58 results about "Scoring Engine" patented technology

The invention relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.

The present invention relates to a system and method for deterministically generating reproducible evaluative scores for a subject of analysis (e.g., a security). The system comprises a processor and memory storing instructions to: receive verified data describing the subject; store this data in a fixed and version-controlled corpus to define a static analytical context; execute a large-language model (LLM) under a structured prompt framework that directs a controlled scratch-pad reasoning process for preliminary interpretations and evidence extraction; perform a multi-pass deterministic analysis of the fixed corpus to produce structured, synthesized statements as reproducible evidentiary outputs; and finally, apply a rubric-based scoring engine that converts these statements into calibrated alignment scores and aggregates them to generate a composite deterministic score. This architecture ensures reproducibility, transparency, and auditability by anchoring the flexible analysis of the LLM and the final scoring logic to a known, unchanging evidence corpus.

A system and method for comprehensive data utilization and tracking comprising an ontological engine which in some embodiments is configured to create and curate various industry-specific ontologies which can be used to provide deeper context to an enterprise's network traffic and data transmission. The system and method further comprise a tagging and tracking engine configured to inspect network packets, apply a first tag associated with an authentication object, apply a second tag associated with an identified ontology, and track the tagged packets as they traverse the enterprise network, generating data utilization tracking information as the packets move through the network. A scoring engine may leverage the data utilization tracking information in combination with user entity and behavior data to compute a risk score associated with data utilization on the enterprise network.

Described herein are methods and systems for evaluating datasets through a multi-faceted scoring approach that assesses data quality across multiple dimensions. A trust score for a dataset may be generated by a scoring engine and displayed on a user interface, providing a comprehensive assessment of the dataset's readiness for use in artificial intelligence applications. The trust score incorporates multiple dimensions including diversity, timeliness, accuracy, security, discoverability, and LLM-readiness, offering users quantitative insights into dataset quality. This scoring system enables organizations to identify high-quality datasets suitable for AI model training, reducing the risk of poor model performance due to inadequate data. The visualization of trust scores through intuitive interfaces allows data scientists, analysts, and other stakeholders to quickly assess and compare datasets, facilitating more informed decision-making in AI development processes.

The invention discloses a circular region quantum communication channel redundancy configuration system based on AI-block chain collaboration. The system comprises a quantum transmission layer, an AI optimization layer and a block chain layer. The quantum transmission layer is based on a circular area sensor network topology, and comprises a central node, a quantum relay ring and a sensor node; the quantum transmission layer adopts ring topology, a center node is located at the center of a circle, and relay nodes are uniformly distributed according to polar coordinates; bidirectional quantum communication is realized between the sensor node and the central node through a quantum relay ring; the AI optimization layer comprises a channel state sensing module, a quantum graph neural network module, a QoS analysis module, a service priority mapping module, a dynamic scoring engine and a resource arbitration module; the block chain layer constructs a dynamic trust center to form an evaluation-evidence storage-control closed loop. According to the invention, the problems of limited transmission distance, high equipment cost, low key distribution efficiency and low resource utilization rate of quantum communication in a circular area scene are solved.

The invention relates to a medical quality control dynamic scoring system, device and equipment based on multi-modal perception and federal evolution, and the method comprises the steps: obtaining original scene information through multi-modal input based on a medical equipment recognition model, carrying out the scene analysis and semantic fusion, generating structured medical scene data, and carrying out the dynamic scoring of the medical quality control according to the structured medical scene data. Multi-source evidence verification is carried out through a medical scoring rule base, a quality evaluation result is generated through weight dynamic adjustment, difference analysis is carried out through comparison with a manual correction result according to the quality evaluation result, and model parameters and scoring rule updating are generated. Multi-modal input is fused based on a medical special perception layer, anti-interference scene analysis and equipment semantic binding are achieved, multi-source evidences are verified in real time through a dynamic scoring engine, deduction weights are dynamically adjusted, subjective item scoring floating is compressed, an identification model is updated through federation, and a rule base is thermally loaded in real time, so that the anti-interference scene analysis and equipment semantic binding is achieved. And automatic closed-loop processing of problem items is realized.

