918 results about "Decision level" patented technology

The invention relates to a coal mine safety production intelligent decision-making system based on digital twinning, and the system comprises a physical sensing layer which collects coal mine environment parameters, equipment states and personnel positioning data through the deployment of a multi-mode sensor network, and generates a structured data flow; the edge calculation layer is used for operating an incremental multi-objective evolutionary algorithm, quickly generating a cache strategy in combination with a strategy cache pool preloading mechanism, uploading the processed data to the digital twinborn layer, receiving a global instruction of the intelligent decision-making layer and decomposing the global instruction into a device-level control signal; the digital twinborn layer is used for receiving the real-time data uploaded by the edge calculation layer, updating the state of a digital twinborn body and feeding back an optimization demand to the intelligent decision-making layer; and the intelligent decision-making layer is used for generating a global strategy by means of digital twin-guided hybrid optimization and a special FPGA acceleration card for a coal mine, and fusing the cache strategy of the edge calculation layer and the global strategy of the intelligent decision-making layer to generate a global instruction.

Provided is a method for training a scene recognition model. The scene recognition model includes a core feature extraction layer, a global information feature extraction layer, an LCS module of at least one level with an attention mechanism, and a fully-connected decision layer. The method includes: acquiring parameters of the core feature extraction layer and the global information feature extraction layer by training based on a first scene label of a sample image and a standard cross-entropy loss; training a weight parameter of the LCS module of each level, based on a loss value acquired by performing a pixel-by-pixel calculation on a feature map output from the LCS module of each level and the first scene label of the sample image; and acquiring a parameter of the fully-connected decision layer by training based on the first scene label of the sample image and the standard cross-entropy loss.

The invention relates to the technical field of town sewage pipe network detection, and discloses a town sewage pipe network leakage detection system and method. The system comprises a data acquisition module which uses a multi-source sensor to acquire real-time operation data such as pipe network pressure, flow and the like; the feature extraction module extracts spatio-temporal features based on the multi-scale convolutional neural network, and generates a pipe network state feature matrix; the anomaly detection module inputs the feature matrix into a pre-training model and marks a potential leakage area; the optimization analysis module constructs a multi-constraint dynamic optimization model, and pipe network pressure parameters are optimized by using an adaptive particle swarm algorithm; the hierarchical execution module generates a global regulation and control sequence, dynamically matches local pressure parameters and adjusts the valve opening and the pump station power through a decision layer, a region coordination layer and an execution layer. The system and the method are accurate in detection and reasonable in regulation and control optimization, leakage risks can be effectively reduced, the operation management level of a pipe network is improved, and water resource waste and environmental pollution are reduced.

The invention relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

The invention relates to the technical field of industrial production lines, and discloses an industrial production line multi-device dynamic collaborative scheduling method based on reinforcement learning, comprising the following steps: S1, modeling a three-dimensional state space; s2, hierarchical reinforcement learning architecture; and S3, edge-cloud cooperative execution. According to the industrial production line multi-device dynamic collaborative scheduling method and system based on reinforcement learning, device states, task constraints and resource occupation are integrated into a structured matrix through three-dimensional state space modeling, and a global decision-making layer captures production time sequence dependence by using a bidirectional long-short-term memory network; modeling equipment space association and process constraints through a graph attention network, and generating a global strategy including task allocation, capacity adjustment and resource pre-allocation; and after the edge layer detects the dynamic event, the cloud platform generates a candidate scheme through Monte Carlo tree search, and realizes dynamic event response and multi-target collaborative optimization by combining multiple targets such as global value network evaluation task completion time and equipment load balancing.

The invention discloses a BIM (Building Information Modeling)-based hoisting construction safety monitoring and early warning system. The system comprises a terminal sensing layer which is used for collecting environmental parameters and personnel behavior data in a closed space in real time; the edge computing layer is used for carrying out cleaning, compression and encrypted transmission on original data by utilizing an explosion-proof edge computing gateway; the cloud collaboration layer is used for storing full data based on a BIM digital twinborn platform, constructing a'danger mode-construction feature-disposal measure 'three-dimensional meta-knowledge graph by adopting an MAML + + algorithm, meanwhile, coupling a physical mechanism data enhancement engine with a multi-physics field coupling model and a physical constraint generative adversarial network, generating virtual data conforming to mass conservation and energy conservation, and sending the virtual data to the cloud collaboration layer; performing mixed training with real data; according to the intelligent decision-making layer, a space-time adaptive threshold evolutionary algorithm encodes a space-time context through a graph attention network and Transform, an alarm threshold is dynamically optimized through deep reinforcement learning, meanwhile, a digital twin deduction engine calculates a shortest safety path in real time, and rescue resource allocation is optimized.

