594results about How to "Quick extraction" patented technology

The invention provides a communication network operation and maintenance fault positioning and tracking method and system, relates to the technical field, and belongs to the technical field of communication network fault diagnosis. According to the method, historical operation data, including network flow data, performance index data and alarm log data, of communication network equipment are collected, fault feature vectors are generated through training of a deep learning model, and a fault feature knowledge base is constructed; receiving operation data reported by equipment in real time to perform feature extraction, and performing similarity matching based on the fault feature knowledge base to determine a fault type; and analyzing a fault propagation path by adopting a graph traversal algorithm, determining fault root cause equipment by calculating a fault probability score of an equipment node, and generating a fault tracking report. According to the invention, accurate positioning and rapid diagnosis of communication network faults are realized, the fault processing efficiency is improved, and the network operation and maintenance cost is reduced.

The invention discloses an intelligent finance and tax question answering method based on an artificial intelligence question answering system, and the method comprises the steps: S1, generating a structured semantic representation through a natural language processing technology, and generating an intention tag through a machine learning algorithm; s2, constructing and maintaining a knowledge graph containing finance and tax field related information, storing the information in the knowledge graph in a structured manner, and reasoning by using a logical reasoning algorithm to generate a preliminary answer; s3, designing a document retrieval and indexing mechanism, and verifying and supplementing the preliminary answer; s4, training the system based on the finance and tax related data and question and answer pairs, and continuously updating the model; s5, identifying common finance and taxation question modes by using a mode identification technology, and providing standardized answers; and S6, supporting multiple rounds of dialogues with the user, and generating more detailed and accurate answers by gradually clarifying the questions of the user. The method has the advantages of high accuracy, high response speed and capability of continuously learning and adapting to new problems.

The invention discloses an image generation method based on a mixed quantum classical condition generative adversarial network. The method comprises the following steps: firstly, converting a random condition label and a sample real condition label into one-hot codes, processing input noise and the random condition label by using a quantum circuit, performing measurement to convert a characteristic quantum state into a classical characteristic vector, and obtaining generated image data from the characteristic vector through a classical single-layer neural network. And transmitting the sample real image data and the real condition label into a discriminator to obtain a real image discrimination result, and transmitting the generated image data and the random condition label into the discriminator to obtain a generated image data discrimination result. And calculating discrimination loss based on the two discrimination results, training a discriminator and a quantum condition generator, obtaining a trained mixed quantum classical condition generative adversarial network, and generating a condition image. Based on the above scheme, the problems of unstable classical GAN training and many training parameters are solved, the limitation of the quantum GAN in a condition generation task is broken through, and the application of the quantum generative adversarial network in scenes such as image generation and pattern recognition in the field of real artificial intelligence is further promoted.

The invention discloses a power grid facility three-dimensional model fitting method and system based on panoramic point cloud, and relates to the technical field of three-dimensional model fitting. The method comprises the following steps: collecting panoramic point cloud data and high-definition image data of a power grid facility, and carrying out data preprocessing; importing a BIM model of a power grid facility, and establishing a homologous coordinate system with the panoramic point cloud data; performing data alignment on the panoramic point cloud data and the BIM model, and extracting key features of the power grid facility from the aligned point cloud data; adjusting vertexes and edges of the BIM model based on the extracted key features to enable the BIM model to be matched with the point cloud data, and ensuring the precision and reliability of the BIM model through consistency verification; and generating a three-dimensional model consistent with an actual power grid facility based on the corrected BIM model for real-time monitoring and defect detection. The operation and maintenance risk caused by data deviation can be reduced, human intervention is reduced, and the automation level of the system is improved.

The invention discloses a data management method and system for workshop production, relates to the technical field of data management, and solves the technical problems that the data volume is large, unified management is inconvenient, and when the data are stored, different types of data are stored disorderly, and subsequent data extraction is inconvenient. According to the method, the data are classified according to the data types and the generation time, the data are classified in a unified mode, unified management of the data is facilitated, in the process of storing the data, unified format conversion is conducted on the data, on one hand, compatibility of different types of data can be guaranteed, and the data storage efficiency is improved. On the other hand, rapid extraction of different types of data can be achieved, meanwhile, repeated data in the data are analyzed and extracted and analyzed, when the data are stored, storage analysis is conducted according to the characteristics of the data, storage is conducted according to the relevance between the data, and the data storage efficiency is improved. And overall extraction of different types of data is facilitated.

