55 results about "Perception risk" patented technology

The invention relates to a risk perception and early warning method and a system for cascading failures of a wind power base. The method provided by the invention comprises the steps of acquiring real-time operation mode data of a power grid and a wind power plant through SCADA (supervisory control and data acquisition) and a WAMS (wide area measurement system), acquiring possible wind power disturbance of the current power grid by combining with historical data of the wind power plant so as to establish a reasonable failure set, establishing a wind power generating unit dynamic model and a fan safety protection model, simulating dynamic operating conditions of the wind power plant after the power grid breaks down, carrying out power grid transient stabilization analysis after the wind power base breaks down and carrying out static safety analysis after a failure is removed, carrying out calculation and evaluation on the safety stabilization condition of the current power grid after occurrence of the wind power disturbance through a series of indexes which are applicable to early warning for the cascading failures of large wind power bases, analyzing the risk of the occurrence of cascading failures of the large wind power base, and realizing online safety early warning for an electric power system after the wind disturbance. The method and the system provided by the invention are easy to implement, and can visually reflect safe and stable operation conditions of the electric power system including the large wind power base.

A method for assessing and managing health risks of heavy metal pollution. The method includes: 1) investigation and analysis of environmental pollution; 2) population exposure analysis; 3) risk assessment and representation; 4) risk perception and estimate of acceptable level of risk; 5) identification of an overall boundary of risk management; 6) identification of priority pollutants; and 7) identification of a key population and a key managing point. The method panoramically analyzes the health risk taking into account multiple sources, multiple environmental media, multiple exposure routes, and multiple receptors. The method also integrates the risk assessment, public risk perception, public acceptable risk level, and risk management to establish a comprehensive risk management strategy.

The embodiment of the invention provides a risk control classification and recognition method for processing user complaint information, which comprises the following steps: carrying out neural network classification and recognition on the user complaint information, dividing the user complaint information into risk control type output, and recognizing a new risk control type by matching with an unsupervised learning sample. Therefore, the labor cost of risk control case review is greatly reduced, automatic risk perception and prevention in advance are achieved, and adverse effects of variousrisks on the whole system are reduced.

The invention discloses a power grid equipment risk perception method, which comprises the steps of acquiring real-time operation data of power grid equipment, extracting key information, evaluating the power grid equipment, carrying out index calculation, judging whether the state of the power grid equipment exceeds a set safety threshold range or not, and pushing early warning information when the state of the power grid equipment exceeds the set safety threshold range. According to the method, by obtaining the real-time operation data of the power grid equipment, efficient data fusion is carried out on mass real-time data, key information is automatically analyzed, and the problem that monitoring personnel cannot effectively analyze a large amount of data is solved; and real-time sensing, intelligent alarming and equipment health state evaluation are carried out on the power grid equipment, so that the power grid equipment management level and the power grid safety situation supervision capability are effectively improved, the equipment fault frequency is reduced, and the operation safety is ensured.

The invention relates to a driving risk evaluation system based on a vision characteristic. The driving risk evaluation system comprises a first hot computer, an information exchange unit and a secondhost computer. The information exchange unit is provided with a media playing module, a driver control module and a data acquisition module. The second host computer is provided with a quantifying processing module and a result display module. The first host computer is used for generating and transmitting required multimedia information. The media playing module is used for receiving and playingthe multimedia information. The driver control module is used for receiving and transmitting a driver reaction signal. The data acquisition module is used for acquiring and transmitting real-time reaction data of a driver. The quantifying processing module is used for performing quantification processing of the driver reaction signal and the real-time reaction data. The result display module outputs a driver risk perception capability detecting result. According to the system of the invention, a simulated true driving environment is adopted for performing risk perception capability detectionon the driver, thereby facilitating analysis to vision factors of an accident driver, and performing vision-based risk perception training on the driver.

