Financial customer digital archive management method and system based on block chain, and medium

Abstract

The invention provides a financial customer digital archive management method and system based on a block chain, and a medium, and belongs to the field of archive management. The problem of low efficiency of financial customer archive management is solved; the method comprises the following steps: acquiring identity information and financial information of a customer; designing an identity label according to the identity information; constructing a Merkle tree to store financial information, and deploying the financial information on a block chain; clients are divided into high-net-value clients and common clients; the credit score of the high-net-value customer is calculated, and the loan interest rate of the high-net-value customer is drawn up; the transaction time, the transaction object and the transaction amount of the common customers are analyzed, the frequently-taken shop of each common customer is determined, then the common customers are classified, and a customer consumption behavior report is obtained; customer consumption behavior reports are summarized and fed back; synchronously updating financial information on the block chain; the financial information of the customer is digitally processed and stored, so that the safety of the financial information is improved, and a financial institution can know the consumption change of the customer in real time.

Description

Technical Field

[0001] The present invention relates to a blockchain-based digital archive management method, system, and medium for financial customers, and relates to the field of archive management. Background Art

[0002] The existing digital file management methods for financial customers have the following shortcomings: Storage efficiency issues: Blockchain operations built using existing management methods have high data redundancy. Taking Hyperledger Fabric as an example, each node in its default configuration must store complete blockchain data. When a large bank piloted blockchain archive management, the storage requirements for a single node reached PB levels, increasing hardware costs.

[0003] Lack of blockchain protection: Existing management methods have a single function, which is only to record financial information. During the recording process of high-frequency transactions, it is easy for the speed of change of financial information to exceed the speed of data confirmation on the blockchain. For example, when a stock exchange was testing blockchain stock trading file management, it found that the confirmation time for a single transaction was in seconds, while the traditional centralized system was only in milliseconds. This situation not only leads to a waste of blockchain computing resources, but also causes data errors.

[0004] Talent shortage: Blockchain development requires proficiency in languages such as Go and Solidity, making it difficult for traditional financial institutions' IT teams to quickly transition; at the same time, blockchain nodes need to run continuously and stay synchronized, resulting in high operation and maintenance costs. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a blockchain-based financial customer digital file management method, system and medium, aiming to solve the problem of low efficiency in financial customer file management.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A blockchain-based financial customer digital archive management method includes: Obtain customer credit information, eliminate low-quality customers with unqualified credit, and obtain high-quality customers; obtain identity information and financial information of high-quality customers; Design a customer identity based on the identity information of high-quality customers; construct a Merkle tree based on the number of high-quality customers, and create three storage spaces in each node of the Merkle tree, one for storing the identity and encrypted financial information of the high-quality customer corresponding to the node, and the other for the identity of the high-quality customer corresponding to the adjacent node or the root node; deploy the Merkle tree on the blockchain to protect and store the customer's financial information; Based on the account balances in their financial information, customers are categorized as high-net-worth and average customers. Credit scores for high-net-worth customers are calculated, and loan interest rates are determined based on these scores. Transaction times and partners for average customers are analyzed to identify the stores each customer frequently visits. Combined with transaction amounts, the number of target customers, potential customers, and average customers for each store is counted to generate a customer spending behavior report. Summarize customer consumption behavior reports and provide feedback to users; collect updated financial information of high-quality customers and update it synchronously on the blockchain.

[0007] Furthermore, the specific steps of constructing the Merkle tree are as follows: Get the number of customers na; get the identity information of all customers and generate verifiable credentials to get VC （1） ~VC （na） ; For VC （1） ~VC （na） Encrypt, and then convert the encrypted verifiable certificate into a hash value to obtain the identity hVC （1） ~hVC （na） ; Calculate the depth bh of the Merkle tree: ; Construct a complete binary tree with a depth of bh and a number of nodes na as tree A （1） ; In tree A （1） At the storage location of each node, three storage spaces are created, denoted as space a, space b, and space c; Tree A （1） Starting from the root node, from left to right and from top to bottom, store hVC in the space a corresponding to node 1 to node na （1） ~hVC （na） , get tree A （2） ; Define the storage conditions of space b; according to the storage conditions of space b, use tree A （2） Based on hVC （1） ~hVC （na） Store it twice in the space b corresponding to node 1 to node na, and get tree A （3） ; Obtain and encrypt the financial information of the first to the nath customer, and obtain the ciphertext emi （1） ~emi （na） ; Tree A （3） Based on the ciphertext emi （1） ~emi （na）Store them in the space c corresponding to node 1 to node na in sequence to obtain a Merkle tree; deploy the blockchain in space D, and synchronously deploy the Merkle tree to the blockchain.

[0008] Furthermore, the storage condition of the space b is: Let node ki be tree A （2） Any node in ; If ki is an odd number, then node ki is the right node; The adjacent node of node ki is node (ki-1), and the root node corresponding to node ki is node gki （1） :gki （1） =[(ki-1) / 2]; Store the hash value hVC of the (ki-1)th customer in the space b of node ki （ki－1） ; Store the hash value hVC of the ki-th customer in the space b of node (ki-1) （ki） ; Node gki （1） The space b is divided into space b （1） and space b （2） ; In node gki （1） Space b （2） In, store hVC （ki－1） and hVC （ki） ; Judging gki （1） The parity of space b （1） Storage objects; If gki （1） is an odd number, then in space b （1） Store in (gki （1） -1) The value of the customer's HVC （gki（1）－1） ; If gki （1） is an even number, then in space b （1） Store in (gki （1） +1) Customer's HVC （gki（1）＋1） ; If ki is an even number, reset the storage condition of space b.

[0009] Furthermore, the storage condition of the reset space b is: If ki is an even number, it means that node ki is a left node; The adjacent node of node ki is node (ki+1), and the root node corresponding to node ki is node gki （2） :gki （2） =ki / 2; Store the hash value hVC of the (ki+1)th customer in the space b of node ki （ki＋1） ; Store the hash value hVC corresponding to the ki-th customer in the space b of node (ki+1) （ki） ; Node gki （2） The space b is divided into space b （3） and space b （4） ; In node gki （2） Space b （4） In, store hVC （ki＋1） and hVC （ki） ; Judging gki （2） The parity of space b （3） Storage objects; If gki （2） is an odd number, then in space b （3） Store in (gki （2） -1) The value of the customer's HVC （gki（2）－1） ; If gki （2） is an even number, then in space b （3） Store in (gki （2） +1) Customer's HVC （gki（2）＋1） .

