605 results about "Remote diagnosis" patented technology

The invention provides a remote fault diagnosis method and system for operation and maintenance of a power grid. The method belongs to the technical field of power system operation and maintenance, and comprises the following steps: collecting operation state data of power grid equipment in real time through a sensor network, and carrying out real-time processing and preliminary analysis on the operation state data through an edge computing node; uploading the preliminary diagnosis result of the edge computing node and the key sensor data to a block chain platform, and storing and verifying the preliminary diagnosis result and the key sensor data through a distributed account book of a block chain; the operation state data of the power grid equipment are collected in real time, and the edge computing nodes are used for preliminary analysis, so that potential fault signals can be quickly found.

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 provides a vehicle fault remote diagnosis and repair method and device and a storage medium. The method comprises the steps of receiving vehicle data of a target vehicle within a preset time period; determining a first fault code corresponding to the fault data; performing fault diagnosis on the first fault code and the operation data through a remote diagnosis module; processing the fault diagnosis information through a fault repair module according to the vehicle identifier to obtain a software repair package of the target vehicle; detecting whether the vehicle-mounted client carries out fault repair or not; after the software repair package is sent to the vehicle-mounted client and fault repair is carried out to obtain a fault repair result, a fault detection instruction is sent to the vehicle-mounted client to carry out fault diagnosis, and a first fault diagnosis result is obtained; determining whether the first fault repair result is completely repaired according to the first fault diagnosis result; if the first fault is not completely repaired, repairing the target vehicle according to the first fault through the remote assistance module; otherwise, it is confirmed that fault repair of the target vehicle is completed.

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 an air purification system for a clean workshop. The air purification system comprises a self-adaptive control unit, an air quality monitoring module, an air flow control assembly, an environmental parameter adjusting assembly, a self-optimization filtering assembly, a human-computer interaction interface and predictive maintenance system, an energy efficiency optimization module and a remote diagnosis and early warning system. On the basis of intelligent technologies such as machine learning, reinforcement learning and a data fusion algorithm, real-time monitoring, dynamic optimization and energy efficiency management of the air quality of the clean workshop are achieved. Based on real-time data and historical data, an algorithm is used for carrying out self-adaptive optimization on the air purification system, and manual intervention is reduced. And the running state of the system is dynamically adjusted through the energy efficiency optimization module, so that the energy consumption is reduced while the cleanliness is ensured. The system can diagnose equipment faults in advance, reduce downtime and prolong the service life of equipment. And due to a man-machine interaction interface and a remote monitoring function, an operator can know the state of the system at any time and carry out regulation and control.

The invention discloses a special transformer state management method and system based on intelligent monitoring and remote diagnosis. The method comprises the steps that electrical parameters of a special transformer user are collected through monitoring equipment; the collected data parameters are analyzed in real time, and the operation state and the abnormal condition of the equipment are judged; monitoring data are transmitted through a communication network, and remote monitoring is achieved; and when an abnormality is detected, triggering an alarm mechanism, and providing fault diagnosis information. According to the invention, the safety and operation efficiency of the power system are improved. Key parameters such as voltage, current, power and frequency can be collected in real time, abnormal conditions such as overload and undervoltage can be rapidly detected through an intelligent algorithm, and an alarm is triggered in time. The system adopts AES encryption to guarantee data transmission security, supports 4G / 5G network to realize remote monitoring, and ensures that operation and maintenance personnel can quickly obtain fault diagnosis information and personalized processing suggestions. The fault response speed and the system stability are obviously improved, and the operation and maintenance cost is reduced.

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 discloses an intelligent power equipment fault diagnosis system and method based on image recognition and partial discharge feature extraction. The system comprises the following modules: a data acquisition module, a signal preprocessing and time-frequency image generation module, a hybrid deep learning network module and a fault classification and diagnosis module. By combining the CNN and the Transform network, local and global features of a partial discharge signal can be captured at the same time, the detection precision and reliability are improved, a self-attention mechanism of the Transform network enables the system to still accurately identify an effective signal under a high-noise background, the false alarm rate is reduced, the system can monitor the operation state of the power equipment in real time, and the system is suitable for popularization and application. The real-time fault early warning and diagnosis report is provided, the method is suitable for being applied to on-line monitoring and remote diagnosis scenes, and accurate recognition and real-time fault early warning of complex partial discharge signals are achieved. Compared with a traditional method, the detection accuracy, the anti-interference capability and the real-time performance are remarkably improved, and the method has a wide application prospect.

