Multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data

Abstract

The present invention relates to the field of big data analysis technology, and specifically discloses a multi-dimensional dynamic supervision and analysis system for urban renewal based on big data. The system sets a data quality processing module, a topology verification and evaluation module, a conflict repair and matching module, and an interactive management feedback module. First, data is extracted from a multi-source urban data set and coordinate conversion is performed to form a unified urban spatial data set. Then, a topology verification engine is started through a data processing port to detect and evaluate the topological conflict degree value of the data set. According to the degree of conflict, the corresponding repair plan is matched and executed, and the repaired data set is marked as a valid urban data set. Finally, the valid data set is uploaded to the urban renewal interactive port for subsequent interactive management operations to ensure the accuracy and availability of the data and provide support for urban renewal projects.

Description

Technical Field

[0001] The present invention relates to the field of big data analysis technology, and in particular to a multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data. Background Art

[0002] In recent years, urbanization has advanced rapidly, cities have continued to expand, and the demand for land has increased. Urban renewal has become an important means of meeting the demand for urban development space and optimizing urban functional layout, and land acquisition and demolition is a key component of urban renewal. Traditional land acquisition and demolition management methods have gradually exposed problems such as inefficiency, lack of transparency, and insufficient scientific decision-making when faced with large-scale and complex acquisition and demolition projects, making them unable to meet the requirements of rapid urbanization.

[0003] For example, the invention patent CN117910957A discloses a design management system, device, and storage medium for urban renewal projects. The system includes a task module, a communication module, a process module, a method module, and / or a flexible module. Each module is used for design task management, design communication management, design process management, design method management, and / or specific element management in urban renewal projects. Furthermore, each module is closely interconnected, complementary, intertwined, and mutually reinforcing.

[0004] For example, the invention patent with announcement number CN114418556B announces a smart city renewal and land preparation system and method, including a perception and acquisition module, a background data module, a capital management module, a base plate construction module, a land preparation module, an urban service module and a transmission and call module. The output end of the perception and acquisition module controls the input end of the background data module. The real-time dynamic base plate of the city is combined with the corresponding space-time information through the set spatiotemporal data module to establish a dynamic city base plate that unifies time and space. The data information in the background cloud data module is updated through the iteration processing module to remove outdated information. The preliminary preparation plan is compared and analyzed through the set project comparison module, and then the auxiliary decision-making module generates an auxiliary decision-making plan, and the preliminary preparation plan is improved in combination with historical data.

[0005] Combining the above technical solutions, it is found that the existing urban renewal management solutions mostly lack unified collection rules and standards for multi-source data. Since urban renewal management involves multi-faceted integration and multi-dimensional management, the existing solutions have insufficient data integration and classification capabilities, and are prone to ignoring role interaction needs, resulting in low information transparency and inefficient interest coordination on the urban renewal platform. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a multi-dimensional dynamic supervision and analysis system for urban renewal based on big data, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-dimensional dynamic supervision and analysis system for urban renewal based on big data, including a data quality processing module, which is used to extract urban multi-source data sets and perform coordinate conversion, record them as urban spatial data sets, and upload them to a preset data processing port to perform data quality processing; a topology verification and evaluation module, which is used to start the topology verification engine on the data processing port, extract the topological relationship of the city according to the urban spatial data set, perform spatial topology conflict verification, and evaluate the conflict degree value of the urban spatial data set; a conflict repair matching module, which is used to match the corresponding conflict repair plan according to the conflict degree value of the urban spatial data set, and perform conflict repair on the urban spatial data set; an interactive management feedback module, which is used to record the urban spatial data set for which conflict repair has been completed as a valid urban data set, upload it to the preset urban renewal interactive port, and perform urban renewal interactive management.

[0008] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0009] (1) The present invention provides a multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data. First, data is extracted from the urban multi-source dataset and coordinate conversion is performed to form a unified urban spatial dataset. Then, the topology verification engine is started through the data processing port to detect and evaluate the topological conflict degree value of the dataset. According to the conflict degree, the corresponding repair solution is matched and executed, and the repaired dataset is marked as the urban valid dataset. Finally, the valid dataset is uploaded to the urban renewal interactive port for subsequent interactive management operations to ensure the accuracy and availability of the data and provide support for urban renewal projects.

[0010] (2) By collecting initial feature information of urban spatial datasets and evaluating their initial data quality indicators, the present invention can ensure the consistency of data in terms of geometry, topological relationships, and attribute descriptions. By identifying and addressing data quality issues, it can reduce duplication of work and error correction time in subsequent data processing, thereby improving data processing efficiency. High-quality data can improve the accuracy of subsequent analysis and processing, and reduce erroneous results caused by data quality issues.

[0011] (3) By collecting conflict information of urban spatial data sets and evaluating the conflict degree values ​​of urban spatial data sets, the present invention can detect potential conflicts in the data in advance. Timely discovery and resolution of conflicts can prevent errors that may occur during data processing and ensure the accuracy and consistency of the data. By evaluating the conflict degree values, multi-source data can be integrated more effectively, reducing the difficulty and time cost of integration caused by data conflicts. By evaluating the conflict degree values, multi-source data can be integrated more effectively, reducing the difficulty and time cost of integration caused by data conflicts.

[0012] (4) By collecting resource pre-call data from the urban renewal interactive terminal and determining the execution efficiency evaluation value of the urban renewal interactive terminal, the present invention can reasonably allocate computing resources to ensure that high-priority tasks receive sufficient resource support. By optimizing resource allocation, the system's resource utilization rate can be improved, resource waste can be reduced, and the overall performance of the system can be improved. By evaluating the execution efficiency evaluation value, resource bottleneck problems can be discovered and resolved in a timely manner, ensuring that the system responds quickly to user requests. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of system module connections of the present invention.

[0015] Figure 2 Perform process mapping for data quality.

[0016] Figure 3 Execute the flow chart for conflict repair. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] See Figure 1 As shown, an embodiment of the present invention provides a technical solution: a multi-dimensional dynamic supervision and analysis system for urban renewal based on big data, including a data quality processing module, a topology verification and evaluation module, a conflict repair and matching module, an interactive management feedback module and an urban renewal management library.

[0019] The urban renewal management library is used to store data reference entropy, clustering reference coefficient, transmission delay variation boundary coefficient, storage density reference gradient and preset values ​​of various factors.

[0020] The data quality processing module is connected to the topology verification and evaluation module, the topology verification and evaluation module is connected to the conflict repair and matching module, the conflict repair and matching module is connected to the interactive management feedback module, and the data quality processing module, topology verification and evaluation module, conflict repair and matching module and interactive management feedback module are all connected to the urban renewal management library.

[0021] The data quality processing module is used to extract urban multi-source data sets and perform coordinate conversion, record them as urban spatial data sets, and upload them to the preset data processing port for data quality processing.

[0022] Specifically, the data is uploaded to the preset data processing port for data quality processing. The specific judgment process is as follows:

[0023] Extract urban multi-source datasets and perform coordinate conversion. Specifically, the conversion is as follows: collect urban multi-source datasets through big data servers, where urban multi-source datasets include but are not limited to remote sensing image data, geographic information system (GIS) data, Internet of Things (IoT) sensor data, socio-economic data, etc., use the preset coordinate system standard as the benchmark of the spatial coordinate system corresponding to the urban multi-source dataset, and use the coordinate conversion tool to uniformly convert the urban multi-source dataset into the benchmark coordinate system. During the conversion process, the coordinate conversion tolerance of the urban multi-source dataset is controlled to be less than or equal to the tolerance threshold during the coordinate conversion. In this way, the spatial coordinate set corresponding to the urban multi-source dataset is filtered and recorded as the urban spatial dataset.

