436 results about "Remote diagnosis" patented technology

The invention discloses a storage hard disk remote diagnosis system and method based on the Internet of Things, and relates to the technical field of health management of the Internet of Things and storage equipment. The system comprises a data fusion module, a causal analysis module, a risk analysis module and a map construction module. The data fusion module collects SMART parameters, IO operation time sequence data and environment data through Internet of Things equipment, dynamically distributes multi-source data weights by using an attention mechanism, and extracts hard disk health state features. And the causal analysis module is combined with the multi-scale time sequence convolutional network and the Bayesian causal network to identify periodic abnormal fluctuation and generate a fault root cause analysis report. The risk analysis module matches historical cases through federal incremental learning, calculates a hard disk fault risk index and generates an early warning signal. And the atlas construction module optimizes resource isolation, data migration and response paths according to the risk indexes, generates a visual operation and maintenance atlas, provides fault positioning, risk links and repair priorities, and improves the operation and maintenance management efficiency.

The invention discloses a brain-computer hybrid intelligent closed-loop swallowing rehabilitation system for treating dysphagia after stroke, and relates to the field of artificial intelligence medical instruments. The system is composed of a plurality of key modules: an intelligent swallowing brain-computer interface module for accurately identifying a swallowing intention through multi-modal physiological signal acquisition and processing; the intelligent swallowing functional electrical stimulation module is used for finely regulating and controlling stimulation parameters according to the condition of the patient; the AI auxiliary online integrated module is used for realizing non-invasive brain-controlled swallowing and high-precision AI regulation and control; the closed-loop intelligent feedback regulation and control module is used for real-time monitoring, early warning and self-adaptive optimization of electrical stimulation; the cloud platform remote monitoring and data storage module supports remote diagnosis and treatment of doctors; and the man-machine interaction interface display and printing module optimizes doctor-patient interaction. According to the system, a brain-computer interface and FES are combined, a personalized, safe and efficient rehabilitation scheme is provided for a patient based on data driving and an artificial intelligence algorithm, swallowing function recovery can be accelerated, complications are reduced, and the system has great clinical and social values.

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

The invention discloses a verifiable medical data query method based on a DAG block chain architecture, and belongs to the technical field of block chains and medical information management. According to the method, medical events such as diagnosis and treatment records and image reports are modeled into DAG nodes, an on-chain topology with time sequence and causal association is constructed by encrypting a hash pointer, dynamic indexing and multi-keyword verifiable query are achieved in combination with an off-chain authentication data structure, a verification object containing a matching node set and a verification path is received, and the verification object is subjected to dynamic indexing and multi-keyword verifiable query. The authenticity, integrity and timeliness verification of the result is independently completed based on the authentication root stored on the chain without depending on a third party. According to the method, through an on-chain and off-chain collaborative storage and incremental authentication updating mechanism, on the premise of ensuring privacy and tamper-proofing of medical data, query efficiency, verification performance and expandability under high concurrency are remarkably improved, communication and resource expenses are lower, and the method is suitable for large-scale popularization and application. The method is suitable for application scenes such as cross-institution medical data sharing, remote diagnosis and treatment and compliance auditing.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention relates to the technical field of remote medical treatment, in particular to a remote ultrasonic diagnosis support system based on cloud computing, which comprises an echo feature analysis module, a dynamic focus screening module, a case image comparison module, a focus trend classification module and a remote diagnosis support module. According to the method, by analyzing echo signals in a remote ultrasonic image data stream and calculating the echo change rate between image frames, the dynamic characteristic capturing capacity of a lesion area is improved, calculation of a gray gradient change value is combined, a suspected lesion area conforming to a change rate stable interval is screened, lesion shape edge curve contour parameters are utilized, and the dynamic characteristic capturing capacity of the lesion area is improved. The method comprises the following steps: performing matching analysis with an ultrasonic case image database, enhancing the accuracy of lesion category attribution, analyzing the morphological change trend of a lesion area, combining trend stability index calculation, realizing lesion evolution classification, ensuring the reasonability of lesion type division, and marking a progressive lesion area, so that remote diagnosis is more intuitive; and the auxiliary decision-making capability is improved.

