751 results about "Data adaptive" patented technology

The invention provides a vehicle-mounted display personalized regulation and control method and system based on deep learning, and relates to the technical field of vehicle-mounted display regulation and control, and the method comprises the steps: obtaining multi-dimensional data of a driver state, and adaptively determining personalized configuration parameters of a vehicle-mounted display interface; acquiring historical preference data of the driver from the cloud knowledge base, generating a vehicle-mounted display optimization strategy based on the driving scene and driver preference, and generating a personalized vehicle-mounted display interface according to the vehicle-mounted display optimization strategy; obtaining visual behaviors and man-machine interaction behaviors of a driver, performing continuous optimization on the state detection model and the personalized display rule by using a self-adaptive optimization algorithm according to subjective and objective feedback data of the personalized vehicle-mounted display interface, taking the subjective and objective feedback data of the driver as reward and punishment signals in the self-adaptive optimization algorithm, and outputting the reward and punishment signals to the personalized vehicle-mounted display interface. Weight parameters of the state detection model and personalized rules of the display template library are updated, and self-adaptive evolution of the vehicle-mounted display system is achieved.

The invention relates to the technical field of intelligent manufacturing, in particular to a rolling method, system and equipment in a full-closed mode and a storage medium, and the method comprises the steps that plate thickness and temperature data are collected in real time through a thickness measuring device integrated with a temperature sensor; predicting a future thickness change trend by using a temperature-sensitive deep learning model and combining historical working condition parameters; calculating a dynamic compensation gap sequence based on the prediction result, the target thickness and the compression roller characteristics, and driving a compression roller control system to adjust the gap; and feedback data after rolling are synchronously collected, model parameters are updated in a self-adaptive mode, and closed-loop control is formed. According to the full-closed mode rolling method, high-precision rolling is achieved through closed-loop control, and the quality and efficiency are improved.

The invention relates to the field of power data management, and discloses a power big data adaptive management method and system fused with spatial-temporal feature mapping, and the method comprises the steps: obtaining a real-time operation data flow from a multi-source power terminal, and constructing an original power data set with a time sequence label and a device identifier; the method comprises the following steps: dividing an original power data set into parallel processing units based on a storage-while-computing architecture, performing dynamic index updating by adopting an event-driven index mapping rule, and constructing a multi-dimensional data index cache system with real-time responsiveness; identifying a key abnormal trajectory through a time-varying feature nesting mechanism, and performing hierarchical measurement and entropy disturbance analysis on a data fluctuation degree in the key abnormal trajectory by using a streaming feature aggregation network; identifying potential security risk nodes in combination with the structure matching degree between the historical abnormal event evolution graph and the key abnormal trajectory; and generating a multi-level response instruction chain based on the risk assessment result. The method has the advantage of improving the operation safety of the power grid.

The invention provides a frequency difference compressor based on precision perception, a gradient compression method, equipment and a medium, which are used for carrying out data compression and transmission between a server and a client so as to reduce communication overhead in personalized federated learning, and relates to the technical field of data compression. The frequency difference compressor comprises an information bottleneck rarefaction unit, a frequency domain compression unit, a dynamic quantization unit and a differential coding unit. Non-key gradient redundant components of the original gradient data are removed through an information bottleneck rarefaction unit to generate sparse gradient data; generating a metadata packet containing the first N high-energy frequency domain coefficients and the positions and the number of the first N high-energy frequency domain coefficients through a frequency domain compression unit; performing adaptive bit width mapping on the high-precision floating point gradient data into low-order integer representation through a dynamic quantization unit to generate dynamic quantization gradient data; and the differential gradient is transmitted to the client through the differential coding unit, so that data compression and transmission are completed. The method solves the problems that the communication overhead is large and gradient information cannot be reserved as far as possible in gradient transmission.

The invention relates to the technical field of artificial intelligence and virtual reality, and discloses a virtual digital human generation and interaction optimization system based on multi-modal data fusion, and the system comprises a multi-modal input fusion module which is used for collecting and fusing multi-modal data from audio, video and touch; the adaptive behavior adjustment module is used for adaptively adjusting the behavior and response strategy of the virtual digital human according to the user input and the interaction history; the real-time dynamic rendering module is used for generating and rendering an image of the virtual digital human and synchronizing actions, expressions and language output of the virtual digital human in real time; the personalized customization interface module is used for providing personalized customization functions of the appearance, the sound and the behavior of the virtual digital human for the user; and the system integration and optimization module is used for integrating the modules. By fusing data (such as audio, video and touch) of multiple modes, the system can comprehensively understand behaviors and emotional states of the user, interaction delay is reduced, and the user can experience a smoother interaction process.