The invention discloses an expressway bend safety monitoring and early warning method, a system and a medium. The method includes the following steps that: the image of a road segment where a currentvehicle runs is obtained, whether a road segment ahead is a bend is judged through feature recognition; if the road segment ahead is a bend, bend information is obtained, and the traffic flow state ofthe road segment where the vehicle is located is obtained; the personal information of a driver before the driver enters the bend and the vehicle speed information of the current vehicle are acquired, and judgment is carried out, so that the level of the perception risk of the driver at the current moment is obtained; and the driving safety level of the current bend is obtained according to the physiological and psychological information of the driver, the bend information and the traffic flow state through an SVM neural network-based expressway safety level monitoring model, and corresponding operations are performed according to different levels. With the expressway bend safety monitoring and early warning method, the system and the medium of the invention adopted, the safety of the driving of the vehicle can be fully guaranteed.

The invention relates to a test and evaluation method of the latent risk perception ability of a driver, and belongs to the technical field of road traffic safety. The method mainly comprises the following steps: (1) classifying latent risk information; (2) determining risk levels of different types of latent risk information; (3) determining a latent risk degree of the latent risk information in a test road section or region; (4) determining a driving risk degree under the latent risk information in the test road section or region based on the latent risk degree; (5) constructing a virtual experiment test scene for testing the latent risk perception of the driver; (6) testing latent risk perception data of the driver through a driving simulation experiment; (7) evaluating the latent risk information perception of the driver. The method can test and evaluate the latent risk perception ability of the driver and assist in realizing the assessment of the latent risk perception ability of the driver, so that the risk cognition ability of the driver is improved, the safety awareness of the driver is enhanced, and the possibility of accident occurrence is reduced.

The invention provides methods and devices for improving risk perception capability based on machine learning, computer equipment and a memory medium. A method comprises the steps of carrying out at least one piece of blacklist and whitelist mechanism processing, regular rule processing and artificial recognition processing on data of a data center, thereby obtaining the data marked with maliciousfeatures; writing the data marked with malicious features into a malicious sample data set for the machine learning; generating a malicious traffic template at least through utilization of the malicious sample data set and through utilization of the a machine learning algorithm; judging matching property of detection data and the malicious traffic template; and determining whether the detection data is malicious data or not according to a matching result. According to the method provided by the invention, the machine learning algorithm for risk perception is continuously optimized, risk perception capability is improved continuously, a conventional attack or a new-type attack can be rapidly perceived in advance, and a corresponding defense scheme or protection measure is established in advance, so the attack of an attacker cannot exert an influence or influenced losses are minimized.

The invention provides a network risk monitoring system of a transformer substation. The system comprises a collection monitoring module used for collecting monitoring data in real time, a security analysis perception module used for performing transformer substation network security risk perception and comprehensive analysis on the collected monitoring data and further evaluating threat degrees of different network security events which the transformer substation network is faced with, a defending and alarming module used for realizing graded early warning and active defense for different network security risks based on the evaluation result of the security analysis perception module. The network risk monitoring system of the transformer substation is used for monitoring the transformer substation network in real time, analyzing threat degrees of different network security events to the transformer substation network rapidly and making out a response rapidly, realizes graded early warning and active defense for different network security events in the transformer substation network, removes security hazards in time and guarantees that the transformer substation has a secure and clean network environment.

The invention provides a method for establishing a ship collision avoidance model based on non-Euclidean conformal transformation. Eyeballs of a driver on duty are used as a research basis of lookoutobservation under a real condition; a spherical model is established on the ship by using a Riemannian spherical theory, one-to-one corresponding projection points are found on the spherical surface by using a spatial projection mode for all information in the plane where the ship is located, and the motion trail of the ship can correspond to an arc on the spherical surface by using the method soas to be more in line with the perception of human vision on risks. The research on the collision risk of the ship in the three-dimensional space situation can effectively reflect the relative speed and the relative distance of the ship under the real condition, and a ship operator on duty can effectively perceive the collision risk of the ship. According to the method, spherical surface mapping of a non-Euclidean space is carried out through projection transformation to carry out modeling, the method for calculating the relative speed and the relative distance of the two ships meeting the risk perception better is provided, and technical support is provided for drivers on duty to carry out correct collision avoidance decisions.