The invention relates to the technical field of terminal management, and particularly discloses a remote diagnosis management method and system based on a vehicle-mounted terminal, and the method comprises the steps: obtaining multi-source heterogeneous data collected by the vehicle-mounted terminal in real time; performing dynamic priority classification on the multi-source heterogeneous data to obtain a multi-source data priority set; constructing an edge-cloud collaborative multi-modal model according to the multi-source data priority set, and obtaining multiple pieces of dynamic data information according to the edge-cloud collaborative multi-modal model; and acquiring data association information and semantic information of each piece of dynamic data distribution information, and constructing a vehicle data association topological graph according to the data association information and the semantic information. According to the invention, the analysis and utilization capability of the vehicle data is effectively improved. The constructed edge-cloud collaborative multi-modal model can efficiently process multi-source data, reduce transmission delay and cloud burden, and improve real-time performance and decision accuracy.

The invention belongs to the technical field of image processing, and discloses a remote digital image analysis cooperation system. The virtual diagnosis module is used for collecting a continuous pathological section sequence and carrying out space registration to obtain a registration section; the model building module is used for performing three-dimensional voxel reconstruction on the registration slices to obtain a three-dimensional voxel reconstruction model, and performing semantic enhancement to obtain a three-dimensional pathological voxel model; the collaborative interaction module is used for generating virtual avatars of G experts based on the VR technology, labeling the three-dimensional pathological voxel model and obtaining space anchor point labeling data of the experts; the graph driving module is used for constructing a structured semantic tree according to the space anchor point annotation data, detecting annotation conflicts and obtaining an annotation conflict detection result; the conflict resolution module is used for performing conflict resolution on the marking conflict detection result to obtain a consensus suggestion; the collaborative efficiency and conclusion reliability of remote diagnosis are improved, and a systematic solution is provided for precise diagnosis of complex pathological cases.

The invention relates to the technical field of equipment maintenance, in particular to a distribution box remote diagnosis and maintenance system which comprises a voltage deviation detection module, a node hot stacking identification module, a channel stability classification module, a contact aging trend evaluation module and a period adjustment trigger module. According to the method, peak value change of effective voltage in each channel period in the distribution box is collected and compared with a preset criterion so as to accurately identify an abnormal discharge unit and accurately position a potential risk channel, and the change rate of equivalent series capacitance, contact conductance and contact resistance of a node is continuously sampled in a non-power-on state so as to accurately identify the potential risk channel. According to the method, multi-parameter same-trend change characteristics are determined, the contact aging process is accurately evaluated by integrating the parameter trend, the node trend and the maintenance plan period remaining time are subjected to cross comparison, the maintenance task advance opportunity is automatically judged, the maintenance plan is actively adjusted, the node fault missing detection risk is effectively reduced, and the maintenance timeliness and accuracy are remarkably improved.

The invention discloses a fault remote diagnosis and digital process maintenance method for water treatment equipment, and relates to the field of water treatment equipment operation and maintenance. The method comprises the steps of unifying time and semantics, generating a quality marking time sequence, and performing working condition benchmark and tracing; according to self-adaptive identification mechanism parameters, constructing a digital twinning and trusted domain, and comparing to obtain a residual error and anomaly in real time; forming root cause evidence and sorting under topology and energy and material closed constraints; generating scripts and rollback, gray scale issuing, sentry monitoring and transaction records through steady / transient simulation and constraint checking; and convergence, energy consumption, chemical dosing, water quality evaluation, precipitation versioning template recharging and closed-loop updating are carried out. Through unified working condition data and digital twinning mechanism-data fusion diagnosis, simulation-driven gray reversible execution and knowledge template recharging under quantitative evaluation, a traceable, reusable and migratable full-link remote diagnosis and process maintenance closed loop is constructed.

The invention relates to the technical field of charging equipment, and discloses a charging pile fault remote diagnosis and early warning system, which comprises the following steps: step 1, multi-source data acquisition, step 2, data preprocessing, step 3, feature extraction engine, step 4, fault diagnosis and early warning, and step 5, fault diagnosis and early warning. 5, dynamic early warning judgment; 6, early warning information generation; 7, diagnosis result storage; and 8, man-machine interaction feedback. Through multi-source data fusion acquisition, standardized processing, multi-dimensional feature extraction, cloud collaborative diagnosis and dynamic early warning mechanism design, early accurate recognition and active protection of faults are realized, discrete data are converted into actionable early warning information, operation and maintenance response delay is greatly reduced, and data traceability is ensured through block chain evidence storage.

