93 results about "Risk matrix" patented technology

The invention relates to the technical field of chemical enterprise production management, in particular to a chemical enterprise safety production informatization management system which comprises a sensing network layer architecture, a digital twin management platform, a risk management and control and decision center, a process closed-loop management center and an emergency cooperative processing platform. According to the chemical enterprise safety production informatization management system and method, through construction of a chemical knowledge graph, on the basis of a dynamic risk matrix and an AI prediction model library, equipment remaining service life prediction, process tiny anomaly capture and accident consequence simulation processing are realized; meanwhile, a root cause analysis module is used for associating low-level alarm and traversing a knowledge graph to automatically infer a fault root, physical equipment and a virtual model are bound through an entity-model mapping engine, the model state is dynamically updated in combination with a real-time rendering technology, and technological parameter optimization, accident deduction and scheme verification can be completed in an analogue simulation engine; and the management intuition and the decision-making scientificity are effectively improved.

The invention relates to the technical field of new energy power system instability risk assessment, in particular to a new energy power system frequency instability risk assessment method based on a heterogeneous graph attention network. The method comprises the following steps: constructing a wind power-photovoltaic heterogeneous graph model according to a power grid topology; performing combined sampling on different operation modes and anticipated disturbances, and determining corresponding input feature vectors; calculating a frequency stability index value label of the sample set; expanding the training set through an active learning iteration process, and carrying out model training; and inputting the collected operation data into the trained heterogeneous graph attention model, outputting a frequency stability index value, and evaluating the system frequency instability risk in combination with the risk matrix. By adopting the frequency instability risk assessment method for the new energy power system based on the heterogeneous graph attention network, the problem of low efficiency of risk assessment in a high-dimensional uncertain scene is solved, and the heterogeneous graph attention network can reflect the influence of different types of devices at different positions and disturbance types on the dynamic frequency of the system; and the accuracy of frequency instability risk assessment is improved.

The invention provides a disaster evolution path deduction method and system based on a multi-modal space-time atlas, and the method comprises the steps: obtaining the attribute data of a shopping mall, and constructing a deduction static atlas based on the attribute data of the shopping mall; the deduction static map initializes a state based on a set instruction, updates based on physical simulation, and outputs a deduction dynamic map; generating a comprehensive risk matrix based on the deduction static atlas and the deduction dynamic atlas, and clustering the comprehensive risk matrix to obtain a classification region cluster; performing physical prediction and digital prediction according to the classification region cluster and the deduction dynamic map, respectively obtaining physical determination and judgment data and digital determination and judgment data, and obtaining spreading probability data through the physical determination and judgment data and the digital determination and judgment data; according to the method, re-deduction is carried out through an intervention instruction, a deduction method capable of carrying out step-by-step deduction and backtracking deduction is provided, a deduction dynamic map is fused, and the deduction accuracy is improved in a physical and digital cooperation mode, so that making of a better emergency plan is assisted.

The invention relates to the field of emergency response, in particular to an emergency lighting evacuation path dynamic planning method and device based on multi-source event triggering, and the method comprises the steps: obtaining multi-source event data in real time; according to the multi-source event data, an information fusion algorithm is adopted for analysis and fusion, and a comprehensive event level comprehensively representing disaster situation development and personnel conditions in the building is generated; dynamically distributing risk weight values for each path node in the building by combining the comprehensive event level and pre-stored building structure topological information, and generating a dynamic risk matrix changing along with time; according to the dynamic risk matrix, performing real-time calculation on personnel at different positions in the building to generate personalized dynamic evacuation paths; and converting the calculated dynamic evacuation path into a control instruction, driving the intelligent emergency lighting lamp to change the indication direction, and guiding the personnel to evacuate along the planned path in a dynamic light and shadow mode. The dynamic risk matrix generates personalized optimal paths for persons at different positions, and accurate evacuation is performed from the perspective of global optimization.

