A supply chain financial industry financial integration data synchronization system and method

Abstract

The application belongs to the technical field of supply chain finance and discloses a supply chain finance industry finance integrated data synchronization system and method.The method comprises the following steps: based on a trusted agency, key generation and identity authentication are performed;based on a supply chain finance system, data encryption and signature are performed, and the data are uploaded to a data synchronization platform;based on a data processing unit, signature verification, decryption and data processing are performed;based on a data analysis unit, data classification is performed using a data classification model;based on the data analysis unit, data analysis is performed using a business data analysis model and a financial data analysis model;based on a data synchronization strategy generation unit, data synchronization strategy generation is performed using a data synchronization strategy generation model; and based on a privacy computing unit, data synchronization and distributed storage are performed.The application solves the problems of insufficient security, low synchronization efficiency, poor data accuracy, lack of transparency and traceability in the prior art.

Description

Technical Field

[0001] This invention belongs to the field of supply chain finance technology, specifically relating to a supply chain finance business and finance integrated data synchronization system and method. Background Technology

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

[0003] Deficiencies of existing technology:

[0004] 1) Insufficient security: Existing data synchronization technologies often lack effective security measures. Data may be subject to security risks such as theft and tampering during transmission. Sensitive business information and financial data may be involved in the data synchronization process, and sufficient data privacy protection mechanisms are not provided.

[0005] 2) Low synchronization efficiency: Existing methods such as direct database connection and file exchange are inefficient when dealing with large amounts of data, which may lead to 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 lead to data accuracy problems, affecting authenticity and reliability;

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

[0008] To address the problems of insufficient security, low synchronization efficiency, poor data accuracy, and lack of transparency and traceability in existing technologies, the present invention aims to provide a supply chain finance business and finance integrated data synchronization system and method.

[0009] The technical solution adopted in this invention is as follows:

[0010] A supply chain finance business and 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 each connected to several supply chain finance systems. The data synchronization platform is equipped 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.

[0011] Furthermore, the data processing unit is equipped 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 is connected to several supply chain finance systems, and the data standardization processing module is connected to the data analysis unit.

[0012] Furthermore, the data analysis unit is equipped 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 equipped with a data classification model based on clustering algorithms, the business data analysis module is equipped with a business data analysis model based on deep learning algorithms, and the financial data analysis module is equipped with a financial data analysis model based on deep learning algorithms.

[0014] Furthermore, the data synchronization strategy generation unit is equipped with a data synchronization strategy generation module, which is equipped with a data synchronization strategy generation model built based on a reinforcement learning algorithm.

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

[0016] A method for integrated business and finance data synchronization in supply chain finance, based on an integrated business and finance data synchronization system for supply chain finance, wherein the data synchronization platform is configured with a data processing unit, a data analysis unit, a data synchronization strategy generation unit, and a privacy computing unit connected in sequence, and 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 and private key pairs and signature information of each supply chain finance system. The private key and signature information in the public and private key pairs are returned to the corresponding supply chain finance system, and the public key in the public and private key pairs 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 encrypted real-time uploaded data and real-time signed data, and then 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 successful, it decrypts the encrypted real-time uploaded data according to the public key to obtain the decrypted real-time uploaded data, and processes the data 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 then 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 a business data analysis model to analyze real-time business data and obtain real-time business data analysis results. It also uses a financial data analysis model to analyze real-time financial data and obtain real-time financial data analysis results.

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

[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 to obtain real-time integrated business and financial data, generates real-time transaction data, and performs distributed storage of the integrated business and financial data and real-time transaction data.

[0024] Furthermore, the data processing unit based on the data synchronization platform verifies the real-time signature data. After successful signature verification, it decrypts the encrypted real-time uploaded data using the public key to obtain the decrypted real-time uploaded data, and then processes the data to obtain the processed real-time uploaded data. The process includes the following steps:

[0025] The data processing unit based on the data synchronization platform calls a trusted institution to verify the real-time signature data.

[0026] After the signature verification is successful, the encrypted real-time uploaded data is decrypted using the public key of the corresponding supply chain finance system to obtain the decrypted real-time uploaded data.

[0027] Based on the data format of the data synchronization platform, the decrypted real-time uploaded data is mapped and transformed to obtain the transformed real-time uploaded data.

