Intelligent customer management and system docking method for personal numbers

Abstract

The invention relates to the technical field of information, and particularly discloses an intelligent customer management and system docking method for personal numbers, which comprises the following steps: S1, receiving a personal number registration request sent by a user terminal, and establishing a personal number data space which comprises a customer information database, a behavior record database and a system docking configuration library; s2, collecting customer data, and uniformly storing the customer data in a customer information database; s3, performing intelligent analysis on the customer data in the customer information database, and generating a customer value evaluation score and a demand prediction label; s4, based on the customer value evaluation score and the demand prediction label, generating a personalized customer management strategy, calculating a similarity matrix between customers, and generating recommendation; and S5, establishing a connection with an external service system, synchronizing the client management strategy to the external service system, and simultaneously obtaining the latest client state update. According to the invention, accurate customer management and efficient system connection can be realized, a data island is broken, and the service efficiency and customer experience are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a method for intelligent customer management and system interfacing of a personal number. BACKGROUND

[0002] In today's business environment, effective customer relationship management is the core of enterprise service quality improvement and competitiveness enhancement. There are various customer management systems on the market, which are usually deployed on the enterprise side and managed by the enterprise to unify customer data, interaction records and transaction history. However, this enterprise-centered centralized management model has obvious limitations: customer data is scattered in independent systems of different enterprises, forming data islands, making it difficult for individual users to obtain unified and continuous cross-platform service experience. At the same time, most existing systems rely on rule engines or statistical analysis for customer segmentation, lack dynamic learning and prediction capabilities based on artificial intelligence, and are difficult to adapt to real-time changes in customer needs.

[0003] In terms of system integration, traditional customer management platforms usually interface with external business systems (such as ERP, e-commerce platforms, social media) through predefined interfaces. This type of interface is often rigid and lacks flexibility, requiring a lot of customized development for different systems, and is difficult to support dynamic and lightweight service composition and data exchange. When individual users need to transfer their personal data or preferences between different service providers, existing technologies lack a secure and standardized interconnection mechanism, resulting in fragmented user experience and low operational efficiency. SUMMARY

[0004] To solve the problems in the prior art that most existing customer management methods rely on rule engines or statistical analysis for customer segmentation, lack dynamic learning and prediction capabilities based on artificial intelligence, and are difficult to adapt to real-time changes in customer needs, and lack a secure and standardized interconnection mechanism for interfacing with external business systems, resulting in fragmented user experience and low operational efficiency, the present application provides a method for intelligent customer management and system interfacing of a personal number.

[0005] The technical solution adopted by the present application is:

[0006] A method for intelligent customer management and system interfacing of a personal number, comprising the following steps:

[0007] S1, receiving a personal number registration request sent by a user terminal, generating a unique personal number identifier after verification, and establishing a personal number data space, the personal number data space including a customer information database, a behavior record database and a system interfacing configuration library;

[0008] S2, collecting customer data and storing it in the customer information database, and performing cleaning and standardization processing on the data;

[0009] S3. Use neural network models to intelligently analyze customer data in the customer information database to generate customer value assessment scores and demand prediction tags.

[0010] S4. Based on customer value assessment scores and demand prediction tags, a collaborative filtering algorithm is used to generate personalized customer management strategies. The collaborative filtering algorithm calculates a similarity matrix between customers and generates recommendations based on the customer management strategies for similar customers.

[0011] S5. Establish connections with external business systems through standardized API interfaces, synchronize customer management strategies to external business systems, and obtain the latest customer status updates from external business systems.

[0012] Preferably, in step S3, the training process of the neural network model specifically includes the following steps:

[0013] S31. Construct a training dataset by extracting historical customer data from the customer information database. Each data sample contains a set of static customer attributes, a sequence of dynamic behaviors, a customer value assessment score, and a demand prediction label. The set of static customer attributes includes customer age, occupation, and income level. The sequence of dynamic behaviors includes customer purchase records, consultation frequency, and service usage duration.

[0014] S32. Preprocess the training data, including missing value imputation, outlier removal, and feature standardization; use the min-max standardization method to scale continuous features to the [0,1] interval, as shown in the formula:

[0015]

[0016] Among them, X norm X represents the standardized feature values, while X represents the original feature values. min and X max These are the minimum and maximum values ​​of the feature, respectively.

