1008 results about "Screening Result" patented technology

The present disclosure relates to a multimodal computing-based early intelligent graded screening system for a brain disease. The system comprises: a multimodal data acquisition unit, configured to acquire multimodal data of a target patient under a specified screening grade to form a screening data set; a multimodal feature generation, completion, fusion and calculation unit, configured to generate and complete feature data of modal features in the screening data set to obtain complete modal features, and extract pathological features for calculation to obtain a first screening result; a knowledge-based intelligent screening unit, configured to encode the feature data of the modal features in the screening data set into corresponding graph structure data features, use a multimodal association graph optimized by expert knowledge to match the graph structure data features to obtain knowledge-based association features, and obtain a second screening result on the basis of the knowledge-based association features; and an intelligent graded screening unit, configured to carry out weighted calculation on the first screening result and the second screening result to obtain a final screening result. The present disclosure achieves accurate early screening of brain diseases of patients.

The invention relates to the technical field of micro-services, can be applied to the field of science and technology finance / digital medical treatment, and discloses a high-guarantee link automatic guarantee method and device, electronic equipment and a storage medium. The method comprises the following steps: performing series recording on a calling link of a service interface of a first micro-service through a proxy probe to obtain service interface calling link data; rendering a screening result of the calling link data as a high-guarantee link panorama; analyzing the high-guarantee link panorama to obtain a high-guarantee link dependency relationship, and calculating a micro-service dependency score of the first micro-service; screening the first micro-service according to the micro-service dependency score to obtain a second micro-service; when the service interface of the second micro-service generates an alarm, automatically collecting alarm associated data; and inputting alarm data to the high-insurance decision large model for analysis, and automatically issuing and executing an alarm self-healing strategy output by the high-insurance decision large model through the model context protocol server. According to the method, high-guarantee link automation and system stability are improved.

The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a waste incineration large model domain knowledge self-adaptive extraction method and system based on dynamic confidence evaluation, and the method comprises the steps: carrying out the dynamic word segmentation of preprocessed text data, and obtaining a dynamic word segmentation result; performing domain term confidence analysis according to a dynamic word segmentation result to obtain a dynamic term confidence score; generating a candidate domain term set based on the dynamic term confidence score; screening the candidate domain term set according to a confidence score threshold, and constructing a waste incineration domain knowledge dictionary according to a screening result; the waste incineration domain knowledge dictionary is merged into a preset large model bottom layer dictionary, and a merged knowledge dictionary is obtained; analyzing the target working condition document based on an AI algorithm model corresponding to the merged knowledge dictionary, and generating an operation instruction question and answer pair; and performing parameter fine adjustment on the AI algorithm model by adopting the operation instruction question and answer to obtain a target algorithm model.

The invention discloses a BERT model-based resume screening method and system, and belongs to the technical field of deep learning and text classification. The method comprises the following steps of: obtaining post demand text data and job seeker resume text data, and preprocessing the post demand text data and the job seeker resume text data; based on a TFIDF-LDA model, subject term extraction and category feature expansion are carried out on the preprocessed text vector; performing word extraction vocabulary filtering on the subject word vector after category feature expansion based on N-gram of information entropy; a deep learning matching degree calculation module is combined to calculate the comprehensive matching degree of the posts and the job seekers, and the resumes of the job seekers are preliminarily screened in combination with a set threshold value; and carrying out resume text classification based on the FE-BERT model to obtain a resume screening result. According to the method, the problems that the ability of candidates is difficult to accurately evaluate and different post requirements cannot be adapted or the talent screening accuracy is low due to the reasons of inaccurate keyword matching, incapability of understanding contexts and the like in the existing resume screening method can be solved.

The invention discloses a marketing copywriting generation model optimization method and device and a storage medium, and the method comprises the steps: obtaining a to-be-optimized data model, and carrying out the incremental pre-training of a base model through marketing knowledge data, and obtaining the to-be-optimized data model; subjective indexes are screened through a preset evaluation index system, and screening results are integrated into objective indexes; constructing a sample data set based on the objective indexes, and training the data model by adopting the sample data set to obtain a target model; and generating a generation logic path of the target model by using a preset thinking chain decomposition model, and optimizing the target model through the generation logic path. According to the method, efficient injection and objective quantitative evaluation of marketing knowledge are realized by constructing a technical path combining a vertical field evaluation index system and incremental pre-training, and the technical effects of improving the brand integrating degree, the selling point accuracy and the commercial availability of the generated copywriting are realized in combination with an iterative optimization mechanism guided by a thinking chain.

