Steel water slag market prediction system

Abstract

The invention discloses a steel grain slag market prediction system. The system comprises the following related systems and models: a global intelligent sensing system, a space-time big data analysis system, an integrated learning model, a knowledge graph and causal reasoning module, a meta-learning and adaptive adjustment module and a supply chain intelligent decision module. The beneficial effects are that the method collects multi-source data through a global intelligent sensing technology, captures the space-time correlation between market demands and production conditions through a space-time big data analysis technology, carries out the fusion analysis of the data through an integrated learning model, forms an accurate market prediction model, combines a knowledge graph and a causal reasoning technology, and achieves the real-time prediction of the market demands. The influence of external factors on the market is analyzed, the prediction result is further optimized, the meta-learning and evolutionary neural network technology is adopted, the prediction model is adaptively adjusted, the prediction precision is improved, dynamic scheduling and optimization of the steel granulated slag supply chain are achieved through an intelligent decision-making system, and the resource allocation efficiency and the market response speed are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of integrated learning and market prediction, and particularly relates to a steel slag market prediction system. BACKGROUND

[0002] With the continuous development of the global steel industry, the by-product of the steel production process, slag, has gradually become an important link in the industrial chain. Slag is not only a solid waste of steel production, but also has certain resource value and can be used in building materials, mineral extraction and other fields. However, the market demand for slag fluctuates greatly, and is affected by many factors such as production capacity, transportation cost, policy and regulation, market supply and demand changes, etc. In order to realize the accurate prediction and effective management of the market supply and demand of slag, traditional market prediction methods face many challenges.

[0003] Currently, many steel industries rely on empirical methods to predict the demand of slag market, which usually ignores the dynamic changes and complex interactions of the market, and lacks sufficient real-time and accuracy. In addition, traditional market prediction models are mostly based on single factor or linear regression analysis of historical data, which is difficult to fully consider the nonlinear characteristics and multi-dimensional factor interaction of the slag market. Under this background, how to use modern artificial intelligence technology to improve the accuracy and real-time of slag market prediction has become a key problem faced by the steel industry. At the same time, the production and logistics chain of the steel industry is large and complex, involving multiple links and participants, and the traditional supply chain management mode cannot meet the growing market demand and volatility, especially in the supply chain optimization of slag, a special product, there are generally problems such as information lag, resource allocation not timely, slow supply chain response, etc. With the rapid development of big data, Internet of Things (IoT) and artificial intelligence (AI) technology, the traditional supply chain management mode is gradually transformed to intelligent and data-driven, which provides a new technical path for the accurate prediction of steel slag market and supply chain optimization. However, existing technical means mainly focus on market data collection and analysis, and there is still a lack of systematic and innovative solutions on how to extract effective information from complex and dynamic market environment, make accurate prediction, and realize intelligent scheduling and optimization of supply chain based on these predictions. Therefore, how to build a market prediction method based on integrated learning, combined with advanced technologies such as spatiotemporal big data, edge computing and knowledge graph, to comprehensively and dynamically cope with the challenges of steel slag market, has become a technical problem that needs to be solved urgently.

[0004] Despite the current application of some big data and artificial intelligence-based market prediction models in the steel industry and other commodity markets, there are still significant technical deficiencies and challenges in the prediction and supply chain optimization of the steel slag market. First, although traditional ensemble learning methods can improve prediction accuracy to some extent, they still have limitations when facing complex market environments. Existing ensemble learning algorithms, such as random forests, gradient boosting machines (GBDT), and XGBoost, can handle large amounts of data and achieve good prediction results, but they are mostly based on pattern recognition of historical data and lack a deep understanding of market dynamics. The steel slag market is influenced by multiple factors, including policy adjustments, environmental changes, global economic fluctuations, etc. The complexity and nonlinearity of these factors greatly reduce the predictive ability of a single model. Traditional ensemble learning methods are difficult to fully capture these complex spatio-temporal characteristics and causal relationships, so their accuracy and real-time performance in dynamic environments cannot meet actual needs.

[0005] Second, current market prediction models mostly rely on static data analysis and lack the ability to process real-time data streams. The volatility and suddenness of the steel slag market make it difficult for traditional market prediction methods to respond to changes in actual demand in a timely manner. For example, sudden policy changes, unexpected natural disasters, or economic crises often lead to sharp changes in market demand, and traditional models are difficult to quickly adapt to these changes, often relying on historical experience for prediction. In current supply chain management, rapid response and adjustment are crucial, so how to combine real-time data streams, high-frequency market feedback, and traditional prediction models to improve the model's adaptability is a major shortcoming in current technology.

[0006] Many current market forecasting methods rely primarily on a single perceptual dimension, such as price or production data, neglecting the multi-dimensional information in the slag market. Modern markets are multimodal, involving market dynamics, production processes, logistics, and other aspects. Traditional forecasting methods often fail to comprehensively analyze this heterogeneous information, making it difficult to form a comprehensive market insight. Therefore, how to effectively integrate multimodal data (such as images, text, and environmental monitoring data) into market forecasting models to enhance their perception and prediction capabilities regarding market changes remains a crucial unresolved issue. Furthermore, supply chain management in the slag market typically faces the problem of lag and mismatch between production processes and market demand. Most existing supply chain management systems are based on static optimization models, lacking adaptive adjustment mechanisms and unable to respond to market changes in real time. Even some intelligent supply chain management systems employ real-time data monitoring and scheduling optimization, but they often rely on simple decision rules or preset strategies, lacking in-depth intelligent reasoning and decision support. For special commodities like steel slag, the uncertainty of demand and the complexity of each link in the supply chain require systems with stronger autonomous learning, intelligent reasoning, and dynamic optimization capabilities, which current technologies have not yet effectively achieved.

[0007] In summary, current methods for forecasting the steel slag market have significant technical shortcomings in dealing with complex market environments, real-time data processing, multi-dimensional data fusion, and intelligent decision-making. Therefore, there is an urgent need for an innovative market forecasting method that can combine multiple advanced technologies to address these technical challenges and achieve accurate forecasting, intelligent supply chain optimization, and rapid response in the steel slag market. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings and defects of existing technologies by providing a steel slag market forecasting system. This method collects multi-source data through full-domain intelligent sensing technology, captures the spatiotemporal correlation between market demand and production conditions using spatiotemporal big data analysis technology, and fuses and analyzes the data through an integrated learning model to form an accurate market forecasting model. Combining knowledge graph and causal reasoning technologies, it analyzes the impact of external factors on the market, further optimizing the forecasting results. It also employs meta-learning and evolutionary neural network technologies to adaptively adjust the forecasting model, improving forecasting accuracy. Through an intelligent decision-making system, it achieves dynamic scheduling and optimization of the steel slag supply chain, improving resource allocation efficiency and market response speed.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: a steel slag market forecasting system, comprising the following related systems and models: a full-domain intelligent sensing system: acquiring multi-source data from various stages of steel production, transportation, market, and environment in real time through holographic sensing technology; a spatiotemporal big data analysis system: capturing the spatiotemporal correlation of market demand, price fluctuations, and production status based on multi-source sensor data and spatiotemporal big data stream analysis technology; an ensemble learning model: employing multiple ensemble learning algorithms to fuse and analyze the spatiotemporal big data streams to form a market demand forecasting model; a knowledge graph and causal reasoning module: constructing a knowledge graph of the slag market and combining causal reasoning technology to analyze how policy changes, natural disasters, and other external factors affect market supply and demand and key variables of price fluctuations; a meta-learning and adaptive adjustment module: introducing meta-learning algorithms and evolutionary neural network technology to quickly adjust the parameters of the forecasting model based on a small amount of historical data or sudden events, improving the model's adaptive capability; and a supply chain intelligent decision-making module: automatically optimizing the supply chain scheduling and resource allocation of the steel slag market based on the above market forecasting results, realizing dynamic scheduling and intelligent optimization of the supply chain.

