A Geographic Information Data Analysis Method and System Based on the Internet of Things

Abstract

The application discloses a kind of geographic information data analysis method and system based on Internet of Things, is related to Internet of Things technical field, obtains Internet of Things sensing measurement and carries out space-time alignment and window aggregation, obtains regularized space-time measurement table;Hierarchical recursive space segmentation structure is constructed to generate spatial partition, and the state table of region is obtained by mapping and summarizing measurement value;Adjacent partition state difference is constructed based on pre-exchange volume and constraint exchange volume, and exchange volume balance discrete equation is established and solved, and spatial state field following physical conservation is obtained;Based on consistent deviation field, perform dynamic subdivision and merging operation on space structure, iteratively output high-precision spatial state field distribution data and node layout adjustment instruction set.The application breaks through the limitation that traditional mathematical interpolation lacks physical mechanism, considers multi-scale analytical accuracy and computational efficiency, and realizes closed-loop dynamic optimization of Internet of Things sensing hardware.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a geographic information data analysis method and system based on IoT. Background Technology

[0002] In recent years, with the large-scale deployment of IoT devices and the rapid development of spatial information technology, the deep integration of massive IoT sensing data with geographic information systems to efficiently manage and dynamically analyze multi-source spatiotemporal data has become an important research direction in fields such as urban environmental monitoring, physical field simulation, and situational awareness.

[0003] Currently, Chinese invention patent application CN117036621A discloses a data management method for geographic information mapping instruments based on the Internet of Things (IoT). This method constructs an octree based on a collected 3D point cloud dataset; obtains the terrain hazard of the leaf nodes of the octree; obtains the terrain information purity of the leaf nodes of the octree; further obtains the terrain criticality of the leaf nodes; compresses the octree based on the terrain criticality of the leaf nodes; stores the geographic information data contained in the compressed octree in a database and establishes an index; and uses geographic information system software or visualization tools to visualize the topography of the target mapping area. However, related technologies lack a unified spatiotemporal regulation mechanism for multi-source heterogeneous IoT data, the division and management of spatial structures are divorced from the actual physical mechanisms, and there is a lack of adaptive bidirectional grid evolution and hardware node optimization closed loops. Summary of the Invention

[0004] The technical problem solved by this invention is that existing technologies are unable to effectively eliminate the spatiotemporal fragmentation and inconsistencies of multi-source IoT devices in terms of sampling frequency, coordinate reference system and network transmission, and break through the limitations of pure mathematical statistical interpolation in spatial analysis. They lack quantitative constraints on the real physical exchange mechanisms (such as material migration and heat conduction) and quantity conservation between adjacent spatial partitions, and it is difficult to perform bidirectional adaptive evolution (subdivision and merging) of spatial structure based on the consistency deviation of dynamic spatiotemporal field, resulting in uneven allocation of computing resources and insufficient local feature analysis.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: Firstly, a geographic information data analysis method based on the Internet of Things, comprising the following steps:

[0006] Step S1: Obtain IoT-sensed geographic measurement values ​​and target area range information; perform spatiotemporal alignment and window aggregation processing on IoT-sensed geographic measurement values ​​to obtain a normalized spatiotemporal measurement value table.

[0007] Step S2: Map the normalized spatiotemporal measurement table to each spatial partition, and summarize and process it within each spatial partition to obtain the regional state table.

[0008] Step S3: In the current hierarchical recursive spatial partitioning structure, construct the pre-exchange quantity of the spatial partition boundary segment based on the regional state differences of adjacent spatial partitions, and apply constraints to the restricted boundary segment to generate the constraint exchange quantity.

[0009] Step S4: Based on the regional state table and constraint exchange quantity of each spatial partition, construct the exchange quantity balance discrete equation between the spatial partitions, and solve the exchange quantity balance discrete equation simultaneously to obtain the spatial state field.

[0010] Step S5: Compare the spatial state field with the regional state table, calculate the consistency deviation value of each spatial partition, perform subdivision and merging operations on the spatial segmentation structure based on the consistency deviation value and iterate until the stopping condition is met, and output the spatial distribution data of the spatial state field and the node layout adjustment instruction set.

[0011] As a preferred embodiment of the geographic information data analysis method based on the Internet of Things described in this invention, step S1 specifically includes:

[0012] Step S11: Obtain IoT sensing geographic measurement values ​​and target area range information;

[0013] The IoT-sensing geographic measurements include device spatial coordinates, sampling timestamps, measurement range, and device identifiers;

[0014] The target area range information includes spatial boundary coordinates, area division hierarchy information, and spatial unit identifier field;

[0015] Step S12: Perform data cleaning on the IoT-sensed geographic measurement values ​​to obtain valid measurement data;

[0016] Step S13: Perform spatial coordinate unification processing on the valid measurement data, and convert the spatial coordinates under different coordinate reference systems into a preset coordinate reference system to obtain unified coordinate measurement data;

[0017] Step S14: Perform time alignment processing on the unified coordinate measurement data and divide it according to the preset time window length. The processing logic is as follows:

[0018] The sampling timestamps in the unified coordinate measurement data are converted to a unified time base to obtain a standard time series;

[0019] The standard time series is divided into intervals according to the preset time window length, and the time axis is divided into consecutive time window intervals.

[0020] For each record in the unified coordinate measurement data, determine the corresponding time window identifier field based on the time interval in which the sampling timestamp of the record is located, and then assign the record to the corresponding time window identifier field;

[0021] For adjacent sampling timestamps under the same device identifier with a time interval greater than the preset time window length, linear interpolation is performed to complete the measurement amplitude corresponding to the missing time window identifier field according to the time order. The calculation method is as follows:

[0022] ;

[0023] in, For interpolation at time The corresponding measurement range, In time The corresponding measurement range, In time The corresponding measurement range, The target time point to be interpolated is located at time [time range]. With time between, The sampling timestamps are adjacent to the target time point and less than the target time point. The sampling timestamps are adjacent to and greater than the target time point;

[0024] Step S15: Aggregate the measured data within each time window to obtain a normalized spatiotemporal measurement table.

[0025] As a preferred embodiment of the geographic information data analysis method based on the Internet of Things described in this invention, step S2 specifically includes:

[0026] Step S21: Construct a hierarchical recursive spatial segmentation structure based on the target region range information. The processing logic is as follows:

[0027] The initial spatial range is determined by the spatial boundary coordinates in the target area range information, and the initial spatial range is recursively divided according to the area division hierarchy information until the preset division hierarchy is reached, forming a hierarchical recursive spatial segmentation structure.

[0028] Step S22: Divide the hierarchical recursive spatial partitioning structure to obtain spatial partitions, and assign a spatial partition identifier field to each spatial partition;

[0029] Step S23: Map the IoT-sensing geographic measurements in the normalized spatiotemporal measurement table to each spatial partition. The processing logic is as follows:

[0030] Based on the spatial coordinates of the devices and the spatial boundary coordinates of the spatial partitions in the IoT sensing geographic measurement, the spatial partition to which each IoT sensing geographic measurement belongs is determined, and the IoT sensing geographic measurement is assigned to the corresponding spatial partition.

[0031] Step S24: Within each spatial partition, the IoT sensing geographic measurement values ​​under the same time window identifier field are summarized and processed. The processing logic is as follows:

[0032] Under the same time window identifier field, the weighted average of the measured values ​​of IoT-sensing geographic measurements belonging to the spatial partition is calculated to obtain the IoT-sensing geographic measurement value of the spatial partition under the time window. The calculation formula is as follows:

[0033] ;

[0034] in, For the first The first time window identifier field, the first Each spatial partition identifier field corresponds to a Polymer Interconnected Sensing geographic measurement value. For the first Weighting coefficients corresponding to the geographic measurements of IoT sensing. To be classified under the time window identifier field and the spatial partition identifier field The measurement range of the IoT-sensed geographic measurements. The number of valid IoT-sensing geographic measurements that are classified under the time window identifier field and the spatial partition identifier field;

[0035] When a spatial partition has no IoT-sensing geographic measurement value under a certain time window identifier field, the IoT-sensing geographic measurement value corresponding to the adjacent spatial partition is used for interpolation to fill the gap.

[0036] Step S25: Associate the aggregated sensing geographic measurement values ​​of each spatial partition with the spatial partition identifier field and the time window identifier field to form a regional status table.

[0037] As a preferred embodiment of the geographic information data analysis method based on the Internet of Things described in this invention, step S3 specifically includes:

[0038] Step S31: In the hierarchical recursive spatial partitioning structure, determine the adjacency relationships between spatial partitions. The processing logic is as follows:

[0039] Based on the spatial boundary coordinates of each spatial partition, determine whether two spatial partitions have a common boundary. If a common boundary exists, establish the corresponding adjacent spatial partition relationship and record the corresponding boundary segment identifier.