[0024] If the coordinate transformation tolerance of the multi-source dataset is greater than the tolerance threshold during coordinate transformation, the multi-source dataset is evenly divided into several small data blocks. For each small data block, coordinate transformation can be performed separately to control the coordinate transformation tolerance of the urban multi-source dataset to be less than or equal to the tolerance threshold during coordinate transformation.

[0025] Specifically, the preset coordinate system standard may preferably adopt the China Geodetic Coordinate System 2000 (CGCS2000) or the Xi'an 80 coordinate system, with the projection method selected based on the city's location. The coordinate conversion tool may be GDAL / OGR, which supports multi-format conversion.

[0026] Urban multi-source datasets often come from different departments, sensors, or information systems, each with its own distinct coordinate system. By extracting and transforming these heterogeneous data, we can unify them into a common coordinate system, enabling seamless data fusion and integrating previously dispersed and isolated data for comprehensive analysis. A unified coordinate system ensures accurate spatial alignment between different data, avoiding data bias or errors caused by differences in coordinate systems. This improves the spatial accuracy and consistency of data, providing a reliable foundation for subsequent spatial analysis and decision-making.

[0027] Collect the initial feature information of the urban spatial dataset, evaluate the initial data quality indicators of the urban spatial dataset, and determine whether to perform data quality processing on the urban spatial dataset. Specifically:

[0028] The initial data quality indicators of the urban spatial dataset are compared with the predefined data quality reference indicator interval. If the initial data quality indicators of the urban spatial dataset belong to the data quality reference indicator interval, it is determined that no data quality processing is required for the urban spatial dataset. If the initial data quality indicators of the urban spatial dataset do not belong to the data quality reference indicator interval, it is determined that data quality processing is required for the urban spatial dataset.

[0029] After data quality processing, it becomes more stable during storage, transmission, and use, making it less prone to system errors or abnormal analysis results caused by data quality issues. This helps ensure the normal operation of the urban renewal management system, ensures the consistency and stability of data at all stages, and provides stable data support for the long-term planning and management of urban renewal. Data quality processing can remove redundant and irrelevant data and simplify the data structure, thereby optimizing the data processing process and improving its speed and efficiency. In urban renewal projects, the amount of data is often very large. Efficient processing can save a lot of time and computing resources, accelerating the progress of urban renewal projects.

[0030] The above data processing ports perform data quality processing, the specific process is as follows Figure 2 As shown, Figure 2The data quality execution flow chart first extracts data from a multi-source urban dataset and performs coordinate transformation to form a unified urban spatial dataset. During the coordinate transformation process, the system controls the coordinate transformation tolerance to not exceed a preset threshold to ensure data accuracy. The system then evaluates the initial data quality indicators of the urban spatial dataset, including data entropy, clustering coefficient, transmission delay coefficient of variation, and storage density gradient. By comparing the indicators with predefined reference indicator ranges, the system determines whether data quality processing is required. If the data quality indicators exceed the reference range, the system will perform appropriate data quality processing operations, such as simplifying coordinate nodes or aligning short line segment endpoints, to improve data consistency and accuracy.

[0031] Furthermore, the initial data quality indicators of the urban spatial dataset are evaluated. The specific evaluation process is as follows:

[0032] The initial feature information of the urban spatial dataset includes the data entropy of the urban spatial dataset, the clustering coefficient of the urban spatial dataset, the transmission delay variation coefficient of the urban spatial dataset, and the storage density gradient of the urban spatial dataset. The data entropy can be obtained by calculating the Shannon entropy; the clustering coefficient can be directly calculated using the NetworkX library; the transmission delay variation coefficient is calculated by calculating the mean and standard deviation of the timestamp of each transmission, and the transmission delay variation coefficient is specifically the ratio of the standard deviation to the mean; the storage density gradient can be obtained by obtaining the ratio of the file volume of the urban spatial dataset to the number of elements in the urban spatial dataset.

[0033] Data reference entropy, clustering reference coefficient, transmission delay variation coefficient and storage density reference gradient are extracted from the urban renewal management database.

[0034] The deviation degree between the data entropy of the urban spatial dataset and the data reference entropy, the deviation degree between the clustering coefficient of the urban spatial dataset and the clustering reference coefficient, the deviation degree between the transmission delay variation coefficient of the urban spatial dataset and the transmission delay variation definition coefficient, and the deviation degree between the storage density gradient of the urban spatial dataset and the storage density reference gradient are successively weighted and aggregated to obtain the initial data quality indicators of the urban spatial dataset. The specific analysis method is as follows:

[0035]

[0036] Where, is the initial data quality indicator of the urban spatial dataset, is the data entropy of the urban space dataset, is the data reference entropy, is the clustering coefficient of the urban space dataset, is the clustering reference coefficient, is the coefficient of variation of transmission delay of urban spatial dataset, Define the coefficient of variation for the transmission delay, is the storage density gradient of the urban space dataset, is the storage density reference gradient, is the weight parameter corresponding to the data entropy predefined in the urban renewal management library, is the weight parameter corresponding to the clustering coefficient predefined in the urban renewal management library, is the weight parameter corresponding to the transmission delay variation coefficient predefined in the urban renewal management library, It is the weight parameter corresponding to the storage density gradient predefined in the urban renewal management library.

[0037] It should be explained that the above-mentioned data entropy is used to measure the information richness of data classification in urban spatial datasets. A reasonable entropy value indicates that the data classification is refined and information-rich, which can support multidimensional analysis; the clustering coefficient refers to the degree of connection between a node and its neighboring nodes in an urban spatial dataset. Moderate clustering reflects the rationality of the spatial structure; the transmission delay variation coefficient is used to measure the relative fluctuation of an urban spatial dataset during data transmission delay, specifically an urban spatial dataset; the storage density gradient refers to the number of spatial elements or the amount of information in a unit physical storage space of an urban spatial dataset. Moderate density indicates that the data compression is reasonable and the storage efficiency is high.

[0038] The weight parameters corresponding to the data entropy, the weight parameters corresponding to the clustering coefficient, the weight parameters corresponding to the transmission delay variation coefficient, and the weight parameters corresponding to the storage density gradient are all extracted from the urban renewal management library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the data entropy, the clustering coefficient, the transmission delay variation coefficient, and the storage density gradient respectively form a mapping set with the weight parameters corresponding to the data entropy, the weight parameters corresponding to the clustering coefficient, the weight parameters corresponding to the transmission delay variation coefficient, and the weight parameters corresponding to the storage density gradient preset in the urban renewal management library. The real-time data entropy, clustering coefficient, transmission delay variation coefficient, and storage density gradient are brought into the mapping set to obtain the weight parameters corresponding to the data entropy, the weight parameters corresponding to the clustering coefficient, the weight parameters corresponding to the transmission delay variation coefficient, and the weight parameters corresponding to the storage density gradient.

