18 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.

This invention discloses an intelligent tool selection method based on k-nearest neighbor entropy estimation and kriging, comprising the following steps: S1, formulating a rule-based pre-screening method for cutting tools to obtain a set of candidate tools for machining; S2, performing tool performance reasoning and tool ranking based on the candidate tool set obtained in S1; S3, combining the analysis of tools and user behavior to obtain a comprehensive cutting tool recommendation method with process scenario adaptability. This invention employs the aforementioned intelligent tool selection method based on k-nearest neighbor entropy estimation and kriging, and by mining tool selection experience from cutting records, obtains a comprehensive cutting tool recommendation method with certain process scenario adaptability. This method can provide predicted machining results, possible user behaviors, and possible tool combinations based on user input when users formulate process manuals, thus achieving intelligent selection of cutting tools.

The application provides a recommendation model selection method and device, electronic equipment and program product, and relates to the field of recommendation models. The method comprises: determining a channel recommendation probability set and a customer group recommendation probability set corresponding to each recommendation model based on a recommendation effect evaluation parameter of each recommendation model in a historical unit time period; determining a comprehensive recommendation probability set corresponding to each recommendation model based on the channel recommendation probability set and the customer group recommendation probability set corresponding to each recommendation model; and determining a target recommendation model corresponding to a target customer group in a target traffic channel from a plurality of recommendation models based on the comprehensive recommendation probability set corresponding to each recommendation model. By calculating the traffic channel recommendation probability and the customer group recommendation probability, the selection range of the recommendation model can be refined, and the recommendation model traffic can be allocated according to different customer groups, thereby effectively improving the accuracy of model selection.

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 relates to an equipment type selection method and system. The method comprises the following steps: acquiring equipment category information corresponding to equipment demand information; performing clustering analysis on the equipment category information to obtain an equipment application scene; and reasoning the equipment category information and the equipment application scene by the knowledge base to obtain target equipment meeting a preset type selection condition. By acquiring the equipment category information corresponding to the equipment demand information, the natural language input of the user can be automatically understood and processed, the demand of manual intervention is reduced, and the risk of human errors is reduced; the equipment type information is subjected to clustering analysis, so that accurate understanding of user requirements is ensured, an accurate equipment application scene is obtained, and a more reliable decision is made. The pre-trained knowledge base constructs the structured query condition according to the equipment category information and the equipment application scene, rapid reasoning and positioning can be carried out in mass information, the target equipment meeting the preset type selection condition is obtained, and therefore the use experience is greatly improved.

The present invention provides an end effector selection device that contributes to selecting an appropriate end effector with respect to a target environment in which work is to be performed by an end effector, a target article on which the work is to be performed by the end effector, and the content of the work. The end effector selection device comprises an end effector selection unit that receives information pertaining to an obstacle and an article arrangement in a target environment in which work is to be performed by an end effector and information pertaining to the operation of the end effector, and outputs, on the basis of the information pertaining to the obstacle and the article arrangement and the information pertaining to the operation of the end effector, information pertaining to the type of the end effector to be selected.

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 partial multi-mark feature selection method and device based on adaptive manifold and mutual information, and relates to the field of data mining, and the method comprises the steps: S1, constructing a feature matrix and a logic mark matrix through employing a data set; s2, a feature selection model construction step; s3, constructing a manifold regular item and a mark similar graph learning item of the target function based on the feature matrix and the logic mark matrix; s4, training the feature selection model by using the target function; and S5, inputting the partial multi-mark data set into the trained feature selection model to obtain a mark confidence coefficient matrix and a weight matrix of the feature selection model, calculating mutual information of a feature matrix and the mark confidence coefficient matrix, calculating a feature importance degree matrix according to the mutual information, and screening out a final feature selection result according to importance sorting. According to the method, the influence of noise marks on mark correlation is eliminated by dynamically learning a mark similar graph weight matrix, and feature selection is realized in combination with mutual information of the weight matrix and mark confidence.

The application discloses a population selection method and electronic equipment for optimizing natural gas liquefaction process parameters. According to the application, when a population is screened, a preliminary search is conducted in a large sample phase space, as many samples in the phase space as possible are covered under the premise that calculation conditions are allowed, then the number of samples in the phase space is reduced through appropriate grouping rules, new populations are formed, meanwhile, the diversity of each variable in the population is preserved as much as possible, then the new population numbers generated by each grouping rule are respectively optimized, and a global optimal solution is searched. The technical scheme of the application can cover as many points in the phase space as possible, preserve the uniqueness of each point in the population, especially the uniqueness of strong correlation variables of a target function, and achieve the purpose of greatly reducing the calculation amount.

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.