373 results about "Diagnostic information" patented technology

The invention relates to the technical field of power distribution protection cabinet fault diagnosis, and discloses an intelligent fault diagnosis and rapid isolation method for a power distribution protection cabinet, and the method comprises the steps: synchronously collecting electrical parameters, environment states and equipment motion signals in the cabinet as multi-source monitoring data when the power distribution protection cabinet operates; parameter fluctuation, state change and action time sequence features are extracted after preprocessing, cross-data association analysis is carried out through a multi-dimensional association model to generate an association feature set, a fault diagnosis information set is formed based on the identified abnormal types, fault positions and development trends, an isolation strategy is formulated according to severity levels and influence ranges, and a fault diagnosis result is obtained. And generating a rapid isolation instruction and dynamically adjusting along with fault evolution. According to the method, multi-source data fusion analysis is realized, faults can be accurately diagnosed and rapidly isolated, the operation safety and reliability of the power distribution protection cabinet are improved, and the method is suitable for fault processing of the power distribution protection cabinet in a power system.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.

The invention relates to the technical field of medical data processing, in particular to a lung cancer risk early warning system based on remote four diagnosis information, which comprises an acquisition module, a generation module, a calculation module, an early warning module and a correction module. According to the method, through multi-modal time sequence fusion and causal map driven closed-loop regulation and control, the more stable, interpretable and adaptive early warning capability for the lung cancer risk is realized, heterogeneous evidences from observation, listening and interrogation are aggregated into a dynamic alarm index according to time and causal paths, instantaneous noise is filtered by using trend consistency and adversarial test, and the early warning effect is improved. Therefore, false alarms caused by short-time fluctuation are remarkably reduced while the sensitivity to real abnormity is kept; the method achieves balance among false alarm reduction, missed diagnosis risk reduction, positive prediction value improvement and user sampling burden optimization, and effectively solves the problem of insufficient early warning accuracy caused by risk identification lag due to dependence on single static medical record information.

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 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 provides an intelligent medical multi-round dialogue diagnosis reasoning method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: constructing time sequence features through employing an attention mechanism for time sequence symptom description, and carrying out bidirectional sequence modeling to generate comprehensive symptom features; using the medical knowledge graph to detect logic contradictions and information loss to generate standardized features; calculating information gain of an inquiry direction based on a deep neural network to generate optimal inquiry content; and iteratively updating according to user feedback until the diagnosis information entropy is lower than a threshold value, and outputting a diagnosis result. According to the invention, the accuracy and efficiency of medical diagnosis are improved.

The invention belongs to the technical field of artificial intelligence, and relates to a distributed photovoltaic system abnormity monitoring and intelligent early warning method. Key parameters of the distributed photovoltaic system are collected at high frequency, noise filtering and signal normalization processing are carried out on data, then multi-domain feature extraction is carried out, anomaly detection is carried out through auto-encoder reconstruction error analysis, convolutional neural network feature classification and a single-class support vector machine, and an anomaly detection result is output. Establishing a multi-dimensional anomaly evaluation model, and synthesizing obtained evaluation results of all dimensions to obtain a final evaluation result; the method comprises the following steps: carrying out hierarchical classification to identify specific anomaly types, carrying out diagnosis of different types of anomalies, identification of typical anomalies and evaluation of fault severity, outputting the specific anomaly types and severity, and providing detailed diagnosis information for early warning decision making. The system state is comprehensively evaluated, corresponding measures are taken according to the grading early warning mechanism and the abnormity severity degree, and complete distributed photovoltaic system abnormity monitoring and intelligent early warning are formed.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention relates to the technical field of heating, ventilation and air conditioning control, in particular to an intelligent laboratory air quality control system and method.The intelligent laboratory air quality control system comprises a data acquisition module, an intelligent decision module, a fault diagnosis module and an execution control module; meanwhile, feedback type multi-target collaborative operation parameters are generated through a plurality of heterogeneous strategy models; the fault diagnosis module analyzes equipment health data through an AI health degree baseline model, and outputs diagnosis information as a dynamic constraint; and the execution control module fuses the compensation control sequence, the cooperative parameters and the diagnosis information, generates a final physical control instruction verified by a safety boundary rule, and drives a heating ventilation air conditioner execution mechanism. According to the invention, through a control strategy integrating prediction, optimization and diagnosis, the stability of the laboratory environment, the operation economy and the long-term operation reliability of the system are significantly improved.

