209results about How to "Fully reflect" patented technology

The invention provides a semiconductor equipment fault prediction and maintenance method and system based on data fusion, and relates to the technical field of semiconductor equipment fault prediction and maintenance, and the method comprises the steps: collecting the data of a multi-source sensor, constructing a process-stress-material multi-dimensional state model, and calculating the failure probability of a key part. And when the failure probability exceeds a threshold value, a fault diagnosis process is triggered, fault mode identification is performed based on a digital twin model, and a fault development trend is predicted in combination with historical data. And finally, constructing a maintenance objective function, determining an optimal maintenance scheme by using an adaptive optimization algorithm and a maintenance decision rule base, and carrying out maintenance guidance. According to the method, the maintenance knowledge graph is constructed, so that continuous optimization of fault prediction and maintenance capability is realized, and the reliability and production efficiency of semiconductor equipment are improved.

The invention discloses an electric power facility visual monitoring system and method, and relates to the technical field of electric power facility operation and maintenance, the system comprises the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; according to the method, the electric parameters, the environment data, the image video data, the audio data and other multi-source heterogeneous data are integrated, the intelligent fusion algorithm and the association analysis model are utilized, the potential association relationship among different types of data is deeply mined, and therefore the operation condition of the electric power facility can be reflected more comprehensively and accurately, and the operation efficiency of the electric power facility is improved. According to the method, the accuracy and integrity of monitoring data are improved, the system can timely find and pre-judge potential risks, powerful decision support is provided for operation and maintenance personnel, and safety risks and maintenance cost increase caused by incomplete or inaccurate monitoring are effectively avoided.

The invention discloses a wind field wind resource fine simulation method, system and device based on WRF-CFD medium and micro-scale coupling and a storage medium, and belongs to the technical field of wind energy resource evaluation.The method comprises the steps that mesoscale meteorological analysis data is collected and calculated through a mesoscale WRF numerical simulation technology; performing fitting processing to obtain a fitting function between the wind speed component and the height; extracting wind speed contour information at a micro-scale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining a boundary condition of a micro-scale CFD model; performing directional calculation and correction on the wind speed of each sector by using a virtual anemometer tower; performing grid division on the wind power plant area to generate a refined grid corresponding to the wind power plant area; and driving numerical simulation of the micro-scale CFD model, and carrying out wind power plant regional range stroke resource evaluation. According to the method, the complex characteristics of the wind field can be comprehensively reflected, the wind resource evaluation precision and the calculation efficiency are remarkably improved, and the resource utilization is optimized.

The invention discloses an equipment operation evaluation method based on multi-source data fusion, and relates to the technical field of equipment operation evaluation, and the method comprises the following steps: S1, data collection: collecting multi-source data of equipment in a real-time operation state, the multi-source data comprising sensor data, operation logs, environment data and maintenance history of the equipment; according to the equipment operation evaluation method based on multi-source data fusion, accurate equipment state monitoring and fault prediction can be provided, and the performance and accuracy of the equipment can be continuously optimized according to actual equipment operation data and environmental conditions of the equipment, so that efficient and intelligent equipment management and maintenance strategies are realized; according to the method, specific steps and features of the equipment operation evaluation method based on multi-source data fusion are clearly defined, comprehensiveness and high efficiency of the method are ensured, and the method is suitable for various complex industrial environments so as to improve reliability and maintenance efficiency of equipment.

The invention discloses a substation equipment defect detection method based on multi-modal normal sample learning, which is suitable for unmanned aerial vehicle data acquisition. The method comprises the following steps: step 1, acquiring multi-modal data of substation equipment and preprocessing the multi-modal data; 2, extracting and fusing features from the multi-modal data preprocessed in the step 1 to obtain a feature set; dividing the feature set into a training set and a verification set; 3, adopting a machine learning model, and performing normal sample learning by utilizing the training set and the verification set to obtain a normal classification model; 4, features are extracted from the to-be-detected substation equipment multi-modal data and then compared with the normal classification model, and whether defect features exist in the to-be-detected substation equipment multi-modal data or not is judged through difference analysis. The detection accuracy can be improved, manual intervention is reduced, dependence on defect samples is reduced, and the method has the advantage of being high in adaptability.