The invention discloses a batch composition scoring system, method and device based on multi-dimensional quantization and a storage medium. The system comprises an input processing module, a scoring module and a scoring module, wherein the input processing module extracts basic information of each composition from a batch composition picture based on multiple threads; the large model module extracts composition text feature information from the corresponding composition pictures according to the basic information of each composition based on the message queue, analyzes the composition text feature information according to a preset special constraint rule, a penalty factor rule and a basic score rule, and outputs a multi-dimensional score of each composition picture; and the multi-dimensional scoring engine performs multi-dimensional quantitative scoring on each composition picture through a multi-dimensional quantitative scoring formula according to the multi-dimensional scores. According to the method, the multi-dimensional quantification rule is fused to the large model, the objectivity of traditional rule quantification is reserved, a dimension self-defining mechanism is introduced, a user is allowed to expand the scoring dimension and adjust the quantification standard or the weight proportion according to the requirement, and fair and accurate automatic batch scoring is achieved.

A systems and methods for determining a risk score for lookalike domains are disclosed. A plurality of candidate domains are generated from a seed domain using a genetic algorithm that applies deception techniques. Registered candidate domains are analyzed by multiple independent scoring engines that produce an internal deception score, a reputation score, a visual similarity score based on rendered webpage analysis, and a favicon similarity score. The individual scores are dynamically weighted and combined using conditional logic to calculate a final risk score. The weighting adapts based on signal strength, including prioritizing visual cloning indicators or malicious infrastructure signals. Unregistered domains are assigned reduced priority. When the final risk score exceeds a predefined threshold, an alert is generated. The disclosed approach integrates lexical, infrastructure, and content-based signals to reduce false positives and improve detection of sophisticated phishing domains.

Systems and methods for detecting human impersonations on the internet, comprising receiving, by an ingestion layer of a detection system, a plurality of web data; filtering, by a processing engine of the detection system, the plurality of web data to identify relevant websites; extracting, via a scoring engine of the detection system, categories of data of a website of the relevant websites; assigning a score, via the scoring engine, for each category of the categories of data; and determining a risk score for the website, via the scoring engine, based on the score for each category.

Techniques for the secure evaluation of an artificial intelligence engine are disclosed. A computer system transmits, to a testing environment by a scoring engine, a dataset associated with an input for an artificial intelligence model to execute within the testing environment. The scoring engine comprises at least one transformer. The computer system receives, by the scoring engine and from the testing environment, a response to the dataset generated by the artificial intelligence model. The computer system determines, by the scoring engine and based on the response, a score representing a quality of the response. The computer system generates, by the scoring engine, an output based on the score.

The invention discloses an intelligent question answering and examination integration method, system and device based on retrieval enhancement generation. According to the method, two major functions of question answering and examination are deeply fused under the same technical framework, and accurate knowledge question answering and automatic examination evaluation are synchronously realized through a shared vectorization knowledge base by utilizing a large language model and a retrieval enhancement technology. According to the intelligent examination process, a question type planning algorithm, knowledge point diversity control, a question type professional scoring engine and a streaming output interaction mechanism are creatively integrated, examination paper with balanced questions and wide knowledge point coverage can be automatically generated according to preset distribution, and high-accuracy automatic scoring is achieved for complex question types such as multi-choice questions and simple answers. According to the method, the problems of information lag and answer non-traceability of a traditional large model and one-sided question setting, rough score, low efficiency and the like of an existing examination system are effectively solved, and the scientificity, efficiency and user experience of evaluation are remarkably improved.

An AI system (100) for evaluating donor affinity from public and institutional data, comprising: a data entry module configured to capture structured and unstructured donor-related data from external public sources and internal institutional databases; a data preprocessing module designed to clean, normalize, and convert the input data into a machine-readable format; a feature extraction and enrichment module designed to identify, extract and enhance relevant donor features by using natural language processing, graph-based analysis and metadata tagging; an affinity assessment engine that uses machine learning algorithms to analyze the extracted features and generate a dynamic donor affinity assessment; an explainable AI module that is operationally connected to the scoring engine and configured to generate interpretable, human-readable justifications for each donor affinity score; a visualization and reporting module that is customized to present donor scores, behavioral profiles, and campaign recommendations through interactive dashboards; and an orchestration and feedback module configured to manage real-time data updates, automate model retraining, incorporate user feedback, and enable seamless integration with existing donor management or CRM systems.

The invention relates to the technical field of business evaluation and the field of smart medical and financial science and technology, and provides a business compliance risk evaluation method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining business compliance risk evaluation data from a business operation system based on an evaluation matrix; performing multi-dimensional scoring processing on the business compliance risk evaluation data based on an automatic scoring engine to obtain a compliance risk evaluation result; under the condition that the compliance risk assessment score is lower than a score threshold value, the compliance risk assessment item information is analyzed and processed based on a risk analysis module, and compliance problem root information is obtained; generating compliance risk optimization suggestion information according to the compliance problem root information and the historical compliance problem case database; and adjusting the business operation system according to the rectification step information and the supervision basis information. According to the technical scheme, the compliance of the business system can be evaluated and processed intelligently.