[0040] Step S32: Based on the difference between the aggregated sensing geographic measurements corresponding to adjacent spatial partitions under the same time window identifier field, construct the pre-exchange quantity of the boundary segment. The processing logic is as follows:

[0041] Under the same time window identifier field, extract the polymer-linked sensing geographic measurements corresponding to adjacent spatial partitions, calculate the corresponding differences, and generate the pre-exchange quantity of the corresponding boundary segment based on the differences. The calculation formula is as follows:

[0042] ;

[0043] in, For the first Spatial partitioning under a time window identifier field Spatial partitioning The pre-exchange amount at the boundary between them For the first Spatial partitioning under a time window identifier field The corresponding polymer-linked sensing geographic measurements, For the first Spatial partitioning under a time window identifier field The corresponding polymer-linked sensing geographic measurements, This is a preset proportional coefficient. Where is the diffusion coefficient. This represents the equivalent exchange cross-sectional area corresponding to the boundary segment. The time window length, The characteristic distance between spatial partitions;

[0044] Step S33: Apply constraint processing to the pre-exchange quantity to obtain the constraint exchange quantity.

[0045] As a preferred embodiment of the geographic information data analysis method based on the Internet of Things described in this invention, step S33 specifically includes:

[0046] The pre-exchange amount for each boundary segment is subject to range restrictions and directional consistency adjustments to obtain the constrained exchange amount, the calculation formula of which is:

[0047] ;

[0048] in, To constrain the exchange quantity, This is the preset upper limit for the amount of data that can be exchanged.

[0049] As a preferred embodiment of the geographic information data analysis method based on the Internet of Things described in this invention, step S4 specifically includes:

[0050] Step S41: Based on the constraint exchange volume between the regional status table of each spatial partition under the identifier field of each time window and the adjacent spatial partitions, construct the exchange volume balance relationship. The processing logic is as follows:

[0051] In the Under the time window identifier field, for any spatial partition, the constraint exchange volume between it and all adjacent spatial partitions is counted, and the exchange volume income and expenditure relationship centered on that spatial partition is established.

[0052] Step S42: Construct a discrete equation for the balance of exchange volume based on the exchange volume balance relationship. The calculation expression is as follows:

[0053] ;

[0054] in, For spatial partitioning Adjacent spatial partition sets, Spatial partitioning Spatial state values, To partition space Point to the current spatial partition The constraint commutative function, Spatial partitioning Spatial state values, From spatial partitioning Point to the current spatial partition The constraint commutative function;

[0055] Step S43: Combine the discrete equations of exchange balance for all spatial partitions under each time window identifier field to obtain the exchange balance equation set.

[0056] Step S44: Solve the exchange quantity balance equation set to obtain the spatial state values ​​of each spatial partition under the time window identifier field, and organize the spatial state values ​​into a spatial state field.

[0057] As a preferred embodiment of the geographic information data analysis method based on the Internet of Things described in this invention, step S43 specifically includes:

[0058] Step S431: The exchange quantity balance discrete equations corresponding to each spatial partition under each time window identifier field are numbered and organized according to the spatial partition identifier field to obtain the equation index relationship;

[0059] Step S432: Based on the adjacency relationship between spatial partitions, fill the constraint commutative terms involving adjacent spatial partitions into the corresponding commutative equilibrium discrete equations to construct the equation coefficient relationship.

[0060] Step S433: Based on the equation index relationship and equation coefficient relationship, summarize the discrete equations of exchange balance under the same time window identifier field for all spatial partitions to obtain the exchange balance equation set under the corresponding time window identifier field.

[0061] As a preferred embodiment of the geographic information data analysis method based on the Internet of Things described in this invention, step S5 specifically includes:

[0062] Step S51: Based on the corresponding aggregated sensing geographic measurements in the spatial state field and regional state table, calculate the consistency deviation value of each spatial partition under the identifier field of each time window. The calculation is expressed as follows:

[0063] ;

[0064] in, For the first Spatial partitioning under the time window identifier field Consistency deviation value, These are the corresponding polymer-linked sensing geographic measurements in the regional status table. The value of the corresponding spatial state in the spatial state field;

[0065] Step S52: Determine the threshold for consistency deviation value, mark spatial partitions with consistency deviation values ​​greater than or equal to the preset subdivision threshold as subdivision objects, mark spatial partitions with consistency deviation values ​​less than the preset merging threshold as merging objects, perform adjacency consistency verification on spatial partitions marked as merging objects, and retain only spatial partitions with adjacent merging objects as valid merging objects.

[0066] Step S53: Perform subdivision operation on the spatial partition marked as subdivision object, perform merging operation on the effective merging object, and perform quantity conservation mapping and adjacency scale coordination rules in the subdivision operation and merging operation to obtain the updated spatial partition and its corresponding spatial partition identifier field.

[0067] Step S54: Reconstruct the hierarchical recursive spatial partitioning structure based on the updated spatial partitions and their corresponding spatial partition identifier fields, and update the adjacency relationships between spatial partitions.

[0068] Step S55: Remap the updated spatial partition to the normalized spatiotemporal measurement table, repeat steps S2 to S4 until there are no subdivision objects or merging objects, and stop the loop to obtain the spatial distribution data of the spatial state field and the node layout adjustment instruction set.

[0069] The spatial state field spatial distribution data includes a spatial partition identifier field, a time window identifier field, and spatial state values ​​corresponding to each spatial partition identifier field and time window identifier field.

[0070] The node deployment adjustment instruction set includes instructions for subdividing spatial partitions and instructions for merging spatial partitions.

[0071] As a preferred embodiment of the geographic information data analysis method based on the Internet of Things described in this invention, step S53 specifically includes:

[0072] The spatial partitions marked as subdivision objects are divided into sub-regions according to their spatial boundary coordinates to obtain sub-spatial partitions, and a new spatial partition identifier field is assigned to each sub-spatial partition.

[0073] In the subdivision operation, when there is corresponding numerical field data in the target inspection observation dataset under the corresponding time window identifier field in the subspace partition, the aggregation calculation is performed based on the observation data to obtain the aggregated measurement value of the subspace partition.

[0074] When there is no corresponding numerical field data in the target test observation dataset under the corresponding time window identifier field in the subspace partition, the aggregated measurement value of the parent space partition under the corresponding time window identifier field is mapped to the subspace partition and used as the aggregated measurement value of the subspace partition.

[0075] Define the parent space partition as the space partition that is above the subspace partition;

[0076] The geometric values ​​of the polymer-linked sensing system corresponding to each time window identifier field of the spatial partition are allocated according to the spatial coverage ratio of each sub-spatial partition, and the allocated geometric values ​​of the polymer-linked sensing system are summarized to obtain the geometric values ​​of the polymer-linked sensing system corresponding to each sub-spatial partition under each time window identifier field.

[0077] The adjacent spatial partitions marked as merging objects are merged. The merged spatial partitions are generated based on the spatial boundary coordinates of the spatial partitions involved in the merging, and a new spatial partition identifier field is assigned to the merged spatial partitions.

[0078] In the merging operation, the polymer-linked sensing geographic measurement values ​​corresponding to each time window identifier field of the spatial partitions involved in the merging are weighted and summarized to obtain the polymer-linked sensing geographic measurement values ​​corresponding to each time window identifier field of the merged spatial partitions.

[0079] After the subdivision and merging operations are completed, the subspace partitions obtained from the subdivision operation and the spatial partitions obtained from the merging operation are integrated to obtain a new set of spatial partitions.

[0080] The adjacency relationship between spatial partitions is re-determined based on the spatial boundary coordinates in the new spatial partition set, and the common boundary between adjacent spatial partitions is made consistent. The sub-spatial partitions obtained from the subdivision operation are integrated with the spatial partitions obtained from the merging operation to obtain the updated spatial partitions, and the corresponding spatial partition identifier fields are summarized.

[0081] Secondly, a geographic information data analysis system based on the Internet of Things includes a regularization module, a segmentation module, an exchange module, a solution module, and an evolution module.

[0082] The regularization module is used to acquire IoT-sensing geographic measurements and target area range information, and to perform spatiotemporal alignment and window aggregation on the IoT-sensing geographic measurements to obtain a regularized spatiotemporal measurement table.

[0083] The block module is used to map the normalized spatiotemporal measurement table to each spatial partition, and to summarize and process the data within each spatial partition to obtain a regional state table.

[0084] The exchange module is used to construct the pre-exchange quantity of the boundary segment of the spatial partition based on the regional state difference of adjacent spatial partitions in the current hierarchical recursive spatial partitioning structure, and to apply constraints to the restricted boundary segment to generate the constraint exchange quantity.

[0085] The solution module is used to construct a discrete equation for the balance of exchange quantities between spatial partitions based on the regional state table and constraint exchange quantities of each spatial partition, and to solve the discrete equation for the balance of exchange quantities simultaneously to obtain the spatial state field.

[0086] The evolution module is used to compare the spatial state field with the regional state table, calculate the consistency deviation value of each spatial partition, perform subdivision and merging operations on the spatial segmentation structure based on the consistency deviation value and iterate until the stopping condition is met, and output the spatial distribution data of the spatial state field and the node layout adjustment instruction set.

