306 results about "Preliminary diagnosis" patented technology

The embodiment of the invention relates to the technical field of image processing, in particular to a TP laminating process production process defect diagnosis method and system based on image analysis, and the method comprises the steps: firstly collecting a real-time image sequence corresponding to a to-be-detected laminating assembly continuously transmitted on a TP laminating process production line; the sequence comprises component surface images and edge region images at different fitting stages; secondly, performing defect sensitive feature enhancement processing on the real-time image sequence to obtain a defect sensitive feature set containing surface texture features, edge contour features and regional gray features; then performing feature correlation analysis processing on the defect sensitive feature set through a trained TP fitting defect diagnosis model to generate a defect preliminary diagnosis result; and finally, determining defect types and position distribution information according to the preliminary diagnosis result, and further generating a production process defect diagnosis report containing defect diagnosis contents, thereby realizing accurate diagnosis of the TP laminating process production process defects.

The invention discloses a power distribution equipment remote diagnosis method based on edge computing, and particularly relates to the technical field of intelligent monitoring of power equipment, and the method comprises the steps: an edge computing node collects the operation state data of the power distribution equipment in real time; performing diagnosis analysis locally at the node to generate a preliminary diagnosis result and key data; uploading the structured data to a cloud according to a preset strategy, and checking the integrity; and the cloud platform performs association analysis on the multi-node data to identify common anomalies, dynamically optimizes a diagnosis algorithm, automatically triggers alarms and work order distribution in a grading manner, and realizes rapid closed-loop processing in combination with the positions and skills of operation and maintenance personnel. Through cooperation of the edge and the cloud, communication bandwidth occupation is reduced, diagnosis accuracy and real-time performance are improved, fault response time is shortened, and the method is suitable for line-level monitoring and operation and maintenance management of the power distribution network.

The invention discloses a defect diagnosis method and system based on multi-modal data cooperative training, and belongs to the technical field of defect diagnosis, and the method specifically comprises the steps: constructing a multi-modal cooperative diagnosis model comprising a feature extraction sub-network and a cross-modal attention module; after multi-modal data is collected and preprocessed, initial features are obtained through the feature extraction sub-network, attention weights are generated through the cross-modal attention module, weighted multi-modal features are obtained, multi-scale fusion features are obtained through the multi-scale feature extraction sub-network, and a preliminary diagnosis result is given through cascade processing. Meanwhile, the data integrity is detected, and a modal missing scene is coped with through cascade collaborative diagnosis; and finally, comparing the two types of diagnosis results with a defect labeling sample to obtain a multi-modal collaborative diagnosis model after training optimization, inputting to-be-diagnosed sample data, outputting a final diagnosis result and updating the defect labeling sample, and realizing efficient and accurate defect diagnosis.

The invention relates to the technical field of equipment operation and maintenance intelligent retrieval, and provides an equipment operation and maintenance data enhanced retrieval method based on a knowledge graph and a large language model, which comprises the following steps: (1) constructing and dynamically maintaining the knowledge graph containing equipment operation and maintenance field entities and relationships; (2) analyzing natural language query of a user, performing multi-hop association retrieval in the knowledge graph, and screening out a related evidence set; (3) constructing a structured cue word based on the evidence set, and driving a large language model to generate a preliminary diagnosis report; and verifying, correcting and formatting the preliminary diagnosis report into a final visual report. According to the method, deep intention understanding and multi-hop association mining of natural language query of a user are realized by constructing a dynamically evolved equipment operation and maintenance knowledge graph, and a reliable evidence chain and a structured cue word constraint mechanism based on the knowledge graph are introduced to ensure that the generated diagnosis report is strictly based on field professional knowledge; the problem that the real semantic intention of user query cannot be deeply understood in traditional retrieval is effectively solved.

