Intelligent decision support system based on big data and block chain

Abstract

The invention discloses an intelligent decision support system based on big data and a block chain, and the operation method of the system comprises the following steps: 1, uploading the price of a product to a block chain for storage, and achieving the transparency of the price; 2, price data is extracted, and product cost is checked through big data; 3, comparing the relationship between the cost and the market price to realize the optimization of the supply chain; 4, price suggestions are generated for supply chain manufacturers by integrating historical data and market trends, and a supply chain optimization and dynamic adjustment module is used for comparing cost with market prices and triggering different mechanisms according to different results; the historical data comprehensive analysis and price suggestion module is used for collecting historical price data and performing price suggestion according to historical prices, and the system has the advantages of being more efficient in price decision and more real-time and intelligent in cost accounting.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to an intelligent decision support system based on big data and blockchain. Background Art

[0002] In recent years, with the increasing complexity of global supply chains and rapid changes in the market, supply chain management has become increasingly critical. Traditional supply chain management methods often face problems such as opaque information, complex cost accounting, and lack of real-time performance. The technologies currently existing in supply chain systems are mainly concentrated in the fields of enterprise resource planning, the Internet of Things, and blockchain to achieve data collection, storage, and sharing. However, these technologies still have some limitations and cannot fully meet the needs of complex supply chain management. First, the current cost accounting is mainly based on historical data and static models, which is difficult to cope with dynamic market changes. The lack of real-time cost accounting methods affects decision makers' accurate grasp of supply chain costs. Secondly, the traditional supply chain system responds slowly to market changes, making it difficult to adjust the supply chain process in a timely manner, resulting in the inability to quickly adapt to market demand and optimize supply chain efficiency. In addition, existing technologies are difficult to provide real-time supply chain optimization suggestions, and cannot quickly adjust supply chain strategies based on the dynamic relationship between market prices and costs, affecting the flexibility of the supply chain.

[0003] In summary, the shortcomings of current technologies make it difficult for supply chain management to respond flexibly in the face of a rapidly changing market environment. There is an urgent need for a new technology that can solve information opacity, improve the real-time nature of cost accounting, and achieve supply chain optimization. Therefore, the present invention aims to overcome the shortcomings of the prior art and provide a more efficient, real-time, and intelligent intelligent decision support system based on big data and blockchain. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent decision support system based on big data and blockchain to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent decision support system based on big data and blockchain, the operation method of the system comprises the following steps:

[0006] Step 1: Achieve price transparency by uploading product prices to blockchain storage;

[0007] Step 2: Extract price data and calculate product costs through big data;

[0008] Step 3: Optimize the supply chain by comparing the cost with the market price;

[0009] Step 4: Generate price recommendations for supply chain manufacturers by integrating historical data and market trends.

[0010] According to the above technical solution, the step of achieving price transparency by uploading product prices to blockchain storage includes:

[0011] Supply chain manufacturers first obtain access rights through the system's identity authentication mechanism. Only when they are authorized can they upload price information. During the price information upload process, the system uses an asymmetric encryption algorithm to generate the manufacturer's digital signature and embeds the digital signature into the uploaded data. In addition, the uploaded data is hashed to form a data summary. These steps are integrated into a smart contract, which contains product pricing information, timestamps, and key elements of digital signatures. The system uses the TLS transport layer encryption protocol for data transmission. When the data upload is completed, the system writes the data to the blockchain through a smart contract. Then, a consensus mechanism based on PoW or PoS is used to ensure that the nodes in the network reach a consensus, verify and record the uploaded smart contract.

[0012] According to the above technical solution, the steps of extracting price data and calculating product costs through big data include:

[0013] The system uses multiple regression analysis to establish the cost model;

[0014] The system introduces machine learning algorithms to more comprehensively consider nonlinear relationships and potential cost savings points.

