420results about How to "Achieve recognizability" patented technology

The invention discloses a building pipeline intelligent flaw detection and leakage positioning system, which relates to the technical field of building pipeline detection and comprises a data acquisition module, a multi-modal fusion module, a defect identification module, a leakage positioning module, a digital twinborn module, a health prediction module and a decision control module. The data acquisition module is used for deploying multiple types of sensors along a building pipeline, acquiring acoustic emission signals, infrared thermal imaging images, pressure fluctuation data and fluxgate detection signals in a running state of the pipeline, and outputting original sensing data; the multi-modal fusion module is connected with the data acquisition module and is used for performing feature extraction on the original sensing data, constructing a multi-modal feature vector, dynamically adjusting each modal weight based on environmental parameters and outputting a weighted fusion feature vector; and the defect identification module is connected with the multi-modal fusion module and is used for inputting the weighted fusion feature vector into a deep neural network model.

The invention discloses a cold-chain pork quality dynamic evaluation method and system based on multi-source data fusion, and relates to the technical field of data processing and intelligent evaluation, and the method comprises the following steps: collecting multi-source signal information of a whole cold-chain transportation process, sorting according to a unified time sequence, continuously arranging all signals on a time axis, and carrying out data processing on all the signals; a complete dynamic record is formed; and extracting each time interval and signal amplitude change track from the formed dynamic record, analyzing a signal section with repeated wave crests in a short time, and generating a preliminary data manuscript for reflection interference judgment. According to the invention, through time sequence arrangement and dynamic association of multi-source signals, a wave crest track analysis mechanism is constructed, real-time identification and correction of reflection interference are realized, and continuity and accuracy of a gas concentration curve are ensured; and by generating a reflection source position list and adjusting numerical value distribution of repeated fragments, false fluctuation is eliminated, and the stability and reliability of cold-chain pork quality evaluation are improved.

The invention relates to the field of electrical variable measurement, in particular to an insulating layer compactness detection method for a mineral cable, which comprises the following steps: firstly, acquiring a time domain reflection signal of the cable, and carrying out denoising pretreatment by utilizing synchronous average and wavelet transform; and then, carrying out spectrum analysis on the discrete signal segments, calculating a spectrum centroid offset rate to determine high-frequency energy attenuation, and constructing a dynamic compensation gain coefficient according to the high-frequency energy attenuation. And then, improving a traditional layer stripping algorithm by using the gain coefficient, eliminating calculation deviation caused by transmission loss, and accurately reconstructing characteristic impedance distribution along the cable. And finally, by comparing the deviation between the reconstructed impedance and the nominal impedance, positioning and judging the abnormities of the compactness of the insulating layer of the cable and the eccentricity defect of the cable core are realized.

The invention discloses a homeland space planning data intelligent analysis method based on big data, and relates to the technical field of homeland space planning, and the method comprises the steps: collecting multi-source data, carrying out the quality screening, and forming a standardized data set. And setting a space element extraction rule based on a specific planning target, and mining key space elements from the standardized data. Constructing a big data analysis model, inputting key elements to calculate and obtain a spatial development tendency index, and generating a preliminary planning scheme according to the spatial development tendency index; and performing compliance verification on the scheme, generating evaluation data, and adjusting the scheme according to the evaluation data. And finally, performing space efficiency deduction calculation on the adjusted scheme, optimizing the scheme according to deduction result data, and outputting a final planning scheme. According to the method, the accuracy of data mining is improved through target-driven element extraction, and the perspectiveness and scientificity of a planning scheme are enhanced through dynamic efficiency simulation.

The invention belongs to the technical field of e-commerce logistics management, and particularly relates to an e-commerce product storage full-process monitoring management method, which comprises the steps of synchronously acquiring multi-source heterogeneous monitoring data in real time through a sensing equipment network; performing space-time alignment preprocessing on the original data, and converting heterogeneous data into a uniform data format; semantic fusion is carried out on the standardized data, and a job behavior knowledge graph with a complete spatio-temporal context is constructed; inputting the atlas into a pre-training hybrid model, and detecting an inventory abnormal event; and generating a strategy instruction in combination with the real-time order and resource data, carrying out joint analysis on an operator, and outputting a compliance score and a specific violation operation type. Through adoption of the technical scheme, complete reconstruction of the context of the operation behavior can be realized, the anomaly detection accuracy is remarkably improved, the abnormal event response delay is shortened, and the inventory loss rate caused by human errors is reduced.

