84results about How to "Effective diagnosis" patented technology

The invention relates to the technical field of intelligent fault diagnosis systems, in particular to an intelligent fault diagnosis system and method for multi-sensor data fusion, and the system comprises an online diagnosis module and an offline training module, the online diagnosis module comprises a self-adaptive balance sub-module and a prototype network sub-module, and is used for processing sensor data in real time and carrying out fault diagnosis; the offline training module comprises a data preprocessing sub-module, a data fusion sub-module, a fault diagnosis sub-module, a data balance sub-module and a data interpretation sub-module, and is used for optimizing a diagnosis model; the data interpretation sub-module comprises an SHAP value extraction unit which is used for interpreting a diagnosis result; an online diagnosis module and an offline training module of the system form a closed-loop optimization mechanism, so that the diagnosis model can continuously learn and adapt to a new fault mode. The design ingeniously balances the requirement of real-time diagnosis and the requirement of continuous optimization of the model, and the long-term effectiveness of the system is greatly improved.

The invention discloses an industrial equipment operation monitoring and early warning system based on an infrared temperature distribution technology, and the system comprises a temperature distribution image correction module which calculates a compensation coefficient of heat reflection on the surface of industrial equipment, and obtains a temperature distribution image after the reflection effect is corrected; the image displacement correction module is used for correcting temperature distribution image offset caused by displacement of the displacement component of the industrial equipment; the temperature abnormal area identification module is used for identifying a temperature abnormal area; the temperature anomaly region maintenance strategy making module is used for making a maintenance strategy of the temperature anomaly region; the temperature imbalance area identification module is used for identifying a temperature imbalance area in the non-temperature abnormal area; the temperature imbalance area maintenance module is used for formulating a preventive maintenance strategy of a temperature imbalance area; and the maintenance strategy execution and optimization module is used for continuously optimizing the maintenance strategy according to the maintenance task execution result. According to the method, the efficiency and effect of industrial equipment maintenance work are improved by combining an infrared thermal imaging technology and a multi-data correction and analysis method.

The invention belongs to the technical field of medicine, and discloses an active targeting atherosclerotic plaque liposome and application thereof in diagnosis, a carrier material for forming the liposome comprises targeting peptide R8 modified methoxy polyethylene glycol phospholipid (R8-DSPE-PEG), polypropylene sulfide-polyethylene glycol (PPS-PEG) and cholesterol (CH), and the targeting peptide R8 modified methoxy polyethylene glycol phospholipid (R8-DSPE-PEG), the polypropylene sulfide-polyethylene glycol (PPS-PEG) and the cholesterol (CH) are combined to form the active targeting atherosclerotic plaque liposome. The sequence of the targeting peptide R8 is Asp-Ser-Ser-Ser-Gln-Cys-His-Arg, and the sequence of the targeting peptide R8 is Asp-Ser-Ser-Gln-Cys-His- The liposome provided by the invention can improve the enrichment concentration of a fluorescent dye or a drug in local lesion, and has the advantages of low toxicity and high efficiency, and the obtained targeted liposome has a good effect on diagnosis of atherosclerosis.

The invention provides a camera quality centralized diagnosis method and system. The method comprises the following steps: regularly obtaining video frames or video streams of a transformer substation camera; determining a quality state of the camera based on the video frame or the video stream; the method specifically comprises the following steps: analyzing the video frame or the video stream, and diagnosing the quality state of the camera according to the quality state of the video frame or the video stream; and if the quality state of the camera is abnormal, triggering an alarm. According to the method, additional equipment is not needed, the state of the camera body is effectively diagnosed by processing and analyzing the pictures or videos output by the camera and combining related judgment rules, the abnormal state of the camera can be found in time, related personnel can be notified, the working reliability and stability of the camera are guaranteed, and the working efficiency is improved. The method can be widely applied to the fields of security monitoring and the like.

The invention relates to the technical field of equipment fault detection, and discloses an equipment fault prediction and diagnosis method which is applied to an ATE system, and the method comprises the steps: collecting the real-time operation data of equipment through a plurality of sensors, and dynamically scheduling the sensors through an adaptive algorithm, so as to adjust the data collection frequency and range according to the state of the equipment; the collected data is preprocessed; training a plurality of machine learning models based on the historical fault data and the operation data, and deploying the models to an ATE system for real-time analysis; predicting an equipment fault according to the model, generating alarm information when a potential fault is detected, and sending the alarm information to an operator through a user interface or a notification system; generating a fault diagnosis report according to an analysis result and providing a repair suggestion; recording a fault instance and data, and updating and retraining the machine learning model to improve prediction and diagnosis accuracy; and generating a digital twin model of the equipment based on real-time and historical data, simulating the running state of the equipment, predicting future faults, and evaluating the health condition of the equipment.

