26results about How to "Early warning" patented technology

This invention belongs to the fields of water environment monitoring, watershed pollution simulation, and smart water management. Specifically, it provides an integrated air-space-ground small watershed water pollution monitoring system, comprising: an airborne monitoring module for collecting spatial distribution data of water quality across the entire small watershed; a ground-based monitoring module for collecting continuous temporal water quality and hydrological baseline data of the small watershed; a multi-source data management module for data processing; a model engine module for constructing and running a distributed hydrological and water quality mechanism model of the small watershed; an adaptive parameter optimization module for automatically calibrating and dynamically updating the parameters of the distributed hydrological and water quality mechanism model of the small watershed; an early warning and source tracing module for obtaining early warning and source tracing results; a long-term prediction module for obtaining water quality prediction results; and a visualization decision module for displaying early warning and source tracing results as well as water quality prediction results. This invention realizes the monitoring, early warning, source tracing, and long-term prediction of water pollution in small watersheds.

The application discloses a tire pressure anomaly early warning method and device and a storage medium, and relates to the technical field of vehicles.The application comprises the following steps: correcting a tire pressure static threshold based on the current speed, tire temperature and load state of a vehicle to obtain a tire pressure dynamic threshold; determining a tire pressure jump rate based on the current tire pressure of the vehicle; determining a tire pressure risk level based on the tire pressure dynamic threshold, the current tire pressure and the tire pressure jump rate; and outputting early warning prompt information and / or executing a vehicle control action based on the tire pressure risk level.The application dynamically adjusts the tire pressure threshold according to the current speed, tire temperature and load state, thereby improving the early warning sensitivity in high-speed, heavy-load and other scenarios; the tire pressure risk is identified by combining the tire pressure dynamic threshold and the tire pressure jump rate, so as to distinguish between slow tire pressure leakage and high-speed tire burst instantaneous jump, realize rapid identification in a high-risk scenario, and improve the accuracy of tire pressure early warning.

The application provides a dangerous area recognition method and device based on machine vision, and relates to the technical field of dangerous area recognition.The application obtains monitoring image data of a danger source in a historical monitoring area, forms a training image set through preprocessing, constructs a danger recognition model, and trains the danger recognition model, performs danger source recognition on real-time monitoring image data according to the trained danger recognition model, demarcates a basic danger area according to a basic safety distance set according to the recognition result, calculates a danger coefficient of each type of static area according to historical data of each type of static area, adds an early warning distance, calculates a dynamic danger coefficient according to historical data of each type of dynamic equipment, calculates a buffer distance in front of a moving direction, obtains historical danger accident data of a to-be-recognized area, extracts an equipment feature set, and identifies a future safety state of a sub-area through a Bayesian prediction model.

This application relates to an image feature recognition experimental method and apparatus, belonging to the field of image feature recognition technology. It involves real-time acquisition of images of an experimental scene and preprocessing them to obtain clear, continuous frame images. Based on these, a tracking and locking operation is performed on a dynamic target to ensure the target remains within the effective monitoring field of view. Subsequently, precise target feature data is extracted from the locked target detection image, and hierarchical cross-matching evaluation is performed using a dual-library standard feature library, including both local and cloud-based libraries. Finally, a corresponding feedback signal is generated based on the evaluation results. This application, through a dynamic tracking and locking mechanism, can adjust the image acquisition angle in real time, ensuring the dynamic target remains continuously within the monitoring field of view. This avoids field-of-view shift and target loss under fixed-angle acquisition, effectively solving the defect of dynamic target field-of-view loss. It also addresses the problem of delayed response in security status determination caused by existing technologies relying on a single local feature library.

