2784 results about "Test scene" patented technology

The invention discloses an unmanned aerial vehicle flight control system vulnerability detection method based on data flow analysis and LLM, and belongs to the technical field of intelligent software testing. Comprising the following steps: extracting a code function module associated with user operation in an unmanned aerial vehicle flight control system through a data flow analysis method, and establishing an operation-code mapping relation library; generating a structured natural language semantic description for each function module code by adopting a large language model LLM, and forming a multi-dimensional semantic feature vector; based on correlation analysis of multi-module semantic features, a combined test scene is constructed, and a natural language test case is generated; the natural language test case is converted into an executable test code through reverse semantic mapping, and coding reconstruction of test logic is completed; and executing a test code and capturing a runtime log in an unmanned aerial vehicle simulation environment, and performing vulnerability feature extraction and root cause positioning by using a large language model. According to the method, the efficiency is improved, and meanwhile, the deep coverage test of a complex interaction scene is supported.

The invention discloses an intelligent software testing method and system based on automatic script generation, and relates to the technical field of software testing, and the method comprises the following steps: deploying a distributed data acquisition probe, dynamically adjusting sampling frequency, collecting software operation data, and extracting data features based on a deep learning model; based on the data features, establishing a behavior pattern classification system, and mapping behavior patterns to realize hierarchical behavior classification; the method comprises the steps of constructing a test scene, generating test data, executing a test task, monitoring a test execution state in real time, calculating a test efficiency index, evaluating and feeding back a test effect, and dynamically adjusting a feature extraction strategy. According to the method, deep learning and reinforcement learning technologies are utilized, accurate recognition and classification of software behavior modes are achieved, and comprehensiveness and effectiveness of testing are guaranteed through multi-dimensional coverage rate analysis and a closed-loop optimization mechanism; and meanwhile, the automation level and the intelligent degree of software testing are remarkably improved, and a powerful guarantee is provided for improving the software quality.

The invention discloses an intelligent vehicle extreme test scene generation method based on deep learning. The method comprises the following steps: building a hybrid generative adversarial network scene; collecting and preprocessing data; performing dynamic confrontation training; according to the extreme test simulation, an intelligent vehicle extreme test scene generation system based on deep learning comprises a hybrid generative adversarial network model, a data input module, a preprocessing module, an edge cloud module and an interaction module. A double-channel confrontation mechanism enables the risk controllability of a generated scene to be high, and dynamic confrontation training is carried out; a real multi-source data set is enhanced and labeled, the integrity of point cloud data is high through a point cloud missing region feature completion algorithm, and dynamic risk entry is effectively simulated by introducing a social force model into dynamic obstacle modeling; the multi-modal risk assessment network improves the assessment accuracy.

The invention discloses an electromagnetic environment simulation method and system of a display module, a medium and a product, and relates to the field of electromagnetic variable measurement, and the method comprises the steps: receiving a test identification code inputted by a user, extracting a plurality of target scenes corresponding to the test identification code from a preset scene library, and generating a scene change sequence; determining a radio frequency interference source combination and a mechanical vibration parameter corresponding to the target scene; setting test parameters of the test time periods corresponding to the multiple target scenes, and generating a dynamic test sequence; receiving a key test scene marked by a user, and adjusting a test time period and test parameters corresponding to the key test scene to obtain a target test sequence; simulating an electromagnetic environment of the target display module, and recording simulation test parameters of the target display module; the simulation test parameters comprise display brightness, contrast and response time; and determining an electromagnetic compatibility evaluation result based on the target test sequence and the simulation test parameters. According to the invention, the accuracy of an electromagnetic compatibility test result can be improved.

The invention relates to the field of chip verification, and provides a chip verification method, device and equipment, a medium and a chip. The method comprises the following steps: establishing a dynamic simulation verification environment of a to-be-verified design, and generating a plurality of test scenes and corresponding test excitation according to a design specification definition; sending the test excitation to a driver through a sequencer, converting the test excitation into a signal conforming to a to-be-verified design interface protocol by the driver, and transmitting the signal to the to-be-verified design; the monitor collects an input signal and an output signal of a to-be-verified design in real time, converts the input signal and the output signal into transactions, and transmits the transactions to the scoreboard; comparing the reference model with an output signal of the to-be-verified design to generate a dynamic simulation verification result; according to the dynamic simulation result, the state space is reduced, state space traversal is carried out on a design part which is not covered by dynamic simulation in the design to be verified, and a formal verification analysis result is generated; and evaluating the to-be-verified design according to the formal verification analysis result.

