2685 results about "Rapid response" patented technology

An Internet-of-Things-based intelligent safety monitoring method and system for power plant operation. The method comprises: deploying sensors and collecting monitoring data in real time; transmitting the collected data to a central server, performing data processing, integrating the monitoring data, calculating a comprehensive environmental index, and preliminarily assessing an environmental monitoring condition; and comprehensively determining a device fault condition, remotely monitoring device status, starting a device emergency response mechanism, and performing intelligent safety monitoring of power plant operation. Power plant devices and environment parameters are monitored in real time, so that device anomalies and potential safety hazards are promptly captured to achieve rapid response. The stability of the power plant is improved, and a video monitoring system is integrated in combination with image recognition technology to monitor anomalous events in the power plant area. The level of safety management is enhanced, and decisions can be quickly made in emergency situations, improving flexibility and efficiency and enhancing management effectiveness and the accuracy and stability of the monitoring system.

The invention belongs to the technical field of power grid regulation and control, and particularly relates to a park-level adjustable load-oriented resource regulation and control method and system, and the method comprises the steps: formulating an initial energy scheduling scheme according to the predicted energy demand in combination with the output characteristics of distributed energy in a park and the load condition of a power grid; and training a load regulation and control strategy model by adopting a reinforcement learning algorithm based on the initial energy scheduling scheme, and generating a personalized load regulation and control instruction for each energy consumption unit. According to the scheme, the real-time energy consumption data of the plurality of energy consumption units in the park, the output characteristics of the distributed energy and the power grid load condition can be comprehensively considered, a scientific and reasonable energy scheduling and load regulation and control strategy is formulated, and efficient utilization and green low-carbon development of park energy are realized. Meanwhile, the method also needs to have the characteristics of data sharing, accurate prediction, intelligent scheduling, rapid response and the like so as to adapt to complex and changeable energy requirements and environmental protection requirements of the park.

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention provides a perception collaborative decision-making method and system based on multi-modal heterogeneous data fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting a global environment situation perception graph into a pre-trained multi-target collaborative decision-making model; the multi-target collaborative decision-making model forms a multi-target decision-making feature set by analyzing the resource entities and the incidence relation in the graph; based on the multi-target decision feature set, decision optimization is carried out to obtain a comprehensive collaborative scheduling scheme; performing instruction analysis and packaging on the comprehensive collaborative scheduling scheme to obtain an executable instruction sequence; and issuing the executable instruction sequence to a corresponding decision node and a control terminal in parallel through a distributed communication architecture to complete real-time scheduling of resources and collaborative issuing of control instructions. According to the invention, by constructing a linkage mechanism of multi-modal data fusion, dynamic environment perception and collaborative decision execution, intelligent perception and quick response to a complex environment are realized.

The invention relates to the technical field of logistics management, in particular to an intelligent logistics terminal equipment collaborative management and control system based on AI edge computing, and the system comprises an edge data fusion and state recognition module which is deployed at an edge node, collects multi-source data of an operation state, environment perception, a communication link and the like, and generates an equipment state representation vector through fusion; the intelligent prediction and task scheduling decision module uploads the state vector to a cloud, predicts task completion capability and fault probability, and generates a task scheduling decision packet; the scheduling strategy issuing and edge execution collaboration module issues a scheduling packet through a multi-protocol gateway, and an edge node completes task distribution, communication switching and resource scheduling and caches a key strategy. According to the invention, the real-time sensing of the state of the logistics terminal equipment, the intelligent prediction and resource optimization of task scheduling, and the quick response and fault-tolerant control in a fault scene are realized, and the operation efficiency, the intelligent level and the stability of the system are remarkably improved.

