Land space planning management method and system based on big data

Abstract

The invention belongs to the technical field of territorial space planning, and particularly relates to a territorial space planning management method and system based on big data. The method comprises the steps of obtaining planning plot data and dividing the planning plot data into a plurality of planning areas; monitoring each area, comparing current data with predicted data to judge abnormity, and establishing a data management node to realize disaster recovery; optimizing the planning parameters and re-dividing regions according to the planning parameters to generate a plurality of optimized planning regions; and finally, determining a preferential adjustment area based on an adjacent relation between optimization planning areas, calculating a supervision score of the preferential adjustment area, and outputting a territorial space planning scheme. According to the method, scientificity and accuracy of a planning scheme are improved through prediction analysis and parameter optimization, accurate identification and efficient management of key areas are realized by using a multi-dimensional priority rule, and high reliability and continuity of system operation are guaranteed by means of a disaster recovery mechanism based on actual measurement performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of national land space planning, and specifically relates to a national land space planning management method and system based on big data. Background Art

[0002] Scientific and rational planning relies on the precise analysis and application of massive amounts of spatial data, which form the cornerstone of planning decisions. With the deepening of the concept of refined governance, how to use advanced technologies to ensure that planning schemes accurately reflect and dynamically adapt to the actual development conditions of the region has become a core issue in improving national land space governance capabilities and achieving high-quality development.

[0003] However, existing land and space planning and management technologies have exposed numerous inherent flaws in practice. Traditional methods often rely on a simple comparison of planning blueprints with existing remote sensing imagery, overlooking the numerous dynamic factors that influence spatial evolution, such as regional climate change, hydrological conditions, geological structure, population migration, and industrial layout. These multi-source, heterogeneous data are often treated as isolated pieces of information, failing to undergo effective integration and correlation analysis. This leads to blind spots in the comprehensive understanding of regional development and severely undermines the comprehensiveness and scientific nature of planning assessments. Current technologies often treat planning boundaries as fixed, rigid constraints, failing to dynamically respond to and adapt planning schemes based on real-time monitoring of environmental, social, and economic parameters. This causes planning schemes to rapidly lag behind rapidly changing realities, easily leading to problems such as misallocation of land resources and overload of ecological carrying capacity, ultimately impacting the efficiency and long-term stability of land and space utilization.

[0004] In view of this, the industry urgently needs a new national land space planning and management solution that can deeply integrate multi-source heterogeneous data and realize dynamic evaluation and intelligent adjustment of planning area boundaries based on comprehensive analysis results, thereby overcoming the staticness and limitations of existing technologies and significantly improving the scientific nature, dynamic adaptability and foresight of planning decisions. Summary of the Invention

[0005] The purpose of this invention is to provide a land space planning management method and system based on big data, which can ensure the real-time and dynamic nature of planning data, thereby ensuring excellent planning quality.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0007] A land space planning management method based on big data, comprising the following steps:

[0008] S1. Obtaining land information and planning data:

[0009] Obtain the land information and planning data of the required planning plot, determine a planning center point in the plot, and then construct multiple planning areas based on the center point;

[0010] S2. Planning data evaluation:

[0011] Monitoring the multiple planning areas, collecting planning data within the current area, and obtaining corresponding historical planning data, calculating predicted planning data based on the historical data, and comparing the predicted data with the current data to determine whether there is any anomaly in the current data;

[0012] S3. Build management nodes and disaster recovery mechanisms:

[0013] Based on the needs of data disaster recovery, multiple data management nodes are established; the data management nodes include at least one master node and one monitoring node;

[0014] S4. Optimize planning parameters and regional division:

[0015] Optimize and adjust the planning parameters to obtain a set of optimized data with a smaller deviation from the original parameters. Use this optimized data to replace the existing planning parameters and re-divide the planning area based on the new planning parameters.

[0016] S5. Output planning scheme:

[0017] According to the above steps, the planning scheme of the national land space is output, and the corresponding supervision score is calculated based on the optimized planning area; in addition, by comparing the adjacent relationships between each area, the area that needs priority adjustment is determined, and the supervision score of the area is used as a reference.

[0018] In a preferred technical solution, the determination of the planning center point in S1 includes the following steps: obtaining the coordinates of the edge points of the plot to form the boundary of the plot; screening out two diagonal points from the boundary and measuring their distance, setting a path under the condition that the surface structure allows, and then calculating the equal division points of the path, and setting the point close to the center of the two diagonal points as the planning center point.

[0019] In a preferred technical solution, the comparison of the planning data in S2 includes the following steps: calculating the change trend of the current planning data, and constructing a prediction function in combination with historical data to output the predicted planning data; then comparing the predicted data with the current data, calculating the deviation value, and judging whether the deviation exceeds the deviation threshold. If it exceeds, it is considered abnormal.

