873 results about "Device failure" patented technology

Provided are a method, a system, and a storage medium for intelligent diagnosis of a device failure based on an IIoT. The method includes: obtaining device-related data of an abnormal device from a total sensor database; predicting failure warning information of the abnormal device and a confidence level corresponding to the failure warning information based on the device-related data; determining a failure processing parameter based on the failure warning information and the confidence level, and generating a failure processing instruction; and sending the failure processing instruction via the device sensor network platform to a maintenance personnel terminal of the device perception and control platform or to a failure shooting device, and controlling the failure shooting device to perform failure shooting.

The invention discloses a power distribution cabinet fault detection method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: laying a multi-modal sensor array, constructing a multi-dimensional data collection network, collecting various types of sensing data in real time, and carrying out the data preprocessing; establishing a multi-modal data fusion model by using a deep learning algorithm, and forming a fault feature mapping relation through feature extraction, data correlation analysis and cross validation to realize collaborative perception of a data level; and constructing a multi-dimensional fault diagnosis decision tree based on a collaborative perception result, accurately positioning a specific device position where a fault occurs in combination with weight distribution and confidence evaluation of data of each sensor, and giving out evaluation results of a fault type and severity. The system effectively reduces the equipment failure rate, prolongs the service life, reduces the maintenance cost, improves the power supply reliability, promotes the development of an electric power system towards the intelligent operation and maintenance direction, and is of great significance to the improvement of the operation reliability and maintenance efficiency of a power distribution system.

The invention discloses an operation and maintenance debugging monitoring system suitable for an electric field, and relates to the technical field of operation and maintenance debugging monitoring, and the system comprises a collection unit which collects the operation data of an electric field device at a multi-dimensional monitoring point, the operation data comprises an electrical parameter, a mechanical vibration parameter, an environment parameter and chemical gas component data, and a multi-modal monitoring data set is constructed; the feature analysis module is used for carrying out spatial-temporal feature extraction on the multi-modal monitoring data set, generating a multi-dimensional feature tensor containing a time domain feature, a frequency domain feature and a spatial distribution feature, obtaining a spatial-temporal coupling relationship among the features through a tensor decomposition technology, and sending the spatial-temporal coupling relationship to the multi-modal monitoring data set; according to the method, a whole-process intelligent solution from monitoring, early warning to decision making is provided for electric field operation and maintenance, the equipment fault risk is greatly reduced, the power failure time is shortened, and the safety and economical efficiency of power grid operation are improved.

The invention relates to the technical field of equipment management, and discloses an industrial park equipment operation state management method and system. The method comprises the following steps: acquiring equipment basic information, historical fault data and real-time operation data of equipment; according to the basic information of the equipment, performing category analysis on the equipment to obtain an equipment category; according to the historical fault data and the equipment category, performing fault period prediction on the equipment based on a pre-trained support vector regression model to obtain an equipment fault period; according to the real-time operation data and the equipment category, performing health index calculation on the equipment to obtain an equipment health index; and according to the equipment category, the equipment fault period and the equipment health index, performing fault prediction to obtain a predicted fault probability, and sending the predicted fault probability to an operation and maintenance end, so that the operation and maintenance end performs equipment maintenance according to the predicted fault probability. According to the method, the equipment management efficiency of the industrial park can be improved, and the equipment state of the industrial park can be effectively analyzed and managed.

The invention discloses a method and system for predicting the health state of equipment based on large model AGENT capability. According to the method, equipment vibration, temperature, pressure, current and other data are acquired in real time through a multi-mode sensor, and data fusion and self-supervised learning are realized in combination with virtual data generated by digital twin simulation, so that equipment operation characteristics are extracted, and an equipment health index is calculated. According to the method, the health index and the change rate thereof are comprehensively considered, the equipment fault risk is dynamically evaluated, meanwhile, a dynamic weight self-adaptive updating mechanism is introduced, model parameters are optimized on line, and it is ensured that the model keeps high-precision prediction in long-term operation. The system can be widely applied to various heterogeneous devices, realizes full-process closed-loop management from data acquisition, intelligent analysis to risk early warning, and provides a scientific basis for device maintenance and preventive overhaul, so that the device failure rate is reduced, and the service life of the device is prolonged.

