Customer portrait generation method and device based on block chain, and storage medium

Abstract

The invention discloses a customer portrait generation method and device based on a block chain and a storage medium. The method relates to the fields of block chains and artificial intelligence, and comprises the following steps: determining a plurality of nodes on a block chain; for each node, controlling the target node to receive the sub-customer portraits of the target customer transmitted by other nodes; inputting the sub-customer portraits of the other nodes and the sub-customer portraits of the target node into a target expert model to obtain a predicted customer portraits of the target node to the target customer; and obtaining predicted customer portraits of all the nodes on the block chain, determining a portrait prediction weight of each node, and performing weighted summation on the predicted customer portraits of all the nodes based on the portrait prediction weight of each node to obtain a target customer portrait. Through the method and the device, the problem that the predicted customer portrait of the user is inaccurate in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the fields of blockchain and artificial intelligence, and specifically, to a customer portrait generation method, device and storage medium based on blockchain. Background Art

[0002] In the field of financial products, customer portraits (or user portraits) are used to model the characteristics of potential customer groups of financial products. Customers are portrayed by collecting and analyzing customer attribute information, behavioral characteristics and other data, thereby providing decision support for the precision marketing of financial products. For example, in bank loan business, customer portraits can be used to help banks identify loan applicants, conduct risk assessments based on the loan applicants' repayment ability and credit records, and determine loan approval and loan amounts.

[0003] The customer portrait generation method in the related art only uses the data of a single institution or department. It is difficult for a single institution or financial institution to obtain comprehensive customer data, and the information island problem is serious. In addition, manual modeling and analysis are usually used, which is highly subjective and difficult to conduct large-scale customer grouping analysis. It is impossible to achieve automation and personalized customization of customer portraits. This results in the low accuracy of the customer portraits generated by the constructed portrait generation model.

[0004] Currently, no effective solution has been proposed to the problem of inaccurate customer profiles in user predictions in related technologies. Summary of the invention

[0005] The main purpose of this application is to provide a customer portrait generation method, device and storage medium based on blockchain to solve the problem of inaccurate customer portrait predicted by users in related technologies.

[0006] In order to achieve the above purpose, according to one aspect of the present application, a method for generating a customer portrait based on blockchain is provided. The method includes: determining multiple nodes on the blockchain, wherein each node represents an institution that collects user information, and the types of user information collected by institutions corresponding to different nodes are different; for each node, controlling the target node to receive the sub-customer portrait of the target customer transmitted by other nodes, wherein the other nodes are nodes other than the target node on the blockchain, and the sub-customer portrait is a customer portrait generated by each node based on the collected user information and the portrait generation model of the node; inputting the sub-customer portraits of other nodes and the sub-customer portraits of the target node into the target expert model to obtain the target node's predicted customer portrait of the target customer, wherein the target expert model is a model constructed based on the portrait generation model of each node through a preset aggregation strategy, and the predicted customer portrait is used to characterize the target customer's purchase tendency for the recommended product; obtaining the predicted customer portraits of all nodes on the blockchain, determining the portrait prediction weight of each node, and weighted summing the predicted customer portraits of all nodes based on the portrait prediction weight of each node to obtain the target customer portrait.

[0007] Optionally, the portrait generation model of each node is trained in the following manner: for each node, the historical user information of each user within a preset period of the node is obtained, and the historical sub-customer portrait of each user is determined; the historical user information and historical sub-customer portrait of each user are determined as a group of first training samples to obtain multiple groups of first training samples; based on the multiple groups of first training samples, a preset machine learning model is trained to obtain the portrait generation model of the node.

[0008] Optionally, the target expert model is constructed in the following manner: control the target node to receive model parameters of the portrait generation model transmitted by other nodes to obtain a set of model parameters; perform weighted summation on a set of model parameters by using the preset parameter weights of each portrait generation model to obtain target model parameters; obtain historical sub-customer portraits of other nodes of multiple users, and obtain the historical predicted customer portraits of each user; determine the historical sub-customer portraits and historical predicted customer portraits of each user as a set of second training samples to obtain multiple groups of second training samples; train the preset machine learning model using multiple groups of second training samples and target model parameters to obtain the target expert model.

[0009] Optionally, the sub-customer portraits of other nodes and the sub-customer portrait of the target node are input into the target expert model to obtain the predicted customer portrait of the target node for the target customer, including: extracting target features from the sub-customer portrait of each other node to obtain a target feature set, wherein the target feature is a data feature associated with the purchase tendency of the recommended product; processing the target feature set to obtain input features, and inputting the input features into the target expert model to obtain the predicted customer portrait of the target node for the target customer.

[0010] Optionally, processing the target feature set to obtain input features includes: determining whether there are duplicate data or conflicting data in the target feature set; if there are duplicate data or conflicting data in the target feature set, fusing the duplicate data or conflicting data according to a preset data ratio to obtain fused data features; replacing the duplicate data or conflicting data in the target feature set with the fused data features to obtain input features.

[0011] Optionally, before determining multiple nodes on the blockchain, the method also includes: determining multiple institutions for generating customer portraits, and building a blockchain network architecture with each institution as a node; for each node on the blockchain, collecting user information of the target user in the institution corresponding to the node through a preset smart contract; inputting the user information into the portrait generation model of the node to obtain a sub-customer portrait of the target user at the node; encrypting the sub-customer portrait of the node through a preset encryption algorithm, and transmitting the encrypted sub-customer portrait to other nodes on the blockchain.

[0012] Optionally, the method also includes: for each node on the blockchain, collecting new user information of the organization corresponding to the node at preset periods, updating the portrait generation model of the node according to the new user information, and obtaining an updated portrait generation model; determining model parameters of the updated portrait generation model to obtain updated model parameters, and controlling each node to transmit the updated model parameters of the node to other nodes; controlling each node to update the target expert model of the node based on the updated model parameters to obtain an updated target expert model.

