1950 results about "Diagnostic methods" patented technology

The invention discloses a multi-modal knowledge graph construction and intelligent fault diagnosis method and system for a communication power supply system. The method comprises the steps of multi-modal knowledge graph construction, graph increment updating and intelligent fault diagnosis. The multi-modal knowledge graph construction adopts a unified data acquisition semantic specification and a heterogeneous data fusion strategy, a dynamic evolution heterogeneous graph is established, and equipment full life cycle state perception and causal link modeling are realized; the efficient, atomicity and consistency updating of the atlas is realized by the hierarchical atlas increment through shadow composition, structural difference rate calculation and a subgraph replacement mechanism; according to the intelligent fault diagnosis, an alarm propagation sub-graph is constructed, a path convergence and multi-dimensional attribute scoring mechanism is adopted, and deep joint verification is carried out by using a multi-modal evidence fusion network; and in combination with a reinforcement learning optimization strategy embedded based on a graph structure, adaptive scheduling and diversity constraint of a diagnosis path are realized, and the fault positioning accuracy and the system intelligence in a complex scene are remarkably improved.

The invention belongs to the field of power system engineering, and discloses a power distribution automation terminal diagnosis method and system based on multi-source wave recording feature fusion, and the method comprises the steps: obtaining the electric quantity data and equipment operation state data collected by a power distribution automation terminal; performing adaptive decomposition on the electrical quantity data by using a variational mode decomposition algorithm to obtain an intrinsic mode function; constructing a deep residual network model, carrying out fusion analysis on the time-frequency domain features of the intrinsic mode function, and generating a fault feature vector; establishing a multi-dimensional evaluation matrix based on the fault feature vectors, and integrating a plurality of indexes to output fault types and credibility scores; according to the fault type and the credibility score, generating a fault isolation strategy based on a Petri network model; and executing a dynamically adjusted self-adaptive self-healing control algorithm. According to the method, complex and changeable fault modes can be effectively identified, a complete collaborative verification mechanism is formed, seamless connection from fault diagnosis to self-healing control is realized, and the operation reliability of the power distribution network is remarkably improved.

The invention discloses a multi-granularity knowledge graph auxiliary diagnosis method based on DeepSeek and Agent. The multi-granularity knowledge graph auxiliary diagnosis method comprises the following steps: step 1, receiving electronic medical record text data and medical image data of a patient; 2, constructing a knowledge graph; updating the knowledge graph every day to reflect the latest medical research result; step 3, analyzing the text data of the electronic medical record through DeepSeek-R1; 4, extracting spatial structure feature nodes of the medical image data through a multilayer three-dimensional convolution kernel; the method comprises the following steps: segmenting medical image data into sequence blocks through a Vision Transform; 5, the output of the DeepSeek-R1, the output of the multi-layer three-dimensional convolution kernel and the output of the Vision Transform are input into a multi-modal fusion module; step 6, outputting a high-confidence diagnosis conclusion and probability distribution; and step 7, generating an intelligent report of the structured text. According to the method, by combining natural language processing, computer vision and the knowledge graph technology, accurate and efficient medical examination is achieved.

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

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 relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The invention discloses a deep learning-based three-dimensional ground penetrating radar pavement damage multi-dimensional diagnosis method, which comprises the following steps: collecting and sorting three-dimensional ground penetrating radar road detection data, the data comprising N B-SCAN maps, M C-SCAN maps, road positions and point coordinates, and preprocessing the B-SCAN maps and the C-SCAN maps; based on a YOLO v12 model, obtaining a first identification result for the preprocessed B-SCAN atlas; obtaining a second identification result based on the preprocessed C-SCAN map; the first recognition result and the second recognition result are further judged through the optimized multi-dimensional cross recognition rule to determine the final recognition result, in the diagnosis method, the recognition model is optimized, and the recall rate and the accuracy rate of the mode in graph recognition are improved to the maximum extent; and the false abnormal signals are further screened, so that the model is improved, the recognition precision is improved, the multi-dimensional cross recognition rule is optimized, and the omission ratio and the accuracy of abnormal defects are reduced.

