87 results about "Test algorithm" patented technology

The invention discloses an air conditioner host load regulation and control parameter self-adaptive learning method. The method comprises the steps that multi-source real-time data of an air conditioner host is obtained and preprocessed; generating a steady-state identification sequence by adopting layered progressive steady-state analysis, wherein the short-term analysis adopts self-adaptive window variance calculation based on the instantaneous change state of the system; based on the steady-state identification sequence, constructing an event causal relationship graph by adopting an improved causal test algorithm, and determining causal association between regulation and response; integrating the causal atlas, and generating a self-adaptive regulation and control strategy library capable of online learning through density clustering and regression modeling; and responding to a power grid dispatching instruction, and matching and generating an optimal regulation and control instruction sequence from the strategy library. According to the method, through the technical means of hierarchical progressive steady-state analysis, improved causal test, density clustering and regression modeling, an online learning strategy library is constructed, the precision of steady-state detection and load prediction can be improved, and the generalization and self-adaption capability of the model under complex working conditions can be enhanced.

The invention discloses an index transaction monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of transaction monitoring. The threshold range is dynamically generated based on the historical business data to adapt to the change of data distribution, the adaptive statistical test algorithm is selected in combination with the data sample size and the distribution characteristics to improve the accuracy of anomaly judgment, and personalized early warning output is performed according to the region and the business type of the auditing point. According to the technical scheme, data dynamic change and business scene diversity can be flexibly coped with, so that the technical problem that the reliability of an early warning result is reduced in the face of missing data, abnormal values and complex seasonal fluctuation, and then the real-time control and decision-making efficiency of an operator on a business operation state is influenced can be solved; the technical effects of improving the accuracy and reliability of business development index abnormity identification, reducing the false report and missing report rate, and enhancing the real-time control capability and decision-making efficiency of the operator for the business operation state are achieved.

The invention relates to a storage test vector generation method and device, equipment and a storage medium, and is applied to the field of semiconductor testing, and the method comprises the steps: obtaining a chip manual and a chip test plan of a to-be-tested chip, and generating a chip description file according to the chip manual and the chip test plan; carrying out abstract processing according to the chip description file, and generating a chip abstract model; converting test logic in a test algorithm into a test sequence according to the chip abstract model; and obtaining a target file format requirement of a target ATE equipment platform, and converting the test sequence according to the target file format requirement to generate a storage test vector. The method has the technical effects that the reusability of cross-platform use of the test vector is improved, the test development efficiency is improved, and the time is saved.

The invention discloses a test method, device and equipment, a medium and a product. The method comprises the following steps: acquiring original CAN bus data acquired by a real vehicle road side; the original CAN bus data are screened based on a to-be-measured algorithm, a positive scene slice set and a negative scene slice set of at least one driving scene corresponding to the to-be-measured algorithm are obtained, scene slices in the positive scene slice set are correctly recognized based on the to-be-measured algorithm, and scene slices in the negative scene slice set are correctly recognized based on the to-be-measured algorithm. The scene slices in the negative scene slice set are scene slices with errors identified based on a to-be-measured algorithm; sending input data and an output result of a to-be-measured algorithm corresponding to the negative scene slice set to a target terminal, and receiving an updated to-be-measured algorithm fed back by the target terminal; verifying the updated to-be-measured algorithm based on the positive scene slice set and the negative scene slice set of the at least one driving scene; and if the verification is passed, replacing the to-be-measured algorithm deployed on the real vehicle with the updated to-be-measured algorithm.

A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include maneuvering, autonomously, a vehicle along a test track, monitoring, via at least one audio sensor, audio data from the vehicle during the maneuvering of the vehicle along the test track, and monitoring, via a monitoring system disposed along the test track, monitoring data of the vehicle along the test track. The operations also include executing, based on the monitored audio data, a sound detection function of an autonomous test algorithm, detecting, via the sound detection function, an error noise, categorizing, via a categorization function of the autonomous test algorithm, the detected error noise, identifying, based on the categorized error noise, a maintenance station and executing, in response to the identified maintenance station and categorized error noise, a spontaneous path planning function of the autonomous test algorithm.

