Intelligent management system of service consultation platform

Abstract

The invention discloses an intelligent management system for a service consultation platform, and the system comprises the steps: processing data through a data collection and processing module; using a data fusion unit to construct a feature matrix for the cleaned multi-source data; based on a demand prediction module, generating a prediction result of the user consultation demand; user input is recognized through a semantic analysis and question and answer module; the analyzed semantic information is transmitted to a knowledge management module, matched content is retrieved from a knowledge base based on a semantic similarity matching algorithm, and answers are generated after ranking according to priorities; information in the service process is collected through the service quality dynamic evaluation module for scoring, an optimization scheme is made through the service optimization strategy generation module, and the optimization scheme is fed back to the evaluation module to achieve closed-loop management. According to the method, the prediction capability and the service efficiency of the platform are remarkably improved, and more efficient and more personalized consultation experience is provided for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of platform intelligent management, and in particular to an intelligent management system for a service consulting platform. Background Art

[0002] With the rapid development of Internet technology and data processing capabilities, service consulting platforms have become an important way for users to acquire professional knowledge and solve problems. However, traditional service consulting platforms face significant challenges in demand prediction and resource matching. Existing technologies usually rely on static rules or a single data dimension to judge demand, and cannot dynamically capture the temporal characteristics, group characteristics, and cross-dimensional interactive relationships of user behavior. In addition, traditional collaborative filtering methods are limited to simple user-service interactions and lack in-depth analysis of the temporal dimension and user behavior characteristics; while clustering analysis models are mostly based on fixed features, ignoring the dynamics and relevance of user needs. The above shortcomings lead to low accuracy and efficiency of service recommendations, which cannot meet the personalized and diversified needs of users. To this end, there is an urgent need for an innovative technical solution that can integrate temporal dynamics, behavioral characteristics, and group demand characteristics to achieve accurate prediction of user consulting needs and efficient resource matching. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides an intelligent management system for a service consulting platform, which can solve the problems that the existing platform is insufficient in capturing the temporal dynamic characteristics of user behavior, and cannot reflect the short-term changes, periodic laws and long-term trends of user needs; the interactive relationship between users and services is not sufficiently modeled, and lacks high-level expression capabilities for complex interactive patterns; traditional clustering methods are not accurate enough in mining the demand characteristics of user groups, and ignore the dynamic correlation of user characteristics. At the same time, the result fusion mechanism of the existing prediction model lacks dynamic adjustment capabilities and is difficult to adapt to the needs of different scenarios. Through the improved temporal dynamic weight function, high-order tensor decomposition method and user demand clustering model based on spectral clustering of the present invention, combined with the multi-model dynamic weight fusion mechanism, the dynamic, accuracy and adaptability of user demand prediction are effectively improved, providing a more efficient prediction and resource matching technical support for the service consulting platform.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions, a service consultation platform intelligent management system, comprising: Through the data collection and processing module, user behavior data, historical consulting data and external industry trend data are collected, and the collected data is cleaned and formatted to obtain multi-source data; The data fusion unit is used to construct a feature matrix based on the cleaned multi-source data to generate a multi-dimensional feature vector of user needs; Generate forecast results of user consultation needs based on collaborative filtering algorithm and cluster analysis model in demand forecast module; The semantic parsing and question-answering modules are used to segment, parse and identify the natural language input by users. The parsed semantic information is passed to the knowledge management module, and matching content is retrieved from the knowledge base based on the semantic similarity matching algorithm, and answers are generated after sorting by priority; Through the service quality dynamic evaluation module, user feedback data, expert behavior data and platform operation data in the service process are collected and scored. Based on the scoring results, an optimization plan is formulated through the service optimization strategy generation module, and the resource scheduling module is used to adjust the expert resource allocation and optimize the recommended content. The optimization plan is then fed back to the evaluation module to achieve closed-loop management.