The invention discloses a power network security defense method and device based on digital twinning, and belongs to the technical field of network security, and the method comprises the steps: obtaining the power data of a power network in real time; constructing a digital twinborn simulation environment of the power network according to the power data; extracting electric power characteristic data in a digital twinborn simulation environment; performing layer-by-layer risk analysis on the power feature data according to each layer of hyperplane function of a preset decision tree, and determining a risk type and a risk matrix of the power network; in a digital twin simulation environment, reconstructing a propagation path of the current attack according to the risk matrix; and generating a security defense strategy of the power network according to the risk type and the propagation path. Therefore, by implementing the method and the device, the problem of low accuracy of power network risk detection in the prior art can be solved.

The invention relates to a road maintenance scheme generation method fusing a risk priority number and a risk matrix, and the method comprises the steps: S1, generating an initial feature code, initializing an optimal solution to be null, and enabling a target function value of the optimal solution to be infinite; s2, on the basis of the current feature codes, the probability of occurrence of risk events in each year of each road section unit is determined; s3, calculating the risk priority number of each year of each road section unit; s4, based on the obtained risk priority number, a target function value is obtained through calculation in combination with road maintenance basic parameters; s5, judging whether the current target function value is smaller than the target function value of the optimal solution or not, and if yes, updating the current feature code into the optimal solution; s6, judging whether an exit condition is met or not, if yes, mapping the current optimal solution into a road maintenance scheme to be output, and if not, executing the step S7; and S7, modifying the feature codes, and returning to the step S2. Compared with the prior art, the method has the advantages of high accuracy and the like.

The application discloses a railway foreign matter intrusion recognition decision method and device, equipment and medium, relates to the image recognition technical field, and includes: through collecting and preprocessing the inspection image, obtaining the foreign matter category and position information through the lightweight detection model, relying on the two types of information to quantify the risk level, fusing multiple information to construct a query vector, and calling regulations from the railway knowledge graph with the help of a search enhancement generation framework, and generating standard disposal suggestions combined with the search content. Through the coupling of the risk matrix and the knowledge graph, a closed loop from perception to decision is realized, and the accuracy and compliance of foreign matter disposal are improved.

The invention discloses a pumped storage power station high-fall cable tunnel fire risk assessment method and system, relates to the technical field of fire science and safety, and aims to solve the problem that an existing cable tunnel fire risk assessment method is not suitable for a high-fall scene and lacks consequence quantification capability. Comprising the following steps: identifying a hazard source through multi-mode investigation and in combination with a high-fall cable tunnel structure and the like; a fire accident tree is constructed based on the method, and the occurrence possibility of a top event and the importance of various factors are determined through qualitative and quantitative analysis; a fire consequence severity grade standard is formulated, fire parameters are obtained through CFD simulation, and the fire consequence severity is determined from the two angles of transverse spreading and longitudinal spreading; adopting a risk matrix method to complete risk quantitative evaluation; and according to the accident tree index and the evaluation result, proposing prevention and control suggestions from multiple aspects. According to the invention, accurate quantitative evaluation of the cable tunnel fire risk in the high-fall scene is realized, and a scientific basis is provided for risk prevention and control.

The invention discloses a natural gas pipeline failure risk assessment method and system based on data driving, and relates to the technical field of pipeline safety. The method comprises the following steps: firstly, calculating and correcting the failure probability by establishing a pipeline failure probability evaluation model, extracting key features by constructing a corrosion and design five types of failure factor index systems and principal component analysis, and calculating the failure probability by utilizing an improved correction coefficient formula; secondly, establishing a consequence evaluation model based on a risk matrix, and realizing differentiated evaluation of failure consequences in different geographical environments by combining GIS data; setting an online learning mechanism, and automatically updating a risk matrix when sensor data triggers a threshold value; the problems that a traditional method is high in subjectivity, insufficient in data fusion and low in simulation precision under complex conditions are solved, the accuracy and dynamic adaptability of risk assessment are improved through machine learning and space-time correlation analysis, accurate decision support is provided for pipeline operation and maintenance, and the safety accident rate is effectively reduced.

The invention relates to a method and computer program product for providing a neural network and system for classifying objects for a vehicle. The method according to the invention comprises the steps of providing a hazard and risk matrix for objects to be classified (S10), determining and assigning a safety weight to a misclassification depending on a risk rating (S20), determining and assigning a training weight to a misclassification (S30), and training a neural network by means of a plurality of iterations (S30). In this process, assigned training weights are dynamically adjusted to optimize a safety index, and the safety index is determined depending on misclassifications and assigned safety weights.

