3114 results about "Sample collection" patented technology

The invention discloses a security event log real-time monitoring and analyzing system and method for a power grid communication network, and relates to the technical field of network security management. The causal relationship graph building module is used for building a causal relationship graph of the target power grid communication network; the multi-dimensional correlation analysis module is used for collecting and analyzing multi-source heterogeneous log data in real time; the abnormal security event identification module is used for identifying an abnormal security event according to the causal relationship graph and the multi-dimensional correlation analysis result; and the attack chain tracking response module is used for tracking the attack chain. According to the method, the technical problem that the existing power grid communication network security monitoring lacks tracking of abnormal event evolution from the time dimension and cannot accurately identify and track a multi-stage attack chain is solved, and the effects of dynamically tracking the evolution process of the abnormal event and identifying a potential attack chain by establishing a time causal chain graph are achieved. And the detection precision and the response speed of the attack behavior are improved.

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention relates to the technical field of artificial intelligence rescue, and discloses an unmanned aerial vehicle and robot dog rescue emergency system and method, and the system comprises an unmanned aerial vehicle cluster module which is used for air data collection, communication guarantee and emergency material distribution; the robot dog cluster module is used for ground environment detection, sample collection and special task execution; the communication cooperation module is used for ensuring information intercommunication and task cooperation between air-ground equipment; the central control module provides task instructions for the unmanned aerial vehicle cluster and the robot dog cluster; the human-computer interaction interface module is used for realizing effective communication between rescue workers and the system; the modules work cooperatively, multi-source data acquisition, three-dimensional scene reconstruction, semantic segmentation, risk assessment, fire spreading prediction and decision support information generation of a disaster site are realized, and a reliable basis is provided for rescue command decision.

A remote test apparatus is configured to simulate an environment of at least one remotely located room and configured to receive at least one air sample from the at least one remotely located room. The apparatus includes a sealed enclosure forming an interior cavity and including an inlet in connection with the at least one remotely located room and an outlet in connection with a sample collection unit. The sample collection unit is configured to communicate the at least one air sample from the remotely located room at a first flow rate. The apparatus further includes at least one sensor disposed in the interior cavity and at least one air transfer unit configured to transfer test air from the interior cavity of the sealed enclosure to the at least one sensor at a second flow rate.

The invention discloses a remote intelligent beer fermentation monitoring platform based on the Internet of Things, and relates to the technical field of fermentation control, a fermentation sample collection module is arranged to collect fermentation characteristic data and progress label data of a fermented beer sample in a fermentation test environment, and a progress model training module is arranged to train a progress prediction model; a progress judgment module is arranged to obtain the expected progress after the p moment output by the progress prediction model, and collect the actual fermentation progress after the p moment, and a fermentation regulation and control module constructs the state space, the action space and the reward function of a fermentation parameter decision model, and judges whether parameter regulation and control need to be performed based on the expected progress and the actual fermentation progress. If the parameters need to be regulated and controlled, generating an action decision of a subsequent fermentation process for the to-be-monitored fermentation tank through a fermentation parameter decision model; the automation and intelligence of the fermentation process are realized, the stability of the beer quality is improved, and the labor cost is reduced.

The invention belongs to the field of food detection, and discloses a pork tenderloin freshness multi-modal fusion detection method based on deep learning, which comprises the following steps: making a sample of a pork tenderloin freshness classification model, collecting HSI data, ultraviolet fluorescence image data, electronic nose data and LF-NMR data in the pork tenderloin storage process, and preprocessing the data; respectively designing feature extraction modules for the ultraviolet fluorescence image data and the HSI data; designing a data enhancement module based on a variational auto-encoder to enhance the sample freshness boundary recognition capability of each category; designing an improved dung beetle optimization algorithm to enhance the hyper-parameter optimization ability of the model; a UV-CNN module, an HSI-CNN module, an AAVAE module and an EDBO optimization algorithm are introduced into a BiLSTM network structure, and a CNN-AAVAE-EDBO-BiLSTM classification model of the freshness of the pork tenderloin is constructed; and testing and evaluating the accuracy of the pig tenderloin classification model.

