79 results about "Total risk" patented technology

The invention discloses an intelligent contract risk recognition method and system, and relates to the technical field of text recognition, and the method comprises the steps: extracting a semantic vector of a to-be-recognized file; obtaining a first risk identification result based on the semantic vector and the review list; performing semantic matching on the semantic vector and the review knowledge graph to obtain a potential risk; obtaining a first risk category based on the potential risk and the review knowledge spectrogram, obtaining a preset risk judgment rule based on the first risk category, and obtaining a second risk category based on the preset risk judgment rule; obtaining a second risk identification result based on the contract category and the second risk category; constructing a clause rule base, and obtaining a compliance result based on the semantic vector and the clause rule base; obtaining a complete result based on the semantic vector and the standardized contract template library; and obtaining a total risk identification result based on the above identification result, thereby solving the problems of low risk identification efficiency and low accuracy caused by the fact that an existing contract term risk identification method depends on the determination of the license experience of professionals.

The invention discloses a method and system for driving SOAR automatic disposal based on system dynamic risk assessment. According to the invention, a closed-loop protection system including data acquisition and preprocessing, dynamic risk assessment, disposal decision triggering, SOAR automatic disposal execution, system total risk value calculation, system-level risk determination, asset risk weighted upgrading, manual intervention triggering, manual disposal feedback and alarm level overall improvement is constructed; and accurate identification and efficient response of network security threats are realized. According to the method, the total risk value of the system is calculated through a multi-dimensional dynamic risk assessment model in combination with an asset value weight algorithm, and real-time upgrading of the asset risk level is realized through a risk threshold dynamic adjustment mechanism, so that a two-level response mechanism (automatic disposal-manual takeover) is realized, and the automation efficiency and the manual reliability are balanced. According to the invention, precise and efficient disposal of network security threats and intelligence of security protection are realized, and the method can be widely applied to core service systems of various industries.

The invention relates to the technical field of circuit risk assessment, in particular to a risk assessment method for an intelligent inspection circuit, and the method comprises the steps: obtaining a topological structure and real-time operation data of a circuit network, and dynamically calculating a risk propagation factor between any two nodes; the factor is used as a weight, risk propagation is iteratively simulated to calculate a final risk accumulation value of each node, and a global risk map is generated, so that risk distribution is evaluated from a global perspective of the network; meanwhile, a dynamic risk propagation network model is constructed based on the same risk propagation factor, the inspection sequence and frequency are dynamically adjusted according to the node total risk association strength, and an optimized inspection path is generated; and finally, an evaluation result fusing the global risk situation and the accurate inspection strategy is output, and a task is executed, so that the problem of insufficient coordination of risk evaluation and inspection decision is effectively solved, and the integrity, accuracy and adaptive ability of circuit risk management are improved.

The invention relates to the technical field of biomedical engineering, and discloses a VTE system execution method and system combined with a standardized scale, and the method comprises the steps: carrying out the standardization processing of original data, and obtaining the standardized data; constructing a risk intensity flow; carrying out integral operation to obtain an accumulated integral value of the risk factor; calculating a dynamic risk trajectory; fusing the baseline score value of the standardized scale with the dynamic risk trajectory to obtain a total risk trajectory; calculating an instantaneous risk probability, and judging whether VTE risk early warning is triggered or not according to a self-adaptive threshold value; according to the method, continuous quantitative monitoring of VTE risk factors is achieved by constructing the body position, anesthesia, hemostatic and circulation parameter four-dimensional risk intensity flow, early recognition and timely early warning of VTE risks are achieved through double mechanisms of continuous triggering and acute triggering, and the accuracy, the real-time performance and the individualized level of risk assessment are remarkably improved.

The invention relates to the technical field of databases, in particular to a database circulation track auditing method based on multi-source data fusion. The method comprises the following steps: collecting multi-source data of different logs, preprocessing to obtain preprocessed multi-source data, extracting key features of the preprocessed multi-source data, and serially connecting the key features according to a time sequence to generate a database circulation track; collecting operation behavior data of a user, calculating a mean value and a standard deviation corresponding to the operation behavior data to determine a normal range and generate an initial baseline, and designing an automatic trigger updating mechanism to dynamically update the initial baseline to generate a dynamic baseline; and calculating the deviation degree between the actual value of each operation behavior dimension and the dynamic baseline, carrying out weighted summation to calculate a total risk value, comparing the total risk value with a risk threshold, and classifying the total risk value into low risk, middle risk and high risk. According to the invention, the false alarm rate can be reduced, and multiple times of missing report of few hidden anomalies can be avoided; the auditing resources can be focused on the high risk preferentially, manual intervention of low-risk abnormity is reduced, and compared with single-dimension grading, the auditing efficiency is improved.