[0013] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a customer portrait generation device based on blockchain is provided. The device includes: a first determination unit, which is used to determine multiple nodes on the blockchain, wherein each node represents an institution that collects user information, and the types of user information collected by the institutions corresponding to different nodes are different; a control unit, which is used to control the target node to receive the sub-customer portrait of the target customer transmitted by other nodes for each node, wherein the other nodes are nodes other than the target node on the blockchain, and the sub-customer portrait is a customer portrait generated by each node based on the collected user information and the node's portrait generation model; an input unit, which is used to input the sub-customer portraits of other nodes and the sub-customer portraits of the target node into the target expert model to obtain the target node's predicted customer portrait for the target customer, wherein the target expert model is a model constructed by a preset aggregation strategy based on the portrait generation model of each node, wherein the predicted customer portrait is used to characterize the target customer's purchase tendency for the recommended product; an acquisition unit, which is used to obtain the predicted customer portraits of all nodes on the blockchain, determine the portrait prediction weight of each node, and perform weighted summation on the predicted customer portraits of all nodes based on the portrait prediction weight of each node to obtain the target customer portrait.

[0014] In an embodiment of the present application, a plurality of nodes on a blockchain are determined, wherein each node represents an organization that collects user information, and the types of user information collected by organizations corresponding to different nodes are different; for each node, a target node is controlled to receive sub-customer portraits of target customers transmitted by other nodes, wherein other nodes are nodes other than the target node on the blockchain, and sub-customer portraits are customer portraits generated by each node based on the collected user information and a portrait generation model of the node; sub-customer portraits of other nodes and sub-customer portraits of the target node are input into a target expert model to obtain a predicted customer portrait of the target customer by the target node, wherein the target expert model is a model constructed based on the portrait generation model of each node through a preset aggregation strategy, and the predicted customer portrait is used to characterize the target customer's purchasing tendency for the recommended product; the predicted customer portraits of all nodes on the blockchain are obtained, the portrait prediction weight of each node is determined, and the predicted customer portraits of all nodes are weighted summed based on the portrait prediction weight of each node to obtain the target customer portrait, thereby realizing the distributed generation and sharing of customer portraits through blockchain technology. It improves data security and privacy protection, and can also integrate multi-source data to build a more comprehensive and accurate customer portrait, thereby achieving the technical effect of improving the accuracy of the generated customer portrait, and further solving the technical problem of inaccurate customer portrait predicted for users due to a single data source. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0016] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a customer portrait generation method based on blockchain provided in an embodiment of the present application;

[0017] Figure 2 It is a flowchart of a method for generating a customer portrait based on blockchain provided in an embodiment of the present application;

[0018] Figure 3 It is a schematic diagram of a blockchain-based customer portrait generation device provided according to an embodiment of the present application;

[0019] Figure 4 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making; if the user chooses to refuse, the expert decision-making process will be entered.

[0023] Example 1

[0024] According to an embodiment of the present application, a method embodiment for generating a customer portrait based on blockchain is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0025] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 This is a hardware structure block diagram of a computer terminal for implementing a customer portrait generation method based on blockchain according to an embodiment of the present application. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (shown as 102a, 102b, ..., 102n in the figure) processors 102 (the processor 102 may include but is not limited to a processing device such as an MCU (Microcontroller Unit) or an FPGA (Field-Programmable Gate Array), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a USB (Universal Serial Bus) port (which may be included as one of the ports of the bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0026] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0027] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the customer portrait generation method based on blockchain in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the above-mentioned customer portrait generation method based on blockchain is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0030] In the above operating environment, Figure 2 is a flowchart of a method for generating a customer portrait based on blockchain according to an embodiment of the present application, such as Figure 2 As shown, the method includes:

[0031] Step S201, determine multiple nodes on the blockchain, where each node represents an organization that collects user information, and the types of user information collected by organizations corresponding to different nodes are different.

[0032] In step S201, first, determine which types of institutions will participate in building customer portraits. These institutions may include financial institutions, retailers, social media platforms, telecom operators, online service providers, etc., each of which has its own specific data sources and user information. Classify user information by type, such as basic identity information, transaction records, browsing history, social media interactions, location data, device information, etc. Different types of user information can adopt different privacy protection measures and data processing technologies. According to the data collection capabilities of the institution, assign the type of user information that each node should process. For example, financial institutions can be responsible for collecting transaction records, social media platforms are responsible for collecting user interaction data, retailers collect shopping behavior data, etc. Ensure that each node focuses on data within its professional field to improve the efficiency and accuracy of data processing.

[0033] It should be noted that in the blockchain, it is necessary to define how different nodes share processed customer profile information instead of directly transmitting raw user data. For example, use smart contracts to encode data sharing rules to ensure that the use of data complies with preset privacy policies and regulatory requirements. Implement data encryption and privacy protection technologies for each node, such as homomorphic encryption, zero-knowledge proofs, or secure multi-party computing, to protect user information transmitted on the blockchain from being leaked or abused. Design a mechanism for how nodes collaborate to generate expert models. For example, using federated learning, each node can jointly train an expert model without sharing the original data to produce a more comprehensive customer profile. In addition, it is necessary to select a suitable consensus mechanism to ensure that all nodes reach a consensus on the consistency and validity of the data. Configure blockchain software and smart contracts for each node to ensure that all nodes can operate according to network rules. Conduct network testing to verify the effectiveness of data sharing, privacy protection, and collaboration mechanisms.

