134 results about "Morphological analysis" patented technology

The invention provides a road bridge crack detection method and system, and the method comprises the steps: collecting a bridge surface multi-view image, and constructing a training data set containing crack feature labeling through quality screening and standardized labeling; preprocessing the image by using a multi-scale feature fused deep convolutional neural network and carrying out semantic segmentation, initially identifying a suspected crack region and generating a segmentation mask; and constructing a BeNNS proxy model based on the mask, and establishing a mapping relationship between the detection result and the bridge structure topology, the stress flow field and the service function chain so as to evaluate the result reliability. And inputting an evaluation result into a hybrid evaluation mechanism, performing online real-time detection and offline batch verification to optimize precision, and outputting a verified crack region. Finally, morphological analysis is conducted on the area, geometric parameters and danger levels of cracks are extracted and integrated to a bridge health monitoring system, a crack evolution tracking algorithm and an early warning mechanism are established, and dynamic tracking early warning is achieved. The problem of low detection precision in a complex environment can be solved.

The invention relates to the field of geographic information technology and natural resource management, and discloses an automatic analysis system for land utilization flow change in land change investigation. The system comprises an object-level spatiotemporal spectrum tree reconstruction and data initialization module, a topological potential energy field construction module based on evolution entropy, a multi-scale geometric difference extraction and morphological analysis module, a variable stiffness topological adsorption and decoupling module based on a stiffness ratio, and a flow damping filtering and event serialization module driven by semantic rules. The system constructs a topological potential energy field by using a historical state vector and semantic stiffness, performs one-way adsorption, adjustment or retention on a difference polygon according to relative stiffness, and cleans invalid traffic based on a damping matrix. According to the method, through physical field simulation and semantic logic verification, high-frequency disturbance misjudgment is effectively eliminated, pattern spot boundary correction is ensured to conform to the legal weight, and the authenticity and logic self-consistency of change survey data are improved.

The invention discloses a control method and system of a servo press, and relates to the field of press control, a force displacement curve is obtained in real time from a current punching period, a key feature vector of the force displacement curve is extracted, the key feature vector is compared with an ideal golden feature vector, and a multi-dimensional deviation vector is obtained. Particularly, a deviation decoupling method based on morphological analysis is introduced, and the total deviation can be accurately decomposed into a thermal drift compensation amount caused by the thermal effect of equipment and a material characteristic compensation coefficient caused by plate performance fluctuation by analyzing translation and zooming differences of a current curve and a gold curve in form. Therefore, independent and accurate compensation can be carried out for a physical source, the problems of information aliasing and compensation failure caused by the fact that disturbance sources cannot be distinguished in the prior art are effectively solved, intelligent and feed-forward type cooperative compensation for the next stamping period is achieved, and long-term stability and high precision of the production process are ensured.

The invention relates to the technical field of image analysis, in particular to a temporal bone and gallbladder sebaceous tumor detection method and system based on multi-modal medical image fusion. The method comprises the following steps: acquiring a multi-modal temporal bone and gallbladder sebaceous tumor image of a patient, carrying out multi-modal image time sequence space alignment, carrying out feature vector change rate fitting, and constructing a multi-time-point image feature vector map; performing multi-modal image voxel decomposition on the multi-time-point image feature vector map, and performing three-dimensional morphological analysis to generate a geometric morphological parameter set; carrying out potential pathological hierarchical structure mining based on the geometric morphology parameter set, carrying out semantic label labeling, and constructing a multi-modal lesion semantic labeling image; and carrying out multi-time-point image change analysis on the multi-modal lesion semantic marking image, and carrying out parallel lesion evolution state prediction so as to generate a cholangioma evolution state prediction map. According to the method, accurate positioning, effective segmentation and visualized display of the temporal bone and gallbladder sebaceous tumor focus are realized.

The invention relates to the technical field of industrial vision, in particular to an automobile part surface defect detection method and system based on machine vision, and the method comprises the steps: firstly loading a CAD three-dimensional model, and rendering an ideal fringe reflection map in a virtual environment; calculating an optical distortion correction matrix by comparing the phase distortion with the phase distortion of an actual initial reflection image, driving a programmable light source to project a compensation pattern, and generating a normalized reflection intensity image; signal object decoupling is achieved through multi-scale wavelet transform, and high-frequency components and low-frequency components are separated; homomorphic filtering is applied to the low-frequency component to construct a homogenized background model, the high-frequency component is reversely corrected, and a high-signal-to-noise-ratio defect signal image is output; generating a coarse segmentation mask by the high-frequency signal through an adaptive local threshold, generating a morphology anomaly mask by the low-frequency signal through Hessian matrix morphological analysis, and fusing to form a collaborative segmentation mask; and extracting multi-dimensional geometric attributes of connected domains in the masks, and inputting the multi-dimensional geometric attributes into a decision tree to realize accurate classification and confidence output of defects.

