Customer information comprehensive management method and system based on block chain technology

Abstract

The invention discloses a customer information comprehensive management method and system based on a block chain technology, and relates to the technical field of information comprehensive management. Comprising a block chain network construction module, an information acquisition and preprocessing module, an analysis model construction module, an information calling request module, an information intelligent query module, an information decryption and return module and an information updating and maintenance module, and the block chain network construction module constructs a network and a contract and definitely defines a storage structure and a consensus mechanism; the information collection and preprocessing module collects information in multiple channels and encrypts and stores sensitive data, the analysis model building module builds a model-to-rule algorithm according to business, and the other modules are sequentially responsible for information calling request, intelligent query, decryption return and information updating maintenance. The multi-channel preprocessing and model application enable the decision to be accurate, and break the data island, thereby improving the operation efficiency and service quality, and promoting the business innovation.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated information management, and specifically to a method and system for integrated customer information management based on blockchain technology. Background Art

[0002] In today's digital business wave, companies have accumulated massive amounts of customer information. However, traditional customer information management faces many difficulties.

[0003] On the one hand, centralized storage makes customer information vulnerable to hacker attacks. Once leaked, such as credit card information and identity information, it will bring privacy violations and property risks to customers. On the other hand, customer information comes from multiple channels online and offline, with messy formats, errors, and redundant data, which affects the company's accurate customer portrait and analysis decisions. For example, marketing strategies based on inaccurate data may lead to resource mismatches, and customer information cannot effectively reach target customers. On the other hand, information flow between internal departments of the company is not smooth, forming data islands. It is difficult for marketing, sales, customer service and other departments to share and collaboratively use customer information, which reduces overall operational efficiency and service quality, and hinders business innovation and expansion.

[0004] Therefore, people need a comprehensive customer information management method and system based on blockchain technology to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a customer information comprehensive management method and system based on blockchain technology to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a customer information comprehensive management method based on blockchain technology, comprising the following steps:

[0007] S1. Build blockchain network and smart contracts, define customer information storage structure and consensus mechanism;

[0008] S2, collect customer information through multiple channels and pre-process it, encrypt and store sensitive information, and store some data on the middleware platform;

[0009] S3. Determine analysis requirements based on business objectives, build models and convert them into smart contract executable rules and algorithms;

[0010] S4. After identity verification, the business personnel initiate an information call request through the portal and describe the task;

[0011] S5. After the blockchain network receives the request, the smart contract parses it and intelligently queries and filters the information in the ledger according to the model;

[0012] S6. The encrypted information is transmitted to the decryption server, which is formatted and returned to the requester after decryption.

[0013] S7. When customer information changes, the blockchain and middleware platform information will be updated and network performance will be maintained after review and verification.

[0014] Further, in step S1, a blockchain platform is selected to build a distributed network, and a smart contract is deployed at each node. The smart contract determines the storage structure of customer information data. The customer information data includes basic customer information, business interaction data, and feature tags for data analysis. The system ensures data consistency between nodes by establishing a consensus mechanism of the blockchain network;

[0015] Furthermore, in step S2, customer information is collected through online platform registration, offline business processing, and market research channels, and the collected original information is cleaned, organized, and standardized to remove noise data and erroneous data to ensure information accuracy and integrity. The Advanced Encryption Standard AES encryption algorithm is used to encrypt sensitive information and store it in the blockchain node. The index information of customer information and some non-sensitive data are stored in the enterprise's internal data analysis middleware platform. The data analysis middleware platform has data caching and preliminary analysis capabilities and works in conjunction with the blockchain network.

