225 results about "Preliminary diagnosis" patented technology

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 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 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 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 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 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 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 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 provides a cable fault detection method based on an intelligent sensor, and relates to the technical field of power cable monitoring and fault diagnosis, and the method comprises the steps: enabling each sensor node to independently collect and analyze local data, and immediately carrying out the local preliminary diagnosis and triggering a local reflection action when a transient fault feature is detected; an adjacent node receiving the consensus request packet serves as a consensus verifier, independently verifies the consensus request based on data collected by the consensus verifier in the same time window, votes a verification result and feeds back the verification result to the consensus initiator; and according to the received voting result, if a sponsored ticket exceeds a preset threshold value, the consensus initiator confirms that fault diagnosis is established, and reports a final consensus confirmation result to a cable fault monitoring center. According to the invention, extremely low power consumption, ultra-low delay, efficient communication and high reliability of cable fault monitoring are realized, and real edge intelligence is realized.

The invention discloses a knowledge graph-AI fusion fault diagnosis method for operation and maintenance management of a power distribution network, and relates to the technical field of artificial intelligence. The problems of low initial diagnosis accuracy, insufficient diagnosis adaptation capability in different grounding forms, long fault positioning time and the like caused by external interference of zero-sequence current signals are solved, and the conditions that the operation and maintenance efficiency is reduced and the power supply loss is increased are finally avoided. The method comprises the following steps: acquiring multi-source data and constructing a dynamically updated power distribution network knowledge graph; multi-mode fault feature extraction is carried out; using an adaptive AI model to identify a fault type and performing primary judgment; and accurately positioning a fault section and analyzing a spreading path in combination with a knowledge graph. According to the method, the single-phase earth fault preliminary diagnosis accuracy is improved to reduce invalid troubleshooting of operation and maintenance, cross-scene diagnosis errors are reduced to adapt to diversified operation and maintenance scenes, average positioning time consumption is shortened, and the supporting capacity of fault diagnosis on operation and maintenance management of the power distribution network is remarkably improved.

The invention relates to the technical field of intelligent agriculture, and discloses a farmland pest image recognition inspection robot which comprises a moving and navigation platform, a wide-area sensing and screening system, a multifunctional diagnosis and marking mechanical arm and a data processing and control unit. The wide-area sensing and screening system of the robot is used for primarily screening images to lock suspicious target plants; an active physiological probe and an in-situ micro-marking nozzle module are integrated at the tail end of the multifunctional diagnosis and marking mechanical arm. The method comprises the following steps: performing contact measurement on a suspicious target plant to obtain multi-modal physiological information and performing preliminary diagnosis; when the diagnosis state is suspected, performing in-situ micro-labeling on the plant; and after a preset time interval, repositioning the plant and retesting. According to the method, wide-area image screening and contact type time sequence physiological diagnosis are combined, and the accuracy of disease and pest diagnosis, the early warning capability and the decision reliability are remarkably improved.

The invention discloses a forestry intelligent pest control system based on multi-sensor cooperation, and relates to the technical field of intelligent pest control. In order to solve the problems of low diagnosis precision and poor cooperation efficiency in the prior art, the invention provides a hypothesis-verification cooperation strategy: a system firstly generates a preliminary diagnosis hypothesis by using macroscopic sensor data, and adaptively regulates and controls a microscopic sensor according to the preliminary diagnosis hypothesis to carry out targeted verification so as to realize high-confidence diagnosis; the system further establishes a disease and insect pest spatio-temporal diffusion risk map for trend prediction, and formulates graded and partitioned precise prevention and control strategies. According to the invention, through prevention and treatment effect evaluation and closed-loop self-optimization learning of the model, intelligent prevention and treatment from passive response to active prediction are realized, and the diagnosis accuracy and the resource utilization efficiency are remarkably improved.

The invention provides a holographic inspection information enhancement method, system and device based on DPO and RAG, and relates to the technical field of holographic inspection information enhancement, and the method comprises the steps: constructing a traditional Chinese medicine original corpus and a traditional Chinese medicine knowledge graph; generating a cold start data set from the traditional Chinese medicine original corpus through an external teacher model; on the basis of the cold start data set, performing supervised fine tuning training on the Qwen2.5 7B pre-training model to obtain a preliminary diagnosis model Model1; based on a new reasoning path generated by Model1, performing multi-dimensional quality evaluation in combination with an RAG mechanism to obtain a human preference comparison data set; performing optimization training on the Model1 through a DPO method based on a human preference comparison data set to obtain an optimization diagnosis model Model2; and carrying out backtracking correction on an output result of the Model2 in combination with an RAG mechanism. According to the scheme, the theoretical preciseness of training data is ensured through a dual data purification mechanism of sampling rejection and RAG enhancement; and a dynamically adjusted DPO algorithm is adopted to realize fine-grained alignment of traditional Chinese medicine syndrome differentiation logic.

