18results about How to "Accurate service" patented technology

The invention discloses a cultural heritage digital display platform, which is mainly applied to the fields of museums, cultural heritage protection institutions, education institutions and the like, and specifically comprises a central control module, and a user interaction module, a multimedia display module, a virtual reality rendering module, a database management module and an intelligent recommendation module which are connected with the central control module. The cultural heritage digital display platform has remarkable beneficial effects in the aspects of improving cultural heritage protection and inheritance efficiency, enhancing user experience and interactivity, realizing personalized recommendation and accurate service, promoting cultural education and propagation, optimizing data management and analysis capability and the like, and is beneficial to protection and inheritance of the cultural heritage; and the cultural literacy and aesthetic level of the public can be improved, and a positive contribution is made to the development of cultural heritage.

The invention relates to the technical field of intelligent voice interaction, discloses an intelligent voice interaction method and system for an osteoporosis therapeutic instrument, and aims to improve the convenience of equipment and the user experience. According to the method, user voice interaction data is acquired, a user intention is identified by using an intention identification model, and a therapeutic apparatus response strategy is generated based on a dialogue management model constructed by a deep reinforcement learning A3C algorithm. The system converts the response strategy into voice output, and provides efficient and intelligent interaction experience for the user. The dialogue management model is trained by adopting a dialogue data set containing multiple rounds of dialogue scenes, so that the intention of the user is accurately understood and proper response is given. Through intelligent voice interaction, the user can easily know information such as the treatment principle, the operation process and the treatment effect, and the usability of the equipment and the user satisfaction degree are remarkably improved. According to the invention, the operation process of the osteoporosis therapeutic instrument is simplified, and the intelligent level of voice interaction is improved through the deep reinforcement learning technology.

The invention provides an unmanned special vehicle operation-oriented scene updating method and a storage medium, the unmanned special vehicle operation-oriented scene updating method comprises a cloud end, a vehicle end and a vehicle cloud intermediate service library, the vehicle cloud intermediate service library comprises middleware arranged at a cloud service end and middleware arranged at a vehicle-mounted client, and the middleware of the cloud service end comprises a cloud end-scene service scheduling component; the cloud-scene service scheduling component comprises cloud-scene analysis and a cloud local service library; the middleware of the vehicle-mounted client comprises a scene service scheduling assembly, and the scene service scheduling assembly comprises scene analysis, vehicle cloud service scheduling and a vehicle cloud local service library; the middleware of the cloud server side and the middleware of the vehicle-mounted client side are both provided with vehicle cloud interconnection assemblies for vehicle cloud communication. The cloud end further comprises a cloud end micro-service, and the cloud end micro-service configures a scene API and other components. According to the invention, the vehicle cloud middleware is taken as a core, creation, updating, synchronization and issuing of the scene mode file are realized, and accurate scene operation is supported.

The invention provides an RTK unmanned aerial vehicle laser radar-based exploration beacon application system, and the system comprises an RTK unmanned aerial vehicle platform which is used for carrying laser radar equipment and carrying out the high-precision positioning; the laser radar surveying and mapping module is installed on the RTK unmanned aerial vehicle platform and used for emitting laser pulses and receiving reflection signals so as to obtain space information of a ground surface target; the data processing module is used for receiving the data acquired by the laser radar surveying and mapping module, processing and analyzing the data, and generating a high-precision map and information required by a survey beacon; and the reconnaissance beacon module is used for automatically or semi-automatically setting a landmark at a specified position according to the information generated by the data processing module. According to the invention, the advanced unmanned aerial vehicle flight platform, high-precision laser radar surveying and mapping, intelligent data processing and automatic exploration beacon functions are integrated, and efficient and accurate services are provided for various scenes requiring accurate boundary delimiting and identification setting.