The invention relates to the technical field of database management, in particular to a test data management and analysis system, which comprises a data self-adaptive analysis module for receiving an RNA (Ribonucleic Acid) sequencing data set, analyzing connectivity of node features and edges in the data set, adjusting weights of nodes and edges, and updating a node feature processing mode and edge weight configuration. According to the method, the genetic map is updated in real time, and the weight configuration of the nodes and the edges is adjusted, so that the dynamic analysis capability of the data is enhanced, the data analysis is more in line with the complexity of biological change, the structural features in the data are allowed to be deeply identified by constructing and comparing the multi-scale topological fingerprints, and the analysis comprehensiveness and the prediction accuracy are improved; according to the method, time sequence data matching and parameter setting are optimized, it is ensured that an analysis result is close to real data, analysis quality and efficiency are improved, spatial and geometric topological features in biological data are extracted, the method is applied to training a neural network model, the learning speed is increased, and prediction accuracy and applicability of the model are improved.

The invention provides a campus direct drinking water energy consumption optimization method and system based on energy-saving control. The method comprises the following steps: collecting key data of campus direct drinking water to construct a water demand prediction model based on a multilayer long short-term memory network; according to a prediction result of the water demand prediction model, generating a dynamic equipment scheduling strategy and executing the dynamic equipment scheduling strategy on the campus direct drinking water equipment; monitoring the data after the dynamic equipment scheduling strategy is executed in real time, and calculating the power deviation between the dynamic equipment scheduling strategy and the actual power of the real-time monitoring equipment; adaptively optimizing the corrected dynamic equipment scheduling strategy according to the actual feedback data for executing the corrected dynamic equipment scheduling strategy; and performing dynamic closed-loop feedback adjustment according to an equipment scheduling strategy in combination with power deviation and water consumption deviation in actual operation. The intelligent energy-saving system effectively overcomes the defects that in the prior art, equipment operation is not matched with the water demand, and water leakage is not detected in time, and intelligent energy-saving optimization of the campus direct drinking water system is achieved.

The invention discloses an electrical equipment state monitoring system and method based on the Internet of Things, and belongs to the field of the Internet of Things, and the method comprises the following steps: installing an intelligent sensor node with a built-in edge calculation and self-diagnosis function, and carrying out the self-adaptive adjustment of frequency collection data according to the working state and historical data of electrical equipment; an edge server is added between the gateway device and the cloud server, and hierarchical data analysis is carried out; training the historical data of the electrical equipment by using a machine learning algorithm, constructing an equipment health state model, and performing fault prediction and health assessment; an early warning condition value is set according to the prediction result, and the intelligent alarm system carries out fault processing according to the early warning condition value. The maintenance cost can be reduced, and the efficiency and accuracy of maintenance work can be improved.

The invention relates to the technical field of unmanned aerial vehicle cooperative control, and discloses a geological surveying and mapping method and system based on an unmanned aerial vehicle and a storage medium. The method comprises the following steps: processing real-time wind field data and a compensation control instruction set to obtain a wind field compensation model, and optimizing the wind field compensation model to obtain adaptive route data; analyzing and processing the battery state data, the adaptive route data and the task completion degree data of the unmanned aerial vehicle through an energy consumption prediction model to obtain task optimization parameters, and dynamically distributing and processing the task optimization parameters to obtain a task execution instruction set; and processing the state information and the task execution instruction set of the multiple unmanned aerial vehicles to obtain cooperative control data, and performing real-time calculation processing on the cooperative control data to obtain a multi-vehicle cooperative control instruction set. According to the invention, by establishing a distributed cooperative control mechanism, intelligent task allocation and anti-collision control during multi-machine cooperative operation are realized, and the efficiency and reliability of geological surveying and mapping are remarkably improved.

