Multi-dimensional dynamically updated intelligent customer portrait construction method and device

Abstract

The invention discloses a multi-dimensional dynamically-updated intelligent customer portrait construction method and device, computer equipment and a computer readable storage medium, and relates to the technical field of customer operation management in the insurance industry, and the method comprises the steps: obtaining various types of data such as customer basic information, purchase history and behavior data through a multi-source module; portrait dimensions are dynamically adjusted based on customer life cycle states, and related characteristic parameters are automatically increased, decreased and optimized through an engine; modeling demand changes in future 6-12 months by using a predictive machine learning model, and generating an enhanced customer portrait containing predictive features; and cross-channel data are synchronously integrated in a minute level through a real-time data processing framework, so that real-time updating and multi-dimensional fusion of portraits are realized. According to the method, multi-dimensional dynamic intelligent customer portrait construction is realized, the comprehensiveness and real-time performance of customer information are improved, the service recommendation accuracy and risk assessment perspectiveness are enhanced, and the customer operation efficiency and satisfaction are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of customer management in the insurance industry, and in particular to a multi-dimensional dynamic updating intelligent customer portrait construction method and device. BACKGROUND

[0002] As a core link of the digital transformation of the insurance industry, life insurance customer management is widely used in customer relationship management, precision marketing and risk control fields. In related technologies, through the collaborative work of API interface, data crawler and Internet of Things devices, a full-process technical system covering data collection, processing and analysis, and decision control is constructed. Specifically, the system includes multi-dimensional data integration, dynamic portrait updating, context-aware processing, cross-channel service synchronization and other key links, among which the customer portrait construction module and the risk assessment model module form a data closed loop to support personalized service recommendation and real-time early warning functions. With the development of artificial intelligence technology, traditional customer management systems have evolved from static data storage to dynamic prediction modeling, but existing technologies still have systemic defects such as insufficient multi-source data fusion and weak real-time response capability.

[0003] However, in the existing customer portrait construction method, structured data is directly used for storage, and unstructured data (such as social media text and health monitoring time series data) is not effectively integrated, which may result in incomplete identification of customer behavior characteristics and affect the accuracy of service recommendation. Specifically, traditional systems usually use fixed-dimensional static portraits, but have limitations such as poor portrait timeliness and insufficient prediction ability, for example, the portrait weight cannot be automatically adjusted when the customer's life cycle changes, resulting in that the family responsibility coefficient of newly married customers is not timely included in the assessment. In addition, although existing service recommendation algorithms introduce collaborative filtering technology, they lack the cooperation of context-aware processing modules and feedback optimization mechanisms, making it difficult to achieve precise matching in dynamic scenarios. In the field of risk assessment, traditional methods only rely on health indicators and financial data, without establishing multi-dimensional assessment models of occupation characteristics and living habits, resulting in significant lag in risk warning response, which may cause mismatch between customer protection needs and product design. SUMMARY

[0004] The main purpose of the present application is to provide a multi-dimensional dynamic updating intelligent customer portrait construction method.

[0005] Another purpose of the present application is to provide a multi-dimensional dynamic updating intelligent customer portrait construction device.

[0006] A third purpose of the present application is to provide a computer device.

[0007] A fourth purpose of the present application is to provide a non-transitory computer readable storage medium.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a method for constructing a multi-dimensional, dynamically updated intelligent customer profile, comprising: S1 acquires basic customer information, purchase history, behavioral data, social media data, geolocation data, and health monitoring device data through a multi-source heterogeneous data acquisition module; S2 dynamically adjusts the profile dimensions based on the customer lifecycle status. The dynamic profile dimension adjustment engine automatically adds or optimizes feature parameters related to the current lifecycle stage. S3 uses predictive machine learning models to model changes in customer demand over the next 6-12 months, generating enhanced customer profiles that include predictive features. S4 uses a real-time data processing framework to synchronize and integrate cross-channel data within minutes, enabling real-time updates of customer profiles and multi-dimensional data fusion.

[0009] Optionally, customer basic information, purchase history, behavioral data, social media data, geolocation data, and health monitoring device data can be acquired through a multi-source heterogeneous data acquisition module, including: S11 obtains real-time data on customer interaction behavior on the insurance platform through API interfaces, including page dwell time, click hotspot distribution, and service preference records; S12 uses OCR technology to automatically extract image information, including ID card and insurance policy, uploaded by customers, and completes structured data conversion through the image recognition module.

[0010] Optionally, profile dimensions can be dynamically adjusted based on customer lifecycle status, including: S21, When a customer is detected to be in the newlywed stage, the family responsibility coefficient is automatically increased. Characteristics of marital status ,in Calculated based on the number of family members Verification was conducted using marriage registration data. S22, When a customer is detected to be in retirement, the health risk weights are dynamically adjusted. The value was reduced to 0.8, and a model for predicting elderly care demand was introduced. Calculate long-term care insurance demand.

[0011] Optionally, predictive machine learning models can be used to model changes in customer demand over the next 6-12 months, including: S31, uses an LSTM network to analyze customer historical behavior sequences. Perform time series modeling and output the probability distribution of future demand. ; S32, using the Transformer architecture to analyze cross-channel behavioral characteristics Attention-weighted fusion is performed to generate a multi-dimensional demand prediction vector. .

[0012] Optionally, cross-channel data can be synchronized and integrated within minutes using a real-time data processing framework, including: S41 uses the Flink streaming engine to process data streams from IoT devices. and social media data streams Perform joint window calculations, with the time window length set to... minute; S42, Constructing customer association features through knowledge graphs ,in Indicates the first Embedding vectors of related entities , For feature dimensions.

[0013] Optionally, S5 uses a natural language processing module to process the interaction text between the customer and the intelligent customer service. Conduct sentiment analysis to extract customer emotional characteristics and based on Adjust service recommendation strategy .

[0014] To achieve the above objectives, a second aspect of the present invention provides a multi-dimensional, dynamically updated intelligent customer profile building device, comprising: The multi-source heterogeneous data acquisition module is used to acquire customers' basic information, purchase history, behavioral data, social media data, geolocation data, and health monitoring device data; The dynamic profile dimension adjustment engine module is used to dynamically adjust profile dimensions based on customer lifecycle status, automatically adding or optimizing feature parameters related to the current lifecycle stage. The predictive machine learning model module is used to model changes in customer demand over the next 6-12 months and generate enhanced customer profiles that include predictive features. The real-time data processing framework module is used to achieve real-time updates of customer profiles and multi-dimensional data fusion by synchronizing and integrating cross-channel data at the minute level.

[0015] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, for implementing the multi-dimensional dynamically updated intelligent customer profile construction method as described in the first aspect embodiment.