[0034] Step S202, for each node, controls the target node to receive the sub-customer portrait of the target customer transmitted by other nodes, where the other nodes are nodes on the blockchain other than the target node, and the sub-customer portrait is a customer portrait generated by each node based on the collected user information and the node's portrait generation model.

[0035] In step S202, before data transmission, each node encrypts the sub-customer portrait generated based on local data. The encryption method can adopt elliptic curve encryption or homomorphic encryption technology to ensure that even if the data is intercepted during transmission, it cannot be interpreted by unauthorized parties. The sub-customer portrait can adopt a standardized data structure, including basic characteristics, behavior patterns, preference tags and other information. Each sub-customer portrait is accompanied by a digital signature to verify the source and integrity of the data. The rules and processes for data transmission are defined through smart contracts. Smart contracts can set what types of data the target node is allowed to receive, as well as the conditions for receiving data. For example, the smart contract will only allow data transmission if the target customer explicitly agrees that his data will be used for portrait generation.

[0036] After other nodes generate sub-customer portraits, they encrypt and sign the data according to the smart contract rules. The data is sent to the target node through the blockchain network. The distributed nature of the blockchain ensures the consistency and immutability of the data during transmission. After receiving the data, the target node uses the corresponding key to decrypt the data and verify the digital signature to ensure that the data comes from a legitimate node and has not been tampered with.

[0037] Step S203, input the sub-customer portraits of other nodes and the sub-customer portraits of the target node into the target expert model to obtain the predicted customer portrait of the target node for the target customer, wherein the target expert model is a model constructed based on the portrait generation model of each node through a preset aggregation strategy, and the predicted customer portrait is used to characterize the target customer's purchase tendency for the recommended product.

[0038] In step S203, the target node receives the sub-customer portraits of the target customer from other nodes. These sub-customer portraits contain the analysis results of different institutions on the target customer's behavior, preference, historical record and other dimensions. Due to the diversity of data sources, all sub-customer portraits can provide a more comprehensive information perspective. Before inputting the sub-customer portraits into the target expert model, the target node pre-processes the collected sub-customer portraits, including data cleaning, format unification, feature selection and other steps to ensure that all sub-customer portrait data formats are consistent and can be correctly parsed and used by the target expert model. Among them, the target expert model can be constructed based on the portrait generation model of each node through a preset aggregation strategy. For example, using federated learning, ensemble learning or other collaborative learning methods. The pre-processed sub-customer portrait data, including the sub-customer portrait of the target node itself, is input into the target expert model. The model learns and predicts the target customer's purchasing tendency, interest preference, etc. by analyzing the features in these sub-customer portraits, and finally generates a comprehensive predicted customer portrait.

[0039] Step S204, obtain the predicted customer portraits of all nodes on the blockchain, determine the portrait prediction weight of each node, and perform weighted summation of the predicted customer portraits of all nodes based on the portrait prediction weight of each node to obtain the target customer portrait.

[0040] In step S204, each node generates a predicted customer portrait for the target customer based on its local data and algorithm model. The predicted customer portrait of each node may contain different features, such as consumption habits, social behavior, investment preferences, etc. Through the blockchain network, the target node ensures that these predicted customer portraits are collected from all other nodes. The target node sets the portrait prediction weight based on the correlation between the purchase tendency of the recommended product and the predicted customer portraits of other nodes. For example, based on the historical prediction results of each node, the accuracy of the expert model of each node is evaluated, and the model with high accuracy has a higher corresponding portrait prediction weight. Alternatively, the data uploaded by the node is evaluated for quality, and the node with high data quality has a higher portrait prediction weight. Or, the contribution of the predicted customer portrait data to the prediction of the purchase tendency of the recommended product is evaluated, and the portrait prediction weight of the data source node with high contribution is higher.

[0041] After determining the predicted customer portraits of all nodes and their corresponding weights, the target node performs a weighted sum operation. The predicted customer portraits of all nodes are integrated into a comprehensive target customer portrait. Throughout the process, measures need to be taken to ensure the privacy and security of user data, such as using encrypted transmission, homomorphic encryption, zero-knowledge proof and other technologies to ensure that sensitive information is not leaked when sharing and processing data on the blockchain. The generated final target customer portrait can be used for subsequent marketing activities and product recommendations. By analyzing the target customer portrait, the target node can more accurately predict the target customer's purchasing tendency and interest preference, thereby formulating personalized marketing strategies.

[0042] The blockchain-based customer portrait generation method provided in the embodiment of the present application determines multiple nodes on the blockchain, wherein each node represents an organization that collects user information, and the types of user information collected by organizations corresponding to different nodes are different; for each node, the target node is controlled to receive sub-customer portraits of target customers transmitted by other nodes, wherein the other nodes are nodes other than the target node on the blockchain, and the sub-customer portraits are customer portraits generated by each node based on the collected user information and the node's portrait generation model; the sub-customer portraits of other nodes and the sub-customer portraits of the target node are input into the target expert model to obtain the target node's predicted customer portrait of the target customer, wherein the target expert model is a model constructed based on the portrait generation model of each node through a preset aggregation strategy, and the predicted customer portrait is used to characterize the target customer's purchasing tendency for the recommended product; the predicted customer portraits of all nodes on the blockchain are obtained, the portrait prediction weight of each node is determined, and the predicted customer portraits of all nodes are weighted summed based on the portrait prediction weight of each node to obtain the target customer portrait, and the distributed generation and sharing of customer portraits are realized through blockchain technology. It improves data security and privacy protection, and can also integrate multi-source data to build a more comprehensive and accurate customer portrait, thereby achieving the technical effect of improving the accuracy of the generated customer portrait, and further solving the technical problem of inaccurate customer portrait predicted for users due to a single data source.

