1373 results about "Hotspot (Wi-Fi)" patented technology

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

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

The invention particularly relates to a unit test case rapid generation method based on an AI large model. According to the unit test case rapid generation method based on the AI large model, structure information is extracted through a static AST, an execution path is tracked through dynamic instrumentation, and code analysis is achieved; an analysis result is converted into cue words through AI, a multi-scene use case is generated through reasoning, and the multi-scene use case is matched with a language framework; counting row / branch / path coverage, identifying gaps and optimizing loop logic; hot spots and paths are displayed through a visual report, IDE one-key repair and manual correction of a feedback model are supported, intelligent retrieval and reuse of historical cases are realized by relying on FAISS, and the generation accuracy is continuously improved. According to the unit test case rapid generation method based on the AI large model, high-coverage-rate test case generation is achieved through the AI large model, the test case generation efficiency is remarkably improved, the test period is shortened, the software quality is improved, the workload of manually compiling and maintaining the test case is reduced, and the development cost is reduced.

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

The invention discloses an intelligent self-monitoring temperature management system for a box-type substation, and relates to the technical field of intelligent power grid equipment monitoring. The problems that an existing system is large in temperature measurement deviation, low in reliability, delayed in early warning, inaccurate in hot spot positioning and extensive in heat dissipation control are solved. According to the scheme, multi-source signals are acquired in parallel through a data acquisition module, and a temperature time sequence is extracted; a boundary calibration module is adopted to fuse data to generate a three-dimensional boundary condition; the multi-physical field solving module obtains an internal temperature / stress field; the physical information prediction module is fused with a heat transfer physical constraint training graph neural network to predict a hotspot migration trend; the hierarchical scheduling module is used for solving a fan and oil pump collaborative optimization instruction in real time based on model predictive control; according to the invention, the accuracy of internal temperature monitoring of the box transformer substation, the reliability of hot spot prediction and the accuracy of heat dissipation control are remarkably improved, the insulation life of equipment is effectively prolonged, and the operation safety and reliability of the system are improved.

The invention relates to the technical field of electric vehicle charging management, and discloses an electric vehicle charging optimization management system. The system comprises a dynamic charging demand prediction module which constructs a model based on historical data, collects voltage fluctuation, current phase deviation and charging power pulse characteristics of a charging pile in real time, and outputs a theoretical charging load value; the multi-dimensional demand difference analysis module is used for performing three-dimensional comparison of time accumulation deviation, frequency response difference and pulse characteristic similarity on theoretical and actually measured load values to generate a charging pile group level difference characteristic tensor; the hot spot region association positioning module inputs the difference feature tensor into a spatial topology mapping network, generates a fault probability thermal distribution diagram in combination with power grid node capacity and charging station position information, and positions a charging bottleneck region; and the self-adaptive regulation and control strategy generation module is used for starting millisecond-level monitoring on a high fault probability region according to the thermal distribution diagram and applying a power disturbance test on adjacent nodes. The system can improve the accuracy and effectiveness of charging management.

The invention relates to the technical field of heat management of energy storage systems, and discloses a heat management method of an intelligent distributed liquid cooling energy storage system. The method comprises the following steps: acquiring operation state parameters of a plurality of single batteries and optimizing timestamp synchronization; and processing the temperature data by adopting small-batch time series decomposition, and extracting change trend characteristics and abnormal hot spot positions. And based on the information, optimizing a heat exchange control rule base parameter set through a non-dominated sorting strategy, and generating optimized control parameters and priority rules. And in combination with inlet temperature and flow parameters of the liquid cooling system, a tabu search mechanism is utilized to decide a cooling liquid flow distribution scheme, and a branch control instruction is output. And finally, a flow constraint boundary is dynamically adjusted by adopting an approximate gradient projection technology, and a branch and bound strategy is fused to discretize and optimize a distribution path, so that the branch flow is finely regulated and controlled, and the thermal management precision and reliability of the energy storage system are improved. According to the method, the accuracy, adaptability and stability of thermal management of the liquid cooling energy storage system are improved.

The invention discloses a mobile communication load collaborative scheduling system and method based on energy efficiency perception, and belongs to the technical field of mobile communication, energy consumption data of each functional module in a base station and external network environment parameters are collected in real time through a multi-source sensor network in the base station, and a dynamic energy efficiency map is generated; based on historical energy efficiency data and real-time load characteristics, constructing an energy efficiency construction prediction model, and predicting an energy efficiency hotspot area; based on the historical movement track of the user and the spatial and temporal distribution characteristics of the service flow, the movement trend of the user and the change trend of the service flow are analyzed, and in combination with an energy efficiency prediction model, a potential overload base station is identified, and overload early warning is triggered; after receiving the early warning information, screening candidate target base stations according to a preset radius by taking the overload base station as a core; and generating a load idle list in a descending order according to the real-time energy efficiency ratio of the candidate base station, and performing energy efficiency sensitivity priority grading on the to-be-migrated service, thereby generating a collaborative scheduling strategy and executing load migration.