The invention discloses a karst underground river system multi-source pollution comprehensive quantitative traceability method, which comprises the following steps: S1, multi-dimensional sample collection and new pollutant screening: carrying out multi-period sampling on a water body, measuring conventional water quality indexes and nitrogen and oxygen isotopes, screening new pollutants, and carrying out targeted quantification on characteristic new pollutants; s2, principal component analysis dimension reduction and pollution factor identification: carrying out principal component analysis on conventional indexes and new pollutant data, extracting principal components of which the cumulative contribution rate exceeds 85%, identifying composite pollution factors, and eliminating secondary interference factors; s3, nitrogen and oxygen isotope auxiliary verification and denitrification elimination: eliminating denitrification interference by utilizing the ratio of delta 15N-NO3 <-> to delta 18O-NO3 <-> in combination with underground water burial depth and runoff velocity, and distinguishing the nitrate source as a chemical fertilizer, soil nitrogen or sewage and wastewater; and S4, PMF model optimization and pollution source quantification: inputting the conventional indexes and the new pollutant concentration matrix into a PMF model, and analyzing the contribution rate of each pollution source.

The invention discloses a soil heavy metal content estimation method and device, and relates to the technical field of soil heavy metal estimation.The method comprises the steps of sample collection and data acquisition, specifically, based on geographic information of a research area, soil samples are collected through a layered and partitioned sampling method; according to the method, comprehensive and accurate collection and data acquisition of soil samples are realized through a layered and partitioned sampling method in combination with a high-precision positioning and measuring instrument, the sample representativeness and the data accuracy are ensured, and through an improved correlation analysis algorithm and a depth model based on feature fusion, the soil sampling accuracy is improved. Accurate analysis and efficient modeling of correlation between environmental magnetic parameters and heavy metal content are realized, prediction precision is improved, dynamic optimization of model parameters is realized by applying a self-adaptive step length strategy in model verification and optimization, model stability and generalization ability are guaranteed, and all modules of the device work cooperatively, so that the reliability of the device is improved. And deep analysis of heavy metal sources and migration and transformation rules is realized by using a geochemical analysis module and related models.

The invention belongs to the technical field of high-performance composite materials, and discloses a carbon fiber reinforced thermoplastic composite material performance database, a construction method and application thereof, and the method comprises the following steps: S1, database structure construction; s2, experimental sample collection and data standardization; s3, feature engineering and variable reduction; s4, training a machine learning model; s5, constructing and verifying an adaptive model; and S6, data expansion and feedback optimization. According to the method, material performance prediction and formula parameter reverse design under target performance are realized through systematic acquisition and normalization processing of three types of data of material components, preparation process and performance characterization and building of a nonlinear mapping model among a material structure, a process and performance through a machine learning method. The database can be used for intelligently recommending a high-performance composite material combination scheme, is suitable for rapid screening and customized development of various thermoplastic composite materials, effectively reduces the research and development cost and development cycle, and improves the material design efficiency.

The invention relates to the technical field of biomedicine, in particular to a hepatic cell differentiation degree evaluation method based on multi-omics data, which comprises the steps of sample collection and preprocessing, transcriptomics, proteomics and metabonomics data analysis, multi-omics data integration and modeling and result output. By integrating multi-level biological information, a multi-dimensional scoring model is constructed, the liver cell differentiation state is quantitatively evaluated, and a standardized grading system is provided for clinic. The method can solve the limitation of single omics analysis, improves the evaluation accuracy and reliability, has universality, can be popularized to other malignant tumor research, and assists precise medical development.

The invention belongs to the technical field of aircraft oil leakage monitoring, and relates to an aircraft oil leakage detection method based on a high-sensitivity mass spectrometry technology, and the method comprises the steps: obtaining a time-resolved mass spectrogram of aviation kerosene molecules in a sample testing stage, and storing the time-resolved mass spectrogram in a database; then oil gas molecules are captured at leakage-prone points of an aircraft pipeline system through a gas collection probe, the captured oil gas molecules are transmitted to an ion source end of a proton transfer reaction mass spectrometer through a negative pressure transmission system to be ionized to generate ions, and the ions are analyzed by a mass analysis system to obtain a mass spectrum; comparing the mass spectrogram obtained by analysis with a time-resolved mass spectrogram in a database, and judging whether leakage occurs or not; based on the method, the problems of insufficient anti-interference capability, difficulty in sample collection and the like of a high-sensitivity mass spectrum oil gas leak detection method can be solved in a targeted manner, and the functions of environmental interference suppression, efficient collection of oil gas molecules, improvement of spatial scanning resolution and the like can be realized at the same time.