The invention provides a small sample tympanic membrane image recognition method based on meta-prompt and knowledge driving, and the method comprises the steps: inputting a small sample training set into an initial multi-mode pre-training model, obtaining a prediction category, comparing the prediction category with a real category, and screening misclassification samples in combination with confidence to construct a meta-task set; and inputting the meta-task set into the primary diagnosis model, and outputting a primary diagnosis report. And then, optimizing the medical description text sample based on the preliminary diagnosis report by utilizing a knowledge refining model, and replacing the original text sample, so as to obtain an updated sample. And finally, iteratively training the initial multi-modal pre-training model by using the updated sample until a termination condition is met. According to the method, a closed-loop optimization system composed of a primary diagnosis model and a knowledge refining model is constructed. Under the condition of small samples, the system dynamically optimizes the visual-semantic understanding ability of the model by using error samples generated by the model, and the accuracy of small sample tympanic membrane image recognition is effectively improved.

The invention provides a medical auxiliary diagnosis method and system based on time sequence and semantic weighting, and belongs to the technical field of medical information processing. Constructing the preprocessed current and historical medical record information of the patient into patient medical record data, and inputting the patient medical record data into a pre-trained medical language model to generate a preliminary diagnosis result; the comprehensive weight of the patient medical record data vector sequence is calculated through a time decay function and the content correlation weight, a medical record fusion vector is obtained through fusion, and the medical record fusion vector and the preliminary diagnosis result are spliced into a query vector; searching candidate fragments in a clinical guide knowledge base based on the query vector, calculating semantic evidence scores and coverage scores based on patient medical record data and tokens of the candidate fragments, obtaining sorting probabilities of the candidate fragments in combination with metadata prior scores of the candidate fragments, and screening out target guide fragments; and the preliminary diagnosis result and the target guide fragment are fused to generate final auxiliary diagnosis and treatment information, so that auxiliary diagnosis and treatment suggestions with traceability, verifiability and authoritative basis are provided.

The invention discloses a distribution transformer monitoring system and method, and relates to the technical field of electric power intelligent monitoring, and the method comprises the steps: obtaining the three-dimensional temperature gradient field distribution and mechanical stress distribution of a distribution transformer based on a standardized multi-source data set, and outputting a comprehensive field matrix through a field coupling analysis method; performing spatial analysis on the comprehensive field matrix by adopting spatial hot spot analysis, identifying and marking temperature and mechanical stress abnormal areas, performing spatial aggregation and noise filtering through an OPTICS density clustering algorithm, and outputting abnormal feature vectors; inputting the abnormal feature vector into a pre-trained intelligent diagnosis model, outputting a comprehensive fault risk score and performing preliminary diagnosis; searching fault data in a historical case library based on the preliminary diagnosis result for comparison verification, and optimizing the preliminary diagnosis result through a machine learning algorithm to generate a final diagnosis result; according to the method, a field coupling analysis method is adopted to establish a heat-force bidirectional action model, and the spatial distribution characteristics of the temperature gradient and the stress tensor are accurately reflected.

The embodiment of the invention provides an energy storage system fault processing method and device, equipment, a medium and a product, and relates to the field of photovoltaic power generation, the method solves the problem of low fault processing efficiency in the prior art by constructing a cloud-side-end cooperative fault processing architecture, changes the traditional static threshold judgment logic, and improves the fault processing efficiency. A decision-making mode combining multi-source information fusion and dynamic prediction is adopted, firstly, preliminary diagnosis of real-time data is achieved through edge calculation, the instantaneity of response is ensured, and then historical trend analysis, equipment health prediction and multi-dimensional real-time state evaluation are fused through a cloud platform. According to the method, the dynamic fault evaluation capable of accurately reflecting the actual severity and development trend of the fault is generated, so that the system can be adaptive to equipment aging and environment change, and a grading response strategy accurately matched with the fault grade is triggered, thereby realizing the crossing from passive alarm to active and accurate fault management and control, and improving the fault processing efficiency.

The invention provides a medical agent middleware-based full-specialized collaborative diagnosis and treatment method and system, and relates to the technical field of medical agents, and the method comprises the steps: building a basic medical knowledge graph through reinforcement learning, converting expert diagnosis and treatment experience into diagnosis and treatment probability distribution through a personalized treatment decision model, and forming an enhanced medical knowledge graph; and calculating a matching probability with a disease based on the symptom feature vector of the patient to generate a preliminary diagnosis decision, and selecting and adjusting a treatment scheme through reinforcement learning. According to the invention, general and specialized collaborative diagnosis and treatment can be realized, and medical decision accuracy and diagnosis and treatment efficiency are improved.