The invention discloses a nuclear power device fault diagnosis method based on feature vectors and Bayesian reasoning, belongs to the technical field of Bayesian reasoning, and particularly relates to nuclear power device fault diagnosis. The method solves the problem that a traditional Bayesian model only uses parameter values as a fault reasoning basis and ignores a large amount of parameter time-frequency feature information, so that the model is difficult to distinguish feature similar faults and realize accurate positioning of the faults, and the fault diagnosis accuracy and the model robustness are affected. The method comprises the following steps: a model learning step: performing structure learning and parameter learning on a Bayesian model according to fault historical operation data to obtain a Bayesian vector reasoning model; the nuclear power device fault diagnosis method based on the feature vector and Bayesian reasoning is suitable for fault diagnosis of a nuclear power device.

The invention discloses a magnetic hydrogel coated bacterial biosensor platform and application, and belongs to the field of synthetic biology and clinical diagnosis. The hydrogel delivery and recovery system comprises a hydrogel-coated whole-cell biosensor and magnetic particles, the hydrogel is constructed by sodium alginate and calcium chloride, and the biosensor is a bacterium for sensing heme. More specifically, engineering bacteria YES601 and magnetic Fe3O4 particles are packaged by sodium alginate hydrogel to form magnetic hydrogel microspheres, the magnetic hydrogel microspheres are delivered to intestinal tracts as a biosensor and can sense heme to emit light, and then complete microspheres are efficiently recovered from excrement in a magnetic adsorption mode for detection. The intestinal bleeding detection efficiency and safety of the whole-cell biosensor are improved. According to the overall design optimization strategy of the hydrogel delivery and recovery system, the diagnosis efficiency and the practical application value can be improved.

The invention relates to the technical field of molecular biological detection, and particularly discloses a quadruple fluorescent PCR primer probe combination, a reaction system and a detection method for identifying a mycoplasma gallisepticum wild strain and a vaccine strain. The quadruple fluorescent PCR primer probe combination for identifying the mycoplasma gallisepticum wild strain and the vaccine strain comprises a universal primer probe group for detecting mycoplasma gallisepticum and a specific primer probe group specially aiming at three vaccine strains F-36, TS-11 and 6 / 85 respectively. According to the quadruple fluorescent PCR primer probe combination, the reaction system and the method for identifying the mycoplasma gallisepticum wild strain and the vaccine strain, the purpose of simultaneously detecting and distinguishing DNA of the mycoplasma gallisepticum wild strain and DNA of three different vaccine strains (F-36, TS-11 and 6 / 85) of the mycoplasma gallisepticum wild strain can be achieved, so that detection and differential diagnosis are more efficient, economical, simple and convenient; the kit has the advantages of high sensitivity, good specificity, rapidness, high flux, simplicity and convenience in operation and the like.

The present invention belongs to the field of medical technology, and discloses a liposome that actively targets atherosclerotic plaques and its application in diagnosis. The carrier material forming the liposome includes a targeting peptide R8 modified with methoxy polyethylene glycol phospholipid (R8-DSPE-PEG), polysulfide propylene-polyethylene glycol (PPS-PEG) and cholesterol (CH), and the sequence of the targeting peptide R8 is Asp-Ser-Ser-Ser-Gln-Cys-His-Arg. The liposome of the present invention can increase the enrichment concentration of fluorescent dyes or drugs in the local lesion, has the advantages of low toxicity and high efficiency, and the obtained targeted liposome has a good effect on the diagnosis of atherosclerosis.

The invention provides an asynchronous motor speed sensor fault diagnosis method, which comprises the following steps of: acquiring a rotating speed signal analog quantity, analyzing the acquired rotating speed signal analog quantity, and realizing asynchronous motor speed sensor fault diagnosis according to different rotating speed signal analog quantity analysis results. Analysis of the analog quantity of the collected rotating speed signals comprises a logic sequence of level conversion, the duty ratio of signals of the same channel, the period deviation ratio of signals of different channels, edge signal time prediction of different channels and high and low level voltage values. According to the method, the fault of the speed sensor can be effectively diagnosed by comprehensively analyzing the analog quantity of the collected rotating speed signal from different angles, and the method is simple to implement, small in calculated amount and high in practicability.