The invention relates to the technical field of data expansion, in particular to a malicious prompt data set expansion method based on Mongolian. According to the method, high-frequency roots and affixes in a Mongolian basic malicious prompt corpus and a universal corpus are extracted, morphological analysis is combined, targeted malicious prompt samples can be constructed, the diversity and authenticity of a Mongolian safety evaluation data set are enhanced, candidate malicious prompt samples are optimized by adopting a genetic algorithm, and the safety evaluation accuracy of the Mongolian safety evaluation data set is improved. According to the method, the expansion sample is enabled to better conform to syntax and semantic rules of the Mongolian, effectiveness of the attack sample in the Mongolian scene is ensured, the expansion data set is enhanced by using the antagonism generation strategy, robustness and antagonism of the data set are improved, attack behaviors possibly occurring in a real scene can be simulated, and the robustness and the antagonism of the data set are improved. Therefore, an effective sample generation tool is provided for safety evaluation in a Mongolian scene, and the anti-attack capability of the Mongolian model is improved.

An information processing method includes: obtaining, as text information, information related to communication by a person; performing emotion analysis on a plurality of words or phrases after breaking down the text information into the plurality of words or phrases by performing morphological analysis on the text information; and visualizing an analysis result of the emotion analysis according to each row and column of a matrix table by forming the matrix table by arranging, in a first direction, a plurality of emotional expression-related items that represent human emotions and arranging, in a second direction, an attribute category-related item that indicates an attribute of the person or an organization.

The application discloses a recruitment interview evaluation method and system based on artificial intelligence and a storage medium, relates to the technical field of recruitment interview evaluation, and solves the technical problem that the prior art only completes morphological analysis and pragmatic reasoning through voice recognition and corpus comparison, does not deeply mine dynamic characteristics of voice signals, and does not perform correlation analysis on personality descriptions in resumes and real-time emotional performances in interviews, so that the evaluation dimension is single, and it is difficult to comprehensively and objectively reflect the language expression ability, psychological state and personality matching degree of a job seeker; the application obtains voice data and emotional data of a job seeker; the voice data is preprocessed to obtain a formant sequence corresponding to the voice data; a voice evaluation coefficient is calculated based on the formant sequence; an emotional similarity is calculated based on the emotional data; an interview evaluation coefficient is calculated based on the voice evaluation coefficient and the emotional similarity; and whether the job seeker is qualified is judged based on the interview evaluation coefficient, so that the above technical problem is solved.

The invention relates to a water treatment system and method based on computer vision and multi-agent reinforcement learning. The water treatment system based on computer vision and multi-agent reinforcement learning comprises a multi-source sensing module used for synchronously acquiring a mixed solution image sequence of an A2O process aerobic tank and sensor data of each process unit; the feature extraction and fusion module is used for performing semantic segmentation and morphological analysis on the image sequence, extracting microbial visual feature parameters, and fusing the visual feature parameters with sensor data to generate a fusion state vector; the intelligent decision-making module is a module based on a multi-agent reinforcement learning model and is used for outputting a combined control action vector according to the fusion state vector; and the execution control module is used for adjusting the aeration rate, the internal reflux ratio and the external reflux ratio of the A2O process according to the combined control action vector. According to the water treatment system disclosed by the invention, a complex biochemical process is realized, closed-loop control of multi-target collaborative optimization is carried out, the treatment efficiency is improved, and the energy consumption is reduced.

The invention provides an alumen ustum form monitoring method based on optical detection, belongs to the technical field of water treatment, and solves the problems that impurities such as silt, organic matters, algae and the like and bubbles generated in a stirring process can be overlapped with alumen ustum in an image, so that a target cannot be accurately distinguished during image segmentation, and the image quality is influenced. Therefore, the technical problem of misjudgment of the form parameters of the alumen ustum is solved. An alumen ustum form monitoring method based on optical detection comprises the following steps: constructing an impurity and bubble optical characteristic database of a multispectral light source, scanning a three-dimensional space of a water sample to obtain optical signal data, carrying out multi-characteristic fusion analysis to extract optical characteristics of impurities and bubbles, and establishing a multispectral recognition model to classify and distinguish various substances. And finally, accurately segmenting the alumen ustum area and extracting morphological parameters of the alumen ustum area According to the method, various substances can be accurately distinguished, the alumen ustum recognition and morphological analysis accuracy is improved, reliable data support is provided for automatic control and optimization of a water treatment process, and water quality safety is guaranteed.