[0087] The beneficial effects of this invention are as follows: This invention innovatively introduces a discrete expression mechanism for exchange volume driven by state differences (such as concentration difference and temperature gradient), transforming isolated discrete measurement point data into dynamic exchange relationships between partitions. During the construction process, by introducing parameters such as equivalent diffusion coefficient, feature distance, and time window length, the physical correlation between exchange volume and spatiotemporal scale is achieved. Furthermore, in the equilibrium modeling stage, the spatial state field is solved by solving a set of discrete equations for commutative equilibrium. This not only overcomes the limitations of traditional pure mathematical statistical interpolation, which lacks mechanistic constraints, but also ensures that the spatial state values ​​strictly satisfy physical evolution and conservation relationships between partitions, eliminating spatial artifacts that violate physical common sense. Simultaneously, this invention constructs a spatial partitioning subdivision and merging mechanism based on consistency deviation, and combines adjacency consistency verification and quantity conservation mapping rules to achieve multi-scale iterative updates of the spatial computing grid. The grid is densified in drastically changing regions and merged in calmer regions, balancing computational efficiency and analytical accuracy. Finally, this invention creatively transforms the iterative results of spatial partitioning into a "node deployment adjustment instruction set," completely opening up a decision-making closed loop from software-side spatial analysis to underlying sensing hardware deployment. This accurately guides the dynamic addition and removal of IoT nodes, significantly reducing system operation and maintenance costs and improving the dynamic adaptability of the monitoring network. Attached Figure Description

[0088] Figure 1 A flowchart illustrating the steps of an Internet of Things-based geographic information data analysis method according to an embodiment of the present invention;

[0089] Figure 2 This is a basic flowchart of a geographic information data analysis system based on the Internet of Things, provided as an embodiment of the present invention. Detailed Implementation

[0090] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0091] Example 1, referring to Figure 1 This paper provides a geographic information data analysis method based on the Internet of Things, which includes the following steps:

[0092] Step S1: Obtain IoT-sensed geographic measurement values ​​and target area range information; perform spatiotemporal alignment and window aggregation processing on IoT-sensed geographic measurement values ​​to obtain a normalized spatiotemporal measurement value table.

[0093] Step S2: Map the normalized spatiotemporal measurement table to each spatial partition, and summarize and process it within each spatial partition to obtain the regional state table.

[0094] Step S3: In the current hierarchical recursive spatial partitioning structure, construct the pre-exchange quantity of the spatial partition boundary segment based on the regional state differences of adjacent spatial partitions, and apply constraints to the restricted boundary segment to generate the constraint exchange quantity.

[0095] Step S4: Based on the regional state table and constraint exchange quantity of each spatial partition, construct the exchange quantity balance discrete equation between the spatial partitions, and solve the exchange quantity balance discrete equation simultaneously to obtain the spatial state field.

[0096] Step S5: Compare the spatial state field with the regional state table, calculate the consistency deviation value of each spatial partition, perform subdivision and merging operations on the spatial segmentation structure based on the consistency deviation value and iterate until the stopping condition is met, and output the spatial distribution data of the spatial state field and the node layout adjustment instruction set.

[0097] In specific implementation, step S1 includes:

[0098] Step S11: Obtain IoT sensing geographic measurement values ​​and target area range information;

[0099] IoT-based geographic measurements include device spatial coordinates, sampling timestamps, measurement range, and device identification.

[0100] The target area information includes spatial boundary coordinates, area division hierarchy information, and spatial unit identifier fields;

[0101] Step S12: Perform data cleaning on the IoT-sensed geographic measurement values ​​to obtain valid measurement data;

[0102] Step S13: Perform spatial coordinate unification processing on the valid measurement data, and convert the spatial coordinates under different coordinate reference systems into a preset coordinate reference system to obtain unified coordinate measurement data;

[0103] Step S14: Perform time alignment processing on the unified coordinate measurement data and divide it according to the preset time window length. The processing logic is as follows:

[0104] The sampling timestamps in the unified coordinate measurement data are converted to a unified time base to obtain a standard time series;

[0105] The standard time series is divided into intervals according to the preset time window length, and the time axis is divided into consecutive time window intervals.

[0106] For each record in the unified coordinate measurement data, determine the corresponding time window identifier field based on the time interval in which the sampling timestamp of the record is located, and then assign the record to the corresponding time window identifier field;

[0107] For adjacent sampling timestamps under the same device identifier with a time interval greater than the preset time window length, linear interpolation is performed to complete the measurement amplitude corresponding to the missing time window identifier field according to the time order. The calculation method is as follows:

[0108] ;

[0109] in, For interpolation at time The corresponding measurement range, In time The corresponding measurement range, In time The corresponding measurement range, The target time point to be interpolated is located at time [time range]. With time between, The sampling timestamps are adjacent to the target time point and less than the target time point. The sampling timestamps are adjacent to and greater than the target time point;

[0110] Step S15: Aggregate the measured data within each time window to obtain a normalized spatiotemporal measurement table.

[0111] Specifically, the IoT-sensing geographic measurements originate from IoT devices deployed within the target area, including device spatial coordinates, sampling timestamps, measurement ranges, and device identifiers. This data is transmitted back to the data center in real time via a wireless network. The target area range information is extracted from a geographic information system or urban planning database, containing spatial boundary coordinates, regional division hierarchy information, and spatial unit identifier fields, used to define the spatial range and hierarchical structure of the analysis area. After acquiring the data, it undergoes initial data cleaning to remove outliers caused by device malfunctions, eliminate duplicate records, complete missing identifiers or timestamps, and mark or remove data that significantly deviates from physical principles, thereby ensuring the reliability and accuracy of the valid measurement data used in subsequent analysis. Next, the valid measurement data needs to undergo spatial coordinate consistency processing. This involves selecting a preset coordinate reference system. In this embodiment, WGS-84 is chosen to ensure that spatial coordinates from different sources and under different coordinate reference systems can be compared and accurately mapped, avoiding spatial misalignment caused by inconsistencies in coordinate systems. The method to achieve coordinate consistency is to call a coordinate transformation algorithm to convert the original coordinates in batches into coordinate values ​​under the preset coordinate system, generating unified coordinate measurement data. After spatial unification is completed, the data also needs to be time aligned. The sampling timestamps in the unified coordinate measurement data are converted to a unified time reference, such as Beijing time, to eliminate time zone differences and obtain a standard time series.

[0112] The standard time series is then divided into intervals according to the preset time window length, and the time axis is divided into consecutive time window intervals. Each record is assigned to the corresponding time window identifier field according to its sampling timestamp. The specific value of the preset time window length is set according to the application scenario. For example, it may be set to 5 minutes or 15 minutes in real-time monitoring, and 1 hour in daily variation analysis. This improves the regularity of the data, reduces sparsity, provides a unified time benchmark for aggregation, and facilitates subsequent spatiotemporal analysis and interpolation processing.

[0113] For adjacent sampling timestamps with time intervals exceeding the preset time window length under the same device identifier, a linear interpolation method is used to fill in the measurement amplitude of the missing time window, ensuring the continuity and integrity of the time series. Finally, the measurement data within each time window are aggregated, and the average value of the measurements within the same spatial partition is calculated within each time window to obtain the representative measurement value under that window. This ultimately generates a normalized spatiotemporal measurement table, which includes time window identifiers, spatial partition identifiers, and corresponding aggregated sensing geographic measurements, providing high-quality, normalized input data for subsequent spatial partition mapping and state field construction.

[0114] The normalized spatiotemporal measurements are shown in Table 1;

[0115] Table 1

[0116] Compared with traditional IoT data analysis methods, this application significantly improves the spatiotemporal consistency, integrity and representativeness of data through systematic spatiotemporal alignment, cleaning, interpolation and aggregation, laying a solid data foundation for subsequent complex geographic information analysis. At the same time, it enhances the method's adaptability to different devices, different coordinate systems and different sampling frequencies, thus demonstrating higher reliability and interpretability in multi-scale, multi-source IoT data fusion analysis.

[0117] In specific implementation, step S2 includes:

[0118] Step S21: Construct a hierarchical recursive spatial segmentation structure based on the target region range information. The processing logic is as follows:

[0119] The initial spatial range is determined by the spatial boundary coordinates in the target area range information, and the initial spatial range is recursively divided according to the area division hierarchy information until the preset division hierarchy is reached, forming a hierarchical recursive spatial segmentation structure.

[0120] Step S22: Divide the hierarchical recursive spatial partitioning structure to obtain spatial partitions, and assign a spatial partition identifier field to each spatial partition;

[0121] Step S23: Map the IoT-sensing geographic measurements in the normalized spatiotemporal measurement table to each spatial partition. The processing logic is as follows:

[0122] Based on the spatial coordinates of the devices and the spatial boundary coordinates of the spatial partitions in the IoT sensing geographic measurement, the spatial partition to which each IoT sensing geographic measurement belongs is determined, and the IoT sensing geographic measurement is assigned to the corresponding spatial partition.