The invention provides an enterprise product intelligent quotation collaborative decision-making platform. A platform architecture comprises a user interaction layer, a data processing layer and a decision-making layer, and platform functions are realized by relying on an intelligent agent. When a user releases a product quotation task, an authority management agent verifies the identity and access authority of the user, and a user interaction and demand analysis agent in a user interaction layer converts the natural language demand of the user into a structured instruction; the data processing layer receives an instruction, basic data and a special cost agent in the layer pull data from an external system through a cross-system calling agent, and a price calculation agent integrates the data through multi-objective optimization to generate candidate quoted prices; and finally, a man-machine cooperation and intelligent decision-making auxiliary agent in a decision-making layer displays all candidate schemes, and an expert performs fine adjustment to select an optimal quotation. According to the invention, rapid and accurate quotation is realized for enterprise products, the quotation model is continuously optimized through continuous feedback and self-learning, and finally a highly autonomous intelligent quotation system is formed.

The invention relates to the technical field of robot scheduling, in particular to a distributed robot collaborative scheduling system and method in a dynamic environment, and the system comprises an environment sensing layer which is used for collecting environment dynamic data in real time; the distributed decision-making layer comprises local task scheduling modules of a plurality of robots; the cooperative communication layer is used for realizing task state synchronization and conflict detection among the robots based on a low-delay communication protocol; the dynamic weight calculation module is used for generating a real-time optimization weight according to the task emergency degree, the robot energy consumption and the path risk factor; according to the invention, by using a completely distributed collaborative scheduling architecture, through a decentralized task distribution mechanism and a distributed consensus protocol, a single-point fault risk existing in a traditional centralized scheduling system is thoroughly eliminated, and even if a part of robot nodes have faults or communication is interrupted, the system can work normally. And the system can still run continuously through autonomous negotiation of the remaining nodes, so that the reliability of the system in a complex environment is remarkably improved.

The invention provides a stamping production line self-organizing production system and method based on a twin intelligent agent, and the twin intelligent agent comprises a data collection layer which is used for collecting the operation state data and production environment parameters of physical equipment; the virtual-real mapping layer is used for constructing a digital twin model of physical equipment and realizing real-time state synchronization and bidirectional control instruction transmission of a physical space and a virtual space; the optimization decision-making layer is used for predicting the performance degradation trend of the equipment based on a deep reinforcement learning algorithm and generating a game parameter adjustment strategy and a preventive maintenance scheme; and the collaborative arbitration layer generates a compromise optimization scheme based on Pareto frontier analysis when the multi-agent strategy conflicts, and the multi-dimensional targets of the production efficiency, the equipment life and the energy consumption are balanced. According to the invention, autonomous task allocation, real-time state monitoring and global resource balance of the stamping production line are realized.

The invention provides an intelligent security comprehensive management and control system, and relates to the field of intelligent security management and control. Comprising a monitoring layer, a regional decision-making layer, a cloud analysis layer and an execution control layer, the monitoring layer collects data through fixed-point monitoring equipment, mobile monitoring equipment and a simulation management node; the regional decision-making layer calculates a local risk index based on the data and generates a local service topology sub-graph, and triggers a simulation management node to generate an abnormal operation record; the cloud analysis layer integrates the local service topology sub-graphs, corrects risk index errors, constructs a dynamic priority service topology network and generates a risk gradient; and the execution control layer schedules the mobile monitoring equipment according to the risk gradient, closes a redundancy process, deploys an interception strategy and an optimization means, and verifies the running state of the equipment through a full-stack self-check protocol.

The invention relates to the technical field of space governance, and discloses a multi-agent space collaborative governance method which comprises the following steps: processing multi-source heterogeneous data such as satellite images and sensor readings, establishing cross-type semantic association through a geographic space data embedding technology, generating a unified structured text after optimizing the satellite images through vLLM, and synchronizing the unified structured text to a central database; an agent role portrait is dynamically generated by the central server large language model based on a preset Prompt template, and generation does not depend on a fixed rule; then, selecting a target node in the edge-center architecture, disassembling a total task into sub-tasks, establishing semantic mapping, calculating a matching probability, and performing optimal distribution by a reward borrowing function; generating a governance scheme in a perception layer-decision layer-execution layer framework, and outputting a coded operation instruction; based on an execution feedback updating strategy, a multi-level mechanism is set, roles are automatically redistributed, and the governance continuity is guaranteed. According to the invention, the overall efficiency and reliability of space governance can be improved in the face of dynamic scenes or emergency situations.