The invention provides a method for dynamically constructing a coal mine geology digital twinborn body based on multi-source heterogeneous information. Comprising the steps of AIGC-assisted geological result reading, end-to-end structured information self-adaptive generation, primary geologic body model construction through a generalized triangular prism algorithm, geometric smooth subdivision based on Kriging interpolation, special geological element dynamic updating and coal mine digital twinborn matrix generation. According to the method, the AIGC is introduced to assist in reading the geological result innovatively from the remarkable characteristics of the data intensive normal form of the geological information, and intelligent generation of structured information matched with an automatic modeling updating algorithm is realized through big data assimilation of multi-source heterogeneous information; a quick response process of information processing, primary geologic body generation, geometric smoothing, abnormal geologic body encryption and global geologic body construction is formed, and a high-precision coal mine geologic digital model is dynamically constructed by utilizing geologic information of different geologic survey stages. And the requirements of high precision, renewability and automation of the coal mine digital twinborn body are met.

The invention provides a railway fastener anomaly detection method and system based on computer vision, and the method comprises the steps: a fastener data arrangement step: carrying out the screening and combination in a track area image, and obtaining a fastener data set; a fastener anomaly detection network construction step: matching a threshold value through a random floating-point number, selecting to carry out false anomaly and label generation strategy processing or data enhancement strategy processing on the fastener image to obtain a first image, extracting features in the first image, and reconstructing through a reconstruction network to obtain a second image, obtaining a comparison score of all corresponding associated pixels between the two images through a preset comparison function; a fastener anomaly model training step: substituting the fastener data set into the fastener anomaly detection network for training to obtain a fastener anomaly model; a fastener abnormality output step: substituting the real-time fastener image into the fastener abnormality model to output an abnormal contour mark and a comparison score at an abnormal contour; according to the invention, it can be ensured that the fastener can be detected in different abnormal states, and various different fastener failure forms are covered.

The invention discloses a spring operating mechanism fault detection and optimization method and a related device, and the method comprises the steps: carrying out the real-time collection of key data of a spring operating mechanism through a stress sensor, a speed sensor and a vibration sensor; based on the collected key data, constructing a multi-dimensional data framework to obtain a multi-dimensional data set; fault feature extraction and data analysis are carried out on the obtained multi-dimensional data set, an adaptive fault model is constructed based on the extracted fault features, and then a real-time fault detection result is obtained through multi-modal data fusion; and optimizing a stress sensor, a speed sensor and a vibration sensor by utilizing automatic calibration and optimization of reinforcement learning according to the real-time fault detection result. Compared with a traditional fault detection method, high-precision real-time monitoring and fault prediction of the spring operating mechanism are achieved through the multi-modal data fusion and deep learning technology, and comprehensive and reliable technical support is provided for safe operation and maintenance optimization of equipment.

The utility model relates to an automatic sorting device for lens disassembly, which relates to the technical field of lens disassembly and comprises a storage bin used for storing lenses to be disassembled and a feeding and discharging Y shaft arranged on one side of the storage bin and used for moving the lenses in a tray out to a working area. The jacking and clamping double-station assembly comprises a clamping head for clamping a to-be-disassembled lens, a lead screw motor for ejecting out to-be-sorted components in the lens in the direction below the clamping head, a component pre-pressing plate piece for pre-pressing the products to prevent the products from scattering and inclining, and a pre-pressing mechanism switching shaft for adjusting the position of a pre-pressing mechanism. An inverted U-shaped support plate is arranged on the top surface of the free end of the component pre-pressing plate piece, the inner wall of the U-shaped support plate is connected with a vacuum chuck tailstock through a lifting component, the bottom surface of the vacuum chuck tailstock is connected with a vacuum chuck piece through a pipeline longitudinally penetrating through the component pre-pressing plate piece, and the vacuum chuck piece adsorbs a component in a lens in a product.

