776 results about "Resource distribution" patented technology

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 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 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.

Systems and methods disclosed herein automatically detect, analyze, and mitigate anomalous resource distribution among artificial intelligence (AI)-based agents within a distributed computational network. The system receives a resource allocation request specifying computational resources, agent parameters, and performance objectives for a network of agents. A first AI model set monitors agent activity by tracking resource consumption and behavioral deviations from baseline profiles. The system compares resource usage of agents with historical norms and / or predetermined thresholds to generate an anomaly score for each agent. A second AI model set aggregates scores to construct a multi-dimensional data structure that indicates the comparison and anomaly score. The system ranks agents by anomaly severity to isolate agents with high scores (e.g., misaligned agents), and reallocates resources and updates access privileges for the misaligned agents.

The invention relates to the technical field of audio control, and discloses a low-delay audio input switching method and system, a storage medium and equipment, and the method comprises the steps: carrying out the parallel pre-initialization of a plurality of pieces of audio input equipment, and building an independent parallel audio data cache for each piece of equipment; monitoring the state of each audio input device and the audio stream quality in real time, and judging whether switching is triggered or not based on a multi-factor decision model; after the switching decision is triggered, seamless audio data stream switching is executed, and format unification processing and cross fade-in and fade-out transition are included; a unified equipment operation interface is provided through the hardware abstraction layer, and system resources are optimized and managed; a delay sensing closed-loop control mechanism is constructed, processing delay of each link is monitored in real time, a caching strategy, a processing algorithm and resource allocation parameters are dynamically adjusted, self-adaptive balance of low delay and high tone quality is achieved, and through the method, quick and smooth switching of audio input equipment is achieved, delay is remarkably reduced, and real-time audio experience is improved.

The invention relates to the technical field of industrial data, discloses a mining equipment state monitoring method and system based on the Internet of Things, and effectively solves the problem of insufficient model generalization ability caused by scarcity of fault samples of mining equipment. The virtual sample generation technology expands the available training data volume by 3-5 times, so that the early fault detection rate is improved to 85% or above. The self-adaptive feature selection mechanism reduces the consumption of computing resources by more than 30%, and maintains the integrity of key fault features at the same time. The multi-stage early warning system realizes accurate grading of fault severity, so that the maintenance resource distribution efficiency is improved by about 40%. The closed-loop optimization mechanism enables the model to continuously evolve in the operation process, and the annual false alarm rate is reduced by about 15%. The explainable diagnosis report provides a clear technical basis for field maintenance, and the average troubleshooting time is shortened by about 50%.

The invention relates to an inter-basin water saving amount transaction and scheduling optimization method, which comprises the following steps of: performing dynamic contrast analysis on water saving behaviors and water resource response on the basis of historical water consumption behaviors, resource regulation and control records and meteorological basic data, judging the autonomy and continuity of the water saving behaviors, and obtaining the right-confirmable water saving amount; according to the watershed water resource tension degree, the water ecology sensitivity and the water transfer cost, a cross-watershed conversion rule is adopted, a conversion scale factor is set, and resource value transverse butt joint of water saving behaviors of different areas is achieved; calculating the flow attenuation, the regional bearing threshold and the water quality compatibility in the allocation path based on the water-saving amount obtained by the water-saving transaction, and determining the available transmission range and the sustainable allocation amount of the water-saving amount; and according to the distribution of the water-saving quotas and the corresponding value grades, carrying out combined screening according to the use priority, the basin compensation relationship and the space-time adaptation requirements, outputting a resource allocation scheme, and forming a cross-basin water-saving-quantity-driven water resource optimal allocation structure.

The invention provides an electric power system cross-source safety intelligent monitoring and dynamic response method and system based on mimicry defense, and relates to the technical field of electric power system network safety. According to the method, multi-source data are acquired by deploying an acquisition agent and a sensor and are encrypted and transmitted, parallel analysis and threat judgment are realized by utilizing a heterogeneous executor pool and dynamic scheduling, an attack chain is identified by combining cross-domain association modeling, disposal is performed according to grades, credible recovery is supported, and reinforcement learning and federated learning are assisted to optimize a strategy and resource allocation. And lightweight adaptation is realized through intensity control in a resource limited scene, a closed-loop safety system covering data acquisition, threat analysis, dynamic disposal and continuous evolution is formed, and the detection accuracy, response efficiency and robustness of a power system are improved.

