21 results about "Contingency plan" patented technology

The invention discloses a power distribution network toughness improvement method based on virtual power plant cooperation and transaction settlement, and the method comprises the steps: S1, constructing a multi-type virtual power plant which comprises a virtual power plant management platform, a power transaction platform and a scheduling platform, and enabling the multi-type virtual power plant to participate in a power grid scheduling and power transaction whole-process management frame, so as to carry out the plan of a VPP disaster accident; s2, the scheduling platform constructs a pre-disaster and post-disaster multi-type VPP collaborative optimization power distribution network power supply recovery model so as to formulate a network reconstruction strategy of each time period, a real-time scheduling strategy of emergency resources and an adjustment strategy of the VPP; and S3, according to the VPP actual response amount issued by the scheduling platform, and the declaration response amount and the VPP historical participation degree reported by the virtual power plant management platform, the power transaction platform calculates the power supply recovery participation excitation unit price and compensation income of each VPP. According to the invention, through the whole-process closed-loop management integrating pre-disaster plan generation, post-disaster dynamic adjustment and transaction settlement, the key problems of insufficient resource collaboration, stage splitting, excitation stiffness and the like are solved.

The invention discloses a distributed purchase and inventory cooperative control system based on multi-agent collaboration, and relates to the technical field of purchase and inventory cooperative processing, and the system comprises an intelligent purchase unit, an intelligent inventory unit, a logistics transportation unit and a cooperative processing unit. The data acquisition unit is used for acquiring data and extracting features after the data acquisition is completed, the intelligent inventory unit is used for monitoring the inventory state, the logistics transportation unit is used for establishing a purchase transportation scheme, an inventory transportation scheme and an emergency scheme, and the purchase transportation scheme is used for planning a transportation mode and a transportation path during purchase. According to the invention, in the cooperative processing process, the corresponding purchasing scheme, transportation scheme and inventory scheme can be formulated according to the types of different materials, the accuracy in the implementation process is ensured, the emergency scheme is established to deal with the problems generated by abnormal conditions, and the stability in the control implementation process is improved.

This application discloses a dynamic decision-making method and system for flash floods and debris flows based on multi-agent and LLM (Limited Language Model), belonging to the field of emergency management and intelligent decision-making technology. This method constructs an intelligent decision-making architecture based on a large model and multi-agent collaboration. It solves the execution challenges of complex disaster tasks through the division of labor and cooperation among multiple agents, and overcomes the decision-making limitations of traditional systems by leveraging the deep reasoning capabilities of a large language model. Specifically, the multi-agent collaborative framework decomposes disaster prevention and control decisions into sub-tasks such as intent parsing, contingency plan retrieval, spatial analysis, data acquisition, and analysis and prediction. Each functional agent focuses on its area of ​​expertise, improving the efficiency and accuracy of task execution. The agent communication protocol ensures efficient information interaction and collaborative scheduling between modules, realizing fully automated processing from task input to data output.

This application discloses a method and electronic device for estimating data storage capacity. The method includes: acquiring first data of a target device, the first data including the data storage capacity of the target device at various times within a first time interval; based on the first data, detecting abnormal change events in the data storage capacity of the target device, acquiring abnormal change data, the abnormal change data being able to characterize the change pattern of the data storage capacity of the target device during the duration of the abnormal change event; and based on the first data and the abnormal change data, estimating the data storage capacity of the target device under the condition that an abnormal change event occurs at a target time. This method can estimate the data storage capacity of the target device under the condition that an abnormal change event occurs at a target time, enabling users to develop targeted contingency plans or purposefully avoid such events, thus improving the stability and robustness of the system.

A computer-implemented method includes accessing route data indicative of a route for a first aircraft to fly within a geographic area. The method includes accessing data indicative of a plurality of contingency landing locations within the geographic area. The method includes processing such data to compute a contingency landing plan that assigns at least one respective contingency landing location to each route segment of the first aircraft. The method includes accessing data indicative of a change in flight operations of a second aircraft that is in-flight within the geographic area and computing an updated contingency landing plan for the first aircraft based on the change in the flight operations of the second aircraft. The method includes transmitting, over a network to a computing device associated with the first aircraft, one or more signals indicative of the updated contingency landing plan.