[0010] Furthermore, the comprehensive intelligent perception system is achieved through the following means: UAV imagery: using UAV imagery to monitor changes in the production area and its surrounding environment; satellite remote sensing technology: using satellite remote sensing technology to capture the real-time operating status of production facilities and their environmental impact; and IoT sensors: deploying IoT sensors in the production process to collect various key data in real time.

[0011] Furthermore, the integrated learning model is implemented through the following steps: multi-model fusion: weighted fusion of the results of multiple single prediction models to optimize the overall prediction accuracy; spatiotemporal data stream analysis: adjusting the prediction model according to the dynamic changes of spatiotemporal data to improve stability and response speed in the event of sudden events or market fluctuations.

[0012] Furthermore, the knowledge graph and causal reasoning module are implemented through the following steps: causal relationship analysis: analyzing the influence chain between various factors through a causal reasoning model; event prediction and adjustment: inferring the possible impact of external events on the slag market and adjusting the prediction results accordingly.

[0013] Furthermore, the Meta-Learning and Evolutionary Neural Network (EvoNN) module can automatically update the prediction model parameters through incremental learning to adapt to new market data or sudden events.

[0014] Furthermore, the intelligent supply chain decision-making system includes the following methods: real-time data feedback mechanism: based on real-time market data, production data, and transportation data, automatically adjust production plans and transportation routes to optimize inventory management; edge computing and real-time scheduling: using edge computing technology to perform preliminary processing of real-time data at the data source end, reducing data transmission latency and improving response speed.

[0015] Furthermore, the aforementioned method for forecasting the steel slag market is characterized by the following: it further includes an intelligent logistics sensing module for real-time tracking of slag transportation status, including transportation routes, logistics bottlenecks, and delay information.

[0016] After adopting the above technical solution, the beneficial effects of this invention are as follows: This method collects multi-source data through full-domain intelligent sensing technology, captures the spatiotemporal correlation between market demand and production conditions through spatiotemporal big data analysis technology, and integrates and analyzes the data through an integrated learning model to form an accurate market prediction model. Combining knowledge graph and causal reasoning technology, it analyzes the impact of external factors on the market and further optimizes the prediction results. It adopts meta-learning and evolutionary neural network technology to adaptively adjust the prediction model and improve prediction accuracy. Through an intelligent decision-making system, it realizes the dynamic scheduling and optimization of the steel slag supply chain, thereby improving resource allocation efficiency and market response speed. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Appendix Figure 1 This is the overall flowchart of the steel slag market forecasting and supply chain optimization system.

[0019] Appendix Figure 2 This diagram illustrates the implementation of a technology for predicting dynamic changes in the water slag market based on spatiotemporal big data analysis.

[0020] Appendix Figure 3 This is a diagram illustrating the technical implementation of a water slag market forecasting system based on multimodal perception and adaptive cognitive decision-making.

[0021] Appendix Figure 4 This is a technical implementation diagram of a water slag market decision-making and optimization scheme based on intelligent reasoning and knowledge graphs.

[0022] Appendix Figure 5 This is a diagram illustrating the technological implementation of a distributed autonomous supply chain system based on blockchain and artificial intelligence.

[0023] Figure labeling: S101, Full-Domain Intelligent Sensing Module; S102, Spatiotemporal Big Data Analysis Module; S103, Integrated Learning and Intelligent Prediction Module; S104, Intelligent Decision-Making and Resource Scheduling Module; S1011, Sensor Data Acquisition; S1012, Satellite Remote Sensing Data Acquisition; S1013, External Data Access; S1014, Data Preprocessing and Fusion; S1021, Spatiotemporal Data Fusion and Feature Extraction; S1022, Multimodal Data Processing; S1023, Data Modeling and Market Forecasting; S1024, Model Evaluation and Optimization; S1031, Intelligent Reasoning and Causal Analysis; S1032, Evolutionary Neural Network Training; S1033, Meta-Learning and Self-Evolution; S1034, Decision Reasoning and Strategy Generation; S1035, Virtual Digital Twin Execution. 041. Smart Contract and Blockchain Execution S1042. Data Sensing and Preprocessing Layer S201. Feature Extraction and Modeling Layer S202. Prediction and Inference Layer S203. Decision and Optimization Layer S204. Sensor Data Acquisition and Fusion S2011. External Market Data Access S2012. Data Cleaning and Denoising S2013. Spatiotemporal Data Fusion S2014. Spatiotemporal Feature Extraction S2021. Ensemble Learning Modeling S2022. Deep Learning Model Training S2023. Model Optimization and Parameter Tuning S2024. Market Fluctuation Prediction S2031. Causal Inference and Decision Analysis S2032. Multi-Dimensional Market Trend Prediction S2033. Event-Driven Market Adjustment S2034. Model Optimization and Parameter Tuning S2041. Virtual Data The following are the key concepts and methods for implementing blockchain technology: Twin Execution (S2042), Smart Contract and Blockchain Execution (S2043), Meta-Learning and Rapid Adaptation Layer (S301), Self-Organizing Neural Network and Pattern Recognition Layer (S302), Causal Reasoning and Intelligent Decision-Making Layer (S303), Adaptive Reasoning and Intelligent Feedback Layer (S304), Meta-Learning Algorithm Design (S3011), Rapid Adjustment and Self-Evolution Mechanism (S3012), Learning and Transfer from Limited Data (S3013), Self-Organizing Neural Network Construction (S3021), Pattern Recognition and Classification (S3022), Dynamic Pattern Update and Adaptation (S3023), Knowledge Graph Construction and Causal Relationship Modeling (S3031), Causal Reasoning and Market Prediction (S3032), Intelligent Decision Generation and Reasoning (S3033), Intelligent Feedback and Policy Correction (S3041), Real-time Feedback... The system should include: Self-optimization (S3042), Continuous learning and model updating (S3043), Event recognition and early warning system (S401), Event-driven prediction model (S402), Automated decision-making and adjustment engine (S403), Automated execution and supply chain scheduling layer (S404), Event recognition algorithm (S4011), Event occurrence probability prediction (S4012), Event triggering condition modeling (S4013), Event impact assessment model (S4021), Event-driven market prediction model (S4022), Short-term and long-term impact modeling (S4023), Automated decision-making engine (S4031), Dynamic decision feedback and optimization (S4032), Emergency resource allocation and path optimization (S4033), Execution and scheduling module (S4041), and Supply chain bottleneck detection and adjustment (S4042).The blockchain architecture comprises the following layers: Multi-objective optimization and task execution (S4043), Blockchain data layer (S501), Artificial intelligence decision-making layer (S502), Distributed autonomous management layer (S503), Supply chain full-chain optimization and feedback layer (S504), Blockchain node deployment (S5011), Smart contract management (S5012), Blockchain data synchronization and verification (S5013), Machine learning model training (S5021), Deep learning and pattern recognition (S5022), Intelligent reasoning and automatic decision generation (S5023), Autonomous decision-making and resource scheduling (S5031), Supply chain bottleneck detection and adaptive adjustment (S5032), Blockchain transparency and compliance assurance (S5033), Full-chain feedback and self-learning (S5041), Multi-objective optimization and decision adjustment (S5042), and Smart contract execution and scheduling feedback (S5043). Detailed Implementation

[0024] The technical solution adopted in this specific implementation method includes the following related systems and models:

[0025] (a) Full-domain intelligent sensing system: Real-time acquisition of multi-source data from all aspects of steel production, transportation, market and environment through holographic sensing technology. The data includes, but is not limited to, production data, transportation data, market price data, policy change data and environmental monitoring data.