[0010] Furthermore, the specific steps for deploying cloud servers and encrypting financial information in space D are as follows: Create storage space D on the cloud server. In space D, create a blockchain network through the cloud platform console, select the consensus algorithm, number of nodes, and data storage method. Set up a node IP whitelist and firewall rules, write business logic using an Ethereum-compatible chain, and complete local testing using Remix or Truffle. Upload the contract to the cloud platform blockchain service and call the contract method through the API / SDK to complete the blockchain deployment and setup. According to hVC （1） ~hVC （na） , using secure communication protocols such as TLS / SSL to obtain the security parameters of the first to the nath customers, rc （1） ~rc （na） ; rc （1） ~rc （na） As the input of the Setup algorithm, get the public key PK （1） ~PK （na） , the master key is denoted as MSK （1） ~MSK （na） ; The public key, master key and ne （1） Use the KeyGen algorithm according to PK （1） ~PK （na） and MSK （1） ~MSK （na） Generate the secret keys SK for the 1st to nath customers （1） ~SK （na） , ID number id （1） ~id （na） ; Use the RSA algorithm to generate information mi （1） ~mi （na） The symmetric key is obtained as SYK （1） ~SYK （na） ; Use Encrypt algorithm as encryption algorithm and PK （1） ~PK （na） , SK （1） ~SK （na） and SYK （1） ~SYK （na） , for information mi （1） ~mi （na） Encrypt and get the ciphertext emi （1） ~emi （na） .

[0011] Furthermore, the specific steps of classifying customers into high net worth customers and ordinary customers are as follows: The account balances of customers 1 to na are recorded as mon （1） ~mon （na） ; will mon （1） ~mon （na） The maximum value in the mon （max） ; Define the relationship S-1: ; Among them, mon （k） represents the account balance of the k-th customer; Customers who meet the S-1 relationship are considered high-net-worth customers, while customers who do not meet the S-1 relationship are considered ordinary customers; Count the number of high net worth customers nca and the number of ordinary customers ncb; Calculate the credit score of high net worth customers （1） ~cre （mca） , and formulate loan interest rates for high net worth customers based on their credit scores （1） ~loa （nca） ; Analyze the transaction time and transaction objects of ordinary customers to determine the stores that each ordinary customer frequently visits; then combine the transaction amount to classify ordinary customers; Count the number of target customers, potential customers and general customers of each frequently visited store.

[0012] Furthermore, the loa （1） The proposed steps are as follows: The market type to which the first high-net-worth client belongs is regarded as the quasi-market; Other high-net-worth customers belonging to the quasi-market are regarded as quasi-customers, and the number of quasi-customers ncl is counted; Get the account balance of all potential customers moy （1） ~moy （ncl） ; Calculate mon （1） with moy （1） ~moy （ncl） of and sum; In pr （1） ~pr （mca） and upr （1） ~upr （mca） Extract the expected profit rate po of the first to ncl-th level users （1） ~po （ncl） , calculate the quasi-market profit rate Apr; extract the expected negative interest rate upo （1） ~upo （ncl） , calculate the quasi-market profit rate Aupr; Calculate the quasi-market net interest rate AUp: AUp = Apr - Aupr; Calculate the quasi-market portfolio return REm: Among them, moy （l） Indicates the account balance of the first prospective customer; Calculate the expected profit Qpr of the first high net worth customer and the expected profit Qpo of the first nclth prospective customer （1） ~Qpo （ncl） ; Qpr, Qpo （1） ~Qpo （ncl） Normalize to the range of 0 to 1 to get rpr, rpo （1） ~rpo （ncl） ; Calculate RPR and RPO （1） ~rpo （ncl） The variance SSpr of REm is calculated and then compared with rpr and rpo （1） ~rpo （ncl） The covariance SXpr of Calculate the beta coefficient β of the quasi-market, β = SXpr / SSpr; Calculate the risk premium coefficient δ for the first high-net-worth customer, δ = β × (REm - rpr); will cre （1） Normalize to the range of 0 to 1 to get rcre; Assume that the bank's initial loan interest rate is roa, and propose loa （1） : loa （1） =roa+(rcre×δ).

[0013] Furthermore, the specific steps for classifying ordinary customers are as follows: Define consumption labels: high frequency, low frequency, large amount, small amount; Determine the shops frequently visited by the first general customer and classify the first general customer; According to the transaction object, count the total number of stores visited by the first ordinary customer in the past month (shp); Count the number of times the first ordinary customer visits the first to shp shops on the first day: fr （1，1） ~fr （1，shp） Similarly, the number of visits to shops 1 to shp on day dat: fr （dat，1） ~fr （dat，shp） ; Constructed matrix B （1） , and calculate the covariance matrix C: Split the matrix C into columns to get the matrix Cc （1） ~Matrix Cc （shp） ; Define the matrix Cc （1） ~Matrix Cc （shp） The weight λ （1） ~λ （shp） and bias μ （1） ~μ （shp） , construct the relation S-2: ; Calculating λ （1） ~λ （shp） and μ （1） ~μ （shp） The value of Calculate the frequency coefficient ba of the first ordinary customer about the first store （1） :ba （1） =λ （1） / μ （1） ; Similarly, the frequency coefficient ba of the shp-th shop （shp） :ba （shp） =λ （shp） / μ （shp） ; Compare ba （1） ~ba （shp） The size of the frequency coefficient is selected as the shop frequently visited by the first ordinary customer. If the consumption tags of the first ordinary customer are: high frequency and large amount, then the first ordinary customer is the target customer of the store; If the consumption labels are: high frequency and small amount or low frequency and large amount, then the first ordinary customer is a potential customer who often visits the store; If the consumption label is: low frequency and small amount, then the first ordinary customer is a general customer who often visits the store.