The invention provides a false tooth image intelligent identification method fusing defect morphological characteristics, and relates to the technical field of computer vision and medical image processing. The false tooth image intelligent identification method fusing defect morphological characteristics comprises the following steps: performing multi-scale morphological operation on a false tooth image to generate a multi-scale morphological characteristic graph; the morphological features and the deep learning features are fused, and a defect area is highlighted through an attention module; using a pre-training model and a data enhancement strategy to improve the small sample defect identification capability; and outputting a defect category probability and a positioning thermodynamic diagram. Through multi-scale morphological analysis, a feature fusion network and an attention mechanism, high-precision identification of the false tooth surface defects is realized. Experimental results show that the F1 value of the method on a public data set reaches 0.92, the method is remarkably superior to an existing method, and the method is suitable for automatic quality detection in the false tooth production process.

The invention discloses a copper-clad plate copper-clad quality detection method and system, and the method comprises the steps: applying an ultrasonic excitation signal with preset frequency and power to a to-be-detected copper-clad plate, and synchronously employing a high-speed camera to collect a dynamic micro-vibration response image sequence of the surface of the copper-clad plate under the excitation of ultrasonic waves; performing phase locking analysis on the dynamic micro-vibration response image sequence, and constructing a copper-clad plate dynamic response feature map; inputting the dynamic response feature map of the copper-clad plate into the trained defect identification model, and outputting a preliminary defect area distribution map; performing multi-scale morphological analysis and region connectivity verification on the preliminary defect region distribution map to generate an accurate positioning defect region map; and generating a comprehensive evaluation report of the copper coating quality according to a preset quality scoring rule on the basis of the accurately positioned defect area graph. According to the embodiment of the invention, the method can achieve the high-precision positioning and classification of the defects of the copper-clad plate, and provides an efficient and reliable automatic solution for the quality evaluation of the copper-clad plate.

The present application relates to the technical field of medical image processing, in particular to an image intelligent analysis system for corneal nerve fibers of dry eye patients, which comprises an image acquisition module, an image preprocessing module, a nerve fiber segmentation module, a nerve morphological analysis module based on differential geometry, a nerve regeneration monitoring module, a data management module and a clinical auxiliary decision module. The nerve morphological analysis module based on differential geometry is the core innovation, which contains a nerve fiber curvature analysis unit, a multi-scale differential invariant extraction unit and a nerve network topology structure quantification unit. The system introduces the theory of differential geometry, represents the nerve fiber through a parameterized curve and calculates the local curvature characteristics. A Gaussian scale space is constructed to extract multi-scale differential invariants. The graph structure representation of the nerve network is established to analyze the topological characteristics. The morphological characteristics of the corneal nerve fibers can be quantified comprehensively from the local curvature to the global topological structure, and the accurate detection of the slight changes of the nerve fibers is realized.

The invention relates to the technical field of industrial detection, in particular to a pin defect detection method, device and equipment and a medium. According to the method, based on pin target positioning, image segmentation is carried out on a pin image, form extraction is carried out on a pin area, an actual included angle between a central axis and a reference axis is calculated, whether the actual included angle is in a preset angle range is detected, and if the actual included angle is detected not in the preset angle range, the pin area is determined. And if so, determining that the corresponding pin has a bending defect. The method comprises the following steps: acquiring a pin image from an original image based on pin target positioning, segmenting to obtain a pin area, extracting morphological characteristics of the pin area to determine a central axis, determining a reference axis according to the pin image, and calculating an actual included angle between the reference axis and the pin image; and finally, determining whether the pin has a bending defect or not by judging whether the included angle is within a preset range or not. And performing morphological analysis on the pin image to determine an included angle between the central axis and the reference axis so as to complete defect detection of the pin.

