Supply chain financial industry and financial integration data synchronization system and method

Abstract

The invention belongs to the technical field of supply chain finance, and discloses a supply chain finance industry and financial integration data synchronization system and method. The method comprises the following steps: carrying out key generation and identity authentication based on a trusted mechanism; performing data encryption and signature based on a supply chain financial system, and uploading the data to a data synchronization platform; performing signature verification, decryption and data processing based on the data processing unit; performing data classification by using a data classification model based on the data analysis unit; based on the data analysis unit, a business data analysis model and a financial data analysis model are used for data analysis; generating a data synchronization strategy by using a data synchronization strategy generation model based on a data synchronization strategy generation unit; and performing data synchronization and distributed storage based on the privacy computing unit. According to the invention, the problems of insufficient security, low synchronization efficiency, poor data accuracy and lack of transparency and traceability in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of supply chain finance, and specifically relates to a supply chain finance business-finance integrated data synchronization system and method. Background Art

[0002] In the field of supply chain finance, companies usually need to synchronize data between the supply chain management system and the financial system to ensure the consistency of business information and financial information, and to ensure that the business data in the supply chain (for example, sales orders, purchase orders, inventory levels, logistics information, etc.) matches the data in the financial system (for example, accounts receivable, accounts payable, inventory book value, cost calculation, etc.) to avoid data contradictions or errors.

[0003] Defects of the existing technology:

[0004] 1) Insufficient security: Existing data synchronization technologies often lack effective security measures, leaving data vulnerable to theft, tampering, and other security risks during transmission. The synchronization process may involve sensitive business and financial information, and therefore fails to provide adequate data privacy protection mechanisms.

[0005] 2) Low synchronization efficiency: Existing methods such as direct database connection and file exchange are inefficient when the data volume is large, which may cause data synchronization delays and affect business processing speed;

[0006] 3) Poor data accuracy after synchronization: During the data synchronization process, various reasons (such as data format mismatch, conversion logic errors, etc.) may cause data accuracy issues, affecting authenticity and reliability;

[0007] 4) Lack of transparency and traceability: Existing technologies often lack transparency and traceability of the data synchronization process. Once a data synchronization error occurs, it is difficult to quickly locate and resolve the problem. Summary of the Invention

[0008] In order to solve the problems of insufficient security, low synchronization efficiency, poor data accuracy, and lack of transparency and traceability in the existing technology, the purpose of the present invention is to provide a supply chain financial business and financial integrated data synchronization system and method.

[0009] The technical solution adopted in the present invention is:

[0010] A supply chain finance business-finance integrated data synchronization system includes a trusted institution, a data synchronization platform and several supply chain finance systems. The data synchronization platform and the trusted institution are respectively communicated with the several supply chain finance systems, and the data synchronization platform is provided with a data processing unit, a data analysis unit, a data synchronization strategy generation unit and a privacy computing unit connected in sequence. The trusted institution is also communicated with the data synchronization platform.

[0011] Furthermore, the data processing unit is provided with a signature verification and data decryption module, a data mapping and conversion module, and a data standardization processing module which are connected in sequence. The signature verification and data decryption module is respectively communicated with several supply chain financial systems, and the data standardization processing module is connected with the data analysis unit.

[0012] Furthermore, the data analysis unit is provided with a data classification module, a business data analysis module and a financial data analysis module. The data classification module is connected to the data processing unit, the business data analysis module and the financial data analysis module respectively. The business data analysis module and the financial data analysis module are both connected to the data synchronization strategy generation unit.

[0013] Furthermore, the data classification module is provided with a data classification model constructed based on a clustering algorithm, the business data analysis module is provided with a business data analysis model constructed based on a deep learning algorithm, and the financial data analysis module is provided with a financial data analysis model constructed based on a deep learning algorithm.

[0014] Furthermore, the data synchronization strategy generation unit is provided with a data synchronization strategy generation module, and the data synchronization strategy generation module is provided with a data synchronization strategy generation model constructed based on a reinforcement learning algorithm.

[0015] Furthermore, the privacy computing unit is provided with a calling interface, a smart contract, an IFPS system, and a blockchain constructed by distributed connection of several data servers as nodes.

[0016] A method for data synchronization in the integrated business and financial aspects of supply chain finance is based on a data synchronization system for the integrated business and financial aspects of supply chain finance. The data synchronization platform is provided with a data processing unit, a data analysis unit, a data synchronization strategy generation unit, and a privacy calculation unit connected in sequence. The method includes the following steps:

[0017] Based on a trusted institution, key generation and identity authentication are performed on the supply chain finance system to obtain the public-private key pair and signature information of each supply chain finance system. The private key and signature information in the public-private key pair are returned to the corresponding supply chain finance system, and the public key in the public-private key pair is published to the data synchronization platform.