The invention provides a vehicle fault root cause diagnosis method, equipment, equipment and a medium, and the method comprises the steps: obtaining a vehicle target fault phenomenon, determining a to-be-detected fault event set based on a preset fault logic relation, constructing an initial scoring matrix, enabling a row vector to correspond to a fault event, enabling a column vector to correspond to the state feature parameters of a plurality of evaluation dimensions, and carrying out the detection of the fault event set; executing the detection task of the highest comprehensive score fault event, obtaining feedback data, updating the initial score matrix parameters based on the feedback data to obtain an updated score matrix, and calculating the comprehensive score of each event based on the updated score matrix again. Iteratively executing the detection task corresponding to the updated highest score event to identify whether the detection event is a fault root cause or not until the fault root cause of the target fault phenomenon is determined; according to the method, through a dynamic priority scheduling mechanism, real-time feedback data is fused in multi-dimensional evaluation, a high-value diagnosis task is executed preferentially, the resource consumption of traditional traversal diagnosis is remarkably reduced, and the fault positioning efficiency is effectively improved.

The invention discloses a dynamic equipment fault progressive diagnosis method based on a knowledge enhancement large model, and relates to the technical field of equipment intelligent fault diagnosis and operation and maintenance, and the diagnosis method comprises the following specific steps: S100, building a domain knowledge graph: based on historical fault data and equipment manual information, carrying out data arrangement and labeling, and carrying out mapping on the domain knowledge graph; the method comprises the following steps: constructing a knowledge graph which takes equipment key parts, sensor measuring points, typical fault modes, fault symptoms, reason mechanisms and maintenance measures as nodes, associates the parts with the fault modes and takes a causal relationship between the fault symptoms and potential reasons as edges, and defining knowledge expression specifications; according to the method, the knowledge graph and the large-scale pre-training language model are fused, an intelligent diagnosis engine with equipment domain knowledge is constructed, the accuracy of equipment fault diagnosis is remarkably improved, the introduction of the knowledge graph enables the diagnosis process to refer to rich domain knowledge and historical experience, and the diagnosis efficiency is improved. And the defects of a pure data driving model in the face of rare or complex faults are made up.

The invention provides an osteoporosis automatic diagnosis method and system based on position specific attention, and the method comprises the steps: carrying out the dual-channel enhancement preprocessing of a lumbar CT image, and generating an enhanced image taking the bone mineral density and the bone trabecula structure characteristics into consideration; inputting the enhanced image into a deep convolutional neural network for multi-scale feature extraction to obtain a feature map containing spatial semantic information; on the basis of the anatomical position index information, generating attention weighted features which highlight the key diagnosis area of the vertebral body; global pooling is carried out on the attention weighted features, and then comprehensive diagnosis features containing anatomical priori knowledge are constructed; and inputting the comprehensive diagnosis features into a classifier to realize centrum-level accurate diagnosis. According to the invention, the workload of radiologists can be effectively relieved, and the efficiency and coverage of osteoporosis screening can be improved. Especially under the condition that primary medical institutions are lack of experienced radiologists, the primary medical service level can be improved.

The invention relates to the technical field of power electronic device diagnosis, and discloses a reverse conducting IGBT intelligent power module fault automatic diagnosis method and system. The method comprises the following steps: acquiring multi-source monitoring data including a grid voltage waveform, a collector current waveform and a shell temperature change curve when the power module operates; then establishing a dynamic feature extraction model, performing time domain and frequency domain conjoint analysis on the multi-source monitoring data, and generating a feature parameter set; then constructing a fault feature space, and mapping the feature parameter set to a high-dimensional space to form a feature vector distribution diagram; carrying out regional division on the feature vector distribution map by adopting a self-adaptive clustering algorithm, and identifying an abnormal feature aggregation region; and finally, comparing the abnormal feature gathering area with a preset fault feature library through a mode matching engine, and outputting a fault type identification result. According to the method, multi-source data can be integrated to realize dynamic feature extraction and adaptive fault identification, and the real-time performance and accuracy of fault diagnosis are improved.

