16results about How to "Comprehensive diagnosis" patented technology

The invention relates to a multi-mode heart and cerebral hemodynamics monitoring method and device, and belongs to the technical field of medical equipment. According to the method, brain absorbance information and pulse wave information at the middle artery of the brain are obtained based on a near infrared spectrum technology by utilizing the relevance among the cardiac output, blood pressure, intracranial pressure and cerebral perfusion pressure of a human body, cardiac output information and cuff-free blood pressure information are obtained based on a thoracic impedance method, and physiological signals are obtained based on the two methods; and carrying out fusion processing to obtain a multi-modal signal, and further constructing a non-invasive monitoring model of non-invasive intracranial pressure, cardiac output, blood pressure and cerebral perfusion pressure, so as to realize continuous non-invasive monitoring reflecting human heart and cerebral hemodynamic parameters. According to the invention, through fusion application of multi-mode signals, the mechanism of cardio-cerebral hemodynamics interaction can be effectively disclosed, and a more effective, safer and noninvasive monitoring means is provided for clinic.

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 provides a high-voltage circuit breaker partial discharge monitoring and early warning method and system, and relates to the technical field of partial discharge monitoring, and the method comprises the steps: obtaining the feature information of a target circuit; a circuit analysis model is called for analysis, and a target analysis result is obtained and comprises a plurality of target point locations; dynamically monitoring the first target point location through an ultrahigh frequency sensor to obtain first partial discharge information; calling a signal analysis model for processing and analysis to obtain a first signal feature set; activating a fault diagnosis model to analyze the first signal feature set to obtain a first diagnosis tag; and when the partial discharge fault of the target high-voltage circuit breaker at the first target point location is displayed, a first early warning signal is sent out. The technical problems that in the prior art, partial discharge monitoring mostly depends on artificial experience or fixed rules, intelligent analysis means are lacked, misjudgment or missed judgment is prone to occurring, accurate positioning is difficult, and the safety of the high-voltage circuit breaker is reduced are solved.

The invention discloses a circulating cooling water system integrating big data and artificial intelligence, and relates to the technical field of big data and artificial intelligence, and the circulating cooling water system comprises an information monitoring module which is used for monitoring the parameter change condition during the operation of a unit; the data processing module is used for cleaning and integrating original data; the artificial intelligence prediction module is used for predicting a future cooling demand based on historical data and a current condition; the optimization control module is used for formulating an optimal system operation strategy according to the prediction result; the execution module is used for applying the optimized control strategy to an actual system; the monitoring and alarming module is used for monitoring the running state of the system in real time and giving an alarm in time when an abnormality is found; and the performance evaluation module is used for evaluating the energy efficiency of the system and providing a basis for long-term optimization. According to the invention, comprehensive monitoring, diagnosis, prediction and optimization functions are realized, the operation efficiency and reliability of the system are greatly improved, and the intelligent and adaptive characteristics of the system enable the system to maintain optimal performance under various working conditions.

The invention provides an intelligent traditional Chinese medicine diagnosis system based on an AI large model, and relates to the technical field of traditional Chinese medicine auxiliary diagnosis and treatment. Comprising an information collection module, a data processing module, a generation diagnosis module and a traditional Chinese medicine special large model diagnosis module. The information collection module is used for collecting user information through face recognition, tongue picture recording, patient self-examination and traditional Chinese medicine diagnosis; the data processing module is used for preprocessing the five kinds of collected information; the generation diagnosis module diagnoses the data output by the data processing module; and the traditional Chinese medicine special large model diagnosis module takes the comprehensive diagnosis generated by the generation diagnosis module as input, and outputs corresponding scheme suggestions through a pre-trained traditional Chinese medicine special large model. Five kinds of information collected by the system can be collected on line, a user does not need to go to a designated place or use related machines, remote diagnosis of the whole system is truly achieved on the premise that the functions are achieved, and the problem that a traditional system is single in diagnosis and suggestion is solved by means of the generation capacity of a large model.

