171 results about "Intelligent city" patented technology

The invention relates to a smart city ESG multi-source data visualization analysis method, device and equipment and a medium, and the method comprises the steps: carrying out the standardized semantic annotation and space-time alignment of city multi-source heterogeneous data, and generating a semantic triple set with a space-time label; the real-time intensity of the causal relationship in the ESG dynamic knowledge graph is constructed and quantified; semantic binding is carried out on the map and the city information model, and a visual descriptor is generated; discovering and rendering a causal relationship chain in the three-dimensional scene based on user interaction; and deducing simulation intervention and optimizing model parameters. According to the method, dynamic and integrated presentation and interactive deduction of complex causal relationships of environment, society and governance in a multi-dimensional space-time scene are realized, and the problems of surface analysis, disjunction of data and space and insufficient decision support in the prior art are effectively solved.

The invention discloses a smart city planning three-dimensional scene reconstruction optimization system combined with semantic segmentation, and belongs to the technical field of three-dimensional scene reconstruction optimization. The system comprises a data sensing module which collects aerial images and laser point clouds, and complements a sheltered area to obtain city modeling data; the preprocessing module performs denoising, data registration and data fusion on the city modeling data to generate texture point cloud data; the semantic understanding module realizes multi-modal semantic segmentation of the texture point cloud data through a fine-tuned SAM network and an improved RandLA-Net, and semantic tag data is obtained through restoration and optimization; the reconstruction optimization module constructs an initial three-dimensional grid model based on the texture point cloud data, and optimizes a ground feature boundary and a missing region of the model in combination with a semantic tag; and the output application module converts the optimized model into a standard format and outputs the model. Through deep coupling of semantic segmentation and reconstruction optimization, the precision of the three-dimensional scene model is improved, and reliable support is provided for smart city planning.

The invention discloses an intelligent video monitoring system and method supporting AI strategy cloud side end cooperative execution. According to the system, unified management of computing power resources and dynamic deployment of an AI algorithm are realized based on an extended standardized protocol through a three-level architecture of a cloud side center platform, an edge computing node and an end side camera. The method comprises the following steps: a cloud platform acquires computing power information of edge equipment; an AI algorithm execution strategy is generated and issued according to service requirements; if the required algorithm is not deployed in the equipment, the equipment is controlled to download and automatically deploy an algorithm module; and finally, the driving equipment executes AI analysis and recovers the structured data. According to the invention, AI computing tasks are reasonably distributed through a cloud side-end cooperative computing architecture, so that the computing power construction cost of a central platform is effectively reduced; through algorithm and hardware decoupling and a standardized protocol interface, ecological binding is broken, on-demand loading and flexible scheduling of the algorithm are realized, and the economical efficiency, the flexibility and the intelligent level of the system in large-scale scenes such as smart cities and the like are remarkably improved.

The invention relates to the technical field of smart city services, and discloses a city service agent scheduling method and system based on intention recognition, and the method comprises the steps: collecting the multi-modal original request data of a city complex, and carrying out the preprocessing and feature extraction; identifying a multi-level intention, extracting an intention entity and a constraint condition, and evaluating an intention priority; constructing an intelligent agent capability model and a knowledge base, calculating a semantic matching degree between the intention and the intelligent agent, and generating an intelligent agent combination scheme according to a matching result; constructing a multi-objective optimization model and generating a scheduling scheme of a single intention and a composite intention; task issuing is carried out, execution abnormity is processed, fault-tolerant recovery is carried out, task completion quality is verified, and user feedback is collected; intention recognition optimization is carried out, scheduling strategy parameters are adjusted, and intelligent agent capability evaluation is updated; according to the method, the multi-dimensional intention of the user can be accurately understood, and multi-agent efficient collaboration and resource optimization configuration are realized.

The invention provides a data processing method and device based on a smart city, and relates to the technical field of data processing.The method comprises the steps that a city-level spatial data base is constructed through oblique photography of an unmanned aerial vehicle, and a unified and real spatial foundation is formed by generating a live-action three-dimensional model, a digital orthoimage and a digital elevation model; on this basis, a building base and a building white model are extracted, spatial logic verification is carried out on multi-source business basic data, a standard address system stably associated with a building three-dimensional entity is further constructed, precise spatial anchoring of governance objects such as population, houses and units is achieved, and the method is suitable for mass production. And finally, the associated data is uniformly converged to a city information model platform, and reliable and updatable spatial data support is provided for smart city application. According to the invention, the timeliness of smart city data processing in a dynamic earth surface deformation scene can be improved.