The invention relates to a steel bridge disease detection and identification method based on a large language model, and belongs to the technical field of artificial intelligence and civil engineering crossing. According to the method, a cross-modal feature alignment mechanism is constructed through a pre-trained multi-modal large language model by fusing a steel bridge image and a field customized text prompt, and a cascade detection process of'component identification-disease classification-region segmentation 'is realized. Comprising the following steps: designing a structured text prompt word bank to enhance semantic consistency, and dynamically fusing general knowledge and instance features in combination with a mixed prompt mechanism; a multi-level cross-modal alignment strategy is adopted to generate an anomaly graph, and a disease area is accurately positioned; a visual prompt enhancement module is introduced to improve the multi-scale feature discrimination ability, and the robustness in a complex environment is adjusted and optimized through data self-adaption. Under the condition of few samples or even zero samples, high-sensitivity detection and pixel-level segmentation of steel bridge cracks, corrosion and other diseases are achieved, and the problems that a traditional method is low in efficiency, poor in generalization, high in labor cost and the like are effectively solved.

The invention discloses a high-frequency power data acquisition adaptive frequency and priority processing method and device based on resource load feedback, and a storage medium, and belongs to the technical field of high-frequency power data processing. The method comprises the following steps: acquiring resource load information of a distribution network side intelligent terminal node in real time; according to the real-time resource load information, judging whether a preset sampling period of the power monitoring data needs to be adjusted, if so, dynamically correcting through a double-layer fast and slow ring adjusting mechanism to obtain a final sampling period, and otherwise, maintaining an original period; acquiring power monitoring data acquired by the node in the corresponding sampling period; and on the basis of a preset priority queue grading rule, a tube queue algorithm is utilized to adopt a differential transmission strategy for the data, so that priority processing of different levels of data is realized. The method does not need to depend on a prediction model, and realizes acquisition link elastic control and key data delay guarantee through real-time quantification of node loads, dual-time-domain closed-loop adjustment of a sampling period and hierarchical queue management messages.

The invention belongs to the technical field of blast furnace system prediction and health management, and particularly discloses a blast furnace system residual life prediction method based on an adaptive calibration model. The method comprises the following steps: firstly, establishing an initial degradation model by utilizing partial observable blast furnace wall temperature data; estimating hyper-parameters in the degradation model based on maximum likelihood estimation, and designing a trigger mechanism based on a dynamic threshold to judge whether to perform adaptive calibration on parameters or structures of the degradation model; when the model is not triggered, updating the model parameters by adopting a Kalman filter; when the triggering condition is met, the model structure is calibrated based on the prediction error, parameters of the prediction error model are identified through a strong tracking filter and an expectation maximization algorithm, and the optimal structure of the prediction error model is determined through an akaike information criterion. According to the method, the parameters and the structure of the degradation model can be adaptively calibrated based on online data, the prediction precision of the residual life of the blast furnace system can be improved, and the online adaptability is high.

The invention relates to the technical field of air target prediction, in particular to an air target trend prediction method, which comprises the following steps: S1, multi-source heterogeneous data adaptive fusion filtering processing; s2, manifold learning is constructed in the high-dimensional spatial-temporal feature space; s3, performing semantic modeling on the dynamic behavior pattern recognition intention; and S4, multi-dimensional threat situation assessment warfare area modeling is carried out. According to the method, high-precision space-time synchronization and noise suppression of radar sensor data, infrared sensor data and other sensor data are achieved through the multi-source heterogeneous data self-adaptive fusion filtering technology, target micro-Doppler features are effectively reserved, missing data are repaired, and through the combination of third-order Savitzky-Golay differential filtering and short-time Fourier transform, high-precision space-time synchronization and noise suppression of radar sensor data and infrared sensor data are achieved. An 18-dimensional compression feature space containing kinematics and electromagnetic characteristics is constructed, a nonlinear topological relation is reserved through t-SNE and an automatic encoder, the signal-to-noise ratio and feature expression capacity of original data are remarkably improved through the function, and a high-precision and low-redundancy input basis is provided for follow-up behavior recognition and prediction.

The invention relates to the technical field of distributed storage, and particularly discloses a distributed storage data self-adaptive load balancing method and system, and the method comprises the steps: distributing data to each node in distributed storage based on a consistent Hash algorithm; key index values of all the nodes are collected in real time, whether the key index values of all the nodes exceed a first set threshold value or not is judged, or whether the response time efficiency of key data obtaining exceeds a second set threshold value or not is judged; if yes, evaluating and sorting the performance of each node in the distributed storage based on an entropy weight method and TOPSIS to obtain a sorting result; positioning nodes with better performance based on a sorting result; migrating the data or the key data under the nodes of which the key index values exceed a first set threshold value to the nodes with better performance; according to the method, intelligent migration of the data to the low-load node is realized, so that the problem of non-uniform data distribution is effectively improved, and the overall performance and reliability of the system are improved.