The invention discloses a landslide hidden danger risk assessment method and system based on a three-dimensional dynamic matrix, and relates to the technical field of landslide hidden danger risk assessment, and the method comprises the steps: when a preset region is in a non-flood season, obtaining a three-dimensional dynamic matrix of a weight coefficient corresponding to each landslide hidden danger factor and a quantized value of each landslide hidden danger factor; calculating to obtain a hidden danger risk matrix calculation value of the preset area, and taking the calculated hidden danger risk matrix calculation value as a final hidden danger risk matrix calculation value; and according to the final hidden danger risk matrix calculation value, carrying out landslide hidden danger risk assessment on the preset area to obtain a landslide hidden danger risk assessment result of the preset area. According to the method, objective and dynamic risk assessment is realized by means of quantitative analysis of the three-dimensional dynamic matrix in combination with deformation characteristics of the landslide hidden danger in different stages, and scientificity and reliability of an assessment result are remarkably enhanced.

The invention discloses a cold region tunnel disaster multi-field coupling deduction and lining response prediction method and device, and the method comprises the steps: collecting temperature, stress and seepage pressure monitoring data containing time sequence and space dimensions; finishing data intelligent repair and anomaly recognition by adopting an algorithm of combining a deep learning model-Kriging interpolation based on time sequence analysis with an isolated forest-mahalanobis distance to obtain preprocessed data; constructing a multi-field coupling deduction model based on a finite element-lattice Boltzmann method; using a support vector machine and fuzzy logic to diagnose disaster types and severity, and determining disaster risk levels based on Monte Carlo simulation and a risk matrix; and finally, predicting the evolution trend of the lining structure of the medium-high risk part under different time scales by adopting a physical information neural network and taking the risk level as a constraint. According to the method, the accuracy and multi-scale of multi-field coupling deduction and lining response prediction of the tunnel in the cold region are realized, and reliable technical support is provided for tunnel construction monitoring, operation and maintenance scheduling and disaster prevention and control.

A method for monitoring pathogen load in livestock. The method uses a risk matrix to evaluate the risk of a pathogen load in livestock to cause a disease. A computer program product comprising code portions adapted for performing the method and a system for monitoring pathogen load in livestock are also described.

The invention provides an urban rainstorm waterlogging risk assessment method, device and equipment and a storage medium, and belongs to the field of risk assessment, and the method comprises the steps: obtaining multi-source data of different regions of a target city; based on an analytic hierarchy process, screening evaluation indexes from two criterion layers of disaster-inducing factors and disaster-bearing body vulnerability to construct a structured evaluation index system, and allocating weights to the indexes in the evaluation index system; carrying out weighted calculation on the multi-source data of each region by utilizing the structured evaluation index system and the weight thereof, and respectively obtaining comprehensive evaluation values of each region on the occurrence probability dimension and the influence degree dimension of the rainstorm waterlogging risk; and inputting the comprehensive evaluation value of each region into a predefined risk matrix model, and calculating to obtain a quantitative risk level result of each basic evaluation unit through matrix mapping.

The invention provides a fireproof design method and device which are suitable for plant phenotypic facility or indoor facility agriculture, and the method comprises the steps: determining the safety distance of each functional region based on critical radiation heat simulation; according to the safety distance, the fire scale of each functional area under the most unfavorable working condition is obtained by combining the characteristics of combustible materials in each functional area or through analogue simulation; calculating a fire probability prediction value of each functional area under different second-class hazard sources and a fire loss prediction value under different fire scales, and evaluating a fire risk evaluation level of each functional area through the risk matrix; and in combination with the fire scale, the fire risk evaluation grade and the safety distance, matching fire prevention control measures corresponding to each functional area. The method can effectively improve the fireproof control of plant phenotypic facility or indoor facility agriculture, and achieves the real-time monitoring and prediction functions of fire risks.