The invention relates to an automatic derivation method, system and device for an automatic driving test scene and a medium, and the method comprises the steps: analyzing a test demand of an automatic driving system, and mapping the test demand to a multi-dimensional scene element hierarchical architecture, so as to construct a multi-layer road scene; reasoning a scene condition dependency relationship by using a dual conditional probability, and generating an initial scene fragment set in combination with probability distribution sampling; establishing a multi-objective optimization function based on the scene type distribution weight, the element redundancy penalty factor, the physical consistency constraint and the security rule, screening and recombining the multiple initial scene fragment sets, and outputting an optimal scene set; extracting a causal logical relationship of the optimal scene set according to the directed acyclic graph, and generating a scene evolution path in combination with topological sorting; and executing multiple safety verification on the scene evolution path, and determining the optimal scene passing the verification as the automatic driving test scene. The technical problems that an existing test scene is low in generation efficiency, poor in authenticity and unreasonable in event chain logic are solved.

The invention provides an integrated test scene prediction method and system based on multi-dimensional data, and the method comprises the steps: carrying out the multi-source data collection of an integrated test scene based on a dynamic sampling strategy, obtaining integrated test multi-dimensional data, carrying out the multi-dimensional data preprocessing of the integrated test multi-dimensional data, and obtaining a pre-trained multi-dimensional data analysis model; inputting the pre-processed integrated test multi-dimensional data into the multi-dimensional data analysis model to carry out sub-module data analysis, obtaining a pre-trained intelligent regulation and control model, importing a system diagnosis report and a key index deviation degree report into the intelligent regulation and control model as inputs, generating an intelligent regulation and control decision, and carrying out intelligent regulation and control. By means of the method, system faults, performance bottlenecks and potential problems occurring in the integrated test scene can be accurately predicted, intelligent regulation and control decisions are provided for solving the problems, and accurate system performance evaluation and optimization services can be provided for users.

A method of evaluating performance of a chatbot includes identifying a test scenario including a request and an expected outcome, initiating an automated conversation between a first machine learning model and the chatbot based on the test scenario, storing a recording of the automated conversation, providing the recording of the automated conversation and the test scenario to a second machine learning model, and receiving an evaluation of the automated conversation from the second machine learning model based on the recording of the automated conversation and the expected outcome.

The invention discloses a real-time three-dimensional fusion reconstruction method for a ship comprehensive test scene, and the method comprises the steps: carrying out the preprocessing of a target object image data flow, converting an image coordinate system into a camera coordinate system, obtaining a sparse point cloud model, and constructing a voxel three-dimensional grid; the method comprises the following steps: constructing a scale space by using a Gaussian difference pyramid to detect key points of an image under different scales, calculating a gradient direction histogram around the key points and taking the gradient direction histogram as a descriptor, matching the descriptor by using an Euclidean distance, and estimating relative pose transformation between the images to complete a local matching process. Projecting and mapping the local frame block to a reference frame to form a key frame, and entering a global matching process; and extracting a surface grid of the three-dimensional model from the dense body grid by adopting a Marching Cubes algorithm, traversing each cube unit in the surface grid, and obtaining distance information from a vertex to a nearest surface based on the cube units, thereby generating a final three-dimensional model of the test target object.

The invention relates to the technical field of software testing, in particular to a software testing management framework and measurement system construction method and system. The method comprises the following steps: acquiring code structure data of a software system, wherein the code structure data comprises code level data, calling relation data and business function data; identifying and analyzing a key path of the software system according to the code structure data, performing test coverage rate evaluation, and mapping coverage effects under different test scenes to obtain coverage feature map data; performing simulation analysis on the execution efficiency of the different test paths according to the coverage feature map data so as to obtain execution efficiency data; and establishing a test resource allocation model according to the test parameters corresponding to the different paths and the execution efficiency data. According to the method, the key path of the software system can be identified and specifically tested by acquiring the code structure data and constructing the coverage feature map, so that the accuracy and comprehensiveness of the test coverage rate are improved.