[0014] The blockchain-based financial customer digital archive management system includes: Data acquisition module: used to obtain customer credit information, eliminate low-quality customers with unqualified credit, and obtain high-quality customers; obtain the identity information and financial information of high-quality customers; Data storage module: This module is used to design customer identities based on the identity information of high-quality customers. A Merkle tree is constructed based on the number of high-quality customers, and three storage spaces are created in each node of the Merkle tree to store the identity and encrypted financial information of the high-quality customer corresponding to the node, as well as the identity of the high-quality customer corresponding to the adjacent node or the root node. The Merkle tree is deployed on the blockchain to protect and store the customer's financial information. Customer analysis module: This module is used to classify customers into high-net-worth and ordinary customers based on account balances in financial information; calculate the credit scores of high-net-worth customers and set loan interest rates based on their credit scores; analyze the transaction times and transaction partners of ordinary customers to determine the stores that each ordinary customer frequently visits; and then, based on the transaction amounts, calculate the number of target customers, potential customers, and ordinary customers of each store that frequently visits, generating a customer consumption behavior report. Data update module: used to summarize customer consumption behavior reports and provide feedback to users; collect updated financial information of high-quality customers and update it synchronously on the blockchain.

[0015] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program runs any one of the steps in the method for managing digital archives of financial customers based on blockchain.

[0016] Compared with the prior art, the present invention has the following beneficial effects: Ensuring data security: The blockchain constructed by this invention uses asymmetric encryption and hash algorithms to encrypt and store customers' financial information. A unique identifier is generated from the customer's identity information and uploaded to the chain. The original data is stored on the cloud platform, which is then linked to transaction records through a Merkle tree structure to synchronously update the customer's financial information. This design not only prevents data leakage but also ensures the security and integrity of customer financial information through hash verification.

[0017] Preventing data tampering: Each block designed by the present invention contains the hash value of the previous block, forming an irreversible chain structure. When the customer's transaction information is recorded in the financial information and uploaded to the chain, any modification requires recalculating the hash values of all subsequent blocks. The computational complexity increases exponentially, significantly increasing the cost of tampering. At the same time, the blockchain constructed by the present invention also supports hot deployment, further reducing usage pressure.

[0018] Understanding customer consumption: After saving the customer's financial information, the present invention divides the customer into high-net-worth customers and ordinary customers according to the customer's actual financial situation, formulates loan interest rates for high-net-worth customers, and recommends consumption types for ordinary customers. This helps financial institutions better understand changes in customers' consumption behavior and provides a reference for subsequent financial decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings: Figure 1 Schematic diagram of the method of the present invention; Figure 2 Schematic diagram of the system of the present invention; Figure 3 Schematic diagram of the complete binary tree of the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Example 1