The invention discloses a control method and a control system of an ultrasonic therapeutic apparatus and the ultrasonic therapeutic apparatus, and the method comprises the following steps: S1, transmitting ultrasonic waves to skins at a plurality of target positions one by one for sampling, and receiving an ultrasonic echo signal of each target position, the plurality of target positions are obtained by a transducer of the ultrasonic therapeutic apparatus moving along a skin surface area directly facing the end face of a probe of the ultrasonic therapeutic apparatus; s2, converting the received ultrasonic echo signal into an ultrasonic image; s3, skin features of each target position in the ultrasonic image are extracted, the distance between the skin of each target position and the end face of the probe is calculated, the calculated maximum distance is compared with a target threshold value, and if the maximum distance is smaller than or equal to the target threshold value, it is judged that the end face of the probe is effectively attached to the surface of the skin; otherwise, judging that the end face of the probe is not effectively attached to the skin surface. Positioning is carried out through ultrasonic imaging, it is ensured that operation is carried out in the state that the end face of the probe is effectively attached to the skin surface area, and the using effect and safety are ensured.

The invention discloses a fault detection method and system for a coal conveying programmed control system of a thermoelectric company, and relates to the technical field of coal mine fully-mechanized mining, and the method comprises the steps: building a fault intelligent diagnosis model based on historical fault data; collecting field data in real time, and judging whether a fault occurs or not through the fault intelligent diagnosis model; and executing a corresponding strategy according to the type and degree of the fault. According to the method, multi-dimensional feature analysis is carried out according to multiple groups of sensors, comprehensive analysis of coal level height data is realized by extracting basic features, statistical features, time domain features, frequency domain features and other features, and the accuracy of fault detection is remarkably improved. The deep learning network is utilized to train and classify the feature data, so that the fault types of blockage, coal interruption and the like can be identified more accurately, and misinformation and missing report are reduced. The coal level height data is collected in real time, features are rapidly extracted, and the running state of the coal drop pipe is monitored in real time.

The invention discloses an electrostatic protection characteristic analysis method, device and equipment of a TVS (Transient Voltage Suppressor), and relates to the field of electrostatic discharge protection analysis. The method comprises the following steps: acquiring information data of a TVS tube; inputting the information data into the combined model to obtain electrostatic protection characteristic data; the electrostatic protection characteristic data is used for representing the electrostatic protection characteristic of the TVS tube; the combined model comprises an RLC circuit model and a double-capacitor model; the RLC circuit model is a physical simulation model obtained by carrying out optimization fitting by adopting a particle swarm optimization algorithm based on a parasitic effect; the double-capacitor model is a simulation model which adopts simulation software and realizes current waveform reproduction corresponding to electrostatic excitation based on electrostatic voltage; according to the invention, the accuracy and universality of electrostatic protection characteristic analysis can be improved.

The invention provides an effective signal filtering and noise reduction method and system for a micro-seismic event of a tunnel water and mud inrush disaster, and relates to the technical field of signal processing. Comprising the following steps: acquiring original signals of water-inrush and mud-inrush micro-seismic events; performing modal decomposition on the original signal by using a variational modal decomposition method to obtain a plurality of modal components; calculating a cross correlation coefficient between the original signal and each modal component; based on the center frequency of each modal component and in combination with the cross correlation coefficient, performing primary noise reduction on the plurality of modal components; performing de-trend fluctuation analysis on the modal components after the primary noise reduction screening to obtain scale indexes of the modal components after the primary noise reduction screening, and performing secondary noise reduction based on the scale indexes; and reconstructing the modal component after the secondary noise reduction screening to obtain a micro-seismic signal after noise reduction filtering. According to the method, the problem that noise and effective signals in part of modal components are mixed is solved, and the weak signal extraction capacity is improved.

The invention provides a medical information data query method and system based on artificial intelligence, and relates to the field of data processing.The system comprises a data acquisition module used for acquiring medical data; the data processing module is used for preprocessing the medical data; the data encryption module is used for encrypting the preprocessed medical data; the data storage module is used for storing the encrypted medical data; the data query module is used for receiving a data query request of the patient end or the medical care end, performing permission verification on the patient end or the medical care end based on the data query request, determining target medical data corresponding to the data query request through artificial intelligence after the permission verification, and feeding back the target medical data to the patient end or the medical care end; the method has the advantage of improving the intelligence of medical data query.

The invention relates to a crude oil pipeline flow characteristic mining method and system, processing equipment and a storage medium, and the method is characterized in that the method comprises the steps: building a candidate set of a pipeline transportation crude oil flow characteristic representation model of a to-be-tested crude oil pipeline based on a pre-built basic symbol library in combination with a large language model and a symbol regression method; and based on a scoring standard for characterizing the quality of the model, scoring the candidate set of the pipeline transportation crude oil flow characteristic characterization model, and based on a scoring result, optimizing and iterating the structure of the pipeline transportation crude oil flow characteristic characterization model to obtain an optimal pipeline transportation crude oil flow characteristic characterization model of the crude oil pipeline to be tested. According to the method, the flow characteristic model can be rapidly and accurately extracted, the time and cost of experimental data and model construction are greatly reduced, and the method can be widely applied to the field of crude oil pipeline flow characteristic analysis.

