1365 results about "Resource distribution" patented technology

A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.

The invention discloses a composite interference-oriented cultivated land system toughness evaluation and optimization method, and relates to the technical field of cultivated land toughness optimization, and the method comprises the steps: collecting and preprocessing multi-source cultivated land data, constructing a toughness evaluation index system, and calculating the weight of each index; and calculating a comprehensive toughness score based on weight distribution, drawing a time sequence change curve based on the score, and performing region division to generate a toughness distribution diagram. According to the method, the single-factor pollution index, the Nemo comprehensive index and the interaction effect index of heavy metal pollution are combined, the soil pollution condition is accurately quantified through a weighted summation method, a scientific and reliable basis is provided for toughness evaluation, the dynamic toughness distribution diagram is generated in combination with the LSTM model and the GIS technology, the spatio-temporal dynamic characteristics of a cultivated land system under composite interference are revealed, and the method has the advantages of being high in reliability and high in reliability. According to the method, the defects of an existing model in space-time analysis are overcome, differentiated optimization strategies are formulated based on region division and weak region labeling, and the resource allocation efficiency and the pertinence of an optimization scheme are remarkably improved.

The invention relates to a computing power network resource scheduling method. The method comprises the following steps: acquiring floating point operation performance parameters and operation states of node processors and energy index data of data centers where the node processors are located, and calculating to generate a node list; constructing a global resource pool based on the list, and generating a resource distribution table containing the total calculation power of the region; obtaining calculation requirements and time delay constraints of the task queue, extracting feature vectors in combination with the resource distribution table, and generating a resource utilization rate table; obtaining network link flow data, predicting a link congestion probability through a long short-term memory network, and generating a flow control strategy table; and finally, updating resource pool network constraints according to the resource utilization rate table and the flow control strategy table, and remapping tasks by taking node effective computing power as a weight to generate a scheduling execution scheme. According to the method, accurate quantitative evaluation of the computing power resources is realized, the matching precision of tasks and the computing power resources is effectively improved, the resource utilization rate of the computing power network can be remarkably improved, and the overall scheduling efficiency and stability of the system are enhanced.

Provided in the present invention are an energy-storage synchronous and coordinated management method and system based on a virtual synchronization technique. The method comprises: selecting a suitable energy storage device on the basis of grid requirements, and collecting parameters of the energy storage device; on the basis of the parameters of the energy storage device, establishing a dynamic model of a virtual rotator, and setting a rotator rotational inertia and a torque parameter; establishing an adaption layer, so as to realize the interaction between the virtual rotator and a physical energy storage device; providing a line monitoring apparatus in a grid so as to collect real-time data of the energy storage device and detect, in real time, whether a fault occurs, and the adaption layer executing a safety operation when a fault occurs, and performing dynamic control and prediction planning when no fault occurs, so as to solve the problem of the uneven distribution of resources; and the adaption layer determining whether grid nodes and the real-time data of the energy storage device are normal, and if the grid nodes and the real-time data of the energy storage device are abnormal, performing detection and setting again, and if the grid nodes and the real-time data of the energy storage device are normal, saving a log and ending the process. In the present invention, an intelligent means is used to replace a conventional experiential control method, such that the dependence on experience is reduced, and dynamic optimization can be performed to increase the level of automation of energy storage control.

The invention relates to the technical field of power distribution network source website collaborative planning, and particularly discloses a power distribution network source website collaborative planning method and system considering a V2G mode. The method comprises the following steps: acquiring an existing topological structure of a power distribution network, traffic road network structure data, electric vehicle number space-time distribution data, photovoltaic resource distribution data and a historical daily load power curve set; predicting to obtain a node load power set, a V2G charging and discharging station maximum schedulable power set and a PVG station maximum output power set in various typical day scenes in the future; establishing an upper-layer planning model by taking the minimum total economic cost of the system as a target, and establishing a lower-layer planning model by taking the minimum annual comprehensive operation cost as a target; and solving the bilayer planning model by adopting a nested iterative algorithm, and outputting a power distribution network source website collaborative planning scheme. According to the method, the economical efficiency and reliability of collaborative planning of the power distribution network, the PVG station and the V2G charging and discharging station are improved, the resource utilization efficiency is remarkably improved, and the total economic cost is reduced.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

