20 results about "Abnormal test result" patented technology

The invention discloses a voiceprint-based power transformation equipment anomaly detection method and device, relates to the field of power transformation equipment monitoring, and can solve the technical problem of low accuracy of existing voiceprint-based power transformation equipment anomaly detection. The voiceprint-based power transformation equipment anomaly detection method comprises the steps of obtaining historical voiceprint feature data of a plurality of power transformation equipment, and based on a two-dimensional convolutional network, obtaining a plurality of historical voiceprint feature data of the plurality of power transformation equipment; extracting a local time-frequency feature of the historical voiceprint feature data to obtain a first voiceprint feature; performing feature extraction on the first voiceprint feature based on a bidirectional recurrent neural network to obtain a second voiceprint feature; according to the second voiceprint feature, multiple times of iterative training is carried out on a preset initial voiceprint detection model, joint optimization is carried out on model parameters based on comparative learning and perceptron classification during iteration, training is completed until the model parameters meet preset values, and a voiceprint detection model is obtained; and obtaining real-time voiceprint features of the to-be-detected power transformation equipment, and inputting the real-time voiceprint features into the voiceprint detection model to obtain an anomaly detection result of the to-be-detected power transformation equipment.

The invention relates to the technical field of test result abnormal mode intelligent early warning systems, in particular to a multi-mode biochemical test result abnormal mode intelligent early warning system and method, and the system comprises a dynamic threshold module, a multivariable mode module, a time evolution module and a self-adaption module. Through fusion of a dynamic threshold technology, multivariable pattern analysis, time sequence modeling and an interpretability mechanism, comprehensive, dynamic and intelligent analysis of biochemical test results is realized. According to the method, the accuracy and timeliness of anomaly detection are remarkably improved, and visual and credible decision support is provided for doctors. The innovative solution is expected to play an important role in the aspects of improving medical diagnosis efficiency, reducing missed diagnosis and misdiagnosis rates, promoting precise medical treatment and the like, and substantive improvement is brought to health management of patients.

The invention discloses an industrial chain node anomaly detection method and system based on electric power time sequence characteristics, and belongs to the technical field of industrial chain data processing, and the method comprises the steps: obtaining electric power data, social and economic data of different industries, and upstream and downstream associated data in the industries in real time; performing season adjustment on the index time sequence in the primary selection index pool by using a time sequence model; performing association analysis to obtain an industrial node association rule, analyzing a time difference earlier stage or lagging stage of indexes of upstream and downstream nodes according to the industrial node association rule, and dividing into consistent, first and lagging index groups; calculating a diffusion index and a synthesis index, and analyzing supply and demand conduction indexes of upstream and downstream of the industry to form a prosperity sequence; comparing the distribution difference between the current sequence and the reference business sequence through KS test to judge whether abnormity exists or not; and tracing the source link with the abnormal condition according to the abnormal test result, and quantifying the influence degree of the source link on the whole industry chain.

The invention discloses a method and a device for testing a pressure-sensitive device and a storage medium thereof. The method comprises the following steps: establishing communication among a pressure device, the pressure-sensitive device and a calibration device; sending a calibration instruction to the pressure-sensitive device through the calibration device to control the calibration device to perform calibration operation on the pressure-sensitive device to obtain calibration information; controlling the pressure device to apply test pressure to the pressure-sensitive device, and obtaining a test result of the pressure-sensitive device according to the reading of the pressure-sensitive device, the test pressure and the calibration information; and under the condition that the test result is abnormal, re-executing the calibration instruction to obtain updated calibration information. The calibration instruction is re-executed according to the test result of the pressure-sensitive device to obtain the updated calibration information, so that the calibration operation of the test process is flexibly adjusted, and the test accuracy and the test efficiency of the pressure-sensitive device are improved.

