Method for constructing super-resolution urban model based on multi-source heterogeneous spatio-temporal data

Abstract

This invention discloses a method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data, specifically relating to the fields of urban digital twins and geographic information databases. The method includes acquiring urban spatiotemporal records; reading the time observation values ​​corresponding to the collection location from the geographic information database according to the source code; calculating the geographic grid number and intra-grid coordinates from the collection location; calling the grid-based operator according to the source code to convert the time observation values ​​into grid values ​​and writing them into a base grid; and outputting a grid-based record table. Using the target grid number in the grid-based record table as the read key, the method reads the extended grid values ​​from the base grid; mapping the intra-grid coordinates to a grid value field indexed by the source code based on an implicit neural representation algorithm; and by uniformly incorporating multi-source urban spatiotemporal records into the geographic information database, generating a grid value field and calculating the query attribution value using an implicit neural representation algorithm, and then using the attribution residual to trigger Riemann gradient descent for sub-grid number boundary optimization and digital twin query simulation verification.

Description

Technical Field

[0001] This invention relates to the field of urban digital twin and geographic information database technology, and more specifically, to a method for constructing a super-resolution urban model based on multi-source heterogeneous spatiotemporal data. Background Technology

[0002] Existing urban digital twin model constructions mostly aim to improve the accuracy of surface representation and unify multi-source geographic data. The common process is to perform coordinate registration, vector-raster transformation and scale normalization on remote sensing images, POI vector data and traffic flow data, then overlay various rasterized layers with remote sensing images to form fusion features, use super-resolution networks to generate high-resolution reconstruction results, and build an urban grid model with spatial index and attribute fields based on this. In densely populated areas such as commercial streets in old city districts, subway station entrances and exits, and transportation hubs, a low-resolution grid often contains building boundaries, road service areas, street-side POI entrances, and vehicle turning positions. However, urban digital twin platforms need to accurately return POI attribution, road-related objects, and traffic activity locations based on spatial indexes, and cannot rely solely on visual clarity to judge the effectiveness of the grid. In this scenario, when relying solely on image super-resolution and multi-channel overlay to generate fine meshes, it is easy to produce results such as the same spatial index returning multiple candidate objects, POI entrances falling into road grids, and vehicle turnaround points falling into building boundary grids. This is because the generation of fine meshes is based on image texture enhancement rather than on object attribution ambiguity in spatial queries. The technical problem this application aims to solve is: how to trigger dynamic grid subdivision and spatial index encoding based on spatial query ambiguity when constructing a super-resolution city model from multi-source heterogeneous spatiotemporal data, thereby avoiding object attribution errors in the high-resolution city digital twin model. Summary of the Invention

[0003] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data. This method unifies multi-source urban spatiotemporal records into a geographic information database, uses an implicit neural representation algorithm to generate a lattice value field and calculate the query attribution value, and then uses the attribution residual to trigger Riemann gradient descent sublattice boundary optimization and digital twin query simulation verification to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data, comprising: S1. Obtain urban spatiotemporal records. Read the time observation value corresponding to the collection location from the geographic information database according to the source code. Calculate the geographic grid number and grid coordinates from the collection location. Call the grid operator according to the source code to convert the time observation value into grid value and write it into the basic grid. Output the grid record table. S2. Using the target cell number in the grid record table as the read key, read the extended cell value from the basic grid. Based on the implicit neural representation algorithm, map the cell coordinates to the cell value field of the source code index. Calculate the object attribution function from the cell value field, generate the query attribution value, and output the query simulation result. S3. Based on the query simulation results, subtract the query assignment values ​​of any two coordinates within the target grid number and take the absolute value. Accumulate the absolute values ​​to obtain the assignment residual. If the assignment residual is zero, write it into the definite grid table; if it is not zero, write it into the ambiguous grid table and record the coordinates of the grid that produced the non-zero difference. S4. For grid numbers in the ambiguous grid table, the subgrid number boundary is used as the Riemann manifold optimization variable with the meaning of dynamic grid design. The Riemann gradient descent algorithm is executed with the assignment residual as the objective function. The subgrid number boundary is updated according to the Riemann gradient. The algorithm stops when the assignment residuals of two adjacent rounds are equal. The output is a set of subgrid numbers containing high-resolution reconstruction blocks. S5. Based on the subgrid set, perform digital twin query simulation verification in the geographic information database. If the attribution residual is zero, write it into the spatial index code with fusion attributes. If it is not zero, write it back into the ambiguous grid table to generate the digital twin model of the target city.