The invention provides a fault diagnosis and positioning method and device based on an intelligent irrigation gateway, and relates to the technical field of agricultural irrigation, and the method comprises the steps: obtaining multi-source sensor data collected from an irrigation system controlled by the irrigation gateway, and carrying out the condensation hierarchical clustering, and obtaining a hierarchical clustering tree; determining a cut-off point in the hierarchical clustering tree according to a preset distance threshold, and cutting the hierarchical clustering tree to obtain a plurality of clusters; abnormal data identification is carried out on the multi-source sensor data in the cluster, and an abnormal fault point is determined; and tracing the abnormal fault point in the tree topology structure network of the irrigation system, and carrying out path planning to obtain a shortest path for tracing the abnormal fault point so as to position the abnormal fault point in the irrigation system. Through the method and the device, the defects of low fault positioning precision and efficiency and influence on the maintenance efficiency of the irrigation system caused by possible misjudgment or missed judgment in fault detection of the irrigation system and difficulty in timely fault positioning are overcome.

The invention relates to a nuclear magnetic resonance image classification diagnosis method for adult diffuse glioma. The method comprises the following specific steps: step 1, respectively acquiring T2-fluid attenuation inversion recovery T2-FLAIR and contrast enhancement T1 weighted imaging CE-T1WI magnetic resonance images of glioma labeled by a doctor according to histological and genomics information; step 2, carrying out data preprocessing operation on the T2-FLAIR image data and the CE-T1WI image data; step 3, each pair of T2-FLAIR and CE-T1WI images after preprocessing is sent to a decision tree diagnosis model, and an Attention-DenseNet model is used for learning and extracting the characteristics of the T2-FLAIR and CE-T1WI images; 4, obtaining a prediction result based on a plurality of independently trained Attention-DenseNet models, fusing the outputs of the plurality of models by adopting a voting mechanism or a weighted average strategy, and generating a final classification decision; and 5, sending the obtained final classification to a platform, and making final diagnosis based on a classification result.

The invention discloses a vehicle controller diagnosis method, device and system, electronic equipment and a vehicle, and belongs to the technical field of vehicles. The method comprises the steps that a diagnosis request message sent by diagnosis equipment is acquired, the diagnosis request message comprises a request address, and the request address is an address distributed from a first addressing interval; the request address is used for identifying a target controller needing to be diagnosed; the diagnosis request message is sent to a target controller identified by the request address, a diagnosis response message returned by the target controller is obtained, the diagnosis response message comprises a response address, and the response address is an address allocated from the second addressing interval; the response address is used for identifying a target controller sending the diagnosis response message; and sending the diagnosis response message to the diagnosis equipment, so that the diagnosis equipment diagnoses the target controller which responds to the address identifier according to the diagnosis response message. The configurable number of the controller diagnosis addresses is increased, more controller diagnosis can be supported, and the diagnosis efficiency of the controller is improved.

The present invention relates to the technical field of chip monitoring, and provides a chip monitoring method, device, equipment and storage medium, including collecting the electrical signal amplitude, frequency characteristics and waveform distortion of the chip; generating a digital quantization signal according to the electrical signal amplitude, calculating the frequency characteristics based on the digital quantization signal, and obtaining frequency domain characteristic data; generating a state characteristic vector and a fault indication vector based on the frequency domain characteristic data; processing the state characteristic vector to obtain a working state index, combining and analyzing the working state index and the fault indication vector to obtain the current working state and potential fault points, based on which, abnormal detection is performed on the waveform distortion, waveform abnormality characteristic data and fault root cause analysis results are obtained and analyzed, and fault warning information and maintenance suggestions are obtained. The chip is monitored by the above scheme to improve the problems of insufficient analysis accuracy and limited fault diagnosis capabilities in complex signal processing and fault diagnosis.

This invention discloses a real-time evaluation method for photovoltaic panel defects and performance based on multispectral imaging, belonging to the field of computer processing technology. The method includes the following steps: S01, emitting multispectral light including visible light, near-infrared, and thermal infrared bands to the photovoltaic panel, and simultaneously receiving reflection and radiation signals based on the multispectral light to obtain a multispectral image dataset; wherein, the dataset includes visible light images, photoluminescence images in the near-infrared band, and thermal infrared images; S02, performing registration and fusion processing on the multispectral image dataset to generate a registered multispectral fused image. This invention, through multispectral information fusion and intelligent analysis, achieves simultaneous diagnosis of internal and external defects of photovoltaic panels and quantitative evaluation of power generation performance, significantly improving detection efficiency and maintenance accuracy.

