185 results about "Risk source" patented technology

The invention belongs to the technical field of engineering progress tracking and early warning, and particularly discloses an AI-based engineering progress tracking and early warning method and system. According to the method, the problem of information islands in multi-modal data integration of a traditional method is solved by constructing the engineering space-time knowledge graph, and the comprehensiveness of construction site state sensing is improved; an intelligent mechanism of deviation analysis and trend deduction is established, and accurate prediction of the engineering state evolution trend is realized through multi-dimensional comprehensive evaluation of geometric morphology, construction process topology, resource in-place time sequence and quality compliance attributes; through structured early warning generation and multi-level risk conduction path modeling, the risk source and the influence range thereof are clarified, and a subsequent construction scheme is optimized in combination with a dynamic adjustment strategy, so that the scientificity and execution efficiency of project progress management are improved.

The invention provides a park environment risk traceability and grading early warning management and control method, and belongs to the technical field of risk early warning based on computer data processing. The method comprises the following steps: firstly, collecting video monitoring, environment and equipment state sensor and real-time meteorological heterogeneous data in a whole domain and a peripheral specific buffer zone of a park, and preprocessing the data to obtain standardized multi-source data; dividing grid units by taking a park boundary coordinate as a reference and distributing a unique standardized code, associating standardized multi-source data to a corresponding grid according to a rule, and performing fusion calculation on a grid-level risk characteristic value of each grid; then combining real-time meteorological data, tracking a risk diffusion path through a reverse derivation algorithm, positioning a risk source position and a core range, and outputting related parameters; and finally, grid risk levels are divided according to the grid risk characteristic values and a preset threshold interval, a corresponding early warning mechanism is triggered, and early warning information is output. According to the invention, the precision of environmental risk positioning, the reliability of traceability analysis and the refinement degree of an early warning result are improved.

The invention relates to the technical field of power systems and automation thereof, in particular to a generative adversarial network-based source-load joint scene generation method, which comprises the following steps of: constructing a multi-source time sequence database and extracting weather, time, space and historical state driving factors; establishing a joint probability distribution model based on a vine connection function; taking a numerical weather forecast path and a date type as conditional input, constructing a generative adversarial network embedded with a physical constraint microloss function of the power system, and forming a physical information generator; performing dependent structure fidelity verification on the generated scene by using the joint probability distribution model; generator parameters are fixed, potential space vectors are optimized through a gradient ascending method to maximize power grid risk indexes, and a high-risk source-load joint scene set is generated. According to the technical scheme, accurate generation of the source-load joint scene which is physically feasible and reasonable in statistics and focuses on the high-risk working condition is realized, and the safe operation toughness and the risk early warning capability of the novel power system are remarkably improved.

The invention discloses a thermal power plant limited space operation safety and environment multi-parameter monitoring system, and relates to the technical field of operation safety monitoring. A structure parameter model is constructed based on a space volume structure, a gas exchange path, a risk type and an operation position, and multi-parameter sensing nodes are configured; extracting abnormal fluctuation characteristics of the environmental parameters through edge computing nodes; a gas flow model and a heat distribution model are combined to carry out space-time inversion, a risk source is positioned, a space risk heat map is dynamically generated, the position and action state of an operator can be obtained in real time, the exposure probability is calculated, and automatic identification and graded early warning of a high-risk area are realized; the system has high-precision, low-delay and full-coverage monitoring capability, and the intrinsic safety of limited space operation of the thermal power plant is improved.

The invention belongs to the technical field of image recognition and power grid capital construction sites, and particularly relates to a power grid capital construction dynamic risk recognition method based on a multi-modal image and LLM deduction, and the method comprises the steps: limiting the relation between an inspection object and a design based on a stress chain and an equipotential chain, and generating a constraint framework; performing multi-channel sampling on objects in the constraint framework; after the channels are aligned, a state evidence packet is generated, and the design relation and the state evidence packet are aligned; a stress chain and an equipotential chain are assembled, and a local topological graph is obtained through cutting and minimum necessary path selection; based on the local topological graph, mapping mechanism elements of the nodes and the edges into a fixed semantic slot position and action mode template, and generating mechanism description; and outputting a dynamic risk source list based on the mechanism description and the local topological graph. According to the method, the multi-modal image is subjected to constraint mapping to form the mechanism elements and the minimum necessary topology, and deduction is carried out on the stress chain and the equipotential chain, so that the dynamic risk source is accurately identified, and evidence-level traceable decision output is realized.