The invention discloses a hydroelectric generating set upper guide shoe temperature intelligent prediction method based on multi-source data fusion, and relates to the technical field of hydroelectric generating set state monitoring and intelligent early warning, and the method comprises the steps: collecting and preprocessing multi-source operation data of a hydroelectric generating set, and constructing a unified time sequence data set; according to a preset working condition zoning rule, the operation working condition type of the current hydroelectric generating set is judged, and a working condition label corresponding to the thermal state of the upper guide shoe is obtained; performing feature importance sorting on the multi-source fusion feature set to obtain an optimal feature subset; on the basis of the optimal feature subset, an upper guide shoe temperature integrated prediction model is established and trained; and predicting an upper guide shoe temperature prediction value, and judging whether a temperature abnormal rise risk exists or not. According to the method, high-precision prediction and real-time abnormal trend identification of the temperature of the guide shoe on the hydroelectric generating set are realized through working condition classification, feature optimization and integrated prediction strategies, and meanwhile, reliable data support and decision basis are provided for intelligent operation and maintenance.

The invention relates to an engineering potential safety hazard detection method based on a multi-modal large model. The method comprises the following steps: acquiring an on-site monitoring image of a target scene; identifying a key entity in the field monitoring image, and extracting positioning information of the key entity; generating a depth map based on the on-site monitoring image; based on the positioning information of the key entity, combining with the depth map, associating the positioning information and the depth information for the key entity, and generating a scene description text; based on the on-site monitoring image, the depth map corresponding to the on-site monitoring image and the scene description text, constructing a multi-modal input packet; inputting the multi-modal input packet into a learned engineering safety domain knowledge base and a visual language large model subjected to instruction fine tuning, and outputting a hidden danger judgment result; the instruction fine tuning of the visual language large model is based on a hidden danger inference chain data set, the data set has hidden danger images corresponding to hidden danger items in an engineering safety field knowledge base and annotation data of each hidden danger image, and the annotation data comprises step result data corresponding to each hidden danger detection step.

The invention provides a multi-band composite high-integration TR assembly. The multi-band composite high-integration TR assembly comprises a shell, a front multi-layer board, a back multi-layer board, a C / X / Ku band receiving and transmitting circuit, a C / X / Ku band single receiving circuit and a Ka band receiving and transmitting circuit, wherein the front multi-layer board and the back multi-layer board are connected in the shell, the C / X / Ku band receiving and transmitting circuit is connected with the front multi-layer board, and the Ka band receiving and transmitting circuit is connected with the back multi-layer board. The low-cost scheme design of an all-solid-state microwave multilayer board suitable for batch production and testing is adopted, ports and transmission line layout are reasonably designed, multi-band composite mode transmission is achieved in the form of the front face and the back face, a C / X / Ku-band transmitting circuit and an H-polarization receiving circuit are integrated on the front face, and the transmission efficiency is improved. A C / X / Ku frequency band V polarization receiving circuit and a Ka frequency band receiving and transmitting circuit are integrated on the reverse side, different functional circuits are respectively transmitted through a microstrip line and a strip line through an interface circuit structure covering 1-40 GHz and an in-board vertical transition structure, the isolation degree of different circuits is improved, mutual crosstalk between different signals is avoided, and the transmission efficiency is improved. And the functions of high integration, high consistency, ultra wide band, numerical control phase shift, numerical control attenuation and the like of the assembly are realized.

The invention provides a cross-border trade supply chain risk early warning system based on digital twinning, and relates to the technical field of cross-border trade. The cross-border trade supply chain risk early warning system based on digital twinning comprises an entity layer, a data acquisition layer, a digital twinning modeling layer, a data processing layer, a risk early warning layer, a safety protection layer and an application output layer, and the entity layer is a monitoring object basis of the system and covers all key physical objects in a cross-border trade supply chain; comprising suppliers, logistics carriers, storage facilities, customs nodes and purchasers in the cross-border trade supply chain. The system integrates a multi-source data acquisition module, covers full-dimensional data of policies, logistics, finance, markets, Internet of Things perception and the like, ensures the comprehensiveness of risk factor extraction, improves the coverage of risk identification, greatly improves the accuracy and real-time performance of early warning, and solves the problem that a traditional early warning mode is inaccurate and lagged in evaluation.