[0043] In order to obtain the sub-customer portrait of each node, it is necessary to train a portrait generation model at each node. Optionally, in the blockchain-based customer portrait generation method provided in the embodiment of the present application, the portrait generation model of each node is trained in the following manner: for each node, the historical user information of each user within a preset period of the node is obtained, and the historical sub-customer portrait of each user is determined; the historical user information and historical sub-customer portrait of each user are determined as a group of first training samples to obtain multiple groups of first training samples; based on the multiple groups of first training samples, a preset machine learning model is trained to obtain a node portrait generation model.

[0044] In some examples, each node collects all historical user information of its users within a preset period (e.g., the past year, the past quarter, etc.). The historical user information may include the user's transaction records, web browsing history, social media interactions, shopping preferences, location data, device usage, etc. Based on the collected historical user information, the historical user information of each user is combined with the corresponding historical sub-customer portrait into a set of first training samples.

[0045] Using these first training samples, each node trains its preset machine learning model. For example, logistic regression, support vector machine, random forest or deep learning model, the training process can include feature selection, parameter tuning, cross-validation and other links to ensure the performance and generalization ability of the model. After the model training is completed, the node should evaluate it, test it with data that has not participated in the training, and check the model's accuracy, recall and other indicators. Based on the evaluation results, the node adjusts the model parameters, optimizes the feature selection or improves the data processing process to improve the accuracy of the model prediction.

[0046] This embodiment trains a portrait generation model for each node, so that each node can build a sub-customer portrait based on its historical data and train a targeted portrait generation model. This reflects the node's understanding of user behavior and preferences, and supports business activities such as personalized marketing and product recommendations.

[0047] After the portrait generation model of each node is trained, the target expert model is constructed by the model parameters of the portrait generation models of each node. Optionally, in the blockchain-based customer portrait generation method provided in the embodiment of the present application, the target expert model is constructed in the following manner: control the target node to receive the model parameters of the portrait generation model transmitted by other nodes to obtain a set of model parameters; perform weighted summation on a set of model parameters by the preset parameter weights of each portrait generation model to obtain the target model parameters; obtain the historical sub-customer portraits of other nodes of multiple users, and obtain the historical predicted customer portrait of each user; determine the historical sub-customer portraits and historical predicted customer portraits of each user as a set of second training samples to obtain multiple groups of second training samples; train the preset machine learning model by the multiple groups of second training samples and the target model parameters to obtain the target expert model.

[0048] In some examples, the target node receives the model parameters of their portrait generation from other nodes on the blockchain. These parameters may include weights, biases, structural information, etc. A parameter weight is set for each node's model parameter based on expert experience, and the parameter weight can be determined based on factors such as the node's data volume, data quality, and model performance. The target node uses these weights to perform a weighted summation of the collected model parameters to generate the target model parameters.

[0049] The target node collects historical sub-customer portraits and historical predicted customer portraits of multiple users and combines them into a second training sample set. The historical sub-customer portraits come from various nodes and reflect the user's behavior and preferences under different data sources. The historical predicted customer portrait is the prediction result of the user's behavior under the past model version of the target node. The preset machine learning model (which can be a deep learning model, an integrated learning model, etc.) is trained using the target model parameters and the second training sample set. The goal of model training is to learn the pattern of user behavior based on historical data and use the integrated model parameters to improve the accuracy of the prediction.

[0050] This embodiment integrates the advantages of multiple data sources by building an expert model and uses a weighted sum strategy to balance the contributions of different node models. The customer portrait ultimately generated is more comprehensive and accurate, providing strong data support for personalized recommendations and marketing strategies.

[0051] After the target expert model is trained, the target expert model is used to predict the predicted customer profile of the target customer. Optionally, in the blockchain-based customer profile generation method provided in the embodiment of the present application, the sub-customer profiles of other nodes and the sub-customer profile of the target node are input into the target expert model to obtain the predicted customer profile of the target customer by the target node, including: extracting target features from the sub-customer portraits of each other node to obtain a target feature set, wherein the target feature is a data feature associated with the purchase tendency of the recommended product; processing the target feature set to obtain input features, and inputting the input features into the target expert model to obtain the predicted customer profile of the target customer by the target node.

[0052] In some examples, data features closely related to the purchase tendency of the recommended products are extracted from the sub-customer portraits of each other node and the sub-customer portraits of the target node. These target features may include the user's consumption history, product preferences, purchase frequency, historical purchase amount, credit score, number of similar products involved in browsing history, and any other information that can reflect the user's purchase tendency. The purpose of extracting target features is to reduce the dimensionality of the input data and avoid interference from irrelevant features. The target features extracted from all sub-customer portraits are integrated together to form a target feature set. Ensure that the features in the set are unified and standardized so that the model can correctly interpret and use the data. At the same time, check whether there is redundancy or conflict between features, and perform feature selection or data cleaning.

[0053] The target feature set is further processed, such as normalization, standardization, feature encoding, etc., to meet the input requirements of the target expert model. The processed feature set is called input features, and the processed input features are input into the target expert model. The target expert model generates a predicted customer profile for the target customer based on the input features. This profile contains the model's prediction results on the user's purchase tendency, which can include information such as purchase probability, interest level, and potential demand.

[0054] This embodiment extracts target features from the sub-customer portraits of the nodes and generates predicted customer portraits of target customers using the target expert model, thereby providing support for precision marketing and personalized recommendations, thereby improving the accuracy of the generated customer portraits.

[0055] In order to avoid data conflicts, the target feature set needs to be processed. Optionally, in the blockchain-based customer portrait generation method provided in the embodiment of the present application, the target feature set is processed to obtain input features, including: determining whether there are duplicate data or conflicting data in the target feature set; when there are duplicate data or conflicting data in the target feature set, fusing the duplicate data or conflicting data according to a preset data ratio to obtain fused data features; replacing the duplicate data or conflicting data in the target feature set with the fused data features to obtain input features.

