2585 results about "Instruction set" patented technology

The invention discloses an AI-based smart park multi-source data fusion method and system, and the method comprises the steps: generating a time-space aligned standardized data flow according to environment parameters, energy consumption waveforms, security signals and personnel trajectory data collected by a heterogeneous sensor network; generating a multi-modal fusion feature matrix based on the standardized data stream; according to the multi-modal fusion feature matrix, generating a three-dimensional twinborn body including the equipment state, the people flow density and the energy consumption hot spot in real time; inputting the three-dimensional twin into a multi-target constrained reinforcement learning algorithm, and fusing real-time data and prediction data to generate a Pareto optimal solution set; and based on the Pareto optimal solution set, generating a final instruction set for driving park equipment regulation and control, and triggering collaborative response of a security and protection system and an energy consumption system at the same time. According to the embodiment of the invention, intelligent upgrading of park management can be realized through cross-modal feature extraction, dynamic digital twin modeling and reinforcement learning optimization.

The invention discloses a special equipment life cycle supervision method and system based on characteristic parameter monitoring, and the method comprises the steps: collecting a multi-dimensional characteristic parameter data flow containing a real-time operation parameter, an accumulated damage parameter and a performance degradation parameter, and carrying out the trend analysis through employing a time sequence prediction model, and generating a trend deterioration early warning signal; an association rule mining algorithm is adopted to carry out association analysis to generate an associated fault early warning signal, then the two early warning signals are fused, inherent attribute data and historical operation and maintenance data are combined, and a real-time dynamic risk score is calculated through a dynamic risk portrait model; and finally mapping to a preset discrete supervision level and automatically executing a corresponding differential supervision instruction set. According to the method, the problems of risk identification lagging and strategy static solidification in traditional supervision are effectively solved, the transformation from passive response to active early warning and from average supervision to accurate strategy implementation is realized, and the foreseeability, pertinence and resource configuration efficiency of special equipment safety supervision are remarkably improved.

The invention provides an AI-based laboratory equipment scheduling optimization method and system, and the method comprises the steps: firstly obtaining a state monitoring data set containing the characteristics of equipment operation power consumption, idle time length, environment interference factors and the like in real time, and then carrying out the multi-dimensional analysis of the state monitoring data set; generating an availability evaluation index set containing characteristics of equipment load fluctuation, maintenance period prediction, compatibility matching and the like, and an experiment task priority queue, performing cross decision analysis on the availability evaluation index set and the experiment task priority queue based on a preset dynamic resource allocation model, and obtaining a scheduling strategy set containing a task allocation path, a cooperative operation rule and a conflict resolution mechanism; scheduling strategy parameters are calibrated according to experimental task operation log data, an optimized execution instruction set is generated, instructions are fed back to an equipment control system to adjust the equipment state, a dynamic resource allocation model is iteratively updated according to execution feedback data, and efficient scheduling optimization of laboratory equipment is achieved.

The invention relates to the technical field of data center heat dissipation, and particularly provides a single-phase and two-phase immersion liquid cooling method and system based on AI intelligent decision, and the method comprises the steps: injecting coupled data into a dynamic feature extraction engine, and outputting a thermodynamic state evolution tensor which comprises the characteristics of a temperature change rate, a load-heat flux density coupling coefficient and the like; the thermodynamic state evolution tensor is input into the deep neural network model, the temperature and pressure matched with the current thermodynamic state evolution tensor are calculated, and a closed-loop control instruction set capable of being executed by equipment is generated; a closed-loop control instruction set is injected into an execution mechanism set, execution mechanisms execute power reconstruction and flow channel switching according to instructions, gaseous fluorinated liquid is liquefied and flows back through an efficient condenser in a two-phase mode, and heat dissipation mode self-adaptive switching and heat cycle reconstruction are achieved. The system comprises a server, an AI algorithm controller, a cooling liquid storage device, a condenser, a circulating pump, an electric valve, a pressure release valve and a temperature sensor. The heat dissipation efficiency and the system reliability are remarkably improved.