The invention relates to the field of digital marketing, and discloses a video understanding-oriented hierarchical spatio-temporal semantic extraction method, which analyzes video content through a three-stage progressive analysis framework: firstly, detecting an object instance in a key frame sequence and constructing a spatio-temporal trajectory to ensure object identity continuity; modeling an object state evolution law by using a recurrent neural network, and quantifying an interaction relationship between objects; and finally, constructing an event causal graph based on the state mutation points, and generating a directed causal chain through a statistical causal test algorithm. Video content is abstracted into event causal logic from pixel features, a semantic path of'track-state-causal 'is formed, structured semantic description of decisions can be directly driven, video semantics and user behavior data are coupled, user preference portraits are dynamically constructed, and personalized material generation is guided.

The invention relates to the field of artificial intelligence chips, and provides a memory aging test method, device and equipment, a storage medium and a program product.The method comprises the steps that according to the number of kernels of a processor to be tested, the memory space of the processor is divided into a plurality of memory address blocks to be distributed to the kernels of the processor; performing permutation and combination on the plurality of memory test algorithms to generate a plurality of first algorithm sequences; issuing the first algorithm sequence to a processor core by adopting a rotary full distribution mechanism; and each kernel runs the issued first algorithm sequence based on the allocated memory address block, and executes the aging test task. By permutation and combination of the memory test algorithms and issuing of the memory test algorithms to the kernels for operation and execution of the aging test task by adopting a rotation type full distribution mechanism, it can be ensured that memory address blocks of different kernels operate different algorithms at the same moment, the complexity of a memory operation scene is improved, and the efficiency is improved. And the test result can effectively guide the screening and quality control of the memory bank.

The application provides a road slope stability prediction method and device, a storage medium and an electronic equipment, which comprises the following steps: collecting multi-dimensional environmental parameters of a road slope in real time to form an original parameter set; analyzing the original parameter set based on a Granger causality test algorithm to determine the causal influence relationship between parameters, construct a parameter causal network, and screen out key parameters; calculating the similarity of each key parameter in the time and space dimensions through a dynamic time warping algorithm to generate a space-time correlation matrix; weighting and fusing the key parameters based on the space-time correlation matrix to obtain a fused parameter; inputting the fused parameter into a pre-trained slope stability prediction model to obtain a predicted value of slope stability; and outputting a slope instability warning information when the predicted value exceeds a dynamically updated warning threshold. In the application, the defects of inaccurate prediction and data redundancy of the current road slope stability prediction method are overcome.

The invention relates to the technical field of Web component testing, in particular to an automatic test case generation method, system and device for a Web component and a medium, and the method comprises the following steps: extracting a front-end frame type and code features to which the Web component belongs, constructing a visual state transition network, and constructing a unified data flow diagram; based on the code features, the visual state transition network and the unified data flow diagram, semantic reasoning is carried out by utilizing a large language model to generate a test path combination, and a minimum complete test case set is generated by applying a combination test algorithm; generating a composite assertion logic; frame type conversion is executed, and the minimum complete test case set and the composite assertion logic are converted into test codes capable of being executed by a front-end frame type; the test code is executed, and the minimum complete test case set and the composite assertion logic are updated and circulated based on Web component change or test failure in the execution process. According to the method and the device, the Web component automatic test which is high in accuracy, cross-frame adaptive and capable of realizing continuous self-healing can be realized.

The invention discloses an infrared camera low-power-consumption triggering method based on dynamic PIR detection and AI verification, and aims to solve the problem of high power consumption in a traditional infrared camera monitoring method. And carrying out dynamic PIR monitoring and AI target identification. Specifically, the method has a double-layer judgment mechanism of hardware-level PIR triggering and software-level AI verification, and a dynamic infrared triggering algorithm is a multi-mode self-adaptive triggering algorithm which integrates three data modes of PIR signal strength, time sequence analysis and environment temperature compensation and dynamically adjusts a triggering threshold value according to historical false triggering records. In addition, different field examples are used for testing algorithms and generating detailed monitoring reports, the availability and value of data are further improved, intelligent triggering of the infrared induction camera can be achieved through the method, the method is suitable for unattended field detection scenes, powerful support is provided for ecological protection and environment monitoring data collection, and the method is suitable for popularization and application. And deep research and wide application in the field of wild animal monitoring are promoted.

