131 results about "City region" patented technology

The invention discloses a low-carbon city subjective and objective mixed planning interval entropy weighting evaluation method, and relates to the technical field of city management and low-carbon evaluation, and the method comprises the steps: collecting and preprocessing index data in real time, building a user behavior prediction model according to the index data, and predicting the index data of a user; dynamically adjusting weight distribution of subjective and objective evaluation indexes based on the predicted index data, and constructing a comprehensive evaluation model to calculate a comprehensive score of each region in the city; and formulating optimization measures of each region according to the comprehensive score. A prediction model is established by using a support vector machine, an evaluation result is highly matched with real-time data through dynamic weight adjustment, the hysteresis problem of traditional static evaluation is avoided, and more accurate and flexible low-carbon city evaluation is realized through dynamic adjustment of weight distribution and construction of a comprehensive evaluation model. And the comprehensive score of the urban area is calculated by adopting a nonlinear transformation function, so that the marginal effect change of the evaluation index under different load conditions can be better reflected.

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 discloses an autonomous decision-making system of a multi-level intelligent agent in an urban area and an operation method thereof. Each end-side decision-making unit broadcasts an equipment state variable to a side-side decision-making unit; the side decision-making unit determines a global reference state and generates a control instruction of each end decision-making unit; after the equipment executes the control instruction, each end-side decision-making unit updates an equipment state variable according to the real-time operation data of the equipment, and when an abnormal event is judged to occur, a side decision-making unit predicts a decision-making variable of each piece of equipment based on a collaborative optimization model of an equipment target and an urban area target; the cloud regulation and control platform predicts a global consistency variable based on the collaborative optimization model of all the side decision units; predicting a regulation and control instruction of each side decision-making unit according to a global dynamic optimization target under a system operation constraint; and the side decision-making unit decomposes the regulation and control instruction into the control instruction of each end decision-making unit, so that the problems that the global cooperative capability of the urban regional integrated energy system is weak and cooperative scheduling is not timely under extreme events are solved.

The invention discloses an urban construction intelligent monitoring system integrating geographic information and the Internet of Things technology, and belongs to the technical field of urban construction. In order to solve the problems that response to urban problems is lagged and development imbalance among urban areas is difficult to effectively reveal and solve, environment monitoring and infrastructure state evaluation are fused, the intelligent degree, real-time monitoring and quick response of urban management are remarkably improved, the processing speed of the urban problems is effectively improved, and the urban management system is suitable for urban management. The urban area is comprehensively evaluated through the evaluation index system, meanwhile, policy making is more refined through the area grading function, data support is provided for resource allocation, the timeliness of information is kept through continuous data updating, and the evaluation result is combined with a G IS system, so that the system is more efficient and reliable. The problem area and the dominant area are visually displayed, the balanced development of a city is promoted, and the overall living environment quality is improved.

The invention provides a method for forecasting sand and dust weather in a customs region based on an environment monitoring station and a sensitive city. According to the method, environmental data are collected by setting and maintaining environmental monitoring stations spread all over cities around a sand source and historical sand and dust transmission paths. A sensitive city is set on a sand and dust transmission path, and a corresponding environment monitoring station is set in the city to serve as a front forecasting city for sand and dust in the Guanzhong region. A mathematical model between the sand and dust weather and sensitive city environment monitoring data is established by combining historical experience data of the sand and dust weather. Through real-time monitoring and data analysis, when sand and dust weather occurs in a sensitive city, whether the sand and dust weather affects a central region or not is predicted according to the mathematical model, and the transmission time, the concentration and the continuous condition of the sand and dust weather are predicted. The method can improve timeliness and accuracy of sand and dust weather forecast, and has important value for environmental protection and public life.

The invention discloses an urban scale rainfall absorption and vegetation configuration collaborative optimization evaluation method, which belongs to the field of urban rainfall flood management optimization, and comprises the steps of performing integration and rainfall event identification on basic data of an urban region; physical mechanism inference is carried out by constructing multiple physical gating such as energy consistency, wet surface dissipation and geometric radiation sensitivity, and underlying surface hydrological response characteristics are obtained; constructing and solving a robust optimization model in combination with the underlying surface hydrological response characteristics and a preset planning target, and determining an optimal vegetation configuration scheme; and carrying out back substitution on the optimal vegetation configuration scheme to carry out closed-loop check and effect evaluation. According to the method, the hydrological response characteristics of the urban underlying surface can be accurately quantified, the vegetation configuration scheme with the definite achievement probability and the controllable risk is formulated under the uncertain condition, and the scientificity and the reliability of urban water resource toughness planning are improved.

