342 results about "Scene analysis" patented technology

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention discloses a data quality intelligent auditing system and method based on a dynamic rule base, and belongs to the technical field of data auditing, and the system comprises a rule base construction module which is used for analyzing business scene parameters through a scene analysis unit according to business scene demands and data type features to generate a rule configuration instruction; the multi-source monitoring engine module is connected to the rule base construction module and is used for collecting multi-source data in real time and loading corresponding checking rules; the automatic verification execution module is used for executing normalized quality verification on the multi-source data based on the verification rule base; and the feedback optimization module analyzes a rule hit rate and a false alarm rate in a verification result through a reinforcement learning algorithm, and dynamically iteratively updates a rule threshold value and a logic combination in the rule base. By constructing a full-automatic process of rule generation, execution, feedback and updating, the problems that a traditional system depends on manual intervention, response is slow, the industry average rule updating period is 3-7 days, and real-time updating is achieved through the scheme are solved.

The invention provides a self-adaptive scene analysis and target generation method and system based on deep learning, and relates to the technical field of computer vision, and the method comprises the steps: extracting the depth information of a scene image sequence, constructing three-dimensional point cloud data, calculating the distance and direction matrix between objects, and generating a scene space feature vector. Meanwhile, a semantic label graph is obtained through semantic segmentation, a semantic incidence matrix is constructed, and a scene semantic vector is calculated. Inputting the two into a variational auto-encoder to obtain a target feature vector, calculating an attention score based on the target feature vector, and generating a target description vector with a weight; and finally, target contour features are generated through a probability graph reasoning network, and a final target result is generated in combination with the regional attention graph. According to the method, the accuracy and efficiency of target generation are improved.

The invention relates to the technical field of camera remote control, and discloses a camera remote control method and system based on portable WiFi. The method comprises the following steps: acquiring position information, coverage range and signal intensity of camera nodes, and constructing a distributed network topology structure; according to the distributed network topology structure, scene analysis is carried out on video content collected by a camera, and an optimal coding parameter configuration table is calculated; dividing the video frame into different priority regions according to the optimal coding parameter configuration table, and executing differential compression processing to form a video data packet with a priority mark; according to the method, video data packets with priority marks are distributed to a multi-level transmission queue, data transmission is scheduled according to the current bandwidth state of the portable WiFi, and an ordered transmission stream is generated, so that the computing load and energy consumption of each camera node are effectively balanced, stable video transmission and control response can still be kept under the condition of network fluctuation, and the video transmission efficiency is improved. And the video lagging rate and the control delay are obviously reduced.

The invention discloses an intelligent cabin interaction method and system combined with digital twinning, and relates to the technical field of man-machine interaction, and the method comprises the steps: collecting the multi-dimensional perception data of a cabin, and constructing a layered cabin digital twinning model; performing scene analysis on the cockpit digital twin model, extracting driver state features, predicting driver potential interaction demands, generating a multi-modal interaction strategy set in combination with the driver potential interaction demands and a current driving scene, and performing simulation evaluation on the multi-modal interaction strategy set to obtain an interaction evaluation index set; performing multi-objective decision optimization under the driving safety constraint based on the interaction evaluation index set, and determining and adaptively executing an optimal interaction strategy; and acquiring actual execution data and driver feedback data of the optimal interaction strategy, and incrementally updating the hierarchical cabin digital twin model. According to the invention, the interaction adaptability and the safety reliability of the intelligent cabin system in a complex driving scene are obviously improved, and an effective technical support is provided for realizing intelligent man-machine cooperative driving.

The invention relates to the field of computer vision and the field of image processing, in particular to a blind guiding scene recognition method based on a multi-modal visual large model. Comprising the following steps: data acquisition: acquiring image or video data of a typical blind guiding scene, marking, constructing a data set, introducing an image prompting mechanism, and dividing a training set and a test set; constructing a multi-modal visual large model which comprises an image coding module, a semantic prompt coding module, a visual language fusion module and a scene analysis semantic decoding module; the model is tested and optimized, and a multi-task loss function is adopted for optimization; a blind guiding auxiliary system is constructed, image acquisition, visual understanding and voice broadcasting functions are integrated, and a closed-loop process is realized. According to the method, the problems of strong target dependence, poor universality and weak semantic comprehension ability of the existing blind guiding identification technology are solved, accurate identification and semantic feedback of complex scenes are supported, and the intelligent level and the environment adaptability of a blind guiding system are improved.

