301 results about "Land use" patented technology

The invention discloses a soil heavy metal distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining topographic factor land use industrial activity data, carrying out the fusion remote sensing information processing, and determining a multi-source data set; performing standardization processing according to the multi-source data set, and performing space-time registration if the scale difference after standardization processing exceeds a preset threshold value to obtain data in a unified format; key features are extracted according to the unified format data, and a dimension reduction feature set is obtained through principal component analysis; a random forest model is constructed according to the dimension reduction feature set, parameters are optimized, and a heavy metal content prediction model is determined; inputting the sampling finite point location data into a heavy metal content prediction model, judging an industrial activity influence area, and obtaining a preliminary distribution estimation result; based on the preliminary distribution estimation result, high-resolution grid data are obtained by fusing topographic factors for the space complex region; and generating a pollution distribution diagram according to the high-resolution grid data, judging a low-prediction-precision region, and obtaining a final optimized layer.

The invention relates to the technical field of water resource management, in particular to a flow hydrograph simulation method based on coupling of a Xinanjiang model and a depression, and the method comprises the steps: obtaining the geographic space data and meteorological data of a target drainage basin; the geospatial data at least comprises a digital elevation model, land utilization data and soil type data; and carrying out sub-drainage-basin division on the target drainage basin based on the digital elevation model, identifying depressions in the digital elevation model by adopting an improved priority flood algorithm, and constructing a spatial topological relation of the depressions. According to the method, the hydrological characteristics of the depression are fused into the Xinanjiang model, the regulation and storage function of the depression group on the drainage basin runoff is quantified, the hydrological simulation precision of the depression dominant drainage basin is remarkably improved, and by improving the depression recognition algorithm, pre-construction of the slope confluence time matrix based on the cells and rapid judgment of the depression merging mechanism, the stability of the depression confluence time matrix is improved. The calculation speed of the distributed hydrological model is greatly improved, and the efficiency requirement of the real-time hydrological model can be met.

The invention relates to the technical field of wind power plant design, in particular to a wind power plant booster station site selection optimization method and system based on a multi-objective genetic algorithm and three-dimensional terrain constraint. The method comprises the following steps: firstly, constructing unified program grid data fused with geographic constraints; establishing a static land use cost model containing an orientation weight by using a rotation convolution algorithm; then, dynamic cost calculation logic of fan-power grid collaborative topology based on candidate coordinates, multi-constraint road dynamic path finding and site earth-rock balance iteration is defined; and finally, constructing a multi-target optimization model of comprehensive multi-dimensional cost, and performing iterative optimization through a genetic algorithm to obtain a Pareto optimal site selection coordinate. Through coupling of geographical constraints, the cost model and the intelligent algorithm, the problem of complex mountain site selection is solved, global collaborative optimization of land, earthwork, road and electrical cost is realized, and scientificity, economy and construction feasibility of wind power plant design are remarkably improved.

The invention discloses an intelligent redevelopment research method for low-efficiency land use, which comprises the following steps of: acquiring multi-source data including land change survey data, topographic data, urban planning data, remote sensing data, point-of-interest data, mobile signaling data and social economic data in a research area, and inputting the multi-source data into a land use identification model; obtaining a spatial distribution diagram of low-efficiency land in the research area and corresponding low-efficiency factors; inputting the spatial distribution diagram and the low-efficiency factors into a redevelopment potential calculation model to obtain a redevelopment comprehensive potential value of the low-efficiency land; a plurality of redevelopment schemes are obtained based on the redevelopment comprehensive potential value, the rigid constraint of the low-efficiency land during redevelopment and the redevelopment mode library; and obtaining respective corresponding benefit index difference values after the low-efficiency land is redeveloped by using the plurality of redevelopment schemes, and obtaining an optimal redevelopment scheme from the plurality of redevelopment schemes by using a multi-attribute decision algorithm and the benefit index difference values.