[0123] Step S24: Within each spatial partition, the IoT sensing geographic measurement values ​​under the same time window identifier field are summarized and processed. The processing logic is as follows:

[0124] Under the same time window identifier field, the weighted average of the measured values ​​of IoT-sensing geographic measurements belonging to the spatial partition is calculated to obtain the IoT-sensing geographic measurement value of the spatial partition under the time window. The calculation formula is as follows:

[0125] ;

[0126] in, For the first The first time window identifier field, the first Each spatial partition identifier field corresponds to a Polymer Interconnected Sensing geographic measurement value. For the first Weighting coefficients corresponding to the geographic measurements of IoT sensing. To be classified under the time window identifier field and the spatial partition identifier field The measurement range of the IoT-sensed geographic measurements. The number of valid IoT-sensing geographic measurements that are classified under the time window identifier field and the spatial partition identifier field;

[0127] When a spatial partition has no IoT-sensing geographic measurement value under a certain time window identifier field, the IoT-sensing geographic measurement value corresponding to the adjacent spatial partition is used for interpolation to fill the gap.

[0128] Step S25: Associate the aggregated sensing geographic measurement values ​​of each spatial partition with the spatial partition identifier field and the time window identifier field to form a regional status table.

[0129] Specifically, the pre-defined values ​​for the hierarchical division are not fixed, but dynamically determined based on the actual range of the target area, the distribution density of IoT devices, and the analytical needs of the application scenario. For example, in urban heat island effect monitoring, it may be divided to the street level, while in watershed hydrological analysis, it may be divided to the sub-basin level. This achieves a balance between computational accuracy and resource consumption, ensuring that the spatial resolution is sufficient to capture key geographical features while avoiding excessive computational burden due to over-refinement. At the same time, it provides a multi-scale initial segmentation basis for subsequent adaptive subdivision and merging.

[0130] The process of recursively dividing the initial spatial range according to the hierarchical information of the region division involves defining the initial range using the spatial boundary coordinates in the target region range information, and then, based on the hierarchical information of the region division (such as a quadtree), dividing the current spatial unit into smaller sub-units, either uniformly or non-uniformly, layer by layer, until a preset number of levels is reached, thus forming a hierarchical recursive spatial partitioning structure. This structure is organized in a tree-like form, with each node representing a spatial partition. The parent node covers a larger area, and the child nodes cover their internal sub-regions. The identifier field of each spatial partition is usually assigned using hierarchical coding or a unique number, for example, by combining the hierarchical number, row number, and column number to generate a structure like "L". 2_3_4 The system uses either a unique identifier (UUID) or a globally unique identifier to ensure that each partition can be uniquely identified and indexed throughout the analysis process. When a spatial partition lacks IoT-sensing geographic measurements within a certain time window, interpolation is performed using IoT-sensing geographic measurements from adjacent spatial partitions. Scale interpolation is then performed using the parent partition's downscaling values, thus maintaining the continuity and integrity of the data spatially and preventing gaps in the subsequent state field construction due to missing data.

[0131] By associating and organizing the aggregated IoT-sensor geographic measurements of each spatial partition with the spatial partition identifier and time window identifier under each time window identifier field, a two-dimensional spatiotemporal data table is constructed. Each row of the table corresponds to a specific combination of time window and spatial partition, and the columns include time window identifier, spatial partition identifier, and aggregated IoT-sensor geographic measurements. In addition, auxiliary information such as partition boundary coordinates and weight coefficients can be added. This step transforms the discrete IoT sensing data into a structured and regularized regional state table, providing a standardized input format for subsequent exchange quantity calculation and state field solution.

[0132] This involves mapping IoT-sensing geographic measurements from a regularized spatiotemporal measurement table to various spatial partitions, and then weighting and summarizing these measurements within each partition according to a time window to form a structured data table. Each row of this table corresponds to a specific time window and a specific spatial partition, recording the IoT-sensing geographic measurements for that partition within that time window. Below is an example table based on the previously generated regularized spatiotemporal measurement table data, assuming four spatial partitions (P001, P002, P003, P004).

[0133] The regional status table is shown in Table 2;

[0134] Table 2

[0135] Compared with traditional methods that rely on fixed grid division or simply raw measurements for spatial analysis, this application achieves multi-scale description of geographic regions through hierarchical recursive spatial segmentation, which can adaptively adapt to different land cover densities and geographic features. By mapping measurements to spatial partitions and performing weighted aggregation, not only is data noise reduced, but regional representativeness is also improved. By interpolating between adjacent partitions to fill in missing data, the integrity of spatiotemporal data is ensured. The resulting regional state table transforms the original disordered IoT data into ordered, computable spatiotemporal field data, laying a solid foundation for the subsequent construction of physically consistent exchange balance equations and state field evolution, thereby improving the robustness, interpretability, and multi-scale adaptability of the entire geographic information data analysis method.

[0136] In specific implementation, step S3 includes:

[0137] Step S31: In the hierarchical recursive spatial partitioning structure, determine the adjacency relationships between spatial partitions. The processing logic is as follows:

[0138] Based on the spatial boundary coordinates of each spatial partition, determine whether two spatial partitions have a common boundary. If a common boundary exists, establish the corresponding adjacent spatial partition relationship and record the corresponding boundary segment identifier.

[0139] Step S32: Based on the difference between the aggregated sensing geographic measurements corresponding to adjacent spatial partitions under the same time window identifier field, construct the pre-exchange quantity of the boundary segment. The processing logic is as follows:

[0140] Under the same time window identifier field, extract the polymer-linked sensing geographic measurements corresponding to adjacent spatial partitions, calculate the corresponding differences, and generate the pre-exchange quantity of the corresponding boundary segment based on the differences. The calculation formula is as follows:

[0141] ;

[0142] in, For the first Spatial partitioning under a time window identifier field Spatial partitioning The pre-exchange amount at the boundary between them For the first Spatial partitioning under a time window identifier field The corresponding polymer-linked sensing geographic measurements, For the first Spatial partitioning under a time window identifier field The corresponding polymer-linked sensing geographic measurements, This is a preset proportional coefficient. Where is the diffusion coefficient. This represents the equivalent exchange cross-sectional area corresponding to the boundary segment. The time window length, The characteristic distance between spatial partitions;

[0143] Step S33: Apply constraint processing to the pre-exchange quantity to obtain the constraint exchange quantity.

[0144] Specifically, the value of the preset proportional coefficient needs to be determined based on the actual physical scenario and spatiotemporal discrete scale. Taking urban river water pollution diffusion monitoring as an example, an environmental protection department deploys IoT monitoring equipment on a river approximately 20 kilometers long, dividing the river into four spatial zones, each approximately 5 kilometers long, to monitor ammonia nitrogen concentration. After processing in steps S1 and S2, the regional status table for each time window is obtained. During the river pollutant diffusion process, the exchange process between adjacent spatial zones is characterized using a diffusion discrete form driven by concentration gradients. The exchange amount is related to the pollutant diffusion coefficient, the equivalent exchange cross-sectional area of ​​the boundary section, the characteristic distance of the spatial zone, and the length of the time window. For adjacent spatial zones, the pre-exchange amount is calculated as follows:

[0145] ;

[0146] The diffusion coefficient is taken as an empirical value of 50 m² / s, the spatial partition characteristic distance is taken as 5000 m, the average river width is 20 m, the average water depth is 3 m, the equivalent exchange cross-sectional area of ​​the corresponding boundary section is 60 m², and the time window length is taken as 3600 seconds.

[0147] Substitute the above parameters The corresponding scale coefficient can be obtained, which, together with the preset proportional coefficient, forms the exchange intensity of the boundary segment. Taking the time window of 08:00-09:00 as an example, the ammonia nitrogen concentrations of spatial partitions P1 and P2 are 2.5 mg / L and 1.8 mg / L, respectively. Substituting the corresponding concentration difference into the above expression, the pre-exchange amount between spatial partitions P1 and P2 is obtained. This pre-exchange amount characterizes the quality of pollutant migration driven by the concentration difference within this time window.

[0148] By clarifying the adjacency relationships between spatial partitions in a hierarchical recursive spatial segmentation structure and constructing pre-exchange quantities based on regional state differences, a quantitative description of the physical quantity exchange between adjacent partitions is achieved. This provides a physically driven input for the subsequent construction of the exchange quantity balance equation. Compared with traditional methods that rely solely on spatial interpolation or statistical smoothing, this application introduces a physical process-based exchange mechanism, which enables the evolution of the spatial state field to not only reflect the distribution of data but also the interaction between adjacent regions. This improves the physical consistency and interpretability of the analysis results and lays a solid physical foundation for the subsequent generation of constraint exchange quantities and the solution of the spatial state field.

[0149] In specific implementation, step S33 includes:

[0150] The pre-exchange amount for each boundary segment is subject to range restrictions and directional consistency adjustments to obtain the constrained exchange amount, the calculation formula of which is:

[0151] ;

[0152] in, To constrain the exchange quantity, This is the preset upper limit for the amount of data that can be exchanged.

[0153] Specifically, the upper limit of the preset exchange volume is set according to the constraints of the actual physical scenario. Taking the monitoring of urban river water pollution diffusion as an example, the range of ammonia nitrogen concentration in the river is limited by environmental capacity, monitoring equipment range, and hydrodynamic conditions, and there is an upper limit to the concentration difference between adjacent spatial zones.