The invention discloses an intelligent rapid magnetic resonance imaging reconstruction and pathological positioning method. The method is specifically implemented according to the following steps: step 1, constructing a pathological focused nuclear magnetic resonance image reconstruction model; 2, constructing a pathological focused nuclear magnetic resonance image reconstruction network according to the pathological focused nuclear magnetic resonance image reconstruction model constructed in the step 1; step 3, training a pathological focused nuclear magnetic resonance image reconstruction network; and step 4, using the trained pathologically focused magnetic resonance image reconstruction network to carry out magnetic resonance imaging and pathology positioning. According to the method, dependence on an accurate pixel-level label can be reduced, the pathological part of the image can be positioned while the reconstruction effect of the nuclear magnetic resonance image is enhanced, a better reconstruction effect is achieved on the part, and a clinician is effectively assisted in diagnosis.

The invention discloses an aptamer electrochemical biosensor based on an AuNPs / MXene nanometer material and a preparation method and application of the aptamer electrochemical biosensor. According to the electrochemical biosensor, AuNPs particles grow on the surface of MXene, an AuNPs / MXene composite material is prepared, then the composite material is fixed to the surface of an electrode through chitosan, aptamer probes P1 and P2 are hybridized with MCH, and then the aptamer probes P1 and P2 are self-assembled to the surface of the electrode through Au-S bonds. According to the invention, the AuNPs / MXene nano material is prepared, and the AuNPs / MXene nano material and the AuNPs / MXene nano material play a synergistic effect, so that the performance of the sensor is further improved, the sensor has a wide detection range and a low quantitative line, and rapid, sensitive and specific detection on a sample can be realized. The sensor can be used for in-situ detection of inflammatory factors at a wound, can be applied to non-invasive instant wound nursing monitoring equipment, and provides reliable and timely monitoring data to realize more effective diagnosis and treatment.

The invention relates to the technical field of medical detection, and provides a mental disease screening system, a chip, a device and a computer readable storage medium. The system for screening mental diseases comprises the following modules: an image acquisition module, an ROI detection module, an ROI extraction module, a cutting module, a standardization module, an erythema segmentation module, a statistical feature extraction module and a report generation module. The system provided by the invention can be used for detecting or diagnosing whether a patient suffers from depression or not. The system for screening mental diseases is simple in use method, high in accuracy and capable of conveniently and quickly completing the diagnosis process.

The invention discloses an application server monitoring method, system and device based on event triggering and a medium, and the method comprises the steps: monitoring the state data of a plurality of application servers in real time; the state data is obtained based on a preset monitoring index; monitoring abnormal events of the plurality of application servers according to the state data; marking an application server corresponding to the abnormal event as a potential fault application server, and collecting operation data of the potential fault application server; and analyzing and diagnosing the operation data through a preset analysis algorithm to obtain a solution and a diagnosis result of the potential fault application server. Thus, in combination with a monitoring mechanism triggered by an event, collection and diagnosis are triggered only when an abnormal event occurs, rapid response to the abnormal event is realized, problems are rapidly analyzed, a solution is provided, the problems of data redundancy and untimely response caused by frequent timing polling are avoided, and the accuracy of data processing is improved. The application server can be monitored and diagnosed efficiently in real time.

The invention relates to the technical field of nuclear power plant safety, and discloses a nuclear power plant fault diagnosis method based on interpretable deep transfer learning, which comprises the following steps: acquiring and preprocessing operation data or simulation data under multiple power levels, and dividing a source domain and a target domain; constructing and preliminarily training a multivariable interpretable gating cycle unit model, and screening features; training a source domain fault diagnosis model based on an adaptive focus loss function; training by adopting a self-adaptive focus mixed maximum mean value difference loss function, and migrating the source domain model to a target domain; and finally, integrating the source domain model and the target domain model to form a diagnosis system, and inputting to-be-tested data clusters and allocating the to-be-tested data clusters to the corresponding models to obtain a fault diagnosis result. According to the method, fault diagnosis tasks under different power levels can be effectively processed, operation condition data under the power levels are integrated and transmitted into a single model for training diagnosis, the accuracy and interpretability of the model are improved, and nuclear power plant operators can be further assisted in decision making.

