294 results about "Scoring system" patented technology

The invention belongs to the technical field of automobile parts, and discloses an automobile part enterprise supply chain risk early warning method based on artificial intelligence. Comprising the steps that supply chain data are collected and processed, and a graph is constructed; evaluating the suppliers based on the atlas to generate a portrait matrix; on the basis of the portrait matrix and in combination with the production parameters, model training is performed, a prediction engine is constructed, and a part quality risk prediction result is generated; performing anomaly detection on the nodes to form a monitoring network, and generating a risk assessment result; constructing a supply chain network topology model based on the map, and performing risk propagation path analysis to generate a risk conduction map; establishing a risk assessment model, integrating the risk prediction result, the risk assessment result and the risk conduction diagram, and performing integrated assessment on the risk of each link of the supply chain to form a scoring system; based on a scoring system, a dynamic risk early warning threshold is generated, a risk response decision tree is constructed, intelligent risk response suggestions are provided, and the enterprise risk disposal efficiency is improved.

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

A computing system for adaptive electronic message classification employs a multi-agent architecture comprising a media feature analysis system, a user context refinement system, and a response synthesis system. The media feature analysis system generates pillar scores including message type, intent, and link risk scores with associated confidence values using trained classification models. When pillar scores and confidence values do not satisfy predetermined threshold conditions, the user context refinement system dynamically constructs contextual prompts using the pillar scores and confidence values as input parameters. User responses generate score modification data that refines the pillar scores and contextual response data for recommendation generation. The response synthesis system generates refined classifications and personalized recommendations using the refined pillar scores and contextual response data. An orchestration system coordinates agent interactions using learned uncertainty points and implements asymmetric influence algorithms with variable weighting based on content and URL analysis concordance.

In some embodiments, a configured artificial intelligence system includes a plurality of intelligence models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards. The intelligence controller may be configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics.

The invention relates to an intelligent interview scoring system capable of explaining decisions based on a large language model, and the system comprises a multi-mode resume analysis and feature coding unit, a resume feature adaptive matching unit, an interactive scoring and knowledge enhancement unit, and an answer quality evaluation unit. Text, image and audio features are extracted through a cross-modal attention mechanism of a multi-modal large language model, resume features are encoded into dynamic word vectors, and entity-level feature vectors are extracted; the post description text is encoded into a demand feature vector by a resume feature adaptive matching unit; calculating semantic similarity between the resume entity feature vector and the demand feature vector; the interactive scoring and knowledge enhancement unit dynamically retrieves knowledge fragments to generate a preliminary evaluation report containing a scoring basis; and the answer quality evaluation unit fuses the information density, the fluency and the integrating degree to generate a final score. And the whole-process intelligence from demand analysis to final decision making is realized.

The invention relates to the technical field of data visualization, in particular to a multi-modal fusion anorectal data visualization analysis method and system, and the method comprises the following steps: synchronously collecting an anorectal image and a pressure signal, aligning a timestamp, carrying out the preprocessing, outputting a standardized image and a coded physiological signal, and extracting edge and texture features. After LSTM coding, splicing with image features, and fusing into a joint feature map; generating a multi-scale heat map by the feature pyramid network, and coarsely positioning a lesion; after boundary optimization, U-Net recovers details, and a pixel-level lesion probability graph is output; superposing the Jet color gradation to the original image in a semitransparent manner, and marking a pressure abnormal time period; and performing weighted scoring to generate a fourth-level clinical suggestion. According to the anorectal disease diagnosis method, through multi-modal space-time alignment, multi-scale feature fusion, boundary optimization and a quantitative scoring system, the problems of data splitting, insufficient precision and low efficiency of a traditional method are solved, a high-precision and high-robustness intelligent diagnosis tool is provided for anorectal diseases, and the scientificity and efficiency of clinical decision making are remarkably improved.

The invention relates to the technical field of voice processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a multi-speaker dialogue voice analysis method, device and equipment and a medium, and the method comprises the steps: obtaining a to-be-analyzed multi-speaker dialogue voice, determining a naturalness score based on an acoustic feature, and obtaining a multi-speaker dialogue voice analysis result; determining a semantic consistency score based on voice embedding and semantic embedding corresponding to a preset text, determining a speaker consistency score based on embedding of a plurality of speakers of the same speaker, determining an interaction rationality score based on voice alternate overlapping duration, determining a diversity score based on a variance of voice features, and fusing the scores, the comprehensive mass fraction is obtained. According to the invention, through quantitative evaluation of five dimensions of naturalness, semantic consistency, speaker consistency, interaction rationality and diversity, a comprehensive quality scoring system is established, so that the evaluation result simultaneously reflects voice fluency, content matching degree, identity stability, interaction rhythm rationality and feature richness.

