12results about How to "Reduce diagnostic costs" patented technology

The invention provides an electric power system main equipment defect diagnosis method and system based on a large language model, and relates to the technical field of electric power system operation and maintenance and intelligent diagnosis, and the method comprises the steps: collecting defect description information of electric power system main equipment; preliminarily understanding the defect description information by using a pre-constructed natural language representation model, obtaining corresponding embedded representation, executing a defect level classification task and a defect basis classification task, and outputting a defect level classification result and a defect basis classification result; and summarizing by utilizing a large language model, fusing the summarized classification information into a prompt text, exciting the reasoning ability of the large language model through a prompt project, and finally obtaining a diagnosis basis and a diagnosis result corresponding to the defect description information. According to the method, excellent results are obtained in defect level classification tasks and question and answer tasks, and high-quality classification evidences and guidance information can be generated.

The invention provides an engine fault diagnosis method and device, electronic equipment and a storage medium. The method comprises the steps that audio data of an engine on a to-be-diagnosed vehicle and voice data of a driver are acquired; performing information extraction on the transliteration text corresponding to the voice data to obtain keywords and semantic information; keywords and semantic information are taken as references to guide audio data to perform feature extraction, so that the feature extraction process pays more attention to audio clips related to faults, irrelevant information is reduced, and accurate and effective audio features are obtained; according to the invention, the fault diagnosis is carried out based on the audio characteristics, the fault diagnosis result of the engine is obtained, comprehensive and accurate engine fault diagnosis is realized, the diagnosis efficiency is improved, the diagnosis range is widened, the dependence on professional equipment and personnel is reduced, and the vehicle can carry out self-diagnosis more conveniently, so that the diagnosis cost is reduced, and the driving safety is improved.

The invention provides a method and device for predicting diabetic nephropathy, electronic equipment and a storage medium. The method comprises the following steps: acquiring questionnaire data of a target diabetic patient, and a first fundus image and a second fundus image of the same type; constructing a target Bayesian network according to the questionnaire data; based on the target Bayesian network, calculating joint probability distribution of the target diabetic patient concurrent diabetic nephropathy to obtain a first probability value; performing difference feature extraction on the first fundus image and the second fundus image to obtain target difference feature data; performing concurrent diabetic nephropathy probability prediction on the target difference feature data to obtain a second probability value; and according to the first probability value and the second probability value, determining a target probability of complicating diabetic nephropathy of the target diabetic patient. The invention aims to construct a simple, economic and low-trauma early screening system for diabetic nephropathy.

The invention discloses a smart agricultural product pest diagnosis system and method based on the Internet of Things, and relates to the technical field of smart agricultural products. The method aims to solve the problems that crop diseases and insect pests are various, and the recognition effect on rare or newly appearing diseases and insect pests is possibly poor. The intelligent agricultural product disease and pest diagnosis system based on the Internet of Things comprises an image acquisition unit, a feature extraction unit, a diagnosis decision unit and a remote interaction unit, the image acquisition unit captures crop growth environment and pest and disease damage image data in real time through a camera and sensors deployed in a farmland, the timeliness of pest and disease damage diagnosis is ensured, the feature extraction unit realizes intelligent processing of pest and disease damage images by establishing a pest and disease damage feature database and constructing a key feature recognition model, and the accuracy of pest and disease damage diagnosis is improved. The remote interaction unit improves the convenience of a user, reduces the cost of manual inspection and diagnosis, improves the production efficiency of agricultural products, and improves the yield and quality of crops.

The present application provides a facial nerve motion data capture system and method, the system comprising: a head stabilizing device, a focusing and lighting device, a designated camera and a seat; the seat is arranged below the head stabilizing device and cooperates with the head stabilizing device to provide a seat for a patient with facial paralysis; the designated camera is arranged directly in front of the seat and faces the seat; the focusing and lighting device is symmetrically arranged on the left and right sides of the designated camera and faces the seat; the head stabilizing device is used to provide support for the head position of the facial paralysis patient and calibrate the head position of the facial paralysis patient when the facial paralysis patient sits on the seat; the focusing and lighting device is used to adjust the shooting environment for the facial paralysis patient to a shooting environment that meets the target shooting requirements; the designated camera cooperates with the focusing and lighting device to record the facial nerve motion image of the facial paralysis patient when the head position of the facial paralysis patient and the shooting environment are calibrated.

One aspect of the present disclosure relates to a method for determining an operational state of a vehicle component of a vehicle. The method comprises providing a first knowledge graph containing information about a plurality of vehicle components of a vehicle, wherein each vehicle component provides one or more signals. The method further comprises determining an operational state of a vehicle component from the plurality of vehicle components, wherein the determining comprises selecting, using the first knowledge graph, a plurality of signals and one or more state parameters required for determining the operational state of the vehicle component. The determining further comprises calculating the one or more state parameters of the vehicle component based on the selected plurality of signals. Finally, the determination of the operational state of the vehicle component comprises determining the operational state of the vehicle component in dependence on the information contained in the first knowledge graph and based on the calculated one or more state parameters.