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

The invention relates to the technical field of PC (Personal Computer) terminal AI (Artificial Intelligence) computing, and discloses a method and a system for dynamically scheduling generative AI heterogeneous computing resources of a PC terminal. The system comprises a resource state acquisition module, a scheduling graph generation module, a resource fluctuation entropy analysis module and a scheduling decision engine module. The resource state acquisition module captures running state parameters of a GPU kernel, a CPU thread and a memory block in real time, and generates a resource state feature tensor through normalization processing; the scheduling atlas generation module analyzes and computes the node connection topology, extracts the correlation between the devices, and constructs a multi-dimensional scheduling atlas; the resource fluctuation entropy analysis module separates the load feature vectors, calculates the entropy of each calculation unit and generates a heterogeneous resource entropy matrix; and the scheduling decision engine module jointly analyzes the atlas and the matrix, identifies bottleneck node resource competition characteristics, generates a dynamic scheduling instruction set, adapts to generative AI task requirements, and ensures efficient and stable operation of the task.

The invention discloses a grassroots society digital governance method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through an Internet of Things sensing network disposed in a grassroots community, and generating a standardized multi-mode sensing data flow; based on the multi-modal perception data stream, outputting a time-space associated cleaned data set; inputting the cleaned data set into a multi-scale space-time encoder, and generating a feature tensor containing regional hotspot distribution and a risk propagation path; based on the feature tensor, constructing a dynamic risk knowledge graph, and outputting a decision matrix including a risk level and an optimal intervention path; inputting the decision matrix into a strategy optimization engine to obtain a hierarchical governance instruction set; and on the basis of real-time governance feedback data after the hierarchical governance instruction set is executed, a conflict instruction in the strategy chain is autonomously corrected through a fuzzy reinforcement learning algorithm. By utilizing the embodiment of the invention, an efficient, intelligent and traceable digital governance scheme can be realized, so that the social governance efficiency and the service quality are improved.

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.

The invention relates to the technical field of vehicle scheduling, in particular to a multi-terminal vehicle scheduling system based on reinforcement learning. The system comprises a heterogeneous data fusion module, a resource allocation module, a hierarchical reinforcement learning module, an optimization feedback module and a man-machine cooperative control module. Data of vehicle operation, operation tasks, environment monitoring and the like are collected and uniformly packaged into a structured data set, an upper-layer manager model generates a global scheduling instruction set based on a PPO algorithm, and a lower-layer worker model outputs a specific vehicle control instruction based on multi-agent reinforcement learning. The system also evaluates and optimizes a historical scheduling execution effect through an NSGA-II algorithm, selects a Pareto optimal solution set, and realizes continuous iteration of a scheduling strategy. The man-machine cooperative control module supports visual display and manual intervention operation, and improves the adaptability and controllability of the system in a complex operation scene.

The invention relates to the field of power system dispatching control, in particular to a virtual power plant collaborative optimization dispatching method, system and device based on multiple spatial-temporal scales and a storage medium. The method comprises the following steps: acquiring real-time supply and demand data of a multi-energy data source, constructing a dynamic operation data set by adopting distributed data acquisition, and performing time sequence analysis on the data set to extract a multi-energy fluctuation feature set; the fluctuation feature set constructs a network topology model in a spatial dimension, and a resource allocation weight of each energy node is determined through graph calculation to generate a resource allocation optimization scheme; when the real-time demand fluctuation exceeds a threshold value, a reinforcement learning algorithm is adopted to carry out optimization adjustment to obtain a real-time scheduling instruction set; in combination with real-time data of the electricity market, an optimized economic signal set is obtained through multi-objective optimization, and an equipment control instruction set is generated by adopting distributed control; and performing real-time monitoring by utilizing edge calculation according to the equipment control instruction set, and dynamically updating the scheduling instruction set through adaptive adjustment based on the system operation deviation to obtain a final resource optimization configuration scheme.

The invention relates to the technical field of intelligent agricultural robots, and provides a multi-agent collaborative anti-collision picking method based on digital twinning and deep reinforcement learning, which comprises the following steps: constructing a digital twinning model of a picking scene, and generating environmental geometric parameters, agent kinetic parameters and fruit position parameters through three-dimensional point cloud reconstruction; acquiring environment state data in real time and inputting the environment state data into the digital twin model for space-time alignment processing to generate synchronous state data; based on the synchronous data, a collaborative strategy containing a collision avoidance priority matrix, a path planning sequence and a task allocation weight is generated through a deep reinforcement learning network; an action instruction set is generated according to the strategy, and multiple agents are controlled to execute a picking task after virtual-physical space bidirectional verification of the digital twin model. According to the invention, efficient collision avoidance and accurate picking of multiple agents in a dynamic environment can be realized, and the picking efficiency, safety and system robustness are improved.

