28 results about "Test query" patented technology

Systems and methods are described for building artificial intelligence (“AI”) pipelines. A user interface (UI) includes selectable pipeline objects, such as a dataset and an AI model, that a user can position and connect on the screen. This causes execution linking between the selected pipeline objects, with the execution linking being visually displayed in the UI. A management policy can be applied to the pipeline, including user or device requirements for accessing the dataset in the pipeline. Then the UI can present a simulated execution of the AI pipeline, in which a test query is input and the pipeline objects execute in an order displayed in the UI. The pipeline can then be deployed for access at an endpoint.

Methods, systems, and devices for data management are described. The method may include generating, for a database table and using a language model, candidate structured query language (SQL) queries and candidate query intents using a set of SQL query templates and table metadata for the database table, where each candidate SQL query is associated with a respective candidate query intent. The method may include validating the respective candidate query intents by translating the candidate query intent into a test SQL query and determining whether the test SQL query corresponds to a candidate SQL query associated with the candidate query intent. The method may include augmenting each query intent with contextual cues resulting in anchor query candidates for each query intent and generating anchor queries for the database table based on whether the anchor query candidates for each query intent results in identification of the database table by the language model.

The invention discloses an object surface defect segmentation method based on small samples, and the method specifically comprises the steps: collecting images of different types of object surface defects and corresponding segmentation masks, constructing a small sample data set, and dividing the small sample data set into a training set and a test set; constructing training and testing tasks, and respectively extracting a corresponding support set and a query set for each task; constructing and training a defect segmentation network, wherein the defect segmentation network comprises a query prior mask generator, a prior-guided bidirectional feature interaction module, a prototype-loss compensation module and a context-aware attention-guided feature aggregation decoding module; and after training is completed, inputting the support set and the to-be-tested query image into the converged defect segmentation network, and outputting a query image prediction result. Experiments prove that the method realizes remarkable performance improvement on an FSSD-12 data set, and shows that the method has obvious advantages in a small sample defect segmentation task.

Provided in the embodiments of the present disclosure are an application debugging method and apparatus, an information query method and apparatus, an application evaluation method and apparatus, and a device and a storage medium. The application debugging method comprises: executing a test query for a target application; presenting corresponding first operation information of a plurality of stages for executing the test query, wherein the operation information of the plurality of stages at least indicates respective outputs of the plurality of stages; and generating an operation reference case for the target application on the basis of the corresponding first operation information of the plurality of stages for the test query, wherein the operation reference case comprises the test query and the operation information of the plurality of stages, and the operation reference case is referenced for determining a result of a user query during the operation of the target application. In this way, an application-creating user can improve the quality of services provided by a target application without the need for modifying code, thereby simplifying the debugging process of the target application.

The embodiment of the invention provides a database pressure testing method and device, electronic equipment and a storage medium, and belongs to the technical field of computers. The method comprises the steps of performing statement analysis on preset test query statements to obtain a statement execution sequence of the test query statements, performing recombination on the test query statements according to the statement execution sequence to obtain service query statements, obtaining simulation service data, and storing the simulation service data in a test database, obtaining a load mode according to the business query statement, obtaining thread concurrency and thread execution duration according to the load mode, starting a target thread according to the thread concurrency, executing the business query statement on the simulated business data through the target thread to obtain reference execution duration, and executing the business query statement on the simulated business data according to the reference execution duration. And performing pressure testing on the test database according to the reference execution duration and the thread execution duration. The accuracy of database pressure testing can be improved.

The invention relates to the technical field of artificial intelligence, and discloses a knowledge base-based question and answer method, device and equipment and a medium, and the method comprises the following steps: obtaining document data and input query in a knowledge base, and carrying out preprocessing operation on the document data to obtain a preprocessed document set; constructing a text vectorization representation based on the preprocessed document set, generating a vectorization feature matrix, and analyzing the vectorization feature matrix through a language model to obtain a high-dimensional context representation; performing question answering on the high-dimensional context representation through an initial question answering assembly line based on the input query, outputting initial answer information, and determining an initial performance score through a test query set; and determining an optimization strategy according to the initial performance score, and optimizing the initial question and answer assembly line based on the optimization strategy to obtain a target question and answer assembly line so as to output and obtain target answer information. The method can be applied to financial science and technology or medical care service program systems, and the accuracy and efficiency of question and answer results can be improved.