The invention provides an osteoporosis auxiliary diagnosis method fusing CT image features and a semantic knowledge graph, and the method is characterized in that the method comprises the steps: multi-source heterogeneous data collection and standardization processing; constructing a modal exclusive depth coding network; mapping and aligning a unified semantic space; carrying out multi-level cross-modal attention fusion; self-adaptive segmentation and feature enhancement of anatomical perception are carried out; semantic reasoning guided by the knowledge graph; performing multi-task collaborative diagnosis output; and training optimization and interference elimination. Through automatic multi-modal analysis and intelligent diagnosis, the workload of doctors in the imaging department is remarkably relieved, and the diagnosis time of a single example is shortened to be within 3 minutes from the average 15-20 minutes. The standardized diagnosis process of the system improves the diagnosis consistency among different doctors, reduces the diagnosis deviation caused by experience difference, and helps primary hospitals to improve the osteoporosis diagnosis and treatment level.

The invention belongs to the field of medical image classification, and discloses a novel dual-path network architecture combining Mama and a convolutional neural network, and the novel dual-path network architecture comprises a Patch Embedding module, a plurality of Conv-OSS layers, a Patch Merging layer, and a final feature classifier. An OSS module is developed, local features are extracted through parallel convolution branch, overall representation is enriched, and global features are effectively captured; an OSSM module is designed, spatial features are modeled from multiple directions, and the extraction capability of global features is enhanced; an MSAConv network is designed, and the overall feature representation quality is improved by aggregating eight diagonal features reserved after 8DScan; an FFM module is developed, Mama output is further optimized, and fine features related to pneumonia in a medical image are emphasized. Experimental results of a pneumonia data set show that the method is superior to an existing computer-aided pneumonia diagnosis method in multiple evaluation indexes, and the huge potential of the method in actual clinical application is highlighted.

The invention discloses a pig farm abortion attribution diagnosis method based on a porcine reproductive and respiratory syndrome risk propagation knowledge graph, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a pig farm porcine reproductive and respiratory syndrome attribution knowledge graph, pre-defining a risk propagation mode according to a porcine reproductive and respiratory syndrome risk propagation mechanism, searching a path according with the risk propagation mode through graph mode matching, and carrying out the diagnosis of the abortion attribution of a pig farm. Integrating into a risk sub-graph; performing representation learning on the risk sub-graphs by adopting a graph attention network fused with PRRS risk propagation knowledge, and quantifying the contribution degree of each risk sub-graph to the abortion rate of the pig farm in combination with context representation learning and a time difference attenuation mechanism; and based on the contribution proportion of each risk event in the attention score decomposition risk sub-graph, generating a quantitative attribution result, and outputting a diagnosis result including risk event identification, a risk propagation link and a quantitative attribution contribution degree. Risk attribution of the porcine reproductive and respiratory syndrome in the pig farm is realized, and contribution of specific attribution risk points to the abortion rate of the pig farm is quantified.

The invention discloses a deep learning-based valve opening and closing state recognition and diagnosis method, which comprises the following steps of: acquiring and synchronizing multi-source signals, and generating a standardized multi-mode time sequence sample; extracting modal features by multiple branches and fusing the modal features into a joint feature vector sequence; the combined features are input into a phase change layered decoder, and a layered recognition result is output; constructing a plurality of types of abnormal events in a point process layer modeling phase change stage, and outputting an event modeling result; constructing a condition reversible generation diagnostor, and outputting a consistency checking result; and fusing a result output state and diagnosis information, and executing alarming and filing. Through multi-mode deep learning feature fusion, phase change hierarchical decoding and conditional modeling, accurate recognition of the opening and closing state of the valve, fine division of the phase change stage and intelligent diagnosis of early faults are achieved.

The invention discloses a switch equipment performance aging intelligent diagnosis method and device based on multi-parameter fusion, relates to the technical field of power equipment abnormal defect diagnosis, and solves the problem of low precision of aging judgment of switch equipment depending on a single temperature and humidity threshold value or a single gas parameter in the prior art. The method comprises the following steps: acquiring internal and external humiture and internal gas characteristic parameters of equipment; constructing multi-channel standardized monitoring data through time synchronization, filtering and de-noising and exception elimination processing; calculating an absolute humidity difference inside and outside the cabinet, constructing a humidity expectation model, and obtaining a standardized residual error; performing offset detection and trend smoothing processing on the standardized residual error to obtain a cumulant and a smoothing trend quantity, and fusing the cumulant and the smoothing trend quantity to calculate an offset index; outputting a moisture seepage risk score, and triggering early warning when the moisture seepage risk score and the characteristic gas concentration exceed a threshold value. According to the invention, real-time high-precision evaluation of the aging state of the equipment is realized, early hidden dangers are effectively identified, and the safety and reliability of a power system are improved.

