501 results about "Network deployment" patented technology

The invention relates to the field of artificial intelligence hardware acceleration, and discloses a memristor-based multilevel compression collaborative optimization neural network deployment method and device, and a storage medium. The method comprises the following steps: carrying out progressive structured pruning on a pre-training model based on a dual-drive scoring mechanism of an L1 norm and gradient sensitivity and hardware feedback, and generating a hardware-friendly sparse weight structure; the characteristics of the memristor are simulated through a micro-nonlinear conductance modeling function, and network weight and conductance parameters are synchronously optimized to reduce errors by adopting mixed precision quantification of four bits of a convolutional layer and two bits of a full-connection layer; a conductance drift and read-write noise model is injected, and the anti-interference capability of the model is improved in combination with adaptive noise enhancement and KL divergence loss; and mapping the optimized model to a memristor memory architecture to complete weight coding and reasoning. Through collaborative optimization of pruning, quantification and distillation, the problems of insufficient storage density, non-ideal characteristic interference and algorithm and hardware mismatch in memristor deployment are solved.

The invention provides a multilevel dynamic authorization and access control method and system based on an identity token, and relates to the technical field of network security, and the method comprises the steps: generating a user main token of a binding terminal, constructing a resource access domain knowledge graph, carrying out the feature coding, predicting an access intention based on knowledge enhancement features and user historical behaviors, and achieving the dynamic grouping of resources. A permission certificate is generated through homomorphic encryption, verifiability is ensured through zero-knowledge proof, a verification program is deployed in a distributed network, and collaborative detection and certificate revocation of abnormal access are achieved. According to the invention, the security and flexibility of access control are improved, and the dynamic adaptive capacity of authority management is enhanced.

The invention discloses a cross-platform distributed synchronous simulation method adopting RoCEv2, and relates to the technical field of communication simulation, and the method comprises the following steps: configuring parameter information of a switch and nodes, and constructing a heterogeneous distributed network of a RoCEv2 enhanced network protocol; deploying a master node global logic time reference, calibrating a slave node clock, starting a sliding window to continuously monitor offset, dynamically detecting clock offset to trigger adjustment, optimizing a time step length in combination with an ECN mark, and dynamically compensating the clock offset of the heterogeneous platform; sequencing events according to logic timestamps to generate DAG topology, and preloading high-frequency events; event data are transmitted through cross-node RDMA, and the main node is sorted and scheduled according to a logic timestamp, so that cross-platform distributed parallel simulation and consistency verification are realized. The method can improve the processing efficiency and fault-tolerant capability of the communication simulation system on large-scale simulation, and can be widely applied to the technical field of communication simulation.

The invention relates to the technical field of mining area pollution monitoring, and discloses an atmospheric pollution monitoring system based on a mining area and a monitoring method thereof.The atmospheric pollution monitoring system based on the mining area comprises the steps that firstly, geographic environment data, outdoor environment data and well area environment data are obtained through a network deployment module and stored; the real-time transmission module is used for preprocessing the acquired data and transmitting the preprocessed data to the comprehensive analysis module, and the comprehensive analysis module is used for analyzing the atmospheric pollution index, judging whether to send out pollution early warning or not according to the calculation result of the atmospheric pollution index, and sending out a pollution alarm signal under the condition that the pollution early warning needs to be sent out; the mining area air pollution monitoring system can comprehensively monitor the mining area air pollution in a multi-dimensional manner, accurately identify the main pollution source and the dynamic change of the main pollution source, break through the limitation of traditional fixed site monitoring, flexibly capture the spatial and temporal change of the mining area pollution, accurately identify the pollution source, improve the monitoring accuracy and improve the monitoring efficiency. And an alarm signal is sent timely.

