21results about How to "Guaranteed personalization" patented technology

The invention relates to the technical field of medical care, in particular to an intelligent query method for a nursing knowledge base, which comprises the following steps of: performing multi-form standardized processing on a nursing problem input by a user, deeply analyzing a query request by utilizing a semantic understanding model, and obtaining an intelligent query result based on an inference algorithm; the method comprises the following steps: automatically selecting related literatures, clinical data and nursing experience data from a nursing knowledge base, generating a preliminary nursing scheme candidate set in combination with a nursing professional knowledge graph, and performing personalized screening and optimization on the scheme by using an intelligent recommendation model in combination with the current illness state, treatment progress and historical nursing data of a patient; the intelligent recommendation model is self-optimized through user feedback, and the nursing scheme recommendation precision is continuously improved. The nursing knowledge base automatically fuses newest medical research results and clinical nursing practice data. The problems that in the prior art, nursing scheme recommendation accuracy is low, updating is not timely, and the individuation degree is insufficient are effectively solved.

The invention discloses an intelligent financial question and answer system based on a large language model, and relates to the technical field of financial question and answer, the system solves the problems of financial term ambiguity and context ambiguity through cooperative work of a plurality of modules, and the system comprises the following modules: a context recognition module, a query module and a query module, a natural language processing algorithm is combined with a context analysis technology in the financial field to automatically extract context clues in user questions; and a financial term knowledge base module. According to the method, context recognition and a financial term knowledge base are combined, and the accuracy of understanding the term ambiguity and the complex context is improved through the graph neural network. And the intelligent recommendation module dynamically provides personalized financial suggestions by using collaborative filtering and content recommendation algorithms in combination with user history and market data, and performs real-time adjustment. The risk assessment and prompt module assesses risks through a Bayesian network and a logistic regression model, and the adaptive learning module continuously optimizes risk prediction and helps users to avoid financial risks.

The invention discloses a digital human personalized shaping method and device based on user psychology and a medium. The method comprises the following steps: acquiring multi-modal data when a user interacts with a digital human; based on the multi-modal data, according to the style preference of the user, establishing a psychological feature model through an adaptive learning mode; dialogue voice of the digital person is generated according to the psychological feature model, and matched facial expressions and limb actions of the digital person are generated according to the dialogue voice and the psychological features of the user. According to the digital human personalized shaping method and device based on the psychology of the user and the medium, the psychological feature model is established based on the style preference of the user, and the interaction content matched with the conversation voice, the facial expression and the body movement is generated, so that the digital human can show more real and natural personalized behaviors, and the user experience is improved. The problem that it is difficult to carry out personalized behavior shaping on the digital human according to psychological characteristics and interaction preferences of the user can be solved.

The invention relates to the technical field of farm disinfection, in particular to an intelligent disinfection system for a farm. The system comprises an initial determination module, a periodic disinfection module, an environment monitoring module, a first analysis control module, a second analysis control module and a feedback correction module. According to the method, the initial disinfection parameter cluster of each breeding area in the target breeding farm is determined according to the actual growth stages of the breeding targets and the actual number corresponding to each actual growth stage, each breeding area is periodically disinfected according to the initial disinfection parameter cluster, and each breeding area is monitored in real time so as to be singly judged; and performing first-class adjustment and second-class adjustment on the initial disinfection parameter cluster based on a first verification result and a second verification result in combination with the first verification result and the second verification result to determine whether the initial disinfection parameter cluster is subjected to first-class adjustment and second-class adjustment. And according to the first analysis control result and the second analysis control result, determining whether to perform feedback correction on a preset initial disinfection period of each breeding area.

The invention provides a method for generating a self-supervised learning contract template based on a trade system platform, and aims to automatically generate and continuously optimize a personalized contract template by using a self-supervised learning technology and a Transform model. The method comprises the following steps: extracting unlabeled data through a historical contract data input module; utilizing a self-supervised learning module to identify the relevance of contract terms; modeling a long-term and short-term dependency relationship of the contract text through a multi-head self-attention mechanism of a Transform model; and generating a high-dimensional semantic embedding representation of the contract terms, and automatically generating a personalized contract template based on the high-dimensional semantic embedding representation. The system continuously optimizes a generation rule through historical data and real-time service feedback, and dynamically adjusts the priority and content sorting of contract terms. According to the method, the contract generation efficiency and accuracy are improved, the dependence on manual labeling is reduced, the method is suitable for the iron and steel industry and other large-scale contract management scenes, the contract template can be flexibly adjusted according to market changes and business requirements, and the individuation and adaptability of the contract are remarkably enhanced.