The invention provides a road disaster detection method and system based on air-ground cooperative unmanned inspection, and the method comprises the steps: obtaining a multi-source road monitoring data set, carrying out the time-space alignment processing of the multi-source road monitoring data set, generating a road monitoring data set integrated with a time-space label, and carrying out the recognition of a road disaster based on a preset multi-mode disaster recognition model. Disaster feature extraction processing is carried out on the road monitoring data set fused with the space-time labels to obtain a road disaster feature set, a disaster detection result set is generated according to the road disaster feature set, the disaster detection result set comprises a plurality of disaster detection units, and an inspection optimization strategy set is generated based on the disaster detection result set. According to the method, the automation degree and the response efficiency of road disaster detection are comprehensively improved under the condition of not depending on artificial experience intervention, and the problems of high misjudgment rate, large monitoring blind area and rigid resource allocation caused by data splitting and time-space asynchronization in a traditional method are solved.

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention provides a multi-mode tailing pond dam break digital twinning emergency deduction and decision optimization method, which comprises the following steps of: firstly, constructing a high-precision three-dimensional scene model, performing simulation calculation on the high-precision three-dimensional tailing pond model based on a finite element model to obtain a dam body stress-strain field, and performing simulation calculation on the dam body stress-strain field based on ANSYS + reinforcement learning. And predicting a dam break probability distribution diagram by using the dam body stress-strain field and the current environment state, generating a rescue scheduling scheme based on the dam break probability distribution diagram and the resource distribution data, building a dynamic virtual deduction scene for exercise, and verifying and optimizing the rescue scheduling scheme according to exercise record data. And a final rescue scheduling scheme is obtained. According to the method, the ANSYS + reinforcement learning hybrid engine is utilized to reinforce the deduction precision, dam break path prediction, optimal resource scheduling and plan self-optimization closed loop are realized, the problems of data fusion distortion, deduction stiffness, decision lag and the like in the traditional technology are solved, and the method is particularly suitable for variable working conditions such as tailing pond seepage field sudden change and extreme weather.

The invention relates to the technical field of data processing, and discloses a comprehensive management method and system for a smart park, and a storage medium. The method comprises the steps that environment, energy consumption and people flow data are collected through the Internet of Things, and a basic data set is obtained through edge node processing and semantic alignment; constructing a digital twin system by using a spatio-temporal data cube and a graphical mode; constructing a land digital identity system by adopting space-time coding and block chain technologies; performing anomaly identification and risk analysis on the activity data to obtain supervision early warning data; performing multi-dimensional analysis on the enterprise operation data to form an enterprise portrait; and presenting resource distribution through mixed reality, and analyzing cross-department data to obtain operation efficiency data. According to the invention, holographic perception, intelligent supervision and cooperative service of park elements are realized.

The invention provides a low-orbit satellite resource allocation method and device based on beam and frequency domain collaborative optimization, and the method comprises the steps: dividing the overall bandwidth of a user region into a central sub-band and an edge sub-band through employing a dynamic soft frequency reuse scheme; all ground users are subjected to spatial clustering according to geographic positions and traffic demands, coverage cells are dynamically divided according to clustering results, and balanced service is achieved; on the basis of dynamic cell division, user clusters with high service volume are selected according to priorities, a beam activation set is generated, and a hopping beam matrix is updated; establishing a channel model between a user and a beam and a switching matrix between satellites; constructing a system utility target, and constructing a multi-target optimization problem model by taking the system utility target as a main part and integrating interference, switching overhead and delay penalty; and the multi-dimensional resource collaborative optimization framework based on deep reinforcement learning reconstructs a multi-objective optimization problem model, and outputs a solution of the multi-objective optimization problem model, including an optimal bandwidth and a power allocation result. According to the invention, resource allocation can be carried out on low-orbit satellites.

