68 results about "Urban region" patented technology

A method is provided that includes: obtaining first urban data of a first sample urban region; inputting the first urban data into a multi-modal foundation model to obtain respective predicted vector representations of a plurality of first data segments; obtaining a plurality of general-purpose foundation models that are pre-trained; for each general-purpose foundation model: generating a vector representation label of a first data segment of a corresponding data modality by using the general-purpose foundation model; and determining a knowledge distillation loss of the general-purpose foundation model based on the vector representation label and a predicted vector representation of the first data segment; and adjusting parameters of the multi-modal foundation model based on at least respective knowledge distillation losses of the plurality of general-purpose foundation models.

The invention discloses an urban heat island effect decision-making method based on satellite data and artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining satellite remote sensing data and urban basic data of a target urban region, the urban basic data comprising population density distribution, historical intervention effects, infrastructure information and urban function partition data; preprocessing the satellite remote sensing data to generate a standardized surface temperature distribution image, and based on the surface temperature distribution image, identifying an urban heat island area and dividing the intensity grade of the urban heat island area; the method has the advantages that satellite remote sensing data and urban multi-dimensional basic data are fused, dynamic scoring and optimization algorithms are combined, the heat island area is accurately recognized, the optimal intervention strategy is generated, the problems that a traditional method is insufficient in data utilization and poor in decision-making scientificity are solved, and the method has the advantages of improving treatment accuracy and resource utilization efficiency.

The invention provides an ecological environment dynamic monitoring method based on big data, and relates to the technical field of environmental pollution detection, and the method comprises the steps: collecting multi-source sensor data of a fixed monitoring station in an urban region, and carrying out the time alignment and space registration of the multi-source sensor data, thereby generating a standardized environmental monitoring data set. And on the basis of the data set, constructing a pollutant transmission network between the monitoring points to obtain a regional pollutant distribution characteristic graph, calculating a diffusion trend coefficient of pollutants in combination with regional wind direction data, constructing a pollutant migration path network, and generating a key supervision region map. According to the method, the pollutant concentration distribution can be dynamically partitioned, and the mobile monitoring equipment is scheduled to carry out gradient type point distribution monitoring, so that the accurate positioning of the pollution source is realized; the accuracy of pollutant diffusion trend analysis and the precision of pollution source positioning are improved, dynamic supervision of key areas is facilitated, the efficiency of environment monitoring and the fine management level are improved, and therefore the risk of environmental pollution is effectively reduced.

The invention relates to the technical field of robot scheduling, and discloses an environmental sanitation robot scheduling method, device and equipment based on environmental sanitation task distribution, and the method comprises the steps: collecting the garbage accumulation information and people flow density data of a plurality of urban environmental sanitation regions, and carrying out the task distribution prediction through a space-time convolution network model, an environmental sanitation demand prediction result is obtained; according to the environmental sanitation demand prediction result, creating an urban region division scheme of a plurality of urban environmental sanitation regions and robot initial grouping configuration information; according to the urban area division scheme and the initial grouping configuration information of the robots, dynamic scheduling grouping of the sanitation robots is calculated in combination with the real-time garbage accumulation event data; the path planning and cleaning operation instruction of each environmental sanitation robot is output in groups based on dynamic scheduling of the environmental sanitation robots, intelligent planning of the paths of the environmental sanitation robots is realized, and the collaborative operation efficiency and stability of a multi-environmental sanitation robot system are ensured.

The invention discloses an urban low-altitude wind field monitoring method and system, and relates to the field of urban environment forecasting, and the method comprises the steps: building a four-dimensional variation assimilation system based on a four-dimensional variation target extensive function formula by taking a wind field state variable of a target urban region as a state variable in the four-dimensional variation target extensive function formula; inputting the wind field observation data of each observation point in the target city area into a four-dimensional variational assimilation system for initial field assimilation to obtain assimilated wind field data; taking the assimilated wind field data as initial meteorological field data, performing wind field simulation calculation by using a preset WRF-LES mode, and obtaining wind field forecast data in the target urban area; real-time monitoring data of each observation point in a target city area is assimilated into an initial and boundary meteorological condition input field of a preset WRF-LES mode through a four-dimensional variational assimilation system, so that wind field information in the target city area is reflected, and city low-altitude wind field forecast data can be acquired in near real time with high resolution.

