A digital intelligence empowerment method based on a large language model

Abstract

The present invention relates to the field of AI teaching technology, and discloses a digital intelligence empowerment method based on a large language model, comprising the following steps: obtaining the current teaching strategy, learner acceptance information, and learner learning information; constructing first plane data based on the acquired current teaching strategy, constructing a first serial data stream and a second plane data based on the acquired learner acceptance information, and constructing a third plane data and a second serial data stream based on the acquired learner acceptance information. The present invention better takes care of the learners' emotions and psychological needs by integrating psychological counseling strategies with teaching strategies, providing them with all-round support and guidance, thereby optimizing teaching effects and promoting the all-round development of learners. This humanized intelligent teaching model is closer to actual educational scenarios, is conducive to giving full play to the advantages of AI teaching, and is conducive to learners' learning and the advancement of AI teaching.

Description

Technical Field

[0001] The present invention relates to the field of AI teaching, and more specifically, to a digital intelligence empowerment method based on a large language model. Background Art

[0002] Traditional, one-size-fits-all teaching models no longer meet the individual needs of diverse learners. Therefore, AI-powered teaching is becoming a new option for more learners. AI teaching applies artificial intelligence technology to education, optimizing the teaching process through intelligent means, improving teaching efficiency and learning outcomes. AI teaching leverages technologies such as machine learning, deep learning, and natural language processing to provide learners with a personalized, adaptive learning experience.

[0003] Existing AI teaching only arranges learning plans based on some basic needs of learners, and learners are required to strictly implement the learning plans. However, learners may lack trust in AI teaching, or feel disgusted and uncomfortable with the teaching format, causing psychological frustration and abandonment of learning plans, or failing to achieve the expected results after learning, which is not conducive to learners' learning. Summary of the Invention

[0004] The present invention provides a digital intelligence empowerment method based on a large language model to solve technical problems in related technologies.

[0005] The present invention provides a digital intelligence empowerment method based on a large language model, comprising the following steps:

[0006] Step 100: Acquire the current teaching strategy, learner acceptance information, and learner learning information; learner learning information includes learning preference information, motivation type information, plan execution information, and self-discipline evaluation information; learner acceptance information includes feedback information, emotional state information, and acceptable teaching time information; the current teaching strategy includes several teaching units, each of which includes incentive and reward mechanisms, teaching content presentation and feedback methods, learning plans and schedules, teacher-student interaction methods, and image information;

[0007] Step 200: constructing first plane data based on the collected current teaching strategy, constructing a first serial data stream and a second plane data based on the collected learner acceptance information, and constructing a third plane data and a second serial data stream based on the collected learner acceptance information;

[0008] Step 300: Input the first plane data and the second plane data into the strategy update model, and output a result representing the updated teaching strategy. The updated teaching strategy includes all updated teaching units, and the updated teaching units include incentive and reward mechanisms, teaching content presentation and feedback methods, learning plans and schedules, teacher-student interaction methods, and image information. Input the updated teaching strategy and the second serial data stream into the psychological counseling model, and output a result representing the psychological counseling strategy. The psychological counseling strategy includes emotional acceptance, empathic feedback, guiding a positive mindset, emotional regulation, building a "growth mindset," and guiding self-dialogue.

[0009] In step 400, the AI teacher performs teaching according to the updated teaching strategy. At the same time, when the learner's feedback is poor and the emotional state deviates from normal, the AI teacher stops the execution of the teaching strategy according to the output psychological counseling strategy until the feedback and emotional state return to normal, and then resumes the execution of the teaching strategy.

[0010] Furthermore, the first plane data includes a first facet for storing data, each first facet indexing a data object, and the first plane data further includes a relationship list for storing association relationships between the data objects indexed by the first facet, wherein the element value in the i-th row and j-th column of the relationship list represents a quantized value of the association relationship between the data objects indexed by the i-th and j-th first facets;

[0011] The correlation relationship includes high and low, corresponding to two quantitative values respectively.

[0012] Data objects include learners, teaching units, AI teachers, teaching subjects, and image types;

[0013] Data objects with high correlation are defined as: learners and their AI teachers, learners and their teaching subjects, teaching units and their AI teachers, teaching subjects and their AI teachers, and AI teachers and their image types.

[0014] Obtaining a first serial data stream based on the learner learning information sorting, the first serial data stream including n data units sorted by time, the t-th data unit storing the second plane data generated by the learner learning information collected at the t-th moment;

[0015] The second plane data includes a second facet for storing data, each second facet indexing a data object. The second plane data also includes a relationship list for storing association relationships between the data objects indexed by the second facet, wherein the element value in the i-th row and j-th column of the relationship list represents a quantitative value of the association relationship between the data objects indexed by the i-th and j-th second facets.

