17 results about "Event risk" patented technology

The invention discloses an insurance short video content generation method and device, a medium and a program product, and relates to the technical field of artificial intelligence. Semantic analysis is performed on hot event data to extract core risk elements, so that the system can capture risk features concerned by the current society. The core risk elements are matched with the guarantee demands of the potential users, the guarantee gap is determined, and the association mapping between the hot event risks and the guarantee demands is established, so that the determination of the target insurance product has risk guidance. And determining a cue word generation strategy from a preset strategy library and inputting the cue word generation strategy into the insurance field large language model to generate a first draft of the content, thereby realizing automatic generation of the content. And in combination with a preposed compliance detection mechanism, the compliance of the generated content is ensured. Quick response and generation of the insurance short video content are realized, and the content generation efficiency is improved.

The invention belongs to the field of big data processing, and particularly relates to an event analysis method, device and equipment and a storage medium, target event information is obtained, the target event information is converted into event feature vectors, the event feature vectors are input into multiple analysis models respectively, and the analysis models output corresponding event types and initial risk levels respectively. Performing similarity matching on the event feature vector and a historical event feature vector of a historical event in a preset knowledge base, screening one or more pieces of historical event information of which the similarity score is greater than a preset threshold, and determining the credibility of each analysis model based on the historical event information and the event type, performing weighted fusion on the initial analysis result based on the credibility of each analysis model, and determining a target risk level corresponding to the target event information; according to the method, by combining multi-model analysis, historical data similarity matching and credibility weighted fusion, the accuracy of risk level judgment of the target event is improved.

This invention belongs to the field of overflow events, specifically relating to a hierarchical multi-joint learning-based method, system, and equipment for predicting well control overflow events. It aims to address the problems of equipment damage, production interruption, safety accidents, and environmental pollution caused by overflow event risks. The invention includes: training multiple well control overflow event prediction base models based on the training set; obtaining prediction results based on the test set; calculating the accuracy of the base models and selecting retained models to obtain a final feature set; integrating the retained models based on the final feature set to construct a voting-based ensemble model; and inputting the data to be predicted into the ensemble model to obtain the well control overflow event prediction result. This invention improves prediction accuracy and robustness by constructing an ensemble model, optimizes resource utilization, enhances operational safety and decision-making efficiency, effectively prevents well control overflow events, and ensures the safety and economic benefits of oilfield operations.

A computer system for analyzing and mitigating risks associated with an event is provided. The computer system is configured to: (i) receive at least one of a city risk profile and a building risk profile from a database; (ii) receive city systems data from the city services computer system; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the event; (iv) generate an event risk profile that includes the at least one potential risk associated with the event; and / or (v) generate a risk mitigation output based upon at least one of the city risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. Computer systems for analyzing and mitigation risks associated with a city, a building, and a user are also provided.

The invention discloses a chronic disease whole-process digital medical treatment cloud service and medical insurance real-time supervision unified large model. The model comprises a technical architecture and a combination engine; the technical architecture comprises an infrastructure layer, a data base layer, a core technology layer, a business application layer and an ecological collaboration layer. The combination engine comprises federated learning and block chain combination, digital twinning and AI and big data combination, edge calculation and 5G and Internet of Things combination, NLP and knowledge graph and AR combination, quantum encryption and zero knowledge proof combination, and Web3.0 and DAO combination. Through cross-layer cooperation of a five-layer technical architecture and six technology combination engines, balance between data sharing and privacy protection is realized, cross-mechanism data islands are broken, data security compliance is guaranteed through technologies such as federal learning block chain quantum encryption, and the contradiction between data intercommunication and privacy protection in the prior art is solved; according to the digital twinborn AI big data combination, a full-process risk control system is constructed, and risk pre-prediction in-event intervention and post-event optimization are realized.

The embodiment of the invention provides a risk early warning method for medicine production, electronic equipment and a medium, and belongs to the technical field of medicine production and artificial intelligence. The method comprises the following steps: acquiring at least one of personnel behavior data, equipment operation data, process data, material data and environment data in a drug production area, performing risk detection on each source data, and generating a reference event according to other source data in multi-source data to predict the risk of a risk event, and executing an early warning operation matched with the risk event and the event risk degree corresponding to the source data. According to the embodiment of the invention, real-time risk event detection and early warning operation are carried out on the drug production area, so that the final influence of risk events such as non-standard operation on the drug quality can be reduced fundamentally.