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 relates to the technical field of image processing, in particular to an urban built-up area extraction method based on regional difference enhancement and local adaptive segmentation, which comprises the following steps of: preprocessing a regional remote sensing image data set, and constructing a luminous built-up area index image for multi-source data adjustment; optimizing the segmentation threshold of the segmented noctilucent built-up area index image by using an optimization algorithm; performing image processing on the obtained to-be-analyzed noctilucent built-up area index image by using the optimized segmentation threshold to obtain an urban built-up area image sequence; and performing time consistency processing on the obtained city built-up area image sequence to obtain a long-time sequence city built-up area result. According to the invention, the multi-source data is fused through the index image of the noctilucent built-up area, so that the difference between the urban area and the non-urban area is enhanced; and the segmentation threshold values of different cities are adaptively adjusted in combination with an optimization algorithm, so that the recognition accuracy is improved.

The invention provides a remote sensing building group depth detection and change intelligent evaluation method for urban planning, and the method comprises the steps: carrying out the multi-modal data collection of a target urban region, and carrying out the standardization processing, abnormal value elimination, multi-modal alignment and interference suppression processing, and obtaining a multi-modal data set; constructing a multi-branch hybrid network, and performing triplet feature extraction on the multi-modal data set through the multi-branch hybrid network to obtain a building group fusion feature map; performing change area detection and change type classification on the two building group fusion feature maps with different time phases to obtain building group change information; and constructing a dynamic evaluation index based on the urban planning core demand, and performing building group change evaluation in combination with the building group change information. According to the method, through multi-source and multi-modal data acquisition and processing, multi-branch hybrid network triplet feature extraction and change detection and dynamic evaluation index application, urban planning core requirements are adapted, and building group detection precision and change evaluation effectiveness are improved.

The invention discloses an urban population flow prediction method based on multi-source data, and the method comprises the steps: collecting and preprocessing mobile equipment position data, public transport card swiping data and POI point location data, and extracting time and space features; calculating a regional attraction coefficient, an inter-regional distance attenuation function, a regional connectivity matrix, a time dynamic parameter and an external influence parameter, constructing a comprehensive population flow prediction mathematical model, and optimizing model parameters through a machine learning method to realize accurate prediction of population flow among urban regions in a future time period; the urban population flow prediction method comprehensively utilizes multi-source heterogeneous data, fully considers the influence of a space factor, a time factor and an external environment factor on population flow, introduces an innovative index of a regional connectivity matrix, realizes adaptive updating of parameters, can effectively improve the accuracy of urban population flow prediction, and improves the urban population flow prediction efficiency. And a scientific basis is provided for smart city construction and urban traffic planning.

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 method, a device and equipment for determining a toughness index of an urban water environment, and belongs to the technical field of urban toughness calculation. The method for determining the toughness index of the urban water environment comprises the following steps: determining risk levels corresponding to M time periods according to hydrological data of a target urban area in the M time periods; according to the water quality data of the target city region in the M time periods, obtaining respective water quality indexes of N predetermined risk grades; and determining the toughness index of the water environment of the target urban area according to the respective water quality indexes and hydrological indexes of the N predetermined risk grades. The toughness index of the water environment determined by the method provided by the invention is more accurate, and the practicability and operability are high.

The invention provides an urban facility updating method and device based on a large language model, equipment and a medium, and relates to the technical field of urban planning, and the method comprises the steps: inputting a division cue word into a plan large language model, and obtaining a plurality of communities corresponding to a to-be-planned urban region outputted by the plan large language model; the division prompt words are used for indicating the plan big language model to divide the to-be-planned city area into a plurality of communities; the plan big language model is constructed based on a general multi-modal big language model; for each community, determining a target facility updating sub-scheme corresponding to the community through a plan big language model; and based on the target facility updating sub-schemes corresponding to all communities, determining an urban facility updating scheme corresponding to the to-be-planned urban area. According to the technical scheme, the urban facility updating scheme is constructed through the large language model, participation of experts and residents is not needed, negative influences caused by human factors are eliminated, and the efficiency and accuracy of planning the urban facility updating scheme are improved.