The invention relates to the technical field of coal mining equipment, and discloses a mining roadway stability real-time monitoring and early warning system which comprises the following steps: S1, multi-source sensor network deployment, S2, data transmission and preprocessing, S3, stability index dynamic calculation, S4, fusion early warning model construction and S5, graded early warning triggering. Roadway surrounding rock deformation, stress, vibration and environmental parameters are collected in real time through a multi-source sensing network, efficient data transmission and preprocessing are achieved in combination with an industrial looped network, surrounding rock strain energy density, displacement convergence rate, support failure coefficient and other key indexes are dynamically calculated, a fusion early warning model is constructed based on machine learning, and the early warning accuracy is improved. According to the method, accurate evaluation of the stability level is achieved, grading alarm and emergency control are automatically triggered when a threshold value is reached, a'perception-analysis-decision-response 'closed loop is formed through the design, the real-time performance of roadway stability monitoring and the early warning reliability are remarkably improved, and accidents such as roof fall and wall caving are effectively prevented.

The invention provides a coal seam surrounding rock stability monitoring method based on micro-seismic monitoring, which relates to the technical field of coal seam surrounding rock stability monitoring and comprises four steps of micro-seismic sensor network arrangement, real-time data acquisition and processing, surrounding rock stability evaluation model construction and real-time early warning and feedback of coal seam surrounding rock stability. According to the invention, a roof-side wall-support body cooperative monitoring architecture is adopted to cooperate with the micro-seismic sensor, and three-dimensional sensing network deployment is established, so that the micro-fracture activity in the surrounding rock of the whole stope can be continuously monitored in real time in a three-dimensional space full-coverage manner, the limitation of traditional point-type monitoring is overcome, and the dynamic evolution law of the micro-seismic activity is analyzed, so that the micro-fracture activity in the surrounding rock of the whole stope can be monitored in a real-time manner. According to the method, before macroscopic physical damage occurs, an area with abnormal stress concentration and accelerated damage accumulation can be identified, advanced early warning is realized, and precious time is gained for active prevention and control.

The invention relates to the technical field of production management in the injection molding industry, and discloses a method and system for automatically generating a production scheduling plan based on production in the injection molding industry, and the method comprises the steps: obtaining a production order, an equipment state and material inventory data, generating original production parameter data, and deploying a distributed production scheduling network; collecting and analyzing multi-stage equipment load data and equipment state data, and generating corresponding influence factors; order priority data are collected, and production task areas are divided; and optimizing and calculating a production scheduling plan based on the task area data, integrating the multi-factor data to generate comprehensive production scheduling data, and further generating and outputting a production scheduling instruction. The system comprises a production scheduling network deployment module, an equipment load analysis module, an equipment state analysis module, a production task area analysis module and a production scheduling instruction generation module. Through multi-data integration and scientific modeling, the intelligent precision of injection molding production scheduling is realized, the equipment management efficiency, the order delivery capability and the overall production efficiency are improved, and the system is suitable for automatic production scheduling management in the injection molding industry.

The invention relates to the technical field of cloud computing, in particular to a cross-domain network node synchronization method and system for deploying a cloud platform. The method comprises the following steps: acquiring a cross-domain network deployment requirement of a target cloud platform and determining a node distribution strategy; designing a network topology structure supporting cross-domain communication and data synchronization; a distributed storage system supporting cross-domain data consistency is selected; realizing a cross-domain network node synchronization mechanism which comprises a heartbeat detection mechanism, an arbitration mechanism, a data synchronization mechanism and a configuration and state synchronization mechanism; and performing cross-domain network optimization and node fault switching and recovery according to a synchronization mechanism. Through a deployment strategy of multiple data centers, resource redundancy and a fault switching mechanism are realized, and the risk of a single point of fault is reduced; a network topology structure supporting cross-domain communication and data synchronization is designed, and the requirement of cross-domain virtual resource scheduling is met; and a distributed storage system supporting cross-domain data consistency is selected, so that the reliability and availability of the data are ensured.