[0028] The converted real-time uploaded data is standardized to obtain the processed real-time uploaded data.

[0029] Furthermore, the data classification model is built based on the FCM clustering algorithm, the business data analysis model and the financial data analysis model are both built based on the N-GAN-Attention-LSTM algorithm, and the data synchronization strategy generation model is built 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 integrated business and financial data, which generates real-time transaction data. The real-time integrated business and financial data and real-time transaction data are then distributed and stored, including the following steps:

[0031] The privacy computing unit based on the data synchronization platform calls the smart contract through the interface, generates a real-time data synchronization request based on 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 blockchain, the node that receives the real-time data synchronization request is designated 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 process proceeds to the next step; otherwise, a consensus failure signal is issued and the 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 integrated business and financial data, real-time transaction data is generated, the real-time integrated business and financial data is stored in the IPFS system, and the real-time data hash value returned by the IPFS system is received.

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

[0035] The beneficial effects of this invention are as follows:

[0036] This invention discloses a supply chain finance integrated data synchronization system and method. It employs a key generation and authentication mechanism based on a trusted institution to ensure the security of data uploaded to the supply chain finance system. Real-time uploaded data is encrypted and signed to effectively prevent theft or tampering during transmission. A data processing unit decrypts and verifies the encrypted data, ensuring its integrity and accuracy. A data analysis unit classifies and analyzes the data, using business and financial data analysis models to improve the intelligence and efficiency of data synchronization. Based on the business and financial data analysis results, a data synchronization strategy generation model dynamically generates data synchronization strategies to adapt to different business scenarios, improving the reliability of data synchronization and ensuring the accuracy of synchronized data. A privacy computing unit is introduced to protect user privacy during data synchronization. Combined with blockchain technology, the data synchronization process is secure, transparent, and traceable. Distributed storage technology is used to store synchronized and transaction data, improving data reliability and system robustness. The system has good scalability, adapting to the rapid development and diversified needs of supply chain finance businesses, and is easy to integrate with new systems or add new data synchronization requirements.

[0037] Other beneficial effects of the present invention will be further explained in the specific embodiments. Attached Figure Description

[0038] Figure 1 This is a structural block diagram of the integrated financial and business data synchronization system for supply chain finance in this invention.

[0039] Figure 2 This is a flowchart of the supply chain finance business and finance integrated data synchronization method in this invention. Detailed Implementation

[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 and 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 several supply chain finance systems. 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.

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

[0044] The supply chain finance system is used to encrypt and sign real-time uploaded data based on private keys and signature information, to obtain encrypted real-time uploaded data and real-time signed data, and then 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 verify the real-time signature data. After the signature verification is successful, it decrypts the encrypted real-time uploaded data according to the public key to obtain the decrypted real-time uploaded data, and processes the data to obtain the processed real-time uploaded data.

[0046] The data analysis unit is used to classify the processed real-time uploaded data using a data classification model 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 business data analysis model is used to analyze the real-time business data to obtain the real-time business data analysis results, and the financial data analysis model is used to analyze the real-time financial data to obtain the real-time financial data analysis results.

[0047] The data synchronization strategy generation unit is used to generate a real-time 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.

[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 to obtain real-time integrated business and financial data, generate real-time transaction data, and perform distributed storage of the real-time integrated business and financial data and real-time transaction data.

[0049] As a preferred embodiment, 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 is respectively connected to several supply chain finance systems, 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 verify the real-time signature data; after the signature verification is successful, 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.

[0051] The data mapping and conversion module is used to map and convert the decrypted real-time uploaded data according to the data format of the data synchronization platform, so as to obtain the converted real-time uploaded data.

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

[0053] As a preferred embodiment, the data analysis unit is equipped 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.

[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] The business data analysis module is used to perform data analysis on 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 equipped with a data classification model based on clustering algorithms, the business data analysis module is equipped with a business data analysis model based on deep learning algorithms, and the financial data analysis module is equipped with a financial data analysis model based on deep learning algorithms.

[0058] As a preferred embodiment, the data synchronization strategy generation unit is equipped with a data synchronization strategy generation module, and the data synchronization strategy generation module is equipped with a data synchronization strategy generation model built based on a reinforcement learning algorithm.

