251 results about "Radiometric data" patented technology

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The invention relates to the technical field of radiation measurement, in particular to a radiation data calibration method for a regional radiation monitor, which comprises the following steps of: according to the variation trend of data in a local range of each moment in a radiation slope sequence of each position and the difference of time intervals between all adjacent extreme points in a radiation data curve, calculating the radiation data of the regional radiation monitor; obtaining the interference degree of the radiation data at each moment of each position; according to the correlation degree between the radiation data sequence of each position and the radiation data sequence of other positions and the interference degree, the corrected interference degree of the radiation data of each moment of each position is obtained; and correcting the radiation data of all positions at each moment according to the distribution of the radiation data of all positions at each moment in the region and the corrected interference degree to obtain the radiation data after region calibration. According to the invention, the accuracy of regional radiation data monitoring is improved.

The invention discloses a radioactive substance multi-point synchronous detection and identification method and system, and relates to the technical field of radioactive substance monitoring. Comprising the following steps: step 1, collecting multi-point radiation data in real time, and preprocessing and storing the data; step 2, constructing a spatial adjacency matrix, estimating a local spatial gradient, calculating an anomaly degree value, taking optimization measures, and performing collaborative anomaly evaluation on the monitoring nodes; 3, calculating a total fitting error value, reversely deducing a leakage source position and leakage intensity in real time, and taking corresponding measures in real time; 4, abnormal trend identification is carried out, an early warning judgment value is calculated, and graded early warning and multi-node cross confirmation measures are implemented; and 5, receiving and storing various types of data, carrying out visualized analysis on the data, and supporting event processing and feedback. The problems that due to the fact that some devices are good in shielding and have physical obstruction, trace leakage is difficult to detect by a single-point monitor, and the risk of long-term retention pollution exists are solved.

The invention relates to the technical field of photovoltaic module detection, in particular to a hot spot detection system and method for a photovoltaic module. Comprises: a multispectral imaging module configured to synchronously collect image data of a photovoltaic module in a visible light band, a near-infrared band and a long-wave infrared band; the electroluminescence analysis module is in signal connection with the multispectral imaging module and is used for acquiring carrier recombination radiation data of the photovoltaic module in a reverse bias state; the dynamic threshold value processing module is configured to establish a dynamic correlation model of the environment parameters and the hot spot characteristic parameters; the three-dimensional reconstruction module is configured to perform spatial registration on the multispectral imaging data and the electroluminescence data and construct three-dimensional temperature field distribution of the photovoltaic module; the defect positioning module is configured to determine the accurate position of a hot spot according to temperature field distribution and carrier recombination characteristics; according to the scheme, a novel detection system architecture with multi-mode collaboration, high dynamic adaptability and three-dimensional analysis capability is constructed.

The invention relates to the technical field of power system load prediction, in particular to a photovoltaic output prediction method and device integrating satellite and ground data, equipment and a medium, and the method comprises the steps: carrying out the preprocessing of data, which comprises remote sensing inversion radiation data, ground observation radiation data and satellite remote sensing data; space grid division is carried out based on the spatial continuous remote sensing inversion radiation data, grid division is carried out based on geographic positions and meteorological characteristics, areas with different meteorological characteristics are divided into different grid units, and each grid unit at least comprises one ground observation station; establishing a solar radiation prediction model for each grid unit; according to the trained solar radiation prediction model, calculating a solar radiation prediction value of each grid in combination with a real-time satellite cloud picture and ground observation radiation data; the limitation that a traditional foundation method cannot accurately predict radiation fluctuation caused by cloud layer changes is overcome, and therefore the prediction precision of solar radiation and photovoltaic output is remarkably improved.

The invention relates to the technical field of prediction modeling, in particular to an ultra-short-term prediction method and system based on a satellite inversion technology, and the method comprises the following steps: obtaining satellite inversion earth surface solar incident radiation data and ground observation data, calculating the matching degree, classifying and extracting stability indexes according to meteorological conditions, and screening short-term prediction applicable data. An applicable radiation data set is obtained. According to the method, a stability index is calculated by using satellite inversion and ground observation matching degree, an error mapping relation is extracted by combining an error trend and probability density, data stability is enhanced, noise is reduced by numerical adjustment and smoothing processing, prediction continuity is improved, multi-mode meteorological data is fused, dynamic weight optimization is performed, and precision is improved. Photovoltaic prediction is combined with radiation characteristics, error adjustment and abnormity elimination are carried out to reduce extreme fluctuation, regional fragmentation is combined with error feedback to dynamically optimize prediction parameters, adaptability is enhanced, and timeliness and accuracy of short-term prediction are improved.

