54results about How to "Improve early warning efficiency" patented technology

The invention relates to the technical field of power grid monitoring, in particular to a power grid abnormal state monitoring and early warning system which comprises a historical reference establishment module, an operation deviation analysis module, an abnormal threshold adjustment module and an abnormal early warning module. According to the invention, through real-time data acquisition and historical data analysis, the monitoring and early warning efficiency of power grid operation is improved, and through collection of power grid operation records, extraction of data of differential time periods, evaluation and analysis of the average load and volatility of the data, and construction of a historical power grid operation reference, the operation trend and potential abnormity can be captured more accurately. The method reduces the uncertainty of judgment depending on experience, can recognize a deviation mode and an adjusted load detection threshold value by comparing current data with a historical reference, enables the abnormal early warning to be more accurate, avoids excessive false alarms and missed alarms, optimizes the evaluation of early warning levels and the implementation of early warning notification, guarantees the safety and stability of a power grid, and improves the safety and reliability of the power grid. And the economic efficiency and the reliability of power grid operation are also improved.

The invention provides a safety early warning method and device for industrial equipment, electronic equipment and a computer readable storage medium, and relates to the technical field of service intelligent fault diagnosis. The safety early warning method comprises the following steps: acquiring actual multi-modal data of all components of industrial equipment at the current moment; predicting predicted multi-modal data of all the components at the next moment based on the multi-modal collaborative prediction model and the actual multi-modal data of all the components at the current moment; and on the basis of the actual multi-modal data of all the components at the current moment and the predicted multi-modal data of all the components at the next moment, abnormal components of the industrial equipment at the next moment are determined, and early warning is given out. The problems that in the prior art, the intelligent level is low, abnormal early warning cannot be achieved, and the effect of nipping in the bud is at least solved. The method is suitable for equipment fault detection and early warning scenes.

The invention relates to the technical field of visual feature perception, in particular to a dangerous behavior early warning method based on driver visual feature fusion. Comprising the steps that a multi-modal data packet is generated according to collected multi-source image data, and the multi-modal data packet comprises first-level modal data, second-level modal data and third-level modal data; processing all data in the multi-modal data packet according to a preset fusion model, and generating a fusion feature image according to a fusion result; and establishing a visual perception model, and generating a driver visual perception result according to the visual perception model and the fused feature image. According to the method, the time sequence is established, the acquired image data in the driving process of the driver is continuously acquired, and the image data in the driving process of the driver is accurately processed according to the preset visual processing model, so that the sight direction of the driver and the accurate prediction of dangerous driving behaviors are improved; therefore, the early warning efficiency of potential driving risks is improved.

The invention relates to the technical field of early warning broadcasting, and discloses a gridding earthquake early warning broadcasting method for emergency broadcasting, which comprises the following steps: performing gridding partition on a target early warning area to obtain N sub-areas; configuring a broadcast file and a calculation model for the alarm horn in each sub-region; obtaining concurrent information of the early warning center to the N sub-regions; the concurrent information comprises the following steps: respectively inputting the concurrent information into calculation models of different alarm horns in the sub-regions to obtain corresponding broadcast query information; and querying the broadcast file based on the broadcast query information to obtain alarm broadcast information of the alarm horns in the corresponding sub-regions, and broadcasting the alarm broadcast information.

The invention discloses a recommendation optimization method based on multi-modal feature clustering, and aims to improve diversity and user experience of a recommendation system. According to the method, firstly, features are extracted from a commodity title and a cover picture through a text and visual feature extraction model, and text and visual feature representation is obtained; then, the features are subjected to feature alignment and standardization processing to ensure the consistency of different modal features, and then the features are fused into a multi-modal embedded vector to serve as unified feature representation of commodities. And then, clustering is carried out on the fused multi-modal embedded vectors by using a K-Means clustering algorithm, so that clustering identifiers of the commodities can be formed. In the recommendation process, the system monitors the clustering identifiers of the displayed commodities, adjusts the recommendation strategy according to the identifiers, and preferentially recommends the commodities of different clusters from the displayed commodities, so that excessive distribution of similar contents is avoided, and the diversity of the recommended contents is ensured. According to the method, by integrating multi-modal information, the performance of a recommendation system is optimized, and the problem of excessive distribution is effectively solved.