[0018] Based on the supply chain finance system, the real-time uploaded data is encrypted and signed according to the private key and signature information to obtain the encrypted real-time uploaded data and real-time signed data, and the encrypted real-time uploaded data and real-time signed data are uploaded to the data synchronization platform;

[0019] The data processing unit based on the data synchronization platform performs signature verification on the real-time signature data. After the signature verification is passed, the encrypted real-time uploaded data is decrypted according to the public key to obtain the decrypted real-time uploaded data, and the data is processed to obtain the processed real-time uploaded data.

[0020] The data analysis unit based on the data synchronization platform uses a data classification model to classify the processed real-time uploaded data to obtain real-time business data and real-time financial data. The real-time business data is input into the business data analysis model, and the real-time financial data is input into the financial data analysis model.

[0021] The data analysis unit based on the data synchronization platform uses the business data analysis model to analyze the real-time business data to obtain real-time business data analysis results, and uses the financial data analysis model to analyze the real-time financial data to obtain real-time financial data analysis results;

[0022] The data synchronization strategy generation unit based on the data synchronization platform generates data synchronization strategies based on the real-time business data analysis results and the real-time financial data analysis results using the data synchronization strategy generation model to obtain a real-time data synchronization strategy.

[0023] The privacy computing unit based on the data synchronization platform synchronizes real-time business data and real-time financial data according to the real-time data synchronization strategy, obtains real-time business and financial integrated synchronization data, generates real-time transaction data, and distributes the real-time business and financial integrated synchronization data and real-time transaction data.

[0024] Furthermore, based on the data processing unit of the data synchronization platform, the real-time signature data is signature verified. After the signature verification is passed, the encrypted real-time uploaded data is decrypted according to the public key to obtain the decrypted real-time uploaded data, and data processing is performed to obtain the processed real-time uploaded data, including the following steps:

[0025] The data processing unit based on the data synchronization platform calls the trusted organization to perform signature verification on the real-time signature data;

[0026] After the signature verification is passed, the encrypted real-time uploaded data is decrypted according to the public key of the corresponding supply chain financial system to obtain the decrypted real-time uploaded data;

[0027] According to the data format of the data synchronization platform, the decrypted real-time uploaded data is mapped and converted to obtain the converted real-time uploaded data;

[0028] The converted real-time uploaded data is subjected to data standardization processing to obtain processed real-time uploaded data.

[0029] Furthermore, the data classification model is constructed based on the FCM clustering algorithm, the business data analysis model and the financial data analysis model are both constructed based on the N-GAN-Attention-LSTM algorithm, and the data synchronization strategy generation model is constructed based on the DQN algorithm.

[0030] Furthermore, based on the privacy computing unit of the data synchronization platform, according to the real-time data synchronization strategy, real-time business data and real-time financial data are synchronized to obtain real-time business-finance integrated synchronization data, generate real-time transaction data, and distributely store the real-time business-finance integrated synchronization data and real-time transaction data, including the following steps:

[0031] The privacy computing unit based on the data synchronization platform calls the smart contract by calling the interface, generates a real-time data synchronization request based on the real-time business data and real-time financial data, and sends the real-time business data, real-time financial data, real-time data synchronization strategy and real-time data synchronization request to the blockchain;

[0032] Based on the blockchain, the node that receives the real-time data synchronization request is used as the master node. The PBFT consensus algorithm is used to reach a consensus on the real-time data synchronization request. If the consensus is successful, the next step is entered. Otherwise, a consensus failure signal is issued and data synchronization ends.

[0033] Based on the master node, according to the real-time data synchronization strategy, real-time business data and real-time financial data are synchronized to obtain real-time business and financial integrated synchronization data, generate real-time transaction data, store the real-time business and financial integrated synchronization data in the IPFS system, and receive the real-time data hash value returned by the IPFS system;

[0034] Based on the master node, real-time transaction data and real-time data hash values are converted into real-time data blocks, and a real-time block chain request is generated. The PBFT consensus algorithm is used to reach a consensus on the real-time block chain request. After the consensus is successful, the real-time data block is chained and data synchronization ends. Otherwise, a consensus failure signal is issued and data synchronization ends.

[0035] The beneficial effects of the present invention are:

[0036] The present invention discloses a supply chain finance business and financial integrated data synchronization system and method. The system adopts a key generation and identity authentication mechanism based on a trusted institution to ensure the security of data uploaded by the supply chain finance system, encrypts and signs the real-time uploaded data, and effectively prevents the data from being stolen or tampered with during transmission. The system uses a data processing unit to decrypt and verify the encrypted data to ensure the integrity and accuracy of the data. The system adopts a data analysis unit to classify and analyze the data, and uses business data and financial data analysis models to improve the intelligence level and processing efficiency of data synchronization. The system uses a data synchronization strategy generation model based on the business data analysis results and the financial data analysis results to dynamically generate data synchronization strategies to adapt to the needs of different business scenarios, improve the reliability of data synchronization, and ensure the accuracy of synchronized data. The system introduces a privacy computing unit to ensure the protection of user privacy during the data synchronization process, combines blockchain technology to ensure the security, transparency and traceability of the data synchronization process, and uses distributed storage technology to store synchronized data and transaction data to improve the reliability of data and the robustness of the system. The system has good scalability, can adapt to the rapid development and diversified needs of supply chain finance business, and is easy to connect to new systems or add new data synchronization needs.