The invention belongs to the technical field of magnetic suspension centrifugal compressor control, and particularly relates to a magnetic suspension centrifugal compressor constant flow and constant pressure control method and system.The method comprises the steps that multi-parameter data of a compressor are obtained, and a working condition analysis model is constructed through the multi-parameter data and historical operation data; training and updating the constructed working condition analysis model; adjusting a working condition demand weight according to a judgment result of the working condition analysis model, introducing a prediction control algorithm according to historical data and working condition characteristics, and performing loss compensation through a dynamic response model established by predicting a current change trend; dynamic adjustment and optimization are carried out based on the real-time working condition and the change rate of pressure and flow; and accurately controlling the output power of the compressor through a dynamic optimization result. Dynamic adjustment and optimization are carried out based on the real-time working condition and the change rate of the pressure and the flow, the precision of pressure and flow control is further improved, and adaptability is improved.

The invention relates to the technical field of carrier evaluation, in particular to a carrier portrait evaluation system and method based on multi-dimensional data fusion, and the system comprises a data source obtaining module which is used for collecting a plurality of carrier related data sources in real time; the data preprocessing module is used for carrying out normalization preprocessing on the collected data; the multi-dimensional data fusion module is used for constructing a dynamic carrier portrait by adopting a multi-modal fusion algorithm and a hierarchical data modeling technology so as to provide a basis for evaluation; the scene performance evaluation module is used for carrying out comprehensive evaluation on the carriers in a plurality of business scenes, carrying out comprehensive scoring on the plurality of carriers through a multi-index evaluation method, and obtaining a comprehensive score ranking of the plurality of carriers; and the carrier relationship detection module is used for analyzing the association relationship between the preferred carrier and other carriers. According to the invention, the accuracy of evaluation is improved, and the adaptability of the system to a complex business environment is enhanced, so that an enterprise can make a more scientific decision when selecting a carrier.

The invention discloses a power equipment defect identification method, system and device and a storage medium, and belongs to the technical field of power equipment defect identification. The method comprises the following steps: acquiring and processing a target infrared light image and a target visible light image of target power equipment to obtain an infrared light sub-image set and a visible light sub-image set; obtaining a feature similarity matrix, obtaining a target element set with the maximum sum of similarity values in the feature similarity matrix, carrying out key point calculation and optimization processing to obtain a target key point set, and constructing a mapping matrix of the target key point set; registering and fusing the target infrared light image and the target visible light image to obtain a target detection image; inputting the target detection image into the trained defect detection model for detection, and outputting a defect detection result of the target detection image; by combining the information of the infrared light and visible light images, the state of the power equipment can be reflected more comprehensively, so that the defect detection precision is improved.

The invention provides a pile foundation buried pipe design optimization method, medium and system, and belongs to the technical field of ground source heat pump pile foundation buried pipe, and the method comprises the following steps: building a geologic model and a vibration parameter matrix by obtaining actually measured geology and vibration data of a to-be-placed pile foundation area; and a feature matching technology is utilized to find optimal matching in a preset typical soil database, clustering analysis is carried out, and the soil deformation amount of the region is determined. And in combination with load and vibration information of the pile foundation and the building, soil deformation in the whole using process is predicted. On the basis, an underground heat transfer model of the pile foundation buried pipe is established, and the heat exchange capacity of the ground source heat pump is calculated; meanwhile, the influence of the buried pipe on soil deformation is analyzed. And finally, the heat exchange capacity and soil deformation change serve as optimization targets, a game model is adopted for solving, and an optimal pile foundation buried pipe design scheme is obtained. In the whole process, the mode of combining measured data and modeling analysis is fully utilized, and intelligent optimization of pile foundation design is achieved.

The invention relates to the technical field of image processing, in particular to a static plantar pressure image data processing method based on an improved YOLOV5 model. Compared with traditional floor table type equipment and desktop level equipment, the static plantar pressure diagnosis system for auxiliary diagnosis of the flatfoot based on the YOLOV5 model can remarkably improve the flatfoot diagnosis effect. The improved YOLOV5 model added with the attention mechanism module can further improve the flatfoot diagnosis effect, early diagnosis and intervention are carried out on the patient, and good prognosis is achieved. Based on an advanced deep learning model and a comprehensive data analysis method, the accuracy and efficiency of flatfoot evaluation are remarkably improved through automatic auxiliary diagnosis. Through a highly automatic process, manual intervention is not needed in the whole process from data acquisition, preprocessing to diagnosis output, personal errors are reduced, and the reliability and consistency of diagnosis are improved. A non-professional user can easily use the foot sole health management system through a friendly user interface and simplified operation steps, the foot sole health management system is widely suitable for various scenes such as families, clinics and hospitals, and intelligentization and popularization of foot sole health management are achieved.