Some examples described herein related to automated functional-safety evaluation and deployment of a software package. As one example, a system can execute an automated scoring engine configured to analyze data associated with different phases of a software development process for a software package. Based on the data, the automated scoring engine can generate a score indicating compliance of the software package with a functional safety standard issued by a standard-setting organization. The system can then automatically control deployment of the software package to an entity based on the score.

The present invention relates to a method and system for automatically sanctioning users in an online community based on risk scores derived from user-generated content and behavior data. The system includes a machine learning risk score engine that processes segmented sessions of user activity, assigning risk scores according to predefined violation categories.These risk scores are aggregated and compared to configurable thresholds specific to each violation category. If the aggregated risk score exceeds the threshold, the system automatically triggers a sanction, such as a warning, muting, temporary suspension, or permanent ban. The system continuously monitors user activity, recalculating risk scores with new data, ensuring ongoing evaluation and appropriate sanctioning. The invention offers flexibility through customizable thresholds and violation categories, enhancing the effectiveness and precision of automated moderation in online communities.

A machine learning-driven system for pattern deviation detection and autonomous correction of data integrity errors in digital networks, comprising: (a) a data acquisition interface configured to receive digital data elements from a variety of network-connected sources and to associate the digital data elements with source metadata; (b) a preprocessing and feature extraction engine configured to transform the digital data elements into feature representations that include at least structural features, temporal features, protocol compliance features, and origin features; (c) a pattern modeling engine configured to maintain a reference behavior representation derived from baseline-consistent feature representations;(d) an anomaly scoring engine configured to calculate a composite anomaly score for an incoming feature representation based on at least one reconstruction deviation and one consistency deviation relative to the reference behavior representation; (e) a fault classification engine configured to assign an integrity fault category and an affected area, which may include a field, record, packet, block, or stream segment; (f) a corrective orchestration engine configured to generate and evaluate a variety of corrective candidates and select a corrective action according to a confidence score derived from the agreement of the fault category, the origin state, the source trustworthiness, and the expected validation success;(g) a validation engine configured to check a corrected data element against integrity constraints; (h) a rollback control configured to restore a saved previous state if the check fails; and (i) an audit log generator configured to record artifacts relating to anomalies, corrections, validations, and rollbacks.

The invention relates to the field of highway tunnel engineering, and discloses a highway tunnel engineering construction safety risk intelligent assessment system and method, and the system comprises a data layer which serves as a data warehouse of the system and stores basic index rules, weight calculation rules and work order and index associated data required by assessment. Standardized data and rule support are provided for the whole evaluation; and the data layer is connected with an algorithm layer. Standardized evaluation rules are established through the index dictionary table of the data layer, a unified judgment basis is provided for risk evaluation, and the subjective deviation of'standard determination by experience 'in traditional manual evaluation is avoided; through combination of a dynamic scoring engine and a special risk engine of an algorithm layer, quantitative and hierarchical processing of risk assessment is realized, dependence on artificial experience is thoroughly eliminated, index determination logic consistency under different tunnels and different assessment scenes is ensured, and objectivity and reliability of an assessment result are improved.

The application provides a kind of enterprise tax data visualization method and system based on blockchain, belong to the technical field of enterprise financial monitoring based on blockchain, including: graph construction and data preprocessing unit, for obtaining maximum semantic matching score and rule index;Risk quantitative evaluation unit, for by risk scoring engine, rule conflict factor, historical mode anomaly factor and financial impact factor are aggregated into compliance risk degree;Chain before decision control unit, for obtaining approved chain signal, marked chain signal or rejected chain signal, and generating corresponding state mark;Risk perception visualization unit, for receiving compliance risk degree and state mark, dynamically rendering the transaction data that has been chained.The application ensures the compliance of chained data, solves the problem of permanent solidification of garbage data.