The invention provides an excrement sample microscopic image detection method and system based on morphological prior constraint, and the method comprises the steps: obtaining a global background region and a potential target candidate region through separation, so as to generate a standardized category morphological constraint parameter set; inputting the enhanced candidate region feature map into a preset category constraint double-branch instance segmentation network to generate an initial instance mask set corresponding to each independent visible component; performing closed-loop verification of mask integrity and category consistency on the initial instance mask set, performing iterative optimization correction on a mask region which is verified to be unqualified so as to generate an instance segmentation map, and synchronously performing spatial positioning and attribute marking on each independent mask region in the instance segmentation map; and based on an attribute marking result, performing multi-dimensional feature extraction and subdivision category identification on each independent mask region, and synchronously generating a corresponding excrement sample detection report. According to the invention, the precision, stability and complex scene adaptation capability of excrement microscopic detection can be greatly improved.

The invention relates to a water leakage detection method and device, a water purifier, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring historical water consumption data of a target water consumption area; determining normal water consumption parameters of the target water consumption area in any water consumption time period in the future according to the historical water consumption data; wherein the normal water consumption parameter is used for representing the water consumption condition of the target water consumption area when water leakage does not occur; acquiring real-time water consumption data of the target water consumption area; and detecting whether water leakage occurs or not according to the water consumption time period of the real-time water consumption data and the normal water consumption parameters corresponding to the water consumption time period. Through the technical scheme provided by the invention, the normal water consumption parameters for water leakage detection are combined with the historical water consumption data to determine and reflect the actual water consumption condition, the defects that a traditional detection scheme depends on a fixed threshold value and is difficult to adapt to complex and changeable water consumption scenes are overcome, and the accuracy of water leakage detection is improved.

The invention discloses an automobile part intelligent production control and quality early warning system based on an AI model. Comprising a data acquisition module, a production process monitoring module, an AI analysis engine module, an adaptive control module, a quality early warning module, an equipment collaborative scheduling module, a data governance and model training module, a user permission and audit module and a remote operation and maintenance and knowledge precipitation module. According to the system and the method, an artificial intelligence algorithm and an industrial automation control technology are fused, and deep perception, dynamic regulation and control and defect pre-recognition of a production process are realized. The prediction precision of the method is superior to that of a traditional statistical process control method in a plurality of actual production scenes.

The invention relates to the technical field of information management, in particular to a material digital management system and method based on a cloud platform. The method comprises the following steps: acquiring material data; distributing a material storage space by using the material data; receiving an operation message of the storage space, and pushing the operation message to an inventory database; creating a circulation record table and an inventory state table; using the operation message in the inventory database to update a circulation record table, and recording circulation data; automatically updating an inventory state table according to the circulation data so as to determine the material inventory capacity; when the material inventory capacity is lower than or equal to a preset safety inventory threshold value, management early warning information is generated; and when the material inventory capacity is higher than a preset safety inventory threshold value, updating the management safety field of the inventory state table. The material inventory is monitored in real time based on the information management technology, the replenishment information is intelligently generated, inventory dynamic management is achieved, and the material management efficiency is improved.

This invention relates to the field of electricity meter operation monitoring technology, specifically to an intelligent analysis system for electricity meter operation faults based on sensor monitoring. The system includes: an operation status acquisition module, which collects the voltage, current, active power, reactive power, and power factor of the electricity meter and obtains the operation status at each moment; a periodic analysis module, which uses active power as a load parameter and obtains a basic period based on the load parameters within a preset time period, then segments the preset time period to obtain different time periods and obtains the period fit at each moment; and a fault monitoring module, which obtains the comprehensive rationality of the load linkage deviation at each moment; and uses the comprehensive rationality of the load linkage deviation at a moment and the period fit to correct the squared Mahalanobis distance corresponding to the data points composed of voltage, current, reactive power, and power factor at that moment, and uses the corrected squared Mahalanobis distance for fault monitoring. This application can effectively monitor the operation faults of electricity meters.

The invention discloses an overload protection method for an audio power amplifier, and relates to the field of audio power amplifiers. According to the invention, the instantaneous output amplitude signal of the power amplifier is divided by the preset overload threshold impedance to obtain the reference current dynamically changing along with the signal, and the actual output current is compared with the reference current in real time, so that the protection criterion is converted from a fixed current threshold to an adaptive impedance threshold; therefore, real-time identification and turn-off of a low-impedance abnormal load are realized, and normal output of a high-peak-factor audio signal is compatible.

