244 results about "Coverage data" patented technology

The invention relates to the technical field of landscape evaluation, and discloses an urban green land landscape evaluation method based on a large model, and the method comprises the steps: carrying out the real-time analysis and standardization processing of a streetscape image, IoT environment data, satellite vegetation coverage data and RTK high-precision positioning information of a target city district, extracting core landscape elements, building cross-scene semantic mapping, and carrying out the calculation of the cross-scene semantic mapping. Transmitting the standardized data to a central database and deploying edge nodes; extracting a multi-dimensional landscape index by adopting an improved semantic segmentation model, dynamically adjusting an index weight in combination with regional features and seasonal changes, and classifying and correcting deviation data by edge nodes; a visual evaluation result is generated based on a cooperative computing architecture and a digital twinborn model, a landscape space to be promoted is identified, and optimization suggestions are generated; and obtaining planner feedback information, updating the model, the weight rule and the suggestion generation strategy, and forming a closed loop iteration mechanism. According to the method, the dynamic and practical evaluation requirements of the current urban green land landscape can be met.

Disclosed are a three-dimensional laser point cloud-based method and system for in-depth vegetation management in transmission corridors. The method comprises: collecting multi-source data by means of an unmanned aerial vehicle- and helicopter-mounted multi-sensor system, and preprocessing the multi-source data; using the processed multi-source data to create a digitalized power grid corridor and construct a vegetation management analysis model; on the basis of data analyzed by the vegetation management analysis model and geofencing technology, designing a hazard vegetation clearing-cost-compensation prediction model; and developing a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows. The described method achieves high-precision and high-coverage data collection, precisely identifies transmission line digital twins and hazard vegetation, standardizes and automates the management of hazard vegetation clearing and related compensation, and develops a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows, thereby achieving the end-to-end digital and mobile management of vegetation management. The organic combination of steps comprehensively improves the intelligence and efficiency of transmission line vegetation management.

The invention relates to the field of industrial automation testing, discloses a multifunctional testing tool and method for an EUROMAP interface of an injection molding machine, and achieves the crossing from single function verification to system-level compliance evaluation. A test network is constructed through a controller, and deep compliance verification, coverage data interaction, exception handling and cross-layer response consistency are realized. The test tool integrates the functions of protocol analysis, signal simulation, fault injection and the like, supports dynamic generation of boundary value test scenes and abnormal injection of signal delay, CRC errors and the like, and verifies the time sequence / logic consistency of a protocol instruction and an execution mechanism response through a hardware-level timestamp alignment technology. According to the method, an evaluation report including compliance rating, fault diagnosis and optimization suggestions is automatically generated, the problems of single coverage dimension, insufficient abnormal simulation, cross-layer verification deficiency and the like of a traditional test are solved, the intelligence and standardization level of the injection molding machine interface test is remarkably improved, and the equipment interconnection and intercommunication performance is guaranteed.

The invention discloses an optimal signal selection intelligent network switching management method based on a super SIM multi-network, and relates to the field of intelligent network switching management, and the main scheme is as follows: analyzing and calculating operation state data to obtain a network delay score, a downloading rate score and a network stability score in a current network environment, and carrying out comprehensive analysis to obtain an optimal signal selection score; obtaining a current network state score; judging whether to execute a network switching instruction or not by evaluating the current network state score; the coverage degree data, the network state data and the switching cost data are analyzed and calculated to respectively obtain coverage matching degree indexes, network state indexes and switching cost-benefit ratios of different operators; switching priority indexes of different operators are obtained through comprehensive analysis, the switching priority is determined, and the problem that switching decisions and requirements are disjointed due to the fact that switching is triggered only based on signal strength or a single rate threshold and core indexes such as network delay and stability which affect user experience are not fused is solved.

The invention provides an individual credit risk assessment method, system and device based on federal learning, and belongs to the technical field of data processing. The method comprises the following steps: in response to a credit evaluation request from a credit evaluation node, determining one or more evaluation participation nodes of a credit evaluation object; generating a corresponding local credit evaluation instruction and sending the local credit evaluation instruction to each evaluation participation node; wherein the local credit assessment instruction comprises a desensitization identifier of the credit assessment object. And determining a global credit score of the credit assessment object based on the local credit score fed back by each assessment participation node and the weight set corresponding to the credit assessment request, and sending the global credit score to the credit assessment node. Through the method, the credit assessment coverage data is improved, and the credit assessment is carried out in time, so that the personal credit risk assessment is accurately completed.