[0154] Within a given time window, based on the pre-exchange amount calculation expression in step S32, the pre-exchange amount is related to the concentration difference, diffusion coefficient, equivalent exchange cross-sectional area of ​​the boundary section, spatial partition characteristic distance, and time window length. When the concentration difference reaches its maximum possible value, the corresponding theoretical maximum pre-exchange amount can be obtained. In actual calculations, the pre-exchange amount is truncated and limited to a preset upper limit. This is done as follows: when the pre-exchange amount is greater than the preset upper limit, the preset upper limit is used as the constraint exchange amount; when the pre-exchange amount is less than the preset lower limit, the preset lower limit is used as the constraint exchange amount. The value of the preset upper limit is set in conjunction with the statistical range of historical monitoring data, hydrodynamic conditions, and spatial partition scale. In the aforementioned river scenario, the theoretical exchange range can be obtained based on the combination of the historical concentration difference upper limit and diffusion parameters, and the corresponding preset upper limit is selected within this range. In other application scenarios, the setting method for the preset exchange volume upper limit is consistent with the corresponding physical process. For example, in urban heat island effect monitoring, it is set based on the temperature difference range and heat diffusion parameters; in traffic flow analysis, it is set based on the road segment flow difference range and traffic capacity parameters; and in atmospheric particulate matter diffusion analysis, it is set based on the concentration change range and wind speed scale. By imposing range constraints on the pre-exchange volume, a constrained exchange volume is formed.

[0155] In specific implementation, step S4 includes:

[0156] Step S41: Based on the constraint exchange volume between the regional status table of each spatial partition under the identifier field of each time window and the adjacent spatial partitions, construct the exchange volume balance relationship. The processing logic is as follows:

[0157] In the Under the time window identifier field, for any spatial partition, the constraint exchange volume between it and all adjacent spatial partitions is counted, and the exchange volume income and expenditure relationship centered on that spatial partition is established.

[0158] Step S42: Construct a discrete equation for the balance of exchange volume based on the exchange volume balance relationship. The calculation expression is as follows:

[0159] ;

[0160] in, For spatial partitioning Adjacent spatial partition sets, Spatial partitioning Spatial state values, To partition space Point to the current spatial partition The constraint commutative function, Spatial partitioning Spatial state values, From spatial partitioning Point to the current spatial partition The constraint commutative function;

[0161] Step S43: Combine the discrete equations of exchange balance for all spatial partitions under each time window identifier field to obtain the exchange balance equation set.

[0162] Step S44: Solve the exchange quantity balance equation set to obtain the spatial state values ​​of each spatial partition under the time window identifier field, and organize the spatial state values ​​into a spatial state field.

[0163] Specifically, the exchange volume relationship centered on spatial partitions is based on the constrained exchange volume between each spatial partition and all its adjacent partitions. Here, a spatial partition is a discrete unit obtained by dividing the target area range information, and each spatial partition serves as a collection unit for IoT sensing geographic measurements. The regional status table is a data table structure formed by filtering and summarizing IoT sensing geographic measurements within a preset time window according to spatial partitions. Each status record is matched one-to-one with the corresponding spatial partition, thereby establishing the correspondence between spatial partitions and the regional status table.

[0164] For a given time window, for each spatial partition, the sum of the constraint exchange volume flowing from that partition to adjacent partitions and the constraint exchange volume flowing from adjacent partitions to that partition is calculated. The constraint exchange volume is determined based on the adjacency relationship between adjacent spatial partitions, and its value is calculated from the difference between the IoT sensing geographic measurement at the boundary location or its aggregation form. It is used to characterize the transmission intensity and direction of spatial state between different spatial partitions.

[0165] According to the principle of physical conservation, in a steady-state or equilibrium state, the net exchange between inflows and outflows should be zero, meaning the exchange between the zone and its surrounding environment is balanced. This constitutes the discrete equation for the exchange balance of the zone, expressed as the constraint exchange between inflows and outflows from all adjacent zones equals zero. It should be noted that the expression "equals zero" is a unified representation of the local conservation relationship; it actually represents the cumulative balance of the exchange interactions between the current spatial zone and multiple adjacent zones, not merely a simple difference between two values. When constructing the discrete equation for the exchange balance, the aggregated sensing geographic measurements in the pre-exchange expression are replaced with spatial state values. Connecting these equations for all spatial zones within the same time window forms a linear system of equations. The unknowns in this system are the spatial state values ​​of each spatial zone within that time window (i.e., the corrected aggregated sensing geographic measurements), while the known quantities are the original measurements in the regional state table and the implicit difference relationships in the constraint exchange quantities. Solving this system of equations can be done using iterative methods such as Gauss-Seidel iteration or direct methods such as matrix inversion, depending on the number of partitions and computational resources. After solving, the spatial state values ​​of each spatial partition under each time window are obtained. These values ​​reflect the physical field values ​​adjusted after considering the exchange of adjacent partitions. Finally, these values ​​are organized into a structured data table, i.e., the spatial state field, according to the time window and spatial partition identifier. The constraint exchange quantity function is a functional relationship constructed based on the difference between the spatial state values ​​of adjacent spatial partitions. Its functional expression is consistent with the calculation expression of the pre-exchange quantity in step S32, and is determined in conjunction with the constraint processing rules in step S33.

[0166] By constructing and solving the exchange balance equation based on physical conservation, the final spatial state field not only depends on the original measurements but also incorporates the interactions between adjacent partitions. This ensures the continuity and physical consistency of the spatial distribution, avoids local anomalies or conservation violations that may be caused by traditional interpolation methods, and provides a reliable foundation for subsequent deviation analysis and adaptive grid evolution.

[0167] In specific implementation, step S43 includes:

[0168] Step S431: The exchange quantity balance discrete equations corresponding to each spatial partition under each time window identifier field are numbered and organized according to the spatial partition identifier field to obtain the equation index relationship;

[0169] Step S432: Based on the adjacency relationship between spatial partitions, fill the constraint commutative terms involving adjacent spatial partitions into the corresponding commutative equilibrium discrete equations to construct the equation coefficient relationship.

[0170] Step S433: Based on the equation index relationship and equation coefficient relationship, summarize the discrete equations of exchange balance under the same time window identifier field for all spatial partitions to obtain the exchange balance equation set under the corresponding time window identifier field.

[0171] It should be noted that, based on the adjacency relationships between spatial partitions, the constraint commutative terms involving adjacent spatial partitions are filled into the corresponding commutative equilibrium discrete equations to construct the equation coefficient relationships, transforming the equilibrium equation of each spatial partition into a linear expression for the unknown spatial state values. Specifically, for each spatial partition... The discrete equation for the balance of exchange quantities is expressed as follows: the sum of the constraint exchange quantities between it and all its adjacent spatial partitions is zero, that is, the constraint exchange quantities flowing into the spatial partition and the constraint exchange quantities flowing out of the spatial partition satisfy a balance relationship.

[0172] The constraint commutative quantity is constructed based on the pre-commutative quantity expression in step S32 and subjected to range truncation. When constructing the linear equation system, the truncation is considered as a limitation on the magnitude of the original commutative quantity, and a linear approximation is used. The constraint commutative quantity is expressed as a linear combination relationship between the constraint and the spatial state values ​​of adjacent spatial partitions, i.e., a linear function of the difference between the corresponding spatial state values ​​of the current spatial partition and those of adjacent spatial partitions. Based on this representation, the equilibrium equation of each spatial partition is expanded into a linear expression about the spatial state values ​​of each spatial partition, and the coefficients corresponding to each unknown are extracted. Equation index relationships and unknown index relationships are established according to the spatial partition identifier field. The corresponding coefficients are filled into the corresponding positions in the coefficient matrix, and the constant term is filled into the right-hand vector, thus constructing the linear equation system.

[0173] In the above linear expression process, the constraint commutative term does not appear directly in the commutative equilibrium discrete equation as an independent variable. Instead, it is transformed into a linear combination term concerning the spatial state values ​​of the spatial partition through its corresponding functional expression, and implicitly embedded into the discrete equation in the form of this linear combination term. Therefore, step S432, "filling the constraint commutative term into the commutative equilibrium discrete equation," specifically refers to filling the equation with the functional expansion form corresponding to the constraint commutative term, and further transforming it into the coefficient term in the coefficient matrix and the constant term in the right-hand vector.

[0174] Based on the established equation index relationship (i.e., each equation corresponds to a partition and has a unique number) and the determined coefficient relationship in each equation, the exchange balance discrete equations of all spatial partitions under the same time window identifier field are summarized. That is, the coefficient row vectors of each equation are combined into a coefficient matrix according to the equation number order, and the right-hand side terms of all equations are combined into a right-hand side vector. In this way, a system of linear equations under the corresponding time window is obtained. This system of equations describes the balance relationship that should be satisfied between the state values ​​of each spatial partition, and provides a mathematical form for subsequent solutions.

