Plug-in-based AI digital human capability extension method

Abstract

The application discloses a kind of based on plug-in AI digital person ability extension method, the method includes according to the data that user submits and interacts, extract user information feature matrix, construct user preliminary portrait;Based on user preliminary portrait, select relevant plug-in, pre-load plug-in and automatically select ability extension strategy;Initialize plug-in function, according to ability extension strategy and user dialogue system gradually enable plug-in, expand the ability of AI digital person, record enabled function information;According to enabled function information, construct AI digital person ability management system, output AI digital person ability matrix;Using the document uploaded by user, parse the task list of user, match ability matrix, customize user personalized task list.The application solves the flexibility and personalization problem of AI digital person function expansion under different user needs and scenarios, realizes the intelligent adaptation and ability improvement of AI digital person in diversified application scenarios, provides more accurate and efficient personalized service for user.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence and AI digital human technology, and in particular to a plug-in-based method for extending the capabilities of AI digital humans. Background Technology

[0002] With the rapid development of artificial intelligence (AI) and virtual assistant technologies, AI digital humans are gradually becoming an important mode of human-computer interaction. AI digital human technology breaks through the physical and temporal limitations of traditional human interaction, providing a virtual, intelligent, and efficient interactive interface capable of providing services and support when humans are not physically present. AI digital humans can perform tasks without emotional bias, providing personalized and customized services, and through continuous learning and adaptation, gradually acquiring interaction capabilities approaching those of real humans. This technology not only drives the evolution of human-computer interaction but also brings profound changes to industries such as healthcare, education, entertainment, and finance, promoting the development of an intelligent society and endowing the digital world with more vivid and meaningful forms of interaction, thus becoming a completely new technological field.

[0003] However, the capabilities of traditional AI digital humans are often preset and fixed, making it difficult to flexibly adapt to the needs of different users and constantly changing task scenarios. Although AI digital humans can perform certain standardized tasks, they often fail to provide personalized and efficient services when faced with complex and varied tasks. In addition, existing AI digital human systems typically lack a dynamic expansion mechanism, making it difficult for the system to quickly load and activate corresponding functions according to different user needs, thus limiting its intelligent and personalized development.

[0004] Plug-in architecture deconstructs the core capabilities of AI digital humans (such as speech recognition, sentiment analysis, natural language understanding and generation, and virtual character behavior modeling and generation) into independent, loosely coupled functional units, endowing the AI ​​digital human system with high flexibility and scalability. This design not only enables dynamic plug-and-play functionality, allowing AI digital humans to be customized according to specific scenarios and needs, but also greatly facilitates cross-platform adaptation, thereby reducing the complexity of adaptation across diverse hardware, operating systems, and application scenarios. Furthermore, this plug-in model achieves high responsiveness during the functional evolution of AI digital humans through the automatic updating and iteration of intelligent plug-ins, rapidly adapting to changes in user needs and optimizing functions based on real-time feedback. This approach makes the interaction of AI digital humans more refined and personalized, enabling multi-level and multi-dimensional emotional expression, behavioral adjustment, and response, ultimately achieving an interaction effect closer to that of real humans. This significantly enhances the immersive experience and emotional resonance of the user experience, laying the foundation for the deep penetration of AI digital human technology in multiple fields.

[0005] Against this backdrop, there is an urgent need for an AI digital human capability extension technology that can flexibly import plugins to improve the quality of dynamic interaction and provide users with customized services. Summary of the Invention

[0006] The purpose of this invention is to provide a plugin-based AI digital human capability extension method, which initially constructs a user profile using user information, preloads plugins, and provides capability extension strategies; the second aspect of this invention is to provide a plugin-based AI digital human capability management system.

[0007] The objective of this invention is achieved through the following technical solution:

[0008] A plugin-based method for extending the capabilities of AI digital humans includes the following steps:

[0009] Step S1: Based on the user-submitted and interacted data, extract the user information feature matrix and construct a preliminary user profile;

[0010] Step S2: Select relevant plugins based on the initial user profile, preload the plugins, and automatically select a capability expansion strategy;

[0011] Step S3: Initialize plugin functionality, gradually enable plugins according to the capability expansion strategy and user dialogue system to expand the capabilities of the AI ​​digital human, and record the enabled function information;

[0012] Step S4: Based on the enabled function information, construct the AI ​​digital human capability management system and output the AI ​​digital human capability matrix;

[0013] Step S5: Using the user-uploaded documents, parse the user's task list, match the capability matrix, and customize a personalized task list for the user.

[0014] Furthermore, step S1 specifically includes:

[0015] Step S101: Based on the user's profile or the basic information filled in during the initial interaction, obtain the user's basic information input set X:

[0016] X = {x1, x2, x3, x4, ..., x} n}

[0017] Where x1, x2, x3, x4, ..., x n These represent user information such as name, age, gender, and job title, where n represents the number of pieces of information.

[0018] Step S102: Divide X into different types of data and process them separately. t ,x d ,xT ,x s These represent categorical, numeric, date, and text data, respectively. t ,x d ,x T ,x s ∈X; Convert the above data into numerical values ​​that can represent features, and combine them in order to generate a feature vector u containing user information features;

[0019] Step S103: Use feature vector u to represent the general description of the user and construct a preliminary user profile.

[0020] Furthermore, step S102 specifically includes:

[0021] (1) The collected categorical user information is converted into a numerical representation using multi-hot encoding. For each category c∈C in the categorical variable C, then C={c1,c2,c3,…,c m}, where m is the number of categories, and for the i-th category c i The forms of expression are as follows:

[0022]

[0023] u l =[u1(x t ),u2(x t ),u3(x t ),…,u m (x t )]

[0024] Where i∈[1,2,3,…,m], x t This represents the information input by the user in this category; the one-hot encoded vectors of all categorical variables are concatenated into a matrix u. l ;

[0025] (2) Use the following formula to process the collected numerical data x d To perform standardization, input the data x that needs to be standardized. d :

[0026]

[0027] Where μ and σ represent x, respectively. d The mean and standard deviation of u d This represents the matrix after standardization of all numerical data, where g represents the number of numerical data points.

[0028] (3) Transfer the collected date data x T Convert the data to time difference ΔT, and then combine them to obtain a date-type data matrix:

[0029] ΔT1=|x T1 -T|

[0030] u T =[ΔT1,ΔT2,…,ΔT h ]

[0031]

[0032] Where T represents the system's set reference time, u T This represents the matrix after converting all date data, where h represents the number of date data items. The matrix is ​​obtained after standardizing the date data. T * ;

[0033] (4) Select the first text data x collected s1 Calculate the weight of each word in the text, when the word W k If it appears once in the text, then count(W) k ) = count(W k )+1, using each word in x s1 The number of times it appears in the text divided by x s1 The total number of words in the corpus is calculated, and the frequency of each word in the document is obtained. All words and their calculated frequencies are stored in the corpus as key-value pairs. The top Ω high-frequency words W are selected. k Calculate its proportion x s1 The weights in the matrix construct the feature matrix u of each text. s1 k = 1, 2, ..., Ω; The weights of high-frequency words in each text data are obtained using this method, forming the corresponding text-type data feature matrix u. s z represents the number of text data items, u s =[u s1 ,u s2 ,…,u sz ];

[0034] (5) Concatenate all data matrices of different types in order of type to obtain the feature matrix u:

[0035]

[0036] Where u represents a matrix containing all information characteristics of the user, and the number of all types of data is equal to the sum of all information data: n = m + g + h + z.