A Predictive Diagnostic Information Capability-Technology (PreDICT™) system (100) enables users including expert and nonexpert users to provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information. The illustrated system (100) generally includes a user device (102) for use by a user assisting a subject (104), a processing platform (108), and a network (106) for connecting the user device (102) to the processing platform (108). The system (100) may also involve an emergency response network (130) that includes public-safety answering points (PSAPs) (132). The processing platform (108) processes the sensor information and other information from the user device (102), determines risk stratification information as well as medical diagnosis and treatment option information based on machine learning technology, and provides output information to the user device to assist the user in treating the subject (104).

The present invention relates to an AI-based system and method for generating enhanced radiology reports. The system comprises a database for storing multimodal patient data, a natural language processing (NLP) module for extracting clinical information, and a machine learning module for correlating the clinical information with radiology images to identify diagnostic insights. An AI-based report generation module analyzes the images and clinical information to generate a preliminary report, which is refined based on radiologist input. The generated report is then integrated into the patient's electronic health record. The system employs techniques such as multimodal deep learning, active learning, explainable AI, and federated learning to enhance diagnostic accuracy, capture expert feedback, provide transparency, and enable multi-institutional collaboration. The invention aims to improve the accuracy, efficiency, and value of radiology reporting in patient care.

The invention relates to a transformer substation switch state monitoring system based on centralized monitoring, and relates to the technical field of power system automation, and the system comprises a data collection module which is used for integrating multi-source heterogeneous data; the state judgment module outputs the unique real-time comprehensive state of each switch according to the communication state, the alarm condition function and the multi-stage judgment logic of the original remote signaling; the outage diagnosis module is used for classifying events when the switch trips and automatically screening event sequence records (SOE) to position core diagnosis information; the influence domain pre-judgment module is used for determining all potential influenced switch sets through a graph traversal algorithm on the basis of a power grid topological structure when accident tripping occurs; and an interface generation module. According to the invention, through automatic integration and visual linkage of the operation state, the fault reason and the influence range of the switch, the monitoring intuition, the diagnosis accuracy and the emergency disposal decision-making efficiency are significantly improved.

The invention provides an AI-driven new energy equipment fault early warning and self-healing method and system, and the method comprises the steps: obtaining multi-source physical data of new energy equipment, carrying out the preprocessing, and extracting a key feature quantity reflecting the health state of the new energy equipment; inputting the key characteristic quantity into a constructed diagnosis model for fault diagnosis to obtain a diagnosis result; if the diagnosis result shows that a fault exists, logic judgment is conducted according to the diagnosis result and a preset intelligent self-healing strategy, and whether self-healing operation is allowed to be executed or not is determined; if the self-healing operation is allowed to be executed, a standardized control instruction is generated and sent to an equipment executing mechanism, and the equipment executing mechanism is driven to complete the self-healing operation; and if the self-healing operation is not allowed to be executed, the diagnosis information, the early warning report and the disposal suggestion are pushed to the operation and maintenance monitoring end, so that accurate prediction, fusion diagnosis and safe self-healing of the fault are realized, and the operation and maintenance efficiency and the equipment reliability are improved.

The invention discloses a thermal power generating unit fault diagnosis method based on a fault knowledge base and an RAG model. The method comprises the steps of 1, data acquisition and preprocessing; 2, real-time diagnosis is carried out, early warning is given out according to conditions, and on the basis of the clean data flow output in the step 1, uninterrupted trend analysis and prediction are carried out on the collected related parameters for 7 * 24 hours through deviation monitoring and a regression algorithm; 3, constructing a fault knowledge base and an RAG model, and giving a complete diagnosis conclusion; and step 4, through a user interaction interface, outputting a visual result for an operator to check the monitoring diagnosis information, and interacting with the system to confirm and process a fault. The invention belongs to the technical field of thermal power plant fault diagnosis, and solves the problem that a thermal power generating unit fault diagnosis method in the prior art is lack of full utilization of an existing knowledge base and a typical fault base of a thermal power plant, so that the interpretability of a diagnosis result is insufficient.

