89results about How to "Improve legibility" patented technology

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention discloses a corrugated pipe inner wall multi-type flaw detection method based on deep learning, and the method comprises the following steps: S1, collecting an initial corrugated pipe inner wall image and corrugated pipe inner wall images in different illumination environments, and generating an original image; s2, preprocessing the original image to generate a preprocessed image; s3, extracting surface geometric overall features of the inner wall of the corrugated pipe from the preprocessed image through a pre-trained ResNet101 network; s4, inputting the preprocessed image and the surface geometric overall features into an improved StyleGAN3 network to generate an enhanced image; s5, inputting the enhanced image into the multi-scale adaptive deep network, and outputting a defect detection result; and S6, performing post-processing on the flaw detection result, and outputting a final flaw detection result. According to the method, deep learning and geometric characteristic embedding technologies are adopted, so that the defects of the inner wall of the corrugated pipe are accurately detected, and the method has the advantages of high precision, high robustness and comprehensive adaptability.

The invention discloses a reservoir four-relation chart processing method, device and equipment and a medium, and relates to the field of oil well exploration, and the method comprises the steps: building a logging information parameter processing interpretation model, and determining parameters in an oil saturation model; based on the neutron-density intersection chart, calculating the distances from the logging response parameters to different lithologic curves as lithologic indication parameters; drawing a reservoir four-relation chart by taking the porosity of the stratum as an abscissa, the resistivity of the stratum as an ordinate and the lithology indication parameters as color mapping, drawing different water saturation scale marks in the reservoir four-relation chart, and in addition, taking the water saturation or the movable hydrocarbon saturation in the reservoir four-relation chart as a bubble radius; and projecting the logging response parameters of various stratum types and the explaining stratum parameter points into the reservoir four-relation chart, and establishing a logging explaining standard. According to the method, the dynamic change and internal relation among the four properties of the reservoir can be comprehensively and accurately displayed.

The invention discloses a visual data construction and display method and system, electronic equipment and a medium, and relates to the technical field of data processing. The method comprises the steps of obtaining asset data of a target enterprise, and generating an asset hierarchical structure based on the asset data; combining the asset distribution layer, the asset association layer and the asset attribute layer to construct an annular visual interface of the target enterprise; in response to a display request of a user, a plurality of asset evaluation paths with different path widths are generated in the annular visual interface, and the path widths represent asset value proportions corresponding to the asset evaluation paths; obtaining the data distribution density of each asset evaluation path in the annular visual interface, and determining a display scale corresponding to each asset evaluation path according to each data distribution density; and displaying each asset evaluation path on an annular visual interface according to the corresponding display scale and path width. By implementing the technical scheme provided by the invention, the effect of improving the efficiency of constructing and displaying the visual data is achieved.

The invention discloses a human voice recognition method and system based on multi-person conference records, and relates to the technical field of human voice recognition, and the method comprises the steps: collecting multi-person conference audio signals through a microphone based on a multi-person conference; framing processing is carried out according to the audio signals, and the short-time energy and the zero crossing rate of the audio signals in the conference are calculated. According to the method, through filtering processing, background noise can be remarkably suppressed, the definition and the discernibility of voice signals are improved, voice activities are analyzed through framing processing, short-time energy calculation and zero crossing rate calculation on audio signals, the accuracy and the efficiency of voice recognition and processing in conference records can be remarkably improved, and the voice recognition and processing efficiency is improved. Time domain signals can be accurately converted to a frequency domain through FFT, basic attributes of voice signals are accurately captured, overlapped voices of the voice signals are further recognized and classified in combination with data collection of a multi-microphone array, conference recognition accuracy is improved, and semantic features are combined.

The invention discloses a polarization degree and light intensity image fusion method based on bidirectional cross attention. The method belongs to the technical field of polarization imaging, computational imaging and image processing. The technical problems that in the prior art, dynamic balance between structural detail keeping and polarization saliency enhancing is generally difficult to achieve, and particularly under the condition of a complex background or a low signal-to-noise ratio, texture missing or noise artifacts are prone to occurring in a fusion result are solved. A fusion mechanism capable of establishing a two-way information interaction relationship between a light intensity image and a polarization degree image is provided, and further processing is carried out aiming at a noise problem, so that more stable and more reliable polarization fusion imaging is realized.