The present application relates to the technical field of data calculation and scheduling arrangement in the power industrial control production scene, and discloses a power industrial control calculation workflow DAG deadlock avoidance method based on incremental topology, comprising: modeling the dependency relationship between workflow input and running state information as a multidimensional dependency graph; locating the affected node set according to the multidimensional dependency graph and the change event, and adjusting the node sequence in the affected node set to obtain an incremental topology sequence, and completing the construction of the affected subgraph; performing explicit loop and implicit loop risk detection processing on the affected subgraph, classifying the risk detection results, combining the classification results with the business target to generate a conflict resolution strategy, so as to realize the directed acyclic graph deadlock avoidance; converting the conflict resolution strategy into executable operations to perform change processing on the multidimensional dependency graph, and performing structure and semantic verification on the change result to verify the deadlock avoidance effect of the workflow directed acyclic graph. The present application can improve the continuous operation capability of the workflow.

The application provides a medical institution safety production dynamic evaluation method and system based on multi-source heterogeneous data fusion, and is applied to the field of data processing. The application first collects multi-source heterogeneous full-dimensional data of equipment operation, environment monitoring, personnel operation, fire safety, logistics energy consumption and business process, realizes standardized conversion through cleaning, normalization and feature extraction, completes data deep coupling through weighted fusion, space-time correlation analysis and deep learning, and forms a fusion data set. Then, according to risk identification, grade determination and trend prediction, three-dimensional correlation nodes are constructed in layers, and a safety feature correlation system is established; combined with the analytic hierarchy process and fuzzy comprehensive evaluation modeling, core features are extracted, and a safety feature vector is generated. Relying on the edge-cloud collaborative engine, real-time updating, early warning and iterative optimization are completed, and through accuracy verification and dynamic correction, the final dynamic evaluation result supporting fine safety management and active risk prevention and control is formed.

The application discloses a campus safety risk identification method and system based on multi-algorithm nested cooperation and a medium, relates to the technical field of campus risk identification, and constructs a dynamic rule library, matches all dynamic rule graphs corresponding to initial event signals from the dynamic rule library, and creates dynamic rule graph instances; each node is activated according to a topological order and an activation condition, the comprehensive confidence and the quasi-truth degree of a potential risk hypothesis are calculated at a convergence node based on DS evidence theory to perform risk early warning; multi-dimensional information is deeply associated and comprehensively judged, and the problems of information silos and low-level misjudgment are solved; a school calendar rhythm is converted into a time weight factor and embedded into a DS evidence fusion process, time background can be perceived, the contribution degree of different types of evidence and the sensitivity of risk decision-making can be automatically adjusted, false positives caused by neglecting context are reduced, and early and accurate early warning can be ensured in a high-risk period.

This invention discloses a method for sensing the operational status of a distribution network, relating to the field of distribution network monitoring and intelligent operation and maintenance. The method includes: synchronous acquisition of multi-source ubiquitous data to construct a low-cost ubiquitous sensing network; edge-side data preprocessing and lightweight analysis; topology-adaptive multi-source data fusion: constructing a distribution network topology map based on neural networks and mining the spatial and temporal correlations of multi-source data; multi-dimensional quantitative assessment of operational status: constructing a multi-dimensional status assessment system; precise fault location and hierarchical early warning: fusing multi-terminal information to achieve precise fault location; and online model iteration and closed-loop optimization. This distribution network operational status sensing method, by employing a low-cost ubiquitous sensing network combined with dedicated sensors and non-intrusive monitoring, covers the entire distribution network—from source to grid to load to storage—solving the problems of large blind spots and high deployment costs associated with traditional sensing methods. It achieves effective monitoring of end nodes and branch lines, realizing the goal of full-area, blind-spot-free sensing.

The invention relates to a rice bacterial leaf blight severity estimation method based on a trigonometric vegetation index, and the method comprises the steps: obtaining an unmanned plane multispectral image and a hyperspectral satellite image of a target region, and carrying out the preprocessing; performing rice bacterial leaf blight disease classification on the field scale, and determining an optimal classification threshold value of disease classification; calculating a disease distribution severity index RBLBI in each satellite pixel; screening out a plurality of characteristic wave bands sensitive to the rice bacterial blight disease; and on the basis of the screened characteristic wave bands, constructing a trigonometric vegetation index BLBTVI. The method has the beneficial effects that the model provided by the invention is light in weight, few in parameters, insensitive to atmospheric residual error, illumination variation and BRDF influence, and higher in robustness and interpretability; after conventional atmospheric correction and cloud / water body masking are completed, a continuous grid and time sequence monitoring result can be directly generated on a satellite-borne hyperspectral product, and the manual patrol and data processing cost is remarkably reduced.

