45results about How to "Improve disposal efficiency" patented technology

The application provides a network security vulnerability automatic management method based on network security intelligence, belongs to the field of network security vulnerability management, and is used for solving the problems of low automation, large risk assessment deviation and poor collaboration in the related art. The method comprises network security intelligence collection, screening, storage and analysis, attack path prediction, key asset identification, hazard scene enhancement and vulnerability management. Through technologies such as dynamic weight, high-order probability graph model and multi-agent game, accurate intelligence processing, dynamic risk quantification, attack forward-looking prediction and cross-enterprise collaborative disposal are realized, the vulnerability management efficiency and accuracy are improved, and the network security of the ECUs is ensured.

The application discloses a kind of based on artificial intelligence's comprehensive pipe gallery intelligent decision system and method, belong to intelligent decision-making technical field.The application is aimed at each pipe gallery area, pre-calibrates ontology parameter and water seepage boundary parameter, real-time collection operation data, after pre-processing, form comprehensive pipe gallery data.Calculate static water head, combine cabin inside and outside air pressure difference correction to obtain effective action water head;Using artificial intelligence algorithm to predict the change process of ground water level, generate prediction water inflow time sequence.Matching water level and volume corresponding relationship, calculate pipe water total inventory sequence, calculate time period water net increment, combined with effective water cross-sectional area to obtain next minute end prediction water level and water level rising rate;Combined with drainage equipment performance to determine the risk of water inflow in the region.Determine the risk of water inflow, preferentially start local drainage equipment full load operation, as needed control regional interval switch opening degree, linkage adjacent pipe gallery area with drainage redundancy collaborative drainage.

The automatic fire extinguishing equipment comprises a protective shell, a lithium battery body is connected to the interior of the protective shell, a heat dissipation sealing mechanism is arranged on the outer side of the protective shell, and a partition fire extinguishing mechanism is arranged on the inner side of the protective shell; the heat dissipation sealing mechanism is arranged on the outer side of the protective shell, and the connecting piece, the multiple sealing blades, the first supporting arm, the second supporting arm, the extending plate, the transmission piece and the adjusting rod are arranged in a series linkage mode, so that the first motor can synchronously adjust the opening or closing state of the multiple sealing blades when being controlled and driven; in the normal working process of the lithium battery body, efficient heat dissipation is achieved in cooperation with the ventilation pipe and the heat dissipation fan, the ventilation channel can be rapidly closed when temperature rises abnormally, external air is prevented from entering the protection shell, and the combustion condition is weakened from the environment level.

This invention belongs to the field of pharmaceutical wastewater treatment technology, and particularly relates to an ultrasonic-coupled electrochemical pretreatment device and method for high-concentration antibiotic production wastewater. The device consists of an ultrasonic treatment first reactor, an electrochemical second reactor, and a flocculation sedimentation third reactor connected in series via pipelines. The first reactor is equipped with a wall-mounted ultrasonic transducer; the second reactor contains a stabilizing electrode and a sacrificial electrode, and multiple disc aerators are installed at the bottom; the third reactor contains an overflow trough, inclined tube packing, and an electromagnet. This invention can achieve efficient desorption and mineralization degradation of adsorbed antibiotics in suspended solids in production wastewater, reduce antibiotic and degradation product residues in flocs, and eliminate toxic effects on subsequent biological treatment processes.

The invention relates to a machine room ceiling condensation and leakage monitoring and processing method, device and equipment and a medium. The method comprises the steps that environment temperature and humidity data and leakage detection signals in a machine room are collected in real time; determining a condensation risk index corresponding to each cabinet in the machine room through the temperature and humidity data and preset equipment arrangement parameters; and determining a corresponding dehumidification strategy and / or drainage strategy based on the condensation risk index and the leakage detection signal, and correspondingly processing the machine room. The device has the effect of improving the machine room ceiling accumulated water and leakage treatment efficiency.