The invention discloses a low-sodium myofibrillar protein flavor prediction method and a low-sodium myofibrillar protein flavor prediction device. The method comprises the following steps: firstly, constructing a protein-flavor interaction system containing myofibrillar protein, potassium chloride, kappa-carrageenan and representative aldehyde flavor compounds; then, by means of spectroscopy, rheometer combination, an electronic tongue and the like, protein conformational change, typical aldehyde molecule binding rate and taste features are represented in a multi-dimensional mode. And establishing a random forest prediction model, taking protein conformation parameters as input, and taking a flavor binding rate and effective taste intensity as output. The model can reveal cross correlation between protein conformations and functions and identify key conformation parameters that affect flavor. And in combination with quantification and prediction capabilities of the model, a conformation-flavor relationship can be systematically analyzed, a basis is provided for optimization of a low-sodium formula, flavor is enhanced while salty taste is increased and bitter taste is inhibited, and healthy and sustainable development of low-sodium meat products is promoted.

The invention discloses an automatic classification design model for surveying and mapping and geographic information achievements, and relates to the technical field of surveying and mapping and geographic information achievement classification. According to the automatic classification design model for the surveying and mapping and geographic information achievements, a terrain classification result is generated by comprehensively analyzing a terrain area to be measured based on surface and deep terrain features, and visual display is achieved through model marking. In combination with gradient, curvature, texture and undulation degree indexes of elevation data, an initial terrain evaluation index is calculated, and a comprehensive terrain evaluation index is generated by using deep rock-soil density, field intensity, energy characteristics and ecological hydrological data, so that the classification accuracy is remarkably improved, the defect of insufficient attention to deep features in a traditional method is overcome, and the method is suitable for large-scale popularization and application. Therefore, the complex characteristics of the terrain area can be comprehensively reflected, a reliable basis is provided for scientific planning, and particularly in a complex geological environment, the high matching degree of a terrain classification result and an actual application requirement is ensured.

The invention relates to the technical field of image processing, in particular to a quasipaa spinosa breeding growth monitoring method and system based on machine vision, and the method comprises the steps: obtaining a monitoring image; determining a quasipaa spinosa motion feature index of a reference region of the monitoring image of the current frame; determining a quasipaa spinosa reference area of the monitoring image of the current frame; determining the quasipaa spinosa enhanced feature information amount of each gray level of the monitoring image of the current frame; determining the gray level mapping relation of the gray level of the monitoring image of the current frame in the histogram equalization process; and performing image enhancement on the monitoring image based on the gray level mapping relation of the gray levels to obtain a target image, and monitoring the growth state of the quasipaa spinosa by using the target image. According to the method, the distinguishability between the quasipaa spinosa and the stone is improved, and the enhancement effect of the image is improved, so that the identification accuracy of the quasipaa spinosa in the image is improved, and the comprehensiveness and accuracy of evaluation of quasipaa spinosa breeding growth monitoring are further improved.

This invention discloses a method and system for simulating and dynamically updating a seepage field throughout the entire process. The invention divides the entire process into several stages using an engineering event table, updates the geometric state, boundary conditions, and source / sink terms of the seepage numerical calculation model according to each stage, and performs unsteady seepage forward modeling. The current stage uses the seepage field state at the end of the previous stage as the initial condition, and the initial values ​​of the parameters to be inverted are inherited from the optimal parameters calibrated in the previous stage. An objective function is constructed based on head and leakage observation data, and an adaptive trust region quasi-Newton optimization method is used for dynamic parameter calibration. The contribution ratio of each supply source to the leakage is calculated using a source-by-source activation / superposition decomposition method. This invention achieves continuous simulation of the seepage field throughout the entire process, dynamic parameter calibration, and quantitative diagnosis of leakage sources, improving simulation accuracy and interpretability.

A method for assessing protein uptake from a subject's gut includes: a. providing a blood sample from the subject; b. substantially removing proteins exceeding a size limit of 40 kDa from the sample to provide a deproteinized sample; c. determining the level of free amino acids in a subsample of the deproteinized sample CF by colorimetry or fluorescence; d. hydrolyzing the subsample of the deproteinized sample to obtain a hydrolyzed sample, which converts any remaining polypeptides and peptides into free amino acids; e. determining the level of free amino acids in the hydrolyzed sample CT by colorimetry or fluorescence; f. optionally, calculating the level of peptide-derived amino acids in the blood sample CP, where CP = CT - CF; and g. assessing protein uptake from the subject's gut based on the values ​​of CF and CT, and optionally CP. Related applications include diagnostic and therapeutic methods.