[0056] In some examples, the data in the target feature set is checked to identify any duplicate data items or conflicting data features. Duplicate data may result from multiple nodes collecting the same type of information, while conflicting data may result from different data sources giving inconsistent values ​​for the same feature. After duplicate or conflicting data is detected, data fusion is performed using a preset data ratio or fusion strategy. The goal of data fusion is to produce a unified, conflict-free data feature to reflect the actual situation of the user. Data fusion methods may include, but are not limited to: Fusion based on data ratio: If there are multiple duplicate or conflicting data features in the target feature set, these feature values ​​can be weighted averaged according to a preset ratio (e.g., the source of the data, the timeliness of the data, the trust of the node, etc.) to obtain a comprehensive feature value. For conflicting data, majority voting, mean, median, mode, or a more credible feature value can be selected based on the source node of the data and the data quality.

[0057] The fused data features replace the original duplicate or conflicting features to ensure the accuracy and consistency of the target feature set. The replaced feature set removes redundancy and retains the most accurate and relevant information. After processing duplicate and conflicting data, feature engineering preprocessing is performed, such as normalizing, standardizing, and one-hot encoding the features to make them suitable for input into the machine learning model.

[0058] This embodiment processes the target feature set to ensure that the feature set input into the target expert model is high-quality, consistent, and non-redundant, thereby improving the accuracy and reliability of model prediction. In a blockchain-based system, data fusion can integrate sub-customer portrait information from multiple nodes, while solving the diversity of data sources and possible data conflicts, thereby generating a more comprehensive and accurate target customer portrait.

[0059] In order to ensure the secure transmission of data, the sub-customer portrait of each node is transmitted through the blockchain. Optionally, in the customer portrait generation method based on blockchain provided in the embodiment of the present application, before determining multiple nodes on the blockchain, the method also includes: determining multiple institutions for generating customer portraits, and building a blockchain network architecture with each institution as a node; for each node on the blockchain, collecting user information of the target user in the institution corresponding to the node through a preset smart contract; inputting the user information into the portrait generation model of the node to obtain the sub-customer portrait of the target user at the node; encrypting the sub-customer portrait of the node through a preset encryption algorithm, and transmitting the encrypted sub-customer portrait to other nodes on the blockchain.

[0060] In some examples, multiple institutions involved in the generation of customer profiles are identified, and each institution is set as a node in the blockchain network, responsible for collecting, processing and sharing the user data it owns. A preset smart contract is deployed on the blockchain network. A smart contract is a code that automatically executes, controls or records digital asset transactions and can automatically execute data collection and processing rules. Smart contracts specify the process of data collection, processing, encryption and transmission, ensuring that all nodes comply with unified data processing standards while protecting user privacy.

[0061] Each node automatically collects the target user's data from its corresponding institution through smart contracts. This data may include user transaction records, social media activities, location information, online behavior, etc. The data collection process must follow the data protection measures defined in the smart contract, such as collecting only non-sensitive information or processing sensitive information through data desensitization technology. The node inputs the collected user information into its local portrait generation model to generate a sub-customer portrait for the target user. The sub-customer portrait reflects the user's behavioral characteristics and preferences from the perspective of a specific institution, and may include consumption habits, points of interest, credit scores, etc.

[0062] The generated sub-customer portrait is encrypted using a preset encryption algorithm. The purpose of encryption is to ensure the security of data during transmission and prevent data from being intercepted and interpreted by unauthorized persons. The encryption algorithm can use symmetric encryption, asymmetric encryption, homomorphic encryption and other technologies to ensure that the data can be correctly processed in the target model even in an encrypted state. The encrypted sub-customer portrait is transmitted to other nodes on the blockchain network. Data transmission follows the blockchain's secure transmission protocol to ensure the integrity and privacy of data during transmission. After receiving the encrypted sub-customer portrait, other nodes can use it as input to build the expert model, and can perform operations such as weighted summation without decryption.

[0063] This embodiment integrates the data of multiple institutions through blockchain, protects user privacy, and generates a more comprehensive and accurate customer portrait. Smart contracts and encryption technology ensure the secure transmission and use of data, while also providing a reliable framework for data collaboration between institutions.

[0064] The target expert model is regularly updated based on the newly added user information to ensure the accuracy of the generated predicted customer portrait. Optionally, in the blockchain-based customer portrait generation method provided in the embodiment of the present application, the method also includes: for each node on the blockchain, collecting the newly added user information of the organization corresponding to the node at every preset period, updating the portrait generation model of the node according to the newly added user information, and obtaining an updated portrait generation model; determining the model parameters of the updated portrait generation model to obtain the updated model parameters, and controlling each node to transmit the updated model parameters of the node to other nodes; controlling each node to update the target expert model of the node based on the updated model parameters to obtain an updated target expert model.

[0065] In some examples, each node collects new user information of its corresponding organization at a preset period (e.g., weekly, monthly, or quarterly). The new user information is input into the node's existing portrait generation model, and the model is retrained or fine-tuned to reflect the changes brought about by the new data. After the model update is completed, the model parameters of the updated portrait generation model are determined. Each node transmits the updated model parameters to other nodes on the blockchain to facilitate the sharing and integration of model parameters.

[0066] Through the blockchain network, each node encrypts the updated model parameters and transmits them to other nodes. The transmission process follows the blockchain's security protocol to ensure the security of data during transmission. After receiving the model parameters, other nodes can use these parameters to update their own portrait generation models and target expert models without directly accessing user data.