The invention discloses a systematized nursing analysis method for intraoperative pressure injury, and belongs to the technical field of medical information systems.The systematized nursing analysis method comprises the steps that patient operation process data, patient body information and patient pressure position condition data are obtained for pressure position abnormity prediction, and patient physical characteristic data are obtained for physical abnormity influence analysis; the method comprises the following steps: analyzing the influence of physical abnormality and the abnormal prediction result of the pressed position, performing injury analysis by combining the influence analysis of physical abnormality and the abnormal prediction result of the pressed position, performing injury early warning based on the injury analysis result, judging whether the pressed position needs to be protected, and performing comprehensive assessment analysis on the body position of a patient and the danger of the pressed position in the operation simulation process before an operation. Meanwhile, compressive damage analysis is conducted on the patient in the operation process by integrating the physique danger information of the patient, and the accuracy and early warning efficiency of compressive damage analysis are improved.

The invention discloses a safety monitoring and early warning method and platform for an electric tower base, and relates to the technical field of safety monitoring, and the method comprises the steps: carrying out the monitoring feature mining according to multivariate safety risk factors, and building a base monitoring feature distribution network; the base is monitored in real time through the Internet of Things sensing array, and a monitoring data stream is obtained; performing error compensation by using the sensing correction multi-level model, and extracting base structure, environment and external force feature information; activating a security risk detection channel, and detecting a risk coefficient in combination with the feature information; if the risk coefficient reaches a threshold value, the early warning device is activated, and a graded early warning signal is generated, so that the technical problems of low base safety risk assessment accuracy and low monitoring response speed caused by lack of comprehensive analysis capability on information in multiple aspects such as base structure state, environmental influence and external force action in existing electric tower base safety monitoring are solved.

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 embodiment of the invention discloses a high-altitude operation protection early warning method and device and a computer readable medium. A specific embodiment of the method comprises the following steps: collecting a real-time monitoring video stream as a to-be-detected video stream; performing target detection on the to-be-detected video stream; for each piece of construction personnel description information in the construction personnel description information set, the following processing steps are executed: carrying out image segmentation on a video image; determining a working face type; determining risk area description information; according to the risk area position information, determining the limb distance of the constructor corresponding to the constructor description information; and according to the risk area protection facility information, the edge distance and the construction personnel dressing information included in the construction personnel description information, performing early warning operation on the construction personnel corresponding to the construction personnel description information. According to the embodiment, the high-altitude operation safety is monitored in real time, automatic early warning for high-altitude operation safety monitoring is achieved, and the situation that the life safety of constructors is directly threatened due to the fact that the detection efficiency of high-altitude operation safety monitoring and the early warning efficiency of high-altitude operation are low is avoided.

The early warning device for the network engineering base station comprises a machine body, supporting rods are installed at the two ends of the machine body, two limiting clamps are installed on the outer sides of the two supporting rods through screws, and a solar cell panel is rotatably installed above the supporting rod at the right end; and a light source detector is mounted above the solar cell panel through screws, the bottom of the light source detector is connected with a servo motor through a wire harness, and a mounting seat is welded and mounted above the supporting rod at the left end. According to the early warning device for the network engineering base station, the solar cell panel is rotatably mounted above the right end supporting rod through the transmission shaft, and the solar cell panel is inclined, so that energy generated by solar irradiation of the solar cell panel can be transmitted to the early warning device; and then, electric energy can be provided through the solar cell panel, so that energy consumption and waste are avoided, and the practicability of the early warning device 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 present invention relates to the technical field of intelligent fire management, and discloses a method for distinguishing the working status of a fire cannon based on an IoT cloud platform, comprising: deploying a high-definition camera in the working area of ​​the fire cannon, and collecting on-site images in real time through the camera; preprocessing the on-site images collected in real time through an edge computing device; transmitting the preprocessed images to the IoT cloud platform, wherein: performing image recognition on the images through a set convolutional neural network to extract the state characteristics of the fire cannon; combining a time series analysis method, using a long short-term memory network to analyze the extracted fire cannon features and generate analysis results; comprehensively distinguishing the analysis results and outputting the distinction results. The present invention significantly improves the accuracy and real-time performance of distinguishing the working status of fire cannons through multi-source data fusion, advanced image processing technology and time series analysis methods.