The invention discloses a blood filter early warning method based on multi-parameter coupling and a blood filter, and aims to solve the problems of lagging and high false alarm rate of the existing single parameter threshold alarm. The method comprises the following steps: acquiring time sequence data of at least two monitoring parameters such as transmembrane pressure and venous pressure in real time; constructing a risk state vector based on the sequential data; calculating a risk covariance matrix of the vector in a set time window so as to quantify a collaborative relationship of variation trends among parameters; performing matching degree comparison on the matrix and a pre-stored standard fault feature matrix; and generating and outputting early warning information at least indicating one equipment abnormal mode based on the comparison result. By analyzing a multi-parameter dynamic cooperative relationship instead of a single parameter absolute value, early warning and intelligent preliminary diagnosis of filter blood coagulation, pipeline abnormity and other faults are realized, and the timeliness, accuracy and clinical decision support capability of alarm are remarkably improved.

The invention discloses a wind generating set fault prediction method and system based on edge computing, and the method comprises the steps: collecting the vibration, temperature, rotating speed and other parameters of a wind generating set in real time through a data collection module deployed by edge computing equipment, carrying out the preliminary screening and packaging through a built-in data processing unit, and synchronizing to a constructed digital twinborn model; establishing an edge calculation fault analysis model based on model data, identifying potential fault features, comparing a physical unit operation state with a digital twin model simulation state parameter by parameter, performing preliminary diagnosis in combination with the fault features, evaluating a unit health state according to a fault mechanism model, and outputting a fault prediction result and part information. The system is correspondingly provided with six units including a data acquisition interaction unit, a data processing unit and a model construction updating unit, and all the units work cooperatively, so that accurate prediction and health management of faults of the wind generating set are realized.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence medical diagnosis system based on multi-dimensional information fusion. Comprising the steps that a self-adaptive diagnosis path planning module judges whether a user request belongs to a preset non-diagnosis and treatment category or not, if yes, a quick response path is activated, and a standardized answer is retrieved and returned to a user; the preliminary diagnosis module generates a candidate disease hypothesis list, verifies the candidate disease hypothesis list and outputs a verified disease hypothesis list; the dynamic knowledge enhancement module generates missing knowledge according to the disease knowledge graph and the query verification disease hypothesis list and supplements the missing knowledge into the medical knowledge graph; a composite confidence evaluation module performs confidence evaluation on each hypothesis disease in the verification disease hypothesis list, and outputs a final disease confidence; the result integration module sorts the final disease confidence in a descending order and integrates the multi-dimensional information of each hypothetical disease to generate a structured differential diagnosis report; the system and the method can assist doctors in realizing high-accuracy, high-reliability and explainable intelligent medical diagnosis.

The invention provides a multi-sensor electric power intelligent monitoring system, and belongs to the field of electric power monitoring. The system comprises a data processing system, a monitoring center server and a plurality of edge computing nodes deployed on different power devices, and the data processing system comprises a multi-source data acquisition module used for acquiring operation data of the power devices; the fault diagnosis module is used for constructing a distributed knowledge graph according to the basic data and the historical operation data of the power equipment, and storing the constructed knowledge graph in each edge computing node and the monitoring center server in a scattered manner so as to perform preliminary diagnosis on the fault of the power equipment; the early warning module is used for mining a data feature association relationship through a quantum heuristic algorithm and establishing a dynamic early warning mechanism; and the life prediction module is used for predicting the residual life of the power equipment based on the chaos theory and the phase-space reconstruction technology. According to the invention, the problems of low data processing efficiency, inaccurate fault diagnosis, untimely early warning and the like in traditional power monitoring are solved.