The invention relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention provides a hydraulic generator inner bore inspection robot based on a dynamic behavior tree and an intelligent decision and operation method thereof, and the robot comprises an upper mechanical arm end executor, a six-axis mechanical arm, an intermediate control system, a mobile platform, a multi-physical quantity fusion detection system, and a central control platform. The method is specially designed for the complex dynamic shielding environment of the inner bore of the hydro-generator. According to the method, by constructing a three-level behavior tree architecture composed of a task decision-making layer, a scene adaptation layer and an execution control layer, inspection requirements are decomposed into environment perception, task execution and resource constraint events, and the events are dynamically mapped to preset behavior nodes. Weight factors are adjusted in real time based on a multi-dimensional evaluation system, and functions of autonomous obstacle avoidance, narrow space operation and anomaly detection are realized in combination with hardware characteristics of the robot. The problems that a traditional inspection method is poor in adaptability and low in efficiency in a dynamic shielding environment are solved, and the intelligent level and safety of hydraulic generator inner bore inspection are remarkably improved.

The invention belongs to the technical field of intelligent traffic system and artificial intelligence crossing, and particularly relates to a traffic multi-agent simulation decision-making method and system based on a large language model. Road network state data are coded into a three-dimensional feature matrix containing channel dimensions, time dimensions and space dimensions, and joint representation of numerical road network data and text event reports is achieved through a hybrid embedding model. The decision-making layer comprises a dynamic Prompt generator which generates a candidate scheme set based on a four-layer progressive prompt structure; the Monte Carlo tree search multi-objective optimization is executed in cooperation with the decision core module; and the execution layer comprises a cross-language communication bridging device which adopts a gRPC bidirectional stream communication protocol and a Protobuf data serialization scheme to realize millisecond-level data interaction between services and support a hot plug mechanism of a strategy injection interface. According to the invention, dynamic optimization of urban traffic resource allocation and breakthrough improvement of simulation deduction efficiency are realized.

The invention relates to the technical field of artificial intelligence, can be applied to service scenes of pension service, financial science and technology, medical health and the like, and discloses a multi-level heterogeneous integrated chip task processing method, device, equipment and medium, and the method comprises the steps: constructing a multi-level heterogeneous integrated chip composed of a perception processing layer, an intelligent decision-making layer and a driving control layer, the layers are connected through a vertical interconnection structure; receiving multi-modal task data and extracting features to generate feature vectors; inputting the feature vector into a neuromorphic processing unit to determine a task decision result; converting the decision result into a driving signal to control an execution device; adjusting synaptic weights based on the feedback signal; and monitoring chip operation state parameters and dynamically adjusting processing frequency and structure parameters. By integrating multi-modal sensing, neuromorphic decision and a dynamic feedback mechanism, data sensing, decision and execution processing are completed in a chip, and the real-time performance and the calculation efficiency are improved by combining operation state monitoring and adjusting frequency and structure.

The embodiment of the invention provides an energy-saving optimization system for heating, ventilating and air conditioning of an indoor ski field. The energy-saving optimization system comprises a multi-source sensing layer, an edge computing layer, a cloud decision-making layer and an equipment execution layer. The multi-source sensing layer is used for collecting multi-source data such as weather, passenger flow, temperature and humidity and equipment state; the edge calculation layer carries out fusion processing on the data and generates a load prediction result through a load prediction mechanism; the cloud decision-making layer generates an optimization control instruction based on a multi-agent deep reinforcement learning and model prediction control optimization strategy; and the equipment execution layer receives and executes the instruction and feeds back the equipment state. Through a multi-layer collaborative optimization architecture, accurate load prediction and equipment intelligent collaborative control are realized, five-stage stepped optimization and a dynamic priority mechanism are adopted, the energy efficiency of the system is remarkably improved, the energy consumption is reduced while the environmental comfort is ensured, and the economical efficiency and the stability of system operation are effectively improved.