[0039] In this embodiment, a multivariate analysis of data entropy, clustering coefficient, transmission delay variation coefficient, and storage density gradient is performed, specifically considering the correlation between these parameters. Within a certain range, urban spatial datasets with higher data entropy have richer classification information, resulting in more and finer clusters during clustering. This results in a higher clustering coefficient within a certain range, which in turn increases the initial data quality of the urban spatial dataset. However, excessively high data entropy and deviation from the reference value may require more data to be processed during data transmission, thereby increasing the transmission delay variation coefficient and reducing the initial data quality of the dataset. A low clustering coefficient and deviation from the reference value indicate poor local connectivity of the data, resulting in a significant decrease in clustering effectiveness and a negative impact on the initial data quality of the urban spatial dataset. A dataset with a high clustering coefficient within a certain range has a reasonable spatial structure and good local connectivity between data, which results in a more uniform distribution of the storage density gradient. Because the data is more spatially concentrated, the initial data quality of the dataset is higher.

[0040] Specifically, data quality processing is performed on the urban spatial dataset. The specific processing process is as follows:

[0041] If the initial data quality index of the urban spatial dataset is less than the minimum value of the data quality reference index interval, the initial data quality index of the urban spatial dataset is subtracted from the minimum value of the data quality reference index interval to obtain the initial data quality index deviation of the urban spatial dataset, and the number of coordinate redundant node corrections is mapped.

[0042] The above mapping obtains the number of corrected coordinate redundant nodes. Specifically, a mapping set between the initial data quality index deviation of the urban spatial dataset and the number of corrected coordinate redundant nodes is obtained from the urban renewal management library. The initial data quality index deviation of the real-time urban spatial dataset is brought into the mapping set to obtain the number of corrected coordinate redundant nodes.

[0043] The number of coordinate nodes of the urban spatial dataset can be extracted from the conversion record of the coordinate conversion tool, and the difference between the number of coordinate redundant node corrections is processed to obtain the coordinate node redundant deviation. The coordinate node redundant deviation is matched with the simplified execution node span corresponding to the predefined coordinate node redundant deviation interval. The specific interval of the coordinate node redundant deviation is determined, and the simplified execution node span of the urban spatial dataset corresponding to the interval is obtained.

[0044] Using the simplification line algorithm, the number of coordinate nodes in the urban spatial dataset is reduced to the corrected number of redundant nodes according to the simplification execution node span of the urban spatial dataset. The simplification execution node span refers to the number of nodes that are reduced each time during the simplification process. For example, if the node span is 1, then one node will be reduced each time the simplification is performed.

[0045] The above line simplification algorithm works as follows: For line features in the urban spatial dataset, find the point farthest from the line connecting the start and end points of the line segment. This point is the most important point on the line segment; retaining it helps preserve the overall shape of the line segment. Using this farthest point as the dividing line, the line segment is divided into two parts. Repeat the above steps for each part, finding the farthest point in each part and recursively simplifying it. During this recursive process, if the distance from the farthest point to the line segment is less than a preset threshold (i.e., the simplification execution node span), the point is considered eligible for simplification and not retained.

[0046] If the initial data quality index of the urban spatial dataset is greater than the maximum value of the data quality reference index interval, the snap function is activated and the short line segment endpoints of the urban spatial dataset are forced to align with the adjacent line segment endpoints of the urban spatial dataset through the snap tool.

[0047] When the snap function is activated as described above, the system defines a snap tolerance range around the current data coordinate editing point, usually in map units (such as meters) or screen pixels. When moving a line segment endpoint, if the endpoint enters the snap tolerance range of other line segments or nodes, the system will automatically adsorb it to the nearest target object to achieve endpoint alignment. The Euclidean distance formula is used to calculate the distance between the current endpoint and the target line segment / node, where the adjacent line segment endpoints, that is, the node with the shortest distance, is selected as the snap target. If the target is a line segment rather than a node, the vertical projection point of the current endpoint on the line segment needs to be calculated as the alignment position.

[0048] By comparing the initial data quality indicators of the urban spatial dataset with the data quality reference index interval, deviations in the data can be accurately identified. For example, when the initial data quality indicator is less than the minimum value of the reference index interval, the data is simplified and redundant coordinate nodes are removed, thereby correcting the excessive complexity of the data. When the initial data quality indicator is greater than the maximum value of the reference index interval, the snap function is activated to force the alignment of short line segment endpoints, correcting data inconsistencies and making the data more accurate and reliable. This processing ensures the consistency of the urban spatial dataset in terms of geometry, topological relationships, and attribute descriptions. Through steps such as coordinate transformation tolerance control and topological verification assessment, data inconsistencies caused by differences in coordinate systems and acquisition accuracy between different data sources are eliminated, allowing the urban spatial dataset to truly and objectively reflect the actual situation in the urban renewal area.

[0049] The topology verification and evaluation module is used to start the topology verification engine at the data processing port, extract the topological relationship of the city based on the urban spatial data set, perform spatial topology conflict verification, and evaluate the conflict degree value of the urban spatial data set.

[0050] Furthermore, the conflict degree value of the urban spatial dataset is evaluated. The specific evaluation process is as follows:

[0051] The conflict information of urban spatial datasets is collected, including the topological modularity difference value of urban spatial datasets, the temporal autocorrelation coefficient divergence of urban spatial datasets, the attribute information entropy difference of urban spatial datasets, and the geometric complexity variance of urban spatial datasets.

[0052] Among them, modularity can be calculated by calculating the ratio between the number of edges within the community where each node in the dataset network is located and the number of edges in the entire dataset network. The difference in topological modularity can be calculated by comparing the modularity values ​​at different time points or in different regions. The divergence of the temporal autocorrelation coefficient can be obtained by calculating the difference in the specific values ​​of the Moran index at different time points. The attribute information entropy difference can be obtained by calculating the difference in the attribute information entropy at different time points or in different regions. The attribute information entropy represents the frequency of attribute values ​​appearing in the urban spatial dataset. The geometric complexity can be comprehensively measured by calculating indicators such as the boundary length, area, and curvature of the geometric shape. The geometric complexity variance is obtained by calculating the variance of these geometric indicators.

[0053] The topological modularity difference value of the urban spatial dataset, the temporal autocorrelation coefficient divergence of the urban spatial dataset, the attribute information entropy difference of the urban spatial dataset, and the geometric complexity variance of the urban spatial dataset are normalized to obtain the normalized results. The normalized results are weighted and aggregated in turn to obtain the conflict degree value of the urban spatial dataset. The specific analysis process is as follows:

[0054]

[0055] Where, is the conflict degree value of the urban space dataset, is the topological modularity difference value of the urban space dataset, is the temporal autocorrelation coefficient divergence of the urban spatial dataset, is the attribute information entropy difference of the urban space dataset, is the geometric complexity variance of the urban space dataset, is the weight parameter corresponding to the topological modularity difference value predefined in the urban renewal management library. is the weight parameter corresponding to the time autocorrelation coefficient divergence predefined in the urban renewal management library. is the weight parameter corresponding to the attribute information entropy difference predefined in the urban renewal management library, It is the weight parameter corresponding to the geometric complexity variance predefined in the urban renewal management library.