The present invention discloses an intelligent measurement and control system for a withdrawable switch cabinet and a control method thereof, which relate to the technical field of automatic control of electric power systems, and solve the problems of slow response speed, inflexible control strategy and difficulty in coping with extreme weather conditions in existing intelligent measurement and control systems when responding to changes in power grids; real-time data acquisition and analysis are realized by integrating an edge processing module, and state prediction and anomaly detection are performed in combination with a long short-term memory network; an adaptive control module is used to optimize the control strategy according to dynamic monitoring results; at the same time, the emergency response module monitors environmental variables through a SCADA system, and an intelligent scheduling algorithm adjusts the switch cabinet operation mode and switches standby equipment; the present invention greatly improves the safety and reliability of the switch cabinet, significantly enhances the rapid response capability and adaptive control capability of the power system to changes in the power grid, and ensures the stability and safety of the power supply.

Disclosed is a system and a method for blockchain-based virtual power plant management, relating to the field of virtual power plants. The system mainly includes three modules: an event-driven based demand rapid response and flexible resource optimal allocation module configured to adjust power supply and demand in real time according to power demand information and available resource information of an industrial park; a blockchain-based data collaboration module configured to establish a data sharing and transaction environment among various participants of a virtual power plant; and a visualization management module configured to provide a user interface for displaying, in real time, operational status and power transactions of the virtual power plant in the user interface. The system can effectively manage virtual power plants, enhancing the efficiency and transparency of power supply.

The invention discloses an intelligent road alarm early warning patrol rescue linkage system and a method thereof, and relates to the technical field of road safety. An intelligent hectometer board, an unmanned aerial vehicle cluster and a mobile terminal are used for collecting accident alarm information and road multi-source data; performing accident target detection classification and abnormal condition classification based on the preprocessed data by utilizing edge calculation, and sending to the cloud; importing real-time road multi-source data into a digital twinborn model to obtain traffic situation spatial-temporal characteristics, and combining accident target detection classification and abnormal condition classification analysis to obtain an accident risk thermodynamic diagram for emergency decision making; and according to the accident risk thermodynamic diagram, generating alarm early warning information of different accident types and different risk levels, according to an emergency decision, generating a linkage mechanism of different departments, and sending the linkage mechanism to different department systems to realize automation of a disposal process. According to the invention, a full-chain linkage system of perception-analysis-early warning-linkage is constructed, rapid response and rescue are realized, the management cost is reduced, and the traffic efficiency is optimized.

The invention relates to the technical field of industrial safety, in particular to a factory safety intelligent management and control method based on a heterogeneous multi-system cross service fusion technology, and the method comprises the steps: collecting factory multi-source heterogeneous data, and carrying out the time-space alignment and fusion; constructing a risk assessment model based on a deep learning algorithm, and performing quantitative risk analysis on personnel behaviors, environmental parameters and equipment states; a dynamic grading early warning mechanism is established, and warning and automatic handling are achieved according to the risk grade; introducing risk trend prediction, and analyzing a risk evolution trend; and a safe closed-loop optimization mechanism is constructed, and self-adaptive adjustment of the management and control strategy is realized. According to the method, a full-process and dynamically-optimized intelligent safety management and control scheme is constructed, and comprehensive perception, quick response and continuous optimization of factory safety management are realized.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

The invention relates to the technical field of process control, in particular to an intelligent environment monitoring and adjusting system for lip fish culture, which comprises an environment parameter monitoring module, a multivariable linkage calculation module, a self-adaptive adjusting module, a fuzzy logic control module and an abnormity emergency response module. According to the method, the trend value is formed through time-phased grouping analysis and relevance calculation of the water quality parameters, dynamic prediction of the breeding environment change is achieved, the cooperative adjustment capacity among the dissolved oxygen, the water temperature and the ammonia nitrogen concentration is optimized through analysis of the multi-parameter linkage relation, the accuracy of environment control is improved, and the method is suitable for large-scale popularization and application. The cooperation efficiency of oxygen pump frequency and temperature control equipment response is improved through real-time matching calculation of equipment operation parameters, the rapid adaptive capacity to water quality changes is enhanced, abnormal fluctuation monitoring is combined with equipment linkage adjustment, rapid response during water quality sudden change is ensured, and the adverse effect of environment fluctuation on fish growth is reduced.