The invention discloses a satellite hyperspectral remote sensing image-based adaptive random forest land utilization classification and identification method, which comprises the following steps of: data preprocessing: loading a vector boundary of any predetermined administrative region, the method comprises the following steps: acquiring surface reflectance data of a certain satellite hyperspectral image in any preset time period from a natural resource satellite remote sensing cloud service platform, and preprocessing; making a sample set; parameter definition and remote sensing index selection: calculating a group of representative remote sensing indexes by using a specific wave band combination of the hyperspectral image; a remote sensing index and an original wave band are used as input feature vectors together to form a complete feature space, and in the training process of the random forest, part of features are randomly extracted from the feature space during construction of each decision tree for node division; performing cross validation and parameter tuning; the visualization of the optimal parameter result is selected; according to the invention, adaptive parameter tuning based on random forest algorithm classification is realized.

The invention discloses a data layer processing method and system for spatial planning land data arrangement, and relates to the technical field of land data, and the method comprises the following steps: obtaining and collecting a plurality of predefined spatial planning constraint layers; the spatial planning constraint layer comprises a stable cultivated land, a permanent basic farmland, an ecological protection red line and a town development boundary; processing each spatial planning constraint layer to generate a site selection base map database of the temporary land; configuring a land use avoidance rule for each constraint layer of the site selection base map database; the avoidance rule comprises a prohibitive rule and a conditional rule, the prohibitive rule is configured for the layers of the permanent basic farmland and the ecological protection red line, and the conditional rule is configured for the layers of the stable cultivated land and the urban development boundary; responding to a temporary land site selection request of the user; the method has the effect of improving the accuracy and compliance of site selection decision.

The invention belongs to ecological environment engineering, and particularly relates to a river and lake wetland zoning and classifying ecological water demand fine accounting method, which comprises the following steps of S1, performing zoning and classifying on a river and lake wetland of a research area; s2, establishing a space-time coupling hydrodynamic model based on a partition classification relationship; s3, constructing a multi-factor ecological response model, and calculating an ecological suitability index of each partition; s4, establishing a multi-objective optimization model, and performing optimization solution on the ecological water demand of the partition by combining ecological benefits, water balance and process smoothness objectives; and S5, performing robust correction on an optimization result by adopting an uncertainty analysis method to obtain a final ecological water demand distribution result. By establishing a river and lake wetland partition classification spatial database based on digital elevation, river network distribution, land utilization and ecological attribute information, fine description and dynamic association of different ecological functional areas are realized, and the effects of ecological process differentiation characterization and remarkable improvement of water demand accounting precision are obtained.

The invention discloses a method for quantifying the influence of vegetation change on regional rainfall and a related device, and the method comprises the steps: carrying out the evapotranspiration simulation of a target region through a remote sensing evapotranspiration model according to meteorological driving data and vegetation and land utilization data, and obtaining an evapotranspiration data set under different vegetation utilization scenes; performing monthly scale correction on the water vapor tracking input data through the evapotranspiration data set to obtain a corrected water vapor tracking input data set; the meteorological driving data and the corrected water vapor tracking input data set are adopted to drive the water vapor tracking model to perform forward tracking, and water vapor tracking results under different vegetation utilization scenes are output; and quantitatively analyzing the downwind rainfall influence of the vegetation change on the target area through the water vapor tracking results under different vegetation utilization scenes. According to the invention, based on the water vapor source-sink relationship of the region, the evapotranspiration change caused by the vegetation change is spatially redistributed, the influence on downwind rainfall is tracked, and the quantitative relationship of the influence of the vegetation change on the regional rainfall is revealed.

The invention relates to the technical field of land data planning, and discloses a reasonable land planning system based on machine learning carbon storage, and the system comprises a multi-source data fusion module, a carbon storage prediction module, a three-generation coordination discrimination module, a land use intelligent optimization module, a scene interaction deduction module, and a planning result generation module. By integrating data such as standardized land utilization and carbon density, a machine learning model is utilized to predict the dynamic state of the carbon density in real time, and the functional coordination degree of the three plants is quantitatively evaluated; and then, by taking the improvement of the carbon reserves and the coordination of the three plants as targets, carrying out spatial simulation and iterative optimization in combination with multi-objective optimization and a cellular automaton, comparing different policy carbon sink effects through scene deduction, and finally outputting an optimization scheme, thereby realizing the real-time closed loop of evaluation and optimization, and synergistically improving the carbon sink benefits and the three-plant function.

