2243 results about "Test report" patented technology

The embodiment of the invention provides a cross-platform compatibility automatic testing method, which comprises the following steps: scanning a target platform architecture specification file to extract instruction set features, collecting chip platform information to construct a testing environment database, constructing a testing case feature model according to the instruction set features and the testing environment database, designing a test case generator based on the test case feature model, and generating a test task scheduling scheme based on the test execution framework; inputting the abnormal behavior characteristics and the performance baseline threshold value into a test result analyzer, constructing a scoring dimension containing functional integrity, a performance standard-reaching rate and a stability index, and mapping the abnormal behavior characteristics and the performance baseline threshold value to the scoring dimension to generate a test result scoring matrix, calculating a weighted average value of the test result scoring matrix and the optimized weight coefficient as a compatibility level, generating a test report based on the compatibility level, and executing regression verification; according to the invention, the comprehensiveness and accuracy of the test can be improved, and more efficient compatibility evaluation is realized.

The invention relates to the technical field of data processing, and discloses a steel structure welding seam quality ultrasonic detection method and system. The method comprises the following steps: scanning a welding seam to obtain digital parameters, constructing a virtual model to generate an ultrasonic response database, optimizing a detection parameter forming scheme, executing distributed acquisition to obtain a signal matrix, extracting defect features by using a neural network, and outputting a digital detection report through transfer learning. According to the method, automatic optimization of detection parameters and accurate extraction and intelligent analysis of defect features are realized, the accuracy, efficiency and reliability of detection are improved, and the dependence on artificial experience is reduced.

The invention discloses an unmanned aerial vehicle electric power inspection image intelligent analysis method and system based on deep learning and multi-modal fusion and a medium thereof, and relates to the technical field of electric power equipment detection. The method comprises the following steps: planning an optimal inspection path by adopting an A * algorithm to realize multi-sensor synchronous data acquisition; adaptive histogram equalization and defogging processing are carried out on the visible light image, non-uniformity correction and temperature calibration are carried out on the infrared image, and filtering and registration are carried out on point cloud data; constructing a multi-scale feature fusion network based on improved VGGNet-16, and introducing deformable convolution and a cross-modal attention mechanism to realize multi-source data fusion; defect detection is carried out based on a three-level template library and a feature map cross-correlation algorithm, and the precision is improved in combination with non-maximum suppression and sub-pixel positioning; and finally generating a detection report containing defect types, positions and maintenance suggestions. According to the invention, the automation level and the detection precision of power inspection are obviously improved.

The invention relates to the technical field of automatic report generation, and provides a test analysis and report generation method and system based on large model retrieval enhancement, and the method comprises the steps: firstly carrying out the semantic task decomposition of user query, and meanwhile, achieving the multi-dimensional information extraction through the butt joint of a knowledge vector library and a structured image-text knowledge system established by a private knowledge base module. And three strategies of RAG, Self-RAG and Graph-RAG are combined to enhance the generation capability of the large model so as to accurately obtain background knowledge. And constructing a standard SQL statement according to a structured query requirement, and performing data filling according to template prompt by an output result fusion module in combination with a query background and an SQL execution result to form a test report. By optimizing a retrieval enhancement generation method, professional knowledge supplement related to query is realized, and the reasoning ability of a large model in a professional scene is effectively enhanced, so that the accuracy and the intelligent level of data analysis are improved.

The invention provides a valve actuator full life cycle intelligent test system and method. The system comprises a combined load generation module used for generating an initial dynamic proportion of axial thrust and radial disturbance force according to a test protocol instruction, simulating a comprehensive stress state of an actuator under the impact of a pipeline medium, and outputting mechanical data containing thrust fluctuation characteristics in real time; the environment coupling module is used for dynamically adjusting an environment stress condition according to the test protocol instruction; the digital twinborn analysis module is used for comparing the actual displacement track of the actuator with the theoretical model track through a space-time alignment algorithm according to the mechanical data and environmental stress conditions to obtain a deviation feature vector; and the intelligent diagnosis module is used for carrying out dynamic time warping matching on the basis of the deviation feature vector and a preset historical fault database, outputting a fault type identification code and confidence, and generating a test report. According to the scheme, the accuracy of accurate evaluation of the full life cycle state of the valve actuator is improved.