[0016] The correlation relationship includes high and low, corresponding to two quantitative values respectively.

[0017] Data objects include learners, preference types, motivation types, execution status, and self-regulation assessment scores;

[0018] The data objects with high correlation are defined as: learners and their preference types in the teaching, learners and their motivation types for learning, learners’ implementation of their learning plans, and learners and their own self-discipline assessment scores;

[0019] Generating third plane data based on the learner's acceptance information includes a third facet for storing data, a second serial data stream where the third facet indexes a data object, and a relationship list for storing association relationships between the data objects indexed by the third facet, wherein the element value in the i-th row and j-th column of the relationship list represents a quantitative value of the association relationship between the data objects indexed by the i-th and j-th third facets;

[0020] The correlation relationship includes high and low, corresponding to two quantitative values respectively.

[0021] Data objects include learners, AI teachers, opinion types, and emotional states;

[0022] Data objects with high correlation are defined as: learners and the types of opinions they express, learners and the emotional states they respond to when receiving instruction, learners and the AI instructors they teach, and opinion types and their corresponding AI instructors.

[0023] The second serial data stream of a data object includes n data units sorted by time, and the t-th data unit represents the learner's acceptance information collected at the t-th moment of the data object.

[0024] Furthermore, the strategy update model includes a first backbone network and a first branch network, the first backbone network includes a first encoder, a third encoder, a first fusion feature layer and a first FC layer, the first branch network includes a second encoder, wherein the first encoder inputs first plane data and outputs a first recognition feature, while the second encoder inputs second plane data and outputs a second recognition feature, the first recognition feature and the second recognition feature are input into the first fusion feature layer, the first fusion feature layer outputs a first fusion state, the first fusion state is input into the third encoder, the third encoder outputs the third recognition feature to the first FC layer, and the first FC layer outputs a result representing the updated teaching strategy.

[0025] Furthermore, the psychological counseling model includes a second backbone network and a second branch network, the second backbone network includes a fourth encoder, a fifth encoder, a second fusion feature layer and a second FC layer, and the second branch network includes a sixth encoder, wherein the fourth encoder inputs a second serial data stream and outputs a fourth identification feature, the fourth identification feature is input into the fifth encoder, the fifth encoder outputs a fifth identification feature, and at the same time the sixth encoder inputs an updated teaching strategy and outputs a sixth identification feature, the sixth identification feature and the fifth identification feature are input into the second fusion feature layer, the second fusion feature layer outputs a second fusion state, the second fusion state is input into the second FC layer, and the second FC layer outputs a result representing the psychological counseling strategy.

[0026] Furthermore, the calculation formula of the first encoder is as follows:

[0027] ;

[0028] in represents the panel feature of the vth first panel in the lth layer, represents the set of first facets with the highest correlation with facet v, represents the first plane recognition weight matrix of layer l, is the attention weight of the first panel u of layer l to the first panel v, which is calculated by the following formula:

[0029] ;

[0030] in is a learnable attention vector, Represents vector concatenation operation, represents the second plane recognition weight matrix of layer l, represents transpose; and Respectively represent the panel features of the vth first panel of the l-1th layer, , E represents the total number of layers, when l=1 , when l=E is equal to the identification feature of the first bin v; and The feature representations of the objects representing the vth and uth first facet indices respectively.

[0031] Furthermore, the calculation formula of the second encoder is as follows:

[0032] ;

[0033] represents the panel feature of the cth second panel in the lth layer, represents the panel feature of the d-th second panel in the l-1th layer, and represent the set of second facets with high correlation with the second facet c and the second facet d, respectively. represents the cardinality of the set, represents the third plane recognition weight matrix of layer l, , E represents the total number of layers, when l=1 , The feature representation of the object with the dth second facet index, when l=E is equal to the second identification feature of the second panel c, is the sigmoid function.

[0034] Furthermore, the calculation formula of the first fusion feature layer is as follows:

[0035] ;

[0036] in, represents the first combination feature, represents the feature combination function (splicing function or summation function), represents the bin feature of the vth first bin of the Eth layer of the first encoder, represents the set of panel features of the first panel, represents the bin feature of the c-th second bin of the E-th layer of the second encoder, A set of panel features representing a second panel;

[0037] The calculation formula of the third encoder is as follows:

[0038] ;

[0039] in represents the tth third identification feature, represents the t-1th third identification feature, , represents the tth data unit of the first combined feature, and is the serial recognition weight matrix of the third encoder, is the serial recognition bias parameter of the third encoder, and ReLU is the ReLU activation function;

[0040] The calculation formula of the first FC layer is as follows:

[0041] ;

[0042] in represents the first output vector, and its cth component value represents the probability value of the cth strategy. The strategy with the largest probability value is selected as the output. The strategy group contains all executable strategies. A strategy includes incentive and reward mechanisms, teaching content presentation and feedback methods, learning plans, schedule arrangements, teacher-student interaction methods, and image information;

[0043] For more complex strategies, vector representation can be used: a strategy is represented as a vector, and the jth component of the vector represents the jth teaching information of the updated teaching strategy;

[0044] represents the last third identification feature output by the third encoder, Indicates full connection.

