547 results about "Decision level" patented technology

The invention discloses a personalized computer-aided decision-making method and system fusing multi-modal data, and relates to the field of personalized computer-aided decision-making, and the method comprises the steps: mapping multi-source heterogeneous modal data to a unified semantic embedding space, and obtaining a multi-modal unified representation vector set; carrying out three-layer progressive fusion on a feature layer, a situation layer and a decision layer of the multi-modal data to generate a global decision context vector; based on a cross attention mechanism, outputting a fused context sensing personalized vector; based on the behavior cloning model, outputting probability distribution on all decision options; according to the user feedback operation data, generating a user personalized decision strategy and performing dynamic optimization; and generating a structured decision report containing visual traceability information based on the hierarchical fusion process and decision reasoning logic. End-to-end intelligent generation from multi-source heterogeneous data to personalized decisions is realized, and a standardized process is converted into personalized customized decisions.

The invention relates to the technical field of data processing, discloses a digital twin adaptive decision platform for TBM tunneling and a control method, and aims to solve the problems of single sensing dimension, mutual separation of physical and data models, disjunction of early warning and control links and low man-machine interaction efficiency in the prior art. According to the scheme, the method mainly comprises the steps that multi-source real-time data is collected through a hardware sensing layer, after the multi-source real-time data is processed through a data management and fusion layer, a digital twin model layer constructs a physical-data fusion model and completes risk assessment, finally risk visualization is achieved through an augmented reality interaction layer, and an intelligent control decision-making layer generates a control instruction and outputs the control instruction to a TBM main control system. According to the integrated intelligent decision-making platform and the control method, multi-source perception deep fusion, physical and data model online coupling, risk assessment and equipment control second-level closed loop can be realized, and visual interaction can be realized through augmented reality.

The invention discloses a BIM-based hydropower station full life cycle design, construction, operation and maintenance integrated control system, and the system comprises an intelligent sensing layer which integrates 5G + Beidou positioning, a LoRa gateway and a sensor, collects multi-source heterogeneous data in real time, and transmits the multi-source heterogeneous data to a digital twinborn layer after the multi-source heterogeneous data is filtered by an edge node; a digital twinborn layer: constructing a parameterized model library through laser point cloud and BIM automatic registration, integrating a geological parameter dynamic correction algorithm, mapping a construction period stress field in real time, and updating model parameters based on sensing data self-evolution; the intelligent decision-making layer performs equipment fault prediction by using an LSTM neural network, optimizes multi-machine load distribution in combination with an improved PSO algorithm, and automatically adjusts a start-stop strategy when the load fluctuates; and the security execution layer is used for triggering equipment operation after virtual twinborn deduction verification through a block chain evidence storage instruction, realizing virtual-real dual verification in combination with an industrial firewall, and finally feeding back a running state to the sensing layer to calibrate and update a model, and supporting intelligent decision.

The invention discloses a sewage plant multi-process-section collaborative intelligent decision fusion system, which comprises a sensing and rule fusion layer used for collecting inlet and outlet water quality parameters, process control parameters and operation state parameters of multiple process sections in real time, preprocessing data and fusing an expert rule base; the mechanism and data driving joint modeling layer is used for establishing a mechanism model and a data driving model under the constraint condition of the expert rule base and predicting control quantities respectively; and the collaborative optimization and fusion decision-making layer is used for executing cross-process-section multi-target collaborative optimization and fusion decision-making based on an output result of the mechanism and data driving joint modeling layer under the constraint condition of an expert rule base, calculating a dynamic fusion weight, generating a final control quantity, and issuing the final control quantity to execution equipment. And in combination with real-time feedback self-adaptive adjustment, closed-loop optimization is realized. According to the system, the sewage treatment stability, decision precision and resource utilization efficiency are improved, and the environmental risk is reduced.