[0016] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the multi-dimensional dynamically updated intelligent customer profile construction method as described in the first aspect embodiment.

[0017] The embodiments of the present invention have the following beneficial effects: The methods, apparatus, electronic devices, and computer-readable storage media of the present invention can realize the construction of multi-dimensional and dynamically updated intelligent customer profiles, significantly improve the accuracy, real-time performance, and forward-looking nature of customer management, effectively support personalized service recommendations and risk warnings, and improve customer satisfaction and business conversion efficiency. Attached Figure Description

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a multi-dimensional, dynamically updated intelligent customer profile construction method provided in an embodiment of the present invention; Figure 2 This is a structural diagram of a multi-dimensional dynamically updated intelligent customer profile building device provided in an embodiment of the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] The following description, with reference to the accompanying drawings, describes a method and apparatus for constructing a multi-dimensional, dynamically updated intelligent customer profile according to embodiments of the present invention.

[0022] Example 1 This embodiment provides a method for constructing intelligent customer profiles that are dynamically updated across multiple dimensions. For example... Figure 1 As shown, the method includes the following steps: S1 acquires customers' basic information, purchase history, behavioral data, social media data, geolocation data, and health monitoring device data through a multi-source heterogeneous data acquisition module.

[0023] Specifically, in some implementations, acquiring basic customer information, purchase history, behavioral data, social media data, geographic location data, and health monitoring device data through a multi-source heterogeneous data acquisition module is a core step in the data acquisition layer of the intelligent life insurance customer management system of this invention. This module adopts a distributed data acquisition architecture, supporting the access and synchronization of various heterogeneous data sources, including but not limited to structured databases, unstructured text data, real-time streaming data, API interfaces, third-party web crawler systems, and IoT device data interfaces.

[0024] In this embodiment, the module performs data collection and preliminary processing through a standardized ETL (Extract, Transform, Load) process. Basic information (such as name, age, gender, occupation, income, etc.) is typically obtained through customer registration information or bank / credit reporting interfaces, with data format conforming to the customer information exchange specification in the ISO / IEC 20022 standard. Purchase history data is extracted from a policy database, supporting mixed access of SQL queries and NoSQL data sources. Behavioral data (such as APP clicks, page dwell time, search keywords, etc.) is collected through a data logging system and processed in real-time using Apache Kafka or Flink. Social media data is accessed through the OAuth 2.0 authorization mechanism to mainstream social platform APIs, supporting JSON / XML format parsing. Geographic location data is obtained through GPS, base station positioning, or IP address resolution technology, with accuracy down to the meter level (e.g., using the WGS-84 coordinate system). Health monitoring device data is accessed through Bluetooth, Wi-Fi, or a dedicated gateway, supporting medical data standards such as HL7 and FHIR.

[0025] In this embodiment, the data acquisition module supports multi-threaded concurrent acquisition, and the maximum concurrency can be configured as follows: The collection frequency can be dynamically adjusted according to the data source type, for example, social media data... Updated every minute, health monitoring data is... Data is collected in real-time at minute intervals. During the data collection process, the system employs data quality assessment indicators. We conduct quantitative assessments of missing data rate, outlier rate, and data consistency to ensure the integrity and reliability of the collected data.

[0026] This step plays a fundamental role in the entire system, providing high-quality, multi-dimensional data support for subsequent modules such as customer profiling, service recommendation, and risk assessment. By integrating heterogeneous data sources, the system can comprehensively capture customer behavior patterns and potential needs, thereby significantly improving the intelligence level of customer management and personalized service capabilities.

[0027] Furthermore, S1 includes: S11 obtains real-time data on customer interaction behavior on the insurance platform through API interfaces, including page dwell time, click hotspot distribution, and service preference records.

[0028] Specifically, in some implementations, acquiring customer interaction data on the insurance platform in real time via API interfaces is a key data collection step in the intelligent life insurance customer management system of this invention for customer profile construction and service recommendation optimization. This step is based on the data collection layer in a distributed architecture, and interacts with front-end applications, mobile apps, smart wearable devices, etc., through standardized API interfaces to achieve real-time capture and transmission of customer behavior.

[0029] In this embodiment, the system adopts a RESTful API architecture, combined with real-time communication protocols such as WebSocket or MQTT, to ensure low-latency data transmission. Customer behavior data on the platform, such as page dwell time, click hotspot distribution, and service preference records, is collected through front-end event tracking technology. For example, page dwell time is recorded using JavaScript's `performance.now()` or `Date.now()` methods to record page load and exit timestamps and calculate dwell time; click hotspot distribution is captured by DOM event listeners, capturing user click coordinates, click frequency, and click areas, and then normalized based on screen resolution and page layout information; service preference records are modeled based on user behavior on the platform, such as service requests, browsing paths, dwell time, and service ratings, to form preference tags.

[0030] In this embodiment, the system sets key performance indicators (KPIs) to evaluate the quality of data collection. For example, the minimum sampling frequency for page dwell time is to record user status every 500 milliseconds, the sampling precision for hotspot distribution is pixel-level (e.g., 100×100 pixel grid division), and the update frequency for service preference records is once every 10 minutes. Furthermore, the system supports standardized data formats, such as using JSON format to encapsulate behavioral data, with fields including user_id, timestamp, page_url, click_coords, service_type, and service_duration, ensuring that the data can be efficiently parsed and processed by subsequent modules.

[0031] In this embodiment, this step is widely applied in scenarios such as customer behavior analysis, personalized service recommendations, and risk warning triggering. For example, when a customer browses a life insurance product page, the system collects the distribution of their click hotspots in real time, identifies the product characteristics they are interested in (such as premiums, coverage, payment methods, etc.), and dynamically adjusts the recommended content based on their historical preferences. In customer lifecycle management, the system continuously collects behavioral data to identify changes in customer status (such as from browsing to hesitation, from hesitation to purchase), thereby triggering profile updates and service strategy adjustments.

[0032] The technical benefit of this step is that it provides high-quality, real-time behavioral data support for subsequent customer profiling and service recommendation algorithms, significantly improving the accuracy and response speed of customer behavior modeling. Through real-time collection and structured processing of multi-dimensional behavioral data, the system can more accurately capture customer intent, enabling immediate service optimization and personalized adjustments, thereby enhancing customer loyalty and satisfaction.

[0033] S12 uses OCR technology to automatically extract image information such as ID cards and insurance policies uploaded by customers, and completes structured data conversion through the image recognition module.

[0034] Specifically, in some implementations, this invention automatically extracts image information such as ID cards and insurance policies uploaded by customers through an image recognition module and converts it into structured data, thereby achieving efficient information processing and system integration. This step is based on OCR (Optical Character Recognition) technology, combined with deep learning models and image preprocessing algorithms, to complete the parsing and structured output of unstructured image data.

