611 results about "Test phase" patented technology

The invention discloses an electric power system abnormal remote signaling detection method based on a graph auto-encoder model, and the method comprises the steps: collecting measurement data of an electric power system, carrying out the preprocessing, obtaining a graph data set with an abnormal label, and dividing the graph data set into a training set and a test set; inputting the graph data in the training set into the graph auto-encoder model for training; after training is completed, abnormal score distribution is counted based on normal edge samples in a training set, a threshold value is set to serve as a follow-up judgment basis, and threshold value selection takes the accuracy rate and the recall rate on a test set as an adjustment and optimization target; in a test stage, image data in a test set are input to carry out edge feature reconstruction and anomaly scoring, anomaly judgment is carried out on edges in combination with a set threshold value, and a preliminary abnormal edge detection result is output; and the output abnormal edge detection result is input into the graph restoration module, the restored edge structure and edge features are output, the damaged remote signaling state in the power grid is restored, and the integrity of the graph structure and the operation credibility of the power system are improved.

The invention discloses a zero-sample industrial anomaly detection method based on knowledge distillation, and belongs to the technical field of image detection. The method comprises a training stage and a testing stage. In the training stage, normal industrial images and abnormal industrial images in any type of industrial products are selected; constructing a knowledge distillation network architecture which comprises a teacher model, a student model, an image encoder and a text encoder; training the teacher model by using the training set data, and transferring knowledge in the trained teacher model to the student model; and in the test stage, constructing an industrial anomaly detection model, and inputting the to-be-detected picture in the test set into the industrial anomaly detection model to obtain a detection result. According to the method, efficient zero-sample anomaly detection can be realized in industrial detection tasks with unknown categories and various anomaly forms, and a solution which is efficient, high in generalization and excellent in robustness is provided for industrial visual detection.

The invention provides an automatic driving freight formation collaborative decision-making method, device and equipment and a storage medium. Specifically, the invention provides a customized partially observable Markov decision-making process (POMDP) model, and the model aims at freight formation collaborative decision-making in an expressway scene, and enhances perception information of freight formation in a training stage by using a formation control method of a staggered structure. And the effect of the agent decision network in the test stage is further improved. Furthermore, a multi-agent reinforcement learning framework based on a centralized training and decentralized execution (CTDE) training normal form is constructed in combination with a QMIX algorithm, and the safety, the passing efficiency and the energy consumption target of the freight formation are optimized, so that the freight formation is promoted to generate spontaneous cooperative behaviors. Compared with a traditional freight formation control method, the provided model and framework show excellent performance in the aspect of improving freight formation collaborative decision-making.

The invention discloses a complex ship license plate identification method, system and device based on mask reconstruction and semantic reinforcement learning. According to the method, through two innovative modes of ship board mask reconstruction pre-training and vision-language cross-modal semantic enhancement, the complex spatial position relation of ship board characters can be effectively learned, and information supplementation can be carried out on ship boards with visual signal loss through semantic information. Meanwhile, the two innovations serve as a ship license plate recognition pre-training mode, configuration is only needed in the training stage of the ship license plate recognition deep neural network model, configuration is not needed in the online testing stage once training of the model is completed, and therefore extra ship license plate recognition calculated amount cannot be brought. The method has good coping and recognition capabilities for complex conditions such as multi-line texts, fuzzy texts, shielding texts, vertical texts and inverted-sequence texts in the ship sign.

The invention discloses a method and system for testing a direct-current offset compensation circuit of an optical transceiving driving chip and a medium, and relates to the technical field of photoelectric integrated circuit testing, and the method comprises the following steps: carrying out the time alignment of a test waveform and an offset track of the optical transceiving driving chip, extracting the phase dislocation amount and the amplitude kick amount between beats, and carrying out the calculation of the phase offset amount and the amplitude kick amount; a continuous change result is mapped into a time sequence conflict observation band which is used for identifying a beat overlapping region; and on the basis of the phase dislocation trend of the time sequence conflict observation band, determining a beat overlapping point of the direct current offset compensation loop, and generating an overlapping beat index according to the duration of amplitude kick and the polarity direction for identifying an interference node. According to the method, millisecond-level beat observation and microsecond-level overlapping identification are realized in a test stage, interference nodes are accurately extracted, a coupling strength graph is constructed, and by combining beat rearrangement and an energy unloading strategy, nonlinear oscillation is inhibited, oscillation and overload risks are reduced, eye pattern quality and error code stability are improved, and chip test precision and robustness are enhanced.

