257 results about "Quality Score" patented technology

The invention discloses an intention classification method and device based on vector retrieval and context awareness and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of extracting business metadata and associating the business metadata with typical problem examples to generate a standardized service description document; encoding the standardized service description document into a high-dimensional semantic vector through a pre-training language model, and constructing a neighbor search index to store the high-dimensional semantic vector; splicing the user identity information and the current question text into an enhanced query statement, and encoding the enhanced query statement into a context-aware dynamic query vector through a semantic model; performing similarity retrieval based on the dynamic query vector to obtain candidate intelligent services, performing business domain filtering, context weighted sorting and dynamic priority rearrangement, and outputting target recommendation services; by collecting interactive behavior data of a target recommendation service, quality scoring is performed on service descriptions and problem examples based on a preset evaluation rule, and the service descriptions and the problem examples of which the quality scores are lower than a quality threshold value are updated.

Methods and systems for assessing a software bill of materials (SBOM) include building a knowledge graph from repositories, using function fingerprints of software packages in the repositories. Dependencies of an application are identified using function fingerprints of the application and comparing to function fingerprints of the software packages. A quality score for an SBOM of the application is generated based on a comparison of the identified dependencies to claimed dependencies of the SBOM. An action is performed responsive to the quality score.

A computerized-method for comprehensive text-summarization quality-assessment with rank-based normalization and weighted-hierarchical-ranking strategy. The computerized-method includes: (i) receiving an original-text and a summary-text that has been generated by a (GPT)-based LLM that has been provided the original-text and a text-prompt; (ii) operating a text-processing NLP module on the original-text and the summary-text to yield a processed-text of both; (iii) measuring the summary-text to assess text-summarization-quality thereof by operating a plurality of metrics to yield a metric-score; (iv) operating ranked-based normalization on each metric-score to yield a normalized-score; and (v) operating an aggregation based on weighted-hierarchical-ranking strategy of the normalized scores to yield an interpreted final-quality score. The interpreted final-quality score indicates a comprehensive text summarization quality assessment of the summary-text, and the plurality of metrics includes: (i) grammatically; (ii) Flesch-Kincaid (FK) readability; (iii) topic coverage; (iv) compression ratio; (v) cosine similarity; (vi) NER accuracy; (vii) BLEU; and (viii) ROUGE.

The invention discloses a privacy protection data sharing and cooperative training method and system based on federated learning. The method comprises the steps of receiving software development log data, adaptively judging the sensitivity degree according to a data type, dynamically adjusting noise disturbance intensity according to the sensitivity degree to perform data desensitization, and generating a sensitivity index; selecting a feature extraction strategy, extracting time sequence correlation features from the desensitization data, constructing a dynamic graph structure with a weight, and obtaining a time sequence feature vector through iterative fusion; calculating the time sequence correlation of the time sequence feature vector to obtain a data quality score, and setting a contribution weight based on the quality score to perform parameter aggregation; combining sensitivity indexes with data quality scores to construct a security sharing domain, decoupling global training parameters into knowledge fragments in the domain, formulating a recombination rule, and selectively acquiring the required knowledge fragments by all parties for local training. According to the method, deep collaboration is realized on the premise of protecting data privacy, and the collaboration training effect is improved.

Methods and systems for assessing a software bill of materials (SBOM) include extracting package names from code of an application to identify first dependencies of the application. Package names are extracted from accesses by the application to identify second dependencies of the application. A quality score is generated for an SBOM of the application based on a comparison of the contents of the SBOM to the first dependencies and the second dependencies. An action is performed responsive to the quality score.

The invention provides a presentation file quality detection method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence, in particular to the field of document quality detection. According to the specific implementation scheme, a to-be-detected presentation file is obtained; wherein the to-be-detected presentation file comprises a plurality of pages of slides; converting each page of slide in the to-be-detected PowerPoint into structured data in a preset format; wherein the structured data represents elements contained in the slide and the distribution condition of the elements; determining a quality score of the to-be-detected PowerPoint according to the structured data corresponding to each page of slide; wherein the quality score represents the quality of the to-be-detected presentation file. And in combination with the element layout condition of each page of slides, the quality detection precision of the PowerPoint is improved.