[0045] Furthermore, the calculation formula of the fourth encoder is as follows:

[0046] ;

[0047] ;

[0048] ;

[0049] in represents the t-th data unit of the second serial data stream corresponding to the v-th third plane element of the third plane data, represents the t-1th fourth recognition feature of the vth third facet, and represent the first and second intermediate features of the vth third surface element, represents the tth data unit of the first and second serial data streams of the vth third bin, and is the serial recognition weight matrix in the fourth encoder, is the serial recognition bias parameter in the fourth encoder, ReLU is the ReLU activation function, Represents the Sigmoid function;

[0050] The calculation formula of the fifth encoder is as follows:

[0051] ;

[0052] represents the panel feature of the third panel of the fth layer, represents the panel feature of the rth third panel in the l-1th layer, and represent the set of facets with high correlation with facet f and facet r, respectively. represents the cardinality of the set, represents the plane recognition weight matrix of the lth layer in the fifth encoder, , E represents the total number of layers, when l=1 , The feature representation of the object representing the rth third facet index, when l=E Equal to the fifth identification feature of the third panel f, is the sigmoid function;

[0053] The calculation formula of the sixth encoder is as follows:

[0054] ;

[0055] in represents the sixth identification feature, represents the updated teaching strategy of the input, Represents the convolution function.

[0056] The present invention provides a digital intelligence empowerment system based on a large language model, comprising:

[0057] Data acquisition module: responsible for collecting current teaching strategies, learners' learning information and learners' acceptance information, and converting the collected data into structured flat data and serial data streams;

[0058] Strategy update model: includes a first backbone network and a first branch network, inputs first plane data and second plane data, and outputs an updated teaching strategy;

[0059] Psychological counseling model: includes a second backbone network and a second branch network, inputs the updated teaching strategy and the second serial data stream, and outputs the psychological counseling strategy;

[0060] Large language model module: Based on the pre-trained large language model, it generates corresponding teaching content and interactive responses according to the updated teaching strategies and psychological counseling strategies;

[0061] AI teaching module: responsible for implementing updated teaching strategies, interacting with learners, monitoring learners' feedback types and emotional states, and providing corresponding psychological counseling based on psychological counseling strategies.

[0062] The present invention provides a storage medium storing non-transitory computer-readable instructions for executing the steps in the aforementioned digital intelligence empowerment method based on a large language model.

[0063] The beneficial effects of the present invention are:

[0064] This invention combines psychological counseling strategies with teaching strategies in parallel to better take care of learners' emotions and psychological needs, provide them with all-round support and guidance, thereby optimizing teaching effects and promoting learners' all-round development. This humanized intelligent teaching model is closer to actual educational scenarios, which is conducive to giving full play to the advantages of AI teaching, and is conducive to learners' learning and the advancement of AI teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flowchart of the digital intelligence empowerment method based on a large language model of the present invention. DETAILED DESCRIPTION

[0066] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0067] like Figure 1 As shown in FIG, a digital intelligence empowerment method based on a large language model includes the following steps:

[0068] Step 100, obtaining the current teaching strategy, learner acceptance information and learner learning information;

[0069] Learners' learning information includes learning preference information, motivation type information, plan execution information and self-discipline evaluation information;

[0070] The information learners receive includes feedback, emotional state, and acceptable teaching time.

[0071] The current teaching strategy includes several teaching units, including incentive and reward mechanisms, teaching content presentation and feedback methods, learning plans and schedules, teacher-student interaction methods, and image information;

[0072] Step 200: Generate first plane data based on the collected current teaching strategy. The first plane data includes a first facet for storing data, each first facet indexing a data object. The first plane data also includes a relationship list for storing associations between the data objects indexed by the first facet, wherein the element value in the i-th row and j-th column of the relationship list represents a quantitative value of the association between the data objects indexed by the i-th and j-th first facets.

[0073] The correlation relationship includes high and low, corresponding to two quantitative values respectively.

[0074] Data objects include learners, teaching units, AI teachers, teaching subjects, and image types;

[0075] Data objects with high correlation are defined as: learners and their AI teachers, learners and their teaching subjects, teaching units and their AI teachers, teaching subjects and their AI teachers, and AI teachers and their image types.