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 discloses a diagnostic data remote transmission method based on a regional remote diagnosis and treatment platform. The method comprises the following steps: S1, collecting diagnostic data of a patient; s2, preprocessing the acquired diagnosis data; s3, carrying out data transmission by utilizing a short-distance wireless communication technology and a long-distance mobile communication technology; s4, performing data encryption and identity verification by adopting AES and RSA encryption algorithms; s5, after the regional remote diagnosis and treatment platform receives the diagnosis data, decryption and identity verification are carried out on the data; s6, performing image data analysis by using a convolutional neural network, processing character data by using a natural language processing technology, predicting and classifying physiological parameters by using regression analysis and a classification algorithm, and performing fusion analysis on multi-modal data in combination with a deep learning model; and S7, transmitting the analyzed and processed diagnosis result back to a local data terminal through a long-distance mobile communication technology. The system has the advantages of high transmission stability, high safety, high transmission efficiency and high diagnosis 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 remote diagnosis and treatment method and system based on a pulse simulator, relates to the technical field of medical care, and discloses a patient pulse acquisition module, a pulse condition parameter simulation setting module, a pulse condition dimension diagnosis module and a pulse remote diagnosis and treatment module. By arranging the patient pulse collection module, the pulse condition parameter simulation setting module, the pulse condition dimension diagnosis module and the pulse remote diagnosis and treatment module, pulse signals of a patient can be periodically simulated through the pulse simulator, and the diagnosis precision of pulse diagnosis on the patient is guaranteed; according to the method, the potential abnormal pulse condition dimension data of the patient can be preferentially diagnosed and analyzed, the pulse condition data of all dimensions does not need to be analyzed at the same time, the pulse condition data processing efficiency of pulse diagnosis of the patient can be improved, and meanwhile the pulse condition data processing difficulty of pulse diagnosis of the patient is effectively reduced.

The invention discloses an intelligent electronic stethoscope system based on wireless transmission and a data processing method thereof, and belongs to the technical field of electronic stethoscopes, the intelligent electronic stethoscope system comprises a heart sound acquisition module, a signal conditioning module, a power supply module, a wireless transmission module, an upper computer module and a database module; the microprocessor module serves as a core control unit, and the system can collect heart sound signals of a patient in real time and conduct amplification, filtering and spectrum analysis processing on the heart sound signals. The processed heart sound data is transmitted to a mobile device or a computer in real time through a wireless transmission module, so that doctors and users can check heart sound waveforms and related data conveniently; the system also supports the uploading of the data to a cloud database for remote diagnosis and query; the method not only can meet the requirements of modern medical treatment for high efficiency and convenience, but also can play an important role in improving the diagnosis precision and the remote diagnosis capability and reducing the infection risk.

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 a diagnosis method, a device for diagnosis and intelligent equipment. The method comprises the following steps: a diagnosis agent node receives a first diagnosis instruction aiming at a node to be diagnosed from a diagnosis node; according to the first diagnosis instruction, verifying whether a diagnosis node has an access control authority for the to-be-diagnosed node; and when the verification is passed, a second diagnosis instruction is sent to the node to be diagnosed, and the second diagnosis instruction is obtained according to the first diagnosis instruction. The technical scheme can be applied to intelligent equipment such as electric vehicles and new energy vehicles, isolation between the diagnosis node and the to-be-diagnosed node in the intelligent equipment can be achieved, and therefore the risk that the to-be-diagnosed node is attacked in the diagnosis process is reduced. Moreover, communication unification of remote diagnosis and near-end diagnosis can be realized by the technical scheme of the application, and external communication of the intelligent equipment for diagnosis is normalized to the diagnosis agent node, so that management and maintenance of the diagnosis system are facilitated.

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 discloses a vehicle cloud collaborative data acquisition method based on a central gateway, and relates to the technical field of information processing. The method comprises the following steps: periodically acquiring CAN / LIN data through central gateway equipment, and filtering, classifying, compressing and storing the CAN / LIN data; the ECU with the fault actively uploads fault information to the central gateway equipment; and the central gateway equipment packages and uploads the collected data and the received fault information to a cloud end in an MQTT transmission mode to carry out real-time monitoring and predict the running state of the vehicle. Through real-time monitoring and analysis of vehicle data, the state of the vehicle is comprehensively mastered, potential problems are found and solved in time, accidents are reduced, and the safety of the vehicle is improved; the data acquisition efficiency and utilization efficiency of the vehicle are improved; based on the remote diagnosis capability, the method is used for more convenient vehicle fault diagnosis and fault prediction analysis, improves the after-sales diagnosis efficiency, and reduces the fault analysis cost.