[0067] Each node updates its target expert model based on the updated model parameters received from other nodes and its own model parameters. The target expert model is retrained or fine-tuned to incorporate new model parameter information and improve the comprehensive prediction ability of the model. The updated target expert model can better reflect the latest user behaviors and preferences, as well as market dynamics.

[0068] This embodiment ensures the timeliness and accuracy of the model in the entire system by allowing each node on the blockchain network to regularly update its own model parameters and promoting the sharing of model parameters between nodes. It improves the quality of customer portrait generation and strengthens the collaboration of the blockchain network, enabling institutions to make decisions based on the latest data insights while protecting the privacy and security of user data.

[0069] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0070] Example 2

[0071] The embodiment of the present application also provides a customer portrait generation device based on blockchain. It should be noted that the customer portrait generation device based on blockchain in the embodiment of the present application can be used to execute the customer portrait generation method based on blockchain provided in the embodiment of the present application. The customer portrait generation device based on blockchain provided in the embodiment of the present application is introduced below.

[0072] According to the embodiment of the present application, Figure 3 is a schematic diagram of a customer portrait generation device based on blockchain provided in an embodiment of the present application, such as Figure 3 As shown, the device comprises:

[0073] A first determining unit 301 is used to determine multiple nodes on the blockchain, wherein each node represents an organization that collects user information, and the types of user information collected by organizations corresponding to different nodes are different;

[0074] A control unit 302 is used to control the target node to receive the sub-customer portrait of the target customer transmitted by other nodes for each node, wherein the other nodes are nodes other than the target node on the blockchain, and the sub-customer portrait is a customer portrait generated by each node based on the collected user information and the node's portrait generation model;

[0075] An input unit 303 is used to input the sub-customer portraits of other nodes and the sub-customer portrait of the target node into the target expert model to obtain the predicted customer portrait of the target node for the target customer, wherein the target expert model is a model constructed based on the portrait generation model of each node through a preset aggregation strategy, wherein the predicted customer portrait is used to characterize the purchase tendency of the target customer for the recommended product;

[0076] The acquisition unit 304 is used to obtain the predicted customer portraits of all nodes on the blockchain, determine the portrait prediction weight of each node, and perform weighted summation of the predicted customer portraits of all nodes based on the portrait prediction weight of each node to obtain the target customer portrait.

[0077] The blockchain-based customer portrait generation device provided in the embodiment of the present application is used to determine multiple nodes on the blockchain through a first determination unit 301, wherein each node represents an organization that collects user information, and the types of user information collected by organizations corresponding to different nodes are different; a control unit 302 is used to control the target node to receive the sub-customer portrait of the target customer transmitted by other nodes for each node, wherein the other nodes are nodes other than the target node on the blockchain, and the sub-customer portrait is a customer portrait generated by each node based on the collected user information and the node's portrait generation model; an input unit 303 is used to input the sub-customer portraits of other nodes and the sub-customer portrait of the target node into the target expert model to obtain the target node's predicted customer portrait of the target customer, wherein the target expert model is a model constructed based on the portrait generation model of each node through a preset aggregation strategy, wherein the predicted customer portrait is used to characterize the target customer's purchase tendency for the recommended product; an acquisition unit 304 is used to obtain the predicted customer portraits of all nodes on the blockchain, determine the portrait prediction weight of each node, and perform weighted summation on the predicted customer portraits of all nodes based on the portrait prediction weight of each node to obtain the target customer portrait, and through blockchain technology, the distributed generation and sharing of customer portraits are realized. It improves data security and privacy protection, and can also integrate multi-source data to build a more comprehensive and accurate customer portrait, thereby achieving the technical effect of improving the accuracy of the generated customer portrait, and further solving the technical problem of inaccurate customer portrait predicted for users due to a single data source.

[0078] Optionally, in the blockchain-based customer portrait generation device provided in an embodiment of the present application, the device also includes: a second determination unit, used to obtain, for each node, historical user information of each user within a preset period of the node, and determine a historical sub-customer portrait of each user; a third determination unit, used to determine the historical user information and historical sub-customer portrait of each user as a group of first training samples, and obtain multiple groups of first training samples; a first training unit, used to train a preset machine learning model based on multiple groups of first training samples to obtain a portrait generation model of the node.

[0079] Optionally, in the blockchain-based customer portrait generation device provided in an embodiment of the present application, the device also includes: a receiving unit, used to control the target node to receive model parameters of the portrait generation model transmitted by other nodes, and obtain a set of model parameters; a summation unit, used to weighted sum a set of model parameters by using the preset parameter weights of each portrait generation model to obtain target model parameters; a portrait acquisition unit, used to obtain historical sub-customer portraits of other nodes of multiple users, and obtain the historical predicted customer portrait of each user; a fourth determination unit, used to determine the historical sub-customer portraits and historical predicted customer portraits of each user as a set of second training samples, and obtain multiple groups of second training samples; a second training unit, used to train the preset machine learning model using multiple groups of second training samples and target model parameters to obtain a target expert model.

[0080] Optionally, in the blockchain-based customer profile generation device provided in an embodiment of the present application, the input unit 303 includes: an extraction module, used to extract target features from the sub-customer portraits of each other node to obtain a target feature set, wherein the target features are data features associated with the purchase tendency of the recommended product; a processing module, used to process the target feature set to obtain input features, input the input features into the target expert model, and obtain a predicted customer profile of the target node for the target customer.

[0081] Optionally, in the blockchain-based customer portrait generation device provided in the embodiment of the present application, the processing module includes: a judgment submodule, used to judge whether there is duplicate data or conflicting data in the target feature set; a fusion submodule, used to fuse the duplicate data or conflicting data according to a preset data ratio when there is duplicate data or conflicting data in the target feature set, to obtain fused data features; a replacement submodule, used to replace the duplicate data or conflicting data in the target feature set with the fused data features, to obtain input features.