The present application relates to the technical field of wisdom community data management, in particular to a wisdom community metadata interaction method and system based on an edge computing framework, comprising: dynamic storage of multidimensional data using hierarchical rolling cache; generation of a uniform resource vector through time sequence progressive mapping; addition of community semantic labels to the uniform resource vector through extensible label slots, and establishment of connections according to association rules to build a resource semantic graph oriented to community services; reception of resident task requests, construction of a multi-factor weighted scoring engine based on the resource semantic graph, and generation of a task data packet with a priority label; construction of a dual-channel hybrid scheduling network, output of resource availability and node correlation degree in the first channel, and output of time sequence characteristics and load prediction parameters in the second channel; and fusion of the dual-channel outputs to obtain task-node correlation and generate an optimal matching strategy. The present application realizes task collaboration and resource adaptive management through semantic resource modeling and hybrid scheduling.

The invention relates to the technical field of machining and intelligent manufacturing, and discloses a heat treatment center hole quality detection method and system for a radial drilling machine machining shaft, and the method comprises the steps: S1, obtaining the processing data of the shaft on a radial drilling machine, and carrying out the cleaning; s2, extracting multi-dimensional features of the cleaned data to generate a quality feature vector; s3, inputting the feature vectors into a quality scoring model to output a comprehensive score and a risk coefficient, and dynamically calculating the scoring model by adopting a decision tree algorithm in combination with a weight factor; and S4, executing response operation by the quality control engine according to the scoring risk mapping rule. The system comprises a data acquisition module, a feature extraction module, a scoring engine, a quality control engine, a context sensing module and a response execution module. According to the method, the quality risk of subsequent processing is reduced, the defect identification accuracy is improved, and the production efficiency and the product percent of pass are improved.

Aspects related to an amalgamation platform providing concealed detection of code-passing using steganography are provided. An amalgamation platform may train a scoring engine to generate suspicion scores for code snippets and an amalgamation engine to generate execution scenarios for the code snippets. The platform may embed code in base code of a network using steganography. The platform may use the steganographic code to detect transmission of a code snippet. The platform may generate a suspicion score for the code snippet. The platform may update the scoring engine based on identifying the suspicion score satisfies a threshold. The platform may generate execution scenarios for the code snippet. The platform may determine a match between an execution scenario and a malicious code scenario. The platform may identify the code snippet as malicious and update the amalgamation engine based on the match. The platform may initiate security actions based on the match.

The invention relates to the technical field of network security, and discloses an SSL / TLS digital certificate health degree intelligent scoring system based on a multi-dimensional weighting model, and the system comprises a scoring engine layer which is used for calculating a health degree score based on the data of each dimension and the weight of the data, and judging a health degree grade; and the intelligent analysis layer is used for diagnosing safety problems and generating improvement suggestions. According to the SSL / TLS certificate security state evaluation method, a weighting model covering five dimensions of encryption strength, validity period, configuration, deployment and compliance is constructed, so that comprehensive and quantitative evaluation of the SSL / TLS certificate security state is realized; a 0-100 score credit score mechanism and a four-level grading system are adopted, so that the security situation is clear at a glance; through a grading alarm mechanism based on comprehensive health degree changes and key indexes, risks can be actively found; and the root cause of the safety problem can be automatically diagnosed, and specific and operable improvement suggestions with priorities are provided, so that the operation and maintenance difficulty and the dependence on professionals are remarkably reduced.

A method and system for location optimization for running application code include a system resource utilization engine collecting mainframe monitoring data. A workload operational key performance indicator (KPI) engine collects the mainframe system monitoring data, application code identifying information, and a workload definition. The system resource utilization engine and the workload operational KPI engine generate a computing workload model for the workload definition. A performance analyzer performs a first analysis of the computing workload model. An overhead analyzer performs a second analysis of the computing workload model. A target venue score engine generates a target venue score for each of one or more target venues using the first analysis and the second analysis.

This invention relates to the fields of ecological environment monitoring and geographic information technology, and particularly to a method and system for quality assessment of coastal wetland soil sampling points based on multi-source environmental data. The method integrates remote sensing, climate, topography, marine, and historical land cover data, extracts and standardizes indicators, and constructs environmental similarity scores (weighted Mahalanobis / nearest neighbor weighted Euclidean), historical consistency scores (multi-temporal cover + migration penalty), and source reliability scores (metadata integrity + anomaly detection + NDVI-SOC residual). An adaptive weighted fusion is used to obtain a comprehensive score and classify the data into A / B / C levels, outputting a report and a high-quality sampling point set. The system includes data access, a scoring engine, and a visualization module, which can significantly improve the accuracy and robustness of soil mapping and carbon storage assessment.

