132 results about "Test algorithm" patented technology

The invention provides a road slope stability prediction method and device, a storage medium and electronic equipment, and the method comprises the steps: collecting the multi-dimensional environmental parameters of a road slope in real time, and forming an original parameter set; based on a Granger causal test algorithm, analyzing the original parameter set, determining a causal influence relationship among the parameters, constructing a parameter causal network, and screening out key parameters; calculating the similarity of each key parameter in time and space dimensions through a dynamic time warping algorithm, and generating a space-time incidence matrix; based on the space-time incidence matrix, performing weighted fusion on the key parameters to obtain fusion parameters; inputting the fusion parameters into a pre-trained slope stability prediction model to obtain a predicted value of the slope stability; and when the predicted value exceeds a dynamically updated early warning threshold value, outputting slope instability early warning information. According to the method, the defects of inaccurate prediction and data redundancy of a current road slope stability prediction method are overcome.

The invention discloses an enterprise credit risk assessment method based on big data collection, and relates to the technical field of big data analysis, and the method comprises the steps: constructing a space-time fusion engine, carrying out the fusion through combining a cross-modal attention mechanism, generating a space-time fusion feature, and carrying out the adversarial training through a gradient inversion layer and a domain classifier, eliminating space-time fusion feature distribution differences; based on space-time fusion features, constructing a causal graph skeleton by adopting a conditional independent test algorithm, quantifying causal effect intensity among nodes through a machine learning model, constructing a risk conduction dynamic model, and predicting risk conduction intensity based on the causal effect intensity; and based on the risk conduction intensity, calculating a risk index weight through a dynamic game network, generating an enterprise risk score in combination with the space-time fusion feature, and generating a dynamic risk score through a time sequence neural network. According to the invention, by constructing the space-time fusion engine and combining the cross-modal attention mechanism, efficient fusion of multi-modal data is realized, and the accuracy and robustness of feature expression are improved.

The method for automatically and intelligently extracting the data of the atlas database comprises the following steps: performing data cleaning on original data to obtain cleaned data; performing cross-modal semantic alignment word segmentation processing on the cleaning data to obtain semantic alignment word segmentation data; performing entity relationship extraction on the semantic alignment word segmentation data, analyzing an influence relationship between entities through a Granger causality test algorithm, verifying the logicality of the influence relationship, and generating entity relationship pair data containing causality intensity; performing graph construction processing of incremental graph optimization on the entity relationship pair data to obtain a dynamic optimization knowledge graph; and carrying out migration fusion of field adaptation on the dynamic optimization knowledge graph to obtain a graph database of field fusion. According to the method, the defect that causal logic is difficult to mine at present, so that the map relationship lacks the deep decision support capability is overcome.

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 embodiment of the invention provides a memory controller, an error address positioning method and a computing system. The memory controller comprises an algorithm control circuit, a data generation circuit, a data comparison circuit and a monitoring circuit, and the algorithm control circuit is configured to generate control information for memory testing based on preset steps of a test algorithm; the data generation circuit is configured to generate test data used for being written into the memory to be tested and expected data obtained after the test data is read; the data comparison circuit is configured to detect whether target data read from the to-be-detected memory is consistent with expected data or not; and the monitoring circuit is configured to respond to the inconsistency of the read target data and the expected data, send a mark for stopping the memory test to the algorithm control circuit so as to stop the memory test and record a test address corresponding to an error and corresponding command data, so that stop and quick positioning are realized when the error occurs, and the repair efficiency is improved.

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.

A method for performing an eye test includes the steps of: receiving a request from a user to perform the eye test; acquiring predetermined user identification data from the user; presenting or providing at least one test image to the user, the at least one test image including at least one test target contained therein; instructing the user to identify at least one of the at least one test target(s) contained within the at least one test image; acquiring response data associated with the user's attempt(s) to identify the at least one test target(s) contained within the at least one test image; aggregating and / or analysing the acquired user identification data and / or the response data utilising a test algorithm which determines result data; and, presenting or providing the result data to the user.