This application relates to the field of public safety decision-making technology, specifically disclosing an artificial intelligence-driven public safety decision support system. First, it utilizes a security event element extraction module to extract event elements from historical public safety events, and then uses an event spatial distribution clustering module to geographically divide the target area based on a planning map. Next, an event temporal distribution sorting module arranges these event elements chronologically, forming a time series. Subsequently, an event semantic information analysis module uses organic propagation analysis of event semantic information to generate organically encoded representations of the semantic information transmission of public safety events in multiple historical regions, capturing the dynamic semantic relationships between events. Finally, an event risk discrimination module combines the above encoded representations and the planning map to identify the public safety risks in the target area. This effectively integrates information from both temporal and spatial dimensions, enhancing the understanding and prediction capabilities of the complex dynamic interactions of public safety events.

The embodiment of the application discloses a kind of event risk detection methods, equipment and medium based on large model, belong to risk detection technical field, solve the problems that prior art scene poor adaptability, rely on sample and artificial rule, so that the risk detection accuracy and efficiency of complex and diverse event are lower. Including, based on the industry knowledge and industry scene information obtained, event risk detection processing template is constructed;Wherein, event risk detection processing template is used to provide detection processing logic for event risk detection model;Obtain the event basic data corresponding to the event to be detected, retrieve event related knowledge data in the preset vector database based on event basic data;Event basic data and event related knowledge data are filled into event risk detection processing template to obtain input prompt word;Call event risk detection model, based on event risk detection model and input prompt word carry out event risk detection, and output detection conclusion.

The present application relates to the technical field of public opinion monitoring, and particularly relates to a public opinion dynamic monitoring and early warning system based on text clustering analysis. In the clustering process, the semantic center of historical event clusters is introduced as a constraint condition, so that the semantic continuity of existing events can be fully considered when the public opinion text is classified, and event misjudgment or repeated generation caused by short-term expression changes can be effectively avoided. By constructing a drift suppression mechanism based on the semantic center change amplitude, the unstable event clusters are adjusted, the stability and accuracy of event evolution modeling are improved, the cross-time window event clusters are associated and determined, the stable event clusters are formed, the tracking management of public opinion events is realized, the text scale, the emotional mean, the emotional heterogeneity, the evolution characteristics of the scale and the emotion are introduced, and the low sample compensation mechanism is combined to model the public opinion event risk, so that the high-risk events are not ignored, the mature event risk is not excessively enlarged, and the timeliness and accuracy of public opinion early warning are improved.

The invention discloses a listed company financial condition intelligent identification and audit simulation analysis platform, and the platform comprises a data fusion module which integrates multi-source data; the financial knowledge graph module is used for extracting an entity relationship to construct a graph; the financial health degree identification module is used for carrying out risk assessment based on a map and a model; the auditing simulation analysis module is used for receiving the auditing hypothesis, performing simulation deduction and outputting an influence path; the visualization module is used for displaying a result and generating an audit report; according to the method, an analysis platform integrating data fusion, knowledge graph, intelligent identification and simulation is constructed, dynamic pre-event and in-event risk early warning is realized, the method can penetrate through a complex company association network, financial statement data and commercial essence, market emotion and association relationships behind the financial statement data are dynamically associated, and the risk early warning efficiency is improved. Therefore, a tool capable of insighting the truth of the business is provided for an auditor and a financial analyst, and the depth and the breadth of financial auditing are greatly enhanced.

The invention discloses a monitoring point layout method, device and equipment, a medium and a product. The method comprises the following steps: acquiring a monitoring point data set and an event data set corresponding to a target area; determining a monitoring thermal grid map corresponding to the target area according to the monitoring point data set; the monitoring heating power is used for representing the coverage density of monitoring points in the grid units; determining an event thermal grid map corresponding to the target area according to the event data set; the event heat is used for representing the risk level intensity of the event occurring in the grid unit; performing intersection negation operation on the monitoring thermal grid map and the event thermal grid map to obtain a to-be-monitored area corresponding to the target area; and determining a monitoring point layout scheme corresponding to the target area according to the to-be-deployed and controlled area. Through the technical scheme of the invention, the monitoring point location layout can be optimized based on the thermal grid map, and the event risk area is reasonably covered while the investment of redundant equipment is reduced.