The invention relates to the technical field of industrial automatic monitoring, and provides a mining equipment remote monitoring method and system and a storage medium. The method comprises the following steps: dividing mining equipment into core equipment and general equipment based on equipment operation parameters; performing fault risk assessment on the core equipment by adopting a dynamic threshold value adaptively generated based on historical data, and performing deviation detection on the general equipment by adopting a fixed threshold value; fusing the fault risk assessment result of the core equipment and the deviation detection result of the general equipment to generate global maintenance decision information; optimizing a field maintenance path according to the geographic position and the emergency degree of the maintenance task; and continuously calibrating system parameters through a closed-loop feedback mechanism. According to the invention, through hierarchical monitoring and adaptive optimization, the problems of uneven distribution of monitoring resources, rigid threshold setting and isolated maintenance decision in the prior art are solved, and accurate configuration of mining equipment monitoring resources and remarkable improvement of fault early warning capability are realized.

The invention discloses an Internet of Vehicles information age optimization method and system based on graph reinforcement learning, and the method comprises the steps: firstly constructing a batch modeling and limited buffering queue structure of a vehicle state, carrying out the modeling of perception data into a multi-data-packet batch, and carrying out the queue management; modeling V2V link topology by using a graph neural network, and extracting large-scale channel embedding representation reflecting a topological structure; a multi-agent reinforcement learning system based on a centralized training distributed execution framework is constructed, each agent makes a decision according to a local state containing graph embedding features, a mixed action space is output, and an AoI opposite number of a receiving end is used as a reward; a graph embedding supervision mechanism based on a dominant function is introduced, so that topological features are aligned with a long-term optimization target; and network parameters are updated through multiple rounds of training, and finally, autonomous optimization control of each agent on packet loss and power is realized. According to the invention, data packet queue management and wireless resource allocation can be effectively coordinated, and efficient and low-overhead AoI minimization is realized in a complex dynamic topology environment.

The invention provides a geological environment monitoring method and system, and the method comprises the steps: constructing an air-space-earth-depth four-dimensional cooperative monitoring network, collecting multi-modal geological environment data, carrying out the preprocessing of the data, and obtaining multi-modal feature data and abnormal data points after dynamic load balance distribution of calculation resources; fusing the data to generate a comprehensive geological environment feature map containing space-time correlation features, abnormal hot spot distribution and a geologic body three-dimensional reconstruction model; and based on the map, performing geological risk prediction by using a geological risk prediction model to obtain a prediction probability. A monitoring network is constructed to integrate multi-modal geological data, resource allocation is optimized by combining edge calculation dynamic load balancing, geological risk prediction is realized by using a prediction model, the problems of single data, poor dynamic adaptability and weak model generalization ability of a traditional method are solved, the prediction precision is improved, the response time is shortened, and the prediction efficiency is improved. And a high-risk area is visually displayed through a three-dimensional risk thermodynamic diagram.

The invention discloses a layered micro-channel liquid cooling method and system based on a magnetic suction interface, and relates to the technical field of micro-channel liquid cooling structures. The layered micro-channel liquid cooling method and system based on the magnetic suction interface comprises the following steps: S1, acquiring and preprocessing temperature characteristic data and fluid flow velocity pressure data, and constructing a standardized thermal load trend data set; s2, the cooling liquid supply state of each layer is evaluated, and the liquid supply rhythm of the corresponding layer is dynamically adjusted; s3, analyzing the cooling demand intensity of each layer, and driving the working frequency of the branch pump of the corresponding layer to change; and S4, evaluating the coordination state of each layer in the process from liquid supply to heat dissipation to liquid return, and dynamically adjusting the liquid cooling control strategy. The problems that in a multi-layer server structure, an existing liquid cooling distribution path cannot achieve dynamic flow adjustment according to the thermal load difference of each layer of equipment, so that cooling resource distribution is uneven, and energy consumption waste is serious are solved.