The invention discloses a converter valve internal temperature monitoring and early warning method and system, and particularly relates to the technical field of converter valve temperature monitoring. Temperature sensors are arranged on a plurality of key heating units, local temperature data are collected in real time, a time sequence is built in combination with cooling water inlet and outlet temperature, a temperature trend prediction model is built based on temperature rise rate and temperature gradient change, local thermal runaway risks are accurately recognized, and a prediction result is compared with a grading threshold value, so that the local thermal runaway risk is determined. Multi-stage early warning signal output is realized, and a corresponding processing strategy is linked; under the extreme conditions that the local temperature is continuously and abnormally increased and the response of the cooling system is lagged, the system can trigger emergency shutdown protection in an override mode, device failure is avoided, the sensing capacity and response speed of local thermal faults are effectively improved, and the operation safety and the intelligent level of the converter valve system are remarkably improved.

The invention relates to the technical field of partial discharge detection, in particular to a leak detection and partial discharge detection method and system based on artificial intelligence, and the method comprises the following steps: obtaining a partial discharge pulse signal, extracting a phase mutation coordinate, analyzing an incremental feature, reconstructing a propagation waveform, constructing an amplitude gradient matrix, and screening a direction vector; and a backtracking path calculates a change rate to generate a time track, and track mapping and space positioning are completed in combination with image data. According to the method, the accuracy and the response speed of partial discharge detection are improved through multi-stage signal processing, extreme points and phase abrupt change are analyzed, more accurate discharge feature capture and equipment defect positioning are achieved, phase and amplitude data are combined, the discharge mode is recognized, a signal propagation path is reconstructed, the fault source positioning accuracy is improved, and the fault source positioning accuracy is improved. Through dynamic rendering, the intuition of a detection result is enhanced, real-time decision is supported, the method is more intelligent and efficient, the equipment fault risk is greatly reduced, and the safe operation level of power equipment is improved.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The embodiment of the invention provides an equipment fault prediction management method and device based on a large model, and the method and device achieve the accurate evaluation of a state through the innovative design of a multi-modal feature fusion model, data integration and feature learning. A fault diagnosis system is constructed, and a reliable root cause analysis mechanism is established in combination with semantic analysis and case retrieval. Maintenance guidance is introduced, and the feasibility of a maintenance scheme is ensured through experience precipitation and priority evaluation. According to the method, the defects of the traditional technology in the aspects of feature extraction, fault diagnosis, maintenance guidance and the like are effectively overcome, and technical guarantee is provided for equipment management.

The invention discloses an equipment preventive maintenance and diagnosis method based on multi-dimensional data verification, relates to the technical field of equipment preventive diagnosis, and solves the technical problems that fault reasons are difficult to comprehensively and deeply analyze, and maintenance measures are lack of pertinence due to the fact that the fault reasons are often misjudged or cannot be positioned. According to the method, the fault diagnosis model is constructed, the adaptation algorithm is adopted for different types of data, the diagnosis accuracy of the model to equipment faults and the adaptability of the model to complex working conditions are improved, the equipment fault type and the abnormal state can be rapidly and accurately recognized, and the fault diagnosis accuracy is improved through the methods of fault phenomenon matching, parameter correlation analysis, historical data deep backtracking and the like. According to the method, fault reasons can be accurately determined, the performance degradation trend and potential risks of the equipment can be effectively identified in combination with a hybrid similarity measurement method, correlation among data is further mined through a secondary analysis mechanism, the potential risks are accurately pre-judged in combination with historical data, and real preventive maintenance is achieved.

The invention discloses an electrical cabinet with strong anti-electromagnetic interference capability, and relates to the technical field of electrical cabinets, the electrical cabinet comprises a cabinet body and further comprises a protection device, a sealing door slidably penetrates through the right side of the cabinet body, a moving hole is formed in the front side of the sealing door, and a limiting hole is formed in the front side of the sealing door. The protection device comprises an operation hole, an operation rod, a T-shaped rod, a protection rod, an L-shaped plate, a filter, a reset spring and a protection disc, the sealing door moves to enable the limiting hole to be aligned with the T-shaped rod, and after the T-shaped rod is aligned with the limiting hole, the T-shaped rod moves in the direction away from the protection rod under the elastic force effect of the reset spring; the T-shaped rod moves in the direction away from the protection rod to make contact with the inner wall of the limiting hole and restore limiting of the sealing door, the sealing performance of the cabinet body is kept through the L-shaped plate, the influence of external electromagnetic interference can be prevented, the equipment failure rate is reduced, and therefore the usability of the whole system is improved.