This invention discloses a 3D visualization modeling method and system for data center equipment operating status, relating to the field of big data processing technology. The method includes: classifying multi-source heterogeneous data based on metadata features to obtain static and dynamic equipment datasets; 3D reconstructing the static equipment dataset and merging it with the dynamic equipment dataset to construct a hierarchical association model; triggering a linkage response to generate joint audit instructions when the equipment operating status exceeds the security boundary; determining abnormal execution paths and abnormal tracking paths based on the hierarchical association model and joint audit instructions; deducing fault conditions by combining temporal change trends; and dynamically rendering and displaying the equipment status within the abnormality's influence range using a 3D visualization engine-driven hierarchical association model. This achieves deep fusion of multi-source heterogeneous data, 3D visualization of equipment operating status, and intelligent deduction of fault conditions, improving monitoring personnel's ability to perceive equipment anomalies and the efficiency of fault handling.

This invention discloses an AI-powered big data-based enterprise management data visualization and analysis system. The system includes a multi-source management data acquisition module, used to collect revenue, cost, profit, cash flow, inventory, procurement, orders, customers, projects, human resources, and supplier performance data from enterprise financial systems, ERP systems, CRM systems, inventory systems, procurement systems, contract systems, project management systems, and human resource systems. This invention relates to the field of enterprise management data processing technology. This AI-powered big data-based enterprise management data visualization and analysis system, when abnormal result indicators occur, can trace intermediate transmission indicators and cause indicators backward along the operational causal chain, and delete pseudo-correlation paths based on business rule verification values ​​and time sequence verification values, solving the problem that existing visualization systems can only display abnormal results but cannot explain the root causes of anomalies.

The application provides a multi-scene safety inspection method and system based on a UAV, and belongs to the technical field of UAV safety inspection, and comprises the following steps: constructing a multi-scene safety sample library; training a multi-task computer vision model based on the multi-scene safety sample library to obtain a safety inspection algorithm model integrating multiple recognition capabilities; the multi-task computer vision model comprises a shared feature extraction backbone network and multiple independent task head networks; a training set is input into the shared feature extraction backbone network to extract general feature maps; the general feature maps are respectively input into the multiple independent task head networks for parallel training to obtain the safety inspection algorithm model; and the safety inspection algorithm model is loaded on a UAV flight platform to perform parallel inference analysis on inspection images collected by the UAV in real time, and to identify preset safety hidden danger targets.

The invention provides an AI video monitoring platform based on an RISC-V framework. The AI video monitoring platform comprises eight levels of frameworks including an equipment perception layer, an edge computing layer, a data access layer, a data processing layer, a data storage layer, an algorithm engine layer, a platform application layer and an industry application layer. The invention belongs to the technical field of monitoring platforms, particularly relates to an AI video monitoring platform of an RISC-V architecture, and aims to at least solve the problems that traditional security depends on manual screen staring and inspection, abnormal event recognition lags behind and the missing report rate is high, and the existing system is high in authorization cost, insufficient in edge scene energy efficiency ratio and poor in adaptability. The risks that the data transmission delay is high, real-time early warning cannot be supported and privacy leakage exists in original video cloud storage due to a front end-cloud framework are avoided; the method avoids the phenomena that different brand devices and platform protocols are not uniform, the compatibility is poor, the deployment and maintenance cost is high, and the expansion capability is weak, and overcomes the defects that a general algorithm is insufficient in precision, lacks an industry exclusive algorithm, and cannot cover the multi-scene safety protection requirement.

This invention relates to the field of smart meter technology, specifically a method and system for big data AI analysis and control of quality in the manufacturing process of electricity meters. The method includes: collecting quality-related data; performing field mapping, object binding, standardization conversion, missing data completion and anomaly filtering, and version association; constructing a unified quality object model; invoking a key indicator statistics engine, a rule-based early warning engine, an AI anomaly identification engine, a multi-dimensional difference analysis engine, and an order quality evaluation engine; fusing and calculating the results from each engine to generate a comprehensive risk value and risk level; triggering meter locking, process interception, stricter testing, equipment inspection, and quality closed-loop actions based on the risk level; and writing back retesting, rework, repair, factory release, and after-sales feedback to the quality data warehouse to update the experience base and optimize subsequent strategies. This invention achieves real-time analysis of quality data, anomaly early warning, intelligent decision-making, rapid traceability, and in-process interception throughout the entire electricity meter manufacturing process.

