957 results about "Alarm message" patented technology

The invention discloses an ultrasonic weak scattering enhancement detection system and method for micro leakage of a liquid pipeline, and relates to the technical field of industrial pipeline state monitoring and nondestructive testing. By performing enhancement processing on a bubble weak scattering signal generated by micro-leakage in the pipeline and inverting bubble parameters, online monitoring, graded early warning and positioning analysis of micro-leakage of the liquid pipeline are realized. The system comprises a clamping type transducer assembly and a control processing unit which are electrically connected, the transducer assembly is mounted on the outer wall of the pipeline; the control processing unit is configured to collect a baseline reference signal and construct a background model; transmitting a coding excitation signal, and driving an ultrasonic transducer assembly to transmit ultrasonic waves; synchronously acquiring ultrasonic echoes or transmission signals; carrying out preprocessing and weak scattering enhancement processing on the original sampling signal; inverting a bubble parameter and a gas content parameter; carrying out leakage judgment, and outputting micro leakage monitoring quantity and graded alarm information; the invention further discloses a method based on the system.

The invention relates to the technical field of operation and maintenance, and discloses an equipment fault processing method and system. The method comprises the following steps: acquiring alarm messages of a plurality of devices in real time, identifying associated alarm messages caused by the same fault according to an alarm association rule, and compressing the associated alarm messages and an existing fault group to obtain new alarm messages; determining the work order type and the circulation process of the new work order according to the alarm type of the newly sent alarm message, and carrying out order dispatching priority configuration on each process link based on the alarm level of the newly sent alarm message and the pre-configured authority information of each process link; and calling a corresponding work order template based on the work order type, configuring the work order template to generate a new work order, monitoring the full life cycle of the new work order, and synchronously updating the processing state of the new work order. According to the method, the fault processing efficiency of the machine room intelligent connection equipment is effectively improved, resource configuration is optimized through real-time monitoring and data analysis, and the method can meet the fault troubleshooting requirements of equipment of different scales and types.

The invention discloses a petroleum drilling machine equipment intelligent alarm method based on video real-time display, and belongs to the technical field of petroleum exploitation equipment monitoring, and the method comprises the steps: obtaining the operation video of a key part of petroleum drilling machine equipment, and transmitting the operation video of the key part to a monitoring center; in the monitoring center, preprocessing the operation video of the key part to eliminate the influence of illumination disturbance, and carrying out key part identification and behavior analysis on the preprocessed operation video of the key part by using an equipment part detection and behavior analysis algorithm based on deep learning to obtain a detection result of the key part; and based on the detection result of the key part, the display alarm and the sound-light alarm of the abnormal type and the occurrence position are triggered in real time, and the alarm information is sent to the handheld terminal of the driller. According to the method, intelligent fault detection and alarm are realized, manual intervention is reduced, the comprehensiveness of equipment monitoring is improved, and the accuracy and timeliness of alarm are improved.

The invention discloses a traffic incident detector system based on a domestic intelligent DSP and a detection method. The system comprises a video acquisition unit, a data exchange and storage unit, an intelligent processing core and a communication interaction unit. The intelligent processing core adopts a heterogeneous SoC chip integrating a multi-core DSP, an NPU and an FFT hardware acceleration module, the FFT module is responsible for video frame frequency domain enhancement, the NPU runs a deep learning model to carry out target detection, and the multi-core DSP executes multi-target parallel tracking and event judgment. And the data exchange and storage unit supports direct carrying of data among the modules through a unified physical address space and a DMA mechanism. The method comprises the following steps: carrying out NPU target detection on a video subjected to environment self-adaptive preprocessing; a multi-core DSP is used for tracking a target in parallel and judging an event; and finally, alarm information is generated and reported. Through heterogeneous hardware division and software and hardware cooperation, high-real-time and high-precision edge analysis is realized, and the detection reliability is improved.

The invention discloses a communication network fault diagnosis method and related equipment based on multi-modal vector fusion, and the method comprises the steps: carrying out the vector structuring processing of historical heterogeneous telecommunication operation and maintenance data through a multi-modal vectorization engine, and obtaining a historical multi-modal vector set; performing vector fusion processing on the historical multi-modal vector set through a vector fusion center to obtain a historical multi-modal fusion vector; an alarm message queue in a telecommunication network is monitored through a diagnosis decision module, and if an unknown composite fault is detected from the alarm message queue, cosine similarity calculation is carried out on a current fault fusion vector corresponding to the unknown composite fault and a historical multi-mode fusion vector in a hierarchical storage module; and outputting a network fault diagnosis decision result. According to the method, the accuracy and comprehensiveness of network fault diagnosis can be improved, efficient data storage, retrieval and diagnosis decision are realized, the operation efficiency and diagnosis capability of the system are improved, and the method can be widely applied to the technical field of communication network operation and maintenance.

