Information processing method and system based on enterprise digital transformation, system design method, equipment and medium

Abstract

The invention provides an information processing method and system based on enterprise digital transformation, a system design method, equipment and a medium, and belongs to the technical field of intelligent response, and the method comprises the following steps: obtaining user query input of enterprise digital transformation business related problems, and triggering session establishment; performing text processing on user query input in the session by using a natural language model, performing consciousness identification and entity extraction by using an intelligent reasoning model, performing logical reasoning and intelligent matching in combination with a knowledge base, generating response content, and returning the response content in the session; and obtaining an evaluation result of the user on the response content, optimizing the natural language model and the intelligent reasoning model according to the evaluation result, counting query historical records of the user, and recommending a digital transformation scheme for the user. According to the method, the intention of the user can be accurately understood, the response content meeting the requirement can be quickly generated, and the performance and the user experience are improved through continuous learning and optimization.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically relates to an information processing method, system, system design method, equipment and medium based on enterprise digital transformation. Background Art

[0002] Enterprise digital transformation has become the key to improving enterprise competitiveness. However, in the process of digital transformation, enterprises face many problems. Among them, how to efficiently and accurately process user query input and respond to and meet user needs in a timely manner has become one of the key factors restricting the effectiveness of enterprise digital transformation.

[0003] Traditionally, companies rely on manual customer service or simple automated tools to respond to user inquiries. However, these methods are unable to cope with complex, changing and high-frequency user needs. Limited by labor costs, response speed and professional knowledge level, manual customer service is difficult to ensure the timeliness and accuracy of service; while simple automated tools often lack sufficient intelligence and personalization, and cannot accurately understand user intentions and provide targeted answers.

[0004] In recent years, intelligent information processing systems based on natural language processing and artificial intelligence technologies have emerged. These systems can use advanced algorithms and models to perform in-depth analysis and intelligent reasoning on user query inputs, thereby generating accurate and personalized responses. However, existing intelligent information processing systems still have many shortcomings when dealing with issues related to enterprise digital transformation. For example, some systems lack the training of professional terminology and logical rules for enterprise digital transformation business, which makes it easy to make errors in term recognition and intent classification; some systems are not accurate enough in logical reasoning and intelligent matching, and the generated response content often cannot fully meet user needs; in addition, some systems lack a mechanism for continuous optimization and improvement based on user feedback, resulting in the inability to continuously improve system performance. Summary of the invention

[0005] In a first aspect, an embodiment of the present application provides an information processing method based on enterprise digital transformation, comprising the following steps: S1. Obtain user query input related to enterprise digital transformation business issues and trigger session establishment; S2. Use the natural language model to process the text of the user query input in the session, then use the intelligent reasoning model to perform consciousness recognition and entity extraction, and combine the knowledge base to perform logical reasoning and intelligent matching to generate the answer content, and then return the answer content in the session; S3. Obtain the user's evaluation results on the response content, optimize the natural language model and intelligent reasoning model based on the evaluation results, and count the user's query history to recommend digital transformation solutions to the user.

[0006] Furthermore, the specific steps of step S2 are as follows: S21. The conversation calls the natural language model to perform word segmentation, part-of-speech tagging, and syntactic analysis on the user query input to complete text processing; S22. The conversation calls the intent classification model in the intelligent reasoning model to identify the user query intent of the processed text, and performs logical reasoning based on the user query intent, and then calls the knowledge base for intelligent matching to generate the response content; S23. The session returns the response content to the user; S24. The session identifies whether a new user query input is received within a set time period after returning the response content; If yes, return to step S21; If not, go to step S3.

[0007] Further, step S22 includes the following steps: S221. Use the labeled user query data related to the digital transformation of the enterprise in advance to train the neural network model to obtain the intent classification model; S222. Build a rule base containing rules and logic related to enterprise digital transformation, use a rule-based logical reasoning model and perform logical reasoning based on the user's query intent and the rules in the rule base; S223. Use an intelligent matching algorithm based on semantic matching and perform precise matching based on the user's query intent, context, logical reasoning results, and knowledge in the knowledge base to generate response content that best meets user needs.