The present application relates to an array electrode intelligent sensing system for tunnel gushing water monitoring and a method thereof. The system comprises a flexible sensing array implanted inside or on the surface of a tunnel lining structure, an intelligent acquisition node and a data analysis platform. The flexible sensing array is composed of multiple composite sensing units arranged in a predetermined topological structure, each unit integrating a main electrode for measuring resistivity / potential, a temperature sensor for measuring temperature and a flexible strain sensor for measuring deformation; the intelligent acquisition node connects the sensing array to synchronously acquire resistivity, temperature and strain data in multiple channels, and preliminarily processes and extracts features from the original data; the data analysis platform receives data from multiple nodes, and through a multi-parameter spatio-temporal fusion analysis model, realizes the collaborative intelligent analysis from "water-bearing state perception" to "structure deterioration evaluation" to "gushing water risk early warning". The present application realizes the fine, three-dimensional and real-time monitoring of the incubation process of tunnel water disasters.

This invention discloses a collaborative management system and method for river cyanobacteria based on AI-based early warning and targeted drug delivery. The system comprises an intelligent sensing and early warning layer, a central decision-making and control layer, and a precise targeted execution layer. The intelligent sensing and early warning layer collects environmental data of the target river through an air-ground-water three-dimensional monitoring network and uses an AI prediction model to provide early warning of cyanobacteria outbreak risks. The central decision-making and control layer receives the risk warnings and, in conjunction with algae spatial distribution, hydrological conditions, and weather forecasts, generates management strategies and control instructions based on a preset rule base and optimization algorithms. The precise targeted execution layer includes at least one unmanned targeted drug delivery vessel that receives the control instructions and executes drug delivery to complete the river cyanobacteria control. This invention achieves a shift from passive response to active intervention and from extensive management to precise control, offering advantages such as high efficiency, environmental friendliness, and long-term effectiveness.

A cloud-based online early warning method and system for smart water meter errors, relating to a measurement field not specifically designed for particular variables, is disclosed. The method includes: extracting a baseline water usage event sequence uploaded by the smart water meter during its initial operating cycle; extracting clustering features and performing unsupervised clustering to generate multiple water usage feature models; extracting a test water usage event sequence uploaded by the smart water meter after the initial operating cycle; performing feature matching between the test water usage event sequence and the water usage feature models to calculate the water consumption drift sequence corresponding to each model; calculating the drift ratio difference of multiple water consumption drift sequences within the same time window to obtain cross-validation results; and extracting the metering error offset rate to generate an online early warning command containing the metering error offset rate. Implementing this application can improve the accuracy of smart water meter error early warning prompts.

The utility model belongs to the technical field of power distribution network early warning, and particularly relates to a rapid early warning device for transient stability of a power grid, which comprises a bracket swing arm mounted on a sensor bracket, an assembly component slidably connected to the bracket swing arm, an adjusting component arranged on the assembly component, an early warning module arranged in the assembly component, a hinge rod arranged at one end of the bracket swing arm, and a power transmission mechanism arranged at the other end of the bracket swing arm, the assembling assembly comprises a swing arm sliding block, a protruding shaft is fixedly installed on the swing arm sliding block, an assembling box is slidably connected to the outer side of the swing arm sliding block, a lifting sliding groove is formed in the assembling box, and a side clamping plate is further arranged at the top of the assembling box. According to the rapid early warning device for the transient stability of the power grid, when a line swings in a small amplitude, the support swing arm can swing freely through the hinge rod, the swing arm sliding block slides in the axial direction of the support swing arm, the assembly box is further adapted to the position change of line swinging through the lifting sliding groove under the limitation of the protruding shaft, and therefore mechanical damage caused by line swinging is effectively reduced.