The invention provides a substation simulation test method based on self-supervised learning, and relates to the field of substation simulation test. The method comprises the following steps: acquiring data of a transformer substation in a normal operation state to form an unmarked training data set; forming positive and negative sample pairs based on the data set, and constructing a time sequence context comparison prediction task; based on a time sequence context comparison prediction task, training to obtain a causal expansion convolution feature extraction network; a data set depth feature vector set is extracted, and a multivariate Gaussian normal feature reference is calculated; constructing a transformer substation digital twinborn simulation model; generating a simulation fault data set based on the transformer substation digital twin simulation model; and generating an abnormal test result of the simulation fault data set through the causal expansion convolution feature extraction network and the multivariate Gaussian normal feature reference. According to the invention, self-supervised learning and a digital twinborn simulation model are combined, the transformer substation fault detection method is optimized, and the method has the advantages of high efficiency, accuracy and self-adaption.

The present invention relates to the technical field of servo drive testing, and specifically relates to a testing device and a testing method for a servo drive, including a frame, and further including: two groups of movable conveying rollers, which are distributed left and right on the frame; an opening and closing control mechanism, which is arranged on the frame; a plurality of fixed conveying rollers, which are arranged on the frame and evenly distributed on the front and rear sides of the two groups of movable conveying rollers; a lifting plate, a lifting mechanism is connected between the lifting plate and the frame; a support plate, the support plate is hinged to the lifting plate; a flipping mechanism, which is arranged on the lifting plate; a centering and clamping mechanism, which is arranged on the support plate. The present invention can automatically convey and test the servo drive, and can conveniently collect the servo drives with abnormal test results uniformly, greatly improving the testing speed of the servo drive and reducing the labor cost.

The application discloses a kind of test operation management systems suitable for automobile temperature sensor, belong to automobile temperature sensor test evaluation technical field, the application is by static test module, dynamic test module respectively to temperature sensor by static to dynamic many aspects are tested, rely on static test obtains temperature sensor in stable condition under temperature difference float value, voltage difference float value, and comprehensive analysis will test object be divided into qualified object and object to be detected, that is, before carrying out dynamic test, screening is carried out, then rely on dynamic test obtains temperature sensor in unstable condition under normal thermal response coefficient, normal linear coefficient, and dynamic performance coefficient is generated from the two, whether the performance detection of qualified object meets the requirement according to dynamic performance coefficient is judged and analyzed, in addition, in the process of testing, abnormal test results are checked and analyzed by static investigation module, dynamic analysis module, effectively improve test accuracy.

The invention discloses a test feedback method, and relates to the technical field of test feedback, and the method comprises the steps: determining a corresponding first test task and a first test server after a first voice instruction is received, sending a first test instruction carrying a test script to the first test server, and enabling the first test server to execute the first test task, monitoring the execution state of the first test task in real time, determining a first feedback strategy according to a target exception type when the target exception type is detected, and sending the corresponding feedback content to a specified first feedback device; according to the method, by establishing an automatic feedback mechanism of voice instruction triggering, state monitoring, anomaly classification, strategy matching and active feedback, the technical problem that in the prior art, due to the fact that a test result is manually inquired, anomaly information cannot reach a tester in time, and anomaly processing is delayed is solved; the technical effect of improving the abnormal response speed and the test feedback efficiency is achieved.

The invention discloses a precision and accuracy analysis method for quality control data in a plurality of laboratory rooms, which performs statistical calculation on quality control test result information in the plurality of laboratory rooms through big data analysis and a statistical algorithm to realize identification of abnormal test results of the same quality control batch in different laboratories. Equipment system errors (inter-equipment variation) and random errors (intra-equipment variation) are distinguished through indexes and graphs, and the problem of precision or correctness can be distinguished, so that the problem can be conveniently, accurately and efficiently positioned, hospital teachers, customer service personnel and research and development personnel are assisted to quickly analyze and process the problem, and the problem can be accurately and efficiently solved. The efficiency of checking the problems of the experimental equipment by personnel is improved, and the labor cost is saved.

The present invention proposes an automated testing system, method, device, and storage medium for a power flow rate (PFR) function. The system includes: an external port management module for data communication with a device under test and temporary storage management of some upstream and downstream data; a data decoding module for parsing instructions and feedback data between the device under test; an interactive interface for providing an interactive environment between the automated testing system for the PFR function and a user; an AC management module for simulating abnormal power outages during a BIOS firmware refresh; a test database for recording user settings and storing test steps and test pass criteria for test cases; a test abnormality alarm management module for alarming abnormal test result information; a PFR automated management system for information transmission and test process management; and a data analysis system for retrieving data judgment steps and test pass criteria for test cases from the test database to determine test results.