[0021] See also Figure 1 , the blockchain-based financial customer digital file management method includes: Step S1: Obtain customer credit information, eliminate low-quality customers with unqualified credit, and obtain high-quality customers; obtain identity information and financial information of high-quality customers; It should be noted that the “customer” in the present invention refers to the customer who uses the present invention (blockchain-based financial customer digital archive management method, system and medium) to perform digital archive management; In the present invention, only the "customers in step S1 'obtaining customer credit information'" represent all customers, i.e., high-quality customers and low-quality customers; the "customers" appearing in subsequent steps S2 to S3 all represent "high-quality customers", for this purpose; It should be noted that the “customers” appearing in claims 2 to 8 above all refer to “high-quality customers”; Step S2: Design a customer identity based on the identity information of high-quality customers; construct a Merkle tree based on the number of high-quality customers, and create three storage spaces in each node of the Merkle tree to store the identity and encrypted financial information of the high-quality customer corresponding to the node, as well as the identity of the high-quality customer corresponding to the adjacent node or the root node; deploy the Merkle tree on the blockchain to protect and store the customer's financial information; The specific steps of step S2 are as follows: Step S21: Obtain the number of customers na; Obtain all customer identity information and use Hyperledger Fabric's identity management components (such as Identity Mixer) to generate verifiable credentials for customers to obtain VC （1） ~VC （na） ; VC （1） ~VC （na） Stored in the corresponding customer's digital wallet; Use AES or RSA algorithm to encrypt VC （1） ~VC （na） Encrypt, and then convert the encrypted verifiable certificate into a hash value to obtain the identity hVC （1） ~hVC （na） ; Step S22: Please refer to Figure 3 , construct a Merkle tree to store customer financial information; (It should be noted that the present invention Figure 3 For demonstration purposes only); Calculate the depth bh of the Merkle tree: ;in, is a positive integer, rounded down; Use the build_binary_tree function to build a complete binary tree with a depth of bh and a number of nodes na as tree A （1） ; In tree A （1） At the storage location of each node, three storage spaces are created, denoted as space a, space b, and space c; Tree A （1）Starting from the root node, from left to right and from top to bottom, store hVC in the space a corresponding to node 1 to node na （1） ~hVC （na） , get tree A （2） ; Define the storage conditions of space b; Let node ki be tree A （2） According to the parity of ki, determine the adjacent nodes and root node corresponding to node ki, and fill data in the space b between the adjacent nodes and root node (corresponding to node ki); If ki is an odd number, it means that node ki is a right node; The adjacent node of node ki is node (ki-1), and the root node corresponding to node ki is node gki （1） ;gki （1） The calculation formula is: gki （1） =[(ki-1) / 2]; Store the hash value hVC corresponding to the (ki-1)th customer (after the verifiable certificate conversion) in the space b of node ki （ki－1） ; Store the hash value hVC corresponding to the ki-th customer (after the verifiable certificate conversion) in the space b of the node (ki-1) （ki） ; Node gki （1） The space b is divided into two storage spaces, and space b is obtained. （1） and space b （2） ; In node gki （1） Space b （2） In, store hVC （ki－1） and hVC （ki） ; Judging gki （1） The parity of space b （1） Storage objects; If gki （1） is an odd number, then in space b （1） Store in (gki （1） -1) The hash value hVC corresponding to the customer (after conversion of the verifiable credential) （gki（1）－1） ; If gki （1） is an even number, then in space b （1） Store in (gki （1） +1) The hash value hVC corresponding to the customer (after conversion of the verifiable credential) （gki（1）＋1） ; If ki is an even number, it means that node ki is a left node; The adjacent node of node ki is node (ki+1), and the root node corresponding to node ki is node gki （2） ;gki （2） The calculation formula is: gki （2） =ki / 2; Store the hash value hVC corresponding to the (ki+1)th customer (after the verifiable certificate conversion) in the space b of node ki （ki＋1） ; Store the hash value hVC corresponding to the ki-th customer (after the verifiable certificate conversion) in the space b of node (ki+1) （ki） ; Node gki （2） The space b is divided into two storage spaces, and space b is obtained. （3） and space b （4） ; In node gki （2） Space b （4） In, store hVC （ki＋1） and hVC （ki） ; Judging gki （2） The parity of space b （3） Storage objects; If gki （2） is an odd number, then in space b （3） Store in (gki （2） -1) The hash value hVC corresponding to the customer (after conversion of the verifiable credential) （gki（2）－1） ; If gki （2） is an even number, then in space b （3） Store in (gki （2） +1) The hash value hVC corresponding to the customer (after conversion of the verifiable credential) （gki（2）＋1） ; According to the storage conditions of space b, tree A （2） Based on hVC （1） ~hVC （na） Store it twice in the space b corresponding to node 1 to node na, and get tree A （3） ; Step S23: Obtain and encrypt the financial information of the first to nath customers; Create a storage space on the cloud server, designated as Space D. In Space D, create a blockchain network through the cloud platform console, selecting a consensus algorithm (e.g., PBFT for consortium chains), the number of nodes (3-5 nodes are recommended initially), and a data storage method (e.g., cloud object storage + local cache). Set up a node IP whitelist and firewall rules, write business logic (e.g., product traceability rules) using Solidity (Ethereum-compatible blockchain) or Go (Hyperledger Fabric), and perform local testing using Remix or Truffle. Upload the contract to the cloud platform blockchain service and call the contract method through the API / SDK to complete blockchain deployment and setup. Return the blockchain access interface to the first to the nth customers; Get the financial information of customers 1 to na, and get information mi （1） ~mi （na） ; Financial information includes: account balance, transaction time, transaction object, and transaction amount; Information （1） ~mi （na） Perform encryption and define the decryption process; Encryption process: According to hVC （1） ~hVC （na） , using secure communication protocols such as TLS / SSL to obtain the security parameters of the first to the nath customers, rc （1） ~rc （na） ; rc （1） ~rc （na） As the input of the Setup algorithm, get the public key PK （1） ~PK （na） , the master key is denoted as MSK （1） ~MSK （na） ; The public key, master key and ne （1） (i.e. the IP address of the first user) using the KeyGen algorithm, based on PK （1） ~PK （na） and MSK （1） ~MSK （na） Generate the secret keys SK for the 1st to nath customers （1） ~SK （na） , ID number id （1） ~id （na） ; Use the RSA algorithm to generate information mi （1） ~mi （na） The symmetric key is obtained as SYK （1） ~SYK（na） ; Use Encrypt algorithm as encryption algorithm and PK （1） ~PK （na） , SK （1） ~SK （na） and SYK （1） ~SYK （na） , for information mi （1） ~mi （na） Encrypt and get the ciphertext emi （1） ~emi （na） ; Tree A （3） Based on the ciphertext emi （1） ~emi （na） Store them in space c corresponding to nodes 1 to na in sequence to obtain a Merkle tree; and then synchronously deploy the Merkle tree to the blockchain in space D; Define the decryption process: The public key is recorded as PK, the master key is recorded as MSK, and the secret key is recorded as SK; Assume the public key entered by the visitor is PK`, the secret key is SK`, the master key is MSK`, and the ID number is id`; Use PK`, MSK` and SK` as parameters of the Keycheck algorithm to detect the format of PK`, MSK` and SK` and PK （1） ~PK （na） , MSK （1） ~MSK （na） and SK （1） ~SK （na） Is the format consistent? If there is any inconsistency, the access will be terminated and the police will be called immediately; If they are consistent, PK`, MSK`, and SK` are used as inputs of the Decrypt algorithm to obtain the symmetric key SYK`; the Decrypt algorithm is used to output the customer IP address iid corresponding to MSK` and SK`; Determine whether iid is consistent with id`; If they are consistent, use PK`, SK` and SYK` to decrypt the ciphertext corresponding to iid; If there is any inconsistency, the access will be terminated and the police will be called immediately; It should be noted that the “visitor” in the present invention refers to a person who accesses the “financial information in space D”; Step S3: Based on the account balance in the financial information, customers are classified into high-net-worth customers and ordinary customers; the credit scores of high-net-worth customers are calculated, and loan interest rates for high-net-worth customers are formulated based on the credit scores; the transaction time and transaction objects of ordinary customers are analyzed to determine the stores that each ordinary customer frequently visits; and then, based on the transaction amount, the number of target customers, potential customers, and ordinary customers of each store that frequently visits is counted to obtain a customer consumption behavior report; The specific steps of step S3 are as follows: Step S31: Classify customers into high-net-worth customers and ordinary customers based on the account balance in the financial information; The account balances of customers 1 to na are recorded as mon （1） ~mon （na） ; will mon （1） ~mon （na） The maximum value in the mon （max） ; Define the relationship S-1: ; Among them, mon （k） Indicates the account balance of the k-th customer, where k ranges from 1 to na. Customers who meet the S-1 relationship are considered high-net-worth customers, while customers who do not meet the S-1 relationship are considered ordinary customers; Count the number of high net worth customers nca and the number of ordinary customers ncb; Step S32: Calculate the credit scores of the first to nca high net worth customers using the Z-Score model （1） ~cre （mca） ; Among them, the mathematical calculation formula of the Score model is: Customer's credit score = (1.2 × ZX （1） )+(1.4×ZX （2） )+(3.3×ZX （3） )+(0.6×ZX （4） )+(0.99×ZX （5） ); Among them, ZX （1） Indicates the asset liquidity indicator of high net worth customers, ZX （1） = (Current assets - Current liabilities) / Total assets; ZX （2） Indicates the cumulative profitability of high net worth customers, ZX （2） =Retained earnings / total assets; ZX （3） Indicates the operational efficiency of high net worth customers, ZX （3） = EBIT / total assets; ZX （4）Represents the capital structure of high net worth clients, ZX （4） = Shareholders' equity / Total liabilities; ZX （5） represents the asset turnover rate of high net worth clients, ZX （5） = Sales revenue / Total assets; Obtain the year-end account balances of the 1st to ncath high net worth