The invention relates to the technical field of smart city public safety, in particular to an urban gas pipeline leakage risk intelligent early warning and positioning method and device.Firstly, a digital twinborn construction and dynamic coupling module constructs and drives a virtual image of a gas pipe network, then, a multi-modal data acquisition and fusion module comprehensively senses the state of the pipe network, and finally, the leakage risk of the gas pipeline is monitored. The intelligent early warning and positioning decision module realizes a core intelligent algorithm; a cloud-edge-end collaborative architecture is adopted, and a terminal sensor is responsible for data acquisition; the edge computing node is deployed in a regional pressure regulating station and is used for carrying out primary data processing and local real-time alarming; the cloud center deploys a digital twin platform and a core AI algorithm model, and performs big data analysis, simulation deduction and global decision. Compared with the prior art, the method can realize early and accurate identification and prediction of gas leakage and potential risks (such as abnormal stress) of the pipeline.

The invention relates to the field of data processing, in particular to an information retrieval method for a smart city system, which comprises the following steps of: obtaining a historical data block of a business system and user interaction session data, and performing word segmentation to obtain an original word and a to-be-processed word set; calculating topic probabilities and topic correlation degrees of original words and historical data blocks through a topic model, screening candidate words and calculating importance degrees of the candidate words; expansion words and historical perception words are generated and spliced into a dynamic sequence, the semantic similarity of the sequence and historical data blocks is calculated, a preset number of data blocks are sequenced and displayed, and retrieval is completed. The topic probability of the original word and the historical data block and the corresponding topic correlation degree are calculated through the topic model, the candidate word inherits the topic attribute of the original word, the topic probability distribution consistency of the candidate word and the original word is quantified in combination with the JS divergence to screen the extension word, the topic deviation risk of semantic extension is avoided from the source, and the semantic extension efficiency is improved. And the retrieval matching precision is improved.

The invention discloses a smart city event perception and emergency scheduling method based on artificial intelligence. The method comprises the following steps: S1, constructing a standardized structured sample; s2, forming a city sub-event graph; s3, constructing a Bayesian event inference network, and synchronously recording intermediate layer feature response; s4, outputting a dynamic thermodynamic diagram as an event perception model; s5, constructing a multi-target scheduling graph, and generating an emergency scheduling task; s6, taking training of a lightweight student model as a target, constructing a fusion type knowledge distillation mechanism for edge deployment, and fusing teacher model output, intermediate layer feature response and Bayesian causal structure consistency as a distillation target; and S7, adjusting an event state association structure in the Bayesian event inference network and a response weight distribution mechanism of the lightweight student model. The method has the advantages of high reasoning precision, low response delay and edge deployment.

The invention relates to the technical field of smart city information management, in particular to a smart city information management method and system based on GIS and AI fusion, and the method comprises the steps: carrying out the data mining and statistical analysis of the basic information of each economic entity in a city, and achieving the overview of economic development situations; collecting geographic position data of an economic body and matching coordinates, and constructing a three-dimensional digital city model associated with a GIS system; a hierarchical comprehensive evaluation algorithm is adopted to calculate the economic innovation value, the contribution degree of the economic innovation value is analyzed, and a long-short memory network is utilized to carry out space-time dynamic evolution analysis and trend prediction; and intelligent semantic understanding and personalized question and answer feedback are realized by analyzing a multi-modal query request of a user. The problems that an existing system is difficult in data fusion, static state analysis lags behind, and decision support is insufficient are solved, unified perception, intelligent analysis and scientific decision support of the urban operation state are achieved, and the urban fine management and service level is remarkably improved.

The invention relates to the technical field of smart city operation and maintenance, in particular to a smart city-oriented municipal facility state operation and maintenance analysis method. The method comprises the following steps: collecting multi-source monitoring data of municipal facilities; performing linkage facility state load feature analysis under each collaborative operation based on the municipal facility multi-source monitoring data, and generating linkage facility state load feature data; performing linkage facility state load characteristic trend prediction processing on the linkage facility state load characteristic data to generate linkage facility state load characteristic trend data; establishing a load energy efficiency multi-agent decision model according to the linkage facility state load characteristic data and the linkage facility energy efficiency data; and transmitting the state load characteristic trend data of the linkage facility to a load energy efficiency multi-agent decision-making model to carry out linkage facility intelligent control parameter analysis of global energy efficiency optimization, and generating linkage facility intelligent control parameters. According to the invention, state linkage intelligent operation and maintenance control of smart city municipal facilities is realized.