The application relates to the technical field of motor assembly quality image detection, and discloses a visual-based motor wire harness connection part abnormality detection method and system, wherein the method comprises the following steps: acquiring a motor wire harness connection part image; constructing a connection part illumination state vector; performing adaptive brightness correction; suppressing reflection and pollution interference; determining an effective wire harness connection part area; and outputting a wire harness connection abnormality detection result. Compared with the method in the prior art which mainly relies on single detection confidence for judgment, especially under complex working conditions such as strong reflection of a metal shell, oil stain adhesion and wire harness shadow superposition, the technical problem that it is difficult to stably distinguish between real structure abnormality and illumination artifact interference is solved. Due to the introduction of adaptive brightness correction and the joint mechanism based on attention enhancement target detection and assembly geometric constraint, the accuracy of motor wire harness connection part abnormality detection is improved.

The application discloses a kind of Hourglass visual Transformer dual-path encoding and decoding network camouflage target detection methods, first, Hourglass visual Transformer (Hourglass-VIT) is used to extract low-level features and high-level features of multi-scale simultaneously;Second, the feature fusion of top-down and bottom-up is carried out to multi-scale feature map using dual-path feature pyramid decoder;Then, high-low interactive enhancement module is used to mine low-level and high-level feature correlation, and obtain multi-scale correlation enhanced feature map;Finally, the prediction map of camouflage target is generated by simultaneously inputting multi-scale correlation enhanced feature into network detection head, and the detection of camouflage target in image is realized.The method of the application can significantly improve the distinguishability and recognition ability of camouflage target in multi-class complex scene, especially for small target, target boundary detection effect is good.

The application discloses a dark underwater image processing method based on color space conversion, which comprises the following steps: firstly, pre-acquired underwater images are down-sampled to reduce the image size and copied into two images; secondly, color space conversion is performed on the two parts of underwater images respectively; thirdly, an underwater optical imaging image model is established, and the first converted underwater image is processed by using an image deblurring algorithm to obtain a clear image with high contrast; fourthly, an improved white balance algorithm is used to process the deblurred image; fifthly, a nonlinear channel prior is introduced into a traditional image deblurring model to minimize the item to eliminate the motion blur problem caused by water flow fluctuation; sixthly, the obtained accurate blur kernel and potential clear image are iterated with the initial restored image to obtain a final restored image; and finally, the two images are fused, re-sampled and outputted to obtain a final clear underwater image. The application can better realize image restoration, improve the brightness and contrast of the image and improve the recognition degree.

The invention relates to the technical field of industrial robot load identification, in particular to a six-degree-of-freedom robot end load parameter identification method, which comprises the following steps: establishing a six-degree-of-freedom series robot dynamical model, reconstructing a load minimum parameter set, and determining that identification can be completed only by exciting a third joint, a fifth joint and a sixth joint. Designing four groups of symmetric excitation trajectories combined by Fourier series and quintic polynomials, and performing discrete sampling to generate an observation matrix; optimizing a load parameter set and a regression matrix, and reducing the condition number of an observation matrix; a load minimum parameter set is obtained through difference calculation of the no-load identification result and the on-load identification result; and based on the parameter set, solving the mass, the mass center coordinate, the inertia moment and the inertia product of the load. The method solves the ill-conditioned problem of a regression matrix by reconstructing the minimum parameter set, reduces the number of excitation joints, does not need to depend on a robot body kinetic model, and has the advantages of high identification precision, high stability, low experiment complexity and high efficiency.

The present specification provides a point cloud processing method, an electronic device and a program product, acquiring a point cloud collected by a point cloud collection device and an image collected by an image collection device, wherein the collection range of the image matches the collection range of the point cloud. The first pixel position of the target object in the image is obtained, and according to the relative position relationship between the two collection devices, the target point cloud matched with the first pixel position is determined from the point cloud as the point cloud of the target object, thereby completing the process of labeling the target object in the point cloud. Since the image collected by the image collection device is more intuitive than the point cloud data and has higher recognizability, the image with the labeled target object is used to assist the point cloud in labeling the target object, which can effectively reduce the difficulty of direct labeling in the point cloud and improve the labeling accuracy and the accuracy of the high-precision map constructed.