[0056] It should be explained that the above-mentioned topological modularity difference value is used to measure the degree of difference in community structure division of urban spatial datasets; the temporal autocorrelation coefficient divergence refers to the KL divergence quantifying the difference in autocorrelation patterns of time series of urban spatial datasets; the attribute information entropy difference is used to measure the degree of heterogeneity of urban spatial datasets in the attribute classification system; the geometric complexity variance is used to quantify the degree of fluctuation of the geometric shape differences of spatial elements in urban spatial datasets.

[0057] Among them, the weight parameters corresponding to the topological modularity difference value, the weight parameters corresponding to the temporal autocorrelation coefficient divergence, the weight parameters corresponding to the attribute information entropy difference, and the weight parameters corresponding to the geometric complexity variance are all extracted from the urban renewal management library, and the mapping relationship therein can be said to be one-to-one or many-to-one. For example, the topological modularity difference value, the temporal autocorrelation coefficient divergence, the attribute information entropy difference, and the geometric complexity variance respectively form a mapping set with the weight parameters corresponding to the topological modularity difference value, the weight parameters corresponding to the temporal autocorrelation coefficient divergence, the weight parameters corresponding to the attribute information entropy difference, and the weight parameters corresponding to the geometric complexity variance preset in the urban renewal management library. The real-time topological modularity difference value, temporal autocorrelation coefficient divergence, attribute information entropy difference, and geometric complexity variance are brought into the mapping set to obtain the weight parameters corresponding to the topological modularity difference value, the weight parameters corresponding to the temporal autocorrelation coefficient divergence, the weight parameters corresponding to the attribute information entropy difference, and the weight parameters corresponding to the geometric complexity variance.

[0058] In this embodiment, through multivariate analysis of topological modularity difference value, temporal autocorrelation coefficient divergence, attribute information entropy difference and geometric complexity variance, specifically considering the correlation between these parameters, if the topological modularity difference value is large, it means that the community structure has changed significantly, which may cause the temporal autocorrelation coefficient divergence to increase, because the change in community structure will affect the spatial autocorrelation, and will also cause the attribute information entropy difference to increase, because the change in community structure will also affect the distribution of attributes, thereby increasing the degree of conflict in the urban spatial dataset; changes in geometric complexity may cause changes in community structure, thereby affecting the topological modularity difference value, changes in spatial autocorrelation, affecting the temporal autocorrelation coefficient divergence, and changes in attribute distribution, affecting the attribute information entropy difference, and also bringing a greater negative impact on the degree of conflict in the urban spatial dataset.

[0059] The conflict repair matching module is used to match the corresponding conflict repair solution according to the conflict degree value of the urban spatial dataset and perform conflict repair on the urban spatial dataset.

[0060] Urban spatial datasets are divided into three basic elements: points (such as building turning points), lines (such as road centerlines), and surfaces (such as plot boundaries). Spatial indexes (such as R-trees) are used to accelerate retrieval, and topological relationships such as adjacency and connectivity are defined. Topological relationships include: adjacency (surfaces share boundaries (such as adjacent plots)), connectivity (line endpoints coincide (such as road intersections)), inclusion (points / lines are located within a surface (such as bus stops located within a plot), and intersection (line-surface intersections (such as subway lines passing through plots)). Set operations and spatial indexing techniques are used to detect conflict types such as surface overlap and line dangling. Topological relationships that do not meet the specifications are defined as conflicts. Specifications can include requirements such as all surface elements must be closed and road centerlines must intersect plot boundaries at nodes. Algorithms such as Spark parallel computing are used to optimize detection efficiency, and the conflict level is quantitatively assessed based on parameters such as conflict density and topological modularity difference.

[0061] Specifically, conflict repair is performed on the urban spatial dataset. The specific execution process is as follows:

[0062] The conflict degree value of the urban spatial dataset is matched with the predefined conflict degree value intervals, the specific interval of the conflict degree value of the urban spatial dataset is determined, and the conflict repair plan corresponding to the interval is obtained by matching.

[0063] Each conflict degree value interval includes a first conflict degree interval, a second conflict degree interval, and a third conflict degree interval.

[0064] If the conflict degree value of the urban spatial dataset belongs to the first conflict degree interval, the conflict data blocks in the urban spatial dataset are extracted, the conflict data blocks are automatically merged using the spatial aggregation function, and the topological relationship of the city is updated; specifically, the conflict data blocks in the urban spatial dataset need to be detected first. Common topological conflict types include inconsistent shared boundaries of polygons and mismatched line segment endpoints. The conflict data blocks are merged using the selected spatial aggregation function. The specific execution code can be: After merging conflicting data blocks, the topology of the city needs to be updated. Topology is determined based on: adjacency, shared boundaries between polygons, connectivity, coincidence of line endpoints, containment, points or lines within polygons, and intersection, where lines intersect polygons. Use topology analysis tools in GIS software or spatial databases to recalculate the topology between the merged geometric objects.

[0065] If the conflict degree value of the urban spatial dataset belongs to the second conflict degree interval, the number of redundant coordinate nodes is corrected.

[0066] If the conflict degree value of the urban spatial dataset belongs to the third conflict degree interval, the coordinate transformation is corrected.

[0067] The above urban spatial dataset performs conflict repair. The specific process is as follows: Figure 3 As shown, Figure 3 The conflict repair process begins by matching the conflict repair solution to the conflict severity value. The conflict severity value is divided into different intervals, each corresponding to a different repair strategy: For low conflict severity (interval 1), the system automatically merges conflicting data blocks and updates the topological relationship; for medium conflict severity (interval 2), the system corrects the number of redundant coordinate nodes; and for high conflict severity (interval 3), the system corrects the coordinate transformation. When correcting redundant coordinate nodes, the system calculates the node adjustment factor by calculating the proportion of conflict severity values. Combined with the predefined number of node boundaries, the system adjusts the number of nodes to optimize data quality. For coordinate transformation corrections with high conflict severity, the system uses the Iterative Closest Point (ICP) algorithm. Through multiple iterations, the coordinate transformation tolerance is gradually reduced to the adaptation threshold, ensuring data accuracy and consistency. This series of operations ensures efficient repair and optimization of urban spatial datasets at different conflict severity levels, improving overall data quality and system stability.

[0068] Furthermore, the number of redundant coordinate nodes is corrected. The specific analysis process is as follows:

[0069] The conflict degree value of the urban spatial dataset is proportionally processed with the second conflict degree interval to obtain the first proportion of the conflict degree value of the urban spatial dataset. The coordinate redundant node adjustment factor is mapped and data coupled with the number of coordinate redundant node corrections. Specifically, the coordinate redundant node adjustment factor is multiplied by the number of coordinate redundant node corrections to obtain the coordinate redundant node adaptation number, which is used to reduce the number of coordinate redundant node corrections.

[0070] The above-mentioned proportion processing specifically performs ratio processing on the conflict degree value of the urban spatial dataset and the corresponding span of the second conflict degree interval to obtain the first proportion of the conflict degree value of the urban spatial dataset, wherein the corresponding span of the second conflict degree interval refers to the difference between the maximum value and the minimum value of the second conflict degree interval.

[0071] The above mapping obtains the coordinate redundant node adjustment factor, specifically: extracting the mapping set between the first proportion of the conflict degree value of the urban spatial data set and the coordinate redundant node adjustment factor from the urban renewal management library, bringing the real-time into the mapping set to obtain the coordinate redundant node adjustment factor.

