A multi-level, scenario-based integrated processing method, system, terminal, and storage medium for urban stock spatial data.

Abstract

This invention relates to the field of data processing technology, and discloses a multi-level scenario-based integrated processing method, system, terminal, and storage medium for urban stock spatial data. The method includes: acquiring urban stock spatial data, extracting dynamic and static elements, and establishing corresponding spatial semantic relationships; integrating the dynamic and static elements with plot scene units according to the spatial semantic relationships to obtain element integration results; calculating multi-dimensional indicators for the dynamic and static elements, integrating them into plot scene units to obtain indicator integration results, and performing fusion processing to obtain scenario-based integrated results; and integrating the constructed scene unit hierarchical structure with the urban stock spatial data at multiple levels to obtain multi-level scenario integration results. This invention enables comprehensive and accurate urban stock spatial analysis through multi-level scenario-based integration of dynamic and static elements and their indicators from urban stock spatial data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a multi-level, scenario-based integrated processing method, system, terminal, and computer-readable storage medium for urban stock spatial data. Background Technology

[0002] Currently, urban development has entered the era of stock renewal. The renewal and transformation of urban stock space requires spatiotemporal big data from a wide range of sources and of diverse types, facing the severe challenges of integrating multi-source heterogeneous data and modeling dynamic correlations throughout the entire process. Traditional geographic information systems (GIS) data models centered on static elements (e.g., land use) or isolated events (e.g., planning adjustments) are insufficient to depict the dynamic interactions of multiple elements, the multi-stage evolution process, and the cross-scale coupling needs in the transformation of inefficient spaces. Geographic scene-based modeling methods provide a dynamic representation framework for complex spatial phenomena, but their application in the field of urban stock space renewal is still in the exploratory stage, especially in terms of system integration throughout the entire process of stock space renewal.

[0003] Urban space management scenarios are often multi-element, multi-dimensional, multi-scale, and multi-scenario. Existing research is mostly limited to the spatial overlay of static elements or the simple mapping of dynamic indicators, resulting in a disconnect between the relationships between static and dynamic elements and the logic between elements and scenarios. This makes it difficult to support the collaborative analysis needs of multiple dimensions and scales, and lacks a unified scenario-based integrated processing method. At the same time, as the basic analytical unit of urban space analysis, the scenario unit usually considers the spatial relationships of multiple levels such as plots, districts, and urban areas, but a multi-level integrated processing and analysis system has not yet been formed.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a multi-level scenario-based integrated processing method, system, terminal, and storage medium for urban stock spatial data. This aims to solve the problem that existing technologies are unable to meet the needs of complex application scenarios, and that scenario unit analysis is often limited to a certain level, failing to form a multi-level integrated analysis system.

[0006] To achieve the above objectives, the present invention provides a multi-level scenario-based integrated processing method for urban existing spatial data, which includes the following steps:

[0007] Acquire multi-source heterogeneous urban stock spatial data in the target city, extract elements from the urban stock spatial data to obtain dynamic elements and static elements, and establish spatial semantic association between the dynamic elements and the static elements.

[0008] Based on the spatial semantic association, the dynamic elements, the static elements, and the land scene units of the target city are integrated in a scenario-based manner to obtain the element integration result of the land scene units;

[0009] Multi-dimensional index calculations are performed on the dynamic and static elements to obtain an index set. The indexes are then integrated into the attribute structure of the land parcel scene unit to obtain the index integration result of the land parcel scene unit. The element integration result and the index integration result are then fused to obtain the scene-based integration result of the land parcel scene unit.

[0010] Based on the scenario integration results, a hierarchical structure of scenario units is constructed. Based on the scenario unit hierarchical structure, the existing urban spatial data is integrated into scenarios at multiple levels to obtain multi-level scenario integration results.

[0011] Optionally, the multi-level scenario-based integrated processing method for urban stock spatial data, wherein, after acquiring multi-source heterogeneous urban stock spatial data in the target city, further includes:

[0012] The dynamic and static elements of the urban stock spatial data are defined to obtain dynamic element labels and static element labels.

[0013] The dynamic elements include crowd activity elements, vehicle activity elements, and IoT sensing device elements, while the static elements include natural entity elements and artificial entity elements.

[0014] Optionally, the multi-level scenario-based integrated processing method for urban stock spatial data, wherein acquiring multi-source heterogeneous urban stock spatial data in the target city, extracting elements from the urban stock spatial data to obtain dynamic and static elements, and establishing spatial semantic relationships between the dynamic and static elements, specifically includes:

[0015] Obtain multi-source heterogeneous urban stock spatial data in the target city, and perform standardization cleaning and normalization processing on the urban stock spatial data to obtain the target city stock spatial data.

[0016] Based on the dynamic element tags, the existing spatial data of the target city is used to extract elements to obtain dynamic elements; based on the static element tags, the existing spatial data of the target city is used to extract elements to obtain static elements.

[0017] Based on the spatiotemporal behavior characteristics of the dynamic elements and the spatial topology of the static elements, an attribute system is constructed to obtain an attribute system. The static elements and the dynamic elements are regarded as entity nodes to obtain static entities and dynamic entities. The attribute system includes static attributes, dynamic attributes, semantic attributes and evolutionary attributes.

[0018] A core relationship is preset, and a spatial semantic association relationship is established based on the static entity, the dynamic entity, the attribute system and the core relationship. The core relationship includes spatial association relationship, spatial topological relationship, spatiotemporal mapping relationship and semantic dependency relationship.

[0019] Optionally, the multi-level scenario-based integration processing method for urban stock spatial data, wherein the scenario-based integration of the dynamic elements, the static elements, and the land parcel scene units of the target city based on the spatial semantic association to obtain the element integration result of the land parcel scene units specifically includes:

[0020] Obtain scene unit division criteria, and divide the target city into scene units according to the scene unit division criteria to obtain land parcel scene units;

[0021] Obtain the spatial attributes of the static elements and the spatiotemporal coordinate sequence of the dynamic elements. Integrate the static elements into the plot scene unit according to the spatial attributes, and anchor the dynamic elements to the plot scene unit according to the spatiotemporal coordinate sequence to obtain the element integration result of the plot scene unit.