The invention provides a septal tumor accurate diagnosis system based on artificial intelligence, a multi-scale attention fusion module receives multi-modal data, focuses a septal region, dynamically adjusts the weight and outputs a key feature vector, an adversarial self-supervision pre-training module digs tumor morphological features based on the vector, robustness and generalization ability are enhanced, and the accuracy of diagnosis is improved. And the causal intervention diagnosis decision-making module constructs a decision-making tree by using enhanced features to perform preliminary diagnosis and transmits causal information to the meta-learning enhanced diagnosis module, and the meta-learning enhanced diagnosis module adjusts parameters by using a small number of samples for rare subtypes, outputs a final diagnosis result and feeds back the final diagnosis result. According to the system, multi-mode and dynamic attention, cooperative confrontation self-supervision pre-training and causal reasoning are fused, rare tumor diagnosis is optimized through meta-learning, a closed-loop feedback and shared knowledge graph is formed, and the diagnosis precision, interpretability and rare case diagnosis capacity are integrally improved.

The invention discloses a machine fault maintenance method and system based on multi-source data fusion. The method comprises the following steps: S1, collecting a vibration time sequence signal, a temperature time sequence signal, an equipment state parameter signal and a historical maintenance text signal of a machine; s2, generating vibration and temperature feature vector signals, and generating text feature vector signals; s3, constructing a multi-source feature dynamic fusion module, and splicing the vibration and temperature feature vector signal, the equipment state parameter signal and the text feature vector signal; s4, inputting the weighted fusion feature signal into a fault diagnosis model, and outputting a preliminary diagnosis signal containing fault type prediction and corresponding confidence; and S5, constructing a decision arbitration module, and when the uncertainty quantization signal is lower than a preset threshold value, outputting a preliminary diagnosis signal as a final maintenance decision signal. According to the multi-source data fusion-based machine fault maintenance method and system, the problems of low fault prediction accuracy and unreliable maintenance decision of industrial equipment can be solved.

The invention discloses an informatization process processing method for a thoracic pain stroke trauma treatment mode, and the method comprises the following steps: 1, information collection and preprocessing: collecting the physiological sign data of a patient through a mobile terminal, and analyzing the physiological sign data based on a deep learning model, so as to obtain a preliminary diagnosis result of an illness state; classifying and marking the patient according to the preliminary diagnosis result of the illness state to obtain a classification and marking result, and uploading the classification and marking result, the physiological sign data and the geographic position information of the patient to a regional medical collaborative server; step 2, multi-center cooperative distribution: after receiving the classification marking result, the physiological sign data and the geographic position information of the patient, a regional medical cooperative server performs calculation processing based on a preset medical resource scheduling model; and step 3, performing a treatment process: sending the optimal treatment path scheme to a medical information system of a target medical center by the regional medical cooperation server, triggering the target medical center to start an emergency plan, and deploying medical resources.

The invention relates to a smart hospital collaborative diagnosis and treatment management method and system, and the method comprises the steps: receiving the appointment registration information of a patient, building a data mapping relation with a hospital information system based on the appointment registration information, and generating a corresponding treatment identifier; after the patient arrives at the hospital, information of the sign-in patient is collected, a sign-in sequence is established, and according to appointment registration information and diagnosis and treatment service data parameters corresponding to the sign-in patient, a treatment serial number is dynamically distributed and a waiting prompt is pushed; after the doctor receives the diagnosis, generating an examination item list based on the preliminary diagnosis information input by the doctor, obtaining the real-time state and queuing data of equipment required by each examination in the examination item list, constructing an execution time window of examination items, generating an optimal examination sequence, and pushing an examination guide path to the patient terminal; and after the patient finishes seeing a doctor, generating a personalized propaganda and education task list according to the diagnosis and treatment information of each stage, and pushing the personalized propaganda and education task list to the patient terminal. The method has the effect of improving the hospital operation efficiency.