[0008] Furthermore, the specific steps of step S3 are as follows: S31. Obtain the user's evaluation results on the response content; When the evaluation result is that the terminology recognition of the enterprise digital transformation business is wrong, proceed to step S32; When the evaluation result is that the intent classification recognition of the user query input is wrong, proceed to step S33; When the evaluation result is that the logical reasoning of the user query input is wrong, the process proceeds to step S34; S32. Add the misidentified enterprise digital transformation business terms to the training data set of the natural language model, retrain, and proceed to step S35; S33. Add the user query input with intent classification recognition error to the training data set of the intent classification model, retrain, and proceed to step S35; S34. Determine the correct reasoning logic of the user query input with logical reasoning errors, and use the correct reasoning logic to correct the intelligent reasoning model; S35. Count the conversation history of user queries, identify user behavioral characteristics and corporate attributes, and recommend relevant digital transformation solutions, products or services to users.

[0009] In a second aspect, the embodiment of the present application further provides an information processing system based on enterprise digital transformation, including: The user interaction module obtains the user's query input, converts it into a format recognizable by the system, and returns the generated response content to the user; The session management module establishes a session for the user's query input and calls the natural language processing module, the knowledge base management module and the intelligent reasoning model module in the session to generate the response content; The natural language processing module uses a natural language model to process text input from user queries; The knowledge base management module stores the knowledge base related to the enterprise's digital transformation and performs dynamic management; The intelligent reasoning model module uses the intelligent reasoning model to perform consciousness recognition and entity extraction based on the text processing results, and combines the knowledge base for logical reasoning and intelligent matching to generate response content.

[0010] Furthermore, the session management module includes: A session establishing unit, responding to a user query input in the user interface to establish a session; The conversation management unit accesses the natural language processing module, the knowledge base management module and the intelligent reasoning model module in the conversation and calls and uses them according to the preset logic to generate the response content; A session closing unit, which closes the current session when no new user query input is received within a set time period after the response content is returned to the user; A session saving unit saves the completed session into the session history record and adds it to the database; The natural language processing module includes: A word segmentation unit performs word segmentation processing on the text in the query request input by the user to obtain a number of words; A part-of-speech tagging unit is used to tag the words obtained by word segmentation; The syntactic analysis unit performs syntactic structure analysis on the text in the query request input by the user and extracts the relationship between the sentence components; The knowledge base management module includes: The knowledge base construction unit obtains the knowledge base content related to answers to questions, product descriptions, and service processes related to the digital transformation of the enterprise input by users or administrators through a graphical interface or API interface, and saves the knowledge base content into the database; The knowledge base update unit regularly updates the knowledge base content based on user feedback, system logs or external data sources; The intelligent reasoning model module includes: The intent classification unit recognizes the user query intent of the processed text through the pre-trained intent classification model; Logical reasoning unit, which performs logical reasoning based on the user's query intention; The intelligent matching unit matches the logical reasoning results with the knowledge base to generate response content.

[0011] Furthermore, it also includes: Personalized recommendation module, which counts the conversation history of user queries, identifies user behavior characteristics and enterprise attributes, and recommends relevant digital transformation solutions, products or services to users; The learning optimization module uses machine learning algorithms to regularly optimize the natural language processing model and intelligent reasoning model based on the response content in the conversation.

[0012] In a third aspect, the embodiment of the present application further provides a method for designing an information processing system based on enterprise digital transformation users, comprising the following steps: SS1. Use Vue front-end framework to build web user interface; SS2. Build a backend service platform using the Flask backend framework; SS3. Set up a natural language model interface on the backend service platform to access the natural language model trained with terms related to enterprise digital transformation; SS4. Set up an intelligent reasoning model interface on the backend service platform and access the pre-trained intelligent reasoning model; SS5. Set up a knowledge base interface on the back-end service platform to access the knowledge base involving answers to questions, product descriptions, and service processes related to the digital transformation of the enterprise.