The invention relates to the technical field of big data intelligent decision making, and provides a household appliance commodity dynamic pricing system and method based on multi-source data fusion, and the system comprises a data collection server, a feature extraction engine, a model training cluster, and a strategy execution terminal. The feature extraction engine comprises a time sequence feature constructor used for generating sliding window statistics; the semantic analysis unit is used for analyzing a commodity detail page text description to generate a brand premium coefficient; and the image recognition module is used for predicting the willingness to pay by the user through the commodity main image quality score. Through collaborative design of the feature extraction engine and the model training cluster, the dynamic response capability and the data application depth which are deficient in a traditional pricing mechanism are realized, traditional household appliance merchants are assisted to adapt to market changes, the customer satisfaction and the market share are improved, and the survival and development of the merchants are guaranteed.

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.

The invention belongs to the technical field of front-end performance optimization. According to the webpage resource preloading method and system based on multi-factor dynamic evaluation, a network quality score is obtained according to an obtained bandwidth factor, a network delay factor and a resource stability factor of a current webpage; dynamically adjusting the behavior weight score of each webpage resource according to the user behavior; determining corresponding loading priority weight scores according to the types of different webpage resources; and taking the weighted sum of the network quality score, the behavior weight score and the loading priority weight score as a final loading score, and carrying out corresponding webpage resource loading according to the final loading score. According to the method, the network quality evaluation and the user behavior prediction are combined by monitoring the network quality score and the user interaction behavior in real time and dynamically adjusting the loading priority and the strategy of the key resource, the limitation of a traditional static resource loading strategy is broken through, and the page loading speed and the overall performance are remarkably improved.

The invention discloses an intelligent question and answer method and terminal based on a retrieval-generation model. The method comprises the steps that a first target answer matched with a target question sent by a user is retrieved in a question and answer pair knowledge base; if the first target answer is not retrieved, generating a second target answer matched with the target question through a retrieval-generation model, and returning the second target answer to the user; obtaining feedback data of the user about the second target answer, and calling an evaluation index model according to the target question and the feedback data to analyze and process the second target answer to obtain a quality score; and if the quality score of the second target answer is greater than a preset target score, updating the target question and the second target answer to the question and answer pair knowledge base. According to the method, computing resources consumed by the RAG question-answering system can be reduced, the response speed is increased, and the reliability of generating answers by questions and answers is improved.

Techniques are described for training and use of machine learning models to determine objective video quality scores. Video quality scores predict the quality of video content perceived by viewers. Quality scores have various uses, including the selection of encoding profiles and determination of encoding ladders. A core model and residual model may be used to determine quality scores.

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 present invention belongs to the field of data processing and discloses a multimodal intelligent AI classification system and method for archive organization. The system and method include introducing a cross-modal consistency loss function and a structured constraint loss in a deployed dual-channel generator, and performing mutual generation scoring on original multimodal data and evolved multimodal data based on a modal quality assessor. The system performs cyclic optimization through a conflict identification and resolution mechanism, extracting features based on the enhanced multimodal data to obtain a quality score triplet. The system uses a PPO algorithm for dynamic routing decisions and introduces a closed-loop optimization for quality assessment. According to the decision action execution path rule, a multimodal feature matrix is ​​constructed based on comprehensive features. A multimodal classifier is designed based on an Attention-Transformer, a modal weight adaptive adjustment module is introduced, and a classification result verification mechanism is added to form a closed-loop optimization. The system realizes intelligent AI classification of multimodal archives.