The invention relates to the technical field of live broadcast, and discloses a live broadcast scene perception recommendation system, which is characterized in that a data acquisition module is used for acquiring multi-source data in a live broadcast process in real time; the scene analysis module is connected with the data acquisition module and is used for receiving and integrating the acquired data and analyzing the live broadcast scene; the user portrait construction module is used for tracking a behavior track of a user on a live broadcast platform in real time, and constructing a dynamic user portrait in combination with historical watching data, a collection record, a consumption record and an interactive behavior in current live broadcast; a recommendation strategy generation module generates a personalized recommendation strategy according to the result of the scene analysis module and the user portrait constructed by the user portrait construction module; the recommendation content pushing module pushes the recommendation content generated by the recommendation strategy generation module to the user; according to the method, personalized and precise recommendation is realized, and the watching experience and participation degree of the user are effectively improved.

The invention discloses a big data platform asset intelligent sensing method based on LLM and a customizable MCP, and the method comprises the steps: defining a standard resource library of a model context protocol, constructing a model context protocol server, integrating a plurality of scanning tools, and carrying out the automatic adaption; receiving a natural language demand input by a user, and calling a pre-trained LLM model; the scene analysis module converts a natural language demand into a standardized scene description object; the intelligent scanning module is used for describing an object according to a standardized scene, generating an optimal tool execution chain, executing a complete scanning task, obtaining a task scanning result and unifying formats of heterogeneous data in the task scanning result, and the conflict resolution module is used for removing conflicts; the multi-source data association module fuses network topology resources and flow data captured by a probe to construct a dynamic asset atlas including asset attributes, service dependence and vulnerability information, and the report generation module generates a structured report. The method can provide a global risk perspective and decision support.

The invention relates to the technical field of artificial intelligence, in particular to a spatio-temporal context extraction method and system for real world model training, and the method comprises the steps: extracting all image information and text semantic information through content feature vectors; structural information such as spatial layout of a scene and relative positions of people and objects in the collected image is extracted through the spatial structure feature vector, and feature information such as appearance, posture and action of a target object is extracted through the entity feature vector; obtaining a time sequence feature vector according to the content feature vectors, the spatial structure feature vectors and the entity feature vectors in a plurality of preset time periods, capturing multi-dimensional dynamic changes of the target physical place in a period of time, and analyzing the extracted vectors to obtain scene analysis feature data; all feature information is integrated to obtain the spatio-temporal context, the representation richness and comprehensiveness of the target physical place are improved, and the representation degree and adaptability of the real world model obtained through subsequent training to the target physical place are achieved.

The invention relates to the field of image processing, and discloses an object monitoring method and device based on multi-source data and a storage medium, and the method comprises the steps: synchronously collecting multi-channel video streams, environment parameters and equipment position information, and carrying out the time alignment and data association processing, and forming associated data; performing feature matching on the multiple paths of video streams, fusing position information and environment parameters, and establishing a mapping relation between video feature points and a unified space coordinate system; splicing the multiple paths of video streams in real time according to the mapping relation to generate a panoramic video stream; performing dynamic scene analysis based on the panoramic video stream and the environmental parameters, and identifying a target type and a state to obtain an analysis result; and generating an equipment regulation and control strategy based on the analysis result, and generating and issuing a regulation and control instruction for adjusting the working parameters of the multi-view image acquisition device based on the equipment regulation and control strategy. According to the invention, the splicing precision, the real-time performance and the target identification accuracy of panoramic monitoring can be improved, and the dynamic adaptive regulation and control of the equipment can be realized.