The embodiment of the invention provides a bearing fault diagnosis method and system for optimizing filtering MCKD, and the method comprises the steps: collecting a vibration signal, and carrying out the Fourier transform of a frequency spectrum of the vibration signal, and obtaining a key function; setting an initial interception length, performing inverse Fourier transform on the key function based on the initial interception length to obtain a frequency spectrum trend, dividing filtering frequency bands with different bandwidths and center frequencies by taking a minimum value of a trend spectrum as a boundary of frequency spectrum segmentation, increasing the interception length, repeating the steps, and recording all results; designing a Mayer wavelet filter based on the obtained filtering frequency band, and taking a designed Mayer wavelet filter coefficient as an initial filter coefficient of MCKD; mCKD filtering results of different initial filter coefficients are calculated, the fault characteristics of all filtering signals are quantized through harmonic spectrum kurtosis HSK, the filtering result corresponding to the maximum HSK value is selected as the global optimal solution of MCKD, and fault diagnosis is completed.

The invention provides a diagnostic marker for metabolic syndrome and preclinical stage thereof, and the diagnostic marker or a plasma small molecule metabolism biomarker consists of the following substances: pyrimidine, (S)-beta-aminoisobutyric acid, 3-hydroxybutyric acid, L-serine, guanidine acetic acid, 3-hydroxyisovaleric acid, L-homoserine, L-cysteine, piperidine acid, hypotaurine and creatinine. The beverage is prepared from phenylacetic acid, erythritol, picolinic acid, taurine, pyroglutamic acid, lemonic acid, malic acid, tyramine, isonicotinic acid, o-phosphoethanolamine, L-glutamine, salicylic acid and D-glucose. The invention also provides a kit for detecting the plasma small molecule metabolism biomarker composition, the spectrum peak intensity of the obtained plasma small molecule metabolism biomarker is detected, the risk score of whether each subject is MetS or pre-MetS is calculated, and then whether the subject is MetS or pre-MetS is predicted. According to the invention, efficient and rapid diagnosis of the metabolic syndrome and the preclinical stage thereof is realized.

The invention discloses a PCIe link state monitoring system and a monitoring method thereof. The PCIe link state monitoring system comprises a PCIe monitoring subsystem, a PCIe controller subsystem and a PHY (Physical Layer) module, wherein the PCIe controller subsystem and the PHY module are connected through a PIPE interface, and the PCIe monitoring subsystem is used for monitoring state information of a PCIe link; four levels of leaky bucket modules are integrated on the same layer of the PCIe monitoring subsystem, and the four levels of leaky bucket modules are used for dropping correctable error information in the state information of the PCIe link into leaky buckets, carrying out overflow counting to obtain leaky bucket overflow times, carrying out error pattern recognition according to the leaky bucket overflow times and preset different execution thresholds, and outputting the error pattern recognition result. Link recovery is carried out by adopting a progressive link recovery strategy; therefore, extra special equipment does not need to be introduced into the PCIe link, the observability of the PCIe link can be improved, and correctable errors caused by the signal integrity problem on the link can be analyzed and diagnosed in real time, so that a corresponding link recovery strategy is adopted.

The invention discloses a diagnosis and treatment probe of a targeted HER2 receptor and a labeled precursor, a preparation method and application of the diagnosis and treatment probe, and belongs to the technical field of radiopharmaceuticals. According to a biological orthogonal click chemical strategy, diagnosis and treatment are organically combined, HER2 is used as a target spot to design the nuclear medicine diagnosis and treatment integrated probe, the probe can achieve efficient and high-specificity tumor marking and treatment, and the accuracy and effect of clinical diagnosis and treatment are improved; meanwhile, by reducing accumulation of non-specific radioactive substances in the body, radiation damage to healthy tissue is remarkably reduced, a safe and effective treatment scheme is provided for patients, and potential clinical transformation prospects are achieved.

The invention discloses an open-circuit fault diagnosis method and equipment applied to an inverter power supply of a hybrid power device. The method comprises the following steps: acquiring an output voltage or an output current in a current state; obtaining a current state, and obtaining each harmonic amplitude and a total harmonic distortion rate of a normal state; obtaining each harmonic amplitude offset and the total harmonic distortion rate offset of the current state; respectively comparing each subharmonic amplitude offset and the total harmonic distortion rate offset of the current state with the judgment threshold of the corresponding subharmonic amplitude offset and the judgment threshold of the total harmonic distortion rate offset; and determining that the open-circuit fault exists in the current state if the same specific subharmonic amplitude offset of the current state is greater than the judgment threshold of the corresponding subharmonic amplitude offset, or the total harmonic distortion rate offset of the current state is greater than the judgment threshold of the total harmonic distortion rate offset. According to the invention, the open-circuit fault of the inverter power supply of the hybrid power device can be diagnosed.

