50 results about "Scoring algorithm" patented technology

The invention relates to the technical field of risk management, and discloses a financial AI large model-oriented multi-dimensional test intelligent risk management and control method, which comprises the steps of integrating channel test, data test, AI test and AI large model test by constructing a financial AI large model test special database; traditional financial sensitive fields and AI test specific sensitive fields are accurately identified based on data consanguinity analysis, and dynamic desensitization is realized in combination with a lightweight desensitization engine; calling a preset risk scene library, defining a risk index system of each scene, dynamically calculating a risk level through a weighted scoring algorithm, and carrying out modeling by using a graph database to identify hidden linkage risks; triggering a grading response strategy according to the risk grade, and dynamically optimizing resource allocation; and after the test is finished, a multi-dimensional risk thermodynamic diagram and a root cause analysis report are generated, and processing experience is fed back to the scene library optimization strategy. According to the invention, the efficiency and safety of risk management and control can be improved.

The invention provides a user portrait method and device for driving training learning, electronic equipment and a medium, and belongs to the technical field of information recommendation, and the method comprises the steps: collecting multi-source heterogeneous data of a student, carrying out the data preprocessing, and obtaining a student data file, the multi-source heterogeneous data comprises learning stage data, capability evaluation data and basic attribute data; based on the trainee data file, extracting two-dimensional features of trainees, including obtaining learning stage features according to division of passing states and learning progress completion rates of driving test subjects, and quantifying the mastering degree of subdivided test points under weak subjects based on a weighted scoring algorithm to obtain weak subject features; and fusing the two-dimensional features and the basic attribute data, and constructing a student portrait including a learning stage label and a weak subject label. According to the invention, the accuracy of the user portrait is improved.

The invention discloses a remote monitoring and scheduling management and control platform for signal shielding equipment, which comprises a general control center and four linkage sub-modules, wherein the four linkage sub-modules realize data interaction with the general control center through an encryption special communication protocol; the four linkage sub-modules are respectively a secret-involved subject permission shielding linkage module, a multi-scene shielding mode adaptation module, a precise anti-secret-stealing shielding module and a full-link monitoring traceability module; the general control center is internally provided with a multi-dimensional confidential risk decision-making engine, the decision-making engine is integrated with a weighted scoring algorithm, and accurate binding of confidential subject qualification and a shielding strategy is achieved. Based on a double-biological-characteristic cross verification mechanism and carrier circulation management and control logic, personnel confidentiality qualification, carrier confidentiality-related state and shielding permission are deeply associated, confidentiality-related risks caused by access of non-qualified personnel are completely eradicated, meanwhile, shielding strategy automatic switching of the whole carrier access process is achieved, the problem that platform personnel permission is disjointed from a protection strategy is solved, and the safety of the platform is improved. And the basic protection safety of a secret-related area and a carrier is greatly improved.

The invention discloses an AI-based schizophrenia clinical thinking training simulation system, which relates to the technical field of artificial intelligence, and is characterized by comprising the steps of constructing a dynamic knowledge base, receiving and preprocessing multi-modal input, performing standardized mapping by extracting keywords, and then performing judgment of a rule layer and a semantic layer. A student question method and a standard answer method in the dynamic knowledge base are sent into a model for response generation, then four-dimensional quantitative scoring and clinical operation multi-dimensional scoring are carried out, error codes and positioning sentences thereof are output, and finally personalized training courses are recommended to students; and hot-updating the dynamic knowledge base continuous optimization model. According to the method, four dimensions of the ICD-10 diagnostic standard are converted into a quantitative scoring algorithm capable of being automatically executed, quantitative assessment of clinical operation specifications is integrated, a diagnosis and operation double-track evaluation system is constructed, the problems that a traditional method is lack of evaluation standards and high in subjectivity are solved, and the evaluation efficiency is improved. Therefore, the evaluation of the clinical thinking ability of the medical students becomes objective, comprehensive and evidence-based.

