2228 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 relates to a thermal management control method and system for an energy storage power supply, and the method comprises the steps: obtaining the real-time temperature data of the energy storage power supply, and carrying out the heat source distribution analysis, and obtaining the corresponding temperature distribution information; heat dissipation operation parameters of the energy storage power supply are obtained, adjustment analysis is carried out in combination with the temperature distribution information, and a corresponding initial heat dissipation control strategy is obtained; acquiring real-time monitoring data of the energy storage power supply, and performing charge and discharge dynamic analysis in combination with the temperature distribution information and the initial heat dissipation control strategy to obtain corresponding charge and discharge adjustment information; based on the real-time temperature data and the thermal management strategy, hot spot identification is carried out, corresponding potential hot spot information is obtained, early warning processing is carried out, and a corresponding hot spot early warning result is obtained; and performing comprehensive control analysis on the thermal management strategy, the initial heat dissipation control strategy and the hot spot early warning result to obtain a corresponding global thermal management control scheme. According to the invention, comprehensive and intelligent control of thermal management of the energy storage power supply can be realized.

The invention provides a private domain live broadcast room hotspot prediction and adaptive routing method based on swarm intelligence, and relates to the technical field of data processing, and the method comprises the steps: constructing a dynamic interest community through a dynamic graph neural network, extracting group interaction mode features through a multi-layer graph attention network, and carrying out the group interaction mode features; the method comprises the following steps: generating time sequence correlation features in combination with an improved bidirectional gating loop unit, extracting high-order semantic features, performing hotspot prediction through a multi-task learning framework, constructing a multi-level network topology model according to a prediction result, and performing path exploration in combination with an improved heuristic search algorithm to obtain an optimal transmission path. Flow distribution is carried out through a weighted polling algorithm, a deep learning distributed load balancing controller and a congestion control algorithm are combined, the data transmission rate is dynamically adjusted, and self-adaptive routing pushing of live broadcast content is achieved.

The invention discloses an interdisciplinary scientific research potential assessment method based on a dynamic multi-modal knowledge graph, relates to the technical field of scientific research management and intelligence analysis, and aims at the dynamic requirements of college scientific research ecology by relying on an incremental assessment framework. Comprising the steps of multi-source data acquisition and incremental knowledge graph construction, time sequence trend identification, online and transfer learning optimization, scene simulation and multi-scene strategy verification and multi-modal data fusion. The map structure is updated in real time through concept drift detection and ontology extension, and emerging hotspots are identified by combining time sequence difference indexes such as citation acceleration and a cooperative network; and the accuracy and continuous iteration capability of the evaluation model are kept in multiple environments by means of online learning and a cross-domain migration strategy. Finally, potential evolution is quantified through scene simulation, strategy suggestions are output, the sensitivity to the cross domain is further enhanced with the assistance of multi-mode embedding, a closed-loop evaluation system from data to decision is formed, and college subject cross layout and resource allocation are assisted.

The invention relates to the technical field of charging pile power dispatching, in particular to a service area new energy charging management method and system based on multi-device data analysis. The method comprises the following steps: acquiring real-time operation monitoring parameters of all charging piles in a service area; multi-dimensional feature perception and charging behavior time popularity distribution analysis are carried out, and a dynamic charging behavior hotspot map is constructed; carrying out multi-time-point power sampling according to the dynamic charging behavior hotspot map, carrying out power cooperation path topology evolution, and constructing a power cooperation path network; performing charging pile power trend prediction and power peak congestion evolution according to the power cooperation path network, and generating a power peak congestion state feature of the charging pile; and obtaining new energy traffic flow data of the service area, carrying out unit time traffic flow average calculation, and carrying out traffic flow prediction to obtain a traffic flow prediction thermodynamic diagram. Charging power scheduling is carried out through demand prediction, and the operation efficiency and stability of charging equipment are improved.

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 knowledge graph-based method for accurately positioning and breaking through discipline weak points, which comprises the following steps of: predicting knowledge points possibly accessed by students in the next step by using a deep learning model based on an updated knowledge graph, capturing a multi-dimensional dependency relationship among the knowledge points through a graph neural network, and combining historical learning track data of the students to obtain a knowledge point database; generating a personalized learning resource recommendation list; dynamically adjusting the priority of the recommendation list according to the comprehensive association degree of the subject weak point and the key path, and improving the priority of the knowledge point of the key path highly associated with the subject weak point; and according to the priority of the recommendation list, adopting a multi-level caching strategy to pre-cache learning resources, maintaining a hotspot knowledge point index in a memory, storing secondary hotspot resources in a solid state disk, storing cold data in a mechanical hard disk, and dynamically adjusting cache content of each level through a cache replacement algorithm.