This invention discloses an integrated sorting device for infectious medical plastics and metal sharps, belonging to the field of medical waste treatment technology. It includes a main shell, which is a hollow cylindrical structure. Inside the main shell, three sets of sieving mechanisms for sorting medical syringes are arranged from top to bottom. Three mounting arms are fixedly connected to the inner wall of the main shell at the position corresponding to each set of sieving mechanisms, and these three mounting arms are equidistantly distributed radially along the main shell. This invention achieves automated grading and sieving of different types of medical syringes and efficient integrated separation of needle tips and syringe barrels, significantly improving the efficiency and safety of medical waste disposal. Simultaneously, through the nested structure of the hollow main shaft and the built-in secondary shaft, combined with a bevel gear transmission pair driven by a closed-loop stepper motor, the working angle of all scraping blades can be adjusted synchronously at one time, enabling precise and efficient shearing and separation of the needle root.

The application provides a kind of expressway emergency response method and system based on management and control unit, by obtaining the event logic knowledge network of expressway emergency event and the driving event data in the control unit monitoring flow, the knowledge vector data of each control unit driving event in multiple emergency response stages with logical time sequence order can be systematically analyzed and processed. By fusing the initial knowledge vector data of each emergency response stage and the prior transmission knowledge vector data, more accurate and comprehensive transmission knowledge vector data is generated, and the corresponding response scheme of each emergency response stage is determined. This method not only improves the accuracy and efficiency of emergency response, but also optimizes the allocation and scheduling of emergency resources by determining the priority information of candidate control unit driving events. Finally, according to the priority information of each control unit driving event, the disposal event corresponding to the expressway emergency event can be quickly and accurately determined.

The invention discloses a wind power intelligent station security inspection system based on deep learning. The system comprises a multi-source data access and synchronization module, an environment contrast evaluation module, a deep learning identification and event generation module, a multi-evidence reliability evaluation module, a weak network abstract evidence collection package generation module and a recheck feedback self-learning module. Synchronous snapshots are generated through access videos, door magnets, access control, vehicle access, weather and network quality information; calculating an environment interference value E based on the physical contrast point location and the virtual photo segment library; obtaining purification characteristics and generating event description by adopting layered identification and interference decoupling; fusing the video confidence coefficient, the trigger confidence coefficient, the legal exclusion confidence coefficient, the E and the network quality and uncertainty gating to calculate alarm reliability R, and performing graded output; generating a key frame evidence obtaining packet and outputting a Hash chain check code in a weak network, and complementarily transmitting a short video when the network is good; and station parameters are updated according to the rechecking conclusion.

This invention relates to the field of toll station management technology and provides a remote operator monitoring and emergency vehicle handling system for highway toll stations. The system includes: a front-end perception layer for real-time vehicle status monitoring, composed of lane equipment including photoelectric sensors, infrared height limit alarms, license plate recognition cameras, ETC antennas, audible and visual warning lights, and self-service device sensors; a network transmission layer connecting lane equipment, toll booth cabinets, the core switch in the computer room, and the monitoring center via a fiber optic network; a remote control layer consisting of a remote operator center capable of video monitoring, video intercom, and remote control; and an anomaly alarm layer for issuing warnings for vehicle congestion, vehicle height exceeding limits, ETC malfunctions, toll evasion, and barrier anomalies. This invention enables real-time vehicle status perception and data aggregation to the remote operator center via the network transmission layer, breaking down information silos and automating emergency handling.

The utility model discloses a positive pressure type air respirator myopia mask with an alarm function, which comprises a positive pressure type air respirator mask body, and a convex positive pressure type air respirator mask transparent observation window is arranged at one end of the positive pressure type air respirator mask body. The positive pressure type air respirator mask comprises a positive pressure type air respirator mask body, a positive pressure type air respirator mask transparent observation window is arranged on the positive pressure type air respirator mask body, a near-sighted glass support is arranged in the positive pressure type air respirator mask transparent observation window, the positive pressure type air respirator mask body is further connected with a positive pressure type air respirator mask breathing connector, a detection device is embedded in the positive pressure type air respirator mask breathing connector, and a power module is arranged in the detection device. According to the utility model, the vision problem of a myopia person wearing the mask can be solved, the content of oxygen and sulfur hexafluoride in an operation environment can be monitored in real time, and the safety and the treatment efficiency of the operator are improved.