The invention discloses a Yellow River basin heritage area ecological corridor environment design system based on digital twinning, belongs to the technical field of ecological corridor environment design, and aims to solve the problem that the Yellow River basin heritage area ecological corridor design is insufficient in accuracy and scientificity. The system comprises a multi-source ecological data acquisition module, an ecological data preprocessing module, a digital twin construction module, an ecological corridor planning design module and a visual output module. According to the method, terrain data, ecological element data and land utilization data are collected and preprocessed, ecological digital twin bodies of the heritage areas of the Yellow River basin are constructed, and in combination with an InVEST model, a minimum cost path algorithm, an RUSLE water and soil loss model and a CASA carbon sink model, ecological node recognition, optimal ecological corridor path planning and corridor environment design simulation are completed, so that the ecological digital twin bodies of the heritage areas of the Yellow River basin are constructed. And visually displaying the result. According to the method, accurate mapping of the ecological scene is achieved, the scientificity of ecological corridor design is improved, and support is provided for ecological corridor construction.

The invention discloses a curing agent for curing and damming solid waste and a layer-by-layer cofferdam damming process, and relates to the technical field of damming filler devices. The curing agent is characterized in that a guide and discharge device with a guide and regulation effect is arranged on the front side of a preparation device; a reclaimed water device with filtering and flow guiding functions is arranged on the side face of the preparation device, and the industrial solid waste environment-friendly treatment, solidification and accumulation technology is adopted, so that equipment machining automation is achieved; according to the process of stirring industrial solid waste and reclaimed water according to a ratio, adding a curing agent according to a classified ratio of the industrial solid waste, conveying by a pipeline, curing and building a weir, repairing a pool on a pool, forming a central emergency pool, stacking and storing, and greening the solid, the dust pollution after stacking is reduced, the storage capacity can be increased, the service life is prolonged, and the environmental pollution is reduced. The effect after layered solidification and accumulation is similar to that of a terraced field, vegetation greening is carried out after soil covering, and the industry is recovered and improved step by step.

The invention discloses a water nutritive salt remote sensing inversion method based on multi-source fusion and a machine learning model. According to the method, non-optical environment factors such as optical remote sensing features, meteorological features and land utilization data are subjected to unified formatting and structured processing, so that multi-source data are input into an inversion model in a unified feature vector form. A self-adaptive screening strategy based on feature importance is adopted, the contribution degree of each feature can be dynamically calculated in the model training process, and variables with low contribution or large noise are automatically deleted. Temperature attenuation, a Metropolis criterion and disturbance generation are introduced, and a globally optimal model structure and hyper-parameter combination is automatically searched. Through joint design of data fusion, feature screening and an optimization algorithm, a stable, generalizable and easy-to-deploy nutritive salt inversion system is constructed, the problems that an existing method depends on a single spectrum, generalization is poor and parameter optimization is difficult can be effectively solved, and inversion of the concentration of the nutritive salt in the water body can be directly carried out.

The invention discloses a construction and verification method of a watershed water resource comprehensive simulation and configuration model, and belongs to the technical field of water resource planning and configuration. On the basis of a natural-social binary water circulation structure, flow generation, flow concentration and allocation modules are coupled, a topological relation is determined by dividing calculation units, and a model is constructed with the minimum water shortage rate and the optimal water supply fairness as targets; establishing a multi-source database containing meteorological information, land utilization information, reservoir information and water consumption unit information; performing iterative calibration and verification on natural water circulation and social water circulation parameters by using historical data; and coupling the model which is verified to be reliable with underlying surface and climate scene data of a future planning year, and operating and outputting a water resource optimal configuration scheme. The method solves the problems that an existing model is low in coupling degree and poor in precision, and high-precision simulation and scientific configuration of a drainage basin water resource system are achieved.

The invention discloses an artificial intelligence-driven urban potential updating area identification and division method, which comprises the following steps of: firstly, constructing an index system according to three dimensions of building space, industry form composition and crowd vitality by using the collected building number, land function data, POI (Point of Interest) industry form data, LBS (Location Based Service) crowd data, high-definition remote sensing image data and road network data; the method comprises the following steps: carrying out hierarchical classification identification on an update object through a natural discontinuity point method and a K-Means clustering algorithm, carrying out intelligent division and optimization on a city update unit through a DBSCAN clustering algorithm and a reinforcement learning model of a graph network structure database, and finally carrying out division and boundary optimization on a city update area. According to the invention, automatic identification and division of potential updating objects, updating units and updating areas of the city can be realized, and digital and intelligent application conversion of city planning work is promoted.