The invention discloses an urban block green land planning method and device based on thermal environment big data, and the method comprises the steps: obtaining the thermal environment data, vegetation coverage data and a vegetation database of a target block; constructing a thermal environment map of the target block based on the thermal environment data, and obtaining a thermal environment feature vector of each sub-block according to the thermal environment map; acquiring a microclimate influence value of each vegetation type; performing correlation analysis on the thermal environment feature vector and the microclimate influence value of each sub-block to obtain the microclimate adjustment contribution degree of each vegetation type to each sub-block; and according to the microclimate adjustment contribution degree, determining a target vegetation type to be planted in each sub-block and a planting proportion of each target vegetation type, and obtaining a vegetation configuration scheme of the target block. The microclimate adjusting capability of the urban green land can be improved.

The invention provides a wind power data determination method and device based on geographical constraint attention. The wind power data determination method comprises the steps of obtaining topographic data, surface vegetation coverage data and wind power data; for the wind power data, combining the topographic data and the surface vegetation data, performing feature fusion through an adaptive attention mechanism to obtain a fusion feature containing multi-source information; performing shallow feature extraction, deep feature extraction and image up-sampling on the fusion features in sequence, wherein the deep feature extraction realizes multi-scale feature extraction and geographic constraint fusion through a multi-stage residual fusion module RFSTB, a wind field frequency domain decoupling module CFB and a geographic perception global attention module GAB; verifying the effectiveness of the model by using a peak signal-to-noise ratio (PSNR) and a structural similarity index (SSIM); and performing super-resolution reconstruction on the wind power data based on the trained model to generate wind power data with predetermined precision. The method provides support for fine evaluation and efficient development of wind energy resources in complex terrain areas.

The invention discloses a method for reducing line loss and improving power supply reliability based on the Internet of Things, and the method comprises the steps: carrying out the full-dimension load and line state monitoring: deploying an Internet of Things sensor, constructing an end-to-end monitoring network, and collecting key parameters in real time; a low-delay and wide-coverage data channel is constructed; a layered communication architecture is adopted, and efficient data transmission is realized; the real-time analysis and intelligent decision engine is used for constructing a load-line-equipment three-dimensional data model based on a cloud end and an edge platform, and realizing line loss real-time diagnosis, load prediction, situation awareness and rapid fault positioning; dynamic optimization and self-healing control: based on a data layer analysis result, source-network-load coordinated regulation and control are realized through an Internet of Things terminal, line loss is reduced, and stable power supply is guaranteed; user interaction and global optimization: realizing power grid-user interaction and peak load shifting through a user-side Internet of Things terminal to reduce the pressure of a power system; the system has the advantages of being capable of reducing line loss, guaranteeing power supply reliability, having various functions and being good in comprehensive application effect.

The invention relates to the technical field of electronic design automation, in particular to a coverage rate-based index database management method, coverage rate-based index database management equipment and a storage medium. Meanwhile, extracting design level information in the coverage rate database to establish an index database consisting of a plurality of index tables; when the visual query window initiates a query request, the index database queries the identifier of the target object according to the query request, and processes and updates the target window of the coverage rate visual system according to the identifier in the coverage rate data structure, so that the search efficiency is greatly improved, and the search time is shortened. And the purpose of real-time searching is achieved.

The invention discloses a cloud management and intelligent distribution method for classroom teaching resources, and relates to the technical field of cloud computing software. The classroom teaching resource cloud management and intelligent distribution method comprises the steps that S1, multi-source teaching resource data and auxiliary distribution data are acquired and preprocessed, and a knowledge content database is constructed; s2, performing semantic fusion analysis through text semantic association, structural consistency and professional term coverage data; s3, carrying out dependency evaluation by fusing pre-association, knowledge co-occurrence, sequence association and semantic fusion data; s4, performing resource adaptability distribution analysis through the maximum knowledge dependence value in combination with the learning state data and the historical feedback data; and S5, evaluating the teaching resource quality by collecting classroom feedback, teacher evaluation and resource use data, and implementing content optimization. The problems of semantic distortion, path breakage, distribution mismatching and resource updating inconsistency in the teaching resource data fusion and intelligent resource distribution process are solved.