[0175] In specific implementation, step S5 includes:

[0176] Step S51: Based on the corresponding aggregated sensing geographic measurements in the spatial state field and regional state table, calculate the consistency deviation value of each spatial partition under the identifier field of each time window. The calculation is expressed as follows:

[0177] ;

[0178] in, For the first Spatial partitioning under the time window identifier field Consistency deviation value, These are the corresponding polymer-linked sensing geographic measurements in the regional status table. The value of the corresponding spatial state in the spatial state field;

[0179] Step S52: Determine the threshold for consistency deviation value, mark spatial partitions with consistency deviation values ​​greater than or equal to the preset subdivision threshold as subdivision objects, mark spatial partitions with consistency deviation values ​​less than the preset merging threshold as merging objects, perform adjacency consistency verification on spatial partitions marked as merging objects, and retain only spatial partitions with adjacent merging objects as valid merging objects.

[0180] Step S53: Perform subdivision operation on the spatial partition marked as subdivision object, perform merging operation on the effective merging object, and perform quantity conservation mapping and adjacency scale coordination rules in the subdivision operation and merging operation to obtain the updated spatial partition and its corresponding spatial partition identifier field.

[0181] Step S54: Reconstruct the hierarchical recursive spatial partitioning structure based on the updated spatial partitions and their corresponding spatial partition identifier fields, and update the adjacency relationships between spatial partitions.

[0182] Step S55: Remap the updated spatial partition to the normalized spatiotemporal measurement table, repeat steps S2 to S4 until there are no subdivision objects or merging objects, and stop the loop to obtain the spatial distribution data of the spatial state field and the node layout adjustment instruction set.

[0183] The spatial distribution data of the spatial state field includes a spatial partition identifier field, a time window identifier field, and the spatial state values ​​corresponding to each spatial partition identifier field and time window identifier field.

[0184] The node deployment adjustment instruction set includes instructions for subdividing spatial partitions and instructions for merging spatial partitions.

[0185] Specifically, the values ​​of the preset subdivision threshold and preset merging threshold need to be determined comprehensively based on the variation range of physical quantities, measurement accuracy, and analysis requirements in the actual application scenario. For example, in urban heat island effect monitoring, if the temperature measurement accuracy is 0.1℃ and the temperature difference in the hotspot area of ​​interest can reach more than 5℃, the subdivision threshold can be set to 0.5℃. That is, when the consistency deviation is greater than 0.5℃, the current partition is considered insufficient to characterize the details of temperature changes and further subdivision is required. The merging threshold can be set to 0.1℃, that is, when the deviation is less than 0.1℃, the temperature change within the partition is considered extremely gradual and can be merged with adjacent partitions. In river water quality monitoring, the ammonia nitrogen concentration variation range may be 0.1~5mg / L, with a measurement accuracy of 0.05mg / L. The subdivision threshold can be set to 0.3mg / L, and the merging threshold can be set to 0.1mg / L. The advantage of setting a subdivision threshold is that it can identify areas with drastic changes in the field. By refining the mesh, it can improve the resolution of fronts, boundaries, or anomalies, avoid the loss of key features due to insufficient resolution, identify areas with gentle changes in the field, and reduce unnecessary computational burden by merging coarsened meshes. This achieves a dynamic balance between computational accuracy and resource consumption, making adaptive mesh evolution more efficient.

[0186] For spatial partitions marked as merge objects, an adjacency consistency filter is performed. The processing logic is as follows: for each spatial partition marked as a merge object, its adjacent spatial partition set is searched to determine whether there is a spatial partition in the adjacent spatial partition set that is also marked as a merge object; if not, the merge object mark of the spatial partition is canceled; if it exists, the merge object mark of the spatial partition is retained.

[0187] The process of remapping the updated spatial partitions to the normalized spatiotemporal measurement table involves redistributing the original IoT sensing measurements (or the data aggregated in the previous round) to the newly generated spatial partitions in each iteration based on the new spatial segmentation structure. Based on spatial overlay analysis, each measurement point is assigned to a new partition according to its coordinates. For newly subdivided sub-partitions, if there are multiple measurement points in the original partition, a new weighted average is applied; if there are no measurement points in the original partition, the parent partition value can be inherited or interpolated from adjacent partitions. For the merged new partitions, the measurement values ​​in each original partition participating in the merger need to be summarized by area or weight to ensure that the data remains conserved and consistent during the spatial scale transformation. This mapping provides input for the construction of the regional state table and the calculation of exchange balance in the next round.

[0188] By introducing an adaptive grid evolution mechanism driven by consistency deviation, the spatial segmentation structure can be dynamically adjusted according to the actual changes in the physical field. While maintaining overall computational efficiency, it significantly improves the ability to resolve areas with drastic local changes. The final output spatial state field not only has multi-scale consistency, but also provides clear adjustment instructions for the optimized deployment of IoT devices. For example, it is recommended to add sensors in subdivided areas and consider reducing monitoring nodes in merged areas, thereby realizing closed-loop optimization from data to decision.

[0189] In specific implementation, step S53 includes:

[0190] The spatial partitions marked as subdivision objects are divided into sub-regions according to their spatial boundary coordinates to obtain sub-spatial partitions, and a new spatial partition identifier field is assigned to each sub-spatial partition.

[0191] In the subdivision operation, when there is corresponding numerical field data in the target inspection observation dataset under the corresponding time window identifier field in the subspace partition, the aggregation calculation is performed based on the observation data to obtain the aggregated measurement value of the subspace partition.

[0192] Define the parent space partition as the space partition that is above the subspace partition;

[0193] When there is no corresponding numerical field data in the target test observation dataset under the corresponding time window identifier field in the subspace partition, the aggregated measurement value of the parent space partition under the corresponding time window identifier field is mapped to the subspace partition and used as the aggregated measurement value of the subspace partition.

[0194] The geometric values ​​of the polymer-linked sensing system corresponding to each time window identifier field of the spatial partition are allocated according to the spatial coverage ratio of each sub-spatial partition, and the allocated geometric values ​​of the polymer-linked sensing system are summarized to obtain the geometric values ​​of the polymer-linked sensing system corresponding to each sub-spatial partition under each time window identifier field.

[0195] The adjacent spatial partitions marked as merging objects are merged. The merged spatial partitions are generated based on the spatial boundary coordinates of the spatial partitions involved in the merging, and a new spatial partition identifier field is assigned to the merged spatial partitions.

[0196] In the merging operation, the polymer-linked sensing geographic measurement values ​​corresponding to each time window identifier field of the spatial partitions involved in the merging are weighted and summarized to obtain the polymer-linked sensing geographic measurement values ​​corresponding to each time window identifier field of the merged spatial partitions.

[0197] After the subdivision and merging operations are completed, the subspace partitions obtained from the subdivision operation and the spatial partitions obtained from the merging operation are integrated to obtain a new set of spatial partitions.

[0198] The adjacency relationship between spatial partitions is re-determined based on the spatial boundary coordinates in the new spatial partition set, and the common boundary between adjacent spatial partitions is made consistent. The sub-spatial partitions obtained from the subdivision operation are integrated with the spatial partitions obtained from the merging operation to obtain the updated spatial partitions, and the corresponding spatial partition identifier fields are summarized.

[0199] Specifically, the implementation of subdivision and merging operations involves the redistribution of spatial partition identifiers, the conservation mapping of measurements during scale transformation, and the reconstruction of adjacency relationships between partitions. The core of this process is ensuring the continuity and consistency of physical quantities during spatial segmentation adjustments. When subdividing a spatial partition marked as a subdivision object, the partition is first divided into sub-regions according to preset division rules based on its spatial boundary coordinates. For example, a uniform grid quartering method can be used for rectangular partitions, while for irregular polygons, sub-partitions can be generated based on the centroid or Thiessen polygon method. Each sub-spatial partition needs to be assigned a new spatial partition identifier field, typically by adding a hierarchical suffix to the original parent partition identifier, such as subdividing parent partition P001 into P001-1, P001-2, P001-3, and P001-4, to ensure the uniqueness and traceability of the identifier. In the measurement allocation stage, the IoT-enabled geographic measurements corresponding to each time window identifier field of the original spatial partition need to be allocated according to the spatial coverage ratio of each sub-spatial partition. The spatial coverage ratio can be the area ratio or the ratio based on the measurement point density weighting. For example, if the area of ​​the sub-partition is 1 / 4 of the area of ​​the parent partition and the measurement points are evenly distributed, then the IoT-enabled geographic measurements of the parent partition are allocated to each sub-partition in a 1 / 4 ratio. If there are actually IoT measurement points in the sub-partition, the measurement value of the sub-partition can be obtained by re-weighting the measurement points in the sub-partition, so as to reflect the local features more accurately. If there are no measurement points, the parent partition value is inherited and multiplied by the area weight. After allocation, the measurement values ​​of each sub-partition under each time window need to be summarized to form the IoT-enabled geographic measurements of the sub-partition itself. For adjacent spatial partitions marked as merging objects, the merging process first requires generating merged spatial partitions based on the spatial boundary coordinates of the participating spatial partitions. The merged boundary is typically the union of the boundaries of all participating partitions, forming a new continuous polygon. The merged spatial partitions need to be assigned new spatial partition identifier fields, which can be achieved by combining existing identifiers (e.g., P001_P002) or by assigning entirely new numbers (e.g., M001). In the measurement merging stage, the aggregated geographic measurements corresponding to the participating spatial partitions under each time window identifier field are weighted and summarized. The weight can be the area percentage of each partition, the number of measurement points within the partition, or the confidence level. The calculation formula is as follows:

[0200] ;

[0201] in, For the first Under the time window identifier field, the merged spatial partitions Polymer-sensing geographic values, For the first The first time window identifier field, the first Each spatial partition identifier field corresponds to a Polymer Interconnected Sensing geographic measurement value. For the first The weighting coefficients of each spatial partition. The number of spatial partitions participating in the merger.