The invention discloses a new energy automobile intelligent network connection remote diagnosis and fault early warning system, and relates to the technical field of new energy automobiles, the system comprises a data acquisition module, a data transmission module, a data analysis and processing module, a personalized early warning model construction module and a fault diagnosis and early warning module; the data acquisition module is used for acquiring driving habit data, vehicle use environment data and historical fault record data of the new energy vehicle; according to the method, a personalized fault early warning model is established for each vehicle by collecting and analyzing multi-dimensional data such as user driving habits, vehicle use environments and historical fault records, accurate early warning of new energy vehicle faults is realized, and by collecting and analyzing the multi-dimensional data, the personalized difference of vehicle use is fully considered, so that the accuracy of new energy vehicle fault early warning is improved. Compared with a traditional general fault early warning system, the accuracy and timeliness of fault early warning are greatly improved, and the situations of false alarm and missing alarm are effectively avoided.

The invention relates to the technical field of oral health management, and discloses an oral health management system based on big data, which comprises a data acquisition module, a data processing module and a data processing module, the intelligent diagnosis module is used for identifying oral health problems and diagnosing oral diseases through deep learning and an artificial intelligence algorithm, the health management suggestion module is used for predicting the future oral health trend of a user based on historical health data according to an intelligent diagnosis result, and the intelligent prediction module is used for predicting the future oral health trend of the user based on the historical health data. And the remote diagnosis and treatment module is used for providing auxiliary services for remote dental diagnosis and treatment. Through a data acquisition module, a data fusion module and an intelligent diagnosis module, oral cavity data and medical image data of a user are integrated, so that formulation of a personalized health management scheme is achieved, personalized oral cavity nursing schemes, diet guidance and living habit adjustment suggestions are provided for each user, and the user experience is improved. Therefore, the accuracy and effect of oral health management are improved.

The invention relates to the technical field of Internet of Things, and discloses a remote diagnosis system and method for a complex system in an Internet of Things environment, and the system comprises a data collection module which is used for collecting the operation state information of the complex system; the data transmission module is used for transmitting the running state information to the cloud processing center; the cloud processing center is used for performing fault analysis according to the received operation state information based on a machine learning algorithm, generating a fault diagnosis result and a corresponding maintenance suggestion and transmitting the fault diagnosis result and the corresponding maintenance suggestion to the user terminal; and the user terminal is used for receiving the fault diagnosis result and the maintenance suggestion, remotely accessing the system state report of the complex system and remotely controlling the complex system. According to the invention, efficient remote monitoring, accurate fault diagnosis and real-time maintenance guidance of the complex system in the Internet of Things environment can be realized, the maintenance cost is reduced while the maintenance efficiency is improved, and thus the maintainability and safety of the complex system are enhanced.

The invention discloses an intelligent medical system suitable for an internet hospital, and relates to the technical field of intelligent medical treatment, and the system comprises the following steps: building an electronic health record, and obtaining the information of a patient; performing remote diagnosis and treatment according to the patient information to obtain a diagnosis and treatment result; performing medicine management according to the diagnosis and treatment result to obtain medicine management information; performing patient health monitoring according to the medicine management information to obtain patient health data; personalized health suggestions are made according to the health data of the patient, and health suggestions are obtained; performing patient health tracking and feedback according to the health suggestions to obtain health feedback; making and adjusting a rehabilitation plan according to the health feedback to obtain the rehabilitation plan; and performing health assessment and diagnosis and treatment feedback according to the rehabilitation plan. The accuracy of drug recommendation is improved, the safety and effectiveness of a treatment scheme are ensured, the scientificity and individual adaptability of health suggestions are improved, and the effectiveness and sustainability of a health management scheme are enhanced.

The invention discloses an intelligent medical remote diagnosis and treatment system based on artificial intelligence, which adopts a convolutional neural network to extract ultrasonic, computed tomography and magnetic resonance imaging characteristics, adopts a long-short term memory network to extract electrocardiogram, blood pressure and blood sugar characteristics, and subsequently fuses the characteristics with an electronic medical record. And finally, the multi-modal local large model is utilized to diagnose the structured data. The specific implementation steps of the system are as follows: S1: data acquisition; s2, data preprocessing; s3, feature extraction; s4, data transmission; s5, performing diagnosis; s6, remote interaction; s7, data analysis; s8, manual verification is carried out; s9, updating the model; the defects that traditional medical image and physiological signal detection is long in consumed time and large in error are overcome, the problem that medical resources are not evenly distributed is effectively solved, and improvement of multi-mechanism cooperation and the overall diagnosis capacity is promoted.