The embodiment of the invention relates to the field of voice control, and discloses an equipment control method and device, equipment and a storage medium, and the method comprises the steps: determining identity information and a target execution task from obtained control voice information sent by at least two users; performing scene analysis on each target execution task to determine target scene information; analyzing whether task conflicts exist among the target execution tasks or not by utilizing the identity information and the target scene information; when the task conflicts exist, screening the target execution tasks with the task conflicts as conflicting execution tasks, and determining the priority ranking of the conflicting execution tasks; and determining the working state of the corresponding target equipment according to the priority ranking of each conflict execution task. According to the equipment control method disclosed by the invention, the problem that multi-user voice control conflicts and cannot be coordinated is solved, multi-user voice cooperative control can be carried out in combination with the user identity information and the actual scene, and the user experience is improved.

The invention discloses a self-adaptive sound effect management method and system with a scene analysis module, and relates to the field of self-adaptive sound effect management, and the method comprises the following steps: S1, collecting environmental acoustic characteristics and user behavior data in real time through the scene analysis module; s2, dynamically extracting user hearing preference parameters and scene classification labels; s3, constructing a virtual audition sweet zone matched with the physical space; and S4, dynamically adjusting the phase delay and the energy distribution proportion of each frequency band, and generating an optimal loudness equalization curve meeting the multi-scene constraint. According to the method, adaptive matching of sound field characteristics is realized in a complex dynamic scene through a multi-level perception-optimization closed-loop architecture, acoustic feature extraction and physical space modeling are deeply fused, the technical boundary of traditional pure signal processing is broken through, and the problem of multi-target conflict is solved based on cross-modal collaborative learning and a global optimization algorithm.

The invention discloses a building facade three-dimensional surveying and mapping system and method based on an unmanned aerial vehicle and AI, and relates to the technical field of building surveying and mapping, and the system comprises a scene analysis module which is used for executing the information analysis of calibration scene information, and marking each surveying and mapping sub-region as a to-be-collected node; the acquisition planning module is used for calculating the node transition probability of each node to be acquired; the surveying and mapping search module is used for configuring a virtual unmanned aerial vehicle and performing selective iteration of surveying and mapping sub-regions by using pheromone concentration and heuristic factors; the clustering sharing module is used for carrying out three-dimensional surveying and mapping on the exterior facade of the building and establishing a shared information pool in a clustering manner; and the surveying and mapping optimization module is used for carrying out collaborative decision optimization on the local path and updating the acquisition path according to a collaborative decision optimization result. The technical problems of low surveying and mapping efficiency and large measurement error caused by difficulty in meeting large-scale and high-precision surveying and mapping requirements of modern buildings in the prior art are solved, and the technical effect of improving the surveying and mapping efficiency and precision of the buildings is achieved.

The invention discloses an intelligent shooting range training system based on real-time data acquisition, relates to the technical field of intelligent shooting range training based on real-time data acquisition, and is used for solving the problem that an existing intelligent shooting range training system based on real-time data acquisition only pays attention to a single aspect of a training effect. The system is difficult to provide personalized training suggestions according to specific conditions of trainees; and a traditional training method may lack scientific verification. Comprising a real-time information acquisition module, an information analysis module, a scene analysis module and an optimization processing module. The real-time information is acquired through the real-time information acquisition module, and the behavior and physiological data are analyzed by the information analysis module to obtain comprehensive and deep analysis, so that the pertinence and effectiveness of training are improved; the method comprises the following steps: identifying different scene mode factors and setting corresponding evaluation weights; personalized improvement suggestions are provided for trainees, the best physiological state can be kept, the training effect is comprehensively improved, and the training experience is optimized.

The invention particularly relates to an atomic business intelligent arrangement device and method. The atomic business intelligent arrangement device comprises an atomic business relation graph intelligent construction module, an atomic business fine management module, a business intelligent arrangement and visual arrangement module and a man-machine interaction module. According to the atomic service intelligent arrangement device and method, full-chain intelligentization from user demand identification, service scene analysis to process automatic arrangement is realized, the service handling efficiency and the service quality are improved, manual correction is supported, the error rate is reduced, the reusability of atomic services is improved, policy scenes can be helped to rapidly fall into the ground, and the service quality is improved. And diversified operation requirements of the user are met, so that the user satisfaction is improved.