The invention discloses a new energy station operation evaluation model equivalent modeling method, and belongs to the field of power systems. The problem that in the existing equivalent modeling process of the new energy station, the identifiability of data is too low is solved. The method comprises the steps of extracting initial variable indexes of a new energy unit; establishing a factor analysis model of the initial variable indexes; obtaining factor score data as a grouping index; performing similarity measurement on the grouping indexes by using correlation analysis and significance test, and obtaining a correlation relationship of the new energy units to complete group division; and calculating equivalent parameters based on a grouping result, and establishing a new energy station equivalent model. The method is mainly used for carrying out equivalent modeling on the new energy station.

The invention discloses a pipeline abnormal state recognition and positioning method and an inspection robot thereof, relates to the technical field of machine vision recognition, and can solve the problems of low recognition precision and high false detection and omission ratio of structural posture abnormity such as falling, inclination and hanging drooping of an underground coal mine pipeline in the prior art. The pipeline abnormal state identification and positioning method comprises the following steps: S1, constructing a global map of a coal mine tunnel, and establishing a coordinate corresponding relation between an image acquisition position and the global map; s2, acquiring a pipeline image in the roadway and position information corresponding to the pipeline image, and performing noise reduction processing on the pipeline image to obtain a to-be-identified image; s3, judging whether the pipeline is in an abnormal posture state or not, and outputting a pipeline abnormity recognition result; and S4, according to the position information corresponding to the pipeline abnormity identification result, completing the positioning of the abnormal pipeline.

This invention relates to the fields of medical data analysis and artificial intelligence, proposing a dual-latent variable decoupled data analysis method based on plasma Netrin-1 and clinical scale features. First, plasma Netrin-1 test data and clinical characteristics such as UPDRS scale scores and disease duration are acquired from the subjects. Batch effect correction is applied to the raw test values, and an input feature vector is constructed. Then, a variational autoencoder model is established. By setting orthogonal constraints, Netrin-1 regression constraints, and disease duration regression constraints, the latent space is structurally constrained, enabling the model to learn latent variables D representing disease progression and C representing adaptive changes during training. Based on this, an efficiency index η composed of latent variables C and D is calculated, and relevant trend indicator parameters are obtained by combining longitudinal follow-up data. This method can achieve decoupled representation of disease-related features and adaptive change features in multi-source clinical data, providing a new data analysis tool for statistical analysis and research of neurodegenerative disease-related data.

The invention discloses an ultrasonic treatment control system and method based on medical image data, and relates to the technical field of ultrasonic data management.The method comprises the steps that a template image of an examined object is established, and in the ultrasonic examination process of a patient, a target detection area is selected from the template image; extracting common features of the ultrasonic image of the certain ultrasonic inspection item, forming a feature reference image by using the common features, calculating a first evaluation coefficient of various ultrasonic probe states through a difference value of state retention time, and removing the common features from the current ultrasonic image to obtain a target image; the definition evaluation values of the target images of the ultrasonic probes in different states are evaluated, the proportion of the image definition evaluation values to the input energy serves as a second evaluation coefficient of the states of the ultrasonic probes, the states of the multiple ultrasonic probes are selected as reference states, and the states of the ultrasonic probes are pushed to related ultrasonic inspection operators.

The invention discloses a low-illumination image dynamic noise reduction method for a daytime effect night vision device based on image recognition, and the method comprises the following steps: obtaining a low-illumination image sequence, and determining a current frame as a target frame; performing noise level estimation and image recognition on the target frame to obtain a noise parameter and a recognition confidence map; generating an initial edge set based on the target frame, and determining an initial edge weight; performing semantic boundary constraint reconstruction on the initial edge set, and outputting a reconstructed edge set; performing Felzenszwalb graph segmentation on the reconstructed edge set, sorting the reconstructed edge set according to the reconstructed edge weight, taking edges in sequence according to the sorting sequence, and outputting a final region label graph and a segmented region set according to a merging criterion; determining a region-level noise reduction intensity parameter for each region, and generating a noise reduction control chart; and performing regional dynamic noise reduction processing on the target frame according to the noise reduction control chart to obtain a noise reduction output frame. According to the method, the target edge and detail fidelity is improved, and the excessive smoothing risk of cross-semantic boundaries is reduced.