The utility model discloses a sample collection and detection device. The sample collection and detection device comprises a shell, a sampling head and a detection assembly, the sampling head is installed on the shell, the sampling head is provided with a capillary channel, and the capillary channel has a preset volume and is used for quantitatively collecting a sample. The detection assembly is arranged in the shell, and the detection assembly is matched with the sampling head and is used for receiving the sample and detecting the sample. According to the sample collection and detection device, the operation steps can be simplified, the risk that the blood sample is polluted can be reduced, the sample can be quantitatively collected, and therefore the accuracy of the detection result can be improved.

The invention relates to the technical field of agricultural planting, and discloses a fertilization ratio optimization method for a pitaya growth cycle, which comprises the following steps: sample collection: collecting root soil from a plurality of pitaya plantations according to the pitaya growth cycle, and collecting soil layers with different depths in a layered manner; soil plant analysis: detecting the content of elements in the soil; strain separation: using a plate streak separation method; material screening: testing a carrier material, and evaluating adsorption of the material to microorganisms; organic raw material treatment: waste pitaya branches and pomace in the plantation are collected; according to the growth nutritional requirements of young trees, the technology is integrated; and according to the growth cycle result, the technology is optimized to improve the technical details. Soil and plant nutrition conditions are accurately mastered through sample collection and analysis, a scientific basis is provided for fertilization, and blind fertilization is avoided. According to the technology integration, all links are integrated and perfected, and an efficient closed loop is formed. Finally, the fertilizer utilization rate is effectively improved and high-quality and high-yield pitaya is realized.

The present invention discloses a three-dimensional fluorescence portable water quality analyzer for pollution component determination and source tracing, and relates to the technical field of water quality analysis, the analyzer comprises the following components: a sample collection and processing module for collecting a water sample from a to-be-detected water body, filtering to remove impurities, and diluting the water sample according to a self-adaptive dilution multiple algorithm; fluorescence intensity information of a water sample under different excitation wavelengths and emission wavelengths is obtained through the three-dimensional fluorescence detection module, rich three-dimensional fluorescence spectrum data is formed, the real-time fluorescence lifetime imaging module is introduced, and by measuring the fluorescence lifetime of a fluorescent substance and utilizing the principle that different fluorescent substances have different fluorescence lifetime characteristics, the real-time fluorescence lifetime of the water sample is obtained. Various pollution components in the water sample are further distinguished and recognized, the pollution components can be accurately distinguished through fluorescence lifetime measurement and imaging, the accuracy of pollution component judgment is greatly improved, and a reliable basis is provided for accurate treatment of water quality pollution.

The invention discloses a deep sampling device for ecological restoration soil investigation, and relates to the technical field of sampling. The linkage mechanism is mounted on the sliding part of the first linear sliding rail and can automatically move along the guide rail part of the first linear sliding rail, and a rotatable connecting sleeve is arranged at the bottom of the linkage mechanism; the end portions of the sampling barrels are detachably connected in a sealed mode, a plurality of liquid injection holes are formed in the barrel wall of each sampling barrel from one end in the axis direction in a penetrating mode, and a plurality of non-penetrating liquid injection holes are formed in the side wall of the drilling barrel from the end, connected with the sampling barrels, of the drilling barrel inwards in the axis direction. The multiple sampling barrels are detachably connected in a sealed mode and suitable for sampling soil of different depths, sampling pollution is avoided through sealed sampling, after sampling is completed, a polymer curing agent is injected into the liquid injection hole to cure soil at the bottom end, the risk that the sampled soil slides down in the lifting process is avoided, and the sampling and collecting quality is guaranteed.

The invention discloses a Raman spectrum fluorescence baseline correction method and device, and the method comprises the steps: collecting a time domain signal of a to-be-detected sample, and obtaining an IRF function of a to-be-detected sample collection system; pre-processing the time domain signal, and carrying out deconvolution on the pre-processed time domain signal and an IRF function to obtain a deconvolution result; fitting the second half data of the deconvolution result by using a support vector regression model to obtain a fitted curve; wherein a radial basis function is adopted as a kernel function, and hyper-parameters of the support vector regression model are optimized through a grid search and cross validation method; performing difference operation on the deconvolution result and the fitting curve to obtain a Raman attenuation curve; and windowing the Raman attenuation curve and averaging to obtain a Raman spectrum. According to the method, a fluorescence attenuation model and parameters thereof do not need to be preset, the IRF deconvolution result is processed and the time domain baseline is predicted by utilizing the characteristics of the time domain signal data and the support vector regression model, the signal-to-noise ratio of the extracted Raman signal can be greatly improved, a good effect is achieved, and the method is suitable for practical application.