The invention discloses an operation risk early warning method and device for power distribution network equipment and electronic equipment, and relates to the technical field of electric power systems or other related fields, the method comprises the steps that real-time operation data of target power distribution network equipment are collected, the real-time operation data comprise operation parameters of N equipment parts in the target power distribution network equipment, and the N equipment parts in the target power distribution network equipment are connected with the real-time operation data; n is a positive integer; determining the equipment risk level of the target power distribution network equipment through a preset risk assessment model and the operation parameters of all the equipment parts; and determining a risk early warning strategy and risk early warning information based on the equipment risk level, and sending the risk early warning information to the risk prevention and control client based on the risk early warning strategy. The technical problem that safe operation of the power distribution network cannot be guaranteed due to inaccurate operation risk early warning of the power distribution network equipment in related technologies is solved.

The invention relates to the technical field of intelligent data analysis, in particular to an intelligent community fire-fighting big data analysis system and method based on artificial intelligence, and the system comprises a data collection module, an artificial intelligence module, a screening module, a first determination module, a calculation module, a second determination module, a judgment module, an alarm module and an adjustment module. By monitoring combustible heaps, fire hydrant water levels and water tank water pressures of all grids in a community in real time, the system can quickly identify potential fire risks and give an alarm in time, the probability of fire occurrence is reduced, the dynamic adjustment function of the system flexibly optimizes grid spacing according to the number of abnormal grids and a preset standard, and the safety of the system is improved. The configuration efficiency of fire-fighting resources is improved, so that a community can accumulate and analyze fire-fighting data, and the problems of low accuracy and slow response of a fire-fighting safety analysis result caused by single monitoring means and insufficient real-time processing capability are effectively solved.

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 invention relates to a navigation rescue method and system based on Beidou short message rapid positioning, comprising the following steps: step S1, decoding the data of the Beidou navigation system received by the reference base station to obtain the Beidou short message; step S2, the rapid positioning platform performs position calculation on the decoded Beidou short message to obtain positioning information; step S3, judging the calculated positioning information and issuing an early warning. The differential platform in the present invention can perform fine tracking processing based on multiple reference message messages of the reference base station to obtain baseband observations for differential calculation that eliminate the transmission error between the base station and the differential platform, and multiple reference channels are finely tracked to obtain a carrier phase error with a smaller error, which can achieve accurate positioning in a rescue environment.

The invention discloses a storage medium, and an underground water pollution early warning method, device and equipment. The method comprises the following steps: constructing a tracing test well group for a tracing test; sampling analysis of underground water is carried out through observation points at different monitoring depths in the tracer test well group, and tracer agent concentration penetration curves at different monitoring depths are obtained; a groundwater flow and solute transport coupling model is established and calibrated, and a spatial distribution field containing hydraulic conduction coefficients of aquifers of different depths is obtained; identifying groundwater priority channels of the target plot at different depths; determining the hydraulic gradient from the pollution source to the potential risk receptor, and calculating the average underground water flow velocity on the priority channels at different depths according to the average value of the hydraulic conduction coefficients of the priority channels and the effective porosity of the stratum; determining a priority channel for installing a monitoring device according to the sequence of the average underground water flow velocity; according to the invention, the installation number of monitoring devices can be reduced, so that the layout cost and the maintenance cost are reduced.