The invention discloses a chain transmission system fault detection and diagnosis test device, method and system, and relates to the technical field of chain transmission system fault detection.The chain transmission system fault detection and diagnosis test device comprises a driving system, a transmission system, a load system, a signal detection system and an auxiliary system; the real-time monitoring and data acquisition of each key component of the chain transmission system are realized; more comprehensive fault information can be provided, the accuracy and reliability of fault detection can be improved, timely discovery and early warning can be realized, and the fault occurrence probability and loss can be reduced; through the adjustable load system, the running states of the chain transmission system under different working conditions can be simulated, the performance and reliability of the system under different loads can be verified, efficient and accurate fault detection and diagnosis of the chain transmission system are achieved, and the safety of equipment running is improved.

The present invention provides a method, system and electronic device for identifying a diseased area, including: obtaining initial image data of a region to be identified; preprocessing the initial image data to determine image data to be segmented; segmenting the image data to be segmented based on a preset grayscale threshold parameter to obtain a preset type of tissue region, and determining a grayscale digital signal corresponding to the tissue region; and, collecting Raman molecular information of the tissue region through Raman spectroscopy acquisition technology to obtain spectral data of the tissue region; mapping the spectral data to a grayscale digital signal according to the position information of the tissue region corresponding to the spectral data to obtain mapped spectral data; inputting the mapped spectral data into a pre-trained convolutional neural network model, and outputting the diagnostic result of the mapped spectral data. The method combines image segmentation, Raman spectroscopy acquisition and convolutional neural network analysis to achieve accurate positioning and efficient diagnosis of diseased tissues.

The invention discloses application of a substance for detecting the content of iron ions in preparation of a product for diagnosis and prognosis of a fever with thrombocytopenia syndrome. The invention further discloses application of the substance for detecting the content of the iron ions in screening patients with the fever with thrombocytopenia syndrome, evaluating prognosis survival conditions or death risks or tissue damage degrees of the patients with the fever with thrombocytopenia syndrome and monitoring change of disease degrees of the patients with the fever with thrombocytopenia syndrome. It is found for the first time that the content of iron ions in serum can effectively assist in diagnosing the fever with thrombocytopenia syndrome, meanwhile, the method has an important assisting and guiding effect on prognosis of development of the fever with thrombocytopenia syndrome, doctors can conduct definite diagnosis conveniently, and a reasonable treatment scheme can be made according to prognosis.

The embodiment of the present invention discloses a method, device, electronic device and storage medium for diagnosing a non-sleeping vehicle network. The method includes: obtaining network management messages generated by each controller in the target vehicle network, and determining each non-sleeping controller in the target vehicle network based on each network management message; if there is an association relationship between each non-sleeping controller, constructing a directed graph according to the association relationship between each non-sleeping controller; determining the source controller among each non-sleeping controller according to the directed graph, and generating a diagnostic report of the non-sleeping target vehicle network according to the identification information of the source controller. The technical solution of the embodiment of the present invention solves the technical problems of the existing diagnostic technology with long diagnostic time and low diagnostic efficiency, and realizes a faster and more effective diagnosis of the non-sleeping situation of the vehicle network, thereby achieving the technical effect of improving vehicle quality.

The invention discloses a device for precisely adjusting the inclination and spin angle of a blade group of a turbine machinery rotor system, comprising a brake motor, a rotor, a rotor driving mechanism and a blade shaft; a brake motor is provided at at least one end of the rotor; a rigid cam is provided on a reduction shaft connected to the brake motor, a flexible wheel is connected to a motor connection seat, an outer gear ring is provided on the end of the flexible wheel facing the rigid cam, and a flexible bearing is provided between the outer gear ring and the rigid cam; the blade shaft is supported in the rotor through a bearing; an inner gear ring is provided at the end of the reduction hemispherical gear on the blade shaft, and the inner gear ring is meshed with the outer gear ring on the flexible wheel; a plurality of blades are mounted on the rotor, and an adjusting hemispherical gear meshed with the reduction hemispherical gear is provided at the inner end of the blade. The invention proposes methods such as a blade tiltable design, a large reduction ratio design, a hemispherical gear transmission and an independent drive of a blade adjustment device, which can realize the online and precise adjustment of the blade group tilt and the spin angle of the blade group during high-speed operation of the rotor system.