The invention discloses a server mainboard regional distributed liquid cooling heat exchange adjusting method, which comprises the following steps of: acquiring a temperature parameter set and a power consumption parameter set of each monitoring region in real time, and generating a thermal load parameter feature vector; identifying a high heat load core area set according to the heat load parameter feature vector; generating a liquid cooling branch flow distribution instruction set based on the position distribution of the high heat load core area set; executing the liquid cooling branch flow distribution instruction set, and obtaining an adjusted temperature feedback parameter set; according to the deviation degree of the temperature feedback parameter set and the target temperature interval, dynamic feedback adjustment is carried out by applying the heat conduction compensation coefficient and the fluid viscosity-temperature compensation coefficient, and an updated liquid cooling branch flow distribution instruction set is generated. The method has the following advantages and effects that by sensing the thermal load space distribution characteristics of all areas of the mainboard in real time, cooling flow distribution is dynamically adapted, real-time compensation of the fluid viscosity-temperature effect is integrated, local hot spots are eliminated, and the heat dissipation effect is improved.

The invention relates to a multi-mode large language model fine tuning method, system and device and a medium, and belongs to the technical field of artificial intelligence and computer vision crossing. The fine tuning method comprises the steps that an original business scene image is acquired and preprocessed, and a preprocessed image is obtained; performing bounding box coordinate labeling and semantic label definition on the entity target in the preprocessed image through a labeling tool, and outputting a structured labeling file; based on the preprocessed image and the structured annotation file, constructing a training sample set comprising multiple rounds of image-text dialogues; loading the pre-trained multi-modal large language model, configuring low-rank matrix decomposition parameters, and generating a fine tuning instruction set; and inputting the training sample set into a pre-trained multi-modal large language model, carrying out joint training operation based on the fine tuning instruction set, and outputting the fine-tuned multi-modal large language model. According to the method, the identification accuracy, the interaction capability and the system availability of the visual question-answering system in an actual application scene are improved.

The invention discloses a precision compensation method of a composite numerical control machine tool, and particularly relates to the technical field of precision control of the numerical control machine tool, which comprises the following steps: synchronously acquiring temperature, vibration and force information under the driving of a unified clock through multiple types of sensors deployed at key nodes of the machine tool, and generating a synchronous multi-source sensing data set with aligned timestamps; inputting the data set into a multivariable coupling error model obtained through data driving training, and calculating and generating a comprehensive space volume error predicted value of a tool nose point of the tool; performing inverse calculation based on a machine tool kinematic chain model, and generating a multi-dimensional micro-compensation instruction set containing the compensation amount of each motion axis and the sequential relationship; and finally, dynamically writing the instruction set into a servo control ring of the numerical control system through a real-time data interface, and driving each motion shaft to execute synchronous compensation motion. According to the method, comprehensive compensation of multi-physics field coupling errors such as temperature, vibration and force load is achieved, and the machining precision and stability of the numerical control machine tool under complex working conditions are improved.

The invention discloses a photovoltaic power station distributed construction management method and system based on load analysis, and the method comprises the steps: outputting a dynamic load intensity thermodynamic diagram according to the terrain elevation data, meteorological historical data and assembly mechanical parameters of a photovoltaic power station planning region; based on the dynamic load strength thermodynamic diagram, generating a construction zoning scheme for wind pressure resistance optimization; outputting a conflict-free construction resource allocation matrix according to the construction partitioning scheme; on the basis of the construction resource allocation matrix, construction parameters are dynamically corrected by adopting an adaptive particle swarm optimization algorithm, and an anti-deformation construction instruction set is generated; and driving the construction machinery to execute operation according to the anti-deformation construction instruction set, and iteratively updating the global load distribution map by using the reinforcement learning model to form a closed-loop construction management link. By utilizing the embodiment of the invention, the balance of construction precision and efficiency in a complex environment can be realized by fusing multi-source data, optimizing partition planning, intelligently scheduling equipment and controlling closed-loop quality.