The invention discloses a photochromic rare earth polymer as well as a preparation method and application thereof, belongs to the field of photochromic anti-counterfeiting material research, and particularly relates to a photochromic rare earth polymer as well as a preparation method and application thereof. The problems that an existing anti-counterfeiting technology is prone to being counterfeited, high in cost, difficult to visually identify and the like in practical application are solved. The preparation method of the photochromic rare earth polymer is carried out under mild conditions, is simple, rapid, energy-saving and efficient, is wide in raw material and reagent source and low in price, effectively reduces the overall manufacturing cost of an anti-counterfeiting technology, and is beneficial to large-scale production and application. The color generation rate of the photochromic rare earth polymer in a strong light environment is 1t; in one second, the color fading rate in a weak light environment is lt; the unique optical characteristics of the rare earth polymer material come from the molecular structure and the characteristics of rare earth elements, so that the anti-counterfeiting bar code prepared from the photochromic rare earth polymer has higher safety and is difficult to copy.

The invention discloses a data-driven foaming product foaming cavity root cause analysis method and system. The method comprises the following steps: firstly, acquiring original data of links such as premixing, preheating, material injection and foaming related to a cavity infrared detection link and a foaming process through a data acquisition module of a foaming product foaming cavity analysis system; secondly, preprocessing the data in an integrated processing module, and constructing a structured big data sample based on an equipment production timestamp; thirdly, constructing a mapping model from process characteristic parameters to infrared detection cavity results by utilizing a decision tree algorithm, realizing main functions of a model construction module, and achieving the purpose of predicting a foaming cavity state; and finally, on the basis of the established model mapping mechanism, providing process feature factor importance ranking and feature discrimination rules of different cavity states in a root cause analysis module. The system constructed by the invention is simple and light in weight, can quickly and effectively reduce the investment of manpower, material resources and financial resources of a random test under high-dimensional process characteristics, realizes foaming process test simulation and provides root cause analysis of different state categories of foaming cavities, thereby providing reference and support for a foaming technologist to improve the process.

The application discloses a kind of multi-instrument collaborative synthesis method and related equipment based on MATLAB.The method comprises the following steps: setting standard sampling frequency, and constructing time vector corresponding to note duration based on the standard sampling frequency, establishing the frequency mapping relationship of scale covering the preset sound range, and determining the target fundamental frequency based on the input pitch parameter;For at least two target instruments, respectively call the acoustic parameter configuration corresponding to the target instrument, generate the single-instrument synthesis signal of the target instrument based on the target fundamental frequency;The audio signals of each target instrument are aligned on the multi-track time axis, and the audio signals of each target instrument are mixed based on the alignment result to generate a multi-instrument collaborative synthesis audio signal.Various solutions on the market can solve the problem that the operation threshold is high, lacks a unified integrated framework, and restricts the industry innovation and upgrading.

The present application relates to the field of intelligent transportation systems and vehicle-mounted information processing technology, and discloses a device and method for processing and dynamically displaying vehicle graphic data, the device comprising an input module, an information receiving module, an information processing module, a graphic information module and a display control module; the method comprising the following steps: receiving data in real time, and applying a hybrid model preprocessing to the received data, using a composite neural network model to generate semantic tags of graphic content in real time based on the received data, using an adaptive machine learning model to dynamically identify specific scenarios and potential risks in the current driving environment based on the processed data; according to the identified scenarios and risks, selecting the most appropriate information from a preset graphic information library through an advanced decision algorithm for display, and by applying the device and method provided by the present invention, the operability and accuracy of the graphic information display are ensured, thereby improving driving safety and the effectiveness of information transmission.