The invention discloses a quality management and control system for fabricated decoration construction, and the system comprises a sensing layer which constructs a multi-modal data collection network, carries out the three-dimensional real-time data synchronous collection through combining logistics API docking and an OCR recognition system, and constructs a construction process digital twin bottom plate; in the edge calculation layer, an edge node carries out lightweight processing on the original data; the cognitive layer is used for calling a Prolog rule through a process knowledge graph engine to reasone the feature snapshots, carrying out defect instant diagnosis and dynamic constraint propagation, outputting a root cause path with probability weight through three-stage verification, and quantifying intervention influence; the decision-making layer is used for constructing a dynamic prediction model based on a bidirectional LSTM and an attention mechanism, automatically activating a compensation mode when key interference is detected in combination with an anti-fact memory bank and a case-based reasoning compensator, and generating an alternative scheme of optimal cost / optimal construction period / comprehensive balance through a multi-target optimizer; and in the application layer, a Unity engine is utilized to develop the digital twinborn billboard.

The invention discloses a coal yard inventory scheduling system based on real-time data, which comprises a sensing layer, an edge calculation layer, a digital twinborn layer, a decision-making layer and an execution layer, and is characterized in that the sensing layer is used for acquiring basic data; the edge calculation layer can collect and clean data of the sensing layer and can extract feature vectors of environment and equipment states; a three-dimensional density field can be constructed; the digital twinning layer receives data obtained by the edge calculation layer and constructs a three-dimensional dynamic digital twinning engine; after the decision-making layer receives the order information, a control instruction is generated after calculation; and after receiving the instruction issued by the decision-making layer, the execution layer performs operation according to the instruction. According to the invention, through multi-source real-time data perception, edge computing node rapid processing and digital twinborn simulation, accurate dynamic scheduling and management of coal yard inventory are realized; the digital twinborn layer is set for simulation rehearsal and mechanical analysis, the collapse risk is warned in advance, and the accident rate is reduced.

The invention provides a generative adversarial-driven intelligent security defense method and system, and solves the problem of dynamic network security defense through three-layer architecture innovation: 1, data fusion layer reconstruction: employing a multi-modal feature extraction engine driven by an MoE architecture, dynamically allocating computing power resources to a plurality of expert models, and improving the heterogeneous data distillation efficiency; an LLM for fine adjustment in the security field is introduced, a cross-modal semantic similarity matrix is constructed, and the accuracy of unstructured threat intelligence analysis is improved; a second dynamic attack and defense layer is constructed, a GPT-4 architecture attack generator is deployed, and generation of a multi-stage APT attack chain is simulated; a double-agent reinforcement learning framework is designed, and the confrontation training efficiency is improved; upgrading a three-cognitive decision-making layer, constructing a dynamic threat map based on a time sequence diagram neural network, and updating an adjacent matrix in real time; a plurality of agent clusters are deployed, the capabilities of encrypted traffic analysis and attack blocking are improved, and the problems of data layer defects, attack and defense confrontation limitation and decision-making layer bottleneck in the prior art are solved.

The invention relates to the technical field of new energy, in particular to a fan blade composite deicing system and method.The fan blade composite deicing system comprises a sensing layer, a decision-making layer, an execution layer and a feedback layer, and the decision-making layer comprises a graph neural network ice type recognition module, a CFD-icing coupling simulation module, a health management module and an energy consumption scheduling algorithm unit; the execution layer comprises a control unit and an execution unit, the control unit comprises a dynamic partition heating controller and a self-adaptive vibration controller, and the execution unit comprises an electric heating system and a vibration deicing system; compared with the prior art that a single deicing mode is usually adopted, and the adaptability to different ice type physical characteristics is poor, the scheme adopts a differential execution strategy, and partition electric heating control is carried out on different chord length areas of the blade based on the ice type recognition result; and high-frequency piezoelectric standing wave vibration is adopted for glaze ice, low-frequency eccentric wheel vibration is adopted for glaze ice, and the beneficial effects that the deicing efficiency is remarkably improved, invalid energy consumption is reduced, and damage to blades is reduced are achieved.

The invention relates to the technical field of agricultural planting, and discloses a crop planting suggestion generation method adapting to different climate conditions. Real-time climate data are collected through a multi-source climate sensor, climate dynamic feature data are generated through a spatial-temporal feature fusion algorithm, and planting strategy parameters are obtained by inputting the climate dynamic feature data into a pre-trained mixed decision model. And constructing a multi-objective optimization model taking the crop adaptability matching degree and the resource utilization efficiency as objectives, and globally optimizing a planting scheme by adopting a hierarchical dynamic programming algorithm integrating fuzzy logic constraint and an adaptive decomposition strategy. A multi-level regulation and control framework comprising a decision-making layer, an adaptation layer and an execution layer is constructed, the decision-making layer generates a global planting sequence, the adaptation layer uses a sliding window optimization algorithm to adjust local parameters, the execution layer regulates and controls soil humidity and illumination intensity based on a robust feedback control algorithm, and finally a planting control instruction is output. And accurate and efficient climate adaptability planting decision is realized.