The invention relates to the technical field of mineral resource management, in particular to a mineral resource dynamic prediction and mining management system which comprises a data perception and fusion layer, a unified digital twinborn model, a dynamic prediction and decision intelligent agent and a visualization and interaction control layer. The data perception and fusion layer collects structured data such as geological exploration and mining environment and market unstructured data, and generates a unified space-time tensor through processing; the unified digital twinborn model generates a dynamic comprehensive mining area situation map containing resource reserve risk economic indicators through a three-dimensional convolutional neural network embedded with an attention mechanism; the dynamic prediction and decision-making agent predicts future reserves and geological risks, and constructs a dual-objective optimization model to generate an optimal mining path equipment scheduling and resource allocation scheme; and the visualization and interaction control layer presents the mining area state and the decision scheme in a three-dimensional manner and provides an interaction interface. According to the invention, the data utilization rate and decision scientificity are improved, the safety risk is reduced, and mine management intellectualization is promoted.

The invention discloses a multi-beam cooperative scheduling method and a multi-beam cooperative scheduling system for low-orbit satellite communication, which mainly solve the problems of poor beam scheduling flexibility and difficulty in adapting to time-varying requirements in a high-dynamic environment in the prior art, and the implementation scheme of the multi-beam cooperative scheduling method comprises the following steps of: collecting online user information by a low-orbit satellite through a wide beam; a hopping beam position is generated by using adaptive clustering under the constraint of channel resources; a hopping beam scheduling algorithm based on deep reinforcement learning outputs an optimal scheduling strategy according to the real-time environment variable; and optimizing a beam hopping time frame structure in each scheduling period, covering accessed users in a beam position through spot beams, and sending service data and control information to complete efficient service transmission. The method can adapt to changes of user distribution and service requirements in low-orbit satellite communication, the utilization rate of spectrum resources is improved, communication delay is reduced, the overall performance and service quality of the system in a high-dynamic environment can be enhanced, and the method can be used for dynamic beam position division, hopping beam scheduling and hopping beam resource allocation in a multi-beam low-orbit satellite communication scene.

The invention discloses a Direct3D memory model compatible method based on adaptive placeholder resources, which comprises the following steps: establishing resource metadata, accessing a scene library, checking an instruction template and double sandboxes when a DXVK is started, describing related resources by the DXVK through the metadata after a D3D application is started, completing resource mapping and metadata dynamic updating, and allocating the resources to the corresponding sandboxes; the DXVK compiles an application shader code, identifies a resource access instruction, matches an access scene and a check template by combining a pipeline stage and binding slot query metadata, instantiates the check instruction and adds the check instruction to the front of a target instruction; according to the unbound resources, adaptive generation of corresponding occupied resources is carried out, the authority and the state are configured, the unbound resources are bound to a Vulkan descriptor set to replace VKNULLHANDLE, and adaptation of the shader interface and the pipeline state parameters is completed to create PSO (Particle Swarm Optimization); and intercepting an application rendering instruction analysis parameter to construct a command buffer area, submitting the command buffer area to a Vulkan queue to trigger a GPU (Graphic Processing Unit) to execute rendering, and finally completing complete conversion and adaptation from the D3D rendering logic to the Vulkan.

The invention relates to the technical field of fishery management, in particular to an intelligent fishery supervision method and system based on multi-source data fusion, and the method comprises the steps: obtaining the multi-source trajectory data of a fishing boat, and carrying out the data fusion, and obtaining the precise trajectory data of the fishing boat; performing trajectory feature analysis, trajectory behavior pattern analysis and high-sensitivity point identification on the accurate trajectory data to obtain trajectory distribution of the fishing boat; obtaining multi-source fishery resource data and performing data fusion to obtain a fishery resource evaluation vector; the fishery resource prediction model processes the fishery resource evaluation vector to obtain fishery resource distribution; performing spatial coincidence analysis, time coincidence analysis and intensity coincidence analysis on the trajectory distribution of the fishing boats and the distribution of fishery resources to obtain analysis results; and an analysis result and management data are obtained and processed, and if abnormal behaviors exist, early warning is carried out.