[0016] Further, in step S3, by determining data analysis needs based on the enterprise's customer segmentation, precision marketing, and risk assessment business goals, a clustering model is designed using machine learning and data mining technology to classify customer groups, a rule model is designed to mine customer purchase behavior correlations, and a prediction model is designed to estimate customer churn risks. The clustering model, rule model, and prediction model are converted into rules and algorithms that can be recognized and executed by blockchain network smart contracts and deployed in smart contracts, so that smart contracts have the ability to make information call decisions based on data analysis results, thereby realizing the organic integration and coordinated operation of enterprise multi-channel data collection, analysis model construction, and blockchain smart contracts, so as to achieve intelligent and efficient enterprise data-driven business decision-making. In the data collection stage, data is collected through multiple channels and the original data is cleaned, sorted, and standardized to ensure data accuracy and integrity. Sensitive information is encrypted and stored in blockchain nodes, and index information and some non-sensitive data are stored in the enterprise's internal specific data analysis middleware platform;

[0017] The clustering model is established by: the database includes a customer classification data set {x1, x2, ..., x q}, for x p For analysis, x p is an m-dimensional data point, that is, x p ={x p1 ,x p2 ,…,xpm}, thus establishing an objective function:

[0018]

[0019] Among them, K is the number of clusters, X k is the center of the kth cluster, X k ={X k1 ,X k2 ,…,X km}, establish a judgment variable z pk , when x p When it belongs to the kth cluster, z pk =1, when x p When it does not belong to the kth cluster, z p k =0, thus establishing the cluster center X k The update formula is:

[0020]

[0021] During the iteration process, the variable z is judged ik and cluster center X k Keep updating until the objective function A converges;

[0022] The method for establishing the rule model is as follows: the database includes a commodity set I = {i1, i2, ..., i m}, including transaction database D = {T1, T2, ..., T n}, establish the support coefficient formula:

[0023]

[0024] Analyze project set Support coefficient, set the minimum support coefficient threshold min s , thereby filtering out the values ​​that satisfy support(X) greater than or equal to min s The number of frequent items

[0025]

[0026] Formula for establishing trust factor:

[0027]

[0028] The trust coefficient of association rule X→Y is calculated by the trust coefficient formula, where and Set the minimum trust factor threshold min c , confirm that

[0029] confidence(X→Y)≥min c The association rule set is:

[0030]

[0031] In this way, rules with strong correlations between corporate customer purchase behaviors are discovered, thereby providing a basis for business decisions such as precision marketing and product recommendations, and realizing the intelligent correlation analysis capabilities of corporate data-driven business decisions. The system and method work in coordination with the company's multi-channel data collection, other data analysis model construction, and blockchain smart contracts. In the data collection stage, data is collected through multiple channels and the original data is cleaned, sorted, and standardized to ensure data accuracy and integrity. Sensitive information is encrypted and stored in blockchain nodes, and index information and some non-sensitive data are stored in the company's internal specific data analysis middleware platform.

[0032] The method for establishing the prediction model is: analyzing the e-th customer, the database includes f feature vectors g of the e-th user e = {g e1 ,g e2 ,…,g ef}, when h e =1, it means the e-th customer is lost. e = 0, it means that the e-th customer has not been lost, thus establishing the predicted probability P(h e =1|g e )’s logistic regression model:

[0033]

[0034] Among them, β={β0,β1,β2,…,β f} is the model parameter, g eα Represents the αth feature vector of the user. The log-likelihood function is obtained by taking the logarithm of the likelihood function, and then the optimal β value is obtained by maximizing the log-likelihood function using the gradient descent optimization algorithm.