The invention discloses an Alzheimer's disease early screening method based on magnetic resonance imaging, and relates to the technical field of magnetic resonance imaging, and the method comprises the steps: obtaining information identity data and magnetic resonance scanning image data of a to-be-detected person; preprocessing the magnetic resonance scanning image data, and calculating a brain area volume and a DTI-ALPS index according to the preprocessed magnetic resonance scanning image data; constructing a brain network and calculating global network attributes and regional node attributes according to the brain network; substituting the information identity data, the brain area volume, the DTI-ALPS index, the global network attribute and the area node attribute of the to-be-tested person into an Alzheimer's disease screening model to calculate diagnosis parameters; and comparing the diagnosis parameter with the optimal diagnosis threshold value to obtain a screening result. According to the method, the brain colloidal lymphatic network function is evaluated by means of multiple high-sensitivity nerve image indexes, and the early screening capability of the Alzheimer's disease is improved.

The invention relates to the technical field of image retrieval, in particular to an image retrieval method based on big data mining, which comprises the following steps: collecting images and metadata, screening metadata fields, determining a candidate image set and obtaining a metadata screening result, according to the method, in the image retrieval process, image metadata screening, geographic information clustering, visual feature fusion, index construction and matching strategy optimization are combined, and the retrieval accuracy and response efficiency are improved. In the metadata screening link, data is screened by using information such as time, places and equipment types, so that interference of irrelevant data is reduced, and a candidate image set is more targeted. Geographic information and timestamps are combined for classification, and clustering analysis is performed by utilizing a geographic information system, so that spatial distribution information plays a greater role in a retrieval process, and a more accurate result can be obtained for query of a specific region.

The invention discloses a heterogeneous resource scheduling method and device of a cloud data center, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the following steps: receiving a scheduling task of an application instance containing source architecture resource information; screening the processing nodes according to the computing power demand of the application instance and the specification reference values of the processing nodes, and determining candidate nodes according to the screening result; constructing an application performance portrait of the application instance according to the performance data of the application instance; according to the application performance portrait and the scheduling task, equivalent resource configuration corresponding to deployment of the application instance on the candidate node is determined, a score is determined based on the equivalent resource configuration and the specification reference value, and a target node is determined based on the score; and modifying the resource request of the application instance based on the equivalent resource configuration to obtain a modified resource request, and deploying the application instance to the target node, so that the target node performs resource configuration based on the modified resource request to complete heterogeneous resource scheduling. And equivalence quantization and intelligent scheduling of heterogeneous resources are realized.

The invention provides an equipment state data analysis method and system applied to automated electrical equipment, and the method comprises the steps: obtaining a historical operation data set of target equipment, and constructing a multi-level state analysis model based on the historical operation data set, the multi-level state analysis model comprises at least one first analysis level and at least one second analysis level, key operation parameters are extracted from the real-time operation data flow of the target equipment through an operation parameter screening strategy in the first analysis level, and a parameter screening result is generated according to the key operation parameters; inputting a parameter screening result into a state evaluation strategy in a second analysis hierarchy, and generating a state evaluation index of the target equipment in combination with a dynamic threshold range corresponding to the key operation parameter; and outputting a real-time operation state grade of the target equipment according to a comparison result of the state evaluation index and a preset state threshold value. According to the invention, efficient monitoring and accurate early warning of the equipment health state can be realized.

The invention relates to the technical field of data processing, and discloses a traffic site screening method and system for marketing and a storage medium. The method comprises the following steps: collecting multi-dimensional data through a monitoring system, and calculating a quality evaluation index; extracting user features to establish a portrait, and calculating a matching degree; predicting an effect in combination with operation data; screening sites based on prediction evaluation; and monitoring and analyzing the screening result, and dynamically adjusting to obtain an optimization scheme. According to the application, multi-dimensional evaluation of shopping mall quality, accurate matching of user portraits and dynamic optimization of screening results are realized, so that scientificity and success rate of merchant site selection decision are improved.