The invention relates to the field of intelligent operation and maintenance, and provides a communication interruption fault monitoring and analyzing method for an interlocking logic part and a control and display machine, which comprises the following steps: acquiring first maintenance data of an interlocking system and an automatic train monitoring system, and performing data analysis on the first maintenance data to obtain a communication state of the interlocking logic part and the control and display machine; according to the communication state, communication interruption between the interlocking logic part and the control display machine is determined, and communication interruption alarm information is generated; and querying second maintenance data of the interlocking subsystem and the train automatic monitoring subsystem according to the communication interruption alarm information, analyzing the second maintenance data, and generating a fault analysis result. According to the invention, the problems of low efficiency and high misjudgment rate caused by dependence on manual troubleshooting of communication interruption faults in the prior art are solved, and automatic fault monitoring and intelligent analysis of the interlocking logic part and the control display machine are realized.

The invention discloses a server hardware abnormity alarm method, system and device, and relates to the technical field of server hardware abnormity alarm, and the method comprises the steps: obtaining the characteristic parameters of an abnormal state mode, carrying out the risk grade division of a detected abnormal state according to an abnormity severity grading standard, and obtaining an abnormal state; a comprehensive anomaly scoring result is obtained through multi-dimensional feature weight calculation; triggering an alarm mechanism of a corresponding level according to a comprehensive anomaly score result, if an anomaly score exceeds an emergency threshold, immediately generating high-priority alarm information, and performing alarm information aggregation processing to obtain a complete hardware anomaly diagnosis report; an alarm information deduplication algorithm is adopted to carry out merging processing on repeated alarms of the same hardware component, and the stability and persistence characteristics of an abnormal state are judged through alarm frequency statistics and abnormal duration analysis in a time window.

The invention discloses a detection method and system for judging the eccentricity of an SF6 circuit breaker contact based on dynamic resistance, and relates to the technical field of high-voltage electrical equipment detection, and the method comprises the steps: obtaining an eccentricity dynamic resistance oscillogram and a standard dynamic resistance oscillogram of a to-be-detected eccentric SF6 circuit breaker through employing an SF6 circuit breaker contact eccentricity detection device; obtaining key index data according to the eccentric dynamic resistance oscillogram and the standard dynamic resistance oscillogram, wherein the key index data comprises a fluctuation index, a misalignment index, an initial value over-standard rate and a peak value over-standard rate; and generating alarm information according to the key index data, and starting maintenance operation in combination with different alarm information. The determined key index data is compared with a standard requirement value, and then whether the detected SF6 circuit breaker needs to be overhauled due to the eccentric problem of the movable and static contacts is judged, so that the potential quality hazard caused by the eccentric problem of the movable and static contacts is eliminated, and the unnecessary overhaul workload of the SF6 circuit breaker can be greatly reduced.

The utility model discloses a conveyor belt deviation detecting and correcting device, which relates to the technical field of belt conveyors, is used for being matched with a carrier roller component and a conveyor belt arranged on the carrier roller component, and comprises a correcting device and a detecting device, the deviation rectifying device comprises vertical rollers symmetrically arranged on the two sides of the conveying belt, the vertical rollers are slidably and elastically connected with the placement frame, guide limiting grooves matched with the side edges of the conveying belt are formed in the working faces of the vertical rollers, and the guide limiting grooves and the conveying belt are in elastic dynamic contact fit, so that the vertical rollers can push the conveying belt back to the initial center line position; and the detection device is arranged on the placement frame and is configured to be capable of detecting the value of the lateral pressure borne by the vertical roller in real time so as to obtain the deviation working condition of the conveying belt, and alarm information is sent out to remind workers in time.