[0035] Furthermore, in step S4, when an authorized user needs to call customer information, he / she initiates a request through the enterprise's unified information call portal. The request clearly states the data analysis task description and specific business scenario requirements, such as "obtaining high-value potential customer information for marketing promotion" or "querying detailed information of customers with specific risk characteristics for risk management", etc. Before the user initiates the request, the system authenticates the user through a multi-factor authentication mechanism including password verification, dynamic verification code verification and biometric recognition, so as to ensure that only legally authorized subjects can initiate information call requests and ensure customer information security;

[0036] Further, in step S5, when the blockchain network receives the information call request, the system parses the request through the smart contract to extract the data analysis requirements and task descriptions therein, and then performs intelligent data query and screening in the blockchain account book based on the built-in data analysis model and rules. If the request is to obtain a specific type of customer information, such as high-value potential customer information, the customer information is evaluated and screened based on a customer value evaluation model constructed based on multi-dimensional factors such as consumption amount, purchase frequency, and product diversity; the system uses the distributed characteristics of the blockchain to enable multiple nodes to simultaneously execute data query and analysis tasks to improve the speed and efficiency of information acquisition, and at the same time dynamically adjusts the query strategy and optimizes the query path according to the data distribution through the smart contract to further improve the query performance;

[0037] Further, in step S6, the encrypted customer information retrieved from the blockchain account book is transmitted to a secure decryption server within the enterprise, and the secure decryption server uses a pre-stored decryption key to decrypt the encrypted customer information to restore the original customer information data, and organizes and formats the decrypted customer information according to the request of the requester, and the formatting includes conversion into a data table in CSV format or structured data in JSON format, and returns the processed customer information to the requester through the information call portal for subsequent business operations or in-depth data analysis by the requester;

[0038] Furthermore, in step S7, when customer information changes, the system automatically triggers the information update process, and first reviews and verifies the updated information within the enterprise to ensure its authenticity and legality. After the review is passed, the updated information is encapsulated into a specific data structure and new transaction information is generated according to the blockchain protocol specifications.

[0039] A customer information integrated management system based on blockchain technology, the system comprising: a blockchain network construction module, an information collection and preprocessing module, an analysis model construction module, an information call request module, an information intelligent query module, an information decryption and return module, and an information update and maintenance module;

[0040] Build a blockchain network and smart contracts through the blockchain network building module, and define the customer information storage structure and consensus mechanism;

[0041] The information collection and preprocessing module collects and preprocesses customer information, encrypts and stores sensitive information, and stores some data in the middleware platform;

[0042] Determine the analysis requirements according to the business objectives through the analysis model building module, build the model and convert it into smart contract executable rules and algorithms;

[0043] Initiate an information call request through the portal by means of the information call request module and describe the task;

[0044] Intelligently query and filter information in the account book according to the model through the information intelligent query module;

[0045] The encrypted information is transmitted to the decryption server through the information decryption and return module, and after decryption, it is formatted as required and returned to the requester;

[0046] The information update and maintenance module is used to review and verify changes in customer information, thereby updating blockchain and middleware platform information and maintaining network performance.

[0047] Compared with the prior art, the beneficial effects achieved by the present invention are: on the one hand, blockchain decentralized storage and encryption technology reduces the risk of customer information being attacked by hackers, protects privacy data such as credit card and identity information, prevents damage to customer rights and interests and corporate legal and reputation crises caused by leakage, and effectively ensures data security; on the one hand, multi-channel information preprocessing ensures accuracy, combines multiple models to transform into smart contract rule algorithms, accurately analyzes customer groups, behavioral associations and churn risks, avoids resource mismatch, helps companies make scientific decisions, and enhances decision-making effectiveness; on the other hand, it breaks the internal data islands of the company, and each department can easily call up information, and smart contracts respond quickly, reducing communication and time costs, improving operational efficiency and service quality, and promoting business innovation and expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 It is a structural diagram of a customer information comprehensive management system based on blockchain technology of the present invention;

[0050] Figure 2 It is a flow chart of a customer information comprehensive management method based on blockchain technology of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 and Figure 2, the present invention provides a technical solution: a customer information comprehensive management method based on blockchain technology, comprising the following steps:

[0053] S1. Build blockchain network and smart contracts, define customer information storage structure and consensus mechanism;

[0054] S2, collect customer information through multiple channels and pre-process it, encrypt and store sensitive information, and store some data on the middleware platform;

[0055] S3. Determine analysis requirements based on business objectives, build models and convert them into smart contract executable rules and algorithms;

[0056] S4. After identity verification, the business personnel initiate an information call request through the portal and describe the task;

[0057] S5. After the blockchain network receives the request, the smart contract parses it and intelligently queries and filters the information in the ledger according to the model;

[0058] S6. The encrypted information is transmitted to the decryption server, which is formatted and returned to the requester after decryption.