The invention relates to the technical field of voiceprint monitoring, and discloses a fan monitoring method and system based on voiceprint recognition, and the method comprises the steps: deploying a plurality of voiceprint sensors at a blade root layer and a cabin layer of a to-be-monitored fan, constructing a fan monitoring identifier for the to-be-monitored fan according to the relation between voiceprint data and standard voiceprint data, the method comprises the steps of determining an association result between fan monitoring areas based on fan monitoring identifiers and a causal test algorithm, determining a risk association area and a risk independent area based on the association result, comparing voiceprint characteristics of voiceprint data corresponding to the risk independent area in a historical database, determining an alarm strategy or a model strategy according to a comparison result, and sending the alarm strategy or the model strategy to a server. And according to the convolutional neural network model of the attention mechanism, outputting a monitoring risk value for the voiceprint feature of the voiceprint data corresponding to the risk associated area or the voiceprint feature determined as the model strategy, and based on the monitoring risk value, judging whether to send out an early warning, thereby ensuring the reliability of fan monitoring.

The invention discloses an industrial multi-source data fusion method based on causal inference in the technical field of computer data processing, and the method is applied to an aero-engine, and comprises the steps: obtaining the original performance data of the aero-engine, carrying out the normalization processing of the original performance data, and obtaining a target variable, the original performance data comprises rotating speed, exhaust temperature, oil pressure, oil temperature and metal abrasive particle component data; constructing a causal inference algorithm to calculate a dynamic threshold value; the causal intensity of each variable pair is calculated from the candidate causal set, variables with the first M ranks of the causal intensity are selected to form an optimization condition set, and a test algorithm is constructed; constructing a symmetric causal intensity matrix; and on the basis of the target variable and the feature matrix, performing quadrature to obtain a new data set after dimension reduction. By constructing the causal inference algorithm, the interpretability and generalization ability of the model can be enhanced, the performance stability is improved, the application range in the industrial field is expanded, and the calculation burden is relieved.

The invention requests to protect an ADC dynamic parameter test method based on spectrum correction, and the method comprises the following core steps: a primary spectrum correction stage: carrying out the spectrum error correction of an ADC output signal through employing a spectrum three-interpolation algorithm, and extracting a fundamental frequency; the sine wave amplitude, the phase and the direct current offset are solved through least square fitting, an initial ideal sine reference sequence is constructed, and noise separation is carried out for the first time. In the secondary spectrum optimization stage, a classic Hanning window function and a frequency domain two-point interpolation algorithm are adopted; and a sine wave error compensation sequence is constructed, and secondary noise extraction is carried out. In the coherent parameter reconstruction stage, the ADC output sequence is restored to a standard test state through amplitude and frequency conversion. According to the invention, the risk of test failure caused by the fact that the amplitude of the input signal exceeds the ADC range can be effectively avoided. On the basis, the secondary spectrum optimization significantly improves the recognition precision of the test algorithm on the harmonic distortion of the ADC output sequence, and effectively reduces the measurement error caused by nonlinear distortion.

The invention provides an abnormal time series data detection method and device and a storage medium, relates to the technical field of data processing, and can improve the accuracy of time series data anomaly detection. The method comprises the following steps: processing original time sequence data based on a spectrum entropy dynamic period detection algorithm, and determining whether the original time sequence data is period time sequence data; and under the condition that the original time series data are non-periodic time series data, determining a target anomaly detection model based on a normality test algorithm KS in combination with a first algorithm and the original time series data, performing anomaly detection on the original time series data based on the target anomaly detection model, and determining an anomaly detection result, the anomaly detection result comprises whether the original time sequence data is abnormal data or not; and under the condition that the original time sequence data is periodic time sequence data, performing anomaly detection on the original time sequence data based on a second algorithm, and determining an anomaly detection result.