The invention discloses an emergency command control method and system based on a Beidou emergency patrol car, and the method comprises the steps: obtaining various signal data from a complex environment through the deployment of a multi-source sensor network, carrying out the data fusion processing under the condition of communication signal interruption, and determining a preliminary position estimation result; according to the preliminary position estimation result, a positioning algorithm based on multi-source data correction is adopted to optimize and adjust the positioning precision, and high-precision positioning data is obtained; through high-precision positioning data, a live-action dynamic model framework is constructed, terrain and obstacle distribution characteristics are fused, related information of environment change perception is acquired, and three-dimensional scene description updated in real time is determined; and according to the three-dimensional scene description updated in real time, analyzing the demand of dynamic resource allocation, identifying key nodes and resource distribution states in multi-party cooperative command, and obtaining a priority sequence of resource allocation. According to the invention, full-process automation and intelligentization are realized, the manual intervention cost is reduced, and the emergency response speed is improved.

The invention discloses a PON-based multi-protocol communication method and device, and the method comprises the steps: distributing a corresponding internal scheduling time slot to each network protocol processing unit for data uploading to each network protocol processing unit when a data processing module detects that the network protocol processing units connected with each field bus device have to-be-uploaded service data; and when one internal scheduling time slot is used up, interrupting the uploading of the service data until the next internal scheduling time slot allocated to the internal scheduling time slot is recovered from interrupting the uploading of the data. And the PON module monitors a global scheduling time slot allocation instruction issued by the optical line terminal in real time, converts an uploaded data frame into an electric signal, transmits the electric signal to the optical module through an internal link, converts the electric signal into an optical signal through the optical module in an allocated global scheduling time slot, and uploads the optical signal to the optical line terminal through an optical distribution network. Real-time performance and orderliness of transmission of service data in multiple field bus devices in a PON network are achieved, the requirement of industrial communication for data transmission is met, and transmission resource distribution is optimized.

The invention discloses an online order full-process automatic processing system and method. The system comprises an order state analysis module, a cross-platform collaborative adaptation module, a resource scheduling planning module, an environment load evaluation module and a task scheduling optimization module. According to the invention, by analyzing the trend of the key time sequence change of the order life cycle, the circulation bottleneck can be identified and the time section exceeding the threshold value can be generated, so that the task sequence is matched with the system load, the scheduling coordination and the response precision are enhanced, the execution capability is dynamically compared to form the optimal distribution instruction, and the cooperation efficiency and the transmission continuity are improved. Based on a utilization rate change identification trend, resource distribution is balanced, scheduling flexibility and controllability are enhanced, resource and energy consumption cooperative adjustment is realized through environment load evaluation, operation stability and environment adaptability are improved, high efficiency and continuous optimization of an order flow are realized, and processing capability and operation toughness of a system under high concurrency are enhanced.

The invention relates to the technical field of power distribution automation operation and maintenance, in particular to a power distribution automation operation and maintenance method based on electric power big data. The method comprises the following steps: collecting and processing power distribution operation data, calling a fault feature rule set to analyze the power distribution operation data, and identifying and detecting a power distribution fault event; based on the electrical topological structure, the protection action information and the power distribution node on-off state of the current power distribution network, a fault feeder line and a fault section are identified, a switching value logic relation and a topological accessibility model are used for power distribution positioning of a fault event area, and a fault diagnosis result data set is output; and generating a fault isolation operation and maintenance strategy and an overhaul task priority list by using an operation and maintenance scheduling model based on the fault diagnosis result data set and the load transfer capacity, the field operation capacity and the historical overhaul resource distribution information of the power distribution station. According to the method, fault diagnosis and an intelligent scheduling mechanism are fused, and the fault response speed and the operation and maintenance cooperation efficiency of the power distribution system under complex working conditions are improved.