The invention relates to the technical field of financial risk control, and discloses a telecommunication fraud risk identification method based on a bank card transfer scene. The method comprises the following steps: a basic feature construction stage: acquiring multi-source data of historical victim users, extracting multi-dimensional features, and processing the multi-source data into standardized time sequence data through feature coding and a DTW algorithm; in the multi-modal fusion and adversarial learning stage, a core layer containing a multi-modal analysis engine, a risk reasoning model and an adversarial generation model is constructed, and multi-modal feature fusion, risk probability output under an RLHF framework and simulated fraud feature generation driven by WGAN-GP are achieved; in the strategy output stage, risk scores are mapped through a double-layer scoring system, and three-level interception is triggered; in the model management stage, model iteration is achieved by means of a monitoring instrument panel and a rolling time window, the online effect is guaranteed by combining gray release and A / B testing, the telecommunication fraud in the transfer scene can be accurately recognized, and the risk control efficiency is improved.

The invention discloses a method for reconstructing an ultrahigh-resolution pathological full slice. The method comprises the following steps: acquiring an effective tissue area image of a pathological tissue fragment by utilizing a fusion mask; an edge classification model and an image enhancement strategy are adopted to obtain an effective splicing edge of the effective tissue area image; constructing a multi-dimensional matching scoring system to calculate a geometric matching degree of a splicing edge, and screening out an optimal Top-N splicing scheme in combination with splicing quality evaluation of a deep neural network; and based on a genetic algorithm, combining global topological optimization, sub-pixel level fine tuning and a multi-dimensional matching scoring system to evaluate splicing quality, realizing iterative stitching of the effective tissue area image, and finally outputting an optimal splicing scheme. Reconstructing a full-slice image of the pathological tissue based on the optimal splicing scheme; the method has efficient and accurate automatic splicing and iterative optimization capability, and rapid reconstruction of the ultrahigh-resolution pathological full slice is realized.

The invention provides a contaminated site remediation technology scoring system which comprises a multi-dimensional evaluation index system construction module, a situational weight dynamic adjustment module, a remediation technology applicability scoring module, an uncertainty analysis and sensitivity test module, a scoring result visualization module, a technical scheme sorting and screening unit and the like. Through four innovations of a dynamic weight adaptation mechanism, a multi-model fusion scoring algorithm, a technology combination scheme evaluation module and uncertainty robust analysis, the problems of single dimension, weight solidification, difficulty in quantification of a synergistic effect and the like in pollution remediation technology evaluation are systematically solved; and an objective, efficient and transparent intelligent decision-making tool is provided for repairing a complex polluted site.

The invention discloses an equipment intelligent health state evaluation method, which comprises the following steps: S1, collecting data, and preprocessing multi-source heterogeneous data to form a standardized relational data set; s2, performing multi-index gradient assignment, and performing assignment on core indexes through reference value setting and a linear interpolation method; s3, carrying out comprehensive weight distribution on each core index by adopting a mixed weight distribution mechanism; and S4, constructing a CNN-LSTM hybrid prediction model, extracting key time sequence features in the equipment production operation data through a CNN layer, processing time sequence data through an LSTM layer, learning a long-term dependency relationship, predicting a future equipment health state score, and providing a maintenance suggestion. By constructing a multi-dimensional and dynamic equipment health state scoring system, the problems that a traditional evaluation method is high in subjectivity and insufficient in quantification are solved, transformation from passive maintenance to predictive maintenance is achieved, meanwhile, a visual health report can be generated, and a manager can be assisted in rapidly positioning weak links of equipment and making an optimization strategy.

The invention discloses a multi-source heterogeneous data flow real-time fusion and weight adaptive correction system and method, and relates to the technical field of multi-source data fusion. Comprising a path monitoring data extraction module, an initial fusion weight determination module, an abnormal transportation event analysis module, an initial correction strategy table generation module, a response updating module and a real-time weight adjustment fusion module. According to the method, a multi-dimensional data quality scoring system is established, so that strong association between weight distribution and data quality is ensured, and fusion precision loss caused by'fixed weight 'is avoided. For an abnormal monitoring point, through indexes such as a data missing rate, a deviation change rate, an auxiliary positioning deviation rate and the like, an abnormal data source type is accurately identified, a weight adjustment scheme of'target data source decreasing amplitude + increasing amplitude data source equal proportion complementation 'is formulated until abnormity is eliminated, and closed-loop correction of'abnormity positioning-weight adjustment-error elimination' is realized.