The invention belongs to the technical field of equipment early warning, and particularly relates to a multi-source information fusion real-time monitoring and data analysis method and system, and the method comprises the steps: achieving the automatic recognition and data collection, remote assistance and support, and intelligent path planning and navigation functions in the inspection process of a chemical device, so as to standardize the inspection process; the inspection efficiency and the result accuracy are improved; establishing a chemical device image recognition model, collecting and preprocessing chemical device image data, performing model training by using a convolutional neural network deep learning model, and deploying the model to actual equipment; an abnormal feature extraction algorithm is developed, the operation state of the equipment is monitored in real time in combination with an intelligent early warning and processing system, early warning is quickly given out, and corresponding processing measures are taken; by adopting the anomaly detection and classification technology, an anomaly detection algorithm is realized, an efficient alarm information transmission mechanism and an intelligent alarm information processing system are developed, and an alarm information feedback and closed-loop management mechanism is established, so that the accuracy and timeliness of anomaly detection are improved.

Embodiments of the invention disclose an object abnormal behavior prompting method and apparatus, a wearable device and a medium. The method comprises the steps of collecting and obtaining device motion change data of the wearable device based on a first frequency; when it is detected that the equipment movement change data meet the risk action detection condition, environment perception data are obtained, and the equipment movement change data and object physiological change data of the equipment wearing object are collected based on a second frequency; if any abnormal behavior detection condition is satisfied, determining that the equipment wearing object has an abnormal behavior state; and performing cooperative detection on the multi-modal sensor data, determining abnormal level information corresponding to the abnormal behavior state, and prompting a corresponding target user based on a target prompting mode corresponding to the abnormal level information, the problem of response lag caused by the fact that a traditional wearable device depends on manual alarm information triggering and single-detection-dimension anomaly detection is effectively solved, and the accuracy of object risk behavior judgment and the timeliness of emergency response are improved.

The utility model relates to an electric control cabinet intelligent safety alarm device, which comprises an electric control cabinet main body, an ultrasonic ranging module, an alarm module and a communication module, and is characterized in that the ultrasonic ranging module measures the time difference of transmitting and receiving ultrasonic waves to determine the actual distance between an obstacle and the electric control cabinet main body, so that non-contact ranging is realized; the timeliness of alarm triggering is ensured, and accurate measurement can be carried out even in a severe environment full of dust, smog or corrosive substances and the like; the multi-mode alarm module and the ultrasonic ranging module are combined for use, multi-mode alarm is carried out when the ultrasonic ranging module triggers the alarm function, a multi-level verification and confirmation mechanism is realized, the possibility of false alarm is reduced, diversified alarm signals are provided, and the alarm can be carried out even in a noisy or poor-sight environment. Normal receiving of alarm information can also be ensured; by means of the communication module, the intelligent safety alarm device of the electric control cabinet has remote monitoring and management functions, and the quick response capability is remarkably enhanced.

The invention relates to a limited space operation safety monitoring method based on multi-sign parameter intelligent judgment, which comprises the following steps of: training a single-class support vector machine model by utilizing historical data, and establishing a normal physiological state multi-dimensional reference model under each operation content; the method comprises the following steps: acquiring current operation content in a limited space, determining a vital sign parameter set needing to be monitored, and collecting real-time vital sign data of an operator through a user terminal; calculating a comprehensive abnormal score AS of the real-time vital sign data, and when the comprehensive abnormal score AS exceeds a dynamic threshold value delta, marking the data as comprehensive abnormal data; marking the vital sign parameter with the maximum contribution degree absolute value in the abnormal parameters as a key vital sign; and sending alarm information to a monitoring terminal, wherein the alarm information comprises the identifier of the key vital sign. By comprehensively analyzing the real-time vital signs and comparing the real-time vital signs with the dynamic threshold value, the accuracy of abnormal person recognition is effectively improved.

The invention discloses an anti-prying alarm information processing method and system for an intelligent lock, relates to the technical field of information processing, and constructs a behavior sequence capable of completely representing a continuous interaction process of a user by integrating original data streams from multiple sensors of the intelligent lock and performing unified coding and time sequence windowing processing on the original data streams. On the basis, an intention recognition model is introduced, and the behavior sequence is deeply analyzed to deduce the potential malicious degree of the behavior sequence. Furthermore, the initial malicious probability is dynamically calibrated by fusing external situation information such as time, user authorization state and the like, so that a more accurate and context aware comprehensive risk score is generated. And finally, intelligent grading response decision making is carried out based on the score. Therefore, the problem of false alarm caused by similar physical characteristics of benign abnormal operation and hostile attack can be avoided, so that the accuracy and reliability of the security and protection system are remarkably improved.