The invention provides a near infrared spectrum qualitative discrimination method and system, and the method comprises the steps: collecting a near infrared spectrum data set, and converting a one-dimensional spectrum sequence into a polar coordinate image; constructing a lightweight convolutional network architecture, and processing the polar coordinate image; k samples of each type are dynamically extracted from the training set, a support set is constructed based on the K samples and a few-sample strategy, and a query support similarity matrix is constructed through a cosine similarity algorithm; and judging the test set based on the query support similarity matrix, the support set and a preset threshold to obtain the highest similarity between the test set and the support set, and if the highest similarity exceeds the preset threshold, judging that the test set is a corresponding category. According to the method, the cost and time of sample collection and preparation can be reduced, the feature information of the spectral data is effectively extracted, and the method has relatively high flexibility during discrimination.

The invention discloses a gene detection system for an algae biological gene, and relates to the technical field of gene detection, the gene detection system for the algae biological gene comprises a sample collection module, a feature extraction module, an amplification module, a sequencing module, an analysis module and a report module, according to the system, the accuracy and comprehensiveness of environmental data are ensured through an efficient sampling method of the sample collection module and fine data processing of the feature extraction module, and the sensitivity and accuracy of gene data are improved through efficient gene processing and sequencing technologies of the amplification module and the sequencing module. And finally, an integrated calculation method of an analysis module and visual display of a report module enable gene anomaly detection to be more accurate and timely, a user can make scientific intervention conveniently, the system can evaluate the gene state of algae organisms more comprehensively by integrating different data sets, and compared with single data set analysis, the system has the advantages that the analysis efficiency is improved, and the system is more convenient to use. And a more accurate and comprehensive evaluation result can be provided.

The invention discloses an ion adsorption type rare earth ore prospecting method based on a multi-element composite mineralization mode, and belongs to the technical field of rare earth ore geological exploration. The method comprises the following steps: firstly, collecting the existing exploration and research data of the known granite weathered crust type rare earth deposit; analyzing the ore-forming basic elements of the known granite weathering crust ion adsorption type rare earth ore deposit; then field geological survey and sample collection are carried out on the known granite weathered crust ion adsorption type rare earth ore deposit; determining each metallogenic mode element by utilizing analysis test data and field survey data; analyzing the formation cause of the ore deposit, establishing a prospecting mark, and constructing a multi-component composite mineralization mode of the granite weathering crust type rare earth ore deposit. And finally, prospecting target area prediction is carried out. For the known high-altitude granite weathering crust ion adsorption type rare earth deposit, multiple exploration technologies are organically fused, the special environment of the high-altitude area is optimized, a multi-element composite mineralization mode is constructed, and brand-new theoretical guidance and technical means are provided for prospecting of ion adsorption type rare earth ore in the high-altitude area.

The invention discloses a sampling device for engineering geological survey, which is used for solving the problems that the sampling device is low in sampling efficiency and cannot obtain a plurality of samples at one time, and relates to the field of geological survey, the sampling device comprises a drilling barrel for drilling, a sampling mechanism is arranged in the drilling barrel, and the sampling mechanism comprises a sampling disc; the sampling disc is provided with at least two pipe seats which are distributed in an annular array, the pipe seats are used for installing sampling pipes, each sampling pipe is composed of an outer pipe and an inner pipe, a driving part is arranged on one side of each outer pipe and used for driving the corresponding inner pipe to move in the axial direction of the outer pipe, the sampling disc is connected with the first rotating part, and at least one sampling hole is formed in the drilling cylinder; through cooperation of the sampling mechanism, the rotary sampling disc and the multiple independent sampling pipes, the sampling pipes can be triggered to sequentially stretch out of the sampling holes at different depths by one-time drilling of the drilling barrel, continuous layered sample collection and single-time or multi-time sampling at the same position are achieved, sampling pipe replacement or secondary drilling in the midway is not needed, and the sampling efficiency is improved. And the sampling efficiency, accuracy and diversity are improved.

