13results about How to "Timely diagnosis" patented technology

According to the laser signal coupling misalignment detection and alignment adjustment method, coupling alignment detection is realized through laser alignment, a spinor factor is introduced based on a perturbation method to establish a coupling alignment calculation analysis and adjustment model, and coupling misalignment measurement and alignment are performed; coupling misalignment faults are classified into parallel misalignment, angle misalignment and comprehensive misalignment, and features of the coupling misalignment faults are identified through a frequency spectrum mechanism, motion and dynamics; detection of coupling centering is realized through laser centering, and non-centering measurement and alignment of the coupling are realized by introducing a spinor factor based on a perturbation method, so that axial leads of a unit meet coaxial requirements, undue mechanical stress of the coupling is eliminated, a reasonable centering state of the coupling during operation is guaranteed, and the coupling accuracy is improved. Therefore, normal, stable and long-term continuous operation of the laser machine is guaranteed. And centering dynamic data display is achieved, the operation time is shorter, the precision is higher, and the adaptability is higher.

The invention relates to the technical field of power system fault diagnosis, and discloses an intelligent fault diagnosis method and system for a power system, and the method comprises the steps: collecting the recording data of a preset number of continuous sampling points under different working conditions according to a preset fault starting time and a preset collection time window, and determining the fault matrix data corresponding to each sampling point; adding a label vector representing a fault type to each piece of fault matrix data, determining a data set, and dividing the data set into a training set and a test set according to a preset proportion; training the rule embedded CNN model through the training set, testing the trained rule embedded CNN model by using the test set, and determining a fault diagnosis model; and deploying the fault diagnosis model in the power system, collecting fault data when the power system has a sudden current change, diagnosing the fault data through the fault diagnosis model, and determining a fault type. The method can diagnose faults in time, and is suitable for various novel power systems.

The embodiment of the invention discloses a transformer invisible fault diagnosis method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring voltage and current recording data of a transformer measured on line; performing influence factor elimination operation on the voltage and current recording data, specifically comprising the following steps of: eliminating a higher harmonic component, a direct current component and an attenuation direct current component by adopting a full-wave differential Fourier algorithm to obtain a fundamental wave voltage and a fundamental wave current; when the elliptical orbit is calculated, normalization processing is carried out on different loads; eliminating the influence of abnormal numerical values by adopting a Gaussian regression algorithm; constructing a voltage difference and current elliptic track by using a transformer port relational expression compensated by the exciting current; correcting the elliptical trajectory by using the primary side voltage data, the secondary side voltage data and the primary side current data of the two groups of transformers measured within the sampling time; and after the operation of eliminating the influence factors, realizing the recognition and diagnosis of the invisible fault of the transformer through Lissajous ellipse fitting, characteristic quantity extraction and an artificial intelligence inversion algorithm.

The utility model relates to the technical field of charging systems, and belongs to an OBC surge voltage protection circuit, which is characterized by comprising a power grid voltage sampling and diagnosing circuit and a switch control circuit connected with the power grid voltage sampling and diagnosing circuit and used for controlling on-off of an input power grid, and the front section of the power grid voltage sampling and diagnosing circuit is connected with a power grid live wire L and a zero wire N; the rear end of the switch control circuit is connected with a rectifier bridge BD1, the rectifier bridge BD1 is connected to an output ground PGND, and the power grid voltage sampling and diagnosis circuit comprises a sampling circuit module used for sampling power grid voltage, a diagnosis circuit module used for diagnosing the power grid voltage and a latch circuit module used for outputting surge voltage protection signals. An RC filtering circuit module is arranged between the voltage diagnosis circuit module and the latch circuit module, and the surge voltage protection signal is connected to the switch control circuit. According to the utility model, the timely diagnosis of the surge voltage can be realized through the network voltage sampling and diagnosis line, and the switch control line is used for quickly disconnecting the power grid, so that the damage of internal devices of the OBC is effectively avoided, the protection of the surge voltage is realized, the stability of the OBC circuit is improved, and the service life of the OBC circuit is prolonged.

The present invention provides a molecular beacon probe and a kit thereof for detecting isoniazid-resistant Mycobacterium tuberculosis. The molecular beacon probe is composed of a single-stranded nucleic acid molecule labeled with a fluorescent excitation group and a quenching group, wherein the fluorescent excitation group is selected from any three of FAM, VIC, TEXRED, CY5, JOE, NED, TET, HEX, and TAMARA; and the fluorescent quenching group is selected from BHQ1, BHQ2, and / or TAMARA. The detection kit provided by the present invention rationally distributes primers and probes to two reaction systems, effectively avoiding mutual interference during the PCR reaction process, and at the same time has high sensitivity and specificity to pathogens, reduces the cost of detecting isoniazid resistance of Mycobacterium tuberculosis, improves detection efficiency and accuracy, and is conducive to rapid and accurate clinical diagnosis.

The invention discloses a heart state detection method and device, equipment and a medium. The method comprises the steps that target electrocardiogram data of a patient to be diagnosed are collected; according to the actual lead number corresponding to the target electrocardiogram data, selecting a target diagnosis neural network model matched with the actual lead number from a plurality of pre-trained standard diagnosis neural network models; the target electrocardiogram data are input into the target diagnosis neural network model, at least one target heart state label determined by the target diagnosis neural network model from a plurality of pre-calibrated heart state labels according to the target electrocardiogram data is obtained, and the at least one target heart state label is used for representing the actual heart state of the patient to be diagnosed. The neural network model is used for automatically analyzing the electrocardiogram data, the states of cardiac hypertrophy, expansion, enlargement and the like can be found in time through the electrocardiogram, heart abnormity can be found in time before symptoms such as heart failure, sudden death and arrhythmia are generated, and a patient can be diagnosed and treated in time.