The invention discloses a ceramic product defect detection and analysis method and system, and relates to the technical field of intelligent detection and analysis, and the method comprises the steps: collecting the multi-modal data of a ceramic product, and carrying out the data preprocessing; for the preprocessed data, identifying a defect area existing on the surface of the ceramic by adopting an image analysis method, and marking the position and the range of the defect area; constructing a classification model, taking the identified defect region data as input, dividing defect types, and extracting features of each defect; the classified defect features are compared with a preset production process parameter rule base, and possible causes of defect formation are analyzed; and generating a detection report according to classification and analysis results. According to the invention, accurate identification and classification of the surface defects of the ceramic product are realized. 2D images and 3D shape data are combined, so that the comprehensiveness and the accuracy of detection are greatly improved; and meanwhile, through a cause analysis model, defect characteristics are associated with production process parameters, and clear cause inference and optimization suggestions are provided.

Methods and systems for artificial intelligence (Al) assisted automation of testing of a software functionality related to a user intent on a software platform is provided. In one embodiment, the method includes receiving user action data indicating the user intent related to a software platform and generating a human-readable test scenario. The test scenario, which includes a sequence of human-readable testing steps and an expected outcome, is generated using a scenario machine learning (ML) model. The method includes generating an interpretable test script based on the test scenario using a script ML model. The test script is interpreted to implement testing steps on the software platform, generating a test outcome. A human-readable test report is also generated to evaluate the test scenario on the software platform, based on the test outcome and the expected outcome.

The invention discloses a solid-state battery performance testing method and system based on data analysis, and relates to the field of battery performance testing, and the method comprises the steps: building a multi-physics field coupled digital twinborn model, collecting a strain signal and temperature field distribution of a failure region according to a three-dimensional physics field state map of the failure region, and when abnormally abruptly changed, carrying out the testing of the performance of a solid-state battery. Triggering multi-stage early warning, matching physical failure evidences with a historical database, extracting an optimal test parameter combination through a meta-learning framework, dynamically adjusting a charging and discharging strategy and monitoring frequency, generating a real-time data stream, and updating parameters of a multi-physics field coupled digital twin model through a back propagation algorithm. According to the method, the test parameters are dynamically optimized through the meta-learning framework, the performance of the solid-state battery is monitored and predicted, the safety and the reliability of the solid-state battery are improved, and a charging and discharging strategy is optimized to prolong the service life of the battery.

The invention discloses a hardware basic service component test system, which comprises a test management module, a hardware simulation module, an automatic test module, a data acquisition module, a data analysis module, a report generation module and a feedback adjustment module, the hardware simulation module is used for simulating hardware environments under different load conditions; the automatic test module is used for executing predetermined test tasks; the data acquisition module is used for monitoring hardware states and performance data in real time; the data analysis module is used for analyzing the acquired data; abnormal behaviors are identified and performance is evaluated, a report generation module generates a test report, and a feedback adjustment module dynamically adjusts test parameters and resource allocation according to an analysis result. The hardware basic service component testing efficiency, accuracy and comprehensiveness are improved, and the method can be applied to the technical field of hardware testing.

The invention provides a consistency test method and device for single-to-wire communication, and the method comprises the steps: receiving a communication signal transmitted by a tested device, extracting a time domain feature, a frequency domain feature and a modulation feature of the communication signal, generating a protocol feature value, carrying out the dimension reduction processing, and generating a protocol feature vector; performing matching degree analysis on the protocol feature vector and a predefined mapping dictionary, and determining a target communication protocol type with the highest matching degree; based on the determined communication protocol, dynamically generating a consistency test flow through a pre-trained neural network model; configuring a signal generator to inject an excitation signal according to the test flow, and synchronously acquiring various parameters of the tested equipment; based on the real-time feedback data, dynamically adjusting the signal amplitude, frequency and judgment threshold through a pre-trained neural network model; and summarizing feedback data of multiple rounds of tests, and generating a test report of the consistency index. According to the method, the protocol of the tested equipment can be automatically identified, the test flow is generated for the protocol, and the test efficiency is effectively improved.