[0059] S7. When customer information changes, the blockchain and middleware platform information will be updated and network performance will be maintained after review and verification.

[0060] In step S1, a blockchain platform is selected to build a distributed network, and a smart contract is deployed at each node. The smart contract determines the storage structure of customer information data. The customer information data includes basic customer information, business interaction data, and feature tags for data analysis. The system ensures data consistency between nodes by establishing a consensus mechanism of the blockchain network.

[0061] Furthermore, in step S2, customer information is collected through online platform registration, offline business processing, and market research channels, and the collected original information is cleaned, organized, and standardized to remove noise data and erroneous data to ensure information accuracy and integrity. The Advanced Encryption Standard AES encryption algorithm is used to encrypt sensitive information and store it in the blockchain node. The index information of customer information and some non-sensitive data are stored in the enterprise's internal data analysis middleware platform. The data analysis middleware platform has data caching and preliminary analysis capabilities and works in conjunction with the blockchain network.

[0062] In step S3, the data analysis needs are determined based on the enterprise's customer segmentation, precision marketing, and risk assessment business goals. A clustering model is designed using machine learning and data mining techniques to classify customer groups, a rule model is designed to mine the correlation between customer purchase behaviors, and a prediction model is designed to estimate customer churn risks. The clustering model, rule model, and prediction model are converted into rules and algorithms that can be recognized and executed by blockchain network smart contracts and deployed in smart contracts.

[0063] The clustering model is established by: the database includes a customer classification data set {x1, x2, ..., x q}, for x p For analysis, x p is an m-dimensional data point, that is, x p ={x p1 ,x p2 ,…,x pm}, thus establishing an objective function:

[0064]

[0065] Among them, K is the number of clusters, X k is the center of the kth cluster, X k ={X k1 ,X k2 ,…,X km}, establish a judgment variable z pk , when x p When it belongs to the kth cluster, z pk =1, when x p When it does not belong to the kth cluster, z p k =0, thus establishing the cluster center X k The update formula is:

[0066]

[0067] During the iteration process, the variable z is judged ik and cluster center X k Keep updating until the objective function A converges;

[0068] The method for establishing the rule model is as follows: the database includes a commodity set I = {i1, i2, ..., i m}, including transaction database D = {T1, T2, ..., T n}, establish the support coefficient formula:

[0069]

[0070] Analyze project set Support coefficient, set the minimum support coefficient threshold min s , thereby filtering out the values ​​that satisfy support(X) greater than or equal to min s The number of frequent items

[0071]

[0072] Formula for establishing trust factor:

[0073]

[0074] The trust coefficient of association rule X→Y is calculated by the trust coefficient formula, where and Set the minimum trust factor threshold min c , confirm that

[0075] confidence(X→Y)≥min c The association rule set is:

[0076]

[0077] In this way, rules with strong correlations between corporate customer purchase behaviors are discovered, thereby providing a basis for business decisions such as precision marketing and product recommendations, and realizing the intelligent correlation analysis capabilities of corporate data-driven business decisions. The system and method work in coordination with the company's multi-channel data collection, other data analysis model construction, and blockchain smart contracts. In the data collection stage, data is collected through multiple channels and the original data is cleaned, sorted, and standardized to ensure data accuracy and integrity. Sensitive information is encrypted and stored in blockchain nodes, and index information and some non-sensitive data are stored in the company's internal specific data analysis middleware platform.