The invention discloses a small watershed nitrogen and phosphorus load accounting method based on key process parameter calibration, and the method comprises the steps: obtaining the basic geographic hydrological data of a target small watershed, the data comprising the areas of different land utilization types in the watershed, the flow length of a water collection region river channel, and the average flow velocity; based on the hydrology and water quality monitoring data of the long-time sequence of the catchment area, adopting a Bayesian inversion model to quantitatively identify the nitrogen and phosphorus output coefficient of each land utilization type; calculating a soil nitrogen and phosphorus output load in the catchment area, determining a natural reduction coefficient of a nitrogen and phosphorus first-order river, and calculating transport time of nitrogen and phosphorus from a starting point of a river channel of the catchment area to a water outlet and transport time of soil output nitrogen and phosphorus from a tributary estuary to the water outlet of the catchment area; and calculating the water collection area outlet nitrogen and phosphorus load. According to the method, the overestimation error caused by the fact that a production and pollution discharge coefficient method is adopted in a traditional method is remarkably reduced through fusion of'pollution source nitrogen and phosphorus entering the river-remaining soil output-along-the-way natural reduction 'ternary accounting, the accuracy of load accounting is improved, the feasibility and advantages of the technical scheme are verified, and the method is particularly suitable for small watersheds with deficient basic data.

This invention discloses an intelligent optimization method and system for the structure of green spaces in urban blocks, relating to the fields of urban planning, landscape architecture, and artificial intelligence. The method includes: acquiring a planning area; delineating units based on the planning area to obtain multiple block units; acquiring relevant indicator data for each block unit; inputting the relevant indicator data for each block unit into a pre-established regression model; and outputting an optimized regression model. The method further includes: calculating the importance of variables in the optimized regression model; performing weight correction and coupling degree calculation based on variable importance to obtain corrected weights and block unit coupling degree; acquiring images of vacant land; performing raster recognition on the vacant land images and overlaying them to obtain an expanded vacant land area; predicting the vacancy probability of the land use raster based on the optimized regression model to obtain the vacancy probability; generating an initial database of urban blocks based on the vacancy probability; generating an initial population from the initial database using a genetic algorithm; and determining the optimized green space structure of the urban blocks based on the fitness of the best individual in each generation of the initial population using a reinforcement learning algorithm. This method can provide diverse options for urban green space planning.

This invention relates to the field of ecological environment management technology, specifically disclosing a refined method and system for visualizing regional vegetation carbon sequestration. The method includes: extracting land use types of a target area based on remote sensing data of land use types and biomass; determining forest areas based on land use types and establishing growth curves for the forest areas based on preset fitting curves; calculating the carbon sequestration amount of the forest areas based on the growth curves and determining the carbon sequestration ratio of different forest areas; allocating the total carbon sequestration amount to different sub-regions of the target area based on the carbon sequestration ratios, and visually converting the allocated target areas. This invention first selects a target area, calculates the carbon sequestration amount based on the forest areas within the target area, then redistributes the carbon sequestration to different areas according to land use types, and visualizes the redistribution results, facilitating the management of the area.

This invention relates to a snowmelt flood prediction method and system based on a physical information neural network, specifically in the field of snowmelt flood prediction. By embedding physical constraints such as shallow water equations and energy conservation equations into the neural network model, the accuracy and stability of prediction are improved while maintaining physical consistency. It can effectively compensate for the shortcomings of traditional empirical models in terms of spatiotemporal continuity and generalization under extreme scenarios. At the application level, this invention can realize the fully automated operation of the entire process from meteorological data input, snowmelt and runoff calculation, flood evolution prediction to risk level assessment. The system can output multi-time step water depth, flow velocity and inundation range results based on real-time or forecast meteorological conditions, and combine them with population density, land use and infrastructure distribution data to generate flood risk zoning maps and early warning information, providing a scientific basis for watershed flood control scheduling, engineering design and disaster emergency management.