The invention discloses a multi-modal self-adaptive clamping system for assembling key components of a humanoid robot and a control method of the multi-modal self-adaptive clamping system, and the system comprises a multi-modal sensing module which is used for collecting assembling parameters; the edge calculation module is used for generating a control instruction by utilizing a multi-mode deep learning model according to the assembly parameters; the cloud service module is used for generating distillation data according to the assembly parameters; the self-adaptive clamping module is used for clamping and moving key components of the humanoid robot; and the programmable logic control module is used for controlling the self-adaptive clamping module to execute component assembling operation according to the control instruction and the distillation data. According to the invention, a multi-mode self-adaptive clamping system is realized, and the efficiency and the clamping stability are improved. The robot can be widely applied to the technical field of industrial robots.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line monitoring data transmission method based on Lora and 5G hybrid networking, and the method comprises the steps: determining a network access mode through a hybrid networking selection algorithm, and generating a communication link through dual-module networking configuration; original monitoring data are processed by using a data framing compression algorithm, and a path is selected and verified through dynamic routing optimization; the modulation format is adjusted through the space-time alignment data of the multi-modal data fusion model and the adaptive modulation technology; optimizing transmission by using an abnormal data filtering algorithm and a dynamic priority scheduling program; and feedback learning mechanism evaluation performance and updating algorithm parameters. According to the invention, network adaptability, data transmission efficiency and reliability are improved, data fusion processing is optimized, adaptive optimization of transmission performance is realized, operation and maintenance cost is reduced, and safe and stable operation of a power system is guaranteed.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses a three-dimensional space self-adaptive unmanned aerial vehicle accurate spraying method and system and an unmanned aerial vehicle. The method relates to the technical field of unmanned aerial vehicle spraying, and comprises the following steps: scanning a spraying area on the bottom surface of a viaduct through an unmanned aerial vehicle to obtain unmanned aerial vehicle spraying quality data, and analyzing, optimizing and adjusting the unmanned aerial vehicle spraying quality data. The method comprises the following steps: scanning a spraying area on the bottom surface of a viaduct through an unmanned aerial vehicle to obtain three-dimensional point cloud data, adaptively generating a flight path and a spraying track of the unmanned aerial vehicle based on the data, flying according to a planned path and spraying, collecting spraying quality data through a sensor, analyzing the spraying quality data, and determining the spraying quality of the viaduct. The dynamic liquid level monitoring error quantized value and the unmanned aerial vehicle precise spraying method stability quantized value are obtained and analyzed with threshold values in a database, the unmanned aerial vehicle precise spraying method is optimized and adjusted, the stability of the unmanned aerial vehicle precise spraying method is improved, and the problem that in the prior art, an unmanned aerial vehicle precise spraying method is insufficient in stability is solved.

The invention discloses a high-speed camera data self-adaptive two-stage caching system and method and a storage medium, and relates to the technical field of machine vision high-speed imaging and high-speed data storage, a preprocessing unit is used for performing deserialization and parallel connection, ROI cutting and gain adjustment on collected item number data, and writing incremental FrameID and cyclic redundancy check codes into each frame; the DDR annular cache unit comprises a monitoring module, a decision module and an execution module; the decision module is used for calculating an instantaneous cache demand Cnew and a dynamic cache occupancy U; the execution module expands and shrinks an annular buffer space in a single shot through a DDR4 controller, and outputs a Hi / Lo mark in real time through an occupancy rate comparator to drive an NVMe queue manager. According to the invention, on the premise of zero extra burden in a normal working condition, millisecond-level automatic capacity expansion and high-speed flood discharge can be realized in a sudden working condition, and real-time frame-level integrity verification can be completed on a local FPGA (Field Programmable Gate Array) side. The patent is subsidized by national key research and development plans, and the project number is 2023YFF0719700.

The invention relates to the technical field of data processing, discloses a novel spatio-temporal stream data distributed computing load balancing method and system, a terminal and a storage medium, and aims at dynamically adjusting task distribution of distributed computing nodes through distribution prediction of spatio-temporal stream data, effectively relieving the problem of data skew in a distributed system and improving the distributed computing efficiency. The method mainly comprises the following steps: 1, space-time stream data adaptive network division and distributed node mapping; 2, carrying out deep modeling and distribution prediction on spatio-temporal flow data features; and 3, deducing distributed computing resource demands and realizing load balancing. According to the method, future laws can be effectively mined from the space-time stream data, dynamic load balancing of the computing nodes is achieved, the data skew phenomenon is effectively relieved, and the space-time stream data computing processing efficiency is improved.