The invention discloses a battery cluster fault diagnosis method and system and a medium, and belongs to the technical field of energy storage system safety. Comprising the following steps: acquiring multi-modal time sequence data of a battery cluster module in a first diagnosis period, inputting the multi-modal time sequence data into an A-ConvNeXt model cascaded with a channel attention module through depth separable convolution for preliminary diagnosis, and outputting a preliminary diagnosis result, confidence and a feature vector; fusing the feature vectors of a plurality of continuous initial judgment periods of the battery cluster to form longer second diagnosis period data, inputting the longer second diagnosis period data into a TCN-TF fusion network formed by connecting a time sequence convolutional network and a Transform encoder in parallel, and outputting a final diagnosis result and confidence after feature splicing and fusion through parallel extraction of local features and long-range dependence; when the final confidence exceeds a preset threshold value, a real fault is judged, the EMS is immediately triggered to execute a power reduction or shutdown instruction, and safe closed-loop control is achieved; according to the invention, accurate, rapid and adaptive diagnosis of the fault of the energy storage battery cluster is realized.

The invention relates to the technical field of power equipment fault detection, in particular to a power equipment fault diagnosis method and system based on a large power model, and the method comprises the steps: converting the multi-modal data of a power cable in operation into a feature vector, and inputting the feature vector into a pre-trained fault diagnosis model to obtain a preliminary diagnosis result and confidence; if the confidence coefficient is not lower than the threshold value, the preliminary diagnosis result is reserved; and if the confidence coefficient is lower than a threshold value, taking the feature vector as a current potential fault feature vector to perform secondary discrimination, namely obtaining a significance index by calculating the similarity and volatility of the current potential fault feature vector and a historical potential fault feature vector, analyzing a time change trend to obtain a cumulative trend index, and performing secondary discrimination on the cumulative trend index. And comprehensively determining a potential fault index through the significance index and the cumulative trend index, and determining a final diagnosis result according to the potential fault index. According to the scheme, the diagnosis accuracy of the fault diagnosis model on low-confidence potential faults and unknown faults is improved.

The invention relates to the technical field of power data analysis, in particular to a line loss abnormity associated mutual inductor insulation fault positioning method. Collecting multi-source operation data of a target station area, and obtaining a datum line loss rate sequence, a residual component sequence and an auxiliary feature vector based on the multi-source operation data; according to the reference line loss rate sequence, obtaining a comprehensive sensitivity vector representing the influence degree of the mutual inductor on the line loss; obtaining a preliminary diagnosis result according to the comprehensive sensitivity vector, the residual component sequence and the real-time load rate; performing spatial logic verification according to the preliminary diagnosis result and the electrical topological graph of the transformer area to obtain a fault positioning list; generating a correction amount according to the fault positioning list and the corresponding comprehensive sensitivity vector, and correcting the line loss rate according to the correction amount; and meanwhile, feedback optimization is performed on the machine learning model according to complete data of the diagnosis. According to the invention, accurate and efficient diagnosis and correction of the line loss abnormity of the target station area can be realized.

The invention discloses a medical diagnosis-oriented multi-modal credible dialogue type retrieval enhancement generation system, and relates to the field of artificial intelligence, in the system, a multi-modal image analysis module generates a structured image feature data packet based on a multi-modal large model according to an original medical image; the knowledge database construction module is used for constructing a multi-center collaborative medical knowledge database; the image knowledge retrieval module is used for obtaining image feature associated knowledge; the clinical knowledge retrieval module is used for obtaining a candidate knowledge set; a multi-dimensional weighting reordering module obtains a knowledge list; the medical inquiry generation module is used for generating structured preliminary diagnosis based on the user questions, the image abstract and the knowledge list; the credibility verification module is used for verifying the structured preliminary diagnosis to obtain a final diagnosis report; according to the method, the medical knowledge retrieval precision can be improved, the image information fusion capability is enhanced, and high-credibility and traceable intelligent diagnosis and treatment auxiliary service oriented to doctor-patient scenes is realized.