[0037] Furthermore, step S2 specifically includes:

[0038] Step S201: Based on the feature matrix u of the initial user profile, convert it into the user's current state space B, and define the action space A, where A represents the action of selecting a plugin:

[0039]

[0040] a j ∈A

[0041] Among them, u i Let μ represent the features in u, Φ represent the threshold for selecting p features, and U represent the feature matrix selected from u that is greater than the threshold Φ. U , σ U Let Vp and Vp represent the mean and standard deviation of each feature, respectively. Vp represents the selection of the first p features from the selected features as the principal component matrix, and j represents the number of plugins.

[0042] Step S202: In the state space and action space, calculate the fitness difference of the progressively selected plug-in actions, and use the fitness difference to evaluate the optimization benefits brought by the state function evaluation of the currently selected plug-in combination:

[0043] ΔΠ=Π a+1 (b,a)-Π a (b,a)

[0044] Π a+1 -Π a =λ(β+μ(Π) a (b',a'))-Π a (b,a))

[0045]

[0046] Among them, Π a+1 (b,a), Π a (b, a) represent the fitness values ​​before and after updating action a in state s, respectively; λ represents the update step size; β represents the fitness value generated when action a is executed; μ represents the fitness value parameter that will be increased by the next action; s' represents the next state entered after executing action a; and a' represents the action selected in the next state. q This represents the state function at this point, where q represents the selected round, and f(Π) represents the state function. a q ) represents the fitness value function for selecting the a-th action in the q-th round, and κ represents the importance of this state change;

[0047] Step S203: Select s q Plugins with values ​​greater than the threshold ν are considered highly relevant to the user and are preloaded. A swap operator is set to exchange or merge different parts of existing operations, allowing previously unconsidered regions to be explored in the solution space, thus expanding the solution space. The plugin selection strategy is then updated using gradient descent.

[0048]

[0049] in, Let λ represent the exchange operator in the q-th round of selection. θ Indicates the step size for the chosen strategy update. It is the state function s q For the policy parameter θ a The partial derivatives of s q Indicates the relationship with θ a Sensitivity to change.

[0050] Step S204: Output the trained capability expansion strategy to automatically select model Ψ.

[0051] Furthermore, step S3 specifically includes:

[0052] Step S301: Initialize all available plugins, establishing initial state and capability information for each plugin, specifically including assigning an initial configuration to each plugin:

[0053] a={id,name,state,description}

[0054] id = Category + Index

[0055] Where 'a' represents a plugin, the plugin content includes the plugin's id, name, state value, and description; the plugin's id is composed of the plugin's category and index, padded with 0s at the beginning; when state = 0, it means the plugin is not enabled, and when state = 1, it means the plugin is enabled.

[0056] Step S302: Use a text clustering model to cluster the user dialogue content, divide the user dialogue content into categories η, use the capability extension strategy to automatically select model Ψ for the plugin of category η, and set the plugin status to 1.

[0057] Step S303: Identify plugins with a plugin status of 1, execute the plugin function, display the corresponding card according to the returned category value, and generate a vector set F containing all enabled functions.

[0058] Furthermore, the specific process for classifying user dialogue content categories in step S302 is as follows:

[0059] (1) Collect user dialogue information and convert it into a numerical feature vector Y. Each dialogue sample is represented as a feature vector y. i Y = [y1, y2, ..., y i ,…,y M ],Y∈R M×DM represents the number of dialogue samples, D is the feature dimension of each sample, and R represents the feature vector matrix;

[0060] (2) Initialize the text clustering model parameters: Set the number of clusters, i.e. the number of clusters, to η = 1, the convergence accuracy to ε = 0.001, and the number of iterations to o = 0;

[0061] (3) Initialize the prior probability ω i =1 / η;

[0062] (4) Given the prior probabilities, calculate the posterior probability that each sample y belongs to each cluster:

[0063]

[0064] in, Let μ represent the posterior probability that the Mth sample comes from the i-th cluster at the o-th iteration, μ and σ represent the mean and variance respectively, e represents the exponential operation, and π represents pi.

[0065] (5) Update the parameter values ​​of each cluster according to the posterior probability. The specific steps are as follows:

[0066]

[0067] (6) Determine whether the model has converged. Calculate whether the change of each parameter is less than the set convergence accuracy. If the change of any parameter is greater than the convergence accuracy, the iteration count o = o + 1, and return to step (4); otherwise, proceed to step (7).

[0068] (7) At this time, η = η + 1. If η ≤ l, proceed to step (3) to ensure that the category level is within level l.

[0069] (8) Calculate the profile coefficient of the samples when η = 1, 2, 3, ..., l, and compare the profile coefficients of the samples with different values ​​of η. The profile coefficient is the similarity of each sample with the samples in the same cluster divided by the similarity of each sample with the nearest cluster. Then select the η with the largest profile coefficient as the optimal category.

[0070] (9) Return the posterior probability r for each dialogue. i (M) and its category η.

[0071] Furthermore, step S4 specifically includes:

[0072] Step S401: Based on the enabled function vector set F, construct the AI ​​digital human capability management model Θ. The model includes: a feature vector embedding model, a convolutional neural network model, a multilayer perceptron model, a feedforward neural network model, and a capability management model.

[0073] Step S402: Input the function vector into the AI ​​digital human capability management model Θ to obtain the AI ​​digital human's capability scores for different tasks, and generate the AI ​​digital human capability matrix D, where rows represent different tasks and columns represent different capabilities:

[0074]

[0075] Where H represents the number of tasks, I represents the number of capabilities, and d ij This represents the AI ​​digital human's score for the i-th task on the j-th ability.

[0076] Furthermore, the specific structure of the AI ​​digital human capability management model Θ constructed in step S401 is as follows:

[0077] The input of the AI ​​digital human capability management model Θ serves as the input of the feature vector embedding model; the output of the feature vector embedding model is connected to the input of the convolutional neural network; the output of the convolutional neural network is connected to the input of the multilayer perceptron model; the output of the multilayer perceptron model is connected to the input of the feedforward neural network model; the output of the feedforward neural network model is connected to the input of the capability management model; and the output of the capability management model serves as the output of the AI ​​digital human capability management model Θ.

[0078] The feature vector embedding model includes: a fully connected layer, a ReLU activation function layer, and a batch processing layer; the input of the feature vector embedding model serves as the input of the fully connected layer; the output of the fully connected layer serves as the input of the ReLU activation function layer; the output of the ReLU activation function layer is connected to the input of the batch processing layer; the output of the batch processing layer serves as the output of the feature vector embedding model and is connected to the convolutional layer of the convolutional neural network model.

[0079] The convolutional neural network model includes: a convolutional layer, a pooling layer, and an activation function layer; the input of the convolutional neural network model serves as the input of the convolutional layer; the output of the convolutional layer is connected to the input of the pooling layer; the output of the pooling layer is connected to the input of the activation function layer; the output of the activation function layer serves as the output of the convolutional neural network model and is connected to the fully connected layer of the multilayer perceptron.

[0080] The multilayer perceptron model includes: a fully connected layer, a ReLU activation function layer, and a Dropout layer; the input of the multilayer perceptron model serves as the input of the fully connected layer; the output of the fully connected layer is connected to the input of the ReLU activation function layer; the output of the ReLU activation function layer is connected to the input of the Dropout layer; the output of the Dropout layer serves as the output of the multilayer perceptron model and is connected to the fully connected layer of the feedforward neural network model.