The invention discloses a suburban transition area range and type identification method, and relates to the technical field of city attribution, and the method comprises the steps: collecting and preprocessing multi-source city big data, and determining the characterization of different expansion modes through the multi-source city big data; the method comprises the following steps of: detecting and extracting different urban development boundaries by using a Densis-Graph threshold extraction method; identifying a city core area UCZ and a suburban transition area USTZ driven by different factors through a city development boundary detected by superposition and dislocation, and subdividing the USTZ into a planning-dominant USTZ and a market-dominant USTZ; the spatial balance of suburban transition areas is analyzed by utilizing raster data, the heterogeneity and the spatial development balance of the suburban transition areas are analyzed, and the expansion mode of metropolitan areas is defined. According to the method, USTZ recognition is independent from urban and rural transition area recognition, and on the basis of USTZ recognition, a power mechanism and spatial development balance analysis are fused, so that the defect that the existing technical scheme can only analyze a static spatial pattern but cannot dynamically analyze spatial formation and spatial evolution is overcome.

The invention provides a risk assessment method and device for main control factors of an urban thermal environment, and the method comprises the steps: constructing an index system of multi-scale spatial feature analysis through preset multi-source big data, and obtaining the thermal environment and spatial form information of an urban region through the index system according to the weight ratio of each data source; according to the plant sensitization potential indexes, carrying out classification planning on the regional plant field samples to obtain distribution information of the plant samples; according to the distribution information and the thermal environment and spatial form information, calculating a trade-off relation of plant sample distribution to cooling benefits to obtain a correlation degree between multi-scale spatial characteristics and plant planning; thermal environment influence factors of the to-be-analyzed city under different vulnerability levels are obtained through statistical analysis according to the correlation degree, and a risk assessment result of the corresponding city is obtained according to the thermal environment influence factors.

The invention provides an unmanned aerial vehicle live-action three-dimensional model construction optimization method and system for urban planning, and relates to the technical field of live-action modeling, and the method comprises the steps: preliminarily constructing a three-dimensional model of an urban region; determining a geometric deviation value of the spatial structure units in the same scene semantic unit according to the geometric boundary features and the image projection errors of the different spatial structure units in the same scene semantic unit; determining semantic conflict loss of the three-dimensional model in a scene semantic extraction process based on a spatial constraint relationship between adjacent spatial structure units and color gradients and transition characteristics of category boundaries; and determining a feature constraint value of the spatial structure unit in the three-dimensional model optimization process through the geometric deviation value and the semantic conflict loss, and carrying out geometric and semantic optimization on the three-dimensional model based on the feature constraint value to obtain a real scene three-dimensional model for urban planning application. By adopting the scheme of the invention, collaborative optimization of the urban area live-action three-dimensional model on geometric accuracy and semantic consistency can be realized.

The invention provides an urban area dynamic representation learning method based on context representation learning, and belongs to the field of urban function and spatio-temporal data mining, and the method comprises the steps: S1, constructing a continuous dynamic graph according to public transport data; s2, an urban area dynamic representation extractor under multiple time dimensions is constructed, the extractor is used for extracting structure information and semantic information in the continuous dynamic graph, nodes in the continuous dynamic graph are converted into representation vectors of fixed dimensions, and the representation vectors are dynamically updated along with time; s3, pre-training the extractor by using a plurality of prediction tasks, including designing a corresponding loss function to guide the extractor to learn more universal urban region characterization; and S4, utilizing the trained plurality of extractors to obtain the dynamic representation of the urban area, and combining the dynamic representation of the urban area with context representation learning to be applied to a plurality of downstream tasks. According to the method, comprehensive space-time semantic information can be captured, and the dynamic region representation of the city is dynamically captured from multiple time granularities.

The invention provides a flexible interconnection device site selection method and system based on distributed resource clustering, and the method comprises the steps: constructing a photovoltaic output feature map Laplacian matrix and a load feature map Laplacian matrix according to the photovoltaic output and load data of a planning region, decomposing the constructed matrixes through DGRNMF, and obtaining a distributed resource cluster; solving a photovoltaic clustering center curve and a load clustering center curve according to a decomposition result through a k-means clustering algorithm, and dividing the photovoltaic clustering center curve and the load clustering center curve into an urban region and a rural region; constructing constraint conditions of the power supply grid segmentation model to obtain the power supply grid segmentation model; and solving the power supply grid segmentation model to obtain an optimal power grid partitioning scheme, and determining a candidate installation position of the flexible interconnection device according to the optimal power grid partitioning scheme. According to the invention, the precision and reliability of the flexible interconnection device site selection planning decision are improved.