[0015] According to the above technical solution, the system uses multiple regression analysis to establish a cost model, including:

[0016] The system is based on smart contracts on the blockchain, automatically retrieving and extracting price data uploaded by various supply chain manufacturers. Through the timestamp information in the smart contract, the system can trace back to each upload operation. Secondly, the system adopts a distributed big data processing framework. By storing data in a distributed file system, the system can process data from different supply chain links in parallel and accelerate the cost accounting process. Specifically, the system first extracts the price data uploaded by various supply chain manufacturers from the blockchain smart contract, including raw material costs, production costs, and transportation costs. Subsequently, the system extracts features from the price data, including analyzing timestamps and considering the potential impact of seasonal changes on costs. In addition, the feature engineering steps cover the processing of missing data and data standardization operations. For each supply chain link i, feature engineering can generate a feature vector X including timestamp processing and data standardization. After extracting price features, the system uses multivariate regression analysis to establish a cost model. Taking into account the interaction between various factors, the following mathematical model is established: C i =β0+β1*Xi1 +β2*X i2 +...+∈ i , where C i represents the cost of the ith link, β is the regression coefficient, X ij is the jth feature of the ith link, ∈ i is the error term.

[0017] According to the above technical solution, the system introduces a machine learning algorithm to more comprehensively consider nonlinear relationships and potential cost-saving points, including:

[0018] The system uses principal component analysis to identify the most influential features, thereby reducing the complexity of the model. Cluster analysis uses autoencoders in deep learning to help the system discover similarities in potential supply chain links: Autoencoder = Encoder (X1, X2, ..., X n ), where X1, X2, ..., X n It is the characteristic vector of each supply chain link. After the cost model is established, the system will continuously adjust the model parameters to improve the accuracy of the cost model. Where n is the number of samples, C i is the actual cost, C 预测 It is the cost predicted by the model. Through this method, the system can deeply analyze the expenses of each link, comprehensively consider the influence of various factors, and provide more accurate and comprehensive accounting results for the supply chain cost. Finally, the cost accounting results are presented in a visual form to provide decision makers with a comprehensive supply chain cost situation, including the specific expenses of each link, the proportion of cost composition and predicted future trends.

[0019] According to the above technical solution, the step of optimizing the supply chain by comparing the relationship between cost and market price includes:

[0020] The system establishes a real-time connection with the market price data source to obtain real-time market price information. Secondly, the system will determine the relationship between cost and market price. When the system detects that the cost is greater than the market price, the trigger mechanism will be activated. When triggered, the system will start the process of re-evaluating the supply chain process, including re-evaluating the efficiency of each supply chain link and looking for optimization opportunities. Optimization methods include renegotiation of supplier cooperation agreements, optimization of production processes, and improvement of inventory management. The system will provide specific suggestions and directions for optimization based on previous data mining and cost accounting results.

[0021] According to the above technical solution, the step of generating price recommendations for supply chain manufacturers by integrating historical data and market trends includes:

[0022] The system collects and integrates historical data, including the manufacturer's production cost, transportation cost information, and historical price trends in the market, and then performs data cleaning, deduplication and alignment steps. It then analyzes historical data and market trends through a deep learning model and finally generates price recommendations. When generating price recommendations, the system considers the manufacturer's cost, market demand, and competitor price factors, and uses multiple regression analysis to establish a mathematical model that comprehensively considers various factors: P 建议 =f(C 生产 , C 运输 ,..., market trends, competitor prices,...), where P 建议 represents the best price recommendation generated by the system, C 生产 , C 运输 , ... represents the manufacturer's production cost and transportation cost. The system will also adopt online learning technology to continuously optimize the price recommendation model through a feedback mechanism to make it better adapt to the changing market environment. Finally, the system will present the generated best price recommendation to the manufacturer in an intuitive and easy-to-understand form by adopting visualization tools such as charts or reports so that the manufacturer can clearly understand the basis and conclusion of the recommendation.

[0023] According to the above technical solution, the system includes:

[0024] Blockchain upload and secure storage module, used to securely upload prices of various supply chains to the blockchain;

[0025] Supply chain optimization and dynamic adjustment module, which is used to compare costs with market prices and trigger different mechanisms based on different results;

[0026] The historical data comprehensive analysis and price recommendation module is used to collect historical price data and make price recommendations based on historical prices.

[0027] According to the above technical solution, the blockchain upload and secure storage module includes:

[0028] Authentication and data upload module, used to achieve secure price data upload through asymmetric encryption and smart contracts;

[0029] Big data cost accounting module, which is used to accelerate cost accounting through a distributed big data processing framework based on smart contracts and provide comprehensive supply chain cost information;

[0030] The visual cost presentation module is used to display the supply chain cost situation in a visual way, including expenses, cost ratios and trends, to provide basic information for decision makers.