The invention relates to the technical field of machine learning, and particularly discloses an intelligent parking guidance and reverse vehicle searching system based on multi-source heterogeneous data fusion. The system comprises a data perception fusion layer, a dynamic prediction decision-making layer and a user service interaction layer, realizes multi-step advanced probability prediction and dynamic optimal path planning of a parking space state through fusion of geomagnetic detection, video identification, payment flow, traffic situation and activity information, and realizes the optimal path planning of the parking space state based on multi-mode induction and live-action AR reverse vehicle searching guidance. And the parking efficiency and the user experience are improved.

The invention requests to protect a dual-core decision-making examination reservation system based on a rule engine and deep reinforcement learning. The dual-core decision-making examination reservation system comprises a data infrastructure layer, an intelligent decision-making layer and a decision-making output layer, wherein the data infrastructure layer is used for forming reservation data according to data of equipment and patients; the intelligent decision-making layer is used for constraining patient appointment according to the appointment data; and the decision output layer provides a scheduling scheme according to the reservation data and the constraint conditions. The dynamic state space constructs a higher-dimensional state vector space, the reservation data comprises a patient feature domain, an equipment state domain and an environment dynamic domain, and comprehensive dynamic data support is provided for decision making in combination with personalized features of the patient and real-time environment information. The intelligent decision-making layer has a hybrid decision-making mechanism and comprises a rule decision-making module and an optimization module, the rule decision-making module verifies hard rules of the reservation data, the optimization module performs weighted integration on non-hard rules of the reservation data, and finally the decision-making layer outputs a scheduling scheme.

The invention provides an energy storage electrical intelligent system of a self-adaptive control strategy, and relates to the technical field of electric power grids. According to the invention, multi-source operation data of a power grid, a load, new energy and an energy storage system are collected in real time through the sensing layer, the decision-making layer realizes accurate synchronization and updating of the system operation state by using a digital twin model, and distributed optimization of swarm intelligence and extreme scene testing of confrontation deduction are fused. Dynamically generating and screening out an optimal control instruction which shows robustness under various working conditions; and finally, the execution layer accurately controls the energy storage system to form a sensing-decision-execution-feedback adaptive closed loop, so that the control strategy of the energy storage system can be automatically and accurately adjusted along with the fluctuation of the running state of the power grid, the stiffness defect of a fixed strategy is overcome, the capacity of the power grid for dealing with source load fluctuation and sudden faults is enhanced, and the energy storage system is ensured to be more stable. The self-adaptive matching of the energy storage system control strategy and the power grid operation state is realized, and the dynamic stability and the operation safety of the power grid are improved.

The invention discloses a transformer internal multi-parameter information fusion discharge diagnosis and evaluation method, and relates to the technical field of power equipment state monitoring and fault diagnosis. According to the method, eight types of sensors are deployed in a transformer, and electrical, mechanical, thermotechnical and insulating multi-physical-field parameters are cooperatively collected; thirdly, performing three-stage preprocessing of abnormal value elimination, standardization and differential noise suppression on the data; then 12 types of fault features are extracted; carrying out feature layer fusion by adopting a dynamic weight distribution method based on AHP-working condition correction, and constructing a fusion feature vector; performing decision-making layer fusion through a five-layer fuzzy logic-neural network hybrid model, and outputting a fault probability and a fault type; and finally, dividing five fault levels according to the fault probability and outputting specific processing suggestions. According to the method, multi-parameter adaptive fusion and high-precision diagnosis are realized, the accuracy is high, the average time delay is low, and the problems of incomplete fault coverage and poor working condition adaptability of a traditional method are effectively solved.

The invention relates to the technical field of artificial intelligence, in particular to an interaction system and method with an emotion dynamic evolution memory function, a medium and a processor. The interactive system comprises a sensing layer, a processing layer and a decision-making layer, the perception layer comprises a semantic text coding module and a fusion processing module; the semantic text coding module outputs the acquired audio as semantic features and high-dimensional features, and performs linear space conversion processing; the fusion processing module and the data output by the semantic text encoding module are linearly spliced to form multi-modal feature vectors, and the multi-modal feature vectors are classified into the current emotion state of the intelligent agent and the emotion state of the user; the processing layer is used for correcting the data output by the sensing layer; and the decision-making layer is used for carrying out information decision-making and storage on the data output by the processing layer. The technical problem that in the prior art, the number of labels is limited, undefined interaction modes are difficult to process, and consequently the character of a robot is limited is solved.

