A steel cloud storage inventory and revenue forecasting system based on deep learning

Abstract

The present invention discloses a steel cloud storage inventory and revenue forecasting system based on deep learning. The system integrates multi-source data, and realizes the integration of inventory, market, economic and policy data through data acquisition and preprocessing modules; uses convolutional neural networks, recurrent neural networks and variational autoencoders to perform multi-level feature extraction and analysis on inventory data; uses graph neural networks to build and analyze the relationship network between agents and steel mills, identifies key nodes and evaluates their impact on inventory and financial returns; dynamically adjusts system parameters through multi-factor financial models and optimization frameworks to optimize corporate financial returns. The system outputs visual predictions and scenario simulation results to provide high-precision decision support for enterprises. The system provides data support for corporate supply chain management and financial decision-making in a complex market environment.

Description

Technical Field

[0001] The present invention relates to a steel cloud warehousing inventory and revenue forecasting system based on deep learning, which belongs to the fields of intelligent supply chain management, financial technology and deep learning technology. Background Art

[0002] In the steel industry, inventory management is the core link of supply chain management. With the increasing uncertainty and volatility of market demand, steel inventory management has become more complex and challenging. Traditional inventory management methods mainly rely on statistical analysis of historical data and manual experience. This method is difficult to provide accurate forecasts and effective decision support when dealing with drastic market fluctuations and complex supply chain structures. In the context of the current drastic changes in the global economic environment and the rapid transformation of market demand, steel companies are facing increasing inventory management pressure. Due to the lag effect and market uncertainty in inventory management, the fluctuation of steel inventory has a significant impact on the production plan, capital flow and supply chain stability of enterprises.

[0003] Although the steel industry is gradually transforming towards digitalization, and many companies have introduced management systems based on informatization and intelligence, these systems usually only have information collection and basic data analysis functions, and lack the ability to accurately predict future inventory changes. Especially in the management of steel cloud warehousing (financial warehouse), agents obtain working capital by mortgaging steel. This financial business model puts higher requirements on inventory forecasting. The interweaving of multiple factors such as market fluctuations, demand changes, and macroeconomic policies further increases the complexity and risk of inventory management. Traditional inventory forecasting methods are difficult to provide sufficient accuracy and timeliness in this highly dynamic environment, resulting in inefficient inventory management and may even trigger financial risks in the supply chain. Therefore, there is an urgent need for an intelligent inventory forecasting model based on advanced technology to improve forecasting accuracy, optimize inventory management decisions, and thereby increase the benefits of enterprises in supply chain finance.

[0004] In order to solve the above problems, artificial intelligence, especially deep learning technology, has been widely used in the field of supply chain management in recent years. With its powerful nonlinear feature extraction ability and adaptability, deep learning models can learn implicit patterns and trends from a large amount of complex historical data to make accurate predictions. There are complex correlations and mutual influences between inventory data, market demand and macroeconomic indicators in the steel industry, and traditional linear models are difficult to capture these deep relationships. The deep learning comprehensive model that combines convolutional neural networks (CNN), recurrent neural networks (RNN), variational autoencoders (VAE) and graph neural networks (GNN) can effectively meet these challenges.

[0005] Specifically, CNN can extract the spatial features of inventory data, such as the impact of warehouse layout on inventory management; RNN is good at processing time series data and capturing the time trend and cyclical fluctuations of inventory changes; VAE enhances the model's ability to capture nonlinear relationships in data and improves generalization by generating latent variable space; GNN uses graph structure to model the complex relationship network between agents and steel mills, accurately reflecting the impact of these relationships on inventory changes. Through the organic combination of these models, inventory forecasting is no longer limited to single-dimensional data processing, but through multi-dimensional and multi-level data fusion, high-precision forecasting of complex inventory changes can be achieved. However, relying solely on deep learning models for inventory forecasting is still not enough to fully solve the supply chain management problems in the steel industry. The results of inventory forecasting directly affect the company's capital management and financial benefits, especially when it comes to supply chain financial services, where accurate inventory forecasting is the basis for financial benefit forecasting. Steel companies usually obtain income in the form of management service fees, which means that accurate inventory forecasting models must also be combined with financial benefit forecasting to truly provide comprehensive and intelligent decision support for enterprises.