The invention provides a semantic intention recognition method and device fusing context information, a medium and equipment, and the method comprises the steps: obtaining a text input by a user, and carrying out the extraction and integration of the context information of each vocabulary and sentence in the text input by the user, so as to construct a context model; according to the context model, obtaining an incidence relation between vocabularies in the text input by the user, a logic relation between sentences and an overall text theme and focus; and obtaining a semantic intention recognition model, recognizing the semantic intention of the text input by the user according to the association relationship between the semantic intention recognition model and the vocabularies, the logic relationship between the sentences and the theme and focus of the text input by the user, and outputting a semantic intention recognition result. Through semantic intention recognition fused with context information, the system can more intelligently understand instructions and requirements of users, so that more personalized services and support are provided.

The present invention relates to a kit and method for simultaneously detecting IL-6, IL-21 and T cell CD4 surface molecules in lymphoid tissue, belonging to the field of biomedicine; the kit comprises quantum dots for labeling IL-6 antibodies, quantum dots for labeling IL-21 antibodies, and quantum dots for labeling CD4 antibodies; the antibodies labeled by quantum dots specifically bind to antigens in lymphoid tissue respectively; CD4T cells have the ability to secrete IL-6 and IL-21, and the relationship between them and IL-6 and IL-21 reflects the tissue immune activity. The present invention provides a kit for simultaneously detecting IL-6, IL-21 and T cell surface molecules in lymphoid tissue, locating the spatial relationship between two cytokines and T cells, calculating and analyzing the corresponding fluorescence intensity, and evaluating the immune situation; the kit has the advantages of high fluorescence intensity, accurate detection results, wide spectrum selection, wide applicability, stable fluorescence structure, long storage time, high sensitivity, good specificity, etc.

A computer-implemented method for determining one or more application-specific control parameters of a pump operating in a system for moving a fluid, the method comprising: monitoring one or more operational variables of the pump during operation of the pump in said system; applying a trained machine-learning model to automatically determine an application type from the monitored operational variables, the application type representing a type of system the pump operates in and / or representing a type of operation performed by the pump when operating in said system, each application type being associated with a respective set of one or more application-specific control parameters; controlling the pump based on the set of one or more application-specific control parameters associated with the determined application type.

The invention discloses a computer intelligent service management system, method and device and a storage medium, and the method comprises the following steps: obtaining related information of computer service used by a user, and storing the related information in a system database; analyzing input data and use behavior data of the user, and generating decision support information based on an analysis result; user portraits are completed by means of big data mining, user service demand features are clarified, and personalized service recommendation is provided; personalized service recommendation effects are monitored, user evaluation is collected, summarized and arranged, and a data analysis mode and a service strategy are optimized according to evaluation service quality and improvement effects; user precise service effects are collected, operation is repeated, a management method is optimized, and precise service management is achieved. According to the method, the use data of different services are processed in a centralized manner, the use behaviors, input data and preferences of the user are analyzed, a final analysis result of the heterogeneous information network relation graph is constructed by analyzing and mining the data, and accurate personalized recommendation is provided for the user.

The invention provides a domestic artificial intelligence computing power adaptation test service platform, which belongs to the technical field of data management, and comprises a user login module used for carrying out identity authentication and entering a platform main interface after the authentication is passed; a plurality of function options for a user to select are provided in the main interface, and each function option is used for executing preset logic when being selected so as to obtain an execution result and display the execution result; the question and answer module is used for receiving questions related to chips or manufacturers and proposed by users, converting the questions into question embedding vectors, generating cue words based on the question embedding vectors and embedding vectors matched with the question embedding vectors and retrieved from a preset data knowledge base, and sending the cue words to a server; and inputting the cue word and the question embedding vector into the large language model so as to feed back and obtain a question answer. The system has the beneficial effects that through integrated information management, comprehensive and accurate domestic chip information service is provided for enterprises, and the information retrieval efficiency is improved; and the use experience is improved through interactive questions and answers.