The invention discloses a quantitative early warning method for coupling risks of an urban lifeline system, and the method comprises the steps: collecting the real-time data of each system, and constructing a multi-level network which reflects the physical connection and function dependence relation between the systems; determining risk source nodes in the network, and traversing all communicated potential risk transmission paths along a dependency direction; for each path, a path cascade failure probability is calculated by fusing a node historical fault probability and a real-time state; quantitatively evaluating the risk value of each path in combination with the key level of the affected node; and finally, according to the risk value sorting, outputting the path in the front of the sorting and the key node thereof as early warning information. According to the method, global identification and quantitative evaluation of the cross-system risk conduction chain are realized, weak links of the network can be accurately positioned, and a scientific decision basis is provided for targeted risk prevention and control.

The invention provides an intelligent Internet of Things safety monitoring method and system, and the method comprises the steps: obtaining an equipment operation record and a behavior operation record, carrying out the event serialization coding of the equipment operation record, obtaining a sensor event sequence vector, carrying out the behavior mode vectorization processing of the behavior operation record, obtaining a behavior event sequence vector, and carrying out the monitoring of the behavior event sequence vector. Inputting the sensor event sequence vector and the behavior event sequence vector into a pre-trained association analysis model, carrying out cross-sequence causal relationship mining processing, generating an abnormal event association graph, and based on event node attributes and association edge weight values in the abnormal event association graph, executing risk conduction path analysis to obtain a risk conduction path; and obtaining a key conduction path set and a risk accumulation intensity value of the house safety risk, and generating a safety monitoring result containing the risk level identifier and the risk source positioning information according to the key conduction path set and the risk accumulation intensity value. According to the invention, the accuracy and comprehensiveness of intelligent internet-of-things safety monitoring are effectively improved.

The invention relates to the technical field of industrial Internet of Things and edge computing, in particular to an intelligent risk identification method and system for an energy increasing station. The method comprises the following steps: deploying a sensor network at an energy adding station and synchronously acquiring signals, extracting multi-scale time-frequency and physical response characteristics, mapping heterogeneous characteristics to energy flow fingerprints through a hybrid projection model, and performing self-calibration enhancement by using a differential baseline; energy flow fingerprints are decoded and mapped into semantic features, candidate abnormal modes are recognized through short-term trend analysis and multi-source cross correlation, and local abnormal scores are calculated; constructing a physical semantic constrained local causal network based on candidate anomalies, deducing an anomaly propagation path, quantifying node risk contribution, grading and positioning a key risk source; and packaging the risk information into a token matching strategy library, executing a transactional action chain according to a hierarchical intervention principle, monitoring an effect in real time and supporting rollback upgrading. According to the invention, real-time identification, causal deduction and intelligent dynamic response of the energy station risk are realized.

The embodiment of the invention discloses a safety assessment method and device for a receiving station, equipment and a medium, and the method comprises the steps: determining a risk event according to a hazard source in a to-be-assessed receiving station, and enabling the risk event to represent a hazard source out-of-control event; determining a safety index of the to-be-evaluated receiving station according to historical data associated with the risk event of other receiving stations and the technological process of the to-be-evaluated receiving station; a comprehensive influence matrix is determined according to the index scores of the safety indexes, a safety evaluation graph of the to-be-evaluated receiving station is determined based on the comprehensive influence matrix, and the safety evaluation graph is used for investigation operation of risk events of the receiving station and characterization of the causal relationship and the hierarchy of the safety indexes. Based on the safety assessment diagram provided by the embodiment of the invention, accident cause factors, causal relationships and levels can be determined, the accuracy of risk disposal can be improved, and meanwhile, through the safety indexes and the safety assessment diagram, the accident occurrence mechanism can be known, the accident prevention capability can be improved, and the occurrence of accidents can be avoided.