The invention relates to the technical field of data product pricing, and discloses a data pricing method based on multi-dimensional value evaluation, a terminal and a storage medium. The method comprises the steps of performing desensitization processing on data needing to be priced based on a differential privacy mechanism, generating a privacy budget value and a privacy risk value of the data, and obtaining a privacy score according to the privacy budget value and the privacy risk value; generating a quality score of the data according to the evaluation index of the desensitized data on the specified task; calculating a dynamic weight coefficient meeting the optimal privacy budget value by using a dual-objective optimization algorithm, wherein the dynamic weight coefficient is used for balancing a privacy protection objective and a data quality objective; and based on the privacy score, the quality score, the dynamic weight coefficient and the cost information of the data provider, generating a data pricing formula by using a meta-learning model, and outputting a pricing result. According to the invention, fair and reasonable data pricing is realized, the expectation of the data purchaser for the data and the leakage risk of the data provider are balanced, and the healthy development of the data transaction market is promoted.

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.

A large language model-based target sequence generation method, which belongs to the field of artificial intelligence technology, specifically to the fields of large language models, natural language processing, deep learning and other technologies are provided. The large language model-based target sequence generation method includes: determining quality scores of candidate paths corresponding to candidate sequence elements based on prediction probabilities of the candidate sequence elements obtained by a large language model; pruning the candidate paths based on the quality scores to obtain one or more pruned paths; determining a target search width based on the prediction probabilities, and determining one or more target sequence elements from one or more candidate sequence elements corresponding to the one or more pruned paths according to the target search width; and generating one or more target sequences based on the one or more target sequence elements.

The invention discloses a self-evolution emerging type data governance method and system, and relates to the technical field of data governance, the method comprises the following steps: inputting a multi-source heterogeneous initial data set into a large model, and executing the following processing by the large model: performing quality evaluation and defect identification on the initial data set to obtain a ternary data set; based on a preset quality score threshold and the data source knowledge graph, performing cyclic self-emerging data supplementation according to the ternary data set to obtain a complete data set; performing data governance on the complete data set by adopting a preset governance rule set to obtain a high-quality data set, performing self-evolution on governance rules, and updating parameters of a large model through closed-loop feedback so as to update the preset governance rule set; the updated preset governance rule set is used for participating in data governance of the next initial data set. According to the method, full-automatic data management can be realized in a self-adaptive, self-optimized and self-explored manner.

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.

The invention discloses a multi-modal data quality evaluation and optimization method and system for the Internet of Vehicles, and relates to the technical field of data quality evaluation and optimization, and the method comprises the steps: carrying out the calculation of the preprocessed multi-modal data through five dimension factors, and obtaining a corresponding quality score and a comprehensive score; obtaining multi-modal data needing to be optimized according to the comprehensive score; obtaining all quality scores and corresponding weights of the multi-modal data needing to be optimized, inputting the quality scores and the corresponding weights into a factor perception optimization network, outputting an optimization strategy suggestion value, and calculating corresponding quality scores of the optimized multi-modal data and the non-optimized multi-modal data through five dimension factors after optimization; and judging whether a convergence condition is met or not according to the comprehensive score variation and the marginal income. According to the method, efficient evaluation and closed-loop optimization of the Internet of Vehicles data set are realized through quality factor calculation, dynamic weighted scoring, and data completion and convergence control strategy of strategy perception optimization.

Systems and methods for using artificial intelligence to generate optimized parameters for a work tool based on tracked haptic feedback and job quality are disclosed. In one embodiment, a method includes an artificial intelligence agent accessing from entries in a database, quality scores and haptic feedback corresponding to a completed instance of a work task. The method further includes the artificial intelligence agent generating optimized operating parameters based on the haptic feedback data and the quality scores, and transmitting the optimized parameters to a tool used to perform the task.