The invention relates to a protocol fuzz testing method and system based on a long-life-cycle enumeration variable state. The method comprises the following steps: S1, a static enumeration declaration statement instrumentation analysis stage; s2, a short life cycle enumeration variable dynamic analysis stage; s3, a static assignment statement instrumentation analysis stage; and S4, a fuzzy test stage. The system comprises a static analysis module, a dynamic analysis module, a network IO module and a variation feedback module. According to the method, the source code of the protocol entity program to be tested is statically analyzed to capture distribution and use of enumeration type variables in the source code. State transition is represented by monitoring changes of enumeration variable value domains, and a corresponding state tree structure is constructed, so that stateful grey box fuzz testing is carried out on any protocol entity program to be tested under the condition that extra manual analysis is not needed.

The invention provides an aging test method and device of equipment, the equipment, a medium and a product, and relates to the technical field of laser test. The method comprises the following steps: acquiring a plurality of operation parameters of target equipment; determining the equipment performance stability of the target equipment based on the plurality of operation parameters; determining an aging test parameter combination for the equipment aging test stage of the target equipment according to the equipment performance stability; performing virtual verification of the aging test parameter combination on the target equipment to obtain an evaluation result; based on the evaluation result, correcting the aging test parameter combination to obtain a target aging test parameter combination; and according to the target aging test parameter combination, performing an equipment aging test on the target equipment. According to the invention, the accuracy of the equipment aging test is improved.

The invention discloses a method, a system and equipment for predicting the stability of integrated circuit test equipment and a medium, and belongs to the technical field of integrated circuit test. The method comprises the following steps: firstly, carrying out preprocessing and feature selection on historical data in an FT test stage, and adopting a Gaussian mixture model (GMM) to cluster and identify different operation condition clusters of a test machine; then, establishing a health state GMM reference for each working condition cluster, calculating a KL divergence value of a normal sample and the reference, and setting a dynamic anomaly detection threshold by 99.73% quantile of the KL divergence value; and finally, in real-time monitoring, calculating a KL divergence value of real-time data and a corresponding working condition cluster benchmark, and comparing the KL divergence value with a dynamic threshold value to realize accurate anomaly marking. The method effectively solves the problem of abnormal detection of the test data of the integrated circuit under complex and changeable working conditions, and improves the monitoring accuracy and working condition adaptability.

The embodiment of the invention provides a transmitting circuit, a receiving circuit, an integrated circuit chip and a test method. The transmitting circuit comprises a test data generation module, a to-be-tested transmitting module, a test receiving module and a test data detection module, wherein the test data generation module is configured to generate first test data; the to-be-tested sending module is configured to generate second test data based on the first test data; the test receiving module is configured to generate third test data based on the second test data; the test data detection module is configured to receive third test data and detect whether the third test data is consistent with the first test data, and the to-be-tested sending module is further configured to receive a first clock signal with an adjustable phase. The test receiving module and / or the test data detection module are / is further configured to receive a phase-adjustable second clock signal. According to the sending circuit, the circuit performance can be prevented from being affected, and performance testing, screening and grading of a physical layer circuit are achieved in the wafer testing stage.

The invention discloses a high-capacity solid state disk single event effect testing method and device based on high-energy particles. The testing method comprises the steps that an SSD user data preset experiment is carried out; based on the data preset experiment, utilizing a device to carry out an irradiation test, judging whether the SSD module has errors such as single event locking and function interruption under an irradiation condition, determining a judgment result, carrying out quantitative calculation on the errors, and outputting a function test result; based on the function test result, performing a silent data destruction test in a post-irradiation test stage; recording silent data destruction and fixed bit error data according to the silent data destruction test; and based on the silent data destruction and the fixed bit error data, carrying out error characteristic quantification calculation, and outputting a calculation result. According to the method, parameterized evaluation of the radiation resistance of the SSD can be realized.