The invention provides an unmanned aerial vehicle group wide area suppression method and system based on multi-band frequency hopping interference. The method comprises the following steps: analyzing a frequency hopping rule and an encryption feature of a communication link of a target unmanned aerial vehicle group, and constructing a protocol feature library containing a multi-band frequency hopping sequence; deploying a directional focusing antenna array, and generating a directional interference energy signal covering a multi-band frequency hopping channel of the hotspot area of the unmanned aerial vehicle group; based on the encryption features and the frequency hopping sequence in the protocol feature library, performing parameter setting and subcarrier modulation adaptation on a preset interference signal, generating an adjustable interference signal, and converting the adjustable interference signal into an interference injection signal; extracting a time distribution rule of the frequency hopping sequence, dynamically and cooperatively adjusting an injection frequency band of interference energy and a signal, and generating a frequency band coordination strategy; and performing multi-band dynamic distribution on the interference energy and the signal to generate a wide-area suppression signal. According to the technical scheme provided by the invention, the blocking efficiency of unmanned aerial vehicle group cooperative control in a complex electromagnetic environment is improved.

The invention discloses a hotspot acquisition equipment adaptive control system based on multi-modal data fusion, and relates to the technical field of intelligent sensing and adaptive control, the hotspot acquisition equipment adaptive control system comprises a heterogeneous sensor array and a space-time alignment engine, each sensor clock is synchronized through an event trigger mechanism, and asynchronous data is processed by adopting B-spline interpolation; the feature pyramid fusion network extracts multi-scale features, and key information is enhanced through a channel and a space attention mechanism; the self-adaptive hot spot tracking module predicts a trajectory by adopting volume Kalman filtering; the dynamic resource allocation module is used for optimizing equipment scheduling according to a hotspot priority evaluation result; the multi-agent cooperative control layer is used for realizing autonomous negotiation among equipment through reinforcement learning; according to the edge-cloud cooperative computing architecture, computing resources are dynamically allocated according to task complexity. According to the method, the omission ratio is reduced through multi-modal data fusion, space-time alignment, temperature measurement errors and a self-adaptive resource allocation mechanism, and the reliability and practicability of the system are remarkably improved.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention relates to the technical field of edge computing, and discloses a network data efficient caching method and system based on edge computing, which realize dynamic identification of hotspot data and optimal caching position decision by analyzing edge node data flow characteristics and network topology states in real time. Firstly, data packet meta-information is extracted from an edge node, a content feature vector is generated, and data popularity is evaluated; secondly, analyzing a network topology structure and a link state, and screening candidate cache nodes; then, factors such as service priority, network reliability and topology stability are fused, and an optimal cache position is determined by adopting a reinforcement learning algorithm; and finally, the cache content is updated, the cache state is synchronized between the edge nodes, and the cache distribution is optimized. The data access efficiency and the resource utilization rate of the edge network can be effectively improved, and the method adapts to dynamically changing network environments and user requirements.

The invention provides an electric energy metering box temperature field simulation analysis method based on finite elements, and relates to the technical field of electrical equipment fault diagnosis. The method comprises the following steps: constructing a three-dimensional structure model considering asymmetric layout and material attributes of internal elements; identifying key heating elements and establishing a dynamic heat source; constructing an unsteady state heat conduction model by combining a Fourier law and an energy conservation equation; setting environment related boundary conditions; and outputting a temperature distribution diagram and an abnormal hot spot early warning result, thereby realizing accurate modeling and risk pre-judgment of the thermal behavior of the electric energy metering box.

The invention discloses a server resource dynamic scheduling method for dealing with video stream high concurrent access, and particularly relates to the technical field of computer network and intelligent scheduling. A user access behavior data set is constructed; a deep learning model is adopted to train an access hot spot prediction model based on the time sequence features to predict a future access hot spot area and a peak trend; acquiring response delay, CPU / GPU occupancy rate and bandwidth load information of the heterogeneous server group, and generating a resource state multi-dimensional parameter set; carrying out joint modeling on the model and the parameter set, constructing a resource scheduling priority model by adopting a graph neural network, and generating an optimal scheduling path graph based on an A * improved algorithm; task transfer, instance elastic expansion, cache preheating and other scheduling operations are executed according to the model; according to the method, the resource utilization efficiency and the service quality of the video system in a high-concurrency scene can be improved, and the method has the advantages of being high in real-time performance, intelligent in scheduling and high in self-learning capability.

