586 results about "Dynamic decision-making" patented technology

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 an AI agent emergency order insertion dynamic decision production scheduling method, a medium and a system, and belongs to the technical field of industrial agents. A dynamic weight adaptive optimization model is adopted to calculate a target weight coefficient and construct a multi-target function set, an improved non-dominated sorting genetic algorithm is adopted to solve and output a Pareto optimal solution set, and a delay risk assessment correlation matrix is combined to start an incremental re-planning algorithm to generate a local adjustment scheme. The Pareto optimal solution set and the local adjustment scheme are combined to generate a final production scheduling scheme, a real-time monitoring module is started to track the execution deviation condition, and when it is detected that the deviation degree exceeds a threshold value, a rapid rescheduling mechanism is triggered to conduct scheme correction; the technical problem of poor production scheduling scheme quality caused by low multi-agent cooperation efficiency in the emergency order insertion dynamic decision process is solved.

The invention discloses a dynamic task distribution system based on multi-source data fusion, belongs to the technical field of intelligent task scheduling, and aims to solve the problems of response delay and execution failure caused by undifferentiated rule verification, fuzzy resource matching logic and inaccurate real-time state evaluation in a traditional task distribution system. According to the system, a task request is obtained through a task receiving module, multiple indexes are converted through a data standardization processing module, a rule matching engine carries out multi-rule verification and outputs a matching degree score, a real-time capability evaluation module calculates a current capability value of an executor, and a dynamic decision module dynamically adjusts double weights according to a task emergency degree and generates a comprehensive score. And time, resource and skill conflicts are eliminated through the conflict detection unit, and finally the optimal executor is selected by the task allocation unit to issue the task. The system is suitable for various fields requiring efficient and accurate task allocation, such as grid command center order distribution, emergency response, logistics distribution, maintenance service and the like.

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

The invention relates to the technical field of data fusion, and discloses an animal scene-oriented adaptive multi-modal data fusion method, which comprises the following steps of: extracting spatio-temporal characteristics from multi-source heterogeneous data such as visual sense, auditory sense and physiological sensing, constructing an animal-environment-group ternary spatio-temporal relation graph, and constructing an animal-environment-group ternary spatio-temporal relation graph; a pilot frequency sampling problem is solved through an adaptive interpolation algorithm, cross-modal projection alignment is completed in a public semantic space, unified space-time representation is output, and confidence coefficient weight is dynamically calculated based on uncertainty measurement of each modal feature. According to the method, accurate alignment of multi-modal data is realized through the cross-modal space-time attention network, the multi-modal feature alignment error is reduced compared with that of a traditional LSTM method, the training data volume of a federated element migration reinforcement learning framework is reduced compared with that of a traditional migration learning method, and the cross-species generalization performance of the model is improved. A multi-level causal inference engine quantitatively reveals causal association between environmental factors and animal diseases, and in combination with a dynamic decision tree visualization technology, the decision recognition degree is improved.

The invention relates to a multi-agent cooperative recruitment method, system and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a target recruitment demand, and generating a task tree for the target recruitment demand through a preset dynamic decision-making mechanism in a main agent; determining a time sequence relationship and logic constraint information of each sub-task in the task tree, and calling target sub-agents in the sub-agents in sequence based on the time sequence relationship and the logic constraint information; the target sub-agent is used for executing the sub-task in the task tree; and generating a target recruitment task result until task execution results of the sub-tasks are obtained. By adopting the method, the recruitment efficiency can be improved.

The invention discloses a bus and station interactive scheduling method and system based on V2X and deep reinforcement learning decision, a medium and equipment, and the method comprises the steps: collecting bus operation dynamic data, station passenger flow and environment data in real time, and combining V2X network interaction information to construct a full-dimension perception system; a deep reinforcement learning model is adopted for dynamic decision making, intelligent closed-loop control of vehicle scheduling, station service and traffic signal cooperation is achieved, the bus punctuality rate is remarkably increased, the waiting time of passengers is shortened, operation safety is guaranteed through active early warning of abnormal conditions, and intelligent upgrading of a bus system from passive response to active prevention is achieved.