The present invention relates to the technical field of data communications, and provides an apparatus and method for rapid recovery from failures of some devices in an optical data switching or computing device cluster. A management device confirms the identifier m of a second-layer device that sends an alarm message; on the basis of a pre-established mapping relationship between the identifier m of the second-layer device and an optical path port Pm in each protection optical switch device, the management device controls an optical switch in each protection optical switch device in the protection optical switch array, such that a common port thereof is connected to the optical path port Pm, thereby replicating an optical signal, imported from a first-layer device library into the second-layer device m, to a second-layer redundant device by means of the optical switch in each protection optical switch device and an optical coupler m. In the present invention, when some devices fail, functions of the devices can be rapidly switched to a redundant device, thereby reducing failure recovery time and saving cluster device resources.

The invention provides an intelligent monitoring method and system for hanging basket operation, relates to the technical field of safety production, and solves the technical problems that in the prior art, warning is not timely, and abnormal behaviors cannot be effectively avoided. The method comprises the following steps: acquiring an operation image at the current moment and an operation instruction at the current moment; the operation image comprises a hanging basket device and an operator; identifying the state data of the hanging basket equipment and the operating personnel in the operation image; predicting a target state at the target moment based on the operation instruction and the state data; the target moment is any moment after the current moment; determining a state type at the target moment according to the target state; the state type comprises a safety state and a risk state; and if so, sending an alarm message. The method and the device are used in the intelligent monitoring process of hanging basket operation.

The invention relates to a crane operation monitoring method and device, equipment and a storage medium. The method comprises the following steps: collecting signal data of a crane through an edge node equipment sensor, analyzing the signal data into structured frame data, dividing the structured frame data into a plurality of samples to be identified based on a preset time window, determining state score values of the samples to be identified based on a locally deployed self-encoding model, and verifying the state score values of the samples to be identified according to a preset verification rule. The to-be-recognized sample with the state score value smaller than a preset threshold value is verified to obtain a verification result, and if the verification result represents that the crane operation corresponding to the to-be-recognized sample is the operation of an alarm event, alarm information is fed back to a preset user, and end-to-end real-time monitoring and abnormal protection of the crane operation state are achieved; and quick response is ensured when the operation of the crane is abnormal.

The invention relates to the technical field of image recognition, in particular to a personnel abnormal behavior supervision method and system for important places. The method comprises the following steps: acquiring a monitoring video frame image in real time, matching in combination with a preset abnormal foreign object image library, recognizing an abnormal behavior image, and confirming the identity of an abnormal person through face recognition to generate first alarm information; then, through analyzing the action sequence of the abnormal behaviors, identifying a second abnormal behavior image and a third abnormal behavior image, and calculating a situation response deviation degree and a behavior coherence imbalance index, generating second alarm information; finally, based on the two pieces of alarm information, the system performs alarm processing on the abnormal personnel, and abnormal behavior monitoring and timely response are effectively realized. According to the invention, through accurate behavior identification, intelligent mode analysis and time sequence association evaluation, an efficient and accurate abnormal behavior monitoring and alarm function is provided, and the place safety is improved.

The invention discloses a mining industry multi-system joint alarm method, which comprises the following steps of: receiving alarm information from a plurality of alarm systems, performing protocol conversion according to a communication protocol format of the alarm information, and verifying in a unified standardized data format; based on a preset rule base, distributing a basic priority score for the alarm information verified to be valid; generating a comprehensive priority value of each alarm through weighted calculation in combination with the area weight and the time weight of the alarm, and forming a global priority queue; and according to the global priority queue, an alarm signal instruction is generated according to a priority sequence, and a multi-mode output device is driven to output an alarm signal. According to the invention, multi-system independent alarm is changed into multi-system intelligent joint alarm, and an automatic alarm center capable of getting through a data barrier, carrying out intelligent analysis and decision making and realizing ordered output is constructed, so that the efficiency and reliability of mining safety monitoring and emergency response are integrally improved.

The invention discloses a data processing method and device applied to edge computing equipment. The method comprises the following steps: accessing a plurality of different models and algorithms through model driving middleware; acquiring at least one path of data and caching the data to a pre-processing queue; performing standardization preprocessing on the multi-modal data by calling a standardization interface; according to the configuration information, distributing the preprocessed data to a corresponding model queue, obtaining a reasoning result through model reasoning, and sending the reasoning result to a post-processing queue for caching; or sending the preprocessed data to a post-processing queue for caching; reasoning results or data are taken out from the post-processing queue, and whether alarm needs to be triggered or not is determined through algorithm calculation, so that an alarm message is generated. According to the method, the difference in multiple aspects of each third-party model is shielded, the workload and the debugging period of developing adaptive codes are reduced, the problems of data transmission disorder and format mismatching during multi-model cooperation are solved through a standard data flow model architecture, and the stability of the system architecture is improved.