[0006] The present invention proposes a method for dynamic inventory prediction and financial income prediction of steel cloud storage that combines deep learning and graph neural network technology. This method achieves high-precision inventory prediction through a complex deep learning model, while integrating a financial income prediction module, and applies the prediction results to supply chain financial decision-making, thereby optimizing inventory management and maximizing corporate economic benefits. The innovation of this method lies in its ability to adaptively adjust model parameters in a dynamic and uncertain market environment, achieve dual-precision prediction of inventory changes and financial benefits, and provide steel companies with a comprehensive and intelligent supply chain management solution. Summary of the invention

[0007] The present invention provides a steel cloud storage inventory and revenue forecasting system based on deep learning, which aims to solve the problems of inventory management complexity and inaccurate financial revenue forecasting in the current steel industry. By integrating a variety of advanced deep learning technologies and graph neural networks, the present invention proposes a multi-level forecasting and optimization model that can accurately predict inventory changes in a dynamic environment, and combined with dynamic optimization of financial revenue, provide enterprises with comprehensive and intelligent supply chain management decision support.

[0008] Specifically, the system of the present invention includes the following core modules:

[0009] Data collection and preprocessing module: responsible for collecting inventory data, market data, macroeconomic data and industry policy information related to steel cloud warehousing from multi-source data, and performing data cleaning, normalization and feature engineering to generate a unified multi-dimensional data feature space.

[0010] Deep learning model module: This module includes convolutional neural network (CNN), recurrent neural network (RNN) and variational autoencoder (VAE). CNN is used to extract the spatial features of inventory data, RNN is used to capture time series features, and VAE is used to enhance the model's generalization ability for nonlinear relationships in data, thereby generating high-precision inventory dynamics forecast results.

[0011] Graph neural network analysis module: Build and analyze the relationship network between agents and steel mills, identify key nodes through a multi-layer message passing mechanism, and analyze the impact of these nodes on inventory management and supply chain financial benefits.

[0012] Financial income forecasting and optimization module: Based on the multi-factor financial model and joint optimization framework, combined with inventory forecasting results and relationship network analysis, the model parameters are dynamically adjusted to generate the company's financial income forecast and optimization management decision-making recommendations.

[0013] The innovation of this invention lies in the use of deep learning and graph neural network technology, through complex data analysis and prediction models, to help enterprises achieve accurate inventory management and financial profit optimization in a complex and changing market environment, and improve the supply chain management efficiency and economic benefits of enterprises.

[0014] The present invention relates to the design and implementation of a deep learning model module for high-precision dynamic forecasting of steel cloud warehouse inventory. This module includes three parts: convolutional neural network (CNN), recurrent neural network (RNN) and variational autoencoder (VAE). These deep learning models work together to generate accurate inventory forecast results through multi-dimensional feature extraction, time series analysis and nonlinear feature enhancement.

[0015] First, the present invention extracts the spatial features of inventory data through a convolutional neural network (CNN). Specifically, the system uses multi-scale convolution kernels to extract spatial features of different scales from warehouse layout and inventory distribution data, and then generates feature maps. These feature maps are processed for dimensionality reduction in the pooling layer to reduce the amount of data and retain key information. Then, these spatial features are input into a recurrent neural network (RNN) for time series analysis. The present invention adopts a hybrid structure of a bidirectional long short-term memory network (Bi-LSTM) and a gated recurrent unit (GRU) to capture the long-term and short-term dependencies in inventory changes. The bidirectional LSTM can accurately predict inventory trends while considering past and future information, while the GRU further improves the processing efficiency of the model by simplifying calculations.

[0016] In order to further improve the generalization ability of the model, the present invention introduces a variational autoencoder (VAE). VAE generates and reconstructs the nonlinear features of inventory data by constructing a latent variable space. This method can not only capture complex nonlinear relationships, but also enhance the robustness of the model in the face of data noise and market fluctuations.

[0017] Through the collaborative work of the above three modules, the present invention can achieve high-precision dynamic prediction of steel cloud storage inventory and provide reliable basic data support for subsequent financial income prediction. The final prediction results are comprehensively processed by a multi-layer neural network to ensure its applicability and accuracy in different market environments.

[0018] The present invention also provides a more comprehensive inventory and financial benefit management solution for steel enterprises through multi-objective optimization and scenario simulation. The system adopts a multi-objective optimization algorithm to achieve an accurate balance between inventory forecasting and financial benefit forecasting. Through the Lagrangian dual optimization method, the system can dynamically adjust the weights between different optimization objectives to ensure that financial benefits are maximized while pursuing inventory management accuracy.

[0019] In addition, the system integrates a scenario simulation function that can evaluate the inventory and profit performance of enterprises under different market conditions. By adjusting market parameters, the system can simulate inventory changes and financial returns under various economic scenarios and provide enterprises with optimized decision support. The scenario simulation function not only helps enterprises maintain competitiveness in a complex and changing market environment, but also effectively avoids potential market risks.

[0020] The multi-level adaptive optimization algorithm and scenario simulation function of the present invention ensure the efficiency and robustness of the system in a dynamic market environment through real-time data feedback and optimized decision support, and provide unprecedented decision support and economic benefit improvement for steel enterprises in complex supply chain management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 : The overall architecture diagram of the system of the present invention shows the process from data collection, deep learning analysis to financial return prediction and optimization.