The present disclosure provides a network element device fault early warning method and device, equipment and storage medium, and relates to the technical field of communication. The method comprises the following steps: collecting device attribute data of a plurality of network element devices; performing classification processing on the collected device attribute data to obtain first attribute data and second attribute data corresponding to each network element device; constructing a digital twin model with a hierarchical structure according to the first attribute data and the second attribute data corresponding to each network element device, wherein the digital twin model comprises a plurality of virtual objects, each virtual object corresponds to a network element device, and the first attribute data and the second attribute data of each network element device correspond to different hierarchical layers of the digital twin model; and using each virtual object in the digital twin model to perform fault early warning on each network element device. The present disclosure can quickly perform fault early warning on network element devices.

The invention relates to a metabolic biomarker for malignant transformation of chronic hepatitis B and application of the metabolic biomarker, and belongs to the technical field of biomedicine. The invention provides aldehyde oxidase 1 (AOX1) and its downstream metabolite pyridoxal phosphate (PLP) as metabolic biomarkers for chronic hepatitis B malignant transformation. Through metabonomics and transcriptomics analysis, a'first-rising and second-falling 'dynamic trajectory of PLP rising in a chronic hepatitis B stage and falling in a liver cancer stage is found, and HBx reduces the PLP level by inhibiting AOX1 expression. The marker can be used for preparing a detection reagent, a kit or an integrated device, and dynamic monitoring and early warning of the vicious transformation risk are realized by detecting the AOX1 / PLP level in samples such as serum. The marker has the advantages of being clear in mechanism, high in dynamic relevance, suitable for clinical transformation and the like, and a new means is provided for liver cancer risk monitoring of chronic hepatitis B patients.

The invention belongs to the technical field of fastener looseness monitoring, and discloses a tower crane standard knot fastener self-looseness monitoring device and method. The tower crane standard knot fastener self-looseness monitoring device comprises a self-looseness monitoring device body, an anti-looseness fixing device and a magnetic attraction fixing device. The self-loosening monitoring device is located at the head position of a standard knot bolt and can be used for monitoring the self-loosening degree of a standard knot fastener. The anti-loosening fixing device is located at the position of the standard knot nut, further fixes the standard knot nut and can be used for improving the self-loosening resistance of the standard knot fastener. A magnetic plate is attached to the magnetic fixing device and used for fixing the device and a standard knot connecting groove and providing a connecting part of the self-loosening monitoring device and the anti-loosening fixing device. On the basis that the structural integrity of the tower crane standard knot fastener is not damaged, self-loosening real-time monitoring of the tower crane standard knot fastener is achieved, anti-loosening measures to a certain degree are provided, meanwhile, the shell further plays a good role in protecting the tower crane fastener, the service life of the tower crane standard fastener is greatly prolonged, and the safety of the tower crane standard knot fastener is improved. And the service safety of the tower crane is improved.

The invention provides a fire alarm mycelium material and a preparation method and application thereof, and belongs to the technical field of fire alarm materials, and the fire alarm mycelium material comprises hemp stalks, ganoderma lucidum, ethanethiol and the like. The preparation method of the mycelium material comprises the following steps: carrying out mixed culture on a hemp stalk culture material and ganoderma lucidum; dissolving polyvinyl alcohol and lauryl sodium sulfate in deionized water; adding ethanethiol and polymethyl methacrylate into an organic solvent; dropwise adding the water phase into the oil phase, and emulsifying to obtain ethanethiol microcapsules; and mixing the pure mycelium material with ethanethiol microcapsules, culturing, and demolding to obtain the fire alarm mycelium material. According to the invention, intrinsic integration of a material structure and an early warning function is realized through a mycelium biological assembly and microcapsule embedding and fixing technology, so that the material becomes a sensing unit. According to the material, rapid and active fire early warning without external energy is realized by utilizing the temperature-triggered fracture characteristic of the microcapsules and the extremely low odor threshold value of ethanethiol.