The invention discloses an anti-falling robot and an anti-falling method and equipment, relates to the field of intelligent robots, and is used for realizing cliff identification and falling protection in a full scene. Identifying a falling risk based on a detection result of the sensor module by at least detecting whether a cliff exists in a first preset range and a second preset range around the robot body; and controlling the motion state of the robot body based on the falling risk. According to the method, the cliff is synchronously detected through the near end and the far end, cliff identification and falling protection in a high-speed moving state and in a full scene where the cliff is initially located at the edge of the cliff or runs close to the edge are achieved at the same time, reliability and safety are high, system operation is smooth, and universality is high.

This invention discloses a lightweight method for detecting foreign objects (FOOs) in power transmission lines, relating to the fields of power line inspection and computer vision. A YOLOv10-based FEO detection model is constructed, comprising a feature extraction part, a feature interaction part, and a detection head part. The C2f module in the feature extraction and feature interaction parts is replaced with an expert dynamic extraction module, and the detection module in the detection head part is replaced with a parameter-sharing detection module. The trained FEO detection model is obtained by collecting and evaluating a calibrated FEO dataset of power transmission lines. FEO keyframes are input into the trained FEO detection model to obtain detection results. The expert dynamic extraction module acquires feature maps from multiple receptive fields, achieving rich representation in FEO detection environments with varying scales. The parameter-sharing detection module, based on this, reduces redundant computation and enhances the semantic representation of small targets through parameter sharing and grouping normalization, effectively improving the efficiency of FEO detection in power transmission lines.

The invention discloses a waste gas degradation treatment effect evaluation method and system, and relates to the technical field of waste gas treatment.The waste gas degradation treatment effect evaluation method comprises the steps that working condition parameters, pollutant concentration and reaction product spectral signals are synchronously collected in the operation process of a waste gas treatment device, and multi-source data are preprocessed to form a time sequence feature set; based on a time sequence feature set, carrying out feature modeling on the spectral signal, extracting main features of a product, comparing the main features with a standard fingerprint database, identifying an abnormal offset degree generated by a by-product, constructing a digital twin model by using working condition parameters, generating an ideal operation state, and calculating a deviation value between reality and ideal; the removal rate is calculated according to the pollutant concentration, the energy efficiency index is generated in combination with the energy consumption parameter, and the removal rate, the energy consumption index, the by-product deviation and the working condition deviation are integrated to form a multi-dimensional operation state vector. The controllability and response speed of the waste gas degradation treatment efficiency are effectively improved, and the intelligent adjusting capacity of the waste gas degradation process is remarkably enhanced.

The invention relates to the technical field of digital twinning simulation, in particular to a digital twinning-based temperature and humidity verification box multi-physics field real-time monitoring method, which comprises the following steps of: constructing a digital twinning grid index system, and solving a no-load flow field by using CFD (Computational Fluid Dynamics) simulation to obtain reference turbulent energy; obtaining geometric data of a to-be-detected device, mapping the to-be-detected device to the digital twin grid index system by using a voxelization algorithm, and calculating a thermal inertia correction coefficient; the flow field is iteratively solved based on the thermal inertia correction coefficient, a source item is generated by applying the virtual resistance density and the turbulent energy, and real-time corrected turbulent energy is obtained through calculation; calculating a reliability weight according to the ratio of the real-time corrected turbulent energy of the grid unit where the sensor is located to the reference turbulent energy, wherein the reliability weight is in negative correlation with the ratio; and weighting the monitoring data by using the reliability weight to realize closed-loop control. According to the method, the problem of monitoring misalignment caused by flow field topology change is effectively solved, and the monitoring accuracy is improved.

This invention belongs to the field of materials testing technology and discloses a deep learning-based method and system for identifying the interface of SiC modified asphalt. It acquires multi-dimensional interface images through a layered time-series acquisition method using four devices: optical microscope, SEM, EDS-Mapping, and Raman spectrometer. Adaptive algorithms are used for targeted denoising, normalization, and registration, while attention mechanisms and weighted fusion strategies are combined to enhance interface features. Simultaneously, a multi-condition dataset containing a high proportion of minor defects is constructed to achieve deep fusion and accurate identification of multi-modal features, eliminating subjective errors from manual judgment and improving the fine-grained accuracy and detection efficiency of SiC modified asphalt interface identification. The U-Net model is improved for specific scenarios and designed to be lightweight. By optimizing the encoder-decoder structure, introducing a dual-channel attention mechanism and a condition-adaptive branch, and combining a loss function, the feature extraction capability of interface boundaries and minor defects is enhanced.