The invention relates to the field of risk management and control of insurance underwriting business, and discloses an underwriting risk assessment method and system, and the method comprises the steps: collecting insurance data of a target customer, and recognizing risk factors affecting underwriting in the insurance data; the risk factors comprise discrete factors; performing feature conversion on the discrete factors according to a preset factor splitting election or factor splitting expansion method, and encoding the discrete factors into a numerical form; selecting key features suitable for different risk scenes from the risk factors, and inputting the key features into a plurality of trained sub-models to obtain scores of the target customer in different risk scenes and interpretable results corresponding to the scores; the plurality of sub-models comprise a BMI intelligent identification sub-model, a claim settlement history risk control sub-model, a disease risk level classification sub-model and a delayed risk re-mining sub-model; and performing weighted calculation on the scores obtained by the sub-models, and finally obtaining the total risk score of the target customer in the current business.

The invention provides a BIM (Building Information Modeling)-based electromechanical project progress risk early warning and coping method. The method comprises the following steps: step 1, establishing a dynamic BIM model; 2, risk factor quantification: extracting a design risk, a resource risk and a process risk based on a BIM model, respectively calculating risk scores, and comprehensively quantifying a total risk through weighting; 3, real-time data fusion: collecting position data of constructors and equipment through an Internet of Things sensor, associating the position data with the BIM model, and updating the process completion rate and the resource distribution state in real time; and 4, dynamic risk early warning: in combination with the BIM model and sensor data, quantifying design, resources and process risks through a weighting formula, realizing risk score dynamic updating, realizing multi-dimensional risk quantification, and mapping a physical construction state in real time by constructing a digital model of a construction site so as to realize dynamic risk early warning. The effectiveness of the optimization strategy is verified through 4D simulation time and space dimensions, and whether construction conflicts and construction period delay are reduced or not can be quickly checked after the optimization strategy is generated.

Disclosed herein is a method for diagnosing a medical condition in a patient. The method comprises: obtaining, from the patient, a plurality of physiological values; implementing a first model configured to determine risk values for at least one of a plurality of medical conditions, based on the physiological values. Implementing the first model comprises: obtaining a first risk value for the at least one medical condition, based on a first one of the obtained physiological values; and weighting the first risk value based on a second one of the obtained physiological values, to determine a total risk value of the at least one medical condition for the patient.

The invention relates to the technical field of artificial intelligence and channel disease monitoring, and discloses a channel revetment multi-dimensional disease intelligent monitoring method and system based on multi-source fusion, and the method comprises the steps: obtaining multi-source data of a target revetment region; constructing a three-level index system; performing DERU evaluation on each third-level index, and calculating a risk category score corresponding to each second-level index according to an evaluation result and a second-level index classification result; dividing the secondary indexes into a disease class and an environment class according to influence types, calculating a total risk value according to the risk class score of the secondary indexes of the disease class, and calculating an environment correction coefficient according to the risk class score of the secondary indexes of the environment class; calculating a health value score of the target bank protection area according to the total risk value and the environment correction coefficient; and outputting a disease intelligent monitoring result and a maintenance strategy according to the multi-source data and the health value score. According to the scheme, the health state of the revetment structure can be comprehensively, timely and accurately mastered, and identification monitoring and intelligent early warning of various diseases are realized.

The invention provides a network security risk assessment method and device, and the method comprises the steps: determining a risk factor of an alarm event according to the received alarm event; then, for each risk factor of the alarm event, performing normalization processing on the risk factor to obtain an initial risk value of the risk factor; then, according to all the alarm events and the risk factors of the alarm events, determining the weight of each risk factor of each alarm event; determining the risk value of the alarm event according to the initial risk value of the risk factor of the alarm event and the weight of the risk factor of the alarm event; and finally, performing aggregation analysis according to the risk values of all the alarm events to obtain the total risk of the cloud network assets and the total risk of the external attack IP. Therefore, risk assessment is carried out on network security comprehensively and accurately.