[0031] According to the above technical solution, the supply chain optimization and dynamic adjustment module includes:

[0032] The market price comparison and real-time connection module is used to connect to the market price data source in real time, determine the relationship between cost and market price, and trigger the real-time supply chain optimization mechanism;

[0033] Real-time monitoring and optimization module, which is used to re-evaluate the supply chain process when the cost is greater than the market price, and provide real-time optimization suggestions for data mining and cost accounting;

[0034] The real-time adaptation module to market changes is used to improve the system's ability to adapt to market changes and ensure that the supply chain remains in the optimal state.

[0035] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention realizes supply chain price transparency and secure uploading through blockchain, including identity authentication, encrypted uploading, smart contracts and distributed ledger storage; then, a comprehensive cost accounting is performed on the uploaded price data, and a cost model is established by combining smart contracts, big data processing, feature extraction, and machine learning; after the cost accounting, the system triggers the supply chain optimization mechanism by connecting to the market price data source in real time, re-evaluates the process, and provides real-time suggestions to ensure the optimal state of the supply chain; finally, the system analyzes historical data and market trends through deep learning, generates the best price suggestions, and continuously optimizes the model through online learning, providing a scientific basis for manufacturers and promoting a competitive market. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0037] Figure 1 A flowchart of an intelligent decision support method based on big data and blockchain provided in Embodiment 1 of the present invention;

[0038] Figure 2 A schematic diagram of the module composition of an intelligent decision support system based on big data and blockchain provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0039] 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 creative work are within the scope of protection of the present invention.

[0040] Embodiment 1: Figure 1This is a flowchart of an intelligent decision support method based on big data and blockchain provided in Example 1 of the present invention. This embodiment can be applied to the scenario of industrial cost optimization. The method can be executed by an intelligent decision support system based on big data and blockchain provided in this embodiment, such as Figure 1 As shown, the method specifically comprises the following steps:

[0041] Step 1: Achieve price transparency by uploading product prices to blockchain storage;

[0042] In the embodiment of the present invention, each supply chain manufacturer uploads key information such as product prices to the blockchain, including but not limited to production costs, transportation costs, market prices, etc., and the uploaded data will be stored in a distributed ledger to form a transparent and traceable data chain;

[0043] Exemplarily, the supply chain manufacturer first obtains access rights through the system's identity authentication mechanism. Only when the supply chain manufacturer is authorized can it upload price information. During the price information upload process, the system uses an asymmetric encryption algorithm to generate the manufacturer's digital signature and embeds the digital signature in the uploaded data. In addition, the uploaded data is hashed to form a data summary. These steps are integrated into a smart contract, which contains product pricing information, timestamps, and key elements of digital signatures. The system uses the TLS transport layer encryption protocol for data transmission. When the data upload is completed, the system writes the data to the blockchain through a smart contract. Then, a consensus mechanism based on PoW or PoS is used to ensure that the nodes in the network reach a consensus, verify and record the uploaded smart contract. In the blockchain, a highly decentralized node network is used to ensure the security and traceability of price data. The execution results of the smart contract are also recorded in the blockchain, providing a verifiable execution history.

[0044] Step 2: Extract price data and calculate product costs through big data;

[0045] In the embodiment of the present invention, after each supply chain manufacturer uploads the price data to the blockchain, the system performs a comprehensive cost accounting;

[0046] Exemplarily, the system is based on smart contracts on the blockchain to automatically retrieve and extract price data uploaded by various supply chain manufacturers. Through information such as timestamps in smart contracts, the system can trace back to each upload operation. Secondly, the system adopts a distributed big data processing framework. By storing data in a distributed file system, the system can process data from different supply chain links in parallel and accelerate the cost accounting process. Specifically, the system first extracts price data uploaded by various supply chain manufacturers from the blockchain smart contract, including raw material costs, production costs, and transportation costs. Subsequently, the system extracts features from price data, including analyzing timestamps and considering the potential impact of seasonal changes on costs. In addition, the feature engineering steps cover operations such as processing missing data and data standardization. For each supply chain link i, feature engineering can generate a feature vector X including timestamp processing and data standardization. After extracting price features, the system uses multivariate regression analysis to establish a cost model. Taking into account the interaction between various factors, the following mathematical model is established: C i =β0+β1*X i1 +β2*X i2 +...+∈ i , where C i represents the cost of the ith link, β is the regression coefficient, X ij is the jth feature of the ith link, ∈ i is the error term;