The application discloses a kind of high-precision vector map automatic generation method, mainly according to laser point cloud intensity, geometric structure feature extraction lane marking, through ground filtering, ground, non-ground point cloud is segmented;Adopt block grating to convert three-dimensional point cloud into two-dimensional projection image;Using image filtering, edge extraction operator, dynamic adaptive threshold segmentation, morphological analysis and other processing extraction road marking pixel, based on point cloud image mapping relationship extraction marking point cloud;Using the point cloud contour matching method based on knowledge template library carries out marking classification.Based on deep learning convolutional neural network, road sign detection classification is carried out to road image, and the point cloud region of interest is established by image point cloud fusion;Using clustering screening candidate point cloud, based on principal component analysis carries out local projection, based on knowledge template library point cloud matching extraction traffic sign;Finally, based on GPS data, calibration, parameter fitting, vectorization, data association are carried out, and the construction of high-precision vector map is completed.

The invention relates to the technical field of laser-induced breakdown spectroscopy data processing, in particular to an LIBS spectrum processing method and device based on residual skewness estimation and a storage medium. The invention discloses an LIBS (Laser-induced Breakdown Spectroscopy) processing method based on residual skewness estimation. The method comprises the following steps: step S10, acquiring an observation signal of an LIBS; s20, on the basis of morphological analysis of the LIBS spectrum, constructing a spectrum model by using prior distribution of a Bayesian framework; step S30, based on the updated hidden variable observation signal, analyzing the updated hidden variable spectrum model by using variational inference to obtain iteration deviation and posterior analysis data; s40, judging whether the updating hidden variable iteration deviation meets an iteration termination requirement or not; if yes, iteration is stopped, and actual spectrum data is obtained according to the updated hidden variable posteriori analysis data; and if not, executing the step S20. According to the method, the spectrum model is analyzed by utilizing variational inference, the optimal convergence point is judged, effective separation of observation signals is realized, posterior analysis data is obtained, and the spectrum estimation precision and the calculation efficiency are greatly improved.

The application discloses a traditional Chinese medicinal material production quality analysis system and method based on machine vision, and belongs to the technical field of traditional Chinese medicinal material quality analysis, and the method specifically comprises the following steps: collecting images of traditional Chinese medicinal materials, and pre-processing the collected images of traditional Chinese medicinal materials; performing morphological analysis on the pre-processed images of traditional Chinese medicinal materials; analyzing the morphology of the traditional Chinese medicinal materials; analyzing the color, texture and deterioration of the pre-processed images of traditional Chinese medicinal materials; evaluating and analyzing the quality of the traditional Chinese medicinal materials; classifying the quality; storing the quality evaluation and analysis data of each batch of traditional Chinese medicinal materials into a database for quality tracing and analysis; the system can flexibly adapt to traditional Chinese medicinal materials of various morphologies, significantly reduces manual intervention, can detect potential quality defects, and improves the universality, reliability and accuracy of traditional Chinese medicinal material quality evaluation.

To provide devices, methods, and programs that assist users in using AI. [Solution] The AI ​​support device of the present invention comprises: a morphological analysis unit that performs morphological analysis on text information representing a command to be performed by the AI; keyword master data that stores pre-registered words, processing attribute codes corresponding to pre-determined processing attributes for each word, and pre-specified scores associated with the processing attribute codes; a keyword extraction unit that extracts words obtained by morphological analysis from the words stored in the keyword master data as keywords; a score calculation unit that calculates a total score by summing the scores for each processing attribute code; and a first decision unit that determines to proceed to the execution of the processing attribute corresponding to the processing attribute code with the highest total score obtained by the score calculation unit.

The present application relates to the technical field of corn breeding, in particular to a medium-high altitude corn shade-tolerant and disease-resistant nutrient variety breeding device and method, which comprises a material placing plate and a multispectral imaging module suspended above the material placing plate. The top surface of the material placing plate is provided with a plurality of material grooves in a dot matrix pattern for separately placing corn kernels. The multispectral imaging module is composed of a ring array of LED lamps, a visible light camera, a near-infrared sensor, and an ultraviolet excitation light source. The multispectral imaging module synchronously acquires RGB, NIR, and UV three-channel images of the kernels. Through the morphological analysis algorithm built in the image processing unit, the distribution coordinates of unqualified corn kernels are obtained. Then, the image processing unit activates the electromagnetic iron to make the connecting pipe, air hole, and air tube correspondingly communicate. At the same time, the air pump is started to supply air to each air tube, and then the identified corn kernels in the material grooves are blown out, thereby improving the accuracy and efficiency of screening shade-tolerant and disease-resistant nutrient variety kernels.