The invention discloses an automatic driving test scene generation framework and generation method, and the method comprises the steps: a road network generator is used for obtaining a node file and an edge file generated by a multi-modal large language model based on prompt word engineering and scene description, inputting the node file and the edge file into a traffic simulation tool, and obtaining a road network file synthesized by the traffic simulation tool; the vehicle generator is used for acquiring a plurality of initial vehicle models generated by the multi-modal large language model based on prompt word engineering and scene description, inputting the initial vehicle models into a traffic simulation tool, and acquiring a vehicle model generated after the traffic simulation tool adds a path file for each initial vehicle model; and the scene generation module is used for integrating the road network file and the plurality of vehicle models, obtaining an automatic driving test scene, building a bridge for interactive use of the multi-modal large language model and a traffic simulation tool, and constructing a traffic simulation mechanism of the multi-modal large language model based on a prompt project. A customized, real, reliable and targeted automatic driving test scene can be generated.

The invention relates to the technical field of financial services, in particular to a testing method and system based on financial environment stability. The method comprises the following steps: carrying out real-time state monitoring on a financial transaction system, and generating system state basic data; constructing a virtual test environment according to the system state basic data, and performing test scene adaptive configuration to generate test environment parameter data; performing multi-dimensional pressure test analysis on the test environment parameter data, calculating a system stability index, and generating stability reference data; performing dynamic load gradient processing according to the stability reference data to obtain system pressure gradient data; and performing risk level mapping on the system pressure gradient data to generate risk assessment mapping data. According to the invention, the virtual test environment is constructed and self-adaptive configuration is carried out, so that a real transaction scene can be simulated, the correlation and effectiveness of the test are improved, and the stable operation of the system under different pressure conditions is ensured.

The invention belongs to the technical field of automatic driving test, and particularly relates to an intelligent automobile edge scene generation method based on a hierarchical reinforcement framework. Comprising the following steps: 1, processing natural driving data, and constructing an initial test environment; 2, establishing a risk conflict prediction method; step 3, establishing a test scene evaluation method including collision rationality evaluation and space-time similarity evaluation; 4, constructing a scene library based on index management; 5, constructing a basic reinforcement learning environment, and constructing a static environment and a basic interface; step 6, dividing a scene hierarchical scheduling period, and dividing a test period based on scene features; step 7, optimizing a reward mechanism and an action strategy, and performing adjustment according to a scene period and a vehicle behavior; and step 8, model training application and scene data output. Finally, the diversity generation of the edge scene is realized, the local convergence problem is broken through, and the edge scene mining efficiency is improved.

The invention discloses a safety device for reducing the back-up collision risk in the automatic driving test process, and belongs to the technical field of automatic driving test safety. Aiming at the collision risk problem caused by rapid movement of dynamic obstacles and communication delay when a vehicle backs up in a closed test site, the device obtains millimeter-level position coordinates and course angles through positioning units deployed on a test vehicle and a target object; a C-V2X protocol is utilized to construct a real-time data link to share vehicle position, speed, backing track prediction data and target object outer contour feature point coordinates, potential conflicts are identified based on future minimum spacing calculation and dynamic threshold, and a cooperative collision avoidance path is generated in combination with a distributed optimization algorithm. And the high-risk vehicle preferentially preempts a communication channel to issue deceleration, course adjustment and braking instructions to surrounding vehicles. The device can effectively improve the reversing safety in a multi-vehicle cooperative test scene, and is suitable for collision risk real-time early warning and active intervention of an automatic driving vehicle closed test site.

The invention relates to the technical field of automatic driving automobiles, in particular to a scene extraction, generation and evaluation method for a vehicle safety test, which adopts a semantic segmentation network to keep scene element features in original natural driving data to the greatest extent. Based on a high-level decision deep reinforcement learning algorithm, a rough set is used to extract an association generation rule of a typical dangerous scene, the typical dangerous scene can be automatically generated in combination with simulation software, and a certain design thought is provided for automatically extracting and generating the characteristics of the scene by referring to natural driving data; meanwhile, the overall intelligence of the self-driving automobile is evaluated in combination with two dimensions of natural driving evaluation and test scene complexity evaluation, and key index advantages in different aspects are combined, so that the multi-dimensionality of evaluation of the self-driving automobile is improved; and the scene generation efficiency and the intelligent level are greatly improved.