[0022] Optionally, the multi-level scenario-based integrated processing method for urban stock spatial data, wherein the step of performing multi-dimensional index calculations on the dynamic and static elements to obtain an index set, and integrating the indicators into the attribute structure of the land parcel scenario unit to obtain the index integration result of the land parcel scenario unit, specifically includes:

[0023] The data characteristics and target requirements of the urban stock spatial data are obtained, and a multi-dimensional element indicator system is defined based on the data characteristics and target requirements. The multi-dimensional element indicator system includes static spatial indicators and dynamic behavioral indicators.

[0024] Based on the multi-dimensional element indicator system, the spatiotemporal coordinate sequence of the dynamic elements is used to calculate the dynamic behavior comprehensive index to obtain the dynamic index set. Based on the multi-dimensional element indicator system, the spatial attributes of the static elements are used to calculate the static comprehensive index to obtain the static index set.

[0025] The dynamic indicator set and the static indicator set are associated with the target identifier of the land parcel scene unit to obtain the association result. Based on the association result, the dynamic indicator set and the static indicator set are integrated into the land parcel scene unit to obtain the indicator integration result of the land parcel scene unit.

[0026] Optionally, the multi-level scenario-based integrated processing method for urban stock spatial data, wherein fusing the element integration result with the indicator integration result to obtain the scenario-based integrated result of the land parcel scenario unit specifically includes:

[0027] Perform a plot polygon inclusion analysis on the dynamic and static element entities of the element integration result to obtain the element analysis result, and perform spatial location verification processing on the spatial indicators of the indicator integration result to obtain the location verification result.

[0028] Based on the element analysis results and the location verification results, hierarchical fusion modeling is performed to obtain an attribute fusion system. Then, based on the attribute fusion system, the element integration results and the indicator integration results are fused to obtain a scenario-based integration result.

[0029] Optionally, the multi-level scenario-based integration processing method for urban stock spatial data, wherein the step of constructing a hierarchical scene unit structure based on the scenario integration result, and performing multi-level scene-based integration of the urban stock spatial data according to the scene unit hierarchical structure to obtain a multi-level scene integration result, specifically includes:

[0030] Based on the physical attribute information of the target city and the scenario integration result, scenario units at each level are obtained, all scenario units are divided to obtain scenario division results, and a hierarchical scenario unit structure is constructed based on the scenario division results. The scenario unit structure includes a plot scenario unit structure, a district scenario unit structure, and an urban area scenario unit structure.

[0031] Based on the scene unit hierarchy, all scene units are integrated to obtain multiple scene element integration results. Based on the scene unit hierarchy, all scene units are calculated to obtain multiple scene indicators. All scene indicators are then integrated to obtain scene indicator integration results.

[0032] The integration results of all the scene elements and the integration results of the scene indicators are fused together to obtain a multi-level scene-based integration result.

[0033] Optionally, the multi-level scenario-based integrated processing method for urban existing spatial data, wherein the multi-level scenario-based integrated processing system for urban existing spatial data includes:

[0034] The element extraction module is used to acquire multi-source heterogeneous urban stock spatial data in the target city, extract elements from the urban stock spatial data to obtain dynamic elements and static elements, and establish spatial semantic association between the dynamic elements and the static elements.

[0035] The element integration module is used to integrate the dynamic elements, the static elements and the land scene units of the target city in a contextualized manner according to the spatial semantic relationship, so as to obtain the element integration result of the land scene unit;

[0036] The indicator integration module is used to perform multi-dimensional indicator calculations on the dynamic elements and the static elements to obtain an indicator set, integrate the indicators into the attribute structure of the land plot scene unit to obtain the indicator integration result of the land plot scene unit, and fuse the element integration result with the indicator integration result to obtain the scene-based integration result of the land plot scene unit.

[0037] The multi-level scene integration module is used to construct a hierarchical scene unit structure based on the scene integration result, and to perform multi-level scene integration of the existing urban spatial data based on the scene unit structure to obtain the multi-level scene integration result.

[0038] In addition, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a multi-level scenario-based integration processing program for urban stock spatial data stored in the memory and executable on the processor, wherein when the multi-level scenario-based integration processing program for urban stock spatial data is executed by the processor, it implements the steps of the multi-level scenario-based integration processing method for urban stock spatial data as described above.

[0039] In addition, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a multi-level scenario-based integrated processing program for urban stock spatial data, and when the multi-level scenario-based integrated processing program for urban stock spatial data is executed by a processor, it implements the steps of the multi-level scenario-based integrated processing method for urban stock spatial data as described above.

[0040] In this invention, multi-source heterogeneous urban spatial data of a target city is acquired. Elements are extracted from the urban spatial data to obtain dynamic and static elements, and spatial semantic relationships are established between the dynamic and static elements. Based on these spatial semantic relationships, the dynamic and static elements are integrated with the land parcel scene units of the target city in a scenario-based manner to obtain the element integration result of the land parcel scene unit. Multi-dimensional index calculations are performed on the dynamic and static elements to obtain an index set. These indices are integrated into the attribute structure of the land parcel scene unit to obtain the index integration result of the land parcel scene unit. The element integration result and the index integration result are then fused to obtain the scenario-based integration result of the land parcel scene unit. A hierarchical scene unit structure is constructed based on the scenario-based integration result. Multi-level scene integration of the urban spatial data is then performed based on this hierarchical scene unit structure to obtain a multi-level scene integration result. This invention integrates the dynamic elements, static elements, and calculation indicators of urban stock space data in a scenario-based manner through multi-level scenario units of "plot-area-city", providing a key data foundation for comprehensive and accurate urban stock space analysis and realizing comprehensive and accurate urban stock space analysis. Attached Figure Description

[0041] Figure 1 This is a flowchart of a preferred embodiment of the multi-level scenario-based integrated processing method for urban stock spatial data of the present invention;

[0042] Figure 2 This is a schematic diagram of the overall process of the multi-level scenario-based integrated processing method for urban stock spatial data of the present invention.

[0043] Figure 3 This is a schematic diagram illustrating the process of establishing spatial semantic relationships between dynamic and static elements in this invention.