The invention relates to the technical field of power grid identification, and discloses a county power distribution network multi-dimensional operation mode identification method, system and device based on typical scene analysis, and a medium, and the method comprises the steps: obtaining the historical operation data of a county power distribution network, carrying out the extraction of multi-dimensional features based on the preprocessed historical operation data, and obtaining the multi-dimensional operation mode of the county power distribution network; carrying out clustering analysis on the operation data of the power distribution network through an improved time sequence clustering algorithm, and determining a typical scene of each category; and constructing a county power distribution network operation mode recognition model, classifying the operation states of the power distribution network, constructing a mapping relationship between the operation mode and a planning decision, and generating an optimal planning scheme. According to the invention, precise recognition of the operation mode of the county power distribution network is realized, the recognition accuracy of the operation mode of the power distribution network is improved, and precise planning is supported; the power distribution network planning cost is reduced and the resource utilization efficiency is improved.

The invention discloses an unmanned aerial vehicle low-altitude photography image enhancement method and system. The method comprises the steps of data preparation, motion joint deblurring optimization, multi-source data registration, scene adaptive enhancement and image comprehensive enhancement. The invention relates to the technical field of unmanned aerial vehicle image enhancement, and realizes high-precision deblurring, precise registration and adaptive visual enhancement of an unmanned aerial vehicle image by fusing inertial measurement unit guided blurring kernel prediction, cross-modal graph structure matching and elastic transformation optimization and an enhancement strategy dynamic adjustment mechanism based on scene analysis. The image definition, the structure consistency and the scene readability are remarkably improved, and the method is suitable for image processing tasks in a complex low-altitude flight environment.

The invention discloses a violation snapshot method based on image recognition, which relates to the related technical field of traffic video monitoring and comprises the steps of environment perception, scene analysis, scene feature library establishment, multi-modal data acquisition, server model aggregation, model issuing and fine adjustment and violation behavior recognition. The invention further discloses a violation snapshot system based on image recognition. The violation snapshot system comprises an environment sensing module, a data acquisition module, a data processing center, a violation behavior recognition module and a snapshot and recording module. Different from a fixed snapshot strategy of a traditional method, the system can dynamically adjust the snapshot strategy according to real-time understanding of a traffic scene, the system can automatically improve the snapshot frame rate when detecting a high-risk scene in which a traffic accident is about to occur, and the snapshot frame rate can be automatically increased in a road section in which the traffic flow is small and the scene is simple. Snapshot resource consumption can be properly reduced; therefore, a complex and changeable traffic environment can be better dealt with, and the snapshot effectiveness and the resource utilization efficiency are improved.

The invention relates to the technical field of data processing, in particular to a digital twin scene efficient rendering method based on dynamic and static combination and a vertex shader, which comprises the steps of early-stage preparation and scene analysis, three-dimensional modeling and material mapping, vertex shader design and optimization, dynamic and static combination rendering process design, post-processing and optimization, and deployment and integration. The method has the beneficial effects that the static scene is quickly rendered by using the pre-rendered cache data, the dynamic element is dynamically rendered by combining the real-time data source and the vertex shader, and the dynamic element and the real-time data source are seamlessly combined by using the image fusion algorithm, so that the rendering speed of the digital twinning scene is remarkably improved, and the rendering efficiency of the digital twinning scene is improved. The real-time performance and interactivity of dynamic elements are ensured, natural fusion of static and dynamic scenes is realized through fine regulation and control of weight factors, a high-quality and vivid digital twinning rendering image is finally presented, and the user experience and the application effect are greatly enhanced.

The invention discloses a network construction type novel energy storage multi-objective optimization coordinated scheduling method, which comprises the following steps: S1, energy storage classification and data acquisition: (1) according to application scene function requirements, dividing energy storage types according to rated continuous discharge duration; (2) data acquisition; s2, calling an energy storage scene and combining with weight parameter setting to construct a multi-objective optimization model: (1) establishing the multi-objective optimization model with the purposes of minimizing the system operation cost, maximizing the energy storage utilization rate and improving the power grid stability; (2) setting constraint conditions; s3, a dynamic optimization algorithm: (1) adopting an improved multi-target particle swarm algorithm or a genetic algorithm, introducing an adaptive weight mechanism, and combining real-time data to dynamically adjust the weight of a target function; (2) aiming at uncertainty factors, embedding a scene analysis method or a robust optimization strategy, and judging whether the comprehensive efficiency is optimal or not; and S4, scheduling execution and monitoring.