The invention discloses a scene granularity interface test case generation method based on multiple agents, and the method comprises a plurality of large language model agents and related components, namely context memory, an observer, a decision maker, an executor, a supervisor, a recorder and a to-be-tested application. An observer identifies the interface control, detection and attribute identification of the control are effectively focused, and it is ensured that accurate data input is provided for subsequent testing steps. Based on the output of an observer, a decision maker generates a detailed plan of the next operation according to the current page state, and an operation strategy can be independently formulated, so that test generation is supported by business scene logic. And an executor analyzes and executes the generated plan operation, carries out test operation strictly according to the plan, and ensures the traceability and repeatability of the test process. Furthermore, the supervisor verifies whether the operation is executed according to the plan and meets the requirements of the target scene, and makes corresponding feedback according to the check result, thereby ensuring that the wrong operation is corrected in time, avoiding the continuous accumulation of errors, and ensuring the effectiveness of each test step. The recorder is responsible for storing the execution instruction and the context information of the test process to the context memory, and the operation history and result in the test process can be traced through the test context information, so that a better decision can be made in the subsequent iteration, and the result export after the test is completed can be effectively supported. And after the recording is finished, the test generation enters the next iteration until the coverage of the test scene is finished.

The invention relates to a server testing method, electronic equipment, a storage medium and a program product, which are applied to the technical field of computer testing, and the method comprises the following steps: in the process of executing a pressure test on a tested server in a composite testing scene, based on the standard exceeding condition of the performance index of the tested server, according to a degradation strategy matched with the standard exceeding condition, determining the performance index of the tested server according to the degradation strategy matched with the standard exceeding condition; the access traffic injected into the tested server is adjusted, and the degradation strategy is used for performing overload protection on the tested server; converting the acquired running state data of the tested server into multi-dimensional feature data according to a plurality of performance dimensions; determining weights of different performance dimensions based on a test mode of a pressure test; and determining a test result of the tested server according to the weight and the multi-dimensional feature data.

The invention discloses an automatic driving vehicle key signal testing method and system based on real data driving. According to the method, data are collected in a real road scene, test cases are automatically generated, a standard test case library is constructed, hierarchical or parallel testing is carried out in virtual simulation, hardware-in-the-loop and real vehicle environments, and result feedback and closed-loop iteration are achieved through automatic analysis. The system comprises a data collection module, a test case generation module, a standard test case library module, a test execution module and a result feedback module. According to the invention, the problems of insufficient scene coverage and low authenticity and efficiency of the existing test method are solved, the comprehensiveness, authenticity and test efficiency of the test scene are improved, and the safety and reliability of the key signal system of the automatic driving vehicle are guaranteed.

The present application provides a code testing method, applied to a code testing system. When a test code is generated, context information of a code under test is extracted, and the test code is generated on the basis of the context information of the code under test. The context information often has a service meaning and can embody a test scenario, so that the prior knowledge of the service and the test scenario is achieved in the generation process of test codes, thereby generating test codes having higher quality.

The invention relates to the technical field of simulation, and particularly discloses a real-time simulation method for detecting photoelectric tracking equipment, which comprises the following steps of: performing feature decoupling processing on photoelectric signals through a dual-channel adaptive neural network architecture, and constructing an equipment mathematical model by adopting a dynamic gating fusion mechanism based on a processing result; the method comprises the following steps: establishing a multi-physics field coupling simulation environment based on a heterogeneous computing architecture, introducing an equipment mathematical model, performing three-field co-evolution through light transmission modeling, electromagnetic field distribution calculation and target motion prediction, and generating a dynamic test scene containing space-time relevance; according to the method, a mode of combining mixed feature analysis and fuzzy reasoning is adopted, multi-dimensional performance indexes are extracted from simulation data, and multi-target dynamic optimization is carried out through a strategy of combining a quantum evolution algorithm and swarm intelligence optimization; by means of a digital twin platform and a hardware-in-loop interface, real-time verification and closed-loop optimization are achieved, and the response speed and tracking precision of photoelectric tracking equipment in a complex environment are greatly improved.