The invention discloses a personalized federal incremental learning method based on instance-level prompt generation, which realizes finer-grained feature capture of data by deploying a dynamic adjustable prompt generation module for a basic network of each client. The core of the module is to mine hidden attributes in data, and the attributes not only can significantly improve the generalization ability of the model for new classes, but also can realize effective migration of base class knowledge to the new classes. The hidden attribute features learned from the base class can be naturally generalized into unseen classes, so that the adaptability of the model to unknown distribution is enhanced while historical knowledge is kept. According to the method, the bottleneck of the static prompt pool in the aspects of expandability and adaptability is broken through, and a more flexible solution is provided for personalized federal continuous learning.

The invention relates to an unmanned aerial vehicle cluster autonomous crossing method and system. The method comprises the following steps: expanding channel capacity, carrying out time delay analysis, and introducing a non-orthogonal multiple access technology to carry out interference management and resource allocation so as to complete network deployment; establishing a linear state space model of a multi-unmanned aerial vehicle system for the unmanned aerial vehicle cluster, setting a cooperative control strategy and an obstacle avoidance strategy, and performing model performance analysis; acquiring environment information through each sensor arranged on each unmanned aerial vehicle in the unmanned aerial vehicle cluster to obtain an environment state; an artificial potential field method is adopted to complete a dynamic obstacle avoidance algorithm according to environment state design, and the path of the unmanned aerial vehicle is adjusted in real time according to the dynamic obstacle avoidance algorithm; establishing an unmanned aerial vehicle kinematic model, designing an optimization algorithm of path planning based on the unmanned aerial vehicle kinematic model, and performing real-time dynamic path adjustment according to the optimization algorithm; setting a security and fault-tolerant mechanism; and autonomous crossing of the unmanned aerial vehicle cluster is realized. And the high-efficiency collaboration and stability of the unmanned aerial vehicle cluster are improved.

The invention provides a network congestion control method and system based on multi-agent reinforcement learning, computer equipment and a storage medium, and relates to the technical field of network congestion control, a communication network comprising a plurality of communication nodes is constructed, the communication nodes are mapped into independent agents, and the independent agents are used for controlling the network congestion. Designing a weighted composite reward function containing indexes such as throughput and delay by taking a flow-level index as a local state and a data sending rate adjustment coefficient as an action; the intelligent agent selects action execution according to a current state based on a strategy network, calculates an environment reward based on the current state, the action, a next state after the action is executed and a weighted composite reward function, and stores the environment reward in a shared experience playback pool; training a value network and a strategy network by using the data in the pool; the trained strategy network is deployed at each node, distributed congestion control is realized, the stability of a congestion control decision in a complex network environment is improved, and bandwidth resources are distributed fairly while the throughput is ensured.

Beam failure recovery and conditional handover may utilize deployment information regarding cells / beams. A wireless transmit / receive unit (WTRU) may receive a beam recovery configuration. The beam recovery configuration may include an indication of candidate recovery beams, network deployment information, information regarding areas of coverage of the candidate recovery beams, at least one quality criterion, random access channel (RACH) parameters, or any appropriate combination thereof. The WTRU may detect a beam failure. The WTRU may select a recovery beam from the candidate recovery beams based on the beam failure and the beam recovery configuration.

The invention relates to the technical field of airport safety monitoring, in particular to an airport runway intrusion identification method based on an ESNB algorithm, and the method comprises the steps: constructing an airport runway intrusion data set; the method comprises the following steps: constructing an improved Inception U-Net network, and obtaining a teacher network based on the improved Inception U-Net network; based on the teacher network, obtaining an optimal student network through a multi-objective evolutionary algorithm; based on the teacher network and the airport runway intrusion data set, performing knowledge distillation on the optimal student network to obtain an ESNB network; according to the scheme, the ESNB network is deployed in an airport and is used for airport runway intrusion recognition, and the problems that existing airport runway intrusion recognition is low in efficiency and high in computing power requirement can be effectively solved.