Embodiments of the invention disclose a privacy protection-based authentication method, apparatus and device. The method comprises the steps of obtaining authentication information for performing identity authentication on a target user; based on a pre-trained risk perception model, determining a risk type of a leakage risk existing in the process of processing the authentication information by the Internet of Things device, the risk perception model being a model constructed based on a machine learning algorithm and obtained by training historical risk element information; determining a target information processing strategy corresponding to the risk type based on the risk type, and performing privacy protection processing on the authentication information based on the target informationprocessing strategy to obtain processed authentication information; and sending the processed authentication information to a server, so that the server performs recovery processing on the processed authentication information to obtain authentication information for performing identity authentication on the target user so as to perform identity authentication.

The invention provides a method for carrying out security protection on sensitive data through natural language analysis, and belongs to the technical field of e-government data security. According tothe invention, sensitive data is automatically discovered, and sensitive data are found and subjected to security protection according to data grading and classifying rules and corresponding safety protection measures; meanwhile, sensitive data use conditions and data flow direction information are monitored, and rules and models are formed; and the security protection and security risk perception capacity of the sensitive data is enhanced according to user permission.

The invention discloses a method for evaluating difference of risk perception ability of an electrician operating personnel. At present, there is no method suitable for evaluating the risk perception ability of electricians. The invention discloses an electrician worker risk perception capability difference evaluation method. The method comprises the following steps: collecting eye movement characteristics of a tested person during risk search in different working environments; on the basis of the previous step, obtaining the sensitivity degrees of the testee in different danger types; determining eye movement characteristics of the tested person in risk search under different demographic characteristics; performing data processing on the acquired eye movement data; and determining a data set of a risk perception prediction model. Therefore, the eye movement data and the risk perception ability are combined, the difference of the risk perception ability is quantified by using the eye movement data, and the risk perception ability of the operator is identified. Through the technical means of risk perception difference analysis and the risk perception capability prediction model, the risk perception capability evaluation of the electrician operation personnel is realized.

The invention relates to a method for testing and evaluating the dynamic risk perception ability of a driver, and belongs to the technical field of driving behavior recognition. The method comprises steps of: (1) classifying dynamic risk information; (2) determining the risk levels of different types of dynamic risk information; (3) determining a driving risk comprehensive level of the dynamic information in a test road segment or area; (5) building a virtual experimental scene for testing the driver risk perception; (6) testing driver risk perception data through a driving simulation experiment; (7) evaluating the driver's dynamic risk information perception. The invention can realize the related technology and method for testing and evaluating the dynamic risk perception ability of the driver to help to test the risk perception ability of the driver, thereby improving the driver's risk cognition ability, improving the driver's safety consciousness, and reducing the likelihood of accidents.

The invention relates to the technical field of safety monitoring, in particular to a risk perception method and system suitable for communities and construction sites, and the method comprises the steps: obtaining a target entering message, and obtaining the number of entering targets from the target entering message according to preset time; if the number of the entering targets exceeds a preset number threshold value, obtaining and positioning each to-be-monitored target identifier; obtaining face image data of each to-be-monitored target identifier, inputting each piece of face image data into a preset region resident target recognition model for matching, obtaining a corresponding first matching result, and the first matching result comprising matching success and matching failure; and taking the face image data corresponding to the matching failure as to-be-positioned face image data, obtaining a personnel movement track of a to-be-monitored target identifier of the to-be-positioned face image data, and triggering a corresponding risk alarm message according to the personnel movement track. The method and the system have the effect of improving the safety of personnel management in the region.

