154 results about "Optimal test" patented technology

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 relates to the technical field of deep learning, in particular to an AI chip test parameter adaptive optimization method based on deep learning, which comprises the following steps: acquiring historical test data of an AI chip, and calculating correlation strength among different failure modes based on the historical test data; identifying a failure coupling matrix according to the edge weight, and converting a preset static detection parameter constraint boundary into a dynamic constraint space changing along with a failure detection state; a multi-level optimization framework is constructed, the upper layer executes failure type correlation analysis and generates constraint propagation information, the middle layer optimizes a parameter cluster based on the constraint propagation information, and the lower layer adjusts a single detection parameter and outputs a parameter optimization result; establishing a neural network mapping model, and obtaining a nonlinear mapping relationship between the detection parameters and the failure types; based on the physical state parameters, the nonlinear mapping relation is adjusted, the dynamic constraint space is updated, parameter optimization is executed again, a parameter optimization result is output, and an optimal test parameter combination is output.

The invention discloses a test case generation method and system for security and futures software. The method comprises the following steps: converting data in an original file into an embedded vector through an information analysis agent, and storing the embedded vector in a vector database; generating an initial test case set by using the plurality of test points and the data fragments corresponding to each test point through a case generation agent; constructing a business rule atlas to perform rule similarity scoring on each test case, deleting the test cases with low scores, and forming a high-score test case set by the test cases with high scores; semantic similarity scoring is carried out according to the historical test cases, sorting is carried out according to scores from high to low to form an optimal test case set, and the test case with the highest score is marked as the historical test case to be stored in the vector database. According to the method, the test cases of the security futures software can be automatically generated, redundant cases are reduced by evaluating the test cases, the case quality is improved, the workload of testers is greatly reduced, and the test efficiency is greatly improved.

The invention relates to the technical field of power battery health state evaluation and anomaly recognition, in particular to a remote health state analysis and anomaly recognition method for an electric bicycle battery, which comprises the following steps of: under an access working condition, applying small-signal active excitation and synchronously acquiring to form an opportunity diagnosis excitation data window; time-frequency coupling modeling including equivalent impedance modeling, harmonic wave and intermodulation fingerprint modeling, Koopman operator modeling and entropy coefficient difference is carried out, and a spectral measurement geometric distance is constructed through optimal transmission to generate a multi-domain response packet, a physical invariant vector and an uncertainty matrix; according to the geometric distance and consistency score gating and hierarchical standardization, obtaining a health state estimation record and an abnormal event set by adopting a shape-limited health mapping model; and in combination with mapping Jacobian and dual potential, generating an opportunity diagnosis incentive plan of a next period by adopting D-optimal test design, and updating a group health manifold, thereby realizing cross-environment, cross-equipment and traceable remote health assessment and early anomaly recognition.

The invention relates to the technical field of optical communication, and discloses an optical module adaptive test and rapid convergence method based on reinforcement learning, which can dynamically generate and execute an optimal test sequence for optical modules of different types or states through an off-line trained RL intelligent agent module, avoids redundant steps in a fixed process, and improves the test efficiency. On the premise of ensuring the test coverage, the test time is obviously shortened, and meanwhile, the detection probability of potential defects is improved; through the closed-loop test of the integrated RL intelligent agent module, the test action can be adjusted in real time according to the state of a single optical module, so that the test process can quickly adapt to the individual difference and process fluctuation of the device, the online quick convergence of the test strategy is realized, and the accuracy and stability of the test are guaranteed; and through online iterative optimization of the RL agent module, new data and new changes in the production process can be continuously learned, so that a test strategy is continuously evolved, and the optimal performance is kept for a long time.

The invention provides a hydrogen storage unit service performance prediction method and system, and belongs to the technical field of hydrogen storage, and the method comprises the steps: determining an initial grid model through a three-dimensional geometric model of a hydrogen storage unit; constructing a structure objective function; optimizing the structure objective function to obtain an optimal grid model, constructing a simulation model of the optimal grid model, and performing a simulation test on the simulation model to obtain simulation data; calculating difference data between the simulation data and actual optimal test data, and calibrating the simulation data by using the difference data to obtain calibrated simulation data; performing correlation analysis on the calibration simulation data and the influence parameter of the hydrogen storage unit to obtain an influence weight of the influence parameter, determining the service life of the hydrogen storage unit based on the influence weight, and predicting the service performance of the hydrogen storage unit according to the service life of the hydrogen storage unit. The model closer to the actual working condition can be created, the accuracy of the simulation result is improved, and the design error is reduced.