The invention relates to a concrete temperature-stress two-parameter cooperative monitoring and early warning method and system, and belongs to the technical field of concrete structure health monitoring, and the method comprises the steps: activating a composite sensor network disposed at a preset position of a concrete structure, and executing a sensor self-calibration mode; the method comprises the following steps: synchronously acquiring temperature and stress original monitoring data of each node, processing based on an altitude air pressure compensation algorithm, and outputting a temperature gradient field matrix and a stress tensor sequence with aligned time domains; the method comprises the following steps: separately calculating a thermal stress component and an effective stress component, performing integral calculation on hydration reactivity, generating a multi-dimensional feature vector set, inputting a pre-constructed prediction model, executing time sequence evolution prediction, calculating a crack probability value, performing environment correction in combination with real-time environment parameters, outputting a risk level identifier, and matching a preset regulation and control strategy. And generating an equipment control instruction set and executing corresponding regulation and control actions. According to the invention, the accuracy and timeliness of crack risk prediction can be improved.

The invention relates to the technical field of science and technology operation management and control, and discloses a science and technology operation intelligent management and control method and system based on big data. According to the method, firstly, heterogeneous data sources in the scientific and technological operation process are collected, and a standardized operation data set is generated through multi-modal fusion processing; performing dynamic feature classification on the key operation indexes, extracting time sequence features and spatial correlation features of the key operation indexes, and constructing a multi-level operation state graph based on feature importance weights; then matching a service rule base with the atlas, identifying abnormal nodes and resource conflict paths, and generating an optimization instruction set containing node repair priorities and conflict resolution strategies; an executable management and control operation sequence is generated; and finally, collecting a feedback data stream, and updating the service rule base and the feature importance weight through an incremental learning mechanism to form a closed-loop optimization link. According to the method, heterogeneous data can be effectively integrated, the operation problem can be accurately identified, the optimization strategy can be quickly generated, and intelligent management and control and continuous optimization of scientific and technological operation can be realized.

The invention discloses an equipment self-adaptive regulation and control method and system based on multi-mode perception, and relates to the technical field of equipment intelligent control. The method comprises the following steps: acquiring real-time operation parameters of medical equipment, and generating multi-modal equipment state data; performing depth state estimation based on the data and a pre-stored historical operation database, identifying a current equipment operation mode and a performance degradation trend, and generating a dynamic target set point interval; matching the dynamic target set point interval with a preset control strategy library to generate a self-adaptive control instruction set for driving an actuator group so as to achieve a target parameter; issuing an instruction set to drive an actuator group to execute regulation and control actions, and continuously collecting operation feedback data; and the feedback data is compared with the dynamic target set point interval to obtain the operation deviation, and the control instruction set is dynamically optimized by adopting a control algorithm based on the deviation, so that the self-adaptive monitoring and cooperative control of the operation state of the medical equipment are realized.

The invention provides a dynamic reconfigurable battery energy storage system balance control method and device based on PWM duty ratio modulation. Belongs to the battery system control field. Voltage information of all single batteries in the energy storage system is collected in real time, total loop current, system operation modes and temperature distribution are monitored synchronously, a controller dynamically calculates charge state values of all the single batteries based on the information, a target object to be balanced is recognized, and meanwhile a global battery consistency evaluation index is established. The method dynamically adjusts the maximum allowable equalization current and generates an equalization topology. The modulation frequency, the duty ratio spectrum amplitude and the phase distribution are adjusted through PWM, and the maximum duty ratio is limited. All the operation instruction sets take effect synchronously at the starting moment of the next equalization period. Besides, the efficiency and working condition mapping table in the historical learning library is synchronously updated to form a closed-loop self-evolution optimization mechanism, so that the balance efficiency optimization of the system in a dynamic scene is realized, and the voltage overshoot is ensured to be within a set threshold value.

The invention relates to the technical field of smart power grids, and discloses a source-grid load-storage cooperation method, which comprises the following steps: carrying out data fusion on real-time acquired source-side distributed power supply output fluctuation data, grid-side topology constraint parameters, load-side time-sharing load demand data and storage-side charge state data to obtain a multi-dimensional system state set; performing short-medium-long term multi-scale coupling prediction on the multi-dimensional system state set to obtain a source load dynamic balance prediction curve; constructing a multi-target collaborative optimization function according to the power fluctuation interval, and generating a target adjustment instruction set based on the multi-target collaborative optimization function; updating the target running state based on the adjustment instruction set; based on the deviation degree of the updated operation state and the prediction state, correcting a weight parameter of the multi-target collaborative optimization function, and based on the corrected multi-target collaborative optimization function, performing source-network-load-storage collaborative regulation and control on the target power platform; according to the invention, the efficiency of source-network load-storage collaboration can be improved.

