35 results about "Polarized light imaging" patented technology

The invention discloses a directional detection method and system for a power supply shell based on a multi-source heterogeneous sensor, belongs to the field of material characteristic detection, and aims to solve the problems of single dimension, weak anti-interference performance, data isomerism and poor adaptability in traditional industrial detection. Through systematic innovation of multi-source sensor collaboration, dynamic mode control, cross-modal data fusion and self-learning optimization, a high-precision, high-efficiency and high-reliability solution is provided for the precision manufacturing field, and an optical, infrared and ultrasonic multi-modal collaborative detection framework is constructed; the optical unit realizes accurate capture of surface texture and morphology through polarized light imaging and 3D structured light scanning, the infrared thermal imaging unit analyzes the heat conduction characteristic of a material, and the ultrasonic array analyzes internal structure defects to form a surface-material-internal full-dimension detection capability; and nine types of defects such as scratches, pits, weld marks, bubbles, sink marks, material layering, stress cracking, thermal stress abnormity and material pollution are covered.

The invention discloses a magnetic material defect detection system based on image recognition, which adopts a first acquisition module, a first determination module, a second determination module, a second acquisition module and a third acquisition module, and is characterized in that the first acquisition module is used for performing multi-angle illumination control on the surface of a magnetic material by adopting a polarized light imaging device; mirror reflection generated by the strong light reflection characteristic is inhibited by adjusting the angle of a polaroid and the incident angle of a light source, original image data with uniform illumination distribution are obtained, and if it is detected that an overexposure area exists in an image, exposure parameters and the polarization angle are automatically adjusted to obtain a high-quality image suitable for follow-up processing; and the first determination module is used for performing multi-scale wavelet transform decomposition according to the acquired high-quality image, identifying a surface defect area by analyzing texture features and edge information in different frequency components, and judging that the surface defect area is a potential defect area if a wavelet coefficient in the high-frequency component exceeds a preset threshold value. The detection performance is obviously improved.

The invention belongs to the technical field of image processing, and particularly discloses an image digital processing method based on infrared polarized light imaging, which comprises the following steps: setting a polarization angle by considering the category of a target scene, and obtaining an infrared image and a polarization image; setting visual parameters used for distinguishing the target object from surrounding objects, and extracting multi-dimensional polarization information of the infrared image according to the difference of the visual parameters; first weighting information and second weighting information are generated through the multi-dimensional polarization information, so that the polarization image and the infrared image are fused, and a multi-modal image is obtained; identifying a target object in the multi-modal image, generating digital information of the target object, and processing the multi-modal image; the method has the following advantages: through multi-angle polarized light and infrared image acquisition, visual parameter difference analysis and two fusion processes, noise is effectively reduced and target features are highlighted in a complex environment, accurate identification, tracking and digital output of a target object are realized, and efficient real-time support is provided for emergency monitoring.

The invention discloses a semiconductor mechanical arm autonomous navigation and positioning method based on AI vision, and relates to the technical field of mechanical arms, and the method comprises the following specific steps: visual perception and multi-modal information fusion: collecting multi-modal data through configured industrial, depth, light field and polarized light cameras, pre-processing the multi-modal data, and carrying out multi-modal information fusion; realizing information fusion by using a deep learning network containing a feature extraction layer and a fusion layer and an attention mechanism, and outputting a unified visual description; according to the method, a deep learning target detection model is utilized, an attention mechanism is introduced to enhance the attention and extraction capability of small target features, the recognition result is optimized in combination with surface feature information recognized by polarized light imaging, the category and position of a target object can be accurately determined, meanwhile, a mechanical arm motion error model is established, and the recognition accuracy is improved. And the fused visual positioning information is fused with sensor data such as a mechanical arm joint encoder and a gyroscope by utilizing a sensor fusion technology, and a positioning result is optimized and compensated.

The invention provides a door and window glass defect feedback control method and device, electronic equipment and a storage medium, and relates to the technical field of door and window glass defect detection. The method comprises the steps of collecting a first image after a polarized light imaging detection device is adjusted to suppress reflected light on the surface of glass and enhance scattered light of bubbles in the glass; after the polarized light imaging detection device is adjusted to enhance reflected light on the glass surface, a second image is collected; calculating an adaptive weight group by analyzing local characteristics of the second image; weighting the first image according to the self-adaptive weight group, and subtracting the weighted first image from the second image to obtain a difference image; and extracting bubble defect information in the glass by analyzing the differential image. According to the method, interference caused by non-uniformity of glass surface reflection is overcome, the adaptability of image differential processing to surface reflection background suppression is improved, and therefore image features of internal bubble defects are extracted more effectively and more accurately.