The invention relates to a multi-agent collaborative diagnosis system and method for ultrasonic multi-modal data, and the method comprises the steps: employing a cloud edge-end hierarchical deployment structure, and setting an end-side micro-agent; the edge collaboration server comprises a plurality of specialized diagnosis agents aiming at different anatomical parts respectively; each specialized diagnosis agent runs as an independent calculation process, cross-process diagnosis information interaction is carried out through a message bus in a publishing and subscribing mode, and the edge collaboration server is further configured with a logic verification unit which executes intermediate quality control and generates an online diagnosis report based on logic mutual identification; the cloud large model platform is in network connection with the edge collaboration server and is deployed with a general medical large model; the problems of misdiagnosis and missed diagnosis caused by single organ isolated diagnosis are effectively solved, and the intelligent level and logic leakproofness of ultrasonic diagnosis are improved.

The invention discloses a PHP application vulnerability verification sample automatic generation method based on a large language model and RAG, and the method comprises the following steps: 1, recognizing a frame, configuration and routing of a target application, and generating an entry candidate set; 2, extracting a reachable path from an entrance to a sensitive operation by utilizing static analysis, and generating a structured path constraint list; 3, integrating the entry set and the constraint list into a prompt, retrieving an enhanced fragment from a knowledge base based on the constraint list, and driving a large language model to generate a candidate request sequence; 4, executing the sequence, verifying vulnerability triggering through a typed rule, and analyzing and confirming that a request is triggered by a legal entry in combination with a call stack; and 5, when verification fails, extracting diagnosis information as an increment constraint and feeding back the increment constraint to the generation step for iterative optimization until an effective sample is generated. According to the method, misjudgment is reduced through retrieval enhancement, entry constraint and call stack proofreading, feedback iterative optimization is utilized, and the automation efficiency and accuracy of PHP application vulnerability verification sample generation are improved.

Disclosed in the present application are an accelerator card management method and apparatus, and a device, a non-volatile readable storage medium, a product and a system in the technical field of computers. In the present application, a baseboard management controller constructs a corresponding management command in the protocol format of the same target protocol, so as to manage at least one accelerator card, such that unified management over multiple types of accelerator cards can be realized; and for the same accelerator card, the constructed management command comprises at least one of the following: a static-information-type command, a dynamic-information-type command, a diagnostic-information-type command, or a firmware-maintenance-type command. Therefore, comprehensive information management can be implemented for the accelerator card, thereby facilitating stable operation of the accelerator card.

The invention discloses a neural development disorder co-disease identification system based on a residual image neural network. Belongs to the technical field of neurodevelopment disorder co-disease recognition, and particularly relates to the technical field of co-disease recognition based on a neural network. The system comprises a data acquisition module for acquiring a resting-state fMRI image; the data preprocessing module is used for extracting standardized time sequences of 116 brain regions of the set resting state fMRI image; the multi-band division and feature extraction module is used for performing frequency band filtering on the standardized time sequence of each brain region to obtain a time sequence after each frequency band filtering; in each frequency band, constructing a binary topological matrix based on the PLV matrix and calculating a PLV feature vector based on the PLV matrix; the neurodevelopmental disorder co-disease recognition module is used for analyzing the brain function connection diagram through a residual image neural network model to obtain diagnostic information of neurodevelopmental disorder co-diseases; and the interpretable output module is used for outputting an abnormal brain region by taking the amplitude low-frequency fluctuation as a reference index.

A structured ultrasound report generation method based on a large language model belongs to the field of medical image processing, and comprises the following steps: firstly, automatically synthesizing a large-scale and high-quality "oral diagnosis clue-structured report" data pair from unpaired ultrasound report texts through a multi-agent data construction framework; thirdly, aligning the dictation clues with a mechanism standard template by adopting a template-enhanced supervised fine tuning technology, and standardizing the structure and terms of a report by utilizing a preset template library so as to bridge the domain knowledge gap of the model; and finally, through a defect-oriented preference optimization strategy, enabling the model to learn from a defective report generated by the model, and continuously improving the diagnosis accuracy and reliability. According to the method, the fragmented diagnosis information dictated by a doctor in the examination process can be automatically converted into a complete, standard and high-quality structured clinical report in real time, and the report generation efficiency and the standardization level are remarkably improved.