The invention provides a big data artificial intelligence AI comprehensive corrosion collection computing power platform method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting data through a distributed corrosion sensor, carrying out the space-time marking, and calculating a space-time correlation matrix to achieve the adaptive fusion of heterogeneous data; constructing a corrosion environment knowledge migration framework based on a graph neural network, and establishing cross-environment knowledge migration; and inputting the fused data into the prediction model to obtain a corrosion state prediction result. According to the method, efficient fusion and accurate prediction of corrosion data are realized, and the cross-environment corrosion monitoring precision is improved.

The invention discloses a vehicle test data feature extraction method and system, and belongs to the field of vehicle test data processing, and the method comprises the steps: sampling a preset feature extraction strategy to extract time-frequency features from intermediate test data, and constructing cross features according to the time-frequency features; the preset feature extraction strategy comprises the step of extracting time domain features from the intermediate test data by using a first sliding window, and the window size of the first sliding window is adaptively adjusted according to the extracted data; after the time-frequency features and the cross features are scored through multiple preset evaluation methods, weighted summation is carried out on all scoring results, and a comprehensive score of each feature is obtained; based on the scene to which the features belong, adaptively adjusting the weight during weighted summation; and removing low-score features of which the comprehensive scores are lower than a preset score threshold to obtain high-score features, and performing dimension reduction on the high-score features to obtain optimized features. The accuracy of feature extraction is improved by dynamically adjusting the feature extraction window and the scoring standard.

The invention discloses a flexible surgical instrument-oriented multi-source sensing data adaptive fusion method and a related device. The method comprises the following steps: acquiring optical, mechanical and pose multi-mode sensing data based on a timestamp synchronization technology in a working environment; clock compensation and space coordinate transformation based on an instrument kinematics model are carried out on the multi-modal data, and space-time registration is completed; inputting the registered data into a neural radiation field model to generate continuous space implicit scene representation; inputting the implicit representation into a pre-trained RWKV fusion network to obtain a fusion feature; and superposing the fusion feature and the original low-frequency component through residual connection, and integrating to generate three-dimensional voxelization environment state data. According to the method, multi-source information high-fidelity fusion is achieved through a single path, data redundancy is remarkably reduced, and the real-time performance and precision of intra-operative environment perception are improved.

The invention discloses a traffic monitoring video rapid target extraction method for edge equipment, and relates to the technical field of intelligent traffic video processing and edge calculation target detection, and the method comprises the steps: carrying out the adaptive downsampling processing of original video frame data, and generating downsampling video frame data; extracting a foreground target candidate region, and constructing a traffic region-of-interest mask in combination with a lane line detection result; carrying out pixel AND operation on the traffic region-of-interest mask and the foreground target candidate region to generate accurate candidate target region data, and extracting a target feature vector; carrying out weighted fusion on the target feature vector through a lightweight attention mechanism, generating a fusion feature descriptor, and calculating a target confidence score; and carrying out screening and duplicate removal processing on the accurate candidate target area data, and outputting traffic target extraction result data. According to the invention, the target in the traffic video can be rapidly and accurately extracted and processed in a low-delay manner on the edge equipment with limited computing resources.

The invention belongs to the technical field of low-altitude navigation safety monitoring, and particularly relates to a low-altitude navigation multi-source heterogeneous data adaptive fusion method for multiple scenes, which is a low-altitude navigation multi-source heterogeneous data adaptive fusion method for multiple scenes such as urban air traffic, low-altitude logistics and emergency rescue. Efficient integration of multiple types of monitoring data and position output of the trusted aircraft can be achieved, and accurate data support is provided for low-altitude navigation anomaly recognition and risk deduction. The method comprises the following specific steps: constructing a low-altitude navigation scene classification system and fusion demand mapping, collecting and preprocessing multi-source heterogeneous data, quantifying data credibility and resolving conflicts, constructing a hierarchical adaptive fusion framework and outputting a fusion result. According to the method, efficient and accurate fusion of multi-source data in different scenes is realized by constructing a layered scene adaptation framework and a credibility fusion model, and finally, a high-credibility aircraft position is output, so that reliable data support is provided for low-altitude navigation anomaly recognition and risk management and control.