The invention discloses a flexible operation and maintenance early warning device based on artificial intelligence. The flexible operation and maintenance early warning device comprises a multi-source data acquisition module, a data preprocessing and fusion module, an AI state recognition module, a risk reasoning and knowledge graph module, a flexible strategy engine module and an early warning output module. The device realizes cleaning, noise reduction, time alignment and feature fusion of multi-source data by collecting equipment operation indexes, log behaviors, security events and environmental parameters. The AI state recognition module is used for recognizing an operation state, a behavior mode and potential abnormity, and the risk reasoning and knowledge graph module completes risk source positioning and reason verification based on an entity relationship and a semantic link. And the flexible strategy engine dynamically generates an early warning strategy according to a reasoning result, and pushes early warning information in a multi-channel manner through an early warning output module. The device can realize high-accuracy, interpretable and self-adaptive operation and maintenance early warning capability, and is suitable for intelligent operation and maintenance management of hospital machine rooms and key business systems.

The application discloses a kind of tunnel abnormal event-oriented risk assessment method and system, it is related to tunnel traffic safety management technical field.The method includes: collecting tunnel operating state data;Generate abnormal event basic data;According to the available sensing distance and the required braking distance of abnormal event occurrence section and its associated section, generate structure sensitive coefficient, and section correction is carried out to abnormal event basic risk value;With abnormal event occurrence section as risk source section, in combination with structure sensitive correction data, propagation direction, distance attenuation and lane disturbance state calculate the propagation risk potential field value of each tunnel section;Determine abnormal event risk assessment result.The application can convert tunnel abnormal event into section-level risk assessment result from single-point alarm, improve the accuracy of tunnel abnormal event risk influence range identification and emergency disposal decision.

The invention discloses a tunnel engineering construction drawing design risk event safety assessment and standardized prevention and control method, which comprises the following steps: constructing a sample library matrix containing risk sources, risk events and risk prevention and control technologies at the same time, training a random forest model, and extracting risk source parameter weights; weighting the risk source parameters of the tunnel to be built by using the weight, inputting the weighted parameters into the model, and predicting the risk event probability; carrying out binaryzation by adopting a self-adaptive threshold algorithm based on F1-score to obtain a risk event prediction result; calculating multi-dimensional similarity with a sample library in combination with a Gaussian kernel function, normalizing to obtain a sample weight, and recommending an excavation method and an advanced pre-reinforcement measure according to the sample weight; and finally, a field implementation result is fed back to the sample library, and the bandwidth and the threshold are optimized by using gradient descent to realize closed-loop updating. The method reduces human intervention, improves risk assessment objectivity and prevention and control scheme standardization level, and is suitable for safety risk assessment in a complex geological tunnel construction drawing design stage.

The invention relates to the technical field of risk management and control of a data processing center, in particular to a risk management and control system and method suitable for a container type data processing center, and the system and method precisely recognize a hierarchical conduction relationship and hidden association among multiple risk objects by judging whether a risk object connection combination is a crotch risk connection combination or not. According to the method, the problem of missed judgment caused by neglecting the chained influence among risk factors in traditional assessment is avoided, the co-belonging potential risk cabinets in the intersection of the multiple risk cabinet fields are captured, the collaborative amplification effect of multiple risk sources is accurately identified, the traditional assessment error area of simple superposition of multiple risks is broken through, and the risk assessment accuracy is improved. According to the design, the risk management and control index not only can reflect the general influence of a single risk, but also can highlight the high-priority hidden danger under the cross action of multiple risk sources, so that the global risk assessment is more suitable for the actual operation scene of the container.

The application provides a method for identifying risk points of construction quality of a multi-ribbed beam floor based on artificial intelligence; the method organizes three-stage inspection information of formwork installation, support erection and pre-pouring review into point records according to the axis network coordinates, extracts and splices inspection items, measured and photo video semantic features, inputs the point risk score and risk source process prompts in combination with the stage conditions; and performs risk joint checking expansion based on a support chain, a formwork row and a boundary reinforcing belt correlation diagram, generates a review list according to the joint checking score and the on-duty capacity, and forms a closed loop by writing back the review conclusion; the scheme realizes phased and traceable point risk identification and review priority sequencing, improves the risk positioning accuracy and the review efficiency, and reduces the missed detection rate.