[0081] The feedforward neural network model includes: a fully connected layer, a feedforward neural network layer, and a Dropout layer; the input of the feedforward neural network model serves as the input of the fully connected layer; the output of the fully connected layer is connected to the input of the feedforward neural network layer; the output of the feedforward neural network layer is connected to the input of the Dropout layer; the output of the Dropout layer serves as the output of the feedforward neural network model and is connected to the fully connected layer of the capability management model.

[0082] The capability management model includes: a fully connected layer and a Softmax activation function layer; the input of the capability management model serves as the input of the fully connected layer; the output of the fully connected layer is connected to the input of the Softmax activation function layer; and the Softmax activation function layer serves as the output of the capability management model.

[0083] Furthermore, the scoring formula for calculating the ability corresponding to each task in step S402 is as follows:

[0084] E1 = Relu(W1·F + b1)

[0085]

[0086] E4 = Relu(W3·Mask(E3,ρ) + b4)

[0087] E5 = Relu(W4·Mask(E4,ρ) + b5)

[0088] E6 = Softmax(W5·E5 + b6)

[0089] Where E1 represents the output of the feature vector after ReLU(·) activation, W1 represents the bias matrix of the fully connected layer, and b1 represents the bias parameters of the fully connected layer; E2 represents the output of the feature vector embedding model, δ represents the learnable scaling parameter, b2 represents the bias parameters of the batch layer, μ and σ represent the mean and variance, and ξ represents the parameter to prevent division by zero error; E3 represents the output of the convolutional layer, W2 and b3 represent the bias matrix and bias parameters of the convolutional layer, LeakyReLU(·) is the activation function, and len(·) is the length calculation function; E4 represents the output of the multilayer perceptron, W3 and b4 represent the bias matrix and bias parameters of the multilayer perceptron, Mask(·) is the masking function, indicating whether neurons are dropped, and ρ represents the dropout rate, which is usually between 0 and 1; E5 represents the output of the feedforward network layer, W4 and b5 represent the bias matrix and bias parameters of the feedforward network layer; E6 represents the task's ability score, W5 and b6 represent the bias matrix and bias parameters of the ability management module, and Softmax(·) is the activation function.

[0090] Furthermore, step S5 specifically includes:

[0091] Step S501: Standardize the task documents uploaded by users, convert them into a parsable text format, remove extra spaces, line breaks, and special characters that interfere with parsing, use periods as the separator for each sentence, and line breaks as the separator for each task.

[0092] Step S502: For each preprocessed sentence, identify task keywords, extract the complete task description using regular expressions, and parse the tasks sequentially into structured data S1. After parsing all tasks in the document, construct a dictionary list S = [S1, S2, ..., S...]. n ], where n represents the number of tasks;

[0093] Step S503: Match the task dictionary list with the tasks in the capability matrix, calculate the sum of the capability values ​​required for the task in the capability matrix as the score of the task, and output the user's personalized task list in the order of the scores.

[0094] The beneficial effects of this invention include:

[0095] (1) Through systematic feature engineering, different types of data are converted into a unified numerical feature vector, ensuring that the data conversion can accurately express the user's multi-dimensional characteristics, providing rich data support for subsequent capability expansion and personalized task customization, and ensuring that the expanded AI digital human service can better meet the user's real needs.

[0096] (2) AI digital humans select capability plugins that match user needs based on the user feature matrix and preload capability plugins, enabling AI digital humans to dynamically adjust in different situations, avoiding the subjectivity and fixedness of the capabilities given by traditional AI digital humans, and improving personalization and flexibility.

[0097] (3) The capability expansion strategy is based on the user's immediate needs and dialogues to dynamically adjust and gradually enable capabilities. This not only avoids loading too many unnecessary functions at once, but also enables the required capabilities to be accurately enabled according to the user's interaction methods and task requirements, thereby realizing the on-demand expansion of AI digital human capabilities. It does not require all capabilities to be given, thus improving the responsiveness and immediacy of the system.

[0098] (4) The AI ​​digital human capability management system, combined with artificial intelligence technologies such as feature vector embedding models, convolutional layers, and pooling layers, can effectively extract deep feature information from raw data and transform this information into a more intuitive and accurate capability representation. Through this deep feature expression, the AI ​​digital human can obtain a more comprehensive task understanding and capability assessment, avoiding simple rule matching and improving the intelligence level of the AI ​​digital human;

[0099] (5) By accepting user-provided documents, tasks are automatically extracted from the documents, processed in a structured manner, intelligently analyzed, and associated with the capability matrix, thus achieving automated processing from task input to task recommendation. This automated processing of business processes reduces human intervention, improves the work efficiency of AI digital humans, and avoids the problem of inaccurate task matching caused by human error;

[0100] (6) By calculating the capability score for each task, the AI ​​digital human can automatically adjust its capability allocation according to the current task requirements and prioritize its strongest capabilities in different tasks. This dynamic capability adjustment enables the AI ​​digital human to adapt to complex working environments and optimize capability scheduling in real time according to changes in tasks.

[0101] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained from the following description and the foregoing claims. Attached Figure Description

[0102] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0103] Figure 1 This is a schematic diagram of the overall workflow of the present invention;

[0104] Figure 2 This is a schematic diagram of the initial user profile after feature extraction according to the present invention;

[0105] Figure 3 This is a flowchart illustrating the process of classifying user dialogue content using a Gaussian mixture model, as described in this invention.

[0106] Figure 4 This is a structural diagram of the AI ​​digital human capability management system of the present invention;

[0107] Figure 5 This is a schematic diagram of the AI ​​digital human capability matrix generated using data from the embodiments of the present invention;

[0108] Figure 6 This is a diagram of the AI ​​digital human capability management function interface of the present invention;

[0109] Figure 7 The user task function interface diagram is analyzed for this invention. Detailed Implementation

[0110] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0111] This invention provides a plugin-based method for extending the capabilities of AI digital humans, such as... Figure 1 As shown, it includes the following steps:

[0112] Step S1: Based on the user-submitted and interacted data, extract the user information feature matrix and construct a preliminary user profile;

[0113] Step S2: Select relevant plugins based on the initial user profile, preload the plugins, and automatically select a capability expansion strategy;

[0114] Step S3: Initialize plugin functionality, gradually enable plugins according to the capability expansion strategy and user dialogue system to expand the capabilities of the AI ​​digital human, and record the enabled function information;

[0115] Step S4: Based on the enabled function information, construct the AI ​​digital human capability management system and output the AI ​​digital human capability matrix;

[0116] Step S5: Using the user-uploaded documents, parse the user's task list, match the capability matrix, and customize a personalized task list for the user.

[0117] The specific steps of the above method will be further explained below through a specific embodiment.

[0118] In this embodiment, step S1 specifically includes the following steps:

[0119] Step S101: Obtain the user's basic information input set X based on the user's profile or the basic information filled in during the first interaction:

[0120] The input received in this embodiment is as follows: X = {"Name":"Yan","Age":35,"Gender":"Male","Position":"Senior Engineer","Start Date":"2015-08-15","Birth Date":"1987-09-15","Marriage Date":"2015-08-20","Education":"Bachelor's Degree","University":"Tsinghua University","Salary":18000,"Cities of Residence":["Beijing","Shanghai","Guangzhou"],"Hobbies":"Hobbies":["Programming","Running","Music","Movies","Mountain Climbing"],"Last Business Trip Location":["Shanghai","Tokyo"],"Technology Stack":["Python","SQL"],"Years of Work Experience":10,"Family Members]} [Wife, Son, Parents, Brothers], "Personal Signature": "There are only 10 kinds of people in the world: those who understand binary and those who don't.", "Personal Introduction": "I am a full-stack developer, focusing on in-depth exploration of programming and technology. Over the past few years, I have accumulated rich development experience, especially in front-end and back-end development. I am familiar with modern front-end frameworks such as React and Vue.js, and also have extensive back-end development experience, proficient in using frameworks such as Express and Django for server-side development. In my daily work, I am not only passionate about solving technical problems and driving project progress, but also highly value teamwork and experience sharing. In addition to programming, I also enjoy running; inspiration often strikes during runs, helping me gain new insights into programming and the technology field."