[0072] The number of coordinate redundant node adaptations is subtracted from the predefined number of coordinate redundant node definitions to obtain the coordinate node redundant definition deviation. This is matched with the simplified execution adaptation node span corresponding to the predefined coordinate node redundant definition deviation interval, and the specific interval of the coordinate node redundant definition deviation is determined to obtain the simplified execution adaptation node span of the urban spatial dataset corresponding to the interval.

[0073] It should be explained that the simplified execution adaptation node span in this embodiment is different from the simplified execution node span described above, which is based on different data processing stages and different goals. The simplified execution node span is in the preliminary stage of data quality processing, and its main purpose is to reduce redundant nodes by simplifying operations to improve data quality and processing efficiency. At this time, the simplification operation is based on the total number of nodes in the original data set and the number of redundant nodes preliminarily identified. The simplified execution adaptation node span is in the subsequent stage of conflict repair, and its main purpose is to further adjust the number of redundant nodes based on the conflict degree value and related parameters to ensure the quality and consistency of the data after repair. At this time, the simplification operation is based on the adjusted number of redundant nodes and the predefined upper limit of redundant nodes. This difference is due to their different positions and purposes in the data processing process. Through different difference calculation methods, it is possible to better adapt to the data processing needs of different stages, improve the flexibility and accuracy of data processing, and optimize the entire data processing process.

[0074] The simplified line algorithm is used to adapt the node span according to the simplification of the urban spatial dataset, and the number of corrected coordinate redundant nodes is reduced to the number of adapted coordinate redundant nodes.

[0075] At the same time, according to the initial data quality indicators of the urban spatial data set, the adaptive learning rate is mapped, and according to the first proportion of the conflict degree value of the urban spatial data set, the reference learning rate is matched to obtain the real-time learning rate of the data processing port. The real-time learning rate of the data processing port is extracted, and the difference processing and comparison are performed with the adaptive learning rate and the reference learning rate respectively to obtain the first deviation of the learning rate of the data processing port and the second deviation of the learning rate of the data processing port. The first deviation of the learning rate of the data processing port is compared with the second deviation of the learning rate of the data processing port. If the first deviation of the learning rate of the data processing port is greater than the second deviation of the learning rate of the data processing port, the reference learning rate corresponding to the second deviation of the learning rate of the data processing port is taken to replace the real-time learning rate of the data processing port. Otherwise, the adaptive learning rate corresponding to the first deviation of the learning rate of the data processing port is taken to replace the real-time learning rate of the data processing port.

[0076] The above mapping obtains the adaptive learning rate. Specifically, a mapping set between the initial data quality indicators of the urban spatial dataset and the adaptive learning rate is obtained from the urban renewal management library, and the initial data quality indicators of the real-time urban spatial dataset are brought into the mapping set to obtain the adaptive learning rate.

[0077] The above matching obtains the reference learning rate, specifically, by matching the first proportion of the conflict degree value of the urban spatial dataset with the reference learning rate corresponding to each predefined interval of the first proportion of the conflict degree value, determining the specific interval of the first proportion of the conflict degree value of the urban spatial dataset, and obtaining the reference learning rate corresponding to the interval.

[0078] It should be explained that if the first deviation of the learning rate of the data processing port is equal to the second deviation of the learning rate of the data processing port, the adapted learning rate and the reference learning rate are averaged, and the average processing result replaces the real-time learning rate of the data processing port.

[0079] The characteristics and conflict levels of urban spatial datasets may vary over time and space. By selecting a learning rate closer to real time, the system can better adapt to these changes, thereby improving processing efficiency and accuracy of results. Different datasets may require different learning rates for optimal processing. Selecting a learning rate closer to real time ensures sufficient flexibility when processing different datasets. Selecting a learning rate closer to real time as an alternative can improve the system's adaptability, flexibility, and processing efficiency, while reducing errors and resource consumption, ensuring the system's stability and accuracy when processing urban spatial datasets.

[0080] Specifically, the coordinate transformation is corrected, and the specific analysis process is as follows:

[0081] If the conflict degree value of the urban spatial dataset belongs to the third conflict degree interval, the conflict degree value of the urban spatial dataset is proportionally processed with the third conflict degree interval to obtain the second proportion of the conflict degree value of the urban spatial dataset, and the tolerance threshold correction factor is mapped. At the same time, the real-time data quality index of the urban spatial dataset is extracted, and the tolerance threshold adjustment element is found. The tolerance threshold correction factor and the tolerance threshold adjustment element are coupled with the tolerance threshold during coordinate transformation. Specifically, the tolerance threshold correction factor and the tolerance threshold adjustment element are multiplied with the tolerance threshold during coordinate transformation to obtain the tolerance adaptation threshold during coordinate transformation.

[0082] The coordinate transformation tolerance of the urban multi-source dataset is extracted, and the iterative closest point algorithm is used for multiple iterations to reduce the coordinate transformation tolerance of the urban multi-source dataset to the tolerance adaptation threshold during coordinate transformation, thereby completing the coordinate transformation correction.

[0083] The specific correction is as follows: Extract geometric objects (such as points, lines, and surfaces) that need to undergo coordinate transformation from the city's multi-source dataset. For each geometric object, find its nearest corresponding point in the target coordinate system. This can be achieved by calculating the Euclidean distance. If the geometric object is a line or a surface, the nearest point on it to the target geometric object can be calculated. Based on the matching point pairs, the least squares method is used to calculate the optimal translation and rotation transformation matrix. Apply the calculated transformation matrix to the source geometric object to update its coordinates. Calculate the tolerance between the updated geometric object and the target geometric object. If the tolerance is less than or equal to the target tolerance adaptation threshold, the algorithm ends; otherwise, continue to iterate. Repeat the above steps until the tolerance reaches the target tolerance adaptation threshold or the preset maximum number of iterations is reached.

[0084] The above multiple iterations specifically mean that the tolerance will be reduced by a fixed value each iteration, and the number of iterations is determined by the difference between the coordinate transformation tolerance of the urban renewal structured dataset and the tolerance adaptation threshold during coordinate transformation. For example, the number of iterations = Coordinate transformation tolerance Tolerance adaptation threshold / fixed step size ,in, Indicates rounding up.

[0085] The coordinate transformation tolerance is gradually adjusted using the Iterative Closest Point (ICP) algorithm to ensure that the coordinate transformation accuracy of multi-source urban datasets meets the preset tolerance adaptation threshold. This allows spatial data from different data sources to be accurately aligned within a unified coordinate system, reducing data deviations caused by coordinate transformation errors. Over multiple iterations, the tolerance is reduced by a certain amount with each iteration, gradually approaching the target tolerance adaptation threshold. This gradual approximation method can effectively reduce error accumulation and improve overall data accuracy. The corrected coordinate data can better maintain topological relationships, such as shared polygon boundaries and coincident line segment endpoints. This helps ensure the correct topological structure of urban spatial datasets and improve data availability and reliability.

[0086] The interactive management feedback module is used to record the urban spatial dataset that has completed conflict repair as the urban valid dataset, upload it to the preset urban renewal interactive port, and perform urban renewal interactive management.