[0044] Figure 4 This is a schematic diagram of the process for scene-based integration of dynamic and static elements based on land parcel scene units in this invention;

[0045] Figure 5 This is a schematic diagram of the index processing flow for dynamic and static elements in this invention;

[0046] Figure 6 This is a flowchart illustrating the integration of land parcel scene units in this invention;

[0047] Figure 7 This is a schematic diagram of the scenario-based integration of the comprehensive indicator system of this invention;

[0048] Figure 8This is a structural diagram of a preferred embodiment of the multi-level scenario-based integrated processing system for urban stock spatial data of the present invention;

[0049] Figure 9 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0051] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0052] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0053] The preferred embodiment of the present invention describes a multi-level, scenario-based integrated processing method for urban stock spatial data, such as... Figure 1 As shown, the multi-level scenario-based integrated processing method for urban stock spatial data includes the following steps:

[0054] Step S10: Obtain multi-source heterogeneous urban stock spatial data in the target city, extract elements from the urban stock spatial data to obtain dynamic elements and static elements, and establish spatial semantic association between the dynamic elements and the static elements.

[0055] Step S10 includes:

[0056] Step S11: Obtain multi-source heterogeneous urban stock spatial data in the target city, and perform standardization cleaning and normalization processing on the urban stock spatial data to obtain the target city stock spatial data.

[0057] Step S12: Extract elements from the existing spatial data of the target city according to the dynamic element tags to obtain dynamic elements; extract elements from the existing spatial data of the target city according to the static element tags to obtain static elements.

[0058] Step S13: Construct a system based on the spatiotemporal behavior characteristics of the dynamic elements and the spatial topology of the static elements to obtain an attribute system, and regard the static elements and the dynamic elements as entity nodes to obtain static entities and dynamic entities. The attribute system includes static attributes, dynamic attributes, semantic attributes and evolutionary attributes.

[0059] Step S14: Preset core relationships. Establish spatial semantic association relationships based on the static entities, the dynamic entities, the attribute system, and the core relationships. The core relationships include spatial association relationships, spatial topological relationships, spatiotemporal mapping relationships, and semantic dependency relationships.

[0060] Specifically, in this embodiment of the invention, in order to address the problem that existing technologies are unable to meet the needs of complex application scenarios and that scenario unit analysis is often limited to a certain level, failing to form a multi-level integrated analysis system, a multi-level scenario-based integrated processing method for urban stock spatial data is proposed. The corresponding processing flow is as follows: Figure 2As shown, after acquiring multi-source heterogeneous data of the target city's existing urban space (i.e., urban existing spatial data), it is necessary to extract dynamic and static elements from the urban existing spatial data and establish spatial semantic relationships between dynamic and static elements. Element extraction from urban existing spatial data involves obtaining key information from the multi-source heterogeneous data and transforming it into standardized elements. Based on element standardization, the multi-source heterogeneous urban existing spatial data is processed and cleaned. Specific tools are used to convert data of different formats into standardized data to obtain the target city's existing spatial data, thereby eliminating abnormal data, correcting format errors, and unifying the coordinate system to ensure positional accuracy. Element-based extraction of urban existing spatial data, based on the multi-source heterogeneous data, involves extracting basic spatiotemporal data, 3D model data, public thematic data, and... The social perception data extracts static elements such as land parcels, buildings, houses, transportation, urban components, pipelines, mountains, water systems, vegetation, and geology, as well as dynamic elements such as people, vehicles, and IoT devices. Then, spatial semantic associations are established between the dynamic and static elements. Specifically, firstly, the dynamic and static elements of the existing urban spatial data are defined, resulting in dynamic element labels and static element labels. The dynamic elements include human activity elements, vehicle activity elements, and IoT sensing device elements; the state and behavior of these elements change over time and directly affect the city's operational status. The static elements include natural entity elements and artificial entity elements. Natural entity elements include land parcels, mountains, water systems, vegetation, and geology, while artificial entity elements include buildings, houses, transportation, urban components, and pipelines. These elements constitute the main elements of urban space.

[0061] After that, as Figure 3 As shown, dynamic and static elements are identified and extracted from the basic spatiotemporal data, 3D model data, public thematic data, and social perception data of the existing space. Specifically, dynamic elements are extracted from the existing space data of the target city based on the dynamic element tags, and static elements are extracted from the existing space data of the target city based on the static element tags. For example, two-dimensional vectors of buildings are identified and extracted from building census data, 3D models of buildings are identified and extracted from individual building 3D models, and building tags are extracted from building POIs (Points of Interest). These building data are all associated with the same building element. Then, semantic tags are added to each identified element to clarify its attributes, categories, and functions. Necessary attribute values, such as building height and completion time, are assigned to the elements according to the actual situation.

[0062] Then, based on the spatiotemporal behavioral characteristics of dynamic elements (e.g., timestamps and trajectory distribution density of events) and the spatial topology of static elements (e.g., the inclusion relationship between plots and buildings, and the connectivity relationship between roads and bridges), an attribute system containing "entity-attribute-relationship" is constructed. The static and dynamic elements are treated as entity nodes, resulting in static and dynamic entities. The static entities are defined as spatial objects such as plots, buildings, roads, and water systems as core entities, serving as the material carriers of urban space and carrying basic attributes such as geographic coordinates and spatial morphology. The dynamic entities are abstracted from spatiotemporal events such as pedestrian flow, vehicle flow, facility usage events, and disaster impacts, reflecting real-time changes in urban operations, such as "customer gathering in a certain business district during a certain period" or "the impact of heavy rain causing flooding on a certain road section."

[0063] The attribute system includes static attributes, dynamic attributes, semantic attributes, and evolutionary attributes. Static attributes are characteristics that do not change or change extremely slowly over time, including spatial physical features such as spatial coordinates, geometric shapes, and physical parameters. For example, static attributes of a building include its height, floor area, construction date, type of building materials, and property status. Dynamic attributes are characteristics that change significantly over time, including spatiotemporal behavioral parameters such as timestamps, trajectory density, event duration, and radius of influence. For example, dynamic attributes of a building include energy consumption, occupancy rate, and visitor traffic. Semantic attributes are the social significance, functional role, and relationships between urban elements and other elements. For example, semantic attributes of a building include semantic descriptions such as functional positioning, historical significance, and protection level. Evolutionary attributes are the changes that elements undergo over time, such as building renovation and upgrades, occupancy records, migration paths of the floating population, and residency periods.