[0020] In a preferred technical solution, the data disaster recovery in S3 includes the following steps:

[0021] Build a priority list of data disaster recovery nodes, record the response delay of subsequent nodes when each node is the primary node; sort the priorities in ascending order based on the delay; set the node with the highest priority as the primary node, and enable the monitoring node at the same time; the remaining nodes will be replaced and put into working state when needed.

[0022] In a preferred technical solution, the operation of optimizing the planning parameters in S4 includes the following steps: setting an adjustment range for an original planning parameter and performing multiple iterations, obtaining a new parameter after each adjustment and calculating the deviation from the original parameter; summarizing all deviations and screening them, and selecting the value closest to the original parameter as the final optimization data.

[0023] In a preferred technical solution, in the iterative process, the screening step includes determining a deviation threshold, and combining all deviations greater than the threshold and then comparing them.

[0024] In a preferred technical solution, the judgment method for determining the priority adjustment area in S5 includes: obtaining boundary point data of two areas; translating the boundary of one area in four directions in turn to determine the distance from another area; if the distance is less than a set threshold, the two areas are regarded as adjacent areas; and determining an area with multiple adjacent areas as a priority adjustment area.

[0025] In a preferred technical solution, the priority adjustment areas are, from high to low, the area with abnormal planning data, the area where the planning center point is located, the area with the largest number of adjacent areas, and the area with large deviations from the original planning parameters.

[0026] The present invention also provides a national land space planning management system based on big data, which is applied to the above-mentioned national land space planning management method based on big data, and is characterized by including the following modules:

[0027] Acquisition module: used to obtain land information and planning data of planned plots, and determine the center point and multiple planning areas;

[0028] Evaluation module: used to obtain historical data of each region, calculate the deviation between the expected data and the current data, and evaluate the change trend of the current data;

[0029] Disaster recovery module: used to establish multiple data nodes, build a disaster recovery mechanism and monitor the normal operation of the nodes;

[0030] Optimization module: used to replace the optimized planning parameters and re-divide the area;

[0031] Output module: used to output monitoring results and space planning plans.

[0032] Beneficial effects

[0033] This invention provides a big data-based land and space planning management method that integrates predictive analysis with parameter optimization techniques to improve the scientific nature and accuracy of planning schemes. First, the system constructs a prediction function, compares historical and current planning data, and calculates deviations to identify outlier areas. Based on this, an iterative approach optimizes the original planning parameters, identifying new parameters with minimal deviations. These parameters are then used to re-divide the planning area, reducing subjective judgment and ensuring that planning adjustments are based on clear data and sufficient data.

[0034] To improve management efficiency, this invention also introduces a multi-dimensional prioritization mechanism to evaluate the multiple optimized planning areas. The sorting rules consider the degree of data anomaly, whether the planning center point is included, the number of adjacent relationships, and the degree of boundary changes. This automatically identifies key regulatory areas, directing management resources to focus on key issues, avoiding indiscriminate investigations, and improving response efficiency and resource utilization.

[0035] Furthermore, this invention establishes a highly reliable data disaster recovery mechanism. The system comprises a master node, a monitoring node, and multiple backup nodes. During the deployment phase, the response latency of each node during failover is measured and used to set failover priorities. If a master node fails, the monitoring node immediately activates the fastest-responding backup node, ensuring continuous data processing and stable system operation, avoiding the risk of interruption caused by single points of failure and thus supporting the long-term and stable implementation of national land space planning and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of the method of the present invention;

[0037] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] Example 1

[0040] See also Figure 1 This embodiment provides a big data-based land and space planning management method, designed to address the issues of delayed data updates, lack of dynamic optimization, and lack of disaster recovery capabilities in traditional planning methods. This method enables optimized matching with real-time planning data, while generating a supervision score that truly reflects the planning status of land and space, thereby improving the accuracy of supervision and the scientific nature of planning.

[0041] S1. Early acquisition and identification:

[0042] This step first involves acquiring the land information and planning data for the planned plot. The land information may include the plot's geographic coordinates, elevation data, land cover type (such as water bodies, forests, buildings, etc.), and the distribution of existing infrastructure. The planning data includes key indicators such as historical land use characteristics, floor area ratio, and green space ratio.

[0043] Subsequently, based on the land information, a planned center point is determined within the planned plot. The purpose of this step is to establish a stable and representative geographic reference benchmark to provide a basis for subsequent regional division. A specific determination method is: first, through a geographic information system (GIS) or remote sensing image analysis, the coordinates of multiple edge points of the planned plot are obtained. These coordinates together constitute the boundary of the planned plot.