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

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

[0061] API calls serve as a bridge for interaction between the blockchain and the outside world. They allow external applications and users to interact with the blockchain programmatically. API calls 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 used to automatically execute contract terms and achieve automated and decentralized transaction processing. Smart contracts can be used in various applications, such as data block generation, request generation, automatic transaction execution, asset management, and execution of legal agreements.

[0063] The IFPS system is a decentralized file storage and sharing network that allows users to share and transfer files directly without intermediaries. Its decentralized nature is similar to blockchain technology, and it 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 a real-time data synchronization strategy, obtain real-time integrated business and financial data, generate real-time transaction data, and distribute the real-time integrated business and financial data and real-time transaction data.

[0065] Example 2:

[0066] like Figure 2 As shown, this embodiment provides a method for integrated business and finance data synchronization in supply chain finance. Based on an integrated business and finance data synchronization system for supply chain finance, the data synchronization platform is equipped 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, key generation and identity authentication are performed on the supply chain finance system to obtain the public-private key pair and signature information for 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. This includes the following steps:

[0068] S1-1: Based on a trusted institution, perform key initialization to obtain 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] In the formula, GP is the common parameter; MSK is the master key; PK is the initial key; and a is the integer field Z. pRandom numbers; H1, H2, H3, H4, H5, H6, H u All are target hash functions; g, g1, g a All are random numbers generated by the generators of the cyclic group G; e(g,g) a For a bilinear mapping of random numbers g;

[0073] S1-2: Collect attribute information of the supply chain finance system V u Based on the entity ID, attribute information, public parameters, master key, and initial key, an asymmetric encryption algorithm is used to generate a key for the supply chain finance system, resulting in a 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] In the formula, SK u b is the private key for the supply chain finance system u; b is the integer field Z. p random numbers; L u K u H3 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 supply chain finance system indicator; MSK is the master key; PK is the initial key; PK u The public key for the supply chain finance system u; g b g a g ab V is a random number generated by the generator of the cyclic group G; u For the attribute information of u in the supply chain finance system;

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

[0078] The formula is:

[0079]

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

[0081] S1.0: Based on the data synchronization platform, initialization is performed to build a unified data model, a data classification model, a business data analysis model, a financial data analysis model, and a data synchronization strategy generation model, including the following steps:

[0082] S1.0-1: Collect several heterogeneous historical business data and several historical financial data, and perform preprocessing and standardization to obtain several standardized historical business data and several 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 the actual transaction activities in the supply chain;

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

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

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

[0088] Financial data from supply chain finance systems include:

[0089] 1) Accounts receivable: Amounts that a company owes to its customers, including aging analysis and account balances;

[0090] 2) Accounts Payable: Amounts owed by a company to its suppliers, including aging analysis and account balances;

[0091] 3) Cash Flow Forecasting: Based on business and financial data, forecast future cash flow to help companies develop financing plans;

[0092] 4) Financial statements: including balance sheet, income statement, cash flow statement, etc., which reflect the company's financial condition and health.

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

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

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

[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 LSTM networks, where N is the total number of key dimensions.

[0097] Business data analysis focuses on sales orders, purchase orders, inventory levels, logistics information, and other data related to the business operations involved in the supply chain finance system.

[0098] The key dimension feature extraction module includes a generator and a discriminator. The generator aims to produce sufficiently realistic data, while the discriminator aims to distinguish between real data and data generated by the generator. In key dimension feature extraction, GANs can be used to generate data that matches the features of real data, thus achieving feature extraction of key dimensions. The attention weight generation module uses preset attention weights to concatenate key dimension features, integrating scattered features. The LSTM network is a special type of recurrent neural network. Its special cell state design makes it excellent at processing and predicting time series data, capable of capturing long-term dependencies, and suitable for data with time correlation. It is used to predict the features of concatenated data.

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

[0101] Financial data analysis focuses on accounts receivable, accounts payable, inventory book value, cost calculation, and other aspects related to 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, including the following steps:

[0103] S1.0-5-1: Using the data synchronization strategy to generate a scheme as a simulation environment for the DQN algorithm, constructing intelligent agents and experience replay pools;

[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 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 according to the actions required 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 the agent are optimized and trained to build a data synchronization strategy generation model and generate several historical data synchronization strategy generation experiences.