The invention relates to the field of data processing, in particular to a smart city data processing method and system based on big data, and the method comprises the steps: dividing a city region into a plurality of sub-regions, and setting a plurality of monitoring points in the city region; the diffusion fluctuation degree in each sub-region is calculated, wherein the diffusion fluctuation degree represents the influence degree of the sub-region buildings on pollutant diffusion; adjusting diffusion parameters in the Gaussian plume model by using the influence degree to obtain an optimal diffusion coefficient, and obtaining the monitoring concentration of each monitoring point; setting the initial position of a pollution source, calculating the estimated concentration of pollutants at each monitoring point by using a Gaussian plume model, and calculating the difference between the estimated concentration and the monitored concentration; and updating the initial position of the pollution source, and taking the position of the pollution source when the difference is minimum as the final position of the pollution source. According to the invention, precise positioning of the gas pollution source is realized.

The invention belongs to the technical field of three-dimensional modeling, and discloses a city building three-dimensional model refined construction method based on oblique photography. Comprising the steps of collecting inclined image data of a to-be-modeled urban area and performing data cleaning to obtain a modeling image data set; carrying out projection consistency analysis, identifying a boundary dislocation region and removing the boundary dislocation region to obtain a dislocation removal data set; shielding area identification is carried out, shielding compensation is carried out based on an identification result, and an enhanced three-dimensional point data set is output; executing detail reservation to obtain a triangular facet data set; normal optimization is carried out, plane adjustment is carried out on the triangular surface data set based on an optimization result, and a three-dimensional grid data set is obtained; constructing an initial three-dimensional model, and performing map mapping on the initial three-dimensional model to obtain a detail map set; constructing a fine building three-dimensional model in combination with the detail map set and the initial three-dimensional model; and the boundary definition, the structural integrity and the texture fineness of the urban building three-dimensional model are improved.

The invention provides an urban area attribute determination method and device, equipment, a medium and a program product, relates to the technical field of artificial intelligence, and aims to solve the problem that urban area characterization methods in related technologies lack fine-grained sensing ability. The method comprises the following steps: acquiring first group movement data, wherein the first group movement data is used for representing position change information of a target user group in a plurality of areas within a preset time period; performing state updating on initial state vectors of a plurality of regions based on the first group mobile data to obtain region dynamic representations of the plurality of regions at each moment in the preset time period; and performing region attribute prediction based on the region dynamic representation of the plurality of regions at the target moment to obtain target attribute values of the plurality of regions at the target moment. According to the method, fine-grained urban region representation can be generated, and the accuracy of region attribute prediction is improved.

The invention provides an urban multi-modal large model training method, device and equipment and a medium, and relates to the technical field of artificial intelligence. Constructing an image-text interlaced data set according to the satellite image description text corresponding to each satellite image and the streetscape image description text corresponding to each streetscape image; and training the city multi-modal large model based on the image-text interlaced data set, and adjusting perceptron parameters corresponding to the perceptron in the city multi-modal large model and large language model parameters corresponding to the large language model to obtain a trained target city multi-modal large model. According to the technical scheme, the problems that in the prior art, a training data source is single, and images and texts are independently processed are solved, the trained target city multi-mode large model has good understanding on the city environment, and the space structure and function characteristics of a city can be comprehensively captured.

The invention provides an urban extreme rainfall risk assessment and drainage optimization method fusing a composite threshold and a Chicago method, and relates to the field of power grid disaster prevention and reduction planning and meteorological and hydrological analysis, and the method comprises the following steps: employing a quartile method and a composite threshold method to recognize and classify extreme rainfall events; constructing an extreme rainfall pattern model, and determining a minute-level rainfall intensity curve by using the extreme rainfall pattern model; generating an extreme rainfall spatial-temporal distribution characteristic spectrum according to the extreme rainfall event set in combination with a spatial interpolation technology fused with a deep learning model; the drainage capacity of the urban drainage system is evaluated, and the urban drainage system is optimized according to the evaluation result. The method has high adaptability, can be popularized to urban areas with different terrains and climate conditions, provides an efficient technical tool for coping with extreme weather events under the climate change background and improving the disaster prevention and reduction capacity of a power system, and has important social and economic values.

