231 results about "Detection early" patented technology

The invention discloses a bridge health state detection early warning method and system based on image recognition, and relates to the field of bridge health monitoring, and the method comprises the steps: collecting a bridge crack image, carrying out the preprocessing of the bridge crack image, and forming a bridge crack image data set; carrying out manual amplification on the bridge crack image data set through a sliding window algorithm, and carrying out feature extraction to generate a feature data set; performing model training by adopting a convolutional neural network to obtain a crack recognition model; acquiring bridge health state data in real time through a sensor, and performing crack detection by using a crack recognition model; modeling the health state of the bridge by adopting a time sequence analysis method, and generating a dynamic damage prediction result; carrying out bridge health state evaluation, and carrying out early warning on bridge damage; according to the invention, amplification and feature extraction of the bridge crack image are carried out through the adaptive sliding window algorithm, and the precision and robustness of crack identification are improved in combination with the convolutional neural network and the dynamic attention mechanism.

The invention relates to the technical field of water conservancy project detection, and discloses a water conservancy project safety detection early warning method based on artificial intelligence. The method comprises the following steps: acquiring multi-modal monitoring data of a key part through a distributed sensor network, and extracting a dynamic feature sequence in a preset time period through space-time alignment and noise filtering; inputting the image into a deep neural network fused with an attention mechanism, constructing a multi-scale space-time correlation map through hierarchical feature learning, and generating a high-dimensional representation of an engineering structure state; historical accident case data is used as a supervision signal, a hybrid expert model is used for performing multi-task training on high-dimensional representation, and the contribution weight of each monitoring index to the safety risk is obtained; combining real-time environment parameters and structural response characteristics to construct a dynamic threshold adjustment model, adaptively updating an early warning threshold according to a risk probability, and screening out key risk factors of which the contribution weights are greater than the updated threshold; and on the basis of spatial and temporal distribution characteristics, through graph neural network node association reasoning, multi-source early warning information is fused to generate a graded early warning result.

The invention belongs to the technical field of fire safety, and discloses a fire-fighting equipment detection early warning method and system based on a visual camera, and the method comprises the following steps: obtaining multi-focal-length image frames of a fixed spray head and a fire extinguisher edge region, judging the continuous change of gray scale in the continuous image frames, and carrying out the detection early warning of the fire-fighting equipment based on the visual camera. Comparing the gray level distribution difference under the long focus and the short focus, identifying the dynamic trajectory of the contour line angular point of the fire-fighting equipment association structure, analyzing the delay of the function action response, marking as an abnormal region, and generating a fire-fighting equipment collaborative early warning information set; according to the invention, by extracting and analyzing the multi-focal-length image frames of the edge areas of important facilities such as a fixed spray head and a fire extinguisher, fine changes of the fire-fighting facilities under differentiated focal lengths can be captured, working states and potential faults of the fire-fighting facilities can be judged, functional abnormalities caused by equipment aging or damage can be recognized in advance, early warning can be carried out in real time, and the safety of the fire-fighting facilities can be improved. And a higher guarantee level is brought to fire safety.

The invention discloses a superconducting magnet quench detection early warning system and method based on skeleton current, and relates to the field of superconducting magnet quench detection, and the method comprises the steps: constructing an electrothermal coupling finite element model of a superconducting magnet and a conductive skeleton; simulating different quench events based on the electrothermal coupling finite element model; according to different quench events, quench judgment threshold values and different levels of quench severity index grading threshold values are determined; when the superconducting magnet is out of service, acquiring a skeleton current signal that the current of the superconducting magnet is shunted to the conductive skeleton; based on the skeleton current signal, according to the quench judgment threshold value and the quench severity index grading threshold value, grading early warning and protection response are conducted on the superconducting magnet quench, nondestructive testing can be conducted on the superconducting magnet quench, grading evaluation and optimization protection of the quench severity degree are achieved, and the safety of the superconducting magnet quench is improved. And the cost of the quench monitoring system is reduced.

The invention relates to the field of park management, and particularly discloses a smart park management system and method based on the Internet of Things, and the method comprises the steps: firstly obtaining park air quality parameter data collected by a plurality of sensors and park noise parameter data, collected by a sound sensor, at a plurality of preset time points, and then employing a deep learning technology to carry out the recognition of the park air quality parameter data and the park noise parameter data; and feature extraction and correlation analysis are carried out on the two parameters, and finally, whether to send out park environment detection early warning is judged through a classifier, so that the air pollution problem is found in time, related personnel are helped to quickly respond, and the health of park residents is protected.