[0121] Step S102: Divide X into different types of data and process them separately. t ,x d ,x T ,x s These represent categorical, numeric, date, and text data, respectively. t ,x d ,x T ,x s ∈X; x t ={"Gender":"Male","Position":"Senior Engineer","Education":"Bachelor's Degree","University":"Tsinghua University","Cities of Residence":["Beijing","Shanghai","Guangzhou"],"Hobbies":["Programming","Running","Music","Movies","Mountain Climbing"],"Last Business Trip Location":["Shanghai"],"Technology Stack":["Python","SQL"],"Family Members":["Wife","Son","Parents","Siblings"]}; x d ={"Age":35,"Salary":18000,"Years of Work Experience":10}; x T={"Start Date="2015-08-15","Date of Birth="1987-09-15",Date of Marriage="2015-08-20"}; x s ={"Personal Signature":"There are only 10 kinds of people in the world: those who understand binary and those who don't.","Personal Introduction":"I am a full-stack development engineer, focusing on in-depth exploration of programming and technology. Over the past few years, I have accumulated rich development experience, especially in front-end and back-end development. I am familiar with modern front-end frameworks such as React and Vue.js, and also have extensive back-end development experience, proficient in using frameworks such as Express and Django for server-side development. In my daily work, I am not only passionate about solving technical problems and driving project progress, but also highly value teamwork and experience sharing. In addition to programming, I also enjoy running; inspiration often strikes during runs, helping me gain new insights in the programming and technology fields."}; The above data is converted into numerical values ​​that can represent features, and then combined in sequence to generate a feature vector u containing user information features. The specific steps are as follows:

[0122] (1) The collected categorical user information is converted into numerical representation using multi-hot encoding. For each category c∈C in the categorical variable C, C={c1,c2,c3,…,c9}, c1~c9 represent gender, position, education, graduating school, city lived in, hobbies, most recent business trip location, technology stack, and family members, respectively. The specific types for each category are as follows: c1={male,female}, c2={senior engineer, junior engineer, intermediate engineer, project manager, senior manager}, c3={master, doctor, bachelor, junior college}, c4={Tsinghua University, Peking University, Fudan University, Shanghai Jiaotong University}, c5={Beijing, Shenzhen, Shanghai, Guangzhou, Chengdu, Mianyang}, c6={programming, running, travel, music, reading, watching movies, mountain climbing}, c7={Shanghai, Tokyo, Beijing, Guangzhou}, c8={Python, Java, C++, JavaScript, Go, SQL}, c9={wife, son, parents, brothers, sisters, grandfather, grandmother, friends};

[0123] For the i-th category c i The forms of expression are as follows:

[0124]

[0125] u l =[u1(x t ),u2(x t ),u3(x t ),…,u m (x t )]

[0126] Where i∈[1,2,3,…,m], x t This represents the information input by the user in this category; we obtain u1 = [1,0], u2 = [1,0,0,0,0], u3 = [0,0,1,0], u4 = [1,0,0,0], u5 = [1,0,1,1,0,0], u6 = [1,1,0,1,0,1,1], u7 = [1,0,0,0], u8 = [1,0,0,0,0,1], u9 = [1,1,1,1,0,0,0,0]; we then concatenate the one-hot encoded vectors of all categorical variables into a matrix u. l =[1,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,1,0,0,1,1,0,1,0,1,1,1,0,0,0,1,0,0,0,1,1,1,1,1,0,0,0,0];

[0127] (2) Use the following formula to process the collected numerical data x d Standardization is performed, with age ranging from 0-100, salary from 2000-50000, and years of service from 1-40. In this case, g=3.

[0128]

[0129] Where μ and σ represent x, respectively. d The mean and standard deviation can be obtained using the formula: age x' d1 =0.35, salary x' d2 =0.33, years of service x' d3 =0.23, thus obtaining the standardized matrix u d = [0.35, 0.33, 0.23];

[0130] (3) Transfer the collected date data x T Converting the data to time difference ΔT and concatenating them yields a date-type data matrix. The system base time is 1900-01-01, and all subsequent representations will use the base time.

[0131]

[0132] u T =[ΔT1,ΔT2,…,ΔT h ]

[0133]

[0134] Where T represents the system's base time, and according to the formula, the ΔT values ​​for the start date, birth date, and marriage date are 42210, 32033, and 42215, respectively.T =[42210,32033,42215], in days. To maintain consistency with other feature types, the date data is standardized to obtain u. T * = [0.99, 0, 1];

[0135] (4) Select the first text data "personal signature" and ignore the stop words to find the four most frequent words: "world", "binary", "person", and "10 kinds", with probabilities of 0.11, 0.11, 0.11, and 0.11, respectively. Select the second text data "personal introduction" and ignore the stop words to find the most frequent words: "development", "programming", "technology", and "experience", with probabilities of 0.49, 0.37, 0.37, and 0.37, respectively. In addition, store all words and their corresponding probabilities in the corpus.

[0136] The features u of the text data are obtained by synthesis s = [0.11,0.11,0.11,0.11,0.49,0.37,0.37,0.37];

[0137] (5) Using the name as the identifier, concatenate all data matrices of different types in order of type to obtain the feature matrix u:

[0138]

[0139] Where u is a vector containing all informational characteristics of the user, and the number of all types of data is equal to the sum of all informational data: n = m + g + h + z; the feature vector of user "Yan Mou" can be obtained as [1,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,1,0,0,1,1,0,1,0,1,1,1,1,0,0,1,0,0,0,0,1,1,1,1,1,0,0,1,0,0,0,0,0,0.35,0.33,0.23,0.99,0,1,0.11,0.11,0.11,0.11,0.11,0.49,0.37,0.37,0.37];

[0140] Step S103: Construct a preliminary user profile by using feature vector u to represent a general description of the user.

[0141] Based on the feature vector, user "Yan" is male, holds a senior engineer position, has a bachelor's degree from Tsinghua University, resides in Beijing, Shanghai, and Guangzhou, and enjoys programming, running, music, movies, and mountain climbing. His most recent business trip was to Shanghai. His technical skills include Python and SQL. His family includes a wife, son, parents, and siblings. His age is standardized at 0.35, placing him in the lower-middle age group. His salary is standardized at 0.33, placing him in the lower-middle income group. His years of work experience are standardized at 0.23, indicating relatively short work experience. His start date is relatively far from the baseline date, his birth date is relatively close to the baseline date, and his marriage date is relatively far from the baseline date. His personal signature focuses on "world," "binary," "10 kinds," and "people." His personal introduction focuses on "development," "programming," "technology," and "experience." This initial user profile is as follows: Figure 2 As shown.

[0142] Step S2 specifically includes the following steps:

[0143] Step S201: Based on the feature matrix u of the initial user profile, convert it into the user's current state space B, and define the action space A, where A represents the action of selecting a plugin:

[0144]

[0145] a j ∈A

[0146] Among them, u i Let μ represent the features in u, Φ represent the threshold for selecting p features, and U represent the feature matrix selected from u that is greater than the threshold Φ. U , σ U Let Vp and Vp represent the mean and standard deviation of each feature, respectively. Vp represents the selection of the first p features from the selected features as the principal component matrix, and j represents the number of plugins.