[0017] Step S33: Initialize the neural network model parameters. Use the He initialization method to set the hidden layer weight matrix. The weight values ​​start from a mean of 0 and a standard deviation of 0. Random sampling is performed from a Gaussian distribution, where n in This represents the number of nodes in the input layer.

[0018] Step S34: Train the neural network using the backpropagation algorithm. The loss function L is a combination of mean squared error and cross-entropy, and the formula is:

[0019]

[0020] Where N is the number of training samples, y iLet i be the true label of the i-th sample. These are the model's predicted values;

[0021] Step S35: Update the model parameters using the Adam optimizer. Evaluate the model performance on the validation set after each training round. Terminate training early when the validation set loss function no longer decreases for several consecutive rounds.

[0022] Preferably, in step S3, the neural network model employs an attention mechanism to enhance its sequential data processing capabilities, specifically including the following steps:

[0023] An attention layer is added after the second hidden layer of the neural network model to calculate the importance weights at different time points in the behavior sequence; given a customer behavior sequence H=[h1,h2,…,h…] T Attention weight α t The calculation formula is:

[0024]

[0025]

[0026] Where T is the sequence length. and Calculate the original alignment score before attention weights at different positions, where u is the attention parameter vector, W is the weight matrix, and h is the weight matrix. t Let b be the hidden state at time step t, b be the bias vector, and α be the hidden state at time step t. t Let be the attention weight at time step t;

[0027] The weighted sequence s is:

[0028]

[0029] The weighted sequence s is concatenated with the original hidden state and then input into subsequent network layers.

[0030] Preferably, in step S4, the specific implementation of the collaborative filtering algorithm includes the following steps:

[0031] Step S41: Construct the customer-behavior matrix R m×n Where m is the number of customers, n is the number of behavioral features, and r is the matrix element. gl Let g be the performance value of the g-th customer on the l-th behavioral characteristic;

[0032] Step S42: Calculate the similarity matrix between customers using an improved cosine similarity calculation method. The formula is as follows:

[0033]

[0034] Where sim(g,d) is the similarity matrix between customer g and customer d, r gk Let r be the performance value of customer g on behavioral characteristic k. dk Let d be the performance value of customer d on behavioral characteristic k. and The average behavioral scores for customers g and d are respectively;

[0035] Step S43: For the target customer, select the K neighboring customers with the highest similarity and generate a recommendation strategy. The recommendation weight calculation formula is as follows:

[0036]

[0037] Where, p gc Let N(g) be the predicted score of target customer g for strategy c, and let N(g) be the nearest neighbor set of customer g. dc The actual rating of strategy c for neighboring customer d.

[0038] Preferably, in step S5, the implementation of the standardized API interface specifically includes the following steps:

[0039] S51. Define a unified data exchange format, using the JSON-LD standard to represent customer data and business rules, and ensure semantic interoperability with different external business systems;

[0040] S52. Establish a two-way authentication mechanism, exchange digital certificates with external business systems, and use the RSA encryption algorithm to sign and verify transmitted data to ensure the security of interface calls.

[0041] S53. Implement asynchronous message queue processing for connection requests. When the external business system is unavailable, the connection request is stored in a persistent queue and automatically retried after the system recovers to ensure eventual data consistency.

[0042] S54. Perform multi-dimensional monitoring of the status information returned by external business systems, including response time, error rate, and data consistency indicators. Generate an alarm notification when any indicator exceeds the threshold.

[0043] Preferably, the method further includes the following steps:

[0044] S6. Monitor customer interaction data in real time, use time series prediction models to predict customer activity trends, and automatically trigger customer recovery strategies when the threshold for declining customer activity exceeds a preset value, and push reminder notifications through the user terminal.