The invention relates to the field of heterogeneous data storage, in particular to a heterogeneous data storage method and system for a civil aviation data platform, and the method comprises the steps: collecting metadata of each subsystem in the heterogeneous data storage system in real time, inputting the metadata into a pre-constructed metadata unified model, and carrying out standardization processing to generate a global metadata view; establishing a data traceability tracking mechanism according to the global metadata view, and mapping dispersed metadata items and data entity operation records according to the data traceability tracking mechanism to generate a traceable metadata change track; and a metadata quality label is embedded in the metadata unified model, a metadata knowledge graph is constructed according to the metadata change track and the metadata quality label, and a resource allocation strategy of the heterogeneous data storage system is dynamically optimized according to an analysis result of the metadata knowledge graph. The problem of low storage management efficiency caused by difficulty in integrating and tracing metadata in a heterogeneous data storage system in civil aviation data is solved.

The invention discloses a machine learning-based flight wheel block removal time prediction method, and relates to the technical field of flight prediction, and the method comprises the steps: collecting flight preorder state data, airport resource distribution data and meteorological data in real time, and generating an original data set through multi-source heterogeneous data fusion; constructing spatio-temporal features including a preorder flight delay propagation chain, a stand-vehicle conflict energy matrix and a meteorological attenuation factor, and screening and optimizing a feature set through distribution drift detection; training and verifying the Bayesian depth quantile regression model, and outputting a prediction result with a confidence interval; and combining the airport Internet of Things positioning feedback optimization feature set and parameters to generate a prediction deviation diagnosis report. According to the method, a preorder flight delay propagation chain and a stand-vehicle conflict energy matrix are constructed, flight dynamics, resource allocation and weather attenuation factors are embedded into a unified spatial-temporal feature space, and the problem of feature information loss caused by data isolation is solved.

The invention relates to the technical field of wireless semantic communication, and discloses a semantic information transmission method, device and equipment and a storage medium, a sending end extracts features of to-be-transmitted data, and matches the features with vectors in a preset discrete vector codebook to obtain discrete feature vectors; and determining an orthogonal frequency division multiplexing time-frequency resource distribution grid according to the feature importance weights of the discrete feature vector and the initial feature vector, generating a sending signal, and transmitting the sending signal to a receiving end. And the receiving end inputs the semantic feature vector obtained after processing the received signal into the vector quantization variational self-decoder, and the semantic feature vector is matched with the vector in the discrete vector codebook again to correct the transmission error, thereby obtaining the reconstructed data to be transmitted. Discretization processing of the features is achieved through a discrete vector codebook, important semantic feature bits are quantized according to the importance weight of each feature and then distributed to orthogonal frequency division multiplexing for transmission, so that key semantic information is protected, and anti-interference transmission of the semantic information is achieved.

The invention discloses a short temporary rainfall forecasting method and system based on a dynamic neural network architecture. According to the method, training sample distribution is optimized through a space-time gradient driven resampling strategy, a time-frequency double-branch dynamic network architecture is adopted, a time domain branch extracts multi-scale space features through an encoder-decoder structure, a frequency domain branch adaptively activates an FFT calculation module through a content awareness dynamic network to capture multi-scale time features, and the multi-scale time features are obtained. And two key problems of meteorological data distribution imbalance and deep learning model optimization imbalance are solved in combination with a balance regression loss function. According to the method, adaptive computing resource allocation for meteorological events with different complexities is realized, the forecasting precision and computing efficiency of heavy rainfall events are remarkably improved, and an efficient and reliable technical solution is provided for short temporary rainfall business forecasting.