The invention provides a media quality score evaluation system based on AI intelligent data analysis, a data acquisition module is used for acquiring media traffic data, user interaction data and media historical conversion data and performing data preprocessing, a virtual simulation module is used for performing analog simulation and acquiring an analog simulation result, and a data processing module is used for processing the analog simulation result. The media screening module is used for screening media platforms and obtaining a candidate media set, the clustering optimization module is used for carrying out clustering optimization operation on the candidate media set and obtaining an optimized candidate media set, and the media evaluation module is used for carrying out quality evaluation on the optimized candidate media set and obtaining a media quality score. The intelligent auxiliary module is used for generating a media selection auxiliary report according to the media quality score of the optimized candidate media set, and the optimized feedback module is used for obtaining the actual media conversion rate of media selected by a user, generating an optimized feedback report and carrying out optimization according to the optimized feedback report, so that the accuracy of screening a media platform 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 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 text optimization method, computer equipment, a storage medium and a program product, aiming at a target text, a large language model skilled at the class of text can be matched according to text features, and a first model used for optimizing the text and a plurality of second models used for evaluating the text are determined. According to the target text and the evaluation weight of each evaluation index, generating an evaluation prompt word; inputting the evaluation prompt word into each second model to obtain an evaluation result of each second model on the target text; the evaluation result comprises the quality score, the essence of the target text and the modification suggestion, the number of the second models is at least two, the advantages of different target models can be fully utilized, the blind area of a single model is made up, and the reliability of quality evaluation is improved. According to the method, the essence and the modification suggestion evaluated by the second model can be automatically utilized to generate the optimization cue word with rich information amount, so that the target text can be optimized according to the essence and the modification suggestion by guiding the model according to the positive example and the counter example.

The invention discloses a data set quality evaluation method and device, equipment and a storage medium. The method comprises the following steps: acquiring a to-be-evaluated data set, and evaluating the to-be-evaluated data set based on a pre-established data set quality evaluation system to obtain at least one evaluation index; wherein the data set quality evaluation system comprises a compliance attribute evaluation module, a content attribute evaluation module, a scale attribute evaluation module and a value attribute evaluation module; determining a quality score corresponding to the to-be-evaluated data set based on at least one evaluation index and a preset weight corresponding to the evaluation index; and determining a quality grade corresponding to the to-be-evaluated data set based on the quality score and a preset score threshold. And the data set quality can be accurately evaluated.

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 discloses a high-dynamic small target tracking method for real-time dynamic template updating, a medium and equipment, and the method comprises the steps: putting forward a TransTRDT technology for the challenge of apparent information change caused by target transformation and shielding, and designing a real-time dynamic template updating branch comprising a quality score head; the effective information of the targets in the initial frame, the current frame and the historical frame is fully utilized, so that the dynamic template can contain more comprehensive target information, the expression capability is enhanced, and the tracking performance is improved. Compared with the prior art, the technology achieves a leading effect on an experiment on an image data set.

The invention relates to a video quality evaluation method and device, equipment, a medium and a product. The method comprises the following steps: acquiring operation characteristics in a target video and the complexity of the target video; fusing the complexity and the operation feature to obtain a fusion feature of the target video; performing quality evaluation on the fusion features through a quality evaluation model to obtain a quality score of the target video; the quality score is used for representing the fuzzy degree of the target video. According to the scheme, the influence of the complexity of the target video on quality scoring is considered, and the accuracy of quality scoring is improved.

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 discloses a news retouching method based on artificial intelligence and a related device. The method comprises the steps of obtaining a preset news database and a model training target corresponding to a target news mechanism; determining a target retouching AI model based on a preset news database and the model training target; obtaining a manuscript to be polished; determining a target report style and a target audience group corresponding to the target news institution; polishing the manuscript to be polished based on the target report style and the target audience group through the target polishing AI model to obtain a reference manuscript; determining a manuscript quality score corresponding to the reference manuscript; and when the manuscript quality score is greater than or equal to a preset quality score, determining the reference manuscript as a target manuscript. By adopting the embodiment of the invention, the manuscript polishing efficiency is improved.