6 results about "Weighted score" patented technology

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 an automatic test selection method based on a utility function weighted scoring model, and the method comprises the following steps: S1, constructing a comprehensive scoring function which is used for calculating the priority score of a test case; s2, sorting the test cases based on the scores, determining the test cases with the scores higher than a threshold value T as a necessary test group, and determining the test cases with the scores lower than T as a spot check group; and S3, executing the test cases of the necessary test group by 100%, and executing the spot check group by taking a Score (i) normalized value as a probability. According to the method, quantitative evaluation is carried out by introducing three factors of risk, importance and cost, high-value test cases are executed preferentially, and repeated execution of test cases with stable long-term passing rate and low risk is avoided, so that the test time and resource overhead are effectively reduced; besides, factors such as module change frequency, historical defect distribution and function key degree are comprehensively considered, so that the test process is more focused on a potential high-risk path, and exposure of more key defects in an early test stage is facilitated.

Methods, apparatus, and processor-readable storage media for automated request processing using an ensemble machine learning framework are provided herein. An example computer-implemented method includes aggregating interaction data associated with a request; computing a weighted score for the request, wherein the weighted score comprises a first component that is based at least in part on a comparison of the aggregated interaction data to a set of keywords and a second component corresponding to a sentiment predicted by a first machine learning model for at least a portion of the aggregated interaction data; using a second machine learning model to determine whether the request is anomalous based at least in part on the weighted score; and in response to determining that the request is anomalous, initiating one or more automated actions for the request.

The invention relates to a nuclear emergency path and opportunity decision-making method based on reinforcement learning and deep learning, and the method comprises the steps: taking minimization of exposure dose and evacuation time as targets, employing a deep reinforcement learning A3C algorithm to carry out the decision-making of the evacuation of a to-be-evacuated agent, and obtaining a plurality of evacuation paths and corresponding action opportunity schemes as candidate schemes; establishing a training set based on a decision sample formed by the candidate scheme, the corresponding decision variable and the multi-source environment information, training a deep learning model which is formed by Transform and a random forest and is from an environment state tensor to the candidate scheme, and outputting a prior score; and obtaining weighted scores of the candidate schemes by adopting linear weighting of score weights, obtaining a comprehensive score by fusing the weighted scores and the prior scores, and determining an executable scheme from the candidate schemes based on the comprehensive score. According to the method, the defects in the aspects of self-adaptability, real-time performance and benefit cost analysis capability in nuclear accident emergency decision-making and path planning in the prior art are overcome.

The invention relates to the technical field of railway construction, in particular to a railway construction advanced intervention problem classification method based on business feature fusion and a gradient boosting tree, and the method comprises the steps: S1, data collection and data storage; step S2, data preprocessing; s3, carrying out feature engineering; s4, performing model training; and S5, performing model performance evaluation and packaging. According to the method, the time feature, the category feature and the long text feature are mined from the advanced intervention problem data, the weighted score and other business features are fused, the hidden feature information in the data is mined, the business mode in the data can be more intelligently captured, and the pertinence and the accuracy of model learning are improved; by optimizing a tree structure and a search strategy, the classification accuracy is improved while the calculation complexity is controlled; the problems that in the prior art, the data statistical analysis capacity is poor, hidden service features in data are difficult to mine, and problem classification lacks are solved.

A competition evaluation system and an evaluation method that provide a user unit for a plurality of players to respectively create a competition member list. The competition member list is generated by selecting at least one of a plurality of competition members by the player. The competition members respectively have a competition result and an appearance information, and each of the competition members is respectively given a performance score and a contribution degree based on the competition result and the appearance information. The invention uses a statistics unit to positively correlatedly correct the performance score of the competition member based on the contribution degree of each of the competition members to generate a weighted score for the competition member, thereby generating a total score for each of the competition member lists, and performing ranking according to the total score, so that the associated players respectively obtain a competition ranking.