The invention discloses an offshore concentrated photovoltaic detection early warning operation platform, and relates to the field of offshore photovoltaic power generation. Comprising a pile foundation and a supporting frame supported by the pile foundation, a photovoltaic panel is laid above the supporting frame, an anti-deformation support structure is arranged below the supporting frame, a supporting adjusting pile is arranged at the top end of the pile foundation, and the supporting adjusting pile is used for being supported at the bottom of the anti-deformation support structure. When the pile foundation settles and the supporting frame and the anti-deformation support structure deform, so that the supporting stress of the position of the supporting adjusting pile is changed, the supporting height is adjusted through the supporting adjusting pile to recover the supporting stress to the anti-deformation support structure; the supporting adjusting pile comprises a supporting part and an adjusting part, and the adjusting part adjusts the supporting height relative to the axial direction of the supporting part so as to recover the supporting stress on the anti-deformation support structure.

The invention provides an expressway bridge deformation detection early warning method based on a cold region environment, which relates to the technical field of bridge structure health monitoring and comprises the steps of monitoring point arrangement and equipment installation, real-time data acquisition and transmission, data processing and analysis, early warning threshold setting and early warning grading, early warning response and disposal, and maintenance and updating. Through the temperature compensation and protection device, the influence of severe environments such as low temperature, wind and snow in the cold region on the performance of the sensor is effectively solved, long-term stable operation of the monitoring system is ensured, and the micro deformation of the bridge in the complex environment of the cold region can be comprehensively and accurately captured by adopting multi-source sensor data fusion and a deep learning algorithm. Through scientific and reasonable early warning threshold setting and a grading early warning mechanism and in combination with the bridge deformation prediction model, potential safety hazards of the bridge can be found in time, early warning signals are accurately sent out, and powerful guarantee is provided for safe operation of the bridge.

The invention provides a production quality detection early warning method and device for a saw chain gear, and relates to the technical field of quality detection early warning, and the method comprises the steps: analyzing a loss parameter of the saw chain gear, positioning a loss value, and constructing a detection parameter hierarchical structure; performing core detection parameter combination extraction; the positioning parameter is used as a detection target, the comparison parameter is used as a cooperative following quantity and fitted into a quality detection module, and quality detection is carried out on production monitoring data; performing early warning grade analysis according to the parameter deviation value and the hierarchical relationship between the detection target and the detection parameter hierarchical structure; and matching the early warning path, and generating early warning feedback information. The technical problems of low quality detection accuracy and inaccurate early warning information caused by difficulty in quickly and accurately positioning specific quality problems or structure defects in the production process in the prior art can be solved, the structure loss is quickly positioned by combining the positioning parameters and the comparison parameters, and the accuracy of quality detection is improved. And the accuracy of quality detection and early warning is improved.

The invention relates to the field of kiln early warning, and discloses an abnormal behavior detection early warning system in real-time kiln video monitoring, which is used for realizing a leap-type innovation of kiln anomaly detection from single threshold alarm to intelligent hierarchical control. Three-dimensional temperature field data of the surface of a steel pipe is collected through an infrared thermal imager arranged in an annular array, a multi-spectral video stream of a slab stacking area is synchronously obtained in combination with a visible light camera, and a vibration sensor array is deployed to collect roller way vibration phase signals. And environment radiation compensation is carried out based on the Stefan-Boltzmann law, and surface cracks and internal slag inclusion defects are identified through a Kirchhoff law deviation threshold. According to the scheme, through multi-modal data space-time synchronous coding, dynamic weight adjustment and gradient template library self-adaptive correction, the anomaly detection precision and the working condition adaptability are remarkably improved.

The invention relates to a fabric defect spot positioning detection system, method and equipment and a medium. The system comprises an image acquisition module, an image processing module, a defect detection module and a detection result generation module, the image acquisition module is used for acquiring an original detection image of a to-be-detected fabric; the image processing module is used for segmenting the original detection image to obtain a to-be-detected image, and the to-be-detected image comprises a core to-be-detected image; the defect detection module is used for inputting the core to-be-detected image into a core defect point detection model to obtain core defect point detection information; the detection result generation module is used for generating a real-time early-warning defect detection result in response to the condition that the core defect point detection information meets the defect detection early-warning condition; and generating a fabric defect initial judgment grade detection result based on the core defect point detection information. By adopting the system, the fabric defect spots can be automatically detected, the actual distribution condition of the fabric defect spots can be accurately reflected, and the fabric quality is ensured to be in a controllable state.