The application discloses a ground control method and device applied to a single-track railway train control system, and is applied to the technical field of track traffic, wherein the ground control method and device are characterized in that: through receiving multi-source key information such as axle-counting equipment and interlocking equipment, the ground control method and device realize vehicle-ground data interaction by means of 400MHz wireless communication; the ground control method and device divide inter-station separation sections, generate signal authorization and route information packages containing track numbers / branch line numbers, adopt a hierarchical communication mechanism and double-checking to guarantee safety, support multi-vehicle tracking and temporary data real-time updating; meanwhile, the system can identify fault types, enable redundant channels and process according to safety states, realize state monitoring, fault positioning and reporting through a maintenance terminal, and comprehensively improve single-track railway transportation efficiency and driving safety.

This application discloses a detection method and system, as well as a storage medium. The detection system acquires log information from a host; analyzes and processes the log information based on a threat intelligence database and a first analysis model to obtain an initial compromise result of the host; wherein, the first analysis model is used to detect unknown risks; and analyzes and processes the initial compromise result based on a second analysis model to obtain a target compromise result; wherein, the second analysis model is used to verify the compromise result; this method can reduce the false negative rate and false positive rate of compromised hosts and effectively improve the efficiency of handling compromised hosts.

The invention discloses a nuclear power plant cold source safety monitoring and early warning system and an implementation method thereof. The system is sequentially provided with an entrance area, an offshore monitoring area and an open sea early warning area from a water intake to the outside; a sensor array is integrated on the trash holding net in the entrance area; a plurality of layers of offshore monitoring floating bodies which are arranged in an arc line are arranged in the offshore monitoring area and are connected through a first chain; an open-sea monitoring floating body arranged in an arc line is arranged in the open-sea early warning area and is connected through a second chain; and the centers of all the areas are connected in series through a third chain to form a skeleton structure which takes the chain as a spine and spreads towards two sides. Wide area scanning is carried out through the open sea early warning area, and first-level early warning is generated; tracking and risk prediction are performed on a target through an offshore monitoring area, and a second-level alarm is generated; and final confirmation is carried out through the entrance area, and third-level response and interception are triggered. According to the invention, three-dimensional and full-coverage monitoring from the open sea to the water intake is realized, and through intelligent graded early warning and linkage response, the early warning time efficiency and the disposal efficiency are remarkably improved.

The application provides an AI software supply chain-oriented security evidence fusion analysis method and system, and relates to the technical field of AI software supply chain security. The method comprises receiving an AI software supply chain and extracting AI software components, and constructing an AI software supply chain topology network; performing security detection on the supply chain to generate a security scanning report, performing semantic field mapping and spatial coordinate marker alignment on the report based on the topology network, outputting security evidence data and performing high-dimensional space vector projection, calculating the semantic similarity of the security evidence data and historical empirical data, and screening risk evidence data with a semantic similarity higher than a threshold; extracting risk evidence data of the same component and importing a hybrid judgment engine to perform vulnerability existence judgment and vulnerability exploitation accessibility verification, prioritizing the risk evidence data based on vulnerability risk values, and outputting a risk priority evaluation report, thereby improving the security audit accuracy and disposal efficiency of the AI software supply chain.

The present application relates to a kind of surge protector fault research and judgment method and system based on Internet of Things, to solve the problem of fault propagation path positioning and accurate isolation under complex power distribution environment. By deploying microsecond level synchronous distributed edge collection unit at the port of surge protector, real-time capture voltage, current and thermal sensitive parameters, generate five-dimensional transient response fingerprint, and use timing alignment mechanism to eliminate data acquisition and transmission delay, realize the strict alignment of multi-level transient response data set. Based on causal graph neural network model, fuse pulse trigger, response lag and energy attenuation causal characteristics, dynamically correct causal relationship and filter false association, accurately trace back fault propagation chain. Finally realize source positioning, structured diagnostic report generation and isolation strategy push, improve the timeliness and reliability of power distribution system fault disposal.