This invention discloses an online monitoring method for external leakage in aircraft hydraulic systems based on a mechanistic model, belonging to the field of hydraulic system technology. It aims to solve the technical bottlenecks of existing aircraft hydraulic system external leakage monitoring, such as poor real-time performance, high false alarm rate, and inability to detect the leakage amount and rate. The method includes the following steps: First, a mechanistic model is constructed based on the law of conservation of mass. Combined with temperature and pressure data monitored by airborne sensors, the oil volume inside each hydraulic component outside the tank is calculated according to volumetric deformation characteristics, deriving the theoretical value of the oil volume inside the self-pressurized tank. Second, the mechanistic model is corrected by comparing the theoretical value with the actual monitored value using flight parameter data under leak-free conditions, eliminating inherent errors. Finally, real-time flight parameter data is input to recalculate the theoretical value, and compared with the real-time monitoring value from the level sensor to determine if the system is leaking. If leakage occurs, the leakage amount and rate are calculated, and an early warning result is output. This invention relies on existing airborne sensors, requiring no additional equipment or sensors. It can effectively distinguish between normal oil fluctuations and actual leakage, improving monitoring accuracy and real-time performance. It provides data support for the handling and maintenance of aircraft hydraulic system leakage faults and has certain engineering application value.

The application discloses a kind of wind turbine fault prediction and health management system based on multi-parameter fusion, belong to wind power equipment intelligent operation and maintenance and fault prediction and health management (PHM) technical field.The present application takes harmonic diagnosis technology as core, the monitoring mode of multi-parameter fusion, optimizes system architecture and carries out function innovation, solves the technical defects such as traditional diagnosis precision deficiency, real-time poor, early warning ability weak.System includes intelligent data acquisition module, enhanced signal hardware processing module, 5G / fiber double communication module, edge computing node, cloud side collaborative server, intelligent diagnosis and health management module, distributed memory, multi-terminal visualization platform and intelligent power supply module;Each module cooperates, through multi-source data acquisition, high-precision signal processing, cloud side collaborative diagnosis, fault grading early warning, health status evaluation and residual life prediction.

The application discloses a kind of natural reserve adaptive monitoring system and method based on sky-ground cooperation, the system includes: service end, for obtaining current satellite remote sensing data that satellite is collected to be monitored natural reserve;According to current satellite remote sensing data, determine whether there is abnormal area in the natural reserve to be monitored;If yes, determine the target unmanned aerial vehicle closest to the abnormal area, target ground mobile device;Generate coordination instruction and send to target unmanned aerial vehicle, target ground mobile device;Target unmanned aerial vehicle, target ground mobile device are used to go to abnormal area based on coordination instruction, to obtain the detailed information of close range of abnormal area, send to service end;Service end is also used to determine whether to give early warning to abnormal area according to the detailed information of close range;If yes, generate the early warning information of abnormal area and send to client end.Therefore, by using the application, the data processing pressure of service end can be reduced, the computing resources are saved, and the monitoring efficiency is improved.

The invention discloses a flexible monitoring sensor for actively sounding fish and a signal analysis method, and relates to the technical field of aquaculture, and the flexible monitoring sensor mainly comprises a flexible substrate, an interdigital electrode arranged on the substrate, and a piezoresistive sensitive layer covering the electrode. The sensitive layer is made of a PDMS and graphene composite material, and the surface of the sensitive layer is provided with a regularly arranged micro-pyramid structure array. The sensor is attached to the body surface of a fish sounding part, the micro-pyramid structure responds to body surface sound vibration in a preset frequency range to generate deformation, and the sound vibration is converted into a resistance change signal between the interdigital electrodes by utilizing the piezoresistive effect of the composite material. Through the design of the PDMS-graphene composite material and the surface micro-pyramid structure, high-fidelity and direct capture of body surface vibration signals generated by initiative sounding of individual fishes is realized, and the problems of group sound mixing and weak signal relevance in a traditional method are effectively avoided.

The application discloses an AI-based radiographic image critical value pop-up window early warning system and relates to the technical field of medical image diagnosis. The system comprises an existing Lianying intelligent AI auxiliary diagnosis application program, an AI image recognition screening module, a critical value early warning triggering module, a pop-up window display module, an audit feedback tracing module, a multi-terminal information synchronization module, a radiology doctor work terminal and a clinical doctor work terminal. Each module is installed in the radiology doctor terminal in the form of a lightweight plug-in, is connected to the existing Lianying intelligent AI auxiliary diagnosis application program through a standardized interface, does not need to modify the existing Lianying intelligent AI auxiliary diagnosis application program, a PACS system and a RIS system, and does not need to add hardware devices.