The invention provides an industrial anomaly detection and root localization method and system based on data driving, and the method comprises the steps: carrying out the cleaning, feature extraction and normalization processing of original data collected in an industrial production process, and constructing a feature space; based on a local anomaly factor LOF and a mahalanobis distance MD method, jointly detecting local anomaly and global anomaly, and identifying an abnormal working condition; extracting space and time correlation characteristics of the abnormal variables through Pearson correlation weighting and Granger causal test to form a space-time correlation matrix; constructing an abnormal causal network based on the matrix, and tracing an abnormal root and a propagation path through depth-first search and abnormal propagation intensity evaluation; and finally, dynamic optimization of the anomaly detection and diagnosis method is realized based on parameter self-adaption and model incremental learning. According to the method, the anomaly detection accuracy and the anomaly traceability interpretation capability can be effectively improved, and the intelligent level and the self-adaptive capability of data processing are enhanced.

The invention relates to a cloud-edge collaborative medical image intelligent diagnosis method and device, equipment and a medium. The method comprises the following steps: firstly, obtaining original image data, and carrying out calibration processing on the original image data through a preset multi-modal image standardization model to obtain a standard image data set; performing feature extraction and grading processing on the standard image data set at an edge end to obtain a grading feature set, transmitting the grading feature set to a cloud end through a bandwidth allocation strategy, and generating a cloud end receiving feature subset; constructing a feature expression matrix based on the cloud receiving feature subset, using a network topology structure to carry out graph analysis, and extracting a path to generate a diagnosis path set; and finally, feedback diagnosis information is formed based on the diagnosis path set, and a task allocation optimization result is obtained by dynamically adjusting a cloud side task allocation proportion. According to the method, efficient processing and diagnosis optimization of medical image data are realized, efficient collaboration of cloud edge resources is ensured, timeliness and accuracy of medical image diagnosis are effectively improved, and resource scheduling requirements in different scenes are met at the same time.

The invention belongs to the field of characterization learning, and particularly relates to a prostate cancer diagnosis method based on a multimodal large model prompt learning mechanism. Comprising the following steps: step 1, data preprocessing; 2, key frames and similarity are calculated, and irrelevant editing is compressed; step 3, text and image alignment training; step 4, a test stage; according to the method, a similarity-based screening mechanism is provided, ultrasonic videos under coarse-grained labels are preliminarily screened under segmentation of a large model, and focus areas are focused on in time sequence; meanwhile, a pre-processing mechanism based on a large model is provided, and the influence of a large amount of irrelevant information existing in prostate ultrasonic image scanning on the model is compressed at a data end, so that the diagnosis effect of the model is improved.

The invention discloses a heat exchange equipment intelligent fault diagnosis method and system based on digital twinning, relates to the technical field of digital twinning intelligent diagnosis, and is used for solving the problem that concurrent valve jamming and multi-equipment coupling faults are difficult to quickly and accurately position and safely dispose. Design configuration, maintenance history and real-time working condition data are written into a twin library through semantic mapping and unified time synchronization, and virtual-real synchronization is kept. Secondly, fusing the flow, the pressure difference, the temperature sequence and the color-infrared image, generating a health vector by means of a dimension reduction network, and performing real-time early warning; and after early warning occurs, a twin copy is cloned by using the combination of the abnormal equipment and the regulating valve, parallel simulation is performed on the jam disturbance of the injection valve, and a concurrent fault is locked through comprehensive similarity. Finally, a knowledge base is called to evaluate candidate disposal strategies, an optimal scheme is selected to be issued and executed, dynamic correction is monitored through residual errors, and a diagnosis-decision-execution closed loop is achieved. According to the method, the equipment reliability is improved, and the energy utilization rate is increased.