[0076] Obtaining a first serial data stream based on the learner learning information sorting, the first serial data stream including n data units sorted by time, the t-th data unit storing the second plane data generated by the learner learning information collected at the t-th moment;

[0077] The second plane data includes a second facet for storing data, each second facet indexing a data object. The second plane data also includes a relationship list for storing association relationships between the data objects indexed by the second facet, wherein the element value in the i-th row and j-th column of the relationship list represents a quantitative value of the association relationship between the data objects indexed by the i-th and j-th second facets.

[0078] The correlation relationship includes high and low, corresponding to two quantitative values respectively.

[0079] Data objects include learners, preference types, motivation types, execution status, and self-regulation assessment scores;

[0080] The data objects with high correlation are defined as: learners and their preference types in the teaching, learners and their motivation types for learning, learners’ implementation of their learning plans, and learners and their own self-discipline assessment scores;

[0081] Generating third plane data based on the learner's acceptance information includes a third facet for storing data, a second serial data stream where the third facet indexes a data object, and a relationship list for storing association relationships between the data objects indexed by the third facet, wherein the element value in the i-th row and j-th column of the relationship list represents a quantitative value of the association relationship between the data objects indexed by the i-th and j-th third facets;

[0082] The correlation relationship includes high and low, corresponding to two quantitative values respectively.

[0083] Data objects include learners, AI teachers, opinion types, and emotional states;

[0084] Data objects with high correlation are defined as: learners and the types of opinions they express, learners and the emotional states they respond to when receiving instruction, learners and the AI instructors they teach, and opinion types and their corresponding AI instructors.

[0085] The second serial data stream of a data object includes n data units sorted by time, and the t-th data unit represents the learner acceptance information collected at the t-th moment of the data object;

[0086] Step 300: Input the first plane data and the second plane data into a policy update model. The policy update model includes a first backbone network and a first branch network. The first backbone network includes a first encoder, a third encoder, a first fusion feature layer, and a first FC (fully connected layer). The first branch network includes a second encoder. The first encoder inputs the first plane data and outputs a first recognition feature. The second encoder inputs the second plane data and outputs a second recognition feature. The first recognition feature and the second recognition feature are input into a first fusion feature layer. The first fusion feature layer outputs a first fusion state. The first fusion state is input into a third encoder. The third encoder outputs a third recognition feature to a first FC layer. The first FC layer outputs a result representing the updated teaching strategy.

[0087] The updated teaching strategy includes all updated teaching units, and the updated teaching units include but are not limited to incentive and reward mechanisms, teaching content presentation and feedback methods, learning plans and schedules, teacher-student interaction methods and image information;

[0088] Input the updated teaching strategy and the second serial data stream into the psychological counseling model, the psychological counseling model includes a second trunk network and a second branch network, the second trunk network includes a fourth encoder, a fifth encoder, a second fusion feature layer and a second FC layer, the second branch network includes a sixth encoder, wherein the fourth encoder inputs the second serial data stream and outputs a fourth recognition feature, the fourth recognition feature is input into the fifth encoder, the fifth encoder outputs a fifth recognition feature, and at the same time the sixth encoder inputs the updated teaching strategy and outputs a sixth recognition feature, the sixth recognition feature and the fifth recognition feature are input into the second fusion feature layer, the second fusion feature layer outputs a second fusion state, the second fusion state is input into the second FC layer, and the second FC layer outputs a result representing the psychological counseling strategy;

[0089] Psychological counseling strategies include but are not limited to emotional acceptance, empathic feedback, guiding a positive mindset, emotional regulation, building a "growth mindset," and guiding self-dialogue;

[0090] In step 400, the AI teacher performs teaching according to the updated teaching strategy outputted. Meanwhile, when the learner's feedback type or emotional state deviates from the threshold of normal acceptance of learning, the AI teacher stops executing the teaching strategy according to the output psychological counseling strategy until the feedback type or emotional state meets the threshold of normal acceptance of learning, and then executes the teaching strategy again.

[0091] In one embodiment of the present invention, the calculation formula of the first encoder is as follows:

[0092] ;

[0093] in represents the panel feature of the vth first panel in the lth layer, represents the set of first facets with the highest correlation with facet v, represents the first plane recognition weight matrix of layer l, is the attention weight of the first panel u of layer l to the first panel v, which is calculated by the following formula:

[0094] ;

[0095] in is a learnable attention vector, Represents vector concatenation operation, represents the second plane recognition weight matrix of layer l, represents transpose; and Respectively represent the panel features of the vth first panel of the l-1th layer, , E represents the total number of layers, when l=1 , when l=E is equal to the identification feature of the first panel v; and The feature representations of the objects representing the vth and uth first facet indices respectively.