The invention relates to the technical field of communication testing, and provides a multi-mode communication equipment test case generation method and device, equipment and a medium, and the method comprises the steps: obtaining a radio frequency parameter, network flow data and equipment log data of communication equipment, carrying out feature extraction and vectorization processing on the radio frequency parameters, the network flow data and the equipment log data to obtain a multi-modal vector; constructing a communication equipment calling relation graph according to the calling relation and the interaction protocol; constructing a test path evolution model based on a graph neural network, and transmitting the multi-modal vector to the test path evolution model, so as to search an optimal test path in a communication equipment calling relation graph through an attention mechanism; constructing a multi-target reinforcement learning reward function and dynamically adjusting the optimal test path through the multi-target reinforcement learning reward function; and according to the optimal test path, generating a target test case, and according to a test result of the target test case, adjusting model parameters of the test path evolution model, and carrying out multi-target reinforcement learning on the reward function.

The invention provides an electromagnetic environment test stationing optimization method, device and equipment and a medium, and relates to the technical field of electromagnetic monitoring, and the method comprises the steps: obtaining building structure information and machine room arrangement information of a target machine room; a plurality of grid division schemes are obtained; determining test sampling points in each grid based on the machine room arrangement information; obtaining a plurality of test point distribution schemes based on the test sampling points of each grid in the grid division scheme; respectively calculating an evaluation index of each test point distribution scheme; and determining an optimal test point distribution scheme. According to the method, the building structure information and the arrangement information of the target machine room are acquired, and equipment arrangement analysis and sampling point selection are performed based on a plurality of grid division schemes, so that efficient test point distribution for the electromagnetic environment of the machine room can be realized; by evaluating the evaluation index value of the test point distribution scheme corresponding to each grid division scheme, the optimal test point distribution scheme is determined, so that uniform distribution and comprehensive coverage of test points are ensured, and the test accuracy and efficiency are improved.

The invention discloses a dynamic simulation test verification method and system for a pumped storage unit monitoring system, and the method comprises the steps: building a high-dimensional system state matrix of a pumped storage unit based on the physical parameters and structural features of a hydraulic, mechanical and electrical system of the unit, carrying out the dimension reduction of the high-dimensional system state matrix, and obtaining a dynamic simulation test result of the pumped storage unit; a dynamic simulation model of unit multi-physics field coupling is obtained; constructing a multi-dimensional parameter space based on the parameter sensitivity coefficient and the working condition boundary condition of the dynamic simulation model, and calculating optimal test point distribution through a high-dimensional point flow diameter algorithm to obtain a test scene library; based on the dynamic simulation model and the test scene library, obtaining a real-time monitoring signal sequence conforming to a monitoring system communication protocol; and based on the real-time monitoring signal sequence, signal acquisition, logic judgment and alarm control functions of the monitoring system are tested through an automatic test script, and a performance evaluation result of the monitoring system is obtained. The test comprehensiveness and effectiveness of the pumped storage unit monitoring system are improved.

The invention discloses a flora prediction and petroleum pollution remediation method based on machine learning. The method comprises the following steps: collecting multiple groups of experimental data of a diesel oil pollution sample treated by a microbial agent, extracting environmental factors, microbial community characteristics and target response variables to construct a training data set after missing value processing, abnormal value detection and standardized pretreatment, and importing the data set into a preset machine learning model to obtain a training result; carrying out feature learning, classification training and hyper-parameter optimization by adopting a GridSearchCV method in combination with 10-fold cross validation; evaluating the correlation between a target response variable classification result and the features through a multivariable Pearson's correlation matrix, and constructing an optimal test set; and finally, selecting an optimal prediction model according to a preset index. According to the method, the model training quality is improved through data preprocessing and correlation analysis, efficient flora prediction and algorithm application evaluation are achieved by means of multiple machine learning algorithms, scientific support is provided for petroleum pollution remediation, and remediation accuracy and efficiency are improved.