[0013] In a fourth aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the information processing method based on enterprise digital transformation as described in the second aspect are implemented.

[0014] In a fifth aspect, an embodiment of the present application further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the information processing method based on enterprise digital transformation as described in the second aspect are implemented.

[0015] It can be seen from the above technical solutions that the present invention has the following advantages: In the information processing method, system, system design method, device and medium based on enterprise digital transformation provided by the present application, by obtaining user query input related to enterprise digital transformation business issues, triggering session establishment, the system can respond to user needs in real time, provide immediate feedback and answers, and improve user experience and satisfaction; through natural language models and intelligent reasoning models, text processing, consciousness recognition, and entity extraction are performed on user query input, and then logical reasoning and intelligent matching are performed in combination with the knowledge base to generate response content, which can accurately understand user intentions and provide relevant answers, thereby improving the intelligence level and accuracy of the system; by obtaining the user's evaluation results on the response content, and optimizing the natural language model and intelligent reasoning model according to the evaluation results, and counting the user's query history records, relevant digital transformation solutions, products or services are recommended to the user, which can continuously improve the accuracy and intelligence level of the system while meeting the user's personalized needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description 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 It is a flowchart of the information processing method based on the digital transformation of enterprises according to the present invention.

[0018] Figure 2 It is a schematic diagram of the information processing system for enterprise digital transformation according to the present invention.

[0019] Figure 3 It is a flowchart of the design method of the information processing system for enterprise digital transformation based on the present invention. DETAILED DESCRIPTION

[0020] In the information processing method based on the digital transformation of enterprises, various embodiments of the present disclosure will be described in detail below. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0021] For example, enterprise digital transformation has increasingly become a core strategy to enhance enterprise market competitiveness, but in the process of this transformation, enterprises have encountered many difficulties. Among them, how to efficiently and accurately process user queries and respond to user needs quickly and appropriately has become a major bottleneck affecting the effectiveness of enterprise digital transformation.

[0022] In the past, companies mostly relied on manual customer service or basic automation tools to respond to user inquiries. However, these methods are not adequate when dealing with complex, changeable, and high-frequency user needs. Limited by cost, response speed, and expertise, manual customer service is difficult to balance the timeliness and accuracy of services; while basic automation tools are not intelligent and personalized enough to accurately capture user intentions and provide customized responses.

[0023] In recent years, with the rapid development of natural language processing and artificial intelligence technologies, intelligent information processing systems have emerged. With advanced algorithms and models, these systems can deeply analyze user queries and perform intelligent reasoning to generate accurate and personalized responses. However, existing intelligent information processing systems still have significant shortcomings when dealing with issues related to enterprise digital transformation. For example, some systems frequently make mistakes in terminology recognition and intent classification due to the lack of sufficient training in the professional terminology and logical rules of enterprise digital transformation business; some systems are not accurate enough in logical reasoning and intelligent matching, and the generated responses are often difficult to meet user expectations; in addition, some systems lack a continuous optimization mechanism based on user feedback, which makes it difficult to continuously improve system performance.

[0024] In response to the above problems, this embodiment provides an information processing method based on enterprise digital transformation, which realizes efficient and accurate processing of user query input through automated and intelligent means. This method not only triggers the establishment of a session, but also combines the natural language model and the intelligent reasoning model, combines the knowledge base for logical reasoning and intelligent matching, generates and returns the response content, improves the quality and efficiency of enterprise services, reduces the cost of manual customer service, enhances user experience, and promotes the smooth progress of enterprise digital transformation.

