88 results about "Optimal deployment" patented technology

The invention discloses a data management method based on a distributed storage system, relates to the technical field of data management, and is used for solving the problem of storage management behavior strategy mismatch. On the basis of dynamic perception of data access behaviors in a distributed storage system, a behavior feature vector fusing an access mutation rate, periodicity and an access span is constructed, behavior pattern recognition and strategy structure generation are completed, optimal deployment and hierarchical storage of copies are achieved through node resource state perception and construction of a cost function, and the strategy structure generation efficiency is improved. And after compression and consistency configuration are executed and the strategy falls to the ground, the compressibility and the offset trend of the behavior path are analyzed, scheduling management information is extracted, and a stable execution or strategy adjustment signal is generated, so that the problem of strategy execution mismatching caused by behavior perception deficiency in the distributed storage system is reduced, and the strategy execution efficiency is improved. The strategy closed-loop control and the resource scheduling optimization under behavior driving are realized, so that the data management efficiency and the strategy adaptation capability of the distributed storage system are improved.

The invention discloses a mobile energy storage pre-layout and dynamic scheduling system and method oriented to toughness improvement of a power distribution network, and belongs to the technical field of power systems and intelligent power grids. In the pre-disaster stage, a two-stage robust optimization model fusing photovoltaic output uncertainty is constructed, and the configuration cost of mobile energy storage, the load reduction risk and the traffic accessibility of key nodes are comprehensively considered to generate an optimal deployment scheme; in a post-disaster stage, based on a traffic network state and a multi-source output characteristic, a multi-source collaborative dynamic scheduling model is constructed, a migration path, a charging and discharging strategy and charge state control of mobile energy storage are optimized in real time, and power supply loss of a key load is minimized. The system realizes rapid power supply recovery and safe operation in the island power grid by cooperatively controlling distributed resources such as mobile energy storage, electric vehicles, photovoltaic and diesel generators and the like. According to the invention, the quick response capability and recovery efficiency of the power distribution network under extreme disasters are significantly improved.

The invention discloses a distributed service deployment method and a distributed service deployment device based on Kubernetes. According to the method, resource object definition and service metadata are extracted by automatically analyzing a configuration file, and accurate acquisition and standardized processing of deployment information are realized; optimal deployment nodes are dynamically matched based on service characteristics and cluster real-time states, and resource scheduling rationality and service operation efficiency are improved; then automatically creating and starting a service instance at a target node according to an analysis result, and ensuring configuration consistency and standardization of a deployment process; and the service flow is controllably migrated to the new instance through the predefined version switching rule, so that smooth transition and risk controllability of version upgrading are realized. According to the method, the efficiency, the reliability and the version management capability of micro-service deployment are remarkably improved through full-process automatic closed loop from configuration analysis, intelligent scheduling to instance creation and flow switching.

The invention relates to the technical field of small target detection and identification in images in the power industry, and provides an energy storage inverter optimization deployment defense method based on a double-layer Stackelberg game and related equipment. The method comprises the following steps: constructing a LinDistFlow model based on load power disturbance and reactive power disturbance of each node attacked by LAA; maximizing the voltage deviation of all attacked nodes in the whole attack time period as a first objective function, and combining a first constraint condition to construct a dynamic model of the LAA attack; taking minimization of the operation cost of the energy storage inverter and the voltage deviation of the attacked node as a second objective function, and combining with a second constraint condition to construct a dynamic model of LAA defense; introducing confidence parameters based on load power disturbance and reactive power disturbance of each node, and constructing an optimization model based on opportunity constraint; and if the defender knows / knows the injection power of the attacker, solving the model, and making a defense strategy in advance by using the data of the attacker.

