20925results about "Material analysis" patented technology

The invention provides a particulate matter and ozone source monitoring method and system based on a distributed sensor, and relates to the technical field of pollution treatment. According to the invention, sensor nodes with geographic perception capability are deployed in a monitoring area in a high-density manner, pollutant concentration and meteorological parameters are collected in real time, and data are uploaded to a cloud platform for preprocessing and dynamic calibration; a machine learning model is constructed based on the combined features of the pollutants and the meteorological factors, a driving relation is mined, and pollutant influence factors are extracted; further performing joint modeling on the influence factors and regional emission source data, identifying the coupling strength between pollutants and emission sources by adopting a classification or clustering method, and judging the categories of main sources; backward trajectory simulation, source fingerprint analysis and multi-source regression decomposition are combined to realize pollution path inversion and source contribution rate quantification; and finally, constructing a geographic information visualization platform, displaying a pollution thermodynamic diagram, a contribution change diagram and an evolution path diagram, and providing support for multi-source pollution traceability and scientific management and control.

The invention relates to a dynamic adjust gyroscope life forecasting method based on gray model. By data collection of vibration effective value, random drift and environmental temperature parameter which are preprocessed using radial neural networks, influence of environmental temperature on vibration effective value and random drift is eliminated and random drift and effective value just related to time are obtained by subtracting drift constant value term, then trend term of vibration effective value and random drift are extracted by using wavelet transformation and gray model are built separately for their trend term. The smaller data in two values of life predicted of dynamic adjust gyroscope unless two predicted values exceeding performance parameter limitation when dynamic adjust gyroscope is considered losing effect. The invention uses performance parameter of life probative period of product to predict its life, showing discipline of performance parameter and life of dynamic adjust gyroscope. It is easy and convenient economical and reliable.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention relates to the technical field of road surface detection, in particular to a road surface skid resistance detection system based on multi-feature fusion, which comprises the following steps: applying broadband sweep frequency excitation by using a frequency modulation vibration sensor, matching with the inherent frequency of road surface texture to generate local resonance, and collecting the temperature, humidity and rainfall of a road surface in real time; performing fast Fourier transform on the collected vibration signals to obtain a resonance response spectrum, extracting key parameters through Gaussian fitting, and fusing frequency domain, material and environment data to form a comprehensive feature set; the frequency domain features are converted into three-dimensional energy distribution of pavement microtextures, the actual contact area ratio is calculated according to the three-dimensional energy distribution, an environment temperature and humidity compensation factor is introduced, and a dynamic friction attenuation coefficient is calculated; and comparing the calculated dynamic friction attenuation coefficient with a third-level safety threshold, and outputting a corresponding anti-skid performance level. Multi-source data fusion enables a detection result to be more fit with an actual driving scene, and misjudgment caused by single data is avoided.

The invention discloses an acid-resistant plate dark crack defect detection method and system, and relates to the technical field of acid-resistant plate defect detection.The detection method comprises the steps that multiple sets of multi-mode detection data in an acid-resistant plate detection scene are obtained, and the multiple sets of multi-mode detection data comprise polarized light appearance image data and micro-strain vibration data; and respectively extracting an appearance discriminant value, an ultrasonic discriminant value, a stress discriminant value and a micro-strain discriminant value from the modal data based on the improved twinborn attention network. According to the acid-resistant plate dark crack defect detection method and system, four-mode data of polarized light appearance, multi-frequency ultrasonic, flexible stress and micro-strain vibration are synchronously obtained through a multi-mode intelligent acquisition module: the polarized light appearance data captures surface shallow cracks, and the multi-frequency ultrasonic data penetrates through a plate body to identify deep cracks; the problem of missing detection of a traditional single mode is complementarily solved; in the analysis link, through a multi-modal attention and reinforcement learning fusion weight model, the weight of each discriminant value can be dynamically adjusted according to the environmental change, and the misjudgment of the fixed weight is avoided.