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] See also Figure 1 The information processing method based on enterprise digital transformation in a specific embodiment is shown, including the following steps: S1. Obtain user query input related to enterprise digital transformation business issues and trigger session establishment; It should be noted that by obtaining user query input on issues related to the enterprise's digital transformation business and triggering the establishment of a session, the system can respond to user needs immediately, provide instant feedback and answers, and improve user experience and satisfaction; S2. Use the natural language model to process the text of the user query input in the session, then use the intelligent reasoning model to perform consciousness recognition and entity extraction, and combine the knowledge base to perform logical reasoning and intelligent matching to generate the answer content, and then return the answer content in the session; It should be noted that the natural language model and intelligent reasoning model are used to process the user's query input, identify consciousness, extract entities, and then combine with the knowledge base for logical reasoning and intelligent matching to generate response content, which can accurately understand the user's intention and provide relevant answers, thereby improving the intelligence level and accuracy of the system. S3. Obtain the user's evaluation results on the response content, optimize the natural language model and intelligent reasoning model based on the evaluation results, and count the user's query history to recommend digital transformation solutions for the user; It should be noted that text processing of user query input is performed through word segmentation, part-of-speech tagging and syntactic analysis to provide accurate basic data for subsequent logical reasoning and intelligent matching, thereby improving the processing efficiency and accuracy of the system.

[0027] This embodiment can accurately understand user queries, quickly generate response content, and continuously improve system performance and user satisfaction through functions such as dynamic management of knowledge base, personalized recommendations and learning optimization, thereby promoting the process of enterprise digital transformation and improving the competitiveness of the enterprise.

[0028] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, another information processing method based on enterprise digital transformation is provided, which includes: S1. Obtain user query input related to enterprise digital transformation business issues and trigger session establishment; S2. After using the natural language model to process the user query input in the session, the intelligent reasoning model is used to perform consciousness recognition and entity extraction, and logical reasoning and intelligent matching are combined with the knowledge base to generate the response content, and then the response content is returned in the session; the specific steps of step S2 are as follows: S21. The conversation calls the natural language model to perform word segmentation, part-of-speech tagging, and syntactic analysis on the user query input to complete text processing; S22. The conversation calls the intent classification model in the intelligent reasoning model to identify the user query intent of the processed text, and performs logical reasoning based on the user query intent, and then calls the knowledge base for intelligent matching to generate the response content; S23. The session returns the response content to the user; S24. The session identifies whether a new user query input is received within a set time period after returning the response content; If yes, return to step S21; If not, proceed to step S3; S3. Obtain the user's evaluation results on the response content, optimize the natural language model and intelligent reasoning model based on the evaluation results, and count the user's query history to recommend digital transformation solutions for the user; The specific steps of step S3 are as follows: S31. Obtain the user's evaluation results on the response content; When the evaluation result is that the terminology recognition of the enterprise digital transformation business is wrong, proceed to step S32; When the evaluation result is that the intent classification recognition of the user query input is wrong, the process proceeds to step S33; When the evaluation result is that the logical reasoning of the user query input is wrong, the process proceeds to step S34; S32. Add the misidentified enterprise digital transformation business terms to the training data set of the natural language model, retrain, and proceed to step S35; S33. Add the user query input with intent classification recognition error to the training data set of the intent classification model, retrain, and proceed to step S35; S34. Determine the correct reasoning logic of the user query input with logical reasoning errors, and use the correct reasoning logic to correct the intelligent reasoning model; S35. Count the conversation history of user queries, identify user behavioral characteristics and corporate attributes, and recommend relevant digital transformation solutions, products or services to users.

[0029] In an embodiment of the present invention, based on S22, a possible embodiment is given below to illustrate its specific implementation scheme in a non-limiting manner.

[0030] Step S22 includes the following steps: S221. Use the labeled user query data related to the digital transformation of the enterprise in advance to train the neural network model to obtain the intent classification model; It should be noted that first, a large amount of user query data related to the digital transformation of enterprises is collected, and these user query data are annotated to clarify the intention of each query, which includes the concept of digital transformation, seeking advice on specific transformation solutions, and consulting on problems that may be encountered during the transformation process; then, the annotated user query data is used as training to train the neural network model as the intent classification model, and the model performance is optimized by adjusting the learning rate of the cross-validation set during the training process; finally, after the training is completed, an independent test set is used to verify the accuracy of the intent classification model for evaluation to ensure that the consciousness classification model can accurately identify the user's query intention; S222. Build a rule base containing rules and logic related to enterprise digital transformation, use a rule-based logical reasoning model and perform logical reasoning based on the user's query intent and the rules in the rule base; Exemplarily, the rules related to digital transformation include the conditions for using different digital transformation solutions, key steps and precautions in the digital transformation process; It should be noted that the logical reasoning model can receive the user's query intent and the rules in the rule base for logical reasoning, and the context information of the user's query must be combined in the reasoning process. After the logical reasoning is completed, the reasoning result is also verified to ensure that it conforms to the actual situation and logic of the enterprise's digital transformation; S223. Use an intelligent matching algorithm based on semantic matching and perform precise matching based on the user's query intent, context, logical reasoning results, and knowledge in the knowledge base to generate the answer content that best meets the user's needs; It should be noted that during the execution of the intelligent matching algorithm, one or more response contents will be generated for the user to choose based on the semantic features of the user's query and the information in the knowledge base. The response content will not only accurately respond to the user's query intention, but also provide relevant background information and suggestions.