[0045] Preferably, in step S6, the time series prediction model employs a long short-term memory network, specifically including the following steps:

[0046] S61. Construct a customer activity time series dataset, collecting customer login frequency, interaction duration and function usage frequency on a daily basis, forming a historical window sequence of length L;

[0047] S62. Using a Long Short-Term Memory (LSTM) network to model an activity time series, the internal state update formula of the LTM network is:

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] Among them, f t i t o t These are the forget gate, input gate, and output gate, respectively, where σ is the Sigmoid activation function, and W... f W i W C W o and b f b i b C b o h is a model parameter. t-1 Let x be the hidden state at time step t-1. t For the input at time step t, C t Let C be the cell state at time step t. t-1 This represents the cell state at time step t-1.

[0055] S63. Input the final hidden state of the Long Short-Term Memory network into the fully connected layer and output the predicted activity values ​​at the next K time points.

[0056] Preferably, the method further includes the following steps:

[0057] S7. Regularly update the neural network model parameters and retrain the model using the latest collected customer interaction data to optimize the accuracy of customer value assessment scores and demand forecasts.

[0058] Preferably, the method further includes the following steps:

[0059] S8. Combine attention mechanisms with graph neural networks to construct customer relationship graphs.

[0060] Preferably, in step S8, the implementation of constructing the customer relationship graph specifically includes the following steps:

[0061] S81. Construct a customer relationship graph based on the interaction and similarity between customers. In the graph, nodes represent customers and edges represent the strength of the relationship between customers.

[0062] S82. Use a graph attention network to learn the feature representation of the client node, where the feature representation of node g is q. g The update formula is:

[0063]

[0064] in, Let g be the updated feature representation of node g, and γ be the non-linear activation function. q represents the attention weight, indicating the importance of neighbor node d to the current center node g. d The original input feature vector of node d, and the attention weights. The calculation formula is:

[0065]

[0066] in, Let q be the attention parameter vector, || denotes vector concatenation, and q z Let z be the original input feature vector of node z;

[0067] S83. The learned customer node features are used for customer group segmentation and influence analysis to identify high-value customer groups and key opinion leaders.

[0068] The beneficial effects of this invention are:

[0069] 1. By establishing a unified personal number identifier and personal number data space, this invention effectively solves the problem in the prior art that personal customer data is scattered across different platforms and cannot form a unified view. It creates a dedicated and centralized information management hub for each user, laying a solid foundation for realizing cross-platform personalized services and fundamentally breaking down data silos.

[0070] 2. By automatically collecting and cleaning customer data from multiple sources such as user terminals, social media platforms, and enterprise CRM systems, comprehensive and automated integration of customer information is achieved. This avoids the tedious operation of manually switching between different systems and entering data in the traditional way, significantly improving the efficiency and accuracy of data collection and providing a high-quality data foundation for subsequent intelligent analysis.

[0071] 3. By applying neural network models to perform intelligent analysis of integrated customer data, accurate and quantitative assessment of customer value and needs is achieved, surpassing traditional judgment methods based on simple rules or human experience. It can automatically mine deep-seated and potential customer behavior patterns from complex data, making customer insights more forward-looking and scientific.

[0072] 4. Based on the analysis results of the neural network model, the collaborative filtering algorithm is used to generate personalized management strategies, which transforms customer management from a "one-size-fits-all" extensive mode to a highly personalized and refined operation. The system can automatically identify high-value customers and recommend the best interaction strategies, which not only improves the targeting and satisfaction of customer service, but also optimizes the allocation of enterprise resources and achieves a significant improvement in marketing efficiency. Attached Figure Description

[0073] Figure 1 This is a flowchart illustrating the intelligent customer management and system integration method for personal accounts in an embodiment of the present invention. Detailed Implementation

[0074] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0075] Example

[0076] like Figure 1 As shown, an intelligent customer management and system integration method for personal accounts includes the following steps:

[0077] S1. Receive a personal number registration request sent by the user terminal. The registration request includes user identity information and customer management preference settings. Verify the user identity information. After successful verification, generate a unique personal number identifier and establish a personal number data space. The personal number data space includes a customer information database, a behavior record database, and a system integration configuration library.

[0078] S2. Collect customer data from multiple data sources, including local customer information uploaded by user terminals, customer interaction data provided by social media platform interfaces, and customer transaction records provided by the enterprise CRM system; store the collected customer data in a unified customer information database, and clean and standardize the data.