The invention discloses an event automatic allocation method and system based on historical disposal information and a multi-target genetic algorithm, and relates to the technical field of information technology and smart city management. Comprising the steps that 1, event data samples which are processed are extracted from a historical event library, each data sample comprises event features, historical processing departments and multiple processing performance indicators, and a training data set is constructed according to the data samples; step 2, training an adaptation degree prediction model based on the data set, learning a mapping relation from the combination of the event characteristics and the disposal departments to each performance indicator through the adaptation degree prediction model, and predicting each performance result possibly generated by any combination of the event and the disposal departments; step 3, when a new event arrives, predicting performance results generated by multiple performance indicators of each candidate department by using the fitness prediction model, and establishing a multi-objective optimization model; 4, solving the multi-objective optimization model by adopting a multi-objective genetic algorithm to obtain a group of Pareto optimal allocation schemes; and 5, selecting a final allocation scheme from the Pareto optimal solution set according to a preset strategy, and executing the final allocation scheme.

The invention discloses a smart city planning method and system based on digital twinning, relates to the technical field of city planning, and solves the problems that the scheme is not feasible due to dependence on manual interpretation of regulation texts, lack of automatic generation and matching of index constraint lists and possible neglect of key constraint conditions in planning suggestions, and meanwhile, the scheme is high in subjectivity and low in cost. And a unified quantitative model is lacked. Through the NLP technology, planning suggestions and regulation terms are compared in real time, conflict indexes are automatically marked, planning conditions and preliminary index calculation results are combined, the generated project planning suggestions strictly follow a policy framework, and manual interpretation errors or violation risks are avoided. A collaborative quantitative evaluation model of the single building and the regional landscape is established, and the landscape effect of the planning suggestion is evaluated from the dimensions of spatial layout, visual vestibule, skyline, color coordination and the like. The fusion degree of the project and the surrounding environment is visually reflected through quantitative scoring, and the deviation of subjective evaluation is avoided.

The invention discloses an urban inland inundation risk intelligent monitoring and early warning device based on short and temporary rainfall forecast, and relates to the technical field of urban disaster prevention and reduction and smart cities. The device comprises a generative approach rainfall forecasting module, an urban hydrological underlying surface module, an intelligent waterlogging risk deduction module, a high-performance calculation and data base and an intelligent emergency decision support module, the generative approach rainfall forecasting module outputs high-temporal-spatial-resolution rainfall data, and the urban hydrological underlying surface module provides static and dynamic hydrological data. Inputting the two into an intelligent waterlogging risk deduction module to calculate a dynamic risk level; the high-performance computing and data base provides computing power and data support for all the modules, the intelligent emergency decision support module generates a dynamic risk avoiding path and an emergency plan based on a risk result, and the device improves waterlogging early warning accuracy and emergency decision effectiveness and assists active defense of urban waterlogging.

The application discloses a cloud multimodal consistency constraint key abnormal event identification method and system. For the multimodal time series data generated by cameras, environmental sensors and other devices in smart city, industrial internet of things and other scenes, a completely unsupervised event-level abnormal understanding framework is proposed: through a multimodal consistency constraint mechanism, a cooperative abnormal period is accurately positioned; the abnormal period is clustered online to form an event cluster; based on Z-score significance analysis, the most abnormal feature dimension in each cluster is extracted and mapped into a human-readable semantic description; combined with the three indexes of the number of affected devices, duration and state deviation, the keyness of the event is quantified, and a ranked list is output. The application does not need to predefine event types or label data, can discover, name and sort known and unknown abnormal events at the same time, significantly improves the discovery and response efficiency of key abnormal events, and is suitable for lightweight cloud deployment of large-scale sensing networks.