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 present invention discloses a performance risk early warning method and system based on transaction data, which belongs to the field of performance risk early warning. The present invention obtains post-transaction comment data of customers of similar transactions and imports the transaction data into an evaluation of the authenticity of transaction performance to obtain a real performance transaction, imports the evaluated real performance transaction information and the transaction information data of this transaction into a transaction performance risk assessment strategy to conduct a transaction performance risk assessment, and performs a transaction process early warning based on the obtained transaction performance risk assessment result. The present application firstly accurately obtains similar transactions through transaction data, and then accurately obtains the real performance transactions in similar transactions based on the data features in the post-transaction comments, and finally comprehensively analyzes the transaction risks based on the transaction data, transaction similarity values ​​and performance risk values ​​of the real performance transactions, thereby improving the efficiency and accuracy of performance risk early warning.

The present application discloses a method for quantitative analysis and early warning of corporate financial risks, which collects and integrates corporate data disclosed on the Internet, extracts indicator data from it according to a pre-set corporate financial risk indicator system, and uses the corporate financial risk model based on machine learning capabilities to conduct quantitative evaluation of corporate financial risks according to the indicator data. In particular, the method adds a public opinion adjustment parameter to the corporate financial risk model, specifically by collecting and analyzing the network traffic data of the corporate official website, thereby adjusting the weight of the corporate public opinion risk indicator according to the changes in the number of visits to the corporate official website. Ultimately, the accuracy of the quantitative analysis of corporate financial risks is improved, and the efficiency of corporate financial risk early warning is improved. In addition, the present application also provides a device, equipment and readable storage medium for quantitative analysis and early warning of corporate financial risks, and its technical effects correspond to the technical effects of the above-mentioned method.

The invention relates to the technical field of fire-fighting platforms, in particular to an intelligent fire-fighting platform based on the Internet of Things and a control method, and the control method comprises the steps: determining a fire easy-to-occur region of a to-be-monitored building based on a historical fire occurrence region of the to-be-monitored building and a fire occurrence source gathering region; based on the risk factor feature type of the fire easy-to-occur area, determining the type of the fire easy-to-occur area; and determining a fire occurrence time period type based on the fire occurrence region type, wherein the judgment method comprises the following steps: determining a fire occurrence time period according to a comparison result of the operation power of the power equipment and the preset operation power and a comparison result of the inflammable exposure degree and the preset exposure degree; according to the fire occurrence time period type, determining a monitoring method for a fire easy-to-occur area; and determining whether to adjust the judgment parameters of the fire monitoring method based on the early warning efficiency of the fire.

The invention discloses a karst area foundation pit water inrush early warning system and method, which relate to the field of artificial intelligence technology. The system monitors the groundwater dynamics, geological stress and soil moisture data of the karst area in real time through a data acquisition module, performs data cleaning, noise filtering and completion in a data processing module, and compensates for data loss using a multidimensional interpolation algorithm, thereby improving data integrity and accuracy. In a regional division and analysis module, the karst area is divided into independent karst sub-areas, and geological characteristic data is monitored in real time. The model of a chain reaction early warning module is used to analyze the fracture extension trend and water level change, thereby realizing the prediction of the time and area of ​​water inrush, and reducing the system's dependence on historical data patterns. The feedback control module evaluates the joint risk index Tsyz, thereby realizing the early prevention of water inrush risks in adjacent areas, thereby ensuring the real-time response of the system and the safety of construction.

The present application provides a method, system, electronic device and medium for early warning of equipment failure in a power plant unit, and relates to the technical field of equipment failure early warning. The method for early warning of equipment failure in a power plant unit provided by the present application includes: obtaining the fault information and equipment operating parameters of the current equipment when the fault early warning information of the current equipment is obtained; determining the equipment-related parameters corresponding to the current equipment based on the equipment operating parameters and the acquired historical operating parameters; determining the associated fault information of the target equipment based on the fault information and the equipment-related parameters; obtaining the initial alarm threshold of the target equipment, and determining the target alarm threshold corresponding to the target equipment based on the initial alarm threshold and the associated fault information; generating the fault early warning information corresponding to the target equipment when the operating parameters of the target equipment reach the target alarm threshold. The present application can reduce the chain losses of other equipment associated with the current equipment caused by the failure of the current equipment.