[0044] Then, from the multiple edge points of the boundary, by calculating the Euclidean distance between all pairs of points, the two points with the largest distance are screened out, that is, the two diagonal points that constitute the actual diagonal line. Taking into account that there may be obstacles (such as lakes and cliffs) inside the plot that are not suitable as the center, this method does not simply take the geometric center, but sets an accessible path between the two diagonal points based on surface structure data (such as elevation and obstacle information). Then, one or more equal division points are calculated along this path. At the same time, the midpoint of the straight line connecting the two diagonal points is calculated, and the midpoint is used as a pure geometric center reference. Finally, the equal division point on the path with the smallest distance from the geometric midpoint is determined as the planning center point. This method ensures that the planning center point is not only geometrically central, but also accessible and representative in the actual geographical environment.

[0045] The planning center point refers to a representative geographic reference point within the planned plot. It is determined not simply by the geometric center, but by comprehensively considering the actual geographic accessibility of the plot. It is obtained by selecting the closest bisecting point to the geometric midpoint along an accessible path between the actual diagonals, ensuring its dual validity in terms of both geometric centrality and actual representativeness.

[0046] Finally, the planned plot is divided into multiple planning areas using the planned center point as a reference. For example, a quadtree or Voronoi diagram algorithm can be used, with the planned center point as the initial node. Recursive division is performed based on the plot boundaries and natural dividing lines such as internal roads and rivers until the area or a key indicator (such as population density) of each planning area reaches a preset equilibrium level.

[0047] A planning area is a separate management unit formed by spatially dividing a planned plot. This division is based on the planning center point and may incorporate natural or artificial boundaries. It is generated using a specific algorithm (such as a quadtree) to ensure that each unit achieves a predetermined equilibrium in terms of area or key planning indicators.

[0048] S2. Planning data monitoring and anomaly identification:

[0049] After the plurality of planning areas are divided, this step continuously monitors these areas to collect current planning data of each of the planning areas, such as real-time traffic flow, pedestrian flow, air quality index, etc.

[0050] Current planning data is a data set collected in real time or quasi-real time for each planning area, reflecting its current status, such as instant traffic flow, pedestrian flow, air quality index, etc., which is the direct basis for dynamic evaluation and anomaly identification.

[0051] At the same time, the system obtains historical planning data corresponding to the current planning data in time and type from the historical database.

[0052] Historical planning data refers to a historical time series dataset that corresponds to the current planning data in terms of indicator types. It contains similar data from the same planning area at multiple points in the past, providing a benchmark for trend analysis and model forecasting.

[0053] Next, it is necessary to determine whether the current planning data contains anomalies. To avoid misjudgments due to normal seasonal or cyclical fluctuations, this method uses a judgment mechanism based on trend prediction. Specifically, a prediction function is constructed based on the recent trend of the historical planning data and the current planning data. This prediction function can be a time series analysis model (such as an ARIMA model) or a machine learning regression model, which can learn long-term trends, seasonal patterns, and cyclical behaviors in the data.

[0054] Prediction function: It is a computational model used to calculate the expected value of a specific planning indicator based on the time series characteristics of historical data. Its specific definition is as follows:

[0055]

[0056] The input is the time series of historical planning data {P(tn),...,P(t-1)}; the current time point is t; the output is the forecast planning data represents the expected value of the planning data at time point t, representing the normal state when no abnormal events occur; c is the constant term; β is the trend coefficient; n is the model order; φ iis the autoregressive coefficient, which represents the weight of the impact of the historical data point P(ti) on the current forecast value; P(ti) is the historical planning data.

[0057] Subsequently, the prediction function is used to input the historical planning data and the current time point to calculate and generate the predicted planning data, which represents the expected value in the absence of abnormal events.

[0058] The forecast planning data is the output value calculated by the forecast function, which represents the expected normal value of a certain planning data indicator at a given point in time under its historical laws and trends.

[0059] Next, the deviation between the predicted planning data and the actual collected current planning data is calculated, for example, by calculating the absolute difference or root mean square error between the two. The deviation value is used to measure the degree of difference between the actual collected current planning data and the predicted planning data generated by the model, for example, by calculating the absolute difference or root mean square error between the two.

[0060] Finally, a determination is made as to whether the deviation value is greater than a preset deviation threshold. This threshold can be dynamically set based on the statistical distribution of historical forecast errors (e.g., three times the standard deviation) to improve adaptability. If the deviation value is greater than the preset deviation threshold, it means that the actual state of the current data has significantly deviated from its expected historical pattern. Therefore, the current planning data is marked as abnormal, triggering subsequent analysis or an alert.