The invention relates to the technical field of oil and gas equipment operation control, in particular to an oil and gas equipment operation parameter self-adaptive fine tuning method and system, and the method comprises the steps: constructing an operation parameter optimization library driven by reinforcement learning, defining an action space containing equipment types, working condition modes and key parameter combinations, predicting accuracy, response efficiency and safety compliance are optimized in combination with a multi-target reward function, an optimized instruction set is generated, safety perception mixing precision fine adjustment is carried out in parallel, calculation precision is configured in a layered mode, errors are monitored in real time, a high-precision mode is switched if the precision exceeds the limit, and a reservoir engineering equation mechanism constraint matrix is embedded to guide parameter updating. A generative adversarial network is utilized to synthesize fault data, a Darcy seepage causal engine is integrated to correct a loss function, the system comprises an action space construction unit, a precision dynamic scheduling unit, a mechanism constraint embedding unit and a data causal cooperation unit, and the method and the system realize data-mechanism fusion, balance precision and real-time performance, and improve equipment operation safety and adaptability.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The invention provides a dynamic game difficulty self-adaptive adjustment method and system based on user behavior feedback, and relates to the technical field of game design, the method comprises the following steps: carrying out dynamic weighting calculation based on a difficulty adaptation index, generating a dynamic difficulty correction coefficient in combination with user real-time physiological feedback data, and adjusting the dynamic game difficulty according to the dynamic difficulty correction coefficient; the dynamic difficulty correction coefficient comprises a scene complexity adjustment parameter and an interaction response threshold adjustment parameter; inputting the user feedback parameter set into an image feature weight distributor, dynamically adjusting weight distribution of image retrieval feature vectors according to user attention distribution data and operation delay parameters, and generating an optimized scene element retrieval strategy; and performing feature space mapping on the dynamic difficulty correction coefficient and the optimized scene element retrieval strategy, and generating a final game difficulty control instruction set through nonlinear superposition. Game design can be optimized, and game adaptability and flexibility are enhanced.

The invention relates to the technical field of virtualization management, in particular to a virtual machine operation management system based on RISC-V. The system comprises an instruction set mapping module, a resource scheduling optimization module, a virtual machine monitoring integration module, an operation tuning update module and an operation configuration distribution module. According to the method, by analyzing the operation instruction of the virtual machine and combining the RISC-V underlying characteristics, accurate instruction mapping is achieved, the processing efficiency and accuracy are remarkably improved, the performance loss and conversion delay caused by too high abstraction level of a traditional virtual machine on the instruction level are overcome, the optimal allocation scheme is screened, the resource utilization rate and the system response speed are effectively improved, and the system performance is improved. The operation state and performance indexes of the virtual machine are integrated, anomaly detection and resource recovery are embedded, system stability and effective resource reutilization are ensured, basic configuration is continuously and incrementally updated, operation configuration refinement and automatic management and updating are achieved, the overall operation efficiency and management flexibility are improved, and the manual intervention cost is reduced.

The invention relates to the technical field of port ship scheduling, and discloses a ship scheduling carrying method based on space-time constraint scheduling simulation and conflict optimization. Obtaining ship scheduling tasks and port space-time resource data, and generating structured data in a classified manner; constructing a multi-dimensional space-time constraint model, and determining an initial constraint condition; scheduling simulation is carried out on the basis, and the robustness of the scheme is improved by introducing real-time data; detecting conflicts and carrying out iterative optimization by adopting a multi-objective optimization algorithm; and synchronously correcting model parameters with real-time data, generating a scheduling instruction set, and pushing and executing the scheduling instruction set. And meanwhile, path planning is performed by using a deep reinforcement learning algorithm, and a visual interface is constructed to assist scheduling. The method can effectively integrate data, optimize a scheduling scheme, improve the utilization rate of port resources, enhance the ability to deal with emergencies, and realize efficient and intelligent management of port ship scheduling.