customers in the past three years, calculate the profit rate and negative interest rate of each high net worth customer in the past three years, and use Markov chain to predict the expected profit rate pr of the 1st to ncath high net worth customers （1） ~pr （mca） , expected negative interest rate upr （1） ~upr （mca） ; (If the high net worth customer's account balance at the end of the previous year is greater than the account balance at the end of the next year, the negative interest rate of the high net worth customer will be calculated. If the negative interest rate is positive, the profit rate of the high net worth customer is negative. If the negative interest rate is negative, the profit rate is negative. If the high net worth customer's account balance at the end of the previous year is less than the account balance at the end of the next year, the profit rate of the high net worth customer is calculated. If the profit rate is positive, the high net worth customer's negative interest rate is negative. If the negative interest rate is negative, the profit rate is negative. If the high net worth client's account balance at the end of the previous year is equal to the account balance at the end of the next year, the profit rate and negative interest rate are both 0); Obtain the market type to which high-net-worth customers belong and formulate loan interest rates for high-net-worth customers ranked 1 to NCa; Propose the loan interest rate for the first high net worth customer （1） ; The market type to which the first high-net-worth client belongs is regarded as the quasi-market; Except for the first high-net-worth customer, all other high-net-worth customers belonging to the quasi-market are regarded as quasi-customers, and the number of quasi-customers is counted as ncl; Get the account balance of all potential customers moy （1） ~moy （ncl） ; Calculate mon （1） with moy （1） ~moy （ncl） of and sum; (among which, moy （1） ~moy （ncl） ∈{mon （1） ~mon （na）}); In pr （1） ~pr （mca） and upr （1） ~upr （mca） Extract the expected profit rate po of the first to ncl-th level users （1） ~po （ncl） and expected negative interest rates upo（1） ~upo （ncl） ; Extract pr （1） 、po （1） ~po （ncl） The maximum value po in （max） , minimum value po （min） ; Calculate the quasi-market profit rate Apr: ; Extract upr （1） 、upo （1） ~upo （ncl） The maximum value upo （max） , minimum value upo （min） ; Calculate the quasi-market profit rate Aupr: ; Among them, po （l） and upo （l） , respectively represent the expected profit rate and expected negative interest rate of the l-th quasi-user, and the value range of l is: 1~ncl; Calculate the quasi-market net interest rate AUp: AUp = Apr - Aupr; Calculate the quasi-market portfolio return REm: Among them, moy （l） Indicates the account balance of the first prospective customer; Calculate the expected profit Qpr of the first high net worth customer: ; Repeat the Qpr calculation process, according to po （1） ~po （ncl） and upo （1） ~upo （ncl） and moy （1） ~moy （ncl） , calculate the expected profit of the first nclth potential customer: Qpo （1） ~Qpo （ncl） ; Use the logistic function to normalize Qpr to the range of 0 to 1 to obtain rpr; use the logistic function to normalize Qpo （1） ~Qpo （ncl） Normalized to the range of 0 to 1, we get rpo （1） ~rpo （ncl） ; Calculate RPR and RPO （1） ~rpo （ncl） The variance SSpr of REm is calculated and then compared with rpr and rpo （1） ~rpo（ncl） The covariance SXpr of Calculate the beta coefficient β of the quasi-market, β = SXpr / SSpr; Calculate the risk premium coefficient δ for the first high-net-worth customer, δ = β × (REm - rpr); Use the logistic function to convert cre （1） Normalize to the range of 0 to 1 to get rcre; Assume the bank's initial loan interest rate is roa, and propose the loan interest rate loa for the first high net worth customer （1） : loa （1） =roa+(rcre×δ); Repeated loa （1） The process of formulating the loan interest rates for high net worth customers from the 2nd to the NCA level. （2） ~loa （nca） ; It should be noted that, when executing the relevant process in "step S32", the present invention needs to obtain the relevant authorization of the high net worth customer; if the authorization of the high net worth customer is not obtained, step S32 is executed; Step S33: Analyze the transaction time and transaction objects of ordinary customers to determine the stores that each ordinary customer frequently visits; then, based on the transaction amount, count the number of target customers, potential customers, and general customers of each store that frequently visits, and obtain a customer consumption behavior report; Step S331: Define consumption tags: high frequency, low frequency, large amount, small amount; Count the total number of transactions (i.e. total consumption times) of the 1st to ncbth ordinary users in the past month, and get cn （1） ~cn （ncb） ; Count the single largest transaction amount of ordinary users from 1st to ncbth in the past month, and get mmy （1） ~mmy （ncb） ; Extract cn （1） ~cn （ncb） The maximum value cn in （max） , minimum value cn （min） , calculate the weighted average of total transaction times acn: ; Extract mmy （1） ~mmy （ncb） The maximum value in mmy （max） , minimum value mmy （min） , calculate the weighted average of total transaction times amy: ; Among them, cn （v） and mmy（v） , respectively represent the total number of transactions and the single maximum transaction amount of the vth ordinary user in the past month, and the value range of v is: 1 ~ ncb; High frequency: ordinary customers whose total number of transactions in the past month is greater than ACN; Low frequency: ordinary customers whose total transaction times in the past month are less than or equal to ACN; Large amount: The largest single transaction amount in the past month is greater than that of Amy's ordinary customers; Small amount: ordinary customers whose maximum single transaction amount in the past month is less than or equal to Amy's; Determine the shops frequently visited by the first general customer and classify the first general customer; According to the transaction object, count the total number of stores visited by the first ordinary customer in the past month (shp); Get the number of days in the past month dat; count the number of times the first ordinary customer visits the first, second, and finally shp shops on the first day (in the past month): fr （1，1） 、fr （1，2） ~fr （1，shp） (It should be noted that the present invention can count multiple times when counting the number of times an ordinary customer visits a store; for example, if the first ordinary customer visits store A five times on the first day of the past month, that is, the first ordinary customer has five transaction records with store A on the first day of the past month, then the number of times the first ordinary customer visits store A on the first day of the past month is five). Number of visits to the first, second, and so on shops on the second day (in the past month): fr （2，1） 、fr （2，2） ~fr （2，shp） ; And so on, the number of times you visit the 1st, 2nd, and shp shops on the dat day (in the past month): fr （dat，1） 、fr （dat，2） ~fr （dat，shp） ; Construct a (dat×shp) matrix B （1） : ; Calculate matrix B （1） The decentralized matrix of （2） ; Calculate matrix B （1） The covariance matrix of , we get the matrix C: ; Where * represents matrix multiplication and T represents matrix transpose; Split the matrix C into columns to get the matrix Cc （1） , matrix Cc （2） ~Matrix Cc （shp）; Define the matrix Cc （1） , matrix Cc （2） ~Matrix Cc （shp） The weight λ （1） ,λ （2） ~λ （shp） and bias μ （1） 、μ （2） ~μ （shp） , construct the relation S-2: ; According to the relationship S-2, λ is calculated using MATLAB software. （1） ,λ （2） ~λ （shp） and μ （1） 、μ （2） ~μ （shp） The value of Calculate the frequency coefficient ba of the first ordinary customer about the first store （1） :ba （1） =λ （1） / μ （1） ; The frequency coefficient ba of the shp-th shop （2） :ba （2） =λ （2） / μ （2） ; Similarly, the frequency coefficient ba of the shp-th shop （shp） :ba （shp） =λ （shp） / μ （shp） ; Compare ba （1） ~ba （shp） The size of the frequency coefficient is selected as the shop frequently visited by the first ordinary customer. If the consumption tags of the first ordinary customer are: high frequency and large amount, then the first ordinary customer is the target customer of the store (the first ordinary customer corresponds to the target customer); If the consumption tags of the first ordinary customer are: high frequency and small amount or low frequency and large amount, then the first ordinary customer is a potential customer who often visits the store. If the consumption tag of the first ordinary customer is: low frequency and small amount, then the first ordinary customer is (the first ordinary customer corresponds to) an ordinary customer who frequently visits the store; Repeat the same process of determining the shops frequently visited by the first ordinary customer and classifying the first ordinary customer, and determine the shops frequently visited by the second to the ncbth ordinary customers and classify them; Count the shops frequently visited by ordinary customers ranked 1 to ncb, count the number and information of target customers, potential customers, and general customers of each shop frequently visited, and summarize the loan interest rates of the high-net-worth customers ranked 1 to nca to obtain a customer consumption behavior report; It should be noted that "step S3" in the present invention is executed at 24:00 every night by default, and the functions of updating and saving financial information in steps S1, S2 and S4 are executed at other times; Because blockchain data confirmation takes a long time, the data upload function of the blockchain may be limited when customers engage in high-frequency transactions, especially securities trading. That is, the speed of change of financial information is greater than the speed of data confirmation of the blockchain. When the blockchain constructed by the present invention encounters the situation where "the speed of change of financial information is greater than the speed of data confirmation of the blockchain", the uploading of financial information in steps S1, S2 and S4 is temporarily suspended. Only the update time of the financial information and the updated financial data are obtained and temporarily stored. The financial information analysis function of step S3 is executed. When "the speed of change of financial information is less than or equal to the speed of data confirmation of the blockchain", the updated financial data is uploaded to the blockchain to prevent operational failures of the blockchain. Step S4: Summarize customer consumption behavior reports and provide feedback to users; collect updated financial information of high-quality customers and synchronize it on the blockchain; It should be noted that the "user" in step S4 of the present invention refers to a financial institution user who uses the present invention (blockchain-based financial customer digital archive management method, system and medium) to manage customer digital archives. Example 2