[0035] Specifically, the image recognition module first receives image files uploaded by the user, typically in common formats such as JPEG and PNG. An image resolution of at least 300 DPI is recommended to ensure the accuracy of OCR recognition. In the image preprocessing stage, the system employs image enhancement techniques, such as grayscale conversion, binarization, noise reduction, and contrast adjustment, to improve image quality and reduce recognition interference. Subsequently, a convolutional neural network (CNN)-based OCR model is used to detect and recognize text regions in the image. This model can recognize mixed text including Chinese characters, numbers, and letters, and supports the accurate extraction of key fields such as ID card numbers, names, dates of birth, and policy numbers.

[0036] In this embodiment, the OCR recognition module supports a custom field template matching mechanism. For example, when recognizing an ID card, the system can perform format verification on the 18-digit ID card number according to the national standard GB 11643-1999 to ensure that the recognition result conforms to the standard. The recognition accuracy can reach over 98% under standard image conditions, and the recognition speed is controlled within 500ms, meeting the needs of real-time business processing.

[0037] This step plays a crucial role in the entire system, particularly in customer information entry, identity verification, and policy verification. Through automated extraction, the system reduces manual intervention, improves data processing efficiency, and provides high-quality structured data input for subsequent customer profiling, service recommendations, and risk assessment. Furthermore, by combining image recognition and natural language processing technologies, the system can further achieve semantic understanding and data association, enhancing its overall intelligence level.

[0038] S2 dynamically adjusts the profile dimensions based on the customer lifecycle status. The dynamic profile dimension adjustment engine automatically adds or optimizes feature parameters related to the current lifecycle stage.

[0039] Specifically, in some implementations, dynamically adjusting profile dimensions based on customer lifecycle states is a crucial step in intelligent life insurance customer management systems. Its core lies in using a dynamic profile dimension adjustment engine to achieve real-time evolution of customer profiles and intelligent optimization of feature parameters. This step is technically implemented through the collaborative work of the customer lifecycle state recognition module and the profile feature management module, using a pre-defined lifecycle stage classification model (such as customer lifecycle state coding). ,in Indicates the first The system maps each lifecycle stage to a profile dimension mapping rule library, dynamically matching the key feature parameters required for the current stage.

[0040] The specific operation method includes: First, the system uses multi-source information such as customer behavior data, transaction records, and health monitoring data, and employs clustering algorithms (such as K-means) or classification models (such as random forest and XGBoost) to identify the current lifecycle stage of the customer. Subsequently, the dynamic portrait dimension adjustment engine adjusts the image dimension according to the preset portrait dimension mapping table. ,in Indicates the relationship with the first The system automatically activates or optimizes the corresponding feature parameters based on the feature set related to each life cycle stage. For example, when a customer is in the "retirement" stage, the system will add feature dimensions such as "health status", "chronic disease history" and "retirement needs", and adjust the weights of existing dimensions such as "income level" and "occupational risk".

[0041] In this embodiment, this step involves feature selection thresholding. Feature update frequency Image Dimension Priority Ranking Algorithm Key parameters include the feature selection threshold, which controls the significance of newly added features and is typically set to [value missing]. This is to ensure that the newly added features are statistically significant for predicting customer behavior. Feature update frequency. Typically, updates are set to daily or weekly, depending on the real-time requirements of the data source. Image dimension priority ranking algorithm. A weighted ranking mechanism based on information gain or customer value models (such as the RFM model) is typically used to ensure that the dynamic adjustment of profile dimensions aligns with business objectives.

[0042] In the embodiments of this application, this step is widely applied to scenarios such as customer segmentation, personalized service recommendations, and risk assessment model optimization. For example, when a customer is in the "family formation" stage, the system will introduce features such as "number of family members" and "children's education needs" to support the customization of family protection plans. In the "high-risk behavior" stage (such as frequent business trips or abnormal health indicators), the system will enhance dimensions such as "behavioral trajectory analysis" and "health risk scoring" to improve the accuracy of risk warnings.

[0043] The technical advantage of this step lies in significantly improving the timeliness and adaptability of customer profiles, enabling them to dynamically evolve as customer status changes. This provides more accurate data support for subsequent intelligent service recommendations and risk assessments. By introducing a dynamic adjustment mechanism, the system can effectively address the non-linear changes in the customer lifecycle, enhance the intelligence level of customer management, and improve customer satisfaction and business conversion rates.

[0044] Furthermore, S2 includes: S21, When a customer is detected to be in the newlywed stage, the family responsibility coefficient is automatically increased. Characteristics of marital status ,in Calculated based on the number of family members Verification was conducted using marriage registration data.

[0045] Specifically, when the system detects that a customer is in the newlywed stage, it will automatically add two key characteristic parameters: family responsibility coefficient. Characteristics of marital status This process, based on customer lifecycle identification mechanisms and multi-source data fusion strategies, is a crucial step in building intelligent customer profiles.

[0046] In this embodiment, the system first uses an event recognition module to detect whether the customer is newlywed. This module integrates marriage registration data from the civil affairs department, customer-filled questionnaires, social media activity, and status change records from the customer relationship management system (CRM). Once the customer's marital status is confirmed as "newlywed," the system triggers a profile update process, automatically introducing... and Two feature parameters. Among them, The calculation is based on the number of family members in the client's household, and the specific formula is as follows:

[0047] in This indicates the total number of the customer's current family members (including spouse, children, parents, etc.). This parameter is obtained through methods such as the insurance information filled in by the customer and family relationship graph analysis. This coefficient is used to quantify the customer's burden in terms of family responsibility. The higher the value, the heavier the family responsibility, thus serving as an important reference in subsequent insurance product recommendations and risk assessments.

[0048] This is verified through marriage registration data, and its value is a Boolean or categorical variable used to identify the authenticity and stability of a client's marital status. In some implementations, this parameter can be further extended to continuous indicators such as marriage duration and frequency of marital status changes to support more refined client segmentation and behavioral prediction.

[0049] In this embodiment, this step is primarily used to optimize the dynamic adjustment mechanism of customer profiles, particularly in the service recommendation and risk assessment modules. For example, in the service recommendation layer, the system can, based on... Based on the value, family insurance products or supplementary insurance products are given priority; at the risk assessment level, It can be used as part of a customer stability assessment, influencing their risk level classification.

[0050] The technological value of this step lies in improving the timeliness and accuracy of customer profiling by introducing dynamic features closely related to the customer lifecycle, thereby enhancing the intelligence level of subsequent service recommendations and risk assessments, and providing insurance companies with more targeted customer management strategies.