The invention discloses an electric equipment line risk online monitoring method based on multi-source data fusion, and particularly relates to the field of electric line monitoring, and the method comprises the steps: collecting the electric, insulation, physical and environmental data of a line according to a preset frequency, and carrying out the standardization processing to obtain corresponding standardization parameters; dividing the standardized parameters into input and output dimensions, calculating output dimension parameter weights by using an entropy weight method, and constructing a theoretical value model of each output dimension by using multiple linear regression; respectively constructing electrical, insulating and physical risk functions based on the standardized parameters and theoretical values; constructing a collaborative coupling matrix in combination with each risk function value and historical data, and calculating a total risk value through a nonlinear fusion logic construction function; defining a dynamic threshold value based on each risk value, judging an abnormal point when the threshold value is exceeded, and triggering iterative optimization of the model; the fault probability is calculated by combining the total risk value and the abnormal point data, and a three-level early warning mechanism is set to judge the line risk; according to the scheme, multi-dimensional risk collaborative monitoring is realized, and the risk identification accuracy and real-time performance are improved.

The invention relates to a point operation activity risk prevention and control method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring multi-source heterogeneous data including user behavior data, user transaction data and a risk library, and determining a total risk score of a user according to each feature data in the multi-source heterogeneous data and a corresponding feature weight; under the condition that the total risk score is smaller than or equal to a first score threshold value, releasing the user account; when the total risk score is greater than a first score threshold and the total risk score is less than or equal to a second score threshold, marking the user account as an account needing to be manually rechecked; and when the total risk score is greater than a second score threshold, intercepting the user account.

The invention provides a security protection method, system and device based on an API identity portrait and a medium, and belongs to the technical field of network security. The method comprises the steps of obtaining static attributes of a target API, including a data operation type, a transaction integrity requirement and a sensitive field, and determining a basic risk score B according to a preset coefficient; historical request logs are collected and analyzed, traffic baselines at different time points are determined, and the current traffic and the baselines are compared in real time to obtain behavior risk scores A; the method comprises the following steps: acquiring a call log, mining an API co-occurrence set by using FP-Growth, constructing a high-frequency sequence pattern through a PrefixSpan algorithm, and comparing a current session request to obtain a relation risk score N; summarizing attack frequencies and types in a preset time period, and obtaining an attack risk score R in combination with a historical statistical value; a total risk score W is determined based on the above scores, and whether to trigger detection is determined with reference to a preset threshold.

The embodiment of the invention provides a natural disaster risk evaluation method, device and equipment and a computer readable storage medium. The method comprises the steps of determining natural disasters possibly involved in a to-be-evaluated region based on geographical environment information of the to-be-evaluated region; determining each natural disaster risk index based on the survey evaluation data of each natural disaster; based on the natural disaster risk indexes, a total risk index and an overlapping risk index are determined, the total risk index is related to the risk of independent occurrence of each natural disaster, and the overlapping risk index is related to the risk of simultaneous occurrence of all natural disasters; and determining a comprehensive risk index of the to-be-evaluated region based on the total risk index and the overlapping risk index. According to the method, the risk superposition influence of various natural disasters is fully considered, so that the obtained risk assessment result is more suitable for areas influenced by various natural disasters, and scientific and comprehensive data support is provided for comprehensive risk assessment of local disasters, disaster control and emergency rescue work.

The invention discloses an AI psychological counseling method and system, and the method comprises the steps: collecting the physiological signal data, behavior signal data, text signal data and individual attribute data of an individual, and the individual attribute data at least comprises the gender; extracting physiological features from the physiological signal data, extracting behavior features from the behavior signal data, extracting text features from the text signal data, and determining the individual attribute data based on the physiological features, the behavior features and the text features in combination with the individual attribute data. Respectively calculating a depression risk value, an anxiety risk value, a pressure load risk value and a crisis warning risk value to form a four-dimensional theme risk vector; calculating a total risk value according to the four-dimensional theme risk vector; and comparing the total risk value with a preset threshold sequence, and determining individual risk levels including a health level, a mild level, a moderate level, a severe level and a crisis level. According to the method and the system, the technical problem of large single-mode error in the traditional psychological service is solved.