[0006] As a preferred solution of the service consulting platform intelligent management system described in the present invention, wherein: the data collection and processing module includes a user behavior collection unit, a historical consulting data collection unit, an industry trend collection unit, a data cleaning unit, and a feature extraction unit; The user behavior collection unit is used to collect user behavior data of users, including browsing history, click behavior and search keywords; the historical consultation data collection unit is used to collect historical consultation data, including recording users' consultation topics, expert selections and corresponding rating records; the industry trend collection unit is used to obtain external industry trend data, including industry hot topics, time distribution data and external public information; the data cleaning unit is used to remove invalid or duplicate data and unify data formats; the feature extraction unit is used to perform dimension regularization and feature enhancement processing on the collected user behavior data, historical consultation data and external industry trend data.

[0007] As a preferred solution of the service consulting platform intelligent management system described in the present invention, the data fusion unit includes digitizing non-numeric data through a feature embedding algorithm, and unifying user behavior, time dimension and historical records into a multi-dimensional feature representation of user needs by constructing a feature matrix, wherein the non-numeric data includes keywords and behavior labels.

[0008] As a preferred solution of the service consulting platform intelligent management system described in the present invention, wherein: the construction of the feature matrix includes: Using context-aware embedding models: ; in: ; represents the input embedding matrix, E is the word embedding matrix, R is the real number set, is the length of the text, for dimension; represents a BERT-based embedding function for generating context-aware text embeddings; is the weight matrix of query, key and value in BERT model, Q, K, V represent query, key and value matrix respectively; MultiHead represents multi-head attention mechanism, output ; represents the text embedding matrix; Behavior Labels Using graph convolutional network embedding: ; in, Indicates The input feature matrix of the layer, represents the label association matrix; m represents the number of label nodes; Indicates The input feature matrix of the layer; Represents the weight matrix of graph convolution; Represents the activation function, and the output is the label embedding matrix ; m represents the label feature dimension; k represents the dimension of the label embedding space, which is the number of dimensions for each label embedding; ; in, Indicates time The hidden state of the moment; Indicates time Input data; The weight matrix representing the hidden state; represents the weight matrix of the input data; b represents the bias term; Represents the activation function, and the final output is the time series embedding matrix ; h represents the dimension of the state vector at time step t; t represents the time series feature dimension; Extended time weight function , introduce polynomial fitting for dynamic adjustment: ; in, represents the frequency of behavior, Indicates the behavior category, Indicates the behavior category The weight coefficient of represents the time decay factor, is the attenuation parameter, represents the logarithmic processing of the behavior frequency, represents the high-order polynomial terms of the time series, represents the order of the polynomial, represents the coefficients of the time polynomial terms; ; in, represents the fused feature tensor, represents the number of samples in the dataset, Indicates The weighted value of the data point, represents the text feature matrix, represents the label feature matrix, represents the time feature matrix, is the number of samples, is the dimension of time feature, Tensor outer product operation.

[0009] As a preferred solution of the service consulting platform intelligent management system described in the present invention, wherein: the constructing of the feature matrix also includes extracting the core interaction relationship of the multimodal features using HOSVD to reduce the high-dimensional tensor into a dense representation; For multimodal embedding tensors Perform a higher-order singular value decomposition: ; in, Represents the fused feature tensor, with dimension , represents the number of samples, Represents the dimension of text features, Represents the dimension of label features, The dimension representing the time feature, Represents the core tensor after tensor decomposition, is the factor matrix of text features, which represents the low-dimensional representation of the text dimension obtained by tensor decomposition. , is the decomposition rank of the text feature, is the factor matrix of the label feature, which represents the low-dimensional representation on the label dimension obtained by tensor decomposition, with a dimension of , is the decomposition rank of the label feature, is the factor matrix of time features, which represents the low-dimensional representation on the time dimension obtained by tensor decomposition, and its dimension is , is the decomposition rank of the time feature, It is the outer product operation of the tensor, which means that the core tensor Respectively with the factor matrix Perform a product operation to obtain a low-dimensional representation after dimensionality reduction; The core tensor after decomposing the tensor Concatenate with time series features: ; in, Represents the core tensor expanded into a matrix along the first dimension; Represents the concatenated feature matrix, which represents the multi-dimensional features of user needs; The final feature representation is optimized by contrastive learning mechanism, and the loss function is: ; in, represents the loss function of contrastive learning, which is used to maximize the similarity between positive sample pairs and minimize the similarity between negative sample pairs. It is The embedded feature vector of samples, is the paired positive sample feature vector, is the feature vector of negative samples, is the temperature parameter, is an exponential function, is the dot product of the positive sample pair, represents the summation operation of all negative samples, is the total number of negative samples.