The invention provides a shale alternating load and multi-field coupling simulation analysis platform and method, and belongs to the technical field of shale alternating load and multi-field coupling simulation analysis. The shale alternating load and multi-field coupling simulation analysis platform comprises a data acquisition module, a real-time monitoring module, a main control module, an alternating load simulation module, a multi-field coupling simulation module, a data analysis processing module, a cloud storage module and a display module. According to the shale alternating load and multi-field coupling simulation analysis method, a multi-field coupling simulation module, a real-time monitoring module, a data analysis processing module and the like are integrated, and the behavior of shale under the complex geological conditions and the alternating load effect can be more comprehensively analyzed. According to the invention, the maximum stress distribution, strain accumulation and fatigue life of shale under periodic bending alternating load can be accurately predicted through the alternating load simulation module; various shale data analysis methods and algorithms are integrated through the data analysis processing module, and efficient processing of experimental shale data is achieved.

The invention discloses a digital audio watermark processing method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: receiving a watermark adding instruction, and carrying out Fourier transform on original host voice to obtain an audio signal; identifying a watermark information adding bit in the audio signal through the audio watermark model, and embedding a watermark message in the watermark information adding bit; the interfering audio signal and the audio signal carrying the watermark message are combined. Receiving the message extraction signal, and separating an interference audio signal in the combined audio signal; a watermark information adding bit in the audio signal is identified, and a watermark message is extracted from the watermark information adding bit. The invention also relates to the technical field of block chains, and the host voice is stored on a block chain network. According to the method, the watermark information adding position is automatically identified through the audio watermark model, so that the watermark information is added and extracted, the dependence on expert knowledge is remarkably reduced, and the watermark processing speed and efficiency are improved.

The invention relates to a cell differentiation trajectory inference method based on deep hyperbolic manifold embedding. The method comprises the following steps: acquiring single-cell RNA sequencing data; introducing hyperbolic space embedding with negative curvature, performing feature extraction on the single-cell RNA sequencing data through a deep feature extraction network, and mapping extracted high-dimensional features to a low-dimensional hyperbolic space; through track inference in a low-dimensional hyperbolic space, transformation paths of cells from progenitor cells to various differentiation end cell types are constructed, and key genes and pathways are evaluated by utilizing a sensitivity analysis method. The method has the beneficial effects that by introducing hyperbolic space embedding with negative curvature, the model can better capture the hierarchical structure of data, and the accuracy and consistency of differentiation tracks are enhanced. Moreover, high-dimensional single-cell sequencing data can be processed, the limitation of a traditional method under the high-dimensional condition is overcome, and the applicability of the model is improved.

The invention belongs to the technical field of traditional Chinese medicine preparation devices, and particularly relates to a production method and device of traditional Chinese medicine composition oral thick paste. Comprising an extract mixing tank, a tank cover is fixedly connected to the top of the extract mixing tank in an inserted mode, a hydraulic lifting rod is fixedly installed on the tank cover, and the telescopic end of the hydraulic lifting rod is fixedly connected with a motor through a connecting frame; a communicating port is formed in the middle of the top of the tank cover in a penetrating manner, and the top of the tank cover further communicates with a feeding port; according to the invention, the water body not only can circularly clean the extract mixing tank to save water, but also can improve the cleaning efficiency and reduce the energy consumption, and the mixing is easier and more uniform; according to the invention, the extractum in the extractum mixing tank can be uniformly mixed, and the problems of low liquidity and easy layering of the extractum are solved; the stirring direction of the stirring mechanism is quickly switched while the lifting effect of the auger blade is not influenced; the traditional Chinese medicine composition prepared by the method has the effects of enhancing immunity, improving sleep quality, relieving pressure and the like.