The invention belongs to the field of software testing and defect detection, focuses on reliability verification of a physical simulation engine, is suitable for scenes depending on physical simulation, such as robot simulation, automatic driving testing, unmanned aerial vehicle control, education and scientific research, and particularly relates to a variation-based physical simulation engine defect detection method. Potential defects of core modules of collision detection, mechanical calculation, dynamics and the like are efficiently found through a variation test method. According to the method, a feature library is constructed through static library API analysis and AST / IR code analysis, test code expansion is performed by utilizing a large model, a test driver adaptive to a specific data type is generated, a physical semantic constraint mutation seed is combined, a test is executed through libFuzzer, defect and coverage rate data are analyzed, a use case selection strategy is optimized, and the test efficiency is improved. Defects of core modules such as collision detection and dynamic solution are efficiently detected, and the test pertinence and efficiency are improved.

The invention discloses an incremental code coverage rate acquisition device based on a Java bytecode dynamic instrumentation technology, and relates to the technical field of software testing and quality control. Comprising a code difference analysis module, a byte code instrumentation module, a coverage rate data acquisition module, a coverage rate data integration module, a coverage rate report generation module and a test trigger and threshold control module, and a syntax tree structure of incremental codes is generated based on a Java syntax analysis tool, so that line level analysis of code differences is realized. According to the method, incremental codes are accurately analyzed through Git and JavaParser, coverage rate data are collected through dynamic instrumentation, a unified report is generated in combination with path analysis, missing scenes are marked in a highlighted mode, and the test efficiency and the code quality control capacity are improved.

The invention discloses a continuous casting adjustment method and system for crystallizer assembly simulation, and relates to the technical field of continuous casting process optimizing.The method comprises the steps that networking data collection is conducted according to equipment ID and molten steel component information of a crystallizer assembly, and continuous casting historical data are obtained; performing working condition expansion on the continuous casting historical data to obtain continuous casting coverage data; establishing an initial finite element model; calibrating boundary conditions to obtain a continuous casting finite element model; clustering analysis is carried out, and a continuous casting optimization difference starting point is positioned; parallel local iteration optimization is started, and a continuous casting multi-target equilibrium solution set is obtained; and performing global iteration optimization to obtain the continuous casting adjustment control reference. The technical problem that in the prior art, the blank shell uniformity and thickness are difficult to accurately control in the continuous casting process, and consequently the continuous casting quality is unstable is solved, and the technical effects that the blank shell uniformity and thickness are indirectly optimized and controlled in the continuous casting process, and the continuous casting quality is improved are achieved.

The invention provides a satellite remote sensing high-temperature heat wave risk early warning method, device and equipment and a medium. The method comprises the following steps: acquiring satellite remote sensing data, numerical mode forecast data, cloud coverage data and land utilization type analysis data corresponding to a research area; according to a model dynamic allocation mechanism, dynamically allocating a corresponding high-temperature heat wave risk early-warning model for each grid in the research area according to the cloud coverage data, and determining a high-temperature heat wave early-warning result corresponding to each grid based on satellite remote sensing data and numerical mode forecasting data through the high-temperature heat wave risk early-warning model corresponding to each grid; and determining a susceptibility weight corresponding to each grid according to the land utilization type analysis data, and determining a high-temperature heat wave risk level corresponding to each grid in combination with the high-temperature heat wave early warning result corresponding to each grid. According to the method, the high-temperature heat wave prediction precision can be remarkably improved, and meanwhile risk early warning is more regional and targeted.

The invention provides an automobile electronic control unit fuzzy testing method based on partial code coverage rate feedback and a computer, and belongs to the technical field of automobile electronic control unit code testing. The computer performs static analysis on source codes of the automobile electronic control unit, identifies inevitable execution blocks and conditional execution blocks, performs selective instrumentation on the conditional execution blocks, and inserts coverage rate recording instructions of the conditional execution blocks. And the computer source code is loaded into the automobile electronic control unit, and a test input signal is generated through the fuzzy test engine and is injected into the automobile electronic control unit. In the process of executing the program by the automobile electronic control unit, recording the coverage condition of the condition execution block to a bitmap in the memory; the computer reads the coverage rate data of the condition execution block through the debugger every time a complete execution cycle is completed; and the fuzzy test engine adjusts the input generation strategy according to the coverage rate data feedback of the condition execution block and tests again. According to the method, the automobile electronic control unit source code fuzzy test coverage condition is efficiently collected.