[0026] (b) Spatiotemporal Big Data Analysis System: Based on multi-source sensor data, it uses spatiotemporal big data stream analysis technology to capture the spatiotemporal correlation of market demand, price fluctuations and production status;

[0027] (c) Ensemble learning model: Multiple ensemble learning algorithms, including random forest, gradient boosting machine (GBDT), and XGBoost, are used to fuse and analyze the spatiotemporal big data stream to form a market demand forecasting model;

[0028] (d) Knowledge Graph and Causal Reasoning Module: Construct a knowledge graph of the slag market, and combine it with causal reasoning technology to analyze how policy changes, natural disasters and other external factors affect market supply and demand and key variables of price fluctuations;

[0029] (e) Meta-learning and adaptive adjustment module: Introduces meta-learning algorithms and evolutionary neural network (EvoNN) technology to quickly adjust the parameters of the prediction model based on a small amount of historical data or sudden events, thereby improving the model's adaptive capability.

[0030] (f) Supply Chain Intelligent Decision Module: Based on the above market forecast results, automatically optimize the supply chain scheduling and resource allocation of the steel slag market to achieve dynamic scheduling and intelligent optimization of the supply chain.

[0031] More specifically, the global intelligent perception system is implemented through the following means:

[0032] (a) Drone imagery: Using drone imagery to monitor changes in the production area and its surrounding environment;

[0033] (b) Satellite remote sensing technology: using satellite remote sensing technology to capture the real-time operating status of production facilities and their environmental impact;

[0034] (c) IoT sensors: Deploy IoT sensors in the production process to collect key data in real time.

[0035] More specifically, the ensemble learning model is implemented through the following steps:

[0036] (a) Multi-model fusion: Weighted fusion of the results of multiple single prediction models to optimize the overall prediction accuracy;

[0037] (b) Spatiotemporal data stream analysis: Adjust the prediction model based on the dynamic changes of spatiotemporal data to improve stability and response speed in the event of sudden events or market fluctuations.

[0038] More specifically, the knowledge graph and causal reasoning module is implemented through the following steps:

[0039] (a) Causal relationship analysis: Analyze the chain of influence between various factors through causal reasoning models, such as how policy changes lead to production bottlenecks, which in turn affect market demand and price fluctuations;

[0040] (b) Event forecasting and adjustment: Based on external events (policy changes, natural disasters), we infer their potential impact on the slag market and adjust the forecast results accordingly.

[0041] More specifically, the Meta-Learning and Evolutionary Neural Network (EvoNN) module can automatically update the prediction model parameters through incremental learning to adapt to new market data or sudden events.

[0042] More specifically, the intelligent supply chain decision-making system includes the following methods:

[0043] (a) Real-time data feedback mechanism: Based on real-time market data, production data and transportation data, automatically adjust production plans and transportation routes to optimize inventory management;

[0044] (b) Edge computing and real-time scheduling: Using edge computing technology, real-time data is initially processed at the data source end to reduce data transmission latency and improve response speed.

[0045] A method for forecasting the steel slag market is characterized by the inclusion of an intelligent logistics sensing module for real-time tracking of slag transportation status, including transportation routes, logistics bottlenecks, and delay information.

[0046] SeeFigure 1 As shown, a steel slag market forecasting system is provided. This system combines omni-domain intelligent sensing technology with spatiotemporal big data analysis technology to achieve accurate forecasting and intelligent decision-making regarding dynamic factors such as supply and demand relationships and price fluctuations in the steel slag market. The core modules of the system include an omni-domain intelligent sensing module (S101), a spatiotemporal big data analysis module (S102), an integrated learning and intelligent forecasting module (S103), and an intelligent decision-making and resource scheduling module (S104).

[0047] The comprehensive intelligent sensing module (S101) is used to collect multi-dimensional data on steel slag production, transportation, market, and environment. Unlike traditional static sensors, this system introduces holographic sensing technology, employing technologies such as UAV imagery, satellite remote sensing, and optical sensors to acquire real-time information on steel slag production processes, transportation routes, and market dynamics. The collected data is transmitted and processed in real-time through spatiotemporal big data streams, ensuring the comprehensiveness and timeliness of the data. In the production process, data collection is performed through sensor data acquisition (S1011), monitoring the steel slag production process through Internet of Things (IoT) sensors, including key production indicators such as furnace charge consumption, temperature, and pressure. In the transportation process, satellite remote sensing data acquisition (S1012) uses GPS and traffic flow monitoring systems to track the transportation status of slag in real time. External data access (S1013) utilizes natural language processing (NLP) technology to extract information from media reports, government announcements, etc., and combines it with traditional transaction data to form a comprehensive market map.

[0048] The spatiotemporal big data analysis module (S102) constructs a high-precision spatiotemporal correlation analysis framework by combining multi-source sensor data, including time-series data and spatial distribution data. Figure 1 In the spatiotemporal data fusion and feature extraction (S1021) shown, the system can identify the correlation between market demand, price fluctuations, and production changes under different time and spatial dimensions. The spatiotemporal big data analysis module (S102) can capture the spatiotemporal characteristics of market fluctuations and emergencies in real time, ensuring the accuracy and timeliness of market forecast results. In the spatiotemporal big data analysis module (S102), the system dynamically adjusts the model parameters by performing real-time fusion and analysis of multi-dimensional data to ensure its adaptability to the rapid changes in the steel slag market. For example, for market demand fluctuations in different regions, data modeling and market forecasting (S1022) can predict inter-regional price differences based on spatiotemporal analysis results, guiding supply chain scheduling.

[0049] The main task of the Ensemble Learning and Intelligent Prediction Module (S103) is to improve the accuracy and stability of market forecasting by integrating multiple machine learning algorithms. In this module, the system integrates ensemble learning methods such as Random Forest, Gradient Boosting Machine (GBDT), and XGBoost, and achieves comprehensive prediction of market fluctuations through multi-model fusion technology. Specifically, the system dynamically analyzes spatiotemporal big data (primarily trained by evolutionary neural networks (S1032)) to adjust the weights of each ensemble model, enabling it to automatically adapt to short-term market fluctuations and long-term trend changes. For example, when a policy change or sudden event occurs, the meta-learning and self-evolution (S1033) system can quickly adjust the model, promptly correct the prediction results, and improve the real-time performance and accuracy of market forecasting.

[0050] The intelligent decision-making and resource scheduling module (S104) integrates advanced technologies such as knowledge graphs, causal reasoning, and meta-learning, enabling it to automatically make adjustments and optimizations based on market forecasts. This module (S104) constructs a knowledge graph of the slag market to reveal the potential causal relationships between various market factors and analyzes the impact of policies, economic fluctuations, and natural disasters on supply and demand using causal reasoning models. Within this module, the system can generate response strategies in real time and automatically adjust production plans, logistics routes, and inventory allocation. For example, when a resource shortage or surge in market demand is predicted in a certain region, the system can immediately optimize supply chain scheduling to ensure the stability and timeliness of slag supply. Combined with meta-learning and self-evolution (S1033), the system can continuously optimize its decision-making algorithm based on historical data and external feedback, improving its adaptability to new environments.

[0051] Through the collaborative work of the above modules, this system provides a high-precision and highly adaptable market forecasting and optimization solution for steel slag, effectively addressing the complexity and dynamism of the market. This system not only senses market changes in real time but also automatically adjusts supply chain strategies based on forecast results, achieving intelligent optimization of the supply chain and optimal resource allocation.

[0052] See Figure 2 As shown, a water slag market dynamic change prediction system based on spatiotemporal big data analysis is provided. This system combines data perception, feature extraction, market prediction and reasoning, as well as smart contract and blockchain technology. It uses virtual digital twin technology to make real-time optimization decisions for the supply chain, ensuring efficient resource allocation and continuous efficient operation of the supply chain.

[0053] The data perception and preprocessing layer (S201) is responsible for collecting data from multiple sources and performing preliminary preprocessing to provide reliable data support for subsequent market forecasting and supply chain optimization. This layer includes sensor data acquisition and fusion (S2011), external market data access (S2012), data cleaning and denoising (S2013), and spatiotemporal data fusion (S2014). By integrating various sensor data (such as temperature, humidity, material flow, etc.), edge computing is used for real-time data fusion in sensor data acquisition and fusion (S2011), providing a foundation for subsequent modeling and decision-making.