The invention discloses a micro course generation method based on OCR recognition and a server, and relates to the field of image data processing, and the method comprises the steps: collecting maintenance record document image data, and carrying out the optical character recognition processing of the maintenance record document image data, and obtaining digital text data; performing natural language processing on the digital text data according to a preset bidirectional encoder representation model, and integrating the terminology information, the fault code information and the operation step information into structured knowledge data; obtaining a knowledge level parameter and a post information parameter of the target user, and performing difficulty grading on the structured knowledge data according to the knowledge level parameter to obtain difficulty grading knowledge data; performing knowledge range screening in the difficulty grading knowledge data according to the post information parameters to obtain target knowledge data; and selecting teaching materials matched with the target knowledge data from the knowledge resource library, and generating a micro course resource bundle according to the teaching materials. By implementing the method, the updating efficiency of the training data can be improved.

The embodiment of the invention provides a children language behavior rehabilitation teaching system and computer equipment. The system is characterized in that a teacher end determines teaching courses in multiple fields according to a screening result to form an initial personalized rehabilitation teaching plan; the supervision terminal performs auditing and adjustment to form a final personalized rehabilitation teaching plan; the educational administration terminal forms course arrangement according to the final personalized rehabilitation teaching plan and the teaching resource condition; the teacher end allows the teacher to teach after determining that the teacher has prepared the teaching course; in the teaching process, the teaching content is displayed to the target child, and the teaching operation guidance content is displayed to any teacher; in the teaching process, the teacher end receives rehabilitation training process data in each teaching course, teaching summary reports of different time periods are formed, family generalization teaching content suitable for target children is determined, and the teaching summary reports and the family generalization teaching content are sent to the parent end; and the supervision end feeds back teaching suggestions to the teacher end in real time.

The embodiment of the invention provides an insurance pricing method and device based on strategy learning, electronic equipment and a medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring behavior data of a user, and acquiring target behavior data of the behavior data in a preset time period; determining an insurance premium initial pricing according to the data category of the target behavior data; performing feature extraction on the target behavior data through a multi-view learning model to obtain a feature vector; performing pricing prediction on the premium initial pricing and the feature vector through a strategy network; according to the pricing ratio, the premium variance and the premium initial pricing obtained by the pricing prediction, determining an estimated current pricing; inputting the estimated current pricing, the initial premium pricing and the historical feature vector of the previous time step of the current time step into a preset value network for pricing evaluation, and outputting an evaluated value; and adjusting and estimating the current price according to the evaluation value. According to the embodiment of the invention, the pricing can be dynamically adjusted according to the behavior data of the user.

The invention provides a substation terminal network equipment defect elimination checking method and system based on deep learning, and the method comprises the following steps: constructing an equipment configuration heterogeneous graph network by a substation client through combining an equipment configuration file, an equipment operation log and equipment network flow data, all the transformer substation clients utilize a heterogeneous graph network to train a local model, the federated learning server updates a shared learning model by combining local training parameters of all the clients, and the clients needing to be checked continue to update the model according to updating parameters of the server; and identifying a configuration mode of local network equipment through the updated model and obtaining a configuration parameter mapping table, generating an equipment checking strategy in combination with the configuration mode and the configuration parameter mapping table, and finishing checking work of all the network equipment through the equipment checking strategy. According to the invention, the transformer substation can accurately complete network equipment checking.

The invention discloses an internet online diagnosis and treatment data processing system based on a big data model, and relates to the technical field of medical treatment, an association rule analysis module analyzes association between a disease and drug treatment by adopting a weighted improvement degree, can accurately identify a relatively good matching relationship between the disease and a drug, and improves the diagnosis and treatment efficiency. The online recommendation module dynamically analyzes the recovery effect of the medicine in different time periods based on the dynamic conditional probability by utilizing the candidate medicine pair and combining the real-time treatment condition of the patient, and the mechanism not only ensures that the recommended medicine is highly matched with the current illness state of the patient, but also improves the accuracy of the medicine recommendation; and medicine selection can be adjusted according to feedback data of the patient. The system performs weighting processing on historical data and performs optimization in combination with data sparsity, so that the problem of inaccurate recommendation caused by data sparsity or data insufficiency in a traditional system is effectively avoided.