The invention discloses an automobile interior and exterior decoration product size measurement method based on automatic scanning, and belongs to the field of automobile part detection, and the method comprises the following steps: carrying out the non-contact comprehensive scanning of an automobile interior and exterior decoration product through a 3D automatic scanner, and obtaining high-precision point cloud data; preprocessing the point cloud data, including denoising, splicing and three-dimensional reconstruction, and generating a product digital model corresponding to the real object; performing automatic alignment and size comparison on the product digital model and a preset CAD design model, and calculating a deviation error of each feature size; and performing automatic qualification judgment on the deviation error based on a preset tolerance standard, and generating a detection report containing an error analysis result and a judgment conclusion. According to the automobile interior and exterior decoration product size measurement method based on automatic scanning, full-coverage data acquisition can be completed in a short time through 3D automatic scanning, the measurement efficiency is improved, the dependence on the experience of measurement personnel is reduced, and high-precision measurement of the size of the automobile interior decoration product is realized.

A method for delineating an ore body of deep-sea polymetallic nodules based on coverage data includes steps as follows. Ore body data of the deep-sea polymetallic nodules is obtained, the ore body data includes: mining site production scale parameters, mining system parameters, and ore body geological characteristic parameters. A boundary coverage rate and a cumulative weighted average coverage rate are calculated based on the mining site production scale parameters, the mining system parameters, and the ore body geological characteristic parameters. The ore body is delineated, based on the boundary coverage and the cumulative weighted average coverage, through a principle of ore body boundary range delineation to obtain an ore body range. The method delineates ore body of nodule with proper geological continuity based on abundance data. It can be directly applied to deep-sea polymetallic nodule mining, solving the problems existing in the delineation of ore body of deep-sea polymetallic nodule.

The invention relates to the technical field of intelligent robot inspection, and discloses a digital twinning-based intelligent AI inspection method, which comprises the following steps of: analyzing a multispectral image acquired by a sensor carried by a robot by utilizing a dynamic attention multilayer feature extraction network; generating an industrial facility abnormal coverage degree real-time distribution map; analyzing historical abnormal coverage data by using a space-time sequence contrast learning model, and generating abnormal coverage trend prediction; analyzing an abnormal coverage evolution process by using a physical constraint neural differential equation model, and generating an accurate abnormal evolution prediction result; generating an optimal inspection path by using a multi-objective optimization algorithm; dynamically updating the digital twin system, and generating an industrial facility maintenance optimization suggestion; according to the invention, through the dynamic attention multilayer feature extraction network and the illumination condition adaptive enhancement algorithm, the detection problem in the strong light and high reflection environment is effectively solved.

The invention discloses a lawn coverage evaluation platform based on image recognition, which belongs to the technical field of lawn measurement and comprises an image acquisition module, an image preprocessing module, a lawn area recognition module, a coverage calculation module, a scoring and analysis module and a result output module which are connected in sequence. The method comprises the following steps: performing standardized image acquisition on a lawn through an image acquisition module, and preprocessing an image to realize geometric standardization of the image; an artificial intelligence image segmentation model is adopted to improve the accuracy and adaptability of lawn area identification; the sample areas are scored and analyzed by calculating the coverage, and the method is used for comparative analysis of multiple varieties. By adopting the method, the acquisition efficiency, the identification accuracy and the evaluation standardization level of the coverage data in the lawn grass seed regional test can be improved, and the problems of non-uniform acquisition conditions, fragmentation of an identification method, incomparable results and the like in the prior art are solved.