Disclosed herein are systems and methods for generating an Al work score for tasks assisted by artificial intelligence. The score is determined by collecting and processing user data using scoring algorithms that consider at least one of the following: Al integration, task complexity, baseline metrics, and time calibration. The system and methods also generate composite scores across users, tasks, groups or industries and classifications across various metrics, enabling correlation reports. These scores can assist in evaluating Al integration, optimizing task assignments and improving Al usage in supervised environments.

The application relates to a kind of audio and video acquisition terminal position auxiliary installation method and system, wherein the audio and video acquisition terminal position auxiliary installation method includes: audio and video acquisition terminal receives the scoring algorithm program that platform server issues and matches target application scene;In the program is run locally in audio and video acquisition terminal and real-time analysis is carried out to self live video stream, and objective position visual angle score is generated;The score is sent to the client to guide the client user to adjust position.By the application, the problem that the result is not accurate and the feedback is not timely caused by model difference and network delay in cloud side evaluation is solved, and the pain point that evaluation standard is not unified in artificial subjective installation is solved, real-time, accurate and quantitative installation process is realized, so as to improve installation efficiency, quality and recognition accuracy of back-end AI algorithm.

A method is disclosed. The method includes generating a graph with nodes and edges, inputting the node data for the nodes into a first scoring algorithm to obtain node scores, and inputting the edge data into a second scoring algorithm to obtain edge scores. The method also includes forming one or more subgraphs from the graph. Each subgraph is formed by: determining an initial node with a node score that exceeds a threshold, determining an initial set of edges connected to the initial node, determining a set of nodes connected to the initial set of edges, and determining additional edges and additional nodes extending from the set of nodes, until a predetermined number of the additional nodes and edges have edge scores and node scores that do not meet the threshold, thereby forming the subgraph.

The embodiment of the invention discloses an agent knowledge structure determination method and device, equipment, a medium and a product. The method comprises the steps that scene data and task data of a target agent in a current operation task scene are acquired; inputting the scene data and the task data into an agent knowledge structure recommendation model to obtain at least one recommendation knowledge structure corresponding to the target agent; the recommended knowledge structure is a knowledge structure defined in a preset knowledge structure library; determining a scoring result corresponding to the recommended knowledge structure by using a preset scoring algorithm; and according to the scoring result, determining a target knowledge structure adopted by the target agent in the current operation task scene from the recommended knowledge structure. According to the technical scheme, the skills of the intelligent agent are accurately improved, and the ability of the intelligent agent to adapt to different industries and scenes is improved.

The invention discloses a grabbing method, device and equipment based on a motion intention and concentration degree and a medium, and the method comprises the steps: obtaining a training sample set of an intention level and a grabbing power level, and training a support vector machine model through the training sample set; performing standardization processing on the first energy ratio feature sequence, the first original feature sequence and the first intrinsic mode feature sequence by adopting a Z-Score algorithm to obtain a second energy ratio feature sequence, a second original feature sequence and a plurality of second intrinsic mode feature sequences; and performing classification decision by using the target support vector machine model to obtain an intention level, simultaneously obtaining a user concentration average value, and determining a target output holding power according to the user concentration average value and the intention level. And the stress state of the grabbed target is detected in real time, and PID operation is carried out. Fine hierarchical control of the grabbing force is achieved, the recognition precision of the grabbing intention is improved, and the problem that traditional brain control equipment grabs but cannot stably hold is solved by conducting anti-falling PID operation in real time.

The present application provides a professional ability evaluation system based on a skill map, relates to the technical field of human resources and the application field of knowledge maps, and comprises an evaluation main system, which comprises a skill star map generation module, a professional track simulation module, a skill challenge module, a skill certification chain module, a future skill navigation module, a skill coordination ecological module, a skill evolution tree module, a professional story generation module, a skill energy pool module, a professional time capsule module and a control flow management module. The present application can collect user professional experience, learning records and test data in time by constructing a dynamic skill star map and combining 5G network real-time data transmission, and can effectively overcome the problems of strong subjectivity, low efficiency and inability to accurately quantify ability of traditional manual evaluation and online testing by dynamically adjusting node weights through a semantic analysis algorithm and a weighted scoring algorithm.