[0078] The method for establishing the prediction model is: analyzing the e-th customer, the database includes f feature vectors g of the e-th user e = {g e1 ,g e2 ,…,g ef}, when h e =1, it means the e-th customer is lost. e = 0, it means that the e-th customer has not been lost, thus establishing the predicted probability P(h e =1|g e )’s logistic regression model:

[0079]

[0080] Among them, β={β0,β1,β2,…,β f} is the model parameter, g eαRepresents the αth feature vector of the user. The log-likelihood function is obtained by taking the logarithm of the likelihood function, and then the optimal β value is obtained by maximizing the log-likelihood function using the gradient descent optimization algorithm.

[0081] Furthermore, in step S4, when an authorized user needs to call customer information, he / she initiates a request through the enterprise's unified information call portal. The request clearly states the data analysis task description and specific business scenario requirements. Before the user initiates the request, the system authenticates the user through a multi-factor authentication mechanism including password verification, dynamic verification code verification, and biometric recognition.

[0082] In step S5, when the blockchain network receives the information call request, the system parses the request through the smart contract to extract the data analysis requirements and task descriptions, and then performs intelligent data query and screening in the blockchain ledger based on the built-in data analysis model and rules. If the request is to obtain a specific type of customer information; the system uses the distributed characteristics of the blockchain to enable multiple nodes to execute data query and analysis tasks simultaneously, and dynamically adjusts the query strategy and optimizes the query path according to the data distribution through the smart contract.

[0083] In step S6, the encrypted customer information retrieved from the blockchain ledger is transmitted to the enterprise's internal secure decryption server, which uses a pre-stored decryption key to decrypt the encrypted customer information to restore the original customer information data, and organizes and formats the decrypted customer information according to the requester's requirements, and returns the processed customer information to the requester through the information call portal.

[0084] In step S7, when customer information changes, the system automatically triggers the information update process, and first reviews and verifies the updated information within the enterprise to ensure its authenticity and legality. After the review is passed, the updated information is encapsulated into a specific data structure and new transaction information is generated according to the blockchain protocol specifications.

[0085] A customer information integrated management system based on blockchain technology, the system comprising: a blockchain network construction module, an information collection and preprocessing module, an analysis model construction module, an information call request module, an information intelligent query module, an information decryption and return module, and an information update and maintenance module;

[0086] Build a blockchain network and smart contracts through the blockchain network building module, and define the customer information storage structure and consensus mechanism;

[0087] The information collection and preprocessing module collects and preprocesses customer information, encrypts and stores sensitive information, and stores some data in the middleware platform;

[0088] Determine the analysis requirements according to the business objectives through the analysis model building module, build the model and convert it into smart contract executable rules and algorithms;

[0089] Initiate an information call request through the portal by means of the information call request module and describe the task;

[0090] Intelligently query and filter information in the account book according to the model through the information intelligent query module;

[0091] The encrypted information is transmitted to the decryption server through the information decryption and return module, and after decryption, it is formatted as required and returned to the requester;

[0092] The information update and maintenance module is used to review and verify changes in customer information, thereby updating blockchain and middleware platform information and maintaining network performance.

[0093] Example 1: In terms of customer segmentation, a large retail enterprise determines the data analysis needs based on its own business goals and uses the clustering model in machine learning. The customer information database {x1, x2, ..., x q}, where each x p Represents a multi-dimensional data point of a customer. By setting the objective function and continuously iterating and updating the cluster center X k , successfully segmenting numerous customers into different groups such as high-value loyal customers, medium-consumption potential customers, and low-activity customers. This enables companies to develop personalized marketing strategies for different groups, such as providing exclusive customized services and preferential activities for high-value customers, effectively improving customer satisfaction and loyalty.