[0005] In a preferred embodiment, S1 includes: S11. Obtain urban spatiotemporal records. Using the record number as the key, read the source code, collection location, collection time, and time observation value from the geographic information database. Perform coordinate transformation on the collection location according to the coordinate reference registered in the geographic information database. Then, subtract the database start time from the collection time and divide by the database time granularity, taking the integer quotient to obtain the standard spatiotemporal record. Urban spatiotemporal records refer to single observation records that can be written into a geographic information database under the same coordinate reference. The record number is used for unique indexing, the source code is used to determine the grid placement rules, the collection location is used to calculate the geographic grid number and grid coordinates, the collection time is used to calculate the time position, and the time observation value is used to convert the grid value. A geographic information database refers to a database that stores urban spatiotemporal records, coordinate benchmarks, database origin, grid side length, database time granularity, spatial index coding, basic grid and grid record table. It can perform spatial positioning according to the collection location, perform spatial retrieval according to the geographic grid number, and perform temporal writing according to the time position. The source code refers to the type code assigned when the urban spatiotemporal record is entered into the database. It is used to identify the spatial carrying form corresponding to the observation value at a given time and points to the lattice operator that matches the spatial carrying form. The time-based observation value refers to the numerical observation result of the urban spatiotemporal record at the time of collection, which is used to convert it into the grid value written into the basic grid according to the grid ratio in the grid operator; The time granularity of the database refers to the time quantification unit registered in the geographic information database, which is used to convert the time difference between the collection time and the database start time into integer time bits. S12. Based on the collection location in the standard spatiotemporal record, subtract the x-coordinate of the library origin from the x-coordinate of the collection location, divide by the grid side length, and take the integer quotient to obtain the horizontal grid number. Subtract the y-coordinate of the library origin from the y-coordinate of the collection location, divide by the grid side length, and take the integer quotient to obtain the vertical grid number. Encode the horizontal and vertical grid numbers into geographic grid numbers according to the order of the Hilbert curve, and generate the grid coordinates from the remainder of the two divisions. Output the location record. The grid edge length refers to the boundary length of a single grid cell in the basic grid under the coordinate reference, which is used to convert the acquisition location into the horizontal grid number, vertical grid number and grid coordinates; The origin of the grid refers to the starting coordinate point of the basic grid under the coordinate datum, and is used as the coordinate zero point for calculating the horizontal grid number, vertical grid number, and coordinates within the grid. The process of encoding into a geographic grid number is as follows: First, convert the horizontal grid number and the vertical grid number into binary grid numbers of the same width. Then, recursively deduce the curve order bit by bit according to the quadrant rotation and quadrant flipping rules of the Hilbert curve. Finally, use the one-dimensional order number obtained by recursion as the geographic grid number. S13. Read the grid-based operator from the source code in the location record, and determine the record geometry according to the spatial carrying form corresponding to the source code; for point-type records, generate the grid percentage based on whether the collection location falls into the grid cell corresponding to the geographic grid number; for line-type records, calculate the intersection length of the record geometry and the grid cell, and divide the intersection length by the record geometry length to obtain the grid percentage; for area-type records, calculate the intersection area of ​​the record geometry and the grid cell, and divide the intersection area by the record geometry area to obtain the grid percentage; then multiply the time observation value by the grid percentage to obtain the grid value, write the grid value into the basic grid according to the location record, and output the grid-based record table; The grid-based operator refers to the grid value conversion rule that is indexed by the source code and corresponds to the grid carrying form of the recording space. The grid-based operator reads the horizontal lower boundary, horizontal upper boundary, vertical lower boundary, and vertical upper boundary of the grid cell corresponding to the geographic grid number, and determines the recording geometry according to the source code. The point-type record determines whether it intersects with the grid cell based on the positional relationship between the collection location and the above four boundaries. The line-type record calculates the length of the line segment that makes up the recording geometry into the grid cell as the intersection length. The area-type record calculates the area of ​​the closed region formed by the recording geometry falling into the grid cell as the intersection area. The point-type record generates the grid ratio based on whether it falls into the grid cell. The line-type record generates the grid ratio by dividing the intersection length by the recording geometry length. The area-type record generates the grid ratio by dividing the intersection area by the recording geometry area. The grid ratio is used to convert the time-based observation value into a grid value.