This invention relates to a method and system for evaluating the integrity of historical urban landscape based on deep learning. The method includes: acquiring urban street view image data from multiple locations; extracting the sky ratio and green view rate of the urban street view images as street-scale spatial features and natural element spatial features, respectively; calculating the number of shop sign colors and color harmony as architectural color spatial features; and calculating the proportion and consistency of traditional materials as material mechanism spatial features; obtaining expert scores based on the ELO scoring algorithm for the urban street view image data; constructing and training an evaluation model based on an artificial neural network based on the various spatial features and a small-scale expert score dataset; and using the evaluation model to evaluate the integrity of the historical urban landscape.

A method is disclosed. The method includes receiving, by a server computer, a plurality of images associated with one or more service providers. The server computer then receives an inquiry request, and determines an image of the plurality of images. The image is selected in response to a composite score based on a scoring algorithm scoring each image in the plurality of images. The scoring algorithm comprises a conversion component and an uncertainty component. The server computer provides an inquiry response comprising the image to the end user device operated by the end user.

This invention provides a method and system for detecting and warning of anomalies in encryption protocols, belonging to the field of network security technology. The method includes: extracting a multimodal feature set from the target encryption protocol communication stream; inputting it in parallel into an adaptive rule engine and an interpretable artificial intelligence model to obtain rule-triggered events and confidence weights, a first anomaly score, and an attribution explanation report, respectively; calculating a second anomaly score based on the above outputs using an improved scoring algorithm; fusing the first and second anomaly scores to obtain a comprehensive anomaly score and generating a warning decision; and uploading the warning information to a federated collaborative node to drive the collaborative evolution of the global rule base and the model. This invention, employing the aforementioned method and system for detecting and warning of anomalies in encryption protocols, solves the collaborative problems of poor interpretability, lagging knowledge updates, and data silos in encryption threat detection through deep collaboration between an interpretable artificial intelligence model and a rule engine, achieving accurate, reliable, and adaptive anomaly warnings.

Methods and apparatuses for improving the scoring and ranking of documents that are responsive to a search query submitted to a search system are described. The search system may utilize a scoring algorithm to score and rank a set of documents that are relevant to the search query. The scoring algorithm may take into consideration topicality, data source affinity, and document popularity. Estimates for both the data source affinity and document popularity may be computed at a group level for different groups within an organization.

The invention discloses a structured safety assessment report intelligent generation system, method and device, and relates to the technical field of road traffic safety. Comprising a data input and preprocessing module, a multi-modal semantic mapping and understanding engine, a domain knowledge base module, an LLM-based report generation and suggestion engine, a dynamic compliance verification and alignment module and a structured report output and management module. The problems that a traditional road safety assessment report is low in writing efficiency, poor in normalization, insufficient in data utilization, lack of dynamic compliance verification, insufficient in operable suggestions and the like are solved, the system comprises a data input and preprocessing module, a multi-modal semantic mapping and understanding module and the like, and through the technologies of a Z-Score algorithm, LLM field fine adjustment, a graph neural network, dynamic compliance verification and the like, the real-time performance of the road safety assessment report is improved. Functions of data cleaning, multi-modal semantic fusion, compliance report generation and the like are realized, the report period is automatically shortened in the whole process, the content specialty, compliance and suggestion operability are improved, and the method is suitable for highway traffic safety management.