The invention relates to the field of optical material defect detection, and discloses a PC lens defect classification and identification method and system, and the method comprises the following steps: asynchronously collecting lens multi-modal data through polarized light imaging, thermal imaging and an acoustic emission sensor, and constructing a five-order asymmetric tensor containing polarization angle, spectrum, time, space and defect features; performing dynamic dimension reduction on the tensor by using an improved Tucker decomposition method, and extracting a core tensor and a factor matrix; establishing a polarization angle-time correlation coding model based on inverse problem solution of a light field state equation, deducing defect distribution characteristics and optimizing a core tensor; mapping the optimized tensor to the QUBO Hamiltonian of a quantum annealing machine, and carrying out optimization solution; and finally, fusing the multi-mode confidence coefficients of polarized light, thermal imaging and acoustic emission signals, generating defect classification labels and inverting geometric and mechanical parameters of the defects. The lens flaw detection efficiency and the classification accuracy are remarkably improved, and the method is suitable for industrial-grade high-precision quality control scenes.

The invention relates to the technical field of bionic navigation orientation and positioning, in particular to a carrier course angle measuring method based on polarized light imaging in a non-Rayleigh atmosphere. Comprising the steps of real-time sky polarized light imaging, atmospheric polarization mode resolving based on Mueller matrix imaging, non-Rayleigh polarization information interference removal, polarization orientation resolving based on Rayleigh scattering, space projection conversion and sun position information resolving. Alignment information of the sun in a carrier coordinate system is obtained by utilizing a Rayleigh scattering model and actual sky polarization imaging, and then a carrier course angle is obtained according to the orientation of the sun in a local geographic coordinate system, so that an orientation function is realized; meanwhile, the error influence caused by image registration and time-sharing rotation measurement of the multi-view-field polarization detector and the interference of environmental factors on a single point are avoided, the method can adapt to sunny and cloudy weather, is also applicable to cloudy and other non-Rayleigh weather, and is wide in application range.

The invention provides a Faraday sheet performance measurement and polarization imaging system and method, and relates to the technical field of optical testing. The system comprises a light source system, an optical research platform, a polarizer, a first analyzer, a microscopic imaging system and an image acquisition system, the light source system is used for emitting laser; the optical research platform is used for arranging a to-be-measured Faraday sheet and generating a magnetic field; the microscopic imaging system comprises an eyepiece and an objective lens, and the image acquisition system comprises an image acquisition mechanism; in the first propagation direction of the laser, the light source system, the polarizer, the Faraday piece to be measured, the objective lens, the first polarization analyzer, the eyepiece and the image acquisition mechanism are sequentially arranged. According to the Faraday piece performance measurement and polarized light imaging system, the laser polarization state is accurately controlled, the polarization direction change is accurately detected, and the amplification and focusing functions of a microscopic imaging system are combined, so that the accuracy and comprehensiveness of the performance test of the Faraday piece adopting a magneto-optical film are improved, and the test cost is reduced.

This invention discloses a seed germination radicle stress detection device and method based on the principle of polarized light. The seed germination radicle stress detection device includes a culture module, a polarized light imaging system, an adjustment and driving mechanism, and a data processing unit. The culture module includes a transparent culture chamber and a seed fixing support set inside the transparent culture chamber. The advantages of this invention are: utilizing the birefringence effect generated by light passing through the radicle sheath, continuous observation of the entire process from the initial germination to radicle breakthrough can be achieved without contacting or damaging the sample; by acquiring images through a multi-angle rotating analyzer and combining it with a phase delay algorithm, weak birefringence signals can be effectively extracted, realizing quantitative calculation and visualization of stress; the core components are general-purpose optical elements and imaging modules, with a simple structure, controllable cost, and easy to popularize in agricultural research and teaching.

An optical displaying system includes a display screen; a first light splitting unit, wherein a light ray emitted by the display screen is converted into a first-type polarized light or a second-type polarized light after passing through the first light splitting unit; an imaging unit, wherein a light ray exiting from the first light splitting unit forms an enlarged image corresponding to a first focal length after passing through the imaging unit; a second light splitting unit; a first optical-path unit, wherein the first-type polarized light transmitted from the second light splitting unit forms an enlarged image corresponding to a second focal length, and is converted into the second-type polarized light, after passing through the first optical-path unit; and a second optical-path unit, wherein the second-type polarized light reflected from the second light splitting unit is converted into the first-type polarized light after passing through the second optical-path unit.