[0202] For example, if partition P001-1 has an area of ​​10 km² and a measured value of 23.5, and partition P001-2 has an area of ​​15 km² and a measured value of 24.2, then the area-weighted measured value after merging is (10×23.5+15×24.2) / (10+15)=23.92. After subdivision and merging, the subdivided subspace partitions and merged spatial partitions need to be integrated to form a new set of spatial partitions. The adjacency relationship is re-determined based on the spatial boundary coordinates of each partition in the new set of spatial partitions. That is, the two partitions are checked for common edges or common points through geometric calculation. If they exist, an adjacency relationship is established and the boundary segment identifier is recorded. At the same time, the common boundaries between adjacent spatial partitions need to be consistent to ensure that the boundary geometry is accurately matched. For example, the small gaps or overlaps caused by calculation errors are eliminated to ensure that the boundaries of adjacent partitions are strictly aligned. Finally, all the integrated spatial partitions and their identifiers are summarized to obtain the updated set of spatial partitions, which is used to prepare for the next round of regional state table mapping and exchange quantity calculation. This series of operations ensures the conservation of physical quantities under scale transformation and the accuracy of topological relationships between partitions during spatial segmentation structure adjustment, thus supporting stable iteration of adaptive grid evolution. The target verification observation dataset is a collection of valid measurement data formed after data cleaning, spatial coordinate unification, and time alignment of IoT-sensing geographic measurements. It is indexed and organized according to spatial partition and time window identifier fields, and used to provide observational data support for newly generated sub-spatial partitions during spatial partitioning. Numerical field data is used to characterize the values ​​of IoT-sensing geographic measurements, including measurement amplitude data and weighting coefficients related to measurement calculation. In the target verification observation dataset, numerical field data is preferably measurement amplitude data. The spatial coverage ratio is the proportion of spatial units containing valid observation data within the target spatial partition to the total number of spatial units within that spatial partition. A spatial unit is the smallest grid unit obtained by dividing the spatial partition according to spatial resolution, and the valid observation data comes from the numerical field data in the target verification observation dataset.

[0203] Example 2, refer to Figure 2 This paper presents a geographic information data analysis system based on the Internet of Things, including a normalization module, a segmentation module, an exchange module, a solution module, and an evolution module.

[0204] The regularization module is used to acquire IoT-sensing geographic measurements and target area range information, and to perform spatiotemporal alignment and window aggregation on the IoT-sensing geographic measurements to obtain a regularized spatiotemporal measurement table.

[0205] The block module is used to map the normalized spatiotemporal measurement table to each spatial partition, and to summarize and process the data within each spatial partition to obtain a regional state table.

[0206] The exchange module is used to construct the pre-exchange quantity of the boundary segment of the spatial partition based on the regional state difference of adjacent spatial partitions in the current hierarchical recursive spatial partitioning structure, and to apply constraints to the restricted boundary segment to generate the constraint exchange quantity.

[0207] The solution module is used to construct a discrete equation for the balance of exchange quantities between spatial partitions based on the regional state table and constraint exchange quantities of each spatial partition, and to solve the discrete equation for the balance of exchange quantities simultaneously to obtain the spatial state field.

[0208] The evolution module is used to compare the spatial state field with the regional state table, calculate the consistency deviation value of each spatial partition, perform subdivision and merging operations on the spatial segmentation structure based on the consistency deviation value and iterate until the stopping condition is met, and output the spatial distribution data of the spatial state field and the node layout adjustment instruction set.

[0209] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0210] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A geographic information data analysis method based on the Internet of Things, characterized in that, Includes the following steps: Step S1: Obtain IoT-sensed geographic measurement values ​​and target area range information; perform spatiotemporal alignment and window aggregation processing on IoT-sensed geographic measurement values ​​to obtain a normalized spatiotemporal measurement value table. Step S2: Map the normalized spatiotemporal measurement table to each spatial partition, and summarize and process it within each spatial partition to obtain the regional state table. Step S3: In the current hierarchical recursive spatial partitioning structure, construct the pre-exchange quantity of the spatial partition boundary segment based on the regional state differences of adjacent spatial partitions, and apply constraints to the restricted boundary segment to generate the constraint exchange quantity. Step S4: Based on the regional state table and constraint exchange quantity of each spatial partition, construct the exchange quantity balance discrete equation between the spatial partitions, and solve the exchange quantity balance discrete equation simultaneously to obtain the spatial state field. Step S5: Compare the spatial state field with the regional state table, calculate the consistency deviation value of each spatial partition, perform subdivision and merging operations on the spatial segmentation structure based on the consistency deviation value and iterate until the stopping condition is met, and output the spatial distribution data of the spatial state field and the node layout adjustment instruction set.

2. The geographic information data analysis method based on the Internet of Things as described in claim 1, characterized in that, Step S1 specifically includes: Step S11: Obtain IoT sensing geographic measurement values ​​and target area range information; The IoT-sensing geographic measurements include device spatial coordinates, sampling timestamps, measurement range, and device identifiers; The target area range information includes spatial boundary coordinates, area division hierarchy information, and spatial unit identifier field; Step S12: Perform data cleaning on the IoT-sensed geographic measurement values ​​to obtain valid measurement data; Step S13: Perform spatial coordinate unification processing on the valid measurement data, and convert the spatial coordinates under different coordinate reference systems into a preset coordinate reference system to obtain unified coordinate measurement data; Step S14: Perform time alignment processing on the unified coordinate measurement data and divide it according to the preset time window length. The processing logic is as follows: The sampling timestamps in the unified coordinate measurement data are converted to a unified time base to obtain a standard time series; The standard time series is divided into intervals according to the preset time window length, and the time axis is divided into consecutive time window intervals. For each record in the unified coordinate measurement data, determine the corresponding time window identifier field based on the time interval in which the sampling timestamp of the record is located, and then assign the record to the corresponding time window identifier field; For adjacent sampling timestamps under the same device identifier with a time interval greater than the preset time window length, linear interpolation is performed to complete the measurement amplitude corresponding to the missing time window identifier field according to the time order. The calculation method is as follows: ； in, For interpolation at time The corresponding measurement range, In time The corresponding measurement range, In time The corresponding measurement range, The target time point to be interpolated is located at time [time range]. With time between, The sampling timestamps are adjacent to the target time point and less than the target time point. The sampling timestamps are adjacent to and greater than the target time point; Step S15: Aggregate the measured data within each time window to obtain a normalized spatiotemporal measurement table.

3. The geographic information data analysis method based on the Internet of Things as described in claim 2, characterized in that, Step S2 specifically includes: Step S21: Construct a hierarchical recursive spatial segmentation structure based on the target region range information. The processing logic is as follows: The initial spatial range is determined by the spatial boundary coordinates in the target area range information, and the initial spatial range is recursively divided according to the area division hierarchy information until the preset division hierarchy is reached, forming a hierarchical recursive spatial segmentation structure. Step S22: Divide the hierarchical recursive spatial partitioning structure to obtain spatial partitions, and assign a spatial partition identifier field to each spatial partition; Step S23: Map the IoT-sensing geographic measurements in the normalized spatiotemporal measurement table to each spatial partition. The processing logic is as follows: Based on the spatial coordinates of the devices and the spatial boundary coordinates of the spatial partitions in the IoT sensing geographic measurement, the spatial partition to which each IoT sensing geographic measurement belongs is determined, and the IoT sensing geographic measurement is assigned to the corresponding spatial partition. Step S24: Within each spatial partition, the IoT sensing geographic measurement values ​​under the same time window identifier field are summarized and processed. The processing logic is as follows: Under the same time window identifier field, the weighted average of the measured values ​​of IoT-sensing geographic measurements belonging to the spatial partition is calculated to obtain the IoT-sensing geographic measurement value of the spatial partition under the time window. The calculation formula is as follows: ； in, For the first The first time window identifier field, the first Each spatial partition identifier field corresponds to a Polymer Interconnected Sensing geographic measurement value. For the first Weighting coefficients corresponding to the geographic measurements of IoT sensing. To be classified under the time window identifier field and the spatial partition identifier field The measurement range of the IoT-sensed geographic measurements. The number of valid IoT-sensing geographic measurements that are classified under the time window identifier field and the spatial partition identifier field; When a spatial partition has no IoT-sensing geographic measurement value under a certain time window identifier field, the IoT-sensing geographic measurement value corresponding to the adjacent spatial partition is used for interpolation to fill the gap. Step S25: Associate the aggregated sensing geographic measurement values ​​of each spatial partition with the spatial partition identifier field and the time window identifier field to form a regional status table.