[0061] The deviation threshold is a preset numerical limit used to determine whether a deviation constitutes a significant anomaly. When the deviation exceeds this threshold, an anomaly flag is triggered. This threshold can be dynamically set based on the statistical distribution of historical forecast errors (e.g., three times the standard deviation) to accommodate the volatility of different data.

[0062] S3. Establish a data disaster recovery mechanism:

[0063] To ensure the continuity and stability of massive planning data processing, this method establishes multiple data management nodes for data disaster recovery. A data management node is an independent computing unit responsible for data storage, computing, and services in a distributed system architecture. It is usually represented by a physical server, virtual machine, or containerized cluster.

[0064] The multiple data management nodes are distributedly deployed servers or computing clusters, which jointly undertake the tasks of data storage, computing and services. These nodes include at least one master node and one monitoring node. In order to achieve efficient fault switching, the steps of establishing the mechanism further include: the system first enters a benchmark test phase, and for the multiple data management nodes, simulates the scenario where any of the data management nodes fails as the master node, and accurately measures and records the response delay of each of the remaining data management nodes to take over its work at this time. The response delay is a specific measured time value that quantifies the total time required from the moment the failure of the master node is detected to the moment any backup node completely takes over its work and resumes service, and includes the entire process of fault detection, task handover and system recovery.

[0065] This delay includes the total time for fault detection, task handover and service restoration. Based on the response delay, a priority list is constructed, wherein the multiple data management nodes are assigned priorities from high to low according to the order of the response delay from small to large. The priority list provides a clear basis for the order of node replacement when a fault occurs. Priority is the level assigned to each data management node based on the response delay. The smaller the response delay, the higher the priority, which determines its role in the system (master node, monitoring node or backup node) and its order in fault switching.

[0066] The node with the smallest response delay means that it is most efficient in taking over the work. Therefore, the system sets the data management node with the highest priority in the priority list as the master node, which is responsible for processing the current real-time task.

[0067] The master node is the only node in the data management node cluster that is currently designated to handle all core real-time tasks. In this solution, this node is the node with the lowest response latency (i.e., the highest priority) selected through benchmark testing.

[0068] At the same time, in order to ensure the high availability of the monitoring function itself, the node with the second highest priority is usually set as the monitoring node, whose core responsibility is to continuously monitor the health status of the master node (for example, through a heartbeat mechanism).

[0069] A monitoring node is a data management node that is specifically designated to continuously monitor the health of the master node (e.g., through a heartbeat mechanism). In this solution, it is usually the second-best node in the priority list (i.e., the second-highest priority) to ensure the high availability of the monitoring function itself.

[0070] The remaining data management nodes are designated as backup nodes. Backup nodes are all data management nodes other than the master node and monitoring nodes. They are on standby, ready to take over the master node's work if it fails, according to a priority list. They can usually handle secondary tasks such as data backup or offline computing.

[0071] During normal operation, the backup node can handle offline computing or serve as a data backup. Once the monitoring node detects that the primary node has failed (e.g., a heartbeat timeout), it immediately activates the backup node with the highest priority according to the priority list, allowing it to take over the work of the primary node, thereby achieving fast and orderly failover and ensuring the continuity of data management services.

[0072] S4. Optimize planning parameters and re-divide areas:

[0073] This step aims to dynamically optimize the planning scheme to better meet current and future development needs. First, the system optimizes the planning parameters contained in the planning data to generate optimized planning parameters. These parameters may be identified from areas with anomalies or adjusted based on overall development goals, such as land use rate, building density, and public facility coverage radius.

[0074] Optimized planning parameters are new parameter values ​​generated after the original planning parameters are processed by the optimization algorithm of this solution. These new parameter values ​​are designed to better meet one or more planning objectives while keeping their variation within a preset acceptable range.

[0075] The specific process of generating optimized planning parameters is as follows: for an original planning parameter to be optimized, first set a reasonable adjustment range. This range defines the parameter search space and avoids unlimited adjustment.

[0076] The adjustment range refers to the upper and lower limits set for the original planning parameters during the parameter optimization process. It defines the search space for the algorithm to generate new candidate parameters to ensure the convergence of the optimization process and the realism of the results.

[0077] Based on the adjustment range, the system iteratively adjusts the original planning parameters multiple times. In each iterative adjustment, an algorithm (such as a genetic algorithm or a variant of the particle swarm optimization algorithm) generates new planning parameters. Based on these new planning parameters, the system simulates the planning effect and calculates its compliance with one or more planning objectives (such as maximizing traffic efficiency and minimizing environmental impact). At the same time, the system calculates the parameter deviation between the new planning parameters and the original planning parameters.

[0078] Parameter deviation refers to the numerical difference between the newly generated candidate planning parameters and the original planning parameters in a single iterative optimization, which is used to quantify the magnitude of the adjustment.