The invention discloses an intelligent test paper composition and scoring method and system based on a knowledge graph. The method comprises the following steps: S1, constructing a student knowledge graph state sub-graph; s2, constructing a question bank knowledge graph; s3, on the basis of a node embedding modeling method of an improved DeepWalk algorithm, performing joint training and low-dimensional representation learning on knowledge point nodes by utilizing a continuous bag-of-words model of multi-head context aggregation, and constructing a topic adaptation degree scoring index; s4, outputting an optimal test paper scheme set; s5, collecting a current answer record when the student completes the test paper; and S6, extracting a semantic feature vector and a behavior feature vector, constructing a fusion score feature vector, and generating scores of test paper subjective question answers. According to the invention, accurate test paper composition and multi-dimensional subjective question intelligent scoring for individual knowledge states of students are realized, the method is suitable for personalized learning evaluation and teaching feedback scenes in an education evaluation platform, and the method has the advantages of high personalization, fine feedback and automatic scoring.

The invention discloses a radiator multi-objective collaborative optimization design method which comprises the following steps: defining the geometric dimension of a radiator and the air volume degradation coefficient of a fan as optimization objectives, and defining a key temperature point and the volume of the radiator as evaluation indexes; constructing a small number of test parameter combinations covering all value ranges of the optimization target, and performing analogue simulation to obtain simulation data of corresponding evaluation indexes; a mapping model is constructed, training is performed through simulation data, and a large number of test parameter combinations and corresponding evaluation index predictions are output based on the trained mapping model; and performing parameter optimization on an output result of the mapping model through an optimization algorithm to obtain an optimal test parameter combination of an optimization target conforming to the evaluation index constraint. The method has the beneficial effects that uniform coverage of a parameter space is realized under a limited sample, and the number of invalid simulation times is reduced; and a nonlinear coupling relationship among parameters is captured, and the prediction precision of the agent model is improved.

The invention provides a bearing outer ring fault diagnosis test parameter setting optimization method, which comprises the following steps of: firstly, accurately acquiring structural parameters of a bearing by utilizing a measuring tool; secondly, testing and calculating the inherent frequency and the damping ratio of the bearing in the installed state through a modal experiment; selecting test working condition parameters according to the actual operation environment of the bearing, and calculating the fault characteristic frequency of the bearing; bearing fault simulation signal band-pass filtering processing is constructed; meanwhile, constructing a fault signal equivalent model; carrying out resonance demodulation analysis on the equivalent model, extracting an envelope spectrum and evaluating a fault diagnosis effect; and finally, circularly adjusting working condition parameters, iteratively optimizing the test working conditions, and selecting the working condition with the maximum fault characteristic frequency amplitude and the minimum noise interference as the optimal test condition. According to the invention, the accuracy and reliability of bearing outer ring fault detection are effectively improved, and powerful support is provided for fault diagnosis of mechanical equipment.

The invention discloses a test paper generation method and system, and belongs to the technical field of intelligent education, and the method comprises the steps: obtaining student answer records, question-knowledge point mapping, a test question bank and knowledge text information, and constructing a knowledge point graph; constructing a student-question-knowledge three-layer heterogeneous graph, and predicting the answering performance of the students on the test paper; taking the answer expression as environment feedback input of reinforcement learning, taking a question set of the current test paper as a state, replacing questions in the test paper or keeping the questions as an action, determining a comprehensive reward value of the test paper in each dimension by constructing a multi-target reward function, and generating an optimal test paper through iterative optimization by taking maximization of the comprehensive reward value as a target; on the basis of the adjacency relation in the knowledge point graph, candidate test questions are selected from a test question bank to form a candidate sub-question set, and multiple sets of parallel test papers equivalent to the optimal test paper are generated through an equivalent question replacement strategy. Through the method, more efficient, reasonable and fair parallel test paper generation can be realized.

The invention discloses an increment testing method and device, a storage medium and electronic equipment, and the method comprises the steps: constructing a dependency graph for describing the dependency relationship between software components in a software system according to software system data, and carrying out the continuous integration of the software components on the basis of a code change set corresponding to a current continuous integration task; a minimum candidate influence range is determined on the dependency graph, a candidate test set is delineated, tests in the candidate test set are ranked according to the sequence of risk scores of all the tests from high to low, a target candidate test set is obtained, all the tests are processed through a greedy approximation algorithm, and a target candidate test set is selected under the constraint of resource budget; and the minimum and optimal test set of software system defects is found to the maximum extent. According to the method, the minimum candidate influence range is determined on the dependency graph based on the code change set, the greedy approximation algorithm is combined, intelligent and accurate selection of the test set under the resource budget constraint is achieved, it can be guaranteed that the defect detection rate is not reduced, and the continuous integration process efficiency can be improved to the maximum extent.