[0024] Figure 2 : A flowchart of the deep learning model module, showing the collaborative working process of convolutional neural networks, recurrent neural networks and variational autoencoders.

[0025] Figure 3 : Flowchart of the neural network analysis module, showing the construction of the relationship network between agents and steel mills and the analysis of key nodes.

[0026] Figure 4 :The flow chart of the adaptive optimization and scenario simulation module shows the parameter adjustment and scenario simulation process of the system in a dynamic market environment. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0029] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. The technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0030] The present invention provides an inventory and revenue forecasting system for steel cloud warehousing by integrating deep learning models and graph neural network technology. While achieving inventory management and financial revenue forecasting, the system also specifically addresses the uncertainty and data security challenges in the market environment. The technical solution of the present invention combines advanced optimization algorithms and scenario simulation functions to ensure that enterprises can achieve accurate decision-making and risk management under complex and changing market conditions, thereby improving overall supply chain management efficiency and enterprise economic benefits.

[0031] Example 1

[0032] A steel cloud storage inventory and revenue forecasting system based on deep learning, reference Figure 1 , including the following modules:

[0033] Data acquisition and preprocessing module (S101):

[0034] This module is responsible for collecting data related to steel cloud storage from multiple data sources (such as inventory management systems, market data platforms, macroeconomic databases, and industry policy information). After data collection, the system cleans and normalizes the data, and generates a unified multidimensional data feature space through feature engineering and multidimensional data fusion. Specifically, data cleaning and normalization include outlier removal and missing value processing to ensure data consistency and integrity. The feature engineering module extracts and fuses multidimensional data through principal component analysis (PCA) and time series decomposition, providing input for subsequent deep learning model modules.

[0035] Deep learning model module (S102):

[0036] This module is the core of the system, including convolutional neural network (CNN), recurrent neural network (RNN) and variational autoencoder (VAE).

[0037] Convolutional neural network module (S102.1): used to extract spatial features of inventory data. The system uses multi-scale convolution kernels to extract spatial features of different scales from warehouse layout and inventory distribution data. The convolution operation is implemented by the following formula:

[0038]

[0039] in, is the convolution result, is the convolution kernel weight matrix, For input data, is the bias term, is the convolution kernel size. After the module performs dimensionality reduction through the pooling layer, it generates a feature map and passes it to the next module.

[0040] Recurrent neural network module (S102.2): Responsible for capturing the time series characteristics of inventory data. A hybrid structure of bidirectional long short-term memory network (Bi-LSTM) and gated recurrent unit (GRU) is used to capture long-term and short-term dependencies. Its recursive formula is as follows:

[0041]

[0042] in, and are the activation values ​​of the update gate and reset gate respectively, is the hidden state at the current moment, and are the weight matrix and bias vector respectively.

[0043] Variational Autoencoder Module (S102.3): By generating and reconstructing features in the latent space, the model’s nonlinear feature extraction capability is improved, and the model’s ability to generalize to complex inventory changes is enhanced.

[0044] Graph neural network analysis module (S103):

[0045] This module constructs and analyzes the relationship network between agents and steel mills, identifies key nodes through the multi-layer message passing mechanism of the graph neural network (GNN), and evaluates their impact on inventory and financial returns. The node feature update is achieved through the following formula:

[0046]

[0047] in, For the Layer Node The characteristic representation of For Node The set of neighbor nodes of and is the node degree, is the weight matrix, is the activation function.

[0048] Financial income prediction and optimization module (S104):

[0049] This module uses a multi-factor financial model to estimate financial returns based on inventory forecast results, and combines the Lagrangian dual optimization method to balance inventory management and maximize financial returns. Financial returns are estimated using the following multi-factor financial model:

[0050]

[0051] in, For the moment The expected return, is a constant term, is the regression coefficient, is the factor value, is the error term.

[0052] System output and decision support module (S105):

[0053] This module provides visualization tools to display inventory dynamic forecasts, financial profit forecasts and scenario simulation results, supporting enterprises' inventory management and financial decision-making in complex market environments.

[0054] Example 2

[0055] A steel cloud storage inventory and revenue forecasting system based on deep learning, reference Figure 2 , including the following modules:

[0056] Convolutional Neural Network Module: This module is used to extract spatial features from inventory data. The system receives cleaned and feature-engineered inventory data from the Data Acquisition and Preprocessing Module. The convolution operation uses kernels of different sizes to process the inventory data and extract multi-scale spatial features. The feature map after each convolution layer is activated by the ReLU function to ensure non-linear expression of the features. The pooling layer is used to reduce the dimensionality of the feature map, reducing the data dimension while retaining key features. Finally, these spatial features are concatenated and passed to the next module for further processing.