The invention discloses an environmental risk perception evaluation method based on data mining, and relates to the technical field of environmental risk perception evaluation. According to the method, firstly, text information related to public environment risk perception is rapidly mined from massive public environment appeal data, text data irrelevant to environment quality is removed by adopting a data cleaning and preprocessing method, and then keyword retrieval is carried out based on an unsupervised and learning technology; and finally, the complaint text is comprehensively scored by adopting a TFIDF algorithm, so that environmental health influence factors highly concerned by the public can be effectively identified, and the timeliness and accuracy of public environmental risk perception degree evaluation are improved.

The invention discloses a distribution network production differentiation operation and maintenance strategy method based on situation awareness, and the method comprises the following steps: S1, collection of production data: collecting production real-time and non-real-time data, and carrying out the quality evaluation of the data; S2, equipment risk perception: establishing an equipment risk evaluation perception model, and carrying out equipment risk identification; S3, equipment risk assessment: performing risk assessment on the equipment according to the equipment perception health degree in combination with the influence of the equipment on the net rack; S4, establishing a risk coping strategy, and establishing a strategy level and a coping strategy according to a risk assessment result; and S5, operation and maintenance strategy execution: carrying out production operation and maintenance according to the risk coping strategy, and providing data and evaluation support for the steps S1-S4 through information backfilling. According to the invention, the equipment evaluation perception integrity and the result preparation can be ensured, the dynamic adjustment of production operation modes such as a situation perception model and a risk coping strategy is realized, the data evaluation is scientific and effective, the equipment failure rate is reduced, and the power supply reliability of a power grid is improved.

The invention relates to a device and a method for security lateral comparative analysis of the Internet-of-Things. The Internet-of-Things comprises a cloud center and a device for communicating withthe cloud center. The device comprises a communication module, a statistical module, a comparative analysis module and a database. According to preset regional network segments set by a user, the userdivides the cloud center into several regions, obtains the data of the devices in any region, records the data to the database, constructs a white list, obtains the data of any device in the currentregion one by one, compares the data with the white list in the database, and reports the abnormal data to the cloud center. On the basis of collecting the data information of the device of the Internet-of-Things in the region, through the statistical principle, lateral comparative analysis of the multi-dimensional information and status of the Internet-of-Things device is carried out, and the abnormal attack activities of the Internet-of-Things device are explored quickly, so a faster and more efficient risk perception method is provided to users, and the ability of zero day risk perception is exhibited to a certain extent.

The invention relates to an evaluation method for crowd environment risk perception standardized measurement based on a Likt scale. According to the method, a Likt five-component table is adopted; designing a risk perception basic questionnaire; the method comprises the following steps: acquiring basic data of crowds for risk perception from five dimensions through a plurality of questions, performing reliability check on questionnaires, performing factor analysis by utilizing a maximum likelihood method, performing factor rotation, performing dimension reduction processing on all risk perception questions involved in the questionnaires to obtain path coefficients among indexes, and giving out fitting parameters; further correcting the preliminarily obtained model to obtain corrected modelfitting parameters and path coefficients; and finally, regression analysis is performed on each risk perception factor, and a basic model after risk acceptance perception factor optimization is given. According to the evaluation method, a targeted crowd risk perception measurement template is designed for perception dimensions of different types of risks, and a set of standardized evaluation toolis formed.

The invention relates to a distribution network line operation risk perception method based on multi-data fusion. The method comprises the following steps: S1, selecting a distribution network line inan area, and carrying out reasonable segmentation processing; S2, preprocessing and normalizing the state variable data recorded by each segmented distribution network line; S3, performing multi-datafusion; S4, dividing the distribution network line fault risks into a plurality of categories according to the previous fault conditions, verifying the effectiveness of risk state evaluation by utilizing the historical data of the segmented distribution network line state variables, calculating the real-time risk condition of each segmented distribution network line through the online data of thesegmented distribution network line state variables, and effectively evaluating the risk state of the distribution network line; and S5, distribution network line operation condition risk probabilitydistribution: performing risk state evaluation on the segmented distribution network line, and obtaining an analysis probability distribution condition of the segmented distribution network line under the operation condition through calculation. The method is of great significance to real-time monitoring of distribution network lines, avoidance of major distribution network line accidents and improvement of regional power supply reliability.