[0051] S22, When a customer is detected to be in retirement, the health risk weights are dynamically adjusted. The value was reduced to 0.8, and a model for predicting elderly care demand was introduced. Calculate long-term care insurance demand.

[0052] Specifically, when the system detects that a customer is in retirement, it will dynamically adjust the health risk weights. The value was reduced to 0.8, and a model for predicting elderly care demand was introduced. This step is crucial for calculating long-term care insurance needs. It's a key component of the risk assessment and service recommendation module within the intelligent life insurance customer management system, and its technical implementation is based on the integrated application of customer lifecycle identification, dynamic risk weight configuration, and predictive models.

[0053] In this embodiment, the system first identifies the customer's lifecycle stage through a customer profile building module. This module extracts and classifies features based on multi-dimensional data sources (such as age, occupation status, income trends, and health monitoring data), and uses machine learning algorithms (such as random forests, support vector machines, or deep neural networks) to make real-time judgments on the customer's status. Once the system identifies that the customer is in retirement, it triggers a dynamic adjustment mechanism to adjust the health risk weights. The value was adjusted from the default value (e.g., 0.6) to 0.8 to reflect the higher sensitivity of retirees to health risks.

[0054] Subsequently, the system invoked the elderly care demand forecasting model. The model is trained based on historical customer data, with inputs including customer current health status, medical history, family structure, living environment, and financial situation. The model output is the customer's future... The probability of needing long-term care insurance within the year and the expected care costs. Evaluation metrics for the predictive model include mean squared error (MSE) and area under curve (AUC) to ensure high predictive accuracy and stability.

[0055] In this embodiment, this step is primarily used to provide retired clients with customized insurance product recommendations and risk warning services. For example, the system can recommend suitable long-term care insurance products based on prediction results and generate personalized premium adjustment suggestions based on the client's risk preferences. Furthermore, the model can be linked with a real-time monitoring module; when abnormal fluctuations occur in the client's health data, the system can further strengthen the warning mechanism, improving the timeliness and targetedness of the response.

[0056] The technical effect of this step is that by dynamically adjusting health risk weights and introducing predictive models, the system can more accurately assess the health risks and retirement needs of retired customers, thereby improving the suitability of insurance products and customer satisfaction, and enhancing the system's intelligence level and market competitiveness.

[0057] S3 uses predictive machine learning models to model changes in customer needs over the next 6-12 months, generating enhanced customer profiles that include predictive features.

[0058] Specifically, in some implementations, modeling changes in customer demand over the next 6-12 months using predictive machine learning models is a key step in enhancing customer profiles within intelligent life insurance customer management systems. This step utilizes multi-dimensional data sources such as customers' historical behavior, purchase records, social activities, geographic location, and health monitoring to construct time-series prediction models. These models quantitatively predict customers' potential needs in the short to medium term (6-12 months), thereby generating an enhanced customer profile that includes predictive features.

[0059] In this embodiment, this step first involves feature extraction and time series modeling of the raw data. The system extracts customer behavior feature vectors from the feature data warehouse. ,in Indicates a point in time. Features are defined as dimensions, including but not limited to customer purchase frequency, browsing duration, and trends in health indicators. Subsequently, predictive models such as LSTM (Long Short-Term Memory), Prophet, and ARIMA are used to model future customer behavior. Taking LSTM as an example, it captures long-term dependencies through a gating mechanism. The model structure typically includes an input layer, several hidden layers, and an output layer. The number of hidden layer units can be set to... The Adam optimizer is used during training, with a learning rate of [missing information]. The loss function is the mean squared error. ,in This is the actual demand value. These are predicted values.

[0060] In this embodiment, the system sets the prediction window to 6-12 months and the time step to... Typically, the interval is one month or one week, adjusted according to the data frequency. Evaluation metrics for the predictive model include RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R² (Coefficient of Determination), with RMSE needing to be controlled within a certain range. This is to ensure that the prediction accuracy meets business needs. Furthermore, the model needs to have good generalization ability, and the R² value on the test set should be... .

[0061] In this embodiment, this step is widely applied to scenarios such as dynamic recommendation of life insurance products, customer lifecycle management, and risk warning triggering. For example, when a customer is about to enter retirement, the system can predict changes in their needs for pension insurance and health management services, thereby adjusting service strategies in advance. When a customer's health condition fluctuates, the system can predict potential future health risks and trigger a personalized early warning mechanism.

[0062] In this embodiment, this step significantly enhances the predictability and foresight of customer profiles, enabling the system to make intelligent decisions based on future customer behavior trends. By introducing predictive features, customer profiles not only reflect the current state but also anticipate potential needs, providing data support for precision marketing, personalized services, and risk control, thereby enhancing the system's intelligence and customer stickiness.

[0063] Furthermore, S3 includes: S31, uses an LSTM network to analyze customer historical behavior sequences. Perform time series modeling and output the probability distribution of future demand. .

[0064] Specifically, in some implementations, the present invention employs a Long Short-Term Memory (LSTM) network to process customer historical behavior sequences. Perform time series modeling to output the probability distribution of future demand. This step is one of the core components of the customer profile construction and service recommendation algorithm module in the intelligent life insurance customer management system. It aims to capture the time dependence of customer behavior through deep learning technology, thereby achieving accurate prediction of customers' future needs.

[0065] In this embodiment, the LSTM network is a special type of recurrent neural network (RNN) whose structure includes an input gate, a forget gate, and an output gate, effectively alleviating the gradient vanishing problem of traditional RNNs when processing long sequence data. (Customer historical behavior sequence) It is typically composed of dimensions such as timestamp, behavior type (e.g., browsing, purchasing, consulting), behavior frequency, and behavior duration. Each time step... This can be represented as a high-dimensional vector containing the customer's behavioral characteristics at a given time point. During training, the LSTM uses a gating mechanism to selectively memorize and forget historical information, ultimately outputting a hidden state sequence that contains time dependencies. .

[0066] Furthermore, in order to output the probability distribution of demand over the next 12 time steps... The system follows the LSTM output layer with a fully connected (dense) layer. This layer uses the Softmax activation function to normalize the output, ensuring that the output at each time step is normalized. This represents the probability that a customer will choose a certain type of service or product at a given moment. The training objective of the model is to minimize the cross-entropy loss function between the predicted output and the actual label, i.e. ,in This represents the probability value predicted by the model.

[0067] In this embodiment, the hidden layer dimension of the LSTM is typically set between 128 and 256 to balance model complexity and computational efficiency. Input sequence length The value is typically between 30 and 60, corresponding to customer behavior records over the past 1 to 2 months. The output sequence length is 12, representing demand predictions for the next 12 time steps. The Adam optimizer is used during training, with a learning rate set to... The batch size is 64, and the number of training rounds is 20 to 50. The specific values ​​can be adjusted according to the size of the dataset and the convergence of the model.