[0079] After completing the multiple iterative adjustments, all the calculated parameter deviations are aggregated to form a parameter deviation set.

[0080] The parameter deviation set refers to the set of all parameter deviation values ​​calculated during multiple iterative adjustments, which fully records the parameter changes in the entire optimization search space.

[0081] In order to ensure the stability and feasibility of the optimization results, the system will preset a parameter deviation threshold before screening out the parameter deviation with the smallest value.

[0082] The parameter deviation threshold is a preset upper limit used to filter the parameter deviation set. Any parameter deviation greater than this threshold is considered excessive and excluded from the candidate set, preventing drastic and unrealistic changes to the planning scheme.

[0083] This threshold is used to exclude candidate values ​​that, while potentially superior in a single metric, vary significantly from the original parameter, potentially leading to drastic changes in the planning solution or making it unrealistic. The system then identifies all parameter deviations from the parameter deviation set that are greater than the parameter deviation threshold and removes these identified parameter deviations from the parameter deviation set to generate a filtered parameter deviation set.

[0084] The filtered parameter deviation set refers to a new, smaller set obtained by removing all items exceeding a preset parameter deviation threshold from the original parameter deviation set.

[0085] Finally, the step of selecting the parameter deviation with the smallest value is performed on the filtered parameter deviation set. This ensures that the new planning parameter corresponding to the parameter deviation with the smallest value, i.e., the optimized planning parameter, is the optimal choice within the "acceptable" range of variations.

[0086] After obtaining the optimized planning parameters, the system replaces the original planning parameters with the optimized planning parameters, and re-divides the multiple planning areas based on the new parameter set to generate multiple optimized planning areas.

[0087] Optimized planning areas are new geographic units generated by redividing the original planning area after replacing the original parameters with optimized planning parameters. The boundaries and attributes of these areas reflect the optimized planning scheme.

[0088] S5. Generate planning scheme and calculate supervision score:

[0089] After the region is re-divided, the step enters the final solution generation and evaluation phase. First, the adjacent relationship between any two of the plurality of optimized planning regions needs to be determined.

[0090] Adjacency is a binary determination of whether any two optimized planning regions are spatially adjacent. When the minimum distance between the boundaries of the two regions is less than a preset adjacency threshold, they are considered to have an adjacency relationship.

[0091] This step is crucial for understanding the mutual influence between regions. The specific method is: for any first optimization planning region and any second optimization planning region in the multiple optimization planning regions, the system uses a computational geometry algorithm to calculate the minimum distance between the boundary of the first optimization planning region and the boundary of the second optimization planning region.

[0092] The minimum distance refers to the shortest Euclidean distance calculated between the boundary lines (polygons) of two optimization planning areas using computational geometry algorithms.

[0093] If the minimum distance is less than a preset adjacent relationship threshold (for example, set to 0 meters to indicate direct boundary contact, or set to a smaller value such as 10 meters to take into account separators such as roads), it is determined that the first optimized planning area and the second optimized planning area have the adjacent relationship.

[0094] The adjacency threshold is a preset distance value used to determine whether two optimized planning areas are adjacent. If the minimum distance between them is less than this threshold, they are considered adjacent.

[0095] Based on the adjacent relationship, the system determines one or more priority adjustment areas from the multiple optimized planning areas.

[0096] Priority adjustment areas refer to specific areas that need to be given priority attention and management, which are screened out from all optimized planning areas based on a set of preset priority sorting rules.

[0097] This is a multi-dimensional decision-making process. On the one hand, the system will count the number of other optimized planning areas that have the adjacent relationship with each optimized planning area, and identify the optimized planning areas with a number greater than a preset threshold as key nodes in the spatial structure.

[0098] The number threshold is used to identify key nodes in the spatial network. When the number of adjacent regions of an optimized planning region exceeds this threshold, it is marked as a key node.

[0099] On the other hand, the priority adjustment areas are determined based on a more comprehensive priority sorting rule, in which priorities are assigned to the following types of optimization planning areas in descending order:

[0100] In this scheme, the priority sorting rule considers the data anomaly status of the region, whether it contains the planning center point, the number of adjacent regions, and the boundary change.

[0101] First priority: the optimized planning area where the current planning data has anomalies according to the judgment in step S2. Such areas have real problems and require the most urgent attention.

[0102] Second priority: the optimized planning area containing the planning center point. This area is the benchmark and core of the entire planning plot, and its stability is crucial.

[0103] Third priority: The optimized planning area with the largest number of other optimized planning areas with the aforementioned adjacent relationships determined according to the aforementioned method. This type of area is the "hub" in the spatial network, and adjustments to it will have the most extensive chain effects.