[0047] For example, in order to more comprehensively consider nonlinear relationships and potential cost savings, the system introduces a machine learning algorithm. The system uses principal component analysis to identify the most influential features, thereby reducing the complexity of the model. Cluster analysis uses autoencoders in deep learning to help the system discover similarities in potential supply chain links: Autoencoder = Encoder (X1, X2, ..., X n ), where X1, X2, ..., X n It is the characteristic vector of each supply chain link. After the cost model is established, the system will continuously adjust the model parameters to improve the accuracy of the cost model. Where n is the number of samples, C i is the actual cost, C 预测 It is the cost predicted by the model. Through this method, the system can deeply analyze the expenses of each link, comprehensively consider the influence of various factors, and provide more accurate and comprehensive accounting results for the supply chain cost. Finally, the cost accounting results are presented in a visual form to provide decision makers with a comprehensive supply chain cost situation, including the specific expenses of each link, the proportion of cost composition and predicted future trends. Such visual analysis helps decision makers better understand the economic situation of the supply chain and provide a basis for formulating reasonable optimization strategies.

[0048] Step 3: Optimize the supply chain by comparing the cost with the market price;

[0049] In the embodiment of the present invention, after the cost accounting is completed, the system compares the relationship between the cost and the market price. When the cost is greater than the market price, the system triggers the supply chain optimization mechanism, which includes re-evaluating the supply chain process, looking for opportunities to improve efficiency, reducing costs and ensuring the optimization of the supply chain;

[0050] Exemplarily, the system establishes a real-time connection with the market price data source to obtain real-time market price information. Secondly, the system will determine the relationship between cost and market price. When the system detects that the cost is greater than the market price, the trigger mechanism will be activated. When triggered, the system will start the procedure of re-evaluating the supply chain process, including re-evaluating the efficiency of each supply chain link and looking for optimization opportunities. Optimization methods include renegotiation of supplier cooperation agreements, optimization of production processes, improvement of inventory management, etc. The system will provide specific suggestions and directions for optimization based on previous data mining and cost accounting results. Through this step, the system realizes real-time monitoring and adjustment of supply chain optimization, improves the system's adaptability to market changes, and ensures that the supply chain always remains in the optimal state.

[0051] Step 4: Generate price recommendations for supply chain manufacturers by integrating historical data and market trends.

[0052] In the embodiment of the present invention, when the cost price is lower than the market price, the system provides the manufacturer with the best price reference, and by combining historical data and market trends, the system generates appropriate price suggestions;

[0053] Exemplarily, the system collects and integrates historical data, including key information such as the manufacturer's production cost, transportation cost, and historical price trends of the market, and then performs pre-processing steps such as data cleaning, deduplication, and alignment. Then, the system analyzes the historical data and market trends through a deep learning model, and finally generates a price recommendation. When generating a price recommendation, the system considers multiple factors such as the manufacturer's cost, market demand, and competitor's price, and uses multiple regression analysis to establish a mathematical model that comprehensively considers various factors: P 建议 =f(C 生产 , C 运输 ,..., market trends, competitor prices,...), where P 建议 represents the best price recommendation generated by the system, C 生产 , C 运输, ... represents the manufacturer's production cost, transportation cost, etc. The system will also adopt online learning technology to continuously optimize the price suggestion model through a feedback mechanism to make it better adapt to the changing market environment. Finally, the system will present the generated best price suggestion to the manufacturer in an intuitive and easy-to-understand form by adopting visualization tools such as charts or reports so that the manufacturer can clearly understand the basis and conclusion of the suggestion. Through this step, the system provides the manufacturer with a scientific basis based on historical data and market trends, enabling it to formulate the most competitive pricing strategy in the market, which not only helps to maintain the manufacturer's competitive advantage, but also promotes the competitiveness of the entire market.