[0147] Setting Φ = 0.25, we obtain the final 8-dimensional state space vector B = [0.92, -0.12, 0.56, 1.02, -0.73, 0.43, -0.18, 0.61].

[0148] Step S202: In the state space and action space, calculate the fitness difference of the progressively selected plug-in actions, and use the fitness difference to evaluate the optimization benefits brought by the state function evaluation of the currently selected plug-in combination:

[0149] ΔΠ=Π a+1 (b,a)-Π a (b,a)

[0150] Π a+1 -Π a=λ(β+μ(Π) a (b',a'))-Π a (b,a))

[0151]

[0152] Among them, Π a+1 (b,a), Π a (b, a) represent the fitness values ​​before and after updating action a in state s, respectively; λ represents the update step size; β represents the fitness value generated when action a is executed; μ represents the fitness value parameter that will be increased by the next action; s' represents the next state entered after executing action a; and a' represents the action selected in the next state. q This represents the state function at this point, where q represents the selected round, and f(Π) represents the state function. a q ) represents the fitness value function for selecting the a-th action in the q-th round, and κ represents the importance of this state change;

[0153] Step S203: Select s q Plugins with values ​​greater than the threshold ν are considered highly relevant to the user and are preloaded. A swap operator is set to exchange or merge different parts of existing operations, allowing previously unconsidered regions to be explored in the solution space, thus expanding the solution space. The plugin selection strategy is then updated using gradient descent.

[0154]

[0155] in, Let λ represent the exchange operator in the q-th round of selection. θ Indicates the step size for the chosen strategy update. It is the state function s q For the policy parameter θ a The partial derivatives of s q Indicates the relationship with θ a Sensitivity to change.

[0156] Step S204: Output the trained capability expansion strategy to automatically select model Ψ.

[0157] Step S3 specifically includes the following steps:

[0158] Step S301: Initialize all available plugins, establishing initial state and capability information for each plugin, specifically including assigning an initial configuration to each plugin:

[0159] a1 = {"id":"study_01","name":"Learning Suggestions","state":0,"description":"This plugin provides suggestions on learning methods and strategies. Based on the user's learning progress, subjects, learning habits, and other data, it offers personalized suggestions to optimize learning plans, improve learning efficiency, and increase learning motivation. Features include: analyzing learning habits, creating personalized learning plans, recommending learning resources, regularly tracking learning progress, and providing feedback on improvement plans."};

[0160] a2 = {"id":"emergency_01","name":"Emergency Escape Guide","state":0,"description":"This plugin provides users with escape suggestions in emergency situations. Based on different emergency situations (such as fire, earthquake, violent attack, etc.), it provides detailed escape routes, emergency measures, and safety precautions. Features include: designing escape route maps for various emergency situations, providing emergency contact information, and prompting for necessary emergency tools and actions."};

[0161] a3 = {"id":"drawing_01","name":"Smart Drawing","state":0,"description":"This plugin uses AI to help users automatically generate or optimize graphics. It supports various graphic formats such as charts, illustrations, and flowcharts. Users can upload sketches or enter descriptions, and the plugin will generate professional images based on these inputs. Features include: automatically generating charts, artistic paintings, flowcharts and diagrams, optimizing design details, and adjusting graphic style and content according to needs."};

[0162] a4 = {"id":"programming_01","name":"Programming Assistant","state":1,"description":"This plugin provides users with programming assistance, including code generation, debugging, optimization suggestions, and learning resources. It supports multiple programming languages, understands code intent, and provides instant feedback. Features include: automatic code completion, code error checking, performance optimization suggestions, programming technique recommendations, and related tutorial resources."};

[0163] a5 = {"id":"teaching_01","name":"Teaching Plan","state":0,"description":"This plugin designs personalized teaching plans based on user needs. It provides optimal teaching plans, methods, and assessment methods according to teaching content, target audience, and time constraints. Features include: providing a teaching syllabus based on user needs, developing a detailed teaching schedule, designing classroom activities and assessment plans to ensure comprehensive course content and alignment with teaching objectives."};

[0164] a6 = {"id":"legal_01","name":"Legal Consultation","state":1,"description":"This plugin provides users with legal consultation services covering multiple fields such as civil law, contract law, labor law, and intellectual property. It offers concise legal opinions, legal document templates, and litigation process guides. Features include: quick answers to legal questions, generation of legal documents, case analysis and handling suggestions, and recommendations for courts and law firms."};

[0165] a7 = {"id":"leave_01","name":"Leave Application","state":0,"description":"This plugin helps users quickly fill out and submit leave applications. It supports various leave types, such as sick leave, personal leave, annual leave, etc. Based on company or school regulations, it automatically generates application forms and sends them to relevant approval personnel. Features include: automatic leave form filling, viewing leave history, and tracking the leave approval process."};

[0166] a8 = {"id":"diet_01","name":"Dietary Health Analysis","state":0,"description":"This plugin provides a dietary health analysis report based on the user's diet records and health data. By analyzing the nutritional components in the diet, it offers healthy eating suggestions to help users improve their eating habits. Features include: analyzing daily intake of calories, protein, fat, and other nutrients; providing diet plans and suggestions; tracking changes in health indicators; and optimizing personalized diets."}

[0167] When state = 0, it means the plugin is not enabled; when state = 1, it means the plugin is enabled.

[0168] Step S302: Cluster the user dialogue content using a Gaussian mixture model to classify the user dialogue content into categories η, such as... Figure 3 As shown, for plugins of category η, the capability extension strategy is used to automatically select model Ψ for strategy selection, and the plugin state is set to 1;

[0169] Step S303: Identify plugins with a plugin status of 1, execute the plugin function, display the corresponding card according to the returned category value, and generate a vector set F containing all enabled functions.

[0170] (1) Collect user dialogue information and convert it into a numerical feature vector Y. Each dialogue sample is represented as a feature vector y. i Y = [y1, y2, ..., y i ,…,y M ],Y∈R M×DM represents the number of dialogue samples, D is the feature dimension of each sample, and R represents the feature vector matrix;

[0171] (2) Initialize Gaussian mixture model parameters: Set the number of sub-Gaussian distributions, i.e. the number of clusters, to η = 1, the convergence accuracy to ε = 0.001, and the number of iterations to o = 0;

[0172] (3) Initialize the prior probability ω i =1 / η;

[0173] (4) Given the prior probabilities, calculate the posterior probability that each sample y belongs to each sub-Gaussian distribution:

[0174]

[0175] in, Let μ represent the posterior probability that the Mth sample comes from the i-th sub-Gaussian distribution at the o-th iteration, μ and σ represent the mean and variance respectively, e represents the exponential operation, and π represents pi.

[0176] (5) Update the parameter values ​​of each sub-Gaussian distribution according to the posterior probability. The specific steps are as follows:

[0177]

[0178] (6) Determine whether the model has converged. Calculate whether the change of each parameter is less than the set convergence accuracy. If the change of any parameter is greater than the convergence accuracy, the iteration count o = o + 1, and return to step (4); otherwise, proceed to step (7).

[0179] (7) At this time, η = η + 1. If η ≤ 5, proceed to step (3) to ensure that the category level is within 5 levels.