[0031] Taking a manufacturing company that is undergoing digital transformation as an example, its internal employees have generated a large number of query demands on various issues encountered in the digital transformation process, such as ERP system operation, data migration, cloud computing applications, etc. The company has deployed an information processing system based on the company's digital transformation.

[0032] The specific application process is as follows: User A of an enterprise enters a query through the enterprise's internal information processing system: "How can I correctly enter production data into the ERP system?" After receiving the query input, the system immediately triggers the establishment of a session and assigns a unique session ID to user A; The system uses the natural language model to segment the query of user A: "How to correctly enter production data in the ERP system?" Part-of-speech tagging and syntactic analysis were performed to identify key information such as “ERP system”, “input” and “production data”.

[0033] The system calls the intent classification model in the intelligent reasoning model and identifies that user A’s query intention is “ERP system operation consultation”.

[0034] According to the intent classification results, the system calls the ERP system operation-related rules in the rule base for logical reasoning.

[0035] Using an intelligent matching algorithm based on semantic matching, combined with the ERP system operation guide and FAQs in the knowledge base, the answer content is generated: "When entering production data in the ERP system, please ensure that the data is accurate and follow the steps below: 1. Log in to the ERP system; 2. Enter the production data management module; 3. Select the correct data entry template; 4. Enter the production data item by item; 5. Save and review." The system returns the generated response content to user A through the conversation interface.

[0036] After receiving the response, user A does not immediately enter a new query, and the session ends.

[0037] User A expressed satisfaction with the response returned by the system and gave a positive evaluation.

[0038] Since user A gave a positive review, the system did not identify any term recognition errors, intent classification recognition errors, or logical reasoning errors, so there is no need to optimize the natural language model and intelligent reasoning model.

[0039] The system counts the query history of user A and identifies that user A has a high degree of interest in ERP system operations. Based on user A's behavioral characteristics and corporate attributes, the system recommends a set of ERP system optimization solutions and related training courses for user A.

[0040] Finally, user A quickly solved the questions in the ERP system operation through the information processing system, improving work efficiency. The system continuously optimizes the model by collecting user feedback and query history records, improving the accuracy and efficiency of future query processing. By deploying the information processing system, the enterprise has reduced the cost of manual customer service and improved employee satisfaction and participation in the digital transformation process.

[0041] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0042] like Figure 2As shown, the following is an embodiment of the information processing system based on enterprise digital transformation provided by the embodiment of the present disclosure. The system and the information processing method based on enterprise digital transformation in the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the information processing system based on enterprise digital transformation, please refer to the embodiment of the information processing method based on enterprise digital transformation.

[0043] System, including: The user interaction module obtains the user's query input, converts it into a format recognizable by the system, and returns the generated response content to the user; The session management module establishes a session for the user's query input and calls the natural language processing module, the knowledge base management module and the intelligent reasoning model module in the session to generate the response content; The natural language processing module uses a natural language model to process text input from user queries; The knowledge base management module stores the knowledge base related to the enterprise's digital transformation and performs dynamic management; The intelligent reasoning model module uses the intelligent reasoning model to perform consciousness recognition and entity extraction based on the text processing results, and combines the knowledge base for logical reasoning and intelligent matching to generate response content.