The utility model discloses a Beidou positioning device for a life jacket. The Beidou positioning device comprises a Beidou No.3 RDSS communication chip; a single Beidou positioning module; an antenna; a water falling trigger chip; the self-consumption battery is low; an indicator light; a key; a back buckle wearing structure; the life jacket Beidou positioning device is accessed to a network through Beidou No.3, is networked with a background management platform, can send alarm information and position information to the management platform when encountering water or being manually triggered, is applied to a safety guarantee product for water operation personnel, automatically gives an alarm when falling into water, and is convenient to use. And uploading the position information and the state of the equipment to a management platform.

The invention discloses a data processing method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring a feature set; analyzing the feature set by using a random forest model to obtain a risk probability value corresponding to the feature set, and determining a security risk state of the target environment according to the risk probability value; when the security risk state indicates that the target environment has the security risk, generating an alarm message, and sending the alarm message to the target object; receiving an actual behavior sequence and detection data of the gas maintenance personnel, and determining the similarity between the actual behavior sequence and a preset standard behavior sequence; and under the condition that the similarity is greater than a target threshold value, comparing the detection data with a preset standard range, and verifying the security risk state according to a comparison result. According to the method and the device, the technical problem that the capability of coping with the fuel gas safety problem is limited due to the fact that the fuel gas danger cannot be predicted in the related fuel gas safety investigation process is solved.

The invention provides an elastic monitoring alarm method and system based on multistage cooperative verification. The method comprises the step of remarkably improving the alarm accuracy through a three-level verification mechanism. The first-stage verification adopts intelligent anomaly detection, compares a current index with a historical baseline, and identifies a preliminary anomaly; in the second-stage verification, association index analysis is introduced, and pseudo anomalies caused by normal business fluctuation are filtered; and the third-stage verification performs cross-customer collaborative analysis to distinguish individual anomaly from global events. In addition, fault root causes are automatically positioned through dependency link analysis, and structured alarm information containing causes, confidence coefficients and processing suggestions is generated. And a closed-loop learning mechanism is established, operation and maintenance feedback is continuously collected, and alarm parameters are automatically adjusted by adopting Bayesian optimization. According to the method, the false alarm rate is reduced, the root cause positioning accuracy is improved, the average repair time is shortened, and the IT operation and maintenance efficiency is remarkably improved.

The invention discloses a gas detection alarm method and system for mining safety equipment, and relates to the technical field related to gas detection, and the method comprises the steps: obtaining basic gas detection related equipment information of a target mine, and constructing a mine topological graph; performing incremental coding identification on the node gas correlation detection data sequence of the M equipment topology nodes in the preset detection window; performing multi-dimensional structured interaction on the M signal token sequences to obtain a local association sub-graph set; and in combination with the M signal token sequences, carrying out local gas detection anomaly identification on the local association sub-graph set, and obtaining gas detection alarm information according to an identification result set. The technical problems that in the prior art, the semantic relation between signals is difficult to accurately understand in the complex and heterogeneous mine environment, local gas abnormity is difficult to recognize in time, and consequently the accuracy and timeliness of gas detection alarming are poor are solved, and the technical effects that abnormity accurate positioning is achieved, and the accuracy and timeliness of gas detection alarming are improved are achieved.

The invention relates to a power distribution overhead line pole inclination monitoring circuit, device and system. The monitoring circuit is installed on an electric pole and comprises an angle sensing module and an alarm positioning module. The signal input end of the alarm positioning module is connected with the signal output end of the angle sensing module; the angle sensing module is used for outputting a voltage signal according to the inclination change of the electric pole; the variable quantity of the voltage signal and the gradient variable quantity of the electric pole are in a linear corresponding relationship; and the alarm positioning module is used for receiving the voltage signal, outputting alarm information under the condition that the voltage signal exceeds a preset voltage threshold value, and outputting position information including the electric pole. According to the invention, potential safety hazards can be reduced.