[0010] As a preferred solution of the service consulting platform intelligent management system described in the present invention, wherein: the demand forecasting module includes a collaborative filtering algorithm and a clustering analysis model; The collaborative filtering algorithm is used to predict the potential needs of users based on the behavior patterns of similar users; the cluster analysis model is used to extract demand characteristics and divide user groups according to the demand characteristics.

[0011] As a preferred solution of the service consultation platform intelligent management system described in the present invention, wherein: the semantic analysis and question-answering module includes a word segmentation unit, a grammatical analysis unit, a semantic similarity calculation unit, and a context management unit; The word segmentation unit is used to segment the natural language questions input by the user; the grammatical analysis unit is used to extract the grammatical structure and semantic content in the question through dependency syntax analysis; the semantic similarity calculation unit is used to quantify the similarity between the question semantics and the knowledge base content; the context management unit is used to record the context information of multiple rounds of user question interactions to dynamically adjust the parsing results.

[0012] As a preferred solution of the intelligent management system of the service consulting platform described in the present invention, the semantic similarity matching algorithm includes parsing the semantics of user input through a multi-head attention mechanism, and combining image features, time decay weights and user portrait information to fuse multimodal data to construct a user demand representation. At the same time, in the semantic matching process, a similarity calculation method is used to improve the matching accuracy based on text content and combined with graph relationships and contextual information in the knowledge base, and the semantic association between items is optimized through the graph neural network GNN, and context-aware similarity is introduced to aggregate the semantic information of the neighborhood of the knowledge base; The entries in the knowledge base are prioritized through a multi-factor scoring mechanism, and the historical usage frequency, expert ratings and time decay factors of the entries are comprehensively considered. The comparative learning method is used to optimize the answering process to ensure that the system can select the knowledge entries that best match user needs.

[0013] As a preferred solution of the service consulting platform intelligent management system described in the present invention, the semantic analysis and question-answering module is combined with the retrieval results of the knowledge management module, generates answers through priority sorting, supports the decomposition and processing of questions input by users, and combines the context memory function to realize multiple rounds of interactive question answering.

[0014] As a preferred solution of the service consulting platform intelligent management system described in the present invention, wherein: the service optimization strategy generation module includes an expert resource optimization unit, a user guidance optimization unit, a resource scheduling optimization unit, and a feedback unit; The expert resource optimization unit is used to dynamically adjust the matching relationship between experts and consulting topics; the user guidance optimization unit is used to generate recommended service content based on user demand prediction results; the resource scheduling optimization unit is used to adjust the platform resource allocation strategy; the feedback unit is used to re-input the optimized data into the service quality dynamic evaluation module to form a closed-loop management process.

