2032 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 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 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.

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 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 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 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.

The invention discloses an automatic driving test case self-evolution repairing method based on a large language model, and belongs to the technical field of testing and performance optimization of an automatic driving system. According to the automatic driving test case self-evolution repairing method based on the large language model, deep semantic mining is conducted on a performance log of the model by means of semantic comprehension and recommendation capacity of the large language model, and a high-quality repairing scene is recommended from a structured test scene library. The method comprises the steps of performance log collection and pre-evaluation execution, failure mode semantic extraction, a scene semantic retrieval mechanism, initial recommendation construction, reflection type semantic optimization, few-sample fine adjustment adaptation and a closed-loop self-evolution mechanism. The method has obvious advantages in the aspects of automatic driving test case recommendation, failure mode recognition, rapid model capability adaptation and system closed-loop self-evolution, and the safety, generalization capability and test maintainability of the automatic driving system can be remarkably improved.

The invention relates to the technical field of artificial intelligence, and discloses a neural network model training method based on virtual reality and simulation and related equipment. The method comprises the steps of generating a three-dimensional visual virtual reality scene based on a target real scene and constructing a dynamic physical simulation environment; deploying the neural network model to a test vehicle digital twin execution model for reasoning, and outputting a reasoning result; identifying a to-be-optimized target test scene type of the neural network model based on a reasoning result; dynamically adjusting scene parameters of the dynamic physical simulation environment to simulate a target test scene type, and generating a high-fidelity synthetic data set; and performing iterative training on the neural network model by mixing the high-fidelity synthetic data set and the real data set to form a closed-loop optimization mechanism until the generalization ability of the neural network model in the complex scene reaches a preset threshold. Based on the method, the training efficiency and generalization ability of the neural network model in a complex scene can be improved.

The invention relates to the technical field of cloud security verification, in particular to a cloud security verification method and system, electronic equipment and a computer readable storage medium. The method comprises the steps of generating and isolating an AKSK which is an access key pair for identity verification, simulating an attack behavior in combination with the AKSK, and performing security capability verification based on the simulated attack behavior. According to the method, the AKSK is generated to simulate the attack in the real cloud environment, it is ensured that the test scene highly restores the real attack path, and the technical problem that the traditional security test mainly depends on static policy analysis (such as IAM policy grammar check) and artificial penetration test and cannot simulate the complete attack path after AKSK leakage in the real attack chain is solved.

The invention discloses a software test automation report generation optimization method which comprises the following steps: automatically registering and authenticating a multi-source test data access endpoint, uniformly collecting and normalizing test data with a multi-dimensional attribute tag, and forming a high-quality grouped data set through structured modeling and automatic clustering; further extracting event and result feature nodes, and establishing an interpretable tree rule dependency relationship; according to the method, the minimum redundancy and reusable customized rule path is generated by applying a dependency tree traversal and optimization algorithm and automatic reasoning, and the continuous adaptive evolution of the rule base among multiple projects is realized by combining conflict detection and visual interaction, so that the efficiency and expandability of data processing in a complex test scene are improved, and the test efficiency is improved. And the flexibility and automation level of report customization rule configuration are enhanced.

The invention provides a scene simulation optimization method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a test scene with emergency characteristics by adopting reinforcement learning, wherein the test scene comprises a modeling module, an environment disturbance module and a behavior body configuration module; if the input automatic driving algorithm is valid in the risk of the test scene, evolving and updating a trajectory disturbance strategy of the adversarial behavior body; and if the automatic driving algorithm fails in the test scene or the evolved and updated trajectory disturbance strategy, performing continuous pressure measurement and depth verification on the decision vulnerability of the automatic driving algorithm based on a vulnerability amplification mechanism. A good theoretical basis method is provided for testing and verifying decision robustness and limit safety performance of an automatic driving system in a complex urban traffic environment.

The invention discloses a test case generation method and device, a test case test method and device, a medium and a program product, and relates to the technical field of interface test and artificial intelligence, the method comprises the following steps: obtaining flow data of a first interface in a first service and a code corresponding to the first service; based on the traffic data, the code and a target model, a test case is generated, the test case comprises a test scene, test traffic and an assertion rule, and the test scene is used for representing a target field in the traffic data and a combination of values of the target field, the assertion rule is used for representing a target test result of the test case. According to the method, the generation efficiency of the test case can be improved under the condition of ensuring the test coverage and the code coverage.

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.

The invention relates to an automatic driving key test scene generation method fusing a visual language large model and a diffusion model, and the method comprises the steps: constructing a diffusion model which is used for learning the data distribution of natural driving; aiming at an original scene, utilizing a visual language large model to analyze and form a guide strategy; and based on a guiding strategy and a guiding diffusion algorithm, guiding intervention is carried out on the diffusion model in a simulation environment so as to calculate a background vehicle track and generate a key scene. Compared with the prior art, the method has the advantages that the original scene is automatically analyzed and potential risks are inferred by utilizing the visual language large model, so that the generation process of the bottom diffusion model is guided, intervened and corrected, and the key test scene with high interactivity, high risk and authenticity can be generated in closed-loop simulation.

The invention discloses an internet of things data stable transmission optimization method, system and device and a medium, and relates to the field of internet of things, and the method comprises the steps: generating a plurality of test scenes through a multi-network simulation model; inputting the plurality of test scenes into the target Internet of Things to obtain Internet of Things test data; inputting the Internet of Things test data into the transmission performance prediction model to obtain performance prediction data; inputting the performance prediction data into a data mining and analysis model to obtain data mining information; generating a data stability analysis report according to the data mining information; and determining an abnormal point according to the data stability analysis report to form a detection and maintenance scheme. The stability, the reliability and the operation and maintenance efficiency of the Internet of Things system are effectively improved, and the method has wide application prospects and remarkable engineering practice values.