The invention relates to the technical field of traffic flow prediction, and provides an urban traffic flow prediction method based on a graph convolutional network and related equipment. The method comprises the following steps: dividing a target city area into unit areas according to a plurality of division scales; according to all the traffic flow data and all the unit areas under the division scale, traffic flow time characteristics of the target city area are calculated; performing feature calculation on the traffic flow time features by using a graph convolutional network to obtain final traffic flow features of the target city region under each division scale; based on all the final traffic flow characteristics, obtaining predicted traffic flow data of the target urban area under each division scale, and determining a target division scale according to all the predicted traffic flow data; and performing traffic flow prediction on the to-be-predicted urban area according to the target division scale to obtain final predicted traffic flow data of the to-be-predicted urban area. According to the method, the accuracy of traffic flow prediction can be improved.

The present invention provides a sponge city design method and system, relating to the field of sponge city data processing technology. The method comprises obtaining information; simulating the waterlogging situation in the urban area using an agent-based model based on the information to obtain a waterlogging distribution map; constructing a graphical model based on the drainage system data; and constructing a drainage pipeline layout optimization model based on the graphical model and the waterlogging distribution map. The drainage pipeline layout optimization model includes an objective function and constraints, the objective function including minimizing the sum of the construction cost of the drainage pipeline and a penalty for insufficient drainage capacity. The constraints include flow balance constraints at the drainage points, capacity constraints of the drainage pipelines, drainage capacity constraints in the flood-prone areas, and drainage capacity constraints in the drainage bottleneck areas. The method then solves the drainage pipeline layout optimization model based on hypergeometric distribution sampling. The present invention takes into account the differences between different urban areas and significantly improves the design results of sponge cities.

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.

The invention discloses an urban traffic intelligent planning auxiliary system and method based on big data analysis, and relates to the technical field of traffic planning. Historical travel habit data of residents in a city and historical traffic flow data of urban traffic are collected, and feature extraction is performed on the historical travel habit data and the historical traffic flow data; the method comprises the following steps: constructing a historical feature set, training a travel demand prediction model for predicting passenger flow among different urban areas based on the historical feature set, identifying high travel demand stations in each time period according to a prediction result of the travel demand prediction model, and determining the travel demand of each time period by using a shortest path algorithm according to position distribution of the high travel demand stations in each time period. Planning a new travel route; and the service quality and efficiency of urban public transportation are improved.

The invention discloses a vehicle insurance quotation optimization method and device, equipment and a storage medium. The method comprises the steps that first evaluation data are acquired, and the first evaluation data at least comprise vehicle basic data, personnel basic data and behavior basic data corresponding to a target vehicle, and regional risk data and historical compensation data of a city region to which the target vehicle belongs; and according to the first evaluation data and a pre-trained target fusion pricing model, performing comprehensive modeling by combining supervision of the prefecture and city golden supervision bureau, insurance industry collaboration, a prefecture and city active adjustment business structure, previous three to five-year historical compensation payment data of the prefecture and city, head guarantee pricing data and third-party prefecture and city same-industry settled compensation payment data, and determining a quotation evaluation factor. And determining a vehicle insurance quotation corresponding to the target vehicle according to the quotation evaluation factor. The vehicle insurance fine pricing system is optimized, the accuracy, flexibility and efficiency of vehicle insurance pricing are improved, the method is more suitable for second and third companies, and better-quality services are provided for clients.

The invention discloses an anti-seismic toughness evaluation method and system oriented to an urban area. The method comprises the following steps: obtaining a structure vulnerability function strength median variable under each classification; obtaining a median of a structure vulnerability function intensity median value variable; constructing an empirical vulnerability function of the single target structure, and determining damage states of all single structures in the urban area under the action of a preset earthquake; obtaining toughness levels corresponding to all the monomer structures of the urban area; and respectively calculating a region toughness index based on the individual number ratio and a region toughness index based on the building area ratio, and obtaining the seismic toughness level of the urban region. According to the scheme of the invention, two indexes of different dimensions, namely the number of the building individuals and the building area, are combined, and a comprehensive regional toughness level index is formed through weighted summation, so that the overall anti-seismic toughness condition of the urban region is reflected more comprehensively and more accurately, and the evaluation result is systematic and has operability.

The present invention discloses a travel scheduling optimization method based on the rigid demands of residents, comprising the following steps: S1: Obtain administrative division boundary data and the AMap_Adcode_citycode urban coding table of Amap; S2: Determine the required Point of Interest (POI) data based on administrative regions and urban coding; S3: Use Pandas to quickly preprocess the POI data; S4: Construct an average accessible distance model between regions; S5: Classify various types of POIs; S6: Process the obtained SDE results through ImageJ; S7: Use Matlab to construct a difference equation to calculate the equilibrium point; S8: Perform a weighted average on the equilibrium point values; S9: Determine a travel optimization scheduling plan for the result obtained from the weighted average. Based on more objective POI data of various types of transportation, life, and address facilities, the present invention conducts SDE analysis in a selected urban area, calculates the weight coefficient of the travel intensity generated by the rigid demands of residents in the research area, so as to formulate a travel scheduling optimization method suitable for the residents in the research area.