[0094] For precision marketing, when designing a rule model, the product set I = {i1, i2, …, i m} and transaction database D = {T1, T2, ..., T n}. By analyzing a large amount of sales transaction data, the support coefficient formula is used to screen out frequent item sets, such as customers who buy a certain brand of clothing often buy a specific brand of shoes, and then the association rules are determined based on the trust coefficient formula. Based on these rules, the company adjusts the display layout of goods, places related goods in adjacent areas, and carries out joint promotion activities, which significantly increases the average order value and sales.

[0095] In the risk assessment phase, a prediction model is built to estimate the risk of customer churn. Analyze the feature vector g of the e-th customer e = {g e1 ,g e2 ,…,g ef}, such as the time interval between the customer’s last purchase, the return rate, etc. Establish a logistic regression model P(h e =1|g e ), and the model parameter β is determined by the optimization algorithm. When it is predicted that some customers have a high risk of churn, the company promptly launches retention measures, such as issuing coupons and providing personalized recommendations, which successfully reduces the customer churn rate and ensures the sustainable and stable development of the company.

[0096] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A comprehensive customer information management method based on blockchain technology, characterized by: The method comprises the following steps: S1. Build blockchain network and smart contracts, define customer information storage structure and consensus mechanism; S2, collect customer information and pre-process it, encrypt and store sensitive information, and store some data on the middleware platform; S3. Determine analysis requirements based on business objectives, build models and convert them into smart contract executable rules and algorithms; S4. After identity verification, the business personnel initiate an information call request through the portal and describe the task; S5. After the blockchain network receives the request, the smart contract parses it and intelligently queries and filters the information in the ledger according to the model; S6. The encrypted information is transmitted to the decryption server, which is formatted and returned to the requester after decryption. S7. When customer information changes, the blockchain and middleware platform information will be updated and network performance will be maintained after review and verification.

2. According to claim 1, a customer information comprehensive management method based on blockchain technology is characterized by: In step S1, a blockchain platform is selected to build a distributed network, and smart contracts are deployed at each node. The smart contracts determine the storage structure of customer information data. Customer information data includes basic customer information, business interaction data, and feature tags for data analysis. The system ensures data consistency between nodes by establishing a consensus mechanism of the blockchain network.

3. According to claim 2, a customer information comprehensive management method based on blockchain technology is characterized in that: In step S2, customer information is collected through online platform registration, offline business processing, and market research channels, and the collected original information is cleaned, organized, and standardized to remove noise data and erroneous data. The Advanced Encryption Standard AES encryption algorithm is used to encrypt sensitive information and store it in the blockchain node. The index information of customer information and some non-sensitive data are stored in the enterprise's internal data analysis middleware platform. The data analysis middleware platform has data caching and preliminary analysis capabilities and works in conjunction with the blockchain network.

4. According to claim 3, a method for comprehensive management of customer information based on blockchain technology is characterized in that: In step S3, the data analysis needs are determined based on the enterprise's customer segmentation, precision marketing, and risk assessment business goals. A clustering model is designed using machine learning and data mining techniques to classify customer groups. A rule model is designed to mine the correlation between customer purchase behaviors. A prediction model is designed to estimate customer churn risks. The clustering model, rule model, and prediction model are converted into rules and algorithms that can be recognized and executed by blockchain network smart contracts and deployed in smart contracts.