[0044] This embodiment achieves efficient and accurate processing of user query input through the collaborative work of the user interaction module, session management module, natural language processing module, knowledge base management module and intelligent reasoning model module, which not only improves the quality and efficiency of enterprise services, but also reduces the cost of manual customer service, enhances user experience, and promotes the smooth progress of enterprise digital transformation.

[0045] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, another information processing system based on enterprise digital transformation is provided, which includes: The user interaction module obtains the user's query input, converts it into a format recognizable by the system, and returns the generated response content to the user; The session management module establishes a session for the user's query input, and calls the natural language processing module, the knowledge base management module, and the intelligent reasoning model module in the session to generate the response content; the session management module includes: A session establishing unit, responding to a user query input in the user interface to establish a session; The conversation management unit accesses the natural language processing module, the knowledge base management module and the intelligent reasoning model module in the conversation and calls and uses them according to the preset logic to generate the response content; A session closing unit, which closes the current session when no new user query input is received within a set time period after the response content is returned to the user; A session saving unit saves the completed session into the session history record and adds it to the database; The natural language processing module uses a natural language model to process text of user query input; the natural language processing module includes: A word segmentation unit performs word segmentation processing on the text in the query request input by the user to obtain a number of words; A part-of-speech tagging unit is used to tag the words obtained by word segmentation; The syntactic analysis unit performs syntactic structure analysis on the text in the query request input by the user and extracts the relationship between the sentence components; The knowledge base management module stores the knowledge base related to the enterprise's digital transformation and performs dynamic management; the knowledge base management module includes: The knowledge base construction unit obtains the knowledge base content related to answers to questions, product descriptions, and service processes related to the digital transformation of the enterprise input by users or administrators through a graphical interface or API interface, and saves the knowledge base content into the database; The knowledge base update unit regularly updates the knowledge base content based on user feedback, system logs or external data sources; The intelligent reasoning model module uses the intelligent reasoning model to perform consciousness recognition and entity extraction based on the text processing results, and combines the knowledge base for logical reasoning and intelligent matching to generate the response content; the intelligent reasoning model module includes: The intent classification unit recognizes the user query intent of the processed text through the pre-trained intent classification model; Logical reasoning unit, which performs logical reasoning based on the user's query intention; Intelligent matching unit, which matches the logical reasoning results with the knowledge base to generate the response content; It should be noted that the continuity of the session and the management of historical records are achieved through the units of session establishment, management, closing and saving; the deep text processing of user query input is achieved through the units of word segmentation, part-of-speech tagging, syntactic analysis; the dynamic management of the knowledge base is achieved through the units of knowledge base construction and update; and the accurate response to user query input is achieved through the units of intent classification, logical reasoning and intelligent matching. This detailed design improves the stability and reliability of the system and further enhances the user experience; Personalized recommendation module, which counts the conversation history of user queries, identifies user behavior characteristics and enterprise attributes, and recommends relevant digital transformation solutions, products or services to users; The learning optimization module uses machine learning algorithms to regularly optimize the natural language processing model and intelligent reasoning model based on the response content in the conversation; It should be noted that by counting the conversation history of user queries, identifying user behavioral characteristics and corporate attributes, and recommending relevant digital transformation plans, products or services to users; at the same time, through machine learning algorithms, the natural language processing model and intelligent reasoning model are regularly optimized to achieve system self-learning and continuous improvement, thereby improving the system's intelligence level and user experience.

[0046] like Figure 3 As shown, this application also provides the above-mentioned information processing system design method based on enterprise digital transformation users, and the method steps are as follows: SS1. Use Vue front-end framework to build web user interface; SS2. Build a backend service platform using the Flask backend framework; SS3. Set up a natural language model interface on the backend service platform to access the natural language model trained with terms related to enterprise digital transformation; SS4. Set up an intelligent reasoning model interface on the backend service platform and access the pre-trained intelligent reasoning model; SS5. Set up a knowledge base interface on the back-end service platform to access the knowledge base involving answers to questions, product descriptions, and service processes related to the digital transformation of the enterprise.