The invention discloses a reliability-based engine test scheme optimization method, and belongs to the technical field of spaceflight quality and reliability. The test cost of the whole engine and components is considered, a large-scale intelligent differential optimization algorithm is adopted, a global optimal solution can be quickly obtained, the situation that optimization is terminated in advance when a local optimal solution is obtained or the global optimal solution cannot be found for a long time and is caught in a rigid bureau is avoided, and better calculation optimization efficiency is achieved; by limiting the reasonable test time range, the number of test samples and the like of the engine and the components, the basic rule of the reliability test is followed, a test scheme can be customized conveniently, rapid convergence of a large-scale intelligent optimization algorithm is accelerated, and calculation optimization is accelerated to obtain an optimal test scheme; and aiming at the reliability index requirement of the engine, the reliability index requirement of the engine can be verified at the minimum test cost.

The invention discloses a test and output restoration method for an instruction-guided image editing model based on a metamorphic test technology, and the method comprises the steps: collecting an original input image and an editing instruction as an original test case, collecting an output image of the original test case, and comparing the input image with the output image to obtain an editing region of the original test case; converting the input image according to the metamorphic relation to generate a candidate image set, selecting an equivalent derivative test image, and forming a new derivative test case together with an original editing instruction; collecting output images of all the derivative test cases, comparing the input images with the output images to obtain editing areas of the derivative test cases, and judging whether the editing areas of all the derivative test cases are consistent with the editing areas of the original test cases or not; carrying out cross validation on all derivative test cases and the output of the original test case, selecting the output of the optimal test case, and mapping the output of the optimal test case to the original test case to serve as the restored original test output; and summarizing a test result and outputting a repair result.

The present application relates to the technical field of power battery health state evaluation and abnormality identification, and particularly relates to a remote health state analysis and abnormality identification method for an electric bicycle battery, which comprises: applying a small signal active excitation under an admitted working condition and synchronously collecting to form an opportunity diagnosis excitation data window; developing time-frequency coupling modeling, including equivalent impedance, harmonic and intermodulation fingerprint, Koopman operator modeling and entropy coefficient difference, and generating a multi-domain response package, a physical invariant vector and an uncertainty matrix by constructing a spectral measure geometry distance with an optimal transmission; gating and layering standardization according to the geometry distance and consistency score, obtaining a health state estimation record and an abnormal event set by using a shape-constrained health mapping model; combining the mapping Jacobian and the dual potential, generating a next-period opportunity diagnosis excitation plan by using D-optimal test design and updating the population health manifold, so as to realize remote health evaluation and early abnormality identification across environments and devices and with traceability.

The invention belongs to the field of integrated circuit design automation, and discloses a multi-PVT corner establishment retention time characterization method and a related device.The method comprises the steps that firstly, an initial search interval is pre-estimated through a logical effort analysis method, and a key area is quickly locked; secondly, introducing a bias item into a test point generated based on binary interpolation through hypothesis interpolation error distribution to form a bias test point, and solving an optimal bias amount by taking a minimum expected interval length as a target so as to obtain an optimal test point; and finally, executing simulation and outputting a representation result. By adopting the method, the characterization efficiency is remarkably improved, a higher convergence speed is realized in a steep transformation and multi-PVT angular point scene, the problems of interpolation stagnation and low speed of a dichotomy method are effectively solved, the sign-off level precision is ensured, meanwhile, the simulation iteration times and CPU time consumption are greatly reduced, and an efficient and reliable solution is provided for a chip time sequence bottleneck.