[0054] Embodiment 2: Embodiment 2 of the present invention provides an intelligent decision support system based on big data and blockchain. Figure 2 A schematic diagram of the module composition of an intelligent decision support system based on big data and blockchain provided in Embodiment 2 of the present invention is shown in FIG. Figure 2 As shown, the system includes:

[0055] Blockchain upload and secure storage module, used to securely upload prices of various supply chains to the blockchain;

[0056] Supply chain optimization and dynamic adjustment module, which is used to compare costs with market prices and trigger different mechanisms based on different results;

[0057] The historical data comprehensive analysis and price suggestion module is used to collect historical price data and make price suggestions based on historical prices;

[0058] In some embodiments of the present invention, the blockchain upload and secure storage module includes:

[0059] Authentication and data upload module, used to achieve secure price data upload through asymmetric encryption and smart contracts;

[0060] Big data cost accounting module, which is used to accelerate cost accounting through a distributed big data processing framework based on smart contracts and provide comprehensive supply chain cost information;

[0061] Visual cost presentation module, which is used to visualize the supply chain cost situation, including expenses, cost ratios and trends, to provide basic information for decision makers;

[0062] In some embodiments of the present invention, the supply chain optimization and dynamic adjustment module includes:

[0063] The market price comparison and real-time connection module is used to connect to the market price data source in real time, determine the relationship between cost and market price, and trigger the real-time supply chain optimization mechanism;

[0064] Real-time monitoring and optimization module, which is used to re-evaluate the supply chain process when the cost is greater than the market price, and provide real-time optimization suggestions for data mining and cost accounting;

[0065] Real-time adaptation to market changes module, used to improve the system's ability to adapt to market changes and ensure that the supply chain remains in the optimal state;

[0066] In some embodiments of the present invention, the historical data comprehensive analysis and price suggestion module includes:

[0067] The historical data integration and cleaning module is used to collect and integrate historical data and generate the best price recommendations through deep learning models, taking multiple factors into consideration;

[0068] Deep learning model analysis and recommendation generation module, which is used to analyze historical data and market trends through deep learning models to generate the most competitive price recommendations;

[0069] The online learning and visualization presentation module is used to continuously optimize the price recommendation model using online learning technology and present the generated optimal price recommendation to manufacturers using visualization tools.

[0070] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0071] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent decision support method based on big data and blockchain, characterized by: The method comprises the following steps: Step 1: Achieve price transparency by uploading product prices to blockchain storage; Step 2: Extract price data and calculate product costs through big data; Step 3: Optimize the supply chain by comparing the cost with the market price; Step 4: Generate price recommendations for supply chain manufacturers by integrating historical data and market trends.

2. According to claim 1, an intelligent decision support method based on big data and blockchain is characterized in that: The steps of achieving price transparency by uploading product prices to blockchain storage include: Supply chain manufacturers first obtain access rights through the system's identity authentication mechanism. Only when they are authorized can they upload price information. During the price information upload process, the system uses an asymmetric encryption algorithm to generate the manufacturer's digital signature and embeds the digital signature into the uploaded data. In addition, the uploaded data is hashed to form a data summary. These steps are integrated into a smart contract, which contains product pricing information, timestamps, and key elements of digital signatures. The system uses the TLS transport layer encryption protocol for data transmission. When the data upload is completed, the system writes the data to the blockchain through a smart contract. Then, a consensus mechanism based on PoW or PoS is used to ensure that the nodes in the network reach a consensus, verify and record the uploaded smart contract.

3. According to claim 2, an intelligent decision support method based on big data and blockchain is characterized in that: The steps of extracting price data and calculating product costs through big data include: The system uses multiple regression analysis to establish the cost model; The system introduces machine learning algorithms to more comprehensively consider nonlinear relationships and potential cost savings points.