The invention discloses a high-resolution solar radiation data correction method and system based on deep learning, and the method comprises the steps: obtaining meteorological data and digital elevation model data, and carrying out the preprocessing; dynamically optimizing parameter configuration of the WRF model, and simulating and outputting high-temporal-spatial-resolution meteorological data as training data; fusing solar radiation flux values in meteorological reanalysis data and near-real-time product meteorological data as label data of a correction model; inputting the high-temporal-spatial-resolution meteorological data and the label data into a pre-constructed multi-scale feature fused deep residual coding and decoding network model for training, and obtaining an optimal parameter model based on multiple loop iterations; and correcting solar radiation flux data output by the WRF mode by using the optimal parameter model. According to the method, the problem that the radiation variable value output by the WRF in the traditional numerical weather mode is consistent with the real data trend but deviates is effectively solved, and the reliability of meteorological data simulation is enhanced.

The invention discloses a calibration performance evaluation and analysis method for a stationary satellite hyperspectral infrared detector, and the method comprises the steps: firstly obtaining an ERA5 data set and L1-level observation data of the stationary satellite hyperspectral infrared detector, and inputting the data into a rapid transmission radiation model to obtain simulated radiation data; then comparing the simulated radiation data with L1-level observation data of a stationary satellite hyperspectral infrared detector, calculating systematic deviation and carrying out deviation analysis; and finally, performing cross matching verification on L1-level observation data of the stationary satellite hyperspectral infrared detector and observation data of the polar orbit satellite infrared atmospheric detection interferometer. On the basis of an atmospheric radiation model, more accurate and reliable calibration performance daily variation characteristics are provided to replace a calibration technology by using latest high-quality data and combining multi-source reference data, so that the defects of the prior art are effectively overcome, and the quality and the application effect of detection data are remarkably improved.

The invention discloses an intelligent earth surface solar radiation forecasting method based on satellite remote sensing, and belongs to the field of information technology and meteorological technology. The method comprises the following steps: S1, acquiring geostationary satellite observation data, satellite inversion earth surface solar radiation data and terrain factor data, and executing Z-score standardization processing; s2, performing dimensionality reduction on the standardized input sequence and the target data by using a pre-training auto-encoder to obtain latent variables of a historical sequence and a future cloud picture observation sequence; s3, inputting the dimension-reduced historical sequence into a conditional potential space diffusion model, modeling the historical satellite cloud picture sequence, and predicting a probabilistic potential variable of a cloud picture at a future moment; converting the solar radiation prediction result into a corresponding earth surface solar radiation prediction result through an inversion model; and S4, the self-encoder is adopted to perform dimension raising to obtain a final prediction result. According to the method, based on a frequency domain and space domain multi-modal feature adaptive fusion mechanism, while a complex cloud-radiation relation is modeled, predicted structural consistency and physical rationality of radiation intensity are kept.

The invention provides a multi-scale photovoltaic power prediction method and system fusing numerical weather forecast and a physical information neural network, relates to the technical field of photovoltaic power prediction, and realizes rolling prediction of future meteorological parameters and horizontal radiation intensity through numerical weather forecast. The authenticity and the stability of the meteorological data are effectively improved by combining quantile mapping and cascade correction of the time sequence mode attention neural network; and through combined modeling of a physical information neural network and a time sequence characteristic network, meteorological and radiation data subjected to multi-level correction and conversion are deeply fused with a photovoltaic system physical law, and high-precision prediction of photovoltaic power is realized. The whole prediction process has automatic anomaly elimination and deletion complementation capabilities, the prediction robustness under complex meteorological conditions and extreme environments is enhanced, and the sequential response and physical consistency of photovoltaic power are optimized, so that the engineering applicability and intelligent level of the system are greatly improved.

A method for determining a scatter correction for an x-ray radiation signal recorded by an x-ray detector (2) of an imaging modality based on x-ray radiation, the method comprising the following method steps: (a) receiving inside radiation data and outside scattered radiation data, wherein the inside radiation data is data obtained from a directly illuminated partial area (21) of the x-ray detector (2) with a primary x-ray beam (11) directed at an object within an examination area (3), wherein the outside scattered radiation data is data obtained by detecting scattered radiation (12) outside of the directly illuminated partial area (21); (b) based on the outside scattered radiation data, determining a scatter correction, in particular by estimating a part of the inside radiation data that is due to the scattered radiation (12).