The invention discloses an intelligent mold part machining cutting tool control method and system. The method comprises the following steps that S1, a multi-source sensing information collection layer is established; s2, constructing a cutter health knowledge graph; s3, generating a dynamic decision control instruction; s4, executing a bimodal control response; and S5, realizing closed-loop control optimization. Through quadruple mechanism coupling of global coverage of a multi-mode sensing layer, dynamic deduction of a knowledge graph decision-making layer, risk isolation of a double-track execution layer and intelligent evolution of a closed-loop optimization layer, the method is realized in an industrial control domain for the first time: raising a sensing dimension, and converting a physical world fragmentation signal into a cutter full life cycle digital twinborn body; reconstructing decision logic, replacing traditional threshold judgment with topological correlation, and foreseeably inhibiting ill-conditioned failure; the system is ecological and self-consistent, and the control strategy continuously evolves in operation to form the anti-interference capability; and finally, normal form transition of tool wear control from passive remediation to active immunity is achieved.

The invention provides a smart power grid cloud edge collaborative heterogeneous data security access method, which comprises the following steps: collecting multi-source heterogeneous power equipment data in real time based on a deployed multi-mode sensor network; based on a lightweight federated learning edge inference engine deployed on an edge end network, performing adaptive semantic enhancement preprocessing on the multi-source heterogeneous power equipment data to generate an edge encryption feature tensor; performing three-dimensional credible security authentication and knowledge fusion on the edge encryption feature tensor based on an electric power space-time knowledge graph fusion engine deployed on the cloud side to generate secure and credible electric power data; and performing space-time causal reasoning and twin mirror image comparison on the secure and credible power data based on a federated block chain-driven digital twin decision engine deployed at a cloud end to generate a dynamic security policy map and store the dynamic security policy map. According to the scheme, the data security is enhanced, intelligent analysis and dynamic decision can be performed according to the real-time state of the power grid, and optimal scheduling and stable operation of the power grid are realized.

The invention relates to a robot decision control method based on gradient rarefaction and a robot. The method comprises the following steps: acquiring multi-modal sensor data of a robot; based on a preset sparsification strategy, generating a dynamic mask corresponding to the gradient matrix of the multi-modal large model; based on the generated dynamic mask, screening an effective gradient in a back propagation process of the dynamic mask; updating parameters corresponding to the effective gradient in real time, and obtaining the output of the multi-modal large model based on the updated parameters; according to the obtained multi-modal sensor data and the output of the multi-modal large model based on the updated parameters, feature fusion is carried out, and a combined state code including an environment state, a robot body state and historical decision information is generated; and according to the determined joint state code and based on a time sequence model, generating an action sequence, a force control parameter and a path planning dynamic decision instruction of the robot, so that the robot can act based on the generated dynamic decision instruction, thereby realizing decision control of the robot.

The invention relates to the technical field of artificial intelligence knowledge maps, and discloses a customs emergency decision-making method and system based on a multi-agent large language model, and the method comprises the steps: constructing a knowledge map from an unstructured customs emergency procedure document through a multi-agent cooperation framework; the multi-agent cooperation framework at least comprises a regulation analysis agent, a key point screening agent, an emergency element extraction agent and a logic construction agent; and providing support for customs emergency decision-making problems of the user and giving customs emergency decision-making answers through a map navigation agent driven by a large language model on the basis of the constructed knowledge map. According to the application, high-fidelity automatic construction of the customs emergency knowledge graph can be realized, and the accuracy and integrity of knowledge are ensured; a static knowledge graph can be converted into an interactive dynamic decision support tool, and a full link from an unstructured regulation document to intelligent decision support is really opened.