The invention discloses an AI-based linkage response and control system, belongs to the technical field of safety monitoring, and aims at solving the problems of comprehensive and real-time acquisition, accurate analysis, timely early warning and decision making of environmental data so as to realize early warning and quick response of a fire disaster. The system comprises a data acquisition module, an AI analysis module, an AI identification module, an AI decision module and an execution terminal robot. The data acquisition module is used for acquiring environmental data of a monitoring sensor and a monitoring camera in a target scene; the AI analysis module is used for processing and analyzing the environmental data acquired by the monitoring sensor and outputting a sensor abnormal area; the AI identification module is used for analyzing an environment video stream acquired by the monitoring camera in real time and outputting a camera abnormal area; the AI decision module is used for further evaluating the abnormal region and dividing abnormal levels; and the execution terminal robot is used for performing real-time response and control on the abnormal area in the control room according to the abnormal level.

Disclosed in the present invention is a method for automatically measuring and adjusting the refractive index of the cross-section of an optical fiber. The method comprises the following steps: S1, optical fiber alignment; S2, detection of optical signals; S3, automatic compensation for optical fiber fusion splicing; S4, analysis of the optical signals; and S5, automatic adjustment and compensation. Compared with the conventional related art, the automatic compensation technique for optical fiber fusion splicing in the present invention has advantages such as higher quality stability, reduced manual intervention, precise parameter adjustment, rapid response capability and improved production efficiency, and is applicable to production and application in the field of optical fiber communications.

The invention relates to the technical field of logistics management, in particular to a warehouse material storage location distributing and sorting system based on a genetic algorithm, and the system comprises a data access and analysis module which is connected with a main database of a warehouse management system through a security protocol, synchronously updates the information of materials, orders and employees, carries out the establishment of a data index, and transmits the data index to a server; according to the invention, through the design of the sorting system and the combination of simulated annealing and a genetic algorithm, the intelligent configuration of the material storage position is effectively realized, the sorting path is optimized, the material carrying time and the manual error are reduced, the sorting accuracy is improved, the AGV is managed by adopting an asynchronous algorithm, the task scheduling is optimized, and the sorting efficiency is improved. The material processing speed is accelerated, the response efficiency is improved, the inventory configuration is dynamically adjusted, the market demand change is adapted, the high efficiency and the quick response capability of the inventory are kept, the automation and the intelligence of the inventory management are promoted, and the operation efficiency and the service quality of the whole warehouse are greatly improved.

The invention relates to the technical field of steam extraction check valves, and discloses a design method and device for quickly responding to a steam extraction check valve under a high-parameter working condition, and the method comprises the following steps: constructing a valve working condition database and a valve body database; based on the valve working condition database and the valve body database, a valve performance prediction model is constructed, and a valve state quantity prediction value is obtained through performance influence factors collected in real time; correcting the predicted value to obtain a valve state quantity corrected value; a valve control environment based on reinforcement learning is established, a valve optimization control strategy model is constructed, a real-time optimal control action is generated and executed based on a valve state quantity correction value, and efficient optimization control over the valve is achieved; according to the method, through combination of data-driven performance prediction and reinforcement learning optimization control, rapid response and efficient operation of the steam extraction check valve under the high-parameter working condition are achieved, and the reliability and the energy-saving effect of the system are improved.

The invention provides a method and a system for intelligently generating and dynamically adjusting a public emergency plan, which are oriented to the field of public safety emergency. The method comprises the following steps: fusing multi-source heterogeneous data to construct an extensible domain knowledge graph, establishing a standardized emergency instruction library, and carrying out multi-dimensional information labeling; automatic extraction of disaster elements is realized based on an entity recognition model of deep learning; analyzing association rules among the emergency entities through a semantic relationship mining technology; key information such as a disaster chain and resource distribution is rapidly obtained by using a map reasoning mechanism; carrying out cross-department resource collaborative allocation by adopting a multi-objective optimization algorithm to generate an optimal disposal scheme; a high-dynamic adjustment mechanism is constructed, and an emergency plan is dynamically optimized based on situation evolution prediction and real-time monitoring data; and finally, automatic generation and versioning management of the plan are realized. Through multi-mechanism cooperation of knowledge modeling, intelligent element analysis, semantic reasoning, dynamic optimization and predictive adjustment, the emergency plan generation efficiency, situation adaptability and resource allocation rationality are remarkably improved, and support is provided for quick response and scientific decision under emergencies.