The embodiment of the invention provides a chip and electronic equipment, the chip comprises a plurality of chip ports, a port multiplexing logic module and a scanning test logic module, the port multiplexing logic module is provided with a plurality of groups of first ports and a plurality of groups of second ports, and the plurality of chip ports are divided into a plurality of groups according to preset granularity; each group of chip ports is connected to at least one of a plurality of groups of first ports and a plurality of groups of second ports of the port multiplexing logic module, and the plurality of groups of first ports or the plurality of groups of second ports of the port multiplexing logic module are connected to the scanning test logic module; and the port multiplexing logic module is used for controlling gating of a path between the chip port and the scanning test logic module by taking a preset granularity as a unit according to the control instruction. According to the embodiment of the invention, the problems of unreasonable I / O allocation and long test time of large-scale complex chips in related technologies can be solved, the degree of freedom of I / O allocation is improved by setting the granularity of I / O allocation, the I / O resources are dynamically adjusted in the test stage, and the chip test time is shortened.

The invention particularly relates to a hydrogeological analysis system and method based on big data, and relates to the technical field of hydrogeological analysis, and the method comprises the steps: placing a new model and an old model in an A / B test stage, and carrying out the parallel processing of real-time data; performing automatic judgment according to a preset performance index, and deciding whether to upgrade the candidate model to a new production model; and applying the model selected after decision making to hydrogeological analysis to solve the problem of long-term prediction precision under dynamic change of the hydrogeological system. According to the method, dynamic threshold monitoring, K-S outlier sample examination, sub-model optimization and A / B test smooth iteration processes are constructed, and a mechanism and machine learning coupling model and parallel calculation are combined, so that the model drift identification delay is reduced, the prediction precision is improved, scenes such as underground water over-mining prevention and control and pollution emergency are effectively supported, and the prediction accuracy is improved. And an accurate and efficient scientific decision basis is provided for water resource management.

The invention discloses a zero-sample industrial defect detection method and device based on visual enhancement and a medium, and the method comprises the steps: collecting an industrial product image and a text semantic tag corresponding to the industrial product image, and constructing a training set and a test set; building a zero-sample industrial defect detection model based on visual enhancement, and training by using the training set; and inputting the test set into the trained model for reasoning and evaluation. The model is composed of a text encoder and an image encoder, and deep interaction and optimization of text and image features are realized by introducing a bidirectional cross attention mechanism, a multi-task fusion module and an image feature refining module. In the training stage, the model learns uniform defect features from part of industrial product categories; in the test stage, defect identification and positioning are carried out on the types of industrial products which are not contacted. According to the method, the dependence on manual labeling is effectively reduced, the adaptability and detection precision of the model in a complex industrial scene are improved, and the method has good generalizability and industrial deployment value.

The invention belongs to the field of characterization learning, and particularly relates to a prostate cancer diagnosis method based on a multimodal large model prompt learning mechanism. Comprising the following steps: step 1, data preprocessing; 2, key frames and similarity are calculated, and irrelevant editing is compressed; step 3, text and image alignment training; step 4, a test stage; according to the method, a similarity-based screening mechanism is provided, ultrasonic videos under coarse-grained labels are preliminarily screened under segmentation of a large model, and focus areas are focused on in time sequence; meanwhile, a pre-processing mechanism based on a large model is provided, and the influence of a large amount of irrelevant information existing in prostate ultrasonic image scanning on the model is compressed at a data end, so that the diagnosis effect of the model is improved.

The invention belongs to the related technical field of equipment intelligent fault diagnosis, and discloses a cross-domain fault diagnosis method and system based on pseudo-label learning under a time-varying working condition. The method comprises the following steps: an offline training stage: constructing a source domain and a target domain by using collected sample signals; constructing a plurality of prediction units, wherein the input of each prediction unit is a sample signal in a source domain or a target domain; constructing a plurality of loss functions, wherein each prediction unit corresponding to the minimum total loss function is an optimal prediction unit; in the online testing stage, vibration signals collected in real time are input into the optimal prediction unit, the probability corresponding to each fault type is output, and the fault type corresponding to the maximum probability is the fault type corresponding to the sample signals collected in real time. According to the invention, the problem of low fault identification accuracy caused by large intra-class difference and small inter-class difference of the fault under the condition that the time-varying working condition is not considered in the existing fault diagnosis method is solved.