The invention relates to the field of medical information processing and traditional Chinese medicine curative effect evaluation, in particular to a diabetic nephropathy traditional Chinese medicine curative effect evaluation method based on patient report outcome. The method comprises the following steps: searching related symptoms and clinical characterization data of diabetic nephropathy through a medical database, extracting target entries, performing language standardization processing, constructing an initial candidate entry set, calculating the occurrence frequency of each symptom entry, and screening entries meeting a set threshold value to form an entry set; carrying out dimension division on the entry set according to feature attributes to form a preliminary dimension structure set; forming a screening result set through preset logic screening conditions; and carrying out weighted regression fusion on the comprehensive curative effect score and clinical detection data, and outputting a patient curative effect index. In an item weight determination process, dynamic balance between subjective experience and objective statistical information is realized through a mixed weighting model, and the defects of strong subjectivity of expert scoring and large result fluctuation in a traditional method are overcome.

The invention discloses a digital employee collaborative screening method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: receiving multi-user concurrent requests, carrying out semantic isolation, generating an adjustment factor in combination with user portrait features, generating an initial semantic vector through two-channel semantic coding, and carrying out semantic conflict detection, thereby effectively avoiding semantic cross and confusion, and improving the user experience. The intention analysis of multi-user concurrent requests is realized; acquiring request features in real time, calculating computing power demand levels of different requests, sequencing the requests with the same computing power demand level, and performing resource allocation; a user adjustment instruction is obtained through a front end, a demand difference is identified, an incremental calculation scheme is generated, and state synchronization control is carried out; according to the method, digital employees are divided into analytic, computational and feedback intelligent agents, collaborative screening is carried out, feedback verification is carried out, it is ensured that a screening result better meets user requirements, and real-time capability iteration of a digital employee cluster is achieved.

The invention relates to the technical field of text generation, provides an NL2SQL method and system based on thinking reasoning, and aims to enable a model to interact with a database environment by introducing a reinforcement learning mechanism to obtain reward signal feedback. The system comprises a table field recall component and a structured query language generation component. The table field recall component fills table field information into a preset template to construct a structured text, then vectorization processing is carried out, the structured text is stored into an elastic search engine, a relevant table is searched according to user query, and finally specific fields are screened through a field selector model. And the structured query language generation component receives the screening result, generates a structured query language in combination with user query, and performs retrieval replacement on a filtering value. In the aspect of training, the method combines supervised fine-tuning cold start and reinforcement learning reasoning training, and the model performs reasoning and thinking according to an input context through trial and error learning. In the reinforcement learning training process, a reward function iteration optimization model based on task execution result correctness is adopted.

The invention discloses an Internet information service method and system based on cloud computing, and the method comprises the steps: dynamically distributing cloud computing resources based on the number of user requests obtained in real time, and constructing a distributed processing cluster; performing protocol analysis and feature extraction on the request to obtain a feature data set; performing whole network information preliminary screening based on request types in the feature data set, and obtaining a preliminary screening result set at least comprising three matched keywords; constructing an Internet information matching model, performing feature matching degree calculation on the initial screening result set and the request data based on the initial screening result set, and performing re-screening based on a matching result; based on the re-screening result, selecting first N high-matching-degree results to form a query result, and generating a response data packet containing an abstract, source credibility and a preview interface; the method has the advantages that the system performance and the response accuracy are optimized through dynamic resource allocation, accurate request analysis, intelligent information screening and a deep matching model, and efficient and reliable services are provided.

The invention provides a vehicle software code generation method and system based on a large model, and relates to the technical field of computers, and the method comprises the steps: obtaining a control demand text of a vehicle; analyzing the control demand text to obtain functional security semantic features and task cycle semantic features; querying a vehicle code knowledge base according to the functional safety semantic features and the task cycle semantic features to obtain a code preliminary screening result; determining reference code information in each piece of vehicle software code information of the code preliminary screening result according to a multi-modal vector corresponding to the control demand text; generating a model prompt according to the control demand text and the reference code information; the model prompt is input into the vehicle software code large model, and candidate codes output by the vehicle software code large model are obtained; and under the condition that the candidate code passes verification, determining the candidate code as a target code corresponding to the control demand text. By applying the method provided by the invention, the quality of software codes can be improved, and vehicle requirements are met.