[0015] Beneficial effects of the present invention: The present invention introduces an improved time dynamic weight function to fully capture the time characteristics of user behavior, covering short-term changes, periodic laws and long-term trends, and improving the accuracy of demand forecasting. Secondly, based on the high-order tensor decomposition method, a joint model of the three-dimensional interactive relationship between users, services and time is constructed, breaking through the limitations of traditional methods on the expression of complex demand patterns. Thirdly, spectral clustering is used to optimize the user grouping model, and the common characteristics of group needs are deeply explored, providing a solid foundation for accurate resource matching. In addition, the present invention flexibly adjusts the contribution weights of time series, collaborative filtering and clustering analysis through a multi-model dynamic weight fusion mechanism to meet the needs of diversified application scenarios. In summary, the present invention significantly improves the platform's prediction ability and service efficiency, and provides users with a more efficient and personalized consulting experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a service consulting platform intelligent management system framework provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0022] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0023] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and which provides a service consulting platform intelligent management system, including: Through the data collection and processing module, user behavior data, historical consulting data and external industry trend data are collected, and the collected data are cleaned and formatted to obtain multi-source data; the data fusion unit is used to construct a feature matrix based on the cleaned multi-source data to generate a multi-dimensional feature vector of user needs; based on the collaborative filtering algorithm and clustering analysis model in the demand prediction module, the prediction results of user consulting needs are generated; through the semantic parsing and question-answering module, the natural language input by the user is segmented, parsed and the intent is recognized; the parsed semantic information is passed to the knowledge management module, and matching content is retrieved from the knowledge base based on the semantic similarity matching algorithm, and answers are generated after sorting by priority; through the service quality dynamic evaluation module, user feedback data, expert behavior data and platform operation data in the service process are collected and scored, and based on the scoring results, an optimization plan is formulated through the service optimization strategy generation module, and the resource scheduling module is used to adjust the expert resource allocation and optimize the recommended content, and the optimization plan is fed back to the evaluation module to realize closed-loop management.

[0025] The data collection and processing module includes a user behavior collection unit, a historical consultation data collection unit, an industry trend collection unit, a data cleaning unit, and a feature extraction unit; The user behavior collection unit is used to collect user behavior data of users, including browsing history, click behavior and search keywords; the historical consultation data collection unit is used to collect historical consultation data, including recording users' consultation topics, expert selections and corresponding rating records; the industry trend collection unit is used to obtain external industry trend data, including industry hot topics, time distribution data and external public information; the data cleaning unit is used to remove invalid or duplicate data and unify data formats; the feature extraction unit is used to perform dimension regularization and feature enhancement processing on the collected user behavior data, historical consultation data and external industry trend data.

[0026] The data fusion unit includes digitizing non-numeric data through a feature embedding algorithm, and unifying user behavior, time dimension and historical records into a multi-dimensional feature representation of user needs by constructing a feature matrix, wherein the non-numeric data includes keywords and behavior tags.

[0027] The constructing feature matrix includes: Using context-aware embedding models: ; in: ; represents the input embedding matrix, E is the word embedding matrix, R is the real number set, is the length of the text, for dimension; represents a BERT-based embedding function for generating context-aware text embeddings; is the weight matrix of query, key and value in BERT model, Q, K, V represent query, key and value matrix respectively; MultiHead represents multi-head attention mechanism, output ; represents the text embedding matrix; Behavior Labels Using graph convolutional network embedding: ; in, Indicates The input feature matrix of the layer, represents the label association matrix; m represents the number of label nodes; Indicates The input feature matrix of the layer; Represents the weight matrix of graph convolution; Represents the activation function, and the output is the label embedding matrix ; m represents the label feature dimension; k represents the dimension of the label embedding space, which is the number of dimensions for each label embedding; ; in, Indicates time The hidden state of the moment; Indicates time Input data; The weight matrix representing the hidden state; represents the weight matrix of the input data; b represents the bias term; Represents the activation function, and the final output is the time series embedding matrix ; h represents the dimension of the state vector at time step t; t represents the time series feature dimension; Extended time weight function , introduce polynomial fitting for dynamic adjustment: ; in, represents the frequency of behavior, Indicates the behavior category, Indicates the behavior category The weight coefficient of represents the time decay factor, is the attenuation parameter, represents the logarithmic processing of the behavior frequency, represents the high-order polynomial terms of the time series, represents the order of the polynomial, represents the coefficients of the time polynomial terms; ; in, represents the fused feature tensor, represents the number of samples in the dataset, Indicates The weighted value of the data point, represents the text feature matrix, represents the label feature matrix, represents the time feature matrix, is the number of samples, is the dimension of time feature, Tensor outer product operation.