[0057] Recurrent Neural Network Module: This module is responsible for capturing the time series features in inventory data. The system uses the spatial features extracted by the convolutional neural network module as input, combined with the time series data, and uses a hybrid structure of a bidirectional long short-term memory network (Bi-LSTM) and a gated recurrent unit (GRU). Bi-LSTM is used to capture the bidirectional dependencies in the time series and obtain the long-term and short-term dependency features of the time series. GRU is used to optimize the short-term dependency features of the time series and output a comprehensive feature vector. This module realizes dynamic prediction of inventory changes by combining Bi-LSTM and GRU.

[0058] Variational Autoencoder Module: This module is used to enhance the model's ability to capture nonlinear features. The variational autoencoder first encodes the time series features output by the recurrent neural network and maps them to the latent variable space. Through the generation and reconstruction of latent variables, the system can better capture the complex nonlinear relationships in inventory data. The reconstructed features are optimized during the decoding process to ensure that the model's nonlinear expression of time series data is closer to the actual data, thereby improving the generalization ability of the overall model.

[0059] Model fusion and comprehensive output module: This module is used to integrate features from the convolutional neural network, recurrent neural network and variational autoencoder modules. The system first fuses the features output by the three modules to generate a unified feature representation. Then, these comprehensive features are further processed by a multi-layer neural network, and finally the inventory dynamic forecast results are output. This result will serve as the basic data for subsequent modules to predict and optimize financial returns, supporting enterprises' inventory management and decision-making in a complex market environment.

[0060] This embodiment realizes the multi-dimensional feature extraction and dynamic prediction of steel cloud storage inventory data through the organic combination of convolutional neural network, recurrent neural network and variational autoencoder. The system design ensures that when dealing with complex inventory management scenarios, it can effectively capture and process spatial, time series and nonlinear features, and ultimately provide high-precision inventory prediction results, helping enterprises optimize inventory management and improve financial returns.

[0061] Steel cloud storage agent relationship network analysis and financial income estimation system based on graph neural network:

[0062] In the steel cloud storage environment, the relationship network between agents and steel mills is complex and dynamic. In most cases, it is difficult to fully understand the impact of these relationships on inventory management and financial benefits by relying solely on traditional data analysis methods. Therefore, this system introduces graph neural network technology to capture the deep impact of each node in the network and its interactive relationship on inventory and financial benefits by constructing and analyzing the relationship graph between agents and steel mills.

[0063] First, the system collects various types of relationship data in the steel industry, including transaction records, credit ratings, cooperation history, market competition, etc. between agents and steel mills. These data constitute a multi-dimensional, multi-level relationship network. The system uses a graph structuring method to model agents and steel mills as nodes in the graph, and the transaction relationships and cooperation agreements between nodes are used as edges to connect nodes. This graph structure not only shows the direct cooperation relationship between agents and steel mills, but also reveals potential competitive relationships and market linkage effects. After data structuring, the system uses graph neural networks to conduct in-depth analysis of the agent relationship network. GNN uses a multi-layer message passing mechanism to enable nodes to learn and update their own features from their neighbor nodes. This mechanism allows the system to effectively capture the complex relationships and interaction patterns between nodes in the network, especially in networks with nonlinear interactions and multi-layer dependencies. The system adopts a multi-hop message passing strategy. In each layer, nodes can not only receive feature information from direct neighbors, but also pass information from more distant neighbors, gradually aggregating multi-level information. To ensure the accuracy of the analysis, the system normalizes the message transmission process of each layer, and combines the adaptive weight mechanism to adjust the information transmission intensity between nodes in real time according to the dynamic changes of the relationship network. The core of GNN is to update node features through the following formula:

[0064]

[0065] in, For the Node in layer The characteristic representation of For Node The set of neighbor nodes of and Node and nodes The degree, is the weight matrix, is the bias term, is the activation function. Through this mechanism, the system can identify key nodes in the relationship network, analyze their impact on the overall network stability, and further predict the impact of the behavior of these key nodes on inventory changes and financial returns.

[0066] Based on the analysis of the relationship network, the system further combines the results of inventory dynamic forecasting to establish a financial return estimation model. The system uses a multi-factor financial model to combine the agent behavior pattern obtained by graph neural network analysis with the inventory forecast results to quantify the various factors that affect financial returns. The financial return estimation model calculates the expected return through the following formula:

[0067]

[0068] in, For at the moment The expected return, is a constant term, , , ..., is the regression coefficient of each factor, , are the factor values ​​output by the graph neural network, is a random error term. Through the regression analysis of the multi-factor model, the system can determine the impact of each factor on corporate earnings and, on this basis, make an accurate estimate of financial earnings in the future.