The invention provides a base station electromagnetic radiation visual monitoring method and device, and the method comprises the steps: determining a three-dimensional topographic map of a monitoring region, carrying out the electromagnetic radiation measurement of an actual geographic position corresponding to a preset collection point on the three-dimensional topographic map, and obtaining the actual measurement data; obtaining beam characteristic parameters of each base station and space coordinate data of the actual geographic position, and calculating predicted electromagnetic radiation data of the actual geographic position according to the electromagnetic radiation prediction model; correcting the model, and calculating electromagnetic radiation data of a target area on the three-dimensional topographic map based on actually measured electromagnetic radiation data and the corrected electromagnetic radiation prediction model; and visually displaying the actually measured electromagnetic radiation data of the preset acquisition point and the electromagnetic radiation data of the target area. Therefore, accurate monitoring and visual display of the electromagnetic radiation of the base station are realized, so that the accuracy and reliability of electromagnetic radiation evaluation are improved, and the overall change rule of the electromagnetic radiation value of each position in the whole monitoring area can be visually observed.

The invention discloses a broadband oscillation characterization platform of a multi-chip power module and a near-field characteristic evaluation method, and belongs to the technical field of power electronics. The continuous operation condition of the power converter is decoupled into a series of discrete switching points, transient near-field radiation data of a to-be-tested power module under the discrete switching points are obtained through a double-pulse test, and near-magnetic field radiation signals are reconstructed; performing time-frequency analysis on the reconstructed near magnetic field radiation signal to generate a two-dimensional time-frequency matrix; and calculating a radiation energy entropy value based on the two-dimensional time-frequency matrix, fusing the radiation energy entropy value with the peak amplitude in the two-dimensional time-frequency matrix to generate a comprehensive EMI index for evaluating the broadband oscillation electromagnetic interference risk, and triggering EMI risk early warning when the comprehensive EMI index exceeds a threshold value. According to the method, the spectrum complexity is quantified through the radiation energy entropy, the multi-dimensional EMI risk assessment is carried out by fusing the peak amplitude of the time-frequency matrix, and the broadband oscillation in the multi-chip parallel power module can be accurately and efficiently represented.

The invention discloses a polar cloud mask quality inspection and correction method and device, and compared with the prior art, the method carries out the standardized grid projection of active and passive remote sensor data, and carries out the consistency evaluation of the data quality as the weight. Cloud mask quality inspection and correction between different active and passive remote sensing sensors are realized, the limitation that time-space matching needs to be performed one by one in traditional satellite remote sensing data fusion is broken, and a reliable cloud mask detection result is identified by using high precision of a laser radar and consistency of multiple passive satellites. And then the result is transmitted to radiation data for correction, so that the result is not influenced by different parameters among different satellite sensors, flexible adjustment can be performed according to output requirements of different data sources, spatial resolution and the like, and more comprehensive data support is provided for polar cloud mask product optimization. The method is helpful for improving cognition on scientific problems such as polar cloud radiation effect and polar climate change, and has important scientific research and application values.

The invention discloses an earth surface downlink short-wave radiation space downscaling method based on a super-resolution reconstruction technology, belongs to the technical field of meteorological data analysis, and solves the problems of insufficient solar radiation data space resolution and weak earth surface heterogeneity characterization capability in the prior art. Carrying out super-resolution reconstruction on the radiation data set by adopting a super-resolution combination model, and carrying out weighted fusion on a reconstructed data set output by the super-resolution combination model; according to the method, the output results of the multiple models are subjected to weighted fusion, weight optimization of the super-resolution sub-models is performed in combination with the high-resolution terrain factors, spatial downscaling from 5km to 1km resolution is finally realized, the spatial precision of the solar radiation data is remarkably improved on the basis of keeping the hour-level time resolution of the original data, and the accuracy of the solar radiation data is improved. And more earth surface heterogeneity information can be captured, and meanwhile, the high-time-resolution application requirement is met.