[0028] The constructing of the feature matrix also includes extracting the core interaction relationship of the multimodal features using HOSVD to reduce the high-dimensional tensor into a dense representation; For multimodal embedding tensors Perform a higher-order singular value decomposition: ; in, Represents the fused feature tensor, with dimension , represents the number of samples, Represents the dimension of text features, Represents the dimension of label features, The dimension representing the time feature, Represents the core tensor after tensor decomposition, is the factor matrix of text features, which represents the low-dimensional representation of the text dimension obtained by tensor decomposition. , is the decomposition rank of the text feature, is the factor matrix of the label feature, which represents the low-dimensional representation on the label dimension obtained by tensor decomposition, with a dimension of , is the decomposition rank of the label feature, is the factor matrix of time features, which represents the low-dimensional representation on the time dimension obtained by tensor decomposition, and its dimension is , is the decomposition rank of the time feature, It is the outer product operation of the tensor, which means that the core tensor Respectively with the factor matrix Perform a product operation to obtain a low-dimensional representation after dimensionality reduction; The core tensor after decomposing the tensor Concatenate with time series features: ; in, Represents the core tensor expanded into a matrix along the first dimension; Represents the concatenated feature matrix, which represents the multi-dimensional features of user needs; The final feature representation is optimized by contrastive learning mechanism, and the loss function is: ; in, represents the loss function of contrastive learning, which is used to maximize the similarity between positive sample pairs and minimize the similarity between negative sample pairs. It is The embedded feature vector of samples, is the paired positive sample feature vector, is the feature vector of negative samples, is the temperature parameter, is an exponential function, is the dot product of the positive sample pair, represents the summation operation of all negative samples, is the total number of negative samples.

[0029] The demand forecasting module includes collaborative filtering algorithm and cluster analysis model; The collaborative filtering algorithm is used to predict the potential needs of users based on the behavior patterns of similar users; the cluster analysis model is used to extract demand characteristics and divide user groups according to the demand characteristics.

[0030] The semantic parsing and question-answering module includes a word segmentation unit, a grammatical analysis unit, a semantic similarity calculation unit, and a context management unit; The word segmentation unit is used to segment the natural language questions input by the user; the grammatical analysis unit is used to extract the grammatical structure and semantic content in the question through dependency syntax analysis; the semantic similarity calculation unit is used to quantify the similarity between the question semantics and the knowledge base content; the context management unit is used to record the context information of multiple rounds of user question interactions to dynamically adjust the parsing results.

[0031] The semantic similarity matching algorithm includes parsing the semantics of user input through a multi-head attention mechanism, and combining image features, time decay weights and user portrait information to fuse multimodal data to construct a user demand representation. At the same time, in the semantic matching process, a similarity calculation method is used to improve the matching accuracy based on text content and combined with graph relationships and context information in the knowledge base. The semantic association between items is optimized through the graph neural network GNN, and context-aware similarity is introduced to aggregate the semantic information of the neighborhood of the knowledge base. The entries in the knowledge base are prioritized through a multi-factor scoring mechanism, and the historical usage frequency, expert ratings and time decay factors of the entries are comprehensively considered. The comparative learning method is used to optimize the answering process to ensure that the system can select the knowledge entries that best match user needs.

[0032] The semantic analysis and question-answering module combines the search results of the knowledge management module, generates answers through priority sorting, supports the decomposition and processing of questions input by users, and realizes multiple rounds of interactive question answering in combination with the context memory function.

[0033] The service optimization strategy generation module includes an expert resource optimization unit, a user guidance optimization unit, a resource scheduling optimization unit, and a feedback unit; The expert resource optimization unit is used to dynamically adjust the matching relationship between experts and consulting topics; the user guidance optimization unit is used to generate recommended service content based on user demand prediction results; the resource scheduling optimization unit is used to adjust the platform resource allocation strategy; the feedback unit is used to re-input the optimized data into the service quality dynamic evaluation module to form a closed-loop management process.