The invention relates to the technical field of automatic control, and discloses an intelligent telescoping method and system based on a vehicle-mounted cup holder, and the method comprises the steps: defining an induction region of the vehicle-mounted cup holder, analyzing the structural characteristics of the vehicle-mounted cup holder, and building an induction sensor array of the vehicle-mounted cup holder; the induction area and the induction instant coefficient of the induction sensor array are calculated, the induction sensor array is optimized, and an optimized induction sensor array is obtained; performing signal filtering on the sensing signal to obtain a filtered sensing signal, identifying a cup state of a cup to be placed, calculating a cup holder expansion coefficient of the vehicle-mounted cup holder, calculating a clamping force of the cup to be placed, and analyzing a clamp expansion coefficient of a clamp corresponding to the vehicle-mounted cup holder; the pouring value of the cup to be placed is analyzed, the rotation angle of the vehicle-mounted cup holder is analyzed through a cup holder inclination algorithm, driving parameters of a driving motor corresponding to the vehicle-mounted cup holder system are defined, and intelligent stretching and retracting of the vehicle-mounted cup holder are executed based on the driving parameters. By means of the vehicle-mounted cup holder, the experience feeling of the vehicle-mounted cup holder in the driving process can be improved.

The present invention discloses a method, medium and program for calculating enterprise portraits based on big data and multidimensional features. The method includes the following steps: obtaining enterprise data, and performing clustering operations on the enterprise data to generate enterprise labels; using a label scoring model to score the enterprise labels corresponding to each enterprise, and generating a scoring result; based on the scoring result, obtaining the weight of each enterprise label; constructing a first enterprise portrait based on the enterprise labels and the corresponding weights; obtaining the influence factor of each enterprise label, and scoring the influence factor through an influence factor scoring model to obtain the value of the influence factor; constructing a second enterprise portrait based on the influence factor of each enterprise label and the corresponding value; combining the first enterprise portrait with the second enterprise portrait to generate a third enterprise portrait. The present invention improves the comprehensiveness and accuracy of enterprise portrait depiction, and facilitates the enterprise service industry to provide more accurate services to enterprises.

This invention relates to the field of vehicle networking technology and discloses a vehicle networking image recognition and detection method, device, and medium. The method includes: acquiring real-time image data of the vehicle's surrounding environment; obtaining target recognition results from the real-time image data based on the image recognition model of the vehicle's fleet; classifying and storing all real-time image data into positive and negative sample sets in a dynamic sample database based on the confidence level of the target recognition results; when the amount of data belonging to a fleet's positive or negative sample set in the dynamic sample database reaches a trigger threshold, retraining and optimizing the fleet's image recognition model using the corresponding fleet's positive and negative sample sets; using the optimized image recognition model as the new image recognition model for the corresponding fleet, repeating the above steps to continuously optimize the image recognition model of each fleet, and performing target recognition on the vehicle's real-time image data using the continuously optimized image recognition model of the vehicle's fleet.

The invention discloses a motor train unit video collaborative caching method based on multi-agent reinforcement learning. The method comprises the following steps: constructing a motor train unit multi-compartment collaborative caching video service system; defining a request response time cost according to a user request cache hit condition; defining a resource replacement time cost according to the difference of cached video contents in adjacent time periods; the problem of minimizing the video service time cost of the motor train unit is formalized; and calling a multi-carriage collaborative caching algorithm based on multi-agent reinforcement learning, and determining a video content caching strategy of each time period. According to the invention, the video content is cached on the motor train unit carriages, the difference of different carriages and the problem of linear networking are considered, the multi-carriage collaborative caching system and method are designed, the acquisition of the video content by the user is accelerated, and the utilization rate of the caching space is improved.