The invention discloses an equipment management method based on data acquisition, and relates to the technical field of equipment management, and the method comprises the following steps: S1, selecting and configuring a sensor; s2, establishing a data acquisition protocol; s3, deploying data acquisition hardware; s4, designing and applying a data storage system; s5, performing data cleaning and preprocessing; s6, deploying an analysis model; s7, establishing a real-time monitoring system; s8, optimizing a maintenance strategy; and S9, performing regular evaluation and optimization. According to the equipment management method based on data acquisition, the running state of the equipment is monitored in real time through real-time data acquisition and analysis, and potential faults and performance problems are found in time. In combination with an advanced data processing technology, such as a machine learning analysis model, future behaviors of the equipment can be learned and predicted from historical data, so that preventive maintenance is realized, and risks of equipment failure and production interruption are reduced. In addition, a condition-based maintenance policy replaces traditional periodic maintenance.

The invention discloses a wind generating set cabin crane safety control system, and relates to the technical field of wind power generation, the wind generating set cabin crane safety control system comprises a monitoring center, the monitoring center is in communication connection with a monitoring module, a data processing module, a judgment module, a crane motor control module and an alarm and prompt module, all the modules are in electric signal connection, and the monitoring module is in communication connection with the data processing module. The monitoring module is used for monitoring environment data and working state data of the crane in real time, and the environment data comprises wind speed, wind direction, temperature and humidity; the working state information of the crane comprises the speed, the lifting rope position, the load weight and the like. By integrating various sensors and a real-time monitoring technology, the operation state and the surrounding environment of the crane are accurately sensed, and when abnormal conditions are detected, the system immediately triggers an early warning or emergency shutdown mechanism, so that accidents are effectively avoided, and the safety accident risk caused by man-made misjudgment or equipment failure is reduced.

The invention relates to the technical field of fault diagnosis, in particular to an electromechanical equipment fault diagnosis method and system based on data analysis. The method comprises the following steps: extracting a historical operation log of a motor, analyzing a current overload state and constructing a current overload spectrogram; then, carrying out insulation layer performance loss gradient analysis based on the spectrogram, and calculating the failure probability of the motor; finally, a risk level is given to the overload current according to the failure probability, an equipment fault diagnosis framework is constructed, and the framework is sent to a control terminal to execute fault diagnosis. According to the invention, the electromechanical equipment fault diagnosis technology is optimized, so that the electromechanical equipment fault diagnosis technology is more accurate.

The invention discloses a water meter equipment fault self-checking maintenance management system, particularly relates to the field of water affair metering management, is used for solving the problem of fault missing report risk hidden danger pain points caused by self-checking failure, and is characterized in that self-diagnosis drift monitoring and heartbeat frame extremely simple coding are triggered through pseudo-flow rate pulses, and the energy consumption increment of an uplink link is reduced to a milliwatt level; the health score drives the rhythm to be rechecked, so that the deep sampling only occurs at a necessary moment; a differential patch is combined with a digital twinborn model to accurately position a failure branch, pushing and loading are only loaded with micro bytes, and downlink consumption and refreshing time are greatly shortened; shape-frequency complementary indexes are fused with unsupervised boundary learning, so that deviation judgment is sensitive and stable; the dual-redundancy monitoring timer verifies that the patch takes effect and then uploads an abstract, and the maintenance center can carry out remote self-repair under the condition of not interrupting metering and communication; and therefore, instant capture and directional repair of silent faults are realized, the autonomous reliability of the water meter is remarkably improved, and the operation and maintenance cost and the field intervention frequency are greatly reduced.