This invention discloses a method for quantitatively evaluating the edge properties of complex scenarios based on a multi-category risk field model, belonging to the field of autonomous driving technology. The method includes: analyzing and classifying temporal scene elements based on the PEGASUS system; constructing a large-scale risk field for unidentified scene elements; constructing a medium-scale risk field for low-confidence identified scene elements; constructing an anisotropic driving behavior risk field for high-confidence identified scene elements; superimposing multiple risk fields within the interaction area; and quantifying the edge properties of the scenario using the minimum travel cost of the main vehicle's path. This invention achieves accurate quantification of the edge properties of complex scenarios by differentially modeling multiple risk sources and superimposing field strengths, utilizing the optimal path cost. It solves the problem that existing methods struggle to uniformly characterize multiple risk categories and overall travel costs, providing strong support for risk assessment and edge scenario mining for autonomous vehicles.

This invention discloses a novel pollutant source apportionment and dynamic risk quantification assessment method coupling APCS-MLR and PMF, relating to the field of environmental management technology. The method includes: collecting pollutant concentration data from environmental samples, preprocessing the data to construct a data matrix; running various models to perform source apportionment based on the data matrix, extracting source apportionment feature information, and matching the pollution sources identified by each model to obtain high-confidence source matching pairs; fusing the source contribution concentrations of the two models based on the high-confidence source matching pairs to obtain a fused contribution concentration; calculating the ecological and human health risk values ​​of each pollution source based on the fused contribution concentration to obtain source-specific risk values, calculating the risk contribution rate of each pollution source, and outputting a list of high-risk sources. This invention solves the problems of insufficient accuracy and reliability of source apportionment using a single model, and addresses the issues of the disconnect between source apportionment results and risk assessment, making it difficult to identify high-risk pollution sources.

This invention relates to the field of data processing and discloses a method and system for project risk identification, early warning, and control. The method includes: acquiring state parameters of logical state nodes in a multi-dimensional data logical network, constructing state evolution trajectories, and calculating decay acceleration within adjacent time windows; identifying parallel monitoring node pairs sharing a common underlying resource pool and calculating the correlation of decay acceleration; when the correlation is negative and the difference exceeds a threshold, generating directed edges pointing to asymmetric resource offset occupancy in the topology graph and outputting early warning and control instructions. This invention identifies the asymmetry in the decay rate of evolution between parallel nodes, constructs asynchronous decay scissor difference judgment rules, achieves reverse mapping of resource scheduling imbalance, eliminates the blind spots of cross-path resource dependency perception in traditional monitoring systems, locks down the risk source before local fluctuations trigger global cascading failures, and improves the timeliness of early warning.

The application discloses a kind of multi-energy collaborative heating system risk assessment and regulation method and device, based on improved GNOME model, risk source in multi-energy collaborative heating system is analogized as oil spill source, risk propagation is analogized as oil spill diffusion, by defining risk particle and giving its risk type, risk level, initial position and influence range etc. Property, using similar convection and diffusion mechanism in oil spill model, in combination with energy flow and information transmission in heating system and other factors, the propagation path and change of risk in heating network are simulated. The present application helps to master the risk development trend in real time, early warning potential risks, provides strong support for formulating targeted risk control measures, and ensures the safe, stable and efficient operation of multi-energy collaborative heating system.

The invention relates to the technical field of intelligent power distribution networks, and discloses a power distribution network information physical coupling risk propagation deduction method, system and device based on a hidden Markov model, and a medium. The method comprises the following steps: establishing a power distribution information physical system, an information physical coupling risk event model and a power distribution network information physical coupling risk event propagation model to effectively deduce the propagation of the power distribution network information physical coupling risk. According to the method, complex cyber-physical coupling risk events are disassembled into independent risk event sets, the characteristics of each risk event can be deeply analyzed, risk influences are accurately evaluated according to multivariable information such as the starting side, the hazard loss type, the occurrence probability and the influence range, the propagation mechanism of risks in different levels is clearly described, and the risk assessment efficiency is improved. And a worker can quickly position a risk source and take effective measures to block risk propagation.

