357 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 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 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 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 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 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 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 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 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.

The invention relates to the technical field of battery state detection, and discloses a 5G communication base station lithium battery intelligent monitoring and remote diagnosis method comprising the following steps: continuously detecting battery state parameters, and establishing a battery state parameter sequence; identifying a rhythm period of the battery, and reconstructing a state parameter sequence based on the rhythm period to obtain a real-time evolution track of the state parameters of the battery; acquiring historical state data of the battery, and constructing a reference evolution trajectory and a fault evolution trajectory of state parameters of the battery; performing anomaly detection on the battery based on the real-time evolution trajectory and the reference evolution trajectory; identifying the real-time evolution direction of the battery state parameters based on the real-time evolution trajectory and the reference evolution trajectory, and identifying the fault type of the battery based on the real-time evolution direction and the fault evolution trajectory; by identifying the rhythm period of the battery state parameters and constructing the evolution trajectory of the battery state parameters, the accuracy of abnormal detection and fault diagnosis of the lithium battery state can be improved.

The invention relates to the technical field of sensing data processing, and discloses a spark machine remote monitoring and fault diagnosis system and method, and the system comprises a data collection unit which is used for collecting the multi-source operation data of a spark machine, and carrying out the preprocessing and feature extraction of the multi-source operation data; the multi-source operation data at least comprise electrical parameters, motion parameters, state parameters and vibration signals; the remote diagnosis platform is deployed at a cloud end and is in communication connection with the data acquisition unit; the user terminal is used for receiving and displaying the diagnosis result and the maintenance suggestion; the remote diagnosis platform comprises a rapid screening layer, a feature classification layer and a prediction layer; the rapid screening layer firstly judges input data, if the fault cannot be determined, the feature classification layer calculates an input feature vector, and if the fault still cannot be determined, the prediction layer is triggered to carry out deep analysis; through a three-layer progressive architecture, the rapid insight of the operation state of the spark machine and the accurate diagnosis of the fault are realized.

The invention discloses an AI-based deep pulse score remote diagnosis and medical resource allocation system, and relates to the technical field of intelligent medical data security, and the system comprises a data acquisition module which is used for acquiring a deep pulse score diagnosis result of a patient, determining a basic security state of encrypted health data when the encrypted health data is stored in a cloud end, determining dynamic authorization data, and sending the dynamic authorization data to a server; the authorization request processing module is used for acquiring an inter-mechanism sharing request according to the dynamic authorization data, deploying a lightweight smart contract in a block chain network, processing real-time transaction verification logic and generating an authorization confirmation record; according to the AI-based deep pulse score remote diagnosis and medical resource allocation system, a set of intelligent contract-based deep pulse score hosting transaction system is designed on the basis of guaranteeing cloud health data security storage and personalized access authority management, and the trust and efficiency problems of data authorization and sharing among different institutions are effectively solved.

The invention relates to the technical field of brushless motor control systems, and discloses an intelligent redundant brushless motor Hall position correction system, which comprises a multi-sensor redundant layout module used for arranging multiple types of sensors, and each type of sensor provides rotor position information; and the data processing and fusion module is used for receiving and processing the data from the plurality of sensors, removing noise through filtering and denoising algorithms, and outputting an accurate rotor position estimation value. According to the invention, the problem of rotor position error caused by external interference is solved through multi-sensor redundancy layout and a Kalman filtering fusion algorithm; an intelligent fault detection and switching mechanism is introduced, and the fault tolerance capability and the emergency response capability are improved through a standby sensor selection algorithm; and real-time monitoring, remote diagnosis, fault early warning and parameter adjustment of the equipment are realized through cloud platform data transmission and remote monitoring technologies.

The invention discloses a transfer device for ultrasound contrast of an abdominal injury patient, and relates to the field of medical apparatuses and instruments. Comprising an examination bed, a longitudinal sliding adjusting mechanism, an ultrasonic examination mechanical arm, an image acquisition adjusting support, an image acquisition positioning mechanism, an ultrasonic probe pressing force acquisition mechanism, an ultrasonic probe clamping mechanism, an ultrasonic probe, a controller and an upper computer, the ultrasonic inspection mechanical arm is fixed to the second longitudinal sliding adjusting mechanism, the image collecting adjusting support is fixed to the upper portion of the ultrasonic inspection mechanical arm, the ultrasonic probe pressing force collecting mechanism is fixed to the front end of the ultrasonic inspection mechanical arm, and the ultrasonic probe clamping mechanism is arranged in the ultrasonic probe pressing force collecting mechanism in a sliding mode. The device can improve the efficiency of remote ultrasonic inspection and the standardization of the inspection; the device detects the pressing force applied to the abdomen by the ultrasonic probe through the pressure sensor.