The invention discloses an intelligent safety monitoring waistcoat based on multi-sensor fusion and a control method, and relates to the technical field of wearable equipment. A human body infrared spectrum is detected through a human body pyroelectric inductive sensor, and whether the intelligent safety monitoring waistcoat is worn or not is judged; if it is judged that the intelligent safety monitoring waistcoat is worn, a safety data collection program is started, and safety data are collected in real time; the fusion module is used for fusing real-time collected security data; transmitting the fused security data to a microcontroller for processing; comparing with a preset threshold value to judge whether an abnormal condition exists or not; if the comparison result is that the abnormal condition exists, judging whether the abnormal condition is a dangerous state or not; if the judgment result is a dangerous state, the microcontroller sends a protection instruction to the airbag protection equipment; the driving controller starts the air source power device, inflates the air source power device, and sends alarm information at the same time. According to the invention, through a wearable trigger mechanism, multi-source data fusion analysis and hierarchical protection response, the accident risk is effectively reduced.

The application provides a sensitive component life detection method and system, and relates to the field of sensitive component life detection.The method comprises the following steps: recording the factory UTC time of a sensitive component and initial performance data at the factory; based on a preset timing monitoring period, acquiring the current UTC time and average performance data of the sensitive component in the timing monitoring period at the end of each timing monitoring period; adjusting the weight coefficient corresponding to different timing monitoring periods according to environmental data and sensitive component load parameters; performing weighted summation on the average performance data according to the weight coefficient to obtain a total integral value; when it is detected that the total integral value is greater than or equal to a preset integral threshold value or the variable value of the average performance data exceeds a preset amplitude threshold value, triggering alarm information that the life has reached the limit. By implementing the method, the performance change of the sensitive component under different environmental and load conditions can be dynamically responded, and the life end of the sensitive component can be more accurately predicted.

The invention discloses an emergency method and system based on door lock Bluetooth collaboration, and the method comprises the steps: detecting a floor network fault according to a monitoring result of a robot dog on a door lock multicast communication state, triggering the robot dog to carry out autonomous task distribution or platform scheduling distribution, and determining a floor which each robot dog is responsible for; according to the event that the robot dog arrives at the designated floor, Bluetooth communication links are sequentially established between the adjacent door locks according to a preset evacuation path, and relay alarm message transmission between the door locks is achieved through the IP forwarding function of the robot dog; according to the relay alarm message transmitted by the door lock in the Bluetooth link, each door lock whistles and alarms according to a planned time point, and synchronously reports personnel statistical information; and according to the door lock fault condition detected by the robot dog, the emergency pointing link is updated in a dominant skipping or autonomous identification mode. According to the embodiment of the invention, the intelligent level and rescue efficiency of building emergency response can be improved.

The invention relates to the technical field of power system operation and maintenance, and discloses a grounding grid corrosion fault diagnosis method and system, a storage medium and equipment, and the method comprises the steps: arranging a plurality of types of sensors at key points of a grounding grid, collecting the electrical signal and environmental parameter data of the grounding grid in real time, carrying out the preprocessing of the collected original data, and carrying out the calculation of the data; a high-quality multi-source data set is generated; feature extraction is carried out on the multi-source data, normalization and weighted fusion are carried out on extracted multi-source features, and a comprehensive fault feature vector is generated; constructing a diagnosis model suitable for multi-feature input, performing adaptive optimization on parameters and feature fusion weights of the diagnosis model by using a whale optimization algorithm WOA, training the diagnosis model by using a comprehensive fault feature vector, and optimizing the model; and inputting the comprehensive fault feature vector into the optimized diagnosis model, outputting a corrosion fault diagnosis result, and generating a fault report and alarm information, thereby realizing accurate identification, positioning and early warning of grounding grid corrosion.

The invention discloses a multi-source sensing fusion fault diagnosis method for intelligent equipment, and particularly relates to the technical field of current transformer monitoring. Collecting a residual current signal output by the mutual inductor according to a set sampling period, and generating corresponding time sequence data; carrying out time-frequency transformation processing on the signal to construct a two-dimensional atlas image; inputting the atlas into an atlas recognition model containing a convolutional neural network structure, extracting features and performing classification judgment; carrying out similarity or distribution deviation comparison on the current map characteristics and a preset normal category map, and judging whether a wiring abnormal state exists or not; if the real wiring fault is identified, alarm information is sent to a monitoring terminal; according to the method, intelligent, real-time and high-robustness identification of the wiring state of the mutual inductor can be realized, and the method is suitable for an electrical fire early warning system in a complex electrical environment.