[0022] See also Figure 2 The blockchain-based financial customer digital archive management system includes: Data acquisition module: used to obtain customer credit information, eliminate low-quality customers with unqualified credit, and obtain high-quality customers; obtain the identity information and financial information of high-quality customers; Data storage module: This module is used to design customer identities based on the identity information of high-quality customers. A Merkle tree is constructed based on the number of high-quality customers, and three storage spaces are created in each node of the Merkle tree to store the identity and encrypted financial information of the high-quality customer corresponding to the node, as well as the identity of the high-quality customer corresponding to the adjacent node or the root node. The Merkle tree is deployed on the blockchain to protect and store the customer's financial information. Customer analysis module: This module is used to classify customers into high-net-worth and ordinary customers based on account balances in financial information; calculate the credit scores of high-net-worth customers and set loan interest rates based on their credit scores; analyze the transaction times and transaction partners of ordinary customers to determine the stores that each ordinary customer frequently visits; and then, based on the transaction amounts, calculate the number of target customers, potential customers, and ordinary customers of each store that frequently visits, generating a customer consumption behavior report. Data update module: used to summarize customer consumption behavior reports and provide feedback to users; collect updated financial information of high-quality customers and update it synchronously on the blockchain; Example 3

[0023] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, executes the steps of any of the above-described methods for self-checking an operation of a smart shared charging station. Through the above technical solution, when the computer program is executed by a processor, the method of any optional implementation of the above-described embodiment is executed to achieve the following functions: Screen, collect and process the financial information of high-quality customers, save the encrypted high-quality customer financial information in the Merkle tree, and deploy it on the blockchain; analyze the financial information, divide high-quality customers into high-net-worth customers and ordinary customers, analyze customers' consumption habits, formulate loan interest rates for high-net-worth customers, and determine the stores frequently visited by ordinary customers, which helps users understand the consumption changes and consumption potential of high-quality customers in real time.