The present invention discloses a method for comprehensive diagnosis of contact network based on multi-source detection parameters, comprising the following steps: S1, automatic alignment of detection data; S2, standard state generation of detection data after automatic alignment; S3, potential defect analysis of detection data in standard state, including time domain analysis and frequency domain analysis, to obtain potential defects of contact network. The method for comprehensive diagnosis of contact network based on multi-source detection parameters mentioned in this aspect mainly utilizes multiple multi-dimensional detection data to conduct comprehensive diagnosis and evaluation of contact network. Multi-dimensional detection data include but are not limited to pull-out value, guide height, bow-net contact force, and hard points.

The present invention relates to the technical field of electric energy metering fault diagnosis, and discloses an electric energy metering fault intelligent diagnosis method and system, which include the following steps: Step 1: Obtain the total value of electric energy at the power generation end and the total value of electric energy at the user end, and perform analysis and processing to calculate the electric energy metering characterization value; Step 2: Compare the electric energy metering characterization value with the electric energy metering characterization value threshold, and if the electric energy metering characterization value is less than or equal to the electric energy metering characterization threshold, generate an electric energy metering fault signal. The present invention obtains the total value of electric energy at the power generation end and the total value of electric energy at the user end, and performs analysis and processing to calculate the electric energy metering characterization value; compares the electric energy metering characterization value with the electric energy metering characterization value threshold, and generates an electric energy metering normal signal or an electric energy metering fault signal, thereby analyzing the electric energy data at the power generation end and the user end, performing processing and analysis based on the electric energy data, and evaluating the stability of the measured data during the measurement process of the detection cycle.

The invention relates to an FRF-ASSA-SVM-based RV reducer fault diagnosis method under a reciprocating working condition, and belongs to the technical field of signal analysis and fault diagnosis. The method comprises the following steps: S1, collecting current signals and vibration signals of an RV speed reducer swing arm system in different fault states; s2, calculating a frequency response function of the swing arm system of the RV speed reducer by using the current signal and the vibration signal; s3, performing spectral analysis on the signal, and determining the maximum current frequency of the input current; s4, determining the analysis bandwidth of the frequency response function through the maximum current frequency; s5, performing ASSA noise reduction on the frequency response function in combination with spectral analysis; and S6, performing fault diagnosis on the RV speed reducer by using the frequency response function after noise reduction and the SVM. According to the method, useful information in the frequency response function can be accurately selected and subjected to noise reduction, the fault characterization capability of the frequency response function is greatly improved, and the fault of the RV speed reducer under the reciprocating working condition can be accurately diagnosed.

The charging pile fault diagnosis method based on deep self-perception cycle network comprises the following steps: collecting multi-dimensional electrical time sequence signals during the operation of a charging pile power module and preprocessing to obtain preprocessed time sequence signals; simultaneously extracting forward and backward long-time dependence features of the sequence through a long short-term memory network to obtain a fused bidirectional hidden state sequence; inputting the obtained bidirectional hidden state sequence into a self-attention mechanism layer, calculating attention weights through scaled dot product to adaptively enhance the representation of key fault feature periods, and outputting a fault diagnosis result through a fully connected classification layer and a normalized exponential function; using an adaptive matrix estimator optimizer with weight decay to end-to-end train a diagnosis model, updating model parameters through decoupled weight decay and adaptive learning rate, and completing model optimization and deployment. The method has strong bidirectional time sequence dependence modeling capability, high generalization capability and faster convergence speed in the fault diagnosis task.

A fluorescent cross-linked RNase H mutant conjugate, a miRNA combination and an application thereof. The fluorescent cross-linked RNase H mutant conjugate (i) is as represented by RNase Hv-(Lx-SH-F)n or (ii) comprises an RNase Hv-Lx-ligand and receptor-F, wherein the ligand can bind to the receptor, RNase Hv is an RNase H mutant, which can bind to RNA or RNA-DNA hybrid strands, but cannot cleave RNA; L is a linker, and x is 1-10; SH is an amino acid containing a sulfhydryl group; F is a luminescent functional group, and n is 1-7. The fluorescent cross-linked RNase H mutant conjugate can directly recognize DNA / RNA hybrid strands and can be converted to generate detectable signals without PCR amplification, and can be applied to sensitively and quickly detect RNA.