The invention discloses a model deployment method and device combined with hardware deployment, and the method comprises the steps: constructing a hardware topological graph corresponding to equipment nodes according to the equipment nodes in a target model deployment environment and a connection relation corresponding to each equipment node; analyzing a dependency relationship of a target operator in the target model to generate a model calculation graph corresponding to the target operator; calculating a target matching value between the equipment node and the target operator by using a preset evaluation algorithm according to the hardware topological graph and the model calculation graph; and determining a deployment scheme corresponding to the target model according to the target matching value and a preset grading strategy. By sensing a dynamic matching mechanism of a hardware state and a model calculation graph in real time, an optimal deployment strategy is automatically generated, and adaptive optimization is triggered when hardware topology or resource conditions change, so that real-time collaborative adaptation of a model segmentation strategy and a heterogeneous environment is realized, and the deployment automation level and the system elasticity are remarkably improved.

The invention belongs to the cross technical field of crowd sensing, air monitoring and artificial intelligence, discloses an intelligent air monitoring node deployment method based on crowd sensing, and aims to solve the problems of insufficient node deployment density, response lag, monitoring blind areas and the like in an existing air quality monitoring system. Firstly, crowd sensing information related to air pollution is extracted from multi-source data such as social media and a public reporting platform, pollution events and spatial positions of the pollution events are recognized through natural language processing and image recognition technologies, and a dynamic pollution sensing thermodynamic diagram is constructed. On the basis, a deep reinforcement learning algorithm framework is designed, factors such as the perception coverage rate, the deployment cost and the communication connectivity are comprehensively considered, and an optimal deployment scheme of the air monitoring nodes is learned and output. The intelligent deployment method designed by the invention has self-adaptive capability, and dynamically adjusts the deployment strategy of the spatial quality monitoring nodes according to the crowd sensing data, thereby improving the response capability of the system to sudden pollution events.

The invention relates to an aggregation scene unmanned aerial vehicle cluster three-dimensional deployment method and device based on cyclic segmentation and multi-objective optimization. The method comprises the following steps: designing a cyclic segmentation optimization framework and a self-adaptive switching mechanism oriented to an aggregation scene; performing cyclic segmentation optimization on the unmanned aerial vehicle cluster based on a cyclic segmentation optimization framework and an adaptive switching mechanism, and updating a Pareto file; designing a comprehensive fitness function, screening out elite parent individuals based on the Pareto file, and performing cyclic segmentation optimization on the unmanned aerial vehicle cluster by using the elite parent individuals; and performing three-dimensional deployment of the unmanned aerial vehicle cluster in the aggregation scene according to a cyclic segmentation optimization result. According to the method, through multi-objective optimization based on Pareto and an innovative segmented optimization framework, and through dynamic switching of level and height optimization stages, astringency and global diversity of understanding are remarkably improved, optimal deployment of a multi-unmanned aerial vehicle communication system can be realized in a complex scene, and theoretical and method support is provided for practical engineering application.

A data processing system includes a processor and a memory for the processor. The memory stores executable instructions that, when executed by the processor alone or in combination with other processors, cause the data processing system to perform functions of: receive a deployment request to deploy a software change; determine factors corresponding to the deployment request that impact an optimal deployment policy for the software change; query an Artificial Intelligence (AI) trained with a dataset of optimized deployment policies based on corresponding sets of the factors, the query requesting an optimized deployment policy for the software change of the received deployment request based on the determined factors and including a ring rollout policy, ring bake time and deployment time; execute the deployment request using the optimized deployment policy returned by the AI; and update training of the AI based on the determined factors and results of the optimized deployment policy.

The invention discloses an optimal deployment method for wireless sensor network nodes based on a three-dimensional space, and the method comprises the steps: obtaining a to-be-deployed three-dimensional space model of the wireless sensor network nodes, and segmenting the to-be-deployed three-dimensional space through a KD tree; randomly throwing the movable wireless sensor network nodes to the to-be-deployed three-dimensional space, and obtaining the initial position of each wireless sensor network node; position deployment of wireless sensor network nodes is planned by adopting an ant lion optimization algorithm, a differential variation disturbance strategy is introduced, and the global search capability of the algorithm is improved; the RRT algorithm is used for performing path search, and meanwhile, a cost function is introduced for optimization, so that node movement collision is avoided. The problem of deployment of the wireless sensor network nodes in the three-dimensional space is solved by constructing the three-dimensional environment model, optimizing the particle swarm optimization algorithm, adopting path planning and the like, the search efficiency and convergence precision of the algorithm are improved, collision and interference between the nodes and obstacles and between the nodes are reduced, and the deployment efficiency of the wireless sensor network nodes is improved. The stability, the energy efficiency and the communication quality of the wireless sensor network can be effectively improved.