The invention relates to the technical field of logistics storage, and discloses an edge-cloud collaborative 5G logistics storage anomaly detection device and method. The device comprises a logistics data acquisition module, an edge preprocessing module, a lightweight model reasoning module, a cloud collaborative analysis module and a dynamic strategy generation module. The logistics data acquisition module acquires multi-source heterogeneous logistics storage data; the edge preprocessing module performs cleaning and dimension reduction on the related data to generate a standardized edge feature vector; the lightweight model reasoning module constructs a model based on an AutoML framework to perform real-time reasoning; the cloud collaborative analysis module fuses the data to generate a global abnormal event positioning map; and the dynamic strategy generation module generates an instruction and an alarm signal according to the result and issues the instruction and the alarm signal through 5G network slicing. According to the invention, multi-source heterogeneous data can be efficiently processed, real-time abnormal detection, accurate positioning and rapid response are realized, the intelligent level and operation efficiency of logistics warehouse management are improved, and the cost and the cargo loss risk are reduced.

The invention provides a water quality monitoring system model construction method and device based on edge calculation and computer equipment, and relates to the technical field of water quality monitoring. The invention aims to solve the problems of data processing delay and insufficient real-time performance in the traditional water quality monitoring technology. The method comprises the key steps that the collecting end is arranged at a key monitoring point of a water body to collect water quality parameters, the water quality parameters are sent to the edge end for real-time data processing, water quality prediction and anomaly detection are conducted in combination with the BP neural network and the support vector machine, and quick response to water quality changes is achieved. And meanwhile, deep learning and a support vector machine are combined, so that the prediction precision and the anomaly detection capability are improved. And the water quality condition is comprehensively evaluated through a comprehensive weighted index method, so that a manager can identify a pollution source more quickly and take effective countermeasures. The water quality monitoring efficiency and accuracy are improved, and solid technical support is provided for sustainable management of water resources.

The invention relates to the technical field of online car-hailing order dispatching, in particular to an online car-hailing intelligent order dispatching method and system based on a multi-dimensional rule, and the method comprises the steps: building a standardized multi-dimensional feature set through collecting passenger orders, driver states, traffic conditions, environmental weather and regional events, and carrying out the combined modeling of regional order demands and driver online conditions based on a recurrent neural network, thereby achieving the intelligent order dispatching of the online car-hailing. Outputting expected passenger and driver thermodynamic distribution, constructing expected difference thermodynamic distribution according to the expected passenger and driver thermodynamic distribution, training a scheduling strategy through reinforcement learning, enabling an unloaded vehicle to actively migrate to a supply and demand gap area before an order is generated, and further combining an order generation condition and driver expected off-duty information to obtain an order generation result; and calculating the accumulated driving time required by the driver to return to the expected off-duty place after the driver completes the service, and selecting the work order for distribution when the time difference between the accumulated driving time and the expected off-duty place is minimum, thereby realizing accurate matching between the order distribution strategy and the driver work cycle. The empty driving rate is effectively reduced, and the quick response capability and the resource configuration efficiency of the system in a dynamic supply and demand environment are improved.

The invention provides a GNSS (Global Navigation Satellite System) anti-interference receiver system based on radio frequency front end-INS (Inertial Navigation System) combined assistance, which adopts a dual-core ARM Cortex-A9 and a programmable logic unit for layered decoupling, and realizes real-time receiving and processing of a high-bandwidth data stream, closed-loop control of an interference suppression link and quick response of a navigation path and a strategy decision. All the modules efficiently cooperate through AXI-Lite register mapping, interrupt signals, a shared state buffer area and a timer synchronization mechanism. By controlling bus and state buffer mapping to keep consistency and supporting a module-level independent debugging and system-level soft reset mechanism under hot start, the expandability of subsystem cascade debugging, fault module positioning and online updating is realized. In the aspects of module architecture, data channels and cooperation mechanisms, the structure order, efficient communication and control closed loop are realized, and an embedded multi-module cooperation anti-interference platform which is suitable for a high-interference environment, multi-channel fusion processing and navigation task real-time response is constructed.