[0037] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a structural block diagram of the supply chain finance business-finance integrated data synchronization system in the present invention.

[0039] Figure 2 It is a flowchart of the supply chain finance business and financial integrated data synchronization method in the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0041] Example 1:

[0042] like Figure 1 As shown, this embodiment provides a supply chain finance business-finance integrated data synchronization system, including a trusted institution, a data synchronization platform, and several supply chain finance systems. The data synchronization platform and the trusted institution are respectively connected to the several supply chain finance systems in communication. The data synchronization platform is provided with a data processing unit, a data analysis unit, a data synchronization strategy generation unit, and a privacy computing unit connected in sequence. The trusted institution is also connected to the data synchronization platform in communication.

[0043] Trusted institutions are used to generate keys and authenticate identities for the supply chain finance system, obtain the public-private key pair and signature information of each supply chain finance system, return the private key and signature information in the public-private key pair to the corresponding supply chain finance system, and publish the public key in the public-private key pair to the data synchronization platform;

[0044] The supply chain finance system is used to encrypt and sign the real-time uploaded data based on the private key and signature information, obtain the encrypted real-time uploaded data and real-time signed data, and upload the encrypted real-time uploaded data and real-time signed data to the data synchronization platform;

[0045] The data processing unit is used to perform signature verification on the real-time signature data. After the signature verification is passed, the encrypted real-time uploaded data is decrypted according to the public key to obtain the decrypted real-time uploaded data, and the data is processed to obtain the processed real-time uploaded data.

[0046] The data analysis unit is configured to use a data classification model to classify the processed real-time uploaded data to obtain real-time business data and real-time financial data, input the real-time business data into the business data analysis model, and input the real-time financial data into the financial data analysis model; use the business data analysis model to perform data analysis on the real-time business data to obtain real-time business data analysis results, and use the financial data analysis model to perform data analysis on the real-time financial data to obtain real-time financial data analysis results;

[0047] A data synchronization strategy generation unit is used to generate a data synchronization strategy based on the real-time business data analysis results and the real-time financial data analysis results using a data synchronization strategy generation model to obtain a real-time data synchronization strategy;

[0048] The privacy computing unit is used to synchronize real-time business data and real-time financial data according to the real-time data synchronization strategy, obtain real-time business and financial integrated synchronization data, generate real-time transaction data, and distribute the real-time business and financial integrated synchronization data and real-time transaction data.

[0049] Preferably, the data processing unit is provided with a signature verification and data decryption module, a data mapping and conversion module, and a data standardization processing module connected in sequence, the signature verification and data decryption module are respectively connected to a plurality of supply chain financial systems in communication, and the data standardization processing module is connected to the data analysis unit;

[0050] The signature verification and data decryption module is used to call a trusted institution to perform signature verification on the real-time signature data. After the signature verification is passed, the encrypted real-time uploaded data is decrypted according to the public key of the corresponding supply chain financial system to obtain the decrypted real-time uploaded data.

[0051] The data mapping and conversion module is used to perform data mapping and conversion on the decrypted real-time uploaded data according to the data format of the data synchronization platform, thereby obtaining the converted real-time uploaded data;

[0052] The data standardization processing module is used to perform data standardization processing on the converted real-time uploaded data to obtain processed real-time uploaded data.

[0053] Preferably, the data analysis unit is provided with a data classification module, a business data analysis module and a financial data analysis module, the data classification module is connected to the data processing unit, the business data analysis module and the financial data analysis module respectively, and the business data analysis module and the financial data analysis module are both connected to the data synchronization strategy generation unit;

[0054] The data classification module is used to classify the processed real-time uploaded data to obtain real-time business data and real-time financial data;

[0055] Business data analysis module, used to analyze real-time business data and obtain real-time business data analysis results;

[0056] The financial data analysis module is used to perform data analysis on real-time financial data and obtain real-time financial data analysis results.

[0057] Preferably, the data classification module is provided with a data classification model constructed based on a clustering algorithm, the business data analysis module is provided with a business data analysis model constructed based on a deep learning algorithm, and the financial data analysis module is provided with a financial data analysis model constructed based on a deep learning algorithm.

[0058] Preferably, the data synchronization strategy generation unit is provided with a data synchronization strategy generation module, and the data synchronization strategy generation module is provided with a data synchronization strategy generation model constructed based on a reinforcement learning algorithm;

[0059] The data synchronization strategy generation module is used to generate a data synchronization strategy based on the real-time business data analysis results and the real-time financial data analysis results to obtain a real-time data synchronization strategy.

