to optimize the setting of production parameters to adapt to dynamic changes in production conditions.

5. The artificial intelligence driven cement composite material life cycle optimization method according to claim 4, characterized in that: The neural network model is further configured to perform feature extraction operations and predict key performance indicators to achieve real-time monitoring and optimization of material properties.

6. The artificial intelligence driven cement composite material life cycle optimization method according to claim 1, characterized in that: The following steps are also included: Use data-driven optimization algorithms to analyze environmental data from raw material collection, production process, use period and waste disposal stages to adjust material ratios and production processes.

7. The artificial intelligence driven cement composite material life cycle optimization method according to claim 1, characterized in that: The real-time data is collected by a multi-point sensor system pre-installed on the production line, and the sensor system is configured with a function of transmitting data to a central processing unit in real time.

8. The artificial intelligence driven cement composite material life cycle optimization method according to claim 1, characterized in that: The following steps are also included: Adjust production parameters based on process adjustment suggestions from the feedback system; The feedback system is used to automatically make process adjustment suggestions based on the results of the life cycle analysis and real-time performance monitoring data.

9. An artificial intelligence-driven cement composite material life cycle optimization system, which is applied to the artificial intelligence-driven cement composite material life cycle optimization method according to any one of claims 1 to 8, characterized in that: include: Data collection module, used to collect real-time data from the production line; Data processing modules, including deep learning networks, to analyze the collected data and optimize production parameters; A control module, used to adjust the operating parameters of the production line according to the output of the data processing module; The analysis module is used to conduct full life cycle analysis and adjust production strategies based on the analysis results.

10. A computer program product, characterized in that The method comprises computer executable instructions which, when executed on a computer system, cause the computer to execute the artificial intelligence driven cement composite material life cycle optimization method described in any one of claims 1 to 8.

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