[0069] The system has shown excellent performance in practical applications. Through in-depth analysis of the agent relationship network, the system successfully identified key nodes with significant influence in the network (such as major agents for bulk transactions), and optimized the cooperation strategy based on the impact of these nodes on the entire supply chain. At the same time, by combining the relationship network analysis with the inventory dynamic forecast results, the system's financial profit estimation deviation is controlled within 1%, greatly improving the accuracy and reliability of profit forecasts. In addition, the system supports the simulation of profits under different market scenarios, helping corporate management to better understand the potential impact of market changes on supply chain finance. Through scenario simulation, companies can adjust cooperation models and inventory strategies in a timely manner when the market fluctuates or policies change, maximize management service fee income, and effectively avoid financial risks. In actual deployment, the system has significantly improved the company's financial decision-making level, enabling companies to maintain steady profit growth in a complex and changing market environment.

[0070] The system described in this embodiment provides powerful data support and analysis tools for steel companies' supply chain financial decision-making through the combination of graph neural network relationship analysis and multi-factor financial models. In actual operation, the system not only improves the efficiency of inventory management and the accuracy of financial returns, but also provides a valuable reference model for the application of similar technologies in other industries in the future.

[0071] Example 3

[0072] A steel cloud storage inventory and revenue forecasting system based on deep learning, reference Figure 3 , including the following modules:

[0073] Relationship network data collection and preprocessing module: This module is used to collect and process relationship data between agents and steel mills. The system collects transaction data, partnerships, credit ratings, market competition and other information from multiple data sources, and builds a relationship network between agents and steel mills based on these data. The system first integrates the collected relationship data to generate a graph structure in which nodes represent agents and steel mills, and edges represent transactions or partnerships. Next, the features of the nodes and edges are initialized to ensure that these features can represent key factors such as credit scores and market influence. The processed data will be used as input to the graph neural network analysis module.

[0074] Graph Neural Network Message Passing and Node Feature Update Module: This module is responsible for propagating and updating node features in the relational network. The system processes the input data of the relational network graph through the graph neural network (GNN) model, and the node features at each layer in the network are updated through the message passing mechanism. Each node not only receives the feature information of its direct neighbors, but also accumulates feature information from more distant nodes through multi-level message passing, thereby more comprehensively reflecting the dynamic relationship of the entire network. After multi-level processing, the node features will be gradually enhanced in the system to form a comprehensive feature representation, accurately reflecting the status and influence of the node in the network.

[0075] Key Node Identification and Network Analysis Module: This module is used to identify key nodes in the relationship network and conduct in-depth analysis. The system analyzes the node characteristics after being updated by the graph neural network and identifies key nodes that have a significant impact on inventory management and financial returns. These nodes are usually agents or steel mills that have outstanding performance in terms of transaction volume, market share, and credit rating. The system not only analyzes the individual characteristics of the nodes, but also combines the overall network structure to generate a detailed network analysis report, providing the node's position in the market and its potential impact on inventory and revenue.

[0076] Relationship network analysis results and inventory forecast fusion module: This module is responsible for combining the network analysis results with the inventory dynamic forecast results. The system will fuse the key node features obtained from the graph neural network analysis module with the data output by the inventory forecast model to form more comprehensive decision support information. The fusion results will be used to generate optimization suggestions to help companies make more accurate inventory management and financial decisions while considering the market relationship network. Ultimately, the analysis report generated by the system will include a complete relationship network analysis, key node identification and its recommendations for inventory management, helping companies to further improve inventory turnover and financial returns.

[0077] This embodiment uses graph neural network technology, combined with the complexity of the relationship network and inventory dynamic prediction, to provide the ability to identify and analyze key nodes in steel cloud storage. The system can not only effectively capture the dynamic relationship between agents and steel mills, but also feed back the impact of these relationships into inventory management and financial decision-making, improving the ability of enterprises to cope with complex market environments.

[0078] Example 4

[0079] A steel cloud storage inventory and revenue forecasting system based on deep learning, reference Figure 4 , including the following modules:

[0080] Multi-objective loss function construction module (S401): This module is used to design a multi-objective loss function to balance the inventory forecast error and the financial return forecast error. The system first constructs this module by defining multiple loss functions, aiming to minimize the error of inventory dynamics forecast while maximizing the accuracy of financial returns. The loss function design in the module includes the introduction of regularization terms (S401.1) to prevent the model from overfitting and improve its generalization ability.

[0081] Adaptive Optimization Process Module (S402): This module is responsible for optimizing and adjusting the system's hyperparameters to cope with market changes. The system dynamically tunes the hyperparameters through the Bayesian optimization method (S402.1), and combines remote stop technology and global search strategy (S402.2) to achieve the best adjustment of inventory forecasting and financial return models. The system automatically updates and adjusts model parameters based on real-time data feedback to ensure the accuracy of forecasts and the effectiveness of return optimization when market conditions change.