The invention belongs to the technical field of parallel robot controllers, and discloses a high-performance parallel robot controller based on an arm + fpga architecture, through deep heterogeneous fusion of an ARM and an FPGA, the control period is shortened to be within 10 microseconds, the trajectory tracking error is controlled to be 0.1 mm or below, and the performance bottleneck of a traditional architecture in a high-speed scene is solved; the multi-core ARM undertakes complex tasks such as global trajectory planning and dynamics solution, and realizes parallel processing by means of an NEON instruction set; the FPGA fully releases the hardware parallel characteristic of the FPGA, real-time tasks such as multi-axis motion control and sensor data fusion are synchronously completed through a distributed logic unit, and a complex decision-real-time execution assembly line cooperation mode is formed. Inertial parameters and load changes of the mechanical arm are estimated in real time through an LSTM neural network, and feedforward compensation is carried out on interference such as mechanical vibration and load abrupt change in combination with an extended state observer achieved through FPGA hardware; the innovatively designed double closed-loop control architecture supports seamless switching between a force control mode and a position control mode.

The invention belongs to the technical field of intelligent manufacturing and automatic design optimization, and discloses an automatic template generation method and system based on UG software, and the method comprises the steps: capturing a sketch operation event flow, carrying out the deep binding of geometric parameters and tolerance rules, generating a technology enhancement parameter set, and constructing a dual-channel instruction set; synchronously generating a two-dimensional engineering drawing projection and a three-dimensional expansion drawing preview; detecting conflicts in real time, and calling an exception correction plan library for dynamic correction; generating a zero-conflict BREP boundary model and a correction track log, and establishing a log-plan library mapping relation; generating an enhanced BERP model through a template drawing optimization mechanism; further generating a processing path instruction set, and integrating the processing path instruction set into a process compliance template drawing package; generating a cross-platform manufacturing package; constructing a quality index set, and generating an abnormal event association graph; and calculating a rule parameter adjustment amount, dynamically updating the process rule base, generating a global strategy packet, and reversely injecting sketch parameter constraints to form a continuous optimization cycle.

The invention relates to the technical field of intelligent control, in particular to a multi-mode-based intelligent home control method and system, and the method comprises the following steps: S1, synchronously collecting a voice stream and an action video stream of a user, and respectively extracting a semantic anchor point of a voice instruction and a spatial anchor point of an action track; s2, constructing a virtual time axis, mapping the semantic anchor points and the spatial anchor points to a unified space-time coordinate system, and performing anchor point deviation correction on a mapping result based on an environment semantic field to generate an alignment instruction set; and S3, performing intention fusion on the alignment instruction set according to the spatial topological relation of the equipment, generating an equipment control instruction, and triggering execution. According to the method, the fault tolerance and execution rationality of fuzzy instructions or wrong finger behaviors are improved, the method is particularly suitable for family scenes with dense multiple devices and frequent environment dynamic changes, and the practicability and the intelligent level of the system are remarkably improved.

The invention discloses a code reconstruction method, system and device based on function recognition and a medium, and the method specifically comprises the steps: scanning a project file topological structure, and constructing a knowledge graph based on a code import relation and a technology stack feature; performing context enhancement by analyzing the code function description and combining with the knowledge graph to generate a code modification instruction set; based on the code modification instruction set, in combination with data stream sensitivity marking and performance portrait analysis, evaluating the target code to obtain an evaluation result; based on an evaluation result, matching a historical optimal practice mode through a graph neural network, and generating an optimization scheme which retains an original design style; by comparing abstract syntax tree differences between original codes and verified optimization schemes, only functional logic nodes are replaced, and legal code style features are reserved. According to the method, the efficiency and quality of code reconstruction and optimization are improved, and a more convenient, efficient and intelligent code processing tool is provided for software developers.

The invention relates to the technical field of industrial platform data analysis, in particular to a renewable resource recovery data management system based on the Internet of Things, which comprises the steps of synchronously acquiring weight, spectrum and microwave characteristic data of a tested resource through a data acquisition module, executing moisture weight decoupling operation by utilizing a resource matching module, and performing data analysis; then, an inventory evolution module tracks performance loss of resources in real time by using an evolution model of an integrated long and short-term memory network, generates a resource attenuation weight, and constructs a two-dimensional decision matrix containing scheduling priority and preprocessing strength grade instructions in combination with real-time inventory saturation; and finally, the clearance scheduling module executes dynamic pruning and bidirectional optimization through a resource scheduling model, accurately allocates the loading share and the access sequence of each node, and generates a dynamic instruction set containing a delivery sequence. According to the method, feeding homogenization is realized through industrial data multi-dimensional collaborative analysis.