[0079] S3. Use a neural network model to intelligently analyze customer data in the customer information database to generate customer value assessment scores and demand prediction labels. The neural network model is a multilayer perceptron model, which includes an input layer, three hidden layers and an output layer. The number of nodes in the input layer corresponds to the customer feature dimensions, and the output layer outputs the customer value score and demand classification probability.

[0080] The training process of a neural network model specifically includes the following steps:

[0081] S31. Construct a training dataset by extracting historical customer data from the customer information database. Each data sample contains a set of static customer attributes, a sequence of dynamic behaviors, a customer value assessment score, and a demand prediction label. The set of static customer attributes includes customer age, occupation, and income level. The sequence of dynamic behaviors includes customer purchase records, consultation frequency, and service usage duration.

[0082] S32. Preprocess the training data, including missing value imputation, outlier removal, and feature standardization; use the min-max standardization method to scale continuous features to the [0,1] interval, as shown in the formula:

[0083]

[0084] Among them, X norm X represents the standardized feature values, while X represents the original feature values. min and X max These are the minimum and maximum values ​​of the feature, respectively.

[0085] Step S33: Initialize the neural network model parameters. Use the He initialization method to set the hidden layer weight matrix. The weight values ​​start from a mean of 0 and a standard deviation of 0. Random sampling is performed from a Gaussian distribution, where n in This represents the number of nodes in the input layer.

[0086] Step S34: Train the neural network using the backpropagation algorithm. The loss function L is a combination of mean squared error and cross-entropy, and the formula is:

[0087]

[0088] Where N is the number of training samples, y i Let i be the true label of the i-th sample. These are the model's predicted values.

[0089] Step S35: Update the model parameters using the Adam optimizer. The learning rate can be set to 0.001, and the number of training iterations can be set to 1000 rounds. Evaluate the model performance on the validation set after each round of training. Terminate training early when the validation set loss function no longer decreases for 10 consecutive rounds.

[0090] By combining loss functions and adaptive optimization algorithms, the model can simultaneously handle regression-type value scoring and classification-type demand forecasting tasks, improving the comprehensiveness of customer analysis.

[0091] The neural network model employs an attention mechanism to enhance its ability to process sequence data, specifically including the following steps:

[0092] An attention layer is added after the second hidden layer of the neural network model to calculate the importance weights at different time points in the behavior sequence; given a customer behavior sequence H=[h1,h2,…,h…] T Attention weight α t The calculation formula is:

[0093]

[0094]

[0095] Where T is the sequence length. and Calculate the original alignment score before attention weights at different positions, where u is the attention parameter vector, W is the weight matrix, and h is the weight matrix. t Let b be the hidden state at time step t, b be the bias vector, and α be the hidden state at time step t. t Let be the attention weight at time step t.

[0096] The weighted sequence s is:

[0097]

[0098] The weighted sequence s is concatenated with the original hidden state and then input into subsequent network layers.

[0099] By introducing an attention mechanism, the model can focus on key time points in the customer behavior sequence, better capture the changing patterns of customer behavior, and is particularly suitable for identifying key nodes of shifting customer interests and changes in demand, thereby improving prediction accuracy.

[0100] S4. Based on customer value assessment scores and demand prediction tags, a collaborative filtering algorithm is used to generate personalized customer management strategies. Personalized customer management strategies include a priority contact customer list, recommended communication times, and recommended product types. The collaborative filtering algorithm calculates a similarity matrix between customers and generates recommendations based on the management strategies for similar customers.

[0101] The specific implementation of the collaborative filtering algorithm includes the following steps:

[0102] Step S41: Construct the customer-behavior matrix R m×n Where m is the number of customers, n is the number of behavioral features, and r is the matrix element. gl Let g be the performance value of the g-th customer on the l-th behavioral characteristic.

[0103] Step S42: Calculate the similarity matrix between customers using an improved cosine similarity calculation method. The formula is as follows:

[0104]

[0105] Where sim(g,d) is the similarity matrix between customer g and customer d, r gk Let r be the performance value of customer g on behavioral characteristic k. dk Let d be the performance value of customer d on behavioral characteristic k. and The average behavioral scores for customers g and d are respectively.