[0104] Fourth priority: Optimized planning areas whose boundary changes during the redivision process in step S4 are greater than a preset change threshold. Boundary change can be quantified by calculating the symmetrical difference between the old and new boundary polygons. [Term Explanation] Boundary change: A quantitative indicator used to measure the degree of change in the boundary geometry of a planning area before and after redivision. This can be determined by calculating the symmetrical difference between the old and new boundary polygons.

[0105] The change threshold is a pre-set threshold for boundary change. When the boundary change of an area exceeds this threshold, it indicates that the area has undergone significant planning adjustments and thus receives higher priority in the ranking.

[0106] Areas with large changes mean that they have undergone major planning adjustments and require focused supervision to ensure the smooth implementation of the adjustments.

[0107] The system combines the above rules to determine the final list of priority adjustment areas. The system then calculates the regulatory score of one or more priority adjustment areas, which integrates multiple dimensions such as the degree of data anomaly, importance, and parameter optimization effect of the area.

[0108] The regulatory score is a comprehensive quantitative score calculated for each priority area. This score incorporates information from multiple dimensions, including the degree of data anomaly, regional importance (determined by the prioritization rules), and parameter optimization effectiveness, to intuitively reflect the urgency and importance of regulation in that area.

[0109] Finally, the system outputs a national land space planning plan that includes the priority adjustment areas and their regulatory scores, as well as a complete optimized planning area division and parameters, providing decision makers with a clear, quantitative and focused planning management basis.

[0110] The national land space planning scheme refers to the complete output generated by this method for decision support. It at least includes the optimized regional division scheme, the full set of optimized planning parameters, and a list of identified priority adjustment areas and their corresponding regulatory scores.

[0111] In summary, the big data-based land space planning management method provided in this embodiment achieves intelligent management of the entire process, from data acquisition, anomaly identification, disaster recovery, parameter optimization, and solution generation, through a series of interlocking steps. It can identify whether there are anomalies in the current planning data, introduce data disaster recovery strategies during data processing to improve data management stability, and effectively improve the accuracy of planning parameters by cross-comparing historical planning data with current planning data. Ultimately, through a scientific priority sorting and scoring mechanism, it ensures the enforceability of planning results and continuously optimizes planning quality.

[0112] Example 2

[0113] See also Figure 2 This embodiment provides a big data-based national land space planning and management system for implementing the method described in Example 1, enabling the full-process automated processing, optimization, and output of planning area and regulatory data. This system, with a "modular architecture" as its core design concept, implements the division of labor and collaborative operation for data collection, evaluation, disaster recovery control, optimized calculation, and planning output.

[0114] The system includes the following functional modules:

[0115] Acquisition module:

[0116] This module is configured to perform the following operations: obtain land information and planning data for a planned plot; determine a planning center point within the planned plot based on the land information; and divide the planned plot into multiple planning areas using the planning center point as a reference. This module connects to various raw input information through interfaces, providing the basic data structure for subsequent modules.

[0117] Assessment Modules:

[0118] The module is configured to perform the following operations: monitor the multiple planning areas to collect current planning data for each planning area; obtain historical planning data corresponding to the current planning data; construct a prediction function based on the change trends of the historical planning data and the current planning data, and use the prediction function to calculate and generate predicted planning data; calculate the deviation value between the predicted planning data and the current planning data, and if the deviation value is greater than a preset deviation threshold, mark the current planning data as abnormal.

[0119] Disaster recovery module:

[0120] The module is configured to perform the following operations: for multiple data management nodes, measure and record the response delay of each of the remaining data management nodes when any of the data management nodes fails as the main node; build a priority list based on the response delay; set the data management node with the highest priority as the main node, and set the remaining data management nodes as backup nodes; a monitoring node continuously monitors the status of the main node, and if it is detected that the main node fails, activates the backup node with the highest priority according to the priority list to take over its work, thereby ensuring uninterrupted system operation.

[0121] Optimization module:

[0122] The module is configured to perform the following operations: for an original planning parameter, set an adjustment range and perform multiple iterative adjustments, generate a new planning parameter in each iteration and calculate the parameter deviation between it and the original planning parameter, thereby forming a parameter deviation set; remove all parameter deviations greater than a preset parameter deviation threshold from the parameter deviation set to generate a filtered parameter deviation set; filter out the parameter deviation with the smallest value from the filtered parameter deviation set, and determine the new planning parameter corresponding to the minimum parameter deviation as the optimized planning parameter; replace the original planning parameter with the optimized planning parameter; and re-divide the multiple planning areas based on the optimized planning parameter to generate multiple optimized planning areas.