The invention belongs to the technical field of receiving-end power grid evaluation, and particularly relates to a receiving-end power grid active support capability evaluation method and system based on the total risk potential of a system, and the method comprises the steps: firstly constructing an active support capability evaluation index system of a receiving-end power grid; then calculating an individual risk potential function of each index and a coupling risk coefficient between each index and other indexes, and then calculating a system total risk potential based on the individual risk potential function and the coupling risk coefficient, the system total risk potential including a system individual risk potential and a system coupling risk potential; and finally, active support capability evaluation of the receiving-end power grid is realized based on the total risk potential of the system. According to the method, on one hand, a scientific and comprehensive evaluation system is constructed, the deep evaluation of the active support capability is realized, on the other hand, on the basis of quantifying the deviation degree by using the individual risk potential, the non-linear superposition risk additionally generated by the interaction or simultaneous degradation among multiple indexes is quantified through the coupling risk potential field, and the evaluation accuracy of the active support capability is improved. And the diagnosis precision is obviously improved.

The invention discloses an open source component evaluation method and device, computer equipment and a storage medium. The method comprises the steps of obtaining related data of an open source component; converting related data of the open source component into numerical values and calculating risk values of all indexes; calculating a total risk score of the open source component according to the risk value of each index and the corresponding weight; determining the risk level of the open source component according to the total risk score; and outputting the risk level. By implementing the method provided by the invention, the accuracy of open source component risk assessment can be further improved, potential risks can be accurately identified and dealt with, and different types of projects can be better served.

Implementations are directed to methods, systems, and apparatus for automated prioritization of cyber risk to digital identities. Actions include obtaining graph data defining a knowledge graph including nodes and edges, the nodes representing respective objects of the enterprise network including digital identities and resources, each node being associated with an explicit risk score and properties of the represented object, each edge representing a relation between objects; determining priority scores for the objects, including, for a first object represented by a first node: determining an implicit risk score for the first node; determining a total risk score for the first node; and determining a priority score for the first node based on the total risk score and properties associated with the first node; generating a ranking of the objects according to the priority scores; and providing, for presentation on a display, cyber security risk data indicating the ranking of the objects.

The invention relates to the technical field of risk assessment in automatic safety detection, and discloses a construction engineering potential safety hazard detection method and system, and the method comprises the steps: selectively defining a detection blind area or point-by-point path planning according to the definition of data sensed by a sensor, and determining an undetected key check point set which cannot be reached; the method comprises the following steps: selecting a physical blocking state, further accumulating a basic risk weight of the physical blocking state, dynamically generating a quantitative total risk score in combination with peripheral high-risk operation types and risk exposure duration, generating a visual risk report, and pushing the visual risk report to a management terminal. The physical blocking state is coupled with the importance of structural key points and the dynamic influence of external high-risk operation, so that an invisible detection blind area is converted into a quantifiable decision basis capable of reflecting the process that risks are accumulated along with time, and the crossing from physical perception to risk semantics is realized.

The present invention relates to the technical field of wellbore risk assessment, and in particular to a method for quantitatively assessing the wellbore integrity risk of a carbon dioxide injection well, comprising: constructing a hierarchical model for injection well integrity assessment; determining the weight coefficient of each risk indicator; evaluating each risk indicator separately; calculating the total risk level score and determining the risk level. The present invention can comprehensively consider a variety of risk factors and achieve a detailed assessment of wellbore integrity through the determination of a hierarchical model and weight coefficients. The acquisition of real-time data and parameter correction ensure the accuracy and timeliness of the evaluation results. In addition, the method can identify high-risk areas in advance, achieve early warning and timely governance, and effectively solve the problem of low evaluation accuracy caused by the high subjectivity of the integrity analysis and evaluation method in the prior art, which makes it impossible to conduct a more objective quantitative evaluation of the wellbore integrity risk.