[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 the encrypted real-time uploaded data and real-time signed data are uploaded to the data synchronization platform.

[0110] The formula is:

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

[0112] In the formula, M u For the encrypted real-time data upload of the supply chain finance system u; E(*) is the asymmetric encryption function; m u For real-time data uploads to the supply chain finance system; SK u is the private key for the supply chain finance system u; u is the indicator quantity for the supply chain finance system;

[0113] The formula is:

[0114]

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

[0116] S3: A data processing unit based on a data synchronization platform verifies the real-time signed data. After successful verification, it decrypts the encrypted real-time uploaded data using the public key to obtain the decrypted real-time uploaded data, and processes the data to obtain the processed real-time uploaded data. The process includes the following steps:

[0117] S3-1: A data processing unit based on a data synchronization platform, which uses a signature verification and data decryption module to call a trusted institution to verify 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] In the formula, β u For the signature verification parameters of the supply chain finance system u; PK u Let be the public key of the supply chain finance system u; if the left side equals the right side, then the signature verification is successful.

[0121] S3-2: After the signature verification is successful, the encrypted real-time uploaded data is decrypted using 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] In the formula, m' u Real-time data upload after decryption of the supply chain finance system u; E - (*) represents the asymmetric decryption function; PK u M is the public key for the supply chain finance system u; u Encrypted data is uploaded in real time for the supply chain finance system.

[0125] S3-3: Based on the data format of the data synchronization platform, i.e. 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: Use the data standardization processing module to perform data standardization processing on the converted real-time uploaded data to obtain the processed real-time uploaded data;

[0127] S4: 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.

[0128] Specifically, the Euclidean distance between each data point 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 with the closest Euclidean distance center is taken as the data type, thereby realizing the data classification of the processed real-time uploaded data.

[0129] S5: A data analysis unit based on a data synchronization platform. It uses a business data analysis model to analyze real-time business data and obtain real-time business data analysis results. It also uses a financial data analysis model to analyze real-time financial data and obtain real-time financial data analysis results.

[0130] Specifically, the first key dimension feature extraction module is used to extract N first real-time key dimension features from the real-time business data. The first attention weight generation module is used to weight and concatenate the N first real-time key dimension features to obtain the first real-time weighted concatenated features. The business data analysis module is used to perform data analysis based on the first real-time weighted concatenated features to obtain the real-time business data analysis results.

[0131] The second key dimension feature extraction module extracts N second real-time key dimension features from the real-time financial data. The second attention weight generation module performs weighted concatenation on the N second real-time key dimension features to obtain the second real-time weighted concatenation features. The financial data analysis module performs data analysis based on the second real-time weighted concatenation features to obtain the real-time financial data analysis results.

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

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

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

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

[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] In the formula, Q(s' p' ,a' p' ) represents the updated state value s' p' and the updated action value a' p' The corresponding updated Q value; Q(s) p' ,a p' ) represents 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' The highest predicted Q value is ).

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

[0141] S7: A privacy-preserving computing unit based on a data synchronization platform synchronizes real-time business data and real-time financial data according to a real-time data synchronization strategy to obtain real-time integrated business and financial data, generates real-time transaction data, and performs distributed storage of the integrated business and financial data and real-time transaction data, including the following steps:

[0142] S7-1: A privacy computing unit based on a data synchronization platform. By calling the interface, it calls the smart contract, generates a real-time data synchronization request based on 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.

[0143] S7-2: Based on blockchain, the node that receives the real-time data synchronization request is taken 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 carried out. Otherwise, a consensus failure signal is issued and the data synchronization ends.

[0144] S7-3: 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 integrated business and financial data, real-time transaction data is generated, the real-time integrated business and financial data is stored in the IPFS system, and the real-time data hash value returned by the IPFS system is received.

[0145] S7-4: 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 on-chain request is generated. The PBFT consensus algorithm is used to reach a consensus on the real-time block on-chain request. If the consensus is successful, the real-time data block is on-chain and the data synchronization ends. Otherwise, a consensus failure signal is issued and the data synchronization ends.