[0060] Preferably, the privacy computing unit is provided with a calling interface, a smart contract, an InterPlanetary File System (IPFS), and a blockchain constructed by distributed connection of several data servers as nodes;

[0061] The call interface is the bridge between the blockchain and the outside world. It allows external applications and users to interact with the blockchain programmatically. The call interface can provide a variety of functions, such as sending transactions, querying blockchain status, and deploying smart contracts.

[0062] A smart contract is a piece of code on the blockchain that automatically executes contract terms and enables automated and decentralized transaction processing. Smart contracts can be used for a variety of applications, such as data block generation, request generation, automated transaction execution, asset management, and legal agreement enforcement.

[0063] The IFPS system is a decentralized file storage and sharing network that allows users to directly share and transfer files without the need for an intermediary. Its decentralized nature has certain similarities with blockchain technology and can be combined with blockchain to provide a wider range of application scenarios.

[0064] Blockchain is used to synchronize real-time business data and real-time financial data according to real-time data synchronization strategies, obtain real-time business and financial integrated synchronization data, generate real-time transaction data, and perform distributed storage of real-time business and financial integrated synchronization data and real-time transaction data.

[0065] Example 2:

[0066] like Figure 2 As shown, this embodiment provides a supply chain finance business-finance integrated data synchronization method. Based on the supply chain finance business-finance integrated data synchronization system, the data synchronization platform is provided with a data processing unit, a data analysis unit, a data synchronization strategy generation unit, and a privacy computing unit connected in sequence. The method includes the following steps:

[0067] S1: Based on a trusted institution, perform key generation and identity authentication for the supply chain finance system, obtain the public-private key pair and signature information of each supply chain finance system, return the private key and signature information in the public-private key pair to the corresponding supply chain finance system, and publish the public key in the public-private key pair to the data synchronization platform, including the following steps:

[0068] S1-1: Initialize the key based on the trusted institution to obtain the public parameters, master key and initial key;

[0069] GP={g,g1,g a ,e(g,g) a ,H1,H2,H3,H4,H5,H6}

[0070] PK={g,g1,g a ,e(g,g) a ,H u}

[0071] MSK={g a ,a}

[0072] Where GP is the public parameter; MSK is the master key; PK is the initial key; a is the integer domain Z pRandom numbers; H1, H2, H3, H4, H5, H6, H u All are target hash functions; g, g1, g a are all random numbers that are generators of the cyclic group G; e(g,g) a is a bilinear map of random numbers g;

[0073] S1-2: Collecting attribute information of supply chain finance system V u and entity ID, and uses an asymmetric encryption algorithm to generate keys for the supply chain finance system based on attribute information, public parameters, master key, and initial key to obtain the corresponding public-private key pair;

[0074] SK u ={MSK,V u ,K=g a g ab ,L u =g b ,(K u =H3(V u ) b )}

[0075]

[0076] Where SK u is the private key of the supply chain finance system u; b is the integer domain Z p Random number; L u , K u is the private key parameter of the supply chain finance system u; H3 is the target hash function of the public parameter GP; u is the indicator of the supply chain finance system; MSK is the master key; PK is the initial key; PK u is the public key of the supply chain finance system u; g b 、g a 、g ab is the random number of the generator of the cyclic group G; V u is the attribute information of the supply chain finance system u;

[0077] S1-3: Based on the public-private key pair and the corresponding entity ID, use the digital identity authentication method to register the identity and obtain the signature information of the corresponding supply chain finance system;

[0078] The formula is:

[0079]

[0080] Where k' is a random number; k u Registration parameter for the supply chain finance system u; KID u KID is the registration ID of the supply chain finance system u; uand the corresponding K u Constitute the signature information {K u ,KID u}; H1 is the target hash function; ID u The entity ID of the supply chain finance system u; is the prime order; P is the base point of the prime field; mod(*) is the remainder function;

[0081] S1.0: Initialize the data synchronization platform and build a unified data model, data classification model, business data analysis model, financial data analysis model, and data synchronization strategy generation model. This includes the following steps:

[0082] S1.0-1: Collect heterogeneous historical business data and historical financial data, and perform preprocessing and standardization to obtain standardized historical business data and standardized historical financial data;

[0083] The business data of the supply chain finance system includes:

[0084] 1) Transaction data: Purchase orders, sales orders, shipping documents, inventory records, etc., which reflect actual transaction activities in the supply chain;

[0085] 2) Logistics data: data related to logistics activities such as freight transportation, warehousing, and distribution, such as transportation routes, inventory locations, and logistics costs;

[0086] 3) Partner information: including detailed information about partners such as suppliers, manufacturers, distributors, and retailers, such as credit records and historical transaction data;

[0087] 4) Market data: including price fluctuations, market trends, seasonal factors, etc., which help predict future market demand and price changes;

[0088] The financial data of the supply chain finance system includes:

[0089] 1) Accounts receivable: Amounts receivable from customers, including aging analysis, account balances, etc.

[0090] 2) Accounts payable: Amounts payable by the company to suppliers, including aging analysis, account balances, etc.