The invention relates to an online multi-dimensional comprehensive test evaluation method for an automatic driving test. The method comprises the following steps: constructing a test scene library; constructing a test scene complexity model; building an intelligent driving joint simulation test platform, and embedding the intelligent driving algorithm into the intelligent driving joint simulation test platform; constructing an automatic driving test multi-dimensional evaluation index system for performing multi-dimensional performance evaluation on the automatic driving vehicle; an intelligent driving joint simulation test platform is utilized to obtain vehicle test data, an evaluation index system and a test scene complexity model are combined to calculate objective evaluation index weights, then comprehensive performance quantitative scores are calculated, and finally an automatic evaluation report is output and obtained. Compared with the prior art, the subjectivity of manual evaluation can be reduced, and the evaluation efficiency of the automatic driving vehicle is improved; comprehensive and objective quantitative scoring can be carried out on the multi-dimensional performance of the automatic driving vehicle, and the influence of different test scenes on the scoring difference of the automatic driving vehicle is reduced.

The embodiment of the invention discloses a verification platform, method and system for verifying an I2C protocol based on a UVM environment, and relates to the field of function verification. The UVM environment has comprehensive parameterization configuration capability, various test scenes are dynamically matched, and all-directional coverage and deep insight of verification are ensured. And an intelligent mode switching mechanism is adopted, and the role change of the DUT is seamlessly jointed, so that the automation and efficiency of the verification process are improved. And a randomized test strategy is introduced, so that the randomness and robustness of verification are remarkably enhanced, and potential problems are effectively revealed. As standard I2C device simulation, time sequence verification is independently executed in an environment, communication abnormity is accurately captured and analyzed, and fault positioning is accelerated. Through the fusion application of the SystemVerilog and the UVM, the verification complexity is simplified, the simulation process is optimized, and the iteration period is shortened.

The invention belongs to the technical field of self-driving automobiles, and particularly relates to a method for evaluating the complexity of a continuous test scene of a self-driving automobile in real time. Comprising the following steps: step 1, establishing a scene complexity evaluation framework; 2, analyzing the feature weights of the scene elements; step 3, calculating an element-system complexity mapping relation; and 4, synthesizing the complexity characteristics of the scenes obtained in the steps 2 and 3, and obtaining a real-time complexity evaluation result of the continuous test scene. According to the method, the complexity of the surrounding environment can be evaluated in real time according to the vehicle driving process, and the non-real-time problem existing in an existing test scene complexity evaluation method is solved.

The invention discloses an unmanned aerial vehicle dynamic environment 3D target detection method and device based on multi-modal fusion, and the method comprises the steps: calibrating a 4D millimeter-wave radar and a camera, and obtaining a pose relation between the millimeter-wave radar and the camera; the method comprises the following steps: preprocessing 4D millimeter wave radar point cloud data to obtain a denoised point cloud image, and performing feature extraction on the denoised point cloud image; using hidden space normal estimation to obtain a pose matrix between adjacent frames, and then through global discretization analysis, realizing multi-frame point cloud pose correction and fusion in a motion scene, and enhancing point cloud data; processing the radar point cloud and the camera image, and obtaining a BEV feature map through a feature extraction network; using a double-end channel attention mechanism and a cross attention mechanism to fuse BEV features; a detection head module based on an anchor frame is designed, and differentiation setting is carried out for different detection targets; and verifying by adopting different data sets and different test scenes to obtain a 3D target detection result. According to the invention, the hidden space normal estimation method is provided to correlate spatio-temporal information, multi-frame splicing is carried out, the density of point clouds is increased, and the performance of target features is improved.

The invention belongs to the technical field of automobile information safety fuzzy testing, discloses an automobile information safety fuzzy testing method based on a large model, and develops optimization application research of LLMs in an automobile software fuzzy testing scene by utilizing the advantages of an artificial intelligence technology in the aspects of data processing, natural language interaction, generalization ability and the like. The concealment and effectiveness of the test set generated by the fuzz test are optimized, and the fuzz test process efficiency is improved. Through the implementation of the invention, related key technologies are applied to the fuzz test of the automobile, and other related products and enterprises in China are radiated by writing technical standards, so that the fuzz test system can be used as an application demonstration and sample plate of the fuzz test of the intelligent driving automobile. According to market analysis, fuzzy test calculation is carried out on 100 thousand automobiles every year according to the method, and the data safety and the system safety are improved.