[0034] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0035] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that: If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0036] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0037] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0038] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0039] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0040] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A service consulting platform intelligent management system, characterized by: include, Through the data collection and processing module, user behavior data, historical consulting data and external industry trend data are collected, and the collected data is cleaned and formatted to obtain multi-source data; The data fusion unit is used to construct a feature matrix based on the cleaned multi-source data to generate a multi-dimensional feature vector of user needs; Generate forecast results of user consultation needs based on collaborative filtering algorithm and cluster analysis model in demand forecast module; The semantic parsing and question-answering modules are used to segment, parse and identify the natural language input by users. The parsed semantic information is passed to the knowledge management module, and matching content is retrieved from the knowledge base based on the semantic similarity matching algorithm, and answers are generated after sorting by priority; Through the service quality dynamic evaluation module, user feedback data, expert behavior data and platform operation data in the service process are collected and scored. Based on the scoring results, an optimization plan is formulated through the service optimization strategy generation module, and the resource scheduling module is used to adjust the expert resource allocation and optimize the recommended content. The optimization plan is then fed back to the evaluation module to achieve closed-loop management.

2. A service consultation platform intelligent management system as claimed in claim 1, characterized in that: The data collection and processing module includes a user behavior collection unit, a historical consultation data collection unit, an industry trend collection unit, a data cleaning unit, and a feature extraction unit; The user behavior collection unit is used to collect the user behavior data of the user, including browsing history, click behavior and search keywords; the historical consultation data collection unit is used to collect historical consultation data, including recording the user's consultation topic, expert selection and corresponding rating records; The industry trend collection unit is used to obtain external industry trend data, including industry hot topics, time distribution data and external public information; the data cleaning unit is used to remove invalid or duplicate data and unify the data format; the feature extraction unit is used to perform dimension normalization and feature enhancement processing on the collected user behavior data, historical consulting data and external industry trend data.

3. A service consultation platform intelligent management system as claimed in claim 2, characterized in that: The data fusion unit includes digitizing non-numeric data through a feature embedding algorithm, and unifying user behavior, time dimension and historical records into a multi-dimensional feature representation of user needs by constructing a feature matrix, wherein the non-numeric data includes keywords and behavior tags.

4. A service consultation platform intelligent management system as claimed in claim 3, characterized in that: The constructing feature matrix includes: Using context-aware embedding models: ； in: ； represents the input embedding matrix, E is the word embedding matrix, R is the real number set, is the length of the text, for dimension; represents a BERT-based embedding function for generating context-aware text embeddings; is the weight matrix of query, key and value in BERT model, Q, K, V represent query, key and value matrix respectively; MultiHead represents multi-head attention mechanism, output ; represents the text embedding matrix; Behavior Labels Using graph convolutional network embedding: ; in, Indicates The input feature matrix of the layer, represents the label association matrix; m represents the number of label nodes; Indicates The input feature matrix of the layer; Represents the weight matrix of graph convolution; Represents the activation function, and the output is the label embedding matrix ; m represents the label feature dimension; k represents the dimension of the label embedding space, which is the number of dimensions for each label embedding; ； in, Indicates time The hidden state of the moment; Indicates time Input data; The weight matrix representing the hidden state; represents the weight matrix of the input data; b represents the bias term; Represents the activation function, and the final output is the time series embedding matrix ; h represents the dimension of the state vector at time step t; t represents the time series feature dimension; Extended time weight function , introduce polynomial fitting for dynamic adjustment: ； in, Indicates the frequency of behavior, Indicates the behavior category, Indicates the behavior category The weight coefficient of represents the time decay factor, is the attenuation parameter, represents the logarithmic processing of the behavior frequency, represents the high-order polynomial terms of the time series, represents the order of the polynomial, represents the coefficients of the time polynomial terms; ； in, represents the fused feature tensor, represents the number of samples in the dataset, Indicates The weighted value of the data point, represents the text feature matrix, represents the label feature matrix, represents the time feature matrix, is the number of samples, is the dimension of time feature, Tensor outer product operation.