The invention provides an integrated test method for simulating semiconductor equipment and an EAP system and related equipment, key parameters are extracted from an equipment operation manual, test scripts of different test stages are set based on the extracted key parameters, and the key parameters are parameters required by a semiconductor technological process; the test system performs characteristic test on the test equipment by using the characteristic test script and generates a corresponding characteristic test result; based on the technological process testing script, the technological process of the testing equipment is simulated in the testing system, integration testing is conducted on the EAP system through the CIM service simulation interface while the technological process is simulated, and the EAP system simulates the working process of a production line containing the testing equipment according to the EAP configuration file and the integration testing script. And whether the control and response of the EAP system to the workflow are accurate or not is verified. According to the invention, an automatic test based on a test standard process is designed, and the test efficiency and accuracy are improved.

The invention relates to a multi-sampling-rate soft measurement method based on progressive transfer learning, and the method comprises the steps: carrying out the partitioning of training data from high to low according to the sampling frequency at a training stage, and training a variational progressive transfer network model; taking the model as a generator of a generative adversarial filling network, training the generative adversarial filling network by using original training data, and realizing automatic filling of unsampled data; based on the filled data, performing block processing on the original data again according to the original sampling frequency from high to low, and training a new variational progressive migration network model as a soft measurement model; in the test stage, the soft measurement model is used for predicting quality variables, and a prediction result is quantitatively evaluated. According to the method, the generation model is fully utilized to fill up the unsampled data in the soft measurement modeling stage, so that the soft measurement model extracts more valuable data information, and the prediction performance of the soft measurement model can be effectively improved.

The invention discloses a vector frequency conversion phase automatic test system and method, and the system is characterized in that a switch matrix is in communication connection with a frequency conversion TR assembly, and is used for switching a switch link to a test channel corresponding to the current to-be-tested phase test type of the frequency conversion TR assembly; a port of the change-over switch is respectively connected with a vector network analyzer and a reference mixer, and the change-over switch is used for switching a signal connection channel between the vector network analyzer and the reference mixer according to a current to-be-tested phase test type of the frequency conversion TR assembly; and the vector network analyzer is in communication connection with the switch matrix and is used for receiving to-be-tested data from the test channel switched by the switch matrix and completing a phase test on the frequency conversion TR assembly in combination with the reference mixer. The switching of the signal connection channel between the vector network analyzer and the reference mixer can be realized according to the current phase test type without pulling out a cable joint, and the automatic test efficiency is improved.

The invention discloses a lightweight RGB-D rail surface defect detection method based on multi-teacher knowledge distillation, and relates to the technical field of image processing. Comprising a training stage and a testing stage: in the training stage, through a knowledge distillation framework, hyperspherical feature mapping distillation, frequency domain attention distillation and corresponding prediction map distillation are compared, and knowledge in a double-flow multi-teacher network is transferred to a lightweight single-flow student network; the performance of the student model is improved as much as possible while it is ensured that the parameter quantity of the uniflow student model is kept small. Inputting the data-enhanced rail surface defect image into a convolutional neural network for training to obtain a significant rail surface defect prediction map; in a test stage, inputting a to-be-detected rail surface defect image into the uniflow student network training model to obtain a significant rail surface defect prediction map; the method has the advantage of improving the detection efficiency and accuracy of the rail surface defect image.

The invention provides an MES-based production line product quality test method and device, equipment and a storage medium. The method comprises the steps of obtaining product index information, data acquisition parameters and production order information; according to the production order information, equipment operation parameters are generated through the MES so as to control the production equipment unit to execute a production test; in the production test stage, the sensor unit is controlled to collect production test parameters of a product according to data collection parameters and send the production test parameters to the data transmission unit; and the MES compares the production test parameters with the product index information to obtain a production test result of the product. Real-time interaction between the MES software and hardware equipment is realized by simulating a real production environment and a real production process, various functions and performances of the MES software in the production process are comprehensively tested, and the reliability and stability of the software in practical application are ensured.