A data transmission method for a handheld ultrasonic system and the handheld ultrasonic system, the method being applied to a mobile terminal deployed with the handheld ultrasonic system, the mobile terminal comprising a first near field communication module, the method comprising: in response to a first user operation, transmitting data to the handheld ultrasonic system through the first near field communication module, acquiring identification information of a target monitor from the target monitor comprising a second near field communication module; wherein the first user operation comprises the step that a user enables the mobile terminal to be close to the target monitor; and establishing communication connection with the target monitor based on the identification information of the target monitor, and transmitting ultrasonic image data to the target monitor based on the communication connection so as to project an ultrasonic image displayed on the mobile terminal to the target monitor for display. The method and the system can solve the problem that the screen of the mobile terminal is small and is difficult to meet the requirement of multi-user simultaneous viewing, and are simple in user operation and good in user experience.

The invention relates to the technical field of voiceprint recognition and signal processing, in particular to a transformer substation voiceprint recognition system based on a trusted WLAN + centralized control system. The system comprises a voiceprint sensing unit used for collecting sound wave signals generated during operation of substation equipment; the signal processing unit adopts a fast independent component analysis technology, constructs a new objective function by combining and utilizing a complex network analysis idea, de-noising the sound wave digital signal, and extracts a feature vector by using a wavelet packet decomposition algorithm; the WLAN transmission unit transmits the feature vector to the analysis and diagnosis unit by using a trusted WLAN; and the analysis and diagnosis unit stores the voiceprint data and performs fault analysis and diagnosis on the feature vectors through a BP neural network algorithm. According to the transformer substation voiceprint recognition system based on the credible WLAN + centralized control system, the problem of noise interference is solved through complex network analysis and a rapid independent component analysis technology, and equipment fault recognition is achieved in combination with a BP neural network algorithm.

The present disclosure relates to methods and systems for diagnosing a particulate filter sensor. Systems and methods are presented for diagnosing operation of a sensor of an exhaust system. In one example, the systems and methods can diagnose operation of the sensor while the engine is combusting air and fuel. Further, operation of the sensor can be diagnosed while the engine is not combusting air and fuel, such that vehicle occupants are not disturbed by the diagnosis.

The application relates to the technical field of power mode recognition, in particular to a power equipment fault detection method and system based on a deep learning network, which comprises the following steps: collecting and preprocessing multimodal data such as power time series and equipment images; monitoring the resource state of an edge node in real time, and adjusting the configuration parameters of a lightweight feature extraction model according to a preset rule; adopting a bidirectional cross-modal attention mechanism, calculating query, key and value representation and attention weight, dynamically weighting and fusing projection feature vectors of different modes, and generating a unified fault representation vector; calculating a fault probability by using the representation vector, and adaptively optimizing the parameters of a probability calculation model through online incremental learning; finally, calculating a task priority, and determining whether data is uploaded to a higher-level system for more detailed analysis according to the priority and a real-time resource state. The application can effectively improve the real-time performance, accuracy and adaptive capacity of edge-end power equipment fault detection.

The present application relates to the field of electric meter remote monitoring, more particularly, the present application relates to the electric meter remote fault diagnosis method and system based on internet of things, the method comprises: obtaining all historical fault sequences corresponding to each fault type; the period of each historical fault sequence is calculated and the probability of each period appearing, and based on the historical fault sequence corresponding to a single period, the template sequence of the period is generated; the real-time parameter time sequence of the electric meter is collected, the similarity of the real-time parameter time sequence and the template sequence of each period corresponding to the fault type is calculated, the calculated similarity is weighted and summed according to the probability of each period appearing, and the real-time fault probability of the fault type is obtained; when the real-time fault probability is greater than the preset fault threshold, the diagnosis result of the corresponding fault type of the electric meter is output. The present application can more comprehensively and accurately describe the complex evolution law of the fault, and realize the remote and timely diagnosis of the electric meter fault.

The invention provides an ultrasonic diagnosis assisting method and system based on machine learning, and belongs to the field of diagnosis assisting. The problem of low ultrasonic diagnosis efficiency is solved; the method specifically comprises the following steps: S1, obtaining a picture set to be extracted; s2, extracting texture features and shape features of the picture set to be extracted to obtain ultrasonic picture features of the patient; s3, acquiring a reference picture set; constructing an ultrasonic image diagnosis model by taking the reference image set as a training set of computer model training; s4, substituting the ultrasonic image features of the patient into the ultrasonic diagnosis model to obtain a disease name of the patient; by acquiring, extracting and analyzing the ultrasonic image of the patient, the disease of the patient is judged, and the diagnosis efficiency of a doctor is improved.

The invention provides EB (Epstein-Barr) virus nucleic acid detection primers, a probe and a kit, according to a target sequence of an EB virus gene, the sequences of the primers and the probe in an asymmetric primer ERA detection system are designed and screened, and the ratio of upstream and downstream primers and an amplification reaction system are optimized, so that the EB virus nucleic acid sequence can be specifically identified, and the EB virus nucleic acid detection kit has the advantages of high specificity and high sensitivity. Non-specific binding with other irrelevant nucleic acids is reduced, so that the detection specificity is improved; meanwhile, efficient amplification of EB virus nucleic acid is realized, and the EB virus nucleic acid can be accurately detected even under the condition that the content of the virus nucleic acid is extremely low, so that the detection sensitivity and the amplification efficiency are improved.