[0068] This step is widely used in practical applications such as customer behavior prediction, service recommendation, and risk warning. For example, after a customer browses life insurance products, the system can predict their possible purchase intentions within the next 12 days based on their historical behavior sequence and generate a personalized recommendation list accordingly. Furthermore, this method can also be used to identify abnormal changes in customer behavior patterns, thereby triggering a risk warning mechanism.

[0069] By introducing LSTM time series modeling, this invention significantly improves the accuracy and stability of customer behavior prediction, providing high-quality input for subsequent intelligent service recommendations and risk assessments, enhancing the system's predictive capabilities and customer response efficiency, and demonstrating the innovative application value of deep learning technology in insurance customer management.

[0070] S32, using the Transformer architecture to analyze cross-channel behavioral characteristics Attention-weighted fusion is performed to generate a multi-dimensional demand prediction vector. .

[0071] Specifically, in some implementations, this invention utilizes the Transformer architecture to handle cross-channel behavioral characteristics. Attention-weighted fusion is performed to generate a multi-dimensional demand prediction vector. This step is one of the core components of the customer profile construction and service recommendation algorithm module in the intelligent life insurance customer management system. It aims to integrate customer behavioral characteristics across different channels (such as online apps, offline stores, social media, and smart wearable devices) to extract representative demand characteristics, providing data support for subsequent personalized service recommendations and risk assessments.

[0072] In this embodiment, the Transformer model employs a self-attention mechanism to model the cross-channel behavioral features of the input. Specifically, the input features... Encoded as a series of embedding vectors ,in This represents the feature embedding dimension. Through multi-head attention, the model can weightedly combine behavioral features from different channels, capturing their correlation in both time series and semantic space. The formula for calculating attention weights is:

[0073] in, , , These are query, key, and value matrices, respectively. denoted as the dimension of the key vector. Through a multi-head mechanism, the model can extract features from multiple subspaces, enhancing its expressive power.

[0074] In this embodiment, the number of layers in the Transformer model is typically set to 2 to 6, with an embedding dimension of... The number of attention heads is between 128 and 512. The number of output dimensions of the model ranges from 4 to 8. Related to the design of customer profile dimensions, this typically covers key indicators such as customer behavioral preferences, purchasing inclinations, and risk levels. During training, optimization is performed using the cross-entropy loss function or mean squared error (MSE), with the learning rate set to... to The batch size ranges from 64 to 256.

[0075] In this embodiment, this step is widely applied to modules such as customer behavior analysis, service recommendation, and risk warning. For example, when a customer browses life insurance products, the system integrates their online click behavior with offline consultation records to generate a more accurate demand prediction vector, thereby recommending products with a higher degree of matching. Furthermore, in customer lifecycle management, this fusion mechanism can dynamically adjust customer profiles, improving the timeliness and adaptability of predictions.

[0076] The technical advantage of this step lies in its ability to effectively capture the temporal dependencies and semantic relationships of customer behavior through weighted fusion of cross-channel behavioral features via an attention mechanism, thereby improving the accuracy and robustness of demand prediction. Simultaneously, this method possesses good scalability, supporting unified modeling of multi-source heterogeneous data, providing a solid technical foundation for building intelligent customer profiles and personalized service recommendations.

[0077] S4 uses a real-time data processing framework to synchronize and integrate cross-channel data within minutes, enabling real-time updates of customer profiles and multi-dimensional data fusion.

[0078] Specifically, in some implementations, this invention uses a real-time data processing framework to synchronize and integrate cross-channel data at the minute level, thereby achieving real-time updates of customer profiles and multi-dimensional data fusion. This step is a key link in the construction of customer profiles and optimization of service recommendations in the entire intelligent life insurance customer management system. Its technical implementation is based on a distributed streaming data processing architecture, combined with data synchronization mechanisms and feature fusion strategies, to ensure the timeliness and completeness of customer information.

[0079] In this embodiment, the system employs streaming frameworks such as Apache Flink or Apache Kafka Streams to collect and process customer behavior data from multiple channels (e.g., mobile applications, smart wearable devices, offline service terminals, social media platforms, etc.) in real time. Data sources are accessed via standardized API interfaces or data crawlers, and the data format must conform to structured data standards such as JSON or Avro to ensure data consistency and parsability. During data synchronization, the system sets a data latency threshold. Minute-level data integration across channels is achieved through timestamp alignment and an event-driven mechanism. The data integration module employs an ETL (Extract, Transform, Load) process to clean, deduplicatize, and standardize the raw data, then writes the processed data into a feature data warehouse, providing high-quality input for subsequent profile building and recommendation algorithms.

[0080] In this embodiment of the application, the system defines several key performance indicators (KPIs), including data synchronization latency. Data integrity rate Feature update frequency etc. Among them, Keep it under 1 minute. , Times per minute. These metrics ensure the dynamic updating capability of customer profiles, enabling them to quickly respond to changes in customer behavior and improve the accuracy of service recommendations.

[0081] In this embodiment, this step is widely applied in scenarios such as customer lifecycle management, personalized service recommendations, and risk warning triggering. For example, when customers interact through different channels (such as mobile apps and offline counters), the system can complete data synchronization within one minute, ensuring the consistency and real-time nature of customer profiles. Furthermore, when customer health data changes, the system can immediately update its risk assessment model, thereby triggering the corresponding early warning mechanism.

[0082] In this embodiment, this step significantly improves the timeliness and multi-dimensional integration capability of customer profiling, providing a solid data foundation for subsequent intelligent service recommendations and risk assessments. Through a minute-level synchronization mechanism, the system can achieve instant responses to customer behavior, enhancing the consistency and personalization of the customer experience, thereby improving customer satisfaction and business conversion rates.

[0083] The multi-dimensional dynamic updating intelligent customer profile construction method of this invention can realize multi-dimensional dynamic updating of customer profiles, improve the comprehensiveness and real-time nature of customer information, thereby enhancing the accuracy of service recommendations and the effectiveness of risk assessment.

[0084] Furthermore, S4 includes: S41 uses the Flink streaming engine to process data streams from IoT devices. and social media data streams Perform joint window calculations, with the time window length set to... minute.

[0085] Specifically, in some implementations, this invention uses the Apache Flink streaming engine to process data streams from IoT devices. and social media data streams Joint window computation is performed to enable real-time analysis and response to customer behavior and status. This step is technically based on Flink's stream processing capabilities, combined with a time window mechanism, to synchronously process and extract features from two heterogeneous data sources, thereby providing high-quality real-time data support for subsequent customer profiling, service recommendation, and risk assessment.