The invention discloses a building surveying and mapping system based on an unmanned aerial vehicle remote sensing technology, and belongs to the technical field of building surveying and mapping. Comprising the following modules: a multi-sensor data acquisition module and an unmanned aerial vehicle data acquisition platform based on an integrated RGB camera, a laser radar and a multi-spectral sensor; the preliminary image processing module is used for generating standardized image data for scene analysis; the intelligent scene recognition module outputs a scene classification result and preliminary complexity grade evaluation; the self-adaptive decision module is used for generating an initial image processing parameter combination according to a scene recognition result; the dynamic image processing module is used for executing regional image registration, correction and seamless splicing based on the initial parameter combination to generate a building orthoimage; and the real-time quality evaluation module monitors and evaluates geometric accuracy and visual quality in the image processing process in real time.

The invention relates to a multi-time scale scene analysis-based micro-grid day-ahead and intra-day optimal scheduling method, which belongs to the field of micro-grid scheduling, and is characterized in that a variational mode decomposition (VMD)-long short-term memory network (LSTM) multi-scale prediction framework is constructed, ultra-short-term precision is improved through variable mode decomposition and frequency division prediction, a Canopy-spectral clustering-K-means hybrid algorithm is designed, and the optimal scheduling of a micro-grid is realized. A typical scene is generated based on Latin hypercube sampling (LHS), the scene coverage capability is enhanced, a day-ahead and intra-day two-stage optimization model is finally constructed, a high-dimensional problem is rapidly solved by adopting a mixed integer programming algorithm, and theoretical support is provided for high-proportion renewable energy consumption and micro-grid refined scheduling.

The invention provides an environmental protection planning dynamic generation method and system based on a multi-modal large model. The method comprises the following steps: firstly, obtaining environmental protection planning target scene demand information covering text and image forms; calling the multi-modal large model to carry out cross-modal scene analysis, and generating an analysis result containing the core demand elements and priority ranking; generating a preliminary environmental protection planning scheme through a cross-modal planning content generation module based on the analysis result; collecting scheme pilot run feedback information and inputting the information into the multi-modal model for feedback adaptation analysis, and generating an adjustment rule; and finally, dynamically adjusting the initial scheme according to an adjustment rule to obtain a final scheme. According to the invention, scene requirements can be comprehensively and accurately captured, a scientific planning scheme is accurately generated, dynamic optimization is realized, and environmental protection planning efficiency and quality are improved.

The invention belongs to the technical field of power system data processing, and relates to a work ticket information extraction and work video linkage analysis method, which comprises the following steps of: converting a work ticket into a text format by adopting an OCR (Optical Character Recognition) technology, removing irrelevant information by applying a text cleaning technology, performing named entity recognition and relationship extraction by utilizing a pre-trained language model, and generating a structured data table; key information of the work ticket is extracted, framing processing and compression enhancement are performed on the video, and a target detection model of a convolutional neural network and a time sequence convolutional network is applied to identify figures, equipment and important scenes in the video and analyze behavior actions to obtain a video detection result; structured information in a work ticket is matched with a video detection result, a time axis is established, and task arrangement in the work ticket and actual operation in a video are compared, so that dynamic compliance verification and risk assessment are realized; the efficiency and accuracy of information processing are improved, real-time monitoring and dynamic analysis are also realized, and on-site problems are found and processed in time.

The invention provides an acoustic system control method based on a scene type and a related device, and the method comprises the steps: carrying out the comprehensive analysis of a scene based on the collected real-time scene data in a current site, and obtaining the scene type of the current site at each time; when it is monitored that the current site is switched from the first scene type to the second scene type, based on a first acoustic parameter corresponding to the first scene type and a second acoustic parameter corresponding to the second scene type, an acoustic system is controlled to be transitionally switched to a target acoustic parameter; environment data of the current site at the time corresponding to the second scene type are collected; optimizing the second acoustic parameter based on the environmental data to obtain an optimized acoustic parameter; and controlling acoustic output parameters of an acoustic system of the current site based on the optimized acoustic parameters. According to the method, the problem of adapting to dynamic changes of field acoustic characteristics is solved, a good acoustic effect can be obtained in various scenes and environments, and the sound quality of an acoustic system is improved.