[0064] A core relation is established, and spatial semantic associations are built based on the static entities, dynamic entities, attribute system, and core relation. The core relation includes spatial topological relations, spatiotemporal mapping relations, and semantic dependencies. The spatial topological relations describe the geometric connections and positional dependencies between static entities, including adjacency (e.g., building A is adjacent to park B), inclusion (e.g., plot C contains 3 residential buildings), connectivity (e.g., subway line D is connected to bus stop E), and intersection (e.g., river F intersects with road G), etc., and are quantified and identified using spatial analysis algorithms (e.g., buffer analysis, overlay analysis). The spatiotemporal mapping relations establish the association between dynamic events and static spaces. For example, the event "morning rush hour traffic flow" is mapped to the spatial location of "urban main road H," and the event "customer flow during a shopping mall promotion" is associated with "plot J within the influence range of shopping mall I." Geographic coding and trajectory matching technologies are used to achieve accurate mapping between events and spatial objects. The semantic dependencies are the logical connections between facilities, land use and human activities. For example, "bus stop" supports "commuting behavior" (facility-behavior support relationship) and "industrial land" restricts "commercial activities" (land use-activity constraint relationship). The semantic logic is formalized through domain ontology libraries (e.g., urban planning ontology, transportation behavior ontology).

[0065] Step S20: Based on the spatial semantic association, integrate the dynamic elements, the static elements, and the land scene units of the target city in a scenario-based manner to obtain the element integration result of the land scene units.

[0066] Step S20 includes:

[0067] Step S21: Obtain scene unit division criteria, and divide the target city into scene units according to the scene unit division criteria to obtain land scene units;

[0068] Step S22: Obtain the spatial attributes of the static elements and the spatiotemporal coordinate sequence of the dynamic elements. Integrate the static elements into the plot scene unit according to the spatial attributes, and anchor the dynamic elements to the plot scene unit according to the spatiotemporal coordinate sequence to obtain the element integration result of the plot scene unit.

[0069] Specifically, in embodiments of the present invention, such as Figure 4As shown, firstly, to delineate the scene units of a land parcel, it is necessary to obtain the scene unit division standard. The statutory map is the legal basis for planning management. When using it as the scene unit division standard to delineate the scene units of a land parcel, the red line of the land parcel delineated by the map is directly used as the spatial boundary. The land use code, development control indicators, facility construction requirements, etc. are extracted as core attributes to ensure that the unit boundary and functional positioning are completely consistent with the statutory planning requirements, thus obtaining the scene unit of the land parcel. Next, element integration is required for the plot scene units. Specifically, for static elements, the spatial boundaries of the static elements are obtained. Based on the spatial boundaries of the plot scene units, a spatial inclusion relationship algorithm is used to determine the inclusion affiliation of the static elements. If the centroid of a static element or more than 70% of its area is located inside the boundary of a plot scene unit, then the static element is determined to belong to that plot scene unit. For linear elements spanning multiple scene units, they are marked according to their length proportion within each scene unit. For dynamic elements, the spatiotemporal coordinate sequence of the dynamic elements is obtained. Each dynamic event point is extracted based on the spatiotemporal coordinate sequence, and its corresponding plot scene unit is determined using a spatial point query algorithm. This ensures that each dynamic record is accurately anchored to the corresponding plot scene unit, realizing the positional association between "behavioral flow" and "plot unit". For semantic association, based on preset semantic relationships, the semantic attributes of static elements, the behavioral characteristics of dynamic elements, and the functional attributes of plot scene units are automatically associated to form the semantic integration of plot scene units.

[0070] Subsequently, the dynamic and static elements, after spatial matching and semantic integration, are integrated into the plot scene units. Each plot scene unit simultaneously carries spatial coordinates, functional attributes, dynamic behavioral trajectories, and relationships, ultimately forming an element integration result of plot scene units containing "spatial location, functional semantics, and dynamic features." At the node level, the plot scene unit serves as the core node, carrying basic attributes such as spatial coordinates, land use, and development indicators, as well as associated static entity nodes such as buildings and roads, and dynamic event nodes such as pedestrian and vehicle traffic. At the edge level, various element nodes are organically connected through spatial topological relationships (e.g., plots contain buildings), spatiotemporal mapping relationships (e.g., events occur on plots), and semantic dependencies (e.g., plot use supports activity types), forming a multi-dimensional relational network.

[0071] Step S30: Perform multi-dimensional index calculations on the dynamic elements and the static elements to obtain an index set. Integrate the indexes into the attribute structure of the land parcel scene unit to obtain the index integration result of the land parcel scene unit. Then, fuse the element integration result with the index integration result to obtain the scene-based integration result of the land parcel scene unit.

[0072] Step S30 includes:

[0073] Step S31: Obtain the data characteristics and target requirements of the urban stock spatial data, and define a multi-dimensional element indicator system based on the data characteristics and target requirements. The multi-dimensional element indicator system includes static spatial indicators and dynamic behavioral indicators.

[0074] Step S32: Calculate the dynamic behavior comprehensive index of the spatiotemporal coordinate sequence of the dynamic element according to the multi-dimensional element index system to obtain a dynamic index set; calculate the static comprehensive index of the spatial attributes of the static element according to the multi-dimensional element index system to obtain a static index set.

[0075] Step S33: Associate the dynamic indicator set and the static indicator set with the target identifier of the land parcel scene unit to obtain the association result, and integrate the dynamic indicator set and the static indicator set into the land parcel scene unit according to the association result to obtain the indicator integration result of the land parcel scene unit.

[0076] Step S34: Perform a plot polygon inclusion analysis on the dynamic and static element entities of the element integration result to obtain the element analysis result, and perform spatial location verification processing on the spatial indicators of the indicator integration result to obtain the location verification result.

[0077] Step S35: Perform hierarchical fusion modeling based on the element analysis results and the location verification results to obtain an attribute fusion system, and then perform fusion processing on the element integration results and the indicator integration results based on the attribute fusion system to obtain a scenario-based integration result.