The invention discloses an adjustable stomach capsule treatment effect and body fat rate BMI intelligent management evaluation method, and relates to the technical field of stomach capsule treatment management, and the method comprises the following steps: setting an initial capacity adjustment amplitude and an initial capacity adjustment frequency of an adjustable stomach capsule according to basic health data of a patient; dynamic changes of gastric contents are monitored in real time through a built-in sensor of the gastric capsule, and monitoring data are sent to external equipment based on a wireless transmission technology. Through real-time data acquisition, feature quantitative analysis and intelligent prediction of a machine learning model, a sharp increment stage and a smooth increment stage can be distinguished in time, capacity adjustment amplitude and frequency are adaptively optimized through a fuzzy reasoning algorithm, a strategy of small-amplitude and high-frequency adjustment is adopted, and the capacity adjustment efficiency is improved. Not only is the discomfort of a patient possibly caused by sharp increase of stomach contents effectively relieved, but also the stability and individuation of the treatment process are ensured, and the safety, comfort and effect of treatment are improved.

The invention discloses an interactive simulation teaching method and system based on virtual reality, and relates to the field of simulation teaching, and the system comprises a medical teaching simulation platform which comprises a teaching information management module, a medical course teaching module, a virtual teaching construction module, a virtual interaction collection module and a virtual data processing module; the teaching information management module is used for acquiring medical education information and medical knowledge information; the medical course teaching module is used for submitting a medical theory teaching course by a teacher user account and carrying out course theory teaching and assessment; the virtual teaching construction module constructs a virtual interactive analysis model according to the medical theory teaching course, the medical education information and the medical knowledge information; the virtual interaction acquisition module is used for acquiring real-time image information and biomechanical information; the virtual data processing module is used for analyzing and processing the collected data according to a virtual interaction analysis model to obtain virtual feedback information; according to the invention, the interactivity in the teaching process is improved.

The invention relates to the technical field of multi-scene teaching and training AI agent application, and discloses a multi-scene teaching and training AI agent application method, system and device, a medium and a product. Through multi-source teaching and training demand data standardization processing, teaching and training scene adaptation model reasoning, business rule bidirectional verification, deployment optimization and whole-process iteration closed loop logic, target interaction request data output by a cue word engineering model serves as core input of a teaching and training adaptation model, and deep collaboration of the target interaction request data and the cue word engineering model is achieved; and formulating an optimization deployment scheme based on the target interaction result and the feedback data, and iterating the intelligent agent. According to the method, the problems of independence and disjunction of cue word engineering and model fine tuning, insufficient interaction continuity, insufficient data security, poor teaching business adaptability and the like in an existing intelligent agent are effectively solved, the accuracy and the real-time performance of teaching and training services are remarkably improved, the teaching management cost is reduced, and meanwhile, the service effect is continuously optimized through closed-loop iteration.

The utility model provides a shoulder arthroscopy positioning device, which belongs to the technical field of positioning devices and comprises a base, a support arranged at the top of the base, a first connecting hole formed in the support, a knob arranged in the first connecting hole, one end of a lifting rope arranged in the knob, and a rotating part arranged at the other end of the lifting rope. An upper clamping plate is arranged at the bottom of the rotating part, a lower clamping plate is connected to one side of the upper clamping plate through two hinges, a sliding groove is formed in the upper clamping plate, two sliding blocks are arranged in the sliding groove, and a fixing block is arranged at one end of the sliding groove; through the arrangement of the knob, the length of the lifting rope can be adjusted according to the shoulder anatomical structure of a patient, extra tools are not needed to meet the requirements of different patients, through the arrangement of the sliding groove and the sliding block, the positions of the puncture needle and the arthroscope can be fixed, and the position of the arthroscope is adjusted through the universal ball in the sliding block; and hand fatigue caused by long-time operation by holding the puncture needle and the arthroscope is avoided.

This invention relates to a gynecological postoperative rehabilitation guidance system based on 5G and XR, specifically in the field of gynecological postoperative rehabilitation. Through multimodal data fusion and intelligent algorithms, it achieves non-invasive and precise quantitative assessment of pelvic floor muscle contraction quality, and transforms the assessment results into immersive and dynamic visual feedback attached to a virtual coach in real time. This significantly improves the standardization of movements and the intuitiveness of guidance in home rehabilitation training. At the same time, based on the user's historical status and reinforcement learning, it adaptively optimizes subsequent training parameters, forming a complete intelligent closed loop of assessment, feedback, and optimization. This effectively improves user training compliance and ensures the safety, personalization, and efficiency of the rehabilitation process.