The invention relates to the technical field of tobacco equipment supervision, and discloses a cigarette making machine table quality supervision method which comprises the following steps: S1, constructing a multi-source database which comprises a cigarette making machine data acquisition database, a cigarette appearance detection and removal device data table, a machine table self-inspection defect number data table and a physical index database table; s2, querying data in the multi-source database through data query software, and comparing the queried data with corresponding set values or set conditions; s3, the data query software outputs running state information of the cigarette making machine; and S4, when the queried data exceeds the corresponding set value or set condition range, the data query software outputs alarm information. And cross validation and comprehensive analysis are carried out through preset logic rules and algorithms, so that the dependence on artificial experience is reduced. And the occurrence of batch quality accidents is effectively avoided.

The invention discloses a large-scale multi-source panoramic situation awareness system and method based on dynamic digital twinning, and belongs to the technical field of safety monitoring of large carrier platforms. The system takes a digital twinning scene as a unified space-time reference, realizes system-level integration of multi-source sensing, data processing and upper computer application, and comprises a multi-source sensing terminal layer, a digital twinning and data processing layer and an upper computer application and visualization layer; the method comprises the steps of sensing data acquisition and edge processing, equipment installation calibration parameter loading and maintenance, carrier attitude and scene coordinate reference updating, multi-source sensing data mapping and dynamic digital twinborn fusion, target position mapping and multi-view visual output, abnormal situation identification, alarm and data recording. According to the method, unified situation space construction, accurate three-dimensional positioning and visual multi-view display can be realized in the navigation, berthing and on-duty scenes of the large ship, and the method has good method expansibility and platform universality.

The utility model relates to the field of fire-fighting equipment, and discloses an automatic fire-fighting and fire-extinguishing inspection vehicle which comprises a mounting frame, crawler belt assemblies symmetrically mounted on the two sides of the mounting frame, a top shell fixedly arranged at the upper end of the mounting frame, a sensor assembly movably connected to one side face of the top shell, a storage box arranged in the center of the mounting frame, and a fire-extinguishing tank detachably mounted in the storage box. The top of the fire extinguishing tank is connected to the storage box through a tank cover, a stirring device is arranged in the fire extinguishing tank and connected to a speed reduction assembly in the mounting frame through a coupler, the bottom of the fire extinguishing tank is connected with a spray pipe assembly through a bolt, and the spray pipe assembly penetrates through the top shell and extends out along the upper end of the top shell. The driving wheel is connected to a motor arranged in the mounting frame through a transmission assembly.

The invention relates to the technical field of computers and artificial intelligence, and discloses a financial transaction risk early warning processing method, system and device, and the main points of the technical scheme are that original financial data is obtained and preprocessed, and an event data flow of financial transaction data and a historical behavior sequence of a user are obtained; based on the event data flow of the financial transaction data, constructing a dynamic time sequence diagram, and generating a context risk vector for the to-be-analyzed transaction through the dynamic time sequence diagram; an individual financial cognitive model is constructed for the user, the individual financial cognitive model is used for adjusting an attention mechanism of the model according to the historical behavior sequence and the context risk vector of the user, and an expected representation vector of the to-be-analyzed transaction is generated; obtaining an actual representation vector of the to-be-analyzed transaction, and calculating a cognitive deviation degree in combination with comparison with an expected representation vector of the to-be-analyzed transaction; and obtaining a corresponding risk level based on the cognitive deviation degree and a preset risk early warning rule, and sending a risk early warning signal corresponding to the risk level.

The invention relates to the technical field of power transformation equipment monitoring, and discloses a real-time processing system based on abnormal information of power transformation equipment. The system comprises a multi-source data fusion module, an abnormal feature mining module, a state trajectory prediction module, a risk situation quantification module, an equipment cooperative control module and an intelligent execution engine module which are connected in sequence. The system collects operation parameters of power transformation equipment through an Internet of Things sensor, and generates an equipment state sequence in a unified format through denoising and standardization processing. Abnormal feature vectors are extracted by performing multi-dimensional feature analysis on the sequence, and a state change track of the equipment is predicted and a state evolution curve is generated based on the abnormal feature vectors. The system calculates risk probability distribution in combination with the real-time state sequence, forms a risk level map, formulates a regulation and control strategy and generates a cooperative control signal according to the risk level map, and finally drives an execution mechanism to complete exception handling. According to the system, closed-loop management from state monitoring and abnormity prediction to cooperative control is realized.