The application discloses a data processing method in device management and an electronic device, relates to the technical field of data processing, and comprises the following steps: obtaining target monitoring data of each target device in a target device set, and converting the target monitoring data into a target feature vector capable of quantitatively representing data integrity of the target monitoring data according to a preset data model; grouping the target devices according to the target feature vectors corresponding to the target devices, determining a target device group and a target reference vector corresponding to the target device group; determining a target score of each target device by using a preset scoring algorithm and according to the target reference vector corresponding to each target device group and the target feature vector corresponding to each target device; and determining a target abnormal device according to the target scores of the target devices. Compared with judging data integrity by relying on artificial spot checks, the target monitoring data of the target devices is converted into target scores, the target abnormal device is identified according to the target scores, and the efficiency of identifying abnormal devices is improved.

This invention presents a method and system for extracting highly timely time-series data from multi-source heterogeneous networks. The system employs asynchronous message queues to achieve loosely coupled distributed collaborative operation of five functional modules: web crawling, structured parsing, data cleaning and mapping, task scheduling, and data storage. By integrating a multi-mode crawling engine with static, dynamic rendering, and API interface calls, the system solves the compatibility problem of heterogeneous data sources. The structured parsing module uses a hybrid parsing approach combining rule bases and a BERT-based natural language processing model, significantly improving the intelligence and accuracy of entity extraction from unstructured text. The data cleaning module uses the statistical Z-Score algorithm to dynamically detect outliers in time-series data, ensuring the reliability of the stored data. The task scheduling module supports incremental updates and failure retry mechanisms, greatly improving system throughput and operational stability. This invention achieves automated, highly reliable, and highly timely time-series data acquisition, cleaning, and structured storage through an advanced modular architecture, AI-driven parsing technology, and statistical data quality control mechanisms.

The invention discloses an underground parking lot management system based on multi-sensor fusion and computer equipment, and the system comprises a parking space recommendation and navigation module which is used for receiving a parking request of a user, and generating a parking space recommendation result and an initial navigation path through a weighted scoring algorithm based on a preset evaluation index; the data acquisition module is used for continuously acquiring multi-source detection data including vehicle positioning data, parking space state sensing data and visual identification data after the vehicle enters the monitoring area of the parking lot; the fusion judgment module is used for fusing the multi-source data by adopting a preset fusion algorithm and judging the state of the recommended parking space in real time; and the dynamic scheduling module is used for responding to the condition that the parking space state becomes unavailable, re-executing the weighted scoring algorithm to generate a new recommended parking space and updating a navigation path. The technical problem that dynamic guide adjustment cannot be realized when the parking space state is abnormal due to inaccurate perception and decision lag in the prior art is solved.

The invention discloses a national defense mobilization knowledge base question answering method and system based on structured retrieval enhancement, and relates to the technical field of artificial intelligence, natural language processing and national defense informatization. Performing dynamic mode induction on the multi-source heterogeneous mobilization data by utilizing a large language model, and automatically extracting entity, attribute and relationship triples; constructing a hybrid index system comprising structured storage and unstructured storage; performing conflict resolution and confidence evaluation on the multi-source data through a weighted scoring algorithm based on timeliness, authority and consistency; and performing hybrid routing retrieval according to the query intention of the user to generate an answer with a traceability link. According to the method, the accuracy, the traceability and the logical reasoning ability of complex decision query are remarkably improved, and the robustness and the adaptability of mobilization decision are enhanced.

The invention provides a method of obtaining a color sub-palette from a first mood board comprising one or more digital color images, the method comprising the steps of: (1) generating a first color palette having k1 representative colors from the first mood board by a method that takes into account under-represented colors, (2) providing a second color palette having k2 colors, (3) ranking the colors in the first color palette according to a scoring algorithm that assigns a score to each color in the first color palette based on the color difference of each color in the first color palette to each color in the second color palette, and (4) selecting a subset of k sub colors from the first color palette, the subset having scores corresponding to the smallest color differences to the colors in the second color palette, the subset of colors being a color sub-palette. The invention also provides a data processing device and a computer program product.