[0054] The feature extraction and modeling layer (S202) utilizes machine learning and deep learning techniques to extract key features from the cleaned data (spatiotemporal feature extraction (S2021)) and build a prediction model. The ensemble learning modeling (S2022) module employs ensemble learning methods (such as XGBoost and Random Forest) to build a prediction model for market fluctuations and combines it with deep learning methods (such as LSTM networks) to optimize prediction accuracy. The ensemble learning model in ensemble learning modeling (S2022) is calculated using the following formula, aiming to optimize the model's weight allocation and prediction results:

[0055]

[0056] in, The predicted value is K, where K is the number of base learners, and α is the base value. k f represents the weight coefficients of the k-th base learner. k (X) represents the output of the base learner, and X represents the input feature set. By using weighted summation, the ensemble learning model can capture the complex nonlinear characteristics of the slag market and provide more accurate market forecasts.

[0057] The prediction and inference layer (S203) uses a pre-trained model to predict water slag market fluctuations (S2031) and combines causal reasoning and intelligent decision analysis (S2032) to generate corresponding decision recommendations. Based on the market prediction results, the causal reasoning and intelligent decision analysis (S2032) module uses a causal reasoning model to analyze the impact of various factors on market fluctuations through knowledge graphs, generating decision recommendations to help supply chain managers respond quickly to future changes.

[0058] The decision-making and optimization layer (S204) combines market forecasting and inference analysis results to optimize and schedule the supply chain, ensuring optimal resource allocation and efficient operation. The model optimization and parameter tuning module (S2041) optimizes production planning, warehousing arrangements, and transportation routes by solving optimization problems. The optimization objective function is as follows:

[0059]

[0060] Among them, C i Let x be the cost of the i-th stage. i Let A be the constraint matrix, b be the resource limit, and N be the total number of stages, and b be the resource allocation decision variables. By solving this linear programming problem, the optimal resource allocation scheme can be obtained.

[0061] The virtual data twin execution module S2042 uses virtual digital twin technology to simulate the operational status of each link in the supply chain in real time and executes scheduling commands based on the optimization results. The smart contract and blockchain execution (S2043) module combines blockchain and smart contract technologies to automatically execute supply chain operations, ensuring transparency and traceability.

[0062] By combining the above layers, this embodiment can predict fluctuations in the slag market in real time, optimize resource allocation in the supply chain by combining intelligent decision-making and virtual digital twin technology, and ensure the transparency and efficient operation of each link through blockchain technology.

[0063] See Figure 3 As shown, a slag market forecasting system based on multimodal perception and adaptive cognitive decision-making is presented. Through a combination of meta-learning, self-organizing neural networks (SOM), and causal reasoning, it rapidly adapts to changes in the slag market, accurately analyzes the causal relationships of market fluctuations, and automatically adjusts production, supply chain, and logistics strategies. This system can self-optimize using intelligent feedback mechanisms and provide efficient decision support when facing unexpected events or new market situations.

[0064] The Meta-Learning and Rapid Adaptation Layer (S301) utilizes meta-learning technology to enable the system to rapidly adapt to new market environments and make effective decisions in response to different market fluctuations. In the meta-learning algorithm design (S3011) module, the system designs a meta-learning-based rapid adjustment model, enabling it to quickly extract effective information from limited sample data and rapidly update decision-making strategies to cope with market fluctuations. By combining rapid adjustment with self-evolutionary mechanisms (S3012), the system can automatically adjust its model structure, learning rate, and prediction strategy to quickly adapt to changes during sudden market fluctuations. This layer utilizes limited data learning and transfer (S3013) to extract patterns from past market fluctuations and quickly transfer them to new market environments, even with limited historical data.

[0065] The Self-Organizing Neural Network and Pattern Recognition Layer (S302) utilizes SOM technology for unsupervised learning of market data. By identifying potential market fluctuation patterns, it provides a basis for subsequent market forecasting and decision-making. In the Self-Organizing Neural Network Construction (S3021) module, the self-organizing neural network identifies potential patterns in the slag market through unsupervised learning and extracts key market fluctuation features. The Module Identification and Classification (S3022) module classifies the identified market fluctuation patterns to help the system identify changing characteristics under different market environments, assisting the decision-making layer in market forecasting and resource allocation. As market data continuously changes, the Dynamic Pattern Update and Adaptation (S3023) module ensures that the self-organizing neural network can be updated in real time, guaranteeing the adaptability of the pattern recognition mechanism to market fluctuations.

[0066] The Causal Reasoning and Intelligent Decision-Making Layer (S303) analyzes the root causes of market fluctuations and generates corresponding adjustment strategies through causal reasoning and intelligent decision-making models. In the Knowledge Graph Construction and Causal Relationship Modeling (S3031) module, a knowledge graph of the slag market is constructed. The system analyzes the interrelationships between factors such as production, policy, and demand through causal reasoning. Based on this knowledge graph, the Causal Reasoning and Market Forecasting (S3032) module can generate market fluctuation forecasts using causal reasoning and identify the driving factors of market changes. Through the Intelligent Decision Generation and Reasoning (S3033) module, the system automatically generates and executes decision recommendations based on the causal reasoning results and market forecasts, guiding adjustments to production and the supply chain in the slag market.

[0067] The meta-learning algorithm design (S3011) adopts the basic idea of ​​meta-learning, which is to achieve rapid system adaptation to different market scenarios by minimizing the loss function of the meta-learning task. Assume a task set T = {T1, T2, ..., T...} is given. N Each task contains a set of training data and a corresponding loss function, and the loss minimization process of meta-learning can be described by the following formula:

[0068]

[0069] in, This represents the total loss of the meta-learning model. For task T i loss function, For task T i The parameters are defined as follows: R(θ) is the regularization term, and λ is the regularization coefficient. Through this optimization process, the system can extract effective patterns from a small number of samples and quickly adapt to new market environments.

[0070] The Adaptive Inference and Intelligent Feedback Layer (S304) uses an adaptive feedback mechanism to correct market forecasts and decisions in real time, ensuring the system always maintains an optimal decision-making state. The Intelligent Feedback and Strategy Correction (S3041) module utilizes an intelligent feedback mechanism, allowing the system to automatically adjust the market forecasting model and decision-making strategy, correcting forecasts based on changes in real-time market data. Through the Real-Time Reaction and Self-Optimization (S3042) module, the system can react to market fluctuations in real time and automatically optimize the model, ensuring that decisions and forecasts remain efficient and accurate even in the face of unexpected market events. The Continuous Learning and Model Update (S3043) module utilizes a continuous learning mechanism to continuously update the system's cognitive model to adapt to the ever-changing market environment, ensuring the system's long-term effective operation.

[0071] The system workflow is as follows:

[0072] Market data collection and processing: The system extracts market dynamic information from multi-source data, performs unsupervised learning through self-organizing neural networks, identifies potential patterns of market fluctuations, and enables the system to quickly adapt to new market environments through meta-learning technology.

[0073] Market Forecasting and Inference: Based on historical data and causal reasoning, the system can identify the driving factors of market fluctuations and generate forecasts about market changes through intelligent decision-making models, guiding the optimization and adjustment of supply chain resources.

[0074] Dynamic decision adjustment: Based on real-time market feedback, the system makes rapid corrections through an adaptive mechanism, automatically adjusting forecasting and decision-making strategies to ensure efficient response to emergencies.

[0075] See Figure 4 As shown, this paper presents a decision-making and optimization scheme for the slag market based on intelligent reasoning and knowledge graphs. By modeling the causal relationships between market fluctuations and historical events using technologies such as deep learning and graph neural networks, the system automatically adjusts supply chain decisions based on an event-driven model. The system can provide early warnings of potential events and respond quickly through an automated decision engine, thereby ensuring efficient supply chain operation and reducing risks.