The invention discloses a soil slope instability risk perception system based on an active source electric field variation law. The soil slope instability risk perception system comprises an electrodegroup, the electrode group comprises a monitoring unit, the electrode group is arranged on a slope body space, the electrode group covers a slope surface body surface and a slope body, and the electrode group is arranged in an array manner. The soil slope instability risk perception system based on the active source electric field variation law can utilize the soil slope body space to arrange anarray body non-polarized electrode group, measure and reckon the unit body conductivity, determine the instability risk coefficient through perception in combination with the space-time variation lawof the unit body, and give an early warning in time.

The invention provides an object management method based on an urban community. The object management method comprises the steps of obtaining an associated track of a management object in a managementarea; matching the to-be-matched track to obtain a matched track; fusing the matching track and the associated track to obtain a fused track; and determining the relationship between different management objects based on the fusion track. According to the invention, the multi-dimensional risk perception data, such as dynamic risk perception data and static risk perception data, are associated, sothat the density and precision of the fusion track are improved. The risk of emergency management is reduced by determining the companion personnel of the management object.

The invention relates to an intelligent sensing monitoring and early warning system for operation of an underground complex. The system comprises a risk early warning standard module used for configuring an operation safety risk early warning system, a real-time sensing method of various risk indexes and an early warning grade division standard; a real-time risk perception module used for realizing real-time perception of various risk data; a real-time risk early warning analysis module used for calculating and outputting real-time risk early warning information in real time; a real-time risk early warning display and processing module used for displaying the real-time risk early warning information and providing manual early warning; a risk early warning feedback data statistical analysis module used for analyzing the distribution characteristics of historical risk early warning in risk types, risk levels and spatial positions and analyzing the quality of manual early warning; and a BIM three-dimensional model used for assisting a display function and a manual early warning processing function. According to the invention, data support is provided for continuous optimization of operation safety risk management, and integrated management of the operation risk of the underground complex is realized.

The invention discloses an Internet of Things communication method and device, a readable storage medium and computer equipment, and the method comprises the steps: obtaining the risk perception dataof Internet of Things equipment, and calculating the scores of an attack factor index and a threat factor index according to the risk perception data; determining a trust level of the Internet of Things equipment according to the score of the attack factor index and the score of the threat factor index; and when the trust level is higher than a level threshold, sending connection establishment information to a service system, so that the service system establishes a connection with the Internet of Things equipment. By detecting the credibility of the equipment, the data security of the servicesystem is ensured, so that malicious equipment is prevented from entering the service system, and data leakage is prevented.

The invention discloses a driver risk perception ability evaluation system and method based on virtual reality, the system is constructed by adopting a VR driving simulator and a management platform, the management platform comprises a VR road traffic risk driving scene library and an evaluation database, and the system has the advantages of strong immersion, small interference and the like. When evaluation is started, the VR driving simulator selects a plurality of driving scenes from the driving scene library to form an evaluated driving scene combination, and the management platform receives and stores driver driving manipulation and watching position and driving scene information sent by the VR driving simulator in real time. Calculating a risk perception accuracy score, a risk perception speed score and an average driving speed of the driver in each VR road traffic risk driving scene, and calculating a risk perception ability score of the driver in each driving scene one by one; and carrying out weighted average calculation on the risk perception ability scores in all driving scenes to obtain a comprehensive risk perception ability score of the driver. The method has the advantages of accurate evaluation, simplicity, easiness in understanding and the like.