The invention discloses a disaster area robot operation path and energy consumption optimization method and system, and the method comprises the steps: constructing a multi-factor coupled unit path energy consumption prediction model, fusing the terrain, real-time wind resistance and battery attenuation factors of a disaster area, and generating a precise energy consumption map; a hierarchical reinforcement learning path scheduling framework is established, an upper-layer strategy network generates a global path by taking energy consumption and time optimization as double targets, a lower-layer execution network processes sensor data in real time based on MPC, and path stability and low energy consumption are considered during obstacle avoidance; designing task plug-in mechanism optimization scheduling triggered by an event; and compressing an upper-layer strategy network by adopting knowledge distillation, and deploying to a robot edge end. According to the method, the endurance and task completion rate are improved through the precise energy consumption model, global-local control disjunction is cracked through the layered architecture, the high-real-time requirement is met through the plug-in mechanism, the edge deployment adapts to the weak network / broken network environment, and the method is suitable for high-real-time scenes such as post-disaster inspection and material transportation.

The invention provides a flexible interference network deployment method and system based on carbon fiber conductive cloth. The method comprises the following steps: acquiring space field intensity data in a non-uniform electromagnetic field environment, and performing physical field correction on the space field intensity data to generate compensation field intensity data; a metal screen cloth structure is formed on the surface of the carbon fiber, and porosity distribution parameters are obtained through gradient difference generation treatment; forming a metal mesh cloth layer with gradient pore size distribution, and performing deformation adaptation on the pore size distribution in combination with real-time bending curvature data to generate dynamic and stable porosity distribution; based on electromagnetic field intensity associated region division on the metal mesh layer, dynamically adjusting the grounding resistance value of each divided region, and generating a resistance regulation and control parameter; physical connection between the metal screen cloth layer and the grounding end is realized, and electromagnetic shielding effectiveness parameters are output. According to the invention, adaptive shielding effectiveness optimization of the flexible interference network in a complex electromagnetic environment is realized.

The invention relates to the field of low-altitude communication network planning methods, in particular to a 5G three-dimensional coverage and capacity planning design method for low-altitude economy, which comprises the following steps: performing airspace range limitation and application scene division on a target airspace, establishing a mobile model of a low-altitude terminal, and providing dynamic input for simulation; the method comprises the following steps: establishing a dual-frequency heterogeneous network, and determining base station addresses covering targets at different heights and meeting different signal station spacing; performing three-dimensional coverage and quality simulation on the established dual-frequency heterogeneous network, performing network KPI evaluation on the simulation until the standard is reached, and outputting a network deployment planning scheme; deploying according to a network deployment planning scheme, predicting low-altitude service flow, and adjusting a beam direction and a PRB reservation ratio to realize on-demand allocation of resources; and carrying out actual measurement on the deployed network, collecting an air measurement index, comparing a comparison deviation between the air measurement index and a simulation result, and carrying out fine adjustment on the network according to the comparison deviation. According to the invention, diversified communication requirements are met, and the success rate of terminal access is improved.

The invention discloses a dynamic monitoring, regulating and controlling method and system for sealing and storage of a water depth of a mine, and relates to the technical field of sealing and storage monitoring of mine water. The method comprises the following steps: monitoring network deployment: installing related sensors at key positions to form a three-dimensional monitoring network; acquisition and transmission: acquiring original data of sensors in the three-dimensional monitoring network, and performing data preprocessing; aI model construction and training: collecting historical data to construct a training set, and constructing an LSTM neural network prediction model; dynamic risk assessment: constructing a risk assessment model based on an XGBoost algorithm, and calculating a sequestration layer risk index in real time through the risk assessment model; and intelligent regulation and control: generating a regulation and control strategy according to the output of the LSTM neural network prediction model. According to the method and the system, monitoring of the sealing and storage area of the water depth of the mine can be accurately realized in real time, and early warning of risks can be realized.