The present application belongs to the technical field of cooperative control, and particularly relates to a contactless transmission path cooperative control system, which comprises a monitoring module, a risk coupling module, a cooperative control module and a feedback module; the monitoring module collects control parameters, evaluation indexes and process parameters and quality data of a processing process of a contactless transmission process in real time; the risk coupling module constructs a transmission and processing risk matrix by using the data, and generates a risk coupling matrix and a production coupling chain based on an algorithm such as a hidden Markov algorithm; the cooperative control module generates transmission and processing control instructions according to the coupling risk information of each node in the chain; and the feedback module realizes real-time adjustment through reverse abnormality evaluation positioning, so that the production process continuously conforms to the standard process chain; the present application realizes risk cooperative control and closed-loop optimization of the transmission and processing processes, and significantly improves the production yield and the intelligent level of the system.

The invention discloses an artificial intelligence reinforcement learning service platform, particularly relates to the field of computer data processing, and is used for solving the problem that training sample semantics drifts and cannot be rechecked due to data center telemetering backfilling disorder and caliber version change superposition. The method comprises the following steps: generating an original record set by collecting telemetering, establishing double indexes, generating an action bill according to a fixed period, locking a backfill time window, extracting observation to form an aligned observation packet, and labeling missing section entries and late section entries; according to the aperture version identification execution state construction expression and the reward calculation expression, packaging the training sample packet and generating a re-calculation abstract, calculating a backfill link completeness index and an aperture shadow difference risk value, obtaining a release judgment value through a biaxial risk matrix, and writing the training sample packet into a sample library or an isolation area. And generating a strategy release packet, completing admission judgment according to the morphological interval table, triggering the freezing strategy release packet to output a bottom guarantee control instruction, and registering a repair queue.

The invention discloses an AI chip equipment fault prediction system based on a cycle time network, and the system comprises the following modules: an on-chip behavior data acquisition module which is used for collecting on-chip behavior data and constructing an on-chip topology dynamic field; the perturbation response acquisition module is used for applying a perturbation signal to the chip and forming a quantum perturbation response tensor; the cross-scale seepage analysis module is used for executing cross-scale seepage analysis and constructing a cross-scale seepage map; the multi-dimensional feature construction module is used for executing synchronous alignment, feature fusion and dynamic reconstruction and generating a behavior feature sequence; the behavior prediction module is used for obtaining a future behavior prediction sequence based on the improved xLSTM model; and the abnormity judgment and regulation module is used for constructing an abnormity risk matrix based on the prediction sequence and the health baseline, and performing dynamic regulation on the chip. According to the invention, accurate prediction and abnormity identification of the operation state of the AI chip can be realized, and the operation safety and the self-adaptive regulation and control capability of the chip are improved.

The invention discloses a cascade dam group system service risk matrix type evaluation method, which comprises the steps of collecting data information of a drainage basin where a cascade dam group system is located to construct a deep learning model, and performing dam break flood routing calculation; calculating a system failure risk rate of each unit dam, quantifying a risk transfer coefficient between adjacent unit dams, constructing a state transition probability matrix and a state transition loss matrix of the cascade dam group system, calculating a state transition risk matrix of the cascade dam group system, and obtaining a total risk rate of the system and three types of total risks of life, economy and social environment; the corresponding total loss is evaluated; and finally carrying out cascade dam group system risk rating. The method has higher calculation efficiency and precision, and can provide richer and more comprehensive calculation results; according to the system risk quantitative grading standard and method, theoretical preciseness is considered, meanwhile, higher engineering applicability and practical operability are achieved, and higher practical application value is shown.

The invention provides an agricultural product quality safety risk assessment method, and relates to the technical field of agricultural product quality safety risk assessment, and the method comprises the steps: extracting monitoring data from a risk monitoring database, and constructing a monitoring matrix; calculating a dynamic safety threshold value corresponding to the combination of the corresponding variety and safety index in the monitoring matrix; converting the monitoring matrix into a risk matrix based on the dynamic safety threshold; splitting the risk matrix into a plurality of sub-matrixes according to varieties, converting each sub-matrix into a risk entropy vector of each variety according to a risk entropy method, and longitudinally splicing the risk entropy vectors of all varieties to obtain a risk entropy matrix; calculating the weight of each safety index based on the risk entropy matrix; calculating a safety evaluation index of each variety based on the risk entropy vector of each variety and the weight of each safety index, and performing safety evaluation on the agricultural product based on the safety evaluation index; the safety threshold can be dynamically adjusted, so that the evaluation result is more accurate.