The invention belongs to the technical field of intelligent engineering management, and discloses an automatic monitoring and control method of an engineering management system based on the Internet of Things. According to the method, a four-layer Internet of Things architecture comprising a sensing layer, a network layer, a platform layer and an application layer is constructed, and the four-layer Internet of Things architecture is uploaded to a cloud platform through a 5G / narrowband Internet of Things dual-mode transmission channel. A digital twinborn technology is innovatively adopted to construct a three-dimensional visual engineering model, intelligent identification and prediction of abnormal working conditions are realized through a machine learning algorithm, and a multi-stage linkage control mechanism is established. And when construction deviation or equipment failure is detected, the system automatically generates an optimization control strategy and issues the optimization control strategy to the execution terminal, so that accurate regulation and control of the construction machinery and intelligent pushing of early warning information are realized. According to the method, a manual inspection mode of traditional engineering management is broken through, full-process automatic supervision is realized through data fusion analysis and closed-loop control, the construction quality supervision precision is effectively improved by more than 30%, the safety accident rate is reduced by 50%, and the engineering management efficiency is remarkably improved.

The invention discloses an electromechanical equipment fault monitoring and early warning method, which comprises the following steps of: acquiring strong current data and weak current data of subway electromechanical equipment, and respectively carrying out preprocessing and time synchronization on the strong current data and the weak current data to obtain a strong current data sequence and a weak current data sequence; the strong current data sequence and the weak current data sequence are subjected to attention cross fusion and then input into a fault diagnosis model, and a fault type and the confidence coefficient thereof are obtained; inputting the fault type into a pre-constructed fault propagation knowledge graph, and querying to obtain a potential fault propagation path; and inputting the strong current data sequence, the weak current data sequence, the fault type, the confidence coefficient of the fault type and the potential fault propagation path into a Bayesian network, dynamically calculating a fault propagation probability and generating an early warning level.

The invention is suitable for the field of equipment fault prediction, and provides an underground equipment multi-source fault prediction method and system based on a large model, and the method comprises the following steps: synchronously collecting an acoustic signal, a visual image, a vibration signal, environment data and working condition parameters of underground equipment in real time, and integrating the acoustic signal, the visual image, the vibration signal, the environment data and the working condition parameters into a multi-modal time sequence data stream; performing time alignment and cleaning on the multi-modal time sequence data stream, extracting deep features of each modal, and generating a multi-source fusion feature vector; inputting the multi-source fusion feature vector into a preset prediction model to obtain prediction information of the underground equipment, and generating fault traceability information; continuously optimizing parameters of the prediction model based on fault data collected in real time; and dynamically displaying prediction and traceability results, and automatically generating a maintenance strategy and an equipment scheduling scheme based on the results. According to the invention, panoramic perception of the operation state of the equipment and accurate capture of early weak fault features are realized, and the comprehensiveness and accuracy of fault detection are greatly improved.

The invention relates to the technical field of wind power operation and maintenance management, and particularly provides an abnormal state detection method and system for a wind driven generator system. Joint feature extraction is carried out on the multi-source monitoring data flow through a feature coding network, a state evolution feature sequence and a cross-source association feature set are generated, a pre-trained anomaly diagnosis model is called to carry out fusion analysis on the features, and an anomaly diagnosis result containing anomaly confidence distribution information is generated; and after the abnormal type, the time dimension development track and the space dimension influence component identifier are analyzed, an equipment early warning instruction containing time and space positioning information is generated and sent to the target management terminal, so that the abnormal state of the wind driven generator system can be comprehensively and accurately detected, detailed abnormal information is provided in time, and the safety of the wind driven generator system is improved. And the equipment fault risk and the operation and maintenance cost are effectively reduced.

The invention discloses a power supply and a control method and device thereof, a power supply system, a medium and a product, and relates to the technical field of power control, a first corresponding relation between a target control parameter of a converter and a change degree of load current is pre-constructed, so that under the action of the target control parameter, the load current of the converter can be controlled. The output voltage of the converter can reach the required voltage of the equipment within the preset duration, the response speed of the power supply in the high-dynamic load jump environment is remarkably improved through rapid load current detection and dynamic target control parameter adjustment, the output voltage of the power supply is recovered to the required voltage as soon as possible, and the power supply efficiency is improved. Equipment failure or performance reduction caused by too high or too low voltage is avoided; under the condition that the high-frequency load changes, the adjustment of the output voltage can be completed within milliseconds to adapt to the requirements of high-power consumption equipment, so that the adaptability of the PSU to the dynamic load is improved.