[0086] In this embodiment, Flink employs an event-time processing strategy to ensure data consistency across the time dimension. The system first processes... and Data stream access and parsing are performed, among which... This includes real-time data from smart wearable devices, health monitoring devices, smart home devices, etc. This includes information such as user behavior, interest tags, and interaction frequency on social media platforms (such as Weibo, WeChat, and Douyin). Before entering Flink, the two data streams need to be buffered and decoupled through message queues such as Kafka or Pulsar to handle high concurrency and data latency issues.

[0087] In this embodiment of the application, the time window length Set as Minutes is a parameter that can be dynamically adjusted based on business needs. The window sliding step size is typically set to... To ensure data continuity and real-time performance, Flink supports timestamp-based window partitioning and uses a watermark mechanism to handle out-of-order events, ensuring the accuracy of window calculations. Furthermore, the system allows configuration of window triggering strategies (such as processing time triggering, event time triggering, or count-based triggering) to adapt to data processing needs in different scenarios.

[0088] In this embodiment, this step is widely used in scenarios such as customer behavior analysis, health status monitoring, and risk warning triggering. For example, in customer health risk assessment, the system uses joint analysis... Physiological data and The system tracks customer activity to identify trends in health risk over specific time periods. In service recommendations, it combines a customer's current location, social behavior, and health status to generate personalized content in real time, enhancing the customer experience.

[0089] The technical advantage of this step lies in enabling the system to achieve real-time fusion and analysis of multi-source heterogeneous data through Flink's low-latency, high-throughput stream processing capabilities. This provides timely and accurate data support for subsequent dynamic updates to customer profiles, service recommendation optimization, and risk warnings. Furthermore, this method effectively improves the system's response speed and data processing efficiency, enhances the intelligence level of customer management, and provides a solid foundation for real-time decision-making in insurance business.

[0090] S42, Constructing customer association features through knowledge graphs ,in Indicates the first Embedding vectors of related entities , For feature dimensions.

[0091] Specifically, in some implementations, customer association features are constructed using knowledge graphs. ,in Indicates the first Embedding vectors of related entities , Feature dimension is one of the key technical steps in the construction of intelligent customer profiles in this invention. This step aims to semantically model customers and their related entities (such as family members, occupation, health status, purchase history, social relationships, etc.) in a structured manner, thereby constructing a set of customer feature vectors with semantic relevance and improving the comprehensiveness and dynamic adaptability of customer profiles.

[0092] In this embodiment, the first step involves constructing a knowledge graph based on multi-source heterogeneous data (such as basic customer information, behavioral data, health monitoring data, social media data, etc.). Each entity in the knowledge graph (such as customer, product, doctor, hospital, device, etc.) is mapped to an embedding vector. Its dimensions The number of vectors is typically set to 128 or 256 to balance semantic expressiveness and computational efficiency. Embedded vectors can be generated using graph neural network (GNN) methods, such as TransE, GraphSAGE, or Transformer-based entity embedding models. These models are jointly trained based on semantic relationships between entities, ensuring that each entity has an interpretable semantic location in the vector space.

[0093] Furthermore, customer association characteristics It consists of embedded vectors of entities directly or indirectly related to the customer, such as the customer's occupation, family members, and purchase records. These entities are connected by edges in the graph. The system extracts the set of entities related to the customer using graph traversal algorithms (such as BFS, DFS, or attention-based graph walks), and concatenates or weights and fuses their embedded vectors to form the final set of customer-related feature vectors. Optionally, the system can also introduce an entity importance scoring mechanism, which weights the embedded vectors based on the strength of the relationship between the entity and the customer (such as time frequency, interaction depth, etc.) to enhance the influence of key entities on customer profiles.

[0094] In this embodiment, this step is widely applied to modules such as customer lifecycle management, personalized service recommendations, and risk assessment and early warning. For example, when a customer is newly married, the system can dynamically introduce entities such as "family members" and "marital status" to build a customer profile that better reflects their current situation; after a customer retires, entities such as "health status" and "retirement needs" can be introduced to support more accurate insurance product recommendations and risk warnings.

[0095] The technical advantage of this step lies in the fact that, by introducing knowledge graph and entity embedding technologies, the system can capture the complex semantic relationships between customers and their associated entities, thereby constructing a set of customer feature vectors with context-aware capabilities. This not only improves the accuracy and richness of customer profiles, but also provides high-quality input features for subsequent intelligent service recommendations and risk assessments, enhancing the system's predictive capabilities and personalized service levels.

[0096] The multi-dimensional dynamic updating intelligent customer profile construction method of this invention can realize multi-dimensional dynamic updating of customer profiles, improve the comprehensiveness and real-time nature of customer information, thereby enhancing the accuracy of service recommendations and the effectiveness of risk assessment.

[0097] S5 uses a natural language processing module to process the interaction text between customers and the intelligent customer service. Conduct sentiment analysis to extract customer emotional characteristics and based on Adjust service recommendation strategy .

[0098] Specifically, in some implementations, the interaction text between the customer and the intelligent customer service is processed through a natural language processing module. Conduct sentiment analysis to extract customer emotional characteristics and based on Adjust service recommendation strategy This is one of the key technical steps in realizing personalized service recommendations in this invention. The core of this step lies in using Natural Language Processing (NLP) technology to model the semantic and emotional states of customers during their interaction with the intelligent customer service, thereby providing an emotional perception basis for subsequent service recommendations.

[0099] In this embodiment, this step first involves the interactive text. Word segmentation, part-of-speech tagging, and syntactic analysis are performed to extract semantic units. Subsequently, a deep learning-based sentiment analysis model (such as BERT, RoBERTa, or LSTM-CRF structure) is used to classify the sentiment of each semantic unit, outputting a sentiment feature vector. Emotional features can include emotion category (e.g., positive, neutral, negative), emotion intensity (e.g., continuous values ​​within the range of 0 to 1), and emotion time series features (e.g., frequency and duration of emotion fluctuations). Emotion classification models are typically pre-trained on labeled emotion datasets (e.g., IMDB, SST-2, or customized datasets for the insurance industry) and fine-tuned in real-world business scenarios to improve the accuracy of emotion recognition in the context of insurance customers.

[0100] In this embodiment, the input text length for the sentiment analysis model is typically limited to 512 tokens to fit the context window of mainstream pre-trained models. The sentiment intensity threshold can be set to... Expressing positive emotions, Negative emotions are represented by a single negative value, while neutral emotions fall somewhere in between. (Emotion feature vector) The dimensions are usually 1 ,in Dimensions of emotional characteristics (e.g., 3 dimensions: positive, neutral, negative). This represents the number of semantic units in the interactive text.