[0076] The event identification and early warning system (S401) is responsible for identifying potential market fluctuation events in real time and predicting future events based on historical event data. In the specimen identification algorithm (S4011) module, the system uses deep learning and graph neural network (GNN) algorithms to identify potential event patterns from historical market fluctuations and policy changes. Using these patterns, the system can provide early warnings of key events that may trigger market fluctuations and generate real-time warning information. Through the event occurrence probability prediction (S4012) module, the system combines current market data with statistical analysis and machine learning methods to predict the probability of event occurrence, serving as an important basis for subsequent decision-making. The event triggering condition modeling (S4013) module is responsible for building event triggering condition models, analyzing the relationship between factors such as policy changes and economic fluctuations and market fluctuations, and predicting the critical conditions for event occurrence.

[0077] To accurately predict the probability of events and market fluctuations, this system uses a Graph Neural Network (GNN) in the event triggering condition modeling (S4013) to model the causal relationship between historical events and market factors. The event occurrence probability prediction model is described by the following formula:

[0078]

[0079] Where P(E|X) represents the probability of event E occurring given market state X. Let λ be the loss function associated with event E, and let λ be an adjustment parameter that controls the effect of the loss function on the probability. It is a normalization term used to ensure that the total probability of all possible events is 1.

[0080] The event-driven forecasting model (S402) constructs a forecasting model based on identified events and their triggering conditions, providing predictions about the impact of future events and offering a basis for subsequent decision-making. In the event impact assessment model (S4021) module, the system constructs an event impact assessment model, analyzing the impact of events on market fluctuations, price changes, and the supply chain based on historical data and event triggering conditions, quantifying the degree of impact. Through the event-driven market forecasting model (S4022) module, combined with the event identification and impact assessment models, the system can predict market trends, price fluctuations, and supply and demand changes after an event occurs. The short-term and long-term impact modeling (S4023) module distinguishes between short-term and long-term impact models based on event type and market background, predicting the impact of events on supply chain scheduling and strategic planning, and providing corresponding assessment reports.

[0081] In the event impact assessment model (S4021), the system quantifies the impact of events on market fluctuations and uses multidimensional data to assess the specific impact of each event on the supply chain. The model is as follows:

[0082]

[0083] Where I(E) represents the market impact score of event E, β i It is each feature f i The weights of (E), f i (E) represents the impact of event E on the i-th influencing factor (such as price, demand, etc.), and ∈ represents the error term. Through this model, the system can quantify the degree of impact of different events and make reasonable decisions accordingly.

[0084] The automated decision-making and adjustment engine (S403) automatically generates and executes decisions based on event-driven forecasting results and end-to-end data, ensuring the supply chain can quickly adjust and optimize resources. In the automated decision-making engine (S4031) module, the system uses deep learning and reinforcement learning algorithms to automatically generate supply chain optimization decisions based on market warnings, event predictions, and impact analysis, covering multiple aspects such as production, transportation, and inventory. The dynamic decision feedback and optimization (S4032) module continuously optimizes the automated decision-making system through a real-time feedback mechanism, correcting decision plans based on actual market reactions to improve the real-time performance and accuracy of decisions. The emergency resource allocation and route optimization (S4033) module automatically allocates resources and optimizes transportation routes when emergencies occur, ensuring the supply chain can respond quickly and meet market demands.

[0085] The Automated Execution and Supply Chain Scheduling Layer (S404) ensures the effective execution of automated decisions and performs real-time scheduling of each link in the supply chain, reducing resource waste and ensuring rapid recovery after an event. In the Execution and Scheduling module (S4041), the system automatically executes decision results through an intelligent scheduling system, allocating resources to where they are most needed and optimizing the scheduling of transportation, production, and warehousing. The Supply Chain Bottleneck and Adjustment (S4042) module monitors each link in the supply chain in real time, automatically detecting and adjusting bottlenecks to ensure the supply chain remains unimpeded in the face of unforeseen events. The Multi-Objective Optimization and Task Execution (S4043) module uses multi-objective optimization algorithms to automatically optimize multiple decision objectives of the supply chain, such as cost, time, and resource utilization, ensuring the system maximizes overall benefits after an event.

[0086] See Figure 5 As shown, a distributed autonomous supply chain system based on blockchain and artificial intelligence is demonstrated. Through the decentralized nature and smart contract functionality of blockchain technology, the system ensures data reliability and transparency at each stage. Leveraging the intelligent decision-making capabilities of artificial intelligence, it optimizes the entire supply chain and learns autonomously, thereby improving the efficiency, security, and flexibility of the supply chain.

[0087] The blockchain data layer (S501) applies blockchain technology to supply chain management to ensure the immutability and traceability of supply chain information, as well as the synchronization and verification of data at each stage.

[0088] S5011: Blockchain Node Deployment

[0089] Deploy blockchain nodes at various stages of the supply chain (such as production, transportation, and warehousing) to ensure real-time data synchronization and decentralized storage. The blockchain node deployment (S5011) module ensures data integrity and transparency.

[0090] S5012: Smart Contract Management

[0091] Blockchain smart contracts enable automated management and contract execution across all stages of the supply chain, including automatic payments, cargo scheduling, and inventory management. Smart contracts guarantee the execution of terms and the automation of processes throughout the supply chain.

[0092] S5013: Blockchain Data Synchronization and Verification

[0093] By using a blockchain network to synchronize and verify data across nodes, the consistency and transparency of supply chain data can be ensured, and information tampering can be prevented.

[0094] The AI ​​decision layer (S502) uses machine learning and deep learning algorithms to analyze historical data in the supply chain and provide decision support for supply chain optimization.

[0095] At the AI ​​decision-making layer (S502), supply chain decisions are optimized using machine learning and deep learning models, and a predictive model based on historical blockchain data is constructed. Considering multiple factors in the supply chain, assuming the system objective is to minimize costs and maximize benefits, a multi-objective optimization problem can be constructed as follows:

[0096]

[0097] Among them, c i Let x represent the unit cost of the i-th stage. i These are decision variables such as production, transportation, and inventory. i It represents the resource requirements of each stage, and Demand(t) represents the market demand at time t.

[0098] To account for supply chain bottlenecks and adaptive adjustments, the system also incorporates reinforcement learning for dynamic optimization, forming a model based on the state-action value function (Q-learning):

[0099] Q(s t ,a t )=Q(s t ,at )+α(r t +γmax a′ Q(s t+1 ,a′)-Q(s t ,a t ))

[0100] Among them, s t , is the system status (such as inventory, transportation status, etc.), a t It refers to the current action (such as production adjustments, transportation route selection), r t It represents the reward obtained from the current decision (e.g., cost reduction, demand satisfaction, etc.), γ is the discount factor, α is the learning rate, and max is the maximum value. a′ Q(s t+1 ,a′) represents the maximum gain in the next state.

[0101] S5021: Machine Learning Model Training

[0102] Based on historical data provided by blockchain, machine learning algorithms (such as XGBoost, Random Forest, etc.) are used to train supply chain optimization models to predict market fluctuations, price changes and supply and demand trends, and optimize decision-making at each stage.

[0103] S5022: Deep Learning and Pattern Recognition

[0104] By using deep learning algorithms (such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to perform pattern recognition on complex supply chain data, potential market patterns and trends can be captured, providing in-depth analytical support for decision-making.

[0105] S5023: Intelligent Reasoning and Automated Decision Generation

[0106] By combining deep learning and knowledge graphs, the system automatically generates intelligent decision-making solutions and adjusts production plans, transportation routes, and inventory allocation based on market changes and supply chain needs.

[0107] The Distributed Autonomous Management System (S503) combines blockchain and artificial intelligence technologies to achieve decentralized autonomous management and self-optimization of the supply chain.

[0108] S5031: Autonomous Decision-Making and Resource Allocation

[0109] At each stage, based on artificial intelligence and smart contracts, resource scheduling and decision-making are automatically adjusted. For example, production plans and logistics routes are automatically adjusted to ensure the efficient operation of the supply chain.