The invention relates to the technical field of wind power generation, and discloses a cross-scale wind power plant wind resource evaluation method based on a numerical mode, comprising the following steps: step 1, acquiring multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme adaptive to the regional terrain; step 3, through a WRF power downscaling method, generating annual scale wind resource data as initial wind resource data; 4, performing partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling the WRF mode and the wind power plant parameterized model; step 6, optimizing WRF lower boundary conditions; 7, simulating the atmospheric flow of the target area, and quantifying the wake flow evolution characteristics of the wind power plant; 8, constructing a wake flow analysis model; and step 9, formulating a collaborative productivity plan of the wind power plant group. The power generation capacity of the wind power plant to be developed can be accurately predicted, and a reliable basis is provided for scientific decision-making of resource distribution development and planning of the million-kilowatt wind power plant.

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

The invention 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 true orthophoto generation method based on a three-dimensional Gaussian model, a storage medium and equipment, and aims to solve the problems of low image quality, low generation speed, detail loss and the like in the prior art. The method comprises the following steps: firstly, acquiring image data through an unmanned aerial vehicle or aerial photography, and extracting a camera attitude and sparse point cloud by using a motion recovery structure (SfM) technology; secondly, constructing a three-dimensional Gaussian model based on the sparse point cloud, and performing iterative training through top view orthographic projection in combination with a gradient descent algorithm; in the training process, a densification strategy, a point deleting strategy, a blocking strategy and an image pyramid strategy are innovatively introduced, so that the detail expressive force and the overall quality of the image are remarkably improved. Specifically, according to the densification strategy, fine detail reconstruction is achieved by dynamically increasing Gaussian ball density, and according to the point deletion strategy, rendering efficiency is improved and computing resource allocation is optimized by eliminating redundant Gaussian balls. The image pyramid strategy generates a multi-level visual effect through multi-scale training, and the blocking strategy improves the reconstruction precision through local optimization. Finally, on the basis of the trained three-dimensional Gaussian model, real-time generation of a high-quality true orthophoto in a large-scale scene can be realized. The method has remarkable advantages in the aspects of efficiency, precision and practicability, provides important technical support for the fields of geographic information systems, urban planning, disaster monitoring and the like, and has wide application prospects.

The invention relates to the technical field of environment monitoring, in particular to a farmland environment intelligent monitoring system based on multi-source data fusion, which comprises a multi-source data acquisition module for synchronously acquiring original data; the dynamic compensation preprocessing module is used for receiving the original data and generating standard data with aligned time axes through a dynamic timestamp compensation mechanism; the spatial-temporal feature decoupling module is used for performing multi-scale feature extraction on the standardized data and separating spatial distribution features, time sequence features and environment coupling features; the multi-modal fusion analysis module is used for fusing the spatial distribution characteristics, the time sequence characteristics and the environment coupling characteristics by adopting an evidence chain fusion algorithm to generate farmland environment state evaluation parameters; and the edge decision optimization module is used for dynamically adjusting a resource allocation strategy of an edge computing node according to the farmland environment state evaluation parameters. Priority processing of key tasks is achieved under limited computing power, and the response efficiency and the resource utilization rate of the system are effectively improved.

The invention relates to the technical field of construction risk early warning, in particular to a BIM-based building construction risk early warning method and system, and the method comprises the following steps: constructing an incidence matrix between risk nodes based on time and space dimensions for the risk factor of each node in a construction process, calculating the risk propagation intensity value between the nodes, and calculating the risk propagation intensity value between the nodes; and judging the risk association relationship between the nodes, performing grading processing on the risk coupling degree according to the propagation rate, and outputting a risk coupling matrix value. According to the method, a high-risk area is extracted by combining spatial distribution data, dynamic partition management is achieved, dynamic influences of environmental variables such as temperature, humidity and vibration are quantified, the complex relation between environmental conditions and risk states is revealed, the accuracy and real-time performance of risk assessment are optimized, and the risk assessment accuracy is improved by analyzing the risk transfer trend and propagation path changes. And dynamically adjusting node priorities and resource allocation, updating the influence of newly added risk signals in real time, and realizing full-process dynamic optimization of construction risk early warning.