[0101] In this embodiment, this step is widely applied in scenarios such as customer inquiries, complaint handling, and claims communication. For example, when a customer exhibits anxiety or dissatisfaction during a conversation with the intelligent customer service, the system can automatically adjust its recommendation strategy, prioritizing the delivery of reassuring services or suggestions for human intervention. Furthermore, emotional characteristics... It can also be used in business modules such as customer satisfaction prediction and service response strategy optimization.

[0102] In this embodiment, this step significantly improves the personalization and contextual adaptability of service recommendations through an emotion perception mechanism. Emotion-driven recommendation strategy. This technology enables timely adjustments to service content when customers experience emotional fluctuations, thereby enhancing customer experience, improving service conversion rates, and triggering an early warning mechanism when customers exhibit abnormal emotions, providing data support for subsequent risk assessment and intervention. This technical solution demonstrates significant practical value and innovation in customer interaction scenarios within the insurance industry.

[0103] The multi-dimensional, dynamically updated intelligent customer profile construction method of this invention introduces a natural language processing module to perform sentiment analysis on the interaction text between customers and intelligent customer service, extracts customer emotional characteristics, and dynamically adjusts service recommendation strategies. This further enhances the ability to depict the emotional dimension of customer profiles, making service recommendations more aligned with customers' real-time emotional states, thereby increasing customer satisfaction and business conversion rates.

[0104] Example 2 This invention relates to a multi-dimensional, dynamically updated intelligent customer profile building system, which is specifically used for: System Architecture: The system adopts a distributed architecture, including a data acquisition layer, a data processing layer, a business logic layer, a presentation layer, and a security control layer.

[0105] The data acquisition layer is responsible for collecting customer information from multiple data sources, including basic information, purchase history, behavioral data, social media data, geolocation data, and health monitoring device data.

[0106] The data processing layer cleans, integrates, and preprocesses the collected data, providing a high-quality data foundation for subsequent analysis and mining.

[0107] The business logic layer implements core functions such as customer profile building, service recommendation, risk assessment and early warning.

[0108] The presentation layer provides a user interface that displays customer profiles, recommended services, risk warnings, and other information, and allows users to interact with the information.

[0109] The security control layer is responsible for the system's security and privacy protection, including data encryption, access control, and permission management.

[0110] Intelligent customer profile building: Multi-dimensional data source integration: The system collects customer information from multiple data sources through API interfaces, web crawlers, and other technologies. These sources include basic information (such as age, gender, and income), purchase history, behavioral data (such as browsing history and click behavior), social media data, geolocation data, and health monitoring device data. These multi-dimensional data sources provide a rich information foundation for building comprehensive and accurate customer profiles.

[0111] Customer profile dimension design: Based on customer needs and business scenarios, design the dimensions and indicators of customer profiles, such as age, gender, income, purchase history, and behavioral habits.

[0112] Dynamic Adjustment Mechanism: The system dynamically adjusts the dimensions and focus of the customer profile based on different stages of the customer lifecycle. For example, for newlywed customers, the system adds dimensions such as family responsibilities and marital status; for retired customers, it adds dimensions such as health status and retirement needs. This dynamic adjustment mechanism allows customer profiles to be updated as customer status changes, maintaining their timeliness and accuracy.

[0113] Predictive Enhancement: The system dynamically adjusts the dimensions and focus of the customer profile based on different stages of the customer lifecycle. For example, for newlywed customers, the system adds dimensions such as family responsibilities and marital status; for retired customers, it adds dimensions such as health status and retirement needs. This dynamic adjustment mechanism allows customer profiles to be updated as customer status changes, maintaining their timeliness and accuracy.

[0114] Intelligent service recommendation system: Context awareness: The system obtains the customer's current context information, such as time, location, and mood, through client devices (e.g., mobile phones, smartwatches). This information is used to adjust service recommendations to better suit the customer's needs in specific situations.

[0115] Recommendation Algorithm: Based on customer profiles and contextual information, this algorithm utilizes collaborative filtering, content recommendation, and other algorithms to provide personalized service recommendations to customers.

[0116] Feedback Mechanism: This mechanism allows customers to rate and provide feedback on recommended services. Based on this feedback, the system continuously optimizes its recommendation algorithm, improving accuracy and customer satisfaction. This feedback mechanism enables the recommendation system to continuously learn and improve, providing customers with more accurate and personalized services.

[0117] Cross-channel integration: Through a unified interface and protocol, the system integrates online and offline service channels. Customers receive a consistent and seamless experience regardless of which channel they access the service. This cross-channel integration enhances service convenience and consistency, thereby increasing customer loyalty and satisfaction.

[0118] Intelligent risk assessment and early warning: Multi-dimensional assessment: In addition to traditional health and financial conditions, the system also incorporates factors such as the client's occupation, lifestyle, and family structure into the risk assessment. This multi-dimensional approach makes the risk assessment results more comprehensive and accurate.

[0119] Real-time monitoring and early warning: Utilizing IoT technology and real-time data analytics, the system monitors and issues early warnings regarding customers' risk status in real time. For example, it monitors changes in customers' health conditions through smart wearable devices and fluctuations in their financial situation through a financial data platform. Once a potential risk is detected, the system immediately issues an early warning so that the insurance company can take timely countermeasures.

[0120] Personalized Response: We develop personalized risk warnings and response recommendations based on clients' risk preferences and tolerance levels. For example, we can provide more conservative investment advice to clients with lower risk tolerance, and more aggressive investment advice to clients with higher risk tolerance.

[0121] The introduction of new artificial intelligence technologies: Deep learning technology: Utilizing deep learning technology to mine and analyze customer data at a deeper level improves the accuracy of customer profiles and the precision of service recommendations. For example, deep neural networks can be used to mine customer purchase history to discover potential customer needs and purchasing preferences.

[0122] Natural Language Processing Enhancement: Enhancing the capabilities of natural language processing technology to achieve a more natural and intelligent customer interaction experience. For example, using an intelligent customer service system to interact with customers via voice or text, answering customer questions and providing services.

[0123] Image recognition technology applications: Image recognition technology is used to process image information such as ID cards and insurance policies uploaded by customers, improving the efficiency and accuracy of data processing. For example, OCR technology can be used to recognize information on ID cards and automatically fill in basic customer information forms.

[0124] System security and privacy protection: Data Encryption: Advanced data encryption technologies, such as AES and RSA, are used to encrypt and store customer data during transmission, ensuring data security.

[0125] Access control: Establish a strict access control mechanism, allowing only authorized personnel to access customer data. Simultaneously, audit and log access activities for traceability and verification.