The invention belongs to the technical field of artificial intelligence task planning, and provides a task planning PDDL file automatic generation method based on natural language input, and the method comprises the steps: (10) constructing task environment description words: analyzing environment elements and participants, and designing a diversified scene framework; (20) building a knowledge enhancement fine tuning model: fusing a vector RAG knowledge base retrieval result and pre-training model parameters by the model, and inhibiting logic illusion; (30) PDDL file generation and knowledge constraint: driving the fine tuning model to output a correct PDDL file in combination with scene semantics and knowledge base rules; (40) multi-stage dynamic evaluation: executing a feasibility index verification instruction through a knowledge base rule matching degree; and (50) model closed-loop iterative optimization: updating RAG knowledge base content and model parameters based on execution feedback, and constructing a closed-loop iterative optimization model. The method has the beneficial effects that the instruction logic deviation rate is reduced through RAG knowledge base constraint, the complex scene instruction generation accuracy is improved, and the dynamic decision response time is shortened.

The invention discloses a self-adaptive multi-level digital watermarking method based on coding process optimization. The method comprises the following steps: firstly, performing deep preprocessing on an input video stream through a pre-trained multi-modal deep learning model, extracting space complexity, time dynamics and content significance features, and generating a uniform feature vector; and constructing a dynamic decision model based on the feature vectors, adaptively determining the watermark intensity, the embedding position and the type, and realizing accurate matching of watermark parameters and video content characteristics. A multi-level watermark hierarchical embedding mechanism is adopted, a robust invisible watermark, a fragile invisible watermark and a dynamic visible watermark are embedded into a video, and the security is enhanced by combining chaotic encryption or Hash chain encryption. During copyright verification, watermark information of each layer is recovered through an adaptive extraction algorithm, and data verification and infringement traceability are completed in combination with a block chain evidence storage system. According to the method, a full-process copyright protection system of embedding, coding, extraction and verification is constructed.

The invention discloses a complex network disintegration method based on evolution deep reinforcement learning. According to the method, an encoder-decoder model fusing a graph convolutional neural network and a deep Q network is constructed, and is used for efficiently extracting importance features of nodes in a complex network and realizing dynamic decision-making of a node disassembling sequence according to the importance features. In order to optimize model parameters and improve search capability, an evolutionary algorithm is introduced to perform global exploration on the model parameters, and the problem that a directional optimization strategy is easy to fall into local optimum is avoided. Meanwhile, deep mining is carried out on an evolution result in combination with a reinforcement learning strategy, the overall optimization process is accelerated, and advantage complementation of parameter evolution and strategy learning is achieved. Experimental results show that the method significantly improves the efficiency and precision of network disassembly while maintaining the robustness of the model, and has good practical value and wide application prospects.

The invention discloses an intelligent control system and method for a fire fighting reconnaissance robot, and relates to the field of intelligent control, and the system comprises an acquisition processing module, a risk prediction module, a path planning module, a decision module and an optimization module. Carrying out disaster risk prediction and analysis on the space-time synchronous environment state set to obtain a dynamic disaster evolution risk field, carrying out path planning analysis according to the dynamic disaster evolution risk field to obtain a safe navigation track, carrying out fire source characteristic analysis and fire extinguishing agent decision based on the safe navigation track to obtain a basic fire extinguishing agent type, and carrying out fire extinguishing agent analysis. And environment self-adaptive parameter optimization is carried out on the type of the basic fire extinguishing agent to obtain a dynamic decision vector of the fire extinguishing agent, so that quick and accurate fire extinguishing and effective risk avoiding can be realized, and the safety and effectiveness of fire-fighting operation are improved.

The invention provides a multi-modal fusion fatigue screen monitoring recognition and reminding method and system, and the method comprises the steps: data collection: collecting the visual data of a screen monitoring person in real time, and synchronously collecting the physiological data; multi-modal data fusion: adopting a feature weighted fusion method based on an entropy weight method to adaptively distribute weights and generate a fusion fatigue index FFI by quantifying dynamic information entropy of each visual data and physiological data; fatigue grade classification: realizing three-level fatigue state judgment based on a fusion fatigue index FFI obtained by multi-modal data fusion and a dynamic decision tree model; and dynamic intervention: based on a fatigue grade classification result, adopting a double-channel intervention mechanism of bracelet touch alarm and automatic telephone call value length. When fatigue and inattention of a monitoring screen watchman occur, the monitoring screen watchman can be timely and accurately identified and a reminding intervention mechanism is started, so that the continuity and the safety of the monitoring screen work are ensured, and bad safety production events caused by human reasons are avoided.