This invention relates to the field of collaborative control technology for enhancing power grid resilience, and particularly to a collaborative control method for enhancing power grid resilience in extreme weather scenarios. The method includes: collecting data to form dynamic panoramic data of the power grid; injecting the panoramic data into a digital twin model to generate quantitative early warning information including fault probability, impact range, and time window; initiating a parallel computing process based on the early warning information to obtain a set of candidate contingency plans; generating a detailed control command sequence using a distributed collaborative decision-making model; simultaneously simulating the execution of the command sequence in the digital twin model, and triggering a correction mechanism to continuously correct the commands by comparing the simulation results with the actual state in real time; recording scheduling intervention behaviors and control effects, structuring them as cases and storing them in a knowledge base, and recommending historical contingency plans based on similarity matching. This invention improves the efficiency of power grid resilience response through data fusion preprocessing, parallel distributed computing, real-time feedback correction, and knowledge reuse collaboration.

This invention discloses an artificial intelligence-based comprehensive monitoring system for highway bridges, belonging to the field of highway bridge health monitoring technology. The system comprises the following components: a data acquisition module, a dynamic early warning threshold generation module, a multi-level alarm and tracing module, a graded contingency plan generation module, and a push and feedback module. This invention constructs a multi-module collaborative intelligent monitoring architecture, dynamically generates early warning thresholds, accurately perceives real-time early warnings, optimizes threshold calculation logic, and improves early warning accuracy. It presets four alarm levels to locate the causes of anomalies and deduce damage paths; it uses case-based reasoning models to generate appropriate handling solutions, verifies and ensures the reliability and usability of reports, and quickly pushes them to multiple terminals for collaboration; it establishes a data feedback closed loop, forming a full-process closed-loop management system, solving traditional problems, and improving the intelligence and precision of bridge safety monitoring, as well as operation and maintenance efficiency and safety assurance capabilities.

Computer implemented methods, systems, and computer program products include program code executing on a processor(s) generates scenarios based on cognitively analyzing external events. The processor(s) cognitively analyze the process and segmenting the process into components, and for each component: determine an endpoint for each component; determine the scenarios relevant to the component; adjust the endpoint of the component based on the scenarios relevant to the component; applying reinforcement learning to the scenarios relevant to the component to validate impacts of the scenarios on the endpoint and to select a most likely scenario; and implement process changes to terminate the component at the adjusted endpoint.

The invention relates to the technical field of major activity safety guarantee, and particularly discloses a major activity safety guarantee risk management system and method based on risk portray.The system comprises a data footstone layer, an analysis, study and judgment layer, a decision action layer and a verification optimization layer, and the data footstone layer constructs a dynamic hidden danger library following the four-dimensional principle; multi-source data are gathered through a standardized model, risks are quantified based on a weighted RPN formula, and a data injection and dynamic updating mechanism is matched. And the analysis, research and judgment layer generates a risk portrait report containing a structured list through scene modeling and multi-dimensional aggregation analysis. The decision action layer converts the portraits into tactical actions such as special plans, resource presetting and special drilling. And the verification optimization layer realizes risk monitoring regulation and knowledge precipitation through closed-loop management. According to the invention, the conversion from passive response to data-driven active defense is realized, the risk identification and pre-judgment precision is improved, the resource configuration and emergency efficiency are optimized, the system self-learning evolution is driven, and the risk recurrence rate of the same kind is reduced.

This invention discloses a method, equipment, and storage medium for real-time monitoring and intelligent early warning of disasters in open-pit mines, relating to the field of mine safety production monitoring technology. The method includes: deploying a multi-source heterogeneous sensor network in the open-pit mine operating area to collect real-time physical parameter data of various disasters, generating consistency confidence weights for fused sensing data and corresponding data sources; setting scoring models for various disaster types and calculating risk sub-scores for each disaster; using a dynamic weight generation strategy to generate dynamic weights for each disaster, and weighted summing the risk sub-scores to obtain a comprehensive risk score; triggering an early warning by dynamically assembling and issuing executable emergency commands from a contingency plan library based on contingency plan assembly logic. This invention can ensure data reliability from the source, possesses dynamic intelligent risk assessment and end-to-end closed-loop self-optimization capabilities, and achieves a fundamental shift from reactive post-event response to precise pre-event prevention.