The invention relates to an urban fine-scale day-by-day atmospheric pollutant concentration estimation method, which comprises the following steps: acquiring urban region multi-source data, and performing multi-scale space-time matching processing; constructing a global estimation model of the atmospheric pollutants and training the global estimation model; obtaining the annual global atmospheric pollutant concentration under the fine scale of the urban area, and converting the annual global atmospheric pollutant concentration into a global atmospheric pollutant concentration weight; calculating a day-by-day local atmospheric pollutant concentration estimation residual error; and estimating to obtain the urban fine-scale day-by-day atmospheric pollutant concentration. The method has the beneficial effects that global features and local changes are comprehensively considered, the spatial resolution of an existing data product in a city is effectively improved, a high-precision hectometer-level day-by-day atmospheric pollutant data product is obtained, a polluted area can be positioned more accurately, and the method has a wide application prospect. Therefore, formulation and implementation of accurate prevention and control measures of urban atmospheric pollution are more scientifically guided, and the method has important application value.

The embodiment of the invention discloses an urban agglomeration hot area prediction method based on a large model data driven graph convolutional network. The method comprises the following steps: acquiring historical travel data corresponding to a city, and determining a historical popularity tensor corresponding to each region of the city based on the historical travel data; based on the historical heat tensor corresponding to each region, determining a comprehensive cost matrix between any two regions among the regions; acquiring public opinion information corresponding to the city, and determining a time aggregation feature tensor of the city based on the public opinion information; and obtaining a space-time tensor of each region based on the time aggregation feature tensor and the comprehensive cost matrix, and determining a predicted popularity tensor of each region based on the space-time tensor. According to the embodiment of the invention, the prediction popularity tensor can more accurately and comprehensively reflect the popularity level which can be reached by each region of the city in the future, and a very valuable decision basis is provided for the fields of city planning, resource allocation, tourism promotion and the like.

The invention discloses a cooperative scheduling optimization method and system for an urban electric logistics fleet, equipment and a medium, and the method comprises the steps: building a fleet cooperative scheduling model based on a Markov decision model, and bringing charging, distribution, transport capacity scheduling and order receiving into an action space of an intelligent agent, the influence of factors such as path planning, charging, freight demand, transport capacity scheduling and the like on a vehicle scheduling strategy is comprehensively considered, and the collaborative scheduling optimization effect is greatly improved; moreover, by converting a vehicle scheduling action selection mechanism in each area into a 0 / 1 planning problem, the flexibility of real-time action selection is greatly improved, and the method can be well suitable for a scheduling scene with a delivery demand and a charging cost change. And meanwhile, scheduling optimization is carried out by taking each grid region of the urban distribution region as an object, so that the phenomenon of unbalanced power load of the urban region can be relieved.

This invention discloses a method for identifying the scope and type of suburban transition zones, relating to the field of urban demarcation technology. The method includes: collecting and preprocessing multi-source urban big data, and using this data to clearly characterize different expansion patterns; detecting and extracting different urban development boundaries using the Densi-Graph threshold extraction method; identifying the urban core zone (UCZ) and the suburban transition zone (USTZ) driven by different factors by superimposing and dislocating the detected urban development boundaries, and subdividing the USTZs into planning-driven and market-driven USTZs; and analyzing the spatial balance of suburban transition zones using raster data, analyzing their heterogeneity and spatial development balance, and clarifying the expansion pattern of metropolitan areas. This invention separates USTZ identification from urban-rural transition zone identification and, based on USTZ identification, integrates dynamic mechanism and spatial development balance analysis, addressing the shortcomings of existing technical solutions that can only analyze static spatial patterns but cannot dynamically analyze spatial formation and spatial evolution.