5. A service consultation platform intelligent management system as claimed in claim 4, characterized in that: The constructing of the feature matrix also includes extracting the core interaction relationship of the multimodal features using HOSVD to reduce the high-dimensional tensor into a dense representation; For multimodal embedding tensors Perform a higher-order singular value decomposition: ； in, Represents the fused feature tensor, with dimension , represents the number of samples, Represents the dimension of text features, Represents the dimension of label features, The dimension representing the time feature, Represents the core tensor after tensor decomposition, is the factor matrix of text features, which represents the low-dimensional representation of the text dimension obtained by tensor decomposition. , is the decomposition rank of the text feature, is the factor matrix of the label feature, which represents the low-dimensional representation on the label dimension obtained by tensor decomposition, with a dimension of , is the decomposition rank of the label feature, is the factor matrix of time features, which represents the low-dimensional representation on the time dimension obtained by tensor decomposition, and its dimension is , is the decomposition rank of the time feature, It is the outer product operation of the tensor, which means that the core tensor Respectively with the factor matrix Perform a product operation to obtain a low-dimensional representation after dimensionality reduction; The core tensor after decomposing the tensor Concatenate with time series features: ; in, Represents the core tensor expanded into a matrix along the first dimension; Represents the concatenated feature matrix, which represents the multi-dimensional features of user needs; The final feature representation is optimized by contrastive learning mechanism, and the loss function is: ； in, represents the loss function of contrastive learning, which is used to maximize the similarity between positive sample pairs and minimize the similarity between negative sample pairs. It is The embedded feature vector of samples, is the paired positive sample feature vector, is the feature vector of negative samples, is the temperature parameter, is an exponential function, is the dot product of the positive sample pair, represents the summation operation of all negative samples, is the total number of negative samples.

6. A service consultation platform intelligent management system as claimed in claim 5, characterized in that: The demand forecasting module includes collaborative filtering algorithm and cluster analysis model; The collaborative filtering algorithm is used to predict the potential needs of users based on the behavior patterns of similar users; the cluster analysis model is used to extract demand characteristics and divide user groups according to the demand characteristics.

7. A service consultation platform intelligent management system as claimed in claim 6, characterized in that: The semantic parsing and question-answering module includes a word segmentation unit, a grammatical analysis unit, a semantic similarity calculation unit, and a context management unit; The word segmentation unit is used to segment the natural language question input by the user; the grammatical analysis unit is used to extract the grammatical structure and semantic content in the question through dependency syntactic analysis; The semantic similarity calculation unit is used to quantify the similarity between the question semantics and the knowledge base content; The context management unit is used to record the context information of multiple rounds of user question interactions to dynamically adjust the parsing results.

8. A service consultation platform intelligent management system as claimed in claim 7, characterized in that: The semantic similarity matching algorithm includes parsing the semantics of user input through a multi-head attention mechanism, and combining image features, time decay weights and user portrait information to fuse multimodal data to construct a user demand representation. At the same time, in the semantic matching process, a similarity calculation method is used to improve the matching accuracy based on text content and combined with graph relationships and context information in the knowledge base. The semantic association between items is optimized through the graph neural network GNN, and context-aware similarity is introduced to aggregate the semantic information of the neighborhood of the knowledge base. The entries in the knowledge base are prioritized through a multi-factor scoring mechanism, and the historical usage frequency, expert ratings and time decay factors of the entries are comprehensively considered. The comparative learning method is used to optimize the answering process to ensure that the system can select the knowledge entries that best match user needs.

9. A service consultation platform intelligent management system as claimed in claim 8, characterized in that: The semantic analysis and question-answering module combines the search results of the knowledge management module, generates answers through priority sorting, supports the decomposition and processing of questions input by users, and realizes multiple rounds of interactive question answering in combination with the context memory function.

10. A service consultation platform intelligent management system as claimed in claim 9, characterized in that: The service optimization strategy generation module includes an expert resource optimization unit, a user guidance optimization unit, a resource scheduling optimization unit, and a feedback unit; The expert resource optimization unit is used to dynamically adjust the matching relationship between experts and consulting topics; the user guidance optimization unit is used to generate recommended service content according to the user demand prediction results; The resource scheduling optimization unit is used to adjust the platform resource allocation strategy; The feedback unit is used to re-input the optimized data into the service quality dynamic evaluation module to form a closed-loop management process.

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