The invention relates to the technical field of memory chip testing, and particularly discloses a test algorithm for screening security indexes of a memory chip, which comprises the following steps of: firstly, establishing a plurality of groups of monitoring environments with different temperatures according to a fixed temperature difference interval, and determining the security indexes of the memory chip by combining read-write data of each read-write operation of the memory chip in a read-write test; according to the method, the read-write abnormal coefficient of each storage chip in the read-write operation process can be calculated, the data reflect whether the read-write state of the storage chip in one read-write operation in different test environments is qualified or not, and then the read-write stability is analyzed by combining the test data of each storage chip in one read-write test. According to the method and the device, the read-write frequency of each storage chip is estimated in combination with the data, and the security of the storage chips is analyzed in combination with the preset read-write operation frequency threshold, so that a large amount of read-write operation tests can be reduced, and the test efficiency of the security of the storage chips is improved.

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 present invention relates to a programmable memory built-in self-test method based on a finite state machine, and the method is implemented by a programmable Mbist control module driven by three counters, an algorithm module, a comparison and analysis module, and a BRAM to be tested. In the present invention, the algorithm module integrates 8 test algorithms and generates characteristic signals according to the characteristics of each test algorithm, thereby improving the coverage of faults. Secondly, the programmable Mbist control module receives the characteristic signal of the test algorithm generated by the algorithm module, and generates a test vector, address and control signal to test the BRAM under the control of three counters, thereby improving the flexibility of the test. The present invention provides a new programmable memory built-in self-test method based on a finite state machine, which replaces the conventional test method. When the test algorithm needs to be replaced, the proposed test method does not need to reset the Mbist circuit, effectively reducing manpower and configuration time, thereby reducing the test cost.

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 invention relates to a computer-implemented method for criteria-based creation of a configuration (10) of a virtual test (VT) for testing automated driving functions of a vehicle, comprising the steps of defining (S1) a dependency of configuration parameters (12a, 12b) on target variables (14) of the virtual test (VT), determining (S2) a test algorithm (A) and / or parameter intervals (16) for the respective target variable (14), and defining (S3) an application condition (18) of the configuration parameters (12a, 12b), the parameter intervals (16), and / or the test algorithm (A). Furthermore, the invention relates to a system for criteria-based creation of a configuration (10) of a virtual test (VT) for testing automated driving functions of a vehicle.

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 application provides a SINS and GNSS combined navigation system fault-tolerant method based on an LSTM neural network, and a set of fault detection and system fault-tolerant method based on an LSTM neural network auxiliary is provided for sudden faults and slowly changing faults in a SINS / GNSS loosely coupled navigation system. A new information Mahalanobis distance auxiliary chi-square test algorithm is used to detect the sudden faults existing in the system in real time, and a sequential probability ratio algorithm is used to detect the slowly changing faults existing in the system. Robust Kalman filtering based on M estimation is used to complete the robustness enhancement of the system, thereby effectively inhibiting the influence of sudden faults on the system. The LSTM neural network is used in the form of replacement to effectively inhibit the influence of slowly changing faults on the system, and signal repair is realized. Through detection and fault tolerance of the two kinds of faults existing in the GNSS signal, the precision of the combined navigation positioning is improved.

The present invention provides an encryption method and system based on a symmetric cryptography algorithm, including: classifying the obtained sensitive data to obtain structured sensitive data and unstructured sensitive data; selecting key numerical fields in the structured sensitive data, inputting them into a preset Fermat primality test algorithm, screening out large prime numbers as basic key elements, and generating an encryption key by combining the basic key elements with a preset key combination rule; using the symmetric cryptography algorithm to encrypt the structured sensitive data based on the encryption key to obtain encrypted structured data; encrypting the unstructured sensitive data to obtain encrypted unstructured data; and integrating the encrypted structured data and the encrypted unstructured data to generate simulated desensitized data. In the present invention, optimization processing is carried out according to the characteristics of the structured sensitive data, so as to effectively protect the security of the sensitive data.

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 provides a test method and system for a flow token bucket based on SrTCM, which constructs a set flow to be sent to a network device under test, wherein the set flow includes constant rate flow, burst flow, and excess burst flow, etc., samples the processing data of the network device under test and the forwarding rate of the output interface of the network device under test, and finally processes the data, forwarding rate, and sending rate of the set flow to determine whether the flow token bucket test of the single-speed three-color marker algorithm passes. This embodiment is directed to the implementation logic of the single-speed three-color marker algorithm, and performs a special and targeted design on the waveform generated by the flow for the logic, rather than just using constant rate flow for testing, so as to test the correctness of the algorithm's processing logic for burst flow and excess burst flow, and can test its algorithm logic more comprehensively and finely.