The invention discloses a multi-target test selection method and system under a test uncertainty condition based on E domination guidance. The method comprises the following steps of: firstly, explicitly modeling uncertainty in a test process by using a detection probability matrix and a false alarm probability matrix; secondly, establishing a multi-objective optimization model which takes the minimum test cost, the minimum missing report rate and the minimum false alarm rate as objectives and takes the fault detection rate and the fault isolation rate as constraints; introducing an E domination relation fused with the preference of a decision maker to improve a traditional Pareto domination criterion; on the basis, designing an E-GPSO algorithm fusing a genetic algorithm and particle swarm optimization to perform iterative search, and dynamically maintaining a non-inferior solution set by adopting an external file based on an E domination criterion; and finally, outputting an optimal test scheme set meeting the constraint and the preference. According to the method, the test cost and the diagnosis reliability can be effectively coordinated and optimized, the decision space is remarkably reduced, and the efficiency and the quality of testability design are improved.

The invention belongs to the field of integrated circuit testing, and discloses a Shmoo testing method and device and a storage medium. The method comprises the steps of determining to-be-optimized Shmoo test parameters of an integrated circuit and defining a search range; an optimization objective function is established, an initial test parameter set is generated, and initial test or simulation is executed to obtain initial fitness data; executing an iterative loop of the grey wolf optimization algorithm, calculating a fitness value of a current test parameter set in each iteration, identifying an optimal test parameter, a suboptimal test parameter and a third optimal test parameter, and updating an algorithm control parameter; updating other test parameters according to the position information of the three test parameters, and carrying out boundary processing on the updated test parameters to keep the test parameters in a predefined search range; and when the termination condition is met, outputting the optimal test parameter as an optimal Shmoo test parameter combination, thereby determining the performance boundary of the integrated circuit or generating a Shmoo graph, and realizing more comprehensive and accurate Shmoo test.

A method for generating a test coupon specification for predicting fatigue life of a component includes determining a load condition for the component, providing a component design, and performing a strength analysis of the component design under the load condition determining a critical area of the component and a stress-related parameter of the critical area. The method includes providing a material condition of the component at least for the critical area of the component. To assist an end-user in determining which are optimal tests to be performed in order to obtain most relevant data for fatigue prediction of a specific component, the method also includes providing a material model and providing, as an input to the material model, the stress-related parameter, and the material condition. The material model generates, as an output, a test coupon specification for being tested in a testing machine.

The invention discloses a test result generation method and device, equipment and a storage medium, and relates to the technical field of testing. The method comprises the following steps: acquiring a plurality of alternative test cases, and constructing a Markov decision process model according to the alternative test cases; the Markov decision process model is calculated to determine an optimal test case from the alternative test cases, a reward function value of the Markov decision process model is determined according to the execution cost of the test case, and the execution cost is related to the execution time of the test case; and testing the financial system to be tested by using the optimal test case to obtain a test result. According to the technical scheme, the global optimal test case is obtained, the execution cost of the test case is comprehensively considered, the use of test resources is optimized, the test effect is improved, and the method is suitable for large-scale and complex software test scenes.

The invention provides an embedded space security analysis method and device, electronic equipment and a storage medium, and belongs to the technical field of network security, and the method comprises the steps: obtaining a target vector set based on a knowledge base of a to-be-tested RAG system; clustering the target vector set to obtain a topic cluster set, and for each topic cluster in the topic cluster set, calculating a vector center point of the topic cluster; generating a rejection set based on the vector center points of all topic clusters; determining an initial load document and an initial trigger query, and taking the initial load document and the initial trigger query as an initial test combination; based on the rejection set, performing iterative updating on the initial test combination through a greedy coordinate gradient algorithm to obtain an optimal test combination; and taking the optimal test combination as a test sample of the RAG system to obtain a test result, and obtaining a security analysis result based on the test result. According to the invention, the accuracy of RAG system security analysis can be improved.

The present invention belongs to the field of radar signal processing technology and discloses a rapid radar scattering test method based on polarization decomposition. The method comprises the following steps: scanning a 3D target model, simulating scattering field distributions under different incident angles and polarization modes, calculating polarization contrast, and identifying polarization-sensitive areas; establishing a dynamic coordinate system conversion model, fusing RTK data, IMU data, and laser tracking data, and correcting the vehicle position and model by combining a Doppler frequency shift model and a least squares method; using a nature-inspired optimization algorithm to calculate the vehicle's optimal test path based on sensitive areas; establishing a geometric environment model, obtaining backscattered signals through beamforming, analyzing a polarization coherence matrix, obtaining high-entropy areas, and decomposing scattering components. The method forms a closed-loop optimization from the data acquisition source to the scattering component analysis end, significantly improving test efficiency and real-time performance.