[0078] Specifically, in this embodiment of the invention, a multi-dimensional element indicator system needs to be defined by combining the data characteristics (e.g., heterogeneity) of urban stock spatial data and the target requirements (e.g., the requirements of the analysis target). This multi-dimensional element indicator system includes static spatial indicators and dynamic behavioral indicators. The static spatial indicators focus on the physical spatial attributes of the land parcel, including geometric features (e.g., land area, boundary perimeter), development intensity (e.g., plot ratio, building density, green space ratio), facility configuration (e.g., number of public service facilities, road network density), and spatial morphology (e.g., average building height, skyline undulation), used to characterize the physical spatial characteristics and planning compliance of the land parcel. The dynamic behavioral indicators reflect the impact of spatiotemporal events on the land parcel, such as pedestrian flow indicators (e.g., average daily pedestrian flow, peak time coefficient), vehicle flow indicators (e.g., vehicle traffic volume, parking space turnover rate), facility utilization efficiency (e.g., bus stop frequency, average daily park visits), and environmental perception indicators (e.g., noise decibels, air quality index), capturing the dynamic operating status of the land parcel through time series data.

[0079] After that, as Figure 5 As shown, dynamic behavior comprehensive indicators are calculated for the spatiotemporal coordinate sequence of the dynamic elements according to the multi-dimensional element indicator system to obtain a dynamic indicator set. Static comprehensive indicators are calculated for the spatial attributes of the static elements according to the multi-dimensional element indicator system to obtain a static indicator set. For different dimension indicators, appropriate data analysis methods are adopted, including statistical analysis methods and model calculation methods. The statistical analysis method directly generates static spatial indicators through spatial data query and attribute statistics. For dynamic behavior indicators, time period aggregation (e.g., hourly passenger flow statistics) and spatial density calculation (e.g., passenger flow intensity per unit area) are performed based on timestamp data.

[0080] Subsequently, based on the static elements and their spatial attributes assigned to each plot scene unit, static comprehensive indicators are calculated for each plot scene unit. Using the spatial analysis module of the geographic information system, key structural and functional indicators for each unit are calculated, including building density, average plot ratio, green space ratio, road network density, public service facility density, functional mix index, and the proportion of old buildings. All indicator calculation results are generated in structured field format and used as a static indicator set. Based on the spatiotemporal coordinate sequence of dynamic elements anchored to each plot scene unit, dynamic behavioral indicators are calculated for each unit. Dynamic data is aggregated and statistically analyzed according to preset time windows (e.g., hourly, daily), calculating indicators such as average daily pedestrian flow, peak intensity ratio, average dwell time, space utilization rate, and functional activity index for each unit. All dynamic indicator calculation results are generated in time-series structured data format, forming a dynamic indicator dataset.

[0081] After that, as Figure 6 As shown, the static indicator set and the dynamic indicator set are associated by primary key based on the unique identifier (i.e., target identifier) ​​of the land parcel scene unit. A fusion operation is then performed, and the fused static and dynamic indicator sets are written into the attribute structure of the land parcel scene unit. Using the unique code of the land parcel as the fusion primary key, the element entity IDs in the element integration result are linked with the indicator records in the indicator integration result. Through database table association or spatiotemporal knowledge graph node ID mapping, a unique correspondence of "land parcel → element → indicator" is established, realizing the coded primary key association.

[0082] The merged land parcel scene units are stored as semantically rich nodes in a spatiotemporal knowledge graph. Each node integrates four dimensions of information: spatial dimension, element dimension, indicator dimension, and source dimension. The spatial dimension includes the land parcel's boundary coordinates, spatial topological relationships with surrounding entities, and spatial accuracy levels, supporting GIS spatial analysis and visualization. The element dimension integrates static entities (e.g., geometric features and functional labels of buildings within the land parcel) with dynamic entities (e.g., trajectory data and behavioral characteristics of spatiotemporal events), recording the relationships between elements in a "node-edge" structure (e.g., "building → located on the land parcel", "vehicle flow → passing through roads → connecting to the land parcel entrance / exit"). The indicator dimension stores the numerical results, time-series curves, and spatial distribution heatmaps of multi-dimensional indicators, supporting horizontal comparison (between land parcels) and vertical analysis of indicators. The source dimension records the original data sources, algorithm model versions, data update timestamps, and element entity collection methods relied upon for indicator calculation, forming a complete data lineage chain. Finally, based on the attribute fusion system, the element integration result is fused with the indicator integration result and the land parcel scene unit to obtain the scene-based integration result.

[0083] Step S40: Construct a hierarchical scene unit structure based on the scene integration results, and perform multi-level scene integration on the urban stock spatial data based on the scene unit structure to obtain multi-level scene integration results.

[0084] Step S40 includes:

[0085] Step S41: Obtain scene units at each level based on the physical attribute information of the target city and the scene integration result, divide all scene units to obtain scene division results, and construct a hierarchical scene unit structure based on the scene division results. The scene unit structure includes a plot scene unit structure, a district scene unit structure, and an urban area scene unit structure.

[0086] Step S42: Integrate all scene units according to the scene unit hierarchy to obtain multiple scene element integration results; calculate indicators for all scene units according to the scene unit hierarchy to obtain multiple scene indicators; and integrate all scene indicators to obtain scene indicator integration results.

[0087] Step S43: Merge the integration results of all the scene elements and the integration results of the scene indicators to obtain a multi-level scene integration result.

[0088] Specifically, in this embodiment of the invention, scene units at each level are obtained based on the physical attribute information of the target city (including physical spatial attributes and physical functional attributes) and the scene integration results. All scene units are then divided to obtain scene division results. A hierarchical scene unit structure is constructed based on these results. This hierarchical structure includes plot scene unit structures, area scene unit structures, and urban area scene unit structures. The plot scene unit uses the city's statutory plan as its basic spatial unit, and its boundary is defined by building outlines, road networks, and land use lines. Based on this, area scene units are further divided. Based on the scope of the urban renewal area, and comprehensively considering functional relevance, transportation accessibility, and social governance boundaries, multiple complementary and spatially adjacent plot scene units are organically integrated to form functional clusters with clear renewal goals and implementing entities. Further, urban area scene units are constructed, based on legal administrative divisions (such as streets, towns, or district-level administrative boundaries), while being appropriately adjusted in conjunction with urban functional zoning (such as central business districts, industrial parks, and main residential areas). Multiple plot scene units are merged into higher-level spatial governance units to support macro-level decision-making tasks such as district-level urban health checks, public service resource allocation assessments, and carbon emission accounting. Ultimately, a hierarchical structure of scene units, "plot-area-urban area," is formed. Plot scene units constitute the basic spatial units; area scene units, as the basic units for urban renewal implementation, include multiple plot units; and urban area scene units, serving city-level strategy formulation and performance evaluation, include multiple area scene units.