The invention relates to the technical field of artificial intelligence and thermal power production fault detection, in particular to a thermal power production fault detection data measuring point screening method and system based on a clustering algorithm, and the method comprises the steps: obtaining historical fault data of thermal power plant production equipment, and selecting related measuring points to form a multi-dimensional time series data set; and performing embedding processing on the time sequence data by using the time sequence large model to generate a feature vector with a set dimension. And then, carrying out clustering analysis on the feature vectors by adopting a clustering model to obtain a clustering result. And finally, screening out the measuring points related to the fault points of the production equipment of the thermal power plant according to a clustering result, obtaining a corresponding screening result, and providing support for equipment fault prediction and diagnosis.

The invention discloses a decommissioned battery virtual high capacity scoring and screening method and system, relates to the technical field of battery detection and evaluation, and aims to solve the problem that decommissioned battery virtual high capacity identification is difficult, and the method comprises the following steps: S1, performing discharge operation on a battery and standing, and monitoring an open-circuit voltage in a standing process in real time to obtain a voltage recovery curve; s2, extracting a multi-modal dynamic feature from the voltage recovery curve, wherein the multi-modal dynamic feature comprises a voltage recovery time constant and a curve fitting residual error; s3, performing weighted fusion on the multi-modal dynamic features and outputting a standardized score value to obtain a health score; and S4, comparing the health score with a preset threshold value, and outputting a virtual high-capacity risk grade screening result.

The invention provides an exosome automatic identification and analysis method based on microscopic imaging, and belongs to the technical field of exosome identification and analysis, and the method comprises the steps: collecting an exosome fluorescence microscopic image, carrying out the preprocessing, and obtaining an exosome region through adaptive threshold segmentation and morphological processing. A GPU is adopted to process and extract morphological, texture and statistical feature matrixes in parallel, and meanwhile, the CPU recognizes an abnormal region. And combining the features to form a region feature set, inputting the region feature set into the preliminary screening model for scoring and screening high-credibility regions. Deep feature extraction is carried out on the high-credibility region, and multiple features are fused by using an attention fusion mechanism to construct vectors; and inputting a depth classifier to obtain a classification probability matrix and a screening result. And finally outputting the number, position, classification and credibility score of the exosomes. The problems that an existing exosome analysis method often depends on manual recognition, and automatic and high-throughput analysis is difficult to achieve are solved.

The invention discloses a digital-intelligence tumor screening platform and method, and relates to the technical field of digital-intelligence tumor screening, and the method comprises the steps: constructing a multi-modal fusion model of a fusion convolutional neural network and a recurrent neural network based on a deep learning framework, and outputting tumor comprehensive correlation features; a machine learning algorithm is utilized, large-scale tumor data are combined for training to construct a tumor risk prediction model, tumor comprehensive association features are input, and different tumor incidence probabilities are output. By means of the multi-modal fusion model, multi-source data such as images, clinic, genes and living habits are integrated, tumor characteristics are comprehensively captured, and the early-stage missed diagnosis rate of common tumors can be greatly reduced. Multi-source data are accessed in real time through a standardized interface, the multi-modal model completes feature extraction and risk prediction in a short time, and the time of the whole screening process is greatly shortened. The result of the visual report is presented by a visual chart, and by matching with popular character interpretation, the understanding degree of the patient on the screening result is improved, and the doctor-patient communication efficiency is remarkably improved.

The invention relates to the technical field of biomedical data analysis, and discloses a data analysis method and system for genetic disease gene detection and a storage medium, and the method comprises the following steps: collecting a patient sample, and carrying out high-throughput sequencing to obtain original data; performing quality control and comparison processing on the data to generate variation detection data, and calculating variation information amount; suspicious variation sites are automatically screened, variation information amount is analyzed based on information entropy, and key variation is screened according to a preset threshold; performing Bayesian inference analysis on the key variation, calculating pathogenicity probability, and performing pathogenicity judgment based on an ACMG standard; calculating the matching degree of the key variation and the phenotype in combination with the phenotype information of the patient, and screening the variation conforming to phenotype characteristics; submitting the screening result to a doctor for auditing, and generating a final screening result; and generating a standardized gene detection report according to a final result. According to the method, pathogenic variation is efficiently and accurately recognized, and the automation level and clinical application value of genetic disease gene detection are improved.