The invention discloses a man-machine collaborative high-real-time function test cross-domain optimization method and device and a medium, and relates to the technical field of electronic information, and the method comprises the steps: expanding a discrete optimization model into a continuous optimization model, the probability that multiple intermediate events in the fault tree fail at the same time is set to be the minimum value of the test proportion corresponding to all the intermediate events contained in the intersection item, and a continuous optimization model based on intersection item consideration is obtained; the method comprises the following steps: performing theoretical analysis on an inherent reliability calculation formula of a system, and cutting off the inherent reliability calculation formula of the system in combination with expert experience to obtain a continuous optimization model based on real-time consideration; a continuous optimization model based on additional constraints is obtained by additionally establishing a relation between decision variables and inherent reliability of the system; solving the continuous optimization model based on the additional constraint to obtain an optimal test strategy; according to the function test cross-domain optimization method and device and the medium, the real-time problem of model solving is solved, and the cross-domain overall optimization capability of the model is improved.

This invention belongs to the field of automatic generation of EOL (End-of-Life) test programs, specifically involving a reinforcement learning-based method for generating EOL test programs for vehicle domain controllers. It includes the following steps: S1: Obtaining raw component data from the schematic diagram, processing the raw circuit and component connection attributes to form a minimum circuit combination; S2: Constructing an optimal EOL test case selection model on the two-dimensional vector dataset obtained in step S1; S3: Based on the model in S2, generating the highest-priority EOL test program using the Q-Learning algorithm; S4: Selecting the optimal test case according to evaluation criteria. This invention addresses the shortcomings of reward-based test results methods, such as low failure rate, difficulty in convergence, and long test feedback time.

The invention relates to an optimal test position searching system and method in an OTA camera obscura, and belongs to the technical field of position searching, the system comprises the OTA camera obscura, a real-time monitoring module, a self-adaptive scheduling algorithm module, a test control module, a data analysis and evaluation module and a result output module, the OTA camera obscura is used for providing a non-interference test environment; the real-time monitoring module is used for acquiring MCS parameters of the equipment to be tested; the adaptive scheduling algorithm module, the test control module and the data analysis and evaluation module are used for evaluating the performance of a test position according to the MCS parameters, determining whether an optimal test position is reached or not, if the optimal test position is not reached, determining a next test position according to an adaptive scheduling algorithm, and controlling the test probe or the antenna array to move to the position; whether the optimal test position is found or not is judged, and if the optimal test position is not found, searching continues; and if the optimal test position is found, the result output module outputs the optimal test position and the test result.

The invention discloses a thermal out-of-focus compensation method and device, computer equipment and a storage medium, and the method comprises the steps: collecting a plurality of temperature values of at least one heating position of a projection device in sampling time, and obtaining target temperature data; according to the target temperature data and a preset focal length compensation model, obtaining an optimal current focusing position currently corresponding to a focusing motor of the projection device; the focal length compensation model is obtained by training based on test temperature data and a corresponding optimal test focusing position; acquiring a focal length compensation value of a focusing motor according to the optimal current focusing position; and adjusting the focusing position of the focusing motor according to the focal length compensation value. According to the method, through the established focal length compensation model, the real-time optimal focusing position of the focusing motor can be accurately predicted, meanwhile, the temperature change is monitored in real time, preventive fine adjustment is carried out before defocusing, the defocusing problem caused by the temperature change is effectively solved, and rapid and accurate self-adaptive focusing control is achieved.

The application discloses a system function test method, device, equipment and medium, and relates to the field of system test. The method comprises the following steps: acquiring a function description interface from a preset automatic script, and sending the function description interface to a matching bus; determining function information of a function to be tested in the function description interface by using the matching bus, and matching an optimal interface from all current tool description interfaces based on the function information and matching information defined in a preset function set; testing the function to be tested by using an automatic test tool corresponding to the optimal interface, and returning a test result to the automatic script. In this way, the method in the application can automatically match the optimal test tool to test the function to be tested of the system to be tested without manual packaging and adaptation of the tool, greatly reduces the time for selecting and packaging and adapting the test tool in the function test, and further improves the system test efficiency.