111results about How to "Real-time diagnosis" patented technology

The invention relates to a smart agricultural insect disease remote automatic diagnosis system. The system comprises an interaction system, an identification system and an insect disease database management system. The interaction system comprises an information acquisition system, a wireless communication system and an information display system; the identification system comprises a similarity comparison system and a remarkable feature extraction system; and the inset disease database management system comprises an insect disease picture database, an insect disease remarkable feature database and an insect disease diagnosis database. The smart agricultural insect disease remote automatic diagnosis system, based on images of crop growth conditions, through intersection operation of the image similarity and remarkable feature extraction comparison system and a machine learning algorithm of the identification system, can effectively improve the identification accuracy of an insect disease type. According to the insect disease type, the system provides an omni-directional diagnosis scheme so as to improve the survival rate and the quality of crops.

The invention discloses a digital slice-based digital remote pathological diagnosis system and a digital slice-based digital remote pathological diagnosis method. The system comprises a remote consultation platform, a plurality of hospital ends and a plurality of specialist ends, wherein each hospital end comprises a fully automatic microscope system and a computer processing system; the fully automatic microscope system is controlled through the computer processing system; a pathological slice is automatically scanned and processed into a digital slice and is stored in a local machine or transmitted to the remote consultation platform or the specialist end through a network, and then a notice is transmitted to the specialist; and the specialist reads and analyzes the digital slice from an appointed storage position according to the notice and transmits a consultation opinion to the hospital end. After the system and the method are adopted, the specialist can diagnose the digital slice through a fixed or mobile terminal at any time and any place and transmits a diagnosis result back to the hospital end. The system and the method are extremely suitable to be used under conditions of vast territory and higher shortage of specialist resources at remote rural areas like China; and through the system and the method, patients of countries can be quickly, accurately and effectively pathologically diagnosed and treated.

The invention discloses a method for computing the RAC of lithium ion battery packs. The method comprises the steps of firstly computing the charging load state SOC of each single lithium ion battery in each lithium ion battery pack, computing the RAC of each single lithium ion battery, thereby computing the RAC of the lithium ion battery packs, comparing the RAC of the lithium ion battery packs through a lithium ion BMS, defining the maximum value of the RAC of the lithium ion battery packs as the RAC of the lithium ion battery packs, carrying out self-learning and temperature compensation on the current internal resistance R of each single lithium ion battery in each lithium ion battery pack, diagnosing the health condition of the lithium ion battery packs, ensuring that the RAC of the lithium ion battery packs is accurately computed, and releasing the RAC in a maximized mode. Meanwhile, when the working condition changes, the lithium ion BMS still can accurately compute the RAC of the lithium ion battery packs, and provide timely maintenance information.

The invention discloses a machine vision based real-time diagnosis method and system of vegetable leaf diseases. The system comprises a disease image acquiring hardware part and a real-time diagnosis software part, wherein the image acquiring hardware part consists of a lighting box, a light source bracket, a light source, a round hole at the top of a box body, a digital camera, a camera fixing rod, an optical lens, a polarizer, a box body side door, an objective table and a computer. The method comprises the following steps: acquiring the symptom image of the leaf disease of vegetable to be detected; carrying out image treatment and scab division by utilizing real-time diagnosis software; acquiring the characteristic indexes of scab, such as color, texture and shape; and carrying out real-time diagnosis on the vegetable leaf disease by utilizing a disease identification model. The real-time diagnosis method and system can be directly used for detecting diseases in filed, plastic houses or greenhouses so as to achieve the rapid, stable and real-time diagnosis of various vegetable leaf diseases; no chemical reagent is used, therefore detection cost is reduced and no environment pollution is caused. The real-time diagnosis method and system can be well applied to disease monitoring.

The invention discloses a GIS and method with a remote self-diagnosis function. The GIS with the remote self-diagnosis function comprises a process layer, a partition layer and a station control layer; the process layer, the partition layer and the station control layer are in signal connection, and a built-in sensor device is designed on the basis of the GIS. The sensor device comprises a currentsensor, a travel sensor, an SF6 sensor, a partial discharge sensor, an ECVT, a video sensor, a vibration sensor and a lightning arrester sensor; the output end of the current sensor is in bidirectional signal connection with an intelligent terminal, and the output ends of the travel sensor, the vibration sensor, the video sensor, the lightning arrester sensor and the SF6 sensor are all in signalconnection with the input end of an online monitoring IED. Intelligent GIS monitoring point acquisition is integrated, and real-time online state evaluation and diagnosis are formed.