The invention provides a system design method and system supporting automatic testing of multiple radio frequency parameters, and relates to the technical field of testing, and the method comprises the steps: receiving and analyzing a test task, generating a test configuration file according to the information of radio frequency equipment to be tested and multiple radio frequency parameter test items, and using a self-adaptive optimization algorithm to optimize the test configuration file. Dynamically adjusting the test configuration file based on historical test data to generate an optimized test configuration file; controlling a radio frequency signal generator to generate a test signal, inputting the test signal into the radio frequency equipment to be tested, starting a real-time monitoring module, performing multi-dimensional analysis on a digital signal processed by a radio frequency signal analyzer, performing anomaly detection on an analysis result by using a deep learning algorithm, identifying and marking an abnormal data point; and generating a test report according to the analysis result and the anomaly detection result.

The invention relates to the technical field of intelligent control of injection molding machines, in particular to a self-adaptive full-automatic spline injection molding system. Comprising an injection molding data acquisition module, a spline model generation module, a self-adaptive control module, a finished product quality detection module and a monitoring and early warning module. The data acquisition module acquires multi-dimensional data in the injection molding process in real time, the spline model generation module adopts deep learning to construct a nonlinear spline model, and the adaptive control module adjusts equipment parameters in real time based on the model. The finished product quality detection module analyzes quality features such as sizes and defects through machine vision and generates a detection report. And the monitoring and early warning module monitors the running state of the equipment in real time, identifies fault features and provides adjustment suggestions. The spline injection molding process is comprehensively optimized, and the production efficiency, the finished product quality and the safety and reliability of equipment operation are remarkably improved.

The invention relates to a multidirectional machine vision detection system and method for automobile parts, and the system comprises an image sensor module, a self-adaptive light source control module, a mechanical arm system, and a data transmission and analysis module. The self-adaptive light source control module adjusts the intensity and angle of a light source in real time according to the detection environment and the gloss characteristic of the surface of the accessory, interference of reflection and shadow is reduced, the mechanical arm system drives the accessory to move at multiple angles through an inverse kinematics algorithm, and it is ensured that the surface of the accessory is fully covered; and the data transmission and analysis module is used for processing the collected image data, identifying potential defects of the accessories through a deep learning algorithm and a high-dimensional feature analysis technology, and generating a detection report. The method is suitable for quality detection of automobile parts with complex shapes and high glossiness.

The invention discloses a salt mist airflow field prediction method and system in an ocean atmospheric environment, and relates to the technical field of ocean wind power plant salt mist corrosion trend prediction.The system divides a wind power plant into a plurality of salt mist monitoring areas, collects environmental data, constructs a salt mist airflow field model by adopting three-dimensional modeling and CFD simulation, and obtains the salt mist corrosion trend of the wind power plant. A digital twinborn model is generated through multi-physics field coupling, and dynamic deduction is carried out; constructing an AI corrosion prediction model by using historical data and simulated deduction data, extracting a corrosion risk feature vector FCV, and performing dynamic training to predict a corrosion trend; salt mist concentration and wind speed data are monitored in real time, a corrosion coefficient is calculated and compared with a threshold value for analysis, and a corresponding protection strategy is formulated; in combination with real-time data and digital twinborn deduction, evaluating a wind-driven salt mist coupling deposition risk, triggering early warning and optimizing a strategy; and a test report is automatically generated, future test parameters are optimized through an AI prediction model, and the corrosion resistance and the operation efficiency of the wind turbine are improved.