[0089] After that, as Figure 7 As shown, based on the hierarchical relationship, all scene units are integrated to obtain multiple scene element integration results. Static elements are merged step by step according to spatial attributes, and dynamic elements are aggregated step by step according to spatiotemporal range, realizing the integration of static and dynamic elements of multi-scale scene units. For static element integration, the core is based on spatial inclusion relationship and attribute homogeneity, using geometric fusion and attribute inheritance rules to reorganize the elements of the plot scene unit into multi-level spatial semantic entities of higher-level scene units. For dynamic element aggregation, the core is based on spatiotemporal inclusion relationship and pattern continuity, using time window statistics and spatial unit association to map the elements of the plot scene unit into a spatiotemporal continuum of higher-level scene units.

[0090] Then, the multi-level elements and indicators are integrated step by step, including the merging of static elements and dynamic elements. The merging of static elements is carried out by geometrically merging step by step according to the spatial topology relationship of "plot → area → city". For the area level, the building outlines and road centerlines of the plots are spatially superimposed to generate a block-level building group model and road network skeleton, while retaining the plot boundaries as sub-elements. For the city level, the functional zoning and facility list of the area are merged to form a city-level functional layout map.

[0091] Dynamic element aggregation involves multi-granularity aggregation across spatiotemporal ranges, including time and space dimensions. In the time dimension, it aggregates plot-level time-period passenger flow into street-level hourly traffic, district-level daily peak traffic, and city-level weekly trends. In the spatial dimension, through spatial proximity buffer analysis, it aggregates dynamic spatial events within a reasonable range around the plot to district units, generating a street activity heatmap reflecting the intensity of regional activity. Furthermore, it performs spatial-scale aggregation of cross-district personnel flow data to identify district-level job-housing correlation strength indicators that characterize the degree of spatial correlation between residence and employment within a district.

[0092] In the multi-level integration of urban stock spatial data, the calculation of indicators must strictly follow the logic of "element integration → scale adaptation → model derivation" to avoid directly using the indicators of the underlying unit, but to recalculate based on the element integration results of scene units at each scale.

[0093] Finally, the integration results of all the scene elements and the integration results of the scene indicators are fused to obtain a multi-level scene-based integration result. At the database level, foreign key constraints are used to enable cascading queries between the land parcel table and the street table, the street table and the district table, and the district table and the city table. For example, when querying a city, one can trace back to the full element data of the districts and land parcels under its jurisdiction. At the spatiotemporal knowledge graph level, each scale unit is used as a node and connected by inclusion relationship edges (e.g., land parcel C → included in → district X → included in → city Y) to form a hierarchical graph structure.

[0094] This invention extracts static and dynamic elements of urban existing space from basic spatiotemporal data, 3D model data, public thematic data, and social perception data, and establishes spatial relationships between these elements. This allows for a more comprehensive and accurate reflection of the multidimensional characteristics of urban existing space, avoiding the limitations of existing technologies that only process single elements or single data sources, thus improving the accuracy and comprehensiveness of urban spatial analysis. Furthermore, it integrates dynamic and static elements in a scenario-based manner using land parcel scene units as the benchmark. As the smallest spatial analysis unit, the land parcel scene unit considers spatial continuity, adjacency, and functional attributes, accurately describing the physical spatial characteristics and socio-economic functions within the unit. Unlike traditional single-level analysis, this invention achieves refined integration of dynamic and static elements through land parcel scene units, ensuring the accuracy of the analysis and multi-level spatial relationships, thereby providing more detailed decision support for urban management.

[0095] Furthermore, such as Figure 8 As shown, based on the above-mentioned multi-level scenario-based integrated processing method for urban stock spatial data, the present invention also provides a multi-level scenario-based integrated processing system for urban stock spatial data, wherein the multi-level scenario-based integrated processing system for urban stock spatial data includes:

[0096] The element extraction module 51 is used to acquire multi-source heterogeneous urban stock spatial data in the target city, extract elements from the urban stock spatial data to obtain dynamic elements and static elements, and establish spatial semantic association between the dynamic elements and the static elements.

[0097] The element integration module 52 is used to integrate the dynamic elements, the static elements and the land scene units of the target city in a scene-based manner according to the spatial semantic relationship, so as to obtain the element integration result of the land scene unit;

[0098] The indicator integration module 53 is used to perform multi-dimensional indicator calculations on the dynamic elements and the static elements to obtain an indicator set, integrate the indicators into the attribute structure of the land plot scene unit to obtain the indicator integration result of the land plot scene unit, and fuse the element integration result with the indicator integration result to obtain the scene-based integration result of the land plot scene unit.

[0099] The multi-level scene integration module 54 is used to construct a hierarchical scene unit structure based on the scene integration result, and to perform multi-level scene integration of the urban stock spatial data based on the scene unit hierarchical structure to obtain the multi-level scene integration result.

[0100] Furthermore, such as Figure 9As shown, based on the above-mentioned multi-level scenario-based integrated processing method for urban stock spatial data, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 9 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0101] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard drive or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard drive, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a multi-level scenario-based integrated processing program 40 for urban stock spatial data. This multi-level scenario-based integrated processing program 40 for urban stock spatial data can be executed by the processor 10, thereby implementing the multi-level scenario-based integrated processing method for urban stock spatial data in this application.

[0102] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing a multi-level scenario-based integrated processing method for the urban stock spatial data.

[0103] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0104] In one embodiment, when the processor 10 executes the multi-level scene-based integrated processing program 40 for urban stock spatial data in the memory 20, the following steps are performed:

[0105] Acquire multi-source heterogeneous urban stock spatial data in the target city, extract elements from the urban stock spatial data to obtain dynamic elements and static elements, and establish spatial semantic association between the dynamic elements and the static elements.

[0106] Based on the spatial semantic association, the dynamic elements, the static elements, and the land scene units of the target city are integrated in a scenario-based manner to obtain the element integration result of the land scene units;

[0107] Multi-dimensional index calculations are performed on the dynamic and static elements to obtain an index set. The indexes are then integrated into the attribute structure of the land parcel scene unit to obtain the index integration result of the land parcel scene unit. The element integration result and the index integration result are then fused to obtain the scene-based integration result of the land parcel scene unit.

