55results about How to "Reduce missed judgments" patented technology

The application discloses a kind of hybrid power system health management method and system, belong to power system health management field.This application selects the characteristic signal of engine, battery and vehicle in hybrid power system to establish hybrid power system health degree;When hybrid power system fails, by obtaining characteristic signal and carrying out normalization processing, hybrid power system health degree based on grey correlation analysis method GRA is calculated in real time, and the misjudgment and the missed judgment of fault are reduced by the fault confirmation method based on counting;When hybrid power system fault is confirmed, through the power redistribution health management strategy under system fault based on LSTM-MPC, the change of hybrid power system speed, battery temperature and diesel engine exhaust temperature in limited time domain is predicted, and the predictive maintenance of hybrid power system health degree is carried out in combination with the change prediction result of part of the state of hybrid power system, so that hybrid power system can still operate with fault under the condition of meeting constraint.

The application relates to a mine energy consumption monitoring and analysis method based on multi-source data fusion. The method collects multi-source heterogeneous energy consumption data and granularity data from multiple production links of the whole process of mine production, merges and aligns the data with production events as alignment anchors, and correlates the data of the same batch of ores between links, converts the energy consumption and material consumption data into normalized energy consumption values under a unified reference according to energy consumption equivalents, establishes an energy consumption transmission relationship between upstream granularity and downstream normalized energy consumption values by taking granularity as an energy consumption coupling variable between adjacent production links, determines the reverse attribution of downstream high-energy-consumption-link trailing granularity to upstream production links, and obtains the energy consumption transmission contribution proportion of the upstream production link to the high-energy-consumption link. The application can trace high energy consumption of downstream processes to upstream process causes, so that energy-saving reconstruction can be targeted.

The invention discloses a geotechnical engineering geological investigation system and method, relates to the technical field of geological investigation, and solves the problems of difficulty in multi-source data fusion, low geological anomaly intelligent identification precision and weak regional comprehensive evaluation and visual expression ability in the existing investigation method. According to the method, the unified comprehensive parameter vector is constructed, scattered drilling data, in-situ test parameters and real-time sensor monitoring data are subjected to standardized integration and collaborative analysis, the limitation that various types of data are independently stored in traditional exploration, and a data island is formed is broken through, efficient fusion of multi-source heterogeneous information is achieved, and the method has the advantages of being simple in structure and convenient to use. The problem of data fragmentation is solved, and deep cognition and judgment of complex geological conditions can be more comprehensively carried out.

This invention discloses a real-time monitoring method for vehicle lane-crossing behavior based on image semantic segmentation, comprising the following steps: acquiring traffic video data to obtain a road image sequence and performing preprocessing; inputting the image semantic segmentation model for semantic segmentation processing to obtain a semantic segmentation result image; performing morphological processing and connected component analysis to obtain the set of pixel boundaries of the vehicle region and the set of pixel boundaries of the lane lines; calculating the spatial position data of the lane lines and performing perspective correction processing to obtain perspective-corrected lane line spatial position data; generating the position region of the vehicle region in the image coordinate system; performing target association matching to generate vehicle motion trajectory data; establishing spatial relationship data and forming relative position changes; performing temporal consistency analysis and real-time determination, and outputting the vehicle lane-crossing behavior detection result, thereby achieving continuous, accurate, and real-time monitoring of vehicle lane-crossing behavior.

The invention discloses a multispectral imaging-based fundus lesion early screening device and a detection method, particularly relates to the technical field of multispectral fundus imaging, and is used for solving the problems of inconsistent calibers among multispectral fundus data wavebands and insufficient cross-session comparability in a real screening scene. Multi-group perturbation acquisition of light source driving, exposure gain and visual axis offset is adopted in the same inspection session, and waveband identification, acquisition parameters and session identification are bound for each waveband frame, so that layered solution is performed on an illumination field, system response and medium scattering based on endogenous spectrum anchor point constraint, and aperture unification and uncertainty gating are completed accordingly. Therefore, comparable and traceable multispectral features can still be obtained under non-mydriasis, field disturbance and cross-batch conditions, and early lesion screening misjudgment and missed judgment caused by imaging condition drift are reduced.