The invention provides a hybrid cloud resource intelligent scheduling method and device and electronic equipment, and the method comprises the steps: obtaining the price of cloud service of each cloud resource service provider and the performance data of the cloud service, inputting the obtained cloud service state data into a preset dynamic score calculation model, and calculating the performance of the cloud service; and determining a resource score of each cloud service and generating a resource score list. And then based on the resource score list, constructing a deployment path directed graph, and according to an optimal deployment path in the deployment path directed graph, migrating data and / or services meeting an automatic migration condition to a target cloud service according to the optimal deployment path. By selecting the embodiment of the invention, the deployment path directed graph can be automatically and quickly constructed according to the quoted price of the cloud resource service provider and the performance of the provided cloud resource, and the data and / or service are / is migrated based on the optimal deployment path given in the deployment path directed graph, so that the additional consumption of the cloud resource caused by manual scheduling is reduced.

The invention provides a UWB anchor point deployment method and system based on multi-objective optimization and dynamic hotspot prediction. The method comprises the following steps: initially installing a UWB positioning system; constructing a dynamic hot area prediction model; predicting a candidate area of the dynamic hot area; constructing a multi-target performance function, and evaluating the candidate region of the dynamic hot region; performing anchor point position search by adopting a hybrid optimization algorithm; performing feedback adjustment based on a state monitoring result; outputting a result; according to the system, a multi-source data acquisition module is used for acquiring multi-source target data; the dynamic hot area prediction module is used for predicting a dynamic hot area candidate area; the multi-target performance evaluation module is used for screening high-value candidate regions; the optimal deployment module is used for searching an optimal UWB anchor point deployment position; the state feedback module is used for feedback adjustment; and the result output module is used for outputting the optimal UWB anchor point deployment scheme. According to the method and the system, the optimal UWB anchor point deployment scheme can be efficiently and accurately obtained, and the positioning accuracy under the underground complex working condition can be ensured.

The invention relates to the technical field of cloud computing, and discloses a multi-cloud certificate deployment intelligent arrangement engine system, which comprises a unified abstraction layer used for providing a standardized certificate deployment interface; the intelligent arrangement engine is used for analyzing a deployment task dependency relationship and generating an optimal deployment path; the distributed transaction management module is used for coordinating deployment transactions of the cross-cloud platform and ensuring the consistency of operation; and the strategy optimization and learning module is used for optimizing the deployment strategy based on the historical deployment data. According to the method, the cloud platform difference is shielded through the unified abstraction layer, the consistent deployment experience is provided, and the adaptation workload in the multi-cloud environment can be remarkably reduced; the deployment path is optimized through the intelligent arrangement engine, so that the deployment time can be remarkably shortened; through distributed transaction management, transaction consistency of cross-cloud deployment is ensured, and strong fault recovery capability is provided. Through multi-objective optimization and online learning, the system can automatically balance the cost, the performance and the reliability, so that the resource utilization rate is greatly improved.

The invention relates to the field of intelligent reflecting surface deployment position optimization, in particular to an intelligent reflecting surface deployment position optimization system. According to the scheme, a channel estimator cooperates with a transmitter, a receiver and an intelligent reflecting surface to carry out wireless channel estimation, then obtained wireless channel information is sent to a deployment optimizer, and after the deployment optimizer receives an indication signal sent by a coordinator and the wireless channel information sent by the channel estimator, the wireless channel information is sent to the coordinator. The optimal deployment position of the intelligent reflecting surface is obtained according to a deployment optimization algorithm in a single-receiver scene, then the optimal deployment position of the intelligent reflecting surface is sent to a position controller corresponding to the intelligent reflecting surface, and after the position controller receives the optimal deployment position of the corresponding intelligent reflecting surface, a control signal of a driver is generated, and the driver is driven to control the intelligent reflecting surface according to the control signal. And the driver is controlled to move the intelligent reflecting surface to a corresponding optimal deployment position. The method is suitable for optimizing the deployment position of the intelligent reflecting surface.