The invention discloses a new energy vehicle underground garage fire treatment method and system based on the Internet of Things. The system comprises a sensor data acquisition module, an edge calculation and risk assessment module, an early warning and emergency response module and a data storage and cloud platform module. The sensors are arranged at positions such as parking spaces and ventilation systems in a garage, monitor environmental parameters such as temperature, smoke and gas in real time, and transmit data to the edge computing device through the wireless gateway. And the edge calculation module evaluates the fire risk in combination with a fuzzy Bayesian network. And the early warning and emergency response module triggers different levels of alarm signals according to the risk levels, and controls ventilation equipment, fire-fighting equipment and the like in a linkage manner. The data storage module is used for storing monitoring data. The system has the advantages of real-time monitoring, intelligent evaluation, multistage early warning and quick response, is suitable for fire prevention and safety management of closed environments such as underground garages, and effectively improves the accuracy of fire risk prediction and the efficiency of emergency response.

The invention relates to the technical field of unmanned aerial vehicle scheduling, and discloses an emergency scene unmanned aerial vehicle task allocation method based on multi-objective optimization, and the method comprises the steps: obtaining a task parameter set of an unmanned aerial vehicle in real time through a standardized task interface, and obtaining a state data set of the unmanned aerial vehicle in real time through an unmanned aerial vehicle management platform; secondly, calculating an optimal task allocation scheme by adopting a multi-target particle swarm optimization algorithm and an improved PSO algorithm, and dynamically adjusting sudden emergency tasks and priority changes in combination with a task preemption mechanism; three task allocation plans including a time optimal scheme, a resource optimal scheme and a parameter optimal scheme are provided to ensure that tasks can be completed in the shortest time or executed at the lowest cost, and the final scheme is provided for a command center for decision making. According to the invention, the intelligence and flexibility of unmanned aerial vehicle scheduling are improved, and rapid response and global optimal resource configuration of unmanned aerial vehicle scheduling in an emergency scene are realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a target event processing method, device, equipment and medium based on language model collaboration.The method comprises the steps that a target event is monitored and recognized, multi-source data associated with the target event are collected, the multi-source data are fused to generate fused data, and the fused data are sent to a server; and inputting the fusion data into a pre-training language model to generate a decision scheme, performing decision coordination and matching based on the decision scheme, distributing a processing agent, executing a matched decision task by using the processing agent, and generating a decision result. According to the invention, through fusion of multi-source data and pre-training language model deep reasoning and combination of an intelligent agent dynamic cooperation mechanism, intelligent perception, rapid response and cooperative processing of complex events are realized, and the adaptive ability and processing efficiency of the system in response to unknown faults and complex tasks are improved.

The invention discloses a modular reclosing circuit breaker protection system based on intelligent electrical cloud coordination, particularly relates to the technical field of intelligent power distribution network relay protection, and is used for solving the problems that fault diagnosis is delayed and a protection strategy cannot be generated in a fault clearing time window due to resource competition in a cloud coordination protection system. A dynamic preemption priority is generated based on power grid topology vulnerability and associated circuit breaker collaborative requirements by monitoring the load state of cloud computing node resources in real time; credibility attenuation control is carried out in combination with a fault current waveform distortion rate; when the resource preemption necessity exceeds a threshold value, freezing a periodic task thread and releasing computing resources, and opening up a high-priority communication channel at the same time; sending a fault recording compression instruction to a target circuit breaker terminal to obtain an edge preprocessing feature vector; and protection strategy optimization is executed in the released resources to generate reclosing parameters, and the reclosing parameters are issued and executed through a high-priority channel, so that quick response of a fault diagnosis task is realized.