This invention relates to the field of monitoring data processing technology, and particularly to a method and system for processing abnormal data in paddy field pollution monitoring data. The method involves collecting monitoring data from multiple locations within a target paddy field to obtain a monitoring data array; acquiring water flow characteristic parameters and soil characteristic parameters from multiple locations, and configuring multiple anomaly identification factors; classifying the multiple monitoring data within the monitoring data array according to the multiple anomaly identification factors to obtain multiple anomaly identification results, wherein the number of anomaly classification node layers is configured according to the multiple anomaly identification factors for anomaly classification; and labeling the monitoring data array based on the multiple anomaly identification results. This method achieves efficient and accurate identification of abnormal data in paddy field pollution monitoring data.

The application discloses a fund guarantee information processing method and system in a supply chain scene, relates to the technical field of big data information processing, and comprises the following steps: S1, collecting value-added tax invoice information and associated business data in supply chain transactions; S2, performing multi-dimensional feature extraction on the value-added tax invoice information and constructing a multi-dimensional fusion feature vector, wherein the multi-dimensional fusion feature vector extracts a semantic embedding vector of a cargo name based on NLP entity recognition, extracts time sequence statistical features based on invoice time sequence analysis, and extracts transaction subject correlation features based on the taxpayer identification numbers of buyers and sellers; S3, inputting the feature vector into an unsupervised anomaly detection model trained based on historical invoice data, calculating a global anomaly score, outputting a false trade mark, and simultaneously performing interpretable verification on key features based on feature contribution degree. The application breaks through the limitations of traditional manual auditing and single rule comparison, improves the false trade screening efficiency through automatic data collection and multi-dimensional feature fusion modeling.

This invention discloses a method and system for identifying crop pests based on visual recognition, belonging to the field of pest identification technology. A crop pest identification system based on visual recognition includes a crop visual processing module and a crop pest identification module. This invention constructs a multi-source information collaborative judgment mechanism by integrating visual image features, volatile gas concentrations, and environmental parameters, effectively distinguishing between physiological abnormalities and pathogenic diseases, reducing misjudgments and missed judgments caused by environmental interference, and significantly improving the accuracy and reliability of identification results. By combining gas sensor data to analyze crop respiration characteristics and maturity status, it can identify crop physiological abnormalities before visual symptoms appear, achieving early detection and warning of pests. Dynamically adjusting the identification threshold and sensitivity according to different growth stages supports targeted prevention and control decisions, improving the timeliness of prevention and control.

This invention discloses a method and apparatus for identifying landslide cracks based on neighborhood-wide perceptual hierarchical convolution. First, the invention calculates the deep spatial features of each point based on the 3D data of the landslide point cloud. Then, these features and the point cloud data are used as input to the network. Next, the network is used to find the descriptive points with the best detailed structural representation within the neighborhood of the landslide crack and divides them into sub-neighborhoods. Then, by using the sub-neighborhoods corresponding to the determined descriptive points and combining them with the spatial features of the point cloud data in each sub-neighborhood, the fine local features of the landslide cracks are extracted. Finally, based on the extracted fine local features, the landslide cracks can be identified. Therefore, this invention, through spatial features and sub-neighborhood division, extracts local features, focusing on the overall structural morphology of the landslide cracks. This reduces misjudgments and omissions caused by complex terrain variations, thereby improving the accuracy of landslide crack identification.

This invention relates to the field of gas pipeline safety monitoring technology, and discloses a method for predicting the risk situation of underground gas pipelines based on neural network 3D modeling. The method includes: collecting multi-source heterogeneous data of the pipeline network and preprocessing it to generate a standardized fusion dataset; extracting spatial and temporal features based on a fusion neural network of CNN and LSTM, and generating a 3D model of the pipeline network by combining GIS coordinates; analyzing the risk factor weights using the Grey-DEMATEL-ISM method, and using the N-K model to calculate multi-factor coupled risks to generate the coupled risk level of each pipe segment; predicting the risk situation based on the 3D model features and coupled risk results, and outputting graded early warning information.