The invention provides an intelligent charging pile management system and method with a remote diagnosis function, and relates to the technical field of power control, and the method comprises the steps: carrying out the one-dimensional variational mode decomposition and multi-dimensional variational mode decomposition of load data and operation state signals of a charging pile, and obtaining a stationary component, a noise component and a state sub-mode signal set; determining load identification characteristics according to the stationary component, the external environment data and the noise component, and performing joint analysis processing on the state sub-mode signal set to obtain fault diagnosis characteristics of the charging pile; carrying out feature fusion on the load identification features and the fault diagnosis features to obtain a modal fusion matrix, and then carrying out joint diagnosis analysis on the modal fusion matrix to obtain a load prediction curve and a fault diagnosis result of the charging pile; according to the method and the device, the charging piles can be subjected to collaborative analysis on the basis of load prediction and fault diagnosis, so that the accuracy of remote scheduling control is improved.

The invention provides a remote diagnosis method and device for a vehicle, local end equipment, a system and a product, and is applied to the local end equipment, and the method comprises the steps: transmitting a diagnosis request message to a vehicle controller after the diagnosis request message of a diagnosis instrument is obtained; when a negative response message sent by the vehicle controller is received, forwarding the negative response message to the diagnostic instrument, and starting to wait for receiving a response message sent by the vehicle controller; the negative response message indicates that the diagnosis request message is received, but the response is to be processed; and if the response message sent by the vehicle controller is still not received when the local waiting duration reaches the preset waiting duration, generating a first virtual negative response message, and sending the first virtual negative response message to the diagnostic apparatus, so that the diagnostic apparatus performs remote diagnosis according to the overtime waiting duration. The method can avoid vehicle remote diagnosis misjudgment and overtime response of the vehicle controller caused by network delay, and improves the accuracy of vehicle remote diagnosis.

The invention relates to the technical field of power dispatching, and discloses a power supply system dispatching method and a power supply system thereof, and the power supply system comprises a monitoring module, a dispatching module, a protection module, a data prediction module, an energy storage module, a remote control and communication module, an artificial intelligence and adaptive dispatching module, a power quality monitoring module and an intelligent load management module. The power quality monitoring module collects harmonic wave and voltage fluctuation data in real time, gives an alarm immediately when the harmonic wave and voltage fluctuation data exceed the standard, and pushes governance suggestions; and the scheduling module is matched to adjust the non-linear load operation time, so that quality problems are reduced from the source. Scene features are identified through deep learning, and an emergency strategy is automatically generated; and the scheduling module receives feedback in real time and corrects the strategy. The protection module accurately locates a fault point through multi-sensor cross validation and automatically cuts off a fault loop; the remote control module pushes fault information to the operation and maintenance terminal in real time, remote diagnosis is supported, and the on-site inspection frequency is reduced.

The invention relates to the technical field of underground pump fault early warning, in particular to a side cloud collaborative diagnosis early warning system for a tower type pumping unit cloud platform. The system comprises an edge end module and a cloud platform module, wherein the edge end module comprises a data acquisition unit used for acquiring operation data of the tower type pumping unit and a local diagnosis unit used for determining a fault moment; the cloud platform unit comprises a remote diagnosis and decision-making unit used for obtaining a fault response value according to the correlation degree of the same type of operation data and the importance degree of the difference of each type of operation data in an analysis period before and after the fault moment; determining a response characteristic sequence based on the fault response value of the same type of operation data at the fault moment; and training a neural network by using the neural network to obtain an oil pumping unit fault diagnosis model, and further performing diagnosis and early warning on the tower type oil pumping unit. According to the invention, the accuracy of diagnosis and early warning of the fault of the oil pumping unit is effectively improved.

The invention provides a UDS-based data storage and remote monitoring system and a UDS-based data storage and remote monitoring method. The method comprises the steps of collecting fault data within a period of time before and after a fault occurs when the fault occurs; preprocessing the fault data and writing the fault data into an NVM (Non-Volatile Memory) in an array form; calling a first service of the UDS to process the preprocessed fault data in the NVM according to a set format and store the processed fault data to a specified address area in the RAM; and calling a second service of the UDS to remotely access the fault data of the specified address area in the RAM to obtain the preprocessed fault data. By improving the acquisition mode, constructing a remote access mechanism and introducing a basic data analysis function, complete recording, remote diagnosis and preliminary intelligent analysis of fault data are realized, the informatization and intelligence level of the whole vehicle diagnosis system is improved, and the fault diagnosis efficiency is improved. The problems that an existing diagnosis system is insufficient in data integrity, weak in remote access capacity and limited in data analysis capacity are solved.

The invention discloses a remote diagnosis method and device, electronic equipment and a storage medium, the method is applied to remote diagnosis equipment in a remote diagnosis system, the remote diagnosis system comprises a target vehicle, and the remote diagnosis equipment is connected with the storage medium. The method comprises the following steps: acquiring signal intensity data, data transmission rate data and connection interruption frequency between the target vehicle and the remote diagnosis equipment within a preset time period; determining a target network quality value of the remote diagnosis system based on the signal strength data, the data transmission rate data and the connection interruption frequency; determining a target diagnosis mode based on the target network quality value; and performing remote diagnosis on the target vehicle based on the target diagnosis mode to obtain a target diagnosis result. According to the embodiment of the invention, the reliability of vehicle remote diagnosis is improved.