The invention discloses a clinical auxiliary judgment method based on large model fine tuning, and the method comprises the following steps: S1, collecting multi-modal clinical data, and carrying out the preprocessing of the multi-modal clinical data; s2, fusing the preprocessed multi-modal data, and constructing a training sample set conforming to an instruction fine tuning format; s3, performing multi-round instruction fine tuning on the LLaMA model, and updating parameters by adopting a hierarchical freezing strategy; s4, executing a clinical task by applying the fine tuning model, generating a diagnosis text, and analyzing and extracting a diagnosis result; s5, comparing the diagnosis result with a standard knowledge base to generate structured diagnosis information; s6, calculating a confidence score and screening credible diagnosis information in combination with historical medical records and similar medical records; and S7, outputting clinical auxiliary judgment results sorted according to confidence. According to the method, efficient fusion and intelligent analysis of multi-modal clinical data can be realized, and professional accuracy and result credibility of a large model in clinical auxiliary judgment are improved.

In some embodiments, a method for die-level monitoring is provided. The method includes distributing user data throughout a plurality of storage nodes through erasure coding, wherein the plurality of storage nodes are housed within a chassis that couples the storage nodes. Each of the storage nodes has a non-volatile solid-state storage with non-volatile memory and the user data is accessible via the erasure coding from a remainder of the storage nodes in event of two of the storage nodes being unreachable. The method includes producing diagnostic information that diagnoses the non-volatile memory on a basis of per package, per die, per plane, per block, or per page, the producing performed by each of the plurality of storage nodes. The method includes writing the diagnostic information to a memory in the storage cluster.

The present invention relates to a system (1) for detecting a disease in a person, in particular a contagious disease such as COVID-19, this detection system comprising: —an acquisition device (7) for acquiring examination data on the person, this acquisition device comprising, in particular, at least one physiological measurement sensor such as a radar, and a thermal camera for acquiring this examination data, —a data processing device (3) arranged to receive this examination data obtained by the acquisition device, —a display device (30) arranged to display diagnostic information on the disease based on an analysis of the examination data, this diagnostic information possibly being representative of a level of probability that the person has the disease.

The invention discloses a parcel clamping stagnation diagnosis and processing system and method based on a large-scale language model, and the system comprises an acquisition module, a multi-modal fusion layer, a prompt construction module, an LLM large-scale language model, a decision module, an execution module, a feedback collection module and a reinforcement learning module. The matching method comprises the steps of monitoring, sorting, parcel state diagnosis, automatic or human intervention execution of corresponding decisions according to diagnosis results, summarization and feedback, and enhancement of LLM large-scale language model learning. The parcel sorting condition is monitored in a data fusion mode, abnormal numerical values are responded and diagnosed, the model judges whether clamping stagnation occurs or not, measures such as confidence analysis, decision execution of distribution of affected target groups, manual troubleshooting and continuous monitoring are given, an operator is allowed to know diagnosis information and then conduct manual intervention, and the accuracy and the reliability of parcel sorting are improved. And the feedback is recorded in time for the model to update the strategy, the architecture is optimized, and the accuracy and response efficiency of subsequent diagnosis are improved.

A deep learning method, system, device, and medium for drug recommendation are disclosed. The method includes: establishing drug representations; establishing patient representations; and generating drug prediction results. These three steps first utilize an RNN to establish patient representations, then integrate patient diagnostic information, treatment information, and historical medication information, employing dual attention to assign weights at different element levels in patient history and single visits, and finally using a neural network for drug recommendation. Furthermore, patient similarity can be incorporated for drug recommendation, effectively improving the accuracy and safety of drug recommendations. The system, device, and medium, through the storage and utilization of relevant functional modules, realize drug recommendation using deep learning methods. As a medical auxiliary tool, it can significantly improve the discrimination quality and work efficiency of pathologists.