The invention provides an intelligent on-line monitoring system and method of a high-voltage switchgear. The intelligent on-line monitoring system comprises a partial discharge on-line monitoring system, a switchgear temperature measurement system, an intelligent data exchange terminal, a comprehensive monitoring system, a cloud platform diagnostic service system and a background management system. The partial discharge on-line monitoring system is used for monitoring the partial discharge activity of the high-voltage switchgear; the switchgear temperature measurement system is used for collecting temperature parameters of the high-voltage switchgear; the intelligent data exchange terminal can obtain intelligent sensor data in real time; the comprehensive monitoring system selectively anddeeply analyzes the equipment exception causes, finds out equipment failure points, and uploads data and diagnostic results to the cloud platform expert diagnostic system; the cloud platform diagnostic service system is used for comprehensively analyzing the operation state of the equipment by utilizing data uploaded by the intelligent data exchange terminal and data uploaded by the movable comprehensive monitoring system, and evaluating the partial discharge condition of the switchgear by combining with a failure case library; and the background management system logs in according to different user rights, and can use different functions according to the rights, and the system has the main functions of being responsible for data management, statistics and analysis, and classified storageof monitoring data. Interaction between operation and maintenance personnel and equipment management can be achieved, and the practical significance for guaranteeing safe operation of the high-voltageswitchgear is achieved.

The invention discloses a fault diagnosis method based on deep learning and signal analysis. The method includes: acquiring data in normal and faulty states of an industrial process in advance, and dividing the data into a training set and a test set; and training model parameters offline based on the training set, detecting a model through the test set, wherein a performance index refers to the precision of fault diagnosis, and a value thereof represents the generalization performance of the model, namely the online diagnosis capability of faults. According to the method, as a variant of a neural network, physical information of a process operation variable in a time domain can be obtained, and frequency domain information of a process measurement variable can be obtained through introduction of a wavelet analysis method; besides, a depth structure adopted by the method adapts to big, fast, various, and uncertain characteristics of industrial big data, the physical information of theprocess operation variable and frequency characteristics of the process measurement variable are combined, a complex mode of a deep grade of the faults is learned, fault diagnosis can be effectively realized, and excellent generalization capability is displayed in an online diagnosis test.

The invention discloses a concrete crack detector with a reinforcing bar as an electrode. A reinforcing bar emission electrode and a reinforcing bar receiving electrode are embedded in concrete, wherein the emission electrode is connected with an emission circuit through an emission cable; a signal transmitted by the emission circuit is sent out from the concrete through the emission electrode; the receiving electrode is connected with a receiving circuit through a receiving cable; and the receiving circuit is used for amplifying and filtering a signal received by the receiving electrode. An output signal of the emission circuit is connected to a control and computation module through an emission communication control interface, and two-way communication control is performed between the control and computation module and the emission circuit. An output signal of the receiving circuit is connected to the control and computation module through a receiving communication control interface, and two-way communication control is performed between the control and computation module and the receiving circuit. As the reinforcing bar is used as the electrode, the original construction features and stress features of the concrete do not change, a construction process and a construction method have no need to change, and the electrode and the concrete structure have the same service life. The concrete crack detector with the reinforcing bar as the electrode has the advantages of simplicity in design, convenience for construction, low cost and long service life.

The invention relates to a method for fault diagnosis of rotating mechanical equipment based on interval-type evidence fusion, belonging to the technical field of fault monitoring and diagnosis of rotating mechanical equipment. The method is based on statistical analysis of typical fault data, comprising the following steps: building a set of fuzzy membership functions, and modeling each fault model mode in a fault archival repository by using the set of functions; modeling a fault inspection pending mode extracted during on-line monitoring by using a single membership function; matching the inspection pending mode with each model mode to obtain a reliability match interval of each fault supported by the inspection pending mode; providing an interval-type diagnostic evidence method obtained from the reliability interval by using the Monte Carlo Latin Hypercube sampling method; and fusing evidences, and then under the decision criterion, making the fault decision according to the fusion result. The decision based on the multi-evidence fusion result is more accurate than the decision made by the single-evidence fusion.