The invention provides a coating foreign matter defect online monitoring method based on polarized light imaging analysis, and relates to the technical field of imaging analysis, and the method comprises the following steps: determining each monitoring point position of a coating production line, and obtaining a coating process parameter table; carrying out parameter verification on the dual-optical-path polarized imaging module and the linear array camera according to a polarized imaging standard, and carrying out equipment initial configuration; drawing up a coating state association map based on the coating process parameter table, performing association analysis on the coating state association map, foreign matter defects and coating layer defects, and setting a defect identification matrix; and generating a morphological filtering script by combining coating surface imaging data, performing feature extraction on polarization state imaging data acquired by the double-light-path polarization imaging module, and triggering foreign matter defect classification and positioning marking according to a feature matching result. According to the method and the device, the technical problem of poor online monitoring accuracy of the coating foreign matter defect in polarized light imaging analysis in the prior art can be solved, and the technical effect of improving the online monitoring accuracy of the coating foreign matter defect is achieved.

To suppress an influence of a protective film.SOLUTION: An image processing device comprises: detection means 21 which detects a predetermined mark added to a protective film affixed to a lens of a photographing apparatus from an image acquired by the photographing apparatus; specifying means 22 which in a case where the image in which the mark is detected is a polarization photographing image acquired by polarization imaging, specifies the orientation of the protective film relative to the lens, based on a position where the mark in the polarization photographing image is detected; and adjustment means 25 which adjusts the polarization photographing image in accordance with the specified orientation, so as to reproduce an image photographed by the photographing apparatus without the protective film.SELECTED DRAWING: Figure 5

The invention relates to a method and a system for measuring strain of an ice sample in a uniaxial compression test process by using a crystal texture digital image. The method comprises the following steps: preparing the ice sample: preparing the ice sample according to a predetermined direction of the uniaxial compression test; polarized light imaging arrangement: placing the ice sample between a chassis and a pressure plate of a uniaxial compression testing machine; a cold light source with adjustable brightness is arranged behind the ice sample, and a polaroid is arranged between the cold light source and the ice sample; arranging a high-speed camera in front of the ice sample and installing a polarized lens to receive polarized light passing through the ice sample; adjusting image quality: enabling the length direction of the ice sample to be along the loading direction of a tester and the thickness direction to be along the light path direction of polarized light; uniaxial compression test and synchronous acquisition; calculating a strain field: calculating a strain field and a deformation field of the surface of the ice sample according to a digital image correlation method based on the polarized light grayscale images of the ice sample at adjacent moments; and identifying crack propagation.

The invention discloses a welding turbid environment vision sharpening system and method based on circularly polarized light imaging. In the system, an active circularly polarized lighting module comprises a laser, a beam expander, a linear polarizer and a first quarter-wave plate which are connected in sequence; the polarized image acquisition module comprises a second quarter-wave plate, an analysis mirror and a camera which are connected in sequence; the active circular polarization illumination module is located at the position of a welding workpiece, and the polarization image acquisition module is located at the position of a welding gun; and the image processing and control unit controls the on-off of the active circular polarization illumination module and the image acquisition of the polarization image acquisition module, and performs image recovery. According to the invention, the lighting source is innovatively replaced by the circularly polarized light from the traditional linearly polarized light, and a set of self-adaptive image recovery algorithm is combined, so that the imaging problem of the vision of the welding robot in a turbid and high-reflection environment is successfully solved, and reliable visual perception guarantee is provided for realizing high-precision and intelligent welding operation.

The invention relates to the technical field of image recognition and intelligent detection, in particular to a hydraulic power plant transparent medium leakage recognition method based on polarized light imaging, and the method comprises the following steps: S1, carrying out the multi-angle shooting of a target region of a hydraulic power plant through a polarization camera, and outputting original image data; s2, carrying out preprocessing, and outputting an optimized polarization image; s3, extracting polarization characteristic parameters including a polarization angle and a polarization degree; s4, comparing with a historical data threshold value, and outputting an abnormal feature region; s5, segmenting a potential leakage region, and outputting a segmentation result; and S6, verification is carried out in combination with the environmental parameters, and a final leakage identification result is output. According to the method, high-precision identification and interference suppression of the transparent medium leakage area are realized by fusing polarization feature extraction and an environment response verification mechanism, and the stability and reliability of a detection result are improved.