This invention relates to the field of image processing technology and discloses a method and system for monitoring sewage outlets based on unmanned aerial vehicles (UAVs). The method includes: generating a dynamic flight strategy for the UAV based on meteorological information and lighting conditions of the target area; conducting preliminary monitoring of sewage outlets in the target area based on their distribution to obtain effective sewage outlet locations and images; filtering out ineffective sewage outlet locations from the distribution; compensating for ineffective sewage outlet locations using historical pollution data and a pre-set multi-period pollution model to generate a pollution heat map; extracting sewage outlet features from the effective sewage outlet images; marking anomalies in the target area's sewage outlets to obtain abnormal sewage outlet locations; conducting centralized inspections of these abnormal locations to obtain the monitoring results for the target sewage outlets. This invention effectively improves the stability of image acquisition quality, the comprehensiveness of sewage outlet location monitoring, and the accuracy of identification under extreme weather and lighting conditions.

The application discloses a system and method for suppressing parallel large-capacity capacitor bank resonance. The system obtains a series resonance candidate frequency point set and a parallel resonance candidate frequency point set through offline identification before operation or in a maintenance debugging stage, and determines damping module parameters; in an online running stage, a control unit performs online preprocessing and composite resonance risk index calculation around the candidate frequency points, controls corresponding damping modules to be put into use in a first level, a second level or in combination, maintains input and cuts off according to the series resonance risk index and the parallel resonance risk index. The application can selectively put damping modules into use according to different resonance types, candidate frequency points and sensitive partitions, reduce current amplification, node voltage amplification and partition circulating current risk of the parallel capacitor bank in a target frequency band, and reduce additional loss caused by long-term input of the damping branch.

The invention relates to the technical field of power system relay protection and power distribution network automation, and discloses a power equipment real-time fault positioning method based on edge calculation, which comprises the following steps: constructing an edge calculation logic topological graph, presetting a positive sequence impedance module value, and monitoring a zero sequence current signal in real time. And when the sudden change of energy is detected, the edge node is switched to the dominant node, and the spectrum fingerprint seed frequency is extracted by using the VMD unconstrained variational model and is sent to the adjacent node. And the adjacent nodes forcibly decompose local signals in the same frequency band based on the frequency by using a CC-VMD constraint optimization model to obtain frequency domain aligned characteristic mode components. And each node calculates a normalized permutation entropy and exchanges data, and calculates a unit electrical distance entropy dissipation rate in combination with a positive sequence impedance module value, thereby determining a fault section. According to the method, the problem of feature alignment under asynchronous sampling is solved by using a frequency domain locking mechanism, and the recognition sensitivity and the positioning accuracy of the high-resistance grounding fault are improved through the entropy dissipation rate.

This invention relates to the field of intelligent operation and maintenance and fault diagnosis technology for industrial equipment, specifically a fault prediction and health management system for scrap steel processing equipment. The system includes: a data acquisition and preprocessing module for acquiring multi-source sensor data within a sliding time window and calculating a data quality index; a feature extraction module for extracting temporal and spatial feature vectors and aligning them based on timestamps; a feature synthesis module for determining fusion weights based on the data quality index and generating fusion features; a fault diagnosis and reasoning module for determining fault types and reasoning fault propagation paths in the fault logic diagram of the scrap steel equipment; and a coordination and update module for determining changes in operating conditions based on the vibration feature distribution under no-load idling conditions and triggering automatic updates of diagnostic model parameters. This invention can suppress low-quality data interference, improve fault diagnosis accuracy, causal interpretability, and adaptability to equipment aging.

The embodiment of the invention provides a living body electric shock identification method and equipment for electrical equipment and a storage medium, and belongs to the technical field of low-voltage power distribution networks. The method comprises the following steps: judging whether a first electric shock triggering condition is met or not according to the amplitude of a total leakage current signal and a preset triggering amplitude threshold value; determining a first sub-period ratio and a second sub-period ratio according to the first electric shock current signal effective value, the second electric shock current signal effective value and the third electric shock current signal effective value; according to the first sub-period ratio and the second sub-period ratio, judging whether a second electric shock triggering condition is met or not; judging whether a third electric shock triggering condition is met or not according to the phase difference between the three-phase voltage of the electrical equipment and the electric shock current signal; and when the first electric shock triggering condition, the second electric shock triggering condition and the third electric shock triggering condition are simultaneously satisfied, determining that the electrical equipment has a living body electric shock phenomenon. The accuracy of determining the electric shock of the living body can be improved.