The invention relates to the technical field of distribution network management, in particular to a distribution network management electricity meter state analysis method and system. According to the technical scheme, the distribution network management electricity meter state analysis method comprises the following steps that S1, multiple layers of edge computing nodes are deployed in a power distribution network, first-level nodes are deployed in a power distribution station, second-level nodes are deployed in an electricity meter concentrator, and computing resource distribution of the edge nodes is dynamically adjusted based on electricity meter distribution density and real-time loads; s2, through a collaborative acquisition mechanism of the first-level node and the second-level node, acquiring operation data of the electric meter in real time; and S3, transmitting the classified and marked data by adopting a multi-modal compression strategy: compressing the periodic monitoring data by using difference value coding, and extracting the sudden abnormal data by using a feature abstract. By constructing a multi-level edge collaborative acquisition mechanism and a 5G network slice transmission channel, the real-time bottleneck of traditional manual inspection and fixed-period acquisition is broken through.

The invention discloses a cloud management and intelligent distribution method for classroom teaching resources, and relates to the technical field of cloud computing software. The classroom teaching resource cloud management and intelligent distribution method comprises the steps that S1, multi-source teaching resource data and auxiliary distribution data are acquired and preprocessed, and a knowledge content database is constructed; s2, performing semantic fusion analysis through text semantic association, structural consistency and professional term coverage data; s3, carrying out dependency evaluation by fusing pre-association, knowledge co-occurrence, sequence association and semantic fusion data; s4, performing resource adaptability distribution analysis through the maximum knowledge dependence value in combination with the learning state data and the historical feedback data; and S5, evaluating the teaching resource quality by collecting classroom feedback, teacher evaluation and resource use data, and implementing content optimization. The problems of semantic distortion, path breakage, distribution mismatching and resource updating inconsistency in the teaching resource data fusion and intelligent resource distribution process are solved.

The invention discloses a dynamic control method and system for a modularized energy-saving array of a server fan, relates to the technical field of server heat dissipation control, and aims to improve the efficiency of the modularized energy-saving array of the server fan through collaborative optimization of multi-dimensional data acquisition and federated learning in combination with a dynamic speed regulation and redundancy control mechanism. The method achieves the precise prediction of the load change, optimizes the distribution of heat dissipation resources, reduces the energy consumption, and improves the heat dissipation efficiency and equipment operation stability.

The invention provides a low-earth-orbit satellite global beam position distribution and scheduling system based on deep reinforcement learning, and the system comprises a low-earth-orbit satellite scene modeling beam position distribution module which is used for constructing a low-earth-orbit satellite communication scene and completing the global beam position division. A satellite constellation configuration and orbit parameters are set, global beam positions are divided based on a spherical network, a beam ground projection model and a user system model are established, and a scene basis and a beam position division basis are provided for beam resource scheduling; the deep reinforcement learning beam resource scheduling module is used for realizing intelligent allocation scheduling of beam resources, establishing a decision system based on a Markov decision process by constructing a beam resource allocation model, extracting features through a state space model, performing time sequence modeling, and optimizing a scheduling strategy based on deep reinforcement learning. And dynamically matching the user demand and the beam resource. Therefore, dynamic optimization of low-orbit satellite beam resources is realized, the resource utilization rate is improved, and the communication requirements of users are efficiently met.

The invention discloses a coordination control method, system and device for distributed resources of a power distribution network and a medium. The method comprises the steps of obtaining a topological structure, electrical characteristics and resource distribution conditions of the power distribution network; dividing the power distribution network into a plurality of control regions by adopting a self-adaptive partitioning algorithm to obtain a self-adaptive partitioning result; based on the self-adaptive partitioning result, a hierarchical control framework is constructed; different types of distributed resources in the power distribution network are coordinated and optimized through an intra-region resource coordination control algorithm and an inter-region coordination mechanism; and obtaining an optimal power distribution network distributed resource coordination control strategy through a distributed optimization algorithm and a self-adaptive control strategy. Effective control technical support is provided for friendly access of large-scale distributed resources to the power distribution network, and the method has important practical value for improving the bearing capacity of the power distribution network and optimizing the resource utilization efficiency.