The invention belongs to the technical field of computer data processing and automatic control, and particularly discloses and provides a digital configuration and BIM (Building Information Modeling) mapping fabricated electromechanical facility linkage method, which comprises the following steps of: analyzing a building information model and digital configuration, and constructing an initial weighted mapping network; initial virtual pressure of each node is calculated, conduction and superposition of the pressure in the network are iteratively simulated, and a dynamic pressure distribution diagram representing global risks is generated; and when an anomaly is detected, reverse tracing is carried out along a path with the maximum pressure contribution degree gradient so as to locate a risk source. Furthermore, various candidate control strategies are deduced through simulation, scoring is carried out from multiple dimensions, and the optimal strategy is selected. In addition, by comparing simulation and actual execution effects, deviation data is utilized to adaptively optimize the weighted mapping network and simulation model parameters. According to the method, accurate prediction, rapid traceability, optimal decision and continuous self-learning of the risk of the complex facility system are realized, and the intelligence and reliability of operation and maintenance are improved.

The application provides an intelligent atrial fibrillation anticoagulation bleeding risk assessment system and method. The system comprises a patient data acquisition module for acquiring gene mutation site data, drug exposure data, anticoagulation activity data, clinical feature data and follow-up bleeding event data; a data checking and fusing module for quality checking, time alignment and feature fusing of the data to generate anticoagulation bleeding correlation features; a site correlation modeling module for constructing a conduction correlation model between the gene mutation site and the drug exposure offset, the anticoagulation activity offset and the follow-up bleeding event based on the anticoagulation bleeding correlation features; and a risk assessment module for determining the anticoagulation bleeding risk representation of a target atrial fibrillation patient according to the conduction correlation model, decomposing the anticoagulation bleeding risk representation into risk sources, and generating an intelligent atrial fibrillation anticoagulation bleeding risk assessment result containing risk level and risk source information. The application can improve the assessment accuracy, enhance individual identification and improve the result interpretability.

The invention belongs to the technical field of network information security, and particularly relates to an anti-fraud early warning method and system based on cross-modal behavior consistency check, and the method comprises the steps: collecting a cross-modal behavior sequence of a user, and extracting the semantic representation of the cross-modal behavior sequence; constructing a cross-modal behavior consistency test model, and quantifying the cross-modal behavior consistency probability of any two behavior modes in a time sequence; constructing a user behavior association network based on the cross-modal behavior consistency probability matrix; on the network, a depth map clustering algorithm considering node attributes and edge weights is adopted to identify potential fraudulent behavior clusters; aiming at each behavior cluster, extracting a cross-modal consistency violation mode of the behavior cluster, and mapping the cross-modal consistency violation mode into a risk element event; based on the time sequence and logic relation between the risk element events, constructing a dynamic risk propagation map, and identifying a risk source, a propagation path and a current threat level; and according to a map analysis result, generating interpretable early stage grading early warning. According to the invention, early discovery and accurate strike of novel collaborative fraud are realized.

The application provides a kind of network matching not power operation control system based on three-dimensional point cloud, it is related to image processing technical field, the application carries out geometric analysis to three-dimensional point cloud model, and identifies the high curvature geometric discharge candidate point set;And fusion real-time sensing electric field distribution and air charge density data, for each candidate point calculates critical discharge tendency index to carry out physical risk empowerment;Through weighted density clustering algorithm, the discharge risk source area composed of high-risk points is identified.The system also compares the actual electric field with the theoretical simulation electric field, calculates the electric field topological distortion degree, to find out the hidden faults caused by non-geometric factors.Finally, the system executes the hierarchical safety strategy according to the risk source area level or distortion degree result, such as constructing dynamic safety boundary or implementing fault isolation.The application can detect the explicit and hidden risks comprehensively, and can execute fine safety control, which improves the safety and intelligent level of operation.

The application provides a power work ticket generation method based on a relational graph convolutional neural network, comprising: obtaining historical work tickets and equipment account information of a power system; constructing a knowledge graph relational graph according to the historical work tickets and the equipment account information; processing and converting data in the knowledge graph relational graph to obtain a graph structure; constructing a relational graph convolutional neural network model and training the model based on the graph structure to obtain a knowledge reasoning model; obtaining new data and predicting risk factors of related equipment and corresponding safety measures in a work ticket based on the content of the knowledge reasoning model. The application comprehensively considers historical work ticket information in a knowledge base and a standard operation safety measure library, and the relationship between work tickets and safety measures and risk sources; the model outputs optimal safety measures and possible risk sources, thereby assisting workers in the complete filling of work tickets, improving work efficiency and work quality, and optimizing the safety production level of a power plant.