The invention discloses a driving direction icon display method and device, a storage medium and a processor. When the method provided by the embodiment of the invention is executed, firstly, all roads on which the target vehicle can run and the directions of the roads are determined, and then the reference and non-reference driving directions of each road are determined according to the priority. And then determining a reference combination body which corresponds to the reference driving direction and has a clear identification feature as a target combination body, selecting driving direction icon elements corresponding to the reference driving direction and the non-reference driving direction from the combination body, and extracting and combining the driving direction icon elements to form a final driving direction icon combination. If the recommended driving direction exists on the road, relevant icon elements are displayed in an enhanced mode so as to improve identifiability. The driving direction icon elements contained in the reference assembly have clear identification characteristics, so that when the driving direction icon elements are combined into the final driving direction icon combination, shielding or confusion among the elements can be effectively avoided.

The application relates to a motorcycle and a muffler thereof, a catalyst deterioration identification method and a storage medium. The muffler of the motorcycle comprises a muffler body, a first catalyst, a front oxygen sensor and a rear oxygen sensor. An air inlet of the muffler body is provided with a pipeline for communicating with an exhaust port of an engine. The first catalyst is arranged on the pipeline. The front oxygen sensor is located upstream of the first catalyst. The rear oxygen sensor is located downstream of the first catalyst. The interval between the central axis of the front oxygen sensor and the central axis of the rear oxygen sensor is S, S is 350mm-480mm; the volume of the first catalyst is B, the displacement of the engine is V, and the volume of the pipeline region corresponding to the central axis of the front oxygen sensor and the central axis of the rear oxygen sensor is C, B is 0.8V-1.4V, and C is 1.1V-2.1V. If the first catalyst does not deteriorate, the rear oxygen electric signal curve is a straight line or substantially a straight line, that is, the rear oxygen electric signal has a small number of flip times. Therefore, the identifiable degree can be improved, and the judgment accuracy is high.

The home intelligent gateway provided by the invention comprises an upper shell; the lower shell is connected with the upper shell in a covering manner and forms a shell with the upper shell; the light guide part is arranged on the shell; the circuit board is arranged in the shell, and a light source component is arranged on the circuit board; the shading component is provided with a first light guide cavity, a light inlet of the first light guide cavity is coupled with the light source component, and the light guide component comprises a light guide body, a connecting part and a light inlet part; the connecting part is located on the inner side of the light guide body, and the connecting part is connected with the upper shell or the lower shell so as to fixedly connect the light guide component and the upper shell or fixedly connect the light guide component and the lower shell; one side of the light inlet part is connected with the light guide body, and the other side of the light inlet part is located at the light outlet from the first light guide cavity; the light guide body is embedded in the joint of the upper shell and the lower shell, and the side edge of the light guide body is used for extending from the outer side edge of the upper shell to the outer side edge of the lower shell. According to the household intelligent gateway provided by the invention, light can enter the light guide component conveniently, so that the light guide component can be arranged in the gateway conveniently.

This application provides a 3D Gaussian rendering optimization system and method based on CPU hierarchical splitting and GPU driving, relating to the field of 3D graphics rendering technology. This application divides the 3D Gaussian scene into multiple bounding boxes on the CPU side, determines the view distance level, protection level, and reconstruction cost level of each bounding box, and uses the highest of these three levels as the lower limit of the target level. The scheduling module adjusts the current rendering level frame by frame, determining the summary state, partial unfolding mode, or unfolded state based on screen error and GPU budget. On the GPU side, representative Gaussian points and unfolded 3D Gaussian points are uniformly sorted and rendered, and the frame rate and the total number of activated 3D Gaussian points are fed back. This reduces the amount of data submitted, takes into account the protection of key areas, achieves smooth hierarchical transitions, and forms a rendering feedback loop, improving the frame rate stability and resource utilization of real-time rendering.

This application relates to the field of sensor technology, specifically disclosing a photoelectric pipeline level sensor, comprising: a pipeline body; a housing structure mounted on one side of the pipeline body via connecting ribs; a PCBA board: an inner cavity matching the PCBA board is provided on one side of the housing structure, and the PCBA board is disposed within the inner cavity; an arc-shaped optical lens: disposed in the detection area of ​​the pipeline body, with the arc-shaped surface facing the interior of the pipeline body; a photoelectric emitting module and a photoelectric receiving module: mounted on one end of the PCBA board, corresponding to the detection area of ​​the pipeline body, and a telecommunications cable harness connected to the other end of the PCBA board. This application aims to solve the problems of traditional photoelectric pipeline level sensors in terms of light utilization efficiency and environmental adaptability, improving optical detection accuracy and enhancing environmental adaptability.