The invention discloses a personnel emergency evacuation method and system based on an improved vector vector diagram, and belongs to the technical field of emergency simulation, and the method comprises the steps: carrying out the initialization of an evacuation environment, including the initialization of static environment space elements and the initialization of evacuation crowd individual positions; establishing a visual field containing a plurality of sensing points according to individual positions and speed directions of the evacuated crowd; and for each individual in the evacuated crowd, individual movement is carried out based on the established visual field and through continuous perception of the static environment and interaction among the individuals in the crowd, and crowd evacuation is completed. According to the method and the system, the information exchange among the groups and the perception-feedback process of the crowd to the environment are fully considered in the evacuation simulation process, and the authenticity of the simulation process is improved.

The invention relates to the technical field of safety monitoring, and discloses an open-cast mining operator safety monitoring method and system based on the Internet of Things, and the method comprises the following steps: S1, designing and integrating a five-point safety belt which comprises a chest belt, two shoulder belts and two leg belts, and integrating a sensor on the safety belt; s2, integrating various sensors, including a heart rate sensor, a gravity sensor (accelerometer), a position sensor, a gas detection sensor, a temperature sensor and an acoustic sensor; s3, transmitting sensor data to a central monitoring system in real time by using a wireless communication technology; and S4, carrying out data processing and analysis in the central monitoring system. By using the five-point type safety belt and various sensors, the environment and individual health conditions can be monitored in real time in an omnibearing manner, various risk factors are recognized and responded in time, the accident rate and potential health problems are remarkably reduced, and the working efficiency is improved.

The invention discloses a knowledge graph ontology constrained vegetation remote sensing classification method. The method comprises the steps of data acquisition and data preprocessing, deep learning model selection and fusion of a knowledge graph ontology and a deep learning model. According to the method, the knowledge graph ontology and the FCN8s-ResNet 50 model are fused in the two stages of model training and classification post-processing. The result shows that 1) the classification accuracy of the deep learning model can be remarkably improved by fusing the mountain vegetation knowledge graph ontology in the two stages of deep learning model training and classification post-processing: the accuracy of the deep learning model constrained by the knowledge graph ontology is improved by 10%, and the overall classification accuracy of vegetation types reaches 89.2%; and 2) the deep learning model constrained by the mountain vegetation mapping knowledge domain ontology not only obtains excellent results in fine classification of vegetation types, but also improves the universality and interpretability of the model, so that powerful support is provided for wide-range remote sensing vegetation classification and application of the deep learning model.

The invention provides a fee deduction method, system and device of an intelligent traffic system and a medium, and belongs to the technical field of intelligent traffic. The method comprises the following steps: acquiring a vehicle image, vehicle driving information and traffic flow information at a preset position of a traffic system; the vehicle image comprises a license plate image of a vehicle; identifying and analyzing the vehicle image by adopting a deep learning algorithm to obtain license plate number information and vehicle type information; predicting a driving path and passing time of the vehicle based on the historical driving data of the vehicle; dynamically calculating a fee deduction amount according to the driving path, the passing time and the real-time traffic flow information of the vehicle; and sending the deduction amount to the mobile terminal of the user, and providing payment service. According to the invention, the intelligent identification system is constructed, the vehicle information is obtained in real time, dynamic fee deduction is carried out in combination with various factors, and accurate and reasonable traffic and parking management is realized.

The invention provides a borborygmus characteristic parameter real-time extraction method based on an intelligent algorithm, and the method comprises the following steps: signal collection: collecting a borborygmus signal through a high-sensitivity recording device; signal preprocessing: preprocessing the collected borborygmus signal, including noise suppression and signal filtering, so as to reduce interference of environmental noise; and feature extraction: performing spectral analysis by adopting fast Fourier transform and wavelet transform, extracting frequency domain and time domain feature parameters of the borborygmus signal, and calculating statistical features of the signal, including mean value, variance, kurtosis and skewness, and frequency spectrum entropy and power spectrum density based on the frequency spectrum.

The invention provides a flood inundation drawing method and system integrating social media data and SAR images, and relates to the technical field of emergency management, and the method comprises the steps: obtaining text data which is related to a rainstorm flood event and carries POI address information in social media; generating a rough flood inundation range map and a flood inundation probability map of the disaster area based on the DEM data, the POI inundation points and the corresponding water depth information; determining a flood inundation probability threshold interval based on the searched field picture and video data, and obtaining an intersection to obtain a flood inundation range map of the urban built-up area; according to the pre-disaster, in-disaster and post-disaster multi-scene SAR images, water body data in different periods are extracted, and a river flood inundation range map is generated; and combining the flood inundation range map of the urban built-up area and the flood inundation range map of the river channel to obtain a flood inundation range map of the affected area. According to the method, the advantages of social media data and time sequence SAR images in urban flood and river flood detection are fully utilized, and high-precision mapping of large flood events is achieved.