The invention discloses a garlic disease and insect pest dynamic diagnosis system based on temperature and humidity time sequence data, relates to the technical field of agricultural information processing, and solves the problems that in the prior art, a fixed parameter model is prone to sensitivity sudden drop, false alarm sudden increase and systematic deviation under sudden or semi-sudden changes of field environment and management variables. According to the scheme, semantic processing of temperature and humidity and farming events is carried out through an acquisition module, a system coupling module carries out segmented identification on distribution mutation caused by environment and operation, a domain representation module constructs causal threatening features, a prediction calibration module carries out dual-path drift decomposition and rapid correction, and a prediction result is obtained. The sample adding and label collecting module generates anti-fact samples and actively collects labels, and the decision attribution module outputs a structured evidence chain; according to the method, the dynamic adaptive capacity and reliability of the diagnosis system under the conditions of non-stationary distribution and concept drift are remarkably improved.

The invention relates to pipeline circumferential weld detection, and discloses a pipeline circumferential weld defect detection method and system based on deep learning, multiple sensors are arranged on the surface layer of a pipeline to collect multi-modal signals, an inertial measurement device is combined to carry out pose correction, and a multi-modal time sequence data matrix is constructed; performing preprocessing and segmentation, calculating to obtain a dynamic reference band, and outputting upper and lower bounds; performing broken line fitting on each segment to extract a phase change factor triple, and comparing with a reference band to screen event candidate segments; constructing a multi-segment time domain coupling relation graph based on the candidate segments, and generating a dominant response mode candidate set; inputting a multi-branch time sequence coding network to extract a three-mode feature and generate a time sequence fusion feature; and performing similarity matching with a defect prototype library to obtain a defect category probability sequence, performing single-mode ablation and time sequence playback verification to obtain a high-confidence index, mapping the high-confidence index to a spatial position, and outputting a defect category and severity in combination with a feature index. According to the method, high-precision defect detection and reliable classification under the trenchless condition can be realized.

A method of delivering a receptacle to an instrument includes the steps of supporting a receptacle in a carriage, activating an electric motor to move the carriage between a first location and a second location of the instrument while the receptacle is supported by the carriage, applying a clamping force to the receptacle as the carriage moves from the first location to the second location, and releasing the clamping force from the receptacle as the carriage moves from the second location to the first location.

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

The invention relates to the technical field of power transformation equipment state monitoring, in particular to a power transformation equipment multi-parameter intelligent sensing and state monitoring system which comprises a sensing unit, a data processing unit, a state analysis and diagnosis unit and an upper computer monitoring and management unit. Through data collection preprocessing, lightweight AI model anomaly preliminary screening and hierarchical edge cloud cooperative transmission strategies, efficient cleaning of original data, rapid edge end anomaly identification and optimal utilization of network resources are realized, data transmission bandwidth occupation is greatly reduced, monitoring real-time performance is improved, and the method is suitable for large-scale popularization and application. The method integrates technologies such as digital twinning, federated learning and a time-space attention network, realizes equipment cross-time-space fault accurate positioning, fault type reliable identification and residual life dynamic prediction in combination with a quantification algorithm, triggers hierarchical early warning through hierarchical health assessment, and provides scientific and accurate decision support for refined operation and maintenance of power transformation equipment.

The invention discloses a crop environment quantitative evaluation and decision making system based on growth stage self-adaption, and relates to the technical field of agricultural intelligent decision making. Acquiring an environment original data sequence of field multi-type sensors through a data acquisition module; the dynamic identification module analyzes the sequence by using a preset crop growth stage discrimination model, identifies the current growth stage and outputs a corresponding key environment parameter weight template; the feature fusion module performs weighted fusion on the original data according to the template to generate an environment feature vector with stage adaptability; the state evaluation module converts the vector into a growth state quantitative evaluation value through a growth state evaluation model; and the decision trigger judgment module calls a corresponding preset dynamic threshold interval according to the growth stage for comparison, and generates a decision trigger instruction when the growth state quantitative evaluation value deviates. According to the invention, stage self-adaptive intelligentization of crop growth monitoring and decision making is realized, and the accuracy of environment state evaluation and the timeliness of management decision making are improved.