The invention relates to the technical field of power equipment fault positioning, in particular to a power equipment fault positioning method based on a graph neural network, which comprises the following steps of: establishing a Chain-GNN model to be matched with hardware deployment to pre-process data during normal operation of a circuit, and determining normal operation baseline data; step 2, setting an abnormal score threshold and a bidirectional traveling wave fault rule base, and oppositely and synchronously injecting bidirectional traveling waves into a preliminarily alarmed line, and step 4, after a fault point is confirmed, ensuring normal operation of other equipment; after the sublines are erected, first-aid repair is carried out, and after first-aid repair is completed, all the sublines are disconnected, and original circuit connection is recovered; comparing the normal operation baseline data in the step 1 to determine whether the line recovers normal operation, if so, entering a step 6, and if not, returning to the step 3 to re-determine the fault point; and the Chain-GNN model can automatically archive the whole process data of the fault processing at this time.

This invention discloses a lightweight traffic video processing and multimodal intelligent perception system and method, comprising: a streaming media preprocessing module for performing availability detection and parameter configuration on the accessed video stream and outputting a stable video stream; a video polling and frame extraction module for establishing a video polling queue, extracting key frames from the video stream, and splicing them into a lightweight video stream; a deep learning algorithm module for using a lightweight visual model to perform preliminary traffic event detection on the lightweight video stream; a multimodal large model service module for secondary confirmation of detected traffic events, extraction of key information, and generation of event reports; and a perception and analysis platform module for realizing the visualization, hierarchical dispatch, and closed-loop management of traffic events. Compared with existing technologies, this invention reduces the computational power requirements for video processing through video stream polling and key frame extraction mechanisms, enabling lightweight access and processing of large-scale road network videos.

This invention relates to the field of cloud-edge collaboration technology, specifically disclosing a scene-adaptive urban management platform based on cloud-edge collaboration, comprising: an image acquisition module: commanding a drone to fly along a preset route and capture images along the way, preprocessing and recognizing the video stream data, and extracting keyframes; a keyframe cropping module: acquiring the three primary color values ​​of pixels, marking target pixels based on the three primary color channels, constructing a planar coordinate system, and cropping frames based on the maximum and minimum values ​​of the horizontal and vertical coordinates; and an analysis and processing module: dividing the cropped frames into groups, acquiring the maximum proportion of cropped frames in the same group, determining the target event corresponding to the urban management problem, and distributing the data to various drone terminals. By lightweighting the data, the bandwidth pressure during upload is reduced, transmission resources are saved, the efficiency from problem discovery to handling is improved, management resources are targeted, and the transformation of urban management from passive response to proactive prediction is supported.

The invention provides a large model anomaly diagnosis and disposal method and device, equipment and a medium, and relates to the technical field of artificial intelligence. After the large model enters a specified processing stage of the current task, when it is determined that the large model triggers a preset abnormal event based on the monitoring data, current abnormal description information is extracted from the monitoring data; based on a pre-constructed causal graph, determining a target observation node corresponding to the current anomaly description information, determining a target root cause node from root cause nodes connected with the target observation node, the target root cause node being capable of representing an anomaly reason corresponding to the current anomaly description information, and realizing automatic and accurate diagnosis of the anomaly reason; according to the method, the target processing strategy corresponding to the target root cause node is determined from the pre-constructed exception processing strategy library, the target processing strategy is executed, a complete exception self-healing closed loop is formed, manual intervention is reduced, the exception diagnosis and processing efficiency is improved, the probability of successful starting of a large model task is improved, and computing resources are saved.

The application provides a multi-agent cooperative scheduling method and system based on real-time voice state, relates to the technical field of financial business cooperative scheduling, and comprises the following steps: collecting and analyzing voice stream data of each participating node in real time through an adaptive multi-channel voice feature analysis model, and outputting a structured voice feature set; quantizing and fusing the structured voice feature set to generate a multi-dimensional node voice state quantization feature vector; dynamically determining at least three key quantization indexes representing different state dimensions as state anchor points from the node voice state quantization feature vector; and the state anchor points specifically correspond to three quantifiable coordinate positions of a voice emotion entropy value, an instruction throughput rate and a channel occupation ratio in the feature vector. The application is suitable for a financial cooperative scene and can accurately capture the real-time state of a node from the voice dimension, thereby improving the real-time performance, adaptability and dynamic optimization capability of multi-agent cooperative scheduling.