The invention relates to an AEB triggering method for reducing false triggering in a curve scene, and the method comprises the steps: firstly constructing a lane line recognition and lane geometric modeling module, forming a stable lane boundary and a center line, building a Frenet coordinate system, and completing the judgment of the lane attribution of a target vehicle and the compensation of the curve distance; performing out-of-control stage identification on an overline target after an overline event is triggered, and generating a trajectory beam and a three-dimensional dynamic occupation envelope under the constraint of a stage label to obtain a future influence area of the target; a tree-enhanced Bayesian network is introduced into the risk inference layer to describe a dependency relationship among multiple risk variables, and collision point grid posteriori distribution and a high-risk duration window are output; a braking feasible region is constructed by combining a forward collision avoidance demand and a backward rear-end collision prevention constraint at a braking decision-making layer, and an impact rate constraint and a damage cost are introduced to realize AEB graded triggering and braking instruction optimization, so that an out-of-control intrusion risk can be inhibited in advance, and the false triggering probability can be reduced.

The invention discloses a multi-constraint emergency material vehicle path optimization method and system, and belongs to the technical field of vehicle path planning. Comprising the following steps: acquiring input data from a data source, and constructing a multi-objective optimization model containing an objective function and constraint conditions based on the input data; based on the input data and the constructed multi-objective optimization model, generating an initial path scheme meeting all constraint conditions by adopting a heuristic algorithm combining nearest distance distribution and an earliest time window priority rule; and constructing a hierarchical collaborative optimization architecture consisting of an ALNS execution layer and a PPO decision layer, carrying out iterative optimization by taking the initial path scheme as a starting point, terminating the iterative optimization when a preset termination condition is met, and finally outputting an optimal path scheme. Compared with the prior art, the method has the beneficial effects that the key indexes are collaboratively optimized through the multi-objective optimization function, and the global solution quality and the scheduling precision are remarkably improved.

The invention relates to the technical field of production line control, and discloses a production line digital superposition control method, device and equipment and a storage medium, and the method comprises the steps: carrying out the parameter collection and data preprocessing of a production line, obtaining the state information of a production process, and determining a first control parameter of glue superposition positioning; creating a decision layer and an execution layer of production line superposition control, and determining a communication connection structure of each process control unit; based on the communication connection structure, superposition error information fusion processing is executed in the decision layer, and an optimal glue positioning control instruction is generated; the optimal glue positioning control instruction is transmitted to an execution layer, a glue spraying unit, a centering unit and a superposition unit are driven to execute glue superposition operation, and execution state data are collected; and according to the execution state data, the first control parameter is dynamically adjusted to obtain the second control parameter, so that the adaptation period during process switching is greatly shortened, and the flexibility and efficiency of the production line are improved.

The embodiment of the invention provides a logistics scheduling system, and belongs to the technical field of logistics. The system comprises a task layer which is used for generating an order task; the feedback layer is used for collecting dynamic resource data related to execution of the order task in a logistics site; the decision-making layer is respectively in communication connection with the task layer and the feedback layer, and the decision-making layer is used for adjusting a path planning strategy according to the dynamic resource data and generating a task execution instruction according to the adjusted path planning strategy; and the execution layer is in communication connection with the decision-making layer, and the execution layer is used for scheduling corresponding transportation equipment to execute the order task according to the task execution instruction. According to the embodiment of the invention, the scheduling flexibility of the transportation equipment can be improved.