The application discloses a comprehensive index evaluation method and device for a wind turbine device. The comprehensive index evaluation method comprises: obtaining operation data of the wind turbine device and boundary data for each operation index, wherein the operation data comprises device information of the turbine device, turbine device operation data and operation environment data during a preset time period; inputting the turbine device operation data and operation environment data into a preset classification algorithm to obtain a plurality of operation index matrices corresponding to each comprehensive index in a plurality of comprehensive indexes; according to the boundary data for each operation index, the device information and the plurality of operation index matrices are aggregated into operation index data for the preset time period; and inputting the operation index data into a preset weighted scoring algorithm to obtain comprehensive index values of the plurality of comprehensive indexes, which are used as operation performance of the wind turbine device.

The invention discloses an automatic building appearance modeling method and device based on image processing, and relates to the technical field of building image modeling, and the method comprises the steps: generating a region and style fine tuning model based on an AI drawing model, carrying out the lightweight adjustment of the AI drawing model through employing a low-rank adaptation technology, carrying out the pre-training of the AI drawing model through employing a building image data set, and carrying out the modeling of the AI drawing model. The AI drawing model automatically generates a plurality of design scheme diagrams, evaluates the quality of the plurality of design scheme diagrams, deletes the design scheme diagrams which do not meet the requirements, processes the reserved design scheme diagrams, and finally obtains the design scheme diagrams which do not meet the requirements. And displaying the reserved design scheme diagram sequence to a user through a machine end interface. According to the modeling method, through fine adjustment, super-resolution optimization and scoring algorithm evaluation of the AI drawing model, a high-quality and diversified building appearance scheme meeting design requirements can be generated, and the design efficiency and precision are remarkably improved.

The application discloses a mobile application platform optimization decision-making method and system based on evaluation results, and belongs to the technical field of mobile application quality management and control and intelligent optimization.The application constructs a multidimensional comprehensive evaluation system covering user experience, business energy efficiency, running performance, stability, maturity and activity, adopts a non-invasive probe to complete full-quantity data collection, realizes quantitative evaluation through a weighted comprehensive scoring algorithm, completes accurate positioning of abnormal root causes based on a coupling algorithm of an improved analytic hierarchy process-entropy weight method, automatically matches an optimization strategy library to generate a hierarchical optimization decision-making scheme, and forms a full-closed-loop management of evaluation-analysis-optimization-verification.The application solves the problems of fragmentation of the evaluation system, low accuracy of root cause positioning and lack of a closed-loop optimization mechanism in the prior art, is suitable for enterprise-level mobile application platform whole-life-cycle quality management and control, and can significantly improve application stability, operation and maintenance efficiency and user satisfaction.

The invention belongs to the technical field of bioinformatics and computational biology, and particularly relates to a dynamic quantile standardization method and system for cross-platform heterogeneous data and application of the dynamic quantile standardization method and system. Through a gene expression scoring algorithm, the method can effectively integrate RNA expression data of different platforms, batches and formats. Gene expression values are sorted and scored, and batch effects and background differences among different data sets are eliminated, so that more accurate and reliable integrated data are generated. The method is simple, convenient and high in adaptability, can process multiple types of gene expression data, does not need to depend on complex model hypothesis or deep learning technology, and remarkably improves the accuracy, reliability and universality of data analysis.

The invention relates to the technical field of behavior data credibility evaluation, in particular to a behavior data credibility evaluation system based on time sequence row feature fusion, which is characterized in that an input capture unit captures year and month sub-node input time sequence data of starting and ending time of a project filled by a user in real time; the interrupt monitoring unit detects a system pop-up window and a user active pause as external interrupt, and records an interrupt duration and a continuous filling starting point, and the feature fusion unit performs dynamic time sequence window segmentation and cross-modal alignment; a time sequence behavior feature vector containing a year priority mark, a month lagging time consumption ratio, a cross-year modification time consumption effect difference value and a continued time consumption deviation rate is generated, a credibility evaluation unit judges a behavior mode by means of a four-dimensional decision matrix, a bidirectional threshold comparison and scoring algorithm is detected through a logic fault, a filling behavior authenticity probability value is output, and the filling behavior authenticity probability value is calculated. And the output feedback unit feeds back the probability value to the evaluation system according to sectional risk mapping, so that risk domain differentiation warning is realized, and the credibility evaluation precision is improved.