[0047] This embodiment achieves the rapid construction and efficient operation of the information processing system through the construction of front-end and back-end frameworks, as well as the access of natural language models, intelligent reasoning models and knowledge base interfaces. It not only reduces the cost and cycle of system development, but also improves the scalability and maintainability of the system, providing strong technical support for the digital transformation of enterprises.

[0048] The information processing method based on enterprise digital transformation provided in the embodiment of the present application can be applied to electronic devices. It can be understood by those skilled in the art that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown, or combine certain components, or arrange different components. In an embodiment of the present invention, electronic devices include but are not limited to laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0049] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, buttons, a camera, a display, and a SIM card interface, etc.

[0050] It is to be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0051] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated into one or more processors.

[0052] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0053] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0054] The above-mentioned electronic device implements the information processing method based on the digital transformation of the enterprise in this application through automation and intelligent means, and realizes efficient and accurate processing of user query input. It not only triggers the establishment of a session, but also combines the natural language model and the intelligent reasoning model, combines the knowledge base for logical reasoning and intelligent matching, generates and returns response content, improves the quality and efficiency of enterprise services, reduces the cost of manual customer service, enhances user experience, and promotes the smooth progress of the digital transformation of the enterprise.

[0055] The storage medium provided in the present application stores a program product that can implement an information processing method based on enterprise digital transformation.

[0056] The information processing method based on enterprise digital transformation includes: obtaining user query input on business-related issues of enterprise digital transformation to trigger session establishment; using a natural language model to perform text processing on the user query input in the session, and then using an intelligent reasoning model to perform consciousness recognition and entity extraction, and combining the knowledge base for logical reasoning and intelligent matching to generate response content, and then returning the response content in the session; obtaining the user's evaluation results on the response content, and optimizing the natural language model and intelligent reasoning model based on the evaluation results, and counting the user's query history to recommend digital transformation solutions to the user.

[0057] In some possible implementations, the information processing method based on enterprise digital transformation disclosed herein can be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of various exemplary implementations of the present disclosure described in the above "Exemplary Method" section of this specification.

[0058] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0059] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An information processing method based on enterprise digital transformation, characterized in that: The steps include: S1. Obtain user query input related to enterprise digital transformation business issues and trigger session establishment; S2. Use the natural language model to process the text of the user query input in the session, then use the intelligent reasoning model to perform consciousness recognition and entity extraction, and combine the knowledge base to perform logical reasoning and intelligent matching to generate the answer content, and then return the answer content in the session; S3. Obtain the user's evaluation results on the response content, optimize the natural language model and intelligent reasoning model based on the evaluation results, and count the user's query history to recommend digital transformation solutions to the user.

2. The information processing method for enterprise digital transformation according to claim 1, characterized in that: The specific steps of step S2 are as follows: S21. The conversation calls the natural language model to perform word segmentation, part-of-speech tagging, and syntactic analysis on the user query input to complete text processing; S22. The conversation calls the intent classification model in the intelligent reasoning model to identify the user's query intent on the processed text, and performs logical reasoning based on the user's query intent, and then calls the knowledge base for intelligent matching to generate the response content; S23. The session returns the response content to the user; S24. The session identifies whether a new user query input is received within a set time period after returning the response content; If yes, return to step S21; If not, go to step S3.

3. The information processing method for enterprise digital transformation according to claim 2, characterized in that: Step S22 includes the following steps: S221. Use the labeled user query data related to the digital transformation of the enterprise in advance to train the neural network model to obtain the intent classification model; S222. Build a rule base containing rules and logic related to enterprise digital transformation, use a rule-based logical reasoning model and perform logical reasoning based on the user's query intent and the rules in the rule base; S223. Use an intelligent matching algorithm based on semantic matching and perform precise matching based on the user's query intent, context, logical reasoning results, and knowledge in the knowledge base to generate response content that best meets user needs.