The invention relates to the technical field of intelligent control, and discloses a precise dispensing nozzle frictioning control method and system based on fault real-time detection.The method comprises the steps that started dispensing equipment conducts automatic alcohol spraying and frictioning clamping on a glue spraying nozzle, and multi-source state data of non-woven fabric is obtained; performing cross-modal feature fusion on the multi-source state data to generate a fusion feature map of the non-woven fabric; performing fault classification on the fused feature map to obtain a real-time fault type and a fault confidence coefficient of the dispensing equipment; dynamically generating a load control instruction set according to the real-time fault type, the fault confidence coefficient and the current process parameters of the dispensing equipment; according to the load control instruction set, driving the corresponding process node to carry out frictioning operation; after the frictioning operation is completed, performing fault re-verification on the state data of the dispensing equipment, and if the fault confidence is lower than a set threshold value, outputting a fault state and a frictioning completion state; according to the invention, the precision dispensing nozzle frictioning control efficiency based on fault real-time detection can be improved.

This application relates to a communication function safety protection method, apparatus, device, vehicle, and storage medium. The method includes: when the communication cycle of a communication node is adjusted to be greater than a preset original cycle, executing at least one of the following protection strategies: a first protection strategy: determining an extended counting verification window based on the scaling factor N of the communication cycle, and performing counting verification on forwarded messages between communication nodes based on the extended counting verification window; wherein N is a rational number greater than 1; a second protection strategy: smoothly reconstructing the control values ​​in the control commands to be sent out by the communication node based on the original demand values ​​and actual feedback values ​​of historical sampling points. This application can improve the ability to identify message anomalies and the verifiability of control outputs under cycle change conditions, enhance the reliability of actuator-level safety mechanism triggering, and improve the effect of vehicle communication function safety protection.

The application discloses a rail-mounted crane visual anti-lifting detection method, system, device and medium, mainly relates to the anti-lifting detection technical field, to solve the current port rail-mounted crane anti-lifting detection generally depends on the low precision of physical sensor and single visual identification technology. Including: according to the pixel range of each industrial camera corresponding image data in the lifting height interval and the hanging high altitude interval, cutting the processed image into lifting height slices and hanging high altitude slices according to the pixel range; using YOLOv5 algorithm, detecting the lifting height slice, identifying whether the container truck tire is off the ground and whether the spreader is aligned; using the CNN-Transformer hybrid structure, detecting the hanging high altitude slice, identifying whether the container truck head has a preset abnormal lifting behavior; when it is detected that the container truck tire is off the ground and the spreader is not aligned or the preset abnormal lifting behavior exists, triggering the abnormal alarm process.

The application discloses a fault root cause positioning method based on a two-horizontal-one-vertical CMDB topology diagram, comprising the following steps: acquiring transaction data flow of a business system to construct transaction business calling topology and output business layer horizontal topology; acquiring calling log and link tracking data of an application service to construct application service calling topology and output application layer horizontal topology; acquiring configuration data and running state data of underlying IT resources to construct support resource topology and output resource layer vertical topology; performing full-link correlation on the three topologies to establish a data correlation model and output a multi-dimensional application operation and maintenance graph with causal logic; accessing alarm and operation and maintenance data, performing fault root cause positioning based on the graph, and outputting fault root cause, influence range and disposal suggestions in combination with a root cause analysis algorithm. Through construction of a business-application-resource three-dimensional topology model and fusion of multi-source operation and maintenance data, the application realizes rapid and accurate positioning of faults, and significantly improves operation and maintenance efficiency and business continuity.

This invention belongs to the field of tunnel structural health monitoring technology and discloses a method and system for identifying tunnel structural defects and providing early warning of safety risks. The method collects surface image data, structural status monitoring data, and environmental operation data of the tunnel lining, and constructs an evaluation object dataset according to tunnel station number, monitoring section, and circumferential location of the lining. It extracts surface defect characteristic parameters such as cracks, leakage, and spalling from the surface image data, extracts structural response characteristic parameters from the structural status monitoring data, and combines historical time-series data to obtain the defect development trend and structural response change trend. It obtains the surface damage level through a defect level rule base, obtains the physical safety level through a physical assessment model, and then determines the current safety level through level envelope fusion. Based on the current safety level, development trend, environmental operation data, circumferential location of the lining, and the relationship between multiple defects, it determines the risk level and generates graded early warning information.