The present invention discloses a method and system for intelligently identifying contract risks, which relate to the field of text recognition technology. The method includes: extracting semantic vectors of files to be identified; obtaining a first risk identification result based on the semantic vectors and a review list; semantically matching the semantic vectors with a review knowledge graph to obtain potential risks; obtaining a first risk category based on the potential risks and the review knowledge graph, obtaining a preset risk judgment rule based on the first risk category, and obtaining a second risk category based on the preset risk judgment rule; obtaining a second risk identification result based on the contract category and the second risk category; constructing a clause rule library, obtaining a compliance result based on the semantic vectors and the clause rule library; obtaining a complete result based on the semantic vectors and a standardized contract template library; and obtaining a total risk identification result based on the above identification results, so as to solve the problem that the existing contract clause risk identification method relies on the professional experience of professionals to make judgments, resulting in low risk identification efficiency and low accuracy.

The invention relates to the technical field of civil aviation operation risk management and control, in particular to a flight full-chain operation situation risk assessment method, which comprises the steps of constructing a full-chain risk event library, determining trigger time, calculating peak time and standard deviation and amplitude of a Gaussian pulse function, and constructing a Gaussian pulse energy representation model of a full-chain risk event. Calculating a link coupling weight and a risk amplification coefficient of the full-chain functional resonance analysis-cascade model coupling network; and calculating the probability that the risk module enters an abnormal state at the next moment according to the risk amplification coefficient, selecting various parameters through Monte Carlo simulation, calculating the total risk energy according to the various parameters and the amplitude after coupling amplification, identifying the link control priority, and outputting the risk level of each operation stage. According to the method, accurate characterization of a full-chain risk event, quantitative calculation of a cross-stage coupling effect and accurate identification of a high-risk link are realized, and pertinence and effectiveness of flight full-process risk management and control are improved.

The invention relates to a resource-pooling system and to a corresponding method for risk sharing of a variable number of risk exposure components. The risk exposure components are connected to the resource-pooling system by means of a plurality of payment receiving modules configured to receive and store payments from risk exposure components for the pooling of their risks. The total risk of the pooled risk exposure components comprises a first risk contribution associated to risk exposure in relation to loan losses, and a second risk contribution associated to risk exposure based on emergency expenses. The pooled risk is divided in a parameterizable risk part and a non-parameterizable risk part by means of an indexing module. In case of triggering a loss by means of a trigger module, the suffered loss is covered by releasing associated loans and emergency expenses of the risk exposure components.

The invention discloses a security risk assessment method and system based on attack chain deduction, relates to the technical field of network security, and solves the problem that security risks are difficult to comprehensively and accurately assess in the prior art. According to the embodiment of the invention, by establishing the asset-vulnerability-attack stage mapping relationship, the whole-process visual modeling of the attack chain from initial reconnaissance to target achievement is realized, the specific effect of the vulnerability in the attack chain is determined, and the problem that the vulnerability linkage effect is neglected in traditional assessment is solved, so that more comprehensive risk assessment is realized; in addition, the mapping relation can be updated in real time according to asset change and new vulnerability discovery, accurate basic data support is provided for attack path generation, and dynamic construction of an attack surface panoramic view is achieved. Besides, by introducing path selected probability parameters, subjective strategy preferences of the attacker are brought into quantitative evaluation of the risk, so that total risk evaluation better fits real attack decision logic, and a more accurate evaluation result is obtained.

The invention discloses a sports event risk prediction method and system based on a risk level model, and belongs to the technical field of risk prevention and control. The method comprises the following steps: acquiring a historical athlete injury case data set; acquiring multiple data in each historical damage case, and generating a rule containing conditional statements and risk level adjustment values; acquiring multiple body data of the target athlete, and performing preprocessing; obtaining a total risk exposure degree integral based on the preprocessed multiple body data; and if the total risk exposure integral is not less than a preset integral threshold, generating a high risk level and sending an early warning instruction. According to the method, the body data and the training load of the athlete are comprehensively evaluated, the quantitative rule is generated in combination with the historical injury cases, the accurate and dynamic injury risk early warning effect is achieved, and the problems that in the prior art, risk evaluation is not accurate, and early warning is not timely are solved.