The invention discloses a digital-analog collaborative test method, which comprises the following steps of: in a parameter configuration stage, embedding a trigger mark into a preset vector row to generate a test vector, and writing the test vector into a digital board card; configuring a trigger event corresponding to the trigger signal and first delay time; in the test stage, the digital board card detects whether a to-be-executed vector line has a trigger mark or not; a trigger signal corresponding to the trigger mark is sent to the system board card; the system board card sends the trigger signal to a corresponding simulation board card, and the simulation board card determines first delay time and a trigger event according to the trigger signal; delaying the trigger event for a first delay time and then triggering; the digital board card delays a second delay time after sending the trigger signal and sends test data corresponding to the vector line to the to-be-tested device; in the triggering process of the triggering event, the end time of the second delay time is aligned with the end time of the first delay time. According to the invention, the efficiency of digital-analog collaborative testing can be improved.

The invention discloses a short-circuit working condition dynamic resistance test circuit and method based on a clamping circuit, the dynamic resistance test circuit comprises a clamping circuit and a short-circuit test circuit, and the clamping circuit and the short-circuit test circuit are respectively connected with two ends of a device to be tested; the short-circuit test circuit is used for providing a short-circuit working condition for the to-be-tested device in a short-circuit test stage of the to-be-tested device; the clamping circuit is used for providing a stable measurement voltage level for the measurement circuit and providing a test working condition for the to-be-tested device in the dynamic resistance test stage of the to-be-tested device, and the dynamic resistance recession representation after the GaN HEMT short circuit working condition can be effectively measured.

Some examples of the present disclosure relate to risk analysis of test failures that occurred during a testing phase of a continuous integration pipeline. In one particular example, a system can detect a failure of a test during a testing phase of a continuous integration pipeline. The testing phase can involve executing a group of tests in a plurality of configurations prior to merging an update to source code into a code base of a software application. Based on detecting the failure of the test during the testing phase, the system can access historical data associated with prior executions of the test, determine a risk score associated with the failure of the test based on the historical data, and perform an action associated with the source code based on the risk score.

The invention discloses an aero-engine compiler fuzzy test method and system based on a large language model. In a variation prompt generation stage, a seed program library is constructed by using embedded programs of various structures and compiler test suite programs, variation operators are selected through diversified guide strategies, and variation prompts are constructed based on a prompt template. In the test generation stage, a large language model is used for generating a variant program with complex data flow and control flow so as to enhance the test coverage rate of a compiler; global and local variables are monitored by inserting test output for the program, so that the program can effectively find out silent miscompilation errors with the maximum harm; in addition, a front-end error feedback strategy is designed, so that the effectiveness of a large language model generation test program is improved. In the differential testing stage, random difference testing and testing under different optimization levels are combined, so that the defects of the middle and rear ends of the compiler are detected. According to the method, the coverage rate of compiler testing can be increased, and silent miscompilation errors can be detected.

The invention discloses an automatic medium-pressure online detection and closed-loop control method for die steel, relates to the technical field of medium-pressure online detection, and is used for solving the problems that the probability of production deviation is increased and the precision of medium-pressure interval range adjustment is reduced due to abnormality discovery lag. Collecting a pressure fluctuation value and an off-center peak value to comprehensively judge whether the conditions are abnormal; when an abnormal instruction is generated, entering a pressure test stage, calculating a pressure scaling interval based on vibration deviation and fan frequency, adjusting a default pressure interval to obtain a new pressure control interval, and comparing the new pressure control interval with a pressure value; and when the temperature exceeds the range, the surface temperature uniformity of the steel billet is detected, a fault-tolerant proportional adjustment pressure scaling interval is calculated, a control interval is updated and subjected to secondary comparison, whether an abnormal instruction is eliminated or not is determined, production abnormity is found in time, the abnormity judgment accuracy is improved, the abnormity is found in time, and the dynamic adjustment precision of the pressure control interval is improved.