The invention relates to the technical field of grain detection, and discloses a grain broken rice rate online detection method and system based on image analysis. A grain broken rice rate online detection method based on image analysis comprises the steps of intelligent illumination control and high-speed synchronous imaging, self-adaptive image enhancement based on reinforcement learning, multi-stage parallel particle detection and segmentation, multi-modal feature deep fusion, robust classification based on ensemble learning, intelligent quality control and process optimization. And continuously learning and updating knowledge. A clear image is obtained through a multi-angle linear array camera and a stroboscopic light source, a reinforcement learning agent is adopted to carry out adaptive image enhancement, a deep learning network is used to carry out particle detection segmentation, multi-dimensional features are extracted, accurate identification is realized through an integrated classifier, and a digital twin model is established to carry out process parameter optimization. According to the invention, accurate online detection of the broken rice rate of grains can be realized in a high-speed flowing state, and the detection efficiency and accuracy are improved.

The invention relates to the technical field of indoor air quality monitoring, and discloses an indoor air quality joint detection system fused with a space-time diagram neural network. The system comprises a multi-source sensor data acquisition module for acquiring multi-source air quality data flow by configuring various sensors; the space-time diagram construction module constructs space-time diagram data according to the spatial position and the time sequence of the sensor; the fusion processing module adopts a space-time diagram neural network to perform feature fusion on the space-time diagram data to generate an air quality fusion feature set; the prediction strategy module determines a prediction strategy based on the remaining duration of the current time and the end time of the prediction period, and performs air quality prediction in combination with the fusion feature set; the dynamic adjustment module analyzes the real-time change of the data, constructs the pollution change degree and tendency degree, and adjusts the detection control parameters according to the pollution change degree and tendency degree; and the joint detection output module synthesizes the prediction result and the adjustment parameters to generate an indoor air quality joint detection report. The system provides powerful support for indoor air quality evaluation.

A receptacle delivery system for an instrument includes a carriage, a puck supported by the carriage, and a first shelf. The carriage is configured to move from a first location of the instrument to a second location of the instrument. The puck is configured to removably support a receptacle such that a longitudinal axis of the receptacle is substantially coincident with a vertical axis of the puck. The first shelf comprises (a) a base extending substantially transverse to the vertical axis of the puck and (b) a first opening defined by the base, and when the carriage is positioned at the second location, the longitudinal axis of a receptacle seated in the puck extends through the first opening.

The invention discloses an intelligent induction and early warning system for poisonous gas in a limited space, and relates to the technical field of gas induction and early warning. A three-dimensional structure model of the limited space is constructed, and non-uniform grid division is carried out based on barrier characteristics; multiple types of toxic gas sensing units are arranged in the grids, and gas concentration data are collected in real time; constructing a toxic gas three-dimensional cloud picture, and generating a dynamic weight field based on the concentration gradient; fusing operator positioning and trajectory prediction information, and establishing an individual risk exposure model; calculating a space-time intersection of the toxic gas cloud picture and the high-risk track area to form a fusion risk matrix; realizing linkage early warning control of a region level and an individual level through multi-level dynamic threshold judgment and thermal block clustering identification; the system can realize accurate perception and active response of the toxic gas gathering trend and personnel exposure risk in the complex limited space, has the technical advantages of high sensitivity, high intelligence and low false alarm, and is suitable for various limited space scenes such as chemical engineering, tunnels and mines.