The invention discloses a dynamic correction method and system for equipment fault mode occurrence degree, and belongs to the technical field of power plant electrical overhaul and maintenance, and the method comprises the steps: collecting operation parameters, maintenance records and historical fault data of target electrical equipment, and recognizing a potential fault mode set of the target electrical equipment; failure mode and influence analysis is carried out on each failure mode, and the initial occurrence degree, severity and detectability of each failure mode are determined; calculating an initial risk priority number of each fault mode, and determining an initial risk according to a preset risk grade division rule; dynamically adjusting the initial occurrence degree, recalculating the updated risk priority number, and generating a corrected risk level; and constructing a dynamic maintenance decision model, and outputting a maintenance priority and a maintenance time window for each fault mode. According to the method, the defects of real-time performance, comprehensiveness and flexibility of electrical operation and maintenance of the power plant are overcome, and efficient and accurate data support is provided for operation and maintenance of electrical equipment.

The invention provides an equipment fault maintenance method and system based on a large model, and a storage medium. The method comprises the following steps: determining fault equipment according to equipment fault description; obtaining current operation data and a current operation image of the fault equipment, and inputting the equipment fault description, the current operation data and the current operation image into a pre-trained large model for fault identification to obtain a target fault reason; determining a priority troubleshooting step according to the target fault reason, and sending the target fault reason and the priority troubleshooting step to the user; a troubleshooting feedback result for the priority troubleshooting step is received, and a real fault reason is determined according to the troubleshooting feedback result; and generating an equipment fault maintenance result according to the equipment fault description, the current operation data, the current operation image and the real fault reason. According to the embodiment of the invention, the target fault reason and the priority troubleshooting step are sent to the user, thereby effectively assisting the equipment fault maintenance, and improving the equipment fault maintenance efficiency.

The invention relates to the technical field of direct current transmission detection, in particular to a device and method for testing power pipeline connection, and the device comprises a power parameter collection module which is used for collecting current signals and voltage signals in a power pipeline, measuring temperature values at a plurality of positions, obtaining insulation resistance at each node, and measuring grounding resistance of a plurality of grounding points; and summarizing the measurement data to generate an electric power parameter set. According to the invention, through integration and analysis of measurement data of current, voltage, temperature, insulation resistance and grounding resistance, comprehensive monitoring and evaluation of the state of the power pipeline are realized. After the data are integrated, by calculating the mean value, the variance and the peak value of the data and analyzing the spatial distribution of the temperature and the frequency characteristics of the grounding resistance, the understanding of the behavior of the power system is enhanced. According to the data analysis mode, the prediction capability of potential risks and the sensitivity of fault detection are improved, and serious equipment faults and service interruption are effectively avoided.

The invention relates to the technical field of nut adjustment, in particular to an accurate adjustment in-place confirmation system and method. Comprising a parameter acquisition unit, a state judgment unit, a strategy making unit and an optimization unit, a specific adjusting strategy is formulated through the strategy formulating unit, a specific nut tightness degree adjusting strategy is formulated according to an adjusting target, the tightness degree of the nut is adjusted in time according to temperature changes, the problems of loose connection, too tight connection, leakage and the like caused by the temperature can be prevented, equipment faults and maintenance cost are reduced, and the working efficiency is improved. The overall performance of equipment can be improved by keeping the proper connection state of the bolt and the nut, it is ensured that the equipment can normally operate at different temperatures, the production efficiency and the product quality are improved, and it is ensured that the equipment can normally operate at different temperatures, and the production efficiency and the product quality are improved.

The invention relates to a heading machine load self-adaptive control method and system, and the method comprises the steps: a data analysis module is disposed on a heading machine equipment layer, collects the heading data of the heading machine equipment layer in real time, and calculates a load coefficient for self-adaptive adjustment; and the control module is connected with the data analysis module and the heading machine equipment layer, and analyzes the accessed load coefficient into a control signal for adjusting rotation and lifting of a cutting arm in the heading machine equipment layer. The method comprises the following steps: determining a motion trail model of a cutting operation surface of a heading machine equipment layer by acquiring perception data of a manual cutting section, determining a load coefficient generated by the motion trail model according to the state change of the cutting operation surface in the cutting process, and adjusting rotation and lifting of a cutting arm in the heading machine equipment layer based on the load coefficient. The self-adaptive control of the equipment layer of the heading machine is realized, the accuracy and the stability of on-line identification of the heading working face are greatly improved, the equipment failure rate and the safety risk of operation are reduced to the greatest extent, and the method has wide applicability.