[0087] Furthermore, the implementation of interactive management of urban renewal also includes:

[0088] Extract the role code of the urban renewal interaction terminal, map it to obtain the initial proportion of rendering computing power of the urban renewal interaction terminal, pre-call resources of the urban renewal interaction terminal before storing the effective urban data set on the front end, collect the resource pre-call data of the urban renewal interaction terminal, and determine the execution efficiency evaluation value of the urban renewal interaction terminal.

[0089] The role code is a code used to identify and manage different user roles in the multi-dimensional dynamic supervision and analysis system for urban renewal. Each user is assigned a unique role code.

[0090] The above mapping obtains the initial proportion of rendering computing power of the urban renewal interaction terminal. Specifically, a mapping set between the role code of the urban renewal interaction terminal and the initial proportion of rendering computing power is extracted from the urban renewal management library, and the real-time role code of the urban renewal interaction terminal is brought into the mapping set to obtain the initial proportion of rendering computing power of the urban renewal interaction terminal.

[0091] The aforementioned resource pre-call data specifically includes the pre-call data distribution entropy of the urban renewal interaction terminal, the power spectral density of the hardware components of the urban renewal interaction terminal, the fractal dimension of the memory access of the urban renewal interaction terminal, and the data transmission phase noise of the urban renewal interaction terminal. The data distribution entropy can be calculated using the Shannon entropy formula; the power spectral density of the hardware components can be calculated by performing a discrete Fourier transform on the signal and then calculating its power spectral density; the fractal dimension of the memory access can be calculated by dividing the memory access sequence into multiple small boxes and calculating the minimum number of boxes required to cover the sequence; and the data transmission phase noise can be calculated by measuring the phase noise of the data transmission signal with a spectrum analyzer.

[0092] The memory access reference fractal dimension is extracted from the urban renewal management library.

[0093] The pre-call data distribution entropy of the urban renewal interaction terminal, the power spectral density of the hardware components of the urban renewal interaction terminal, and the data transmission phase noise of the urban renewal interaction terminal are normalized to obtain a normalized processing result. The memory access fractal dimension of the urban renewal interaction terminal and the memory access reference fractal dimension are subjected to deviation processing to obtain a deviation processing result. The normalization processing and deviation processing results are weighted and aggregated in turn to obtain the execution efficiency evaluation value of the urban renewal interaction terminal. The specific analysis method is as follows:

[0094]

[0095] Where, is the execution efficiency evaluation value of the urban renewal interaction terminal, The pre-call data distribution entropy of the urban renewal interaction terminal, The power spectrum density of the hardware components at the urban renewal interaction end is Memory access fractal dimension for urban renewal interaction, Refer to the fractal dimension for memory access, The data transmission phase noise of the urban renewal interactive terminal is It is the weight parameter corresponding to the pre-defined pre-call data distribution entropy in the urban renewal management library. The weight parameter corresponding to the power spectrum density of hardware components predefined in the urban renewal management library, The weight parameter corresponding to the fractal dimension of the memory access predefined in the urban renewal management library, It is the weight parameter corresponding to the data transmission phase noise predefined in the urban renewal management library.

[0096] It should be explained that the above-mentioned pre-call data distribution entropy is used to measure the spatial / temporal distribution uniformity of the pre-call urban spatial data set of the urban renewal interaction terminal. A lower pre-call data distribution entropy indicates strong data locality and an improved pre-call hit rate; the hardware component power spectral density is used to measure the power fluctuation frequency of components such as the CPU, GPU, and SSD of the urban renewal interaction terminal during the pre-call stage; the memory access fractal dimension is used to measure the complexity of the memory access pattern during the resource pre-call process of the urban renewal interaction terminal; the data transmission phase noise is used to measure the timing stability of the pre-call urban spatial data set of the urban renewal interaction terminal in network transmission.

[0097] The weight parameters corresponding to the pre-called data distribution entropy, the weight parameters corresponding to the hardware component power spectral density, the weight parameters corresponding to the memory access fractal dimension, and the weight parameters corresponding to the data transmission phase noise can all be extracted from the urban renewal management library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the pre-called data distribution entropy, the hardware component power spectral density, the memory access fractal dimension, and the data transmission phase noise are respectively mapped to the weight parameters corresponding to the pre-called data distribution entropy, the weight parameters corresponding to the hardware component power spectral density, the weight parameters corresponding to the memory access fractal dimension, and the weight parameters corresponding to the data transmission phase noise preset in the urban renewal management library to form a mapping set. The real-time pre-called data distribution entropy, hardware component power spectral density, memory access fractal dimension, and data transmission phase noise are brought into the mapping set to obtain the weight parameters corresponding to the pre-called data distribution entropy, the weight parameters corresponding to the hardware component power spectral density, the weight parameters corresponding to the memory access fractal dimension, and the weight parameters corresponding to the data transmission phase noise.

[0098] In this embodiment, multivariate analysis of pre-call data distribution entropy, hardware component power spectral density, memory access fractal dimension and data transmission phase noise is performed, specifically considering the correlation between these parameters. When the data distribution entropy is low, it indicates that the data locality is strong, the pre-call hit rate is high, and the power fluctuation of the hardware component may be small, thereby reducing the power spectral density. Similarly, when the data distribution entropy is low, the locality of data transmission is enhanced, which may reduce the phase noise in network transmission, so that the execution efficiency of the urban renewal interaction terminal is higher; when the power spectral density of the hardware component is high, it may indicate that the load of the hardware component is unbalanced, which may lead to a more complex memory access pattern and increase the memory access fractal dimension, which may cause the memory access fractal dimension to be larger and deviate from the reference value, thereby reducing the execution efficiency of the urban renewal interaction terminal; when the memory access fractal dimension is high and deviates from the reference value, it indicates that the memory access pattern is complex, which may lead to a decrease in the timing stability of data transmission, increase the data transmission phase noise, and greatly reduce the execution efficiency of the urban renewal interaction terminal.

[0099] The real-time conflict degree values ​​of the urban spatial dataset are extracted and matched with the execution efficiency evaluation reference values ​​corresponding to the predefined real-time conflict degree value intervals. The specific interval of the real-time conflict degree values ​​of the urban spatial dataset is determined, and the execution efficiency evaluation reference value corresponding to the interval is obtained.

[0100] The execution efficiency evaluation value of the urban renewal interaction terminal is compared with the execution efficiency evaluation reference value. If the execution efficiency evaluation value of the urban renewal interaction terminal is greater than or equal to the execution efficiency evaluation reference value, the initial proportion of the rendering computing power of the urban renewal interaction terminal is maintained to execute the role interaction.

[0101] Data compression optimization reduces resource consumption for data storage and transmission, improving system efficiency. Based on real-time data quality indicators and execution efficiency assessments, resource pre-allocation is dynamically adjusted to ensure efficient system operation under varying loads. By comparing execution efficiency assessments with reference values, inefficiencies are promptly identified and addressed. By adjusting the data compression ratio and resource pre-allocation, efficient execution is ensured for various tasks.