The application provides a test scheme for checking data retention performance of NOR type Flash memory, focuses on the influence of erase-write cycles on the data retention performance, unifies the erase-write cycle number of the Flash memory test, stipulates clear temperature selection criteria, provides a reference for the Flash memory related test, and comprises the following steps: step one: determining the basic test algorithm of the NOR type Flash memory as the Checkerboard algorithm from the algorithm design complexity and the fault coverage; step two: improving the Checkerboard algorithm process and determining the data retention fault determination step; step three: carrying out a pre-experiment to determine the data retention performance test temperature, erase-write number and sample number of the NOR type Flash memory; step four: determining the erase-write number of the data retention test at normal temperature and the sample number under each erase-write number; step five: determining the test pattern, failure criterion, test duration and test cycle; step six: carrying out the data retention test combined with the erase-write cycle based on the non-replacement timing truncation test; and step seven: test data processing, fitting the average failure time under different erase-write numbers with the erase-write number to obtain the corresponding relationship between the average failure time and the erase-write number.

This invention relates to the field of collision detection technology, and in particular to a general performance evaluation system for visual collision detection algorithms. The system includes a video data module for calibrating optical variables and storing motion modal videos of approaching collision objects to obtain a standard video dataset; a data transmission module for exporting data from the standard video dataset to a test algorithm model to obtain test results, and also for importing the test results into an analysis module; and an analysis module for analyzing the test algorithm model using performance analysis and algorithm theory analysis to obtain analysis results. This invention can comprehensively evaluate collision detection algorithms, helping to analyze the specific advantages and disadvantages of visual collision detection algorithms, promoting the overall development and performance improvement of such algorithms, and increasing the practical applicability of visual collision detection algorithms.

The application provides an artificial intelligence-based software test case automatic generation system and method, relates to the technical field of software testing, and comprises the following modules: a project management data acquisition module that collects code metadata and project management data; a dynamic risk weight allocation module that inputs the two types of data into a risk prediction model, outputs a risk heat map, and allocates the dynamic risk weight of the input parameters; a basic test suite generation module that generates a basic test suite according to the input parameters and the dynamic risk weight; and a software test algorithm execution module that starts a software test algorithm and iteratively evolves to generate a test case set. The application solves the technical problem that the existing software test case generation mode is difficult to effectively integrate software related data to identify high-risk links, and the generated test cases are insufficient in covering key areas and low in accuracy, achieves the technical effect of accurately identifying high-risk links of software, and the generated test cases are more targeted, thereby improving software testing efficiency and software quality evaluation accuracy.

The invention provides a memory testing method and device, electronic equipment and a computer readable storage medium, and belongs to the technical field of memory testing. The method comprises the following steps: acquiring the number of application cores, and labeling each application core; obtaining a memory test range of a memory to be tested, distributing a plurality of blocks to be tested for each application core according to the memory test range and the number of the application cores, and numbering the distributed blocks to be tested of each application core; according to the mark number of each application core and the serial number of each to-be-tested block, determining a target test algorithm of each to-be-tested block allocated to each application core from at least two preset test algorithms; and executing a corresponding target test algorithm on each distributed to-be-tested block in parallel through each application core, and obtaining a test result of each to-be-tested block. Therefore, each application core runs different types of preset test algorithms in parallel in the allocated to-be-tested block, so that the diversity test of the memory at the same time is realized, the memory test flexibility and accuracy are improved, and the test time is saved.

1. The name of the design product: artificial intelligence comprehensive experiment box. 2. The use of the design product: for teaching simulation test algorithm learning and practice, visual detection, etc. Teaching product. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The present application relates to a kind of benchmark test method and system of network platform manufacturing service cooperation optimization, comprising the following steps:1.resolve network platform manufacturing service cooperation optimization element relationship, construct network platform manufacturing service cooperation optimization complex network model;2.according to actual problem situation, configure network platform manufacturing service cooperation optimization problem model as needed;3.considering test problem demand, import or generate test data as needed;4.considering test algorithm situation, import or configure the algorithm of solving problem as needed;5.solve the problem multiple times, and record the test results of this problem.The present application can effectively simulate and simulate network platform manufacturing service cooperation optimization environment and process, and can be configured and tested according to the actual needs of user to problem, data and algorithm and compare, to solve the problem of lack of unified benchmark test system tool for previous different problems and algorithm.