The application discloses a kind of based on GIS and InSAR's city underground road shield excavation Ground subsidence risk visualization assessment and prediction method, and city ground object grading and intelligent annotation are realized by the multi-source data of GIS and InSAR, using the improved filtering method to extract local subsidence and combine related subsidence influence factor to build susceptibility multi-classification evaluation dataset, in combination with artificial intelligence algorithm, according to the established dataset, realize city subsidence susceptibility multi-classification evaluation, and then based on risk matrix, with susceptibility multi-classification as possibility, city ground object importance classification as severity, realize the visualization assessment and prediction of settlement risk for city underground road construction.The advantage of the present application is to realize the visualization assessment and prediction of settlement risk for underground road construction, improve the rationality of line planning and the safety of construction period.

A computer implemented method includes receiving, by the computer, a request to display a process safety risk matrix associated with a process facility and displaying the process safety risk matrix. The process safety risk matrix includes a 2-dimensional representation of a plurality of risk matrix cells, each of the plurality of risk matrix cells associated with a combination of a likelihood level and a severity level. The computer implemented method includes displaying, by the computer, an indication of a hazardous process event scenario within a first cell within the process safety risk matrix, the cell location determined based on a Target Mitigated Event Likelihood (TMEL) and a Safety Severity. The computer implemented method includes displaying, by the computer, an indication of a second graphic indication on a second cell to identify a change in a hazardous scenario count associated with the second cell.

The invention relates to a collision detection method based on motion uncertainty quantization and multi-modal intention. The method comprises the steps of U1, motion uncertainty ellipse modeling; u2, carrying out obstacle multi-mode behavior probability identification; u3, dynamic virtual lane generation and obstacle screening; and U4, space-time risk matrix judgment. According to the method, the omission ratio of a structured scene (high speed) is reduced, mistaken braking of temporary deviation (such as well lid avoidance) is effectively avoided, the time consumption of single-time detection is greatly reduced, high-frequency collision detection is supported, and decision delay of the high-speed scene is avoided.

The application relates to the technical field of environmental treatment, and discloses a stress and gas composite disaster zoning and grading collaborative treatment method based on a risk matrix method. The stress and gas composite disaster zoning and grading collaborative treatment method based on the risk matrix method can realize real-time monitoring of stress evolution of a working face in a mining process by a stress monitoring system, automatically generate stress contour lines, and effectively divide low stress areas, medium stress areas and high stress areas. A gas geological visualization system can visualize gas occurrence conditions of the working face area, effectively improve the evaluation accuracy of the gas occurrence of the working face, accurately divide low gas areas, medium gas areas and high gas areas, and investigate effective drainage radii of different gas occurrence areas in combination with field conditions. On the basis of comprehensive analysis of the gas geological visualization system and the stress monitoring system, the risk matrix method is used for risk identification, risk judgment and risk evaluation of the working face area, low risk areas, medium risk areas, high risk areas and major risk areas are effectively divided, and different treatment measures are taken for different risk areas.

The application relates to the technical field of artificial intelligence, and provides an intelligent insurance matching method and system combining a user portrait and a clause knowledge base. The method comprises the following steps: constructing a first portrait of a user based on a user data set and optimizing a second portrait; constructing a product portrait by multi-dimensionally extracting features from an insurance clause knowledge base; performing demand coverage risk evaluation on the product portrait based on the second portrait of the user, generating a demand coverage risk matrix; performing guarantee conflict risk evaluation, generating a guarantee conflict risk matrix; introducing a risk analysis channel, combining the second portrait of the user to generate a risk matrix; and fusing the three types of risk matrices to perform multi-level optimization matching, generating an insurance optimization matching graph, so as to solve the technical problem that there is a significant deviation between a recommended result and actual risk preference and guarantee demand of a user, realize dynamic and accurate matching of an insurance product and user demand, reduce guarantee conflict risk and improve the relevance of a recommended result, and achieve the technical effect of meeting the demand of personalized insurance configuration.

