6 results about "Validation test" patented technology

The disclosure provides a pricing test method, device, equipment and medium for a charging system, and belongs to the technical field of function test. The method comprises the following steps: generating verification test sample data, wherein the verification test sample data comprises verification test user data, verification test call data and result verification data; determining whether a first condition is met; providing the verification test user data and the verification test call data to the charging system; obtaining execution result data of the charging system in performing pricing test on the verification test call data according to the verification test user data; and determining a pricing test result of the charging system according to the result verification data and the execution result data. The disclosure can effectively improve the verification accuracy of the verification result of the pricing correctness, and effectively reduce the verification time and the verification workload.

An automated self-precision validation accelerometer is configured for machine diagnosis for efficient and accurate vibration readings from the machine. The accelerometer sensor system conducts self-precision validation test and diagnosis. The accelerometer sensor system comprises two accelerometer sensors for more data acquisition. The usage of two tri-axial accelerometers identify ill-health of the sensors through a self-precision validation test conducted by a firmware module configured in the microcontroller unit that triggers the accelerometers to conduct in built self-test at specific constraints. The system 300 for self-precision validation accelerometer sensor comprises a power supply unit 301, a self-test sensor assembly 302, a wireless interface unit 303, and an external device 304. The firmware module 302c further sends a report on the test to the external device 304 through a wireless interface unit 303 advising for the replacement of sensor.

A diversified validation test suite application (DVTSA) for a generative artificial intelligence (AI) driven tool or a large language model (LLM) tool includes at least a first control logic, a second control logic, and a third control logic. The first control logic receives a seed test input or a user input, analyzes the seed test input or the user input, and extracts varying key elements. The second control logic performs an overlay measurement of the output of the first control logic. The third control logic causes the human verifier to evaluate an output of the second control logic with respect to a predefined coverage metric, and selectively and continuously iterating such that the output of the second control logic increases LLM tool input robustness and output robustness from a first level to a second level greater than the first level during production and pre-production LLM tools.

This invention discloses a drilling lithology identification method based on enhanced dynamic behavioral features. Addressing the shortcomings of traditional methods that rely solely on instantaneous parameters and struggle to capture the dynamic evolution of the drilling process, this method first collects drilling engineering parameter sequences and constructs a depth-domain parameter matrix using a sliding window. Then, it extracts dynamic behavioral features such as parameter change rates, local fluctuation intensity, and dynamic coupling relationships between parameters, and concatenates these features with the original parameters. Next, it constructs a Transformer encoder, utilizing a self-attention mechanism to capture long-range dependencies in the depth sequence. The encoded features are then processed through average pooling and a fully connected layer to output lithology classification probabilities. Finally, a cross-entropy loss function weighted by dynamic responses is used to optimize the model. In validation tests on six wells in the Volve oilfield, this method significantly improved the accuracy of lithology classification and the stability of the model, providing reliable technical support for intelligent drilling.

The present invention relates to a method for validating the ability of a test filter unit to produce a sterile effluent. The method according to the present disclosure has the advantage that it allows simulating cumulative stress incurred by the filter during use with consideration to time and pressure, as closely as is feasible. A further advantage of the method according to the present disclosure is that it allows evaluating the impact of the stress incurred by the process filter during PUPSIT, while factoring in lab scale equipment / limitations and differing objectives, e.g., performing PUPSIT during manufacturing vs. filter validation.

The invention relates to the technical field of solid state disk testing, in particular to a solid state disk testing index optimization method and device, equipment and a storage medium. Comprising the following steps: setting part of to-be-tested products as a sample group, and obtaining a first test parameter set and a multi-environment parameter matrix; performing a preliminary screening test and multiple rounds of verification tests on the sample group, collecting sample test data, identifying sensitive parameters, performing sensitivity grading, and updating a selection probability according to a grading result to obtain a second test parameter set; fitting the sample test data to obtain a bathtub curve, and extracting stage features as a stage feature set; and generating an environment adaptation test strategy according to the stage feature set and the environment parameter matrix, and testing all the to-be-tested products in combination with the second test parameter set and the environment adaptation test strategy to obtain a total test result. According to the method, sensitive parameter precision of solid state disk test index optimization, test strategy full-period and scene coverage diversification can be realized.