The invention discloses a static risk assessment method and system for a power distribution network system, and relates to the technical field of power distribution networks. The method comprises the steps of data acquisition, wind speed influence analysis, sunlight intensity influence analysis and comprehensive risk assessment. According to the invention, multifunctional meteorological monitoring equipment is deployed at key positions of the overhead line, parameters such as wind speed, wind direction, sunlight intensity and environment temperature are synchronously acquired, a multi-dimensional data acquisition system is constructed, and a data foundation is built for risk assessment; a scientific calculation model is applied, conductor galloping and thermal imbalance risks are accurately quantified, and the limitation of single-factor evaluation is avoided. Line risks are divided into high, medium and low levels through comprehensive risk assessment, so that the risk state is clear at a glance; differentiated coping strategies are made according to different risk levels, high risks are maintained preferentially, medium risks are monitored in an enhanced mode, plans are made, low risks are operated and maintained conventionally, and reasonable allocation of resources is achieved.

The present invention discloses a method for generating a contingency plan for icing disposal of power transmission and distribution lines based on the Q-Learning algorithm, belonging to the technical field of power line deicing optimization. The steps are as follows: establish an environmental model for ice accretion growth, development, and disposal; based on the Q-Learning algorithm, construct an intelligent agent learning model for exploring the contingency plan for icing disposal and complete the training; select a specific environmental model as the environment corresponding to the contingency plan; apply the trained intelligent agent model to the contingency plan environment to calculate the optimal treatment strategy; output each change in the above environment and each measure taken to a word document of the contingency plan; construct an animated demonstration of the contingency plan for icing disposal to assist in training the disposal personnel. By constructing data related to the line state into an environment, the present invention uses reinforcement learning to complete decision optimization, constructs a learning model using the Q-Learning algorithm to complete the learning of the intelligent agent and the evaluation function; the intelligent agent interacts with it to output the corresponding contingency plan, and outputs the contingency plan in word format.

This invention discloses a method, system, terminal, and storage medium for typhoon early warning of ships and large equipment in shipyards based on contingency plan activation and rule matching scanning. The method includes: collecting multi-source key parameters through a shipyard multi-dimensional typhoon early warning management platform; filtering valid data through rule matching scanning; sequentially calling a contingency plan hierarchical triggering model, a wind load nonlinear calculation model, and a ship mooring force analysis algorithm to complete early warning threshold generation, wind load calculation, and mooring force analysis; the collaborative linkage of various models to explore parameter coupling relationships; and optimizing the calculation process step by step through indicator system construction, feature parameter extraction, multi-source data substitution, and result verification; the platform integrates the results of multiple modules to generate corresponding level early warning signals and emergency guidance. This invention achieves precise and efficient typhoon early warning, providing full-process technical support for typhoon prevention of ships and large equipment in shipyards, and improving the operability of early warning and risk management capabilities.

This invention discloses a method for predicting open channel water quality by coupling mechanistic and non-mechanistic models, comprising the following steps: Step 1, data collection; Step 2, constructing an open channel mechanistic model; Step 3, constructing an open channel non-mechanistic model; Step 4, predicting the water quality of the open channel. The method of this invention combines the advantages of two types of water quality prediction methods by coupling mechanistic and non-mechanistic models. It uses an LSTM model to predict the upper boundary flow and water quality in the near future, and uses the prediction results as the upper boundary of the hydrodynamic and water quality models to predict the water quality changes at various cross-sections along the channel in the near future. This method improves the accuracy and precision of water quality prediction, and is an important tool for conducting water quality trend prediction and pollution early warning assessment. It provides technical support for intelligent water quality safety management and offers a scientific basis for solving water pollution and formulating relevant contingency plans.

The invention provides a foreign trade information processing method and system, and the method comprises the following steps: S1, extracting heterogeneous data from a customs system, a payment platform and a logistics tracking system in real time, and analyzing an unstructured text through a regular expression and a Bi LSTM model; s2, constructing a foreign trade entity knowledge graph taking the order ID as a core node, and mapping an association relationship among enterprises, commodities, logistics and payment entities; s3, based on a time sequence convolutional network (TCN), fusing an exchange rate fluctuation rate, a geographical political risk index and a buyer historical payment record to generate a dynamic risk score; s4, when the risk score exceeds a threshold value, automatically triggering a plan in a coping strategy library, such as credit clause modification and cargo port transfer; and S5, calculating an optimal purchase order time window by using a reinforcement learning model and taking the shipping price fluctuation curve and the inventory holding cost as constraints. According to the method, multi-source data intelligent extraction is carried out, and a foreign trade entity knowledge graph data model is constructed through butt joint of an AP I gateway and a customs / bank / logistics system.