The invention discloses a hydrological analysis method and system based on SWAT model runoff reconstruction and a storage medium. The hydrological analysis method comprises the following steps: 1, obtaining research basin runoff data, meteorological data, digital elevation model data, land utilization data and soil data; 2, based on the data in the step 1, analyzing the evolution trend and potential mutation points in the runoff time sequence period by adopting a mutability test method; 3, dividing a natural period and a variation period based on the mutation points, and obtaining a natural runoff sequence by using an SWAT model; and 4, based on a SWAT model simulation result, extracting multiple hydrological indexes representing hydrological situation changes by adopting an IHA method, extracting ecologically-related hydrological indexes ERHIs from the hydrological indexes by adopting a PCA method, and calculating contribution rates of climate changes and human activities to the ecologically-related hydrological indexes by adopting a quantitative attribution method. The runoff driving mechanism of climate factors and human activities is disclosed, and the physical interpretability and decision support capability of the SWAT model are enhanced.

The invention relates to the technical field of environment monitoring and big data analysis, and discloses a soil environment quality evaluation method based on big data. The method comprises the following steps: fusing measured data of soil heavy metals with multi-source heterogeneous data such as remote sensing, terrain, land utilization, hydrogeology and the like; a space-attribute joint constraint depth generation network is constructed, an encoder of the space-attribute joint constraint depth generation network extracts multi-scale features, an attribute physical constraint module is embedded into a convection-dispersion equation to model a heavy metal migration mechanism, and a decoder outputs a high-fidelity concentration field; and dividing four pollution risk grades according to national standards, and generating an evaluation diagram. Through data driving and physical mechanism collaborative modeling, the pollution boundary depicting precision is improved, the prediction error under the low sampling density is reduced compared with a traditional method, the recall rate of a high-risk area is increased, and reliable support is provided for precise soil treatment.

The present disclosure establishes a facility in association with another land used by the facility more accurately than in the prior art. The information processing device is provided with a control part which executes the following actions: acquiring cadastral map data of land; and associating the first area with any one of the plurality of facilities on the basis of at least a result obtained by comparing the position information of the first area with the position information of the plurality of facilities. The control unit associates at least the owner information of the land corresponding to the first region and the owner information of the land corresponding to each of the plurality of facilities, which are included in the cadastral map data. At this time, when a land boundary that separates a land corresponding to the first region and a land corresponding to the facility that is the candidate for association can pass through each other, it is considered that the owner of the land corresponding to the first region is the same as the owner of the land corresponding to the facility that is the candidate for association.

The invention relates to the technical field of crowd flow prediction, discloses a crowd OD flow prediction method and related equipment, and effectively overcomes the defects in fine-grained city internal OD flow prediction in the prior art by constructing an urban crowd flow prediction model fusing multi-dimensional key factors. Spatial correlation among geographic units is captured by means of a spatial adjacency encoder, regional function complementarity and geographic distance characteristics are mined through a land utilization complementation encoder, traffic reachability information is integrated by means of a traffic connection encoder, and a characteristic pattern cooperatively output by a multi-dimensional encoder provides comprehensive support for prediction. According to the design, the comprehensive influence of spatial positions, land utilization and traffic conditions on urban travel is fully considered, the defects that a traditional model ignores spatial dependence and an existing machine learning and deep learning model is insufficient in multi-factor integration are overcome, and the prediction accuracy and the fitting degree are remarkably improved.

The application relates to a smart city-based urban planning decision feasibility analysis system, aiming to solve the problems of experience dominance, static solidification, appeal imbalance and technology isolation in traditional urban planning. The system includes four modules: data collection, algorithm processing, decision output and dynamic optimization. The data collection module dynamically collects urban basic space, dynamic monitoring, industry and livelihood data and completes preprocessing; the algorithm processing module realizes spatial structure quantization, resource allocation optimization, planning risk assessment and dynamic feasibility prediction; the decision output module generates a planning scheme containing land use layout, energy configuration and public service facility distribution parameters; the dynamic optimization module updates and threshold triggering mechanism, iteratively adjusts the planning scheme. The system is suitable for new area planning and stock area renovation scenarios, realizes the transformation of planning decision, and improves the scientificity and landing effect of planning decision.

