122 results about "Quality Score" patented technology

The invention discloses a knowledge graph-based search engine relation chain recommendation method, which comprises the following steps of: receiving a relation chain recommendation request submitted by a user through a search engine to obtain initial query information; performing semantic analysis and intention recognition on the initial query information, and determining a corresponding entity type and a relationship type; calling a dynamic knowledge selection and evaluation mechanism according to the entity type and the relationship type, and dynamically screening the original knowledge graph to obtain effective knowledge sub-graphs; performing diffusion type reasoning based on a knowledge graph diffusion model to generate a candidate relation chain path set; evaluating the quality score of each candidate path in real time; and performing dynamic screening according to the path quality score, and outputting a relation chain path recommendation result. According to the method, accurate mining and dynamic optimization of the knowledge graph relation chain path are realized, and the accuracy of a recommendation result and the flexibility of user interaction are improved.

Systems and methods are disclosed for generating an adaptive bitrate ladder for a content item based on the actual quality of the content item. A user uploaded content item is determined to have different quality scores in different time portions of the content item, where the quality score is based on an analysis of visual characteristics of frames of the content item. In response, an adaptive bitrate ladder (ABR) is generated for each time portion which contains a set of bitrates based on the quality score.

The invention discloses an RAG question and answer optimization method and system based on fusion retrieval, and relates to the technical field of large model knowledge question and answer. The method comprises the steps of obtaining user query, executing keyword retrieval and semantic vector retrieval in parallel, and obtaining a keyword retrieval result list and a semantic vector retrieval result list respectively; and calculating the quality score of each text unit in each retrieval result list based on multi-dimensional quality evaluation, and filtering out the text units with the quality scores lower than a preset quality threshold value. And sorting the text units in all the filtered retrieval result lists by adopting a quality weighted reciprocal sorting fusion algorithm. And based on the length of each text unit and the document format to which the text unit belongs, performing context content enhancement and combination on each text unit after sorting, and generating an enhanced context block list. And constructing structured prompt information based on the enhanced context block list and user query, inputting structured prompt words into the large language model, and generating and outputting a final answer.

The invention relates to the technical field of enterprise service digitalization and intelligent management, in particular to an enterprise management method and management platform based on dynamic portraits. The method comprises the following steps: S1, accessing and standardizing multi-source heterogeneous data, calculating a source-level quality score and generating an evidence chain; s2, constructing a portrait map and a time axis taking an enterprise as a main node; s3, the rule engine and the LLM-RAG output numbers in parallel; s4, consistent arbitration and belief propagation; s5, event-driven dependency subgraph increment recalculation is carried out; s6, carrying out portrait-strategy matching and task linkage; s7, carrying out closed-loop write-back and auditable playback; and S8, multi-tenant fine-grained permission. According to the scheme, the problems of data fragmentation and insufficient timeliness and traceability are solved, and the linkage of portrait credibility, interpretability and real-time decision is realized.

The present disclosure provides a method and an apparatus for evaluating the quality of model samples, a storage medium and a computer device. The method includes: inputting sample data into an AI-generated content detection model to obtain a hit probability of the sample data; matching a content evaluation system based on the attribute information of the sample data; processing the sample data based on the evaluation rule in the content evaluation system to determine a test value of the sample data relative to at least one preset evaluation index; and performing a weighted calculation on the hit probability and the test value based on a target weight corresponding to the hit probability and the preset evaluation criterion, to obtain a quality score of the sample data. This method is capable of filtering data that may mislead model training, and also realizing a high-precision evaluation of the training data.

Systems and methods are disclosed for generating an adaptive bitrate ladder for a content item based on the actual quality of the content item. A broadcaster determines a quality score of a content item and multiplexes a message indicating the quality score with data packets of the content item to a headend. The headend uses the quality score to determine an ABR ladder with a set of bitrates for the content item. The headend then transcodes the content item into the set of bitrates. The headend then transmits a plurality of network addresses, each corresponding to a stream at a particular bitrate, to a user gateway to enable the user gateway to make the streams available.

The invention provides a chat question-answering method and device based on knowledge precipitation, equipment and a storage medium, relates to the technical field of data processing, in particular to the fields of artificial intelligence, domain knowledge modeling and the like, and can be applied to application scenes such as chat conversation intelligent question-answering and the like. The specific implementation scheme is as follows: in response to a current message event in a chat dialogue, determining context information of the current dialogue; performing intention recognition based on the context information to obtain an intention recognition result; retrieving related domain knowledge from a knowledge base based on the intention recognition result; using the retrieved domain knowledge and context information to generate candidate answers; and carrying out quality scoring on the candidate answers, screening the answers with scores reaching a preset threshold value as target answers, and sending the target answers to the chat conversation. According to the scheme, the context can be constructed in the chat conversation, the intention can be recognized, the precipitated knowledge can be retrieved, and the high-quality answer can be generated, so that real-time reliable reply is provided, efficiency and accuracy are improved, and knowledge reuse and dynamic update are promoted.