The invention belongs to the technical field of network security, and discloses a network security situation real-time perception and visual display system, which comprises the following modules: a real-time data acquisition module, an intelligent analysis and situation evaluation module, a three-dimensional visual display module and an automatic decision processing module. According to the system, dual mechanisms of unsupervised anomaly detection and a supervised machine learning model are fused through an intelligent analysis and situation evaluation module, the recognition precision of unknown anomaly and known threats is improved, and warning, abnormal traffic and asset vulnerability are deeply correlated by using a time sequence event correlation analysis technology; according to the method and the system, the real-time and accurate global situation portrait is formed, meanwhile, a multi-dimensional safety index quantification system is constructed by means of a situation evaluation unit, and a scientific basis is provided for risk hotspot identification and early warning in combination with threat intelligence, asset vulnerability, service criticality and topology dynamic calculation.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention relates to the technical field of storage, in particular to an SSD wear leveling method and device based on dynamic data migration and a medium. The method comprises the following steps: monitoring access frequency, recording read-write times of a logic block in real time, and identifying hotspot data; tracking a wear state, counting the erasure times of the physical flash memory block, and evaluating the wear degree of the physical flash memory block; data migration decision: determining whether to migrate data based on the access frequency and the wear frequency; preferentially migrating data with high access frequency and high wear degree according to access frequency and wear program sorting; and data migration execution: migrating the hotspot data from the high-wear area to the low-wear area, and updating the mapping relation. According to the method, the data storage position can be intelligently adjusted according to the real-time data access characteristics and the abrasion state, and therefore the service life of the SSD is more efficiently prolonged.

The invention relates to the technical field of storage control, in particular to a memory control method, which comprises the following steps: acquiring a target logic address in response to a reading request from a host system; determining a data access mode associated with the target logic address in real time based on the historical access information, wherein the data access mode at least comprises a continuous mode, a random mode and a hotspot mode; according to the determined data access mode, triggering a data prefetching strategy and a flash translation layer query strategy matched with the access mode; obtaining target data from a memory module based on a data prefetching strategy and a flash translation layer query strategy; and scheduling the cache resources and the input / output bandwidth according to a predetermined resource management strategy, and returning the target data to the host system. The prefetching accuracy and the cache utilization rate are improved.

The invention belongs to the technical field of enterprise risk management, and relates to a multi-dimensional big data analysis risk assessment method and system, and the method comprises the following steps: obtaining enterprise financial data, production operation data and Internet public data; generating an enhanced event stream with dimension marks; an operation early warning identifier is generated when the operation dimension behavior meets a preset continuity condition, and a public opinion early warning identifier is generated when the public opinion dimension behavior meets a preset negative emotion condition; outputting a cross-dimension risk event report when the index exceeds a preset combination threshold value; tracing a historical enhanced event stream according to the cross-dimension risk event report, and constructing a risk evolution link diagram containing a source data event and an early warning event according to a time inverted sequence; and matching a preset response rule base to generate a graphical intervention scheme containing the risk hotspot position and the processing priority. The problem that a static billboard can only display isolated event points and lacks dynamic description of a risk space-time conduction path is solved.

The invention provides a CPU scheduling method and related equipment, and relates to the technical field of computers. The method comprises the following steps: in response to execution of a load process, sampling a last-stage cache failure event of the load process to obtain a memory access hotspot address corresponding to the load process; wherein the last-stage cache failure event is related to a memory access address, and the memory access hotspot address is a first memory address of which the memory access frequency is greater than a first preset threshold value; determining a CPU corresponding to the memory access hotspot address as a first CPU candidate set of the load process; in the first CPU candidate set, determining a target CPU of the load process; and adding the load process into the process scheduling queue of the target CPU. According to the method and the device, the hotspot address is identified by sampling the last-stage cache failure event, the candidate set and the target CPU are determined, the load process is enabled to preferentially run on the CPU with high affinity with the memory hotspot, and the load performance can be effectively improved under a non-uniform cache architecture.