The invention relates to the technical field of ozone precursor traceability, in particular to an ozone precursor collaborative traceability method and system based on a knowledge graph, and the method comprises the following steps: obtaining precursor concentration change, recognizing an abnormal path, extracting path characteristics, carrying out standardized scoring, adjusting graph connection strength, and analyzing sequence offset to obtain collaborative nodes. And screening origin nodes in combination with time sequence meteorology to generate an origin node list. According to the method, pollution response channels are identified through precursor node concentration changes and path connection relations, focusing of key paths is enhanced, paths are scored based on multi-dimensional indexes, connection attributes are dynamically adjusted in combination with monitoring period ozone response intensity, map structure updating is achieved, and concentration response sequence offset is analyzed; according to the method, nodes with co-evolution characteristics are screened, the stable relation identification capability is improved, the path reasonability is evaluated by combining a release time sequence and meteorological conditions, the initial source positioning accuracy is improved, and the response speed and the identification precision of precursor tracing are integrally enhanced.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention relates to the technical field of chemical raw material processing, and discloses a petroleum coke particle size grading and batching system which comprises a raw material pretreatment module, a multi-stage dynamic screening module, a particle size real-time monitoring module, an intelligent batching decision module and a closed-loop control execution module. The classification definiteness of materials with different particle sizes is ensured; meanwhile, through a particle size monitoring feedback mechanism, the particle size distribution abnormal state in the screening process is recognized in real time, the material purity within the target particle size range is guaranteed, the particle size grading accuracy is improved, a closed-loop matching decision module is arranged, particle matching weight parameters are dynamically optimized in combination with the electrolytic aluminum process requirement, and the particle size grading accuracy is improved. The deviation degree between the ingredient proportion and the target performance can be judged in real time; the speed ratio of the conveying belt and the opening degree of the stock bin are automatically corrected through the executing mechanism, it is guaranteed that the mixing proportion of coarse particles, medium particles and fine particles is always in the optimal interval matched with electrolytic aluminum anode production, and the quality stability of anode carbon blocks is improved.

A method and a system for optimization of development by carbon dioxide injection based on incomplete miscibility characteristics are provided, where the method includes following steps: building a three-dimensional reservoir model based on laboratory experiments and reservoir values, where the three-dimensional reservoir model includes: a fluid model and a reservoir model; based on the three-dimensional reservoir model, obtaining incomplete miscibility characteristics of reservoir development by carbon dioxide injection; and completing whole life cycle optimization development of carbon dioxide injection based on the incomplete miscibility characteristics.

The invention belongs to the technical field of air pollution monitoring, and particularly relates to an air pollution monitoring system and method based on big data. According to the method, the pollutant diffusion topological structure is constructed through collection degree fusion of the multi-source environmental data, the prediction accuracy of air pollution monitoring is effectively improved, and when the pollutant diffusion topological structure is constructed, the influence of meteorological parameters and geographic information on a pollutant transmission path is comprehensively considered, so that the prediction accuracy of the air pollution monitoring is improved. The method enables a corresponding prediction mechanism to more accurately describe a pollutant space-time evolution rule in an air environment, introduces a prediction error-based iterative optimization mechanism after a prediction result is output, and compares the pollutant prediction concentration with the actual monitoring concentration in real time, thereby achieving the real-time prediction of the pollutant. And a diffusion weight coefficient and a diffusion probability matrix in the pollutant diffusion topological structure are adjusted, so that a corresponding prediction mechanism can maintain high-precision prediction capability, and relatively accurate data support is provided for early warning of an air environment.

The invention relates to the technical field of quality detection, in particular to a gas atomization powder sphericity degree multi-dimensional online analysis system and a method thereof.The gas atomization powder sphericity degree multi-dimensional online analysis system comprises a dynamic dual-mode imaging module, a transverse wind field auxiliary detection unit and a multi-dimensional feature fusion analysis module; the transverse wind field auxiliary detection unit is used for applying controllable transverse wind power through an airflow nozzle orthogonal to the powder falling direction and measuring the deflection track of gas atomization powder ball particles in combination with a laser displacement sensor. The online calibration module is used for spraying standardized spherical particles to the powder flow according to a preset period; the traditional density detection depends on destructive sampling and off-line measurement, the efficiency is low, and the whole production line particles cannot be covered; through an orthogonal wind field trajectory inversion technology, a density value and an internal defect mark are synchronously output through non-contact dynamic measurement, 100% lossless online full inspection of a production line is realized, and sampling limitation and aging bottleneck of a traditional means are broken through.