The invention provides an OCR (Optical Character Recognition) information dynamic verification method and system based on multi-modal fusion and a medium. The method comprises the following steps: firstly, acquiring OCR identification data and scene environment data, extracting a multi-modal feature set, then training a multi-modal quality evaluation model based on historical data, performing field correctness, format compliance and logic relevance scoring on each modal, calculating confidence to position a problem modal, and finally obtaining a multi-modal quality evaluation result. The method comprises the following steps of: obtaining an OCR (Optical Character Recognition) identification result, performing traceability analysis on a problem mode, implementing mode enhancement, recalculating an enhanced quality score and a confidence score, dynamically distributing each mode weight according to enhanced confidence, obtaining a global quality score after weight fusion, and finally judging whether the OCR identification result is qualified or not based on a global score threshold value. According to the method, the accuracy and the robustness of OCR information verification are remarkably improved through collaborative verification and dynamic weight fusion of the multi-modal information, and identification interference in a complex scene can be effectively coped with.

The invention provides a business handling method, device and equipment for financial business, a medium and a product, and relates to the field of artificial intelligence. According to the method, when a customer carries out business handling, multi-modal features of a face image are extracted by responding to the face image of the customer, and a weight coefficient corresponding to each multi-modal feature is determined. And based on a core cluster reference library, determining a first quality score of each multi-modal feature, the core cluster reference library being obtained by performing clustering processing on historical face images. And determining a total quality score of the face image according to the plurality of first quality scores and the corresponding weight coefficients. And when the total quality score is greater than the quality score threshold, executing a corresponding business handling operation. According to the method, through multi-modal feature extraction and weighted evaluation, the face image quality can be comprehensively and accurately measured, the accuracy and efficiency of business handling are improved, and the business risk and handling error rate caused by the face image quality problem are reduced.

The invention relates to the technical field of multi-modal information processing, and discloses a multi-modal information generation and enhancement method based on an AI large model, comprising the following steps: step 1, acquiring multi-modal original data and task prompts, generating a unified token sequence and constructing a constraint graph; 2, setting a modal quota, a coverage threshold value and a consistency threshold value according to the importance degree of the constraint node; step 3, generating a visibility mask and establishing a quota account book, and controlling the visibility of dependent resources; 4, when the generation section meets a threshold value, determining a commitment section and generating an abstract fingerprint; step 5, weighting candidate output according to the quota account book, and adjusting mask regeneration when no feasible candidate exists; step 6, calculating a quality score, and performing backfill enhancement on the low-quality sub-segment; and 7, carrying out statistics on a modal conflict rate and a semantic drift rate, and adaptively adjusting a quota and a constraint weight. According to the method, semantic consistency maintenance, resource adaptive allocation and generation quality enhancement in the multi-modal information generation process are realized.

The invention provides a data asset quality evaluation method and system, and the method comprises the steps: obtaining an affiliated evaluation implementation object of a to-be-evaluated data asset and preset storage format data in the to-be-evaluated data asset, determining a quantification rule for the preset storage format data according to the affiliated evaluation implementation object, and carrying out the quantification of the preset storage format data. According to a quantification rule, carrying out quantitative evaluation on the preset storage format data to obtain a quality score of the to-be-evaluated data assets; according to the method, after the affiliated evaluation implementation object is determined, the quantification rule for the preset storage format data is determined according to the affiliated evaluation implementation object, so that the to-be-evaluated data assets are quantized and evaluated in multiple dimensions according to the quantification rule, an evaluation basis for quantitative calculation is set for the to-be-evaluated data assets, and the evaluation accuracy of the to-be-evaluated data assets is improved. And subjective evaluation factors are reduced, and the to-be-evaluated data assets are cooperatively evaluated through multiple dimensions, so that the accuracy of evaluating the to-be-evaluated data assets is improved.

Systems and methods are disclosed for generating an adaptive bitrate ladder for a content item based on the actual quality of the content item. A broadcaster determines a quality score of a content item and multiplexes a message indicating the quality score with data packets of the content item to an OTT provider. The OTT provider uses the quality score to determine an ABR ladder with a set of bitrates for the content item. The OTT provider then encodes a plurality of portions of the content item for each bitrate. The OTT provider then generates a manifest indicating a portion of a URL corresponding to each portion and transmits the manifest to a client device.