[0146] This invention discloses a supply chain finance integrated data synchronization system and method. It employs a key generation and authentication mechanism based on a trusted institution to ensure the security of data uploaded to the supply chain finance system. Real-time uploaded data is encrypted and signed to effectively prevent theft or tampering during transmission. A data processing unit decrypts and verifies the encrypted data, ensuring its integrity and accuracy. A data analysis unit classifies and analyzes the data, using business and financial data analysis models to improve the intelligence and efficiency of data synchronization. Based on the business and financial data analysis results, a data synchronization strategy generation model dynamically generates data synchronization strategies to adapt to different business scenarios, improving the reliability of data synchronization and ensuring the accuracy of synchronized data. A privacy computing unit is introduced to protect user privacy during data synchronization. Combined with blockchain technology, the data synchronization process is secure, transparent, and traceable. Distributed storage technology is used to store synchronized and transaction data, improving data reliability and system robustness. The system has good scalability, adapting to the rapid development and diversified needs of supply chain finance businesses, and is easy to integrate with new systems or add new data synchronization requirements.

[0147] This invention is not limited to the optional embodiments described above, and anyone can derive other various forms of products based on the inspiration of this invention. The specific embodiments described above should not be construed as limiting the scope of protection of this invention; the scope of protection of this invention should be determined by the claims, and the specification can be used to interpret the claims.