The invention discloses a multi-target path planning method and device, equipment and a storage medium, and relates to the technical field of traffic path planning, and the method comprises the steps: obtaining solar radiation data and original map data, carrying out the multi-source data fusion based on the solar radiation data and the original map data, and obtaining the radiation road network data; based on the radiation road network data, a total cost function of the path length is constructed through an accumulated cost function of the path length and a heuristic function of the estimated cost, and candidate paths are generated according to the total cost function; on the basis of the radiation road network data, calculating the path average radiation intensity of the candidate path, and further obtaining the energy recovery amount of the candidate path; constructing a dual-target cost function based on the total cost function and the energy recovery amount, adjusting node priorities and screening and updating a non-dominated solution set according to the dual-target cost function, and generating a target candidate path set according to the non-dominated solution set; and determining an optimal balance path in the target candidate path set through multi-dimensional index quantitative evaluation and balance coefficient sorting.

The invention provides a multi-sensor data fusion method and system based on an intelligent pod. According to the method, a radiation data flow of an intelligent pod multispectral sensor in an electromagnetic interference field is collected; based on the data, the quantitative attenuation coefficient of the radiation value of each wave band in the interference field is calculated by using a radiation transfer equation. And generating a directional beam of the phased-array antenna by using the coefficient, and scanning a target area to obtain echo data. And finally, performing joint sparse representation on the original radiation data stream and the target echo data to generate a fusion result. The invention discloses multi-sensor data fusion based on an intelligent pod. According to the method, the anti-interference wave beam scanning target is dynamically generated by quantifying the attenuation of electromagnetic interference on radiation of each wave band, and the sensing performance of the intelligent pod in a complex electromagnetic environment is improved by combining the joint sparse representation and fusing the multi-source data.

The invention provides a wind speed prediction method based on a valley fan, and relates to the field of wind speed monitoring, and the method comprises the steps: data collection: collecting historical meteorological data of a valley, the historical meteorological data comprises air pressure, temperature, humidity, wind direction, wind speed and solar radiation data, and collecting data at the same time interval; the beneficial effects of the invention are that the method is specially designed for the unique wind regime characteristics of the valley region, comprehensively considers the topographic data and various meteorological factors of the valley, such as air pressure, temperature, humidity, wind direction and the like, and employs advanced data processing and feature extraction technologies, thereby achieving the real-time monitoring of the valley region. According to the method, the complex change rule of the wind speed of the valley fan can be more accurately captured through the effective learning of the LSTM on the time sequence features and the CNN on the spatial features in the deep learning model, compared with a traditional prediction method, the prediction error is remarkably reduced, and more reliable data support is provided for the stable operation and efficient management of a wind power plant.

The invention provides an atmospheric water vapor detection method and system based on a satellite, and relates to the technical field of satellite remote sensing, and the method comprises the steps: obtaining satellite remote sensing data and solar radiation angle data of a target area; performing correction processing according to the satellite remote sensing data and the solar radiation angle data to obtain corrected radiation data; establishing an atmospheric radiation transmission model according to the corrected radiation data, and generating radiation simulation data; performing water vapor inversion processing according to the radiation simulation data, and calculating to obtain vertical distribution data of water vapor in the atmosphere; and performing three-dimensional interpolation processing according to the vertical distribution data to obtain three-dimensional hierarchical water vapor monitoring data. According to the method, the radiation error in the satellite remote sensing data is corrected by using the solar radiation projection model, so that the radiation error problem caused by ground reflection and the sunlight angle is solved; morphological prediction is carried out on the missing area through a topology analysis technology, fitting filling is carried out in combination with neighborhood water vapor data, and the accuracy and reliability of a monitoring result are improved.

The invention relates to the technical field of nuclear radiation detection management, and discloses a nuclear radiation detection management system based on artificial intelligence, comprising: a dynamic sensing module which detects nuclear radiation through quantum sensing to obtain radiation data, captures NV color center fluorescence intensity, analyzes to obtain gamma ray dose rate, constructs a nanoscale olfactory receptor simulation structure, and sends the radiation data to a control module; radioactive gas is detected and cooperatively monitored; the reasoning calculation module is used for analyzing space-time correlation based on radiation data, optimizing algorithm to accelerate result output, adjusting calculation complexity by calculating dynamic weight distillation, and correcting data transmission in real time; the decision management module is used for constructing a four-dimensional causal graph based on the radiation data, performing intervention simulation, evaluating a decision long-term effect, screening an optimal decision, and performing adversarial training based on historical repair case data; and the fusion cooperation module is used for establishing a cross-mechanism federated learning framework, carrying out hash operation on the radiation data, and establishing an NFT (Network Fourier Transform) on a block chain through an intelligent contract for query, verification and transaction.