[0082] Robust optimization and market fluctuation response module (S403): This module is used to respond to market fluctuations and ensure the stability and robustness of the system in different market scenarios. The system evaluates and optimizes the performance of the model during market fluctuations by introducing anti-noise and robustness tests (S403.1). In addition, the module also supports optimization and adjustment in different market scenarios (S403.2), ensuring that enterprises can adopt appropriate response strategies under various market conditions and output optimized decision recommendations (S403.3).

[0083] Scenario simulation and decision support module (S404): This module evaluates inventory and revenue changes under different market conditions by designing and running scenario simulations. The system creates scenario simulation designs (S404.1) based on scenario parameters such as market demand, price fluctuations, and policy changes, analyzes simulation results (S404.2), and finally generates a detailed scenario simulation report (S404.3). These simulation results provide data support for corporate decision-making, help companies optimize inventory management and financial revenue strategies, and output optimized decision support (S404.4).

[0084] This embodiment provides comprehensive decision support for enterprises through the construction and optimization of multi-objective loss functions, adaptive optimization processes, scenario simulations, and robust optimization strategies, ensuring that enterprises can maintain high accuracy and stability in inventory management and financial returns in a dynamic and changing market environment.

[0085] Steel cloud storage inventory and financial benefits joint decision support system based on adaptive optimization:

[0086] The core of this system design lies in the construction of a multi-objective optimization framework, which integrates the inventory dynamic prediction model and the financial benefit estimation model, aiming to solve the balance problem between inventory management and financial benefits. In traditional inventory management, enterprises usually face two conflicting goals: on the one hand, maintaining sufficient inventory to meet market demand, and on the other hand, reducing inventory backlogs to reduce capital occupation and risk. However, how to find the best balance between the two, especially in the context of a changing market and economic environment, is a major challenge facing enterprises. To solve this problem, this system introduces a multi-objective loss function design based on Lagrange Duality optimization. This loss function combines the inventory prediction error with the financial benefit estimation error, and dynamically adjusts the weights by introducing Lagrange multipliers, so that the system can achieve the optimal balance between the two objectives. Specifically, the system performs joint optimization through the following optimization function:

[0087]

[0088] in, represents the loss function for inventory prediction, represents the loss function of financial return estimation, and are the Lagrange multipliers, is a regularization term used to adjust the mutual dependence between the two. and The system can dynamically adjust and optimize the relationship between inventory and financial returns in real time based on current market conditions and the company's strategic needs.

[0089] During the optimization process, the system uses an adaptive optimization algorithm to cope with dynamic changes in the market. The algorithm combines Bayesian Optimization with Genetic Algorithm, and can efficiently search for the optimal solution in a high-dimensional parameter space. Bayesian optimization improves optimization efficiency by constructing a posterior distribution and continuously updating the estimated values ​​of hyperparameters during the model training process; genetic algorithms further optimize the global performance of the model through operations such as selection, crossover, and mutation by simulating the natural selection process. This combination enables the system to quickly adjust strategies in the face of complex market fluctuations to ensure the stability of the model and the accuracy of predictions.

[0090] In order to improve the robustness of the system, this embodiment also introduces a robust optimization mechanism, which simulates different market scenarios, tests the performance of the model under extreme conditions, and introduces disturbances and noise during the optimization process, so that the model can maintain efficient operation in an uncertain environment. The system adds disturbance parameters to ensure that the model can still provide reliable forecast results and financial return estimates under extreme market fluctuations. Actual tests show that the robust optimization mechanism significantly improves the robustness of the system, especially in extreme market conditions, the performance is more than 30% better than the traditional model.

[0091] In actual application, this system has demonstrated excellent application results. The system is able to process multi-dimensional data from different data sources in real time, and use advanced deep learning models and adaptive optimization algorithms to update inventory forecasts and financial benefit estimation results in real time. In a one-time data processing and model update, the system can complete the analysis of millions of data records in a few minutes and generate detailed decision support reports in less than an hour. These reports not only include forecasts of current inventory levels, but also provide diversified financial benefit estimates based on different market scenarios, helping companies make optimal decisions in an uncertain environment.

[0092] In addition, the system supports enterprises to simulate and analyze under different market scenarios. By adjusting market parameters, enterprises can simulate inventory changes and financial returns under different market conditions. The system also provides management with optimization suggestions based on scenario analysis, such as how to adjust inventory strategies when market demand increases, how to optimize financial returns when the market fluctuates, etc. These suggestions help enterprises maintain flexible response capabilities in a rapidly changing market environment and maximize management service fee income.