The invention discloses a photovoltaic-energy storage-charging multi-stage scheduling and market bidding optimization method and a photovoltaic-energy storage-charging multi-stage scheduling and market bidding optimization device. The method comprises the following steps: constructing a data-driven random environment model reflecting photovoltaic output, electricity price fluctuation and charging load uncertainty by adopting a mode of combining time sequence clustering and a non-homogeneous Markov chain based on historical operation data; modeling a scheduling and bidding problem of the optical storage and charging integrated station into a multi-stage Markov decision process model which comprises day-ahead decision and joint optimization of multiple intra-day rolling adjustment; a deep reinforcement learning algorithm is utilized to train the network, and a strategy regulation and control network which can adapt to various uncertain scenes and meet equipment physical constraints is obtained; and deploying the trained strategy regulation and control network in an energy management system to realize global coordinated scheduling and bidding of the optical storage and charging integrated station. According to the method, the economic benefit is remarkably improved, the robustness is greatly enhanced, the decision is globally coordinated and optimized, the real-time decision capability is strong, and the expandability and portability are good.

The invention discloses a micro-grid parallel-separation operation switching method, which relates to the technical field of micro-grids and comprises the following steps: S1, collecting real-time state data of a micro-grid, calculating system instability and generating a stability state label; s2, generating an ultra-short-term prediction result, and calculating an energy buffer potential well parameter set and a virtual inertia coefficient in combination with a stability label; s3, triggering a mode switching instruction based on the stability state label, executing the energy buffer potential well parameter set and the virtual inertia coefficient to control the output of the energy storage system, and completing grid-connected and off-grid mode switching; s4, the harmonic content of the power grid is collected, and a protection constant value and reactive power output are dynamically adjusted according to the instability degree of the system; and S5, constructing a system support framework by constructing a hierarchical communication network, deploying a redundancy mechanism and adopting a staged implementation strategy. And through real-time stability evaluation, virtual inertia adjustment and ultra-short-term prediction optimization, the stability, the electric energy quality and the adaptive capacity in the grid-connected and off-grid switching process of the micro-grid are improved, and high reliability and flexibility of the system are ensured.

The invention relates to the technical field of artificial intelligence and robot control, in particular to a reinforcement learning control method and system based on physical space feedback, and the method comprises the steps: training an initial strategy through domain randomization in a simulation environment; fusing multi-modal sensor data in a real environment, and constructing an environment state; deploying the strategy network after the strategy network parameters are optimized into a real environment, combining a model base and model-free reinforcement learning, utilizing simulation data and real data to jointly optimize the strategy network, and compensating a dynamical model error through online fine adjustment; and correcting the action instruction in real time based on the security constraint. The system comprises a sensor module, a strategy network module, a security constraint module and a simulation-real migration module. According to the method, the dynamical model error is compensated through online fine adjustment; through a mixed model base and a model-free reinforcement learning architecture, and in combination with multi-modal sensor data and a security constraint mechanism, high-sample-efficiency and high-security physical system control is realized.

A method comprises receiving a request to predict a deployment configuration for at least one application, analyzing code of the at least one application to identify one or more additional applications on which the at least one application will depend, identifying a plurality of network paths between the at least one application and the one or more additional applications, and using one or more machine learning algorithms to predict execution times for the at least one application over the plurality of network paths. The predicted execution times for the at least one application over the plurality of network paths are inputted to a network graph model. The network graph model predicts the deployment configuration for the at least one application based at least in part on the predicted execution times, wherein the deployment configuration comprises a subset of the plurality of network paths.

The invention belongs to the technical field of bridge construction, and particularly relates to a suspension bridge prefabricated cable strand method construction traction system control method based on reinforcement learning. The method comprises the specific steps that modeling is conducted on the PPWS method cable strand traction process through Simulink; a reinforcement learning environment is built, a state space and an action space are defined, the state space comprises the position, the speed and the acceleration of a puller, and the action space comprises PID controller parameters; a DDPG reinforcement learning model is adopted for training; the trained DDPG strategy network is deployed in hardware control, dynamic optimization of PID parameters is achieved according to state data collected by a sensor, and intelligent control over cable strand traction is achieved. According to the method, the multi-target reinforcement learning framework including the track smoothness reward, the speed stability reward and the speed sudden change punishment is constructed, PID control parameters are dynamically optimized in combination with the DDPG algorithm, accurate regulation and control of the operation speed of the puller are achieved, the control precision and the construction efficiency of the main cable traction process are remarkably improved, and the human intervention requirement is reduced.