The invention provides a high-voltage cable outer sheath insulation state monitoring system and method, and the system comprises a multi-mode sensing layer which monitors the insulation state of a cable outer sheath in real time through a multi-mode sensor; the edge calculation layer is used for carrying out data preprocessing and preliminary abnormity judgment on site; the cloud computing layer is used for carrying out multi-modal data analysis and carrying out fault prediction and risk assessment based on a deep learning technology; and the self-adaptive diagnosis and decision-making layer is used for generating an insulation risk grade and a maintenance suggestion based on a multi-source data analysis result and outputting a decision-making instruction. The multi-mode sensing technology is utilized, edge computing and cloud computing are combined, the limitation of a traditional single detection technology is broken through, and the problems that the false alarm rate is high, fault positioning is inaccurate, and environmental adaptability is lacked are solved.

The invention discloses a smart marketing system and device and a medium, and belongs to the field of big data analysis. The system comprises a data support layer used for collecting multi-source heterogeneous data including equipment data, user behavior data and third-party data, and forming data assets by preprocessing the multi-source heterogeneous data; the intelligent decision-making layer is used for identifying the life cycle stage of the current user based on an intelligent model matrix and the data assets, predicting the transition stage of the life cycle of the user and generating a precision marketing strategy and a channel selection scheme of each stage; and the business application layer is used for converting the precision marketing strategy and the channel selection scheme into a specific marketing task, monitoring marketing effect data and feeding back the marketing effect data to the data support layer and the intelligent decision-making layer. According to the system, the intelligent marketing process of full-link data fusion, dynamic life cycle management and automatic marketing closed loop can be realized.

The invention discloses a multi-dimensional bid evaluation intelligent optimization method based on deep learning, and the method comprises the following steps: obtaining historical and current bid evaluation data, carrying out the preprocessing, and generating a multi-dimensional input feature set; constructing a hierarchical Pareto leading-edge learning network, and outputting a target sub-representation set of each bid evaluation dimension by a bottom-layer target sub-network; executing a multi-task gradient equilibrium and hyper-volume maximization strategy in a middle-layer leading edge aggregator; performing frontier point screening and interpolation fusion on the Pareto frontier point candidate set at a top decision-making layer to generate a comprehensive scoring result and contribution mapping; and outputting a bid evaluation sequence and selection suggestion according to the comprehensive scoring result, and generating a bid evaluation decision result set. According to the method, intelligent optimization of multi-dimensional bid evaluation is realized by adopting the hierarchical Pareto leading-edge learning network, and the method has the advantages of weight self-adaption, result interpretability and evaluation decision stability.

The invention provides a meta-universe virtual-reality interaction method based on causal invariance, belongs to the field of meta-universe virtual-reality interaction technologies, and is used for solving the problems of poor cross-domain adaptation, insufficient long-tail scene coverage and low dynamic robustness in related technologies. According to the method, through sensing layer causal kernel quality evaluation and data enhancement, reasoning layer causal invariance learning and cross-domain parameter migration, decision layer causal attention intention alignment, execution layer causal reinforcement learning control and iteration layer double-threshold knowledge updating, full-link parameter collaborative circulation is realized in combination with a causal data interface; and finally, the accuracy and the stability of virtual-real interaction of the element universe are improved, and efficient adaptation of cross-domain, long-tail and dynamic scenes is realized.

The invention discloses a BIM-based electromechanical installation resource management and control system, and the system comprises a data sensing layer which integrates a laser radar, an RFID, a UWB and an environment sensor, collects multi-modal data, and forms a total-factor digital twin base data stream after the multi-modal data is preprocessed by a mobile edge computing node; the model fusion layer is used for binding the construction progress with BIM component attributes, dynamically deducing a component-level progress path through an AI algorithm, identifying key path conflicts, integrating ERP resource data to generate a thermodynamic diagram and a cost deviation curve, and automatically detecting pipeline collision, progress lag and cost hyper-branched risks through a rule engine machine learning dual-mode engine; triggering multi-stage early warning and dynamically adjusting a threshold value; the intelligent decision-making layer is used for predicting the progress deviation probability of the future seven days by adopting LSTM, generating a non-tampering deviation correction scheme in combination with a block chain, and realizing hierarchical response through a yellow-orange-red three-level alarm system; and the user interaction layer is used for realizing multi-terminal collaboration and executing verification data feedback through AR glasses, a mobile terminal and a large-screen monitoring center.