This application relates to a method, system, and medium for the fusion analysis and automatic handling of multi-source anomaly data in the power industry. The method includes: real-time collection of heterogeneous logs and status data from information network devices via a log collection agent; real-time storage of the aggregated multi-source data in an Elasticsearch cluster to construct a panoramic power data lake; fusion analysis and collaborative anomaly detection of the multi-source data based on Elasticsearch's built-in machine learning engine and combined with third-party analysis algorithms to identify hidden faults and complex threats that are difficult to detect from a single data source, generating anomaly results with confidence levels; and the real-time conversion of analysis results into handling instructions through an integrated alarm and automated handling system to drive the execution of contingency plans or notify maintenance personnel via a messaging platform. This application addresses the problems of high daily maintenance costs and low efficiency in the power grid.

This invention provides an intelligent early warning method and device for urban extreme weather disasters based on data-physical fusion, relating to the fields of meteorological disaster simulation and artificial intelligence decision-making technology. The method includes: simulating windstorms and floods in a target city to obtain numerical simulation feature vectors; processing historical contingency plan texts to obtain contingency plan text vectors; constructing a vector database containing extreme disasters in the target city; designing a retrieval-enhanced generative agent, a disaster assessment agent, a report summarization agent, and a meteorological disaster dialogue agent, and constructing a multi-agent loss reduction decision-making big language model based on an agent scheduling framework; and generating customized loss reduction decision schemes for specific disaster scenarios by combining the vector database with the multi-agent loss reduction decision-making big language model. This invention solves the problem of transforming meteorological forecast information into accurate decision-making, realizing the transformation from single meteorological parameter forecasting to scenario-based loss reduction decision support, and significantly improving the intelligence level of urban disaster early warning.

The invention relates to a security industry scheduling planning method based on a plan scheduling optimization algorithm, and belongs to the technical field of intelligent manufacturing and production planning. The method comprises the following steps: firstly, acquiring order, resource and production data through an industrial sensor and a manufacturing execution system (MES); and then, an improved genetic algorithm model embedded with exclusive constraints of security and protection production is constructed and trained, the model adopts an order-process-resource three-dimensional coding system, a scheduling scheme is ensured to be compliant through a dynamic punishment mechanism, and an optimization result including a basic scheduling, an emergency plan and a cost suggestion is output. According to the invention, the adaptability and flexibility of production scheduling in the security industry are effectively realized, and the production efficiency, the resource utilization rate and the system reliability are remarkably improved.

The application provides a flood defense interactive decision method driven by virtual-real twin contingency plans, comprising the following steps: analyzing a flood control situation of a basin based on a flood control scene, and judging whether there is a flood hidden danger; if not, a normal deduction state is executed; if yes, a real-time mutual feedback deduction state is executed. The application innovatively proposes a real-time mutual feedback deduction of "reality-scene-contingency", through a time progression mode, combining the reality scene of the current situation of each device detection, model deduction of each level hydropower station, flood control section, reservoir and real-time water and rain weather, using the deduction scene of water and rain weather forecast data, dispatching scheme and flood control situation analysis, to generate a dispatching scheme which is verified by hydrodynamic simulation, has good effect risk evaluation, and meets the standard of flood control effect.

Computer implemented methods, systems, and computer program products include program code executing on a processor(s) generates scenarios based on cognitively analyzing external events. The processor(s) cognitively analyze the process and segmenting the process into components, and for each component: determine an endpoint for each component; determine the scenarios relevant to the component; adjust the endpoint of the component based on the scenarios relevant to the component; applying reinforcement learning to the scenarios relevant to the component to validate impacts of the scenarios on the endpoint and to select a most likely scenario; and implement process changes to terminate the component at the adjusted endpoint.

The invention discloses a community emergency safety guarantee platform system based on artificial intelligence, and the system comprises a data collection and processing module which is used for collecting multi-modal data and generating a standardized input data set; the causal graph construction module is used for constructing a structured twin causal graph, establishing a node relationship and hooking an intervention variable; the risk diffusion prediction module is used for operating a risk diffusion prediction model and outputting a risk potential energy field, channel survival time and a risk development trend; the action generating and compiling module is used for generating a candidate action sequence and compiling a plan byte code; the formalized verification module is used for carrying out security fence, mutual exclusion logic and time sequence constraint verification on the plan byte codes; and the linkage execution and rollback module is used for issuing the plan byte code to the edge control equipment for execution and recording whole process data. According to the invention, through multi-modal data processing, causal graph modeling and physical constraint prediction, scientific early warning and reliable linkage disposal of community emergencies are realized.