The invention relates to a fault diagnosis method of a random fuzzy fault characteristic fusion rotating mechanical device. The fault diagnosis method belongs to the technical field of fault monitoring and diagnosis of the rotating mechanical device. The fault diagnosis method can process fault characteristic parameters with randomness and fuzziness, performs statistic analysis on typical data of the fault characteristic parameters under each fault to construct random fuzzy variables, uses the variables to model each fault sample mode in fault archives, uses the random fuzzy variables to model a fault pending inspection mode extracted from on-line monitoring, and enables the pending inspection to be matched with each fault sample mode to obtain degree of the pending inspection mode to support each fault, namely diagnosis evidences. The diagnosis evidences provided by a plurality of fault characteristic parameters are fused, under a decision rule, fusion results are used for performing fault decision, and the decision based on the multi-evidence fusion results is more accurate than the decision made out according to single diagnosis evidence.

The invention provides an electrical communication system fault diagnosis method and device. The method comprises the steps of obtaining basic fault information of a target fault; obtaining a prestored electrical communication system fault case library, wherein the electrical communication system fault case library stores basic fault information, fault phenomenon description, fault reason deduction and diagnosis strategies of a plurality of fault cases; comparing the basic fault information of the target fault with basic fault information of each fault case in the electrical communication system fault case library, and determining the fault case with the target fault similarity exceeding the first preset similarity threshold value as a typical fault case from the electrical communication system fault case library; according to actual information of on-site faults, optimizing the typical fault case, and determining the optimized fault diagnosis case of the target fault; according to theoptimized fault diagnosis case, performing fault diagnosis on the electrical communication system, and the faults of the electrical communication system can be accurately diagnosed in real time.

The invention discloses a blade ice-coating failure on-line monitoring method and system based on a wind generation set operation state. According to the method, operation state data acquired by a wind generation set operation set SCADA system is utilized, and ice-coating state characteristic indexes are extracted after proper processing of the data; a built blade ice-coating diagnosis nerve network is input; and characteristic indexes which represent both the ice-coating degree of blades and the ice-coating position of the blades are output. The method is a damage-free, dynamic and real-timemonitoring diagnosis method, and a theoretical basis and technical support can also be provided for diagnosis of blade ice-coating failure in engineering practice; the whole monitoring system is simple and convenient in operation; and the method and system have large promotion value.

The invention discloses a frequency converter driving embedded permanent magnet motor stator inter-turn short-circuit fault diagnosis method. According to the method, a current signal of an operating motor and an inside signal of a controller are used for realizing inter-turn short-circuit fault detection and degree judgment. The method comprises the flowing steps of firstly, a proper inverter switch signal is selected to be used as a detecting excitation source; the injection of an additional high-frequency signal is not needed; the additional loss caused by a high-frequency injection detecting method is avoided; meanwhile, the early fault detection reliability is enhanced; secondly, three-phase current is subjected to rotational coordinate conversion; the switching current harmonic in a three-phase coordinate system is converted under a rotary coordinate system; the saliency interference factor of the motor is effectively decoupled; finally, a frequency domain multipoint interpolation extraction algorithm is used, and multi-fault feature information is comprehensively used, so that the influence of noise interference and frequency spectrum leakage on the detection result is effectively avoided. The method has the advantages that the application range is wide; the fault detection effect is good; the recognition precision is high; the stator inter-turn short-circuit fault can be diagnosed in real time, and the fault degree index is given.

The invention discloses a liberobacter asiaticum detection method based on visible light images. In the training stage, a large number of images of citrus leaves infected with liberobacter asiaticum and images of normal leaves are collected, the feature values of the texture feature and the color feature are extracted, the above feature values and the feature values of normal leaves are trained and learned through a BP neural network, and the optimal BP neural network model is obtained; in the identification stage, the features of the leaf images to be identified are extracted and input into the optimal BP neural network model, and then whether a citrus tree is healthy is judged. By means of the liberobacter asiaticum detection method based on visible light images, the type of the liberobacter asiaticum and whether the citrus tree is not yellowed due to liberobacter asiaticum are further judged, early, accurate and non-destructive diagnosis can be carried out on the liberobacter asiaticum, and the liberobacter asiaticum detection method based on visible light images has the advantage of being high in detection accuracy.