[0024] The above formulas are all dimensionless and calculated by taking their numerical values. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions. For example, if there are weight coefficients and proportional coefficients, the size of the settings is to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. Regarding the size of the weight coefficient and the proportional coefficient, as long as it does not affect the proportional relationship between the parameter and the quantized value, it is fine.

[0025] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A blockchain-based digital file management method for financial customers, characterized by: The file management method comprises: Eliminate unqualified and low-quality customers and obtain high-quality customers; obtain the identity information and financial information of high-quality customers; Design a customer identity based on the identity information of high-quality customers; construct a Merkle tree based on the number of high-quality customers, and create three storage spaces in each node of the Merkle tree, one for storing the identity and encrypted financial information of the high-quality customer corresponding to the node, and the other for the identity of the high-quality customer corresponding to the adjacent node or the root node; deploy the Merkle tree on the blockchain to protect and store the customer's financial information; Based on the account balances in their financial information, customers are categorized as high-net-worth and average customers. Credit scores for high-net-worth customers are calculated, and loan interest rates are determined based on these scores. Transaction times and partners for average customers are analyzed to identify the stores each customer frequently visits. Combined with transaction amounts, the number of target customers, potential customers, and average customers for each store is counted to generate a customer spending behavior report. Summarize customer consumption behavior reports and provide feedback to users; collect updated financial information of high-quality customers and update it synchronously on the blockchain.

2. The blockchain-based financial customer digital archive management method according to claim 1 is characterized in that: The specific steps of constructing the Merkle tree are as follows: Get the number of customers na; get the identity information of all customers and generate verifiable credentials to get VC （1） ~VC （na） ; For VC （1） ~VC （na） Encrypt, and then convert the encrypted verifiable certificate into a hash value to obtain the identity hVC （1） ~hVC （na） ; Calculate the depth bh of the Merkle tree: ; Construct a complete binary tree with a depth of bh and a number of nodes na as tree A （1） ; In tree A （1） At the storage location of each node, three storage spaces are created, denoted as space a, space b, and space c; Tree A （1） Starting from the root node, from left to right and from top to bottom, store hVC in the space a corresponding to node 1 to node na （1） ~hVC （na） , get tree A （2） ; Define the storage conditions of space b; according to the storage conditions of space b, use tree A （2） Based on hVC （1） ~hVC （na） Store it twice in the space b corresponding to node 1 to node na, and get tree A （3） ; Obtain and encrypt the financial information of the first to the nath customer, and obtain the ciphertext emi （1） ~emi （na） ; Tree A （3） Based on the ciphertext emi （1） ~emi （na） Store them in the space c corresponding to node 1 to node na in sequence to obtain a Merkle tree; deploy the blockchain in space D, and synchronously deploy the Merkle tree to the blockchain.

3. The blockchain-based financial customer digital archive management method according to claim 2 is characterized in that: The storage conditions of the space b are: Let node ki be tree A （2） Any node in ; If ki is an odd number, then node ki is the right node; The adjacent node of node ki is node (ki-1), and the root node corresponding to node ki is node gki （1） :gki （1） =[(ki-1) / 2]; Store the hash value hVC of the (ki-1)th customer in the space b of node ki （ki－1） ; Store the hash value hVC of the ki-th customer in the space b of node (ki-1) （ki） ; Node gki （1） The space b is divided into space b （1） and space b （2） ; In node gki （1） Space b （2） In, store hVC （ki－1） and hVC （ki） ; Judging gki （1） The parity of space b （1） Storage objects; If gki （1） is an odd number, then in space b （1） Store in (gki （1） -1) The value of the customer's HVC （gki（1）－1） ; If gki （1） is an even number, then in space b （1） Store in (gki （1） +1) Customer's HVC （gki（1）＋1） ; If ki is an even number, reset the storage condition of space b.

4. The blockchain-based financial customer digital archive management method according to claim 3 is characterized in that: The storage conditions of the reset space b are: If ki is an even number, it means that node ki is a left node; The adjacent node of node ki is node (ki+1), and the root node corresponding to node ki is node gki （2） :gki （2） =ki / 2; Store the hash value hVC of the (ki+1)th customer in the space b of node ki （ki＋1） ; Store the hash value hVC corresponding to the ki-th customer in the space b of node (ki+1) （ki） ; Node gki （2） The space b is divided into space b （3） and space b （4） ; In node gki （2） Space b （4） In, store hVC （ki＋1） and hVC （ki） ; Judging gki （2） The parity of space b （3） Storage objects; If gki （2） is an odd number, then in space b （3） Store in (gki （2） -1) The value of the customer's HVC （gki（2）－1） ; If gki （2） is an even number, then in space b （3） Store in (gki （2） +1) Customer's HVC （gki（2）＋1） .

5. The blockchain-based financial customer digital archive management method according to claim 2 is characterized in that: The specific steps for deploying cloud servers and encrypting financial information in Space D are as follows: Create storage space D on the cloud server. In space D, create a blockchain network through the cloud platform console, select the consensus algorithm, number of nodes, and data storage method. Set up a node IP whitelist and firewall rules, write business logic using an Ethereum-compatible chain, and complete local testing using Remix or Truffle. Upload the contract to the cloud platform blockchain service and call the contract method through the API / SDK to complete the blockchain deployment and setup. According to hVC （1） ~hVC （na） , using secure communication protocols such as TLS / SSL to obtain the security parameters of the first to the nath customers, rc （1） ~rc （na） ; rc （1） ~rc （na） As the input of the Setup algorithm, get the public key PK （1） ~PK （na） , the master key is denoted as MSK （1） ~MSK （na） ; The public key, master key and ne （1） Use the KeyGen algorithm according to PK （1） ~PK （na） and MSK （1） ~MSK （na） Generate the secret keys SK for the 1st to nath customers （1） ~SK （na） , ID number id （1） ~id （na） ; Use the RSA algorithm to generate information mi （1） ~mi （na） The symmetric key is obtained as SYK （1） ~SYK （na） ; Use Encrypt algorithm as encryption algorithm and PK （1） ~PK （na） , SK （1） ~SK （na） and SYK （1） ~SYK （na） , for information mi （1） ~mi （na） Encrypt and get the ciphertext emi （1） ~emi （na） .