The invention discloses a landslide maneuverability confirmation method and system based on an adaptive difference model, and relates to the technical field of data processing, and the method comprises the steps: obtaining a sub-slope region, and obtaining a vegetation coverage data sequence; obtaining a difference index, obtaining a first to-be-processed data sequence, obtaining a second to-be-processed data sequence, obtaining a difference sequence of the first to-be-processed data sequence, obtaining a first fluctuation index, and if the first fluctuation index exceeds a third preset threshold value, taking a sub-slope region corresponding to the first to-be-processed data sequence as a sliding source region boundary region; acquiring a difference sequence of the second to-be-processed data sequence, acquiring a second fluctuation index, and if the second fluctuation index exceeds a fourth preset threshold value, taking a sub-slope region corresponding to the second to-be-processed data sequence as a landslide region boundary region; and obtaining a target sliding source area, a target landslide area and an area proportion. The method has the advantages of self-adaptive difference distinguishing, stable and accurate boundary and dynamic quantization.

The invention discloses an accumulated snow coverage analysis method and system and a medium, and the method comprises the steps: obtaining a plurality of pieces of to-be-processed satellite data of a to-be-analyzed region; performing feature extraction on the to-be-processed satellite data to obtain multi-dimensional feature data of each preset pixel; analyzing the multi-dimensional feature data, and determining a first accumulated snow identification result; respectively determining a plurality of target preset pixels geographically covering each preset geographic grid; carrying out statistics on distribution of the first accumulated snow identification results of a plurality of target preset pixels in each preset geographic grid, and determining a state statistical index; constructing a time data sequence of a preset geographic grid; analyzing the time data sequence of each preset geographic grid, and determining a second accumulated snow identification result of each preset geographic grid; and generating an accumulated snow coverage data set of the to-be-analyzed area based on the second accumulated snow identification result. The precision of the accumulated snow coverage data is improved.

The invention discloses an automatic vegetation coverage extraction method for field camera survey. The method comprises the following steps: inputting an RGB electronic photo to be processed; calculating the overgreen index and the color saturation of the RGB electronic photo; determining a threshold value of over-green index segmentation vegetation pixel by pixel by using the color saturation; identifying a vegetation pixel based on the over-green index and a pixel-by-pixel threshold value; and counting the proportion of the number of vegetation pixels in the RGB electronic picture to the total number of pixels, thereby obtaining the vegetation coverage. According to the method, the optimal threshold value of ExG segmented vegetation can be determined pixel by pixel for RGB electronic photos imaged under different illumination conditions and ground feature scenes, so that high-precision automatic extraction of the vegetation coverage FVC is realized, and vegetation coverage data of a quadrat scale can be quickly and accurately acquired during field investigation; the method is simple in principle, easy to implement, high in adaptive capacity, high in automation degree and wide in application range, and has wide application value in the fields of ecology, geography, remote sensing and the like.

The invention discloses a chip verification coverage rate improving method, and relates to the technical field of chip verification, and the method comprises the steps: collecting coverage rate data in a chip verification process, and calculating a hash value of the coverage rate data; based on the hash value, dynamically adjusting a current excitation strategy by using a machine learning model, and sending an excitation signal to a verification environment according to the adjusted excitation strategy to trigger execution of a verification scene; and iteratively executing the process from collecting the coverage rate data to sending the excitation signal until an iteration termination condition is reached. According to the method, the hash value of the coverage rate data is calculated, the excitation strategy is dynamically adjusted by utilizing machine learning, and the targeted excitation signal is generated, so that the verification scene is accurately triggered, the coverage rate is gradually improved, the problems of difficulty in coverage rate improvement, high resource consumption and insufficient dynamic adaptive capacity in chip verification in related technologies are solved, and the chip verification efficiency is improved. And a more efficient, intelligent and resource-friendly solution is provided.

Methods and systems for automatically selecting a mobile network rate plan for an asset tracking device based on a set of reported geolocations of the device overtime, coverage data for various mobile networks, and a set of possible network rate plans. A change in mobility rate plans may be implemented through remote provisioning of a new mobile network profile to the asset tracking device.

The invention belongs to the technical field of machine vision measurement, and particularly relates to a semantic segmentation-based orchard green manure coverage machine vision measurement method, which comprises the following steps of: creating a multi-target optimization model and planning an image acquisition path based on a satellite map to obtain an optimal image acquisition path; carrying out orchard image collection based on the optimal image collection path, and carrying out small-amount labeling on the orchard images to construct a training set; creating a semantic segmentation model, carrying out initial training through the training set, then predicting unlabeled images, screening to-be-labeled images, and carrying out labeling and iterative training; the model outputs the probability that each pixel belongs to green manure or non-green manure; marking a green manure area and a fruit tree area in the orchard image through a two-layer image binarization process; and obtaining orchard green manure coverage data and fruit tree data based on a marking result, and formulating and executing a green manure supplementing scheme. The green manure coverage measurement efficiency and the result accuracy are improved, and the data processing burden is reduced while the actual coverage condition of orchard green manure is comprehensively reflected.