[0102] Specifically, the role interaction is performed, and the specific analysis process is as follows:

[0103] If the execution efficiency evaluation value of the urban renewal interaction terminal is less than the execution efficiency evaluation reference value, data compression optimization is started before the front-end storage of the urban effective data set, the real-time data quality indicators of the urban spatial data set are extracted, and the adaptive compression ratio of the data compression is matched. At the same time, the execution efficiency evaluation value of the urban renewal interaction terminal and the execution efficiency evaluation reference value are differenced to obtain the execution efficiency deviation evaluation value of the urban renewal interaction terminal, and the compression ratio correction element of the data compression is mapped to couple it with the adaptive compression ratio of the data compression. Specifically, the compression ratio correction element of the data compression is multiplied by the adaptive compression ratio of the data compression to obtain the adaptive compression ratio of the data compression, which is used to reconfigure the resource pre-call of the urban renewal interaction terminal. Specifically, the urban renewal interaction terminal first compresses the urban effective data set according to the adaptive compression ratio of the data compression, and then executes the resource pre-call.

[0104] After reconfiguring the resource pre-call of the urban renewal interaction terminal, the real-time execution efficiency evaluation value of the urban renewal interaction terminal is extracted and compared with the execution efficiency evaluation reference value. If the execution efficiency evaluation value of the urban renewal interaction terminal is greater than or equal to the execution efficiency evaluation reference value, the role interaction is executed.

[0105] If the execution efficiency evaluation value of the urban renewal interaction terminal is still less than the execution efficiency evaluation reference value, the role code priority determination is enabled to obtain the role code priority determination value, and the idle computing power ratio of the urban renewal interaction terminal is extracted. According to the role code priority determination value, the idle computing power call ratio is mapped and coupled with the initial rendering computing power ratio of the urban renewal interaction terminal. Specifically, the idle computing power call ratio is added to the initial rendering computing power ratio of the urban renewal interaction terminal to obtain the rendering computing power adaptation ratio of the urban renewal interaction terminal, which is used to execute role interaction.

[0106] The specific analysis process of the above role code priority determination value is as follows:

[0107] Obtain the historical resource interaction frequency of the role and the real-time interaction complexity of the role, wherein the historical resource interaction frequency and the real-time interaction complexity can be extracted from the interaction record of the role.

[0108] The first dependency of role interaction is obtained by multiplying the historical frequency of resource interaction of the role with the weight parameter corresponding to the predefined historical frequency of resource interaction. The second dependency of role interaction is obtained by multiplying the real-time interaction complexity of the role with the weight parameter corresponding to the predefined real-time interaction complexity.

[0109] The first role interaction dependency and the second role interaction dependency are added together to obtain the role interaction dependency, which is matched with the priority judgment values ​​corresponding to the predefined role interaction dependency intervals, to determine the specific interval of the role interaction dependency and obtain the role code priority judgment value corresponding to the interval.

[0110] The above mapping obtains the idle computing power call ratio. Specifically, a mapping set between the role code priority judgment value and the idle computing power call ratio is obtained in the urban renewal management library, and the real-time role code priority judgment value is brought into the mapping set to obtain the idle computing power call ratio.

[0111] Priority determination values ​​based on role codes ensure that high-priority tasks are executed first, improving the system's responsiveness and task processing efficiency. Based on real-time execution efficiency assessments, task priorities are dynamically adjusted to ensure the proper allocation of system resources. Priority determination and resource adjustment ensure stable system operation under high loads, avoiding resource overload or idleness. Priority determination values ​​coordinate the execution order of different tasks, reducing task conflicts and resource competition. Priority determination values ​​protect the fundamental rights and interests of low-priority roles. If excessive computing power is initially reserved for high-priority roles, potential problems of resource starvation and priority overload may arise.

[0112] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data, characterized by: include: The data quality processing module is used to extract the urban multi-source data set and perform coordinate conversion, record it as the urban spatial data set, and upload it to the preset data processing port for data quality processing; The topology verification and evaluation module is used to start the topology verification engine at the data processing port, extract the topological relationship of the city based on the urban spatial data set, perform spatial topology conflict verification, and evaluate the conflict degree value of the urban spatial data set; The conflict repair matching module is used to match the corresponding conflict repair solution according to the conflict degree value of the urban spatial dataset and perform conflict repair on the urban spatial dataset; The interactive management feedback module is used to record the urban spatial dataset that has completed conflict repair as the urban valid dataset, upload it to the preset urban renewal interactive port, and perform urban renewal interactive management; The data quality processing is performed on the data uploaded to the preset data processing port. The specific execution process is as follows: The method of extracting the urban multi-source dataset and performing coordinate conversion specifically comprises the following steps: collecting the urban multi-source dataset through a big data server, using a preset coordinate system standard as a reference of a spatial coordinate system corresponding to the urban multi-source dataset, uniformly converting the urban multi-source dataset into a reference coordinate system through a coordinate conversion tool, and during the conversion process, controlling the coordinate conversion tolerance of the urban multi-source dataset to be less than or equal to a tolerance threshold during the coordinate conversion, thereby filtering and obtaining a spatial coordinate set corresponding to the urban multi-source dataset, and recording it as an urban spatial dataset; Collect the initial feature information of the urban spatial dataset, evaluate the initial data quality indicators of the urban spatial dataset, and determine whether to perform data quality processing on the urban spatial dataset. Specifically: The initial data quality indicators of the urban spatial dataset are compared with the predefined data quality reference indicator interval. If the initial data quality indicators of the urban spatial dataset belong to the data quality reference indicator interval, it is determined that no data quality processing is required for the urban spatial dataset. If the initial data quality indicators of the urban spatial dataset do not belong to the data quality reference indicator interval, it is determined that data quality processing is required for the urban spatial dataset.

2. The multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data according to claim 1 is characterized by: The specific evaluation process for evaluating the initial data quality indicators of urban spatial datasets is as follows: The initial feature information of the urban spatial dataset includes data entropy of the urban spatial dataset, clustering coefficient of the urban spatial dataset, transmission delay variation coefficient of the urban spatial dataset, and storage density gradient of the urban spatial dataset; The data reference entropy, clustering reference coefficient, transmission delay variation limit coefficient and storage density reference gradient are extracted from the urban renewal management database; The deviation degree between the data entropy of the urban spatial dataset and the data reference entropy, the deviation degree between the clustering coefficient of the urban spatial dataset and the clustering reference coefficient, the deviation degree between the transmission delay variation coefficient of the urban spatial dataset and the transmission delay variation definition coefficient, and the deviation degree between the storage density gradient of the urban spatial dataset and the storage density reference gradient are weighted and aggregated in sequence to obtain the initial data quality index of the urban spatial dataset.

3. The multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data according to claim 2 is characterized by: The data quality processing of the urban spatial dataset is performed as follows: If the initial data quality index of the urban spatial dataset is less than the minimum value of the data quality reference index interval, the initial data quality index of the urban spatial dataset is subtracted from the minimum value of the data quality reference index interval to obtain the initial data quality index deviation of the urban spatial dataset, and the number of coordinate redundant nodes corrected is obtained by mapping; The number of coordinate nodes of the urban spatial dataset is extracted and the difference between the number of coordinate redundant nodes and the corrected number of coordinate redundant nodes is processed to obtain the coordinate node redundant deviation. The coordinate node redundant deviation is matched with the simplified execution node span corresponding to the predefined coordinate node redundant deviation interval to obtain the simplified execution node span of the urban spatial dataset. By using the simplified line algorithm, the number of coordinate nodes of the urban spatial dataset is reduced to the number of redundant node corrections according to the simplified execution node span of the urban spatial dataset; If the initial data quality index of the urban spatial dataset is greater than the maximum value of the data quality reference index interval, the snap function is activated and the short line segment endpoints of the urban spatial dataset are forced to align with the adjacent line segment endpoints of the urban spatial dataset through the snap tool.