The present disclosure relates to a method for downloading an application resource package and an electronic device. The method applied to a content distribution server comprises: in response to an initial download request initiated by a first application client, returning a total byte number of the application resource package to the first application client; in response to a fragment download request sent by the first application client according to the total byte number, detecting whether a first byte interval requested by the fragment download request hits a second byte interval; in the case of a hit, pulling first fragment data from a source station server and returning the first fragment data to the first application client, so that the first application client obtains a target application resource package containing a current tracking identifier according to the first fragment data, and the first application server establishes a first mapping relationship between a first client identifier and a second client identifier according to the current tracking identifier; wherein the first fragment data is obtained by rewriting data corresponding to the first byte interval of the application resource package according to the current tracking identifier by the source station server.

The application provides a smart meridian therapy instrument, compared with the prior art, the smart meridian therapy instrument comprises an acupuncture module which is in contact with the skin of a patient to perform acupuncture, a data storage module which pre-stores relevant operation information of the acupuncture module, and an instruction generation module which drives the acupuncture module to automatically perform acupuncture treatment on the patient. The smart meridian therapy instrument of the application provides more efficient, safe and comfortable acupuncture treatment experience for the patient through its comprehensive, accurate and automatic design.

The invention belongs to the technical field of intelligent equipment, and discloses a menu generation method and system based on a deep learning model, existing menus in an existing menu set are preprocessed, and the preprocessed existing menus are classified by constructing a menu database, so that the simplification of cooking steps and the reliability of data can be guaranteed; then, the menu database can be subjected to expansion processing, data diversity and individuation of the menu database are guaranteed by generating a new menu, and richer data support can be provided for the deep learning model when the deep learning model is trained in combination with the menu database; then, food material data and seasoning data can be identified from the user information, the food material data and the seasoning data are input into the deep learning model to obtain a more accurate and personalized target menu, and the food material data and the seasoning data can be utilized to guarantee the rationality of each step in the target menu. And the personalized cooking requirement of the user is met.

The application provides a psychological support answer automatic generation method based on LLMs iterative correction, first constructs a seeker character image prompt, then carries out resource library retrieval according to the seeker character image prompt, and obtains a plurality of answers; uses LLMs to generate a corresponding strategy chain for each answer, and guides LLMs to select an optimal strategy chain therefrom; then generates an initial answer based on the optimal strategy chain; iteratively guides LLMs to discover the 'deficiency' information of the initial answer, and finally constructs a correction suggestion based on the 'deficiency' information of the initial answer to guide LLMs to generate a final answer. The application is based on retrieval enhancement, combines the psychological support strategy theory and the internal function of LLMs self-exploration and correction, constructs a dynamic and cooperative triple prompt framework, and guides LLMs to generate a high-quality answer with higher relevance and individualization.

The invention provides an auxiliary diagnosis and treatment method and system for sarcopenia, and relates to the field of medical data processing, and the method comprises the steps: determining a plurality of target sarcopenia diagnosis motions of a to-be-diagnosed patient based on the basic information of the to-be-diagnosed patient and the basic information, motion test information and sarcopenia states of a plurality of sample sarcopenia patients; according to the limb skeletal muscle contents of the multiple sample sarcopenia patients, acquiring the limb skeletal muscle contents of the to-be-diagnosed patient; determining the sarcopenia state of the patient to be diagnosed according to the basal metabolic rate, the grip strength and the skeletal muscle content of the limbs of the patient to be diagnosed and the test data of each target sarcopenia diagnosis movement; and according to the disease and movement association map, the basal metabolic rate and nutrition association map, the psychological intervention knowledge map, and the basic information, diet and movement habits, sarcopenia state and psychological state test data of the to-be-diagnosed patient, a suggested auxiliary treatment scheme of the to-be-diagnosed patient is determined, and the intelligent auxiliary diagnosis and treatment method has the advantage of realizing intelligent auxiliary diagnosis and treatment of sarcopenia.

The present invention discloses a method and device for constructing a dynamic personalized network for federated learning, the method comprising the following steps: creating a neural network model, initializing the neural network model and sending it to a client; dynamically calculating the client's samples according to an early exit strategy, and completing the update of the early exit layer; selecting an early exit strategy execution layer, and selectively sending the parameters in the selected early exit layer; after receiving the nodes and parameters uploaded by the client, the server completes the aggregation of the early exit layer node weights according to the aggregation strategy; receiving the parameters after aggregation from the server, completing the update operation of the model parameters in the client, and completing the construction of a personalized network with the help of the update of the early exit layer. The dynamic personalized network construction method of the present invention improves the reasoning efficiency of the model and the attention of difficult samples, and reduces the cost of federated learning in sample calculation and model reasoning while ensuring the personalization of the client model.