The application relates to an AI-based land planning site selection multi-objective optimization method and system, which comprises the following steps: constructing a city data cube; constructing a cellular automaton model and generating a land use potential map; constructing a game decision model and generating a preliminary site selection scheme set; inputting the preliminary site selection scheme set into the cellular automaton model to generate an evolution simulation data set; constructing a multi-objective evaluation model and calculating the relative closeness of each scheme in the preliminary site selection scheme set to an ideal solution; generating a site selection decision report according to the relative closeness; and in summary, the application integrates multi-source real-time and historical data to construct a city data cube, applies a cellular automaton model to generate a dynamic land use potential map, introduces multi-agent game decision to realize multi-objective scientific trade-off, realizes automatic processing and consistency guarantee of planning rules, and provides a scientific and quantitative trade-off mechanism in a multi-objective conflict scenario, and has the effect of improving the data timeliness and dynamic response capability of site selection decision.

The invention belongs to the technical field of urban planning, and particularly relates to a traffic station region land utilization efficiency evaluation method, which comprises the following steps of: acquiring and preprocessing point-of-interest data and a traffic station region in an analysis region; extracting land utilization layout features based on the null data set; constructing a multi-mode traffic network including walking, bicycles, buses and rail transit based on the layout features; establishing a land coupling simulation model of traffic based on the regional reachability index; causal inference and model calibration are carried out based on historical traffic intervention events of dynamic evolution; adopting a multi-objective optimization algorithm to generate traffic and land utilization configuration schemes meeting different planning objectives; according to the method, the evaluation result is subjected to visual display and decision output, unified modeling of traffic and social economic data is achieved through multi-source heterogeneous data fusion and standardization processing, and the effect of improving data space-time matching precision and calculation reliability is achieved.

The invention discloses a photovoltaic resource screening and sorting method for power grid access, and belongs to the technical field of photovoltaic land screening. The method aims to solve the problem of grid connection failure caused by lack of power grid compatibility evaluation in the prior art. Dynamic matching of a photovoltaic group and a power grid infrastructure is ensured by integrating a power grid permitted access judgment mechanism, including access capacity check, power generation load matching check and light abandoning proportion constraint check. Adjacent developable lands are clustered into a group through land clustering, and parameters such as installed capacity and generating capacity are calculated; combining the plot groups, carrying out power grid access analysis, and judging whether the group combination meets a power grid operation condition or not; and evaluating the contribution degree of the grouping combination to the static stability margin of the power grid, the power supply reliability and the n-1 power flow transfer rate, and recommending the land use grouping combination according to the comprehensive benefit index sequence. According to the invention, through dynamic constraint and engineering optimization, efficient screening of photovoltaic resources is realized, and the grid-connected risk is reduced.

The utility model discloses a farmland and land dual-purpose wheel which comprises a rim, a plurality of spokes in the rim are fixedly connected with a hub, the joints of the spokes and the rim are fixedly connected with a plurality of anti-skid blocks, and the sections of the anti-skid blocks are arranged in a trapezoid shape. The antiskid blocks are arranged on the inner side of the rim, and a certain distance is kept between the tail ends of the antiskid blocks and the outer ring of the rim, that is, when the rim runs on a normal land (non-farmland), the rim is in direct contact with the ground, and the antiskid blocks cannot be in contact with the ground, so that the ground can be prevented from being damaged by the antiskid blocks; when the rim travels on the planting ground, under the pressure effect of agricultural machinery, the rim can sink into part of soil until the anti-skid blocks make contact with the soil, the contact area of the rim and the ground can be increased through contact of the anti-skid blocks and the soil, the anti-skid blocks and the cross section of the rim are arranged in a cross shape, the anti-skid effect on the agricultural machinery can be achieved, and the agricultural machinery can be protected. And the agricultural machinery can normally run on land and in farmland.

The invention relates to the technical field of civil engineering, and discloses a CAD earthwork calculation and land use automatic optimization method and system, and the method comprises the following steps: S1, data input: reading CAD graphic data and configuration parameters, verifying the closeness, validity and legality of the data, and storing the data in a database; the CAD graphic data comprises land use range boundaries, terrain elevation points and road center lines; s2, performing dual-mode adaptive earthwork calculation: counting the number N of elevation points, calculating a land area A and an elevation standard deviation sigma, determining an adaptive threshold Nthreshold = f (A, N, sigma, alpha), adaptively selecting a calculation algorithm according to the relationship between N and Nthreshold, and outputting an excavation amount, a filling amount and a clear soil amount. The CAD earthwork calculation and land use automatic optimization method and system have the advantages of integrating earthwork calculation, slope calculation, land use optimization and cost calculation, and being simple in operation, low in learning cost, high in calculation precision, high in efficiency and the like.