The invention discloses a fall risk real-time assessment system and method based on big data, and relates to the field of risk assessment, and the system comprises a data collection and preprocessing module, a big data processing and integration module, a feature extraction and selection module, a fall risk assessment module and a real-time monitoring and early warning module which are sequentially connected and cooperatively work. Wherein the data acquisition and preprocessing module is responsible for acquiring data from a plurality of sources and converting the data into digital signals; the big data processing and integration module is responsible for secure transmission, convergence and preprocessing of data; the feature extraction and selection module is responsible for extracting features related to the falling risk from the preprocessed data; the tumble risk assessment module is responsible for real-time assessment of tumble risks based on the extracted features; and the real-time monitoring and early warning module is responsible for triggering an early warning signal according to the falling risk assessment result. According to the invention, accurate and real-time evaluation of the falling risk, accurate and timely output of the evaluation result and rapid response of the early warning signal are realized.

The invention discloses a method for a mowing robot to move along a lawn boundary based on a local grid map, and relates to the field of mowing robot edge navigation movement, and the method comprises the steps: recognizing a target region and an obstacle region through a semantic segmentation algorithm, constructing a dynamically updated local grid map through the combination of improved inverse perspective transformation and depth point cloud information, and carrying out the navigation of the target region and the obstacle region; no physical boundary dependence is realized, the deployment cost is reduced, and the real-time processing efficiency of acquired images can be improved; according to the local grid map, the edge navigation deflection angle is calculated through lawn boundary feature extraction in combination with the position of the robot, precise navigation of the robot along the lawn boundary is achieved, rapid response of dynamic obstacles is supported, and the edge movement precision and operation efficiency of the mowing robot are remarkably improved. The problems that an existing mowing robot is low in edge movement precision and poor in operation efficiency due to the fact that an edge track cannot be decided in real time and calculation efficiency cannot be considered at the same time are solved.

The invention discloses a cloud side-end collaborative virtual power plant scheduling method and system based on dynamic task migration, relates to the technical field of virtual power plant resource scheduling, and solves the problem that a general method is difficult to meet the real-time scheduling requirement of a virtual power plant. According to the invention, task migration is realized through a three-level architecture of a cloud planning long-term strategy, regional collaboration of an edge layer and quick response of a terminal layer in combination with a dynamic load balancing algorithm; the first edge node locally processes regional collaborative tasks, reduces the communication frequency of the cloud server, reduces the end-to-end delay, and then combines federated learning to realize training parameter sharing between the edge nodes and avoid redundant data transmission, thereby effectively improving the response speed of the scheduling system, and improving the scheduling efficiency. And the real-time scheduling requirement of the virtual power plant can be better met.

The invention discloses a power grid risk prediction method and system based on network security data and a medium. Relates to the technical field of network security. Performing embedded coding on the power grid security situation data to obtain a power grid security situation embedded coding vector sequence; performing feature enhancement and dynamic aggregation processing on the power grid security situation embedded coding vector sequence to obtain a power grid security situation time sequence significant aggregation representation vector; the method comprises the following steps: analyzing power grid security situation data based on an artificial intelligence technology of deep learning, extracting power grid security situation characteristics at each time point, carrying out information dynamic aggregation according to the relative importance of the power grid security situation characteristics at each time point, mining a global change rule and a potential abnormal mode of the power grid security situation data, and carrying out dynamic information aggregation. Therefore, intelligent identification of the abnormal state of the power grid network is realized. The dynamic change of the power grid network environment can be more accurately captured, early warning and quick response to the abnormal state of the power grid network are achieved, and therefore the defense capacity of a power grid system to network attacks is improved.

The invention belongs to the technical field of environmental atmospheric pollutant assessment, and discloses a method for assessing the influence of fireworks and crackers on atmospheric PM2.5, which is suitable for assessing the particulate matter pollution influence of a fireworks setting-off point source in a specific area on a surrounding environmental air quality monitoring station, and realizes PM2.5 emission diffusion simulation and traceability of centralized setting-off of fireworks and crackers. The characteristics of Gaussian diffusion, puff diffusion and computational fluid mechanics theories are integrated, PM2.5 influence evaluation of areas and cities can be achieved, firework and cracker pollution contribution key areas can be recognized, rapid response of pollution emission reduction measures can be achieved, and then more effective supervision and control over centralized setting-off of fireworks and crackers can be achieved.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.