[0180] (8) Calculate the profile coefficient of the samples when η = 1, 2, 3, ..., l, and compare the profile coefficients of the samples with different values ​​of η. The profile coefficient is the similarity of each sample with the samples in the same cluster divided by the similarity of each sample with the nearest cluster. Then select the η with the largest profile coefficient as the optimal category.

[0181] (9) Return the posterior probability r for each dialogue. i (M) and its category η.

[0182] Step S303: Identify plugins with a plugin status value of 1, execute the plugin function, and display the corresponding card according to the returned category value. When the return value of η is 1, return text; when the return value of η is 2, return audio; when the return value of η is 3, return video; when the return value of η is 4, return a table; when the return value of η is 5, return an image; and generate a vector set F containing all enabled functions.

[0183] Step S4 specifically includes the following steps:

[0184] Step S401: Based on the enabled function vector set F, construct the AI ​​digital human capability management model Θ. The model includes: a feature vector embedding model, a convolutional neural network model, a multilayer perceptron model, a feedforward neural network model, and a capability management model. The specific internal structure is as follows: Figure 4 As shown;

[0185] The input of the AI ​​digital human capability management model Θ serves as the input of the feature vector embedding model; the output of the feature vector embedding model is connected to the input of the convolutional neural network; the output of the convolutional neural network is connected to the input of the multilayer perceptron model; the output of the multilayer perceptron model is connected to the input of the feedforward neural network model; the output of the feedforward neural network model is connected to the input of the capability management model; and the output of the capability management model serves as the output of the AI ​​digital human capability management model Θ.

[0186] The feature vector embedding model includes: a fully connected layer, a ReLU activation function layer, and a batch processing layer; the input of the feature vector embedding model serves as the input of the fully connected layer; the output of the fully connected layer serves as the input of the ReLU activation function layer; the output of the ReLU activation function layer is connected to the input of the batch processing layer; the output of the batch processing layer serves as the output of the feature vector embedding model and is connected to the convolutional layer of the convolutional neural network model.

[0187] The convolutional neural network model includes: a convolutional layer, a pooling layer, and an activation function layer; the input of the convolutional neural network model serves as the input of the convolutional layer; the output of the convolutional layer is connected to the input of the pooling layer; the output of the pooling layer is connected to the input of the activation function layer; the output of the activation function layer serves as the output of the convolutional neural network model and is connected to the fully connected layer of the multilayer perceptron.

[0188] The multilayer perceptron model includes: a fully connected layer, a ReLU activation function layer, and a Dropout layer; the input of the multilayer perceptron model serves as the input of the fully connected layer; the output of the fully connected layer is connected to the input of the ReLU activation function layer; the output of the ReLU activation function layer is connected to the input of the Dropout layer; the output of the Dropout layer serves as the output of the multilayer perceptron model and is connected to the fully connected layer of the feedforward neural network model.

[0189] The feedforward neural network model includes: a fully connected layer, a feedforward neural network layer, and a Dropout layer; the input of the feedforward neural network model serves as the input of the fully connected layer; the output of the fully connected layer is connected to the input of the feedforward neural network layer; the output of the feedforward neural network layer is connected to the input of the Dropout layer; the output of the Dropout layer serves as the output of the feedforward neural network model and is connected to the fully connected layer of the capability management model.

[0190] The capability management model includes: a fully connected layer and a Softmax activation function layer; the input of the capability management model serves as the input of the fully connected layer; the output of the fully connected layer is connected to the input of the Softmax activation function layer; and the Softmax activation function layer serves as the output of the capability management model.

[0191] Step S402: Input the function vector into the AI ​​Digital Human Capability Management Model (DAM) to obtain the AI ​​digital human's capability scores for different tasks. Rows correspond to different tasks, and columns correspond to different capabilities. Tasks, in row order, include: providing suggestions, designing solutions, data analysis, and information consultation. Capabilities, in column order, include: learning suggestions, emergency escape guides, intelligent drawing, programming assistant, teaching plans, legal consultation, leave application, and dietary health analysis. This generates the AI ​​digital human capability matrix as follows: Figure 5 As shown.

[0192] Furthermore, the scoring formula for calculating the ability corresponding to each task in step S402 is as follows:

[0193] E1 = Relu(W1·F + b1)

[0194]

[0195] E4 = Relu(W3·Mask(E3,ρ) + b4)

[0196] E5 = Relu(W4·Mask(E4,ρ) + b5)

[0197] E6 = Softmax(W5·E5 + b6)

[0198] Where E1 represents the output of the feature vector after ReLU(·) activation, W1 represents the bias matrix of the fully connected layer, and b1 represents the bias parameters of the fully connected layer; E2 represents the output of the feature vector embedding model, δ represents the learnable scaling parameter, b2 represents the bias parameters of the batch layer, μ and σ represent the mean and variance, and ξ represents the parameter to prevent division by zero error; E3 represents the output of the convolutional layer, W2 and b3 represent the bias matrix and bias parameters of the convolutional layer, LeakyReLU(·) is the activation function, and len(·) is the length calculation function; E4 represents the output of the multilayer perceptron, W3 and b4 represent the bias matrix and bias parameters of the multilayer perceptron, Mask(·) is the masking function, indicating whether neurons are dropped, and ρ represents the dropout rate, which is usually between 0 and 1; E5 represents the output of the feedforward network layer, W4 and b5 represent the bias matrix and bias parameters of the feedforward network layer; E6 represents the task's ability score, W5 and b6 represent the bias matrix and bias parameters of the ability management module, and Softmax(·) is the activation function.

[0199] Step S5 specifically includes the following steps:

[0200] Step S501: Standardize the task documents uploaded by users, convert them into a parsable text format, remove extra spaces, line breaks, and special characters that interfere with parsing, use periods as the separator for each sentence, and line breaks as the separator for each task.

[0201] Step S502: After preprocessing the tasks, identify task keywords, extract complete task descriptions using regular expressions, and parse the tasks into structured data in sequence: S1 = {"task_id": 01, "task_description": Provide learning suggestions for employees based on their programming skills, task_features: {"Programming Assistant", "Provide Suggestions"}}, S2 = {"task_id": 02, "task_description": I need to design a teaching plan on company law, task_features: {"Legal Consultation", "Teaching Plan"}}, S3 = {"task_id": 03, "task_description": I want to draw a pie chart based on the employees' health status, task_features: {"Dietary Health Analysis", "Intelligent Drawing"}}. After parsing all the tasks in the document, a dictionary list of tasks is formed: S = [S1, S2, S3].

[0202] Step S503: Match the task dictionary list with the tasks in the capability matrix, calculate the sum of the capability values ​​required for the task in the capability matrix as the score of the task, and obtain three task scores of 1.5, 1.6 and 1.2 respectively; output the user's personalized task list [S2, S1, S3] in the order of the scores.

[0203] In this embodiment, the interface diagram of the AI ​​digital human capability management function is as follows: Figure 6 As shown in the diagram, the AI ​​digital human's interface for processing user documents and breaking down user tasks is as follows: Figure 7 As shown, calculating the capability score of the AI ​​digital human plugin ensures that tasks match the AI ​​digital human's capabilities, improving execution efficiency. The capability score can also dynamically adjust task difficulty, supporting the gradual improvement of the AI ​​digital human's capabilities, optimizing task allocation, and enhancing user experience and engagement. Furthermore, using plugins to extend the AI ​​digital human's capabilities significantly improves its flexibility and adaptability, allowing it to dynamically load or modify specific functional modules according to different task requirements. This plug-in design not only improves the AI ​​digital human's work efficiency and professionalism but also facilitates system maintenance and updates, reducing development costs. Through plugins, the AI ​​digital human can be applied across domains, supporting needs in different scenarios, ensuring continuous innovation and expansion, thereby improving user experience and meeting ever-changing demands.