The application discloses an AI Agent-based storage chip mass production test method and device and a medium, relates to the technical field of storage chip testing, and comprises the following steps: based on mass production test double images, utilizing an AI Agent to comprehensively test coverage requirements, life consumption constraints and misjudgment risks, forming a risk constraint mass production test configuration, determining a main test channel for executing a current mass production test task from test channels, when a channel health score of the main test channel reaches a preset execution threshold and a historical test result fluctuation value exceeds a preset fluctuation threshold, selecting a backup test channel with the highest channel health score and a jig contact deviation value lower than a preset contact deviation threshold as a shadow review channel to form a main shadow channel binding test schedule; and the application forms the main shadow channel binding test schedule, calls the shadow review channel to review the same storage chip to be tested, and realizes the re-verification of abnormal test results in an independent review path.

The invention relates to the technical field of servo driver testing, in particular to servo driver testing equipment and a testing method thereof.The servo driver testing equipment comprises a rack and further comprises two sets of movable conveying rollers distributed on the left side and the right side of the rack; the opening and closing control mechanism is arranged on the rack; the plurality of fixed conveying rollers are erected on the rack and are uniformly distributed on the front and rear sides of the two groups of movable conveying rollers; a lifting mechanism is connected between the lifting plate and the rack; the supporting plate is hinged to the lifting plate; the turnover mechanism is arranged on the lifting plate; and the centering and clamping mechanism is arranged on the supporting plate. According to the invention, the servo drivers can be automatically conveyed and tested, and the servo drivers with abnormal test results can be conveniently and uniformly collected, so that the test speed of the servo drivers is greatly improved, and the labor cost is reduced.

The invention provides a power distribution confluence cabinet test method, device and system and a storage medium, and the method comprises the steps: triggering an equipment simulation unit in a test device in response to a test instruction, enabling the equipment simulation unit to simulate the operation condition of triggering equipment in linkage with a power distribution confluence cabinet, and transmitting the operation condition to the power distribution confluence cabinet, feedback information of the power distribution confluence cabinet for the operation condition is obtained; based on the feedback information, determining whether the power distribution confluence cabinet is abnormal or not; and if the power distribution confluence cabinet is abnormal, outputting an abnormal test result of the power distribution confluence cabinet. Therefore, the correctness of electrical loop connection in the power distribution confluence cabinet and the comprehensiveness of functions can be verified, and the normal electrical connection of internal components, the normal functions of the components and the normal linkage strategy of an external component system before the power distribution confluence cabinet is installed in a cabin are ensured, so that the test efficiency, the test comprehensiveness and the reliability of a test result are improved.

The present application relates to the field of medical artificial intelligence and clinical auxiliary decision-making technology, in particular to an abnormal test result combination pattern recognition method and system based on deep learning, comprising: receiving test results output by a hospital test information system to construct a 48-dimensional test index vector; calculating a multi-dimensional joint deviation degree based on a group health joint distribution reference model; constructing a patient individual baseline with stable period test values for baseline drift correction, and inferring an individual baseline and giving a baseline disturbance double-channel representation when the stable period is missing with a meta-learning baseline inference subnetwork output; inputting the individualized joint deviation degree and the baseline disturbance double-channel representation into a multilayer perceptron network classifier to output five types of severe clinical event prediction probabilities of sepsis, acute kidney injury, disseminated intravascular coagulation, acute liver failure and acute exacerbation of chronic diseases, and the training loss contains a pathogenic causal diagram prior constraint term; when the prediction probability exceeds 40%, an orange reminder is pushed to the mobile terminal of the responsible nurse.