The present application provides a kind of multi-source data fusion sea fog detection method, comprising the following steps: comprising the following steps: S1: obtaining the multi-source data for data fusion;S2: the quality control, spatial interpolation and deviation correction processing to the multi-source data obtained in S1, obtain sea fog range data;S3: check sea fog range data, identify the consistency of sea fog range data, and correct sea fog range data;S4: calculate the sea fog detection rate of sea fog range data, with sea fog detection rate as fusion weight, generate sea fog range fusion data;S5: using the data after S4 processing, divide fog grade for sea fog range fusion data, generate specified spatiotemporal resolution multi-source sea fog fusion live data.The present application has beneficial effects: solve the deficiencies that the existing sea fog detection technology cannot be considered in space coverage, data accuracy and multi-layer cloud fog condition applicability, etc., provide high-quality live data for the monitoring and early warning of sea fog, forecast verification, marine disaster prevention and mitigation.

The invention discloses an intelligent fuel gas quality inspection method and device based on vector matching and a dynamic rule engine. The method comprises the steps that data preprocessing is conducted through audio and text cleaning; setting a vectorization and semantic understanding layer for converting the preprocessed text into a high latitude vector; setting a configurable quality inspection rule engine which is used for realizing four layers of configurable quality inspection rule engines of quality inspection operators, quality inspection conditions, quality inspection rules and quality inspection tasks; and a decision and feedback layer is arranged and used for correcting error data in a manual rechecking mode. Vector matching is carried out based on an M3E-Base text embedding model, and quality inspection operators, conditions, tasks and rules which can be dynamically configured are combined. By fusing an advanced semantic understanding technology and a configurable quality inspection rule engine, gas customer service quality management is promoted to evolve towards automation, intellectualization and precision, and powerful technical support is provided for safe operation and excellent service of gas enterprises.

This invention relates to the field of materials determination technology, and discloses a method for detecting the titanium content in a titanium-containing reagent and a method for detecting the titanium content in a polyolefin catalyst. The method includes the following steps: (1) under an inert gas protective environment, the titanium-containing reagent is acid-hydrolyzed and reduced to obtain a trivalent titanium ion sample solution; (2) under an inert gas protective environment, ferric ammonium sulfate solution is added dropwise to the sample solution, and the potential is tested using a redox electrode during the addition process. The titration endpoint is determined based on the redox electrode potential threshold, and the titanium content is calculated based on the amount of ferric ammonium sulfate solution added at the titration endpoint. This detection method is green and environmentally friendly, and improves the accuracy of the detection results while reducing the detection cost.

This invention discloses a few-sample intelligent X-ray defect detection method based on a diffusion-generative model, belonging to the fields of industrial non-destructive testing and artificial intelligence. The method first acquires and preprocesses an X-ray image dataset, constructing a defect condition embedding that preserves physical properties. It then builds a conditional diffusion-generative model optimized by incorporating rectified flow reparameterization and a consistency model distillation mechanism, training it in stages to generate high-fidelity defect samples. A multi-scale Transformer detection network is constructed, and through mixed-sample training, dynamic loss optimization, curriculum learning, and feature consistency constraints, intermediate features from the detection network are fed back to the generative model to adjust the defect condition weights, achieving joint training of the generative model and the detection network. Finally, the input image to be detected completes defect localization and classification. This invention can generate multiple types of realistic defect samples, eliminating sample imbalance and domain bias, and achieving both high detection accuracy and real-time performance under few-sample conditions.

The present application provides a label classification model training method, label classification method, device and equipment. The label classification model training method includes: obtaining an unlabeled data set and a labeled data set, constructing a Gaussian graph based on the unlabeled data set and the labeled data set; using the labeled data set to train an initial label classification model; determining a label propagation algorithm between the labeled data set and the unlabeled data set according to the Gaussian graph; based on the label propagation algorithm, converting the unlabeled data that can be converted in the unlabeled data set into labeled data; adding the converted labeled data to the labeled data set to obtain a new labeled data set; using the new labeled data set to retrain the initial label classification model to obtain a target label classification model. The method of the present application can improve the label classification effect of user consumption data.