The invention discloses a cluster collection communication system applied to AI data stream processing, which relates to the technical field of distributed systems and network engineering and comprises a topology network deployment module, a data stream spatio-temporal data acquisition module, an optimization verification module, a state monitoring and early warning module and a visual traceability module. The topology network deployment module synchronously processes AI data streams, the data stream spatio-temporal data acquisition module extracts an entropy value and a covariance matrix, the optimization verification module outputs optimization parameters and a credible verification result, the state monitoring early warning module sets an adaptive threshold value to realize graded early warning, and the visual traceability module generates a three-dimensional thermodynamic diagram and a traceability graph. According to the method, the data stream processing efficiency is improved through the star-ring hybrid topology and the low-delay protocol, the decision credibility is enhanced in combination with quantum optimization and block chain verification, accurate early warning and three-dimensional visual traceability acceleration fault positioning are realized by using dynamic weighted monitoring and an adaptive threshold, and an efficient and intelligent full-stack solution is provided for AI trunking communication.

The invention discloses a dam safety detection method and system based on multi-source detection data, and relates to the technical field of dam safety detection, and the method comprises the steps: cooperatively collecting dam multi-source data through multiple platforms and multiple sensors; the collected multi-source data are preprocessed; constructing a multi-scale teacher network, and performing high-precision feature learning and risk quantification by using labeled multi-source data; teacher network knowledge migration is carried out through a knowledge distillation technology, and a lightweight student network is trained in combination with cross-domain pseudo data; and deploying the trained lightweight student network to an unmanned aerial vehicle or a robot dog, and carrying out dam safety real-time detection. Through integration of multi-source data acquisition, cross-domain mutual training of teachers and students, lightweight model deployment and automatic early warning decision, pain points of single data, difficulty in cross-domain adaptation, insufficient precision, deployment limitation and low efficiency are solved, the system can be directly deployed on an unmanned aerial vehicle or a robot dog, dependence on cloud computing power is not needed, data acquisition and analysis time delay is reduced, and the system can be widely applied to unmanned aerial vehicles or robot dogs. And emergency scenes are quickly responded.

The invention discloses an adaptive controller tuning method based on reinforcement learning. The method comprises the following steps: S1, constructing a photovoltaic control task environment; s2, inputting the state sequence into a long short-term memory network, and outputting an embedded state vector; s3, constructing a hierarchical policy network comprising a basic policy module and a fast adaptation module; s4, training the hierarchical strategy network, inputting the embedded state vector into the hierarchical strategy network to generate a controller parameter action, interacting with a photovoltaic control task environment by using a multi-layer feedforward neural network to obtain response and reward feedback, and executing target value calculation and strategy optimization; s5, obtaining a general strategy initial parameter by using a meta-learning optimization method; and S6, deploying the optimized hierarchical strategy network to a target photovoltaic control task. The method is suitable for photovoltaic control and other multi-working-condition dynamic environments, and has the advantages of being high in strategy migration capacity, high in robustness and excellent in self-adaptive performance.

A topology-reprogrammable test environment is provided that can support the needs of CI / CD / CV in the field. The system disclosed provides a highly scalable network architecture to simplify the implementation of network slicing, TaaS and network CI / CD, and solves problems related to the complexity of cloud-native network (CNN) deployments. A Network Cell (NC), comprises or consists of a Containerized Network Function (CNF), a Containerized Digital Twin (CDT), and a Containerized Test Agent (CTA). The CDT has at least two personalities, e.g., an emulator of the CNF in the same NC or a nodal of the CNF. The choice of personality of the CDT is controlled by the CTA of the NC. A number of NCs use a 3D IP address to interconnect and form a new kind of CNN over the infrastructure of VRs.