A Candidatus Liberibacter spp. online rapid detection system based on deep learning includes a mobile phone client and a server end; the server end includes a data transmission module, a leaf and fruit detection module, a Candidatus Liberibacter spp. diagnosis module, and a sample database; a model of the leaf and fruit detection module is trained by using the data with instance annotations in a sample database to obtain the optimal model; a model of the Candidatus Liberibacter spp. diagnosis module is trained by using the data with diagnostic annotations in the sample database to obtain the optimal model; the leaf and fruit detection module transmits the output and detected single leaf and single fruit images to the Candidatus Liberibacter spp. diagnosis module. A Candidatus Liberibacterspp. online rapid detection method based on deep learning adopts the Candidatus Liberibacter spp. online rapid detection system based on deep learning. The invention belongs to the technical field ofintelligent identification of Candidatus Liberibacter spp., and has the advantages of rapid, efficient and reliable detection.

The invention relates to computer digital image process technology in aquiculture industry. The device is made up with printer, upper computer on the spot, image collection card, multi-channel video selector and multiple pick-up heads. Use multi-channel video selector to select some pick-up to shoot rearing pond's real-time picture; the picture is sent to video images collection card; the card converts analog image signal to digital image signal that can be processed by computer; process, identification and alert is made by computer according to preset programs; the processed result is memorized to database in computer; original image is binarized to distinguish white belly of sick fish from background color, then statistic quantity and size of the white zone part, judge is made according to area of white zone to take measures. Adopting the invention, fishes' disease can be early diagnosed and predicted; fishes can be prevented from damage and a new method for aquiculture fishes' disease diagnosis.

The invention relates to a deep learning-based Candidatus Liberibacter spp detection method, device and system. The method comprises the steps: acquiring image data of citrus leaves to be identified;Inputting the image data into a neural network model of Candidatus Liberibacter spp detection of a mobile terminal; And determining a detection result corresponding to the image data. The detection method is simple and convenient to operate, lossless, low in cost and popular with citrus producers; Moreover, the diagnosis process is simplified, the diagnosis cost is reduced, fruit farmers can be helped to detect and discover diseases as soon as possible, rapid, real-time, accurate and non-destructive diagnosis on citrus fruit trees in orchards is realized, reference can be provided for fertilization and production of the fruit farmers, great help is provided for the fruit tree yield, and a positive role is played in promoting accurate agriculture and agricultural informatization.

The invention relates to an electric power equipment fault diagnosing method and an apparatus, comprising: obtaining the temperature data of a piece of to-be-diagnosed electric power equipment at the present time; obtaining the temperature data, the temperature differential value and the temperature variation rate of the previous time; calculating the temperature differential value and the temperature variation rate of the present time; conducting data integration between the temperature differential value and the temperature variation rate of the present time and those of the previous time for an integrated result; and determining whether the to-be-diagnosed electric power equipment suffers from any fault or not at the present time. The electric power equipment diagnosing method proposed by the invention is based on the data integration of three variables: the temperature data, the temperature differential value and the temperature variation rate for an electric power equipment diagnosing result, which can diagnose the fault of the equipment in real time. In addition, the entire detection process is fast and highly accurate with low erroneous fault reporting, therefore, reducing redundancy information to a large extent.

The application provides a vehicle failure diagnosis and related equipment. The method comprises the following steps: acquiring to-be-detected data according to a fixed time interval so as to form a to-be-detected data sequence, wherein the to-be-detected data is vehicle failure data; encoding the to-be-detected data sequence to obtain an observation sequence; inputting the observation sequence into a fault classification model, and outputting a fault classification sequence; and acquiring a vehicle fault diagnosis result according to the fault classification sequence.