The invention provides an urban human-water system complex mutual feedback relation identification method based on a system evolution model, and the method comprehensively considers the factors of population change, water resource utilization, water pollution control, urban economic development and the like of a research area. Comprising the following steps: S1, analyzing the evolution characteristics of three subsystems of people, water and city in a research area, and evaluating the coupling coordination degree of the three subsystems; s2, gathering related data, carding causal relationships among the interiors of the three subsystems, constructing a quantification equation, and constructing a mutual feedback evolution model of the urban human-water system by adopting a system dynamics method; and S3, verifying the simulation effect of the model through visual inspection and historical inspection. According to the method, the complex mutual feedback relation among human, water and city multi-elements can be clarified, the human, water and city multi-element mutual feedback evolution process is simulated, finally, a universal model framework is established, the framework can be applied to human, water and city coupling systems in different urban regions, and technical support is provided for achieving coordinated development of the urban economic society and the water resource environment.

The application provides a point cloud filtering method and device for removing background point clouds, and a vehicle. A plurality of point cloud columns and an initial index are obtained. A plurality of shallow feature maps, a plurality of point cloud column features corresponding to the plurality of shallow feature maps, and a first index are determined according to the plurality of point cloud columns and the initial index. The plurality of shallow feature maps and the first index are input into a preset convolutional backbone network to obtain a plurality of splicing features corresponding to the plurality of point cloud columns, wherein the shallow feature maps and the splicing features correspond one by one. The plurality of splicing features are input into a full connection layer to obtain a plurality of classification labels corresponding to the plurality of point cloud columns, wherein the full connection layer is contained in the preset convolutional backbone network, and the classification label is used to represent an object corresponding to a point cloud in the point cloud column. A target point cloud is determined according to the plurality of classification labels, thereby solving the technical problem that the precision of a 3D target detection small model on a laser radar for detecting a target object is not high in a complex urban scene in the prior art.

The invention discloses a regional electric power carbon emission monitoring system, relates to the technical field of carbon emission monitoring, and solves the technical problem that in the prior art, when electric power control is performed on different regions, reasonable electric power configuration is not performed according to the electric power demand conditions of the regions, so that the resource cost is relatively high. According to the method, carbon emission data, power data and time data of different regions in a city are collected, carbon emission use states and carbon emission transmission states of the regions are obtained based on the carbon emission data in the regions, the carbon emission states are obtained through integration, and the power data and the time data are matched to obtain power demand data of the regions; the method comprises the following steps: acquiring a carbon emission state, acquiring a carbon emission monitoring state in combination with the carbon emission state, performing power distribution for urban areas according to the carbon emission monitoring state in the areas, sequencing the areas needing power supplementation to acquire urgent supplementation data and slow supplementation data, and integrating area power configuration data.

The invention relates to the technical field of data evaluation, and discloses an urban vegetation cooling effect evaluation method and system based on multi-source remote sensing data, a terminal and a storage medium, and the method comprises the steps: obtaining urban region data of a city, and carrying out the preprocessing and feature extraction to obtain a feature extraction result; inputting the feature extraction result into a vegetation cooling effect evaluation training model, outputting a feature sorting result, a feature analysis result and an ecological threshold recognition result, obtaining a vegetation cooling effect evaluation prediction result, and adjusting the vegetation cooling effect evaluation training model to obtain a vegetation cooling effect evaluation model; and acquiring to-be-evaluated city data of a target city, performing preprocessing and feature extraction to obtain a target feature extraction result, and inputting the target feature extraction result into the vegetation cooling effect evaluation model to obtain a vegetation cooling effect evaluation result. According to the method, through multi-source remote sensing data fusion, the urban vegetation cooling effect is accurately depicted, so that the accuracy of a vegetation cooling effect evaluation result is improved.

The invention provides an urban railway precast beam lifting and transporting frame construction device and method, and relates to the technical field of precast beam construction.The urban railway precast beam lifting and transporting frame construction device comprises a bottom frame, the bottom frame is used for bearing the bottom of a precast beam, abutting frames are hinged to the two sides of the bottom frame correspondingly, and each abutting frame can rotate around a hinge point so as to abut against the side wall, close to the bottom, of the precast beam in the width direction; each abutting frame is fixedly connected with a steel wire rope; the two sets of top frames correspond to the abutting frames on the two sides respectively, each set of top frame comprises a clamping frame and a swing frame, the clamping frame is of a U-shaped structure and used for being clamped to the top side edge in the width direction of the precast beam, the swing frame is installed on the clamping frame in a hinged mode, an abutting plate used for abutting against the top of the precast beam is arranged at one end of the swing frame, and a lifting lug is arranged at the other end of the swing frame. Safe and stable construction of the whole lifting, transporting and erecting process of the precast beam can be achieved, and the safety and construction efficiency of urban railway precast beam operation are effectively improved.