The invention discloses an electrostatic induction and optical imaging principle-fused online monitoring device and method for wear particles of a water-lubricated bearing, and belongs to the field of state monitoring of mechanical equipment. The device comprises a square insulation detection cavity, a detection electrode array, an electromagnetic shielding shell, a high-speed optical imaging module and a signal processing unit, the square insulation detection cavity is installed in a bearing lubrication water loop. The detection electrode array is fixed to the outer wall of the cavity and used for capturing electrostatic signals generated when the abrasion particles pass through. The electromagnetic shielding shell wraps the detection cavity and is grounded through a wire to eliminate environmental electromagnetic interference. The high-speed optical imaging module integrates a high-magnification microscope lens and a CMOS sensor, is coaxially mounted with the detection cavity, and obtains the morphology characteristics of the wear particles in real time; and the signal processing unit performs multi-modal data fusion analysis on the electrostatic signal and the optical image. Through cooperative detection of electrostatic induction and optical imaging, the monitoring precision and reliability of the wear particles of the water-lubricated bearing are remarkably improved.

The invention relates to the technical field of environmental pollution monitoring, in particular to an on-line environmental pollution monitoring method and system.The method comprises the steps that original acoustic signals of the inner side wall and the outer side wall of a curve are collected in real time in a high-risk monitoring area, propagation path compensation is conducted, and an inner side acoustic characteristic parameter set and an outer side acoustic characteristic parameter set are obtained; analyzing to obtain thickness values and adhesion strength values of biological membranes on the inner side and the outer side of the curve; inner and outer side falling risk factors are obtained through calculation, and a comprehensive falling risk value of the inner and outer side falling risk factors is calculated; when the comprehensive shedding risk value exceeds a preset shedding risk threshold value, generating biological membrane shedding risk early warning information; the monitoring problem of spatial distribution heterogeneity of the biological membrane at the pipe network bend is effectively solved, and accurate early warning of the falling risk of the biological membrane in the bend area is achieved.

The invention provides a physical response data analysis method and system based on aircraft catalytic operation, and the method comprises the steps: obtaining three-dimensional cloud particle distribution data sets of a catalytic operation region before and after the catalytic operation through airborne cloud particle detection equipment, the three-dimensional cloud particle distribution data set comprises particle characteristic data before catalysis and particle characteristic data after catalysis; performing morphological feature extraction processing on the pre-catalysis particle feature data and the post-catalysis particle feature data by adopting a cloud particle AI image shape recognition algorithm to generate a pre-catalysis particle morphological difference feature set and a post-catalysis particle morphological difference feature set; constructing a dynamic physical response evaluation model, inputting the particle morphological difference feature set into the dynamic physical response evaluation model to execute multi-dimensional response correlation analysis, and generating a dynamic response feature set of the catalytic operation; and generating a catalytic strategy optimization instruction set according to the dynamic response feature set, and transmitting the catalytic strategy optimization instruction set to the airborne catalytic control system for catalytic parameter adjustment operation.

The invention relates to the technical field of optical particulate matter sensing, in particular to a particulate matter particle size recognition and concentration detection method. The method comprises the following steps: laser emitted by a laser forms a laser beam which is focused and then diverged through a lens; with a laser focus as a reference, at least two photosensitive devices are distributed in the light beam propagation direction and comprise a perifocus device and a far focus device, and receiving surfaces of the near focus device and the far focus device face laser beams to collect scattered light; scattered light signals of each device are collected, and are input into a processing unit after independent current / voltage conversion, signal amplification and analog-to-digital conversion; based on the characteristics that small-particle-size scattered light is preferentially captured by perifocus high irradiance and large-particle-size scattered light is preferentially captured by far focus large beam diameter, a mapping relation between particle sizes and channels is established; the number concentration and mass concentration coefficient of each channel is calibrated through standard particles, and synchronous detection of PM1.0, PM2.5 and PM10 is realized in combination with linear processing, so that the particle size recognition precision and the concentration measurement reliability are effectively improved.