The invention provides an intelligent automatic network security emergency response method and system based on AI self-learning. The system comprises a data acquisition layer, a data preprocessing layer, an AI analysis layer, a decision layer, an automatic response layer and a monitoring and feedback layer. According to the method, through fusion of a machine learning technology, automatic arrangement and a continuous optimization mechanism, full-process closed-loop management from threat detection to disposal is realized. The core advantage of the method lies in the organic integration of technical modules and the combination of self-learning and continuous optimization mechanisms, and the self-learning and continuous optimization mechanisms enable the system to dynamically adjust the model according to the treatment result through feedback loop and reinforcement learning technologies. The self-learning characteristic embodied in the research, for example, in combination with an RAG (Retrieved-Augmented Generation) technology and a vector database, can effectively alleviate the deficiency of the traditional model in the aspect of adaptability.

The invention relates to the technical field of block chains and artificial intelligence, and discloses an AI-based block chain smart contract cross-domain authentication security enhancement method and system.The AI-based block chain smart contract cross-domain authentication security enhancement method comprises the steps that a hierarchical security architecture comprising a sensing layer, an analysis layer, a decision-making layer and an execution layer is constructed, collecting cross-domain authentication data and transmitting the cross-domain authentication data to an analysis layer; realizing a threat detection model based on deep learning, receiving feature data collected by the sensing layer, and outputting a threat score and an abnormal index; establishing a dynamic trust evaluation mechanism, receiving an output result of the threat detection model, and calculating a real-time trust value between entities; and designing an adaptive authentication strategy optimization algorithm, and dynamically selecting an optimal authentication strategy based on a trust evaluation result, so that the problem of security authentication between heterogeneous block chain networks can be solved, and an efficient and secure cross-domain authentication solution is provided.

The invention relates to an unmanned aerial vehicle group collaboration and task allocation optimization method and system based on edge computing, in particular to the field of communication, efficient task allocation and threat early warning are achieved through dynamic modeling of a multi-modal sequence prediction model and a heterogeneous relation graph, firstly, real-time environment and historical task data are fused, and the real-time environment and historical task data are fused; generating space threat probability distribution and an environment dynamic coefficient; then, a dynamic adjacency matrix is used for adjusting a subgraph embedding vector, a threat-driven topological structure is reconstructed in real time, a decision-making layer outputs a task instruction and value evaluation based on a hierarchical decision-making network, task acceptance, task abandoning and path selection are intelligently optimized, and task conflicts are solved through a federal consensus mechanism; according to the method, the cooperation efficiency and the task execution accuracy of the unmanned aerial vehicle group in a complex environment are effectively improved, and task allocation and resource use are optimized.

The invention discloses an intelligent agent-based automatic operation and maintenance method and system, and relates to the technical field of computer operation and maintenance, and the method comprises the steps: constructing an intelligent agent comprising a data collection layer, a state sensing layer, a decision-making layer and an execution layer, the data collection layer collects IT system multi-source data, the state sensing layer analyzes the state of the data sensing system, and the decision-making layer determines the state of the data sensing system; the decision-making layer generates an operation and maintenance instruction based on a sensing result decision, and the execution layer executes the instruction; relevant knowledge of the IT system is collected and sorted, and a knowledge graph is constructed; the intelligent agent is trained by using historical operation and maintenance data, the intelligent agent is enabled to try and explore in different environments through a reinforcement learning algorithm, the behavior of the intelligent agent is continuously adjusted according to a feedback result, and a decision model and an operation and maintenance strategy of the intelligent agent are optimized; and the intelligent agent monitors the IT system in real time, starts the data acquisition layer, the state sensing layer, the decision-making layer and the execution layer when detecting an abnormal event, and automatically executes corresponding operation and maintenance operation according to a decision-making result. According to the invention, stable and efficient operation of the IT system can be guaranteed.

