15results about How to "Improve early warning efficiency" patented technology

The invention provides a vulnerability early warning method and system based on asset fingerprint driving. The method comprises the following steps: acquiring vulnerability information data from a plurality of vulnerability information sources; asset fingerprint information of assets in the target system is collected; based on semantic analysis, version comparison and a fingerprint matching algorithm, performing fine-grained association matching on the vulnerability information data and asset fingerprint information, and identifying target assets affected by vulnerabilities; performing influence range analysis, risk level evaluation and repair priority ranking on the target assets to generate a vulnerability influence analysis report; based on the vulnerability impact analysis report, sending an early warning notification to a related person in charge, and automatically generating an electronic work order; the processing state of the electronic work order is tracked, the repair progress is recorded, and full-life-cycle closed-loop management of vulnerabilities from recognition to repair is achieved. The missing report rate and the false report rate are effectively reduced, and compared with manual matching, the full-automatic mode can form a complete closed loop, and the early warning efficiency is improved.

The invention discloses an information security vulnerability processing system and method, and particularly relates to the technical field of computer security, and the system comprises a network monitoring module, an intrusion detection module, an abnormal network information detection module, an early warning module and an intrusion tracking module, illegal intrusion behaviors are collected through log records, the intrusion detection module judges the legality of network access by detecting data streams of a network, keyword matching is conducted on the captured data streams, and the abnormal network information detection module conducts abnormal information detection on the received data streams. The early warning module automatically responds to the detected network vulnerability, and the intrusion tracking module adopts information tracking to judge the trace of an attack intruder. Various network risks can be assessed by monitoring various data in the network and centralizing the data captured by the monitoring points in the abnormal network information detection module for processing.

The invention provides a data intelligent analysis method and system applied to carbon emission monitoring, and the method comprises the following steps: executing an NMF load decoupling algorithm to obtain a dynamic non-negative vector which comprises a resident coefficient and an industrial coefficient; according to the non-negative row vector and the space-time database, respectively calculating resident and industrial carbon emission intensities corresponding to the current time period, and updating the carbon emission intensities to the space-time database; calculating and generating future carbon emission predicted values corresponding to different future time periods according to the space-time database; and performing anomaly detection calculation according to the carbon emission intensity and a future carbon emission predicted value corresponding to the current time period. By adopting the method, the carbon emission intensity condition of the whole area can be intuitively and indirectly reflected in real time without tampering.

The invention relates to the technical field of wireless communication, in particular to a risk early warning method, device and equipment, a medium and a product, the method is applied to a computing power server, and the method comprises the following steps: acquiring perception information reported by reconfigurable intelligent metasurface equipment and aircraft information reported by a low-altitude aircraft; matching the sensing information with the aircraft information to obtain a matching result; wherein the matching result is used for indicating whether a sensing target corresponding to the low-altitude aircraft is included in a plurality of sensing targets in a signal coverage range of the reconfigurable intelligent metasurface equipment; based on the matching result, determining collision risks between the low-altitude aircraft and other sensing targets except the sensing target corresponding to the low-altitude aircraft; and under the condition that the collision risk meets an early warning requirement, generating risk early warning information, and returning the risk early warning information to the low-altitude aircraft.

The present application relates to the technical field of cable detection, in particular to a cable insulation state evaluation and early warning method based on transient traveling wave signal characteristics, comprising: distinguishing intra-zone faults and extra-zone faults according to the fault distance and the high-low frequency energy difference of the transient traveling wave signal, combining the topological structure of the current cable to be detected to determine the preliminary identified fault point; performing secondary regional mapping on the fault point, integrating the spatial position of each fault point through the direction information corresponding to the transient traveling wave signal to determine the fault region topological graph of each fault point combination; positioning the fault section corresponding to the current fault point according to the fault region topological graph data updated at any time; combining the identified fault section with the duration, intensity and frequency of fault occurrence to evaluate the insulation problem level of the corresponding fault section; comparing the insulation problem levels of the fault section under multiple analyses to perform early warning according to the coincidence of each fault section. The accuracy and efficiency of cable early warning are realized.