The invention discloses an N-sugar chain marker combination for predicting the curative effect state of IgA and IgG type MM and a prediction scoring system and application of the N-sugar chain marker combination. The N-sugar chain marker combination is composed of the following 11 N-sugar chains: NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4 and NA4Fb. The method has the advantages that (1) the relevance between the N-carbohydrate chain and the curative effect in the blood of the IgA and IgG type MM patients in different curative effect states is found for the first time, and the N-carbohydrate chain can be used as an index for predicting the curative effect states of the IgA and IgG type MM patients for the first time; and (2) the N-carbohydrate chain curative effect prediction scoring system provided by the invention is used for predicting the curative effect states of IgA and IgG type MM patients, and a new technical means is provided for predicting the curative effects of the IgA and IgG type MM patients.

The invention belongs to the technical field of natural language processing, provides a multi-dimensional confidence fusion large language model uncertainty evaluation method and system, designs a multi-dimensional confidence modeling mechanism, and can perform multi-dimensional confidence modeling on the basis of internal information in a large language model generation process without depending on an external knowledge base. And the reliability of the output content is effectively judged. According to the system, on the basis of a semantic clustering mechanism, a Token-level multi-dimensional confidence modeling method is innovatively introduced, a scoring system fusing factors such as probability centrality, context disturbance sensitivity, generation consistency and language rationality is constructed, the confidence structure of each part of content in model output can be evaluated from the Token level, and the evaluation efficiency is improved. And the discrimination capability of the system on the uncertainty difference in the generation process is obviously improved.

The invention discloses a match data processing method and system based on multi-terminal collaboration, relates to the technical field of data processing, can be applied to the field of match live broadcast, and mainly aims at solving the problems that live broadcast data processing of existing match data processing highly depends on manual broadcast director and is low in timeliness. The method mainly comprises the following steps: inputting penalty information through a penalty input interface of a penalty terminal, forwarding the penalty information to a timing and scoring system, calling at least one target triggering pendant matched with a penalty object and a penalty type, generating real-time match condition data according to the target triggering pendant and a real-time video stream, and sending the real-time match condition data to the penalty terminal. The real-time match condition data is displayed in a live broadcast mode through a live broadcast interface of the live broadcast terminal; intercepting a playback video stream according to the marking timestamp and the penalty type of the video point marking instruction; and in response to data updating of the timing and scoring system, generating match condition playback data according to the timing and scoring updating pendant and the playback video stream, and displaying the match condition playback data in a live broadcast manner through a live broadcast interface. The method is mainly used for event data processing.

The invention relates to the technical field of physiological parameter monitoring, in particular to a construction method of a cardiology rehabilitation evaluation model, which comprises the following steps: monitoring a systolic pressure peak value, a diastolic pressure valley value and duration time of a postoperative patient, constructing a waveform parameter set, calculating a peak sequence amplitude change rate through a moving average method, and calculating a peak sequence amplitude change rate. Marking a form abnormal period and a structure drift region; dividing an active phase and a passive phase to extract wave crest characteristics; analyzing time sequence difference to generate a three-dimensional rhythm vector; constructing a regression model; according to the method, heart pressure change is continuously monitored, a waveform structure is identified, compression period change is dynamically captured, a structure drift area is determined and positioned by combining an amplitude change rate and a difference value, multi-dimensional heart rhythm characteristics are extracted, day and night differences are analyzed, and multi-day deviation values are integrated to generate a scoring system; the abnormal recognition, rhythm quantification and trend prediction capabilities are improved, and a high-adaptability quantification basis is provided for rehabilitation evaluation.

The invention relates to the technical field of image processing, and discloses a paper ruler regulation drawing operation image batch processing and cloud collaborative scoring system, which comprises the following steps: acquiring operation images of a paper ruler regulation drawing through a multi-source image, carrying out resolution adaptation and intelligent edge detection, carrying out perspective correction and standardization processing, and carrying out cloud collaborative scoring on the operation images of the paper ruler regulation drawing; reconstructing a brightness component of the operation image and carrying out vectorization processing; constructing a geometric constraint network of the job image to perform complex topological relation automatic constraint verification, after verification is finished, constructing a symbol data set, detecting and classifying symbol data, and performing multi-scale detection on symbol feature values obtained through calculation; constructing a directed acyclic graph, accelerating a key operator by using CUDA, constructing a mapping specification knowledge graph, dynamically adjusting a scoring weight, establishing a dynamic scoring mechanism, and calculating to obtain a processing score; and establishing a distributed storage architecture, analyzing and calculating a final score, and generating personalized improvement suggestions and teaching resource recommendations through error pattern mining.