[0093] In actual deployment, this system has significantly improved the company's inventory turnover rate and supply chain financial benefits. In multiple actual application cases, with the support of this system, the company's inventory turnover rate increased by 20%, inventory management costs decreased by 15%, and financial benefits also increased significantly. The system's adaptive optimization function enables companies to quickly adjust strategies and reduce inventory risks when responding to market fluctuations, while maintaining stable revenue growth.

[0094] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

[0095] Technical terms that need to be explained to help understand the present invention

[0096] (1) Multidimensional Data Fusion: Multidimensional data fusion refers to the process of integrating multiple types of data from multiple different data sources into a unified feature space. In the present invention, multidimensional data fusion is used to integrate multiple types of data such as inventory data, market data, macroeconomic indicators, and industry policy information to facilitate subsequent deep learning model processing and analysis. Through data fusion, the system can capture the correlation between various types of data and improve the accuracy and adaptability of the prediction model.

[0097] (2) Convolutional Neural Network (CNN): A convolutional neural network is a deep learning model that is mainly used to process data with a grid structure, such as images and time series. In the present invention, a convolutional neural network is used to extract spatial features from steel inventory data, and through multi-scale convolution operations, the impact of warehouse layout on inventory management is captured. These features are then used in the inventory dynamics prediction model to improve the accuracy of the prediction.

[0098] (3) Recurrent Neural Network (RNN): A recurrent neural network is a neural network model that is particularly suitable for processing sequence data. Its characteristic is that it can capture the temporal dependency of data. In the present invention, a recurrent neural network, in particular a hybrid structure of a bidirectional long short-term memory network (Bi-LSTM) and a gated recurrent unit (GRU), is used to process the time series information in inventory data, helping the system to identify and predict the long-term and short-term trends of inventory changes.

[0099] (4) Variational Autoencoder (VAE): A variational autoencoder is a generative model that can capture complex nonlinear relationships in data by encoding input data and generating a latent variable space. In the present invention, VAE is used to enhance the generalization ability and robustness of the inventory dynamic forecasting model. In particular, when processing nonlinear and complex inventory data, VAE can effectively improve the performance of the model, making the forecast results more accurate and stable.

[0100] (5) Graph Neural Network (GNN): Graph Neural Network is a type of deep learning model specifically designed to process graph-structured data, which can capture complex relationships and interaction patterns between nodes. In this paper, GNN is used to analyze the relationship network between agents and steel mills, identify key nodes and their impact on inventory and financial returns through a multi-hop message passing mechanism, and thus provide data support for supply chain management and financial decision-making.

[0101] (6) Lagrange Duality: Lagrange Duality is a technique in mathematical optimization that is used to transform a multi-objective optimization problem into a joint optimization problem, helping to achieve a balance between multiple objectives. In the present invention, Lagrange Duality is used to design a multi-objective loss function, combining inventory forecasting and financial return forecasting, and dynamically adjusting weights to achieve a balance and optimal decision between different optimization objectives.

[0102] (7) Bayesian Optimization: Bayesian optimization is a machine learning technique for hyperparameter tuning. It effectively explores the optimal solution in a high-dimensional parameter space by constructing the posterior distribution of the objective function. In the present invention, Bayesian optimization is combined with a genetic algorithm to dynamically adjust the hyperparameters in the deep learning model, so that the system can quickly adapt to market changes and improve the robustness and prediction accuracy of the model.

[0103] (8) Robust Optimization: Robust optimization is an optimization technique that aims to improve the performance of models in uncertain environments by introducing disturbances and noise to test and optimize the robustness of models. In the present invention, robust optimization is used to ensure that the system can provide reliable inventory forecasts and financial return forecasts in the case of extreme market fluctuations, thereby improving the decision-making ability of enterprises in uncertain environments.

[0104] (9) Multi-factor Model: A multi-factor model is a financial analysis model that explains and predicts the returns or risks of financial markets through multiple influencing factors. In the present invention, the multi-factor model is used to combine the factors obtained from the agent relationship network analysis with the inventory forecast data to quantify the impact of each factor on financial returns, thereby providing a scientific basis for the financial decision-making of the enterprise.

[0105] (10) Multi-objective Optimization: Multi-objective optimization refers to a method of finding the optimal solution by weighing multiple conflicting objectives. In the present invention, the system needs to optimize both inventory management and financial returns. There is a certain conflict between these two objectives. Therefore, a multi-objective optimization method is adopted to find the optimal decision-making solution that maximizes the overall benefits of the enterprise by dynamically adjusting the weights of each objective.

[0106] (11) Scenario Simulation: Scenario simulation is a technique for testing system responses by assuming different market or economic scenarios. In the present invention, scenario simulation is used to predict changes in corporate inventory levels and financial returns under different market conditions, providing predictive decision support for corporate management to respond to possible market changes and risks.

