12 results about "Selection method" patented technology

According to the embodiment of the invention, the instrument type selection method comprises the following steps: demand collection: obtaining a spoken type selection demand input by a user; the demand analysis is used for converting the spoken type selection demand input by the user into a demand of applying a standard instrument term; type selection searching: searching in an instrument type selection knowledge graph based on the analyzed demand to obtain an instrument entity meeting the demand; and performing model selection evaluation, calculating weighted scores of the instrument entities meeting the requirements based on weights of different model selection elements, and obtaining the instrument entities of final model selection, based on the method, customer requirements can be more fully and accurately understood, meanwhile, the entity association ability and retrieval ability of the knowledge graph are further enhanced, and the user experience is improved. Therefore, the instrument type which better meets the requirements of the user is recommended for the user. The invention further provides an instrument type selection system and a storage medium.

The invention relates to the technical field of psychological assessment, and particularly discloses a dynamic psychological assessment question selection method and system based on a factor integrity principle, and the method comprises the steps: creating a session, collecting the psychological attention field of a user, mapping the psychological attention field of the user into a corresponding psychological factor, carrying out the duplicate removal, and generating a target psychological factor set; all questions corresponding to each psychological factor are retrieved and loaded from the database, preset answering time and actual answering time are obtained according to the number of the questions and preset average answering time of each question, psychological credibility indexes of the user are analyzed, answers to each question of the user and question fields are obtained, and an answering data set is generated; whether the user completes all target questions or not is verified through the score integrity verification module, score calculation is performed after integrity verification is passed, and the priority of the psychological attention field of the user is determined in combination with the psychological credibility index, so that the reliability of the evaluation data is ensured, and the user experience and the practical value of the evaluation result are also improved.

The invention provides a high-priority test case screening method and device, a storage medium and computer equipment, and relates to the technical field of computers.The method comprises the steps that a demand document and a test case set are received; preprocessing the demand document, and constructing a cue word based on a preprocessing result; inputting the cue word and the test case set into a large language model to obtain an initial high-priority test case; after text normalization processing is conducted on the initial high-priority test case, a target high-priority test case is obtained through text matching processing, and the target high-priority test case is marked. According to the method, high-priority test cases can be automatically and accurately screened out in a rapid iteration software development environment, the coverage rate and efficiency of regression testing and smoking testing are improved, meanwhile, the correctness of core functions is guaranteed, and the reliability and applicability of case screening results are remarkably enhanced.

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.

The invention discloses a test case selection method and device, equipment, a medium and a product, relates to the technical field of artificial intelligence, can be applied to the field of financial science and technology, and comprises the following steps: determining a to-be-tested function point according to acquired test demand information, and determining candidate test cases from a test case set according to the to-be-tested function point; determining a test demand matching degree and / or historical execution statistical data, and determining a target test case meeting the test demand information from the candidate test cases according to the test demand matching degree and / or historical execution statistical data; wherein the historical execution statistical data comprises at least one of a historical defect discovery rate, historical execution time consumption, historical execution complexity and a historical execution reuse rate. According to the method and the device, the effect of screening the test case based on the multi-target screening dimension is realized, the quality of the selected target test case can be ensured, and the test effect of performing software testing by using the target test case is further ensured.

The invention discloses a method and a device for selecting supervised fine-tuning training data of a man-post matching model, and relates to the technical field of human resource data processing based on artificial intelligence. The method comprises the steps of obtaining an initial difficulty score of a post-resume pair through multi-dimensional difficulty assessment; generating a matching label based on the real behavior of the recruitment decision maker; a comprehensive difficulty score is obtained through the comparison model preliminary judgment and matching label consistency calibration; and selecting data adaptive to the difficulty level according to the comprehensive difficulty score to construct a training set of a specific reasoning style. According to the method, accurate screening and optimal configuration of the training data are realized, and the accuracy, generalization ability and training efficiency of the man-post matching model are effectively improved.

Support the behavioral change of the individual in accordance with the characteristics of the environment / group to which the individual belongs. [Solution] The system integrates data on the behavior of each of several subjects and data on the characteristics of the environment / group to which the subject belongs, calculates a cluster index value using this data, and classifies the subject into one of several clusters moving toward the target behavior, in which the cluster index value calculated for the subject belongs is associated with that cluster. For each of the multiple clusters, the system selects a policy selection method from among several policy selection methods that is suitable for the cluster characteristics, which are the characteristics of the environment / group to which the subject classified into that cluster belongs, and selects a policy for the subject classified into that cluster according to the selected policy selection method.

The invention provides a test case selection method and device, electronic equipment and a storage medium, and relates to the technical field of software testing. The method comprises the following steps: determining the coverage degree, depth and breadth, maintainability and test weight index of a test case according to a mapping relationship between the test case and a code; determining a score of the test case according to the coverage degree, the test depth and breadth, the maintainability degree, the test weight index and the manual evaluation index of the test case; and sorting the plurality of test cases according to the test case scores, and selecting the test case which is preferentially tested according to a sorting result. The test case is automatically scored based on multiple dimensions of the coverage degree, the test depth and breadth, the maintainability and the test weight index, and the scoring accuracy and scoring efficiency of the test case are improved. And sorting is performed according to the scores of the test cases, and the high-quality test cases can be selected from the plurality of test cases according to the sorting result to be tested preferentially, so that the test efficiency is improved.

The invention discloses a talent selection method and system based on AI auxiliary interview, and relates to the technical field of talent selection, and the method comprises the following steps: S1, obtaining a recruitment position and a vacancy number, determining an enterprise employment direction according to the recruitment position, and formulating an interview hard requirement and an interview elastic requirement according to the enterprise employment direction and the vacancy number; according to the scheme, a hard requirement scoring mechanism is formulated according to the hard requirements and enterprise employment directions, so that the scoring mechanism meets real requirements of enterprises, potential interviewers can be screened more accurately, special talent interviewers can be obtained through preferential admission of the potential interviewers, and the evaluation efficiency of the special talent interviewers is improved. Whether the elasticity requirement can be reduced according to the job hunting characteristics of the crowd or not can be judged according to the condition of the special talent interviewer subsequently, and the situation that the interviewer is not completely matched for a long time due to the fact that the interview requirement is input too strictly is further avoided.

The invention discloses a multi-modal training sample selection method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps that an original training sample is acquired, and the modals of data in the original training sample are various; inputting the original training sample into a to-be-trained multi-modal large language model, and performing illusion suppression on the to-be-trained multi-modal large language model to obtain a model answer; and determining a quality score of the model answer, and determining a target training sample from the original training samples according to the quality score. According to the technical scheme provided by the embodiment of the invention, the high-difficulty sample with illusion inhibition difficulty is screened out.