The application provides a power distribution operation weather risk dynamic early warning method and system. The method comprises the following steps: querying weather forecast data according to a current operation position, time-aligning the weather forecast data and weather detection data to obtain a synchronous weather data set, extracting features and performing weighted fusion on the synchronous weather data set according to a fusion weight coefficient to obtain weather characteristic values, converting the weather characteristic values into initial risk values according to a numerical mapping rule, performing weighted summation on the initial risk values according to a risk weight coefficient to obtain a basic weather risk value, performing scene correction on the basic weather risk value according to power distribution operation data and the current operation position to obtain a comprehensive risk value, and matching the comprehensive risk value with a risk and instruction relationship to obtain a risk grade and an early warning instruction. Through multi-source weather data synchronous fusion, power distribution operation scene correction and hierarchical mapping early warning, the application realizes scene-based early warning of weather risks, so as to improve the safety of power distribution operations.

The invention relates to the technical field of information system health assessment, and particularly provides an information system health index assessment method and system based on dynamic threshold adaptive adjustment, and the method comprises the steps: collecting a multi-dimensional assessment index and auxiliary decision data of system operation, the multi-dimensional assessment index covering multiple dimensions such as a hardware layer and a service layer, the auxiliary data comprises historical and real-time business scene data; historical data are classified according to multi-level service scene labels, the normal fluctuation range of each index is mined, and a scene historical threshold library capable of being dynamically updated is constructed; based on a real-time service scene matching basic threshold, combining a trend analysis algorithm and a weight adaptation rule, and performing differential adjustment granularity dynamic optimization to obtain a dynamic threshold interval; and comparing the real-time operation parameter with the dynamic threshold interval, quantifying the score of the single index, and performing weighted summation to obtain the health index. According to the invention, the accuracy and timeliness of health index evaluation of the information system are improved, and a reliable basis is provided for real-time health monitoring and operation and maintenance decision making of the information system.

The invention discloses a virus inactivation evaluation method based on image recognition and data processing, and relates to the technical field of biotechnology and medical detection, and the method comprises the following steps: preprocessing a collected microscopic image, and adopting a gray conversion and contrast adjustment method to enhance the light and shade difference between virus particles and polysaccharide residues in the image, so as to obtain a virus inactivation evaluation result; obtaining first image data after primary processing; according to the virus inactivation evaluation method based on image recognition and data processing, through extraction and analysis of morphological characteristics such as boundary integrity and shape regularity of virus particles, objective judgment of a virus inactivation state is realized, so that uncertainty caused by manual subjective judgment is reduced, misjudgment and missed judgment are reduced, and the accuracy of virus inactivation evaluation is improved. The accuracy, the stability and the repeatability of a virus inactivation evaluation result are improved.

The application discloses a mine self-rescuer training behavior evaluation method based on multi-modal fusion, comprising the following steps: step one: acquiring visual behavior data of a training object and sensing behavior data of a mine self-rescuer; step two: aligning the visual behavior data with stage actions and encoding interactive semantics, and analyzing key step states of the sensing behavior data; step three: detecting human key points through an improved RTMPose model; step four: determining spatial relationships through a motion determination algorithm; step five: determining consistency correlation, generating a training behavior fusion recognition result; step six: performing evaluation processing on the training behavior fusion recognition result through a hierarchical time sequence constraint evaluation mechanism; and step seven: generating an evaluation output result and completing result storage. Through the improved RTMPose model and the motion determination algorithm, the objectivity and result reliability of the mine self-rescuer training behavior evaluation are improved.

The application discloses a panoramic data acquisition and master station synchronization method of a relay protection and stability device, relates to the technical field of power communication monitoring, and aims to solve the problem of master station synchronization distortion; the application constructs a cooperative link of displacement checking, configuration analysis, aggregated configuration, communication processing and information processing, realizes unified acquisition and master station synchronization of the state of a pressing plate, the state of a loop and device message information of the relay protection and stability device, checks the mechanical action of the pressing plate and the electrical response of the loop jointly, generates a panoramic data directory in combination with object master keys, relation indexes and version indexes, and executes field mapping, time normalization and difference supplement transmission, so that the data acquisition accuracy, synchronization integrity and operation reliability are improved.