The invention discloses a rolling bearing lightweight fault diagnosis method and system based on multi-source signal fusion. Vibration, temperature and rotating speed signals are synchronously collected, and timestamps are calibrated; respectively carrying out denoising and normalization preprocessing; dividing and aligning windows; differential feature extraction: extracting time-frequency features of the vibration signals by using a one-dimensional residual CNN, and extracting abnormal measurement of the temperature / rotating speed signals by using an LSTM in combination with an isolated forest algorithm; carrying out self-adaptive weighted fusion on the features through an attention mechanism; the lightweight diagnosis model (through knowledge distillation, pruning and quantification) deduces and outputs the fault category, the health index and the confidence coefficient. The system correspondingly comprises an acquisition module, a preprocessing module, a feature extraction module, a fusion module and a diagnosis module. The method improves the early fault sensitivity, enhances the variable working condition robustness, supports the real-time deployment of edge equipment, and is suitable for the intelligent monitoring of industrial bearings.

The invention relates to the technical field of intelligent diagnosis, and discloses a micro-grid fault diagnosis method and system based on data driving and unsupervised learning. The method comprises the following steps: collecting current, voltage, temperature and power data of a micro-grid and constructing a time sequence matrix; inputting a time sequence prediction network and a time sequence reconstruction network, and performing parallel processing to obtain a prediction error and a reconstruction error; carrying out weighted fusion on the two errors and constructing a two-dimensional error space to judge normal fluctuation and fault abnormity; and extracting a state variable to generate a dynamic threshold to judge a fault. The false alarm rate and the missing report rate of fault diagnosis are reduced.

The invention relates to a diagnosis method for judging a loose part of rotating equipment. The diagnosis method comprises the following steps: (1) collecting vibration data; (2) spectrum characteristic analysis; (3) differential vibration testing; (4) positioning a loose part; and (5) dynamic verification and optimization. The device has the advantages that through multi-dimensional vibration data acquisition, vibration sensors are arranged at key parts such as motor feet, a bedplate and foundation bolts, vibration displacement values in the horizontal direction, the vertical direction and the axial direction are measured, multiple test points are divided to synchronously acquire data, and vibration information of rotating equipment can be comprehensively acquired. And combining frequency spectrum characteristic analysis and differential vibration comparison, comparing vibration displacement value differences of different hierarchical structures of the same part, and calculating a difference threshold value, so that the specific loose part of the A-type machine is accurately positioned, the limitation that accurate positioning is difficult to realize by traditional experience-dependent judgment or single vibration amplitude analysis is broken through, the blindness of maintenance is reduced, and the working efficiency is improved. The maintenance efficiency is improved.

The invention relates to a power grid line loss abnormity correlation diagnosis method based on deep learning, and the method comprises the following steps: S1, obtaining power grid line loss related multi-source data, carrying out the fusion of the multi-source data, and obtaining a fused multi-dimensional fact sample table; s2, establishing an abnormal label system, and performing label labeling on the multi-dimensional fact sample table to obtain a labeled sample set; s3, multi-dimensional feature construction is carried out based on the labeled sample set, and a space-time multi-modal feature tensor is obtained; s4, constructing a multi-task deep learning diagnosis model, and performing training based on the space-time multi-modal feature tensor to obtain an end-to-end diagnosis model; and S5, based on the end-to-end diagnosis model, obtaining a multi-dimensional structured alarm according to the implementation data. According to the invention, the full coverage capability and the actual accuracy of line loss anomaly detection are significantly improved.

The invention relates to the technical field of fault diagnosis, and discloses an inertial sensor fault data test diagnosis method and system, and the method comprises the steps: carrying out the noise suppression operation of original output data in a sliding window, and obtaining a purified data flow; calculating an expected value of the purified data stream based on a preset calibration parameter, and performing deviation comparison on the expected value and the purified data stream to obtain a residual sequence; performing time-frequency domain analysis on the residual sequence to obtain a multi-dimensional characteristic parameter; based on the operation state of the inertial sensor, performing nonlinear state space reconstruction on the multi-dimensional characteristic parameters to obtain a comprehensive fault index; performing comparative analysis on the comprehensive fault index and a preset dynamic diagnosis threshold value, and when the comprehensive fault index continuously deviates from the dynamic diagnosis threshold value, determining that the inertial sensor has a potential fault; outputting a fault early warning report according to the potential fault; according to the invention, the efficiency of fault data test diagnosis of the inertial sensor can be improved.