The invention provides a fault repair and task unloading optimization method under a space-air-ground fusion vehicle-mounted network framework, and belongs to the technical field of Internet of Vehicles. According to the first stage, the optimal deployment point location of the unmanned aerial vehicle is searched globally based on the wolf pack algorithm, the flight path is planned dynamically in combination with the near-end strategy optimization algorithm, and network connectivity repair of a fault area is achieved; and in the second stage, a task unloading decision initial population is generated through multiple deep reinforcement learning agents, and a Pareto optimal solution set is obtained through optimization of a non-dominated sorting genetic algorithm II, so that multi-target balance of energy consumption, cost and time delay is realized. According to the method, the fault self-healing capability and task processing efficiency of the SAGVN can be remarkably improved, and the method adapts to the high-dynamic and high-reliability requirements of intelligent traffic.

The invention discloses a card type fast application deployment method, system and device based on a multi-kernel fusion system, and the method comprises the steps: decomposing a fast application into a plurality of card modules with independent functions, determining the dependency relationship between the card modules, and then determining a deployment sequence; configuring and determining a self-adaptive interface and a constraint value of each card module; and determining the adaptation degree of the kernel and each card module according to various indexes, further determining the minimum deployment condition value of each card module and the kernel, performing deployment, and managing the running state of the card module according to the multi-dimensional context data. According to the method, the fast application is decomposed into a plurality of card modules with independent functions, the optimal deployment combination for deploying the card modules on the kernel is determined by comprehensively considering multiple indexes, efficient operation of each kernel and the application module is ensured, and the method can be widely applied to the technical field of fast application deployment.

A method for developing a platform-independent cloud application with a flexible deployment model and performing automated deployment on a target cloud platform is fulfilled in the ongoing description by (i) obtaining user-written application source code targeted to a Logical Application Model, (ii) obtaining from the user an Application Manifest that is agnostic to specific deployment models and target clouds, (iii) combining the user-written application source code with system-generated bindings and a main file to obtain a final application source code, (iv) dynamically determining based on heuristics or user-provided preferences, the optimal deployment model for each specific component of the cloud application, (v) transforming the Application Manifest into a deployment configuration file based on the optimal deployment model and the target cloud platform, (vi) automatically generating IaC scripts based on the deployment configuration file, and (vii) automatically deploying executable components of the cloud application on the target cloud platform.

The invention discloses an intelligent metasurface auxiliary directional charger deployment method based on a heterogeneous graph neural network and deep reinforcement learning, and belongs to the field of wireless energy supplementation of the Internet of Things. The method aims at solving the problems that at present, related work is mostly one-time static geometric deployment, the coverage range is limited, flexibility is insufficient, the optimization granularity is coarse, high-quality charging full coverage is difficult to achieve under the cost constraint, and the overall charging effectiveness of a network is difficult to maximize. According to the method, a heterogeneous network graph fusing the relation of sensor nodes, directional chargers and intelligent super surfaces (RIS) is constructed, a heterogeneous graph neural network is deployed in a base station in a centralized mode to extract the network structure and energy state characteristics, and a near-end strategy optimization algorithm is further combined. And joint optimization of a directional charger deployment position and a charging direction as well as an RIS position and reflection configuration is realized. According to the method, the charging coverage rate, the charging utility and the deployment cost are taken as a comprehensive target, the optimal deployment strategy can be intelligently generated, the charging coverage is expanded, the charging utility is improved and the node failure rate is reduced on the premise of controllable cost, so that the purpose of prolonging the service life of the network is achieved.