4. The geographic information data analysis method based on the Internet of Things as described in claim 3, characterized in that, Step S3 specifically includes: Step S31: In the hierarchical recursive spatial partitioning structure, determine the adjacency relationships between spatial partitions. The processing logic is as follows: Based on the spatial boundary coordinates of each spatial partition, determine whether two spatial partitions have a common boundary. If a common boundary exists, establish the corresponding adjacent spatial partition relationship and record the corresponding boundary segment identifier. Step S32: Based on the difference between the aggregated sensing geographic measurements corresponding to adjacent spatial partitions under the same time window identifier field, construct the pre-exchange quantity of the boundary segment. The processing logic is as follows: Under the same time window identifier field, extract the polymer-linked sensing geographic measurements corresponding to adjacent spatial partitions, calculate the corresponding differences, and generate the pre-exchange quantity of the corresponding boundary segment based on the differences. The calculation formula is as follows: ； in, For the first Spatial partitioning under a time window identifier field Spatial partitioning The pre-exchange amount at the boundary between them For the first Spatial partitioning under a time window identifier field The corresponding polymer-linked sensing geographic measurements, For the first Spatial partitioning under a time window identifier field The corresponding polymer-linked sensing geographic measurements, This is a preset proportional coefficient. Where is the diffusion coefficient. This represents the equivalent exchange cross-sectional area corresponding to the boundary segment. The time window length, The characteristic distance between spatial partitions; Step S33: Apply constraint processing to the pre-exchange quantity to obtain the constraint exchange quantity.

5. The geographic information data analysis method based on the Internet of Things as described in claim 4, characterized in that, Step S33 specifically includes: The pre-exchange amount for each boundary segment is subject to range restrictions and directional consistency adjustments to obtain the constrained exchange amount, the calculation formula of which is: ； in, To constrain the exchange quantity, This is the preset upper limit for the amount of data that can be exchanged.

6. The geographic information data analysis method based on the Internet of Things as described in claim 5, characterized in that, Step S4 specifically includes: Step S41: Based on the constraint exchange volume between the regional status table of each spatial partition under the identifier field of each time window and the adjacent spatial partitions, construct the exchange volume balance relationship. The processing logic is as follows: In the Under the time window identifier field, for any spatial partition, the constraint exchange volume between it and all adjacent spatial partitions is counted, and the exchange volume income and expenditure relationship centered on that spatial partition is established. Step S42: Construct a discrete equation for the balance of exchange volume based on the exchange volume balance relationship. The calculation expression is as follows: ； in, For spatial partitioning Adjacent spatial partition sets, Spatial partitioning Spatial state values, To partition space Point to the current spatial partition The constraint commutative function, Spatial partitioning Spatial state values, From spatial partitioning Point to the current spatial partition The constraint commutative function; Step S43: Combine the discrete equations of exchange balance for all spatial partitions under each time window identifier field to obtain the exchange balance equation set. Step S44: Solve the exchange quantity balance equation set to obtain the spatial state values ​​of each spatial partition under the time window identifier field, and organize the spatial state values ​​into a spatial state field.

7. The geographic information data analysis method based on the Internet of Things as described in claim 6, characterized in that, Step S43 specifically includes: Step S431: The exchange quantity balance discrete equations corresponding to each spatial partition under each time window identifier field are numbered and organized according to the spatial partition identifier field to obtain the equation index relationship; Step S432: Based on the adjacency relationship between spatial partitions, fill the constraint commutative terms involving adjacent spatial partitions into the corresponding commutative equilibrium discrete equations to construct the equation coefficient relationship. Step S433: Based on the equation index relationship and equation coefficient relationship, summarize the discrete equations of exchange balance under the same time window identifier field for all spatial partitions to obtain the exchange balance equation set under the corresponding time window identifier field.

8. The geographic information data analysis method based on the Internet of Things as described in claim 7, characterized in that, Step S5 specifically includes: Step S51: Based on the corresponding aggregated sensing geographic measurements in the spatial state field and regional state table, calculate the consistency deviation value of each spatial partition under the identifier field of each time window. The calculation is expressed as follows: ； in, For the first Spatial partitioning under the time window identifier field Consistency deviation value, These are the corresponding polymer-linked sensing geographic measurements in the regional status table. The value of the corresponding spatial state in the spatial state field; Step S52: Determine the threshold for consistency deviation value, mark spatial partitions with consistency deviation values ​​greater than or equal to the preset subdivision threshold as subdivision objects, mark spatial partitions with consistency deviation values ​​less than the preset merging threshold as merging objects, perform adjacency consistency verification on spatial partitions marked as merging objects, and retain only spatial partitions with adjacent merging objects as valid merging objects. Step S53: Perform subdivision operation on the spatial partition marked as subdivision object, perform merging operation on the effective merging object, and perform quantity conservation mapping and adjacency scale coordination rules in the subdivision operation and merging operation to obtain the updated spatial partition and its corresponding spatial partition identifier field. Step S54: Reconstruct the hierarchical recursive spatial partitioning structure based on the updated spatial partitions and their corresponding spatial partition identifier fields, and update the adjacency relationships between spatial partitions. Step S55: Remap the updated spatial partition to the normalized spatiotemporal measurement table, repeat steps S2 to S4 until there are no subdivision objects or merging objects, and stop the loop to obtain the spatial distribution data of the spatial state field and the node layout adjustment instruction set. The spatial state field spatial distribution data includes a spatial partition identifier field, a time window identifier field, and spatial state values ​​corresponding to each spatial partition identifier field and time window identifier field. The node deployment adjustment instruction set includes instructions for subdividing spatial partitions and instructions for merging spatial partitions.

9. The geographic information data analysis method based on the Internet of Things as described in claim 8, characterized in that, Step S53 specifically includes: The spatial partitions marked as subdivision objects are divided into sub-regions according to their spatial boundary coordinates to obtain sub-spatial partitions, and a new spatial partition identifier field is assigned to each sub-spatial partition. In the subdivision operation, when there is corresponding numerical field data in the target inspection observation dataset under the corresponding time window identifier field in the subspace partition, the aggregation calculation is performed based on the observation data to obtain the aggregated measurement value of the subspace partition. Define the parent space partition as the space partition that is above the subspace partition; When there is no corresponding numerical field data in the target test observation dataset under the corresponding time window identifier field in the subspace partition, the aggregated measurement value of the parent space partition under the corresponding time window identifier field is mapped to the subspace partition and used as the aggregated measurement value of the subspace partition. The geometric values ​​of the polymer-linked sensing system corresponding to each time window identifier field of the spatial partition are allocated according to the spatial coverage ratio of each sub-spatial partition, and the allocated geometric values ​​of the polymer-linked sensing system are summarized to obtain the geometric values ​​of the polymer-linked sensing system corresponding to each sub-spatial partition under each time window identifier field. The adjacent spatial partitions marked as merging objects are merged. The merged spatial partitions are generated based on the spatial boundary coordinates of the spatial partitions involved in the merging, and a new spatial partition identifier field is assigned to the merged spatial partitions. In the merging operation, the polymer-linked sensing geographic measurement values ​​corresponding to each time window identifier field of the spatial partitions involved in the merging are weighted and summarized to obtain the polymer-linked sensing geographic measurement values ​​corresponding to each time window identifier field of the merged spatial partitions. After the subdivision and merging operations are completed, the subspace partitions obtained from the subdivision operation and the spatial partitions obtained from the merging operation are integrated to obtain a new set of spatial partitions. The adjacency relationship between spatial partitions is re-determined based on the spatial boundary coordinates in the new spatial partition set, and the common boundary between adjacent spatial partitions is made consistent. The sub-spatial partitions obtained from the subdivision operation are integrated with the spatial partitions obtained from the merging operation to obtain the updated spatial partitions, and the corresponding spatial partition identifier fields are summarized.

10. A geographic information data analysis system based on the Internet of Things (IoT), applied in a geographic information data analysis method based on the IoT as described in any one of claims 1-9, characterized in that, It includes a regularization module, a block division module, a swapping module, a solution module, and an evolution module; The regularization module is used to acquire IoT-sensing geographic measurements and target area range information, and to perform spatiotemporal alignment and window aggregation on the IoT-sensing geographic measurements to obtain a regularized spatiotemporal measurement table. The block module is used to map the normalized spatiotemporal measurement table to each spatial partition, and to summarize and process the data within each spatial partition to obtain a regional state table. The exchange module is used to construct the pre-exchange quantity of the boundary segment of the spatial partition based on the regional state difference of adjacent spatial partitions in the current hierarchical recursive spatial partitioning structure, and to apply constraints to the restricted boundary segment to generate the constraint exchange quantity. The solution module is used to construct a discrete equation for the balance of exchange quantities between spatial partitions based on the regional state table and constraint exchange quantities of each spatial partition, and to solve the discrete equation for the balance of exchange quantities simultaneously to obtain the spatial state field. The evolution module is used to compare the spatial state field with the regional state table, calculate the consistency deviation value of each spatial partition, perform subdivision and merging operations on the spatial segmentation structure based on the consistency deviation value and iterate until the stopping condition is met, and output the spatial distribution data of the spatial state field and the node layout adjustment instruction set.

Images