The invention provides a transmission system gear fault diagnosis method based on incremental learning, and the method comprises the steps: firstly collecting transmission system monitoring data through a sensor, constructing an incremental data set, and dividing the incremental data set into fault diagnosis tasks of different stages; constructing an initial model, and training the model based on the initial fault data; in the test stage, a generalized entropy index is defined to judge whether the fault is a new fault; when new fault type data appears, a small number of samples are randomly selected from the old type data and fused with the new type data to construct a training set, and in order to avoid forgetting of the model to the old type fault data, knowledge distillation is carried out on attention weight and old type prediction output during training; and when the generalized entropy index continues to judge that an unknown fault occurs, repeating the network training step to update the model, and finally realizing incremental learning of a fault mode. According to the method, continuous updating of the fault diagnosis model can be realized for streaming data, the state monitoring capability of the transmission system is improved, and a guarantee is provided for the service safety of a mechanical system.

The invention discloses a lightweight high-precision human body posture estimation method based on a Mama framework, belongs to the technical field of human body posture estimation, and aims to reduce the calculation cost and realize high-precision human body posture estimation by designing a lightweight neural network framework. The method comprises the following steps: firstly, constructing a human body posture estimation network of a Mama framework, and enhancing the key point detection capability while ensuring the real-time performance by using a lightweight feature extraction module; in the training stage, a diversified human body posture data set is used for training the network, and the adaptability of the network to various postures and scenes is ensured; in the testing stage, real-time human body posture estimation is achieved through the lightweight network model, compared with a traditional method, the network model is simple in design, the parameter quantity and the floating point operand of the network are greatly reduced, the real-time performance of the model is enhanced, meanwhile, the posture estimation precision is remarkably improved, and the real-time performance of the model is improved. The method is suitable for resource-limited equipment or real-time application scenes.

The invention relates to a cross-domain few-sample fault diagnosis method based on a geometric perception coordinated penalty prototype network, and belongs to the technical field of fault diagnosis, and the method comprises the following steps: S1, dividing samples through employing vibration monitoring signals in a data set; s2, obtaining respective support sets and query sets through source domain samples and target domain samples, and generating meta-tasks used in training and testing stages; s3, extracting input sample features by using a feature extractor; s4, mining an internal geometric structure of the data by using an adaptive manifold mapping module (AMMM), and converting sample features to manifolds; s5, calculating to obtain a coordination penalty prototype HPP; s6, classification is carried out through a dynamic high-sensitivity distance DHSD, loss is calculated, model parameters are updated through back propagation, and training of the GAHPPN is completed; and S7, quickly adapting to the meta-task of the target domain through the trained GAHPPN model, and diagnosing the health state of the target domain sample.

The invention discloses a radar HRRP target open set identification method based on hierarchical prototype loss function constraint, and aims at improving the generalization performance of a model for known categories when a test set comprises targets which do not appear in a training stage in an open environment in RATR practical application, and improving the recognition efficiency of the model. And information about the relationship between the unknown category and the known category is provided while the unknown category is identified. Specifically, firstly, a category hierarchical structure is constructed based on prior knowledge to guide training and testing stages. Then, a hierarchical prototype loss (HPL) is proposed to constrain the consistency of the feature space distribution and the category hierarchical structure. And finally, performing prediction according to a hierarchical structure by using the trained prototype network so as to provide information of a relationship between an unknown category and a known category. Wide experiments on actually measured HRRP data verify the effectiveness of the provided method on open set identification.

The invention discloses a wiring detection method, device and equipment of an inverter parallel system and a medium, the inverter parallel system comprises a host and at least one slave, the wiring detection method comprises the following steps: when the voltage of the host reaches a target phase, obtaining a test phase of the voltage of the slave, and then according to the target phase and the test phase, determining the voltage of the slave according to the target phase and the test phase; a master-slave voltage phase difference is obtained; according to the voltage phase difference of the master and slave machines, the wiring result between the master machine and the slave machine is determined, whether the plurality of inverters in the inverter parallel system have the condition of dislocation wiring or not is judged, interphase short circuit caused by inconsistent phases of the inverters is avoided, and therefore it is ensured that the whole inverter parallel system can operate stably.