The invention discloses an atmospheric pollutant detection and early warning device, and relates to the technical field of atmospheric detection. The atmospheric pollutant detection and early warning device comprises a host and further comprises a first rotating mechanism, a second rotating mechanism, an air inlet and an air outlet, the first rotating mechanism is connected with a detection cylinder, and the first rotating mechanism is used for performing rotation adjustment on the detection cylinder; the moving column is inserted into the detection cylinder and is connected with the side wall of the detection cylinder through a moving module; the spiral pipe is communicated with the interior of the detection cylinder through an electromagnetic valve, and one end, far away from the electromagnetic valve, of the spiral pipe is communicated with an air inlet pipe; and a second rotating mechanism. The atmospheric pollutant detection early warning device can mix and stir the detected atmosphere to make the atmosphere more uniform, and can drive the air inlet pipe to rotate synchronously so as to facilitate suction detection of the atmosphere at different angles, and in the detection process, the detection sensor can rotate automatically, so that the detection accuracy is improved, and the detection efficiency is improved. And the detection result is more accurate.

The invention discloses a boiler anti-wear and anti-explosion detection early warning method and system, and relates to the technical field of boiler detection, and the method comprises the steps: arranging a sensor to collect boiler operation data; calculating a temperature field in the boiler through a heat flow coupling equation; calculating a flow velocity field in the boiler by using a fluid mechanics equation; constructing a damage degree equation based on the temperature field and the flow velocity field in combination with the stress field distribution of the boiler component in the working environment, and evaluating the damage condition of the boiler component; based on the calculation result of the damage degree equation, the damage risk level of the boiler component is judged, risk early warning operation is executed, and operation parameters of the boiler are adjusted. According to the anti-abrasion and anti-explosion detection early warning method for the boiler, the multi-physical field effect in the boiler is comprehensively considered, and the damage state and potential risks of boiler parts can be evaluated more accurately. A finite element method is adopted to finely solve a temperature field and a flow velocity field, stress field distribution is combined, the damage degree of the boiler is dynamically evaluated, and the health state of boiler parts is reflected in real time.

The invention relates to the technical field of posture detection, and particularly discloses a method and system for achieving posture detection early warning based on multimode data, and the method comprises the steps: obtaining the multimode data which comprises environment air pressure data, acceleration data of a user, angular velocity data, height data, magnetic field data and weight data; preprocessing the multi-mode data to obtain preprocessed multi-mode data; performing multi-stage attitude detection according to the pre-processed multi-mode data, and performing multi-mode early warning according to the pre-processed multi-mode data in the multi-stage attitude detection process; wherein the multi-stage attitude detection comprises weightlessness detection, impact detection and attitude stability detection which are carried out in sequence; and outputting a multi-stage attitude detection result and multi-mode early warning information. According to the invention, the false alarm rate and the missing report rate can be obviously reduced, and more personalized, more timely and more accurate fall risk management is provided, so that the safety guarantee level and the life quality of the user are greatly improved.

The invention relates to the technical field of electric field detection and early warning. The invention aims to overcome the defect of lack of an electric field detection device under the hot-line working condition of an ultra-high-voltage line in the prior art, and relates to an electric field detection device under the hot-line working condition of the ultra-high-voltage line, which is a non-contact electric field detection device and is used for detecting the electric field under the hot-line working condition of the ultra-high-voltage line in the hot-line working condition of the ultra-high-voltage line. The non-contact electric field detection device takes a microprocessor as a core control unit, and is composed of a double-spherical-shell type electric field sensor, a differential amplifier, a filter circuit, a true RMS conversion circuit, a power supply module and a man-machine interaction circuit. And deep association of data support, function collaboration and scene adaptation is carried out through the structure. The problem that traditional equipment is insufficient in stability in a severe environment is solved, continuous and stable operation of the electric field detection equipment under complex working conditions is achieved, it is ensured that reliable electric field intensity data can be continuously output, and a basic guarantee is provided for operation safety evaluation.