The invention relates to the technical field of environment control, in particular to an intelligent mildew-proof system and a mildew-proof and moisture-proof product, and the system comprises an evaporation trend monitoring module, a wet area path identification module, a retention risk identification module, a stress grade judgment module and a linkage adjustment archiving module. According to the method, through comprehensive collection and difference analysis of environment parameters of air duct nodes and superposition of air velocity and dew point change trends, humidification trend data with time sequence and spatial flow direction characteristics are constructed, wet flow direction consistency and a wind speed disturbance track in a continuous time window are combined, a wet area expansion path is identified, and a key diffusion section is positioned; through extracting deviation information between a regional equipment response state and a risk level intervention demand, linkage intervention of a fan and dehumidification is realized, accurate adjustment and dynamic recording of multi-parameter cooperative control are completed, fine identification capability of a wet region evolution and retention trend is enhanced, and initiative and adaptability of space humidity control response are improved.

The invention relates to a gas sensor environment anti-interference drift compensation method, system and equipment, and the method comprises the steps: collecting a mixed gas response signal in an environment, and obtaining an electromagnetic interference signal under the same timestamp; threshold truncation is carried out on spike pulses in the mixed gas response signals to obtain trend waveform signals caused by gas concentration changes, and denoising processing is carried out on the electromagnetic interference signals to obtain effective electromagnetic interference parameters; performing feature recognition on the effective electromagnetic interference parameters through a preset interference-response cooperative processing model to determine an interference mode and an interference intensity grade, synchronously analyzing the trend waveform signal to obtain a response feature curve of the target gas and the interference gas, and obtaining a drift compensation coefficient of the target gas; and carrying out collaborative optimization on the drift compensation coefficient through a preset interaction influence model to obtain a final compensation result so as to achieve the purpose of outputting a high-precision target gas concentration detection result.

The invention discloses an intelligent monitoring method, device and system for tunnel construction gas, and the method comprises the steps: collecting multi-source gas data in real time through a mixed gas monitoring network disposed in a tunnel construction environment, the mixed gas monitoring network comprises a fixed gas monitoring node, a mobile gas monitoring node and a personnel wearable monitoring node; based on the multi-source gas data, constructing a three-dimensional gas concentration field dynamic model covering the whole tunnel construction area by using a data fusion and machine learning algorithm; on the basis of the three-dimensional gas concentration field dynamic model, the spatio-temporal evolution trend of the gas concentration in a future preset time period is predicted, at least one control instruction is generated and executed according to the prediction result, and the control instruction comprises an intelligent early warning instruction and / or a ventilation system adjusting instruction. According to the intelligent monitoring method, device and system, traditional post-event alarm is converted into pre-warning and active intervention, the safety of tunnel construction is remarkably improved, and the emergency response speed is remarkably increased.

The invention provides a method for automatically centering and assembling a bearing on a long-axis part, and belongs to the technical field of bearing assembly.The method comprises the steps that position information of the long-axis part and the bearing is collected in real time through a binocular camera and a laser distance measuring sensor, a central line equation is calculated through the least square method, and position deviation is determined; a centering adjustment vector is generated to control a multi-degree-of-freedom precise attitude adjustment platform to perform preliminary adjustment, a mechanical arm is matched with high-precision laser ranging to perform precise centering, the centering process is modeled into a weighted undirected graph, an optimal adjustment path is solved, optimal press fitting parameters are calculated based on a bearing press fitting physical mechanics equation, and press fitting is performed. Meanwhile, the assembling process is monitored through a force sensor and a displacement sensor, an assembling quality evaluation matrix is constructed, finally, the assembling result is detected and evaluated, and high-precision automatic assembling of the long-axis part and the bearing is achieved.

The invention relates to the technical field of environment detection, discloses a mine methane detection system based on automatic environment compensation, effectively solves the problem of insufficient methane detection precision in a complex environment, and remarkably improves the reliability of a measurement result. The system can track environmental parameter changes in real time and implement dynamic compensation, and measurement deviation caused by a traditional fixed compensation method is avoided. The optimized data processing flow ensures the timeliness of the detection result, and provides accurate data support for underground safety operation. The safety protection capability of the system is further enhanced through a multi-stage early warning mechanism and redundant communication design, and all-directional monitoring of the mine methane concentration is achieved. The problems that an existing methane detection system is large in measurement error and weak in anti-interference capability due to the single sensor type are solved. And meanwhile, the defects of false alarm and missing alarm caused by incapability of identifying sensor performance degradation in time are overcome.