10 results about "Professional degree" patented technology

The invention discloses an intelligent error correction and style optimization system and method for business English writing, and relates to the technical field of natural language processing and artificial intelligence, and the system comprises the following components: a user interaction module, a data storage module, a language ability evaluation module and a self-adaptive error correction feedback and style optimization module. Through a multi-dimensional evaluation algorithm based on dynamic weight adjustment, historical writing data of a user is deeply analyzed, an evaluation system including four dimensions of grammar errors, vocabulary application, sentence structures and business profession is constructed, the system comprehensively evaluates the language ability of the user, and the user experience is improved. And the evaluation weight is dynamically adjusted according to the capability improvement rates of the users in different dimensions, so that the language capability levels of the users are more accurately divided, and the system can provide personalized error correction feedback and style optimization suggestions for the users with different language levels based on the evaluation result.

The invention relates to the technical field of text generation, in particular to a text generation method and system based on natural language processing. Comprising the following steps: S1, collecting corpus data, preprocessing the collected corpus data, and then performing meaning label analysis on the corpus data; s2, acquiring real-time cue words and historical cue words output by a user, and performing demand language analysis on the real-time cue words and the historical cue words by using a natural language processing technology; the method comprises the following steps: setting a professional degree benchmark, determining a professional degree evaluation dimension according to domain knowledge, carrying out professional degree scoring on candidate texts, analyzing real-time and historical demand languages of a user, determining the professional level of the user, and predicting the professional degree required by the current demand by referring to related historical output texts. The dynamic adjustment mechanism can ensure that the generated text is not too hard and obscure and not too light and brief, and the applicability and practicability of the text are greatly improved.

The embodiment of the invention discloses a recommendation model training method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multiple groups of training data, each group of training data comprises historical behavior data of a first user and preset user features, the preset user features are user features of a second user, and the second user is a user feature of the first user; the historical behavior data of the first user comprises positive feedback behavior data of the user behavior data of the second user; according to the historical behavior data, determining a fused user feature, a professional degree value and an interactive behavior dynamic graph of the first user; based on the interactive behavior dynamic graph, processing the fused user features through a graph convolutional neural network to obtain convolutional user features; inputting the convolutional user features into an initial recommendation model, and outputting predicted user features; and training the initial recommendation model according to the predicted user features, the preset user features and the professional degree value until the initial recommendation model meets a training stop condition, and obtaining a trained target recommendation model.

The invention belongs to the technical field of artificial intelligence and law crossing, and particularly provides a law intelligent question-answering method and system based on multi-agent dynamic role scheduling and mixed retrieval, electronic equipment and a storage medium, and the method comprises the following steps: generating a structured task chain of a plurality of to-be-retrieved sub-tasks; generating a structured evidence set; constructing a multi-intelligent collaborative generation architecture; and generating a legal consultation reply based on the structured evidence set by adopting a multi-intelligent collaborative generation architecture. According to the method, role adapter scheduling logic is innovatively introduced, the system can firstly analyze whether a user request needs'right protection practical operation ', 'judgment prediction' or'theoretical learning 'like'law partners', and then the corresponding target adapter is dynamically activated. The dynamic scheduling mechanism endows the system with adaptive ability for different legal scenes, ensures that each answer is executed by the most suitable expert role, and significantly improves the professional degree of service.

The invention discloses an intelligent customer service method and system, and the method comprises the steps: carrying out the intention recognition and slot filling of user input, and obtaining structured intention information; executing dynamic intention management on the structured intention information to obtain a confirmation intention and knowledge enhancement information; executing the business process based on the confirmation intention, and combining the knowledge enhancement information in the execution process to obtain a business execution result; according to the method, the service logic is solidified through the preset service processing atlas in the execution engine, and then the illusion content is eradicated through accurate scheduling of the intelligent agent, so that the reply content is 100% accurate and reliable; business results, dialogue history and enhanced knowledge are fused, reply information is complete and deep, expert-level complete reply is provided, and user experience and professional degree are improved.