The invention relates to the field of computer systems, in particular to an operation stability testing method and system oriented to a heterogeneous computing system, and the method comprises the steps: constructing a system overall operation logic model comprising a resource topological structure, a task scheduling rule and a communication link matrix; identifying a key communication path according to the communication link matrix, analyzing a resource competition hotspot in combination with a task scheduling rule, and establishing an interference model library; calling a specific model instance in the interference model library according to a test target, configuring injection parameters, and dynamically applying interference in a system operation process; key performance indexes in the system operation process are collected in real time; based on the real-time monitoring data, a multi-index weighted scoring algorithm is adopted to calculate an overall stability coefficient, a robustness grade and a self-healing capability index of the system, and a stability evaluation result is generated; and generating a visual report according to the topological structure of the operation logic model, and supporting comparison and analysis of historical versions. Unified centralized management is realized, and test efficiency and evaluation accuracy are improved.

The invention relates to the technical field of Internet, and discloses an Internet hotspot data mining system and method based on artificial intelligence, and the system comprises a data collection module, a semantic understanding module, a dynamic clustering module, a popularity evaluation module, a visual output module and a feedback optimization module. By setting a multi-source calibration unit, when cross-platform hotspot data acquisition is carried out, standardized processing of multi-source heterogeneous data acquisition is ensured by establishing a dynamic semantic feature library and configuring adaptive weight parameters for different data platforms; meanwhile, by monitoring the semantic offset of the collected data in real time, characterization deviation generated during cross-platform data collection can be detected and eliminated, the accuracy of hotspot clustering similarity calculation is guaranteed, hotspot recognition errors are reduced, and by deploying a semantic tracking engine, when hotspot event propagation analysis is carried out, the analysis accuracy is improved. The deviation degree of event core elements is calculated by constructing spatio-temporal feature vectors, and whether topic semantics are migrated or not is judged in real time.

The invention discloses a new energy access lower harbor area transformer insulation performance and service life evaluation method, which comprises the following steps: firstly, establishing a wind and light storage AC / DC hybrid micro-grid equivalent simulation model, and quantifying a composite action mechanism of new energy fluctuation and harmonic characteristics on dynamic loss and hot-spot temperature rise of a transformer; analyzing a harmonic loss mechanism of the transformer in different operation modes; secondly, constructing a quay crane and field crane operation simulation model, and analyzing the harmonic characteristics of bridge crane equipment and the influence of the harmonic characteristics on the harmonic distortion rate of the power distribution network; then, on the basis of the data, a transformer hot-spot temperature dynamic calculation model is established in combination with bidirectional power flow characteristics; and finally, calculating transient temperature distribution of the transformer through a flow field-temperature field coupling method, and constructing a multi-physical field coupled insulation aging rate model to realize evaluation of the residual life of the transformer. The method solves the problem that the traditional method cannot quantify the bidirectional power flow and harmonic wave composite aging effect.

The invention discloses a new media operation content recommendation system based on big data, and relates to the technical field of new media operation, cross-platform short-time behaviors are collected in real time through edge calculation, multi-platform user interest characteristics are dynamically fused in combination with federal learning, the limitation of single platform data is broken through, and instant interest transfer of users is accurately captured; cross-platform behavior breakpoints are filled by using bidirectional LSTM and an attention mechanism, and prediction weights are dynamically adjusted according to equipment types, so that the response speed of sudden interests is remarkably improved; a multi-platform content resource pool is opened through a semantic association index and a hotspot adjustment matrix, and semantic-level matching and real-time scheduling of cross-platform content are achieved; in addition, multi-platform collaboration is completed through the privacy compliance unit on the premise of guaranteeing data security, and legal risks are avoided.

The invention relates to the technical field of geographic information data, in particular to a GIS-based CRM customer ecosphere dynamic visual management system, which comprises the following steps: acquiring geographical location information, transaction data, customer relationship data and time sequence data of customers; constructing a customer ecosphere space model to analyze the geographic position information and the customer relationship data, obtaining space association strength and business association strength among customers, and generating a dynamic evolution matrix; performing real-time spatial clustering analysis on the transaction data, and identifying a client distribution hotspot area; based on the time sequence data and the geographic position information of the customer, constructing a customer space-time trajectory model, analyzing a space-time behavior pattern of the customer, and obtaining a space-time trajectory prediction result including a customer periodic behavior pattern and a space activity trend; and carrying out three-dimensional rendering on the spatial association strength, the business association strength, the customer distribution hotspot area and the spatio-temporal trajectory prediction result to obtain a three-dimensional visual interface.