The invention discloses a senile dementia early warning, evaluation and health management system and method based on artificial intelligence, and relates to the field of artificial intelligence and medical health. The system comprises a data acquisition module for acquiring Chinese speech, electroencephalogram signals, facial expression images and eye movement data of a patient; the data preprocessing module is used for preprocessing and storing various data; the feature extraction and modeling module is used for extracting a feature set and constructing a one-dimensional classification model; the intelligent diagnosis module inputs the feature set to a one-dimensional classification model to obtain a disease probability and a preliminary diagnosis result, and a final result is obtained through comprehensive diagnosis after a preliminary diagnosis threshold value is compared; and the user interaction module realizes diagnosis visualization, allows a user to define a preliminary diagnosis threshold value, imports data to retrain and updates a one-dimensional classification model. Through multi-modal data fusion and intelligent analysis, potential relations among different modal data are fully mined, and the accuracy of senile dementia diagnosis and the system adaptability are effectively improved.

The invention discloses a clinical intelligent decision-making method based on proxy workflow and a storage medium. Comprising the following steps: acquiring and preprocessing clinical information of a patient, and constructing a candidate disease set; and constructing a proxy directed workflow. In the retrieval stage, the diagnosis criteria corresponding to the candidate diseases are retrieved and aggregated from the diagnosis criteria library rechecked by the experts to form working memory. The preliminary diagnosis stage model node generates a preliminary candidate diagnosis set in combination with work memory and patient medical history and physical examination. And the final diagnosis stage generates a final diagnosis result based on the preliminary candidate diagnosis and the complete clinical information. And when the global confidence is lower than a threshold value, the model node pointedly checks an information source and updates reasoning and confidence. A successful reasoning track forms a demonstration set after manual auditing, the demonstration set is used for supervising a fine tuning model to obtain an initial strategy, multiple structured outputs are generated through grouping sampling, relative strategy updating is carried out in combination with reward signals and reference strategy regularization constraints, and optimization and stable improvement of the model diagnosis capability are achieved.

The invention discloses a multidisciplinary intelligent consultation method for fundus diseases based on structured memory and dynamic reflection. The method comprises the following steps: acquiring medical records of patients and various examination result reports; creating a multidisciplinary consultation hall and deploying a consultation intelligent center; initializing, and synchronously storing the preliminary diagnosis data to a structured memory unit; the secretary-memory agent dynamically screens and directionally calls expert agents with matched specialities and role positioning from an agent expert pool to enter a multidisciplinary consultation hall based on the preliminary diagnosis data, and establishes interactive connection between the expert agents and a consultation intelligent center; driving each expert agent to execute a dynamic reflection algorithm, and obtaining a diagnosis reference scheme through multiple rounds of interaction debate; consultation process evaluation is carried out through a multi-dimensional quantitative evaluation mechanism; and carrying out clinical judgment on the diagnosis reference scheme based on clinical expert experience. According to the method, high efficiency, low cost and standardization of fundus disease multidisciplinary consultation can be realized, and multidisciplinary comprehensive diagnosis opinions formed by fundus diseases can be accurately obtained.

The invention belongs to the field of equipment fault diagnosis, and particularly discloses an equipment remote fault diagnosis method based on a large model. Through multi-modal data fusion, an attention mechanism reasoning model, man-machine cooperation verification and intelligent resource scheduling, the problems that maintenance excessively depends on expert experience and a mature remote scheme is lacked are solved. Specifically, according to the scheme, firstly, multi-source data are aligned and fused to form a unified feature vector, and the defect of information isolation is overcome. Afterwards, a model based on an attention mechanism can automatically focus key features, preliminary diagnosis is generated, and dependence on expert experience is reduced. And then, through a man-machine interaction verification mechanism, an expert can remotely check and correct a result, so that the diagnosis reliability is ensured. And finally, the system automatically schedules resources according to a verified result, so that rapid linkage from diagnosis to disposal is realized, remote guarantee is accurately executed, and equipment deterioration is effectively restrained.

The embodiment of the invention provides an enterprise digital diagnosis device and method, equipment and a medium, and belongs to the field of enterprise digital transformation. The device comprises an enterprise data collection and storage module for collecting enterprise data to construct a data set and storing the data set into a memory; the enterprise digital diagnosis agent is used for receiving and jointly embedding structured, semi-structured and unstructured data sets, performing field fine tuning on a general large model based on an industrial digital transformation evaluation standard document, and constructing the general large model for preliminary diagnosis; and the information extraction and diagnosis module is used for extracting feature information in the unstructured data in the data set based on an NLP natural language processing large model and inputting the feature information to a prediction model to predict potential risks. Through application of the large model technology, comprehensive and accurate evaluation of the enterprise digital level is realized, and through real-time diagnosis and optimization, problems can be rapidly identified and targeted suggestions can be provided to assist enterprises in promoting digital transformation.