[0082] Optionally, in the blockchain-based customer portrait generation device provided in the embodiment of the present application, the device also includes: a fifth determination unit, used to determine multiple institutions for generating customer portraits, and build a blockchain network architecture with each institution as a node; a first collection unit, used to collect user information of the target user in the institution corresponding to the node for each node on the blockchain through a preset smart contract; a sub-customer portrait determination unit, used to input user information into the portrait generation model of the node to obtain the sub-customer portrait of the target user at the node; an encryption unit, used to encrypt the sub-customer portrait of the node through a preset encryption algorithm, and transmit the encrypted sub-customer portrait to other nodes on the blockchain.

[0083] Optionally, in the blockchain-based customer portrait generation device provided in the embodiment of the present application, the device also includes: a second collection unit, used to collect new user information of the organization corresponding to each node on the blockchain at preset intervals, and update the portrait generation model of the node according to the new user information to obtain an updated portrait generation model; a sixth determination unit, used to determine the model parameters of the updated portrait generation model, obtain the updated model parameters, and control each node to transmit the updated model parameters of the node to other nodes; an updating unit, used to control each node to update the target expert model of the node based on the updated model parameters to obtain an updated target expert model.

[0084] It should be noted that the first determination unit 301, the control unit 302, the input unit 303 and the acquisition unit 304 correspond to steps S201 to S204 in Example 1, and the two modules and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the above-mentioned modules may also be part of the device and may be run in the computer terminal 10 provided in Example 1.

[0085] Example 3

[0086] An embodiment of the present application may provide an electronic device, Figure 4 is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (only one is shown) processor 402, memory 404, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0087] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0088] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: determine multiple nodes on the blockchain, wherein each node represents an organization that collects user information, and the types of user information collected by the organizations corresponding to different nodes are different; for each node, control the target node to receive the sub-customer portraits of the target customers transmitted by other nodes, wherein the other nodes are nodes other than the target node on the blockchain, and the sub-customer portraits are customer portraits generated by each node based on the collected user information and the node's portrait generation model; input the sub-customer portraits of other nodes and the sub-customer portraits of the target node into the target expert model to obtain the target node's predicted customer portrait of the target customer, wherein the target expert model is a model constructed based on the portrait generation model of each node through a preset aggregation strategy, and the predicted customer portrait is used to characterize the target customer's purchasing tendency for the recommended product; obtain the predicted customer portraits of all nodes on the blockchain, determine the portrait prediction weight of each node, and perform weighted summation of the predicted customer portraits of all nodes based on the portrait prediction weight of each node to obtain the target customer portrait.

[0089] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: for each node, obtain the historical user information of each user within the preset period of the node, and determine the historical sub-customer portrait of each user; determine the historical user information and historical sub-customer portrait of each user as a group of first training samples to obtain multiple groups of first training samples; train the preset machine learning model based on the multiple groups of first training samples to obtain a portrait generation model of the node.

[0090] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: control the target node to receive the model parameters of the portrait generation model transmitted by other nodes to obtain a set of model parameters; perform weighted summation on a set of model parameters through the preset parameter weights of each portrait generation model to obtain the target model parameters; obtain the historical sub-customer portraits of other nodes of multiple users, and obtain the historical predicted customer portraits of each user; determine the historical sub-customer portraits and historical predicted customer portraits of each user as a set of second training samples to obtain multiple groups of second training samples; train the preset machine learning model through multiple groups of second training samples and target model parameters to obtain the target expert model.

[0091] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: extract target features from the sub-customer portraits of each other node to obtain a target feature set, wherein the target features are data features associated with the purchase tendency of the recommended product; process the target feature set to obtain input features, input the input features into the target expert model, and obtain the target node's predicted customer profile for the target customer.

[0092] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: determine whether there are duplicate data or conflicting data in the target feature set; if there are duplicate data or conflicting data in the target feature set, fuse the duplicate data or conflicting data according to a preset data ratio to obtain fused data features; replace the duplicate data or conflicting data in the target feature set with the fused data features to obtain input features.

[0093] The processor can also call the information and applications stored in the memory through the transmission device to perform the following steps: determine multiple institutions for generating customer portraits, and use each institution as a node to build a blockchain network architecture; for each node on the blockchain, collect the user information of the target user in the institution corresponding to the node through a preset smart contract; input the user information into the portrait generation model of the node to obtain the sub-customer portrait of the target user at the node; encrypt the sub-customer portrait of the node through a preset encryption algorithm, and transmit the encrypted sub-customer portrait to other nodes on the blockchain.

[0094] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: for each node on the blockchain, collect the new user information of the organization corresponding to the node at every preset period, update the portrait generation model of the node according to the new user information, and obtain an updated portrait generation model; determine the model parameters of the updated portrait generation model to obtain the updated model parameters, and control each node to transmit the updated model parameters of the node to other nodes; control each node to update the target expert model of the node based on the updated model parameters to obtain an updated target expert model.

[0095] By adopting the embodiment of the present application, a solution for generating customer portraits based on blockchain is provided. Through blockchain technology, distributed generation and sharing of customer portraits are realized. The security and privacy protection of data are improved, and multi-source data can be integrated to build a more comprehensive and accurate customer portrait, thereby achieving the technical effect of improving the accuracy of the generated customer portrait, and further solving the technical problem of inaccurate customer portraits predicted by users due to a single data source.

[0096] It can be understood by those skilled in the art that Figure 4 The structure shown is for illustration only, and the electronic device may also be a smart phone, a tablet computer, a PDA, a mobile Internet device (MID), a PAD or other terminal device. Figure 4 The structure of the electronic device is not limited. Figure 4 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 4 Different configurations shown.