[0091] 3) Cash flow forecasting: Predicting future cash flow based on business and financial data to help companies develop financing plans;

[0092] 4) Financial statements: including balance sheet, income statement, cash flow statement, etc. These statements reflect the financial status and health of the company;

[0093] 5) Risk assessment data: This includes assessment data on credit risk, market risk, operational risk, etc., used to determine financing conditions and risk management strategies;

[0094] S1.0-2: Based on standardized historical business data and standardized historical financial data, use the Fuzzy C-Means (FCM) clustering algorithm to build a data classification model and generate cluster centers for several business data types and several financial data types;

[0095] S1.0-3: Based on standardized historical business data, a business data analysis model is constructed using the Generative Adversarial Networks (GAN)-Attention-Long Short-Term Memory (LSTM) algorithm, generating analysis results for the historical business data.

[0096] The business data analysis model includes a first key dimension feature extraction module based on N GAN networks, a first attention weight generation module based on the Attention mechanism, and a business data analysis module based on the LSTM network, where N is the total number of key dimensions.

[0097] Business data analysis analyzes sales orders, purchase orders, inventory levels, logistics information, etc. of businesses involved in the supply chain finance system;

[0098] The key dimension feature extraction module includes a generator and a discriminator. The goal of the generator is to generate sufficiently realistic data, while the goal of the discriminator is to distinguish between real data and data generated by the generator. In key dimension feature extraction, GAN can be used to generate data that matches the characteristics of real data, realizing feature extraction of key dimensions. The attention weight generation module uses preset attention weights to achieve splicing of key dimension features and integrate scattered features. The LSTM network is a special recurrent neural network. Its unique cell state design makes it excellent in processing and predicting time series data. It can capture long-term dependencies and is suitable for data with time correlation. It is used to predict spliced data features.

[0099] S1.0-4: Based on some standardized historical financial data, use the N-GAN-Attention-LSTM algorithm to build a financial data analysis model and generate some historical financial data analysis results;

[0100] The business data analysis model includes a second key dimension feature extraction module based on N GAN networks, a second attention weight generation module based on the Attention mechanism, and a financial data analysis module based on the LSTM network.

[0101] Financial data analysis focuses on the accounts receivable, accounts payable, inventory book value, cost calculation, etc. involved in the supply chain finance system;

[0102] S1.0-5: Based on the analysis results of several historical business data and several historical financial data, a data synchronization strategy generation model is constructed using the Deep Q Network (DQN) algorithm. The model includes the following steps:

[0103] S1.0-5-1: Generate a simulation environment for the DQN algorithm using a data synchronization strategy to build an agent and experience replay pool;

[0104] S1.0-5-2: Define the state space of the DQN algorithm based on each data state type corresponding to the historical business data analysis results and the historical financial data analysis results, and the parameters of the state space correspond to each data state;

[0105] S1.0-5-3: Define the action space of the DQN algorithm based on the actions that need to be output by the data synchronization strategy;

[0106] S1.0-5-4: Define the reward function of the DQN algorithm based on the possible impact of each action in the action space, which is used to evaluate the quality or impact of the action;

[0107] S1.0-5-5: Construct the input layer, several hidden layers, and output layer of the deep Q network, connect the input layer to the state space, and connect the output layer to the action space;

[0108] S1.0-5-6: Based on the state space, action space, and reward function, and according to the analysis results of several historical business data and several historical financial data, the deep Q network and intelligent agent are optimized and trained to build a data synchronization strategy generation model, and several historical data synchronization strategy generation experiences are generated;

[0109] S2: Based on the supply chain finance system, the real-time uploaded data is encrypted and signed according to the private key and signature information to obtain the encrypted real-time uploaded data and real-time signed data, and then uploaded to the data synchronization platform.

[0110] The formula is:

[0111] M u =E(SK u,m u )

[0112] Where M u is the encrypted real-time uploaded data of the supply chain finance system u; E(*) is the asymmetric encryption function; m u Real-time data upload for supply chain finance system u; SK u is the private key of the supply chain finance system u; u is the indicator of the supply chain finance system;

[0113] The formula is:

[0114]

[0115] Where r' is a random number; is the prime order; P is the base point of the prime field; H2 is the target hash function; K u For the signature information {K u ,KID u Registration parameters of the supply chain finance system u in}; KID u For the signature information {K u ,KID u Registration ID of supply chain finance system u in}; ID u is the entity ID of the supply chain finance system u; the real-time signature data is {ID u ,M u ,γ'={K u ,R u ,B u}}; R u ,B u ,γ' are the signature parameters of the supply chain finance system u;

[0116] S3: Based on the data processing unit of the data synchronization platform, the real-time signature data is signature verified. After the signature verification is passed, the encrypted real-time uploaded data is decrypted according to the public key to obtain the decrypted real-time uploaded data, and the data is processed to obtain the processed real-time uploaded data. The steps include the following:

[0117] S3-1: The data processing unit based on the data synchronization platform uses the signature verification and data decryption module to call the trusted organization to perform signature verification on the real-time signature data;

[0118] The formula is:

[0119] β u B u P=β u H2(R u ,M u ,ID u ,K u )R u +βu K u +β u H1(ID u ,K u )PK u

[0120] Where, β u PK is the signature verification parameter of the supply chain finance system u; u is the public key of the supply chain finance system u; if the left formula is equal to the right formula, the signature verification is successful;

[0121] S3-2: After the signature verification is passed, the encrypted real-time uploaded data is decrypted according to the public key of the corresponding supply chain finance system to obtain the decrypted real-time uploaded data;

[0122] The formula is:

[0123] m' u =E - (PK u ,M u )

[0124] Where m' u Decrypted and uploaded data in real time to the supply chain finance system u; E - (*) is the asymmetric decryption function; PK u is the public key of the supply chain finance system u; M u Encrypted and real-time data upload for the supply chain finance system u;

[0125] S3-3: Based on the data format of the data synchronization platform, that is, the data format of the unified data model, the data mapping and conversion module is used to perform data mapping and conversion on the decrypted real-time uploaded data to obtain the converted real-time uploaded data;

[0126] S3-4: Using the data standardization processing module, perform data standardization processing on the converted real-time uploaded data to obtain processed real-time uploaded data;

[0127] S4: The data analysis unit based on the data synchronization platform uses the data classification model to classify the processed real-time uploaded data to obtain real-time business data and real-time financial data. The real-time business data is input into the business data analysis model, and the real-time financial data is input into the financial data analysis model.

[0128] Specifically, the Euclidean distance between each data in the processed real-time uploaded data and the cluster centers of several business data types and several financial data types is obtained, and the business data type or financial data type of the distance center with the closest Euclidean distance is used as the data type, thereby realizing data classification of the processed real-time uploaded data;

[0129] S5: The data analysis unit based on the data synchronization platform uses the business data analysis model to analyze the real-time business data to obtain real-time business data analysis results, and uses the financial data analysis model to analyze the real-time financial data to obtain real-time financial data analysis results;

[0130] Specifically, a first key dimension feature extraction module is used to extract N first real-time key dimension features of the real-time business data, a first attention weight generation module is used to perform weighted splicing on the N first real-time key dimension features to obtain a first real-time weighted splicing feature, and a business data analysis module is used to perform data analysis based on the first real-time weighted splicing feature to obtain a real-time business data analysis result;

[0131] Using the second key dimension feature extraction module to extract N second real-time key dimension features of the real-time financial data, using the second attention weight generation module to perform weighted splicing on the N second real-time key dimension features to obtain second real-time weighted splicing features, using the financial data analysis module to perform data analysis based on the second real-time weighted splicing features to obtain real-time financial data analysis results;

[0132] S6: The data synchronization strategy generation unit based on the data synchronization platform generates a data synchronization strategy based on the real-time business data analysis results and the real-time financial data analysis results using the data synchronization strategy generation model to generate a real-time data synchronization strategy, including the following steps:

[0133] S6-1: Randomly extract several historical data synchronization strategy generation experiences from the experience replay pool, and update the action space of the data synchronization strategy generation model based on the several historical data synchronization strategy generation experiences to obtain the updated action space A'=[a'1,...,a' j" ,...,a' I ], where a' j" is the updated j-th "action value", j" is the action indicator; I is the total number of action space dimensions;

[0134] S6-2: Based on the real-time business data analysis results and the real-time financial data analysis results, the state space of the data synchronization strategy generation model is updated to obtain the updated state space S'=[s'1,...,s' i" ,...,s' I' ], where s' i" is the updated i-th state value, i" is the state indicator, and I' is the total number of state space dimensions;

[0135] S6-3: Update the state space S' = [s'1, ..., s' i' ,...,s' I] as the input of the enterprise supply chain optimization model, using the deep Q network to generate the updated action space A'=[a'1,...,a' j' ,...,a' I ] the Q-value of each possible action in;

[0136] S6-4: Use the reward function to obtain the reward value of each possible action in the updated action space, and update the Q value of the possible action according to the reward value to obtain the updated Q value of the possible action;

[0137] The formula is:

[0138] Q(s' p' ,a' p' )=(1-α")·Q(s p' ,a p' )+α"·(R(s p' ,a p' ,s' p' )+γ·Q max (s p' ,a p' ))

[0139] Where, Q(s' p' ,a' p' ) is the updated state value s' p' and updated action value a' p' The corresponding updated Q value; Q(s p' ,a p' ) is the state value s p' and action value a p' The corresponding predicted Q value; α" is the learning rate; Q max (s p' ,a p' ) is the highest predicted Q value;

[0140] S6-5: Repeat the above steps until the iteration threshold is reached. Use the greedy strategy to take the possible action corresponding to the highest updated Q value as the execution action, and output the execution action as the real-time data synchronization strategy;