The coordinates of an intersection at which a vehicle should turn right or left according to route guidance for a vehicle are obtained, and an arrow icon (24) which is a virtual object is superimposed and displayed, via an AR-HUD (1) of the vehicle, on the intersection viewed through a windshield (2). The closer the vehicle is to the intersection, the lower the position at which the arrow icon (24) is displayed is in a display region (21). When the distance between the intersection and the subsequent intersection is long, for example, on a highway, the lower-limit position of the arrow icon (24) is restricted to the inside of an upper region (21A). When the distance between the intersection and the subsequent intersection is short, for example, in an urban area, the arrow icon (24) is allowed to overlap a lower region (21B), and the arrow icon (24) is displayed such that the position of the arrow icon (24) more correctly corresponds to the actual position of the intersection.

The utility model belongs to the technical field of baffles, and particularly relates to a building construction detachable dust fall protection baffle which comprises a baffle body. A suction pipe and a suction head above the suction pipe are additionally arranged on one side of the baffle, a traditional water mist dust falling mode is replaced, and water mist cannot affect pedestrians outside the baffle. A filter screen is arranged in the suction head to filter dust, and a side box for storing and wetting dust is arranged on the side, close to the baffle, of the suction pipe. A traditional water mist dust falling mode is replaced, the situation that water mist affects pedestrians outside the fence is avoided, the stability of pedestrian traffic outside the fence is ensured, and the fence is suitable for being used in urban areas and other areas with the large pedestrian flow rate.

The invention discloses a water supply project operation management and protection system. The system comprises a login management module, a map display management module, a map editing management module, an element query management module, a space query management module, a data export module, a database and a cloud server. By means of the mode, the water supply project operation management and protection system can collect, sort and input all elements and data in a water conservancy water supply project, interconnection and information sharing of water conservancy water supply information of multiple regions are achieved, scientificity, normalization and applicability of water supply project operation management and maintenance are improved, and the water supply project operation management and maintenance efficiency is improved. And synchronous management and protection operation of multiple cities / districts and counties can be carried out through the cloud server, so that the convenience and high efficiency of system use are improved.

The application provides a SCR downstream NOx sensor failure diagnosis method, comprising the following steps: 1) confirming that the SCR downstream NOx sensor, the ammonia sensor and the ECU are in normal communication; installing a PEMS device and confirming that the PEMS device test data is accurate; 2) starting the vehicle to be tested, and performing PEMS emission tests under three working conditions of urban area, suburb and highway; 3) performing data elimination on the PEMS device, aligning the remaining data by working condition, then comparing and obtaining errors under different working conditions, and obtaining a total error; 4) if the total error is greater than the set threshold, the SCR downstream NOx sensor installed on the vehicle to be tested has failed; or if the total error is less than or equal to the set threshold, the SCR downstream NOx sensor installed on the vehicle to be tested is normal. The application can accurately judge the failure of the SCR downstream NOx sensor, has the advantages of scientific rationality, high reliability and wide application range, and can provide a theoretical basis for the normal operation of the vehicle.

The invention relates to a transformer substation ventilation and noise reduction method and device based on a self-adaptive filtering algorithm. The method comprises the following steps: carrying out data acquisition on noise of a transformer substation ventilation system; establishing a filtering-XLMS algorithm model; constructing a model based on an improved variable step size LMS algorithm; and analyzing and processing the noise data of the ventilation system of the transformer substation, and outputting a reverse counteracting signal through the controller to counteract the noise of the ventilation system of the transformer substation. According to the principle of sound wave interference offset, corresponding waveforms can be generated in a self-adaptive mode according to fan noise changes to drive the loudspeaker to form negative signals, fan noise signals are offset, and the purpose of active noise elimination and reduction of the transformer substation ventilation system is achieved. The transformer substation noise pollution abatement problem is solved, the problem that transformer substation noise disturbs residents in an urban area is solved, and the high-quality service level of a power grid is improved.