The invention discloses a multi-modal data fusion-based DC insulator degradation diagnosis method and system. The method comprises the following steps of obtaining infrared image data and current data of a to-be-detected insulator string; constructing an insulator chain model according to the infrared image data, and obtaining the temperature of each insulator according to the insulator chain model and the infrared image data; obtaining the theoretical temperature of each insulator according to the current data; and according to the theoretical temperature of the insulator and the temperature of the insulator, degradation identification of the insulator is realized. Obtaining historical maintenance data of the insulator, constructing a self-adaptive threshold according to the historical maintenance data of the insulator, and performing secondary judgment on a degradation identification result through the self-adaptive threshold; according to the method, the problems of diagnosis one-sidedness, poor environmental adaptability, low degradation positioning precision and the like caused by dependence on a single data source in a traditional method are effectively solved, and the accuracy, robustness and engineering practicability of direct current insulator degradation diagnosis are remarkably improved.

A diagnostic method localizes technical faults in a motion system that includes a base adapted to receive a motion stage for equipment, a machine frame resting on the floor, dampers adapted to support the base, and an active isolation system arranged between the base and the machine frame. The active isolation system and the base form a mechanical system. The active isolation system includes actuators, adapted to impart six degree-of-freedom (DOF) motion to the base in a reference frame, and inertial sensors adapted to provide a six DOF measurement of the base's motion. The method includes: i) applying a control signal for actuating or contributing to the actuation of the actuators of the active isolation system to impart a motion to the base; ii) obtaining, with the inertial sensors, a six DOF measurement of the base's motion relative to a reference point; iii) creating a measured process sensitivity matrix of the mechanical system using the six DOF measurement; and iv) determining, based on the measured process sensitivity matrix, whether all the actuators and sensors of the active isolation system are working as expected and / or whether there is a pivot point impeding the movement of the base.

The invention discloses a measurement laboratory anomaly detection and diagnosis method, system and device based on deep learning and a medium, and relates to the technical field of anomaly detection and diagnos.The method comprises the steps that multi-source real-time data are collected and preprocessed; performing alignment processing based on sampling inconsistency among the data sources, and constructing unified data representation; generating a corresponding prediction result by using the prediction model; calculating a comprehensive abnormal score based on the aligned data and the prediction result; comparing the comprehensive abnormal score with a threshold value, and judging whether a comprehensive abnormal state exists or not; if the judgment result is abnormal, performing abnormal cause decoupling processing and causal inference to obtain a candidate root cause set; and inputting the candidate root cause set into a deep learning causal inference model to obtain an anomaly diagnosis result. A physical perception residual scoring mechanism is introduced, a comprehensive anomaly score is combined on the basis of anomaly detection, a weighted calculation method is adopted, and the contribution degree of each data source to an abnormal state can be accurately evaluated.

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 discloses a traditional Chinese medicine intelligent diagnosis method and system based on a CoT large model. The method and system can be applied to intelligent health consultation service, primary medical auxiliary diagnosis, traditional Chinese medicine education training, traditional Chinese medicine knowledge popularization, remote medical support, traditional Chinese medicine scientific research assistance, health management platforms and other scenes. According to the traditional Chinese medicine intelligent diagnosis method and system based on the CoT large model, the CoT instruction is adopted to guide the large model to carry out information perfection degree evaluation on the context information, when the context information is incomplete, a guiding type question is generated to guide a user to supplement missing information, the missing information serves as supplementary input of large model reasoning, and therefore the accuracy of diagnosis is improved. The large model can obtain complete inquiry information, and then a professional, accurate and interpretable diagnosis result is generated according to the complete inquiry information. Compared with the prior art, the method has the capability of actively guiding the user to provide complete question information, and the CoT instruction is adopted to guide the large model to perform reasoning, so that the traditional Chinese medicine diagnosis capability of the large model is improved.