1514 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 power network topology optimization method based on time sequence data analysis, and relates to the technical field of power network topology optimization, and the method comprises the following steps: dividing a power grid into a plurality of sub-regions, and injecting disturbance signals into the sub-regions; obtaining time sequence disturbance response data, extracting oscillation characteristics, and identifying oscillation nodes; constructing an oscillation weakness evaluation model, and carrying out weakness evaluation on the oscillation nodes; correcting a phase difference matrix of the DTW based on an evaluation result, and screening a cross-regional risk propagation path; and power grid topology reconstruction is carried out according to a screening result, so that the problems of inaccurate weak node identification and low cross-regional high-risk path screening accuracy in a traditional network optimization method can be solved.

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 micro-service full-link gray level traffic management system and method based on PaaS, and relates to the field of traffic management, and the system comprises an intelligent traffic analysis and decision center module, a micro-service arrangement and management platform, a full-link data analysis module, a dynamic safety protection and compliance auditing module, and a distributed configuration center. According to the micro-service full-link gray flow management system and method based on the PaaS, dynamic self-adaptive gray strategy formulation can be carried out, the latest service target, the gray version release plan and the system performance index threshold can be synchronously obtained in cooperation with a distributed configuration center, and the flow management efficiency is improved based on the information and the intelligent screening result of the flow. A routing strategy and a version switching strategy of gray traffic can be dynamically generated, and in the strategy making process, the real-time load condition of a system, the availability of micro-services and key index changes of services are fully considered.

The invention provides a multivariable multi-step runoff prediction method based on dynamic variational mode decomposition, and belongs to the technical field of drainage basin runoff prediction.The method comprises the steps that various hydrometeorological data of a research drainage basin are collected; decomposing an original runoff sequence by adopting a dynamic variational mode decomposition strategy VMD, performing sliding window slicing processing on drainage basin underlying surface and meteorological data and upstream hydrologic station runoff data, and combining target hydrologic station runoff data subsequences obtained by decomposition into a first characteristic variable; meteorological factor screening is carried out on the meteorological station data, a screening result is used as a second characteristic variable, and the first characteristic variable and the second characteristic variable are combined together to form an input characteristic variable; a sparrow search algorithm SSA is adopted to optimize parameters of a long short-term memory network LSTM to construct the long short-term memory network LSTM, and future runoff is predicted. According to the method, the defect that future forecast information is introduced by binding decomposition is effectively overcome, and the applicability of the decomposition-integration forecast model in actual engineering is enhanced.

The invention discloses an information query method, system, device and equipment, a storage medium and a program product, and the method comprises the steps: obtaining a query request input by a user, and determining a user intention according to the query request; screening a pre-accessed data table and a pre-stored API according to the user intention to obtain a data table screening result and an API screening result; determining a target query capability according to the data table screening result and the API screening result; performing query through data table query or API scheduling corresponding to the target query capability to obtain a query result and displaying the query result; according to the method, the data table and the API accessed by the system are screened, fusion use of data table query and API calling is carried out based on the data table and API screening result, intention classification of data table / API scheduling process branches is not introduced in the whole process, and the method is suitable for a mixed demand scene that access services have both information query and API scheduling; and the flexibility and universality of scene support are improved.

The invention discloses a quantitative scientific research project screening method based on patent situation analysis, and the method comprises the steps: obtaining patent data, and carrying out the vectorization of a patent text through a pre-trained Chine-BERT model; the method comprises the following steps: performing dimension reduction processing on patent text feature vectors by using a UMAP algorithm, performing clustering analysis on the feature vectors after dimension reduction by using a K-Means + + algorithm, identifying professional technical themes and technical effect themes, merging documents of the same clustering category into a text unit, calculating the importance of each word by using a TF-IDF algorithm, and extracting the feature vectors of the patent text by using the TF-IDF algorithm. Extracting a theme keyword of each cluster; and a technology-effect matrix is constructed based on a clustering result, and a quantitative screening result of the scientific research project is obtained through calculation of a technology life cycle, technology innovation evaluation and market competitiveness evaluation. The objective of the invention is to provide a comprehensive and efficient scientific research project screening method through an advanced semantic understanding patent clustering method, and help to rapidly screen scientific research projects.