The invention discloses an intelligent fusion processing method, system and device for coal mine multi-source heterogeneous data and a storage medium, and relates to the technical field of data processing and distributed calculation scheduling, and the method comprises the steps: collecting multi-source heterogeneous data, constructing a feature signature, and generating a quality score; and generating a fusion result according to the feature signature and the quality score, collecting a node state through the fusion result, calculating a reward and punishment factor, and executing dynamic scheduling by using the reward and punishment factor. According to the method, format recognition is performed on collected multi-source heterogeneous data, and the multi-source heterogeneous data subjected to format recognition is output, so that unified input of the multi-source heterogeneous data in the same processing link is realized; constructing a feature signature object and generating a quality score based on the multi-source heterogeneous data subjected to format recognition, so that quality information is transmitted along with the data in a computable form; and the fusion processing has clear input, calculation paths and output.

The invention relates to the technical field of artificial intelligence, and particularly provides a model knowledge injection method and device and electronic equipment. The method comprises the following steps: if quality scores of two answers of the same question in a specified field meet the following preference requirements: the quality score of a first answer is greater than a first set threshold value, and the quality score of a second answer is lower than a second set threshold value, taking the two answers as a preference data pair corresponding to the question, constructing a preference data set based on the preference data pair corresponding to each question; in the alignment stage, outputting token prediction distribution of each question based on the input preference data set through the current knowledge answer model; calculating a first loss and a second loss based on the token prediction distribution and the answer of each question; and if the first loss and the second loss meet a preset iteration stop condition, taking the current knowledge answer model as a target knowledge answer model, otherwise, adjusting parameters of the knowledge answer model, and continuing iterative training.

Embodiments of the present application provide a prompt word generation method and device, equipment, storage medium and program product, relating to the technical field of financial technology and artificial intelligence. The method comprises: collecting first interaction behavior data between a user and a large language model and generating a first interaction feature vector based on the first interaction behavior data; inputting the first interaction feature vector into a first model to obtain a quality score of a first prompt word of the large language model; in the case that the quality score of the first prompt word is lower than a first threshold, generating a rewrite trigger signal and generating a new version of the first prompt word based on the rewrite trigger signal. The method of the present application can improve the efficiency of prompt word generation and the performance of the large language model.

The application belongs to the technical field of data asset management, and particularly relates to a data asset operation decision method and system based on artificial intelligence. Firstly, multi-dimensional characteristic data of target data assets is collected, and the collected multi-dimensional characteristic data is vectorized and fused to form a feature representation F. Then, the feature representation F is quality evaluated. Next, the feature representation F, a comprehensive quality score Q and historical data are used to predict and evaluate a future expected value Vp, a predicted risk value Ri and a predicted cost value Zm of the target data assets. Secondly, a multi-objective decision function is constructed with the future expected value Vp maximization, the predicted risk value Ri minimization and the predicted cost value Zm optimization as targets. Strategy optimization is performed through an optimization algorithm, and a recommended operation action Aopt is generated. The prediction result can be automatically converted into an executable operation decision, and the dependence of the decision on artificial experience can be greatly reduced.

The present invention disclosure provides a system for performing root cause analysis, comprising a processor and a memory, the processor is configured to at least one problem associated with running of an application by at least one user, wherein identifying problem includes determining a priority level of one or more services being impacted based on service level agreements, identify at least one root cause associated with the problem, identify one or more root cause corrective actions (RCCA) based on root cause and implement at least one RCCA for troubleshooting the identified problem. Further, the system generates a RCCA form for use by a user group for identifying and validating an additional RCCA and update the database to include the additional RCCA based on a quality score for the identified root cause corrective action. The system is configured to create a RCCA form, auto-fill the RCCA form, identify one or more RCCA implemented for the identified problem, and determine a quality score of the RCCA.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating and updating project plans for software development projects. In one aspect, a method includes obtaining data indicative of initial project plan parameters associated with a software development project; identifying multiple program modules associated with the software development project; obtaining, for a first set of modules of the multiple modules, corresponding source code segments; executing a quality assessment process to compute, for each corresponding source code segment, a corresponding quality score; identifying, based on the quality scores, that at least a subset of the corresponding source code segments are integrable into a corresponding first subset of the first set of modules; and generating a revised project plan by generating updates to the initial project plan parameters by accounting for the corresponding source code segments being integrated in to corresponding ones of the first set of modules.