The application discloses an intelligent division method for police case high-occurrence areas based on density clustering and k-means clustering, and the method comprises the following steps: obtaining police case data of a city; preprocessing original police case data to obtain data after unification and standardization; clustering the data after preprocessing by using a density algorithm to obtain all categories and corresponding data of each category; clustering the police case data of all categories by using a k-means algorithm; then clustering all police cases of each police case data by using the density algorithm to obtain all categories and corresponding data of each category; extracting boundary points of each category data to obtain corresponding boundary contours, that is, to obtain police case high-occurrence areas. The application can accurately identify police case high-occurrence areas, and can effectively balance the uneven distribution of police case high-occurrence areas in urban areas and suburban areas, thereby providing effective data support for subsequent police deployment, unified dispatching and command.

The invention provides a snow sweeping and deicing path control method based on vehicle track dynamic feedback, and belongs to the technical field of smart city traffic management.The snow sweeping and deicing path control method comprises the steps that a kafka technical component and a websocket communication technology are combined, GPS data of a snow removing vehicle are accessed in real time, the vehicle driving track is dynamically displayed, and urban area operation progress is visited; a Q-learning reinforcement learning algorithm is introduced, parameters such as road priority and operation vehicle energy consumption are combined, dynamic allocation of the optimal operation path strategy of the snow removal truck is achieved by constructing a reward function and a continuous iteration state-action value function (Q value function), and the effect of dynamic balance of snow removal operation efficiency and operation vehicle energy consumption is achieved.

This invention relates to the field of spatiotemporal data processing technology, and discloses a method, system, terminal, and storage medium for predicting multi-dimensional urban indicators based on semi-supervised learning with small samples. The method includes: extracting visual, textual, and structural features of urban space using a three-branch encoder, and generating a unified urban region representation vector using a multimodal fusion mechanism; subsequently, constructing a semi-supervised collaborative computing framework that integrates a heterogeneous neural network model and an ensemble tree model as a regressor; and, under conditions of a small number of labeled samples, using an alternating pseudo-label generation and four-fold pseudo-label selection mechanism, performing semi-supervised augmentation training on the model with massive amounts of unlabeled data to improve the accuracy of spatiotemporal data labeling. This invention significantly reduces the dependence on labeled data and achieves high-precision labeling prediction across multiple dimensions of urban indicator calculation.

The invention discloses a traffic planning method based on big data, and belongs to the technical field of traffic planning, and the method comprises the following steps: S1, collecting the passenger volume and passenger transportation turnover data of the whole society, railways, roads, water transportation and civil aviation of a preset region; s2, using a big data analysis technology to predict traffic demands in the target urban area and from the target city to other cities based on the collected passenger traffic volume and passenger traffic turnover data; and S3, according to a traffic demand prediction result, constructing a three-layer rail transit network comprising an intercity line, an urban line and a local line, and endowing a fast and slow line composite function to a newly added line in the rail transit network. According to the method, the rail transit network with clear layers and highly-compounded functions is constructed, and the operation efficiency of a traffic system is comprehensively improved.

The present invention discloses an electrical automation monitoring device for a tunnel substation, which includes a track; an inspection robot main body, and the inspection robot main body is installed on the track through driving wheels and guiding wheels; a detection mechanism, and the detection mechanism is installed outside the track, and the inspection robot main body is detected after moving to the detection device; the detection mechanism includes a frame located outside the track, a lifting mechanism is provided on one side of the frame, a plurality of detection units for detecting a plurality of driving wheels are provided at the bottom of the frame, and a lifting mechanism for lifting the driving wheels is provided on one side of the track. When the distance between the mountain tunnel and the urban area or the power company is far, the inspection robot is used for monitoring. After the inspection robot detects an abnormal signal, the staff can arrive at the substation in time for maintenance, reducing the consumption of manpower and material resources. Moreover, the inspection robot can stably monitor the substation in all directions, making it more convenient to comprehensively monitor the substation.

1. The name of this design product: B&B (Zero Carbon Mobile HS2002). 2. The purpose of this design product: used in the wild, urban areas, scenic spots and other environments without water and electricity. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture. 5. The bottom view of this design product is a part that is difficult to see or cannot be seen when in use, so the bottom view is omitted.