The invention relates to a photovoltaic cell panel invisible defect detection device based on polarized light imaging. The photovoltaic cell panel invisible defect detection device comprises a light source assembly and a detection assembly, wherein the light source assembly induces a photovoltaic cell panel to emit polarized light in a first direction; and the receiving assembly receives the polarized light in the first direction and performs imaging on the photovoltaic cell panel so as to detect the defects of the photovoltaic cell panel. According to the method, the invisible defects of the photovoltaic cell panel can be effectively detected in the sunlight environment, and the problem that the imaging quality of a traditional detection method is poor in the sunlight environment is solved.

The invention discloses a cutting path design system based on workpiece shape recognition, which relates to the field of cutting path design, and comprises a multi-mode optical imaging module for selecting and configuring optical settings based on an input workpiece type; the image processing module is used for expanding a dynamic range by applying HDR processing, reconstructing a background by adopting discrete cosine transform or wavelet transform, and reducing mirror reflection through polarized light imaging data to obtain de-noised image data; the shape recognition module is used for performing edge shape recognition by using a deep learning model to obtain workpiece edge shape data comprising a coordinate point sequence; the cutting path design module adopts a local point cloud processing strategy to obtain 3D shape data; and the feedback regulation module is used for generating a cutting path by adopting an optimization algorithm by taking the minimum cutting length as a target and outputting cutting path data. The method has the advantages that when the sub-pixel-level recognition precision is guaranteed, the path planning efficiency is improved, the cutting length is reduced, and the machining collision risk is avoided.

This invention provides a semiconductor wafer stress distribution measurement system based on polarization imaging, applicable to chip design and process optimization. The system includes a polarization light source module, a wafer processing module, a multi-channel optical system, a polarization phase processing module, a data acquisition and processing module, and a result visualization module. Its core innovation lies in applying differential geometry theory to polarization phase information processing. A polarization state manifold construction unit maps polarization state parameters onto a Poincaré sphere and calculates the local metric tensor on the manifold. A stress-phase mapping unit calculates the connection coefficient and curvature tensor to determine the stress distribution. A multi-scale analysis unit constructs the scale space of the polarization state manifold, enabling seamless analysis of stress distribution from micro to macro scales. This significantly improves stress measurement accuracy and spatial resolution, allowing simultaneous analysis of stress distribution at the micrometer and millimeter scales.

The invention provides a complex amplitude imaging system based on a bifocal liquid crystal lens, and the system comprises a to-be-measured target complex amplitude light field which is located near a left-handed or right-handed circularly polarized light imaging object plane; the bifocal liquid crystal lens comprises a lens layer for providing a propagation phase required by lens imaging and a liquid crystal layer for providing a geometric phase required by left-handed and right-handed circularly polarized light separation; and the polarized photoelectric imaging unit is used for synchronously detecting polarized light intensity distribution along four different linear polarization directions on an image plane after imaging through the bifocus liquid crystal lens, and calculating polarization coding information containing a Stokes parameter based on the measured polarized light intensity distribution. On the basis, the invention further provides a complex amplitude reconstruction method based on the physical driving neural network, and complex amplitude reconstruction is achieved by substituting captured Stokes parameters and a forward physical imaging model into the neural network. According to the invention, the contradiction among the time resolution, the device simplicity and the reconstruction fidelity of the existing complex amplitude imaging system is solved.

This invention provides a multimodal microscopic imaging integrated detection system, relating to the field of microscopic optical detection technology. It includes a bright-field microscopic imaging module sharing a tissue section sampling optical path assembly, and at least two of three modules: a polarized light microscopic imaging module, a fluorescence microscopic imaging module, and a micro-Raman spectroscopy detection module. The bright-field, polarized light, and fluorescence microscopic imaging modules share an imaging optical path assembly. The micro-Raman spectroscopy detection module includes a Raman spectrometer and a laser for irradiating tissue sections to excite Raman scattering light. The beneficial effect of this invention is that, based on bright-field microscopic imaging, it introduces multiple characteristic imaging or spectroscopic technologies such as polarized light imaging, fluorescence imaging, and Raman spectroscopy. Through multimodal information complementarity, it enriches the differential information between cancerous tissues and normal, inflammatory lesions, etc., significantly improving the speed and efficiency of pathological examination and enabling rapid and accurate intraoperative cancer diagnosis.