An artificial-intelligence-based decision-assisting system and method are disclosed for generating ranked recommendations through adaptive multi-source analysis. The system includes a processor and a non-transitory computer-readable storage medium storing executable instructions that implement a scoring engine and a ranking engine. Decision parameters and user-defined weighting factors are received from user devices and combined with system-defined weighting factors retrieved from behavioral, historical, external-context, and scoring-criteria databases. Composite decision scores are computed and used to rank candidate options. The system iteratively updates weighting factors or rankings based on feedback data and dynamically restricts data retrieval to relevant parameters to reduce latency and improve throughput. Results are displayed via a graphical user interface, and anonymization procedures protect user identity. The method supports concurrent processing and adaptive learning to refine decision predictions.

The invention discloses a normal distribution-based standard quantitative score optimization system, method and device and a storage medium, and the system comprises the steps: when a large model module receives batch composition pictures transmitted by an input processing module, carrying out the multi-dimensional feature analysis of each composition picture, and outputting the multi-dimensional score of each composition picture; a multi-dimensional scoring engine calculates a multi-dimensional quantitative initial total score of each composition picture according to the multi-dimensional scores; and the score optimization module performs composition ranking according to a preset ranking rule according to the multi-dimensional quantized initial total score, and optimizes the multi-dimensional quantized initial total score of each composition picture according to a composition ranking result based on normal distribution. According to the method, through a coupling mechanism of AI ranking generation and normal distribution scoring, the problem of scoring stiffness caused by excessive dependence on a fixed scoring rule in a traditional system is solved, and fair and flexible automatic scoring conforming to an education evaluation rule is realized.

The invention belongs to the technical field of computers, particularly relates to a software vulnerability availability evaluation system fusing multiple features, and aims to solve the problems of single evaluation dimension, high false alarm rate, lack of dynamic context perception and the like. The system comprises a vulnerability feature extraction module, an environment context perception module, an attack path modeling module, an availability scoring engine and a dynamic feedback optimization module, constructs an attack path model of environment perception by fusing static code features and a dynamic operation environment, and optimizes an attack path based on multi-dimensional quantitative scoring and online weight optimization. High-precision and self-adaptive vulnerability availability evaluation is realized, the false report and missing report rate is remarkably reduced, and the safety operation and maintenance efficiency is improved.

The application discloses a tumor multidimensional evaluation and report generation system and method based on a unified workflow, relates to the technical field of clinical auxiliary decision and medical workflow optimization, and comprises a unified workflow entrance module, a tumor type identification module, a special evaluation engine scheduling module, a special evaluation engine library module, an evaluation result collection module, an intelligent report generation module, a decision support module and a workflow state visualization module, wherein the modules are connected in series. The unified workflow entrance module provides unified data management; the special evaluation engine library module is internally provided with multiple scoring engines for different tumor types and different diagnosis and treatment scenes; and the intelligent report generation module automatically generates a complete evaluation report. The application can effectively solve the problem of fragmentation of the tumor multidimensional evaluation workflow, effectively realize report integration, and enhance the clinical decision support capability.

The invention relates to the field of competitive scoring, in particular to a competitive scoring system and a competitive scoring method. The system comprises a sensing acquisition module, a bimodal scoring engine, a dynamic compensation module, an action quality evaluation module and a data collaboration bus, the invention further discloses a competitive scoring method executed by adopting the system. The competitive scoring method comprises the steps of bimodal signal synchronous acquisition, pressure-track fusion judgment, dynamic contact area compensation, action normalization correction, comprehensive score generation and the like. According to the method, the technical effects of realizing four-dimensional data real-time interaction of pressure, track, area and normalization in competitive scoring, generating a comprehensive score, dynamically adjusting a force threshold according to the contact area, filtering noise interference, evaluating action normalization, manually correcting and marking dispute fragments and the like are achieved.

The present application belongs to the field of network technology and security, and particularly relates to a scene-based self-learning malicious request detection method and system. The method mainly includes scene-based one-key configuration, detection granularity and self-defined configuration of strategy, and an integrated scoring engine combining abnormal score obtained from different detection strategies. In addition, the method also generates configuration feedback suggestions based on traffic self-learning, and presents them to customers in a clear and operable manner. Customers can adjust the configuration according to the feedback suggestions, generate personalized scene solutions for easy transposition, and perform gray test before delivery to ensure the availability of the current scene configuration. The present application realizes flexible and adaptive configuration of business scene capability by detailed division of different scene requests, and combination of different traffic restrictions and different detection functions for different scenes, thereby improving the scene security detection capability and reducing the false negatives and false positives caused by general configuration.