The invention discloses a Kubernetes micro-service optimal deployment method and device based on award accumulation deep reinforcement learning and a medium, and relates to the field of electric digital data processing, the deployment method comprises the following steps: S1, constructing a multi-target micro-service deployment optimization problem; s2, constructing a micro-service resource preference function and combining with an optimization target; s3, constructing a reinforcement learning model; s4, constructing a reward function, and training the model; and S5, obtaining an optimal deployment strategy on line according to a real state through the trained model. According to the method, a multi-target micro-service deployment optimization problem is constructed, two targets of minimum service response delay and resource load balancing are considered, and a Kubernetes deployment demand is effectively converted into a computable and solvable optimal problem; meanwhile, a micro-service resource preference function is designed and combined with an optimization target, the calculation complexity during model training is reduced, the real-time performance of Kubernetes deployment is guaranteed, and therefore the development requirement of a micro-service architecture is met.

The invention provides an intelligent metasurface optimal position deployment method and system in an electric power scene, and the method is characterized in that the method comprises the following steps: constructing a single-input single-output system model for the inspection of a multi-intelligent metasurface (RIS)-assisted unmanned aerial vehicle, the system comprises a base station, an unmanned aerial vehicle and a plurality of RISs, the RIS is deployed in a candidate site set # imgabs0 # between a base station and an inspection area, the height # imgabs1 # azimuth angle theta i and the number Ti of daughter boards of each RIS are adjustable, and the inspection area is divided into N grids; establishing a channel model, and calculating average power gains of direct connection and cascade channels; an optimization problem with the goal of minimizing the total deployment cost is constructed, the total deployment cost comprises RIS site fixed cost and daughter board hardware cost, and the constraint condition is that the system coverage rate is not lower than a preset threshold value eta min; and converting the optimization problem into an integer linear programming problem, introducing a binary variable to represent RIS deployment configuration, and solving through a branch definition algorithm to obtain the optimal RIS position, height, azimuth angle and daughter board number.

The invention relates to a bridge structure lightweight health monitoring method and system based on cloud side end collaborative optimization, and the method comprises the following steps: S1, arranging a monitoring device and an edge calculation unit at a bridge end, and building a cloud calculation unit at a cloud end; s2, when a task request is received, evaluating processing time delay and energy consumption, and calculating the processing cost of a task k; and S3, defining a selection variable for cloud edge calculation, and scheduling. According to the cloud-side collaborative task scheduling mechanism designed by the invention, the tasks can be reasonably distributed to cloud computing or edge computing according to the processing delay and energy consumption conditions of the computing tasks, so that the system performance and energy consumption are balanced. Based on the cloud edge cooperation mechanism, an optimal deployment strategy of the edge gateways in the system is provided, a linear mixed integer programming formula with the operation cost and the deployment cost as the minimization target is modeled, the optimal deployment number of the edge gateways in the resource-constrained environment is solved, and the deployment efficiency of the edge gateways in the resource-constrained environment is improved. Therefore, the comprehensive optimization of the SHM system among the cost, the energy consumption and the working efficiency is realized.

The invention discloses an enterprise resource supervision system and method based on a cloud platform, and relates to the technical field of enterprise informatization management and cloud computing. Comprising a cloud platform deployment module, a resource collection module, a security management module and a resource supervision module. By constructing a 9-dimensional capability index space, the mainstream cloud platform capability is quantitatively evaluated, and optimal deployment type selection is realized based on the Euclidean distance; the acquisition module supports multi-source data to be accessed and pushed to the processing channel; the security module realizes identity authentication and authority control; the supervision module performs verification around data integrity, time consistency, primary key uniqueness and semantic consistency, and constructs a data quality score and label system; according to the method, abnormal data are automatically classified and processed, the operations including field supplementation, time restoration, major key conflict analysis, semantic standardization and the like are included, an interactive supplementary recording process is started for serious abnormal data, authentication is carried out again, closed-loop supervision of high-credibility data is finally achieved, and the intelligence and compliance level of enterprise resource management is improved.