5. According to claim 4, a method for comprehensive management of customer information based on blockchain technology is characterized in that: The clustering model is established by: the database includes a customer classification data set {x1, x2, ..., x q }, for x p For analysis, x p is an m-dimensional data point, that is, x p ={x p1 ,x p2 ,…,x pm }, thus establishing an objective function: Among them, K is the number of clusters, X k is the center of the kth cluster, X k ={X k1 ,X k2 ,…,X km }, establish a judgment variable z pk , when x p When it belongs to the kth cluster, z pk =1, when x p When it does not belong to the kth cluster, z pk =0, thus establishing the cluster center X k The update formula is: During the iteration process, the variable z is judged ik and cluster center X k Keep updating until the objective function A converges; The method for establishing the rule model is as follows: the database includes a commodity set I = {i1, i2, ..., i m }, including transaction database D = {T1, T2, ..., T n }, establish the support coefficient formula: Analyze project set Support coefficient, set the minimum support coefficient threshold min s , thereby filtering out the values ​​that satisfy support(X) greater than or equal to min s The number of frequent items Formula for establishing trust factor: The trust coefficient of association rule X→Y is calculated by the trust coefficient formula, where and Set the minimum trust factor threshold min c , confirm that confidence(X→Y)≥min c The association rule set is: The method for establishing the prediction model is: analyzing the e-th customer, the database includes f feature vectors g of the e-th user e = {g e1 ,g e2 ,…,g ef }, when h e =1, it means the e-th customer is lost. e = 0, it means that the e-th customer has not been lost, thus establishing the predicted probability P(h e =1|g e )’s logistic regression model: Among them, β={β0,β1,β2,…,β f } is the model parameter, g eα Represents the αth feature vector of the user. The log-likelihood function is obtained by taking the logarithm of the likelihood function, and then the optimal β value is obtained by maximizing the log-likelihood function using the gradient descent optimization algorithm.

6. According to claim 5, a method for comprehensive management of customer information based on blockchain technology is characterized in that: In step S4, when an authorized user needs to call customer information, he / she initiates a request through the enterprise's unified information call portal. The request clearly states the data analysis task description and specific business scenario requirements. Before the user initiates the request, the system authenticates the user through a multi-factor authentication mechanism including password verification, dynamic verification code verification and biometric recognition.

7. According to claim 6, a method for comprehensive management of customer information based on blockchain technology is characterized in that: In step S5, when the blockchain network receives the information call request, the system parses the request through the smart contract to extract the data analysis requirements and task descriptions, and then performs intelligent data query and screening in the blockchain ledger based on the built-in data analysis model and rules. If the request is to obtain a specific type of customer information; the system uses the distributed characteristics of the blockchain, and the nodes simultaneously execute data query and analysis tasks, and dynamically adjust the query strategy and optimize the query path according to the data distribution through the smart contract.

8. According to claim 7, a method for comprehensive management of customer information based on blockchain technology is characterized in that: In step S6, the encrypted customer information retrieved from the blockchain ledger is transmitted to the enterprise's internal secure decryption server, which uses a pre-stored decryption key to decrypt the encrypted customer information to restore the original customer information data, and organizes and formats the decrypted customer information according to the requester's requirements, and returns the processed customer information to the requester through the information call portal.

9. A customer information comprehensive management method based on blockchain technology according to claim 8, characterized in that: In step S7, when customer information changes, the system automatically triggers the information update process, first reviews and verifies the updated information within the enterprise, and after the review is passed, encapsulates the updated information into a specific data structure and generates new transaction information according to the blockchain protocol specifications.

10. A customer information integrated management system based on blockchain technology, the system is applied to the customer information integrated management method based on blockchain technology according to claim 1, characterized in that: The system includes: a blockchain network construction module, an information collection and preprocessing module, an analysis model construction module, an information call request module, an information intelligent query module, an information decryption and return module, and an information update and maintenance module; Build a blockchain network and smart contracts through the blockchain network building module, and define the customer information storage structure and consensus mechanism; The information collection and preprocessing module collects and preprocesses customer information, encrypts and stores sensitive information, and stores some data in the middleware platform; Determine the analysis requirements according to the business objectives through the analysis model building module, build the model and convert it into smart contract executable rules and algorithms; Initiate an information call request through the portal by means of the information call request module and describe the task; Intelligently query and filter information in the account book according to the model through the information intelligent query module; The encrypted information is transmitted to the decryption server through the information decryption and return module, and after decryption, it is formatted as required and returned to the requester; The information update and maintenance module is used to review and verify changes in customer information, thereby updating blockchain and middleware platform information and maintaining network performance.

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