[0204] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable storage medium. The method can be implemented using standard programming techniques, including a non-transitory computer-readable storage medium configured with a computer program within the computer program, wherein the storage medium is configured such that the computer operates in a specific and predefined manner—according to the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Furthermore, for this purpose, the program can run on a programmed application-specific integrated circuit (ASIC).

[0205] Furthermore, the procedures described herein may be performed in any relevant order unless otherwise indicated herein or otherwise clearly contradicted by the context. The procedures described herein (or variations and / or combinations thereof) may be executed under the control of one or more computer systems configured with executable instructions, and may be implemented by hardware or a combination thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that commonly executes on one or more processors. The computer program comprises a plurality of instructions executable by one or more processors.

[0206] Furthermore, the method can be implemented in any type of computing platform operatively connected to the associated device, including but not limited to personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, standalone or integrated computer platforms, or in communication with charged particle tools or other imaging devices. Aspects of the invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. Furthermore, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media comprises instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. When programmed according to the methods and techniques described in the invention, the invention also includes the computer itself.

[0207] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A plugin-based method for extending the capabilities of AI digital humans, characterized in that: The method includes the following steps: Step S1: Based on the user-submitted and interacted data, extract the user information feature matrix and construct a preliminary user profile; Step S2: Select relevant plugins based on the initial user profile, preload the plugins, and automatically select a capability expansion strategy; Step S2 specifically includes: Step S201: Based on the feature matrix u of the initial user profile, convert it into the user's current state space B, and define the action space A, where A represents the action of selecting a plugin: U = {u i | } B = a j ∈A Among them, u i Let μ represent the features in u, Φ represent the threshold for selecting p features, and U represent the feature matrix selected from u that is greater than the threshold Φ. U , σ U Let Vp and Vp represent the mean and standard deviation of each feature, respectively. Vp represents the selection of the first p features from the selected features as the principal component matrix, and j represents the number of plugins. Step S202: In the state space and action space, calculate the fitness difference of the progressively selected plug-in actions, and use the fitness difference to evaluate the optimization benefits brought by the state function evaluation of the currently selected plug-in combination: DP = P a+1 (b, a) - P a (b, a) P a+1 - P a = λ(β + μ(Π a (b', a')) - P a (b, a)) s q = k Among them, Π a+1 (b, a), Π a (b, a) represent the fitness values ​​before and after updating action a in the current state, respectively; λ represents the update step size; β represents the fitness value generated when action a is executed; μ represents the fitness value parameter that will be increased by the next action; after executing action a, the next state must be entered, and a' represents the action selected in the next state; s q This represents the state function at this point; q represents the round selected; f(Π) a q ) represents the fitness value function for selecting the a-th action in the q-th round; κ represents the importance of this state change; Step S203: Select s q Plugins with values ​​greater than the threshold ν are considered highly relevant to the user and are preloaded. A swap operator is set to exchange or merge different parts of existing operations, allowing previously unconsidered regions to be explored in the solution space, thus expanding the solution space. The plugin selection strategy is then updated using gradient descent. i a+1 = θ a + λ θ ×DP a × in, Let λ represent the exchange operator in the q-th round of selection. θ Indicates the step size for choosing the strategy update. It is the state function s q For the policy parameter θ a The partial derivatives of s q Indicates the relationship with θ a Sensitivity to change; Step S204: Output the trained capability expansion strategy to automatically select model Ψ; Step S3: Initialize plugin functionality, gradually enable plugins according to the capability expansion strategy and user dialogue system to expand the capabilities of the AI ​​digital human, and record the enabled function information; Step S3 specifically includes: Step S301: Initialize all available plugins, establishing initial state and capability information for each plugin, specifically including assigning an initial configuration to each plugin: a = {id, name, state, description} id = Category + Index Where 'a' represents a plugin, the plugin content includes the plugin's id, name, state value, and description; the plugin's id is composed of the plugin's category and index, padded with 0s at the beginning; when state=0, it means the plugin is not enabled, and when state=1, it means the plugin is enabled. Step S302: Use a text clustering model to cluster the user dialogue content, divide the user dialogue content into categories η, use the capability extension strategy to automatically select model Ψ for the plugin of category η, and set the plugin status to 1. Step S303: Identify plugins with a plugin status of 1, execute the plugin function, display the corresponding card according to the returned category value, and generate a vector set F containing all enabled functions; Step S4: Based on the enabled function information, construct the AI ​​digital human capability management system and output the AI ​​digital human capability matrix; Step S5: Using the user-uploaded documents, parse the user's task list, match the capability matrix, and customize a personalized task list for the user.

2. The method for extending the capabilities of an AI digital human based on plug-ins according to claim 1, characterized in that: Step S1 specifically includes: Step S101: Based on the user's submitted personal information or the basic information filled in during the initial interaction, obtain the user's basic information input set X: X= {x1, x2, x3, x4, ..., x n } Where x1, x2, x3, x4, ..., x n This represents user information, where n represents the number of pieces of information. Step S102: Divide X into different types of data and process them separately. t , x d , x T , x s x represents categorical, numeric, date, and text data respectively. t , x d , x T , x s ∈X; Convert the above data into numerical values ​​that can represent features, and combine them in order to generate a feature vector u containing user information features; Step S103: Use feature vector u to represent the general description of the user and construct a preliminary user profile.

3. The method for extending the capabilities of an AI digital human based on plug-ins according to claim 2, characterized in that: Step S102 specifically includes: (1) The collected categorical user information is converted into a numerical representation using multi-hot encoding. For each category c∈C in the categorical variable C, then C={c1, c2, c3, ..., c m }, where m is the number of categories, and for the i-th category c i The forms of expression are as follows: u(x t , c i ) = u l = [u1(x t ), u2(x t ), u3(x t ), ..., u m (x t )] Where i∈[1, 2, 3, ..., m], x t This represents the information input by the user in this category; the one-hot encoded vectors of all categorical variables are concatenated into a matrix u. l ; (2) Use the following formula to process the collected numerical data x d To perform standardization, input the data x that needs to be standardized. d : u d = [ , , ..., ] Where μ and σ represent x, respectively. d The mean and standard deviation of u d This represents the matrix after standardization of all numerical data, where g represents the number of numerical data points. (3) Transfer the collected date data x T Convert the data to time difference ΔT, and then combine them to obtain a date-type data matrix: ΔT1 = |x T1 - T| you T = [ΔT1, ΔT2,..., ΔT h ] u T * = Where T represents the system's set reference time, u T This represents the matrix after converting all date data, where h represents the number of date data items. The matrix is ​​obtained after standardizing the date data. T * ; (4) Select the first text data x collected. s1 Calculate W for each word k The weight in the text, when the word W k If it appears once in the text, then count(W) k =count(W k ) + 1, using each word in x s1 The number of times it appears in the text divided by x s1 The total number of words in the corpus is calculated, and the frequency of each word in the document is obtained. All words and their calculated frequencies are stored in the corpus as key-value pairs. The top Ω words W are selected. k As a high-frequency word, its proportion x is calculated. s1 The weights in the matrix construct the feature matrix u of each text. s1 k=1, 2, ..., Ω; Following this method, the weights of high-frequency words in each text dataset are obtained, forming the corresponding text-type data feature matrix u. s z represents the number of text data items, u s =[ u s1 , u s2 ,..., u sz ]; (5) Concatenate all data matrices of different types in order of type to obtain the feature matrix u: Where u represents a matrix containing all information characteristics of the user, and the number of all types of data is equal to the sum of all information data: n = m + g + h + z.