The invention relates to the technical field of transformer substation monitoring, and discloses a transformer substation electrical quantity wireless measurement method and system, and the method comprises the steps: obtaining temperature data of each monitoring point in a target region, analyzing whether the temperature of each monitoring point is abnormal, judging whether equipment in the monitoring point with the abnormal temperature works abnormally, and if yes, determining whether the equipment works abnormally; according to the wireless measurement method and system for the electrical quantity of the transformer substation, whether the equipment in the range of the monitoring points is abnormal or not is judged according to the temperature of each monitoring point in the area, and the equipment in the range of the monitoring points with the abnormal temperature is maintained according to the operation condition of the equipment in the range of the monitoring points with the abnormal temperature. Whether the temperature abnormity of the monitoring point is caused by environmental factors or not is judged, the real acquisition point with equipment fault and fault equipment are determined, the fault equipment is searched and determined in time, the equipment is prevented from being operated in an incorrect use mode for a long time, the service life of the equipment is prolonged, and the reliable operation degree of a transformer substation and even a power grid is improved.

The invention provides a method and a system for evaluating the health degree of audio and video conference equipment, which comprehensively utilize a QFD thought and AHP and AI methods to construct an equipment fault model and a fault risk transfer model, solve the field problem of fault influence dependency analysis among an equipment principle, a real-time operation state and a historical operation state in equipment fault detection, and improve the reliability of the equipment fault detection. The evaluation method comprises the following steps: scanning a basic state in real time and detecting an environment in real time; verifying the integrity of the signal link; carrying out equipment performance standard test; the influence of the hardware loss on functionality is evaluated; performing actual use state testing, including limiting condition testing and fault injection testing; and performing health degree modeling and grading, and establishing a multi-dimensional comprehensive evaluation model. According to the method, a traditional regular maintenance mode is exceeded through multi-dimensional dynamic modeling and a predictive intervention mechanism, real-time environment sensing and hardware loss modeling are fused, a fault window can be pre-judged, the equipment operation fault rate is reduced, and preventive maintenance is achieved.

The invention belongs to the field of new energy battery pack flying welding equipment, and discloses power battery pack flying post-welding detection, dust removal and milling three-in-one equipment which comprises a three-axis rack, a dust removal mechanism and a detection and milling module, and the dust removal mechanism and the detection and milling module are fixed to the moving end of the three-axis rack. The equipment failure rate is low, and the equipment maintenance and operation cost is reduced.

The invention provides a low-voltage current transformer residual life prediction method and device, equipment and a medium, and relates to the technical field of current transformers, and the method comprises the steps: obtaining a multi-stress operation information parameter and a performance index parameter of a to-be-predicted low-voltage current transformer; calculating performance degradation index parameters according to the performance index parameters; training to obtain a preset residual life prediction model based on the multi-stress operation information parameter historical sample value, the performance index parameter historical sample value and the residual life historical sample value, and predicting the residual life according to the multi-stress operation information parameter, the performance degradation index parameter and the preset residual life prediction model. The residual life prediction value of the to-be-predicted low-voltage current transformer is obtained, multiple stress factors are comprehensively considered, the health condition of the transformer is comprehensively evaluated, the accuracy and reliability of residual life prediction of the low-voltage current transformer are improved, production shutdown and safety risks caused by equipment faults are effectively reduced, and the production efficiency is improved. And continuity and stability of production are guaranteed.

The invention provides a rail transit remote monitoring method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the fault causal reasoning and fault propagation simulation of an equipment fault event based on artificial intelligence when an equipment abnormal event occurs in a rail transit scene through remote monitoring; and performing multi-modal early warning based on a fault causal reasoning result and a fault propagation simulation result. According to the rail transit remote monitoring method and system based on artificial intelligence, fault causal reasoning and fault propagation simulation are carried out on the equipment fault event based on artificial intelligence, multi-modal early warning is carried out based on the fault causal reasoning result and the fault propagation simulation result, and the reliability of the system is improved. The system has sufficient monitoring efficiency to a great extent when facing sudden faults, complex fault modes and large-scale data analysis in a rail transit scene, and monitoring accuracy and response timeliness are improved.