The invention provides a smart city data linkage method based on digital twinning, and the method comprises the steps: constructing a fusion modeling layer, constructing a lightweight digital twinning model through CIM, BIM and GIS technologies, packaging a scenarized simulation model through a micro-service architecture, and forming a data linkage space carrier; establishing a space-time-event double-main-line data linkage mechanism by taking the scene as a reference, accessing multi-source data through a stream processing engine and associating the multi-source data to a space coordinate system to form a dynamic data link; a semantic mapping layer based on Ontology unifies data modes and associates and maps the data modes, a cross-domain data linkage channel is constructed, and heterogeneous data semantic-level linkage is achieved; recognizing the state change of the facility through a time sequence model, and generating a cross-department automatic instruction chain in combination with spatial association to form scenarized closed-loop linkage; and constructing a decision support linkage system by using feedback data, integrating a quantitative model and a deduction engine, and converting the data into visual decision information to complete a full-chain scenarized closed loop. The method solves the problems of traditional data dispersion and the like, improves the linkage accuracy and processing efficiency, and provides support for urban governance.

The invention relates to an information interaction method and device fusing a large language model and a digital twinborn platform, and belongs to the technical field of computers.The method comprises the steps that when a large oracle model needs to be called for semantic analysis processing, a vectorized knowledge base is constructed based on digital twinborn space information; performing semantic matching on the natural language instruction and vectorization information in a vectorization knowledge base to obtain enhanced context information related to the natural language instruction; the natural language instruction and the enhanced context information are input into a large language model together, reasoning is carried out through the large language model, and a structured execution instruction is generated; and converting the structured execution instruction into the first control instruction identified by the digital twin platform and executing the first control instruction, thereby facilitating the smart city management platform to support natural language input and context-aware interaction, and further facilitating the improvement of the intelligent level and the man-machine interaction efficiency of the smart city management platform.

The invention discloses a city safety abnormal event image generation method, system and device and a medium, belongs to the technical field of computer vision and smart cities, and aims to solve the technical problem of how to improve the generation quality and diversity of city safety abnormal event images. According to the technical scheme, the method comprises the following steps: constructing and preprocessing a city safety abnormal event data set: collecting monitoring images containing normal scenes and various abnormal events, constructing a labeling graph by labeling abnormal region positions and types of the monitoring images, and dividing a training set and a test set according to a preset proportion; heterogeneous feature fusion is executed, and comprehensive condition features are generated: an original monitoring image is coded into latent layer features, downsampling is carried out on a labeled image, and the labeled image and Gaussian noise features are spliced and fused in a channel dimension; performing field adaptation on the pre-training model based on an intelligent fine tuning framework; integrating multi-scale and space attention mechanism to enhance feature extraction; performing condition-guided image generation; and generating performance is quantified by adopting a systematic evaluation system.

The invention discloses a smart city power marketing service simulation evaluation method and system based on digital twinning. The method comprises the steps of obtaining power marketing service data, performing space-time semantic analysis, extracting attribute dimensions, establishing a cross-domain attribute association graph, establishing a cross-domain attribute association graph, and constructing a digital twinborn model; the target marketing service process is decomposed into service units, the service units are decomposed, a local simulation domain is constructed for parallel simulation, and a combined simulation result is obtained through an inter-domain coupling operator; and comparing a simulation result with real data, identifying virtual and real deviations, performing reverse traceability decomposition on virtual and real deviation distribution, mapping the virtual and real deviation distribution back to each local simulation domain, determining a local contribution magnitude of each local simulation domain, and constructing a hierarchical evaluation index to obtain a service quality evaluation result. According to the invention, accurate evaluation of the power marketing service is realized, and the service quality supervision efficiency is improved.

The invention discloses a large language model alignment method based on multi-user preference dynamic balance, and the method comprises the steps: collecting user interaction data, preference comparison data and public policy question and answer data in a livelihood consultation scene, and dividing the data into structured preference data and unstructured data; preprocessing the divided data to generate a user feature vector and a response feature vector; constructing a user-response bipartite graph, performing multi-hop preference propagation through a graph neural network, capturing potential association among users, and outputting a user preference embedding vector; embedding a vector selection expert path according to user preference through a dynamic gating function, and generating a personalized response; and through an optimization-free embedding aggregation strategy, similar users are retrieved based on a graph structure, and the embedding of the similar users is weighted and aggregated, so that rapid cold start of new users is realized. The precision and efficiency of livelihood consultation can be remarkably improved, and the method is particularly suitable for smart city scenes with high-frequency policy updating and frequent user flow.