The invention relates to equipment (12) for positioning and supporting an upper limb of a patient (14) intended to undergo a medical procedure, the equipment being characterised in that it further comprises: a cavity (22) for accommodating the elbow of the upper limb of the patient, configured to define a space for positioning and supporting the elbow of the patient; gripping means (24) for gripping, by a hand, the upper limb of the patient; means (30a, 30b, 34) for moving the gripping means (24) in translation in a longitudinal direction relative to the cavity (22) for accommodating the elbow of the patient so that the upper limb of the patient can extend, once the gripping means (24) have been grasped by the patient and the elbow is accommodated in the accommodating cavity (22), between the elbow accommodating cavity and the gripping means, regardless of the length of the upper limb of the patient.

The invention relates to the technical field of frozen soil collection, and discloses a plateau frozen soil sample collection device which comprises a mounting table, a sampling outer cylinder is arranged below the mounting table, a heating cavity is formed in the sampling outer cylinder, and electric heating rods are fixedly mounted on the two sides of the top of the sampling outer cylinder; and the heating end of the electric heating rod extends into the heating cavity. When frozen soil is collected and sampled through rotation of the sampling outer cylinder and the sampling inner cylinder, an anti-freezing solution in the heating cavity can be heated through the electric heating rod, and heat is dissipated to the outside through the sampling outer cylinder, so that ice crystals in the frozen soil outside the sampling outer cylinder are melted, and the sampling cylinder can quickly penetrate through a frozen soil layer; and meanwhile, the anti-freezing solution in the water storage tank is circularly pumped into the cooling cavity in the sampling inner barrel to cool the sampling inner barrel, so that the situation that heat is generated due to friction between the sampling inner barrel and the sampled frozen soil is reduced, the original state of the frozen soil sample can be kept, and the sampling accuracy is ensured.

The invention relates to an animal wound multi-modal intelligent identification method based on artificial intelligence, and the method comprises the steps: carrying out the feature extraction and semantic constraint through multi-modal sample collection and metadata extraction, employing an image preprocessing and text natural language processing technology, and combining a mixed visual model of a convolutional neural network and a visual Transform, and a large language model. A cross-modal attention mechanism and a semantic trigger are utilized to realize feature reweighting, clinical standard soft boundary constraints are introduced, and fuzzy semantic rules are converted into learnable constraints in a feature space, so that the accuracy and consistency of model judgment are improved. The method has adaptive optimization and incremental learning capabilities, and is beneficial to improving generalization and clinical applicability of exposure level intelligent judgment under different animals and complex wound types.

The invention discloses a thromboembolism occurrence risk assessment method and assessment system. The method comprises the following steps: acquiring a blood vessel image map of a to-be-assessed patient; inputting the blood vessel image map of the patient to be assessed into a pre-trained risk assessment model; wherein the pre-training of the risk assessment model is based on a ResNet-50 model, and self-supervised contrast learning iteration is carried out by inputting positive and negative samples so as to update model parameters; extracting a feature embedding vector in the blood vessel image; according to the feature embedding vector, calculating probability distribution of embolism occurrence risk levels through a model classification head; and according to the probability distribution, selecting a risk level label and outputting the risk level label as an evaluation result. According to the method based on the time axis symmetry, the image data of the patient is collected before and after the embolism occurrence time point, the positive and negative sample pairs are generated, the problem that the embolism sample is difficult to obtain is solved, and meanwhile the sample collection cost is reduced through self-supervised learning.

The invention discloses a determination method for different origins of metallogenic fluids in a mountain-building type gold deposit in the south of Jiangsan. According to the method, through innovative sample collection and pretreatment, high-resolution sampling and fine treatment are carried out on specific parts of an ore deposit; advanced fluid inclusion comprehensive analysis is adopted, and inclusion information is accurately obtained in combination with multiple technologies; high-precision stable isotope determination and innovative interpretation in combination with a regional database are applied; element geochemical deep analysis and innovation index application are carried out, and SR-XRF and a machine learning algorithm are introduced; and finally, through comprehensive data fusion and intelligent analysis, constructing an intelligent platform and drawing a three-dimensional evolution model. The method can effectively integrate multi-source data, accurately determine the origin of the ore-forming fluid, overcome the limitation of a traditional method, improve accuracy and reliability, provide powerful support for deep exploration of ore deposit causes and prospecting exploration, and have important theoretical and practical significance.