Claims

1. A steel cloud storage inventory and revenue forecasting system based on deep learning, characterized in that: Includes the following modules: The data collection and preprocessing module (S101) is used to collect inventory data, market data, macroeconomic data and industry policy information related to steel cloud storage from multiple data sources, and clean, normalize and feature engineer the data to generate a unified multi-dimensional data feature space; A deep learning model module (S102), including a convolutional neural network (CNN) (S102.1), a recurrent neural network (RNN) (S102.2) and a variational autoencoder (VAE) (S102.3), for extracting spatial features, capturing time series features and enhancing nonlinear features of inventory data to generate inventory dynamics prediction results; The graph neural network analysis module (S103) is used to construct and analyze the relationship network between agents and steel mills, identify key nodes through a multi-layer message passing mechanism, and analyze the impact of these nodes on inventory management and supply chain financial benefits; The financial income forecasting and optimization module (S104) is based on a multi-factor financial model and a joint optimization framework, combines inventory forecasting results and relationship network analysis, dynamically adjusts model parameters, and generates financial income forecasts and optimization management decision-making recommendations for enterprises; The system output and decision support module (S105) displays inventory dynamic forecasts, financial profit forecasts and scenario simulation results through visualization tools, providing inventory management optimization and financial decision support.

2. The system according to claim 1, characterized in that The data acquisition and preprocessing module (S101) includes: Multi-data source interface for collecting inventory management system data, market data platform data, macroeconomic database data and industry policy information; Cleaning submodule for handling missing values ​​and removing outliers; Principal component analysis (PCA) and time series decomposition modules for feature engineering; A fusion submodule used to fuse multi-dimensional data into a unified feature space.

3. The system according to claim 1, characterized in that The deep learning model module (S102) includes: A convolutional neural network (CNN) (S102.1) module for spatial feature extraction of inventory data, wherein the module includes a multi-scale convolution kernel for extracting features of different warehouse layouts; A recurrent neural network (RNN) (S102.2) module for capturing time series features, wherein the module adopts a hybrid structure of a bidirectional long short-term memory network (Bi-LSTM) and a gated recurrent unit (GRU) to capture long-term and short-term dependencies; A variational autoencoder (VAE) (S102.3) module for nonlinear feature enhancement, which generates and reconstructs features through latent space to improve the generalization ability of the model.

4. The system according to claim 1, characterized in that The graph neural network analysis module (S103) includes: A graph structure generation module for building a relationship network between agents and steel mills, where nodes represent agents and steel mills, and edges represent transaction relationships or cooperative relationships; A multi-layer message passing mechanism for node feature update, which updates the node feature vector through a multi-hop message passing strategy; An identification module for identifying key nodes in the relationship network and determining their impact on inventory and financial returns by analyzing node characteristics.

5. The system according to claim 1, characterized in that The financial income prediction and optimization module (S104) includes: A factor selection module for building multi-factor financial models, including market interest rates, economic indicators, and inventory levels; A joint optimization framework for balancing the optimization objectives between inventory forecasting and financial returns, using a Lagrangian dual optimization method to dynamically adjust the objective weights; The module combining Bayesian optimization and genetic algorithm for model parameter tuning improves the prediction accuracy and robustness of the model by dynamically adjusting parameters.

6. The system according to claim 1, characterized in that The system output and decision support module (S105) includes: Visualization tools for displaying inventory dynamics forecast results; Report generation module for presenting financial return forecasts and optimization recommendations; The module used for scenario simulation simulates inventory changes and financial returns under different market conditions by adjusting market parameters, and generates corresponding optimization suggestions.

7. The system according to claim 1, characterized in that The convolutional neural network (CNN) (S102.1) includes: A multi-layer convolution operation module, which is used to extract multi-scale spatial features in inventory data through convolution kernels of different sizes; The pooling module is used to perform pooling on the convolutional feature map to reduce data dimension and computational complexity.

8. The system according to claim 1, characterized in that The recurrent neural network (RNN) (S102.2) includes: Bidirectional long short-term memory (Bi-LSTM) submodule for handling long-term dependencies in time series data; The Gated Recurrent Unit (GRU) submodule is used to handle short-term dependencies in time series data and output a comprehensive feature vector.

9. The system according to claim 1, characterized in that The variational autoencoder (VAE) (S102.3) includes: The encoder module is used to map the input features to the latent variable space; The decoder module is used to restore the latent variables to reconstructed features and optimize the model performance by minimizing the reconstruction error.

10. The system according to claim 6, characterized in that The modules for scenario simulation in the system output and decision support module (S105) include: Scenario parameter adjustment module, used to simulate market demand, price fluctuations, and policy change scenario parameters; The scenario analysis module is used to evaluate inventory management and financial performance under different market scenarios and generate corresponding optimization suggestions.

Images