The invention discloses an emergency response mechanism implementation method and device based on a rooting theory and a three-dimensional risk matrix. The method comprises the following steps: constructing an emergency response model according to collected early warning event cases based on the rooting theory; determining that the safety factors influencing the risk comprise the occurrence probability, the sweep range and the continuous span of the risk, constructing a three-dimensional risk matrix model according to the safety factors, and calculating the risk dimension weight based on the weight adjustment and calculation method of the interval number judgment matrix information; dividing a three-dimensional risk assessment level interval by adopting an improved radar map method according to the quantized risk level and the risk dimension weight, and determining an emergency response level corresponding to the emergency to be assessed based on the three-dimensional risk assessment level interval; formulating a corresponding emergency response strategy according to the emergency response model and the emergency response level; the method does not need to preset hypotheses, can adapt to non-organizational and sudden complex emergency scenes, and remarkably improves the accuracy and efficiency of emergency response.

The invention discloses a municipal drainage pipeline safety risk assessment method and device, and belongs to the technical field of municipal drainage pipeline design. According to the method, a complete assessment process of determining assessment factors, calculating possibility scores, calculating consequence scores and judging risk levels is defined, the assessment factors cover the possibility and consequences, meanwhile, scores are calculated by adopting a specific formula, and four levels of risks are divided through a risk matrix. The method overcomes the defects that a conventional CJJ181 method has no static prediction, is incomplete in possibility factors and does not consider consequences, achieves the dynamic and comprehensive evaluation of the pipeline safety risk, provides a scientific basis for determining the pipeline safety risk level and formulating targeted management and control measures, is standard in evaluation process, is high in operability, and is suitable for popularization and application. And practical requirements of investigation and detection of municipal drainage pipelines are met.

This invention discloses a large-scale grounding grid corrosion status assessment system based on big data analysis, comprising: a data acquisition module for collecting node resistance, potential, conductivity, humidity, and temperature, and recording time and node data; a data processing module for denoising, normalizing, and correcting data to generate a structured data matrix; a feature extraction module for extracting corrosion feature vectors based on historical records; a corrosion assessment module for probabilistic model inference and scoring to generate corrosion levels, risk matrices, and health trends; a corrosion distribution module for interpolation mapping to generate a full-network corrosion distribution map and marking high-risk nodes; a trend prediction module for simulating evolution to generate future corrosion level change curves and high-risk rankings; and a report generation module for generating a status assessment report. This invention utilizes big data analysis technology to achieve accurate corrosion assessment, risk visualization, and health trend prediction for grounding grids, improving maintenance decision-making efficiency.

A method for thermal safety risk assessment of benzoic acid reduction reaction, comprising: performing thermal stability test on benzoic acid, reducing agent and catalyst; mixing the reducing agent with at least two candidate solvents and performing differential scanning calorimetry test to evaluate compatibility and screen target solvents, and then performing thermal analysis on the reaction liquid of the target system; using a full-automatic chemical synthesis reactor to carry out reaction calorimetry and gas release synchronous characterization, obtaining process data of feeding, heat release, temperature, heat conversion rate and gas, and calculating total heat release, specific heat release, adiabatic temperature rise and MTSR; using an adiabatic accelerating calorimeter to perform secondary decomposition test on the reaction completion liquid to determine Td24 and TMR ad ; comprehensively performing decomposition heat, severity, possibility, risk matrix and process hazard degree assessment, outputting acceptability conclusion and control suggestion; and determining and verifying the thermal stability of the product, realizing thermal safety assessment closed loop.

This invention discloses a credit risk assessment method and system based on the fusion of financial steady-state entropy and occupational growth potential. The method and system acquire user-authorized financial time-series data and occupational background data, calculate income entropy, expenditure entropy, and surplus entropy based on information entropy theory, and synthesize a financial steady-state entropy index. It then vectorizes occupational text using NLP, inputs it into a pre-trained occupational trajectory model to output occupational growth potential, and inputs the financial steady-state entropy and occupational growth potential into a nonlinear fusion model to generate a dynamic credit score. Finally, it executes differentiated credit granting strategies based on the clustering results of a two-dimensional risk matrix. This invention achieves a transformation from "static credit" to "dynamic credit," using financial steady-state entropy to characterize short-term repayment stability and occupational growth potential to assess medium- and long-term repayment potential. Through mathematical modeling and data-driven approaches, it achieves multi-dimensional dynamic fusion, making credit assessment more fair, intelligent, interpretable, and forward-looking.