The invention provides an open caisson sinking control method based on dynamic balance of a soil resistance field, which comprises the following steps of: judging whether an open caisson posture dynamic balance condition is met or not according to an open caisson bottom surface soil resistance vector field and an open caisson posture offset, if not, intervening space distribution of the open caisson bottom surface soil resistance vector field, and reconstructing the open caisson bottom surface soil resistance vector field; the dynamic balance condition of the open caisson posture is met; and based on the open caisson bottom surface soil resistance vector field, the height difference vectors of the adjacent bins on the open caisson bottom surface and the soil taking control matrix, a dynamic decision objective function is constructed, the objective function is solved, an optimal soil taking control matrix is obtained, and an actuator cluster is controlled to take soil from the bins on the open caisson bottom surface. According to the method, the influence of dynamic evolution of the soil resistance field of the bottom face of the open caisson on the open caisson attitude offset is considered, the soil resistance field is dynamically adjusted, the open caisson attitude is accurately controlled, and soil taking control is optimized.

The invention relates to the technical field of water and fertilizer application and irrigation, in particular to a water and fertilizer application and irrigation dynamic decision-making method based on a crop model. The method comprises the following steps: acquiring a crop growth parameter complete set and a water and fertilizer demand objective function set; performing dimension reduction on the crop growth parameter complete set to obtain a key crop parameter set; constructing a dynamic constraint factor generation rule; constructing a multi-level agent modeling system based on the key crop parameter set and the water and fertilizer demand objective function set to obtain a hierarchical crop agent model; therefore, through system integration of data dimension reduction, multi-level agent modeling, a parallel evolutionary algorithm and dynamic multi-objective optimization, the problems of data processing redundancy, low modeling efficiency, single optimization objective and insufficient constraint adaptability in a traditional water and fertilizer decision method are solved; the agricultural water resource and nutrient cooperative configuration efficiency and the decision precision are improved.

The invention provides a multi-unmanned aerial vehicle cooperative transportation dynamic decision-making method and system in an emergency environment, and the method comprises the steps: 1, obtaining the material state information reported by a disaster region node after an emergency occurs; step 2, constructing a hunter and prey local decision model taking the disaster-affected node as a main body, and forming a local priority and competition relationship between the nodes; 3, establishing a multi-target minimum cost maximum flow (MCMF) model, and solving to obtain a candidate transportation scheme; and step 4, inputting the candidate transportation scheme as an initial population into an improved non-dominated sorting genetic algorithm NSGA-II, performing non-dominated optimization on energy consumption, time, fairness and risk, and generating an optimal collaborative transportation strategy cluster approaching the Pareto frontier. According to the method, through a mode of combining local decision and global optimization, comprehensive improvement of multi-unmanned aerial vehicle collaborative transportation efficiency, fairness and safety is realized in a complex dynamic emergency environment.

The invention provides a satellite-ground heterogeneous network dynamic switching method and system and electronic equipment, and relates to the technical field of wireless communication, and the method comprises the steps: carrying out the collection of core state parameters from a network side, a user side and a service side, and obtaining the state parameters, related to network switching, of the network side, the user side and the service side; performing dynamic association processing on the state parameters respectively corresponding to the network side, the user side and the service side to generate a unified state vector; performing dynamic decision optimization according to the unified state vector through a reinforcement learning model to obtain a satellite-ground switching decision weight; according to the satellite-to-ground switching decision weight, generating a satellite-to-ground network comprehensive score difference, and according to the satellite-to-ground network comprehensive score difference, determining a network switching target and a decision confidence coefficient of the network switching target; and determining an execution parameter of the network switching target according to the decision confidence through a Bayesian execution engine, and performing network switching according to the execution parameter. According to the invention, the switching effect of the satellite-ground heterogeneous network is improved.