The application relates to a commodity title abstract generation method and device, equipment and medium in the computer technology field, the method comprising: acquiring a title text of a commodity; extracting knowledge entries belonging to product words and belonging to attribute words from the title text; determining information scores of each knowledge entry according to statistical features of the knowledge entries; selecting corresponding product word and attribute word combination texts to construct a first candidate abstract set; calculating the similarity between multiple long texts combined by part word elements in the title text and the title text; selecting long texts with high similarity to construct a second candidate abstract set; inputting data pairs composed of the title text and each candidate abstract in the first candidate abstract set and the second candidate abstract set into a pre-trained text classification model to the convergence, predicting the quality scores corresponding to each candidate abstract, and selecting a candidate abstract with a high quality score as the abstract of the title text. The application can generate high-quality abstracts.

The invention discloses an effect evaluation method for generating a user interface element selector based on a large model, and the method specifically comprises the following steps: S1, marking a target element by a user, capturing page information, obtaining a complete DOM (Document Object Model) structure, and extracting element features from the obtained DOM structure; s2, submitting the extracted element features to a large language model, wherein the large language model generates various CSS selectors based on the obtained element features; s3, multi-dimensional evaluation is carried out on the generated selector, comprehensive quality score calculation is carried out on the selector through multi-dimensional evaluation, and the multi-dimensional evaluation comprises accuracy scoring, stability scoring and performance scoring; and S4, performing an automatic retry generation mechanism based on an evaluation result to obtain a selector with the highest quality score. According to the method, diversified selector candidates based on different characteristics are generated through multi-model selector generation and by using LLM analysis element characteristics.

The application discloses a kind of sequencing FASTQ file quality score sequence's sparse processing method, comprising: S1, extracting quality score data stream from FASTQ file;S2, the quality score data stream to be processed is divided into multiple segments, each segment contains n characters;S3, set the sliding window size of similarity calculation to m characters, every m characters is divided into a segment, and similarity measurement between the segments is carried out;S4, set threshold e, when the average similarity result after similarity measurement is greater than or equal to e, two segments are replaced with each other, and the replaced quality score data stream is obtained.The application can reduce the complexity of quality score data stream by sparse preprocessing, thereby reducing the compression rate of FASTQ file.

Disclosed herein, method and system for generating subject lines for emails. The method may include inputting a subject line generation prompt to an LLM. The subject line generation prompt includes a sample subject line for an email, a target customer segment, and instructions for alternative subject line generation. The method may further include generating a set of alternative subject lines in response to the subject line generation prompt. For each subject line of the set of generated alternative subject lines, the method may further include determining a score corresponding to the subject line, calculating a customer segment weighted IV corresponding to the evaluation parameter, determining a weighted quality score using the calculated customer segment weighted IV and the score for each of a set of evaluation parameters. The method may further include selecting an optimal subject line from the set of alternative subject lines based on the weighted quality score.

The invention discloses a freight data management method and system based on artificial intelligence, and relates to the technical field of logistics data management analysis, and the method comprises the steps: collecting multi-source data, carrying out the time correction, calculating the quality score of the multi-source data, constructing the multi-source data into a fixed variable, and splicing the fixed variable into a vector; calculating a fusion reference value of each fixed variable to obtain a data source consistency residual error and a consistency score, performing fusion to obtain an instant score, updating fixed variable credibility, calculating fixed variable fusion based on the updated credibility, and writing the fixed variable fusion into a semantic knowledge graph; calculating a statistical magnitude for each fixed variable and mapping the statistical magnitude into nodes to construct a variable dependency graph; and constructing a window based on a time bucket, processing a window value by using a two-layer full-connection encoder, training the two-layer full-connection encoder based on an SAE loss function, and outputting a hidden variable based on the trained encoder. According to the invention, probabilistic positioning from abnormal phenomena to decision-making root causes is realized.

The present disclosure provides a document quality evaluation method and device, electronic equipment and medium, relates to the technical field of computers, in particular to the technical field of artificial intelligence and machine learning. The implementation scheme is: obtaining a plurality of document data of a target document; obtaining a document feature vector of the target document based on the plurality of document data; and performing predictive analysis on the document feature vector to obtain a quality score of the target document.