The invention relates to the field of network security, and particularly provides a security cooperation system and method in a hybrid cloud environment. The system comprises a unified security management module, a security solution cooperation module and a security function module. The unified security management module analyzes a self-healing algorithm through a policy graph, carries out graph modeling on a multi-cloud security policy, automatically identifies policy conflicts and drifts through graph difference analysis, and realizes continuous consistency and self-healing of a cross-cloud policy. And the security solution cooperation module is embedded into a reinforcement learning agent, dynamically decides an optimal deployment scheme of security resources according to a reward function by sensing a threat situation, a resource utilization rate and response delay, and realizes intelligent self-adaptive arrangement of protection resources. The security function module is used for micro-servitizing and containerizing security capability based on a cloud native technology, transparent injection is realized through a service grid, an automatic compliance repair and verification mechanism is built in, and a compliance closed loop for detection, repair and verification is formed. And the management efficiency and the protection accuracy of the security policy are improved.

The invention discloses an equipment deployment method based on precoding cross entropy optimization in an Internet of Things system, which comprises the following steps of: (1) defining a physical boundary and an initial parameter of an equipment deployment area, and initializing an equipment deployment space coordinate; (2) discretizing a continuous space into binary codes through a space pre-coding module and a position coding mapper, and compressing search dimensions; (3) generating candidate deployment position samples according to the probability distribution; (4) calculating the system performance of the candidate position by using a complex black box system and an evaluation function; (5) updating probability distribution based on an elite sample, and accelerating convergence to an optimal solution; and (6) judging an optimal deployment position. Compared with a traditional gradient optimization and linear enumeration method, the closed-loop global optimization of the equipment deployment position is realized through space coordinate discretization coding, probability-driven candidate sample generation, system evaluation and a cross entropy probability iteration updating mechanism; and the global optimality guarantee and the calculation efficiency are synchronously improved.

The invention discloses a machine room resource capacity intelligent planning system and method based on multi-source data fusion, and belongs to the technical field of data center management, and the system comprises a multi-source data collection module, a resource manifold modeling module, a geodesic optimization module, a self-adaptive planning module and a verification and inspection module. The method comprises the following steps: mapping multi-dimensional resource parameters such as space, power, heat dissipation and load bearing of a machine room into a Riemannian manifold mathematical model, and constructing a resource measurement tensor representing a resource distribution density and a constraint relationship; calculating a resource allocation optimal path on the resource manifold, constructing a multi-objective function including space utilization rate, energy efficiency, heat dissipation efficiency and cost effectiveness, and generating an optimal deployment scheme of the machine room equipment; in combination with topological characteristic analysis of resource manifolds, potential resource bottlenecks are predicted, and a resource allocation scheme is dynamically adjusted; and the security and compliance of the evaluation scheme are verified through digital twinborn simulation, so that the problem of unbalanced resource allocation caused by independent planning of each system in traditional machine room planning is solved.

The invention discloses a low-voltage power line carrier communication network repeater deployment method and system, and relates to the technical field of power communication. The method comprises the following steps: (1) according to PLC network topology information, acquiring an STA set which cannot directly communicate with a CCO, and inputting model parameters; (2) calling a general optimization solver to carry out model solution to obtain a solution result; (3) if the feasible solution exists, outputting a PCO deployment mark; if not, adjusting the received signal threshold value and the transmitting power, and repeating the step (2); and (5) once a feasible solution is obtained or the model parameter reaches the set limit value, the solution is ended. According to the deployment planning model constructed by the invention, the optimal deployment position of the PCO in the network is solved by taking the minimum weighted sum of the total transmission time delay and the total cost as a target and taking the received signal strength and the single-hop relay as constraints; the problem that seamless coverage of a low-voltage power distribution network is difficult to realize by single-hop communication due to high background noise and frequency selective fading of a PLC channel is solved.

The invention provides a low-altitude unmanned system air node optimal deployment method based on communication-link-energy consumption joint optimization. The method comprises the following steps: initializing an unmanned aerial vehicle group; the system constructs a performance model according to the real-time channel information, constructs a multi-objective collaborative optimization problem, and solves the multi-objective optimization problem by adopting an MOPSO-HGD algorithm to obtain an optimal result; deploying the unmanned aerial vehicle group to the optimal position according to the optimal result to form a virtual distributed MIMO array structure; setting an unmanned aerial vehicle electric quantity threshold value, judging the size of each unmanned aerial vehicle remaining electric quantity and the unmanned aerial vehicle electric quantity threshold value, and if the unmanned aerial vehicle remaining electric quantity is smaller than the unmanned aerial vehicle electric quantity threshold value, recycling the unmanned aerial vehicle; otherwise, re-determining the position of the unmanned aerial vehicle group; through a closed-loop control mechanism of channel perception-multi-target optimization-dynamic adjustment, the unmanned aerial vehicle relay system can still keep a high signal-to-noise ratio and strong robustness in a complex low-altitude environment, is suitable for scenes such as low-altitude broadband communication, emergency rescue and urban information networks, and has a good engineering application prospect.