The invention discloses a cotton irrigation decision-making system based on agricultural knowledge guidance and reinforcement learning. The method comprises the following steps: collecting data such as weather, soil and yield, and establishing a standardized agricultural database; a yield prediction model fusing the crop mechanism and deep learning is constructed, and yield responses of different irrigation states are evaluated; and integrating the prediction model with a crop simulation system DSSAT, constructing a simulation environment, optimizing an irrigation strategy in an iteration process by adopting a reinforcement learning algorithm, and realizing a dynamic decision by taking yield gain and water consumption as objective functions. The innovation point is that a knowledge-guided yield prediction network structure is provided, and the prediction precision and the model interpretability are improved; and a reinforcement learning mechanism based on a reward function is designed to realize joint optimization of yield and water saving. The method has the advantages that the problems that traditional irrigation depends on experience, decision lags and adaptability lacks are solved, the irrigation efficiency and the water resource utilization rate are effectively improved, and an intelligent, efficient and water-saving irrigation scheme is provided for cotton planting.

The invention provides an electric energy hybrid game method for multi-agent collaborative scheduling. By constructing a three-layer collaborative game framework, dynamic decisions of a power distribution system operator, an electric vehicle charging station and an electric vehicle user are incorporated into a unified optimization model. According to the hierarchical iteration mechanism of the three-level game model, firstly, a power distribution system operator publishes node marginal electricity price on an outer layer, a middle-layer charging station is driven to adjust a bidding strategy, and then an inner-layer user is guided to dynamically update a charging station selection proportion. The closed-loop optimization from electricity price conduction to user response effectively breaks the limitation of unbalanced benefit distribution in the traditional two-party game. Through the nested design of the master-slave game, the non-cooperative game and the evolutionary game, the results of the three layers of games are finally converged to an equilibrium state, so that the charging station selection, power distribution interaction and pricing strategies form a collaborative optimal solution, the global optimization of network-station-vehicle three-party resource scheduling is realized, and the power resource allocation efficiency is remarkably improved.

The invention belongs to the technical field of computer fault processing, and discloses an automatic fault processing method based on cooperation of a decision tree and an intelligent agent. The method comprises the following steps: constructing a dynamic decision tree model with a threshold adaptive adjustment mechanism; deploying a cooperative system composed of a diagnosis agent, a strategy generation agent and an execution verification agent; collecting real-time operation data and extracting time-frequency and statistical characteristics through a multi-dimensional sensor; a two-stage diagnosis mechanism is adopted to identify known and novel fault modes; candidate disposal strategies are generated and evaluated in combination with deep reinforcement learning; optimizing an optimal strategy through physical constraint check and state evolution simulation, and issuing and executing the optimal strategy; and finally, online updating of the model and the knowledge base is realized based on treatment effect feedback. According to the technical scheme, high precision of fault diagnosis, self-adaption of the disposal strategy and sustainable evolution of the system capability are realized, and the reliability and the automation level of operation and maintenance of industrial equipment are remarkably improved.

The invention relates to the technical field of intelligent decision, and discloses an intelligent decision support method based on reinforcement learning. The method comprises the following steps: acquiring a historical interaction data set containing a decision action sequence, an environment state sequence and a corresponding instant reward signal in a target decision scene; the data set is input into a state feature extraction network for spatial-temporal feature coding, and a state feature vector set with time sequence relevance is generated; constructing a decision action space mapping table based on the set, wherein candidate decision actions and expected accumulated rewards corresponding to the state feature vectors are recorded in the table; dynamically updating the mapping table by adopting a strategy gradient algorithm to generate an optimized strategy gradient parameter set; and constructing a real-time decision support engine according to the parameter set, wherein the engine can respond to the environment state change and output the optimal decision action. The method adapts to a dynamic decision-making scene, and assists in efficiently outputting decision-making results meeting requirements.