The invention relates to the technical field of temperature control, in particular to a temperature controller nonlinear compensation control method and system based on fuzzy logic, and the method comprises the steps: constructing a double-closed-loop control architecture of a temperature control system; main control data is generated through the outer loop fuzzy decision layer, and feedforward compensation and lag compensation processing is carried out on the main control data through the inner loop compensation execution layer to obtain a final control quantity; and adjusting operation parameters of the temperature control system according to the final control quantity. Through a double compensation mechanism of feed-forward compensation and lag compensation, accurate pre-judgment and self-adaptive adjustment of control data are realized, and the problems of control lag and nonlinear instability caused by sensor delay, inertia of an execution mechanism and environmental interference in a temperature control system are systematically solved.

The invention relates to the technical field of hydrogen energy automobiles, in particular to a method for establishing a hydrogen leakage prediction model of a hydrogen energy automobile. According to the method, training, verification and test data sets are obtained through multi-modal data fusion and screening based on physical significance on source data fusing CFD simulation data and actually measured leakage data, multi-dimensional information is extracted from training data, time domain, frequency domain and physical field feature matrixes are obtained, a time sequence coding layer and a physical decision layer are constructed on the basis, and the time sequence coding layer and the physical decision layer are constructed. Therefore, fluid mechanics is integrated into a model structure, the model can explain and make decisions according to physical laws, the problem that fault attribution analysis conforming to the physical laws cannot be provided in the prior art is solved, a hybrid architecture model integrating data driving and physical constraint is constructed based on a two-layer structure, data set optimization is verified, and a test data set is adjusted. And finally, a hydrogen leakage monitoring model with high-precision prediction capability and reliable physical interpretability is established.

The invention relates to the technical field of building construction, in particular to a foundation pit dewatering prediction method based on a digital twinborn technology, which comprises the following steps of: 1, establishing a multi-source data fusion layer; 2, establishing a digital twin modeling layer; 3, establishing an intelligent prediction algorithm layer; 4, establishing a dynamic optimization decision layer, and generating a precipitation well optimization scheme based on a prediction result; according to the method, engineering geological data of a geological survey report before excavation of the building foundation pit, historical meteorology of a building location and hydrogeological data are utilized, digital twinning is carried out on precipitation during the whole construction period of the proposed building foundation pit on the basis of a digital twinning technology, the precipitation period, daily precipitation and influence of precipitation on foundation pit supporting are predicted, and the foundation pit supporting effect is improved. According to the method, the influence of settlement deformation of surrounding buildings is reduced, digital simulation is provided for smooth construction of a whole project, and measurement and prediction are provided for the project and the surrounding environment, so that the construction cost is greatly reduced.

The invention discloses a computer network management system based on a cognitive digital twinning technology. The computer network management system comprises an intelligent sensing layer, a multi-dimensional fusion layer and an enhanced visualization layer, wherein the intelligent sensing layer is used for outputting a state portrait to the multi-dimensional fusion layer and the enhanced visualization layer; the multi-dimensional fusion layer is used for outputting a knowledge graph to the cognitive decision-making layer and the enhanced visualization layer; the cognitive decision-making layer is used for performing second-level traceability on the fault propagation path, verifying the diagnosis hypothesis in parallel and then feeding back verification results of the fault propagation path and the diagnosis hypothesis to the enhanced visualization layer; carrying out intelligent decision making, and outputting a decision making strategy to a collaborative autonomous layer; the collaborative autonomous layer is responsible for automatically executing configuration change and flow optimization operations and feeding back the operations to the intelligent sensing layer; and the enhanced visualization layer is used for forming a dynamic visual network topology, establishing a visual map and simulating an attack path, when an administrator triggers rapid drilling analysis through an interaction instruction, a visualization verification result is fed back to the cognitive decision-making layer, and the data acquisition priority of the intelligent perception layer is adjusted.

The invention relates to the technical field of artificial intelligence, in particular to a government affair hotline complaint classification and automatic order sending system and method based on a large model, and adopts a four-layer architecture to realize full-process intelligent management of government affair complaints from acceptance to disposal. The four-layer architecture comprises a multi-mode sensing layer, an intelligent decision-making layer, a dynamic single-layer dispatching layer and a closed-loop self-optimization layer; the method has the beneficial effects that an efficient, accurate and intelligent government affair hotline complaint classification and automatic order dispatching system is constructed by fusing a large model technology and an innovative data processing and order dispatching strategy, rapid and accurate classification and automatic order dispatching of complaint work orders are realized, the work order processing flow is remarkably optimized, the processing period is shortened, and the working efficiency is improved. The quality and efficiency of government affair services are improved, and the satisfaction degree of the public to the government affair services is enhanced.