The invention discloses a message-driven enterprise multi-service system centralized to-do processing method and system, and the method comprises the steps: S1, obtaining resource investment and task processing data, and obtaining a resource consumption and task state cluster through clustering; s2, using a regression algorithm to analyze correlation between the two, and determining a resource utilization rate change trend; s3, when the threshold value is exceeded, calculating a task completion rate statistical characteristic to obtain a resource optimization index; s4, constructing an interactive model by using a decision tree in combination with historical data, and judging an input strategy adjustment point; s5, rendering visual resource distribution; s6, abnormal tasks are screened, and an optimization area is determined; and S7, updating an instrument panel to obtain a decision view. Through data clustering, correlation analysis and dynamic adjustment, the resource and task matching degree is improved, and decision scientificity and efficiency are enhanced.

The invention relates to the technical field of intelligent management, in particular to an intelligent management system and a resource cooperative scheduling method for an entrepreneurship space, and the system comprises a dynamic resource topological graph module which is used for collecting resource distribution data in real time through a space IoT positioning system, and generating a dynamic resource topological graph; when the system is used, a resource distribution diagram is drawn in real time through a space IoT positioning system, three-dimensional coordinates and coupling weight are marked for each resource node, and the node state is dynamically updated, so that the coupling relationship between the space path cost and the resources is fully considered; according to the method, the situation of cross-region movement of members is greatly reduced, the resource cooperative utilization rate and the resource average turnover rate are improved, conflict propagation prediction and resource redistribution are carried out through establishment of a resource dependency relationship matrix and dynamic time window splitting operation, delay caused by conflicts is shortened, the resource vacancy rate is reduced, and the emergency response speed is increased.

The invention discloses an Internet of Things monitoring management method and system based on deep learning, and relates to the related technical field of Internet of Things monitoring management, and the method comprises the steps: reading node multi-source data through an intelligent agent, constructing a deep causal structure field through cross-modal deep causal reasoning, and revealing the relationship between environmental inducements, state paths and risk propagation; constructing a space-time causal tensor representing dynamic risk distribution of a monitoring area, quantitatively evaluating monitoring income, energy consumption cost and risk exposure, executing game analysis under a multi-party game strategy, and establishing an intelligent agent behavior strategy; and updating the monitoring network topology, executing deep space-time-cause and effect joint reasoning, generating an exception explanation result, and performing exception report management. According to the invention, the technical problems of lack of causal interpretability, resource allocation rigidness and insufficient abnormal response capability of Internet of Things monitoring in the prior art are solved, and the technical effects of accurate abnormal management of Internet of Things nodes and improvement of resource utilization efficiency and risk decision interpretability are achieved.

The invention discloses an intelligent scheduling control system for computing power and power load of a data center, and the system obtains key data of computing power and power in real time through a data collection module, thereby helping the system to master the operation state in time. The load evaluation module calculates a comprehensive load value based on the data, comprehensively considers calculation power and electric power dimensions, accurately reflects an actual load, clarifies calculation power demand and electric power supply balance, and avoids misjudgment of single-dimension evaluation; the comprehensive load value is compared with a preset threshold range through a scheduling strategy generation module, a strategy is generated in combination with service priority and computing power resource distribution, actual requirements are met, and the problems that in a traditional mode, consideration on the service priority is insufficient, and flexible scheduling cannot be achieved according to resource distribution are solved; computing power and power distribution are adjusted in real time through the scheduling control module according to a strategy, load changes and service requirements can be quickly responded, efficient operation of the data center is ensured, the resource utilization efficiency is improved, the operation cost is reduced, and the defect that traditional scheduling is difficult to adjust in real time is overcome.

The invention relates to the technical field of property management, in particular to a property inspection path planning method, which comprises the following steps of: constructing a task priority weight matrix, generating a priority updating signal by utilizing a dynamic environment sensing module, generating an optimal inspection path in combination with a time sequence analysis algorithm, and dynamically adjusting the path through real-time data feedback. The method can comprehensively consider the multi-target priority, the dynamic environment change and the resource distribution, improves the inspection efficiency and flexibility, reduces the dependence on high-precision data, is suitable for complex scenes, and meets the intelligent and precise inspection requirements of modern property.