6. The blockchain-based financial customer digital archive management method according to claim 5 is characterized in that: The specific steps for classifying customers into high net worth customers and ordinary customers are as follows: The account balances of customers 1 to na are recorded as mon （1） ~mon （na） ; will mon （1） ~mon （na） The maximum value in the mon （max） ; Define the relationship S-1: ; Among them, mon （k） represents the account balance of the k-th customer; Customers who meet the S-1 relationship are considered high-net-worth customers, while customers who do not meet the S-1 relationship are considered ordinary customers; Count the number of high net worth customers nca and the number of ordinary customers ncb; Calculate the credit score of high net worth customers （1） ~cre （mca） , and formulate loan interest rates for high net worth customers based on their credit scores （1） ~loa （nca） ; Analyze the transaction time and transaction objects of ordinary customers to determine the stores that each ordinary customer frequently visits; then combine the transaction amount to classify ordinary customers; Count the number of target customers, potential customers and general customers of each frequently visited store.

7. The blockchain-based digital file management method for financial customers according to claim 6 is characterized in that: loa （1） The proposed steps are as follows: The market type to which the first high-net-worth client belongs is regarded as the quasi-market; Other high-net-worth customers belonging to the quasi-market are regarded as quasi-customers, and the number of quasi-customers ncl is counted; Get the account balance of all potential customers moy （1） ~moy （ncl） ; Calculate mon （1） with moy （1） ~moy （ncl） of and sum; In pr （1） ~pr （mca） and upr （1） ~upr （mca） Extract the expected profit rate po of the first to ncl-th level users （1） ~po （ncl） , calculate the quasi-market profit rate Apr; extract the expected negative interest rate upo （1） ~upo （ncl） , calculate the quasi-market profit rate Aupr; Calculate the quasi-market net interest rate AUp: AUp = Apr - Aupr; Calculate the quasi-market portfolio return REm: Among them, moy （l） Indicates the account balance of the first prospective customer; Calculate the expected profit Qpr of the first high net worth customer and the expected profit Qpo of the first nclth prospective customer （1） ~Qpo （ncl） ; Qpr, Qpo （1） ~Qpo （ncl） Normalize to the range of 0 to 1 to get rpr, rpo （1） ~rpo （ncl） ; Calculate RPR and RPO （1） ~rpo （ncl） The variance SSpr of REm is calculated and then compared with rpr and rpo （1） ~rpo （ncl） The covariance SXpr of Calculate the beta coefficient β of the quasi-market, β = SXpr / SSpr; Calculate the risk premium coefficient δ for the first high-net-worth customer, δ = β × (REm - rpr); will cre （1） Normalize to the range of 0 to 1 to get rcre; Assume that the bank's initial loan interest rate is roa, and propose loa （1） : very （1） =roa+(rcre×δ).

8. The blockchain-based digital file management method for financial customers according to claim 6 is characterized in that: The specific steps for general customer classification are as follows: Define consumption labels: high frequency, low frequency, large amount, small amount; Determine the shops frequently visited by the first general customer and classify the first general customer; According to the transaction object, count the total number of stores visited by the first ordinary customer in the past month (shp); Count the number of times the first ordinary customer visits the first to shp shops on the first day: fr （1，1） ~fr （1，shp） Similarly, the number of visits to shops 1 to shp on day dat: fr （dat，1） ~fr （dat，shp） ; Constructed matrix B （1） , and calculate the covariance matrix C: Split the matrix C into columns to get the matrix Cc （1） ~Matrix Cc （shp） ; Define the matrix Cc （1） ~Matrix Cc （shp） The weight λ （1） ~λ （shp） and bias μ （1） ~μ （shp） , construct the relation S-2: ; Calculating λ （1） ~λ （shp） and μ （1） ~μ （shp） The value of Calculate the frequency coefficient ba of the first ordinary customer about the first store （1） :ba （1） =λ （1） / μ （1） Similarly, calculate the frequency coefficient ba of the shp-th shop （shp） ; Compare ba （1） ~ba （shp） The size of the frequency coefficient is selected as the shop frequently visited by the first ordinary customer. If the consumption tags of the first ordinary customer are: high frequency and large amount, then the first ordinary customer is the target customer of the store; If the consumption labels are: high frequency and small amount or low frequency and large amount, then the first ordinary customer is a potential customer who often visits the store; If the consumption label is: low frequency and small amount, then the first ordinary customer is a general customer who often visits the store.

9. A blockchain-based financial customer digital archive management system, applicable to the blockchain-based financial customer digital archive management method according to any one of claims 1 to 8, characterized in that: The file management system includes: Data acquisition module: used to obtain customer credit information, eliminate low-quality customers with unqualified credit, and obtain high-quality customers; obtain the identity information and financial information of high-quality customers; Data storage module: This module is used to design customer identities based on the identity information of high-quality customers. A Merkle tree is constructed based on the number of high-quality customers, and three storage spaces are created in each node of the Merkle tree to store the identity and encrypted financial information of the high-quality customer corresponding to the node, as well as the identity of the high-quality customer corresponding to the adjacent node or the root node. The Merkle tree is deployed on the blockchain to protect and store the customer's financial information. Customer analysis module: This module is used to classify customers into high-net-worth and ordinary customers based on account balances in financial information; calculate the credit scores of high-net-worth customers and set loan interest rates based on their credit scores; analyze the transaction times and transaction partners of ordinary customers to determine the stores that each ordinary customer frequently visits; and then, based on the transaction amounts, calculate the number of target customers, potential customers, and ordinary customers of each store that frequently visits, generating a customer consumption behavior report. Data update module: used to summarize customer consumption behavior reports and provide feedback to users; collect updated financial information of high-quality customers and update it synchronously on the blockchain.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are executed.

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