The invention relates to the technical field of power transmission line control, in particular to a self-adaptive optimization control system for an ultra-high voltage power transmission line, which comprises a corona effect monitoring and environment sensing module for monitoring and acquiring a corona discharge condition in the power transmission line in combination with a multi-point distributed sensing network and collecting meteorological data in real time; the self-adaptive voltage and electric field control module is used for dynamically adjusting the voltage level of the power transmission line through a self-adaptive voltage adjusting algorithm according to the corona discharge condition and the meteorological data; the corona power loss calculation module is used for analyzing the electric field intensity, the air ion density and the noise level of the power transmission line through a nonlinear model, calculating the power loss caused by the corona effect, generating a dynamic corona loss prediction curve, and finely adjusting the adjusted voltage level in the adaptive voltage and electric field control module; according to the method, the calculation of the power loss is more accurate, the corona effect monitoring precision is improved, and the risk caused by sudden intensification of the corona effect can be effectively avoided.

The invention relates to the technical field of logging evaluation of a shale gas reservoir geologic model, and discloses a logging evaluation method suitable for the shale gas reservoir geologic model, which comprises the following steps: collecting and arranging logging data, including natural gamma, resistivity, interval transit time, density and neutron logging curves, based on geological data and logging data of adjacent wells, an initial geological model of the shale gas reservoir is established, a logging curve is preprocessed, including depth correction, environment correction, data smoothing, utilization of response characteristics of the logging curve, identification of cracks and stratification in the shale gas reservoir, and utilization of the logging data to calculate physical property parameters of the shale gas reservoir. According to the logging evaluation method, a plurality of reservoir parameters including the organic carbon content, the thermal maturity, the porosity, the permeability, the water saturation, the fracture development degree and the like can be obtained, and the comprehensive analysis of the parameters can more comprehensively reflect the reservoir characteristics, so that the accuracy of a geologic model is improved.

The invention relates to the technical field of report generation, and discloses a data report generation method based on deep learning, which comprises the following steps: S1, collecting structured data, unstructured data and multi-modal data, cleaning and preprocessing different types of data, and using a uniform data representation mode; and S2, performing feature extraction on different data modals by using a multi-modal deep learning model, fusing multi-modal features, and performing comprehensive processing on the features through a deep neural network or a self-attention mechanism. By introducing a multi-modal deep learning model, the system can better process and fuse structured data, non-structured data and multi-modal data, which means that the generated report can more comprehensively and accurately reflect information from different data sources, and provides more insight analysis, and the feature extraction and fusion technology can improve the accuracy of the system. The system can capture more detailed and important features, so that report generation is more accurate and customized.

The invention provides a fire extinguishing performance evaluation method, device and equipment for an energy storage battery fire extinguishing system, and belongs to the field of battery energy storage system safety. The evaluation method comprises the following steps: acquiring real-time test data of multiple fire extinguishing performance indexes in different fire extinguishing stages; comparing the real-time test data of each fire extinguishing performance index with historical reference data, and determining an effective score of each fire extinguishing performance index; determining a weight value of each fire extinguishing performance index; based on the effective scores and the weight values of the multiple fire extinguishing performance indexes, the scoring result of the battery fire extinguishing process is determined; and determining an evaluation grade of the fire disposal capability of the fire extinguishing system based on a scoring result of the battery fire extinguishing process. Through the method provided by the invention, the battery fire disposal capability can be reflected more truly and comprehensively, and the calculation result is more comprehensive and accurate.

The invention provides a vehicle fault identification method and apparatus, an electronic device, a vehicle, a chip and a medium. The method comprises the steps of obtaining multi-source fault feature data of the vehicle and a weight corresponding to the multi-source fault feature data; the multi-source fault feature data comprises fault feature data corresponding to a sensor, a vehicle-mounted diagnosis system and a vehicle communication system; determining a first fusion feature sequence based on the multi-source fault feature data and a weight corresponding to the multi-source fault feature data; determining a second fusion feature sequence within a preset duration, wherein the second fusion feature sequence is a feature matrix constructed based on the first fusion feature sequence; respectively inputting the second fusion feature sequence into a plurality of classifiers to obtain a plurality of classification results of the vehicle fault type; and identifying a fault type of the vehicle based on the plurality of classification results. According to the vehicle fault identification scheme provided by the invention, the accuracy and comprehensiveness of vehicle fault diagnosis can be improved.