Systems and methods for improving query creation and AQM are provided. A user interface is provided that allows an analyst to generate and test queries. After the analyst provides a query, one or more subsets of previous call center interactions are selected to test the provided query using methods such as a large language model or string vectorization. Once the subsets of interactions are selected, an LLM is used to evaluate the query against the subset. The evaluations may indicate interactions which were positive and which interactions were negative and may include quotes from the interactions that support the evaluations. The evaluations are presented to the analyst for review, and the analyst may revise the query in view of the evaluations. Once the analyst is satisfied with the performance of the query, the query may be used by the LLM to perform AQM for new interactions in the call center.

The embodiment of the invention discloses a method for generating a test load, which takes all attributes of test data and all operators of a query statement as states of a finite-state machine, so that a query statement template corresponding to a randomly generated state jump path can theoretically traverse all query types, and moreover, all the operators of the query statement can be used for generating the test load. The target query statement template is instantiated according to the required query cardinal number and the generation function, the number of query results obtained by querying the generated test data through the test query statement obtained through instantiation is the required query cardinal number, and therefore the customized test requirement of any query cardinal number needing to be tested can be met.

The invention provides a DSL (Digital Subscriber Line) rule modification method and device and a computing equipment cluster. In an embodiment, a method includes: determining a domain specific language (DSL) rule; the DSL rule is used for querying data in a database; conditions in the DSL rule are modified, and the modified DSL rule is determined; performing a first test on the modified DSL rule based on a query engine and a database; the query engine is used for executing the DSL rule to query data in the database; screening a target DSL rule from the modified DSL rules based on a result of the first test; the query engine executes the target DSL rule and the DSL rule, the data queried from the database is the same, and the execution time of the query engine executing the target DSL rule is shorter than the execution time of the query engine executing the DSL rule. By applying the modified DSL rule, the time for executing the DSL rule to query the database can be shortened, and the query efficiency is improved.

The invention provides a large model version recognition method and device, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as large models, deep learning, natural language processing and low codes. The large model version identification method comprises the steps of obtaining a target time window according to first release time of a target version of a to-be-identified large model; obtaining a plurality of test queries according to the target time window; inserting the plurality of test queries into the service data flow of the to-be-identified large model in real time to obtain a response result output by the to-be-identified large model for each test query; obtaining an abnormal score of the to-be-identified large model according to the plurality of response results; and in response to determining that the abnormal score meets the first preset requirement, obtaining an identification result that the actual version of the to-be-identified large model is not the target version. According to the method and the device, the acquisition accuracy of test query and the identification accuracy and identification efficiency of the large model version can be improved, and the avoidance resistance during identification of the large model version is enhanced.

The embodiments of the present application disclose a test method, related devices and equipment for a target test stub service. The related embodiments can be applied to various scenarios such as cloud technology and artificial intelligence to improve the security of accessing the test stub service and reduce the access cost. The method of the embodiments of the present application includes: receiving a test request for the target test stub service, obtaining a first intermediate code and a second intermediate code, where the first intermediate code carries a priority loading identifier, includes a first function name and first bytecode corresponding to the first function name, loading the first intermediate code and the second intermediate code according to the sequence of the priority loading identifiers to obtain a target code, executing the target code, when receiving a test query message, sending the test query message to the target test stub service for processing according to the target test stub service address in the target code, and if a response message returned by the target test stub service is received, the test of the target test stub service is successful.

Embodiments of the present application provide a laboratory information management method for reducing manual workload and improving sample identification input efficiency. The method comprises: when a sample identification is input to an instrument system, receiving sample test query information sent by the instrument system, the sample test query information at least comprising the sample identification, for obtaining corresponding sample test item information; querying a hospital information system (HIS) according to the sample identification; when the sample test item information is obtained from the HIS, performing local saving on the sample identification and the corresponding sample test item information, and sending the sample test item information to the instrument system, so that the instrument system performs test and management on samples.

The embodiment of the present invention discloses an automated testing method, device, electronic device and storage medium for password controls. The method includes: determining the type of control to which the current atomic test step belongs based on the atomic test step positioning information; if the control type to which the current atomic test step belongs is a password control, using the password control execution logic to perform an assignment operation on the current password control to implement automated testing; querying the current atomic test step status to determine whether to start the next atomic test step. Through the technical solution of the embodiment of the present invention, when processing password controls, the system unifies the specifications with other control types, and automatically implements different assignment methods for different controls based on factors such as the framework type and object positioning information, thereby solving the problem of automated testing of password controls and greatly reducing the workload of testers in maintaining cases.