The invention discloses a fire point detection method and device based on thermal imaging and a medium, and the method comprises the steps: S1, monitoring data collection: collecting thermal imaging data of a monitoring region in real time through a thermal imaging device; s2, image preprocessing, including de-noising processing on the image and enhancement processing on the fire point; s3, fire point feature extraction: segmenting a temperature threshold, and preliminarily judging an area higher than the threshold as a suspected fire point area; shape and texture feature extraction is carried out, and shape features and texture features of the suspected fire point area are extracted; s4, fire point identification: performing morphological analysis on the preliminarily screened candidate areas, eliminating non-fire point interference, and further confirming fire points through shape and size features; and S5, fire point positioning and information output: mapping the identified fire point position to a three-dimensional map or a two-dimensional map based on map positioning, carrying out accurate marking by using latitude and longitude coordinate information, and outputting related information of the fire point.

The application discloses a copper-clad plate copper-clad quality detection method and system, and the method comprises the following steps: applying an ultrasonic excitation signal with a preset frequency and power to a to-be-detected copper-clad plate, and synchronously collecting a dynamic micro-vibration response image sequence of the copper-clad plate surface under ultrasonic excitation by using a high-speed camera; performing phase-locked analysis on the dynamic micro-vibration response image sequence, and constructing a copper-clad plate dynamic response feature map; inputting the copper-clad plate dynamic response feature map into a trained defect recognition model, and outputting a preliminary defect region distribution map; performing multi-scale morphological analysis and region connectivity verification on the preliminary defect region distribution map, and generating an accurate positioning defect region map; and generating a comprehensive evaluation report of the copper-clad quality based on the accurate positioning defect region map and according to a preset quality scoring rule. By using the embodiment of the application, high-precision positioning and classification of the copper-clad plate defects can be realized, and an efficient and reliable automatic solution is provided for copper-clad plate quality evaluation.

The invention is applicable to the field of in-vitro diagnostic equipment, and discloses a sample analyzer. The sample analyzer comprises a sample introduction device, a sample distribution device, a first visible component detection device, a second visible component detection device and a controller, wherein the first visible component detection device comprises a first microscopic imaging device. The controller is configured to at least output a kinetic analysis result of sperms in the semen sample according to an image obtained by executing a first shooting action on the semen sample by the first microscopic imaging device; and at least outputting a morphological analysis result of at least part of the visible components in the first body fluid sample according to detection data obtained by performing the visible component detection action on the first body fluid sample by the second visible component detection device. According to the invention, one sample analyzer can be used for carrying out visible component analysis on a semen sample and another type of body fluid sample, so that the purchase, use and maintenance cost of the sample analyzer for analyzing the semen sample can be reduced.

The invention discloses an airport bird identification and tracking method and system based on visual radar fusion, and the method comprises the steps: carrying out the three-dimensional positioning and morphological analysis of an airport airspace target through the cooperative work of a binocular stereo camera and a millimeter-wave radar; a background difference technology is adopted to extract candidate targets, and multi-dimensional descriptors are constructed through visual features such as morphological asymmetry and wing flapping frequency; performing distance and speed verification on a visual identification result by using micro-Doppler features of radar echoes, and determining a bird identification target through fusion error evaluation; an inter-frame image matching and shielding detection technology is implemented to generate a continuous trajectory sequence, and trajectory fluctuation parameters and periodic features are extracted to reflect flight dynamics; according to the method, identification inconsistency parameters are obtained through consistency checking of multi-frame identification results, bird target identification tracking results are output according to quality grading, and reliable information support is provided for airport airspace safety early warning and bird repelling decision making.

The invention is suitable for the technical field of stem cell grading, and provides a stem cell morphology automatic grading method and system based on a convolutional neural network, and the method comprises the steps: obtaining a preprocessed stem cell image; training a convolutional neural network model based on the marked stem cell image data set; a to-be-graded stem cell image is input into a trained convolutional neural network model, a grading result is automatically output and displayed, cell image features are automatically extracted through the convolutional neural network, a data enhancement technology is utilized to expand a training data set, an advanced optimization algorithm is adopted to carry out model training, and finally the grading accuracy of 95% or above is achieved. The method greatly improves the efficiency and accuracy of morphological analysis of stem cells, and shows huge value in the fields of medical research, clinical diagnosis, drug research and development and the like.