4. According to claim 3, an intelligent decision support method based on big data and blockchain is characterized in that: The system uses multiple regression analysis to establish a cost model, including: The system is based on smart contracts on the blockchain, automatically retrieving and extracting price data uploaded by various supply chain manufacturers. Through the timestamp information in the smart contract, the system can trace back to each upload operation. Secondly, the system adopts a distributed big data processing framework. By storing data in a distributed file system, the system can process data from different supply chain links in parallel and accelerate the cost accounting process. Specifically, the system first extracts the price data uploaded by various supply chain manufacturers from the blockchain smart contract, including raw material costs, production costs, and transportation costs. Subsequently, the system extracts features from the price data, including analyzing timestamps and considering the potential impact of seasonal changes on costs. In addition, the feature engineering steps cover the processing of missing data and data standardization operations. For each supply chain link i, feature engineering can generate a feature vector X including timestamp processing and data standardization. After extracting price features, the system uses multivariate regression analysis to establish a cost model. Taking into account the interaction between various factors, the following mathematical model is established: C i =β0+β1*X i1 +β2*X i2 +...+∈ i , where C i represents the cost of the ith link, β is the regression coefficient, X ij is the jth feature of the ith link, ∈ i is the error term.

5. According to claim 4, an intelligent decision support method based on big data and blockchain is characterized in that: The system introduces machine learning algorithms to more comprehensively consider nonlinear relationships and potential cost-saving points, including: The system uses principal component analysis to identify the most influential features, thereby reducing the complexity of the model. Cluster analysis uses autoencoders in deep learning to help the system discover similarities in potential supply chain links: Autoencoder = Encoder (X1, X2, ..., X n ), where X1, X2, ..., X n It is the characteristic vector of each supply chain link. After the cost model is established, the system will continuously adjust the model parameters to improve the accuracy of the cost model. Where n is the number of samples, C i is the actual cost, C 预测 is the cost predicted by the model.

6. According to claim 5, an intelligent decision support method based on big data and blockchain is characterized in that: The steps of optimizing the supply chain by comparing the relationship between cost and market price include: The system establishes a real-time connection with the market price data source to obtain real-time market price information. Secondly, the system will determine the relationship between cost and market price. When the system detects that the cost is greater than the market price, the trigger mechanism will be activated. When triggered, the system will start the procedure of re-evaluating the supply chain process, including re-evaluating the efficiency of each supply chain link.

7. The intelligent decision support system based on big data and blockchain according to claim 6 is characterized by: The steps of generating price recommendations for supply chain manufacturers by integrating historical data and market trends include: The system collects and integrates historical data, including the manufacturer's production cost, transportation cost information, and historical price trends in the market, and then performs data cleaning, deduplication and alignment steps. It then analyzes historical data and market trends through a deep learning model and finally generates price recommendations. When generating price recommendations, the system considers the manufacturer's cost, market demand, and competitor price factors, and uses multiple regression analysis to establish a mathematical model that comprehensively considers various factors: P 建议 =f(C 生产 , C 运输 ,..., market trends, competitor prices,...), where P 建议 represents the best price recommendation generated by the system, C 生产 , C 运输 , ... represents the manufacturer's production cost and transportation cost. The system will also adopt online learning technology to continuously optimize the price recommendation model through a feedback mechanism.

8. An intelligent decision support system based on big data and blockchain, characterized by: The system comprises: Blockchain upload and secure storage module, used to securely upload prices of various supply chains to the blockchain; Supply chain optimization and dynamic adjustment module, which is used to compare costs with market prices and trigger different mechanisms based on different results; The historical data comprehensive analysis and price recommendation module is used to collect historical price data and make price recommendations based on historical prices.

9. The intelligent decision support system based on big data and blockchain according to claim 8, characterized in that: The blockchain upload and secure storage module includes: Authentication and data upload module, used to achieve secure price data upload through asymmetric encryption and smart contracts; Big data cost accounting module, which is used to accelerate cost accounting through a distributed big data processing framework based on smart contracts and provide comprehensive supply chain cost information; The visual cost presentation module is used to display the supply chain cost situation in a visual way, including expenses, cost ratios and trends, to provide basic information for decision makers.

10. The intelligent decision support system based on big data and blockchain according to claim 9, characterized in that: The supply chain optimization and dynamic adjustment module includes: The market price comparison and real-time connection module is used to connect to the market price data source in real time, determine the relationship between cost and market price, and trigger the real-time supply chain optimization mechanism; Real-time monitoring and optimization module, which is used to re-evaluate the supply chain process when the cost is greater than the market price, and provide real-time optimization suggestions for data mining and cost accounting; The real-time adaptation to market changes module is used to improve the system's ability to adapt to market changes and ensure that the supply chain remains in the optimal state.

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