The invention provides a subjective question scoring system and method based on large language model combination weighting, and the system comprises a key point matching scoring module which is used for recognizing an answer to be scored based on a keyword, and obtaining a key point matching score; the similarity comparison scoring module is used for scoring the similarity between the to-be-scored answer and the standard answer semantics to obtain a semantic similarity comparison score; the language model scoring module is used for obtaining subjective question scores based on a large language model on the basis of a large language model fusion prompt word engineering technology; and the weighted scoring module is used for weighting each scoring result to obtain a final subjective question scoring result. According to the technical scheme, scoring errors caused by limitation of a single method can be effectively avoided, so that the scoring accuracy and credibility are remarkably improved. The integrated design of the method not only enhances the robustness of the scoring system, but also brings a new technical breakthrough for the subjective question scoring field.

The invention designs a scoring method and device for AI competition practical operation questions and computer equipment, and the method comprises the steps: obtaining a plurality of data sets generated by participating users in a pre-constructed competition system corresponding to the current competition practical operation questions in response to the examination operation of the participating users for the current competition practical operation questions; wherein the data set comprises a training data set; obtaining code data input by participating users; obtaining a target competition model obtained by training the participating users by using the training data set, the target competition model being a model associated with the code data; and performing comprehensive scoring processing on the code data, the plurality of data sets and the target competition model by using a pre-constructed scoring system to obtain a competition scoring result of the participating users. By adopting the scheme, the grading consistency of the AI competition practical operation questions can be ensured.

The invention discloses a performance scoring system and method for emergency response of a chemical park, and relates to the field of scoring of emergency response of chemical parks. Comprising an intelligent perception and risk early warning module, an intelligent decision and command scheduling module, an emergency execution and on-site disposal module, a resource management and logistics guarantee module, an evaluation optimization and continuous improvement module and an innovative technology application module. According to the performance scoring system and method for the emergency response of the chemical park, a mutually coordinated organic whole is formed from perception early warning, decision scheduling, field disposal, resource guarantee and continuous optimization to technical enabling, the assessment optimization and continuous improvement module continuously improves the emergency response capability through closed-loop management, and the performance scoring efficiency is improved. And continuous iteration from experience to capability is realized. The post-event analysis sub-module automatically collects data such as early warning response time, resource allocation efficiency and evacuation completion time after the emergency ends, and identifies weak links through data mining.

A Large Language Model (LLM) and scoring system to generate responses or queries in the context of consulting or advisory services (AI advisory system). The system includes a user trust wallet 1,2 for

The invention relates to the technical field of service quality evaluation, in particular to a multi-dimensional after-sales service evaluation scoring system, which comprises a responsibility description comparison module, a node process propulsion module, a scoring dimension generation module, a scoring data acquisition module and a responsibility attribution judgment module. According to the invention, through the description and comparison of responsible persons of audio and text dual channels, the verification of node sequence consistency and conclusion content consistency, the linkage judgment of the fusion task state and personnel identity, and the linkage circulation logic of on-site sign-in data, and the dynamic mapping of the scoring dimension in combination with multivariate parameters such as the service type and the emergency degree. Scoring data are collected in a layered mode, score differences and time sequences are fused, responsibility traceability labels are established, multi-dimensional data synchronous verification, flow node collaborative propulsion, dynamic scoring scene matching and responsibility affiliation transparent management are achieved, the dispute recognition capacity in a complex service scene is enhanced, and the responsibility traceability is improved. And the adaptability of the whole-process tracing fineness of the work order and the service link evaluation is improved.

The invention discloses a method for comprehensively scoring vehicle driving based on multiple factors, and relates to the technical field of vehicle insurance, and the system comprises the following components: a data collection module, a risk scoring model module, a scoring system module, a technical framework support module and a safety guarantee module. By integrating a plurality of data collection modules, including an intelligent sensor and a multi-source data fusion technology, driving records, driving experience, basic information, vehicle types and value data of drivers can be comprehensively collected, and meanwhile, the risk scoring model module applies an improved multi-factor weighted risk assessment algorithm, comprehensively considers a plurality of factors, and improves the risk assessment efficiency. And the psychological and health condition data of the driver is introduced, so that the risk assessment is more accurate and comprehensive, and the insurance company and the traffic management department can identify the high-risk driver and vehicle more accurately, thereby taking corresponding risk management measures.

