364 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 the technical field of medical diagnosis, in particular to a dysphagia detection and automatic scoring system based on a high-resolution acoustic sensor. Comprising a three-dimensional curved surface type acoustic sensing array module, a self-adaptive environment noise compensation module, a swallowing stage dynamic segmentation module, a multi-dimensional feature extraction engine, a swallowing abnormal mode classifier and a dynamic weight scoring model. A self-adaptive environment noise compensation module, a time-frequency domain feature extraction module and a dynamic weight scoring model are combined, the swallowing process is monitored and scored in real time, the system automatically identifies and analyzes acoustic features of an oral cavity preparation period, a pharyngeal period and an esophageal period, and through multi-dimensional feature fusion, dynamic weighting and neural network optimization, the swallowing process is monitored and scored in real time. The method has the advantages of being non-invasive, easy and convenient to operate, high in real-time performance and the like, and can be widely applied to early screening, evaluation and follow-up visit of clinical dysphagia.

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 discloses a cancer early warning management method and system based on a physical examination report, and belongs to the field of medicines.The method comprises the steps that health data of individuals are collected, and unstructured texts in the physical examination report are processed through combination of optical character recognition and a natural language processing technology; calculating a cancer risk based on a single physical examination report, evaluating a specific cancer by using a cancer risk scoring system, and analyzing whether an index exceeds a normal range or not through a rule engine; based on historical data trend prediction risks, index change rates are calculated, and an abnormal trend is analyzed and predicted in combination with a time sequence; multi-modal deep learning is utilized to predict individual cancer risks, and a Bayesian network is combined to perform joint analysis on multiple factors to generate a health management scheme; further examination is arranged according to the early warning level, and a screening strategy is provided for specific cancers. According to the method, the OCR + NLP technology is adopted, and the data quality is improved. And in combination with a multi-modal deep learning model, abnormal changes are found in advance, and early warning is realized.

The invention discloses a coronary intervention postoperative risk prediction model construction method and system, and belongs to the technical field of medical care information and health monitoring. A coronary intervention postoperative risk prediction model construction method comprises the steps of constructing an attending doctor experience scoring system and a nursing personnel ability scoring system, and performing weighted fusion on results of a doctor experience model and a nursing personnel model to form a final additional model for output. According to the coronary intervention postoperative risk prediction model construction method and system provided by the invention, the main model is combined with the additional model, the main model is comprehensively modeled through the regression model and the time sequence model based on the static and dynamic characteristic data of the patient, and the individual health state change of the patient is captured; the additional model evaluates the influence of medical teams and relatives on the postoperative risk by quantifying the experience of doctors and the ability of caregivers, and overcomes the defect that a traditional model only depends on patient data.

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.

An AI-powered real-time patient risk assessment system (100) for predictive health management, comprising: (a) a data acquisition and integration module configured to collect and standardise patient data from multiple clinical sources, including electronic health records, monitoring devices and laboratory systems; b) a feature extraction and data processing module for pre-processing the collected data and extracting clinically relevant features; (c) a machine learning-based risk prediction module configured to generate real-time risk scores based on the extracted features using trained AI models; (d) a clinical decision support and alerting module configured to trigger alerts and suggest interventions based on risk thresholds; (e) a visualisation and dashboard module for displaying patient risk scores and trends via an interactive interface; and f) a feedback learning and model update module configured to incorporate clinical feedback and retrain the AI models to improve prediction accuracy over time.

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 discloses a rehabilitation exercise system and method based on intelligent interaction, and relates to the technical field of medical rehabilitation, intelligent management of basic information, medical history information, treatment information and medication information of patients is realized through a patient information management module; personalized exercise plans and real-time action guidance are provided for the patient through the rehabilitation exercise module; dynamically evaluating the rehabilitation state of the patient through a rehabilitation evaluation module; a friend communication platform is established through the community interaction module, and science popularization education is provided; and real-time monitoring and early warning of the doctor on the rehabilitation process of the patient are realized through the doctor supervision module. The artificial intelligence technology is adopted, the rehabilitation actions of the patient are monitored and corrected in real time through the camera, the rehabilitation state of the patient is evaluated through the electronic scale and the intelligent scoring system, the exercise plan is dynamically adjusted according to the rehabilitation condition of the patient, the rehabilitation exercise accuracy and the patient compliance are effectively improved, and the rehabilitation effect of the patient is improved. And the intelligent and electronic management of the whole rehabilitation exercise process can be realized.