The embodiment of the invention provides a database pressure testing method and device, electronic equipment and a storage medium, and belongs to the technical field of computers. The method comprises the steps of obtaining a database name and a database type of a test database, extracting a database connection parameter from a pre-obtained test configuration file according to the database name, and extracting a test tool parameter from the test configuration file according to the database type, calling a pressure test tool according to the test tool parameters to generate original simulation service data and a test query statement, connecting a test database according to the database connection parameters, and storing the original simulation service data to a test partition of the connected test database to obtain reference simulation service data, and according to the test query statement and the reference simulation service data of the test partition, carrying out pressure test on the connected test database, so that the database pressure test efficiency can be improved.

A method, apparatus and computer program product are provided for generating search engine input queries using point-of-interest information and permutations thereof to assess the quality of search results produced by and to improve the search engine. Methods may include: identifying a reference point-of-interest from a reference data database; extracting at least one point-of-interest attribute from the reference point-of-interest; identifying a geographic location of the reference point-of-interest; calculating a test query search center based on the geographic location of the reference point-of-interest; generating a test query based on the at least one point-of-interest attribute from the reference point-of-interest and the test query search center; querying a search engine using the test query; receiving one or more responses to the test query; analyzing the one or more responses to calculate response quality metrics; and modifying the search engine based at least in part on the metrics.

A method of updating a trained cardinality estimation model includes receiving a cardinality estimation model with cardinality labels and detecting a drift in underlying data or predicates of the cardinality estimation model. The type of the detected drift is determined and new test queries that mimic test queries for the detected drift are synthesized. A portion of the synthesized test queries is selected to reduce annotation cost and used to update the cardinality estimation model.

The embodiments of the present disclosure provide a method for application debugging, a method for information query, a method for application evaluation, an apparatus, a device, and a storage medium. The method for application debugging includes: executing a test query for a target application; presenting corresponding first operation information of multiple stages of executing the test query, the operation information of the multiple stages at least indicating the output of each of the multiple stages; and generating an operation reference case for the target application based on the corresponding first operation information of the multiple stages of the test query, the operation reference case including the test query and the operation information of the multiple stages, and the operation reference case being referenced in the operation of the target application to determine the result of the user query. In this way, the application creator can improve the quality of services provided by the target application without modifying the code, thereby simplifying the debugging process of the target application.

A virtual assistant platform provides a user interface for app developers to configure the enablement of domains for virtual assistants. Sets of test queries can be uploaded and statistical analyses displayed for the numbers of test queries served by each selected domain and costs for usage of each domain. Costs can vary according to complex pricing models. The user interface provides display views of tables, cost stack charts, and histograms to inform decisions that trade-off costs with benefits to the virtual assistant user experience. The platform interface shows, for individual queries, responses possible from different domains. Platform providers promote certain chosen domains.

The invention relates to the technical field of aero-engines, and particularly provides an aero-engine fault diagnosis method and device, electronic equipment, a storage medium and a program product. The method comprises the steps that a meta training data set and a meta test data set are obtained, the meta training data set comprises vibration image sample data of M fault categories, the meta test data set comprises vibration image sample data of N fault categories, and a meta training support set and a meta training query set are obtained from the meta training data set, obtaining a meta test support set and a meta test query set from the meta test data set, training a neural network model based on meta learning by using the meta training support set, verifying the neural network model by using the meta training query set, finely tuning the neural network model by using the meta test support set, and testing the neural network model by using the meta test query set. Based on the neural network model, aero-engine faults are diagnosed. According to the technical scheme, the accuracy of aero-engine fault diagnosis can be improved.

The present invention relates to a few-shot SAR target classification method based on self-supervised learning and nearest neighbor network, including: obtaining a training task set and a test task set from a number of synthetic aperture radar images; constructing a network model in the pre-training stage; iteratively training the network model in the pre-training stage by using the training task set; constructing a network model in the self-supervised training stage; loading the parameters of the trained network model in the pre-training stage into the network model in the self-supervised training stage, and iteratively training the loaded network model in the self-supervised training stage by using the training task set; inputting the test task set into the trained third feature extraction module for feature extraction to obtain a set of test feature vector groups; calculating the similarity between the test feature vector corresponding to the test query sample and the test feature vector corresponding to each test support sample in the set of test feature vector groups to obtain the target classification result. This classification method effectively improves the classification accuracy of few-shot targets.