The invention provides a power distribution network fault handling method and system based on event driving and case inversion. The method comprises the following steps: acquiring power distribution network alarm and topological data in real time; performing semantic standardization and invalid filtering on the alarm; based on alarm synthesis of an event template library, the filtered alarms are synthesized into events according to time, topology and causality; event risk weight calculation and sorting; searching and matching a historical case library; generating a disposal suggestion and outputting an operation order; performing closed-loop learning and case library updating; the fault interval positioning time is shortened, the case matching precision is improved, the automatically generated operation order is available, the daily invalid alarm amount is reduced, and correct handling and the system closed-loop updating period of young watchmen in simulation fault drilling are promoted.

This invention relates to a method and system for detecting anomalies in container system calls based on multi-level context. The technical solution employed is as follows: Low-level context data containing process causal relationships and call parameters is captured through a kernel-mode monitoring mechanism. System calls are deconstructed in parallel into three types of feature vectors: at the parameter level, an Event Risk Index (ERI) is generated by analyzing data entropy and error codes to address the semantic vacuum problem; at the temporal level, a deep sequence model is used to capture long-range dependencies and quantify them into an Anomaly Sequence Index (ASI) to overcome local dependency problems; at the causal level, a Control Index (CI) is generated by constructing a process family graph and applying graph representation learning to overcome the structural flattening problem. The three types of heterogeneous vectors are dynamically fused through an adaptive gating fusion network, input into an unsupervised autoencoder model, and the reconstruction error is compared with a threshold to make a decision. This invention can accurately identify advanced threats such as process spoofing and parameter injection, improving the security of container runtime and possessing significant practical application value.

The embodiment of the invention discloses an event risk detection method and device based on a large model, and a medium, belongs to the technical field of risk detection, and solves the problems of low risk detection accuracy and efficiency of complex and diverse events due to poor scene adaptability and dependence on samples and manual rules in the prior art. Comprising the following steps: constructing an event risk detection processing template based on acquired industry knowledge and industry scene information; wherein the event risk detection processing template is used for providing detection processing logic for the event risk detection model; obtaining event basic data corresponding to the to-be-detected event, and retrieving event-related knowledge data in a preset vector database based on the event basic data; filling the event basic data and the event related knowledge data into an event risk detection processing template to obtain an input prompt word; and calling the event risk detection model, performing event risk detection based on the event risk detection model and the input prompt word, and outputting a detection conclusion.

The embodiment of the application discloses a news event-based information processing method and related device, the method comprises the following steps: collecting news events pushed by multiple news sources, and extracting feature information in the news events; according to the feature information, excluding repeated news events from different news sources to obtain multiple target news events; tagging the multiple target news events; determining a risk score of each target news event in the multiple target news events; calculating and determining a push priority of each target news event in the multiple target news events; according to the push priority, determining a push order of the multiple target news events, and sending the multiple target news events carrying information of the push order to a terminal device. In this way, the event risk analysis of news events and the dynamic adjustment of news push order according to business needs effectively reduce the load of the server, reduce resource consumption, reduce news push delay, and improve information processing efficiency.

Provided is a method for constructing a backtracking analysis model based on an attack chain, including: a kernel-layer security agent collects process, file, and network behavior features in a hardware-isolated environment to generate event tuples; a tensor network pipeline performs three-dimensional decoupling mapping of the tuples into behavior fingerprint vectors, orthogonalized noise features, and asymmetric adjacency tensors, and compresses them into spatio-temporal topological tensor block; a reinforcement learning controller constructs a directed acyclic graph based on the tensor block, calculates connectivity loss, and outputs event risk scores; a dynamic routing engine constructs a decision tree model based on risk markers, burst frequency, and correlation entropy to implement three-level shunting, and a multi-simulation system generates anti-interference index; when the deviation between a physical trajectory and a digital model exceeds a tolerance, a closed-loop feedback weight coefficient is used to updates loss function parameters and adjust channel resource weights.

The invention relates to a public opinion event risk assessment method and device based on a multilayer network, and belongs to the technical field of event risk assessment, and the method comprises the steps: obtaining a total set of a plurality of social platform topic events; for any social platform, calculating text similarity, user similarity and emotion fluctuation similarity; unified dimension and direction processing is carried out; performing empowerment to calculate the final association degree of any two topic events in the total set of the plurality of social platform topic events; for each social platform, constructing a multi-layer network according to the topic event and the final association degree of any two topic events in the total set of the topic events of the plurality of social platforms; determining a set for the multi-layer network, and de-duplicating the set to obtain an infection source set; sequentially taking each node in the infection source set as an infection source to obtain an infection state set, an infection duration set and a neighbor infection proportion set of each node; and determining a risk score of each node, and determining a risk topic event according to the risk score.