The invention discloses a strip mine high and steep slope disaster early warning method and system based on intelligent sensing and multi-source data fusion, and belongs to the technical field of strip mine high and steep slope disaster intelligent early warning. According to video monitoring and multi-source dynamic sensing information, establishing a three-dimensional visualization platform for the high and steep slope disaster of the strip mine; obtaining a slope disaster risk early warning level by using a multi-source data fusion method according to the slope related information; performing early warning reminding according to the slope disaster risk early warning level; and generating a corresponding emergency scheme according to the slope disaster risk early warning level. According to the method, multi-technology, multi-level and multi-scale dynamic sensing of the high and steep slope disasters of the strip mine is carried out, a slope multi-source information integration technology and a three-dimensional visual scene are fused, the comprehensiveness, real-time performance and accuracy of data are guaranteed, and the reliability and timeliness of early warning of the high and steep slope disasters of the strip mine are effectively improved.

The embodiment of the invention provides a pegmatite type lithium ore prediction method and device based on deep learning, and the method comprises the steps: obtaining a plurality of different types of data of a to-be-collected region, converting the space coordinates of various types of data into a same space coordinate system, and generating a geoscience space database; rasterizing various types of data in the geoscience spatial database to generate a prediction element graph, a mask graph and a prediction label graph; generating a training data set and a verification data set based on the prediction element graph, the mask graph and the prediction label graph of the target area; adopting the training data set and the verification data set to train a preset full convolutional neural network to generate a prediction model; and based on the prediction model, finding out the distribution condition of the pegmatite type lithium ores in the selected to-be-collected area. By adopting the technical scheme, a batch of lithium-containing pegmatite veins with a certain form, scale and spatial distribution can be quickly and effectively identified.

The invention discloses an EEG emotion analysis method based on a deep neural network, and the method comprises the steps: collecting multi-channel data, carrying out the multi-dimensional feature extraction of the data, and carrying out the smoothing and standardization processing of the features; inputting the processed features into a CNN-LSTM model, analyzing the features and obtaining emotion features; further analyzing the emotional features and the signal feature dimensions, and outputting accurate emotional scores; according to the method, multi-dimensional feature extraction and emotion quantitative discrimination of all-channel electroencephalogram data are realized, traditional category discrimination is replaced, and the effects of high efficiency, high precision and meticulous effects are achieved.

The invention discloses a method for predicting the concentration of dissolved gas in transformer oil based on an EVO-VMD-LSSVM. An energy valley optimization algorithm EVO, variable mode decomposition VMD and a least square support vector machine LSSVM are combined and used. The method comprises the following steps: decomposing an original dissolved gas concentration signal into a plurality of intrinsic mode functions IMFs with different frequency characteristics through a VMD method, and extracting multi-scale characteristics of the signal; a plurality of factors such as transformer load, operating oil temperature, environment temperature and operating time are introduced into the model, an LSSVM prediction model is established for IMF in combination with characteristic parameter correlation, LSSVM model parameters are optimized by using an EVO algorithm, and prediction precision is improved; reconstructing the prediction result of each IMF to obtain a final dissolved gas concentration prediction value; the prediction performance is obviously better than that of a traditional method, and evaluation indexes such as mean square error (MSE), mean absolute error (MAE) and decision coefficient (R) show that the method has high prediction precision and robustness.

The invention discloses an anomaly detection method and device of an energy storage system, electronic equipment and a storage medium. The method comprises the following steps: in response to a received instruction for performing anomaly detection on the energy storage system, obtaining at least one piece of battery cell state data of a target battery cell in the energy storage system in a current preset time period; feature extraction is conducted on the at least one piece of battery cell state data, a battery cell feature data set is obtained, the battery cell feature data set comprises at least one feature data subset, and the feature data subset is used for representing battery cell feature data generated based on the same battery cell state data; and matching the cell characteristic data set with a target abnormal sample set to obtain an abnormal detection result, the target abnormal sample set being used for representing the corresponding cell characteristic data set when the energy storage system is in an abnormal state, and the abnormal detection result being used for representing whether the energy storage system is in an abnormal state. According to the invention, the technical problem of low efficiency of performing anomaly detection on the energy storage system in the prior art is solved.