The present invention belongs to the field of cross-technology of crowd perception, air monitoring and artificial intelligence, and discloses a method for intelligent deployment of air monitoring nodes based on crowd perception. In response to the problems of insufficient node deployment density, delayed response and monitoring blind spots in existing air quality monitoring systems, crowd perception information related to air pollution is first extracted from multi-source data such as social media and public reporting platforms, and pollution events and their spatial locations are identified through natural language processing and image recognition technology to construct a dynamic pollution perception heat map. On this basis, a deep reinforcement learning algorithm framework is designed to comprehensively consider factors such as perception coverage, deployment cost and communication connectivity, and learn and output the optimal deployment plan for air monitoring nodes. The intelligent deployment method designed by the present invention has adaptive capabilities, and dynamically adjusts the deployment strategy of spatial quality monitoring nodes according to crowd perception data, thereby improving the system's response capability to sudden pollution events.

The invention relates to an intelligent networking planning system based on multi-objective dynamic optimization, which comprises a data input module, a data preprocessing module, a system modeling module, a multi-objective optimization module and a scheme output module, and is characterized in that the data input module is used for receiving geographic information, environmental conditions, equipment models and performance parameters input by a user; the data preprocessing module is used for carrying out standardization processing on the input geographic information and equipment parameters; the system modeling module is used for establishing a terrain and terrain model; the multi-target optimization module is used for carrying out multi-target dynamic optimization and generating an optimal deployment scheme; and the scheme output module is used for generating an equipment deployment scheme report. According to the invention, complementary coverage between devices is realized, the system cost is reduced, and the deployment efficiency is improved. The system has good expansibility and adaptability, can be flexibly adjusted according to different application scenes and requirements, improves the overall performance and adaptability of a low-altitude security system, and meets diversified low-altitude security requirements.

The invention discloses a model pruning method for a heterogeneous cloud edge-end cooperative system, and belongs to the technical field of model pruning of machine learning. The problem that in the prior art, a traditional pruning method based on a deep learning model is low in resource utilization rate is solved. The method comprises the following steps: S1, initializing a to-be-called model, analyzing the structure of the to-be-called model to form a structure set, distributing the structure set to a computing node, and generating an overhead predictor; s2, according to a pruning shape control strategy, calling an overhead predictor, and using a pruning evaluation function to obtain a used pruning evaluation function; and S3, in the pruned model set of different segmentation points, according to a time overhead evaluation function and a network condition, screening to obtain an optimal deployment model. According to the method, the operation efficiency of the pruned model on different hardware is effectively improved, the reasoning efficiency and the resource utilization rate can be controlled, different task requirements are met through dynamic weight adjustment, and the method can be applied to the pruning model.

Embodiments of the present application disclose a non-real-time resource scheduling method and device for heterogeneous computing resources. The method comprises: receiving a to-be-processed task and an optimized performance index sent by a request end; determining target computing resources corresponding to each subtask in the current available computing resources; constructing all deployment schemes according to the correspondence between each subtask and each target computing resource; determining the value of each performance index corresponding to each deployment scheme, and determining the optimal deployment scheme in each deployment scheme and returning to the request end. By applying the scheme provided by the embodiments of the present application, the characteristics of the tasks in the automatic driving platform can be targeted, that is, the tasks on the automatic driving platform have less variability, and periodic tasks are usually deployed, and the scheduling time has no effect on the execution efficiency. When the to-be-processed task is received, all possible deployment schemes are generated, and the performance index corresponding to each deployment scheme is determined, so that the optimal deployment scheme can be selected according to the user's optimized performance index.