4. The multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data according to claim 1 is characterized by: The specific evaluation process of evaluating the conflict degree value of the urban spatial dataset is as follows: Collect conflict information of urban spatial datasets, including topological modularity difference value, temporal autocorrelation coefficient divergence, attribute information entropy difference, and geometric complexity variance of urban spatial datasets; The topological modularity difference value of the urban spatial dataset, the temporal autocorrelation coefficient divergence of the urban spatial dataset, the attribute information entropy difference of the urban spatial dataset, and the geometric complexity variance of the urban spatial dataset are normalized respectively to obtain the normalized results. The normalized results are weighted and aggregated in turn to obtain the conflict degree value of the urban spatial dataset.

5. The multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data according to claim 4 is characterized by: The specific execution process of performing conflict repair on the urban spatial dataset is as follows: Match the conflict degree value of the urban spatial dataset with the predefined conflict degree value intervals, determine the specific interval of the conflict degree value of the urban spatial dataset, and obtain the conflict repair solution corresponding to the interval; The conflict degree value intervals include a first conflict degree interval, a second conflict degree interval, and a third conflict degree interval; If the conflict degree value of the urban spatial dataset belongs to the first conflict degree interval, the conflict data blocks in the urban spatial dataset are extracted, the conflict data blocks are automatically merged using the spatial aggregation function, and the topological relationship of the city is updated; If the conflict degree value of the urban spatial dataset belongs to the second conflict degree interval, the number of redundant coordinate nodes is corrected; If the conflict degree value of the urban spatial dataset belongs to the third conflict degree interval, the coordinate transformation is corrected.

6. The multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data according to claim 5 is characterized by: The specific analysis process of the corrected coordinate redundant node number is as follows: The conflict degree value of the urban spatial dataset is proportionally processed with the second conflict degree interval to obtain a first proportion of the conflict degree value of the urban spatial dataset, mapped to obtain a coordinate redundant node adjustment factor, and data coupled with the number of coordinate redundant node corrections to obtain the coordinate redundant node adaptation number, which is used to reduce the number of coordinate redundant node corrections; The number of coordinate redundant node adaptations is subtracted from the predefined number of coordinate redundant node definitions to obtain the coordinate node redundant definition deviation, which is then matched with the simplified execution adaptation node span corresponding to the predefined coordinate node redundant definition deviation interval to obtain the simplified execution adaptation node span of the urban spatial dataset; Adapt node spans according to the simplification of urban spatial datasets through simplified line algorithm, and reduce the number of corrected coordinate redundant nodes to the number of adapted coordinate redundant nodes; At the same time, based on the initial data quality indicators of the urban spatial dataset, the adaptive learning rate is mapped and matched to the reference learning rate based on the first proportion of the conflict degree value of the urban spatial dataset. The real-time learning rate of the data processing port is extracted and the difference between the adaptive learning rate and the reference learning rate is processed and compared to obtain the first deviation of the learning rate of the data processing port and the second deviation of the learning rate of the data processing port. Compare the first deviation of the learning rate of the data processing port with the second deviation of the learning rate of the data processing port. If the first deviation of the learning rate of the data processing port is greater than the second deviation of the learning rate of the data processing port, take the reference learning rate corresponding to the second deviation of the learning rate of the data processing port and replace the real-time learning rate of the data processing port; otherwise, take the adaptation learning rate corresponding to the first deviation of the learning rate of the data processing port and replace the real-time learning rate of the data processing port.

7. The multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data according to claim 5 is characterized by: The specific analysis process of the corrected coordinate transformation is as follows: If the conflict degree value of the urban spatial dataset belongs to the third conflict degree interval, the conflict degree value of the urban spatial dataset is processed with the third conflict degree interval to obtain a second proportion of the conflict degree value of the urban spatial dataset, and the tolerance threshold correction factor is obtained by mapping. At the same time, the real-time data quality index of the urban spatial dataset is extracted, and the tolerance threshold adjustment element is found. The tolerance threshold correction factor and the tolerance threshold adjustment element are coupled with the tolerance threshold during coordinate transformation to obtain the tolerance adaptation threshold during coordinate transformation; The coordinate transformation tolerance of the urban multi-source dataset is extracted, and the iterative closest point algorithm is used for multiple iterations to reduce the coordinate transformation tolerance of the urban multi-source dataset to the tolerance adaptation threshold during coordinate transformation, thereby completing the coordinate transformation correction.

8. The multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data according to claim 1 is characterized by: The execution of interactive management of urban renewal also includes: Extract the role code of the urban renewal interaction terminal, map it to obtain the initial proportion of the rendering computing power of the urban renewal interaction terminal, pre-call resources for the urban renewal interaction terminal before storing the valid urban data set on the front end, collect the resource pre-call data of the urban renewal interaction terminal, and determine the execution efficiency evaluation value of the urban renewal interaction terminal; Extracting the real-time conflict degree value of the urban spatial data set, matching it with the execution efficiency evaluation reference value corresponding to each predefined real-time conflict degree value interval, determining the specific interval of the real-time conflict degree value of the urban spatial data set, and obtaining the execution efficiency evaluation reference value corresponding to the interval; The execution efficiency evaluation value of the urban renewal interaction terminal is compared with the execution efficiency evaluation reference value. If the execution efficiency evaluation value of the urban renewal interaction terminal is greater than or equal to the execution efficiency evaluation reference value, the initial proportion of the rendering computing power of the urban renewal interaction terminal is maintained to execute the role interaction.

9. The multi-dimensional dynamic monitoring and analysis system for urban renewal based on big data according to claim 8 is characterized by: The specific analysis process of the execution role interaction is as follows: If the execution efficiency evaluation value of the urban renewal interaction terminal is less than the execution efficiency evaluation reference value, then before the front-end storage of the urban effective data set, data compression optimization is started, the real-time data quality indicators of the urban spatial data set are extracted, and the adaptive compression ratio of the data compression is obtained by matching. At the same time, the execution efficiency evaluation value of the urban renewal interaction terminal is differenced with the execution efficiency evaluation reference value to obtain the execution efficiency deviation evaluation value of the urban renewal interaction terminal, and the compression ratio correction element of the data compression is mapped and coupled with the adaptive compression ratio of the data compression to obtain the adaptive compression ratio of the data compression, which is used to reconfigure the resource pre-call of the urban renewal interaction terminal; After reconfiguring the resource pre-call of the urban renewal interaction terminal, extract the real-time execution efficiency evaluation value of the urban renewal interaction terminal and compare it with the execution efficiency evaluation reference value. If the execution efficiency evaluation value of the urban renewal interaction terminal is greater than or equal to the execution efficiency evaluation reference value, then execute the role interaction; If the execution efficiency evaluation value of the urban renewal interaction terminal is still less than the execution efficiency evaluation reference value, the role code priority determination is enabled to obtain the role code priority determination value, extract the idle computing power ratio of the urban renewal interaction terminal, and map the idle computing power call ratio according to the role code priority determination value. This is coupled with the initial rendering computing power ratio of the urban renewal interaction terminal to obtain the rendering computing power adaptation ratio of the urban renewal interaction terminal, which is used to execute role interaction.

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