Systems and methods of determining composite indices such as, e.g., health, safety, and performance indices, of an enclosure from weighted values of contributing metrics including sensor data and / or controlling one or more building systems based on the composite indices.

The invention relates to the field of medical artificial intelligence, in particular to a clinical scoring system combining large language model multi-agents and machine learning and a self-evolution method of the clinical scoring system, and provides the clinical scoring system based on the large language model multi-agents and the self-evolution method of the clinical scoring system. The objective of the invention is to overcome the defects of data missing sensitivity, insufficient interpretability, weak dynamic updating capability and the like of an existing clinical scoring system. According to the system, through a multi-agent collaboration architecture, the natural language interaction capability of a large language model (LLM) and the precise prediction capability of a machine learning model are deeply fused, and a self-evolution mechanism is introduced to realize dynamic optimization, so that the reliability, flexibility and transparency of clinical decisions are remarkably improved.

The invention provides a truss structure autonomous design system and method based on multiple agents. A layered multi-agent architecture is adopted, a general control agent is taken as a core, and a high-level design intention is analyzed and a plurality of downstream special agents are arranged for cooperative work; the whole design process realizes automatic closed loop through a graph-based workflow engine; the core innovation of the method is a hybrid intelligent optimization algorithm of LLM heuristic exploration and genetic algorithm accurate fine tuning, and the algorithm is driven by an original and quantifiable design quality scoring system to efficiently carry out analysis-diagnosis-optimization iteration so as to cooperatively improve the safety and economy of the design. The design time can be shortened from several hours to several minutes, about 7.0% of material consumption is saved on average, it is ensured that the scheme completely conforms to industry specifications, the design efficiency, economy and reliability are greatly improved, and meanwhile the use threshold of professional design software is remarkably reduced through natural language interaction.

The present application describes an electronic scoring system, especially designed for combat sports. The system allows a judge to automatically and consistently record the scoring parameters throughout a combat and its various rounds. In order for this to be possible, the judge evaluation is captured periodically, during the combat, using a scoring device and is sent to, central control unit, which is in charge of all the processing underlying the determination of the final winner of the combat.

The invention discloses a multi-sensor fusion target scoring system high-precision positioning method, and relates to the technical field of target scoring positioning, and the method comprises the following steps: building a transient interference recognition channel based on the electromagnetic response of a shock wave sensor, obtaining the electromagnetic disturbance of the bullet hit front edge, and generating an interference time sequence; performing delay triggering on the image sensor according to the interference time sequence, and avoiding an interference peak value to form a safe exposure time period; performing charge pre-release on the image acquisition chain before safe exposure; carrying out synchronous compensation on the shock wave signal based on energy distribution of charge pre-release, so that the shock wave signal and image energy response are symmetrical and mutually stable in a time domain; and dynamic shielding control is carried out on a space coupling path between the two types of sensors by utilizing an energy balance region formed in the mutual stable state. Through energy symmetric compensation and dynamic shielding control of shock waves and image signals, time-space separation and synchronous mutual stability of the signals are achieved, and high precision and stability of target scoring positioning under high-energy shooting are ensured.

The invention relates to a test scoring system suitable for various subjects, specifically, the technical scheme realizes scoring based on AI, solves the problems of low manual scoring efficiency and difficulty in unification of scoring scales, and meanwhile, compared with an existing scoring system, teachers can flexibly adjust scoring requirements according to actual teaching requirements, so that the scoring efficiency is improved. Examination questions, scoring points, detailed rules and weights can be independently set according to needs, and the use effect is better.

The invention discloses a bus shift extraction optimization method based on multi-dimensional score driving, and relates to the field of urban public traffic scheduling management, and the method comprises the following steps: initializing shift extraction parameters, determining a shift extraction target, defining time period features, and setting a score weight and a penalty coefficient; constructing a shift extraction optimization mathematical model, including defining decision variables and establishing an objective function and constraint conditions thereof; respectively constructing a multi-dimensional scoring system and a constraint verification strategy by utilizing the objective function and the constraint conditions thereof; generating candidate shift drawing schemes, calculating a comprehensive score and selecting an optimal shift drawing scheme; performing iteration processing on the optimal shift extraction scheme in combination with loop control and a scheme rollback strategy to obtain a final shift extraction scheme; and verifying and counting the final shift drawing scheme, and generating a corresponding shift drawing report. On the premise that operation balance and service integrity are ensured, global optimal selection of the shift drawing scheme is achieved, and the intelligent level and operation efficiency of bus dispatching are remarkably improved.