The present invention provides a method and system for constructing a prostate cancer risk prediction model based on random forest and XGBoost. The construction method includes: step S1: collecting real original data of cases from various hospitals, cleaning, preprocessing, filling and standardizing the original data, and dividing it into a training set and a validation set according to a certain ratio; step S2: based on the training set, the prediction model combination with the best performance is selected according to the AUC value as a random forest algorithm combined with an XGBoost algorithm; step S3: using the random forest algorithm for feature screening, and using the XGBoost algorithm to construct a prediction model. The present invention uses the RF algorithm to extract multimodal information, and selects the most discriminative feature composition for the feature set used for modeling; and uses the XGBoost algorithm to construct a multimodal fusion classification model, and performs model training in the classifier. The present application can achieve an accuracy of 92% on the validation set, and can efficiently and quickly realize the risk prediction and diagnosis of prostate cancer.

The embodiment of the invention provides a neurodegenerative disease assessment method, device and equipment and a storage medium, and is applied to the technical field of medical diagnosis. The method comprises the steps that electroencephalogram signals and electrocardiosignals of a testee in the sleep period are collected, and electroencephalogram signal data and electrocardiosignal data are obtained; performing feature extraction on the electroencephalogram signal data and the electrocardiosignal data to obtain electroencephalogram signal features and electrocardiosignal features; and inputting the electroencephalogram signal features and the electrocardiosignal features into a pre-trained neurodegenerative disease evaluation model, processing the electroencephalogram signal features and the electrocardiosignal features by the neurodegenerative disease evaluation model, and predicting whether the subject attacks the neurodegenerative disease in the future. In this way, whether the subject attacks the neurodegenerative disease or not in the future can be quickly and accurately predicted based on the electroencephalogram signals and the electrocardiosignals of the subject in the sleep period, and then a doctor is assisted in making more objective judgment on the subject.

A kind of permanent magnet synchronous motor bearing fault diagnosis method based on motor current analysis method, first acquisition permanent magnet synchronous motor stator U, V phase current signal, calculate W phase current;Three-phase current is obtained by space vector dimension reduction two-phase current under rectangular coordinate system, and two-phase current is carried out vector normalization processing;Based on the mechanism of action of bearing fault to current signal and the characteristics of current signal, the signal after space vector dimension reduction is carried out variation modal decomposition, the approximate entropy of modal component is calculated and the feature matrix is formed;The improved standard artificial bee colony algorithm makes the bee colony type mutually transform according to the optimal solution, and changes the bee colony initialization population generation rule, the optimal classification model is obtained by using the optimized adaptive variable type algorithm to optimize machine learning model parameter;Using optimal model, the test set in sample set is classified according to fault type, and compared with label to obtain test set accuracy rate;The present application has the advantages of high accuracy, high diagnostic efficiency and the like.

The invention discloses a motor fault diagnosis method based on voiceprint feature extraction and transfer learning, and belongs to the technical field of equipment fault diagnosis, and the method comprises the following steps: S1, obtaining a sound signal of a working state of a motor, and extracting voiceprint features through an empirical mode decomposition and Mel-frequency cepstral coefficient method; s2, constructing a voiceprint feature data set according to the extracted voiceprint features; and S3, inputting the voiceprint feature data set into a CNN + ResNet network to obtain a motor fault diagnosis result. According to the method, the limitation of a traditional contact type method can be overcome, the maintenance cost is reduced, more accurate diagnosis information can be provided at the early stage of a motor fault, and the operation safety and reliability of motor equipment are improved. Data analysis and fault classification are carried out through a voiceprint feature extraction technology in combination with a deep learning model, a new direction is developed for motor fault diagnosis, and the method has important research value and application prospect.

The invention provides a voice voiceprint analysis and numeralization method and system for representing a human body qi deficiency state. The method comprises the steps of defining voice feature parameters corresponding to a human body qi deficiency state according to a traditional Chinese medicine theory, and establishing a qi deficiency state voice feature knowledge base; acquiring voice data of the qi-deficiency crowd and the non-qi-deficiency crowd; extracting specific voice global features of the qi-deficiency crowd from the preprocessed voice data, wherein the global voice features include but are not limited to a fundamental frequency mean value, a fundamental frequency variance, an energy mean value, an energy variance, a speech speed and a formant parameter of voice; respectively constructing differentiated voiceprint models of the qi-deficiency crowd and the voiceprint comparison crowd by using the extracted global voice features, establishing a quantitative mapping relation between the global voice features and the human body qi-deficiency state through regression calculation, and generating a qi-deficiency state evaluation model; and verifying the qi deficiency state evaluation model by using a test sample, and optimizing model parameters according to a verification result.

The present invention discloses a vehicle fault remote diagnosis system and method, the system includes a cloud, a vehicle terminal connected to the cloud, and at least one ECU connected to the vehicle terminal; the cloud is used to send diagnostic instructions and process diagnostic data fed back by the vehicle terminal; the vehicle terminal is used to collect diagnostic data of the vehicle and send the collected diagnostic data to the cloud; the ECU is used to obtain the diagnostic data of the vehicle in real time and feed back diagnostic messages to the vehicle terminal; the vehicle terminal includes an MCU and a 4G module connected to the MCU, the MCU is used to parse the diagnostic instructions, convert them into CAN message format, receive the diagnostic message of the corresponding ECU, and send it to the cloud through the 4G module. The present invention solves the limitations of conventional remote diagnosis functions, improves diagnostic efficiency, and reduces diagnostic costs.