This invention discloses a method and system for the operation and maintenance of a shielded pump in a nuclear reactor experimental setup. By monitoring the stress of devices related to the shielded pump within the experimental setup and utilizing a stress assessment model for the shielded pump, the invention enables a clear, comprehensive, and real-time diagnosis of the pump's operating status. This allows for more rational intervention in preventative partial and full disassembly inspections of the shielded pump, enhancing the targeted nature of maintenance interventions, saving manpower and time costs, and increasing the safety and economy of shielded pump operation and maintenance.

The present application relates to a battery pack damage assessment method, device, vehicle, equipment and storage medium. The method comprises: obtaining the measured temperature of the battery pack in the vehicle, the measured temperature of the coolant in the cooling system corresponding to the battery pack, the measured flow rate and the measured flow resistance; determining the standard flow resistance according to the measured temperature of the battery pack, the measured temperature of the coolant and the measured flow rate; determining the damage assessment result of the battery pack according to the difference between the measured flow resistance and the standard flow resistance; wherein, determining the standard flow resistance according to the measured temperature of the battery pack, the measured temperature of the coolant and the measured flow rate comprises: searching in the flow resistance map according to the measured temperature of the battery pack, the measured temperature of the coolant and the measured flow rate to obtain the standard flow resistance. The use of this method can improve the accuracy and timeliness of battery pack damage assessment.

A bearing fault diagnosis method based on multi-sensor, multi-scale feature fusion is proposed. The method involves collecting current and vibration signals to create a dataset, which is then augmented and divided into training, validation, and test sets. A multi-scale feature fusion network model is constructed. The signal data in the training set is transformed using Fast Fourier Transform (FFT) to extract features. An attention mechanism is then introduced to adjust the weights. The fused features are processed alternately using self-calibrating convolutions and dilated convolutions, followed by pooling, feature flattening, and fully connected layers. Finally, a classifier outputs the fault classification result. The validation set is input into the model to fine-tune its parameters. Finally, the test set is used for testing, and the results are output. By using multi-source data such as vibration and current signals as input, and leveraging the ability of neural networks to discover hidden patterns in training samples, this method aims to extract multi-scale features from multi-source data and fuse these features to complete the intelligent fault diagnosis process for bearings.

Provided are a composition, a kit and an application for colorectal cancer detection. The provided composition comprises reagents for detecting the methylation status of ADHFE1 gene, PPP2R5C gene and SDC2 gene, and can be used to detect colorectal cancer with high sensitivity and specificity.

The invention relates to the technical field of spinal deformity auxiliary observation, and discloses a spinal deformity auxiliary observation method, which comprises the following steps: S1, fixedly erecting a plurality of cameras in an evaluation site, calibrating the cameras, and collecting a multi-view video image of an evaluated person; s2, background filtering processing is carried out on the multi-view image, and a pure human body image is extracted; and S3, generating a 3D human body shape model by using the pure human body images under each view angle. Through a multi-camera dynamic capture technology, the back and spine forms of an assessed person can be observed in all directions in the dynamic processes of walking, stooping and the like, compared with traditional static image assessment, the dynamic observation mode can provide more comprehensive and three-dimensional data, doctors are helped to accurately assess the changes of the spine forms, and the assessment efficiency is improved. Therefore, the accuracy and comprehensiveness of diagnosis are improved, a doctor can better understand the spine structure of a patient through multi-angle and multi-action capture, and more effective support is provided for diagnosis and treatment of spine malformation.

The invention discloses a medical ultrasonic image analysis system and method, and relates to the technical field of medical image processing, and the system comprises an ultrasonic image collection module, an image feature extraction module, a Bayesian network diagnosis model construction module, a probabilistic reasoning calculation module and a diagnosis result output module. According to the medical ultrasonic image analysis system, the Bayesian network diagnosis model is constructed, various features in the image and personal information and medical history of a patient can be comprehensively considered, comprehensive and accurate diagnosis of diseases is achieved, the accuracy and reliability of diagnosis are improved, and the diagnosis efficiency is improved. Meanwhile, the influence of subjective judgment of a doctor on the diagnosis result is greatly reduced, the risk of misdiagnosis and missed diagnosis is reduced, meanwhile, due to the fact that the Bayesian network has the high reasoning ability and self-learning ability, the system can continuously optimize and perfect the diagnosis model along with accumulation of clinical data, and the diagnosis precision and efficiency are improved.