4. The information processing method for enterprise digital transformation according to claim 2, characterized in that: The specific steps of step S3 are as follows: S31. Obtain the user's evaluation results on the response content; When the evaluation result is that the terminology recognition of the enterprise digital transformation business is wrong, proceed to step S32; When the evaluation result is that the intent classification recognition of the user query input is wrong, the process proceeds to step S33; When the evaluation result is that the logical reasoning of the user query input is wrong, the process proceeds to step S34; S32. Add the misidentified enterprise digital transformation business terms to the training data set of the natural language model, retrain, and proceed to step S35; S33. Add the user query input with intent classification recognition error to the training data set of the intent classification model, retrain, and proceed to step S35; S34. Determine the correct reasoning logic of the user query input with logical reasoning errors, and use the correct reasoning logic to correct the intelligent reasoning model; S35. Count the conversation history of user queries, identify user behavioral characteristics and corporate attributes, and recommend relevant digital transformation solutions, products or services to users.

5. An information processing system based on enterprise digital transformation, characterized in that: include: The user interaction module obtains the user's query input, converts it into a format recognizable by the system, and returns the generated response content to the user; The session management module establishes a session for the user's query input and calls the natural language processing module, the knowledge base management module and the intelligent reasoning model module in the session to generate the response content; The natural language processing module uses a natural language model to process text input from user queries; The knowledge base management module stores the knowledge base related to the enterprise's digital transformation and performs dynamic management; The intelligent reasoning model module uses the intelligent reasoning model to perform consciousness recognition and entity extraction based on the text processing results, and combines the knowledge base for logical reasoning and intelligent matching to generate response content.

6. The information processing system for enterprise digital transformation according to claim 5, characterized in that: The session management module includes: A session establishing unit, responding to a user query input in the user interface to establish a session; The conversation management unit accesses the natural language processing module, the knowledge base management module and the intelligent reasoning model module in the conversation and calls and uses them according to the preset logic to generate the response content; A session closing unit, which closes the current session when no new user query input is received within a set time period after the response content is returned to the user; A session saving unit saves the completed session into the session history record and adds it to the database; The natural language processing module includes: A word segmentation unit performs word segmentation processing on the text in the query request input by the user to obtain a number of words; A part-of-speech tagging unit is used to tag the words obtained by word segmentation; The syntactic analysis unit performs syntactic structure analysis on the text in the query request input by the user and extracts the relationship between the sentence components; The knowledge base management module includes: The knowledge base construction unit obtains the knowledge base content related to answers to questions, product descriptions, and service processes related to the digital transformation of the enterprise input by users or administrators through a graphical interface or API interface, and saves the knowledge base content into the database; The knowledge base update unit regularly updates the knowledge base content based on user feedback, system logs or external data sources; The intelligent reasoning model module includes: The intent classification unit recognizes the user query intent of the processed text through the pre-trained intent classification model; Logical reasoning unit, which performs logical reasoning based on the user's query intention; The intelligent matching unit matches the logical reasoning results with the knowledge base to generate response content.

7. The information processing system for enterprise digital transformation according to claim 5, characterized in that: Also includes: Personalized recommendation module, which counts the conversation history of user queries, identifies user behavior characteristics and enterprise attributes, and recommends relevant digital transformation solutions, products or services to users; The learning optimization module uses machine learning algorithms to regularly optimize the natural language processing model and intelligent reasoning model based on the response content in the conversation.

8. A method for designing an information processing system based on enterprise digital transformation users, characterized in that: The steps include: SS1. Use Vue front-end framework to build web user interface; SS2. Build a backend service platform using the Flask backend framework; SS3. Set up a natural language model interface on the backend service platform to access the natural language model trained with terms related to enterprise digital transformation; SS4. Set up an intelligent reasoning model interface on the backend service platform and access the pre-trained intelligent reasoning model; SS5. Set up a knowledge base interface on the back-end service platform to access the knowledge base involving answers to questions, product descriptions, and service processes related to the digital transformation of the enterprise.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the information processing method based on enterprise digital transformation as described in any one of claims 1 to 4 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the information processing method based on enterprise digital transformation as described in any one of claims 1 to 4 are implemented.

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