The present application relates to the technical field of lighting control, in particular to a landscape lighting dynamic control method based on environment perception data, the present application collects the reflected light radiation of the lighting object at the preset observation position, and compares the reflected light radiation with the reference radiation to obtain the reflected light radiation deviation rate; the surface optical state of the lighting object is identified according to the time series change trend of the deviation rate, and the intermediate state hosting mode is entered during the state transition; after completing the surface optical state switching, the corresponding projection adjustment parameter is obtained by searching the state-projection geometric relationship database; at the same time, hierarchical power compensation control is performed based on the brightness contribution proportion of the near, middle and far layers to generate device control instructions. The method can improve the adaptability of landscape lighting to environmental changes, enhance the visual level coordination and lighting stability.

The invention discloses a visual prompting method and system oriented to interaction of an image and a real scene, and relates to the technical field of image interaction. The visual prompting method and system for interaction between the image and the real scene comprises the following steps: S1, preprocessing real-time sensing data and task interaction historical data; s2, constructing a spatial structure expression system, and partitioning complex image information; s3, performing multi-modal image feature fusion extraction analysis on the real-time sensing data, improving the image quality, and entering an obstacle recognition and feedback process; s4, based on priority optimization and feedback adjustment evaluation, according to a grading evaluation result, identifying and marking obstacles and feeding back intensity; and S5, carrying out space reachability and risk detection on task interaction historical data and real-time perception data, and carrying out visual linkage. The technical problem that an image recognition system in the prior art is difficult to effectively recognize invisible obstacles and provide accurate real-time feedback in a deep structure and a complex environment is solved.

The application provides a surface enhanced Raman imaging system applied to single cell metabolism detection, comprising a laser emission module, a microscopic Raman generation and imaging module, a Raman collection module and a central control module; the laser emission module generates a laser beam and is coupled to the microscopic Raman generation and imaging module; the laser beam emitted by the laser emission module is sequentially reflected by a Raman dichroic band edge filter, transmitted by an imaging dichroic band edge filter and reflected by a second reflector of the Raman generation and imaging module, and then focused by a microscope objective to a sample to be detected; backscattering Raman spectrum signals generated by the sample to be detected are converted into parallel light by the microscope objective, reflected by the second reflector, transmitted by the imaging dichroic band edge filter and the Raman dichroic band edge filter, and then output to the Raman collection module for spectral spectroscopy and detection; the central control module is connected with the laser emission module, the microscopic Raman generation and imaging module and the Raman collection module and performs data interaction.

The invention discloses a Kubernetes-based GPU fault automatic monitoring method and system, and relates to the technical field of computer monitoring, and the method comprises the steps: running a GPU detection program at each node; indexes are collected, wherein the indexes comprise XID and / or SXID error events in the system logs and the driving state and the equipment running state obtained based on the NVML; standardizing the indexes into an index data structure; generating a fault judgment result associated with the node identifier and the GPU equipment identifier according to a preset evaluation rule; and when the data is abnormal, the data is mapped as a Node Condition and / or reported to a Kubernetes Event, and a Prometheus acquisition interface is exposed to be pulled and stored. Through the technical scheme of the invention, GPU fault automatic identification and card level positioning are realized, cluster perceptibility and alarm traceability are enhanced, manual troubleshooting cost is reduced, and cluster stability is improved.

The application discloses a surface water abnormal sampling triggering method and system based on multi-source information fusion, and relates to the technical field of data processing.The method comprises the following steps: collecting surface water online monitoring, environmental working conditions, historical water quality and external early warning data, and constructing a multi-source associated data set; obtaining a standard data set after preprocessing; identifying data association abnormalities to generate a first abnormality mark, and combining with space-time comparison to identify implicit abnormalities to generate a second abnormality mark; constructing a digital twin model of a river basin water environment, and generating a third abnormality mark through residual analysis of real-time monitoring data and a dynamic water quality baseline; using a graph neural network to aggregate and analyze the three kinds of marks, identifying abnormal propagation paths and tracing directions, and outputting tracing results and comprehensive abnormal confidence; and generating a targeted sampling instruction to automatically trigger associated point abnormal sampling.The application fuses multi-source data and digital twinning, accurately identifies water quality abnormalities and targets tracing sampling, and improves monitoring efficiency.