The invention discloses an automatic driving test scene set optimization method, apparatus and device, and a storage medium. The method comprises the steps of generating a simulation scene file in an OpenSCENARIO format through preprocessing data; analyzing the simulation scene file through a teacher model, outputting a risk description text, receiving a scene feature vector and the risk description text through a student model, and outputting a failure probability; determining a comprehensive value index according to the failure probability, dynamically updating a scene library of automatic driving test scenes, collecting failure data in an AUT test, performing incremental fine tuning on the student model, and obtaining an optimized target test scene set; the problem of gradient estimation variance explosion caused by sparseness disasters can be effectively solved, the stability of model training is improved, the recognition capability of a rare long-tail failure scene is enhanced, the accuracy of failure probability prediction is improved, the judgment accuracy of the model to a boundary scene is improved, and the accuracy and comprehensiveness of scene value quantification are improved.

The invention discloses an evaluation method for an automatic driving road passing regulation conformance test scene. The evaluation method comprises the steps that a seven-layer logic scene framework with scene elements as minimum units is constructed; obtaining a scene set based on the seven-layer logic scene architecture; wherein the seven-layer logic scene architecture comprises a road layer, a traffic infrastructure layer, a road and traffic facility temporary event layer, a traffic participant layer, an environment layer, a communication state layer and a self-vehicle state layer; and constructing a multi-dimensional test scene evaluation model, extracting scene elements of each test scene, and inputting the scene elements into the multi-dimensional test scene evaluation model to obtain a comprehensive evaluation result of each test scene. According to the method, scene elements are taken as units, four dimensions including true rationality, law and regulation coverage, test suitability and danger complexity and 15 indexes are covered, a comprehensive and effective test scene can be screened out from massive scenes, compliance and safety of an automatic driving system are ensured, and development of the technology in a legal framework is promoted.

An intelligent cable bending test device disclosed by the present invention comprises a cable cylinder used for unwinding or winding a cable, a test wheel seat, a traction seat, a first adjusting assembly and a second adjusting assembly, the cable comes out of the cable cylinder, passes through a test wheel on the test wheel seat and then is connected to the traction seat, the test wheel carries out bending limiting on the cable, and the first adjusting assembly is connected with the second adjusting assembly. The test wheel seat moves relative to the base to form a bending test on a cable, the first adjusting assembly is provided with a first contact wheel, the second adjusting assembly is provided with a second contact wheel, and the first contact wheel and the second contact wheel clamp the cable and then limit the cable. The first adjusting assembly and the second adjusting assembly do not rotate synchronously so that the cable can be in a twisted or non-twisted state, bending performance testing of the cable in different twisted states is achieved, more accurate cable bending performance parameters are obtained, and meanwhile more abundant bending testing scenes are provided.

The invention provides a simulation test scene parameter generation method of an automatic driving system and electronic equipment. The method comprises the following steps: acquiring road condition information transmitted by at least one road monitoring device in a target road in real time; the road condition information comprises a long-tail problem identification result; reconstructing road scene parameters based on pre-stored road attribute data and road condition information; searching a target vehicle running on a target road in an automatic driving mode by accessing an automatic driving system, and acquiring sensor configuration parameters of the target vehicle; and generating simulation test scene parameters according to the sensor configuration parameters and the road scene parameters, so as to generate a simulation test scene for the target road, the target vehicle and the automatic driving system based on the simulation test scene parameters. According to the invention, the simulation test scene covers a wide range of driving environments and emergencies, and the diversity and comprehensiveness of the scene are ensured, so that the method is used for realizing the efficient offline verification of the automatic driving system.

The invention discloses a multi-modal driving test scene generation method and system, and the method comprises the steps: obtaining a plurality of groups of model training samples, each group of model training samples comprising a user natural language description, a driving scene visual image, a risk point thermodynamic diagram, and a safety key scene description code; constructing a driving test scene generation framework, and training the driving test scene generation framework by using the multiple groups of model training samples to obtain a trained driving test scene generation framework; wherein the driving test scene generation framework comprises an image content coding model, a risk point mining model, a thermodynamic diagram coding model and a large language model, the risk point mining model and the thermodynamic diagram coding model are connected in series, and the output end of the image content coding model and the output end of the thermodynamic diagram coding model are connected with the input end of the large language model; and generating a multi-modal driving test scene according to the input user natural language description and the driving scene visual image by using the trained driving test scene generation architecture.