The invention discloses an intelligent risk early warning management method for an information system, and the method comprises the steps: taking data obtained from an intelligent data source of a preset information system within a specified time as to-be-analyzed data; wherein the intelligent data source comprises a reference data source and the to-be-determined data; abnormal data and fuzzy data are obtained according to the consistency proportion, the reference data source and the to-be-analyzed data, the fuzzy data are screened based on the anomaly degree, and a screening result is added into the abnormal data; constructing a knowledge graph by adopting the intelligent data source based on a graph attention network, performing knowledge completion on the knowledge graph, obtaining a matched entity through the knowledge graph after knowledge completion according to the abnormal data, and performing causal tracing according to the matched entity to obtain tracing data; and constructing an intelligent risk early warning management model according to the traceability data, inputting to-be-managed data into the intelligent risk early warning management model, and outputting a management result.

The disclosure describes a reading system and a reading method for fundus images, the reading system comprising: an input module for receiving fundus images; a screening module for outputting screening results based on the fundus images, the screening results at least including quality control judgment results and lesion judgment results; a first classification module for classifying the fundus images into screening qualified images and first images to be quality controlled based on the quality control judgment results, and taking at least one image from the first images to be quality controlled and the screening qualified images as an image to be quality controlled; and a quality control module for outputting quality control results based on the image to be quality controlled. According to the disclosure, the screening accuracy of the reading system can be improved.

The invention relates to the technical field of power system operation decision, and discloses a topological optimization reconstruction method and system based on machine learning. The method comprises the following steps: constructing a target power distribution network reconstruction model corresponding to a power distribution network; performing supervised learning on the initial XGBoost model based on historical reconstruction case data to obtain a target XGBoost model; performing line switch prediction on the real-time reconstruction case data based on a target XGBoost model to obtain a switch prediction value of each line in the power distribution network and a prediction probability corresponding to each switch prediction value; screening each switch prediction value based on the prediction probability to obtain a screening result of the corresponding switch prediction value; and solving the target power distribution network reconstruction model based on all the first switch predicted values of which the screening results are low-confidence decisions, and determining a first reconstruction strategy corresponding to the power distribution network according to the first solving result and all the second switch predicted values of which the screening results are high-confidence decisions. According to the method, the solving efficiency of the large-scale power distribution network reconstruction problem can be remarkably improved.

The invention discloses a tracing method and system for atmospheric pollution in a chemical industry park, and relates to the technical field of tracing of atmospheric pollution. The method comprises the following steps: determining at least one first pollution source according to an enterprise-pollutant-raw material incidence matrix and at least one pollutant in the chemical industry park; and according to the real-time pollutant concentration data and a Gaussian diffusion model simulation result, screening out an enterprise with a relatively high fitting degree with the real-time pollutant concentration data, and according to a screening result, determining at least one second pollution source. And determining at least one third pollution source according to the position of the at least one pollution source. And determining at least one fourth pollution source in the high-concentration change area. And determining a pollution traceability result. According to the invention, multi-source data are fused to trace the air pollution, the comprehensiveness and timeliness of the data are ensured, and the accuracy of pollution traceability can be improved.

The invention relates to the technical field of automatic testing, in particular to an automatic software testing method and system based on multi-mode AI cooperation. According to the scheme, optical character recognition, voice recognition and natural language processing analysis tools are packaged into independent containers, and analysis results are input into a multi-mode semantic association model in real time; calculating the semantic similarity between the image description text and the corresponding voice transcription text; constructing a test demand complexity evaluation model; defining use case quality indexes, and establishing an error sample library to store manually labeled problem use cases and correction labels thereof; the intention demand is finally determined according to the screening result of the three-stage filter; and setting a test case template, optimizing a path according to the fusion quality, improving the case generation efficiency, and synchronously updating the optimized path to a knowledge base associated with the error sample library. According to the scheme, automatic software testing is achieved through multi-modal AI cooperation in combination with containerized deployment, model dynamic optimization and the like, and efficiency and recognition accuracy are improved.