[0110] S5032: Supply Chain Bottleneck Detection and Adaptive Adjustment

[0111] By leveraging AI models and blockchain data flow, supply chain bottlenecks (such as production delays, transportation problems, and insufficient inventory) can be detected in real time, and resource allocation and path optimization can be automatically adjusted to eliminate bottlenecks and ensure the smooth operation of the supply chain.

[0112] S5033: Blockchain Transparency and Compliance Assurance

[0113] Blockchain technology ensures the transparency of the supply chain, automatically records transactions, payments, transportation and other operations, ensures that the entire supply chain process complies with regulatory requirements and is tamper-proof, and guarantees compliance and trust.

[0114] The supply chain optimization and feedback layer (S504) improves the efficiency and accuracy of system decision-making through continuous feedback mechanisms and self-learning.

[0115] S5041: Full-Chain Feedback and Self-Learning

[0116] Based on real-time feedback data from all links of the supply chain, the system continuously optimizes the decision-making model through reinforcement learning, improving the accuracy and timeliness of decisions in order to cope with changes in the market and environment.

[0117] S5042: Multi-objective optimization and decision adjustment

[0118] The system uses a multi-objective optimization algorithm to consider multiple objectives (such as cost, time, resource utilization, etc.) simultaneously, and adjusts decisions based on real-time feedback to ensure the overall efficiency of the supply chain is maximized.

[0119] S5043: Smart Contract Execution and Scheduling Feedback

[0120] During the execution of smart contracts, the system can adjust contract terms based on feedback information to ensure optimal execution at each stage, and adjust scheduling strategies based on real-time data to ensure efficient supply chain response.

[0121] The working principle of this invention is as follows: This system first constructs a multi-layered sensing network seamlessly connecting all aspects of steel production, transportation, market, and environment through comprehensive intelligent sensing. Unlike traditional sensing methods based on static sensors and single data sources, this system introduces holographic sensing technology (UAV imagery, optical sensors, satellite remote sensing data, etc.), enabling each production link, transportation route, and market dynamic to acquire an independent "digital identity." This information is collected and fused in real time using advanced spatiotemporal big data analysis technology, ensuring that data on market demand, production status, and environmental changes are accurately captured, providing solid data support for subsequent market forecasting. In a dynamically changing market with frequent unforeseen events, traditional methods struggle to guarantee real-time data updates and accuracy. This system, through its spatiotemporal sensing network, overcomes this shortcoming, ensuring the timeliness and comprehensiveness of the data.

[0122] Secondly, this system integrates learning and intelligent prediction models. Traditional steel slag market prediction methods often rely on linear analysis of historical data, neglecting the diverse nonlinear characteristics and sudden changes in the market. To address this issue, this system introduces various ensemble learning methods, including Random Forest, Gradient Boosting Machine (GBDT), and XGBoost models. These algorithms effectively improve the accuracy and stability of predictions through multi-model fusion technology. Traditional market prediction models typically utilize only historical price fluctuations or single supply and demand data. However, this system captures the inherent patterns of slag market fluctuations through in-depth fusion of multi-dimensional spatiotemporal data. Specifically, through dynamic analysis of spatiotemporal data, the system can automatically adjust the parameters of the prediction model to better adapt to short-term market fluctuations and long-term trend changes. This method not only enhances the system's adaptability but also improves prediction stability in the face of extreme market volatility. To further enhance the system's predictive capabilities, this system combines knowledge graphs and causal reasoning techniques. By constructing a slag market knowledge graph encompassing multiple fields such as policy, production, logistics, and market demand, the system can reveal the potential causal relationships between various factors in the slag market. Based on a causal reasoning model, the system can analyze how external factors such as policy changes, natural disasters, and economic fluctuations affect key variables such as market supply and demand and price fluctuations, thereby enabling timely prediction and adjustment of market changes. For example, when policy changes or unforeseen events affect the production chain, the system can quickly analyze the potential chain reactions and make proactive decisions. Furthermore, the introduction of meta-learning technology allows the system to rapidly adjust its learning algorithm based on limited historical data or unforeseen events, improving the model's adaptability to new environments and data, and enhancing its learning speed.

[0123] This system also incorporates an adaptive cognitive decision-making mechanism. Through continuous self-evolution and feedback adjustments, the system can react quickly to sudden changes in the steel slag market. This mechanism relies on Evolutionary Neural Networks (EvoNN) and meta-learning algorithms, enabling the system to automatically optimize predictive models and supply chain decisions based on market feedback and changes in the external environment. This achieves dynamic scheduling of the slag market and optimal allocation of supply chain resources. The system's evolutionary process not only depends on historical data but also leverages externally perceived information (such as market news and policy changes), thereby enhancing its adaptability to a volatile market.

[0124] This system, through the deep integration of various advanced technologies such as integrated learning, spatiotemporal big data, edge computing, knowledge graphs, and meta-learning, breaks through the technical bottlenecks of traditional market forecasting methods, providing an innovative and feasible solution for steel slag market forecasting and supply chain optimization. The system can not only perceive market changes in real time and capture the relationships between influencing factors, but also achieve intelligent decision-making and full-chain supply chain optimization based on the forecast results, ultimately forming a market forecasting and management system with highly adaptive, real-time responsive, and intelligent optimization capabilities. This system has profound academic significance and broad industrial application value, and is particularly suitable for supply chain management and market forecasting in the steel, energy, and mining industries.

[0125] In this system, data acquisition and sensing technologies form the foundation of the entire market forecasting and supply chain optimization system. The dynamic fluctuations in the steel slag market are not only closely related to the production process but also influenced by multiple external factors, such as policy changes, economic cycles, transportation conditions, and environmental changes. Therefore, traditional sensing methods based on single data sources or static sensors cannot fully reflect the complexity and real-time changes of the market, making it difficult to provide sufficient information support for accurate forecasting. This system solves this problem by introducing full-domain intelligent sensing and spatiotemporal big data streams, providing real-time, comprehensive, and high-precision data support for subsequent integrated learning and intelligent decision-making models.

[0126] This system's comprehensive intelligent sensing system is a highly integrated sensing network encompassing data collection and transmission across multiple levels, including production, warehousing, transportation, market, and environment. Firstly, the sensing network in the production stage monitors the slag production process in real time through Internet of Things (IoT) sensors, including key production indicators such as furnace charge consumption, temperature, pressure, and output. Furthermore, drones and satellite remote sensing technologies serve as important supplements, capturing changes in the production area and surrounding environment through aerial imagery and remote sensing data, further enhancing the comprehensive sensing capability of the production process. For example, satellite remote sensing technology can monitor the operational status of production facilities and changes in the surrounding environment in real time, promptly identifying external factors that may affect production, such as weather disasters and resource supply issues, thereby providing additional contextual data for market forecasting models. Monitoring of the transportation chain relies on advanced intelligent logistics sensing technology. The transportation of steel slag often involves multiple logistics links and modes of transport (such as trucks, railways, and ships). Bottlenecks and delays of varying degrees may exist in these links, affecting the final market supply. This system tracks the transportation status and routes of slag in real time by deploying GPS, temperature and humidity sensors, and traffic flow monitoring systems on transportation equipment. It captures information such as transportation delays and logistical bottlenecks, and transmits this data to a central data platform via edge computing devices. Through comprehensive awareness of the transportation network, the system can more accurately predict the timeliness of slag supply and changes in market supply and demand, and adjust supply chain scheduling plans in a timely manner.

[0127] Market data collection is also a significant innovation in this system. Market demand and price changes are influenced by various factors, such as macroeconomic policies, regional market fluctuations, and public opinion. To comprehensively grasp market dynamics, this system utilizes Natural Language Processing (NLP) technology to extract information from unstructured data sources such as news media, government reports, and public opinion. This information is then combined with structured data such as traditional market transaction data, consumer demand data, and price indices. Through multimodal data fusion technology, a complete market perception map is formed. This fusion method allows the system to not only rely on traditional price and transaction data but also incorporate external factors such as market opinion and policy dynamics, thereby improving the comprehensiveness and accuracy of market forecasting.