[0123] Output Module:

[0124] The module is configured to perform the following operations: determine the adjacent relationship between any two optimized planning areas among the multiple optimized planning areas; determine one or more priority adjustment areas from the multiple optimized planning areas based on the adjacent relationship and preset priority sorting rules; calculate the supervision scores of the one or more priority adjustment areas; and finally output a national land space planning scheme containing the priority adjustment areas and their supervision scores to achieve data docking and interactive closed loop with the control system.

[0125] The big data-based national land space planning management system provided in this embodiment features a complete execution chain of "collection-assessment-disaster recovery-optimization-output," fully adapting to the planning needs of both conventional plots and highly complex areas. The system incorporates a multi-version parameter candidate mechanism within the optimization module, while the disaster recovery module implements a failover mechanism for master nodes for data and nodes. Optimization and disaster recovery operate continuously through the collaborative efforts of monitoring nodes and data screening mechanisms, enabling the gradual optimization and continuous iteration of national land space planning solutions.

[0126] Example 3

[0127] This embodiment provides a computer device for a land space planning management method based on big data, which is used to ensure that the method described in Example 1 has stable execution capabilities, data interaction capabilities and logical operation capabilities. At the same time, it cooperates with the system described in Example 2 to form a "software and hardware integrated" adaptive land planning processing platform.

[0128] The device includes the following hardware resources:

[0129] At least one processor to perform scheduling, transceiver, and control tasks;

[0130] Multiple input and output interfaces for collecting sensor data, geographic information data, historical database content, etc.

[0131] A data storage unit for storing temporary table of regional division, historical planning parameters, abnormal area records and optimization candidate values;

[0132] The communication interface supports two-way communication with external devices or systems such as mobile inspection terminals and the supervision center cloud platform.

[0133] To implement the specific method logic, the device pre-configures multiple functional modules applicable to the present invention. Each functional module is loaded and executed on the processor using an instruction set. These functional modules are loaded and executed on the processor in the form of an instruction set to implement all or part of the functions of the acquisition module, evaluation module, disaster recovery module, optimization module, and output module.

[0134] The specific execution process of the above-mentioned functional modules is loaded into the processor in the form of program instructions and then runs on the processor. Its execution process includes: the acquisition module reads data from external sensors or historical databases; the optimization module performs an iterative parameter optimization process to generate optimized planning parameters; the disaster recovery module executes a switchover instruction in the event of a master node failure based on the monitoring results of the monitoring node on the master node; and the output module invokes the scoring calculation function based on the optimization results to generate and output a national land space planning plan.

[0135] The device can be deployed as a fixed terminal in the national land surveying and mapping center, or it can be embedded in the ground patrol control network through an edge computing node to share data with the drone inspection platform; its efficient data processing capabilities combined with the collaborative scheduling capabilities of multi-functional modules further enhance the digitalization, intelligence and dynamic response capabilities of the entire national land space planning process, providing basic computing power and full-chain solutions for future urban construction, industrial layout, disaster warning, ecological assessment, etc.

[0136] The equipment in this embodiment provides solid computing support for the execution of the method. Through the integrated hardware and software mechanism, it not only improves the automation and real-time responsiveness of the method execution process, but also ensures the continuity of land and space information processing and the reliability of planning result output.

[0137] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A land space planning and management method based on big data, characterized in that: The following steps are involved: S1. Obtaining land information and planning data of the planned plot; Based on the land information, determining a planning center point in the planned plot; Dividing the planned plot into a plurality of planning areas with reference to the planned center point; S2. Monitoring the plurality of planning areas to collect current planning data for each planning area; Acquiring historical planning data corresponding to the current planning data; Based on the historical planning data, calculating and generating forecast planning data; Comparing the predicted planning data with the current planning data to determine whether there is any anomaly in the current planning data; S3. Establish multiple data management nodes to achieve data disaster recovery; the multiple data management nodes include at least one master node and one monitoring node; S4. Optimizing the planning parameters contained in the planning data to generate optimized planning parameters; Replacing original planning parameters with the optimized planning parameters; Re-dividing the plurality of planning areas based on the optimization planning parameters to generate a plurality of optimized planning areas; S5. Determine the adjacent relationship between any two optimized planning areas among the multiple optimized planning areas; Based on the adjacent relationship, determining one or more priority adjustment areas from the multiple optimized planning areas; Calculate the supervision scores of the one or more priority adjustment areas; and output a national land space planning plan including the priority adjustment areas and their supervision scores.

2. The land space planning management method based on big data according to claim 1 is characterized in that: The step of determining a planning center point comprises: Obtaining coordinates of a plurality of edge points of the planned plot to form a boundary of the planned plot; Selecting two diagonal points forming a diagonal line from a plurality of edge points of the boundary; Setting a path between the two diagonal points; calculating one or more bisection points of the path; Calculate the midpoint of the line connecting the two diagonal points; The bisection point with the smallest distance from the midpoint is determined as the planning center point.