The invention discloses an intelligent diagnosis method for power equipment primitive state recognition and rule fusion, and relates to the technical field of power system dispatching automation. The method is used for substation dispatching master station monitoring picture equipment primitive state recognition and abnormity diagnosis, video and graph model library data acquisition, preprocessing denoising, frame synchronization and ROI extraction. Using the improved YOLOv8-Tiny to extract a multi-modal feature to identify a primitive state; establishing an expert experience rule base, a graph-model association rule base and an anomaly judgment rule base; performing primary diagnosis on the forward chain reasoning fusion rule; performing cross checking on telecommand consistency, telemetering relevance and graph model library integrity; and outputting a result coexistence log. The problems that manual checking is low in efficiency and prone to omission are solved, the reliability of primitive recognition and correlation checking is improved, a D5000 system and a domestic operating system are adapted, complex monitoring scenes are coped, the operation and maintenance burden is relieved, it is guaranteed that monitoring pictures of a dispatching master station are accurate, and safe operation and maintenance of a power grid are facilitated.

The invention provides an intelligent pre-inquiry form generation method and system for a child tumor, and relates to the technical field of intelligent medical inquiry, and the method comprises the steps: firstly recognizing the type of a child tumor pre-inquiry scene, and extracting scene demand features to form a set; and obtaining multi-source input data such as a parent preliminary symptom description text, a child past health record and an examination result sheet, and performing association processing. And then adjusting child tumor large model parameter configuration based on the scene demand feature set, and inputting an associated data set to generate an inquiry entry semantic prototype. According to parent expression habit sample optimization expression logic, a preliminary pre-inquiry form is formed, and parent feedback is collected to form a multi-dimensional feedback data set. And finally, inputting the feedback data set into the model to generate a form field optimization scheme and a preliminary diagnosis suggestion draft, and adjusting the preliminary pre-inquiry form to obtain a final child tumor intelligent pre-inquiry form containing a preliminary diagnosis direction. According to the invention, the scene adaptability and accuracy of pre-inquiry are improved.

The invention belongs to the technical field of hydroelectric generating set fault diagnosis, and particularly discloses a hydroelectric generating set intelligent fault diagnosis method based on multi-sensor data fusion. The method comprises the following steps: firstly, constructing two branch convolutional neural networks to respectively extract time domain features and frequency domain features of sensing data, and then constructing a central convolutional neural network to perform feature extraction on fusion features obtained by fusing the shallow time domain features and the frequency domain features; fusing the fusion feature obtained by the convolution of the # imgabs0 # layer and the time domain feature and the frequency domain feature obtained by the convolution of the # imgabs1 # layer in the branch convolutional neural network, and updating the fusion feature obtained by the convolution of the # imgabs2 # layer; outputting a preliminary diagnosis result by the central convolutional neural network; and finally, performing decision fusion based on information entropy on a preliminary diagnosis result obtained by the multiple paths of sensing data to obtain a final diagnosis result. Compared with the existing diagnosis method, the diagnosis method provided by the invention has higher anti-interference capability and robustness.

Various processes, algorithms, and systems are provided herein for assisting physicians in distinguishing among related diseases, such as distinguishing connective tissue associated interstitial lung disease from idiopathic pulmonary fibrosis. Methods for generating such processes, algorithms, and systems are also disclosed. In some embodiments, a preliminary diagnosis of a set of possible diseases is obtained, along with protein count information from a patient's blood sample. Additional, patient-specific information (e.g., age, sex, etc.) may also be obtained. The data is processed by a trained machine learning algorithm, to output a differential diagnosis of which of the set of possible diseases is present for that patient. Based on the diagnosis, a treatment course can be selected, and further information can be tracked regarding the patient's outcome.