The invention belongs to the technical field of artificial intelligence, and relates to a script data processing method, which comprises the following steps: receiving theme and demand information input by a user; performing script generation processing on the theme and the demand information based on a preset large language model to obtain a corresponding initial script; performing content adjustment processing on the initial script based on a preset multi-agent to obtain a corresponding first script; performing content enhancement processing on the first script based on a preset compliance optimization strategy to obtain a corresponding second script; based on a preset target engine, performing content adaptation processing on the second script by using the demand information to obtain a corresponding target script; and outputting the target script. The invention further provides a script data processing device, computer equipment and a storage medium. In addition, the target script can be stored in the block chain. The method can be applied to insurance script generation scenes in the fields of financial science and technology and medical health insurance, and the professional degree, accuracy and compliance of script generation are improved.

The application provides a large language model training method, system and device, and relates to the technical field of electric digital processing, which comprises the following steps: dividing a domain text training set into several training sample sets; calculating the training attention degree corresponding to each training sample set according to the semantic change and frequency of word segmentation in each training sample set; performing maximum minimum normalization based on all training attention degrees to obtain the demand coefficient corresponding to each sample set; and performing multi-round fine-tuning training on a preset large language model through the LoRA method and all training sample sets to obtain a target large language model, wherein the rank in the multi-round fine-tuning training is adjusted by the demand coefficient. The application uses domain text and general text as a comparison. By analyzing the appearance frequency and semantic difference of word segmentation in the two kinds of texts, it is determined whether word segmentation is more used in a professional field, so as to determine the professional degree of the training sample, and learning by targeted allocation of computing resources can improve the accuracy of computing resource allocation.

The application belongs to the technical field of artificial intelligence and law, and specifically provides a legal intelligent question and answer method and system based on multi-agent dynamic role scheduling and mixed retrieval, an electronic device and a storage medium, the method comprising: generating a structured task chain of a plurality of to-be-retrieved sub-tasks; generating a structured evidence set; constructing a multi-intelligent collaborative generation architecture; and generating a legal consultation reply based on the structured evidence set by using the multi-intelligent collaborative generation architecture. The application innovatively introduces role adapter scheduling logic, and the system can analyze whether the user request is "right protection operation", "judgment prediction" or "theoretical learning" like a "law firm partner", and then dynamically activate the corresponding target adapter. This dynamic scheduling mechanism gives the system the ability to adapt to different legal scenarios, ensuring that each answer is executed by the most suitable "expert role", significantly improving the professional degree of service.

The invention discloses an innovation and entrepreneurship education recommendation system based on semantic analysis, and belongs to the technical field of semantic processing. The method comprises the following steps of: acquiring an entrepreneurship education background height, a professional height, a background height and a professional noun set of a to-be-entrepreneurship direction through an evaluation unit, and respectively calculating an education background integrating degree, a professional integrating degree and a background integrating degree in combination with user education background, professional information and family employment information; the related text is mapped into a vector by using a word embedding unit, and semantic analysis processing is performed by means of a multilayer LSTM neural network in combination with the height of each dimension and the integrating degree to obtain the fitness of the to-be-started direction. And finally, the recommendation unit selects the direction corresponding to the maximum fitness as the optimal entrepreneurial direction to be recommended to the user, so that the problem of low entrepreneurial direction recommendation accuracy in the prior art is effectively solved.

The application provides a medical information publishing method and system based on artificial intelligence. The method can input the Nth semantic information and the Nth determined plurality of information types into a medical information conversion model to determine the Nth medical information for medical staff and the Nth medical information for patients corresponding to the original medical information; perform quality assessment on the Nth medical information for medical staff according to the professional degree, the seriousness of the emotional tendency, and the accuracy of the Nth medical information for medical staff to obtain a first quality assessment value; perform quality assessment on the Nth medical information for patients according to the understandability, the friendliness of the emotional tendency, and the accuracy of the Nth medical information for patients to obtain a second quality assessment value; and determine a third quality assessment value according to the matching degree of the medical staff version sub-information of each information type and the patient version sub-information of the corresponding information type, so that the medical information published by the hospital has a high adaptation degree for medical staff and patients.