The invention discloses an unmanned aerial vehicle monitoring system and method for automatic agricultural production, and belongs to the field of general control systems, crop growth images, soil data and future period weather data are integrated, crop growth conditions are comprehensively analyzed, crop lodging conditions under the influence of future weather are estimated, and crop lodging conditions under the influence of future weather are estimated. According to the crop lodging early warning method and system, crop lodging early warning efficiency and accuracy are improved, crop growth conditions are comprehensively analyzed, growth implicit features are analyzed to obtain a growth condition analysis result, and crop lodging early warning efficiency and accuracy are improved. And then the growth condition analysis result is merged into the crop lodging trend analysis model to carry out crop lodging trend comprehensive analysis, so that the crop lodging trend analysis efficiency and accuracy are further improved.

The invention relates to the technical field of electronic navigation, in particular to a navigation-based ground abnormity early warning method, system and device and a storage medium, and the method comprises the steps: obtaining vehicle historical information data, road geographic information data and historical abnormal state data; performing feature extraction on each selected area of each target road according to the data; obtaining a corresponding characteristic value, and constructing an anomaly detection library of each target road according to each characteristic value; in the vehicle driving process, real-time driving data of the vehicle are obtained, and the driving area of the vehicle in the current road and the corresponding target feature value in the anomaly detection library are determined; based on the target characteristic value and the vehicle navigation position information in the real-time driving data, generating an abnormal detection result of vehicle driving, and executing a corresponding early warning strategy; according to the invention, the ground abnormity can be accurately analyzed based on the vehicle driving track, the early warning efficiency and accuracy are improved, and the risk of accidents is reduced.

The utility model discloses a liftable highway traffic incident early warning device and early warning system, and relates to the technical field of road traffic management, the early warning device comprises a telescopic housing, the housing is provided with a control module, a display early warning module, a sound early warning module, a lifting module and a communication module, the control module can display information obtained by the communication module through the display early warning module and the sound early warning module, and controls the lifting module to enable the inner shell and the outer shell to slide in the direction close to each other or away from each other. According to the early warning device, through the control module, the shell is adjusted to ascend and descend, information obtained by the communication module is displayed through the display early warning module and the sound early warning module, the structure is simple, and effective early warning can be achieved. The lifting type road traffic incident early warning system comprises a plurality of early warning devices and a service platform used for sending sound, character and identification early warning information to the early warning devices. The early warning range is reasonable and controllable, and the early warning efficiency is high.

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 present invention relates to the technical field of a gas pressure regulating box, and in particular to a gas pressure regulating box safety early warning system based on the Internet of Things, comprising a data detection unit for detecting gas concentration and gas pressure data, a data processing unit for screening the gas concentration and gas pressure data obtained by the data detection unit based on a preset data screening standard, a cloud storage unit for receiving and storing the filtered gas concentration and gas pressure data output by the data processing unit, a data analysis unit for calculating a comprehensive evaluation value based on the gas concentration and gas pressure data of an internal pipeline of the gas pressure regulating box, and generating an evaluation value curve based on the evaluation value, and judging whether the operating state of the gas pressure regulating box meets the standard based on the absolute value of the slope of the evaluation value curve, and a cloud early warning unit for issuing an alarm, thereby effectively improving the early warning efficiency of the system described in the present invention.

The present invention relates to the field of marine disaster data processing technology, and in particular to a marine disaster data processing method based on remote sensing monitoring, comprising the following steps: S1: collecting data on ocean temperature, salinity, sea level and marine surface biological activities; S2: performing preliminary processing on the data collected in S1; S3: transmitting the processed data to a ground processing center in real time through a satellite communication system; S4: performing advanced analysis on the data received in S3, identifying and predicting minor changes in the marine environment; S5: predicting the disaster risk within the next 48 hours; S6: automatically generating and publishing early warning information based on the prediction results of S5. The present invention significantly improves the real-time and accuracy of marine disaster early warnings by integrating remote sensing technology, data processing algorithms and information release systems, effectively enhances the timeliness and effectiveness of disaster response measures, and reduces the impact of disasters on human society and the environment.