The invention discloses a micro-seismic multichannel data processing method and system based on a DBSCAN algorithm, and belongs to the technical field of micro-seismic monitoring. According to the invention, through feature vector construction and optimization distance calculation, the clustering reliability is enhanced; a multi-dimensional feature vector Xi containing P-wave refinement arrival time ti, an amplitude Ai and space coordinates (xi, yi, zi) is constructed, distance calculation is performed by combining a normalized cosine distance with wave velocity constraint, a micro-seismic wave propagation rule is fully utilized, the discrimination capability of DBSCAN clustering on physical consistency is enhanced, and the robustness of a screening result is improved, so that noise interference can be effectively processed in a complex environment, and the accuracy and the robustness of the screening result are improved. And the stability and the reliability of a micro-seismic positioning result under different geological conditions are ensured.

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 relates to a material screening method and device based on muon transmission imaging, electronic equipment and a storage medium. The method comprises the steps that actually-measured muon track flux data and a cosmic ray muon flux model are acquired; according to the actual measurement muon track flux data, the cosmic ray muon flux model and the voxel discrete grid, determining an actual measurement muon blocking capability matrix; simulating penetration processes of cosmic ray muons in different track directions for multiple times according to the cosmic ray muon flux model to obtain multiple groups of simulated muon track flux data; according to the simulation muon track flux data, the cosmic ray muon flux model and the voxel discrete grid, determining a simulation muon prevention capability matrix; determining the coincidence degree of each prior material combination according to the actually measured muon prevention capability matrix and the plurality of simulated muon prevention capability matrixes of each prior material combination; and determining a material screening result according to the coincidence degree of each prior material combination. Therefore, the actual material composition in the to-be-detected object can be effectively discriminated.

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 provides a supply chain optimization method and system based on procurement source searching, and relates to the technical field of supply chain optimization, and the method comprises the steps: calling historical procurement data, and building a historical procurement data set; market matching is carried out, and a market data set is established; carrying out market general risk evaluation on the purchase demand, setting a general risk level, and configuring a supplier number interval; comprehensive supply evaluation of the cooperative suppliers is carried out, and a supply stability coefficient is established; calling an evaluation model according to the screening result of the non-cooperative suppliers, executing adaptation evaluation of the non-cooperative suppliers, and establishing an adaptation evaluation result; adaptive optimization of the suppliers is carried out, and an adaptive optimization result is established; and carrying out supply chain optimization. According to the method and the device, the technical problem that the supplier performance cannot be comprehensively evaluated due to the lack of analysis on the supplier multi-dimensional data in most of the prior art can be solved, the technical goal of analyzing the supplier multi-dimensional data is realized, and the technical effect of comprehensively evaluating the supplier capability is achieved.

The invention provides a cross-modal retrieval enhancement method based on a visual large language model. The method comprises the following steps: acquiring a preliminary cross-modal retrieval result of a text and an image; performing relevancy scoring on the preliminary cross-modal retrieval result by utilizing a visual big language model; screening the preliminary cross-modal retrieval results according to the relevancy score; and sorting the screening results, and displaying the most relevant result to the user. According to the method, the accuracy and reliability of cross-modal retrieval are remarkably improved, and high data annotation and training cost is effectively avoided. Meanwhile, as a universal retrieval enhancement scheme, the method can be seamlessly integrated into various existing cross-modal retrieval systems, and the application flexibility and economical efficiency of the method are greatly 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 belongs to the technical field of hearing testing, and relates to a self-adaptive hearing screening optimization method based on acoustic feature analysis, which comprises the following steps: generating audio data with different signal-to-noise ratios through a constructed audio library, measuring an acoustic transfer function of a user ear canal by using a probe microphone, and generating a personalized acoustic transfer function in combination with a head-related transfer function. Time domain and time frequency features are extracted by means of EEG data, and the auditory reaction ability and the spatial auditory ability of the user are quantified in combination with the spatial positioning features of the binaural simulation microphones; the method comprises the following steps: constructing a multi-modal auditory feature vector, weighting features in combination with a multi-head attention mechanism, predicting the hearing age of a user through a time sequence Transform encoder in combination with the age of the user and a standard threshold value, and evaluating hearing loss according to a prediction result; the individual difference of each user can be accurately adapted, the defect that individual difference and complex environmental noise are neglected in a traditional hearing screening method is overcome, and therefore the accuracy of a screening result is improved.