The invention belongs to the technical field of electric digital data processing, and particularly relates to a PDC drill bit torsion impact signal collecting and processing method which comprises the steps that torsion data are segmented into a plurality of torsion data windows; determining the torsion waveform complexity of the torsion data window; determining the rotation speed slippage strength and the formation meshing complexity of the torsion data window; determining an adaptive order of an AR model of the torsion data window; and performing predictive filtering on the torsion data in the torsion data window to obtain a predictive residual sequence of the torsion data window, and extracting a torsion impact signal according to numerical characteristics of the predictive residual sequence. According to the method, the numerical characteristics of the torsion data and the rotating speed data are analyzed, the order of the AR model is self-adapted, the limitation of the AR model with the fixed order is overcome, background noise is filtered out, and the accuracy and robustness of the torsion impact signal are improved.

The application provides a three-dimensional point cloud change detection method based on Siamese AdaptConv, and belongs to the field of intelligent scene reconstruction; firstly, three-dimensional point cloud data of a to-be-detected scene at two different time points is acquired, and an adaptive neighborhood is constructed for each point; then, the pretreated three-dimensional point cloud data is input into a Siamese AdaptConv double-branch network, multi-scale features of each layer are fused respectively, corresponding fusion features are obtained, the change probability of the same position point is further calculated, and a candidate change point set is mapped; DBSCAN clustering is performed, and a candidate region is output; finally, semantic verification is performed on the candidate region, a geographical knowledge graph is pre-constructed, and semantic correlation is calculated; when the semantic correlation exceeds a set threshold value, it is considered that the three-dimensional point cloud change of the candidate region is consistent with the existing entity semantics in the knowledge graph, and the reasonable change is determined; and the application realizes end-to-end multi-sequential feature alignment and change identification.

The utility model belongs to the field of comprehensive detector, specifically is four in one comprehensive detector of distribution network patrol, the lower part of patrol instrument casing is provided with handheld handle, the front side of the casing of patrol instrument is provided with display screen, the left side of patrol instrument is provided with external ultrasonic sensor module, the back of patrol instrument is provided with infrared detector module, the top of patrol instrument is provided with air ultrasonic sensor module, the upper portion of patrol instrument is provided with fitting assembly, acoustic detection module is clamped and is connected with fitting assembly, and the utility model discloses an open type working environment adopts acoustic detection module detection, and closed type working environment adopts air ultrasonic sensor module detection, and the local discharge area will show as high energy bright spot or macule on the image, thereby realizes the intuitive positioning and identification of local discharge position, can judge whether the equipment inside exists local discharge and the severity of discharge through the analysis ultrasonic signal amplitude, frequency, phase and so on, and the advantage of air type ultrasonic detection is that the anti-electromagnetic interference ability is strong, is applicable to the environment of high pressure, strong magnetic field.

The invention discloses a laser scanning imaging detection system for a large belt conveyor, and relates to the technical field of laser scanning imaging, a laser scanning module is used for carrying out full-width laser scanning on the running surface of a conveyor belt, and a data acquisition module obtains continuous point cloud data frames and caches the continuous point cloud data frames to form a sliding point cloud sequence; the reference modeling module performs curved surface fitting on the point cloud sequence to establish a band body reference model; the defect positioning module identifies a local abnormal region based on the height deviation; the feature extraction module extracts multi-dimensional morphology parameters of the abnormal region to form defect candidate vectors; the defect identification module inputs the vector into a lightweight convolutional neural network, and outputs a defect type and a confidence score; the alarm output module triggers an alarm when the threshold value and the inter-frame continuity condition are met; the system has the characteristics of high detection precision, strong robustness and intelligent recognition, and is suitable for online detection of surface defects of the conveyor belt in a complex environment.

This invention discloses a GIS-based risk management system and method for pollutant migration in mining area watersheds, relating to the technical field of simulating pollutant migration patterns. It includes a 3D GIS functional module, a hydrodynamic-water quality model coupling module, and a mining watershed pollution risk management module. Based on watershed hydrology theory and pollutant migration and diffusion mechanisms, it fully considers the migration of pollutants in the storage area after rainfall via surface microrunoff. By studying the formation patterns of microrunoff, it simulates the watershed migration process of pollutants in the mining area under different rainfall intensities, thereby more accurately calculating pollutant migration and diffusion fluxes and managing the risk of pollutant migration in the watershed. Based on historical and real-time data and models, it uses a 3D model to display the spatial distribution patterns and development trends of pollutants, achieving accurate pollutant identification and source tracing. It can simulate pollution control measures in mining area watersheds, predict development trends, and provide early warnings for pollution events.