The invention discloses a charging pile metering and billing optimization control method based on an intelligent city management platform, and relates to the technical field of billing optimization, and the method comprises the steps: recognizing the delayable charging capability of each charging request, and dividing the requests into high-elasticity schedulable requests and low-elasticity supportability requests according to the capability; calculating the total schedulable power which can be provided by all the high-elasticity schedulable requests in the target adjustment time period; the total schedulable power is compared with the adjustment demand power of the power grid, the technical problem that the deviation between the declaration duration and the actual duration is too large in charging request elastic classification is solved, and the classification precision is improved.

The invention discloses an urban space type identification method based on a mixed density clustering algorithm, and relates to the technical fields of smart cities, urban planning, tourism planning, urban traffic and the like, and the method comprises the steps: constructing a data set for urban space type identification for a research area; based on the constructed data set, urban space division processing is carried out by using a DBSCAN density clustering algorithm, and an obtained result is marked as a clustering result D; performing unique clustering processing on each category S in the D based on a C-means clustering or Gaussian mixture model (GMM) algorithm to obtain a central point position P of each category S; counting the number of facility POIs in a local spatial range for the central point position P of each category S, and recording an obtained result as a local spatial information counting result; and determining the type of each category in the clustering result D according to a local spatial information statistical result. According to the invention, clustering of the service point space data set and identification of the urban space type can be realized based on the design of the mixed density clustering algorithm.

This invention provides a CIM model generation method and system based on spatial intelligence and visual knowledge fingerprints, belonging to the field of smart city digital twins; it solves the problem of low efficiency in building texture generation; specifically as follows: edge detection is performed on the top view of the original CIM model, and the plot boundaries are synthesized with the Canny edge map to generate the building layout; planning indicators are extracted from the GIS data of the target plot, and a white model is generated using a modeling engine based on the building layout; the white model is automatically UV unwrapped, and a semantic segmentation map is output based on constraint rules and construction generation logic, and then encoded based on the visual features parsed from the target plot CIM model to generate texture maps; the generated texture maps are remapped back to the surface of the CIM3 level white model to obtain a generative reconstruction model; this invention realizes the continuous regeneration of CIM data features based on spatial intelligence, and solves the contradiction between privacy and utility by resolving spatial logic reasoning and generation.

1. Name of the product in this design: Edge Computing Server (RL-3000H4 4U). 2. Purpose of this design: It is used for data computing and processing, and can have functions such as computing, storage and AI acceleration. It is suitable for smart cities, industrial internet, smart retail, smart manufacturing, vehicle networking, cloud computing, 5G and other scenarios. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model. 5. The bottom surface of the product is a part that is not easily seen or cannot be seen during use, so the bottom view is omitted; it is already shown in the perspective view, so the top view is omitted.

The invention discloses a smart city public resource dynamic distribution system based on AI, and the system comprises a data collection and fusion module which obtains and fuses resource state data and public resource demand data of a smart city, and generates multi-source heterogeneous real-time city data; the artificial intelligence demand prediction module is used for outputting a quantitative demand prediction result according to the multi-source heterogeneous real-time city data; the dynamic optimization decision module is used for generating an optimized dynamic resource allocation strategy based on a quantitative demand prediction result, an allocation optimization target and a current public resource constraint condition; and the resource scheduling execution and feedback module is used for converting the optimized dynamic resource allocation strategy into a target control instruction, issuing the target control instruction to a public resource execution terminal to complete resource scheduling and allocation, collecting actual state data after resource allocation and feeding back the actual state data. According to the invention, the dynamic prediction and optimization decision of the resource demand are realized, the emergency response speed and the global optimization capability are improved, and the efficient and intelligent resource allocation demand of the smart city is met.

The invention relates to the technical field of smart city management and planning decision, in particular to a smart city planning decision support system and method based on multi-source data fusion. The smart city planning decision support system based on multi-source data fusion comprises a data acquisition module for acquiring functional area data of a target lag and stagnation area and original city planning data; the data processing module is used for processing the functional area data and the original city planning data to obtain functional area core strength data and planning influence data with time sequence marks; and the model matching module is used for constructing a planning data set according to related planning case data of each region, and acquiring recommended planning data based on the planning influence data and the planning data set. According to the method, a decision support system fusing multi-source data and integrating intelligent matching and dynamic evaluation is constructed, and the key defects that in traditional city planning, the data source is single, analysis is static and one-sided, decision depends on subjective experience, and scheme rigidity lacks elasticity are effectively overcome.