The invention discloses an integrated verification and test system and method for complex rules of a substation monitoring system, and the system comprises a rule editing module which provides a visual interface to support a user to edit the complex rules containing various types of variables and time sequence constraints; the analysis and verification module is used for performing grammar, variable conflict and business compliance multi-level verification on the rules; the intelligent test case generation module is used for automatically generating test cases covering normal, boundary and abnormal conditions based on the rule tree structure; the simulation test interface module converts the use case into a standard test script, submits the standard test script to a simulation tool for execution, and outputs the standard test script to a monitoring system through protocol conversion; the result analysis module is used for comparing an execution result with an expected result and generating a structured test report; according to the invention, full-process automation of rule verification and testing is realized, the verification efficiency is remarkably improved, and the test requirements of various transformer substation business scenes are met.

The invention provides a state self-checking system for an unhooking robot, and relates to the technical field of automatic machinery, and the system comprises a real-time collection module which collects a real-time working data set of the unhooking robot; the operation evaluation module is used for comprehensively evaluating the operation of the unhooking robot based on the real-time working data set; the fault detection module is used for carrying out point cloud matching and data comparison on the unhooking robot based on the operation state evaluation information, carrying out fault detection according to a comparison result and generating a fault self-detection report; the fault tracing module is used for synchronizing the fault self-checking report to a human-computer interface for fault tracing; and the fault processing module is used for formulating fault processing suggestions and sending the suggestions to the human-computer interface to perform remote control on the unhooking robot. The technical problem that in the prior art, due to insufficient fault detection precision, tiny or complex faults cannot be found in time, and the operation stability of the robot is affected is solved, the fault detection precision and the response speed are improved, and the operation reliability and safety are enhanced.

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention provides an intelligent inspection method and system of a work order all-in-one machine for pressure-bearing special equipment, and relates to the technical field of equipment inspection.The intelligent inspection method comprises the steps that an inspection request sent by terminal equipment is received, and inspection standards and inspection work order information corresponding to the pressure-bearing special equipment to be inspected are searched for in a pre-stored equipment information database; according to the inspection work order information, inspection parameters corresponding to the to-be-inspected pressure-bearing special equipment are determined, and a prediction model used for predicting the remaining service life of the to-be-inspected pressure-bearing special equipment is constructed based on equipment historical inspection data and equipment use environment data; and using the prediction model to predict the remaining service life of the pressure-bearing special equipment to be detected, taking a prediction result as additional detection reference information, generating a dynamic optimized detection strategy, and repeatedly executing the detection process according to the optimized detection strategy until all detection processes required in detection work order information are completed. And comprehensively generating a test report and sending the test report to the terminal equipment.

The invention discloses a hospital project intelligent management and collaborative operation optimization system based on an AI algorithm technology, and the system comprises a multi-modal data collection module which is used for obtaining hospital operation data, medical equipment data, patient vital sign data and medical staff operation records in real time; the data fusion engine, the dynamic resource scheduling module, the risk prediction unit and the collaborative work platform provide intelligent task distribution, conflict detection and emergency plan generation functions; the self-adaptive optimization controller is used for continuously updating system parameters through an online learning mechanism; the method breaks through the limitation of traditional single-mode data processing, constructs a cross-department data association model through a graph neural network, integrates heterogeneous data sources such as radiation images, inspection reports and equipment logs, realizes dynamic mapping of diagnosis and treatment paths and resource states, introduces a space-time attention mechanism, and improves the diagnosis and treatment efficiency. Equipment operation cycle characteristics and patient movement tracks are subjected to combined modeling, so that the sensing precision of the system is improved to a minute level.

The invention relates to the technical field of electrical variable testing, in particular to a high-precision performance testing processing method and system for a low-impedance voltage transformer. The method comprises the following steps: constructing a controllable test excitation signal, and synchronously collecting a corresponding primary side voltage and a secondary side voltage of a mutual inductor; space electromagnetism elimination is carried out according to the primary side voltage and the secondary side voltage of the mutual inductor, and frequency domain characteristic decomposition and signal instantaneous offset calculation are carried out at the same time, so that a mutual inductor signal characteristic matrix is constructed; acquiring environment parameters corresponding to the low-impedance voltage transformer; constructing a neural network model based on Bayesian optimization, and predicting and outputting a performance parameter set corresponding to frequency response and load characteristics; and comparing the performance parameter set with a preset standard threshold value, feeding back, generating a calibration compensation coefficient, testing and evaluating, and generating a high-precision performance test report corresponding to the low-impedance voltage transformer. According to the invention, high-precision performance testing of the low-impedance voltage transformer can be realized.