The invention discloses a full-dimension self-interference detection method for a parallel robot platform. The method comprises the following steps: acquiring basic data including six-degree-of-freedom pose information, electric cylinder length and motion state information acquired by a data acquisition layer; whether interference risks exist in multiple risk sources of the parallel robot platform or not is judged on the core detection layer by means of the basic information, if the interference risks exist, hierarchical risk management and control operation is conducted on the parallel robot platform on the response execution layer, and if not, cyclic collection and detection are continuously conducted; wherein the multiple risk sources of the parallel robot platform comprise a Hooke twist detection risk source, a rod length detection risk source, a singularity detection risk source and an OBB collision detection risk source. The method has strong dynamic working condition adaptability and expandability, response conflicts and decision delay are effectively avoided through multi-module collaborative decision, and a systematic solution is provided for safe, efficient and reliable operation of the high-end parallel robot.

This application provides a method and system for determining risk sources of distributed electric aircraft based on flight profiles, applied in the field of aircraft simulation technology. It includes: acquiring the overall construction content of the flight profile and topology of the distributed electric aircraft, and obtaining typical operating conditions for each stage in multiple phases corresponding to the flight profile. The overall construction content includes a high-power-density battery pack, a DC-DC buck converter, an electronic speed controller, a DC brushless motor, and rotors / propellers; simulating the mathematical model of the overall construction content under the target typical operating conditions of the target phase, obtaining simulation results for the target typical operating conditions; and accurately determining the flight test risk sources of the distributed electric aircraft under the target typical operating conditions from the high-power-density battery pack, DC-DC buck converter, electronic speed controller, DC brushless motor, and rotors / propellers based on the simulation results. This significantly reduces the flight risk of new aircraft configurations (i.e., distributed electric aircraft) and improves flight test efficiency.

This invention discloses a big data-based intelligent financial risk analysis method, belonging to the field of big data analysis technology. The method includes: refining a standardized risk event set to form financial risk intensity sequences, contract risk intensity sequences, and supply chain risk intensity sequences within a time window; accumulating these sequences within the time window to form financial risk deposition quantity sequences, contract risk deposition quantity sequences, and supply chain risk deposition quantity sequences, and generating linkage trigger signals; and based on these trigger signals, performing reverse backtracking on the financial risk deposition quantity sequences, contract risk deposition quantity sequences, and supply chain risk deposition quantity sequences along a time dimension to identify risk source sequences and diffusion relationships, thus constructing a risk tracing chain. This invention achieves dynamic tracing of the tracing origin and diffusion relationship chain.

The invention provides a cross-border river water transportation pollution risk acquisition method, which comprises the following steps of: acquiring a first evaluation index according to the ship route traffic volume, the ship route dangerous goods transportation ship volume, the ship route water refueling point number and the ship route loading and unloading wharf number of a water transportation ship route of a drinking water cross-border area, obtaining a second evaluation index according to the water quality standard-reaching proportion and the navigable water flow of the navigable water body of the water transportation route of the drinking water transboundary region, acquiring a third evaluation index according to the shortest distance between the cross-boundary section and the refueling point, the shortest distance between the cross-boundary section and the wharf, the percentage of the area of a drinking water source protection area in the navigable river to the area of the whole river, the precious and rare aquatic species of the water containing body and the population density of the cross-boundary area; and according to the first evaluation index, the second evaluation index and the third evaluation index, obtaining a risk index taking the water traffic route as a risk source so as to indicate the risk of water pollution accidents caused by the water traffic route of the drinking water transboundary region.

The application discloses a kind of intelligent bidding risk control early warning method, belong to artificial intelligence and big data analysis technical field, including the multidimensional correlation degree between rules by constructing rule grid engine calculation, form the dynamic expected correlation table of rule;Standardized metadata model and abnormal mapping visualization mechanism are established simultaneously, can focus on associated risk link after abnormal triggering quickly, and with mapping table form is shown, solve the technical problem that rule is not isolated in bidding risk control system, improve detection efficiency and abnormal result is directly visualized and shown, the correlation degree between rules is calculated by the application, realize the grid coupling between rules, improve the systematicness and accuracy of risk control detection, compare relevant rules using expected correlation table, significantly reduce redundant matching, improve early warning response speed, by mapping relationship, abnormal data is visualized into two-dimensional table structure, it is convenient for manager to determine risk source and influence scope intuitively.