The invention discloses a solar radiation forecasting method fusing a decomposition algorithm and a GraphCast model, and relates to the technical field of solar radiation forecasting. Comprising the following steps: acquiring meteorological variable data in an ERA5 reanalysis open source database; preprocessing the data to obtain meteorological variable data conforming to the input format of the GraphCast model; a GraphCast model is constructed to carry out weather forecast; acquiring actually measured meteorological variable data and solar radiation data of a certain region site; through an XGBoost feature screening algorithm, meteorological variables with high importance relative to solar radiation are screened out; preprocessing the screened meteorological variables by using a VMD decomposition algorithm, and constructing a training set and a test set by using the preprocessed data; taking actually measured solar radiation as a label of the model, and performing training optimization on the RF model by using data of the training set; and on the basis of a GraphCast coupling VMD decomposition algorithm hybrid RF model, solar radiation is predicted according to meteorological data of a test set. According to the invention, solar radiation forecasting with higher precision can be achieved.

The invention provides a hyperspectral remote sensing image generation method and device based on three-dimensional scene radiation simulation, relates to the technical field of remote sensing data processing, and aims at solving the technical problems that in the prior art, hyperspectral remote sensing image data acquisition cost is too high, data sources are few, and the resolution is low. The method comprises the following steps: determining three-dimensional scene data according to a user demand; inputting the three-dimensional scene data into a pre-constructed hyperspectral imaging simulation model, and executing the following operations: based on a global coordinate system, starting from an original point, and emitting randomly distributed light to a three-dimensional scene object through a virtual imaging plane, the three-dimensional scene object being used for simulating optical response of a real ground object structure after light incidence; determining an intersection point of each light ray and the three-dimensional scene object; according to the geometric information and the material information corresponding to the intersection point, combining atmospheric radiation parameters, calculating radiation information of light and radiation data of an entrance pupil of the remote sensor; and generating a hyperspectral remote sensing picture according to the radiation data at the entrance pupil.

The invention discloses a laser powder bed melting thermal scanning chromatography quality evaluation method and system. According to the laser powder bed fusion thermal scanning chromatography quality evaluation method, obtained layer section radiation data is fused and updated in a gradient image mode by using a signal processing technology and an image fusion technology, a layer section fused gradient image is generated, gain noise reduction processing is performed on the layer section fused gradient image, and the layer section fused gradient image is obtained. The method comprises the following steps: acquiring a layer cross section fusion gradient image, converting the layer cross section fusion gradient image into a pseudo-color image, performing gradient anomaly region extraction operation on the pseudo-color image, establishing a correlation model among gradient anomaly, printing anomaly and printing defects, and realizing online quality evaluation by utilizing the established correlation model and combining layer cross section radiation data acquired in real time. Gradient abnormity in the printing process is monitored in real time, and possible printing defects are predicted, so that measures can be taken in time for intervention and adjustment, the defective rate of products is reduced, and cost reduction and efficiency improvement are achieved.

The invention discloses an electric power intelligent monitoring system based on multi-device cooperative linkage and an operation and maintenance method, and belongs to the technical field of electric power intelligent monitoring and operation and maintenance. Radiation data and historical decay records are collected through a sensor, a linear regression algorithm is adopted to fit and establish an energy spectrum purity attenuation model, and the dose loss proportion is calculated in real time; carrying out dynamic compensation on beam intensity and irradiation time based on the proportion, fusing real-time dose monitoring data by utilizing a Kalman filtering algorithm, and optimizing a beam intensity value to suppress noise interference; the irradiation time is further adjusted according to whether the dose loss exceeds a threshold value or not, and a dynamic compensation combination is formed; the effectiveness of the combination in each stage of the source life cycle is verified through the simulation module, and the verified compensation parameters are updated to the control system in real time, so that closed-loop regulation and control are realized. According to the invention, the whole process of dose output in the irradiation process is accurately and stably controlled, and the consistency and reliability of the irradiation process are remarkably improved.

The invention provides an underwater gamma radiation monitoring simulation training method and training device, and relates to the technical field of water body radioactivity monitoring simulation, the method is applied to a management server, and the method comprises the following steps: calculating a space three-dimensional simulation radiation field according to a radiation source item; acquiring positioning data sent by the simulation training equipment; based on the positioning data, determining the relative position of the simulation training equipment in the space three-dimensional simulation radiation field; calculating gamma radiation data at the simulation training equipment according to the relative position; and sending the gamma radiation data to the simulation training equipment. According to the invention, underwater gamma radiation monitoring simulation training can be carried out under the condition that a real radiation source does not need to be used.