[0128] At the environmental sensing level, this system also incorporates intelligent environmental monitoring technology. By deploying environmental sensors (such as temperature and humidity sensors, and air quality monitors), it monitors environmental factors in real time during the production and transportation of slag. The collection of this environmental data can provide predictions of potential market fluctuations. For example, changes in the external environment, such as extreme weather conditions and resource scarcity, may affect the production efficiency, transportation schedule, or market demand of slag, thereby influencing the final market price and supply chain scheduling. All sensing data undergoes preliminary processing and filtering through edge computing, ensuring data real-time performance while reducing data transmission latency and bandwidth consumption. Edge computing can perform preliminary analysis and processing of raw data at the data source, extracting valuable information and transmitting the results to the central data platform. This effectively improves the response speed and processing efficiency of the entire sensing system, avoiding the data latency problems that may exist in traditional cloud computing methods, and ensuring that real-time data can be promptly fed back into market forecasting and supply chain optimization models.

[0129] The steel slag market exhibits distinct spatiotemporal characteristics, with fluctuations not only being a function of time series data but also influenced by regional differences. Static models derived from traditional data analysis methods often overlook the spatiotemporal dependencies in the data, resulting in inflexible and unreal-time predictions. To address this issue, this system employs spatiotemporal big data analytics, combining multi-source sensor data (including time series data and spatially distributed data) to construct a high-precision spatiotemporal correlation analysis framework. Based on spatiotemporal data streams, the system can dynamically identify the spatiotemporal relationships between factors such as market demand, price changes, and production fluctuations. Furthermore, it utilizes analytical tools such as spatiotemporal flow mapping to accurately model key dynamics of the slag market, including supply and demand relationships and price fluctuations.

[0130] Through technological innovation in comprehensive intelligent sensing and spatiotemporal big data analysis, this system enables precise sensing and dynamic monitoring of all aspects and factors in the steel slag market, providing high-quality and timely data support for subsequent integrated learning and intelligent prediction models. This innovative data acquisition and sensing framework breaks through the limitations of traditional prediction methods in terms of single data source and timeliness, further improving the prediction accuracy and response speed of the entire system, and providing strong support for supply chain optimization and intelligent decision-making in the steel slag market.

[0131] In this system, the adaptive cognitive decision-making system combines ensemble learning with intelligent reasoning to achieve flexible decision adjustments and optimizations in dynamic and ever-changing market environments. The system not only makes predictions based on historical data and real-time information, but also optimizes the prediction model and decision-making process through continuous feedback mechanisms, thus demonstrating strong adaptability and long-term market resilience in the face of complex and volatile market environments.

[0132] The adaptive decision-making mechanism in this system relies on meta-learning algorithms and evolutionary neural networks (EvoNN). Meta-learning is a technique that enables models to quickly adapt to new environments and limited data, making it suitable for handling dynamically changing market conditions. In the steel slag market, due to the constant changes in factors such as price fluctuations, supply and demand relationships, and policy changes, traditional static models often struggle to adapt in real time. However, through meta-learning algorithms, the system can respond to new changes in a short period of time using limited data, thereby improving the flexibility and accuracy of prediction and decision-making.

[0133] Specifically, the Evolutionary Neural Network (EvoNN) is an adaptive neural network based on evolutionary algorithms. By simulating natural selection and genetic mechanisms, EvoNN can continuously optimize its structure and parameters based on market feedback. Specifically, as the slag market fluctuates, EvoNN improves its ability to capture complex market changes through self-adjustment of its network structure. For example, when sudden market fluctuations or policy changes occur, the evolutionary neural network can optimize the weights and connections in the prediction model through self-adjustment, thereby recovering and adapting to the new market environment in a short period of time. This adaptive capability enables the system to maintain high prediction accuracy under various extreme conditions, avoiding the lag and inadequacy of traditional static models in the face of sudden events. Knowledge graphs and causal reasoning techniques play a crucial role in this system, enhancing its understanding of the complexity and variability of the slag market. In predicting the steel slag market, simply relying on historical data for linear modeling cannot fully reveal the deep-seated relationships between various factors in the market. Therefore, this system constructs a knowledge graph of the slag market and combines it with a causal reasoning model to deeply explore the causal relationships between various variables within the market. For example, policy changes may directly affect slag production capacity, and changes in production capacity, in turn, will affect market supply and demand, price fluctuations, and so on. Through causal reasoning mechanisms, the system can build more accurate market models, thereby making more targeted and forward-looking decisions.

[0134] Specifically, causal reasoning models play an irreplaceable role in the dynamic forecasting of the slag market. By constructing causal relationship diagrams, the system can identify the causal chains between various factors in the slag market, such as the mutual influence between policy, price, production, and supply, and provide early warnings of potential future changes through reasoning mechanisms. For example, when a region implements a new environmental protection policy, the system can analyze the potential production bottlenecks or changes in market demand through reasoning models, and then estimate its long-term impact on price fluctuations, providing forward-looking guidance for decision-makers. To further improve the intelligence and accuracy of the decision-making system, this system combines Deep Reinforcement Learning (DRL) and Self-Organizing Maps (SOM) technologies, enabling the system to continuously adjust its decision-making strategies based on market feedback. Reinforcement learning technology allows the system to automatically optimize the decision-making process through multiple trial-and-error learning processes, finding the optimal strategy in complex market environments. Self-Organizing Maps, through unsupervised learning, automatically identifies potential patterns and regularities in market data, enabling the system to discover new market trends or changes from data without explicit labeling, further improving the accuracy of the system's decisions. In practical applications, the system combines ensemble learning and deep learning methods to achieve multi-level and multi-dimensional market modeling. For example, in market forecasting, ensemble learning can combine multiple machine learning algorithms (such as Random Forest, XGBoost, and LightGBM) to improve the robustness and accuracy of forecasts by integrating the results of multiple models. Deep learning, on the other hand, uses a more complex neural network architecture to deeply explore the complex nonlinear relationships in the slag market. This combination not only improves the accuracy of market forecasting but also enhances the system's adaptability and its ability to respond quickly to sudden market changes.

[0135] Traditional market forecasting models often rely on a single data source, leading to significant limitations in understanding complex market environments. However, by employing cross-modal perception technology, this system can integrate perceptual information from multiple dimensions, including visual, auditory, environmental, and market data, to form a more comprehensive and three-dimensional market map. Combining deep learning and knowledge graph technologies, the system can deeply analyze the intrinsic relationships between these multimodal data points, providing richer and more accurate information support for the decision-making process.

[0136] In this system, cross-domain intelligent decision-making and end-to-end optimization are among the key technologies for achieving efficient and intelligent supply chain management. With the continuous development of the global market, the supply chain of the steel slag industry is not only affected by production processes, transportation routes, and market demand, but also closely linked to external factors such as policies, the environment, and the economy. Therefore, single-point local optimization cannot cope with the increasingly complex challenges of supply chain management. This system utilizes innovative methods such as virtual digital twins, event-driven models, smart contracts, and blockchain technology to construct an integrated, cross-domain collaborative end-to-end optimization platform, aiming to improve the transparency, responsiveness, and autonomous decision-making capabilities of the slag supply chain.