[0141] S7: Based on the privacy computing unit of the data synchronization platform, according to the real-time data synchronization strategy, synchronizes the real-time business data and real-time financial data to obtain real-time business-finance integrated synchronization data, generates real-time transaction data, and distributes the real-time business-finance integrated synchronization data and real-time transaction data. The steps include the following:

[0142] S7-1: The privacy computing unit based on the data synchronization platform calls the smart contract through the calling interface, generates a real-time data synchronization request based on the real-time business data and real-time financial data, and sends the real-time business data, real-time financial data, real-time data synchronization policy and real-time data synchronization request to the blockchain;

[0143] S7-2: Based on the blockchain, the node that receives the real-time data synchronization request is used as the master node. The Byzantine Fault Tolerance (PBFT) consensus algorithm is used to reach a consensus on the real-time data synchronization request. If the consensus is successful, the next step is entered. Otherwise, a consensus failure signal is issued and data synchronization ends.

[0144] S7-3: Based on the master node, according to the real-time data synchronization strategy, synchronize real-time business data and real-time financial data to obtain real-time business and financial integrated synchronization data, generate real-time transaction data, store the real-time business and financial integrated synchronization data in the IPFS system, and receive the real-time data hash value returned by the IPFS system;

[0145] S7-4: Based on the master node, the real-time transaction data and real-time data hash value are converted into real-time data blocks, and a real-time block chain request is generated. The PBFT consensus algorithm is used to reach a consensus on the real-time block chain request. After the consensus is successful, the real-time data block is chained and data synchronization ends. Otherwise, a consensus failure signal is issued and data synchronization ends.

[0146] The present invention discloses a supply chain finance business and financial integrated data synchronization system and method. The system adopts a key generation and identity authentication mechanism based on a trusted institution to ensure the security of data uploaded by the supply chain finance system, encrypts and signs the real-time uploaded data, and effectively prevents the data from being stolen or tampered with during transmission. The system uses a data processing unit to decrypt and verify the encrypted data to ensure the integrity and accuracy of the data. The system adopts a data analysis unit to classify and analyze the data, and uses business data and financial data analysis models to improve the intelligence level and processing efficiency of data synchronization. The system uses a data synchronization strategy generation model based on the business data analysis results and the financial data analysis results to dynamically generate data synchronization strategies to adapt to the needs of different business scenarios, improve the reliability of data synchronization, and ensure the accuracy of synchronized data. The system introduces a privacy computing unit to ensure the protection of user privacy during the data synchronization process, combines blockchain technology to ensure the security, transparency and traceability of the data synchronization process, and uses distributed storage technology to store synchronized data and transaction data to improve the reliability of data and the robustness of the system. The system has good scalability, can adapt to the rapid development and diversified needs of supply chain finance business, and is easy to connect to new systems or add new data synchronization needs.

[0147] The present invention is not limited to the above optional embodiments. Anyone can derive various other forms of products based on the teachings of the present invention. The above specific embodiments should not be construed as limiting the scope of protection of the present invention. The scope of protection of the present invention shall be based on the scope defined in the claims, and the description can be used to interpret the claims.

Claims

1. A supply chain finance business and financial integrated data synchronization system, characterized by: It includes a trusted institution, a data synchronization platform and several supply chain financial systems. The data synchronization platform and the trusted institution are respectively communicated with several supply chain financial systems, and the data synchronization platform is provided with a data processing unit, a data analysis unit, a data synchronization strategy generation unit and a privacy computing unit connected in sequence. The trusted institution is also communicated with the data synchronization platform.

2. The supply chain finance business-finance integrated data synchronization system according to claim 1, characterized in that: The data processing unit is provided with a signature verification and data decryption module, a data mapping and conversion module, and a data standardization processing module which are connected in sequence. The signature verification and data decryption module are respectively communicated with several supply chain financial systems, and the data standardization processing module is connected with the data analysis unit.

3. The supply chain finance business-finance integrated data synchronization system according to claim 1, characterized in that: The data analysis unit is provided with a data classification module, a business data analysis module and a financial data analysis module. The data classification module is connected to the data processing unit, the business data analysis module and the financial data analysis module respectively. The business data analysis module and the financial data analysis module are both connected to the data synchronization strategy generation unit.

4. The supply chain finance business-finance integrated data synchronization system according to claim 3, characterized in that: The data classification module is provided with a data classification model constructed based on a clustering algorithm, the business data analysis module is provided with a business data analysis model constructed based on a deep learning algorithm, and the financial data analysis module is provided with a financial data analysis model constructed based on a deep learning algorithm.

5. The supply chain finance business-finance integrated data synchronization system according to claim 1, characterized in that: The data synchronization strategy generation unit is provided with a data synchronization strategy generation module, and the data synchronization strategy generation module is provided with a data synchronization strategy generation model constructed based on a reinforcement learning algorithm.