The invention relates to the technical field of motor control, and discloses a low-noise flux linkage adaptive feedback control system of a high-speed motor. The system comprises an acquisition module used for acquiring current, voltage and vibration data of a high-speed motor at various operation rotating speeds to obtain operation parameters and noise characteristic data of the high-speed motor; the transformation processing module is used for performing frequency domain orthogonal transformation processing on the operation parameters and the noise characteristic data of the high-speed motor to obtain flux linkage real-time observation data; the self-adaptive analysis module is used for executing multi-mode working condition self-adaptive analysis according to the flux linkage real-time observation data and generating flux linkage control reference values of different rotating speed intervals; and the output module is used for inputting the flux linkage control reference values of different rotating speed intervals into a magnetic-machine coupling control algorithm, and calculating and outputting a motor low-noise driving control signal. According to the method, the problem of magnetic flux path distortion under high-speed operation is effectively solved, and downtime and efficiency loss caused by a traditional off-line calibration method can be avoided.

The invention discloses an RFID robot tag identification optimization method and device for a dynamic scene, and the method comprises the steps: an RFID robot moves based on a preset identification path, a reader identifies tags and obtains tag information in real time, and the reader is disposed on the RFID robot and is used for reading all tags on the identification path; based on the state evaluation model, the real-time state of the RFID robot is given in combination with the label information; in combination with the real-time state of the RFID robot, a self-adaptive control strategy is adopted, and tag reading parameters of the RFID robot are adjusted; and the tag reading parameters of the RFID robot are analyzed, and the RFID robot is adjusted, so that optimization of tag identification of the RFID robot is completed. Through combination of the state evaluation model and the adaptive control strategy, the state data of the RFID system are received and analyzed in real time, and the adaptive control strategy can output an optimal parameter adjustment scheme, so that the accuracy and the real-time performance of data reading are improved while it is ensured that the RFID system always keeps efficient operation.

The invention belongs to the field of robot and computer vision, and particularly relates to a visual SLAM (Simultaneous Localization and Mapping) back-end optimization technology based on a data self-adaptive iterative reweighted robust kernel. Comprising the following steps: S1, a front-end data reading and processing module: a robot carries a camera to collect an image, performs feature extraction and feature matching on the image, and generates an initial camera pose and map points; s2, a rear-end optimization module: according to the initial camera pose and map point generated at the front end, calculating a median absolute deviation in an adaptive strategy, calculating a robustness parameter, then substituting the median absolute deviation and the robustness parameter into a target function, simplifying the target function by using an iterative reweighting strategy, and finally solving the optimal camera pose and map point by using an L-M algorithm; s3, the loopback detection module detects loopback according to whether the robot reaches a previous position or not, and transmits information to a rear end for processing; and S4, constructing a map module: constructing an environment map according to the optimized camera pose and map points.

The invention relates to the technical field of industrial automatic monitoring, and provides a mining equipment remote monitoring method and system and a storage medium. The method comprises the following steps: dividing mining equipment into core equipment and general equipment based on equipment operation parameters; performing fault risk assessment on the core equipment by adopting a dynamic threshold value adaptively generated based on historical data, and performing deviation detection on the general equipment by adopting a fixed threshold value; fusing the fault risk assessment result of the core equipment and the deviation detection result of the general equipment to generate global maintenance decision information; optimizing a field maintenance path according to the geographic position and the emergency degree of the maintenance task; and continuously calibrating system parameters through a closed-loop feedback mechanism. According to the invention, through hierarchical monitoring and adaptive optimization, the problems of uneven distribution of monitoring resources, rigid threshold setting and isolated maintenance decision in the prior art are solved, and accurate configuration of mining equipment monitoring resources and remarkable improvement of fault early warning capability are realized.

The invention discloses a gas recognition method based on multi-source information fusion and environmental perception, and the method comprises the steps: constructing a deep feature learning framework of multi-source fusion through combining the spatial response features, time sequence features and external environmental information of gas; the method comprises the following specific steps: preprocessing collected gas data, and respectively extracting features of an image mode, a sequence mode and an environment mode; fusing the image features and the sequence features through a cross attention fusion module, and capturing the space-time correlation of the data; a cross-modal attention compensation module is introduced, so that main-modal gas data adaptively gathers key information in an auxiliary-modal environment, and effective compensation of environmental factors on gas recognition performance is realized; and finally, gas category prediction is performed through a classification decision head. According to the method, the problems that the detection is easily interfered by environmental factors, the stability is poor or the qualification is inaccurate due to the fact that modeling depends on single modal data in the existing gas identification technology are solved.