The invention relates to the technical field of intelligent file processing, and discloses an element extraction method and device of a test report, computer equipment and a storage medium, the method comprises the following steps: obtaining scanning document documents of a plurality of paper test reports, and screening out a multi-modal format document from the scanning document documents; identifying and extracting text features, picture features and table features in the multi-modal format document based on an optical character identification technology model; performing paragraph combination on the extracted text features; and extracting structured data from the text features, the picture features and the table features after paragraph combination by adopting a multi-modal element extraction model to obtain a complete electronic document corresponding to the multi-modal format document. Based on an optical character recognition technology, character features, image features and table features in various paper test reports are accurately recognized and extracted by using a number, image and character recognition and extraction technology, and the recognition and extraction accuracy and extraction efficiency are improved.

The invention discloses an automatic test method based on natural language processing, which comprises the following steps: S1, generating a test case: performing semantic analysis on a software requirement document or a test requirement by utilizing a natural language processing technology, extracting key information, and combining the extracted test information and a preset test case template to generate a test case; a preliminary test case is automatically generated; the key information comprises a test purpose, an operation step and an expected result; s2, analyzing the test case; s3, generating a test script; s4, executing the test script; and S5, analyzing a test result. According to the automatic test method based on natural language processing, the test requirements described by the natural language can be automatically analyzed, the test case is generated, the test is automatically executed, and the test report is finally generated, so that the software test efficiency is improved, the workload of manually compiling and executing the test case is reduced, and the test efficiency is improved. And the coverage rate and the accuracy of the test are improved, more software defects can be found, and the quality of software products is guaranteed.

The invention discloses a ship mobile energy storage system CAN communication protocol automatic test system and method, and the system comprises a protocol modeling and test configuration module, a simulation node behavior simulation module, a CAN bus interaction and power closed loop module, and a test analysis and result evaluation module. A CAN communication protocol specification of the ship mobile energy storage system and parameters of tested equipment can be obtained, a protocol analysis model and a test configuration file are constructed for format verification and logic consistency verification, meanwhile, a corresponding test case sequence is automatically generated, and the message sending time is controlled to generate a time sequence simulation message flow; and sending the time sequence simulation message flow to the tested equipment, simulating energy interaction behavior acquisition to generate a power feedback data set, testing to obtain a signal consistency deviation and a power execution error to construct a protocol conformity evaluation matrix, and generating a visual test report. According to the invention, the high coverage rate and automatic closed-loop test of the CAN communication protocol can be realized.

The invention provides a solid state disk testing method and device, equipment and a medium, and relates to the technical field of hard disk detection. The test method comprises the following steps: in a process of writing data into physical areas of a to-be-tested solid state disk according to a physical address in sequence, randomly selecting at least part of areas from the physical areas written with the data at a preset time interval to perform reset operation until the physical areas reach a target area state, the target area state comprises a target filling state and a target fragmentation state; executing a read-write test on the physical region in the target region state to obtain test data; and generating a test report based on the test data.

The invention discloses a photovoltaic inverter test system and method, and the system comprises a photovoltaic simulator module which is used for simulating the output characteristics of a photovoltaic array; the adjustable load module is used for adjusting different types of loads; the data acquisition and monitoring module is used for monitoring and recording operation parameters of the photovoltaic inverter in real time; the control and protection module is used for controlling the test process and providing protection; the environment simulation module is used for simulating working states under different environment conditions; and the test report generation module is used for generating a test report and performing data analysis. Compared with the prior art, the photovoltaic inverter test system has the advantages that remarkable technical breakthroughs are realized in the aspects of improving test precision, enhancing load regulation capability, expanding environment simulation conditions and improving data acquisition and monitoring functions, various defects in the prior art are overcome, the quality and efficiency of photovoltaic inverter test are greatly improved, and the photovoltaic inverter test system has important application value and popularization prospect.