The invention provides a global biodiversity hot spot area threat condition remote sensing monitoring method and system, and relates to the technical field of ecological environment monitoring, spatial analysis and remote sensing and information processing, and the method comprises the steps: obtaining multi-temporal global land coverage data and auxiliary GIS data of a biodiversity hot spot area, and carrying out the preprocessing; extracting forest coverage layers of a reference period and a monitoring period from global land coverage data, and extracting pixels converted from a forest to a non-forest through a layer difference method to generate a forest loss layer; based on the forest loss map layer and the preprocessed auxiliary GIS data, calculating various ecological pressure indexes including a forest loss rate, a protection failure rate and a population pressure index, and performing normalization; and calculating a comprehensive threat index based on each ecological pressure index, and determining an ecological threat level. According to the method, automatic identification, quantitative evaluation and grading of the ecological threats of the hot spot region are realized, and technical support is provided for global ecological protection planning and management.

The invention provides an automatic calculation method, system, equipment and program product for incremental code coverage rate, which are applied to the field of software testing, and the method comprises the following steps: receiving a code version change event, collecting and merging binary coverage rate data, obtaining an incremental code change set, and generating a structured coverage rate report. According to the scheme, the incremental code coverage rate can be accurately calculated, and the code quality measurement efficiency is improved.

The invention discloses a method for predicting the coverage degree of an H ion negative electrode in a Zn aqueous battery, and relates to the technical field of surface catalysis. In order to solve the defect that in the prior art, it is difficult to accurately predict the coverage degree of H ions on the surface of a Zn negative electrode on a multi-scale level, the technical scheme provided by the invention comprises the steps that a three-dimensional atom structure model of H atoms and a Zn electrode adsorption surface is constructed, formation energy and adsorption energy of the H ions and water molecules are calculated, and a reaction potential energy surface is established; constructing a reaction path and an electron transfer behavior, defining potential related parameters, and outputting reaction rate, current density and coverage data; establishing a phase field dilute material transfer model containing temperature, pH and pressure parameters, and completing material migration and charge distribution calculation; interface physical field parameters are fed back to the microdynamics model to form microcosmic-macroscopic bidirectional data interaction and realize self-consistent convergence; and outputting a coupling model result to obtain multi-scale prediction data. The method is suitable for active site identification and performance prediction in electro-catalytic reaction.

The invention relates to the technical field of ecological system service value evaluation, in particular to an intelligent ecological system service value dynamic evaluation system, which comprises a data collecting and processing module for collecting ecological system related data and carrying out data processing on the collected data; the dynamic association module is used for establishing dynamic association among the data based on the processed data; the service value dynamic evaluation module is used for comprehensively evaluating the service value of the ecological system based on the established dynamic relevance between the data; according to the invention, through the dynamic association module, the dynamic association among the biological data, the water quality data and the tree coverage data is constructed, and the model can be dynamically adjusted according to the real-time data; the parameters of the evaluation model are adjusted in real time through a self-adaptive feedback mechanism and based on the actually observed ecological system service value, so that dynamic correction is carried out, the prediction precision of the model is improved, and early warning and real-time management can be carried out on potential service change or environmental deterioration in the ecological system.

The invention relates to the technical field of power grids, in particular to a smart power grid environment adaptive data processing method and system. According to the method, through cooperative work of an environment sensing sensor, a high-definition camera and an unmanned aerial vehicle inspection system, omnibearing environment information including meteorological data, landform data, vegetation coverage data and human activity data is obtained; a distributed storage architecture and a parallel processing technology are adopted to efficiently pre-process the environmental data; constructing a smart grid environment adaptive analysis model covering power planning decision support, power operation and maintenance decision support and power dispatching decision support; an analysis result is visually displayed through a multi-level visual interface based on the GIS technology, and environmental adaptability optimization of active defense, automatic recovery and optimal adaptation is achieved. The problems of uneven data quality, low processing efficiency, insufficient analysis capability and the like of a traditional power grid are solved.