The invention relates to the technical field of spatial and temporal distribution reconstruction and early warning of VOCs, in particular to an urban area VOCs spatial and temporal distribution reconstruction and early warning method, which comprises the following steps: S1, collecting and preprocessing an urban area VOCs original data set; s2, according to the pollution type of the VOCs in the urban area; s3, constructing a regional VOCs reconstruction and prediction model based on the hybrid CNN-LSTM; s4, setting initial parameters of the model, and training the constructed model to obtain optimal model parameters; s5, performing precision verification on 10 DeepAir models generated after training by using the overall verification set in the step 2; s6, deploying the trained regional VOCs reconstruction and prediction model into the urban region; s7, using the regional VOCs reconstruction and prediction model to generate a complete VOCs concentration distribution reconstruction map in the region; according to the method, accurate reconstruction and prediction of the regional VOCs distribution diagram are realized, and urban regional VOCs spatial and temporal distribution monitoring and early warning required by current practical application can be met.

The application provides a kind of urban ecological restoration priority order identification method, device and electronic equipment, the method comprises: obtaining the spatial distribution data of multiple ecosystem service indicators in different years in target urban area and carrying out standardization processing;According to the spatial distribution data of multiple ecosystem service indicators in different years after standardization processing, determine the ecosystem service cluster value ESB of each pixel corresponding to the preset grid scale in different years in target urban area;According to the ESB of each pixel in different years and impervious surface index value ISA, determine the ecological restoration target year period;For any target pixel, according to its ISA change rate in the ecological restoration target year period, and the ESB change rate of its neighborhood pixel in the ecological restoration target year period, determine its ecological restoration priority order. Prior area of urban ecological restoration area can be divided, improve the efficiency and economy of urban area ecological restoration.

The invention discloses a path planning method based on a city engine and an unreal engine. The method comprises the following steps: completing basic data preparation, rule definition, model generation and visual presentation and analysis, and establishing a city model; secondly, constructing a vehicle motion model in an unreal engine, and simulating weather; the three-dimensional static model and a dynamic model in an unreal engine are fused to generate an urban environment; carrying out geographical gridding on the urban area; introducing a trafficability map and physical restrictions of the vehicle, and setting the trafficability map of the vehicle; an improved A * algorithm based on geographic gridding is introduced, path planning is conducted, the improved A * algorithm is used, and path optimization is achieved through a heuristic function and a penalty function; and outputting the generated path plan.

The invention discloses an urban planning and construction management system based on space-time big data, and relates to the technical field of urban planning and management. Analyzing the multi-source heterogeneous data of the urban area to obtain a plurality of ground feature type areas of the urban area; according to the building model rule and the multi-source heterogeneous data, constructing a spatio-temporal data fusion model in the urban area; performing spatial analysis on each three-dimensional model in the spatio-temporal data fusion model to obtain each type of spatial adaptability coefficient of each three-dimensional model; constructing a trend prediction model, and obtaining predicted social statistical data of each surface feature type region; and according to each type of spatial adaptability coefficient of each three-dimensional model and the predicted social statistical data, the recommendation function attribute of each three-dimensional model is output, and the scientificity and efficiency of urban planning and construction management are remarkably improved.

The present invention discloses a city optimization construction method based on the evaluation results of healthy cities in subtropical areas, comprising: constructing a subtropical healthy city evaluation index system based on existing healthy city evaluation indicators and subtropical city health influencing factors; dividing a target city area into multiple grids according to a scale; collecting data corresponding to the subtropical healthy city evaluation index system in the target city area, and calculating the values ​​of several indicators of each grid according to the data; standardizing the values ​​of several indicators and determining the weight of each indicator; calculating the healthy city comprehensive evaluation value of each grid based on the values ​​and weights of various indicators after the standardization of each grid; identifying grids whose healthy city comprehensive evaluation values ​​are less than an evaluation threshold to form several areas to be optimized, and optimizing the areas to be optimized, so as to establish a healthy city evaluation system for subtropical areas, and thus construct cities in subtropical areas.