The application discloses a method and device for monitoring product testing, electronic equipment and a storage medium. The method comprises the following steps: collecting a test result picture and a test environment picture of a product through a camera; inputting the test result picture and the test environment picture into a trained neural network model to output a test condition corresponding to the product; and if the test condition is abnormal, uploading abnormal test result information to a user terminal according to the test condition. The application fully considers the environmental factors during testing, avoids the problem of test failure caused by external environmental factors, improves the reliability of test monitoring, uses a neural network model for monitoring, saves manpower and time for manual monitoring, and automatically uploads the abnormal condition to the user terminal, so that the test personnel or the research and development personnel can understand the test condition, analyze and process the abnormal condition in a timely manner, thereby improving the test efficiency and accelerating the product research and development process.

This invention discloses a sintering material sampling device using a sagger as a sintering carrier, relating to the field of sagger sampling technology. The device includes a cover plate with a fastening assembly on its lower surface. Three circular slots are provided on the cover plate, each containing a sampling component. Each sampling component includes an outer cylinder. By using a fixed cover plate relative to the sagger and with three circular slots on the cover plate, this invention allows for sampling sintering material at three different points. The fixed sampling location significantly improves the reliability of material compaction after firing, reducing the likelihood of abnormal test results due to different sampling points.

The present invention provides a valve detection device, which belongs to the technical field of valve air tightness detection. It comprises an operating platform, one side of the operating platform is fixedly connected to a test platform, one side of the test platform is fixedly connected to a hydraulic telescopic rod, the output end of the hydraulic telescopic rod is fixedly connected to a test movable disk, and the upper part of the test platform near the test movable disk is fixedly connected to a test fixed disk. The present invention supports and fixes the valve to be tested through the mutual cooperation of the first valve bracket and the second valve bracket. The fixing method is simple and easy to operate, which avoids the complicated process that the operator needs to fix the valve to be tested with both hands during the air tightness test. At the same time, the inlet and outlet positions of the valve to be tested are positioned, so that the valve to be tested is placed at the center of the test fixed disk and the test movable disk, without the need for manual positioning by the employee, thereby avoiding inaccurate positioning and abnormal test results.

The invention discloses a test data evaluation and analysis method for a PCBA circuit board, and relates to the technical field of circuit board testing, and the method comprises the steps: obtaining an analysis element library; using a multifunctional analysis method to obtain multiple extreme test intervals; obtaining a to-be-evaluated item; analyzing functions in the circuit board based on the to-be-evaluated item, and evaluating the test data based on an analysis result; the method and the device are used for solving the problems that in the existing test data evaluation analysis for the circuit board, when the test result is abnormal due to the influence of the electronic element in the circuit board, the electronic element causing the abnormal test result cannot be obtained based on the analysis result of the test data; the problem that the repair efficiency of the damaged circuit board is affected due to the fact that electronic components of the circuit board cannot be corrected in time based on a test result is solved.

The invention discloses a quantum key charging machine, a quantum key charging test method thereof, a medium and equipment. The quantum key charging test method comprises the following steps: executing a communication test between a quantum key charging machine and quantum key management network equipment; under the condition that the communication test is normal, executing a quantum key charging function test of the quantum key charging machine; under the condition that the quantum key charging function is normal, executing at least one performance pressure test of the quantum key charging machine; and under the normal condition of the performance pressure test, executing at least one abnormal operation test of the quantum key charging machine. By applying the technical scheme of the invention, normal communication of the network can be ensured from the underlying communication relationship, and meanwhile, pressure test and abnormal test results are obtained under the condition of ensuring normal functions, so that the safety and reliability of quantum key charging are ensured.

Embodiments of the present application provide a kind of intelligent teaching data processing method and related device, applied to intelligent teaching system, the intelligent teaching system includes first teaching equipment and second teaching equipment, the method includes: first teaching equipment receives the first application program code generated by target user;First teaching equipment sends first application program code to second teaching equipment;Second teaching equipment tests first application program code to obtain first test result;If first test result is abnormal test result, then second teaching equipment obtains the abnormal information in first test result;Second teaching equipment determines abnormal correction information according to abnormal information;Second teaching equipment sends abnormal correction information to first teaching equipment;First teaching equipment shows abnormal correction information, can when the test result of application program code is abnormal, corresponding abnormal correction information is determined and shows, improve the reliability when student carries out application program code programming.