The invention relates to a doped TiO2 metasurface integrated optical system. The simulation modeling doping subsystem is used for realizing material directional design through preposed atomic-scale precise simulation, simulating and driving a modified titanium dioxide material to optimize a doping system, and outputting a refractive index dispersion curve and an extinction coefficient curve of a full visible light wave band; the metasurface integration subsystem selectively transmits a set phase incident ray in an original optical ray based on light polarization, and realizes the integration effect of multiple types of functions of polarization conversion, polarization selective reflection and aberration correction through three-dimensional parameter cooperation; dynamic compensation polarization crosstalk and phase deviation are complementary with static regulation and control, residual stray light is filtered, high-contrast vertical-direction linearly polarized light imaging is formed, and a complete optical system is formed; a system light path follows a light path complete process of polarization screening, integrated regulation and control, dynamic compensation and purification; the characteristics of high imaging quality and low loss are kept in a wide view field range; and the light path control precision and accuracy are improved.

The invention discloses an ultra-long focal depth vector polarized light imaging system and method for minimum luminous flux depth imaging, and relates to the field of optical imaging. Comprising a laser used for emitting exciting light; the vector polarized light generator is used for converting the exciting light into vector polarized light; the phase modulation element is used for performing phase modulation on the vector polarized light; the imaging module comprises a high numerical aperture objective lens and a silicone oil immersion objective lens and is used for performing high-precision positioning on fluorescent molecules in the biological tissue; and the detection module is used for receiving and detecting fluorescence signals emitted by the fluorescence molecules so as to realize positioning and imaging analysis of the fluorescence molecules. Through vector polarized light and phase modulation, the imaging focal depth is obviously expanded, and high-resolution imaging can be carried out in tissues with the depth exceeding 1 micron.

The application is suitable for the technical field of automatic detection and sorting of vegetables and fruits, and provides a kind of intelligent sorting system for vegetables and fruits, the system comprises: data acquisition module, for obtaining the collection image of the pitaya to be detected by polarized light imaging, and determining the initial abnormal index of the spot area in the collection image, while obtaining the surface properties of the specific area of the collection image;Image analysis module is used for analyzing the collection image obtained by polarized light imaging based on machine vision technology, and judging whether there is liquid drop phenomenon in the spot area, if there is, determine the corresponding liquid drop parameter.The application adopts deep learning and polarized light imaging technology, combines machine vision, and finely analyzes the spot area in the pitaya collection image, dynamically detects the influence of liquid drop on the reflection component of the spot, so as to realize the accurate correction of initial abnormal index.

The invention discloses a data line surface defect nondestructive detection method based on deep learning image recognition, relates to the technical field of defect nondestructive detection, and aims to solve the technical problems that defect evolution cannot be dynamically monitored and missed detection is caused by bimodal data dislocation in traditional detection. Comprising the following steps: S1, dynamic simulation and defect evolution monitoring: simulating a contact environment in an actual use scene of a data line, driving the data line to perform dynamic bending, monitoring form changes of the data line in different bending states in real time, and capturing a dynamic evolution process of a defect from recessive to dominant; and S2, bimodal cooperative imaging and cross-modal calibration: in the dynamic bending and defect evolution monitoring process, synchronously acquiring photoacoustic imaging data and polarized light imaging data of the data line. The method has the advantages that the hidden defect evolution process of the medical data line in an actual use scene is dynamically captured, and the hidden defect omission ratio of the medical data line is reduced.

The invention discloses a punch forming operation detection method and system based on industrial vision, and the method comprises the steps: triggering a polarized light source to irradiate the surface of a to-be-detected stamping part, and collecting an image of the to-be-detected stamping part at multiple angles through an industrial camera; carrying out image enhancement on the image by using a self-adaptive Retinex algorithm, and segmenting the enhanced image by using an Otsu dual-threshold method; carrying out morphological skeleton extraction on the segmented image, calculating an Hu invariant moment, and generating a feature vector containing seven dimensions; calculating a standard template; and based on the standard template, three-level matching strategy comparison is carried out, the stamping parts are sorted according to the comparison result, and the defect types of the unqualified stamping parts are recorded. Through polarized light imaging, morphological feature fusion and a dynamic compensation mechanism, stable detection without a neural network is realized.