[0108] Based on the scenario integration results, a hierarchical structure of scenario units is constructed. Based on the scenario unit hierarchical structure, the existing urban spatial data is integrated into scenarios at multiple levels to obtain multi-level scenario integration results.

[0109] The acquisition of multi-source heterogeneous urban spatial data in the target city further includes:

[0110] The dynamic and static elements of the urban stock spatial data are defined to obtain dynamic element labels and static element labels.

[0111] The dynamic elements include crowd activity elements, vehicle activity elements, and IoT sensing device elements, while the static elements include natural entity elements and artificial entity elements.

[0112] Specifically, the process of acquiring multi-source heterogeneous urban spatial data in the target city, extracting elements from the urban spatial data to obtain dynamic and static elements, and establishing spatial semantic relationships between the dynamic and static elements includes:

[0113] Obtain multi-source heterogeneous urban stock spatial data in the target city, and perform standardization cleaning and normalization processing on the urban stock spatial data to obtain the target city stock spatial data.

[0114] Based on the dynamic element tags, the existing spatial data of the target city is used to extract elements to obtain dynamic elements; based on the static element tags, the existing spatial data of the target city is used to extract elements to obtain static elements.

[0115] Based on the spatiotemporal behavior characteristics of the dynamic elements and the spatial topology of the static elements, an attribute system is constructed to obtain an attribute system. The static elements and the dynamic elements are regarded as entity nodes to obtain static entities and dynamic entities. The attribute system includes static attributes, dynamic attributes, semantic attributes and evolutionary attributes.

[0116] A core relationship is preset, and a spatial semantic association relationship is established based on the static entity, the dynamic entity, the attribute system and the core relationship. The core relationship includes spatial association relationship, spatial topological relationship, spatiotemporal mapping relationship and semantic dependency relationship.

[0117] Specifically, the step of integrating the dynamic elements, the static elements, and the land parcel scene units of the target city according to the spatial semantic association to obtain the element integration result of the land parcel scene units includes:

[0118] Obtain scene unit division criteria, and divide the target city into scene units according to the scene unit division criteria to obtain land parcel scene units;

[0119] Obtain the spatial attributes of the static elements and the spatiotemporal coordinate sequence of the dynamic elements. Integrate the static elements into the plot scene unit according to the spatial attributes, and anchor the dynamic elements to the plot scene unit according to the spatiotemporal coordinate sequence to obtain the element integration result of the plot scene unit.

[0120] Specifically, the step of performing multi-dimensional index calculations on the dynamic and static elements to obtain an index set, and integrating the indicators into the attribute structure of the land parcel scene unit to obtain the index integration result of the land parcel scene unit includes:

[0121] The data characteristics and target requirements of the urban stock spatial data are obtained, and a multi-dimensional element indicator system is defined based on the data characteristics and target requirements. The multi-dimensional element indicator system includes static spatial indicators and dynamic behavioral indicators.

[0122] Based on the multi-dimensional element indicator system, the spatiotemporal coordinate sequence of the dynamic elements is used to calculate the dynamic behavior comprehensive index to obtain the dynamic index set. Based on the multi-dimensional element indicator system, the spatial attributes of the static elements are used to calculate the static comprehensive index to obtain the static index set.

[0123] The dynamic indicator set and the static indicator set are associated with the target identifier of the land parcel scene unit to obtain the association result. Based on the association result, the dynamic indicator set and the static indicator set are integrated into the land parcel scene unit to obtain the indicator integration result of the land parcel scene unit.

[0124] Specifically, the step of constructing a hierarchical scene unit structure based on the scene integration results, and performing multi-level scene integration on the existing urban spatial data according to the scene unit hierarchy to obtain multi-level scene integration results includes:

[0125] Based on the physical attribute information of the target city and the scenario integration result, scenario units at each level are obtained, all scenario units are divided to obtain scenario division results, and a hierarchical scenario unit structure is constructed based on the scenario division results. The scenario unit structure includes a plot scenario unit structure, a district scenario unit structure, and an urban area scenario unit structure.

[0126] Based on the scene unit hierarchy, all scene units are integrated to obtain multiple scene element integration results. Based on the scene unit hierarchy, all scene units are calculated to obtain multiple scene indicators. All scene indicators are then integrated to obtain scene indicator integration results.

[0127] The integration results of all the scene elements and the integration results of the scene indicators are fused together to obtain a multi-level scene-based integration result.

[0128] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a multi-level scenario-based integrated processing program for urban stock spatial data, and when the multi-level scenario-based integrated processing program for urban stock spatial data is executed by a processor, it implements the steps of the multi-level scenario-based integrated processing method for urban stock spatial data as described above.