The invention discloses a multi-process collaborative detection method and system in a chip packaging process, and the method comprises the steps: constructing a whole-process material pedigree tree with a chip unique code as an index, achieving the time-space alignment and standardization of multi-source heterogeneous detection data, and employing a conditional variation auto-encoder to intelligently interpolate a missing value; cooperative feature coding of process track perception is carried out based on a Transform model with a process mask, and cooperative feature representation containing whole process dependency information is obtained; a specific inter-process early warning transmission mechanism is constructed, and multi-process risk fusion and dynamic threshold adjustment are carried out; and carrying out dynamic fusion and collaborative decision-making on the multi-process detection evidence based on the D-S evidence theory, and outputting a quality judgment result and a disposal instruction. According to the invention, information sharing and intelligent cooperation of the whole packaging process are realized, false detection accumulation between processes is effectively reduced, and the defect root cause tracing efficiency and the quality determination accuracy are improved.

This application discloses an abnormal collision detection and control method and related apparatus for a robot, applied to the robot's control module. The method includes: acquiring first multi-channel sound data through a multi-channel sound acquisition module within a first preset time period; acquiring first state data through a robot state perception module; preprocessing the first multi-channel sound data and the first state data to obtain second multi-channel sound data and second state data; extracting features from the second multi-channel sound data to obtain first feature data; performing abnormal collision detection based on the first feature data and the second state data to obtain a target collision detection result; and optimizing the robot's control parameters based on the first feature data, the second multi-channel sound data, the second state data, and the target collision detection result. Using this application, abnormal collision events during robot operation can be accurately detected, and control optimization can be performed accordingly.

The present disclosure provides a user information security risk assessment method and device, electronic equipment and medium, relates to the technical field of data processing, and particularly relates to the technical field of natural language processing, large language model and information security. The implementation scheme is: obtaining first page data of a first page of a target application program, wherein the first page is used to display a privacy policy describing a user information list of the target application program, and the first page includes at least one first link; processing the first page data using a large model, wherein the large model is used to: screening at least one second link from the at least one first link, wherein each second link in the at least one second link indicates a first sub-page used to display other content in addition to the privacy policy; and extracting an evaluation data list from the first page data excluding the at least one second link; and evaluating the degree of information security risk when a user uses the target application program according to the evaluation data list.

The invention discloses a sintering pallet air leakage rate treatment system and method based on multi-mode perception, and belongs to the technical field of sintering pallets, and the method comprises the steps: S1, collecting contact surface temperature distribution data, sound signals, pressure data and environment dust concentration data; s2, extracting multi-modal features, normalizing the multi-modal features, and mapping the multi-modal features to the same coordinate system of the trolley contact surface; s3, the air leakage probability of each position is calculated in real time through a fusion algorithm; identifying the position with the air leakage probability greater than a set threshold as a high-probability air leakage point, and outputting a coordinate set of the high-probability air leakage point; s4, the coordinates of the air leakage points are converted into a physical coordinate system of a dynamic plugging actuator, plugging force needing to be applied by each target sealing unit is calculated, the target plugging force is converted into a pressure set value, a control instruction is sent to each execution unit, and the action completion state of each target unit is monitored; and S5, multi-modal data after plugging is collected, the air leakage probability is recalculated, and the plugging effectiveness is judged according to the calculation result.

This application discloses a method and system for detecting an open freezer door in a dual-system refrigerator, belonging to the field of smart home appliance fault diagnosis technology. Addressing the problems of existing refrigerator door-open-door detection schemes being susceptible to defrosting interference, not covering the entire fault lifecycle, and lacking adaptability to dual-system refrigerators, this application first acquires the refrigerator's operating data, divides non-defrosting cycles based on the defrosting heater's operating status to isolate defrosting interference, and filters out valid detection data. The open freezer door fault is divided into a temperature rise stage, a steady-state stage, and a recovery stage, and corresponding detection rules are constructed for each stage. The detection logic is dynamically adjusted according to the number of non-defrosting cycles, and after rule matching, a diagnostic result including fault status, fault stage, and fault time interval is generated. This application can achieve accurate detection throughout the entire fault lifecycle, effectively reducing false alarms and false negatives, adapting to the operating characteristics of dual-system refrigerators, and improving the accuracy and reliability of fault diagnosis.