[0096] In one embodiment of the present invention, the calculation formula of the second encoder is as follows:

[0097] ;

[0098] represents the panel feature of the cth second panel in the lth layer, represents the panel feature of the d-th second panel in the l-1th layer, and represent the set of second facets with high correlation with the second facet c and the second facet d, respectively. represents the cardinality of the set, represents the third plane recognition weight matrix of layer l, , E represents the total number of layers, when l=1 , The feature representation of the object with the dth second facet index, when l=E is equal to the second identification feature of the second panel c, is the sigmoid function;

[0099] In one embodiment of the present invention, the calculation formula of the first fusion feature layer is as follows:

[0100] ;

[0101] in, represents the first combination feature, represents the feature combination function (splicing function or summation function), represents the bin feature of the vth first bin of the Eth layer of the first encoder, represents the set of panel features of the first panel, represents the bin feature of the c-th second bin of the E-th layer of the second encoder, A set of surfel features representing the second surfel.

[0102] In one embodiment of the present invention, the calculation formula of the third encoder is as follows:

[0103] ;

[0104] in represents the tth third identification feature, represents the t-1th third identification feature, , represents the tth data unit of the first combined feature, and is the serial recognition weight matrix of the third encoder, is the serial recognition bias parameter of the third encoder, and ReLU is the ReLU activation function.

[0105] In one embodiment of the present invention, the calculation formula of the first FC layer is as follows:

[0106] ;

[0107] in represents the first output vector, and its cth component value represents the probability value of the cth strategy. The strategy with the largest probability value is selected as the output. The strategy group contains all executable strategies. A strategy includes incentive and reward mechanisms, teaching content presentation and feedback methods, learning plans, schedule arrangements, teacher-student interaction methods, and image information;

[0108] For more complex strategies, vector representation can be used: a strategy is represented as a vector, and the jth component of the vector represents the jth teaching information of the updated teaching strategy;

[0109] represents the last third identification feature output by the third encoder, Indicates full connection.

[0110] In one embodiment of the present invention, the calculation formula of the fourth encoder is as follows:

[0111] ;

[0112] ;

[0113] ;

[0114] in represents the t-th data unit of the second serial data stream corresponding to the v-th third plane element of the third plane data, represents the t-1th fourth recognition feature of the vth third facet, and represent the first and second intermediate features of the vth third surface element, represents the tth data unit of the second serial data stream of the vth third bin, and is the serial recognition weight matrix in the fourth encoder, is the serial recognition bias parameter in the fourth encoder, ReLU is the ReLU activation function, Represents the Sigmoid function.

[0115] In one embodiment of the present invention, the calculation formula of the fifth encoder is as follows:

[0116] ;

[0117] represents the panel feature of the third panel of the fth layer, represents the panel feature of the rth third panel in the l-1th layer, and represent the set of facets with high correlation with facet f and facet r, respectively. represents the cardinality of the set, represents the plane recognition weight matrix of the lth layer in the fifth encoder, , E represents the total number of layers, when l=1 , The feature representation of the object representing the rth third facet index, when l=E is equal to the fifth identification feature of the third panel f, is the sigmoid function;

[0118] In one embodiment of the present invention, the calculation formula of the sixth encoder is as follows:

[0119] ;

[0120] in represents the sixth identification feature, represents the updated teaching strategy of the input, Represents the convolution function.

[0121] In one embodiment of the present invention, the calculation formula of the second fusion feature layer is as follows:

[0122] ;

[0123] in, represents the second combination feature, represents the feature combination function (splicing function or summation function), represents the bin feature of the fth third bin of the Eth layer of the fifth encoder, A collection of panel features representing the third panel.

[0124] In one embodiment of the present invention, the calculation formula of the second FC layer is as follows:

[0125]

[0126] in represents the second output vector, where the e-th component value represents the probability value of the e-th strategy. The strategy with the largest probability value is selected as the output. The strategy group contains all executable strategies. A strategy includes emotional acceptance, empathic feedback, guiding a positive mindset, emotional regulation, building a "growth mindset", and guiding self-talk.

[0127] For more complex strategies, vector representation can be used: one strategy is represented as a vector, the jth component of the vector represents the jth psychological counseling method of the psychological counseling strategy; FC means fully connected.

[0128] In one embodiment of the present invention, the training steps of the policy update model are as follows:

[0129] ;

[0130] in represents the loss value, represents the total number of teaching unit classification categories, represents the total number of training samples, It is The symbol value of the training sample, if the The actual teaching unit of the training sample is classified as the t-th classification category and its value is 1, otherwise it is 0. is the first prediction of the policy update model The probability that a training sample belongs to the tth classification category, Represents the logarithmic function with the natural constant e as the base.