The invention relates to the technical field of wind power plant control, and discloses a wind power plant control system. The system comprises a data acquisition module, a feature extraction module, a decision instruction generation module, an optimization model construction module and a hierarchical control execution module. The data acquisition module acquires data through a multi-source sensor; the feature extraction module performs multi-modal feature extraction by using a deep belief network; the decision instruction generation module generates a regulation and control instruction by means of a pre-trained collaborative decision model; the optimization model construction module constructs a multi-target dynamic optimization model, and adopts a decomposition coordination optimization algorithm to adjust operation parameters; the hierarchical control execution module executes a regulation and control instruction through a hierarchical control architecture, and the decision layer, the coordination layer and the execution layer respectively adopt corresponding algorithms to realize global regulation and control, local parameter adaptation and accurate tracking of power and pitch angles. The system can improve the power generation efficiency of the wind power plant, balance the equipment loss, enhance the cooperative operation capability with the power grid, and guarantee the stable and efficient operation of the wind power plant.

The invention discloses an optimization control method for a power system, and relates to the technical field of power control, and the method comprises the steps: constructing a multi-energy state space, and building a layered optimization architecture based on the multi-energy state space; the decision-making layer generates a multi-energy balance scheduling instruction through a multi-target collaborative optimization algorithm to realize multi-target collaborative optimization of the electric heating gas system; the execution layer comprises a plurality of energy subsystem control modules, and collaborative strategy optimization is carried out through a distributed reinforcement learning algorithm; positioning a disturbance source of topology mutation through a causal reasoning algorithm, and generating a reconstruction instruction; training a control strategy of each energy subsystem by adopting a distributed learning framework, and executing a distributed cooperative control action according to the reconstruction instruction; the network connectivity is judged through the communication state of the energy subsystem, and if the communication is normal, collaborative decision making is carried out through a distributed learning framework; and if the communication is interrupted, switching to an edge node offline autonomous mode.

The invention discloses a data governance strategy dynamic execution system based on active metadata and AI recommendation, and relates to the technical field of data governance, and the system comprises an active metadata sensing layer which is used for actively monitoring and capturing metadata real-time changes of various data sources, and reporting the metadata real-time changes in a standardized data format; the intelligent analysis decision-making layer is used for receiving the standardized metadata, taking a preset knowledge base as a basic support, performing metadata association analysis by relying on an AI recommendation module, generating a governance strategy and converting the governance strategy into an executable instruction; the strategy dynamic execution layer is used for receiving an executable instruction, performing task splitting, priority ranking and dependency management through a workflow scheduler, and calling a corresponding executor to automatically complete execution of a governance strategy; and the unified management and control portal layer is used for providing a visual interface for an administrator so as to carry out treatment strategy approval, global monitoring, blood relationship analysis, audit query and manual intervention. According to the invention, intelligent generation and automatic execution of the data governance strategy can be realized.

The invention discloses a BIM intelligent assembly system applied to node construction, and belongs to the technical field of node construction. The BIM intelligent assembly system comprises the following modules: a node-level BIM parameterized model library; a factory-end intelligent manufacturing module; the field end intelligent assembly module comprises a reusable node sensing layer composed of UWB, IMU and a visual fusion sensing suite; an edge decision-making layer in which a lightweight Transform model is embedded; the shape memory alloy lock pin and the self-locking and self-adjusting execution layer of the piezoelectric micro-displacement sliding table are integrated on the node connecting plate; and a block chain quality file sub-module. The parameterized node group is directly issued to the steel bar hoop bending robot, the sleeve welding robot and the 3D printing mold, the space coordinates of steel bars, sleeves and embedded parts can be locked within the millimeter-level range in the factory stage, and the six-degree-of-freedom postures of the nodes are captured in real time during on-site hoisting; once deviation is found, the edge AI gives out a posture adjusting instruction immediately, and correction is completed through an SMA lock pin and a piezoelectric micro-displacement sliding table.