The invention discloses a defect diagnosis system for electric power line unmanned aerial vehicle inspection, and belongs to the technical field of electric power inspection, the defect diagnosis system comprises an unmanned aerial vehicle inspection unit, an edge calculation processing unit, a cloud cooperation unit and a man-machine interaction unit, the unmanned aerial vehicle inspection unit carries multiple types of sensors, and comprehensive acquisition of environment and equipment information is realized; the edge calculation processing unit completes defect preliminary diagnosis at the unmanned aerial vehicle end, and the data transmission amount is reduced; the cloud collaboration unit performs secondary verification on the diagnosis result and updates and optimizes the lightweight model; the man-machine interaction unit provides a visual operation interface and supports a worker to check data and carry out remote control. According to the system, the real-time performance, high precision and multi-scene adaptability of electric power line inspection defect diagnosis are realized through an edge-cloud collaborative architecture in combination with multi-sensor fusion and a lightweight intelligent algorithm, and the operation and maintenance efficiency of an electric power line is effectively improved, so that safe and stable operation of a power grid is guaranteed.

The invention discloses a method for visually detecting grape black root bacteria Ilyonectria sp. Based on RPA-CRISPR / Cas12a and application of the method for visually detecting the grape black root bacteria Ilyonectria sp. Based on the RPA-CRISPR / Cas12a. According to the method, an RPA specific amplification primer, a CrRNA probe and an ssDNA probe are adopted; according to the RPA specific amplification primer, an HIS gene is used as a specific recognition target, and specific amplification can be carried out on the grape black root pathogen Ilyonectria. The method for detecting the grape black root pathogenic bacteria has high specificity and sensitivity, is simple, convenient and rapid in operation flow, and is suitable for rapid screening and preliminary diagnosis of target pathogens in a field environment or a basic detection scene.

The invention discloses a high-altitude photovoltaic power station intelligent operation and maintenance and fault prediction method based on artificial intelligence, and belongs to the technical field of data processing, and the method comprises the steps: 1, estimating the real illumination value of each assembly at any moment; 2, on the basis, combining information such as temperature aging and installation altitude to construct theoretical power output of the assembly; 3, comparing deviation trends of the theoretical power and the actual power in the time window, and determining whether an abnormal trend generation event trigger signal exists in combination with an illumination fluctuation condition; 4, after an event is triggered, key data are collected to generate structural features, and preliminary diagnosis is completed based on rules; and 5, fusing the edge feature initial judgment result and the power deviation information at the center side to output a final diagnosis result. According to the scheme, through combination of physical modeling and rule judgment, an intelligent diagnosis scheme adaptive to the plateau power station environment is constructed.

The invention relates to the technical field of bearing fault diagnosis, in particular to a bearing fault diagnosis method based on a neural network and multi-criterion preference consensus, and the method comprises the steps: data collection and feature extraction, data preprocessing, fault diagnosis model matching, preliminary diagnosis and probability generation, final diagnosis and probability generation, and bearing state decision making. According to the method, a multivariate decision-making auxiliary model fusing a physical marginal value function and a neural network attention mechanism is provided, and a reinforcement learning driven consensus achievement process is combined, so that adaptive optimization of a multi-expert fault diagnosis result is realized; the problems that an existing bearing fault diagnosis method cannot deal with complex working conditions, is insufficient in physical interpretability, depends on a single decision mode and mostly adopts static weight distribution are solved, the accuracy, robustness and interpretability of bearing fault diagnosis are improved, and high-precision and low-round group fault diagnosis is achieved.

The invention discloses a doctor-patient intelligent matching method and system fusing real-time doctor-seeing information, and relates to the field of intelligent medical treatment, and the method comprises the steps: obtaining pre-stored personal information, and recognizing the doctor-seeing intention of a patient based on symptom description information and the personal information; multiple rounds of dialogue questions are sent to the user terminal, and detailed disease information is obtained; generating question options based on the detailed disease information, and sending the question options to the user terminal to receive click feedback performed by the patient through the user terminal; analyzing the disease information of the patient based on the detailed disease information and the click feedback, and determining a preliminary diagnosis and treatment result; recommending a department list for the patient based on the preliminary diagnosis and treatment result, and determining the matching degree between each department in the department list and the disease; and according to the matching degree and the preliminary diagnosis and treatment result, determining a target doctor, obtaining a real-time residual number source number of the target doctor, and sending the target doctor and the corresponding real-time residual number source number to the user terminal. According to the invention, the matching accuracy can be effectively improved.