The utility model provides a smart city novel street lamp which comprises a main body, the main body comprises a base, the upper end of the base is provided with a first bending section, the upper end of the first bending section is fixedly connected with a vertical section, the upper end of the vertical section is fixedly connected with a second bending section, and the upper end of the second bending section is fixedly connected with an inclined section; a camera and an illuminating lamp are arranged on the lower end face of the inclined section, and the camera is located below the illuminating lamp. The intelligent street lamp is attractive in appearance, and meanwhile, the camera, the display screen and the wireless charging module are arranged on the street lamp, so that the multifunctional arrangement meets the construction requirement of a smart city.

The present application relates to the field of smart city environment management, and more particularly to a carbon emission information data analysis method and system based on CIM, which overcomes the drawback of ambiguous spatial location of emission data in traditional methods by implementing precise coding matching and binding of CIM spatial units and carbon emission data. The above spatial characteristics and real-time dynamic data are input into a strength analysis model based on machine learning. Personalized intelligent evaluation is achieved. After identifying high-emission units, the method can use the established precise spatial mapping for reverse intelligent tracing, quickly locating and quantifying specific activities and sources causing high emissions, and achieving precise diagnosis. Based on the clear diagnosis conclusion, further combined with the knowledge base and optimization algorithm, customized and quantifiable emission reduction strategies are automatically generated. Finally, differentiated results are pushed to different roles.

The present disclosure provides a target detection method and a target detection model training method and device, relates to the technical field of artificial intelligence, in particular to the technical field of augmented reality, virtual reality, computer vision, deep learning and the like, and can be applied to scenarios such as smart cities. The implementation scheme is as follows: image data including a target object is obtained, and a target category feature corresponding to a target category of the target object is obtained, wherein the target category feature is fed to a decoder of a target detection model used for target detection; the target category of the target object is predicted based on the target category feature, to obtain a predicted category and a predicted category feature corresponding to the predicted category; the consistency of the predicted category and the target category is determined based on the similarity between the predicted category feature and the target category feature; and the predicted category is taken as a result of target detection on the target object according to the consistency of the predicted category and the target category.

The invention provides an urban green space intelligent identification method and system based on a high-resolution remote sensing image. The method comprises the following steps: step 1, preprocessing the high-resolution remote sensing image to obtain a preprocessed image; 2, performing semantic segmentation on the preprocessed image based on a visual large model to generate an initial city green space mask; 3, constructing a comprehensive vegetation index based on multi-source vegetation index fusion, and extracting an urban vegetation layer through dynamic threshold segmentation; and 4, carrying out space algebraic operation and optimization processing on the initial green space mask and the urban vegetation layer to generate a fine-grained urban green space distribution map. The method provided by the invention provides method support for urban green space distribution based on high-resolution remote sensing images, is suitable for efficient and high-precision urban green space intelligent identification, and can provide reliable technical support for applications such as smart cities and sustainable development targets.

The invention discloses an automatic garbage classification device for a smart city and a using method thereof, relates to the technical field of garbage classification, and solves the problems that in the existing garbage throwing and classification process, classified collection of a garbage truck is inconvenient, solid-liquid separation of garbage cannot be achieved, electric quantity supplementation of the garbage can is inconvenient, and environmental protection is not facilitated. The automatic garbage classification device for the smart city and the using method of the automatic garbage classification device for the smart city comprise a mounting and conveying mechanism, and two classification and storage mechanisms are mounted on the inner side of the mounting and conveying mechanism; and the classified storage mechanism comprises a classified storage box, a movable baffle, a fan box, a liquid injection plate, a disinfectant spraying head, a garbage guide inclined plate, a garbage temporary storage seat, a garbage compression plate and a hydraulic rod. According to the garbage disposal device, garbage which is thrown in a classified mode is automatically compressed and subjected to solid-liquid separation, garbage transfer and disposal are facilitated, cleanliness of a throwing point can be kept, and electric energy supplementation is facilitated by adjusting the angle of the solar cell panel.