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 relates to the technical field of big data and the field of financial science and technology, and discloses a database load optimization method based on dynamic fragmentation, which comprises the following steps: establishing an association relationship between fragments and storage nodes by configuring basic logic fragments and dynamic logic fragments; monitoring a data access record, identifying hotspot data and marking the hotspot data as a to-be-migrated data block; when the fragment load exceeds a threshold value, migrating the high-load data block to the dynamic logic fragment; and when the access frequency is reduced, moving the low-load data block back to the basic logic fragment. According to the method, through configuration and management of the dynamic logic fragments, in combination with real-time monitored access records and load state analysis, the high-load data blocks are recognized and migrated to the dynamic logic fragments for storage, and the overload problem of the basic logic fragments is relieved. Meanwhile, the low-load data blocks are moved back to the basic logic fragments, dynamic adjustment and balance of storage resources are further achieved, the load balancing capacity of the system is improved, and the requirements for high concurrency and real-time load changes in a financial scene are met.

The invention relates to the technical field of data processing, and discloses a communication transmission management method and system for a heterogeneous network and a storage medium. The method comprises the following steps: processing a signal strength parameter and a data priority level in a data hierarchical configuration table to obtain a network resource allocation scheme; performing real-time monitoring processing on a 4G / 5G cellular network base station, a WiFi hotspot and a satellite communication node to obtain a network switching control instruction; performing transmission quality monitoring processing on the engine parameters, the navigation data, the airborne equipment state data and the environment monitoring data to obtain a transmission quality parameter table; and performing data mining analysis processing on the network switching control instruction and the transmission quality parameter table to obtain a transmission strategy optimization parameter containing network resource optimization configuration. In a heterogeneous network environment, intelligent network switching and resource allocation based on data priorities are realized, so that transmission requirements of data with different priorities are ensured, and meanwhile, the utilization efficiency of network resources is improved.

The invention discloses a fresh commodity replenishment method and system, electronic equipment and a storage medium, and relates to the field of commodity management. According to the method, fresh food hot topics are obtained from social media, demand hotspots are determined according to the fresh food hot topics, weather influence factors are determined according to predicted weather data, and holiday influence factors and seasonal influence factors are determined according to the current date; determining sales influence factors according to the second sales data of the competitor and the first sales data of the target store; through a preset prediction model, according to the sales influence factors, the demand hotspots, the weather influence factors, the holiday influence factors and the seasonal influence factors, determining predicted demand quantities of various fresh commodities in a second preset time period after the current moment; and determining the purchase quantity according to the predicted demand quantity and the stock quantity of the target store, and determining a purchase scheme according to the purchase quantity and the supplier information. By implementing the technical scheme provided by the invention, the accuracy and efficiency of replenishment of fresh commodities are improved.

The invention discloses a self-adaptive data caching and synchronizing method, system and device for an edge gateway of the Internet of Things and a storage medium, and the method comprises the steps: obtaining network quality parameters, such as link bandwidth, time delay and packet loss rate, between nodes, detecting and solving instruction conflicts based on a vector clock mechanism, generating an instruction execution sequence, and carrying out the self-adaptive data caching and synchronizing of the edge gateway of the Internet of Things; local caching and incremental synchronization strategies are adopted for hotspot data, a data access mode is dynamically predicted in combination with a machine learning algorithm, and a cache updating period is optimized. The future data is predicted according to the data characteristics of the Internet of Things application, and the data cache updating period of each node is dynamically adjusted, so that each node can keep a relatively consistent updating rhythm when caching a hotspot data copy; therefore, the hot spot data copies of different nodes can be ensured to have similar accuracy and timeliness at the same time point, so that the data consistency is maintained.

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 discloses a sensitive word auditing method, which belongs to the field of auditing, and comprises the following steps of: firstly, preprocessing an input text, such as removing spaces, symbols, stop words and the like, so as to generate a word segmentation result; thirdly, constructing a dynamic synonym and a deformation word library, combining user behaviors and NLP technology updating, and expanding word meanings by applying a word vector model Word2Vec; then, based on BERT, a text context is analyzed, and metaphors and biguan languages are detected. Performing sensitive word primary screening and secondary screening by using a static word bank and an SVM (Support Vector Machine), and analyzing complex semantics by LSTM (Long Short Term Memory); the model is optimized based on a feedback self-learning mechanism, difficult contents are manually checked, an analysis report is generated, and finally a word bank and an algorithm are periodically updated to adapt to hotspots. The method has the beneficial effects that sensitive word auditing is improved through a self-learning mechanism, synonyms and deformed word banks can be dynamically updated, and private expressions are identified. BERT and LSTM model multi-level auditing are adopted, secondary screening is carried out in combination with SVM, and recognition of complex contexts and biguan languages is improved.

According to the panoramic interaction display method and system based on AI multi-modal fusion provided by the invention, efficient and accurate multi-modal fusion is realized by integrating the visual and audio information of the current panoramic material and combining multiple pieces of panoramic interaction hotspot information, a panoramic interaction model with rich interactivity is generated, and the interaction effect of the panoramic interaction model is improved. And hotspot interaction space features are further extracted, so that panoramic display can automatically adjust a visual angle according to a user demand or a preset situation and trigger a corresponding commentary audio or interaction animation, immersion and interactivity of panoramic display are remarkably improved, user experience is optimized, seamless connection of audio playing and user interaction operation is realized, and user experience is improved. Therefore, the dynamic display and interaction of the panoramic content can be realized conveniently.