[0129] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0130] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0131] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A multi-level, scenario-based integrated processing method for urban stock spatial data, characterized in that, The multi-level, scenario-based integrated processing method for urban stock spatial data includes: Acquire multi-source heterogeneous urban stock spatial data in the target city, extract elements from the urban stock spatial data to obtain dynamic elements and static elements, and establish spatial semantic association between the dynamic elements and the static elements. The process of acquiring multi-source heterogeneous urban spatial data in the target city, extracting elements from the urban spatial data to obtain dynamic and static elements, and establishing spatial semantic relationships between the dynamic and static elements specifically includes: Obtain multi-source heterogeneous urban stock spatial data in the target city, and perform standardization cleaning and normalization processing on the urban stock spatial data to obtain the target city stock spatial data. Based on dynamic element tags, the existing spatial data of the target city is used to extract elements to obtain dynamic elements; based on static element tags, the existing spatial data of the target city is used to extract elements to obtain static elements. Based on the spatiotemporal behavior characteristics of the dynamic elements and the spatial topology of the static elements, an attribute system is constructed to obtain an attribute system. The static elements and the dynamic elements are regarded as entity nodes to obtain static entities and dynamic entities. The attribute system includes static attributes, dynamic attributes, semantic attributes and evolutionary attributes. A core relationship is preset, and a spatial semantic association relationship is established based on the static entity, the dynamic entity, the attribute system and the core relationship. The core relationship includes spatial association relationship, spatial topological relationship, spatiotemporal mapping relationship and semantic dependency relationship. Based on the spatial semantic association, the dynamic elements, the static elements, and the land scene units of the target city are integrated in a scenario-based manner to obtain the element integration result of the land scene units; Multi-dimensional index calculations are performed on the dynamic and static elements to obtain an index set. The indexes are then integrated into the attribute structure of the land parcel scene unit to obtain the index integration result of the land parcel scene unit. The element integration result and the index integration result are then fused to obtain the scene-based integration result of the land parcel scene unit. Based on the scenario integration results, a hierarchical scenario unit structure is constructed. Based on the scenario unit structure, the existing urban spatial data is integrated into scenarios at multiple levels to obtain multi-level scenario integration results. The process of constructing a hierarchical scene unit structure based on the scene integration results, and performing multi-level scene integration on the existing urban spatial data based on the scene unit hierarchy to obtain multi-level scene integration results specifically includes: Based on the physical attribute information of the target city and the scenario integration result, scenario units at each level are obtained, all scenario units are divided to obtain scenario division results, and a hierarchical scenario unit structure is constructed based on the scenario division results. The scenario unit structure includes a plot scenario unit structure, a district scenario unit structure, and an urban area scenario unit structure. Based on the scene unit hierarchy, all scene units are integrated to obtain multiple scene element integration results. Based on the scene unit hierarchy, all scene units are calculated to obtain multiple scene indicators. All scene indicators are then integrated to obtain scene indicator integration results. The integration results of all the scene elements and the integration results of the scene indicators are fused together to obtain a multi-level scene-based integration result.

2. The multi-level scenario-based integrated processing method for urban stock spatial data according to claim 1, characterized in that, The process of acquiring multi-source heterogeneous urban spatial data in the target city further includes: The dynamic and static elements of the urban stock spatial data are defined to obtain dynamic element labels and static element labels. The dynamic elements include crowd activity elements, vehicle activity elements, and IoT sensing device elements, while the static elements include natural entity elements and artificial entity elements.

3. The multi-level scenario-based integrated processing method for urban stock spatial data according to claim 1, characterized in that, The step of integrating the dynamic elements, the static elements, and the land parcel scene units of the target city according to the spatial semantic relationship to obtain the element integration result of the land parcel scene units specifically includes: Obtain scene unit division criteria, and divide the target city into scene units according to the scene unit division criteria to obtain land parcel scene units; Obtain the spatial attributes of the static elements and the spatiotemporal coordinate sequence of the dynamic elements. Integrate the static elements into the plot scene unit according to the spatial attributes, and anchor the dynamic elements to the plot scene unit according to the spatiotemporal coordinate sequence to obtain the element integration result of the plot scene unit.

4. The multi-level scenario-based integrated processing method for urban stock spatial data according to claim 1, characterized in that, The process of calculating multi-dimensional indicators for the dynamic and static elements to obtain an indicator set, and integrating these indicators into the attribute structure of the land parcel scene unit to obtain the indicator integration result of the land parcel scene unit, specifically includes: The data characteristics and target requirements of the urban stock spatial data are obtained, and a multi-dimensional element indicator system is defined based on the data characteristics and target requirements. The multi-dimensional element indicator system includes static spatial indicators and dynamic behavioral indicators. Based on the multi-dimensional element indicator system, the spatiotemporal coordinate sequence of the dynamic elements is used to calculate the dynamic behavior comprehensive index to obtain the dynamic index set. Based on the multi-dimensional element indicator system, the spatial attributes of the static elements are used to calculate the static comprehensive index to obtain the static index set. The dynamic indicator set and the static indicator set are associated with the target identifier of the land parcel scene unit to obtain the association result. Based on the association result, the dynamic indicator set and the static indicator set are integrated into the land parcel scene unit to obtain the indicator integration result of the land parcel scene unit.

5. The multi-level scenario-based integrated processing method for urban stock spatial data according to claim 1, characterized in that, The process of fusing the integrated results of the elements and the integrated results of the indicators to obtain the scene-based integrated result of the land parcel scene unit specifically includes: Perform a plot polygon inclusion analysis on the dynamic and static element entities of the element integration result to obtain the element analysis result, and perform spatial location verification processing on the spatial indicators of the indicator integration result to obtain the location verification result. Based on the element analysis results and the location verification results, hierarchical fusion modeling is performed to obtain an attribute fusion system. Then, based on the attribute fusion system, the element integration results and the indicator integration results are fused to obtain a scenario-based integration result.

6. A multi-level, scenario-based integrated processing system for urban stock spatial data, characterized in that, The multi-level scenario-based integrated processing system for urban existing spatial data is used to implement the multi-level scenario-based integrated processing method for urban existing spatial data as described in any one of claims 1-5. The multi-level scenario-based integrated processing system for urban existing spatial data includes: The element extraction module is used to acquire multi-source heterogeneous urban stock spatial data in the target city, extract elements from the urban stock spatial data to obtain dynamic elements and static elements, and establish spatial semantic association between the dynamic elements and the static elements. The element integration module is used to integrate the dynamic elements, the static elements and the land scene units of the target city in a contextualized manner according to the spatial semantic relationship, so as to obtain the element integration result of the land scene unit; The indicator integration module is used to perform multi-dimensional indicator calculations on the dynamic elements and the static elements to obtain an indicator set, integrate the indicators into the attribute structure of the land plot scene unit to obtain the indicator integration result of the land plot scene unit, and fuse the element integration result with the indicator integration result to obtain the scene-based integration result of the land plot scene unit. The multi-level scene integration module is used to construct a hierarchical scene unit structure based on the scene integration result, and to perform multi-level scene integration of the existing urban spatial data based on the scene unit structure to obtain the multi-level scene integration result.

7. A terminal, characterized in that, The terminal includes: a memory, a processor, and a multi-level scenario-based integrated processing program for urban existing spatial data stored in the memory and executable on the processor. When the multi-level scenario-based integrated processing program for urban existing spatial data is executed by the processor, it implements the steps of the multi-level scenario-based integrated processing method for urban existing spatial data as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a multi-level scenario-based integrated processing program for urban stock spatial data. When the multi-level scenario-based integrated processing program for urban stock spatial data is executed by a processor, it implements the steps of the multi-level scenario-based integrated processing method for urban stock spatial data as described in any one of claims 1-5.

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