[0106] Step S43: For the target customer, select the K neighboring customers with the highest similarity and generate a recommendation strategy. The recommendation weight calculation formula is as follows:

[0107]

[0108] Where, p gc Let N(g) be the predicted score of target customer g for strategy c, and let N(g) be the nearest neighbor set of customer g. dc The actual rating of strategy c for neighboring customer d.

[0109] By leveraging collaborative filtering algorithms to generate intelligent management strategies based on customer groups, the limitations of relying solely on single customer data are avoided, thus improving the diversity and effectiveness of recommendation strategies. This approach is particularly suitable for generating strategies for new customers when historical data is lacking.

[0110] S5. Establish connections with external business systems through standardized API interfaces, including enterprise ERP systems, e-commerce platforms, and social media platforms; synchronize customer management strategies to external business systems, and obtain the latest customer status updates from external business systems.

[0111] The implementation of the standardized API interface specifically includes the following steps:

[0112] S51. Define a unified data exchange format, using the JSON-LD standard to represent customer data and business rules, ensuring semantic interoperability with different external business systems.

[0113] S52. Establish a two-way authentication mechanism, exchange digital certificates with external business systems, and use the RSA encryption algorithm to sign and verify transmitted data to ensure the security of interface calls.

[0114] S53. Implement asynchronous message queue processing for connection requests. When the external business system is unavailable, the connection request is stored in a persistent queue and automatically retried after the system recovers, ensuring eventual data consistency.

[0115] S54. Perform multi-dimensional monitoring of the status information returned by external business systems, including response time, error rate, and data consistency indicators. Generate an alarm notification when any indicator exceeds the threshold.

[0116] Through standardized API interface design, reliable connection between the personal account system and heterogeneous external systems has been achieved, ensuring the security and stability of data exchange and solving the problems of protocol differences and network unreliability in system integration.

[0117] S6. Monitor customer interaction data in real time, use time series prediction models to predict customer activity trends, and automatically trigger customer recovery strategies when the threshold for declining customer activity exceeds a preset value, and push reminder notifications through the user terminal.

[0118] The time series prediction model employs a long short-term memory network and includes the following steps:

[0119] S61. Construct a customer activity time series dataset, collecting customer login frequency, interaction duration, and function usage frequency on a daily basis to form a historical window sequence of length L.

[0120] S62. Using a Long Short-Term Memory (LSTM) network to model an activity time series, the internal state update formula of the LTM network is:

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] Among them, f t i t o t These are the forget gate, input gate, and output gate, respectively, where σ is the Sigmoid activation function, and W... f W i W C W o and b f b i b C b o h is a model parameter. t-1 Let x be the hidden state at time step t-1. t For the input at time step t, C t Let C be the cell state at time step t. t-1 This represents the cell state at time step t-1.

[0128] S63. Input the final hidden state of the Long Short-Term Memory network into the fully connected layer and output the predicted activity value at the next K time points. When the predicted value drops by more than 30% compared to the current value, trigger customer recovery strategies. For example, provide high-value feature trials to price-sensitive customers instead of simple discounts, provide targeted tutorials and expert support to customers who are not proficient in using the product, and also reach out to customers based on their channel preferences (such as WeChat, email, and SMS).

[0129] Long Short-Term Memory (LSTM) network models can effectively capture the long-term dependencies and cyclical patterns of customer activity, identify potential churned customers in advance, provide a window of opportunity for proactive customer recovery, and reduce customer churn rate.

[0130] S7. Regularly update the parameters of the neural network model and retrain the model using the latest collected customer interaction data to optimize the accuracy of customer value assessment and demand forecasting.

[0131] S8. Combine attention mechanisms with graph neural networks to construct customer relationship graphs.

[0132] The specific steps involved in constructing a customer relationship graph are as follows:

[0133] S81. Construct a customer relationship graph based on the interaction and similarity between customers. In the graph, nodes represent customers and edges represent the strength of the relationship between customers.