The invention provides a blood sample automatic analysis system based on artificial intelligence. The blood sample automatic analysis system based on artificial intelligence comprises a sample collection module used for automatically collecting a blood sample of a patient, a data acquisition module comprising a sensor and an imaging device and used for acquiring an image of the blood sample or original data in other forms, and a data preprocessing module, and the noise filtering module is used for carrying out noise filtering, standardization and normalization processing on the original data. According to the blood sample automatic analysis system based on artificial intelligence, by introducing a dynamic adjustment mechanism and a self-adaptive learning algorithm, the system can adjust model parameters in real time, and especially under the condition that data is insufficient or a data set has deviation, the recognition accuracy of rare diseases is greatly improved. The dynamic model training method not only improves the generalization ability of the model, but also effectively relieves the limitation of relying on a large-scale annotation data set.

The invention discloses a photo-thermal amplification and real-time PCR (polymerase chain reaction) detection integrated optical fiber system, and relates to the field of pathogen testing, the optical fiber system comprises an optical fiber probe, a photo-thermal photosensitizer and a micro reaction chamber; the micro-reaction chamber is used for extracting and discharging a to-be-detected sample by utilizing air pressure, the optical fiber probe is arranged in the micro-reaction chamber, and the optical fiber probe reacts with the to-be-detected sample in the micro-reaction chamber; the photo-thermal photosensitizer is arranged inside or on the outer surface of the optical fiber probe and is used for receiving heat generated by the heating light to heat the optical fiber probe and controlling the reaction temperature of the optical fiber probe and the to-be-detected sample; the optical fiber probe is also used for receiving the exciting light which excites the to-be-detected sample to generate a fluorescence signal, and the optical fiber probe is used for detecting the fluorescence signal. According to the application, sample collection, photo-thermal cycle amplification and real-time fluorescent quantitative detection can be integrated, and rapid and effective detection of a virus sample is realized.

The utility model relates to the technical field of ecological environment detection, in particular to an underwater plankton sampling device which comprises a main body, a sampling driving component is arranged in the main body, and a control collecting component is fixedly connected in the main body; according to the utility model, the sampling depth can be accurately controlled by utilizing the depth sensor, in order to meet different research requirements, the tube wheel is driven by the motor to wind the sinking tube, and the automatic control driving mechanism can quickly complete sampling and can drive the sampling barrel to move up and down in water to realize sampling at different depths, so that the sampling efficiency is improved. The microprocessor is used for controlling the operation of the depth control mechanism and the driving mechanism, automatic sampling is realized, the inner wall of the sampling barrel is smooth, attachment and damage of plankton in the sampling process are avoided, automatic sampling is realized through the control unit, the operation is simple, and the use is easy; the sample collection container can be used for storing sampled plankton samples, so that the samples are prevented from being polluted.

The invention belongs to the field of coal quality analysis, and discloses a microwave heating type coal quality on-line analysis device and application thereof.The device comprises an on-line sample collecting and preparing module, a microwave heating module, a weight collecting module and an analysis control module, and the microwave heating module comprises a microwave drying cavity, a microwave devolatilization cavity and a microwave ashing cavity; each chamber is connected with a microwave temperature control unit for independent temperature control; the online sampling and sample preparation module is used for collecting coal from a coal conveying line in real time, crushing the coal into standard coal samples, and feeding the standard coal samples into the corresponding treatment chambers for microwave heating treatment; the microwave ashing cavity is further connected with an oxygen supply unit, and the oxygen supply unit is used for providing an oxygen-enriched environment for the microwave ashing cavity; the weight acquisition module is connected with the analysis control module and is used for acquiring the weight of the standardized coal sample before and after heating; and the analysis control module is used for controlling the microwave temperature control unit to adjust the temperature of the corresponding chamber and obtain various indexes of the standardized coal sample. According to the invention, the efficient and full-automatic microwave heating coal quality online sampling analysis effect can be realized.

The invention relates to the technical field of soil pollution detection, in particular to a soil pollution real-time detection and analysis method and system based on spectral analysis, and the system comprises a soil pollution detection platform, a soil database, a regional pollution division unit, a sample check analysis unit, a regional identification analysis unit, a pollution diffusion analysis unit and a display response unit. Analysis is carried out from two points of an unmanned aerial vehicle hyperspectral technology and a sample collection and detection technology, namely, regional pollution division feedback analysis is carried out on a spectral feature image to judge whether a heavy metal pollution area exists in a target soil area or not and the pollution degree condition, and regional pollution matching check analysis is carried out on sample parameters to judge whether the target soil area has a heavy metal pollution area or not. According to the method, the unmanned aerial vehicle hyperspectral detection can be checked and fed back through sample experiment detection, and whether the two types of detection are consistent or not can be visually known through an information feedback display mode, so that the availability of a detection result is ensured.