[0131] In one embodiment of the present invention, the training steps of the psychological counseling model are as follows:

[0132] Step 301: Copy the psychological counseling model to generate the Actor network and the TargetActor network, use the feedforward neural network as the Critic network, copy the Critic network to obtain the TargetCritic network, and randomly initialize the parameters of the Actor network, TargetActor network, Critic network, and TargetCritic network;

[0133] Step 302: Input The learners received information collected Go to the Actor network to get the Psychological counseling strategies implemented ;

[0134] No. Psychological counseling strategies implemented Add random noise to get Psychological counseling strategies for increasing noise ;

[0135] The first Psychological counseling strategies implemented Input into the environment and get immediate rewards from feedback Hedi The learners received information collected ; Experience Deposit into experience pool;

[0136] Step 303: randomly obtain N experiences from the experience pool;

[0137] enter Go to the TargetActor network to get the Psychological counseling strategies implemented ;

[0138] Step 304, calculate the first loss value and the second loss value , gradient descent method updates the Actor network and Critic network;

[0139] ;

[0140] ;

[0141] ;

[0142] in Represents input and The output of the Critic network is Indicates that and The output obtained by inputting the Critic network, Indicates execution The comprehensive Q value when express and The output of the TargetCritic network;

[0143]

[0144] in, 、 are the first and second weight parameters in the instant reward, As a result of emotional stability, For teaching acceptance;

[0145] ;

[0146] ;

[0147] in, is the learner’s emotional state score at time step t, such as the emotion score extracted from the learner’s language expression by a sentiment analysis algorithm, is the psychological state at time step t, which includes the learner’s current emotional state, historical interaction records, and other information. is the action taken by the counseling model at time step t (i.e., the counseling response given), is the new psychological state at time step t+1, reflecting the learner's Changes in emotional state after represents the action taken by the learner at time step t;

[0148] If the learner's emotional state improves after taking action (i.e. > ),but is positive, otherwise it is negative; by maximizing the cumulative reward, the model will learn which types of responses can effectively improve the learner's emotional state.

[0149] ;

[0150] in, Indicates direct feedback from learners (such as satisfaction ratings), Indicates interaction indicators (such as learner response speed, interaction duration), , They refer to the weight coefficients of direct feedback and interaction indicators respectively.

[0151] is the discount factor, a value between 0 and 1, used to balance the weights of immediate rewards and future rewards. The default value is 0.6;

[0152] pass Update the Actor network by Update the Critic network;

[0153] Step 305: every fixed number of network updates, update the TargetActor network so that its parameters are the same as those of the current Actor network, and update the TargetCritic network so that its parameters are the same as those of the current Critic network;

[0154] Step 306: When the Actor network and the Critic network converge, or when the number of loops of steps 303-305 reaches a set value, the Actor network replaces the psychological counseling model and the step is terminated.

[0155] Based on the above-mentioned digital intelligence empowerment method, a digital intelligence empowerment system based on a large language model is proposed, including:

[0156] Data acquisition module: responsible for collecting current teaching strategies, learners' learning information and learners' acceptance information, and converting the collected data into structured flat data and serial data streams;

[0157] Strategy update model: includes the first backbone network and the first branch network, inputs the first plane data and the second plane data, and outputs the updated teaching strategy

[0158] Psychological counseling model: includes a second backbone network and a second branch network, inputs the updated teaching strategy and the second serial data stream, and outputs the psychological counseling strategy

[0159] Large language model module: Based on pre-trained large language models (such as GPT, BERT, etc.), it generates corresponding teaching content and interactive responses according to the updated teaching strategies and psychological counseling strategies.

[0160] AI teaching module: responsible for implementing updated teaching strategies, interacting with learners, monitoring learners' feedback types and emotional states, and providing corresponding psychological counseling based on psychological counseling strategies.

[0161] At least one embodiment of the present disclosure provides a storage medium storing non-transitory computer-readable instructions for executing one or more steps in the aforementioned large language model-based digital intelligence empowerment method.

[0162] The computer program may be stored / distributed on a suitable medium, such as an optical storage medium or solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