4. The method for extending the capabilities of an AI digital human based on a plug-in, as described in claim 1, is characterized in that: The specific process for classifying user dialogue content in step S302 is as follows: (1) Collect user dialogue information and convert it into a numerical feature vector Y. Each dialogue sample is represented as a feature vector y. i Y = [y1, y2, ..., y i , ..., y M ],Y∈R M×D M represents the number of dialogue samples, D is the feature dimension of each sample, and R represents the feature vector matrix; (2) Initialize the text clustering model parameters: set the number of clusters, i.e. the number of clusters, to η=1, the convergence accuracy ɛ=0.001, and the number of iterations o=0; (3) Initialize the prior probability ω i =1 / η; (4) Given the prior probabilities, calculate the posterior probability that each sample y belongs to each cluster: in, Let μ represent the posterior probability that the Mth sample comes from the i-th cluster at the o-th iteration, μ and σ represent the mean and variance respectively, e represents the exponential operation, and π represents pi. (5) Update the parameter values ​​of each cluster based on the posterior probability. The specific steps are as follows: (6) Determine whether the model has converged. Calculate whether the change of each parameter is less than the set convergence accuracy. If the change of any parameter is greater than the convergence accuracy, the iteration number o = o + 1, and return to step (4); otherwise, proceed to step (7). (7) At this time, η=η+1. If η≤l, proceed to step (3) to ensure that the category level is within level l. (8) Calculate the profile coefficient of the samples when η=1, 2, 3, ..., l, based on the comparison of different values ​​of η. The profile coefficient is the similarity of each sample with the same cluster divided by the similarity of each sample with the nearest cluster. Then select the η with the largest profile coefficient as the optimal category. (9) Return the posterior probability r for each dialogue. i (M) and its category η.

5. The method for extending the capabilities of an AI digital human based on plug-ins according to claim 1, characterized in that: Step S4 specifically includes: Step S401: Based on the enabled function vector set F, construct the AI ​​digital human capability management model Θ. The model includes: a feature vector embedding model, a convolutional neural network model, a multilayer perceptron model, a feedforward neural network model, and a capability management model. Step S402: Input the function vector into the AI ​​digital human capability management model Θ to obtain the AI ​​digital human's capability scores for different tasks, and generate the AI ​​digital human capability matrix D, where rows represent different tasks and columns represent different capabilities: D = Where H represents the number of tasks, I represents the number of capabilities, and d ij This represents the AI ​​digital human's score for the i-th task on the j-th ability.

6. The method for extending the capabilities of an AI digital human based on a plug-in, as described in claim 5, is characterized in that: The specific structure of the AI ​​digital human capability management model Θ constructed in step S401 is as follows: The input of the AI ​​digital human capability management model Θ serves as the input of the feature vector embedding model; the output of the feature vector embedding model is connected to the input of the convolutional neural network; the output of the convolutional neural network is connected to the input of the multilayer perceptron model; the output of the multilayer perceptron model is connected to the input of the feedforward neural network model; the output of the feedforward neural network model is connected to the input of the capability management model; and the output of the capability management model serves as the output of the AI ​​digital human capability management model Θ. The feature vector embedding model includes: a fully connected layer, a ReLU activation function layer, and a batch processing layer; the input of the feature vector embedding model serves as the input of the fully connected layer; the output of the fully connected layer serves as the input of the ReLU activation function layer; the output of the ReLU activation function layer is connected to the input of the batch processing layer; the output of the batch processing layer serves as the output of the feature vector embedding model and is connected to the convolutional layer of the convolutional neural network model. The convolutional neural network model includes: a convolutional layer, a pooling layer, and an activation function layer; the input of the convolutional neural network model serves as the input of the convolutional layer; the output of the convolutional layer is connected to the input of the pooling layer; the output of the pooling layer is connected to the input of the activation function layer; the output of the activation function layer serves as the output of the convolutional neural network model and is connected to the fully connected layer of the multilayer perceptron. The multilayer perceptron model includes: a fully connected layer, a ReLU activation function layer, and a Dropout layer; the input of the multilayer perceptron model serves as the input of the fully connected layer; the output of the fully connected layer is connected to the input of the ReLU activation function layer; the output of the ReLU activation function layer is connected to the input of the Dropout layer; the output of the Dropout layer serves as the output of the multilayer perceptron model and is connected to the fully connected layer of the feedforward neural network model. The feedforward neural network model includes: a fully connected layer, a feedforward neural network layer, and a Dropout layer; the input of the feedforward neural network model serves as the input of the fully connected layer; the output of the fully connected layer is connected to the input of the feedforward neural network layer; the output of the feedforward neural network layer is connected to the input of the Dropout layer; the output of the Dropout layer serves as the output of the feedforward neural network model and is connected to the fully connected layer of the capability management model. The capability management model includes: a fully connected layer and a Softmax activation function layer; the input of the capability management model serves as the input of the fully connected layer; the output of the fully connected layer is connected to the input of the Softmax activation function layer; and the Softmax activation function layer serves as the output of the capability management model.

7. The method for extending the capabilities of AI digital humans based on plug-ins according to claim 5, characterized in that: The formula for calculating the score of each task's corresponding ability in step S402 is as follows: E1 = Relu(W1· F + b1) E2 = b2 + δ× E3 = LeakyRelu( ) E4 = Relu(W3·Mask(E3, ρ)+ b4) E5 = Relu(W4·Mask(E4, ρ) + b5) E6 = Softmax (W5· E5 + b6) Where E1 represents the output of the feature vector after ReLU(·) activation, W1 represents the bias matrix of the fully connected layer, and b1 represents the bias parameters of the fully connected layer; E2 represents the output of the feature vector embedding model, δ represents the learnable scaling parameter, b2 represents the bias parameters of the batch layer, μ and σ represent the mean and variance, and ξ represents the parameter to prevent division by zero error; E3 represents the output of the convolutional layer, W2 and b3 represent the bias matrix and bias parameters of the convolutional layer, LeakyReLU(·) is the activation function, and len(·) represents the length function; E4 represents the output of the multilayer perceptron, W3 and b4 represent the bias matrix and bias parameters of the multilayer perceptron, Mask(·) represents the masking function, indicating whether neurons are dropped, and ρ represents the dropout rate, with a value between 0 and 1; E5 represents the output of the feedforward network layer, W4 and b5 represent the bias matrix and bias parameters of the feedforward network layer; E6 represents the task's ability score, W5 b6 represents the bias matrix and bias parameters of the capability management module, and Softmax(·) is the activation function.

8. The method for extending the capabilities of an AI digital human based on plug-ins according to claim 1, characterized in that: Step S5 specifically includes: Step S501: Standardize the task documents uploaded by users, convert them into a parsable text format, remove extra spaces, line breaks, and special characters that interfere with parsing, use periods as the separator for each sentence, and line breaks as the separator for each task. Step S502: For each preprocessed sentence, identify task keywords, extract the complete task description using regular expressions, and parse the tasks sequentially into structured data S1. After parsing all tasks in the document, construct a dictionary list S=[S1, S2, ..., S...]. n ], where n represents the number of tasks; Step S503: Match the task dictionary list with the tasks in the capability matrix, calculate the sum of the capability values ​​required for the task in the capability matrix as the score of the task, and output the user's personalized task list in the order of the scores.

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