[0137] The virtual digital twin technology in this system is one of the foundations for achieving end-to-end optimization. The virtual digital twin simulates various stages in the steel slag supply chain, including production, warehousing, transportation, and sales, providing real-time feedback for actual production and supply. Each physical entity (such as production equipment, transportation vehicles, and storage facilities) has a virtual digital twin that reflects its status, operation, and interaction with the external environment in real time. Through this digital twin model, the system can track the entire process of slag from production to transportation, and from inventory to the market in real time, providing accurate status data and predictive information, thereby optimizing resource scheduling and supply chain decisions. For example, when a delay or bottleneck occurs in a certain stage, the system can quickly identify the problem through the virtual twin model and make adjustments in advance, ensuring the coordination and flexibility of all stages of the supply chain. Through the simulation and optimization of the virtual digital twin, end-to-end collaborative scheduling is achieved. The coordination and scheduling of production, warehousing, transportation, and sales are usually affected by multiple factors, including production capacity, inventory status, transportation timeliness, and market demand. Traditional supply chain scheduling systems typically aim at local optimization and struggle to achieve comprehensive coordination in dynamic and complex market environments. This system utilizes digital twin technology to integrate real-time data from each stage, enabling collaborative scheduling and resource optimization across the entire supply chain. The system can not only adjust production plans based on real-time production data and inventory levels, but also flexibly adjust transportation routes and inventory allocation according to market demand fluctuations and transportation delays. This achieves optimal resource allocation, reduces inventory costs, improves logistics efficiency, and ultimately optimizes the entire steel slag supply chain. Secondly, the event-driven forecasting and automated decision-making system is one of the core technologies that maintain supply chain optimization in a dynamic environment. In practice, fluctuations in the steel slag market are typically triggered by multiple external factors, such as policy changes, natural disasters, and sudden market demands. Traditional supply chain management methods often lack the ability to predict these unforeseen events, leading to slow supply chain response, untimely resource allocation, and even severe market imbalances. This system introduces an event-driven forecasting model, using deep learning, graph neural networks, and other technologies to capture the causal relationship between historical events and market fluctuations, identifying potential events that may affect supply chain operations in advance.

[0138] Specifically, event-driven models, based on historical and spatiotemporal data analysis, can predict the impact of external events (such as policy changes and surges in market demand) on the supply chain and respond in advance. For example, when a region releases a new environmental policy, the system can analyze potential production bottlenecks, transportation difficulties, and price fluctuations through causal reasoning, and adjust production plans and transportation routes in advance to minimize the negative impact of policy changes on the supply chain. Furthermore, event-driven automated decision-making systems can automatically adjust production, transportation, inventory, and market strategies by combining real-time data, reducing human intervention and improving the speed and accuracy of supply chain response. This intelligent decision-making mechanism ensures that the steel slag supply chain can quickly adjust in the face of external uncertainties, guaranteeing market supply stability and supply chain efficiency.

[0139] Furthermore, by combining blockchain and artificial intelligence, blockchain technology provides decentralized trust guarantees for all aspects of the steel slag supply chain, ensuring the transparency and authenticity of data from all parties involved. In traditional supply chain management, information flow typically relies on centralized management systems, making it susceptible to issues such as information distortion and data tampering. However, by introducing blockchain technology, production, transportation, and transaction data at each stage are recorded and updated in real time through smart contracts, ensuring data transparency, immutability, and trustworthiness. Simultaneously, the combination of blockchain and artificial intelligence enables the system to conduct intelligent contract management and automated execution while ensuring data security, optimizing cooperation and resource allocation across the supply chain. Smart contracts can automatically adjust contract terms based on real-time data, executing tasks such as production scheduling, inventory management, and transportation allocation, reducing human intervention and improving the autonomy and efficiency of the supply chain. Finally, the distributed autonomous supply chain system, by combining blockchain and AI technologies, promotes the intelligent and decentralized transformation of the steel slag market supply chain. Decisions at each supply chain stage are not only based on analysis and reasoning from the central system but can also be made autonomously locally. Based on blockchain technology, the system enables self-organization and autonomy among all parties in the supply chain, ensuring efficient operation even with multi-party participation. This significantly reduces friction and uncertainty in the supply chain, ensuring that the supply chain can maintain autonomous operation and achieve efficient optimization when facing complex and changing market environments.

[0140] By combining virtual digital twins, event-driven models, smart contracts, and blockchain, this system achieves end-to-end optimization of the steel slag market supply chain. The system not only senses real-time dynamic changes in the market and production but also enables efficient collaboration between various links through cross-domain intelligent decision-making. Whether responding to external emergencies or in daily supply chain scheduling, the system can automatically adjust and optimize based on its highly integrated intelligent decision-making capabilities, thereby ensuring supply and demand balance, price stability, and efficient supply chain operation in the steel slag market.

[0141] The above description is only used to illustrate the technical solution of the present invention and is not intended to limit it. Any other modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention, as long as they do not depart from the spirit and scope of the technical solution of the present invention, should be covered within the scope of the claims of the present invention.

Claims

1. A steel slag market forecasting system, characterized in that: It includes the following related systems and models: (a) Full-domain intelligent sensing system: Real-time acquisition of multi-source data from all aspects of steel production, transportation, market and environment through holographic sensing technology. The data includes, but is not limited to, production data, transportation data, market price data, policy change data and environmental monitoring data. (b) Spatiotemporal Big Data Analysis System: Based on multi-source sensor data, it uses spatiotemporal big data stream analysis technology to capture the spatiotemporal correlation of market demand, price fluctuations and production status; (c) Ensemble learning model: Multiple ensemble learning algorithms, including random forest, gradient boosting machine (GBDT), and XGBoost, are used to fuse and analyze the spatiotemporal big data stream to form a market demand forecasting model; (d) Knowledge Graph and Causal Reasoning Module: Construct a knowledge graph of the slag market, and combine it with causal reasoning technology to analyze how policy changes, natural disasters and other external factors affect market supply and demand and key variables of price fluctuations; (e) Meta-learning and adaptive adjustment module: Introduces meta-learning algorithms and evolutionary neural network (EvoNN) technology to quickly adjust the parameters of the prediction model based on a small amount of historical data or sudden events, thereby improving the model's adaptive capability. (f) Supply Chain Intelligent Decision Module: Based on the above market forecast results, automatically optimize the supply chain scheduling and resource allocation of the steel slag market to achieve dynamic scheduling and intelligent optimization of the supply chain.

2. The steel slag market forecasting system according to claim 1, characterized in that: The comprehensive intelligent sensing system is achieved through the following means: (a) Drone imagery: Using drone imagery to monitor changes in the production area and its surrounding environment; (b) Satellite remote sensing technology: using satellite remote sensing technology to capture the real-time operating status of production facilities and their environmental impact; (c) IoT sensors: Deploy IoT sensors in the production process to collect key data in real time.

3. The steel slag market forecasting system according to claim 1, characterized in that: The ensemble learning model is implemented through the following steps: (a) Multi-model fusion: Weighted fusion of the results of multiple single prediction models to optimize the overall prediction accuracy; (b) Spatiotemporal data stream analysis: Adjust the prediction model based on the dynamic changes of spatiotemporal data to improve stability and response speed in the event of sudden events or market fluctuations.

4. The steel slag market forecasting system according to claim 1, characterized in that: The knowledge graph and causal reasoning module is implemented through the following steps: (a) Causal relationship analysis: Analyze the chain of influence between various factors through causal reasoning models, such as how policy changes lead to production bottlenecks, which in turn affect market demand and price fluctuations; (b) Event forecasting and adjustment: Based on external events (policy changes, natural disasters), we infer their potential impact on the slag market and adjust the forecast results accordingly.

5. The steel slag market forecasting system according to claim 1, characterized in that: The Meta-Learning and Evolutionary Neural Network (EvoNN) module can automatically update the prediction model parameters through incremental learning to adapt to new market data or sudden events.

6. The steel slag market forecasting system according to claim 1, characterized in that: The intelligent supply chain decision-making system includes the following methods: (a) Real-time data feedback mechanism: Based on real-time market data, production data and transportation data, automatically adjust production plans and transportation routes to optimize inventory management; (b) Edge computing and real-time scheduling: Using edge computing technology, real-time data is initially processed at the data source end to reduce data transmission latency and improve response speed.

7. A steel slag market forecasting system, characterized in that: It also includes an intelligent logistics sensing module: used to track the status of water slag transportation in real time, including transportation routes, logistics bottlenecks, and delay information.

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