The invention provides a power wire breakage fault detection device and a use method, and relates to the technical field, through distributed high-precision acquisition and strong electromagnetic interference resistance and vibration resistance design of a multi-mode sensing module, a dynamic environment noise model is constructed and a background interference intensity factor is generated by combining an environment noise modeling module and utilizing machine learning; complex environment noise is effectively filtered out, the false alarm rate is remarkably reduced, and the real broken line signal capturing capacity is improved; a risk assessment unit of a system adaptive optimization module associates a fault confidence index with a background interference intensity factor, and fits a broken line risk assessment index, so that adaptive detection sensitivity adjustment based on real-time environmental noise is realized; and the strategy optimization unit carries out multi-level risk assessment and grading treatment through an assessment threshold interval, so that the fault response time is shortened, the capturing and early warning efficiency of incomplete or initial weak signals is improved, and the safety of a railway power supply system is effectively guaranteed.

The invention provides a load section early warning method and device and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining the real-time load of a load section; according to the real-time load, whether early warning judgment for the load section is executed or not is determined; if it is determined that the early warning judgment for the load section is executed, a first time period and a second time period are determined respectively; acquiring first integral data of the active power of the load section in the first time period; acquiring second integral data of the active power of the load section in a second time period; determining a load rate prediction value of the load section according to the first integral data and the second integral data; if the load rate prediction value is larger than a preset load rate prediction threshold value, load section early warning is triggered; and the early warning efficiency and the early warning accuracy are improved.

The application provides a mobile internet abnormal behavior early warning method, device and storage medium, the method comprises the following steps: collecting mass flow data of the mobile internet; identifying the mass flow data based on a first identification library, determining abnormal flow data; extracting the value signal element of the abnormal flow data based on a first extraction library; early warning the abnormal behavior of the mobile internet based on the value signal element. The mobile internet abnormal behavior early warning method provided by the application can quickly identify the abnormal flow data in the mobile internet, extract the value signal element of the abnormal flow data, restore the abnormal behavior of the mobile internet, and effectively early warn, thereby improving the early warning efficiency and realizing real-time security check on the abnormal data generated by the mobile internet.

The invention belongs to the field of urban governance and intelligent security and protection, and discloses a personnel trajectory real-time early warning method based on traceable data streams, which comprises the following steps: data access and standardization: a collector mobile phone terminal or terminal equipment reports a JSON message containing a unified ID, a timestamp and position information every several seconds and sends the message to kafka; the method comprises the following steps: accessing Apache NiFi through a Kafka message queue, and carrying out data analysis and standardization; using apache nifi consumption collector track data to carry out traceable streaming processing so as to carry out collector track monitoring and early warning; off-line early warning is generated through a nifi process and inserted into mysql, and then data are displayed through an early warning platform. According to the method, a data flow pipeline is constructed through a visual flow processing framework, grouping is carried out according to a personal unique identifier (user Id), and a data processing mode that the same key is serial and different keys are parallel is realized, so that orderliness of a single personal track and concurrency of multi-personal data are guaranteed.

The embodiment of the present application provides a kind of intelligent early warning method, device and electronic equipment, can select early warning parameter from monitoring parameter, and early warning parameter is preprocessed, clustering, then early warning parameter is combined into different parameter group, as training sample data, finally, early warning model is trained based on training sample data.This embodiment, select multiple early warning parameters, early warning parameter is clustered, so that early warning parameter can be divided into different early warning levels, finally, different early warning parameters are combined into different training samples to train early warning model, so that the early warning model obtained can combine multiple different early warning parameters to warn landslide disaster based on the same standard, realize that multiple parameters are simultaneously warned, improve the early warning efficiency, and there is no different early warning threshold condition, to improve the accuracy of early warning.