3. The land space planning and management method based on big data according to claim 1 is characterized in that: The step of determining whether the current planning data is abnormal includes: Constructing a prediction function based on the change trends of the historical planning data and the current planning data; Using the prediction function, calculating and generating the prediction planning data; Calculating a deviation value between the predicted planning data and the current planning data; Determining whether the deviation value is greater than a preset deviation threshold; If the deviation value is greater than the preset deviation threshold, the current planning data is marked as abnormal.

4. The land space planning management method based on big data according to claim 1 is characterized in that: The step of establishing multiple data management nodes to achieve data disaster recovery further includes: For the multiple data management nodes, measuring and recording the response delay of each of the remaining data management nodes taking over the work when any of the data management nodes as the master node fails; Constructing a priority list, wherein priorities are assigned from high to low to the multiple data management nodes according to the order of the response delay from small to large; Setting the data management node with the highest priority in the priority list as the master node; The remaining data management nodes are set as standby nodes. When the main node fails, the standby nodes are activated by the monitoring node to enter a working state.

5. The land space planning management method based on big data according to claim 1 is characterized in that: The step of generating the optimization planning parameters comprises: For each of the original planning parameters, setting an adjustment range; Based on the adjustment amplitude, the original planning parameters are iteratively adjusted multiple times, and in each iterative adjustment: a new planning parameter is generated; Calculating parameter deviations between the new planning parameters and the original planning parameters; After completing the multiple iterative adjustments, summarizing all calculated parameter deviations to form a parameter deviation set; Selecting the parameter deviation with the smallest value from the parameter deviation set; The new planning parameter corresponding to the parameter deviation with the smallest value is determined as the optimized planning parameter.

6. The land space planning management method based on big data according to claim 5 is characterized in that: The step of screening the parameter deviation set further includes: Before screening out the parameter deviation with the smallest value, a parameter deviation threshold is preset; identifying, from the set of parameter deviations, all parameter deviations that are greater than the parameter deviation threshold; All identified parameter deviations are removed from the parameter deviation set to generate a filtered parameter deviation set.

7. The method for land space planning and management based on big data according to claim 1, characterized in that: The steps to determine adjacency relationships and priority adjustment areas include: For any first optimization planning area and second optimization planning area among the multiple optimization planning areas, calculating the minimum distance between a boundary of the first optimization planning area and a boundary of the second optimization planning area; If the minimum distance is less than a preset neighbor relationship threshold, it is determined that the first optimized planning area and the second optimized planning area have the neighbor relationship; For each optimized planning area, count the number of other optimized planning areas that have the adjacent relationship with it; The optimized planning areas whose number is greater than a preset number threshold are determined as components of the one or more priority adjustment areas.

8. The land space planning and management method based on big data according to claim 1 is characterized in that: The step of determining one or more priority adjustment areas is based on a priority sorting rule, in which priorities are assigned to the following types of optimization planning areas in descending order: First priority: According to the judgment in step S2, the optimization planning area where the current planning data has abnormalities; Second priority: the optimized planning area including the planning center point; The third priority: the optimized planning area with the largest number of adjacent relationships; Fourth priority: an optimized planning area whose boundary change in the re-division in step S4 is greater than a preset change threshold.

9. A national land space planning and management system based on big data, characterized in that: include: A collection module is configured to: obtain land information and planning data of a planned plot; Based on the land information, determining a planning center point in the planned plot; and dividing the planned plot into a plurality of planning areas with reference to the planned center point; an evaluation module configured to: monitor the plurality of planning areas to collect current planning data for each planning area; Acquiring historical planning data corresponding to the current planning data; Based on the historical planning data, calculating and generating forecast planning data; and comparing the predicted planning data with the current planning data to determine whether there is any anomaly in the current planning data; A disaster recovery module is configured to: establish multiple data management nodes to achieve data disaster recovery, wherein the multiple data management nodes include at least one master node and one monitoring node; an optimization module configured to: optimize the planning parameters included in the planning data to generate optimized planning parameters; Replacing original planning parameters with the optimized planning parameters; and re-dividing the plurality of planning areas based on the optimization planning parameters to generate a plurality of optimized planning areas; an output module configured to: determine an adjacent relationship between any two optimized planning areas among the plurality of optimized planning areas; Based on the adjacent relationship, determining one or more priority adjustment areas from the multiple optimized planning areas; calculating a regulatory score for the one or more priority adjustment areas; And output a national land space planning plan that includes the priority adjustment areas and their regulatory scores.

Images