The invention provides a radiation detection early warning method and system for medical equipment, and is applied to the technical field of data processing. The method comprises the following steps: performing preprocessing and feature extraction on radiation feature data in target medical equipment information to generate radiation feature time sequence data; processing the radiation characteristic time sequence data based on a convolutional neural network to generate radiation early warning characteristic parameters; carrying out abnormal value filtering processing on the radiation characteristic time sequence data based on an adaptive threshold filtering method to generate target radiation characteristic data; processing the target radiation characteristic data to generate a radiation intensity dynamic change curve and abnormal radiation risk characteristics; processing the radiation intensity dynamic change curve based on a preset dynamic early warning rule and the abnormal radiation risk characteristics to generate early warning decision data; and processing the early warning decision data and the radiation early warning characteristic parameters, generating radiation standard exceeding early warning information and an equipment abnormal shutdown instruction, and optimizing radiation protection energy consumption management.

The invention provides an AI vision-based coal cinder amount intelligent detection early warning method and system, and the method comprises the steps: synchronously collecting the surface feature information, thermal radiation feature and internal structure information of a slag hopper of a slag conveyor through a dual-spectrum camera, and obtaining a dual-spectrum image; pixel-level coal cinder segmentation is carried out on the double-spectrum image; based on the segmentation result, counting the pixel proportion of the coal cinder area, and converting the pixel proportion into the actual coal cinder total amount proportion; identifying the segmented coal cinder area by adopting connected domain analysis, dynamically adjusting a size judgment threshold value through contour analysis and geometric feature extraction in combination with the bearing capacity of the slag conveyor, and determining whether large coal cinder exists or not; when the actual coal cinder total amount proportion is larger than the preset threshold value or large coal cinder exists, the risk level is generated to trigger the multi-level early warning control action, the actual coal cinder total amount proportion and the large coal cinder are automatically recognized through double-spectrum fusion segmentation, and early warning is given out when abnormity occurs, so that the accuracy and the real-time performance of coal cinder amount detection are effectively improved.

The utility model relates to the field of refrigerator car distance measurement based on laser radar, and discloses a height limit detection early warning unit used for a refrigerator car and based on laser radar, which comprises a shell, a laser radar sensor, a processor and an early warning device, and is characterized in that the laser radar sensor is used for measuring the distance between the refrigerator car and a height limit rod; the laser radar sensor, the processor and the early warning device are electrically connected in sequence, the laser radar sensor, the processor and the early warning device are all located in the shell, and the shell is installed outside the front end of a carriage of the refrigerator car. According to the scheme, the laser radar sensor is applied to the refrigerator car, the distance between the refrigerator car and the height limiting rod is conveniently measured, the distance between the refrigerator car and the height limiting rod is accurately obtained in real time, and therefore the safety and efficiency of the refrigerator car are improved.

The invention discloses a brain-vision fusion driver abnormal state detection early warning method based on an L3 automatic driving technology. The method specifically comprises the steps that a face image and an electroencephalogram signal of a driver are acquired in real time, and face feature points of the driver are extracted from the face image; calculating various face judgment indexes of the driver in real time; performing false removal and noise reduction on the extracted electroencephalogram signals in real time; according to the real-time face judgment index and the noise-reduced electroencephalogram signal, whether the driver enters a corresponding abnormal state under the two driving modes of taking over driving and automatic driving is judged, so that early warning is performed on the driver; the minimum attention concentration degree required in each driving mode can be met when the driver uses the automatic driving vehicle, and the potential safety hazard of L3 automatic driving is reduced to the maximum extent.

The invention belongs to the technical field of power distribution networks, and particularly discloses a power distribution network single-phase earth fault early warning device. Comprising a box body, one side of the top of the box body is rotatably connected with a box cover, the interior of the box body is detachably connected with a fault early warning positioning instrument, the fault early warning positioning instrument is used for fault detection early warning and fault position positioning, the two sides of the fault early warning positioning instrument are each provided with a wedging groove, and the wedging grooves are used for providing installation positions. A protection mechanism is arranged outside the box body, the protection mechanism is used for protecting the device, and a temperature regulation and control mechanism is arranged on the outer side of the fault early warning positioning instrument. Through the arrangement of the protection mechanism, the outside of the device is wrapped by a protection air bag, so that when the device is impacted or falls, the impact force can be greatly reduced, the damage of the early warning device is avoided, the economic loss is avoided, the use is not delayed, and the detection efficiency is further improved.