The invention relates to the technical field of medical equipment, in particular to a nervous system remote monitoring system based on intelligent wearable equipment, which comprises a sensor module, a data processing module, a real-time monitoring module, a remote transmission module, a feedback and intervention module, a data storage and analysis module and a user interface module. The sensor module collects nervous system signals of a human body, the data processing module conducts denoising, feature extraction and mode recognition on the nervous system signals and generates warning signals, the remote transmission module transmits data to a remote server or a medical terminal, and the feedback and intervention module provides health suggestions and triggers intervention measures according to monitoring results. The data storage and analysis module performs comprehensive analysis on long-term monitoring data by using a big data analysis technology, and the user interface module provides visual display through a smart phone or wearable equipment. According to the system, real-time monitoring, remote diagnosis and personalized health management of the nervous system are realized, and the system has wide clinical application value.

The invention belongs to the technical field of battery health detection, and particularly relates to a remote diagnosis and maintenance guiding system for a vehicle battery pack box. The remote diagnosis and maintenance guidance system comprises a data acquisition edge processing module, a multi-mode communication and cache transmission module, a composite fault diagnosis module, a sensor self-calibration and environment compensation module, a cloud big data analysis and model iteration module, a remote maintenance guidance and AR visualization module and a system monitoring module. According to the invention, through multi-mode communication and edge caching, data is ensured not to be lost in an extreme environment; a composite fault is identified through a fusion diagnosis algorithm, and maintenance direction deviation is avoided; according to the invention, a sensor self-calibration and compensation mechanism is provided, false alarms caused by low temperature / high humidity are inhibited, and the accuracy of battery data monitoring is improved.

The invention relates to the technical field of wall-hanging stove temperature control, in particular to a remote diagnosis method and system based on automatic intelligent temperature control. Dividing to obtain a use cycle, and obtaining use parameters of the wall-hanging stove at the end of the use cycle; dividing the water supply pipe into a first pipeline, a second pipeline and a third pipeline; constructing a first incrustation scale prediction model, and identifying the wall-hanging stove use parameters of the first pipeline and the third pipeline to obtain first incrustation scale data and third incrustation scale data; constructing a second scale prediction model, and identifying use parameters of the second pipeline wall-hanging stove to obtain second scale data; obtaining final incrustation data according to the first incrustation data, the second incrustation data and the third incrustation data, and measuring and calculating to obtain an incrustation early warning coefficient for early warning; and a heating parameter correction model is constructed, corrected heating parameters are obtained according to the final-period water scale data, and adjustment is conducted. According to the invention, through identification, early warning and correction of the scale in the wall-hanging stove, the temperature control precision of the wall-hanging stove is ensured.

The present invention discloses a fault diagnosis method, system, device and medium. The method comprises: receiving a vehicle diagnosis push message sent by a remote diagnosis server; wherein the vehicle diagnosis push message comprises at least one of the following: a diagnosis task type and a diagnosis script information; obtaining a corresponding target diagnosis script file and a corresponding target diagnosis condition according to the diagnosis task type and / or the diagnosis script information; wherein the target diagnosis script file is obtained by converting a diagnosis database and a diagnosis sequence, and the target diagnosis script file is an interpretive language file; and performing fault diagnosis on a target vehicle according to the target diagnosis condition and the target diagnosis script file. The present invention eliminates the need for the remote diagnosis client to parse the diagnosis sequence and frequently read the diagnosis database, thereby reducing the load on the remote diagnosis client and improving the operating efficiency of the diagnosis task; at the same time, the diagnosis task type is partitioned to improve the diagnosis efficiency.

The invention provides an edge computing remote medical task offloading method based on an improved PPO algorithm, and the method comprises the steps: dynamically selecting an optimal offloading mode through a PPO-MED intelligent decision module according to the characteristics of a medical task and a system resource state, and enabling the offloading mode to comprise a local computing offloading mode (LCO), an edge server offloading mode (ESO) and an edge intelligent preprocessing offloading mode (EIPO); a PPO-MED algorithm models a medical task unloading problem into a Markov decision process, and an unloading strategy is optimized through deep reinforcement learning. The improved PPO-MED algorithm realizes intelligent decision, can dynamically select the optimal unloading strategy according to the real-time system state, task urgency and resource availability, significantly improves the adaptive capacity and service quality of the system in a complex environment, compresses the original medical data into a plurality of orders of magnitude, and improves the reliability of the system. On the premise that the diagnosis accuracy is guaranteed, the transmission delay is remarkably reduced, and the method is particularly suitable for remote diagnosis application of basic medical institutions under the limited bandwidth condition.