The invention relates to a part generation method based on atomization disassembly and encapsulation data. The part generation method comprises the following steps: step 1, analyzing a demand scene; step 2, carrying out atomization disassembly; step 3, performing standardization verification; step 4, packaging the function logic layer; step 5, packaging the element part layer; 6, packaging an interface layer; according to the method, the problems of function solidification and difficult splitting of a traditional data product are solved, and efficient circulation and value release of data elements are promoted; and reusability and circulation efficiency of data parts are improved through the element part layer.

The invention discloses a road abnormal event real-time monitoring and warning system, and the system comprises a data processing module which is used for collecting and preprocessing multi-source monitoring data; the region segmentation module is used for performing multi-scale region segmentation on the multi-source monitoring data by utilizing a pyramid scene analysis network to form a region segmentation map; the weight calculation module is used for constructing a weighted relation graph of road scene areas and abnormal event types and calculating the weight of the abnormal event in each area; the state evaluation module is used for updating node states in the graph and evaluating the occurrence probability of road abnormal events in different areas; the classification labeling module is used for carrying out risk classification on the abnormal events according to the state updating result and generating real-time alarm information; and the feedback adjustment module is used for dynamically adjusting network parameters according to the real-time monitoring result and alarm information feedback. According to the method, the pyramid scene analysis network, the graph attention neural network and the Bellman equation are combined, so that efficient and accurate road abnormal event real-time monitoring and warning are realized.

The invention provides a real-time video monitoring intelligent analysis system based on deep learning, and relates to the technical field of computers, and the system comprises a video collection unit which is used for obtaining a real-time monitoring video stream to be analyzed, and extracting a target video frame sequence from the real-time monitoring video stream; the scene analysis unit is used for processing the target video frame sequence by using a pre-trained scene analysis model to obtain a dynamic object mask and a scene semantic graph corresponding to the real-time monitoring video stream; the acquisition unit is used for acquiring thermal infrared characteristics and dynamic environment variable information of a monitoring area corresponding to the real-time monitoring video stream; and the semantic segmentation unit is used for inputting the dynamic object mask, the scene semantic graph, the thermal infrared features and the dynamic environment variable information into a pre-trained semantic segmentation model to obtain a semantic segmentation result output by the semantic segmentation model. By applying the method provided by the invention, the monitoring video can be accurately analyzed.

The invention provides a coal mining data management system and method based on video artificial intelligence, and relates to the field of big data, and the system comprises a data collection module which is used for accessing video data of each scene of a coal mine; the identification module is used for identifying equipment and personnel; the analysis module is used for realizing artificial intelligent scene analysis; the information selection module is used for obtaining selected feature scene data; the comprehensive management module is used for realizing resource sharing and information intercommunication; the control linkage protection module is used for realizing intelligent linkage; and the user interface module provides an operation interface. According to the invention, key elements and dynamic changes thereof in a coal mining scene can be accurately identified and understood, and the fusion condition of virtual reality and a real scene can be more intuitively displayed and analyzed.

The invention discloses a charging pile power management system based on the Internet of Things, and particularly relates to the technical field of the Internet of Things, and the system comprises a data collection module which collects the real-time operation data of a charging pile through a sensor, and forms a data set; the data processing module is used for processing the data according to a preset or optimization strategy and generating a processed data set; the scene analysis module performs analysis based on the processing data set and divides the operation scene of the charging pile; the data feature extraction module is used for extracting and analyzing features when an optimization mechanism is triggered; the logic judgment module is used for formulating an optimization strategy through fuzzy logic reasoning and sending the optimization strategy to the data processing module; the power energy efficiency management module is used for adaptively adjusting power distribution and improving the energy efficiency of the system; the data transmission delay can be reduced, the response speed can be improved, a user can obtain smoother experience when using the charging pile, transmission of redundant information is reduced through an optimization strategy, and efficient power management is achieved.