The invention provides an information resource distribution method and system applied to a dynamic network environment, and the method comprises the steps: firstly obtaining a real-time state feature set of a target distribution network, determining a resource distribution constraint condition set in a current network environment based on the real-time state feature set, carrying out the priority analysis of a real-time resource request sequence of a target user group, and obtaining a real-time resource distribution constraint condition set; obtaining resource timeliness grade distribution and a request path optimization feature set, generating a dynamic distribution strategy parameter set matched with a resource distribution constraint condition, triggering a resource redirection operation, and enabling the resource request response rate of the target user group to be improved to a preset threshold interval; and the information resource distribution efficiency and stability in the dynamic network are improved.

Systems, methods, and non-transitory, machine-readable media may facilitate testing within sub-environments with respect to resource capacity data corresponding to resources. Specifications of resource allocations for resources may be collected. Observation data, including resource metrics data, may be consolidated and used to develop resource data composites with mapped resource metrics data. A sub-environment interface may be configured to allow creation of sub-environments based on the resource data composites. Source schemas and data structures corresponding to the resource data composites may be selected. A migration plan to migrate the selected data structures to a destination schema for testing in a sub-environment may be created. A migration according to the migration plan may be executed. The destination schema may be created to match the selected source data structures and the selected source schemas. The selected data structures may be migrated to the destination schema to allow for testing in the sub-environment.

The invention relates to the technical field of Internet of Things control, and discloses an intelligent Internet of Things equipment linkage control method and system, and the method comprises the steps: collecting Internet of Things terminal data, converting the Internet of Things terminal data into a standardized space-time matrix, and carrying out the feature decoupling processing, and obtaining a high-dimensional feature vector; querying equipment information to calculate the energy transfer efficiency and the communication delay sensitivity of the equipment by using the high-dimensional feature vector so as to construct a dynamic knowledge graph of the Internet of Things equipment; carrying out fault signal injection on the dynamic knowledge graph, analyzing an abnormal propagation path of the equipment and the anti-interference capability of the equipment, and carrying out risk assessment on the Internet of Things equipment to obtain an equipment risk degree; an initial linkage strategy of the Internet of Things equipment is constructed, feasible strategies in the initial linkage strategy are identified, and a feasible strategy subset is obtained; and performing directed acyclic graph decomposition on the feasible strategy subset, performing dynamic resource allocation on decomposition task nodes, and executing a target task. According to the invention, the equipment linkage control collaboration of the intelligent Internet of Things can be improved.

The invention provides a local cache resource optimization method and system combined with a node state, and relates to the technical field of cloud storage optimizing.The method comprises the steps that firstly, a state index set, including indexes such as resource use, security levels, survivability and transmission cost, of multiple nodes in a distributed network is obtained; calculating a target cache value of the current node to the target information resource according to the target cache value, generating a local cache decision parameter after correction of a dynamic adjustment factor, if the local cache decision parameter exceeds a node cache trigger threshold value, storing the target information resource into a local cache of the current node and generating a cache resource distribution update instruction, and updating the cache resource distribution update instruction. And adjusting the adjacent node cache synchronization strategy according to the cache resource distribution updating instruction, thereby realizing the cooperative operation of the cache resources, and improving the resource utilization rate and access efficiency of the distributed network.

The invention discloses a hierarchical traffic prediction-based multi-terminal video display adaptive method, which comprises the following steps of: acquiring network traffic, user behaviors and equipment state information in real time by constructing a multi-dimensional feature acquisition module, and generating a dynamic feature matrix containing traffic, interaction and performance features; processing the feature matrix by using a deep learning model, and generating and dynamically adjusting a network traffic grading prediction result; formulating an optimized video content distribution strategy based on a prediction result and equipment performance parameters, and coordinating multi-terminal resource distribution; through user behavior analysis and network state monitoring, a self-adaptive video display control strategy is generated, dynamic adaptation of video resolution, frame rate and playing logic is realized, a full-link collaborative optimization module is constructed, terminal, network and application layer resource allocation is coordinated, a video transmission and display strategy is optimized, and network flow prediction and multi-terminal interaction requirements are combined to realize multi-terminal interaction. And generating a dynamic video layout and content switching strategy.