Test system (100) for checking a plurality of connections of a wiring harness for faultlessness, the test system (100) comprising: a plurality of test adapters (110) for testing respective terminals of the wiring harness; a plurality of control boards (120), each control board being electrically connectable to one or more test adapters (110) of the plurality of test adapters; and a plurality of segment controllers (130) which can be connected to a test computer (140) via a bus system (150), wherein each segment controller (130) can be electrically connected to one or more control boards (120) of the plurality of control boards; wherein a respective test adapter (110) of the plurality of test adapters comprises a microcontroller (111) which is designed to carry out a test query with regard to the respective connection and to forward information thereon to the test computer (140) via the corresponding control board (120) and segment controller (130).

Embodiments of the present disclosure disclose a data processing method, apparatus, electronic device, and computer-readable medium. A specific implementation of the method includes: in response to receiving a project test query request, querying a project database corresponding to the project test query request; screening at least one to-be-processed test data from at least one piece of test data in the project database; setting the test data in the at least one piece of test data other than the at least one to-be-processed test data as at least one target test data; importing the at least one target test data into a project simulator to obtain a test result corresponding to the project test query request, where the project simulator is used to simulate the operation of a data project through test data. This implementation can overall reflect the test effect of the data project and improve the test efficiency of the data project.

The present application relates to remote sensing information processing technical field, for solving the problem of low algorithm accuracy of existing hyperspectral classification method, poor auxiliary training effect of source domain data set and so on, and proposes a kind of small sample hyperspectral classification method based on cross-domain spectral band alignment, comprising: S1, reading the source domain data set D source And target domain data set D target Of hyperspectral data set, calculate the aligned spectral wavelength range and the aligned band number N align ;S2, by parabolic interpolation and resampling alignment method, the spectral wavelength range and band number N align Of two data sets are unified;S3, select training sample, collect training support set and training query set to construct small sample learning task S4, train embedding network F emb Determine network model parameters;S5, collect test support set and test query set from target domain data set, determine the predicted class label of each sample using nearest neighbor algorithm, and then complete the classification of small sample hyperspectral.

Various arrangements for performing health monitoring of cellular network functions in a cloud-computing environment are presented. A cloud computing region may be input for analysis. A cellular network core may be implemented on a cloud computing platform. Instantiations of the cellular core function and pods of the cellular core function executed within the cloud computing region can be identified. A test query can be performed based on the input of the cellular core function. A status of the cellular core function can be output based on the test query.

Various arrangements for performing health monitoring of cellular network functions in a cloud-computing environment are presented. A cloud computing region may be input for analysis. A 5G cellular network core may be implemented on a cloud computing platform. Input of a cellular core function for analysis may also be received. Instantiations of the cellular core function and pods of the cellular core function executed within the cloud computing region can be identified. A customized test query can be performed based on the input of the cellular core function. A status of the cellular core function can be output based on the customized test query.

The invention particularly relates to a small sample image classification method based on a Bayesian normal form, and the method comprises the steps: obtaining enough priori knowledge from an auxiliary training set based on a Bayesian thought, and then obtaining the posterior distribution of each class on the basis of a small number of support set samples through employing a Bayesian updating formula. And for the query set samples, according to the characteristics of the query set samples and the posteriori distribution of each support set category, constructing comparison distribution similarity measurement for classification. Through a Bayesian network, priori knowledge of an auxiliary training set is fully utilized by utilizing a priori updating posteriori idea, and more complete and more discriminative support set feature distribution is learned; the relation between the to-be-tested query sample and different support sets is fully considered by constructing the contrast distribution measurement, and the similarity between the to-be-tested query sample and each support set category is more effectively obtained, so that the accuracy of small sample image splitting is improved.

Systems and methods for improving query creation and AQM are provided. A user interface is provided that allows an analyst to generate and test queries. After the analyst provides a query, one or more subsets of previous call center interactions are selected to test the provided query using methods such as a large language model or string vectorization. Once the subsets of interactions are selected, an LLM is used to evaluate the query against the subset. The evaluations may indicate interactions which were positive and which interactions were negative and may include quotes from the interactions that support the evaluations. The evaluations are presented to the analyst for review, and the analyst may revise the query in view of the evaluations. Once the analyst is satisfied with the performance of the query, the query may be used by the LLM to perform AQM for new interactions in the call center.

A virtual assistant platform provides a user interface for app developers to configure the enablement of domains for virtual assistants. Sets of test queries can be uploaded and statistical analyses displayed for the numbers of test queries served by each selected domain and costs for usage of each domain. Costs can vary according to complex pricing models. The user interface provides display views of tables, cost stack charts, and histograms to inform decisions that trade-off costs with benefits to the virtual assistant user experience. The platform interface shows, for individual queries, responses possible from different domains. Platform providers promote certain chosen domains.