6. The supply chain finance business-finance integrated data synchronization system according to claim 1, characterized in that: The privacy computing unit is equipped with a calling interface, a smart contract, an IFPS system, and a blockchain constructed by distributed connection of several data servers as nodes.

7. A method for data synchronization in integrated business and financial integration for supply chain finance, based on the integrated business and financial integration data synchronization system for supply chain finance as described in any one of claims 1-6, wherein the data synchronization platform comprises a data processing unit, a data analysis unit, a data synchronization strategy generation unit, and a privacy computing unit connected in sequence, and characterized by: The method comprises the following steps: Based on a trusted institution, key generation and identity authentication are performed on the supply chain finance system to obtain the public-private key pair and signature information of each supply chain finance system. The private key and signature information in the public-private key pair are returned to the corresponding supply chain finance system, and the public key in the public-private key pair is published to the data synchronization platform. Based on the supply chain finance system, the real-time uploaded data is encrypted and signed according to the private key and signature information to obtain the encrypted real-time uploaded data and real-time signed data, and the encrypted real-time uploaded data and real-time signed data are uploaded to the data synchronization platform; The data processing unit based on the data synchronization platform performs signature verification on the real-time signature data. After the signature verification is passed, the encrypted real-time uploaded data is decrypted according to the public key to obtain the decrypted real-time uploaded data, and the data is processed to obtain the processed real-time uploaded data. The data analysis unit based on the data synchronization platform uses a data classification model to classify the processed real-time uploaded data to obtain real-time business data and real-time financial data. The real-time business data is input into the business data analysis model, and the real-time financial data is input into the financial data analysis model. The data analysis unit based on the data synchronization platform uses the business data analysis model to analyze the real-time business data to obtain real-time business data analysis results, and uses the financial data analysis model to analyze the real-time financial data to obtain real-time financial data analysis results; The data synchronization strategy generation unit based on the data synchronization platform generates data synchronization strategies based on the real-time business data analysis results and the real-time financial data analysis results using the data synchronization strategy generation model to obtain a real-time data synchronization strategy. The privacy computing unit based on the data synchronization platform synchronizes real-time business data and real-time financial data according to the real-time data synchronization strategy, obtains real-time business and financial integrated synchronization data, generates real-time transaction data, and distributes the real-time business and financial integrated synchronization data and real-time transaction data.

8. The method for synchronizing business and financial data in a supply chain according to claim 7, characterized in that: The data processing unit based on the data synchronization platform performs signature verification on the real-time signature data. After the signature verification passes, the encrypted real-time uploaded data is decrypted according to the public key to obtain the decrypted real-time uploaded data, and data processing is performed to obtain the processed real-time uploaded data, including the following steps: The data processing unit based on the data synchronization platform calls the trusted organization to perform signature verification on the real-time signature data; After the signature verification is passed, the encrypted real-time uploaded data is decrypted according to the public key of the corresponding supply chain financial system to obtain the decrypted real-time uploaded data; According to the data format of the data synchronization platform, the decrypted real-time uploaded data is mapped and converted to obtain the converted real-time uploaded data; The converted real-time uploaded data is subjected to data standardization processing to obtain processed real-time uploaded data.

9. The method for synchronizing business and financial data in a supply chain according to claim 7, characterized in that: The data classification model is constructed based on the FCM clustering algorithm, the business data analysis model and the financial data analysis model are both constructed based on the N-GAN-Attention-LSTM algorithm, and the data synchronization strategy generation model is constructed based on the DQN algorithm.

10. The method for synchronizing business and financial data in a supply chain according to claim 7, characterized in that: The privacy computing unit based on the data synchronization platform synchronizes real-time business data and real-time financial data according to the real-time data synchronization strategy, obtains real-time business and financial integrated synchronization data, generates real-time transaction data, and distributes the real-time business and financial integrated synchronization data and real-time transaction data, including the following steps: The privacy computing unit based on the data synchronization platform calls the smart contract by calling the interface, generates a real-time data synchronization request based on the real-time business data and real-time financial data, and sends the real-time business data, real-time financial data, real-time data synchronization strategy and real-time data synchronization request to the blockchain; Based on the blockchain, the node that receives the real-time data synchronization request is used as the master node. The PBFT consensus algorithm is used to reach a consensus on the real-time data synchronization request. If the consensus is successful, the next step is entered. Otherwise, a consensus failure signal is issued and data synchronization ends. Based on the master node, according to the real-time data synchronization strategy, real-time business data and real-time financial data are synchronized to obtain real-time business and financial integrated synchronization data, generate real-time transaction data, store the real-time business and financial integrated synchronization data in the IPFS system, and receive the real-time data hash value returned by the IPFS system; Based on the master node, real-time transaction data and real-time data hash values are converted into real-time data blocks, and a real-time block chain request is generated. The PBFT consensus algorithm is used to reach a consensus on the real-time block chain request. After the consensus is successful, the real-time data block is chained and data synchronization ends. Otherwise, a consensus failure signal is issued and data synchronization ends.

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