The invention discloses a family resource optimization system based on artificial intelligence, and the system comprises a family digital twinning layer which is used for constructing a dynamic digital twinning layer which is synchronous with a family physical environment; the global sensing layer is used for connecting a physical family and digital twinning and converting original sensor data into semantic understanding capable of being utilized by the digital twinning; the cognitive decision-making layer is used for reasoning and learning the semantic understanding provided by the global perception layer and generating a digital instruction; and the physical execution and closed loop layer is used for converting the digital instruction into an executable specific action and is responsible for collecting consequences and feedback generated by the action so as to form a complete perception-cognition-action-feedback intelligent closed loop. By means of the family resource optimization system based on artificial intelligence, the problems that current family resources are passive in response, lack of unified scheduling and optimization, low in credibility and the like can be solved, and the intelligent and integrated operation capacity of the family resources is remarkably improved.

The invention belongs to the technical field of artificial intelligence, and discloses a smart energy production and distribution system based on artificial intelligence. The system comprises a sensing layer used for collecting energy production parameters, storage capacity, transmission loss and user requirements in real time; the data processing layer is used for performing exception elimination, format standardization and integrated storage according to production, storage, transmission and demand dimensions; according to the AI intelligent decision-making layer, a prediction unit outputs energy production potential and demand peak values in the future 1-24 hours, and an optimization unit generates a production adjustment, storage allocation and transmission path optimization scheme; and the execution layer receives and executes the scheduling operation. According to the system, supply and demand pre-judgment and dynamic regulation and control are realized through AI intelligent decision, the problem of traditional response lag is solved, and the energy utilization rate is increased by more than 15%; accurate input is provided for decision-making through full-chain data acquisition and standardized processing, so that the scheduling scheme is more practical, and the transmission loss is reduced by more than 10%.

The invention provides a micro-grid energy optimization scheduling system fusing digital twinning, and the system comprises a physical micro-grid layer which is used for collecting real-time operation data through a sensor network, and adjusting the operation state of equipment according to an equipment control instruction issued by an execution control layer; the data sensing layer is used for receiving, preprocessing and transmitting the real-time operation data; the digital twinborn platform layer is used for constructing and updating a digital twinborn model based on the preprocessed real-time operation data, performing real-time dynamic simulation, extreme scene simulation and operation state prediction based on the digital twinborn model, and outputting a simulation result and prediction data; the energy optimization decision-making layer is used for receiving the simulation result and the prediction data, solving through an optimization algorithm by taking low carbon and stability as a planning target, and generating an optimal scheduling scheme; and the execution control layer is used for converting the optimal scheduling scheme into an equipment control instruction and issuing the equipment control instruction to the physical micro-grid layer so as to better adapt to a complex micro-grid operation environment.

The invention relates to a zero-carbon park carbon reduction path planning method based on an HJ-PSO hybrid optimization algorithm, and the method comprises the following steps: S1, constructing a master-slave game framework, and defining a multi-level decision logic; s2, constructing an upper leader decision-making layer of the subordinate game framework; s3, constructing a lower response layer of the master-slave game framework; s4, determining a strategy set of a master-slave game and a Nash equilibrium condition; s5, an HJ-PSO hybrid optimization algorithm is adopted to solve the upper-layer robust optimization model; and S6, inputting the dynamic electricity price and load data obtained in the step S5 into the lower follower model in the step S3, and obtaining an optimal response strategy of an energy storage operator ESO and a load aggregator LA through the efficient solving capability of CPLEX. According to the invention, collaborative optimization scheduling of economic, low-carbon and stable operation of the system is realized.