Claims

1. A supply chain finance industry financial integration data synchronization system, characterized in that: The application relates to a supply chain finance system, which comprises a trusted agency, a data synchronization platform and a plurality of supply chain finance systems, wherein the data synchronization platform and the trusted agency are in communication connection with the plurality of supply chain finance systems, 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 which are sequentially connected, the trusted agency is in communication connection with the data synchronization platform, 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 sequentially connected, the signature verification and data decryption module is in communication connection with the plurality of supply chain finance systems, the data standardization processing module is connected with the data analysis unit, 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 with the data processing unit, the business data analysis module and the financial data analysis module, the business data analysis module and the financial data analysis module are connected with the data synchronization strategy generation unit, 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, the financial data analysis module is provided with a financial data analysis model constructed based on a deep learning algorithm, the data classification model is constructed based on an FCM clustering algorithm, the business data analysis model and the financial data analysis model are constructed based on an N-GAN-Attention-LSTM algorithm, the data synchronization strategy generation unit is provided with a data synchronization strategy generation module, the data synchronization strategy generation module is provided with a data synchronization strategy generation model constructed based on a reinforcement learning algorithm, the data synchronization strategy generation model is constructed based on a DQN algorithm, the privacy calculation unit is provided with a calling interface, an intelligent contract, an IFPS system and a blockchain constructed by a plurality of data servers as nodes in distributed connection. The method comprises the following steps: Based on the trusted agency, the key generation and identity authentication of the supply chain finance system are carried out, the public and private key pair and the signature information of each supply chain finance system are obtained, the private key in the public and private key pair and the signature information are returned to the corresponding supply chain finance system, and the public key in the public and private key pair is published to the data synchronization platform; Based on the supply chain finance system, the real-time uploading data is encrypted and signed according to the private key and the signature information, the encrypted real-time uploading data and the real-time signature data are obtained, and the encrypted real-time uploading data and the real-time signature data are uploaded to the data synchronization platform; Based on the data processing unit of the data synchronization platform, the real-time signature data is subjected to signature verification, after the signature verification is passed, the encrypted real-time uploading data is decrypted according to the public key, the decrypted real-time uploading data is obtained, and data processing is carried out to obtain the processed real-time uploading data, which comprises the following steps: Based on the data processing unit of the data synchronization platform, the trusted agency is called to carry out signature verification on the real-time signature data; ​ ​ 2. A supply chain financial industry financial integration data synchronization method based on the supply chain financial industry financial integration data synchronization system of claim 1, wherein 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. ​ ​ ​ ​ ​ After the signature verification passes, the encrypted real-time upload data is decrypted according to the public key of the corresponding supply chain finance system to obtain decrypted real-time upload data; According to the data format of the data synchronization platform, the decrypted real-time upload data is mapped and converted to obtain converted real-time upload data; The converted real-time upload data is subjected to data standardization processing to obtain processed real-time upload data; Based on the data analysis unit of the data synchronization platform, the data classification model is used to classify the processed real-time upload data to obtain real-time business data and real-time financial data, and 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 classification model is constructed based on the FCM clustering algorithm; Based on the data analysis unit of the data synchronization platform, the business data analysis model is used to analyze the real-time business data to obtain real-time business data analysis results, and the financial data analysis model is used to analyze the real-time financial data to obtain real-time financial data analysis results; The business data analysis model and the financial data analysis model are both constructed based on the N-GAN-Attention-LSTM algorithm; Based on the data synchronization strategy generation unit of the data synchronization platform, the data synchronization strategy generation model is used to generate real-time data synchronization strategies according to the real-time business data analysis results and the real-time financial data analysis results; The data synchronization strategy generation model is constructed based on the DQN algorithm; The method comprises the following steps: The experience is randomly extracted from the experience replay pool according to the plurality of historical data synchronization strategies, and the action space of the data synchronization strategy generation model is updated according to the experience generated according to the plurality of historical data synchronization strategies, to obtain an updated action space A' = [a' 1,...,a' j" ,...,a' I ], wherein a' j" is an updated jth action value, j is an action indicator, and I is a total number of dimensions of the action space. According to the real-time service data analysis result and the real-time financial data analysis result, a state space of a data synchronization strategy generation model is updated to obtain an updated state space S'=[s'1,...,s' i" ,...,s' I' ] where s' i" is an updated i" state value, i" is a state indicator, and I' is a total number of state space dimensions. The updated state space S' = [s'1,..., s' i' ,...,s' I ] is taken as the input of the enterprise supply chain optimization model, and the Q value of each possible action in the updated action space A' = [a'1,..., a' j' ,...,a' I ] is generated using a deep Q network. An updated reward value of each possible action in the updated action space is obtained using a reward function, and the Q value of the possible action is updated according to the reward value to obtain an updated Q value of the possible action; The formula is: Q(s p' ,a p' ) = (1 - a") · Q(s p' ,a p' ) + a" · (R(s p' ,a p' ,s p' ) + γ · Q max (s p' ,a p' )) where Q(s p' ,a p' ) is the updated Q-value corresponding to the updated state value s' p' and the updated action value a' p' ; Q(s p' ,a p' ) is the predicted Q-value corresponding to the state value s p' and the action value a p' ; a" is the learning rate; and Q max (s p' ,a p' ) is the highest predicted Q-value. The above steps are repeated until the iteration threshold is reached, the possible action corresponding to the highest updated Q value is used as the execution action using the greedy strategy, and the execution action is output as the real-time data synchronization strategy; Based on the privacy computing unit of the data synchronization platform, the real-time business data and the real-time financial data are synchronized according to the real-time data synchronization strategy to obtain real-time industry and finance integrated synchronization data, real-time transaction data is generated, and the real-time industry and finance integrated synchronization data and the real-time transaction data are stored in a distributed manner, comprising the following steps: Based on the privacy computing unit of the data synchronization platform, an intelligent contract is called through an interface, real-time data synchronization requests are generated according to the real-time business data and the real-time financial data, and the real-time business data, the real-time financial data, the real-time data synchronization strategy, and the real-time data synchronization request are sent to a blockchain; Based on the blockchain, a node that receives the real-time data synchronization request is used as a master node, a PBFT consensus algorithm is used to perform consensus on the real-time data synchronization request, and after the consensus succeeds, the next step is entered, otherwise, a consensus failure signal is sent and the data synchronization is ended; Based on the master node, according to the real-time data synchronization strategy, the real-time business data and the real-time financial data are synchronized to obtain real-time industry and finance integration synchronization data, generate real-time transaction data, store the real-time industry and finance integration synchronization data to the IPFS system, and receive the real-time data hash value returned by the IPFS system; Based on the master node, the real-time transaction data and the real-time data hash value are converted into real-time data blocks, a real-time block chain request is generated, a PBFT consensus algorithm is used to perform consensus on the real-time block chain request, after the consensus succeeds, the real-time data blocks are chained, and the data synchronization is ended, otherwise, a consensus failure signal is sent out, and the data synchronization is ended.

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