[0097] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0098] Example 4

[0099] The embodiment of the present application also provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the blockchain-based customer portrait generation method provided in the first embodiment.

[0100] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0101] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of a blockchain-based customer profile generation method.

[0102] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0103] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0108] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for generating customer portraits based on blockchain, characterized in that: include: Determine multiple nodes on the blockchain, where each node represents an organization that collects user information, and the types of user information collected by organizations corresponding to different nodes are different; For each node, control the target node to receive the sub-customer portrait of the target customer transmitted by other nodes, wherein the other nodes are nodes on the blockchain other than the target node, and the sub-customer portrait is a customer portrait generated by each node based on the collected user information and the portrait generation model of the node; Inputting the sub-customer portraits of the other nodes and the sub-customer portrait of the target node into the target expert model to obtain the predicted customer portrait of the target node for the target customer, wherein the target expert model is a model constructed based on the portrait generation model of each node through a preset aggregation strategy, and the predicted customer portrait is used to characterize the purchase tendency of the target customer for the recommended product; The predicted customer portraits of all nodes on the blockchain are obtained, the portrait prediction weight of each node is determined, and the predicted customer portraits of all nodes are weighted summed based on the portrait prediction weight of each node to obtain the target customer portrait.

2. The method according to claim 1, characterized in that The portrait generation model of each node is trained in the following way: For each node, obtain the historical user information of each user within a preset period of the node, and determine the historical sub-customer portrait of each user; Determine each user's historical user information and historical sub-customer portrait as a group of first training samples, and obtain multiple groups of first training samples; A preset machine learning model is trained based on the multiple groups of first training samples to obtain a portrait generation model of the node.

3. The method according to claim 1, characterized in that The target expert model is constructed in the following way: Controlling the target node to receive the model parameters of the portrait generation model transmitted by the other nodes to obtain a set of model parameters; The set of model parameters is weightedly summed by the preset parameter weights of each portrait generation model to obtain the target model parameters; Obtain historical sub-customer portraits of the other nodes of multiple users, and obtain historical predicted customer portraits of each user; Determine the historical sub-customer portrait and the historical predicted customer portrait of each user as a group of second training samples, and obtain multiple groups of second training samples; The target expert model is obtained by training a preset machine learning model using the multiple groups of second training samples and the target model parameters.

4. The method according to claim 1, characterized in that Inputting the sub-customer portraits of the other nodes and the sub-customer portrait of the target node into the target expert model to obtain the predicted customer portrait of the target node for the target customer includes: Extracting target features from the sub-customer portraits of each other node to obtain a target feature set, wherein the target features are data features associated with the purchase tendency of the recommended product; The target feature set is processed to obtain input features, and the input features are input into the target expert model to obtain a predicted customer profile of the target customer by the target node.

5. The method according to claim 4, characterized in that The target feature set is processed to obtain input features including: Determine whether there is duplicate data or conflicting data in the target feature set; When the duplicate data or the conflicting data exists in the target feature set, the duplicate data or the conflicting data is subjected to data fusion according to a preset data ratio to obtain fused data features; The duplicate data or the conflicting data in the target feature set is replaced with the fused data feature to obtain the input feature.

6. The method according to claim 1, characterized in that Before determining the plurality of nodes on the blockchain, the method further includes: Identify multiple institutions for generating customer portraits, and use each institution as a node to build a blockchain network architecture; For each node on the blockchain, user information of the target user at the institution corresponding to the node is collected through a preset smart contract; Inputting the user information into the portrait generation model of the node to obtain the sub-customer portrait of the target user at the node; The sub-customer portrait of the node is encrypted by a preset encryption algorithm, and the encrypted sub-customer portrait is transmitted to other nodes on the blockchain.

7. The method according to claim 1, characterized in that The method further comprises: For each node on the blockchain, newly added user information of the organization corresponding to the node is collected at a preset period, and the portrait generation model of the node is updated according to the newly added user information to obtain an updated portrait generation model; Determine the model parameters of the updated portrait generation model, obtain the updated model parameters, and control each node to transmit the updated model parameters of the node to other nodes; Each node is controlled to update the target expert model of the node based on the updated model parameters to obtain an updated target expert model.

8. A customer portrait generation device based on blockchain, characterized in that: include: A first determination unit is used to determine multiple nodes on the blockchain, wherein each node represents an organization that collects user information, and the types of user information collected by organizations corresponding to different nodes are different; A control unit, for each node, controls the target node to receive sub-customer portraits of the target customer transmitted by other nodes, wherein the other nodes are nodes other than the target node on the blockchain, and the sub-customer portraits are customer portraits generated by each node based on the collected user information and the portrait generation model of the node; An input unit, used for inputting the sub-customer portraits of the other nodes and the sub-customer portraits of the target node into a target expert model, to obtain a predicted customer portrait of the target node for the target customer, wherein the target expert model is a model constructed by a preset aggregation strategy based on the portrait generation models of each node, wherein the predicted customer portrait is used to characterize the purchase tendency of the target customer for the recommended product; The acquisition unit is used to obtain the predicted customer portraits of all nodes on the blockchain, determine the portrait prediction weight of each node, and perform weighted summation of the predicted customer portraits of all nodes based on the portrait prediction weight of each node to obtain the target customer portrait.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the blockchain-based customer portrait generation method described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory storing an executable program; A processor, used to run the program, wherein the program, when running, executes the customer portrait generation method based on blockchain as described in any one of claims 1 to 7.

11. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the steps of the blockchain-based customer portrait generation method described in any one of claims 1 to 7 are implemented.

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