The invention provides a data processing method and device of a vehicle, the vehicle and a storage medium. The method comprises the steps of obtaining scene data of the vehicle; respectively calling a first to-be-measured algorithm and a second to-be-measured algorithm to process the scene data through an algorithm calling interface to obtain first result data corresponding to the first to-be-measured algorithm and second result data corresponding to the second to-be-measured algorithm; the versions of the first to-be-measured algorithm and the second to-be-measured algorithm are different; comparing the first result data with the second result data to obtain a comparison result of the first to-be-measured algorithm and the second to-be-measured algorithm; and generating a log file of the vehicle according to the scene data, the first result data, the second result data and the comparison result. According to the technical scheme, the algorithms can be called through the same algorithm calling interface to respectively process the data, and then result comparison is performed, so that unified comparison of output results of all the algorithms can be realized, and the problem that respective comparison is required due to non-unified algorithm interfaces at present is solved.

The invention discloses a receiving baseband evaluation test device which comprises a test signal generation unit, a time-frequency matching unit, a high-speed interface, a data processing unit, a parameter configuration unit and an upper computer. According to the receiving baseband evaluation test device provided by the invention, any node data of the tested piece can be acquired by adopting the high-speed interface, so that the tested piece can be comprehensively tested; besides, the data processing unit can call the corresponding test algorithm modules according to the selected nodes and can flexibly configure the connection topology between the modules, so that a customized test architecture adaptive to each node of the tested piece is constructed, systematic verification of the whole machine performance of the tested piece can be realized, process depth debugging in the research and development process is improved, and the test efficiency is improved. And the fault diagnosis efficiency and the parameter tuning capability can be improved, and multi-dimensional test support with depth and breadth is provided for the tested piece.

This invention discloses a passive detection method, apparatus, and electronic device in a non-Gaussian noise environment. The method includes constructing an M-dimensional probability density function of the Rayleigh distribution of the received signal; obtaining a first probability density function of the received signal under assumption H1 and a second probability density function under assumption H0; assumption H1 represents the assumption that the received signal includes a radiating source signal; assumption H0 represents the assumption that the received signal does not include a radiating source signal; based on the first and second probability density functions, a generalized likelihood ratio (GLR) test formula is obtained according to the GLR criterion; the derivative of the M-dimensional probability density function under assumption H1 is taken to obtain a maximum likelihood estimate of the product of the channel coefficient and the Doppler frequency shift; the maximum likelihood estimate is substituted into the GLR test formula to obtain a test calculation value; and the test calculation value is compared with a threshold value to achieve passive detection in the absence of a reference signal through a GLR test algorithm based on the test assumption.

The invention relates to the technical field of traffic management and deep learning, and discloses a key vehicle illegal behavior identification and disposal management method and system based on deep learning. Historical track data, vehicle-mounted sensor data and a driver behavior video sequence of key vehicles are acquired, spatial-temporal features are extracted by using a three-dimensional convolutional neural network and a graph convolutional neural network, and a behavior node embedding vector set is generated; analyzing the time sequence dependency relationship by using a Granger causality test algorithm to generate a causality adjacency matrix; constructing a causal chain for explicitly modeling driving behavior evolution by a time sequence causal map; and after the real-time behavior characteristics are mapped to map nodes, multi-hop reasoning is carried out by using a map attention propagation algorithm, activation probability distribution of each illegal behavior node in a future time window is calculated, and high-probability illegal behavior nodes are extracted to generate a pre-judgment result set and output to a disposal management system. According to the invention, active pre-judgment before illegal behaviors occur is realized, and the initiative and the prevention effect of disposal management are improved.

The invention relates to an API test case generation method and device and a medium. The invention provides an API (Application Program Interface) test case generation method. The method comprises the following steps: extracting parameter metadata of one or more API parameters from an API definition document; a dependency relationship model among the one or more API parameters is established according to the API definition document, the dependency relationship model comprises one or more constraint rules, and the constraint rules in the one or more constraint rules are used for describing constraints among values of the corresponding API parameters in the one or more API parameters; generating one or more value combinations of the one or more API parameters using a combinatorial test algorithm and weighted random sampling according to the dependency model; and inputting the generated one or more value combinations into a predefined test template to generate one or more API test cases.