[0134] S82. Use a graph attention network to learn the feature representation of the client node, where the feature representation of node g is q. g The update formula is:

[0135]

[0136] in, Let g be the updated feature representation of node g, and γ be the non-linear activation function. q represents the attention weight, indicating the importance of neighbor node d to the current center node g. d The original input feature vector of node d, and the attention weights. The calculation formula is:

[0137]

[0138] in, Let q be the attention parameter vector, || denotes vector concatenation, and q z Let z be the original input feature vector of node z.

[0139] S83. The learned customer node features are used for customer group segmentation and influence analysis to identify high-value customer groups and key opinion leaders.

[0140] By analyzing customer relationship networks through graph neural networks, the system can discover potential connections and group characteristics among customers, providing support for group customer management strategies and word-of-mouth marketing, and expanding the dimensions of customer management.

[0141] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for intelligent customer management and system integration of personal accounts, characterized in that, Includes the following steps: S1. Receive the personal number registration request sent by the user terminal, generate a unique personal number identifier after verification, and establish a personal number data space, which includes a customer information database, a behavior record database, and a system interface configuration library. S2. Collect customer data and store it in a unified customer information database, and clean and standardize the data. S3. Use neural network models to intelligently analyze customer data in the customer information database to generate customer value assessment scores and demand prediction tags. S4. Based on customer value assessment scores and demand prediction tags, a collaborative filtering algorithm is used to generate personalized customer management strategies. The collaborative filtering algorithm calculates a similarity matrix between customers and generates recommendations based on the customer management strategies for similar customers. S5. Establish connections with external business systems through standardized API interfaces, synchronize customer management strategies to external business systems, and obtain the latest customer status updates from external business systems.

2. The intelligent customer management and system integration method for personal accounts according to claim 1, characterized in that, In step S3, the training process of the neural network model specifically includes the following steps: S31. Construct a training dataset by extracting historical customer data from the customer information database. Each data sample contains a set of static customer attributes, a sequence of dynamic behaviors, a customer value assessment score, and a demand prediction label. The set of static customer attributes includes customer age, occupation, and income level. The sequence of dynamic behaviors includes customer purchase records, consultation frequency, and service usage duration. S32. Preprocess the training data, including missing value imputation, outlier removal, and feature standardization; use the min-max standardization method to scale continuous features to the [0,1] interval, as shown in the formula: ； Among them, X norm X represents the standardized feature values, while X represents the original feature values. min and X max These are the minimum and maximum values ​​of the feature, respectively. Step S33: Initialize the neural network model parameters. Use the He initialization method to set the hidden layer weight matrix. The weight values ​​start from a mean of 0 and a standard deviation of 0. Random sampling is performed from a Gaussian distribution, where n in This represents the number of nodes in the input layer. Step S34: Train the neural network using the backpropagation algorithm. The loss function L is a combination of mean squared error and cross-entropy, and the formula is: ； Where N is the number of training samples, y i Let i be the true label of the i-th sample. These are the model's predicted values; Step S35: Update the model parameters using the Adam optimizer. Evaluate the model performance on the validation set after each training round. Terminate training early when the validation set loss function no longer decreases for several consecutive rounds.

3. The intelligent customer management and system integration method for personal accounts according to claim 2, characterized in that, In step S3, the neural network model employs an attention mechanism to enhance its sequential data processing capabilities, specifically including the following steps: An attention layer is added after the second hidden layer of the neural network model to calculate the importance weights at different time points in the behavior sequence; given a customer behavior sequence H=[h1,h2,…,h…] T Attention weight α t The calculation formula is: ； ； Where T is the sequence length. and Calculate the original alignment score before attention weights at different positions, where u is the attention parameter vector, W is the weight matrix, and h is the weight matrix. t Let b be the hidden state at time step t, b be the bias vector, and α be the hidden state at time step t. t Let be the attention weight at time step t; The weighted sequence s is: ； The weighted sequence s is concatenated with the original hidden state and then input into subsequent network layers.