The application discloses a similar case recommendation method and system, belongs to the technical field of clinical decision support system, and aims to solve the technical problem that the implementation difficulty of similar case recommendation is extremely great and the feasibility is not high by using a deep learning algorithm and a natural language processing technology.The technical scheme is as follows:the method is specifically as follows: data preprocessing: extracting the symptom and diagnosis information of the case admission record electronic medical record; and processing the case into a standard symptom and standard diagnosis list, and extracting the treatment information of the case for storage in a dictionary to assist in subsequent acquisition of the similarity degree of the case; wherein, the treatment information comprises age and department; obtaining the symptom weight: based on the knowledge graph of the symptom and disease related knowledge, extracting the weight of the symptom in the disease diagnosis; obtaining the similarity degree of the case: according to the symptom and diagnosis list of the target case, and fusing the weight to obtain the similarity degree of other cases and the target case; similar case recommendation.

The application discloses a medical expense insurance simulation decision method and system based on data fusion, and relates to the technical field of medical expense insurance simulation decision. When a target patient has obtained a current stage treatment scheme, but the system cannot determine part of the medication sites to be determined as a unique medication direction according to existing diagnosis information and completed examination results, the candidate medication direction is reserved first, then the supplementary candidate is formed according to the medication category, stage continuation state and subsequent processing path, and the supplementary result of the previous stage corresponding to the same to-be-supplemented site is combined to perform an overlay determination, and only the unoverlaid part generates the supplementary result of this round; when subsequent new data arrives, only the affected unoverlaid part that satisfies the boundary condition and no longer establishes the original support relationship is executed for local reopening, so that the current stage input that can be called by insurance simulation decision is formed.

1. Name of the product in this design: Surge arrester diagnostic instrument controller (10kV). 2. Purpose of this design: To control a surge arrester diagnostic instrument and convert diagnostic data into diagnostic information that can be evaluated. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The application discloses a drug recommendation method and system based on drug mapping and diagnosis enhancement, the natural language processing technical field and the recommendation system field, aiming at the problems of implicit drug and disease association, single diagnosis information and poor interpretability in the existing drug recommendation technology, and realizes accurate recommendation through four core modules: a diagnosis enhancement module utilizes a large language model to associate diagnosis and treatment methods, physical examination, and generates enhanced diagnosis information; a drug mapping module establishes an explicit association of drugs, diagnosis and treatment methods, a drug recommender module combines a cross-attention mechanism and a graph neural network, and respectively models enhanced diagnosis representation and drug external knowledge; and a joint training module balances single-disease and global recommendation loss to optimize model performance. The application is superior to existing mainstream models in various indicators, can significantly improve the accuracy, clinical interpretability and safety of drug recommendation, and is suitable for assisting doctors in formulating personalized medication schemes.

The application discloses a diagnosis monitoring method and system based on a tree topology, and the method comprises the following steps: a controller generates an I / O module diagnosis information acquisition task and an I / O network diagnosis information structure according to a configuration file; the diagnosis information acquisition task acquires diagnosis information periodically, and writes the diagnosis information into the I / O network diagnosis information structure; the controller combines a tree I / O network topology relationship to judge the states of network elements in a step-by-step manner from bottom to top according to the I / O module diagnosis information and the I / O network diagnosis information; and configuration software acquires the states of I / O network topology elements after judgment and collection from the controller, and displays the states in a graphical manner. The technical scheme can realize intuitive display of abnormal elements of each level of the I / O network, realize abnormal collection from bottom to top, and improve the online monitoring experience.

The present invention provides an on-vehicle ECU detection method, device and medium based on ECALL, which relate to the field of on-vehicle information detection. The method comprises: an on-vehicle information service providing platform sends a compiled detection instruction to a network communication service providing platform, the network communication service providing platform generates detection information and sends it to an on-vehicle emergency call system, the on-vehicle emergency call system parses the detection information to obtain a diagnostic instruction message, and sends it to an on-vehicle control module, the on-vehicle control module obtains diagnostic information corresponding to the diagnostic instruction message, and sends it to the on-vehicle emergency call system, the on-vehicle emergency call system compiles the diagnostic information into service information, and sends it to the on-vehicle information service providing platform via the network communication service providing platform, so as to remotely obtain on-vehicle ECU information in a vehicle without T-BOX installed through the on-vehicle emergency call system, and the vehicle ECU information can be obtained without pre-embedded diagnostic configuration items.