The invention discloses a vehicle remote diagnosis control method and device based on a vehicle fault code, and the method comprises the steps: collecting the parameter information of a specified part of a vehicle and the operation parameters of the whole vehicle through a CAN network by a VCU, and generating important parameter information; the VCU sets a trigger condition according to the control logic, and generates a uniquely recognizable vehicle fault code in combination with the monitoring platform; the important parameter information and the vehicle fault code are sent to a vehicle-mounted terminal T-BOX through a CAN bus; the T-BOX performs analysis and recombination according to a CAN communication protocol with the VCU to generate a message; the T-BOX uploads the message to a monitoring platform; the monitoring platform analyzes the message to obtain important parameter information and a vehicle fault code, and performs matching analysis with a whole vehicle fault detail list according to the important parameter information and the vehicle fault code to generate a vehicle fault record; and informing platform supervisors, vehicle after-sale maintenance personnel and drivers of the fault details and the fault analysis conclusions. According to the method, the problems of decisive support, hysteresis quality, rapid flow increase and the like of data in vehicle fault remote diagnosis can be solved.

The invention relates to a vehicle remote diagnosis method and system and a medium, and the method comprises the steps: collecting diagnosis data from a vehicle, and the diagnosis data comprises vehicle controller area network data, vehicle electronic control unit logs, fault diagnosis codes and vehicle driving data; transmitting the diagnosis data to a cloud platform through a wireless network; analyzing the diagnosis data on the cloud platform by using an artificial intelligence model, wherein the artificial intelligence model comprises a fault diagnosis model and a fault prediction model; generating a diagnosis result based on an analysis result of the artificial intelligence model, wherein the diagnosis result comprises current fault diagnosis and potential fault prediction; sending the diagnosis result to the vehicle or the user equipment; and executing corresponding maintenance or preventive maintenance operation based on the diagnosis result. According to the invention, real-time monitoring, remote diagnosis and predictive maintenance of vehicle faults can be realized, so that the diagnosis efficiency is improved, and the maintenance cost is reduced.

The invention provides a TBOX-based vehicle fault diagnosis system, which is characterized in that a TBOX is installed in a vehicle and is used for acquiring vehicle data and judging whether the vehicle has a fault based on the vehicle data, and when the vehicle has the fault, the TBOX enters a diagnosis and maintenance mode, and the diagnosis and maintenance mode comprises a field diagnosis mode and a remote diagnosis mode; the diagnosis end is used for establishing a data communication channel with the TBOX in a diagnosis maintenance mode; and the TBOX is also used for transmitting the vehicle fault data to the diagnosis end through the data communication channel, receiving a vehicle configuration instruction issued by the diagnosis end, and issuing the vehicle configuration instruction to a target ECU unit of the vehicle to realize vehicle fault diagnosis full-link diagnosis operation. According to the scheme, a traditional fault diagnosis instrument is omitted, hardware cost of vehicle service diagnosis is reduced, usability and convenience of vehicle diagnosis are provided, and transmission safety and reliability between the TBOX and a platform or a terminal are guaranteed through a self-developed data transmission protocol.

The invention relates to the technical field of artificial intelligence medical diagnosis, and discloses a remote disease diagnosis system based on artificial intelligence driving. The system comprises five functional modules, a real-time medical data acquisition module acquires multi-modal physiological data of a patient from a remote terminal in real time, and the multi-modal physiological data comprises vital sign time sequence data, medical image data and symptom description text data; the cross-modal correlation analysis module carries out feature fusion on the data, extracts a correlation mode between modals and generates an abnormal feature data set with a weight mark; establishing a dynamic diagnosis baseline by the dynamic diagnosis baseline model, and adapting to individual physiological feature changes by updating parameters; the abnormal behavior detection module is used for comparing the real-time data with a baseline, identifying abnormal indexes and generating a marking result; the deep pathogenesis tracing module analyzes a potential disease mode and traces a pathogenesis path in combination with a patient's historical medical record. The system can optimize the remote diagnosis effect and assist clinical diagnosis and treatment.