The invention relates to the technical field of track maintenance prediction, in particular to a track line maintenance prediction system based on big data analysis, which is provided with a data acquisition module, a data analysis module, a feature recognition module, a feature judgment module and a maintenance regulation and control module. Screening a feature monitoring track section, performing morphological analysis processing on a ballast bed section model of the feature monitoring track section through a feature recognition module, performing section state analysis on a ballast bed morphological representation image, and judging an abnormal tendency category of the feature monitoring track section through a feature judgment module; and a track line maintenance regulation and control mode is determined through the maintenance regulation and control module. According to the method, the track section with the abnormal tendency is rapidly screened, the track line maintenance method is adaptively adjusted according to the actual track bed condition of the track section, and the efficiency and reliability of a track line maintenance prediction system are improved.

A document search system that enables efficient document search regardless of the ability of a user is achieved. Document search is performed using a document search system in which database document data is stored. After first document data and second document data are input to the document search system, the document search system extracts a plurality of terms from the first document data. The extraction of the terms is performed using morphological analysis, for example. Next, the extracted terms are weighted on the basis of the second document data. For example, texts included in a document represented by the second document data are classified into first and second texts. Among the terms extracted from the first document data, the weight of the term included in the first text is set larger than the weights of the other terms. The classification of the texts can be performed in accordance with a rule basis or using machine learning. After that, the similarity of the database document data to the first document data is calculated on the basis of the weighted term.

The application discloses a kind of multi-focus view embryo image morphological analysis method, system and medium, it is related to image processing technical field, including: using the multi-focus view of isomeric fusion segmentation network to the input multiple focal plane embryo image is carried out multi-scale semantic feature extraction and fusion;The isomeric fusion segmentation network fuses Swin Transformer and U-Net structure, utilizes multi-head cross attention module to realize the joint modeling of global and local semantic features, and outputs embryo segmentation result;Wherein, the isomeric fusion segmentation network is by cross-image contrast learning strategy to carry out intra-class compactness constraint and inter-class dispersion constraint optimization, and curvature regularization loss function is introduced to carry out geometric prior constraint training and obtain;The method effectively enhances the analytical ability to weak contrast structure and the recognition accuracy of complex morphological characteristics.

The invention discloses a steel member welding process image analysis method and system, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a steel member welding seam image; the steel member welding seam image is preprocessed, a to-be-analyzed area is recognized, and the to-be-analyzed area comprises a welding seam main body area and a weld toe area at the junction of the welding seam main body area and base metal; performing multi-scale geometric morphology analysis on the to-be-analyzed area, and extracting geometric morphology features; performing texture feature quantitative analysis on the to-be-analyzed region, and extracting texture features; carrying out fusion processing on the geometrical morphology features and the texture features to obtain fusion features; and performing weld defect identification according to the fusion features to obtain defect positions and defect types. According to the method, the welding seam defects can be identified by combining the geometrical morphology characteristics and the texture characteristics, so that image analysis in the welding process is realized, and the accuracy and the product quality are improved.

An information processing apparatus outputs answer information corresponding to inquiry information that is input. The information processing apparatus includes a memory and circuitry. The memory is configured to store a plurality of databases each having at least a first field and a second field. The circuitry is configured to: perform morphological analysis on the inquiry information, to divide the inquiry information into morphemes; perform a first matching process based on the morphemes and the first field of each of the plurality of databases, to determine whether to adopt the database as an extraction source from which the answer information is to be extracted; and perform a second matching process based on the morphemes and the first field of the database, which is determined to be adopted as the extraction source, to output, as the answer information, data in the second field corresponding to data in the first field.

The application relates to the technical field of water quality monitoring, and specifically discloses a water body sample reservation environment multi-parameter monitoring system, which realizes adaptive filtering by using empirical mode decomposition and sample entropy analysis to generate an optimized time sequence by real-time collection of multi-dimensional parameters such as temperature, pH, dissolved oxygen, turbidity and conductivity; identifies pollution source characteristic components by dynamic principal component analysis and blind source separation; constructs a causal correlation network by using dynamic time warping and transfer entropy, and forms a multi-parameter collaborative response spectrum by combining with graph convolution topological feature extraction; realizes pollution type identification based on the spectrum by multi-scale morphological analysis and density clustering, generates an early warning report containing pollution level evaluation and trend prediction by using confidence weighted decision fusion; solves the technical problem of evidence distortion caused by sample state drift in the traditional sample reservation process, and realizes full-chain water quality monitoring from data collection to intelligent diagnosis.