Disclosed herein are system, method, and computer program product embodiments for detecting anomalies during software testing. The methods include generating a plurality of test reports for the software program by executing one or more test cases on a plurality of versions of the software program, generating a control chart based on the plurality of test reports, generating an alert when at least one testing characteristic includes an anomaly over the plurality of versions of the software program as determined based on the control chart. The control chart includes a plot associated with at least one testing characteristic of the software program, and a historical context associated with execution of the one or more test cases on the plurality of versions of the software program.

The invention relates to the field of industrial visual inspection, and provides an automatic test performance benchmark construction method which comprises the following steps: dynamically generating ROI (Region of Interest) parameters; performing pixel cutting on the target area of the production line according to the dynamically generated ROI parameters to generate a high-speed image data stream, and aligning the image acquisition moment with the mechanical motion phase of the production line; selecting a matched visual detection model based on target features in the high-speed image data stream, and outputting a defect detection result by the matched visual detection model; according to defect distribution parameters of a preset test scene, the simulation defect features are embedded into the high-speed image data stream, and a mixed test data stream is generated; performing multi-device time sequence alignment on the mixed test data stream and the defect detection result, and packaging into a standardized data set containing a unified time reference; and analyzing the standardized data set, comparing the simulated defect truth value label with a defect detection result, calculating a multi-dimensional performance index of the visual inspection system, and generating a test report containing parameter optimization suggestions.

The invention relates to the technical field of electronic information, and discloses a high-reliability storage chip packaging test method and system.The method comprises the steps that firstly, a to-be-tested storage chip and original parameter information of the to-be-tested storage chip are obtained, packaging structure data are collected to construct a three-dimensional model, chip feature data are extracted, then the chip feature data are compared with standard reference data, and potential defect information is mined; suspicious packaging parts are screened, the sensitivity of the suspicious packaging parts to environmental parameters is monitored, then fault risk types are evaluated, weak links are positioned, performance is detected, specific problem points are found out, then a test planning target is formulated, test means and standards are clarified, a test operation instruction is generated, finally, packaging testing is executed, electrical performance data are collected, and stability is verified. And generating a packaging test report. The stability of the memory chip can be improved.

The invention discloses a storage chip energy consumption test method and device based on multi-objective optimization. The method comprises the following steps: S1, initializing a test environment; s2, setting a multi-objective optimization model and a fuzzy logic system; s3, constructing an initial test data set; s4, calculating the membership degree of each test target; s5, generating a group of initial optimization parameter combinations; s6, dynamically adjusting the test parameters on the basis of the initial optimization parameter combination; s7, generating an optimized test parameter combination; s8, repeating the steps S6 and S7 until a preset optimization convergence condition is reached, and generating a final test parameter combination; s9, obtaining final test result data; and S10, analyzing the final test result data, comprehensively evaluating the performance of each test target through a fuzzy logic system, and generating a multi-target optimization test report of the storage chip. According to the method, the problems of multi-target test balance, test process optimization and balance between efficiency and accuracy in the prior art are effectively solved.

The invention provides a Web automatic testing method and system based on parameterized input, and the method comprises the steps: capturing the operation of a user in a browser, so as to generate a basic script; analyzing and reconstructing the basic script to generate a test script containing parameter variables for executing different test scenes through different parameter values; modifying the test script to obtain a modified test script; setting a plurality of test cycles, and testing the modified test script by using the test cycles to obtain a test result; and generating a test report according to the test result. According to the method, the user operation is automatically captured, the basic script is generated, and the basic script is analyzed, reconstructed and optimized, so that the workload of manually compiling the script can be reduced, the flexibility and stability of the script are enhanced, and then multi-scene verification is performed through the test cycle, so that the problem of parameter change in the test method is solved, and the test efficiency is improved. And the case writing efficiency is low.