The invention discloses an automatic driving test scene library construction method based on real traffic data, and relates to the technical field of automatic driving, and the method comprises the following steps: based on a selective sensor fusion framework, dynamically adjusting a fusion strategy of a multi-modal sensor according to a current driving environment, and obtaining corresponding scene elements; performing hierarchical classification on scene elements, constructing a risk assessment model, calculating a comprehensive risk score, and preliminarily dividing risk levels; constructing a rule-based classifier by adopting an association rule mining technology on the basis of results of hierarchical classification and preliminary risk grading, and carrying out risk grading on the scene to be evaluated; and the scene elements and the risk levels are stored in a structured manner, and an automatic driving scene library supporting multi-dimensional query is constructed. According to the method, the characteristics of the traffic scene can be captured more comprehensively, scene elements can be identified more accurately, the risk levels of the scene can be divided scientifically, and the scene library is constructed by combining the scene elements and the risk levels, so that the diversity and pertinence of the test scene are improved.

The invention belongs to the technical field of charging detection, and particularly relates to a charging pile extreme environment test method based on composite stress simulation, a terminal and a storage medium, and the method comprises the steps: inputting an identifier corresponding to a target test scene through a human-computer interaction interface of a test cabin, and executing a query operation based on the input identifier, retrieving a preset parameter combination from a preset scenarized stress library by using the mapping function; in a closed test cabin, environment parameter data are collected in real time through a distributed sensor array and fed back to a central controller, and the central controller drives an execution component to cooperatively regulate and control parameters such as temperature and humidity, so that the environment parameters are stabilized in a preset range; a sequential stress scheme is generated based on a pre-stored dynamic loading algorithm, an execution component is controlled to realize stress superposition or stress alternation, and a charging load is synchronously adjusted to simulate a composite stress effect under an actual working condition; simulation of multi-factor combined stress and dynamic loading is realized, an actual use fault can be found in advance, and the matching degree of a test result and a real environment is remarkably improved.

The invention provides an anthropomorphic automatic driving simulation test scene construction method. The method comprises the steps that all vehicles in a traffic scene are divided into two types of intelligent agents including a test vehicle and an environment vehicle, the traffic scene is modeled into a Markov decision process, the test vehicle is a vehicle controlled by an automatic driving algorithm, and the environment vehicle is a vehicle controlled by a GAIL-GRU driving strategy model; a Markov decision process is utilized to extract a driving track from the human driving data set, and an expert track data set is generated; and training a GAIL-GRU driving strategy model by using the expert track data set, realizing interaction between an environment vehicle and a test vehicle by using the trained GAIL-GRU driving strategy model, and constructing a simulation test scene. The automatic driving simulation test scene constructed by the invention can effectively expose decision defects of an automatic driving algorithm in a complex interaction situation, has good anthropomorphism and relatively high risk scene coverage, and provides support for a decision control simulation test of a high-level automatic driving vehicle.

The invention relates to a test case generation method and system based on an industrial agent. The method comprises the steps that an industrial data set is acquired, and a system dependency model is constructed; generating a path coverage test sequence through the first agent; generating a boundary value test data set through the second agent; generating abnormal test scene information through a third agent; and generating a test case set based on the path coverage test sequence, the boundary value test data set and the abnormal test scene information. In conclusion, by constructing the system dependency model and utilizing the multi-agent collaborative analysis process logic path, boundary condition and dependency relationship, the multi-surface coverage of the industrial control system test scene is realized, the automation of the test case generation is realized, the effects of improving the generation efficiency and enhancing the comprehensiveness of the test coverage are achieved, and the method is suitable for industrial control system test. Especially for the coverage of boundary conditions, abnormal scenes and concurrent paths, the manual dependence is reduced, and the reliability and consistency of the test are improved.

The invention relates to the field of industrial automation testing, discloses a multifunctional testing tool and method for an EUROMAP interface of an injection molding machine, and achieves the crossing from single function verification to system-level compliance evaluation. A test network is constructed through a controller, and deep compliance verification, coverage data interaction, exception handling and cross-layer response consistency are realized. The test tool integrates the functions of protocol analysis, signal simulation, fault injection and the like, supports dynamic generation of boundary value test scenes and abnormal injection of signal delay, CRC errors and the like, and verifies the time sequence / logic consistency of a protocol instruction and an execution mechanism response through a hardware-level timestamp alignment technology. According to the method, an evaluation report including compliance rating, fault diagnosis and optimization suggestions is automatically generated, the problems of single coverage dimension, insufficient abnormal simulation, cross-layer verification deficiency and the like of a traditional test are solved, the intelligence and standardization level of the injection molding machine interface test is remarkably improved, and the equipment interconnection and intercommunication performance is guaranteed.

The invention provides an interactive system virtual test method and device based on multi-mode cognitive twin bodies. The method comprises the following steps: 1) constructing user cognitive twin: outputting a behavior decision tree with confidence through an adversarial generative network based on population attributes, cognitive preferences and behavior history; 2) creating a dynamic interaction environment: encoding a to-be-tested interface into a semantic graph structure which can be analyzed by a machine, and associating a physical operation constraint rule base; 3) running a multi-agent test: deploying a twin cluster in the virtual sandbox, and recording an operation path, an attention thermodynamic diagram and decision delay; and 4) generating a design defect map, namely, positioning an interface cognitive friction point by comparing the deviation degree of an expected interaction flow and an actual behavior track. According to the method, the problems that a traditional user test period is long and scene coverage is limited are solved, and early interception of interaction risks and design optimization preposition are achieved. The method can be widely applied to high-risk / high-cost / high-cycle test scenes of the man-machine interaction system.