The invention provides a chemical plant management method and system based on a digital twinborn technology, and the method comprises the steps: setting a sensor at a preset position of a chemical plant, obtaining the basic data of the chemical plant through the sensor, and constructing a chemical plant digital twinborn model through the digital twinborn technology based on the three-dimensional data and basic data of the chemical plant; according to the radiation data, the partition medium refractive index, the radiation field accumulation factor and a chemical plant digital twinborn model, a preset monitoring point location is used as a radiation source, and the radiation quantity of each space point location is calculated through a spherical propagation model; according to coordinate information and orientation information of each spatial point location in the chemical plant digital twinborn model, determining an optimal inspection route from the starting point location to the ending point location by adopting a three-dimensional A * path algorithm; and inspecting the chemical plant according to the optimal inspection route. The embodiment of the invention provides a high-efficiency chemical plant management method, and designs an inspection route with relatively small radiation quantity, so as to ensure the body health of inspection personnel.

The invention discloses a resource evaluation method and system based on multi-landform feature correction, which remarkably improves the accuracy of wind speed and solar radiation data by combining physical constraint correction and machine learning methods, and particularly improves the accuracy of the wind speed and solar radiation data under the conditions of complex terrains and multiple earth surface types. The problem of systematic errors existing in ERA5 reanalysis data in the prior art is solved. Specifically, through elimination of invalid values of radiation data at night and correction of wind speed data based on surface roughness, deviation caused by factors such as terrain shielding, vegetation canopy reflectivity and urban building group wind resistance effect is effectively eliminated. The ERA5 data is trained and optimized by using a machine learning model such as a random forest, so that the wind speed and radiation data at different geographic positions, seasons and time periods can be more accurately corrected, and the corrected data is more in line with an actual observation result.

Lowest cost usage scheduling of an energy system during a time interval of interest is achieved by utilizing two components of learning and optimization. First, several learning methods are used to forecast energy production and if needed energy consumption of a given group of energy components as a function of historical production data, weather, and solar irradiance data collected from weather and geo reports. A classification approach is used to select the learning model. A classification approach is used to select the learning model and approach. Then, an optimization problem is formulated and solved to create the optimal schedule of energy usage during time intervals of interests for the given group of energy components subject to scheduling and equipment constraints. Accordingly, integrated iterative methods, programs, and systems are described aiming at minimizing the cost of energy consumption for the given group of energy components within time intervals of interest.

The invention belongs to the technical field of machine vision automatic identification, and particularly relates to a radioactive article automatic identification method based on machine vision, and the method comprises the steps: labels on radioactive articles are automatically identified and clamped, and the labels are square labels with the same size, different from the background color and different in color; according to the method, the radioactive article is measured, the radiation data of the radioactive article is measured, and the radiation field is reconstructed according to the radiation data, so that the problems that working personnel are affected and the measured and acquired data are insufficient when the space dose rate in the retired environment is acquired are solved. According to the radioactive substance clamping and measuring device, the complexity and danger of radioactive substance processing in the nuclear industry are dealt with, clamping and measuring of radioactive substances are carried out through the mechanical arm, the clamping accuracy is improved, the working efficiency is greatly improved, and the safety of workers is guaranteed.

The invention relates to the technical field of quality control detection, in particular to a radiotherapy radiation amount detection device and method based on multi-sensor fusion, and the device comprises a data obtaining module which obtains the state information of the connection between a functional plug-in and a detection base, and a data processing module which obtains the state information of the connection between the functional plug-in and the detection base and obtains the state information of the connection between the functional plug-in and the detection base, determining first space attitude information of each functional plug-in relative to the detection base; the global space attitude determination module is used for determining global space attitude information of each functional plug-in in a radiotherapy coordinate system based on the first space attitude information and second space attitude information of the detection base; the multi-dimensional radiation data detection module is used for synchronously acquiring multi-dimensional radiation detection data from each functional plug-in based on global space attitude information in the process of executing radiotherapy irradiation; according to the invention, through combined sensing formed by the micro displacement sensor and the inertial measurement unit, real-time accurate measurement and dynamic compensation of the spatial attitude of each functional plug-in are realized.