The invention discloses an intention-driven network configuration template generation method, which is characterized in that automatic translation from a user intention to a network configuration instruction is realized by constructing a comprehensive model fusing a graph neural network and a natural language processing model, and specifically comprises the following steps: S1, constructing a network configuration instruction graph; s2, preparing a data set; s3, finely adjusting the BERT model; s4, instruction embedding generation; s5, intention embedding generation; s6, calculating matching similarity; according to the method, knowledge contained in a network configuration document is mined, an instruction graph capable of representing the relation between network configuration instructions is constructed, an incidence relation data set of a private network service deployment intention and the network configuration instructions is prepared, a graph neural network and a natural language processing pre-training model are fused, and through contrast learning training tuning, the network configuration instructions are optimized. The translation from the private network deployment intention to the network configuration instruction sequence is realized, that is, the network configuration template is automatically generated, and the pressure of a service expert on reasoning a configuration scheme in a new scene is relieved.

The invention discloses an unmanned aerial vehicle cluster collaborative target tracking strategy obtaining and online updating method and device, and the method comprises the steps: deploying a strategy network Actor network and an evaluation network Critic network to each unmanned aerial vehicle flight control system, and creating a mirror image agent for each unmanned aerial vehicle; observation information of each unmanned aerial vehicle is obtained in real time and input to a strategy Actor network and a mirror image Actor network of each unmanned aerial vehicle, and a corresponding flight strategy is generated. After the unmanned aerial vehicle is driven to execute a flight action according to the flight strategy, a first accumulated reward value of the unmanned aerial vehicle is obtained, and after the mirror image agent executes a virtual flight action according to the mirror image flight strategy, a reward value is estimated and a second accumulated reward value is obtained. And after the unmanned aerial vehicle is driven to execute a preset number of flight strategies each time, updating the parameters of the strategy network Actor network, the evaluation network Critic network and the mirror image network of the unmanned aerial vehicle. According to the invention, the accuracy of the tracking strategy can be improved.

The invention discloses a boiler furnace three-dimensional combustion temperature field reconstruction method and system, and the method comprises the steps: S1, multi-mode sensing network deployment, S2, time-space synchronization data collection, S3, sound wave flying time extraction and acoustic modeling, S4, flame image processing and radiation temperature conversion, S5, three-dimensional temperature initial field generation, and S6, multi-mode joint objective function construction. S7, carrying out regularization constraint iteration solution; and S8, carrying out three-dimensional temperature field visualization output. According to the method, high penetrability and sensitivity of sound waves to medium temperature and rich radiation information contained in flame images are fully utilized, and the precision and spatial resolution of temperature field reconstruction are remarkably improved by establishing a joint objective function and performing collaborative optimization. Meanwhile, a regularization method and an adaptive filtering technology are introduced, interference caused by measurement noise and uncertain problems is effectively suppressed, and the stability and robustness of the reconstruction process are enhanced.

The invention provides an air-ground wireless network deployment method, device and equipment, a medium and a program product, and relates to the technical field of wireless communication. The method comprises the following steps: determining the number of air base stations in the air-to-ground wireless network and the positions of a plurality of ground user terminals required to be covered by the air-to-ground wireless network; based on the number of the base stations and the position of each ground user terminal, performing clustering processing on the plurality of ground user terminals to obtain a plurality of clusters; respectively determining the initial position of each air base station based on the central position of the position area represented by each cluster; and based on the initial position of each air base station, solving the optimization problem to obtain the deployment position of each air base station. According to the method, the influence of burst flow caused by dynamic change of user positions in a disaster scene on the network and the condition of dynamic change of interference are fully considered, and the ground user terminals are clustered by adopting clustering so as to reasonably deploy the positions of air base stations, so that the coverage integrity of the air-ground wireless network is improved.