The invention discloses a cloud game video quality evaluation method based on spatial domain depth features and time domain change features. The method comprises the following steps: step 1, obtaining a to-be-evaluated distorted game video; 2, equally dividing the video into a plurality of segments and selecting key frames; 3, extracting a spatial feature vector; and 4, extracting a time domain feature vector. 5, the time domain feature vector and the space feature vector are connected in series, and a fusion feature vector is formed. And step 6, inputting the fusion feature vector into a multi-layer perceptron (MLP) quality regression module, and outputting a scalar value as a prediction quality score of the video sample. According to the method, the inter-frame change features are extracted in a multi-dimensional mode through physical modeling, the time feature vector and the space feature vector are connected in series to obtain the fusion feature vector, the multi-dimensional time information and the space saliency information of the distorted game video can be extracted more efficiently, and therefore the accuracy of game video quality evaluation is improved.

The invention discloses a retrieval enhancement generation method and system based on an iteration self-improvement mechanism, and belongs to the retrieval enhancement generation technology, and the method comprises the steps: extracting a core entity, a relationship category and a query intention in input information through an entity relationship recognition model, and generating a standardized query statement; performing retrieval on the standardized generated statement based on the retrieval strategy, and returning a retrieval result; performing dynamic information fusion processing on the retrieval result to generate a reference text; generating a final query result based on the reference text, and performing quality scoring on the query result; and if the quality score result does not reach the preset threshold value, optimizing the retrieval strategy, and performing retrieval again until the integrity score of the query result reaches the set threshold value. According to the method, the retrieval accuracy can be improved.

The invention provides a data screening method and device, equipment, a medium and a product, and the method comprises the steps: obtaining original data, and determining a target time scale for segmenting the original data; segmenting the obtained original data based on a target time scale to obtain at least one original data interval segment; for each original data interval segment in the at least one original data interval segment, determining a quality score corresponding to the original data interval segment; and if the quality score corresponding to the original data interval segment is greater than or equal to a preset quality score threshold, determining the original data interval segment as target data which is used for system identification. The data screening efficiency can be improved.

The invention discloses a data quality control method for a carbon emission factor database, and relates to the technical field of carbon emission data quality control. When warehousing data information is called by a specific application scene of an application end, the warehousing comprehensive quality score of the data information and characteristic parameters of the specific application scene are calculated; and expanding the representativeness of the evaluation data, obtaining a representative score, calculating an accuracy score, fusing the warehousing comprehensive quality score, the normalized representative score and the accuracy score, and calculating an application end comprehensive quality score in combination with a scene sensitivity coefficient. According to the method, the problem that data formats and semantics are inconsistent is effectively solved, and the association strength and scene requirements of all quality dimensions are fully considered by constructing the dynamic coupling weight matrix; the evaluation deviation caused by the dimension difference is effectively eliminated by means of a collaborative correction coefficient; precise adaptation of data space-time validity is realized based on the comprehensive attenuation factor, so that the warehousing comprehensive quality score can truly reflect the inherent quality of the data and the actual application adaptation potential.

The invention discloses a text segmentation method, and the method comprises the steps: for a plurality of first candidate segmentation points in a to-be-segmented text, calculating a first similarity between two adjacent text blocks corresponding to the first candidate segmentation points according to a preset window; predicting the similarity of each first candidate segmentation point in the to-be-segmented text through a preset similarity evaluation function to obtain a second similarity corresponding to each first candidate segmentation point; calculating a segmentation quality score corresponding to each first candidate segmentation point according to the first similarity and the second similarity corresponding to each first candidate segmentation point; and according to the segmentation quality score corresponding to each first candidate segmentation point, determining a target text segmentation point in the to-be-segmented text. According to the technical scheme, a real semantic fault and accidental similarity fluctuation can be effectively distinguished, the probability of misjudging a false segmentation point as a target segmentation point is remarkably reduced, and meanwhile, missing of a real segmentation point is avoided, so that the accuracy of text segmentation is improved.

The invention belongs to the technical field of quality detection, and particularly relates to a river channel slope protection construction quality detection method based on artificial intelligence. The method comprises the following steps: firstly, presetting a monitoring point at a river channel protection slope, and collecting current and multiple historical time point images; and extracting monitoring point identification and slope protection structure contour features, screening effective feature points, and accurately aligning multi-time-point data to obtain three-dimensional time sequence position data. Fitting a time sequence change curve through an improved least square method, extracting time sequence features and monitoring point space correlation features, and constructing feature vectors; and by taking the monitoring points as nodes, the feature vectors as node features and the spatial distance and displacement similarity as edge weights, constructing a weighted undirected graph, inputting the weighted undirected graph into a graph attention network containing TSA, outputting a quality score and a global score of each monitoring point, and comparing a preset threshold value to judge a quality grade. The defects of traditional manual inspection and existing automatic detection are overcome, the detection precision, objectivity and early warning capacity are remarkably improved, and guarantee is provided for safe operation of the water conservancy project.