The application discloses a super-long focal depth vector polarized light imaging system and method for minimum light flux depth imaging, and relates to the field of optical imaging. The system comprises a laser for emitting excitation light; a vector polarized light generator for converting the excitation light into vector polarized light; a phase modulation element for phase modulating the vector polarized light; an imaging module comprising a high numerical aperture objective lens and a silicon oil immersion objective lens for high-precision positioning of fluorescent molecules in biological tissues; and a detection module for receiving and detecting fluorescent signals emitted by the fluorescent molecules to realize positioning and imaging analysis of the fluorescent molecules. The application significantly expands the imaging focal depth through vector polarized light and phase modulation, and can realize high-resolution imaging in tissues with a depth of more than 1 micrometer.

The embodiment of the invention relates to the photovoltaic field, and provides a solar module junction box detection system, method and device, a product and a medium, and the system comprises an initial detection unit which is arranged at the front end of a corner protector, and shooting equipment which is used for detecting a first defect feature and offset of a solar module junction box; the fine detection unit is arranged at the double feed ports of the grading machine, comprises a coaxial polarized light imaging system and is used for detecting a second defect feature of the solar module junction box, and the defect degree represented by the second defect feature is smaller than that represented by the first defect feature; the production line linkage unit is connected with the assembly line and used for driving the grading machine to move out defective products after defect characteristics are detected; and the data processing unit is used for associating the detection results of the initial detection unit and the fine detection unit with the assembly serial number and feeding back the detection results to a process parameter database so as to adjust the process parameters. According to the invention, the problem of low detection rate in defect detection of the solar module junction box in the prior art is solved.

The application discloses a kind of magnetic material defect detection systems based on image recognition, using first acquisition module, first determination module, second determination module, second acquisition module and third acquisition module, first acquisition module is used to adopt polarized light imaging device to carry out multi-angle light illumination control to the surface of magnetic material, by adjusting polaroid angle and light source incidence angle, specular reflection generated by suppressing strong light characteristic, obtain the original image data with uniform illumination distribution, if it is detected that there is overexposure area in image, then automatically adjust exposure parameter and polarization angle, obtain the high-quality image suitable for subsequent processing;First determination module is used to carry out multi-scale wavelet transform decomposition according to the high-quality image obtained, identify surface defect area by analyzing the texture features and edge information in different frequency components, if wavelet coefficient exceeds preset threshold in high-frequency component, then it is judged as potential defect area.The detection performance of the application is significantly improved.

The invention is applicable to the technical field of automatic detection and sorting of vegetables and fruits, and provides an intelligent sorting system for vegetable and fruit goods, which comprises a data acquisition module used for acquiring an acquired image of pitaya to be detected through polarized light imaging, determining an initial abnormal index of a spot area in the acquired image, and sending the initial abnormal index to a server; meanwhile, acquiring surface attributes of a specific area of the acquired image; and the image analysis module is used for analyzing an acquired image obtained by polarized light imaging based on a machine vision technology, judging whether a liquid drop phenomenon exists in the spot region or not, and determining a corresponding liquid drop parameter if the liquid drop phenomenon exists in the spot region. According to the method, deep learning and polarized light imaging technologies are adopted, machine vision is combined, a spot area in a pitaya collection image is subjected to fine analysis, the influence of liquid drops on spot reflection components is dynamically detected, and therefore accurate correction of the initial abnormal index is achieved.

The invention discloses a lung cancer dynamic monitoring and chemotherapy toxicity early warning device based on a flexible electronic patch, which comprises a double-source noninvasive ctDNA enrichment and detection system for capturing exhaled gas exosome through a nano porous aerogel layer, inducing sweat secretion and enriching ctDNA through nano magnetic beads in combination with micro-current, and detecting the exhaled gas exosome. A methylation detection chip and a nanopore sequencing chip are adopted to realize multi-dimensional molecular characteristic analysis; the multi-modal toxicity monitoring collaborative network integrates a sweat impedance spectroscopy electrode, a fluorescence sensor, a flexible sonic sensor and a miniature polarized light imaging module, and evaluates renal function, lung rale and skin toxicity in real time; a dynamic drug regulation and control framework establishes an elastic dose algorithm based on drug-resistant mutation load, and continuous monitoring and real-time data transmission are realized through split type flexible circuit design. According to the equipment, the lung cancer detection specificity, the toxicity evaluation accuracy and the dose adjustment timeliness are remarkably improved, and the problems of single sample, monitoring lag, scheme rigidity and the like in the prior art are solved.