[0163] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A digital intelligence empowerment method based on a large language model, characterized by: The following steps are involved: Step 100, obtaining the current teaching strategy, learner acceptance information and learner learning information; Learner learning information includes learning preferences, motivation types, plan execution, and self-discipline assessments; learner acceptance information includes feedback, emotional state, and acceptable teaching time; current teaching strategies include several teaching units, which include incentive and reward mechanisms, teaching content presentation and feedback methods, learning plans and schedules, teacher-student interaction methods, and image information; Step 200: constructing first plane data based on the collected current teaching strategy, constructing a first serial data stream and a second plane data based on the collected learner learning information, and constructing a third plane data and a second serial data stream based on the collected learner acceptance information; Step 300: Input the first plane data and the second plane data into the strategy update model, and output a result representing the updated teaching strategy. The updated teaching strategy includes all updated teaching units, and the updated teaching units include incentive and reward mechanisms, teaching content presentation and feedback methods, learning plans and schedules, teacher-student interaction methods, and image information. Input the updated teaching strategy and the second serial data stream into the psychological counseling model, and output a result representing the psychological counseling strategy. The psychological counseling strategy includes emotional acceptance, empathic feedback, guiding a positive mindset, emotional regulation, building a "growth mindset," and guiding self-dialogue. The strategy update model includes a first backbone network and a first branch network, the first backbone network includes a first encoder, a third encoder, a first fusion feature layer and a first FC layer, the first branch network includes a second encoder, wherein the first encoder inputs first plane data and outputs a first recognition feature, while the second encoder inputs second plane data and outputs a second recognition feature, the first recognition feature and the second recognition feature are input into the first fusion feature layer, the first fusion feature layer outputs a first fusion state, the first fusion state is input into the third encoder, the third encoder outputs the third recognition feature to the first FC layer, and the first FC layer outputs a result representing the updated teaching strategy; Step 400: The AI instructor performs teaching according to the updated teaching strategy. If the learner's feedback is poor or their emotional state deviates from normal, the AI instructor will stop executing the teaching strategy according to the output psychological counseling strategy until the feedback and emotional state return to normal, and then resume executing the teaching strategy. The psychological counseling model includes a second backbone network and a second branch network. The second backbone network includes a fourth encoder, a fifth encoder, a second fusion feature layer and a second FC layer. The second branch network includes a sixth encoder. The fourth encoder inputs a second serial data stream and outputs a fourth recognition feature. The fourth recognition feature is input into the fifth encoder. The fifth encoder outputs a fifth recognition feature. At the same time, the sixth encoder inputs an updated teaching strategy and outputs a sixth recognition feature. The sixth recognition feature and the fifth recognition feature are input into the second fusion feature layer. The second fusion feature layer outputs a second fusion state. The second fusion state is input into the second FC layer. The second FC layer outputs the result representing the psychological counseling strategy.

2. A digital intelligence empowerment method based on a large language model according to claim 1, characterized in that: The first plane data includes a first facet for storing data, each first facet indexing a data object. The first plane data also includes a relationship list for storing association relationships between the data objects indexed by the first facet, wherein the element value in the i-th row and j-th column of the relationship list represents a quantized value of the association relationship between the data objects indexed by the i-th and j-th first facets. The correlation relationship includes high and low, corresponding to two quantitative values; Data objects include learners, teaching units, AI teachers, teaching subjects, and image types; Data objects with high correlation are defined as: learners and their AI teachers, learners and their teaching subjects, teaching units and their AI teachers, teaching subjects and their AI teachers, and AI teachers and their image types. Obtaining a first serial data stream based on the learner learning information sorting, the first serial data stream including n data units sorted by time, the t-th data unit storing the second plane data generated by the learner learning information collected at the t-th moment; The second plane data includes a second facet for storing data, each second facet indexing a data object. The second plane data also includes a relationship list for storing association relationships between the data objects indexed by the second facet, wherein the element value in the i-th row and j-th column of the relationship list represents a quantitative value of the association relationship between the data objects indexed by the i-th and j-th second facets. The correlation relationship includes high and low, corresponding to two quantitative values; Data objects include learners, preference types, motivation types, execution status, and self-regulation assessment scores; The data objects with high correlation are defined as: learners and their preference types in the teaching, learners and their motivation types for learning, learners’ implementation of their learning plans, and learners and their own self-discipline assessment scores; Generating third plane data based on the learner's acceptance information includes a third facet for storing data, a second serial data stream where the third facet indexes a data object, and a relationship list for storing association relationships between the data objects indexed by the third facet, wherein the element value in the i-th row and j-th column of the relationship list represents a quantitative value of the association relationship between the data objects indexed by the i-th and j-th third facets; The correlation relationship includes high and low, corresponding to two quantitative values; Data objects include learners, AI teachers, opinion types, and emotional states; Data objects with high correlation are defined as: learners and the types of opinions they express, learners and the emotional states they respond to when receiving instruction, learners and the AI instructors they teach, and opinion types and their corresponding AI instructors. The second serial data stream of a data object includes n data units sorted by time, and the t-th data unit represents the learner's acceptance information collected at the t-th moment of the data object.