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 relates to a lithium ion battery self-discharge consistency screening method. The method comprises the following steps: S1, fully charging n formed battery cells, carrying out constant-current discharge to 2.5 V after shelving, obtaining a discharge curve, and recording inflection point voltage U1 on the curve; s2, recording a corresponding voltage value on the curve, which is less than or equal to U1 and greater than 3% SOC, as U2; charging the battery cell to U2; s3, placing all the battery cells T1, placing the battery cells T2 at a high temperature, continuously placing the battery cells T3 at a normal temperature, and testing an open-circuit voltage OCV2; placing the battery cell at normal temperature for T4, and testing open-circuit voltage OCV3; s4, calculating a K value of each battery cell; s5, calculating an average value alpha and a standard deviation sigma of the K values of all the battery cells; and S6, comparing the K value of each battery cell with the value of alpha + x sigma, and screening out the battery cells of which Ki is greater than alpha + x sigma in the group. According to the method, the test time is shortened, the influence of temperature and polarization on the test is reduced, and the screening result is more accurate.

The invention provides a literature screening method and device, electronic equipment and a storage medium, and the method comprises the steps: extracting all literature data related to a screening topic from a literature library, and downloading a full text of the literature data and a corresponding abstract part; generating a cue word according to the screening theme, inputting an abstract part corresponding to the literature data and the cue word into an intelligent screening model, judging whether the literature data meets a PICO requirement or not through the intelligent screening model, and outputting a first screening result; the cue words and the literature data are input into an intelligent screening model in a full text mode, whether the literature data meet the PICO requirement or not is judged through the intelligent screening model, a second screening result is output, and the intelligent screening model is one or more large language models with the natural language understanding ability; and performing cross validation according to the first screening result and the second screening result, and screening literatures meeting PICO requirements or related to the current theme by using a large language model, thereby improving the accuracy and efficiency of literature screening.

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 freight screening result display method and a computer readable storage medium, and terminal equipment receives freight search information on a freight search interface, and sends a freight search request carrying the freight search information to a server after receiving a search operation on the freight search interface. The server determines a plurality of recall modes according to the freight search information and information of a first user corresponding to the terminal equipment, and sends the recall modes to the terminal equipment according to preference degree information of the first user for recall precision and / or preference degree information of the first user for historical search results obtained by the plurality of recall modes; determining recall numbers corresponding to the plurality of recall modes; a screening result is obtained based on the recall numbers corresponding to the recall modes, the server sends the screening result to the terminal device, and the terminal device displays the screening result. According to preferences of different drivers for transportation demand orders, screening results displayed for the drivers are determined in a personalized manner, so that the order receiving efficiency of the drivers is improved, and the order reaching efficiency is improved.

The invention discloses an LLM-based pre-training language model depolarization method and system, a storage medium and a terminal, and belongs to the field of language processing, and the method mainly comprises the steps: S1, generating social common sense knowledge through a large language model; s2, using a structural causal model to estimate the collision effect of the generated social common sense knowledge, and screening out the social common sense knowledge aligned with the hidden space of the pre-training language model; for the unaligned social common sense knowledge, a prejudice avoidance strategy is used for avoiding the unaligned social common sense knowledge; and S3, training and optimizing the pre-training language model based on a screening result. According to the method, the social common knowledge generated by the LLMs is introduced, so that the data quality problem existing in a traditional method is solved, and the social prejudice in the PLMs is effectively relieved. Meanwhile, by estimating the collision effect, aligned knowledge can be recognized and utilized to achieve positive migration, and the learning burden of PLMs on non-aligned knowledge is reduced.

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.