To automatically monitor the progress in a construction site, and detect a problem early.SOLUTION: A generative AI system for automatically monitoring the progress in a construction site includes: means for acquiring the progress; means for detecting a problem; and means for reporting the problem detected early.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of equipment detection and early warning, and discloses an equipment detection and early warning system and device.The equipment detection and early warning system comprises the steps that historical data of oil storage equipment are collected; processing the historical data, and analyzing the processed historical data to obtain a risk value of the oil storage equipment; constructing an early warning evaluation model of the oil storage equipment in combination with the historical data and the neural network model, and obtaining an early warning evaluation value of the oil storage equipment by using the early warning evaluation model; calculating a comprehensive hidden danger value of the oil storage equipment based on the risk value and the early warning evaluation value, and judging whether the oil storage equipment has potential safety hazards or not according to the comprehensive hidden danger value; and generating an early warning prompt according to a potential safety hazard judgment result, feeding back the generated early warning prompt to related personnel, and instructing the related personnel to maintain the oil storage equipment. According to the invention, through the risk value and the early warning evaluation value, potential faults or problems can be effectively predicted, early warning is realized, and accidents are reduced.

The invention relates to a driver fatigue detection early warning method and device and electronic equipment, and the method comprises the steps: obtaining the image information, physiological information and posture information of a driver, and obtaining a fatigue detection model with complete training, and inputting the driver image information, the driver physiological information and the driver attitude information to the completely trained fatigue detection model for fatigue detection to obtain a driver fatigue detection result, matching the fatigue detection result with a preset fatigue threshold, and if the matching is carried out, carrying out fatigue detection on the driver. And carrying out fatigue driving early warning operation. The method provided by the invention is used for accurately identifying the fatigue driving behavior of the driver and carrying out related early warning on the fatigue driving behavior of the driver, so that traffic accidents caused by fatigue driving are reduced.

The invention discloses an inter-satellite interconnection routing component radiation hardening method and system, and the method comprises the following steps: obtaining preset radiation hardening configuration parameters in response to a single event upset detection instruction started by a user, the configuration parameters comprising a detection time interval, a detection table item range and a single event upset exception processing strategy; in a system initialization stage, register table entries of a switch chip are backed up through a memory error correction mechanism, a reference table entry backup library is constructed, write operation on the table entries of the switch chip is intercepted during system operation, and the reference table entry backup library is synchronously updated in real time. According to the method, through regular scanning and real-time synchronous backup, efficient detection of single event upset errors is realized, and it is ensured that the system can be found and recovered as soon as possible when upset occurs; a user can set a detection interval, a table item range and a self-defined recovery behavior according to requirements, and extremely high flexibility is provided.

A dust explosion detection early warning device disclosed by the present invention comprises a rapid mounting seat, a mounting block, a dust treatment device, a rotating rod and a dust concentration detection unit, the lower end of the dust treatment device is fixedly connected with a warning indicator, the middle part of the upper end of the dust treatment device is fixedly connected with the mounting block, and the mounting block is detachably connected to the middle part of the lower end of the rapid mounting seat. A flange plate is fixedly connected to the upper end of the quick mounting seat, rotating rods are uniformly arranged on the periphery of the dust treatment device, one end of each rotating rod is rotationally connected to a rotating seat, the rotating seats are fixedly connected to the edge of the lower end of the dust treatment device, and buckle seats are fixedly connected to the dust treatment device above the rotating seats; the upper middle portions of the rotating rods are connected into the buckle bases in a buckled mode, and the other ends of the rotating rods are fixedly connected with dust concentration detection units. The invention aims to provide the dust explosion detection early warning device which is convenient to disassemble and assemble, has multi-direction detection and is efficient in dust treatment.

The invention, which relates to the technical field of medical information management, discloses an obstetrical diagnosis and treatment information integrated management and leak detection early warning system comprising a data acquisition module, a diagnosis and treatment trajectory modeling module, a rule knowledge base module, a leak detection identification module, a leak detection risk calculation module, a high-risk cross analysis module and an early warning push module. A gestational week time axis and diagnosis and treatment trajectory model is constructed by collecting basic information of pregnant and lying-in women and previous antenatal care data; matching the trajectory model with a standard obstetrical diagnosis and treatment path rule set, identifying missing or overdue examination, calculating a missing detection weight value, and forming an individualized risk map; further performing cross analysis in combination with high-risk pregnancy indexes, determining high-risk omission items, generating an early warning task queue, and pushing the early warning task queue to a doctor-patient terminal to realize closed-loop management and real-time intervention; the accuracy of missed detection identification and the intelligent level of obstetrical risk management can be effectively improved.