3. The digital intelligence empowerment method based on a large language model according to claim 2 is characterized in that: The calculation formula of the first encoder is as follows: ； in represents the panel feature of the vth first panel in the lth layer, represents the set of first facets with the highest correlation with facet v, represents the first plane recognition weight matrix of layer l, is the attention weight of the first panel u of layer l to the first panel v, which is calculated by the following formula: ； in is a learnable attention vector, Represents vector concatenation operation, represents the second plane recognition weight matrix of layer l, represents transpose; and Respectively represent the panel features of the vth and uth first panels of the l-1th layer, , E represents the total number of layers, when l=1 , when l=E is equal to the identification feature of the first panel v; and The feature representations of the objects representing the vth and uth first facet indices respectively.

4. A digital intelligence empowerment method based on a large language model according to claim 3, characterized in that: The calculation formula of the second encoder is as follows: ； represents the panel feature of the cth second panel in the lth layer, represents the panel feature of the d-th second panel in the l-1th layer, and represent the set of second facets with high correlation with the second facet c and the second facet d, respectively. represents the cardinality of the set, represents the third plane recognition weight matrix of layer l, , E represents the total number of layers, when l=1 , The feature representation of the object with the dth second facet index, when l=E is equal to the second identification feature of the second panel c, is the sigmoid function.

5. The digital intelligence empowerment method based on a large language model according to claim 4 is characterized in that: The calculation formula of the first fusion feature layer is as follows: ； in, represents the first combination feature, represents the feature combination function, represents the bin feature of the vth first bin of the Eth layer of the first encoder, represents the set of panel features of the first panel, represents the bin feature of the c-th second bin of the E-th layer of the second encoder, A set of panel features representing a second panel; The calculation formula of the third encoder is as follows: ； in represents the tth third identification feature, represents the t-1th third identification feature, , represents the tth data unit of the first combined feature, and is the serial recognition weight matrix of the third encoder, is the serial recognition bias parameter of the third encoder, and ReLU is the ReLU activation function; The calculation formula of the first FC layer is as follows: ； in represents the first output vector, and its cth component value represents the probability value of the cth strategy. The strategy with the largest probability value is selected as the output. The strategy group contains all executable strategies. A strategy includes incentive and reward mechanisms, teaching content presentation and feedback methods, learning plans, schedule arrangements, teacher-student interaction methods, and image information; For more complex strategies, vector representation can be used: a strategy is represented as a vector, and the jth component of the vector represents the jth teaching information of the updated teaching strategy; represents the last third identification feature output by the third encoder, Indicates full connection.

6. The digital intelligence empowerment method based on a large language model according to claim 5 is characterized in that: The calculation formula of the fourth encoder is as follows: ； ； ； in represents the t-th data unit of the second serial data stream corresponding to the v-th third plane element of the third plane data, represents the t-1th fourth recognition feature of the vth third facet, and represent the first and second intermediate features of the vth third surface element, represents the tth data unit of the first and second serial data streams of the vth third bin, and is the serial recognition weight matrix in the fourth encoder, is the serial recognition bias parameter in the fourth encoder, ReLU is the ReLU activation function, Represents the Sigmoid function; The calculation formula of the fifth encoder is as follows: ； represents the panel feature of the third panel of the fth layer, represents the panel feature of the rth third panel in the l-1th layer, and represent the set of facets with high correlation with facet f and facet r, respectively. represents the cardinality of the set, represents the plane recognition weight matrix of the lth layer in the fifth encoder, , E represents the total number of layers, when l=1 , The feature representation of the object representing the rth third facet index, when l=E is equal to the fifth identification feature of the third panel f, is the sigmoid function; The calculation formula of the sixth encoder is as follows: ； in represents the sixth identification feature, represents the updated teaching strategy of the input, Represents the convolution function.

7. A digital intelligence empowerment system based on a large language model, used to execute the steps of the digital intelligence empowerment method based on a large language model as described in any one of claims 1 to 6, characterized in that: include: Data acquisition module: responsible for collecting current teaching strategies, learners' learning information and learners' acceptance information, and converting the collected data into structured flat data and serial data streams; Strategy update model: includes a first backbone network and a first branch network, inputs first plane data and second plane data, and outputs an updated teaching strategy; Psychological counseling model: includes a second backbone network and a second branch network, inputs the updated teaching strategy and the second serial data stream, and outputs the psychological counseling strategy; Large language model module: Based on the pre-trained large language model, it generates corresponding teaching content and interactive responses according to the updated teaching strategies and psychological counseling strategies; AI teaching module: responsible for implementing updated teaching strategies, interacting with learners, monitoring learners' feedback types and emotional states, and providing corresponding psychological counseling based on psychological counseling strategies.

8. A storage medium, characterized in that: Non-transitory computer-readable instructions are stored for executing the steps in the digital intelligence empowerment method based on a large language model as described in any one of claims 1-6.

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