4. The intelligent customer management and system integration method for personal accounts according to claim 3, characterized in that, In step S4, the specific implementation of the collaborative filtering algorithm includes the following steps: Step S41: Construct the customer-behavior matrix R m×n Where m is the number of customers, n is the number of behavioral features, and r is the matrix element. gl Let g be the performance value of the g-th customer on the l-th behavioral characteristic; Step S42: Calculate the similarity matrix between customers using an improved cosine similarity calculation method. The formula is as follows: ； Where sim(g,d) is the similarity matrix between customer g and customer d, r gk Let r be the performance value of customer g on behavioral characteristic k. dk Let d be the performance value of customer d on behavioral characteristic k. and The average behavioral scores for customers g and d are respectively; Step S43: For the target customer, select the K neighboring customers with the highest similarity and generate a recommendation strategy. The recommendation weight calculation formula is as follows: ； Where, p gc Let N(g) be the predicted score of target customer g for strategy c, and let N(g) be the nearest neighbor set of customer g. dc The actual rating of strategy c for neighboring customer d.

5. The intelligent customer management and system integration method for personal accounts according to claim 4, characterized in that, In step S5, the implementation of the standardized API interface specifically includes the following steps: S51. Define a unified data exchange format, using the JSON-LD standard to represent customer data and business rules, and ensure semantic interoperability with different external business systems; S52. Establish a two-way authentication mechanism, exchange digital certificates with external business systems, and use the RSA encryption algorithm to sign and verify transmitted data to ensure the security of interface calls. S53. Implement asynchronous message queue processing for connection requests. When the external business system is unavailable, the connection request is stored in a persistent queue and automatically retried after the system recovers to ensure eventual data consistency. S54. Perform multi-dimensional monitoring of the status information returned by external business systems, including response time, error rate, and data consistency indicators. Generate an alarm notification when any indicator exceeds the threshold.

6. The intelligent customer management and system integration method for personal accounts according to claim 5, characterized in that, It also includes the following steps: S6. Monitor customer interaction data in real time, use time series prediction models to predict customer activity trends, and automatically trigger customer recovery strategies when the threshold for declining customer activity exceeds a preset value, and push reminder notifications through the user terminal.

7. The intelligent customer management and system integration method for personal accounts according to claim 6, characterized in that, In step S6, the time series prediction model employs a long short-term memory network, specifically including the following steps: S61. Construct a customer activity time series dataset, collecting customer login frequency, interaction duration and function usage frequency on a daily basis, forming a historical window sequence of length L; S62. Using a Long Short-Term Memory (LSTM) network to model an activity time series, the internal state update formula of the LTM network is: ； ； ； ； ； ； Among them, f t i t o t These are the forget gate, input gate, and output gate, respectively, where σ is the Sigmoid activation function, and W... f W i W C W o and b f b i b C b o h is a model parameter. t-1 Let x be the hidden state at time step t-1. t For the input at time step t, C t Let C be the cell state at time step t. t-1 This represents the cell state at time step t-1. S63. Input the final hidden state of the Long Short-Term Memory network into the fully connected layer and output the predicted activity values ​​at the next K time points.

8. The intelligent customer management and system integration method for personal accounts according to claim 7, characterized in that, It also includes the following steps: S7. Regularly update the neural network model parameters and retrain the model using the latest collected customer interaction data to optimize the accuracy of customer value assessment scores and demand forecasts.

9. The intelligent customer management and system integration method for personal accounts according to claim 8, characterized in that, It also includes the following steps: S8. Combine attention mechanisms with graph neural networks to construct customer relationship graphs.

10. The intelligent customer management and system integration method for personal accounts according to claim 9, characterized in that, In step S8, the specific implementation of constructing the customer relationship graph includes the following steps: S81. Construct a customer relationship graph based on the interaction and similarity between customers. In the graph, nodes represent customers and edges represent the strength of the relationship between customers. S82. Use a graph attention network to learn the feature representation of the client node, where the feature representation of node g is q. g The update formula is: ； in, Let g be the updated feature representation of node g, and γ be the non-linear activation function. q represents the attention weight, indicating the importance of neighbor node d to the current center node g. d The original input feature vector of node d, and the attention weights. The calculation formula is: ； in, Let q be the attention parameter vector, || denotes vector concatenation, and q z Let z be the original input feature vector of node z; S83. The learned customer node features are used for customer group segmentation and influence analysis to identify high-value customer groups and key opinion leaders.

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