The utility model discloses a pressure regulating silencer with a deslagging function, which comprises a silencing pipe, a pressure regulating sleeve sleeved on the silencing pipe and a silencing outer sleeve sleeved outside the pressure regulating sleeve, a silencing chamber is formed between the silencing pipe and the silencing outer sleeve, a deslagging silencing hole communicated with the silencing chamber is arranged on the silencing pipe, and a deslagging hole is arranged on the silencing outer sleeve. The residue discharging holes are formed in the silencing outer sleeve, so that independent residue discharging paths are formed among the residue discharging silencing holes in the silencing pipe, the silencing chamber between the silencing pipe and the silencing outer sleeve and the residue discharging holes in the silencing outer sleeve, and residues do not need to enter the nail fixing device any more. The nail fixing device effectively prevents the residues from entering the nail fixing device to cause corrosion, jamming and other damage to key components (such as a firing pin assembly), greatly prolongs the service life of related equipment components, reduces the maintenance frequency and cost caused by component damage, does not need to disassemble and assemble the nail fixing device main body to clean the residues in the nail fixing device, and improves the working efficiency. And the overall reliability and stability of the equipment are improved.

The invention relates to an unmanned aerial vehicle low-altitude temperature field inversion method and system. The method comprises the steps that original temperature image data collected by an infrared imager of an unmanned aerial vehicle and flight log data of the unmanned aerial vehicle are acquired; processing the relative temperature of each pixel of all original temperature images in the original temperature image data to obtain absolute temperature image data after calibration inversion; analyzing the flight log data to obtain camera attitude data of the unmanned aerial vehicle; performing flight attitude reconstruction according to the camera attitude data to obtain a lens attitude track corresponding to the original temperature image data; according to the absolute temperature image data and the lens attitude track, through a three-dimensional temperature field reconstruction algorithm, reconstruction is carried out to obtain an inversion three-dimensional temperature field point cloud; and performing visualization processing on the point cloud of the inversion three-dimensional temperature field according to the point cloud model, and outputting the inversion three-dimensional temperature field. An unmanned aerial vehicle technology, a temperature inversion technology and a three-dimensional reconstruction technology are utilized to realize three-dimensional temperature field reconstruction of a meso-scale level in an urban building built-up area.

The invention relates to the technical field of oil field equipment management, and particularly discloses an oil field equipment facility three-dimensional modeling method and system based on digital twinning, and the method comprises the steps: constructing a three-dimensional model of oil field equipment through the three-dimensional visualization and virtual-real mapping technology, so as to simulate the operation state of the oil field equipment, and carrying out the real-time monitoring; wherein the oil field equipment comprises an oil pumping unit and an oil field separator; a digital twinborn model of the oil field equipment is constructed through a GA algorithm, and the operation state of the oil field equipment is comprehensively simulated and dynamically monitored; and constructing a three-dimensional dynamic model of the oil field equipment according to the three-dimensional model of the oil field equipment and the digital twinning model based on a digital twinning technology, and optimizing the state of the oil field equipment.

The invention discloses a diaphragm motion state monitoring device and a breathing health assessment method. The device comprises a wearable equipment body, a pair of flexible pressure sensors, a flexible myoelectricity sensor and a main control unit, the wearable equipment body is rectangular, the pair of flexible pressure sensors are arranged on the upper portion and the lower portion of the wearable equipment body, the flexible myoelectricity sensor is arranged between the pair of flexible pressure sensors, and the main control unit is arranged outside the wearable equipment body; the pair of flexible pressure sensors is used for detecting a thoracic cavity pressure signal and an abdominal cavity pressure signal; the flexible myoelectricity sensor is used for detecting diaphragm myoelectricity signals; and the main control unit is used for performing index calculation based on the processed chest pressure signal, abdominal pressure signal and diaphragm electromyographic signal to obtain a plurality of respiratory health indexes, and sending the indexes to the mobile terminal for comparison so as to realize respiratory health assessment. According to the invention, real-time monitoring and evaluation of the breathing health state of the user can be realized.