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 human resource software for a supply chain, and relates to the technical field of human resource allocation, and the human resource software comprises the steps of S1, database construction, S2, model establishment, S3, matching degree matrix calculation, S4, weight allocation, S5, real-time optimization, S6, dynamic adjustment, and S7, scheme output. According to the method, the ability evaluation value is dynamically adjusted based on the employee historical task execution time through the skill attenuation factor, the natural decline rule of the employee skill along with time is reflected, and the mismatching problem caused by outdated skill evaluation is avoided; in combination with a five-dimensional scoring system and skill threshold constraints, the system can automatically filter substandard persons, and meanwhile, the comprehensive level of multiple skills is considered during matching; according to the design, the personnel ability evaluation is always kept synchronous with the real-time state, the accuracy of personnel and post matching is improved in a complex and changeable supply chain environment, and the subjective error of artificial evaluation is reduced while the task execution quality is guaranteed.

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 discloses an intelligent agent-based English composition scoring method and device, equipment and a storage medium. Comprising the following steps: inputting composition topics, composition contents and scoring standards into a scoring standard analysis agent; the scoring standard analysis agent selects a target analysis tool from a preset analysis tool group for analysis to obtain an analysis result; the analysis result comprises analysis of different scoring dimensions and corresponding suggested scores; and inputting the composition question, the composition content, the scoring standard and the analysis result into a scoring agent to obtain an English composition scoring result. Through a staged scoring mechanism, an analysis agent extracts key features to form an intermediate analysis result, and then a scoring agent performs secondary check and comprehensive judgment according to a scoring standard, so that the scoring accuracy and consistency are effectively improved. And meanwhile, the scoring standard is independently used as an external input parameter, so that the system can automatically adapt to different scoring systems, and the universality and the adaptability are enhanced.

The invention provides a sports event real-time score statistical method and system based on AI visual identification, and relates to the technical field of intelligent scoring of sports events, and the method comprises the steps: collecting images through multiple cameras, and carrying out target detection to obtain data of athletes, equipment and scoring areas; generating a joint thermodynamic diagram by using the feature pyramid network, and constructing a dynamic skeleton diagram structure to extract action features; performing periodic detection and abnormity correction on the equipment; and performing feature matching and score calculation through a double-flow feature projection network and a multi-head attention mechanism, generating a score data packet including time, athlete numbers, types and video clips, and sending the score data packet to a scoring system. According to the invention, intelligent real-time scoring of sports events is realized, and the scoring accuracy and efficiency are improved.

A computer implemented preventative health care and health intervention application is described. The application includes a scoring system to score an overall health of the member, including breakdowns scoring the member's initial physical health, mental health, and an initial score for the lifestyle of the member. One or more health care providers are assigned to and / or selected by the user. The health care providers assess which health intervention programs are best suited to meet the goals of the member and to improve the score for the member's health. The score is re-evaluated when the member meets a minimum threshold of participation and attendance in the one or more selected health and health intervention programs. The score evaluation includes review of attendance, participation information, data collected from sensors and wearable devices, as well as based on predictive analytics integrated into the preventative health care and health intervention program.

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 discloses an intelligent timing and scoring system for a match, and relates to the field of match systems, the intelligent timing and scoring system for the match comprises a first data acquisition port used for receiving a standard shadowboxing video as a scoring standard, and a second data acquisition port used for acquiring a real-time action video of an athlete in a shadowboxing match; according to the intelligent timing and scoring system for the competition, the difficulty value ND of each standard action is accurately quantified, and each real-time action of an athlete is compared and scored with the standard action according to a unified standard, so that the subjectivity and the uncertainty in the scoring process are greatly reduced, the credibility and the authority of a competition result are ensured, and the competition quality is improved. Therefore, the competition really becomes the comparison of technology and strength, the competitive consciousness and training enthusiasm of athletes are stimulated, and the benign development of the whole project is promoted.

The invention discloses a multi-modal data-driven teaching evaluation report automatic generation and intelligent analysis method. According to the method, multi-modal data is collected from various education information systems, and examination scores of students, classroom interaction, learning behaviors and feedback data of teachers are covered. The data are standardized, cleaned, preprocessed and the like, and abnormal values, missing values and unstructured data are processed. An evaluation model is constructed based on a multilayer neural network, a teaching evaluation report is generated by using an adaptive weight scoring system, and personalized teaching improvement suggestions are generated through a probability model. Meanwhile, the model is optimized by utilizing historical data and an incremental learning method, and finally, the method performance is measured through prediction accuracy, mean absolute error and comprehensive evaluation indexes, so that a comprehensive, accurate and intelligent teaching evaluation method is provided for the education field.

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.