The invention relates to a folic acid metabolic capability and genotyping detection technology and in particular relates to a kit and a detection method of a folic acid metabolic capability and genotyping as well as application of the kit. The kit comprises specific primers aiming at 677C>T mutation and 1298A>C mutation of an MTHFR (Methylene Tetrahydrofolate Reductase) gene, and 66A>G mutation ofan MTRR (Methionine Synthase Reductase) gene, and a specific mutation detection probe, RT-PCR (Reverse Transcription-Polymerase Chain Reaction) buffer, an RT-PCR reaction Taq enzyme, sterile purifiedwater, a negative quality control product, a wild type quality control product, a mutant quality control product and a packaging box for separating and packaging a reagent bottle or pipe. The detection method of the folic acid metabolic capability and the genotyping, provided by the invention, has the advantages of strong specificity, high sensitivity, small pollution, simplicity and rapidness inoperation, high safety performance and the like; a detection result has relatively good accuracy and repeatability; the detection method is especially suitable for directly taking whole blood as a detection sample and related gene mutation detection is directly carried out; a genome DNA (Deoxyribonucleic Acid) template does not need to be extracted. The folic acid metabolic capability can be accurately judged and individualized folic acid oral administration and supplementing dosages are provided; the kit and the detection method have important value.

A FlexRay bus-based online network diagnosis device comprises a microprocessor MC9S12XF512, a FlexRay bus interface connected with a FlexRay bus network, a CAN (controller area network) bus interface connected with a CAN bus network, and a bus level measuring circuit. The FlexRay bus-based online network diagnosis device allows the status of the FlexRay bus network to be monitored online. The FlexRay bus-based online network diagnosis device is characterized in that the microprocessor monitors data transmission in the FlexRay bus network; when a network anomaly occurs, a warning message is given out in time for fault grading and processing; normal intra-vehicle network communication is ensured. The FlexRay bus-based online network diagnosis device can be integrated to an existing vehicular controller; the problem that when the FlexRay network is interfered or fails, failure of the FlexRay network to normally operate affects vehicle running safety and reliability is solved.

The invention discloses a non-invasive intracranial pressure measurement method and a measurement instrument implementing the method. The method comprises that a vibration signal generated in front of the head portion of a patient is transmitted to the non-invasive intracranial pressure measurement instrument; the resonance frequency of the head portion of the patient is extracted through treatment by the non-invasive intracranial pressure measurement instrument, and a measurement value of the intracranial pressure is obtained; and measurement parameters of the intracranial pressure of the patient at the moment are displayed on an upper computer software interface and a liquid crystal display. By the aid of the method, diagnoses can be conducted in real time and quickly, a high-performance processor is used for converting and analyzing signals, and results can be obtained in a few seconds after scanning is finished, so that measurement can be conducted at any time, additional influence on the patient is prevented, conditions can be monitored in real time, and changes can be grasped.

The invention relates to a rotary mechanical equipment fault diagnosing method based on diagnosing evidence smooth updating and belongs to the technical field of rotary mechanical equipment fault monitoring and diagnosing. The diagnosing evidence during equipment operation is obtained through an evidence acquiring method, a conditioning linear weighting evidence fusion rule is recursively used to update the diagnosing evidence updated at the previous moment with the current-moment diagnosing evidence, and the updated current-moment diagnosing evidence is obtained. Under a certain decision making rule, the updated diagnosing evidence is used for fault decision making. On the basis of the reliability of the current-moment, historical-moment and future-moment diagnosing evidences, the smooth weighting in linear fusion evidence updating is determined, the updated current-moment diagnosing evidence is enabled to have useful diagnosing information of historical and future moments, and the decision made on the basis of the updated diagnosing evidence is more accurate and reliable than the decision made on the basis of diagnosing evidence not updated.

The invention provides an embedded dual server based failure diagnosis method and system for rotation machines in a heat-engine plant. By adopting the method and system, reliability and utilization rate of the rotation machines can be improved. The method includes determining a failure fuzzy relation matrix; collecting vibration signals of the rotation machines and determining failure signals and a failure signal membership matrix; diagnosing the failure types of the rotation machines according to the failure fuzzy relation matrix and the failure signal membership matrix. The system includes a failure fuzzy relation matrix determining unit used for determining the failure fuzzy relation matrix; a failure signal membership matrix determining unit used for determining the failure signals and the failure signal membership matrix; and a failure type determining unit used for diagnosing the failure types of the rotation machines according to the failure fuzzy relation matrix and the failure signal membership matrix and sending early warning information. The method and system provided by the invention is suitable for the field of heat-engine plant monitoring.