The invention discloses a method for testing an embedded Flash memory based on an RISC-V debugging specification, and the method comprises the steps: 1, determining a test algorithm according to a test specification, generating a test program through the test algorithm, and compiling the test program to generate an executable target code; step 2, generating a test vector for controlling a JTAG pin time sequence by using a JTAG communication protocol and the target code; step 3, the automatic test equipment serially inputs the test vector to the chip to be tested through a JTAG interface of the chip to be tested, and waits for a return result; and step 4, the CPU of the chip to be tested executes the target code loaded into the static random access memory under the control of the debugging module, and outputs a test result to the automatic test equipment through the specified GPIO pin.

The present application relates to the technical field of attribute searchable encryption, in particular to a lattice-based puncturable key-policy attribute searchable encryption method and system, which realizes fine-grained and dynamic management of search authority of data receivers through initialization of a key generation center and a key puncture mechanism, and can precisely revoke search capability of a specific keyword without updating all keys; ciphertext generated by a data owner combines attribute encryption and signature technology, which ensures data confidentiality and integrity and resists quantum attacks; a trapdoor generated by a puncture key of a data receiver protects search privacy and realizes efficient ciphertext retrieval; and a test algorithm executed by a cloud server can accurately verify matching of the trapdoor and the ciphertext and return a result under an honest but curious security model. The whole scheme effectively solves the deficiencies of existing schemes in fine-grained dynamic revocation, quantum attack resistance and multi-receiver access control under the support of lattice cryptography theory.

The application relates to the technical field of storage chip testing, and particularly discloses a test algorithm for screening the safety index of a storage chip, which comprises the following steps: first, a plurality of monitoring environments with different temperatures are established according to fixed temperature difference intervals; the read-write data of each read-write operation in the read-write test of the storage chip is combined, so that the read-write abnormality coefficient of each storage chip in the read-write operation is calculated; the data reflects whether the read-write state of the storage chip in the read-write operation is qualified in different test environments; then, the test data of each storage chip in the read-write test is combined to analyze the read-write stability, the read-write times of each storage chip are estimated according to the data, and the safety of the storage chip is analyzed according to the preset read-write operation time threshold value; thus, the read-write operation test can be reduced, and the test efficiency of the safety of the storage chip is improved.

The invention discloses a laboratory record report table automatic generation method and device and a medium, and belongs to the technical field of report automatic generation. The method comprises the following steps: deploying a sensor network to an expressway construction site laboratory; collecting test data of a highway construction site test room; wherein the test data comprises a test position, a test parameter, a test type, an environment parameter and an operator; comparing a preset report table database based on the test type to determine a to-be-filled table and a test algorithm; wherein the report table database comprises a to-be-filled test report table corresponding to the test type and a test algorithm corresponding to the test type; processing the test data based on a test algorithm to generate calculation data and a detection result; and filling the test data and the calculation data into the to-be-filled table to generate a record report table. According to the method, the technical effects of unified management, rapid calculation and automatic report generation of the test detection data are realized.

The invention discloses a computer operation fault rapid identification processing method, and relates to the technical field of data analysis, and the method comprises the following steps: S100, setting a first test parameter according to a mapping relation between a driving square wave and a write-in signal, and testing a memory based on an improved SDRAM test algorithm and the first test parameter to obtain a test result; a cross cubic network of memory addresses is constructed, a numerical matrix is constructed according to the cross cubic network, first calculation is performed based on the numerical matrix, and a fault address and a fault type are output. According to the method, accurate coordinates are provided for subsequent redundancy repair through automatic classification, the number of test steps is reduced by driving the mapping relation between square waves and write-in signals and exciting time sequence and amplitude coupling faults at a time, three-dimensional addresses are subjected to redundancy through the cross cubic network, and the storage fault recognition speed and accuracy are remarkably improved.