The invention discloses a commodity evaluation monitoring method and system based on multi-dimensional data, and relates to the technical field of evaluation monitoring, and the method comprises the steps: collecting the multi-dimensional data, and carrying out the preprocessing of the multi-dimensional data; performing feature extraction on the preprocessed multi-dimensional data, and fusing the extracted features into a comprehensive feature vector; based on the comprehensive feature vector, through a space-time convolutional neural network, identifying and evaluating the space-time distribution of the hot spots; based on the spatial and temporal distribution of the evaluation hotspots, identifying areas and time periods of the evaluation hotspots, and evaluating the trend of the hotspots according to an identification result; and performing dynamic early warning according to the trend of predicting and evaluating the hot spots, and generating an evaluation report. According to the method, spatial-temporal feature modeling is carried out on data through STCNN, and the distribution conditions of evaluation hotspots in different time and spaces are identified. Based on the spatio-temporal characteristics, the area and time period of the hot spot can be accurately positioned and evaluated, and the diffusion trend of the hot spot can be predicted and evaluated.

The invention discloses a database-based space conflict treatment method and device and a medium, and relates to the technical field of electric digital data processing. The method comprises the steps of determining conflicting pattern spots with conflicts in space pattern spots through a preset space analysis function according to a geometrical relationship between the space pattern spots corresponding to a planning land in a space database; analyzing the spatial relationship and the ownership planning relationship corresponding to the conflict pattern spots to determine the conflict intensity corresponding to each conflict pattern spot; based on the spatial relationship network, determining a hotspot conflict area with spatial aggregation according to the conflict intensity; determining treatment priorities corresponding to the conflict pattern spots according to a descending order of the conflict intensity, and adjusting the treatment priorities of the conflict pattern spots according to conflict linkage paths among the conflict pattern spots in the hotspot conflict area; and generating a space governance path in the hotspot conflict area through the adjusted governance priority, so as to realize the space conflict governance of the planned land through the space governance path.

The invention provides a transformer winding state detection method based on temperature characteristics. The method comprises the following steps: (1) collecting operation data; (2) preprocessing the operation data; (3) taking the normalized transformer load current, the normalized active power, the normalized top oil temperature, the normalized ambient temperature and the normalized oil flow speed as input data, taking the normalized winding hot-spot temperature as output data, and constructing a data set; (4) establishing a VSN-TKAN-GRN deep learning network parallel computing model of the transformer winding hot-spot temperature considering the multi-head attention mechanism; (5) predicting the hot-spot temperature of the transformer winding based on a VSN-TKAN-GRN deep learning network parallel computing model; and (6) calculating the average relative error percentage of the transformer winding hot-spot temperature prediction result and the actual measurement result, and judging the winding state according to the change of the average relative error percentage. According to the method, the state of the transformer winding in operation can be efficiently and accurately judged, so that effective transformer operation and maintenance measures can be taken in time, and large faults are avoided.

The invention discloses a robot automatic navigation method and system based on depth vision fusion, and relates to the technical field of intelligent navigation, and the method comprises the steps: carrying out the registration of a point cloud subset of continuous frames through employing an ICP algorithm, distributing weights for a static obstacle and a dynamic obstacle, and carrying out the fusion, and obtaining a fused environment perception data set. The method comprises the following steps: updating a dynamic environment in real time, generating a dynamic environment map, carrying out trajectory tracking on moving targets by using binocular vision equipment, calculating a real-time motion trajectory of each moving target, obtaining an environment hotspot map, generating a global navigation path for a robot by using an A * algorithm, planning a detouring path at the periphery of a hotspot area, and outputting a navigation path sequence. According to the method, intelligent optimization of the motion path of the robot is realized through path planning and an obstacle avoidance strategy, the safety is ensured, the path efficiency is maximized, and unnecessary detour and pause time is reduced.

The invention discloses a complex environment information acquisition method based on a sensor network, and relates to the technical field of sensor networks, and the method comprises the steps: building a complex environment portrait and a service level, and generating a scene configuration comprising an information freshness budget, a risk heat map and an alarm threshold; based on scene configuration, configuring mesh wireless networking, time synchronization parameters and candidate relays, and constructing observation configuration and quality rules; and generating a data stream and a real-time queue state according to a self-adaptive sampling strategy and an event mark, calculating an emergency degree and distributing a forwarding probability according to an information freshness budget by combining a candidate relay and a risk heat map, outputting uploaded data and a quality record, completing convergence and grading alarm according to an alarm threshold, and generating a disposal feedback and audit index. The state set, the uncertainty set, the link idle degree and the energy sufficiency are linked under the constraint of the information freshness budget, so that hot spot encryption, low-risk sparseness, reduction of energy consumption and conflict rate and standard observation time efficiency are realized.