[0126] Access control: Different permissions and access levels are assigned based on users' roles and responsibilities. For example, customer service personnel can only view basic customer information and purchase history, but not customer financial status or sensitive information. Summary of the Invention: Deepening the construction of intelligent customer profiles: Integrate more data sources: Integrate customer basic information, purchase history, behavioral data, as well as social media data, geolocation data, health monitoring device data and other multi-dimensional data sources.

[0128] Dynamically adjust customer profile dimensions: dynamically adjust the dimensions and focus of the customer profile according to different stages of the customer lifecycle.

[0129] Enhance the predictive power of customer profiles: Utilize machine learning algorithms to predict customers' future behavior, needs, or risks.

[0130] Personalization enhancement of intelligent service recommendation systems: Context-based service recommendations: Considering the customer's current context, providing service recommendations that better match the customer's actual needs.

[0131] Introduce a customer feedback mechanism: allow customers to rate and provide feedback on recommended services, and use the feedback to optimize the recommendation algorithm.

[0132] Cross-channel service integration: Integrate online and offline service channels to provide a seamless and consistent service experience.

[0133] Refinement of intelligent risk assessment and early warning: Multi-dimensional risk assessment: Taking into account multiple factors such as the client's occupation, lifestyle, and family structure, we comprehensively assess the client's risk.

[0134] Real-time risk monitoring: Utilizing IoT technology and real-time data analysis, we monitor and provide early warnings about the risk status of our clients in real time.

[0135] Personalized risk warning response: We provide personalized risk warnings and response suggestions based on the client's risk preferences and tolerance.

[0136] Introducing new artificial intelligence technologies: Deep learning technology: Utilizing deep learning technology to conduct deeper mining and analysis of customer data.

[0137] Enhanced Natural Language Processing: Improving the capabilities of natural language processing technology to achieve a more natural and intelligent customer interaction experience.

[0138] Applications of image recognition technology: Utilizing image recognition technology to process customer-uploaded image information improves the efficiency and accuracy of data processing.

[0139] Enhanced system security and privacy protection: Data encryption and privacy protection: We employ advanced data encryption technologies and privacy protection mechanisms to ensure the security and privacy of customer data.

[0140] Access control and permission management: Establish strict access control and permission management systems to prevent unauthorized access and data leakage.

[0141] Example 3 This invention also provides a multi-dimensional, dynamically updated intelligent customer profile building device, such as... Figure 2 As shown, the device includes: The multi-source heterogeneous data acquisition module 100 is used to acquire customers' basic information, purchase history, behavioral data, social media data, geographic location data, and health monitoring device data. The Dynamic Profile Dimension Adjustment Engine Module 200 is used to dynamically adjust profile dimensions based on customer lifecycle status, and automatically add or optimize feature parameters related to the current lifecycle stage. Predictive machine learning model module 300 is used to model changes in customer demand over the next 6-12 months and generate enhanced customer profiles containing predictive features. The Real-Time Data Processing Framework Module 400 is used to achieve real-time updates of customer profiles and multi-dimensional data fusion by synchronizing and integrating cross-channel data at the minute level.

[0142] Example 4 To implement the methods of the above embodiments, the present invention also provides a computer device, which includes a memory and a processor; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, so as to implement the various steps of the methods described above.

[0143] Example 5 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0144] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0145] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0146] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for constructing a multi-dimensional, dynamically updated intelligent customer profile, characterized in that: include: S1 acquires basic customer information, purchase history, behavioral data, social media data, geolocation data, and health monitoring device data through a multi-source heterogeneous data acquisition module; S2 dynamically adjusts the profile dimensions based on the customer lifecycle status. The dynamic profile dimension adjustment engine automatically adds or optimizes feature parameters related to the current lifecycle stage. S3 uses predictive machine learning models to model changes in customer demand over the next 6-12 months, generating enhanced customer profiles that include predictive features. S4 uses a real-time data processing framework to synchronize and integrate cross-channel data within minutes, enabling real-time updates of customer profiles and multi-dimensional data fusion.

2. The method as described in claim 1, characterized in that, The acquisition of customer basic information, purchase history, behavioral data, social media data, geographic location data, and health monitoring device data through the multi-source heterogeneous data acquisition module also includes: S11 obtains real-time data on customer interaction behavior on the insurance platform through API interfaces, including page dwell time, click hotspot distribution, and service preference records; S12 uses OCR technology to automatically extract image information, including ID card and insurance policy, uploaded by customers, and completes structured data conversion through the image recognition module.

3. The method as described in claim 1, characterized in that, The method of dynamically adjusting profile dimensions based on customer lifecycle status also includes: S21, When a customer is detected to be in the newlywed stage, the family responsibility coefficient is automatically increased. Characteristics of marital status ,in Calculated based on the number of family members Verification was conducted using marriage registration data. S22, When a customer is detected to be in retirement, the health risk weights are dynamically adjusted. The value was reduced to 0.8, and a model for predicting elderly care demand was introduced. Calculate long-term care insurance demand.

4. The method as described in claim 1, characterized in that, The method of using predictive machine learning models to model changes in customer demand over the next 6-12 months also includes: S31, uses an LSTM network to analyze customer historical behavior sequences. Perform time series modeling and output the probability distribution of future demand. ; S32, using the Transformer architecture to analyze cross-channel behavioral characteristics Attention-weighted fusion is performed to generate a multi-dimensional demand prediction vector. .

5. The method as described in claim 1, characterized in that, The method of synchronizing and integrating cross-channel data at the minute level through a real-time data processing framework also includes: S41 uses the Flink streaming engine to process data streams from IoT devices. and social media data streams Perform joint window calculations, with the time window length set to... minute; S42, Constructing customer association features through knowledge graphs ,in Indicates the first Embedding vectors of related entities , For feature dimensions.

6. The method as described in claim 1, characterized in that, Also includes: S5 uses a natural language processing module to process the interaction text between customers and the intelligent customer service. Conduct sentiment analysis to extract customer emotional characteristics and based on Adjust service recommendation strategy .

7. A multi-dimensional, dynamically updated intelligent customer profile building device, characterized in that, include: The multi-source heterogeneous data acquisition module is used to acquire customers' basic information, purchase history, behavioral data, social media data, geolocation data, and health monitoring device data; The dynamic profile dimension adjustment engine module is used to dynamically adjust profile dimensions based on customer lifecycle status, automatically adding or optimizing feature parameters related to the current lifecycle stage. The predictive machine learning model module is used to model changes in customer demand over the next 6-12 months and generate enhanced customer profiles that include predictive features. The real-time data processing framework module is used to achieve real-time updates of customer profiles and multi-dimensional data fusion by synchronizing and integrating cross-channel data at the minute level.

8. A computer device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the intelligent customer profile construction method with multi-dimensional dynamic updates as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the intelligent customer profile construction method with multi-dimensional dynamic updates as described in any one of claims 1-6.

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