The utility model discloses a collimation system of neutron nondestructive testing equipment, and aims to overcome the defects of poor shielding effect on neutron beams and poor scattering limiting effect on neutrons in the conventional collimation system. The utility model comprises: a first collimation body; a plurality of shielding layers are stacked on the first collimation body and the second collimation body to form a multi-layer structure; the neutron channel penetrates through the first collimation body and the second collimation body; one end of the neutron channel is an incident end, the other end of the neutron channel is an emergent end, a first reflecting layer is arranged on the inner wall of the neutron channel, and the first reflecting layer extends from the emergent end to the incident end. According to the collimation system of the neutron nondestructive testing equipment, the shielding effect on neutron beams is improved, neutron scattering is limited, and influences on the environment and personnel of a nondestructive testing site are avoided.

The invention provides a cross-scene deployment-oriented intelligent edge calculation method and system, a terminal and a medium, and particularly relates to the technical field of information communication, and the scheme comprises the steps: employing a plurality of Internet of Things devices to collect image frames of the same object at different visual angles in a source domain or a target domain at the same time, and obtaining source domain data and target domain data; based on the source domain data and the target domain data, utilizing an unsupervised domain adaptation algorithm and a knowledge extraction algorithm to finely adjust the semantic edge inference system to obtain an optimized semantic edge inference system, and the semantic edge inference system is constructed based on Internet of Things equipment and at least one edge server; and performing edge calculation on the target domain data by using the optimized semantic edge inference system to obtain a prediction result. According to the scheme, the data distribution and the change of the signal-to-noise ratio of the wireless channel can be adaptively processed without depending on the labeled data of the target domain, and rapid and economical large-scale deployment in a dynamic application scene can be realized.

The invention discloses an inhaul cable detection robot based on visual identification and an inhaul cable detection method.The inhaul cable detection robot comprises a crawling device, a camera detection device, a control center, a driving device and a connecting piece, a shear fork type mechanism with an elastic structure and a crawler-type advancing mechanism are arranged in the crawling device, and on the premise that an inhaul cable is not damaged, the inhaul cable can be automatically detected through the crawler-type advancing mechanism; the cable is held tightly to climb upwards, the condition of the cable is fed back in real time through the camera detection device, the stress of each device and the visual angle of the camera are balanced according to a cable detection method, and the pressure of the crawling device on the cable can be balanced in real time through the holding device and the visual angle control device of the control center. The device not only can ensure that the pressure of each part of the crawling device is balanced and the inhaul cable is tightly held, but also can prevent the inhaul cable from being damaged and deformed due to the fact that the pressure of a certain part of the crawling device on the inhaul cable is too large, can adapt to detection of different inhaul cables, and the visual angle control device controls the camera detection device in the detection process. According to the method, pictures of different positions of the inhaul cable are obtained in real time, details and potential defects of partial areas are not detected, the accuracy and comprehensiveness of detection are ensured, the efficiency and reliability of detection are improved, and inhaul cable detection work is completed more comprehensively and efficiently.

The invention relates to the technical field of green financial project financial feasibility evaluation systems, in particular to a green financial project financial feasibility evaluation system and method based on machine learning, and the system comprises a financial evaluation module, a market evaluation module and an environment evaluation module. The financial evaluation module comprises an analysis evaluation unit, a prediction analysis unit and a model simulation unit; the analysis and evaluation unit is used for collecting and sorting data of items and clients and processing the data; the prediction analysis unit predicts financial indexes under different market scenes by utilizing an optimization algorithm according to the customer data and the market data; the market assessment module provides key external environment information for financial assessment by deeply analyzing market trend and policy changes. Meanwhile, the environment evaluation module quantifies the environment influence of the project, and provides important environment indexes for market evaluation and financial evaluation. The complementary mechanism ensures that all key factors influencing the feasibility of the project are considered in the evaluation process.

The invention belongs to the technical field of swivel bridge detection, and particularly relates to a swivel bridge horizontal alignment detection device which comprises a mounting seat fixed to the upper end of an abutment, a controller is fixedly connected to the side wall of the mounting seat, and two guide boxes are symmetrically and fixedly connected to the upper side wall of the mounting seat. After the swivel bridge construction is completed, mechanical contact detection is arranged on the abutment, compared with a detection assembly arranged between two swivel bridges which are spaced by four meters, a fixed and accurate mounting reference can be provided for the detection device by means of a stable structural foundation of the abutment, and a detection structure does not need to be arranged between the bridges which are spaced by four meters; the problems of inconvenient operation, large assembly fixing difficulty and the like caused by cross-interval installation are avoided, the bending deformation of the detection rod due to the gravity of the detection rod can be effectively avoided, and the detection precision is guaranteed from the installation reference and the detection structure.