The application discloses a membrane shell design method of a dense-rib floor based on numerical simulation and relates to the technical field of the membrane shell design of the dense-rib floor, which comprises the following steps: screening a plurality of longitudinal rib parameter combinations; building a finite element numerical simulation model, carrying out self-adaptive mesh division, and then outputting simulation results of the longitudinal rib parameter combinations; screening an optimal longitudinal rib combination by using a multi-target optimization scoring algorithm, and if the deformation requirement is not met, entering the next step; setting a plurality of transverse rib parameter combinations; adding the numerical simulation model corresponding to the optimal longitudinal rib combination, outputting simulation results of the transverse rib parameter combinations; screening an optimal transverse rib combination; and combining the optimal longitudinal rib combination to form a final membrane shell scheme. By establishing double-level finite element numerical simulation of the longitudinal rib parameter combination and the transverse rib parameter combination, combining self-adaptive mesh division and a multi-target optimization scoring algorithm, the design efficiency and the design precision are improved while the design strength of the membrane shell of the dense-rib floor is ensured.

The application discloses an intelligent medicine recommendation method and device based on semantic vector search, and belongs to the technical field of intelligent medicine recommendation. The method converts medicine information and user natural language queries into high-dimensional semantic vectors through a pre-training language model optimized in the medical field; a comprehensive query representation is constructed by fusing medical exclusive features and personalized preferences; a hybrid strategy combining vector retrieval and keyword indexing is adopted to obtain a preliminary recommendation set; a multidimensional comprehensive scoring algorithm containing medical safety verification is used for secondary screening and sorting; a user feedback reinforcement learning mechanism is introduced to realize dynamic optimization of the model; and the device corresponds to a medicine information processing module, a user query processing module, a vector database and a matching and recommendation engine module. The application improves complex semantic understanding capability, realizes personalized and accurate recommendation, guarantees medicine safety, considers both search efficiency and scene adaptability, and is suitable for internet medical and medical e-commerce scenes.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause a system to perform a method for analyzing media content, the method comprising: ingesting, via one or more web scraping techniques, media content from one or more online sources; preprocessing, via one or more natural language processing tools, the media content comprising the steps of: tokenizing the media content into a plurality of components, removing stop words from the plurality of components, lemmatizing the plurality of components, and normalizing the plurality of components; analyzing, via the one or more natural language processing tools, the preprocessed media content for one or more sentiments; generating, via one or more scoring algorithms, a score for each of the one or more sentiments; compiling the score for each of the one or more sentiments to generate a final score; and displaying the final score on one or more client devices.

The invention discloses a radar fault early warning self-checking method and device based on semantic driving and a medium, and belongs to the technical field of radar fault early warning. The method comprises the steps of obtaining operation state data of a radar system based on a preset acquisition frequency; processing the operation state data based on a trained machine learning algorithm to generate early warning information; after the prediction occurrence time window ends, calling a historical record interface to query alarm information and fault list data in the time window; performing semantic analysis and matching on the early warning information, the alarm information and the fault list to determine a semantic matching result; processing the semantic matching result based on a preset comprehensive weighted scoring algorithm to determine a confidence score; and if the confidence score exceeds a preset threshold, marking as high-confidence risk early warning and pushing to a preset operation and maintenance platform. According to the method, the technical effects of intelligent semantic matching and real-time self-checking of the early warning information and the fault evidence are achieved.