The present invention proposes an intelligent operation and maintenance monitoring system and method based on machine learning optimization. The system includes: a data acquisition module, a data preprocessing module, a model evaluation module, a model interpretation module, a trend analysis module, a fluctuation analysis module, an alarm analysis module, an availability analysis module, an automated response module, and an anomaly detection and early warning module. The present invention optimizes the performance of the model and improves the prediction accuracy through systematic feature engineering and algorithm selection processes. The present invention introduces a model interpretation module to improve the transparency and credibility of the model, making it easier to understand the decision-making process of the model. The present invention can accurately predict long-term performance trends and short-term fluctuations, thereby more effectively performing system capacity planning and resource allocation. The availability analysis module of the present invention ensures the high availability of the cluster system by dynamically analyzing the status of cluster nodes. The present invention achieves rapid response and early warning to abnormal events, and improves the stability and reliability of the system.

The invention belongs to the field of hydrogen gas detection, and particularly relates to hydrogen gas leakage detection early warning equipment and a use method thereof.The hydrogen gas leakage detection early warning equipment comprises a base, the overlook face of the base is concave, the top of the base is fixedly connected with a supporting plate, one side of the supporting plate is fixedly connected with a top plate, and the bottom of the top plate is fixedly connected with a first gas inlet pipeline; the outer wall of the first air inlet pipeline is in sliding connection with a second air inlet pipeline; according to the hydrogen gas leakage detection and early warning equipment and the using method thereof, air near a hydrogen refueling station is pumped into the first gas inlet pipeline through the exhaust fan, detection is conducted through the hydrogen sensor, and when hydrogen leakage is detected, an alarm is given through the buzzer alarm; air exhausted by the first air inlet pipeline enters the cavity and is filtered through the palladium asbestos, hydrogen in the air is purified, the danger of explosion is prevented, meanwhile, the palladium asbestos absorbing the hydrogen becomes black, and a hydrogenation driver needs to drive away from the hydrogen refueling station in time when discovering that the palladium asbestos becomes black or the buzzer alarm gives an alarm.

To provide a system capable of detecting anomalies early while preventing the decline of cognitive and physical functions in the elderly.SOLUTION: A system in an embodiment includes a conversation unit, a measurement unit, a guidance unit, a detection unit, and a notification unit. The conversation unit performs a conversation with a user based on the personal data of the user. The measurement unit measures the temperature and blood pressure of the user. The guidance unit provides training for users. The detection unit detects a specific symptom or behavior based on one or more pieces of information of the conversation results by the conversation unit, measurement results by the measurement unit, and guidance results by the guidance unit. The notification unit notifies detection results by the detection unit to a specific contact target.SELECTED DRAWING: Figure 1

The invention belongs to the field of optical storage and charging integrated equipment, relates to a data analysis technology, is used for solving the problem that an existing operation detection and early warning system of the optical storage and charging integrated equipment cannot detect the state of a photovoltaic power generation process, and particularly relates to an operation detection and early warning system suitable for the optical storage and charging integrated equipment. Comprising a power generation test module, a power generation detection module and an anomaly analysis module. The power generation test module is used for testing and analyzing the power generation amount of the light storage and charging integrated equipment: generating a test period and dividing the test period into a plurality of test time periods, setting a plurality of test time points in the test time periods, and acquiring an illumination coefficient of a photovoltaic power generation area of the light storage and charging integrated equipment at the test time points; according to the invention, the generating capacity of the photovoltaic storage and charging integrated equipment can be tested and analyzed, the parameters influencing the photovoltaic generating capacity are collected and counted, and then a power generation contrast basis is generated according to the generating capacity and the illumination value in the test period, so that data support is provided for the power generation detection process.

The utility model discloses a fault detection and early warning device for a fan in a tunnel. The fault detection and early warning device comprises an assembly shell, the side wall of the assembly shell is fixedly connected with a connecting block, an installation mechanism is fixedly assembled in the assembly shell, a vibration sensor is fixedly assembled on the assembly shell through the installation mechanism, and an alarm is fixedly assembled on the top of the assembly shell. The state of the fan in the operation process can be detected through the vibration sensor assembled in the assembly shell, when the fan operates abnormally, vibration of different frequencies can occur, and when the vibration frequency exceeds the range set by the vibration sensor, the alarm gives an alarm, so that the fan is prevented from being damaged. According to the utility model, the operation state of the fan is monitored in the tunnel, the structure is simpler, the installation process is convenient, the debugging process of the vibration sensor is simpler and more convenient through the arranged installation mechanism, and the time for erecting a ventilation system is saved.