The invention belongs to the technical field of fluid detection and nondestructive detection, and discloses a method for detecting bubbles in fluid (non-gas). According to the method, a multi-channel ultrasonic probe is fixedly installed on a pipeline installation plane, the ultrasonic excitation / receiving module excites the multi-channel ultrasonic probe, echo signals sequentially pass through the adjustable gain amplification module, the filtering module and the high-speed data acquisition module and then are converted into digital quantity, and the digital quantity is read by the FPGA. And the driving touch display module displays the echo signal and detection parameter setting options (detection parameters can be set through a PC host, a peripheral host, a cloud end or an FPGA module), and when the echo signal exceeds a preset alarm threshold value, an alarm signal can be output to the outside. Through an Ethernet TCP / IP protocol, the FPGA can send an echo signal or a detection result to a PC host, a peripheral host or a cloud end, thereby realizing master-slave connection, remote control, remote diagnosis and analysis, fault pre-judgment and full life cycle prediction.

The invention relates to the technical field of industrial robot fault diagnosis, and discloses a fault early warning and remote diagnosis method for an industrial robot electrical control system, and the method comprises the steps: obtaining a mechanical load parameter through a potential load model, an inertia load model and a basic dynamics model, obtaining an electrical state parameter through an electrical efficiency model, and carrying out the fault early warning and remote diagnosis. A correction factor is generated through a correction model in combination with the environment temperature, a dynamic current threshold value is calculated through a threshold value model based on the correction factor and a basic current threshold value, and finally health scores and fault early warning information are output through a diagnosis model in combination with the vibration acceleration and servo ring following errors. According to the method, mechanical, electrical and environmental multi-source parameters are fused, dynamic threshold adjustment and multi-dimensional health assessment are realized, normal working conditions and abnormal states are effectively distinguished, the fault early warning accuracy is remarkably improved, the false alarm rate is reduced, and a reliable remote diagnosis scheme is provided for the industrial robot.

The invention discloses a remote disease diagnosis method based on an audio and video technology, and relates to the technical field of medical treatment. Compared with a previous remote diagnosis method, the problem that diagnosis depends on a single data source in the prior art is solved; processing the physiological signal depending on a fixed rule or a static model; solving the problems of interpretable defects and lack of causal reasoning ability of the black box model; in the signal processing level, the audio causal separation technology based on the dynamic causal diagram improves the fidelity of pathological sound, and the Retinex illumination compensation is combined to reduce the blood oxygen detection error of the low-light scene, so that the bottleneck that the traditional single-mode signal is easily interfered by the environment is broken through; and in combination with the dynamic causal diagram and the causal diagram of the causal hidden space, the trust crisis of the black box model is effectively solved, the limitation of traditional remote diagnosis is broken through, and a technical foundation is laid for constructing an intelligent and credible remote medical system.

The invention provides a vehicle-mounted diagnosis and remote diagnosis combination method and system, and relates to the technical field of vehicle fault diagnosis, and the method comprises the steps: converting diagnosis service data into a diagnosis script, and deploying the diagnosis script to a vehicle-mounted diagnosis unit for generating a preliminary diagnosis result according to fault data; sending the preliminary diagnosis result and the fault data to a regional diagnosis center to match corresponding computing resources for coordinated analysis, and generating an intermediate diagnosis scheme; and when the confidence coefficient of the middle-level diagnosis scheme is lower than a preset threshold value, submitting the preliminary diagnosis result, the fault data and the middle-level diagnosis scheme to a cloud server for deep analysis to obtain an optimized diagnosis strategy, and respectively issuing the optimized diagnosis strategy to a vehicle-mounted diagnosis unit and a regional diagnosis center to complete collaborative diagnosis. By implementing the method, the three-level diagnosis architecture is constructed, and different architectures complete fault diagnosis of different complexity degrees, so that the pressure of a remote platform is relieved, reasonable allocation of computing resources is realized, and the overall diagnosis efficiency is improved.

The invention provides a remote rehabilitation system for a stroke patient, relates to the technical field of stroke rehabilitation, and aims to remotely diagnose the patient, periodically generate a personalized rehabilitation scheme for the patient, and timely adjust and update a training scheme according to the exercise effect of the patient. The system comprises a data acquisition terminal, a data transmission terminal, a control terminal, a doctor monitoring terminal and a patient terminal, a personalized rehabilitation training scheme generation algorithm is applied, multi-dimensional data is fused to form a comprehensive feature vector, and rehabilitation potential and requirements are evaluated. Personalized schemes covering exercise, cognition and psychological intervention are customized for the patient, dynamic adjustment is conducted according to feedback, and precise rehabilitation is achieved. The system has the functions of real-time monitoring, intelligent reminding and abnormity early warning, the rehabilitation effect is optimized, the medical burden is relieved, the life quality of patients is improved, and the system is suitable for medical institutions at all levels.