The invention discloses an intelligent work order dynamic scheduling processing method based on multi-source data fusion. The method comprises the following steps: collecting Internet of Things data, resource data and personnel data through a data fusion layer; the intelligent decision-making layer analyzes the Internet of Things data in real time, creates a pre-diagnosis work order, or manually creates a fault work order and submits the fault work order to the intelligent decision-making layer; after a work order enters the system, an intelligent decision-making layer calculates a comprehensive scheduling score for each maintenance personnel meeting the condition, automatically distributes the work order to the personnel with the highest score, and pushes all information to a communication terminal of the intelligent decision-making layer; during dispatching, warehouse inventory is automatically checked through the resource collaboration layer; after maintenance is completed, results and time are recorded back to the system, and a self-learning closed loop is formed. According to the method, the mode of finding things by people and finding objects by people is changed into the mode of finding people by things and preparing objects, the method is a fundamental change of an operation and maintenance mode, the per capita efficiency can be improved, the cost can be reduced, and excessive dependence on personal experience can be avoided.

The invention discloses a hospital scene-oriented layered obstacle avoidance method for a mobile robot, and aims to solve the problem of insufficient adaptability in a dynamic dense environment in the prior art. According to the method, a set of'perception-decision-control 'collaborative hierarchical navigation framework is constructed. In a sensing layer, an improved ORB-SLAM3 is adopted to realize the construction of a'global sparse and local dense 'hybrid map, and both the positioning precision and the calculation real-time performance are considered. In a decision-making layer, a three-layer progressive architecture of'global A * path planning-local TEB trajectory optimization-upper layer TD3 reinforcement learning self-adaptive parameter adjustment 'is designed; wherein the TD3 intelligent agent can dynamically adjust the cost function weight of the TEB algorithm according to the real-time environment state, so that the robot has the human-like decision-making ability of autonomously balancing between efficiency and safety. The whole system is integrated based on ROS and is verified through simulation and a physical platform. According to the method, the navigation robustness, safety and intelligence of the mobile robot in high-dynamic scenes such as hospitals and the like are remarkably improved.

The invention discloses a pile foundation concrete accurate pouring control system and method based on multi-source sensing fusion. The pile foundation concrete accurate pouring control system comprises a sensing layer and a control layer, wherein the sensing layer comprises a phased array ultrasonic probe, a binocular vision sensor and a laser ranging array; the modeling layer comprises a three-dimensional hole shape reconstruction module and a BIM digital modeling engine; the data layer comprises a real-time monitoring database and a multi-source data fusion center; the decision-making layer comprises a concrete dynamic evaluation model and a BIM visual platform; the execution layer comprises a self-adaptive PID controller used for adjusting the concrete pumping rate according to the decision instruction, and a three-dimensional visualization terminal used for displaying the BIM model with the tolerance zone in real time. According to the method, a BIM model containing dynamic compensation is constructed through ultrasonic three-dimensional scanning, closed-loop control is achieved by fusing multi-source sensing data, and tolerance band penetration early warning and self-adaptive PID adjustment are combined. The hidden defect discovery rate is improved, and the comprehensive construction efficiency is improved.

The invention discloses a power distribution scheduling system and method based on dynamic load prediction, and aims to solve the problems that load prediction and scheduling are disjointed and distributed energy fluctuation is difficult to deal with. The system is composed of a multi-dimensional perception layer, an edge calculation layer, a cloud decision-making layer and an execution feedback layer. The cloud decision-making layer constructs a topological adjacency matrix through a spatio-temporal feature fusion engine, calculates a load evolution trajectory by using a prediction model based on a deep spatio-temporal diagram neural network and an attention mechanism, and generates an optimal scheduling sequence by using a multi-target collaborative optimization scheduling module and a model prediction control algorithm. And a deep closed loop of prediction and scheduling is realized in combination with an adaptive feedback correction unit. According to the method, the prediction precision and the system response speed under the complex working condition are remarkably improved, and the economy and the safety of operation of the power distribution network are ensured.