The invention relates to the technical field of construction project management, in particular to a construction project management system based on cloud computing, which comprises a construction path optimization module, a construction state analysis module, a construction environment monitoring module, a progress plan adjustment module and a green construction optimization module. According to the method, through combination of construction task resource distribution and three-dimensional space information, resource path distribution logic is optimized, material consumption, equipment operation states and key process deviation data are utilized, accurate positioning and prediction of construction dynamic problems are realized, construction interruption is reduced, and equipment and personnel distribution characteristics are extracted based on field image data, so that construction efficiency is improved. The equipment arrangement deviation degree and the potential safety hazard superposition value are quantified, the safety and efficiency of a construction site are improved, and the utilization rate of green construction resources is improved by dynamically adjusting the process time and the resource utilization rate, optimizing the relation between task nodes and factors, improving the flexibility and the response capability of a construction plan and conducting partition modeling energy consumption data and multi-constraint optimization.

The invention relates to a GPU resource space-time slicing and dynamic reconstruction virtualization method and system based on AI training, and the method comprises the steps: receiving an AI training task, obtaining the task feature data of the AI training task, and transmitting the task feature data to a task prediction model; the task prediction model outputs a training demand portrait of the current AI training task based on the task feature data, and the training demand portrait comprises an estimated computing power demand, a peak video memory demand, an expected running time length and a task priority; based on the training demand portrait, a preset AI scheduler generates a vGPU slicing strategy corresponding to the AI training task according to the current GPU resource distribution condition; operation information of each vGPU is obtained in real time and updated to the GPU resource pool, and the computing power or the video memory size of the vGPU in operation is adjusted in real time according to the training demand portrait. The method has the advantages that the dynamic allocation capacity of the GPU load resources is improved, and therefore the task training efficiency of the GPU is improved.

The invention relates to a method for setting power loss of a mountain photovoltaic array. The method comprises the following steps of: establishing a coupling causal spectrum of multi-source factors including terrain, angle, sunlight, temperature, dust and wiring mode to component output; identifying a dominant loss path based on an identification method of a Bayesian network; outputting a structure weight matrix of the power loss, and identifying an inclination angle gt; a temperature gt; a set of influence factors of the wiring topology pair setting; calculating a power loss response index of each component based on the loss path weight; forming a response index thermodynamic diagram by using a space weight interpolation algorithm, and performing array space sensitive partitioning; the high response area is used as a preferential setting target, and the low response area enters an observation maintenance mechanism; by constructing the response index and the correction response potential function based on multi-factor coupling, the power response potential of the component to the setting operation can be accurately identified, the problems of average adjustment and low-efficiency resource allocation in a traditional setting method are effectively avoided, and it is ensured that setting resources are intensively input into a high-response area.

The invention belongs to the technical field of agricultural Internet of Things, and discloses an Internet of Things-based intelligent agricultural cloud platform data acquisition and management system, which comprises the following steps of: acquiring plant rhizosphere microenvironment information, generating rhizosphere original data and performing correlation analysis to obtain rhizosphere electrochemical interaction data; collecting canopy microclimate information, and performing temperature and humidity gradient quantification processing and pore dynamic response characteristic extraction to obtain pore response characteristic data; collecting field illumination information, and carrying out multispectral distribution analysis and photoperiod sensitivity differentiation analysis to obtain a crop photoperiod sensitivity spectrum; performing multi-dimensional association fusion to generate crop physiological status comprehensive evaluation data; agricultural measure analysis and resource allocation optimization are carried out, and finally a resource allocation instruction is generated and agricultural operation scheduling is carried out; according to the invention, intelligent acquisition, analysis and management of data in each link of agricultural production are realized, and the utilization efficiency of agricultural resources and the production accuracy are improved.