The invention discloses a power flow risk perception method for a power grid. The method comprises the steps of obtaining a power grid frame diagram, constructing a Jacobian matrix, calculating the power flow diagram of the power grid and performing risk perception early warning on the power grid. The corresponding Jacobian matrices under different conditions of power flow can be obtained according to the power grid frame diagram, then singular value decomposition is performed on the Jacobian matrix under the power flow of the power grid. The power flow diagram of the power grid is obtained bycalculating the voltage and power distribution of each node in the physical transmission line, and risk perception early warning of the power grid is performed according to the power flow diagram ofthe power grid so as to find out the hidden dangers in the power grid line and eliminate them, and the design scheme of the power grid is optimized through power flow calculation and thus the stability of the power system and the allocation ability of resources can be enhanced.

The invention relates to a network behavior risk perception and defense method based on behavior characteristic big data analysis. The network behavior risk perception and defense method comprises five steps of risk assessment server hardware configuration, risk assessment server software configuration, data risk assessment, risk elimination and recovery and data updating. According to the invention, on one hand, effective monitoring and management among all levels of hardware equipment participating in network data transmission can be effectively realized; on the other hand, the initiative ofillegal data information identification and detection operation and the working efficiency and precision of the detection operation are effectively improved; and relevant illegal data information sources can be correspondingly limited and managed, so that the working efficiency and quality of network data transmission detection are effectively improved, and data propagation threatening the network security can be completely eradicated from the source.

The invention provides a prediction method and device for agricultural product consuming behavior decisions. The method includes the steps that a consuming behavior decision predicting model is set up; according to the risk degrees, in occurring places, of risk events in all map sections, the number of the risk events and actual distances from the occurring places of the risk events to the current map section, the risk perception degree of the current map section is calculated, wherein the map sections are sections which are divided in a map and involved in the risk events; according to the consuming behavior decision predicting model and the risk perception degree of the current map section, the consuming behavior decisions of consumers in the current map section are predicted. According to the actual distances from the occurring places of the risk events to the current map section, the risk perception degree of the current map section is calculated, the consuming behavior decision predicting model is combined to predict the consuming behavior decisions of the consumers, and the prediction method well adapts to the change characteristics that the risk events are territorially related, occur suddenly and develop fast.

The invention provides a cerebral apoplexy recurrence risk perception and behavior decision model construction system and method. The risk perception system comprises a data sensing layer, an edge analysis layer, a cloud early warning layer and a risk assessment layer; the data sensing layer sends the physiological measurement parameters to the edge analysis layer; the edge analysis layer executes edge calculation analysis; when the analysis result meets a first preset condition, a feedback signal is sent to the data sensing layer, and the edge calculation analysis results are stored in groups; the cloud early warning layer comprises a plurality of different types of cloud early warning databases; when the analysis result meets a second predetermined condition, the cloud early warning layer executes cloud query calculation in a cloud early warning database based on the edge calculation analysis result stored in groups; and the risk assessment layer receives a cloud query calculation result of the cloud early warning layer and gives a risk assessment value. The invention also discloses a recurrence risk decision system and method. According to the technical scheme, the recurrence risk of the cerebral apoplexy can be effectively perceived.

The invention relates to the technical field of mobile communication and Internet, and discloses a virtual node and honeypot combined trapping attack method, device and system, the virtual node and honeypot combined trapping attack method comprises the following steps: receiving an attack behavior generated on a virtual node monitored by a honeypot; judging whether the attack behavior belongs to threat perception or not by analyzing the attack behavior; if the attack behavior does not belong to threat perception, ignoring the attack behavior; and when the threatening perception is judged, calling a honeypot to simulate real equipment to make a corresponding response, and trapping an attacker to continue attacking. After the risk perception is judged, the honeypot encrypts the virtual data packet to achieve the effect of trapping an attacker, at the moment, the virtual node is mainly a carrier of the virtual data packet, the honeypot is mainly used for acquiring an attack behavior, and the virtual node and the honeypot are combined, so that the virtual node can also perform trapping response to the attacker in the process of trapping the attacker to attack, and the attack behavior can be effectively trapped. The virtual nodes are more real, and trapping attacks are more effective.