The invention discloses an image reconstruction algorithm, an image processing method and device, a vehicle and a storage medium. The image processing method comprises the steps of obtaining a visible light image and an auxiliary modal image to be fused; and performing fusion reconstruction on the visible light image and the auxiliary modal image by using an image reconstruction algorithm to obtain a target image, the image reconstruction algorithm comprising a step of completing a feature extraction task of the associated visible light image by using a high-frequency attention mechanism and a Fourier convolution mechanism in the high-frequency attention block. Therefore, the high-frequency information in the visible light image with various image qualities can be rapidly and effectively extracted through the image reconstruction algorithm, so that the target image is obtained by fusing and reconstructing the visible light image and the auxiliary modal image based on the effectively extracted high-frequency information; and features in a visible light image and features in an auxiliary modal image are clearer. Therefore, it can be ensured that images with high quality can be obtained when shooting is carried out in various shooting environments.

The invention relates to an environment monitoring equipment small strip printing problem identification method, equipment and a medium, and belongs to the technical field of environment monitoring. The method comprises the steps of obtaining a digital image of a small printing strip of environment monitoring equipment; performing feature extraction on the digital image to obtain to-be-checked data; calling a pre-established knowledge base and an algorithm base; standard data related to environmental monitoring are stored in the knowledge base, and the algorithm library comprises a plurality of calculation methods and logic inspection methods for checking and problem identification of printed small data; performing fitting calculation on the to-be-checked data by adopting a convolutional neural network, comparing an obtained calculation result with standard data in a knowledge base, and generating a checking result of whether the environment monitoring equipment printing strip is abnormal or not based on a comparison result; and feeding back the verification result to the user. According to the method and the device, the problem in the manually monitored printed small strip is automatically checked, and the problem identification capability and efficiency of the monitoring data are improved.

The invention provides an annotation data generation method and system based on a large language model, and the method comprises the steps: S1, obtaining a structured text of a representative contract of the same category, and generating a corresponding information extraction template based on the obtained structured text of the representative contract; s2, generating annotation data in batches based on the information extraction template and all contract texts in the same category; s3, summarizing different types of annotation data and corresponding contract texts, generating an annotation data set, and performing fine adjustment on the large language model by utilizing the annotation data set to obtain a fine-adjusted large language model; and S4, marking the contract text based on the fine-tuned large language model.

The invention discloses a lightweight visual inertia fusion positioning method and system, and relates to the technical field of autonomous positioning. The problems that a traditional visual inertial odometer is not robust enough under the condition of data damage, a sensor fused with many deep learning models in an existing deep learning method is huge in size and needs more computing resources during operation, so that deployment on an embedded system or mobile equipment with limited resources becomes difficult, and the cost is low are solved. And further application of the visual inertial odometer is limited. The method comprises the following steps: acquiring image data and IMU data; extracting visual feature information from the image data by using a FlowNetSimple network, and extracting inertial feature information from the IMU data by using a one-dimensional convolutional network; fusing the visual feature information and the inertial feature information by adopting a hard attention mechanism; a recurrent neural network is used for time modeling, and an output transformation network is used for converting the output of the model into a final prediction result. The method is applied to automatic driving.

The invention discloses an aerosol particle size and particle shape measurement system and method. The system comprises an image acquisition module, an aerosol microscopic imaging device, a system parameter calibration module, a particulate matter acquisition module, a particle size calculation module and a particle shape calculation module. The aerosol microscopic imaging device obtains contour information of aerosol based on a transmission-type microscopic imaging method; the image acquisition module acquires an aerosol particle image through the aerosol microscopic imaging device, and draws a corresponding gray value on each pixel point of the image to obtain a complete aerosol particle image; the system parameter calibration module is used for calibrating system parameters; the particulate matter acquisition module acquires particulate matters of the aerosol particle image; the particle size calculation module calculates the aerosol particle size; the particle shape calculation module calculates the aerosol particle shape. According to the invention, the problems of small depth of field, repeated particle calculation, low calculation efficiency and the like in aerosol optical microscopic imaging are solved, and high-precision and high-efficiency measurement of the particle size and the particle shape of the aerosol is realized.