The invention relates to the technical field of venous thrombosis data monitoring, in particular to a pregnancy venous thrombosis intelligent monitoring method and system.The pregnancy venous thrombosis intelligent monitoring method comprises the following steps that pregnancy venous blood flow data is collected, dynamic indexes of blood flow velocity, blood pressure and blood viscosity are obtained, whether the dynamic indexes fluctuate within a set steady-state range or not is judged by setting the steady-state range, and if yes, the pregnancy venous thrombosis is monitored. And generating feature data of pregnancy health monitoring. According to the method, by dynamically monitoring the indexes such as the pregnancy blood flow speed, the blood pressure and the blood viscosity, the change trend of the vein blood flow state can be efficiently recognized, and in the state transition process, the high-risk blood flow state can be recognized in advance and intervened in time according to the weight analysis of the transition frequency and the duration time between the blood flow states. In addition, by analyzing the blood flow state transition trend, the deviation direction and fluctuation amplitude of the high-risk state can be recognized more visually. The blood flow dynamic characteristics closely related to the high-risk state are screened, and irrelevant redundant information is eliminated, so that characteristic analysis is more specific.

This invention relates to the field of wind energy forecasting technology, and more particularly to a method, system, device, and storage medium for early warning of extreme offshore wind power events based on typhoon forecasting. The method includes acquiring real-time satellite cloud images; identifying and marking regions of typhoons in the satellite cloud images based on their morphological characteristics; obtaining the pixel coordinates of the centroid of these regions and converting them into actual latitude and longitude coordinates to obtain the typhoon's eye coordinates; acquiring infrared satellite cloud images; obtaining the maximum wind speed radius based on the typhoon's eye coordinates; and estimating the current maximum wind speed using a preset wind speed model; calculating the typhoon's short-term trajectory, future maximum wind speed, and maximum wind speed radius using a preset typhoon short-term dynamics integrated prediction model; and predicting the typhoon's impact on a designated location within a future timeframe using a preset typhoon physical model, and calculating the wind speed and direction at that location.

This invention relates to the field of fire protection technology and discloses an audible and visual alarm and a fire self-inspection alarm device. The device includes a light alarm and a horn fixedly installed on the top of a support plate; snap-fit ​​plates are fixedly attached to the outer surface of the support plate around its perimeter; a plug-in rod is fixedly installed at the bottom of the snap-fit ​​plates; a water tank is fixedly installed on the bottom of the inner side of the inspection vehicle; a sealing plate is fixedly installed on the top of the water tank; the sealing plate has plug-in holes on its surface; an installation plate is fixedly installed on the bottom surface of the inspection vehicle; a water outlet pipe is fixedly installed at the bottom of the installation plate; and a nozzle is fixedly connected to the bottom surface of the water outlet pipe. This invention proposes to achieve emergency spraying operations through the nozzles. Simultaneously, the baffle plate on the surface of the guide rail is adjustable and can protect the smoke sensor according to actual needs, preventing it from failing due to dust accumulation or external impact. The water inlet pipe ensures a continuous water supply to the water tank, improving the efficiency of fire early warning and response, and reducing the risk of lingering fire hazards.

The invention relates to the technical field of aviation meteorological aid decision making, in particular to an aviation meteorological aid decision making system and method based on multi-agent collaboration, and the system comprises a sensing layer, a core decision making layer, an execution layer and an iterative optimization layer which are connected in sequence, and the iterative optimization layer is used for carrying out parameter optimization on the core decision making layer. According to the method, the multi-agent collaborative architecture is driven by the adjusted large language model, a knowledge base is generated by combining a prediction module with a physical model and an artificial intelligence model and retrieval enhancement, multi-source data is fused for reasoning, and rapid disassembly and reliable optimization of complex tasks are realized by combining a mixed decision mechanism; according to the method, the response time is remarkably shortened while the decision compliance is guaranteed, the comprehensive cost of flight operation and ground guarantee is effectively reduced through global path and resource optimization, and the early warning efficiency and precision of disaster weather are improved.

An annotation-based controller layer interface real-time monitoring method and device, comprising: weaving monitoring probes into each controller to monitor the calling event information of each interface in the controller; storing the corresponding calling event information into a user-side locally deployed lock-free ring buffer when the interface is called; and in the lock-free ring buffer, aggregating the interface calling parameters of each interface in the current time window according to different monitoring dimensions to obtain multiple aggregation results and uploading the aggregation results to a server. The scheme locally deploys a lock-free ring buffer on the user side to store the interface calling event information, and predefines the time window size and window step to perform sliding window processing on the calling event information, thereby realizing sub-second level real-time index statistics and greatly improving the discovery and early warning efficiency of interface exceptions.