This application relates to a method, system, device, and medium for environmental monitoring decision optimization based on reinforcement learning. The method includes: acquiring multimodal raw monitoring data in a hazardous waste management scenario and performing feature fusion processing to generate a continuous state vector; calculating the current risk potential scalar value based on the continuous state vector and a constructed differentiable risk potential function; correcting the potential scalar value based on expert experience and semantics; constructing a continuous reward function and calculating an immediate reward value based on the continuous reward function and the total risk potential value; inputting the continuous state vector, the immediate reward value, and the next-time continuous state vector after the environmental feedback following the intervention action into a proximal policy optimization network for iterative training until the network converges to obtain the optimal policy. This method can significantly improve the real-time performance, foresight, and intelligent decision-making level of environmental risk monitoring, effectively curbing illegal and criminal activities related to solid waste and preventing environmental risks.

The invention discloses an intelligent early warning method and device for secondary operation of an electric power system, and relates to the field of electric power system analysis, and the method comprises the steps: obtaining a to-be-evaluated operation plan and the equipment state, power grid parameters and environment parameters of an operation site, inputting the parameters into a basic risk evaluation model, and generating an operation plan correlation total risk value; determining an operation plan risk level according to the operation plan association total risk value; when the operation plan risk level is lower than a preset basic risk level, executing according to the operation plan, and obtaining real-time operation data; inputting the real-time operation data into a real-time operation risk assessment model to generate a real-time operation comprehensive risk value; determining a real-time operation risk level according to the real-time operation comprehensive risk value; and when the real-time operation risk level exceeds a preset early warning level, sending out a first early warning signal. By implementing the method, the problem that secondary field operation risk assessment depends on manual operation in the prior art can be solved, and the timeliness of risk management and control is improved.

The invention discloses a risk assessment method for the public security industry, and belongs to the technical field of public security management. The method is cyclically executed in a preset time period, and comprises the following steps of: constructing a multi-dimensional dynamic risk map: gridding an activity site, converging multi-source real-time data associated with geographic grids, and calculating a basic risk value of each grid through a multi-factor risk contribution model; a risk exposure degree deduction step: acquiring a real-time police force position, calculating a police force response delay index of each grid, and performing coupling calculation to obtain a risk exposure degree index of each grid; and a police scheduling suggestion generation step of generating a police scheduling suggestion aiming at minimizing the total risk exposure degree of the whole field through a resource optimization allocation algorithm based on the risk exposure degree index. The technical problem that in the prior art, risk assessment and police dispatching are separated from each other, and on-site dynamic changes cannot be handled is solved.

The invention provides a method and a device for limiting the use of an internet of things card at a region boundary, and the method comprises the steps: under the condition that the internet of things card is connected to an adjacent unauthorized region base station at an authorized region boundary, calculating the use limit of the internet of things card on the basis of a base station abnormal call proportion, a historical abnormal risk coefficient, a geographic position risk coefficient and a corresponding weight coefficient; calculating a base station overall risk coefficient of the uplink base station of the Internet of Things card; calculating an Internet of Things card risk coefficient based on the Internet of Things card risk behavior coefficient, the abnormal use risk coefficient, the abnormal movement risk coefficient, the potential business risk coefficient and the corresponding weight coefficient; calculating a total risk coefficient based on the base station overall risk coefficient and the Internet of Things card risk coefficient; and based on the total risk coefficient, determining whether the current Internet access behavior of the Internet of Things card is legal so as to determine whether to limit the network access of the Internet of Things card. The Internet of Things card of the area boundary is limited to be used in the risk range, the problem that the area boundary is connected to the adjacent area and cannot be used is solved, and the user experience is improved.

The application relates to the technical field of risk monitoring, and discloses a risk early warning method, device and equipment and a storage medium, which comprises the following steps: standardizing enterprise operation time sequence data of a target enterprise collected by the application, and generating a time sequence sequence in a preset format; inputting the time sequence sequence into an improved time sequence coding model, and outputting a deep time sequence feature vector of the target enterprise; predicting an operation characteristic value of the target enterprise according to the deep time sequence feature vector, determining a residual error between the operation characteristic value and an actual operation characteristic value, and obtaining a residual error anomaly score; generating a total risk score based on an attention weight and the residual error anomaly score, and performing risk early warning on the target enterprise according to the total risk score. The position coding based on the real time interval calculation of adjacent time steps is introduced to replace the traditional fixed position coding, so that the time sequence coding model can accurately perceive the uneven time interval characteristics caused by different collection granularities, and the timeliness and accuracy of risk early warning are significantly improved.