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 territorial resource data processing, in particular to a data storage method, system and device for territorial resource planning and a medium, and the method comprises the steps: carrying out the fragmentation processing of territorial resource data, and obtaining data fragmentation information; performing real-time analysis on the data fragment information to obtain hot data and cold data, performing synchronous replication processing on the hot data, and performing asynchronous replication processing on the cold data; obtaining memory state information, dividing the memory state information to obtain grading information, adjusting the grading information according to the hotspot data, and storing the hotspot data to a plurality of grading caches; the data access flow information is acquired in real time, the data fragmentation information is dynamically adjusted according to the data access flow information, and the distribution updating information used for updating the hot data and the cold data is obtained, so that territorial resources can be stably stored, the data consistency is improved, and the system reliability and the user experience are improved.

The invention provides an intelligent content creation method and system based on multi-modal data processing and dynamic task planning. After a user submits social media information, the system automatically captures and collects historical social media data of the user, carries out deep analysis on the data through a multi-modal model, and constructs and stores a user portrait in a database. When a user creates contents, the system dynamically calls an adaptive large model according to a multi-mode identification result: processing video contents by using a video analysis model, converting audio data by using an audio large model, analyzing pictures by using an image analysis model and the like; and the large language model is accurately generated based on user problems and integrated analysis results. And the system can record the interactive dialogue and behavior data of the user. And the dynamic task planning module dynamically analyzes and calls a hotspot tracking tool or a network retrieval tool to analyze contents according to an analysis result of the multi-modal data uploaded by the user, so that personalized, intelligent and precise content creation is realized, and the responsivity and the precision of creation are effectively improved.

The invention discloses a big data read-write adaptive method based on a decision algorithm and a big data read-write system, and belongs to the technical field of intelligent decision of a data center, high-speed read-write operation of data is processed in real time in a service system, and a data read-write strategy and resource allocation are dynamically adjusted by analyzing a data access mode and a system load condition; data are preloaded through an intelligent prefetching strategy, and the storage and access paths of hotspot data are optimized, so that the waiting time of a user and the consumption of system resources are reduced. According to the method, data management can be simplified, data access efficiency can be improved, data consistency and security can be enhanced, the problem of multi-database management and query complexity faced by enterprises can be effectively solved, and more efficient and more consistent data access and management experience can be provided for the enterprises.

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 financial data processing, in particular to a financial marketing platform data storage system and method, and the system comprises a data prediction regulation and control module, a hotspot recognition module, a storage medium adjustment module, a sensitive encryption module, a data backup module and a performance evaluation module. According to the method, by collecting and calculating the average value of the data traffic and the access time points of the financial marketing platform, the time period with high access frequency can be accurately predicted, the data traffic can be effectively managed, the probability of system overload can be reduced, and by matching suitable storage media such as SSD and HDD for different types of data, optimization is carried out according to the access speed requirement, the access speed of the financial marketing platform can be greatly improved. The data processing speed and cost efficiency are remarkably improved, differentiated encryption processing and backup are performed on the data, and a security policy is designed according to the sensitivity level of the data, so that the operation efficiency of data storage is effectively monitored and improved, and the efficiency and security of a data storage system are improved.

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 relates to the technical field of digital marketing, in particular to an intelligent marketing data analysis system and method. According to the invention, through deep analysis of the user interaction data, the user behavior offset is accurately identified, so that the marketing strategy can be dynamically adjusted, and the putting accuracy is improved. By means of the change trend of the residence time sequence, the relative change rate is calculated in combination with the access preference, user interest hotspot distribution information is effectively extracted, and the pertinence of content recommendation is improved. Based on access path stability analysis of interest hotspots, a stable mode of user behaviors is extracted, and an advertisement putting interval is optimized, so that user loss caused by frequent putting is reduced. And user groups with fluency decline or obvious jumping behaviors are screened by utilizing change calculation of interaction fluency, so that the advertisement pushing rhythm is reasonably adjusted, and the advertisement experience is improved. The user access data is synthesized to analyze the conversion change of the user behavior after the advertisement is displayed, and the influence of different advertisement intervals on the user behavior is accurately evaluated.

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 provides a cold start optimization system and a cold start optimization method for a Python Serverless function. The system comprises a dependency analysis module, a module pre-compiling system, a hierarchical module loader, an inert import mechanism, a code cache optimizer and a self-adaptive optimization system. The dependency analysis module analyzes code dependency in a function deployment stage; the module pre-compiling system pre-compiles the source code into an optimized byte code; the hierarchical module loader loads modules of different hierarchies according to priorities; actual loading of an inert import mechanism delay module; the code cache optimizer caches a hotspot function and a module state; and the self-adaptive optimization system dynamically adjusts preloading and caching strategies. The system further comprises a JIT compiling intensifier, a state storage and quick recovery module, a memory management optimizer and a security mechanism. The method has the beneficial effects that the function cold start time is remarkably shortened, the function execution efficiency is improved, the resource utilization is optimized, and meanwhile, the safety and the expandability are ensured.