The invention discloses a muck weighing and shield data sharing method and system, and is applied to the technical field of shield tunneling machine construction. The method comprises the following steps that a weighing sensor measures the weight of muck in real time at a fixed frequency and converts the muck weight into a weighing message sequence, a shield tunneling machine collects shield tunneling parameters and converts the shield tunneling parameters into a shield tunneling message sequence, and a data processing center is symmetrical to the weighing message sequence and the shield tunneling message sequence to carry out data analysis and standardization processing. Performing real-time association by taking a data timestamp as a criterion to form fusion data arranged according to a tunneling time sequence; calculating the deviation ratio of the actual slag tapping quantity and the theoretical slag tapping quantity, and generating early warning information according to the calculation result of the deviation ratio; and sending the standardized muck volume data, the shield volume data, the deviation rate calculation result and the early warning information to a data sharing platform. According to the method, the muck weighing data and the shield machine data are associated in real time, common sharing of the data is achieved, and the method can be used for real-time analysis and early warning of shield construction.

The application discloses a kind of intelligent hoisting monitoring method and system of suspension bridge based on machine vision, which comprises: picture is collected by visual unit being arranged around main cable, sling and component hoisting point, component three-dimensional posture vector of the component to be hoisted is generated, the displacement of multiple sampling points of the component to be hoisted is obtained, the displacement measure in time interval is calculated, the weighted sum of the displacement measure of all sampling points is used as dynamic deformation index, the time sequence stability index of the component to be hoisted is calculated according to component three-dimensional posture vector and dynamic deformation index;The displacement spectrum of each sling is generated, the direction consistency index and peak mutation degree are calculated according to displacement time sequence, and the frequency spectrum cluster degree is calculated according to displacement spectrum;The weighted sum of direction consistency index, peak mutation degree and frequency spectrum cluster degree is used as sling stress trend factor, the sling stress trend factor is coupled with time sequence stability index, the risk quantization index of hoisting process is generated, and monitoring is carried out according to risk quantization index.

The utility model relates to the technical field of electric power engineering equipment, in particular to a transformer convenient to install, which comprises a transformer body, one side of the transformer body is fixedly provided with an oil tank support, and the top of the oil tank support is provided with a quick-release oil level gauge assembly; the oil tank body, the light disc, the limiting column, the floating disc, the strobe lamp body and other components are arranged, the floating disc is fixedly connected with the limiting column, and the limiting column is matched with the light disc in a penetrating mode, so that the floating disc can drive the limiting column to synchronously move along with rising and falling of the insulating oil level in the oil tank body. The stroboscope lamp body at the top end of the limiting column is connected with the limiting column through threads, in combination with the pressure sensing module on the bottom face of the light disc, when the floating disc reaches the preset height, light color change of the stroboscope lamp is triggered, then the effects that the device can sense the oil level in real time through the floating disc and the stroboscope lamp conducts visual feedback are achieved, and rapid early warning of oil level abnormity is achieved.

The present application relates to the technical field of red blood cell quality evaluation, in particular to a visual system for rapidly evaluating highland red blood cell storage damage and application thereof. The present application divides hypoxic stored red blood cells into normal group and damage group, analyzes different cell morphologies thereof, adopts Lasso regression for variable screening, optimizes risk factor parameter dimension, and determines red blood cell morphological damage predictors (SDC, CDC and CSD). Further, a binary classification logistic regression prediction model is constructed, a new red blood cell storage damage early warning model based on morphological characteristics is established, the identification success rate of "high-risk" red blood cells with damage is controlled to be more than 75%, thereby providing a new tool for comprehensive evaluation of highland stored red blood cell damage, and having important significance for rapid early warning and evaluation of highland stored red blood cell quality change.