The invention discloses a public facility reachability evaluation method and system based on geographic multiple agents, and belongs to the field of urban calculation and geographic information. According to the method, a multi-agent system comprising a user agent and a facility agent is firstly constructed, and then a dynamic utility function combined with an evolutionary game theory is introduced to simulate the dynamic decision and congestion avoidance behavior of a user; and user strategies are balanced through multiple rounds of iterative evolution, so that a macroscopic reachability pattern emerging from a microcosmic game is obtained. According to the method, starting from the microscopic individual level, by simulating the dynamic game process of a large number of agents, the defects of a static calculation model are overcome, and dynamic evaluation and predictive simulation of reachability of public facilities are achieved. Compared with the prior art, the evaluation result is more accurate, dynamic feedback of the real world can be reflected, and a scientific decision support tool is provided for urban planning.

The invention relates to the technical field of agricultural irrigation, and particularly discloses an intelligent agricultural water and fertilizer integrated irrigation system, which comprises an environment and crop state sensing network used for collecting soil, weather and crop phenotype data; the multi-source data fusion and demand analysis engine is used for associating data and analyzing water and fertilizer demands of individual crops; the dynamic decision-making unit based on the crop growth model is used for generating a partition variable irrigation and fertilization instruction through rolling optimization; and the partition variable execution mechanism is used for accurately executing the differentiated instructions. By means of the scheme, personalized precise water and fertilizer management of different crops in the field is achieved, the water and fertilizer utilization efficiency is remarkably improved, and balanced growth of the crops is promoted.

The invention provides a micro-grid main grid cooperative switching intelligent regulation and control method and system, and relates to the technical field of power grid regulation and control. According to the method, the historical operation data of the micro-grid and the main grid are collected in real time, and the future operation state parameter set is generated, so that the operation states of the micro-grid and the main grid are accurately predicted. The dynamic stability index of the micro-grid and the power grid health index of the main grid are calculated, the two indexes serve as input, a switching decision management model is applied to generate a switching tendency score, and the switching tendency score is compared with a dynamic decision threshold value in real time to judge whether switching conditions are met or not. According to the method, the collaborative switching control instruction is automatically generated according to the correlation index and the switching mode rule, and the micro-grid is finely pre-adjusted in advance, so that smooth seamless switching between the micro-grid and the main grid is realized when the physical switching action is executed, and the safety, the reliability and the stability of the switching operation between the micro-grid and the main grid are improved.

The invention discloses a spiking neural network driven robot dynamic adaptive control system, which relates to the technical field of robot control, and comprises seven modules: an environment sensing module which integrates various sensors and collects and transmits environment, attitude and interaction information; the signal preprocessing module processes data through composite filtering and feature extraction; the spiking neural network modeling module constructs a three-layer structure and performs training based on a fusion learning rule; the dynamic decision output module converts the pulse signal into a control instruction and adjusts gain; the actuating mechanism driving module drives the actuator to act; the state feedback monitoring module monitors and feeds back motion parameters and system states; and the adaptive optimization module optimizes the network and module parameters based on feedback data, and dynamically matches the environment. The control precision and the response speed of the robot in a complex environment are improved, the adaptive capacity is enhanced, the operation reliability and safety are guaranteed through multi-module cooperation, and the application scene is expanded.

The invention relates to the technical field of project management intelligent decision making, in particular to a project management intelligent decision making system integrating multi-target resource scheduling optimization. The system comprises an equipment state sensing module, an environment monitoring module, a data acquisition module, a conflict evaluation module and a dynamic decision module. The equipment state sensing module is used for collecting operation parameters of key construction equipment in real time, wherein the operation parameters comprise equipment real-time position coordinates, actual speed, fault early warning coefficients, actual working time and actual completion time. The data acquisition module is used for acquiring supply chain data and construction plan data. The equipment state sensing module, the data acquisition module and the environment monitoring module are arranged for cooperative use, so that the system can automatically sense environment changes such as wind speed and wind direction, the equipment offset risk is calculated through the conflict evaluation module in real time, and meanwhile, the resource scheduling instruction is generated, so that the error correction response time is greatly shortened, and the error correction efficiency is improved. And meanwhile, the delay time of the construction period is reduced.