The invention discloses application of an AI-driven target screening technology in anti-obesity treatment of a drug delivery system, and belongs to the field of cross fusion of biological medicine and artificial intelligence. The screening method provided by the invention comprises the following steps: selecting a signal channel target and active small molecules related to lipid metabolism; processing the signal channel target and the active small molecules through the data set to obtain an input matrix; and inputting the input matrix into a deep learning model to obtain the interaction intensity between the active small molecules and the signal path target. According to the method, an artificial intelligence technology is introduced, a deep learning model is constructed to efficiently predict the affinity between a drug and a protein target, the problems that a traditional target screening method depends on experience and is low in efficiency are solved, and target expression is subjected to experimental verification through a molecular biology method, so that the target screening efficiency is improved. A verification closed loop from calculation prediction to molecular demonstration is realized, and the biological credibility of a target screening result is proved.

The invention discloses a portable swallowing function screening system based on multiple modes. The portable swallowing function screening system comprises a signal acquisition device, a data processing device and an interactive display device which are connected in sequence, the signal acquisition device is used for acquiring a laryngeal pressure signal, a laryngeal acoustic vibration signal and a swallowing acoustic wave signal of a patient; the data processing device is used for performing feature extraction and feature fusion according to the throat pressure signal, the throat sound vibration signal and the swallowing sound wave signal to obtain a multi-modal fusion feature, and calculating a swallowing disorder risk probability through a preset deep learning model according to the multi-modal fusion feature; the interactive display device is used for judging the swallowing function according to the swallowing disorder risk probability and displaying the screening result of the swallowing function according to the judgment result.

The invention provides a vehicle charging scheme recommendation method and system and a vehicle, and relates to the technical field of electric vehicle charging, and the method comprises the steps: obtaining user interaction data and user historical behavior data, forming a user behavior portrait, determining a current travel demand of a user and a first charging necessity parameter under the current travel demand, obtaining a first charging station screening result; obtaining the remaining endurance mileage of the vehicle, and determining a second charging necessity parameter; calculating a third charging necessity parameter to obtain a second charging station screening result; calculating a fourth charging necessity parameter according to the historical charging data, the current residual electric quantity and the service life, and obtaining a third charging station screening result; and determining a charging recommendation scheme in combination with user preferences. The charging efficiency of the vehicle in the long-term use process can be improved.

The invention discloses an unmanned aerial vehicle nest control method and system, and the method comprises the steps: obtaining the first configuration data of a flying unmanned aerial vehicle, and obtaining the local configuration data of an unmanned aerial vehicle nest matched with the flying unmanned aerial vehicle; determining second configuration data of the flying unmanned aerial vehicle according to the target task request, and performing comparative analysis on the first configuration data and the second configuration data to obtain a to-be-replaced configuration index; screening the local configuration data based on the to-be-replaced configuration index, and controlling the unmanned aerial vehicle nest to execute an internal configuration component transfer signal according to a screening result, so that the unmanned aerial vehicle nest reaches an initial switching state; acquiring first flight pose data of the flying unmanned aerial vehicle; predicting a drop point position of the flying unmanned aerial vehicle based on the first flight pose data to obtain a predicted drop point coordinate; and controlling the unmanned aerial vehicle nest to execute a target switching state signal generated by the initial switching state and the predicted drop point coordinates. According to the method, powerful support is provided for multi-task application of the unmanned aerial vehicle.

The invention relates to the technical field of spine health, solves the problems that in the prior art, a spine posture abnormity screening mode is single, a screening process lacks grading progressive performance and a correction strategy lacks individuation, and provides a spine posture abnormity three-level screening and correction system and method. The system comprises a data acquisition module used for acquiring first original data, second original data and third original data; the primary screening module is used for obtaining a primary screening result; the secondary screening module is used for obtaining a secondary screening result according to the second original data; the third-stage screening module is used for obtaining a third-stage screening result according to the third original data; the comprehensive evaluation module is used for obtaining a comprehensive evaluation result; the correction management module is used for generating a personalized correction strategy; and the control module is used for managing the working process of each module. The invention provides a spine posture abnormity screening and correction system capable of generating a personalized spine correction strategy.