[0006] In a preferred embodiment, S2 includes: S21. Using the target grid number as the read key, read the grid records corresponding to the target grid number and the outer ring grid number from the geographic information database. Generate query points according to the grid coordinates in the grid records. Generate training samples according to the query points, source codes and grid values. Write empty value positions when the grid record is missing the grid value corresponding to the source code. Output the training sample table. Among them, a grid record refers to a single grid value record stored in the basic grid with the geographic grid number as the index. It includes at least the geographic grid number, the coordinates within the grid, the source code, the grid value, and the time position. It is used to represent the spatial location, data source, numerical result, and time position of the urban spatiotemporal record after it has been converted and written into the grid cell. The process of generating training samples is as follows: using the coordinates within the grid in the grid record as the query point, concatenating the query point with the source code in the same grid record as the sample input, and using the grid value in the same grid record as the sample output; when the same query point is missing a grid value corresponding to a certain source code, write an empty value after the sample input corresponding to the query point and the source code, and use the empty value for S22 to calculate the backfill error; S22. Execute the implicit neural representation algorithm based on the training sample table. Take the query point as the coordinate input, the source code as the condition input, and the grid value as the supervision value. Calculate the squared difference between the coordinate network output value and the grid value, the squared difference between the empty value backfill value and the adjacent grid value with the same source code, and the squared difference between the output values ​​on both sides of the common edge of adjacent grid numbers. Accumulate these values ​​as the field loss. Calculate the gradient of the coordinate network parameters according to the field loss and update them. Stop when the field loss of two adjacent rounds is equal. Output the grid value field. The coordinate network output value refers to the predicted grid value calculated by the coordinate network after the implicit neural representation algorithm is given a query point and source code. It is used to subtract the grid value in the training sample and form the grid value reconstruction error. The backfill value for empty positions refers to the predicted grid value calculated by the coordinate network for sample inputs with empty positions. It is used to replace the position in the grid record where the corresponding grid value of the source code is missing, and the difference between it and the adjacent grid value of the same source code forms the backfill error. Coordinate network parameters refer to the updatable parameters involved in fitting the lattice field in the implicit neural representation algorithm, including coordinate encoding weights, source code embedding weights, and network mapping weights. Among them, network mapping weights include intermediate mapping weights used to form intermediate mapping values ​​from the network input vectors, and output weights used to form predicted lattice values ​​from the intermediate mapping values. The coordinate encoding weights, source code embedding weights, intermediate mapping weights, and output weights together determine the mapping relationship between the query point and the source code to the coordinate network output value. The process of calculating and updating the gradient is as follows: calculate the partial derivatives of the coordinate network parameters with respect to the field loss, use the partial derivatives as the gradient, subtract the product of the learning step size and the gradient from the coordinate network parameters to obtain the updated coordinate network parameters, and recalculate the field loss with the updated coordinate network parameters until the field loss of two adjacent rounds is equal.

[0007] In a preferred embodiment, S2 further includes: S23. Calculate the belonging edge cost between query points based on the grid value field. Add the absolute values ​​of the difference between the grid value fields of the same source code of the two query points to obtain the same source cost. Add the absolute values ​​of the difference between the grid value fields of different source codes of the two query points to obtain the different source cost. Subtract the different source cost from the same source cost to obtain the belonging edge cost. Write the belonging edge cost into the intra-grid query graph. S24. In the intra-cell query graph, starting from each query point in the target cell number, the cost of the belonging edge is accumulated to obtain the path cost. Query points with equal path costs are written to the same belonging number. The belonging numbers obtained by the same query point under different source codes are subtracted and the absolute value is taken to obtain the conflict value. S25. When the conflict value is zero, use the attribution number as the query attribution value and output the query simulation result; when the conflict value is not zero, delete the corresponding source code input and re-execute S22 to S24, subtract the conflict value obtained again from the conflict value before deletion and take the absolute value as the correction difference, and output the query simulation result when the correction difference is zero.

[0008] In a preferred embodiment, S3 includes: S31. Using the query simulation results as input, read the grid coordinates, time position and query value in the geographic information database according to the target grid number, concatenate the target grid number, time position and grid coordinates in the field order to form a coordinate sequence number, and output the coordinate assignment table. The time bit refers to the integer quotient obtained by subtracting the database start time from the collection time and then dividing by the database time granularity. It is used to represent the discrete time position of the city's spatiotemporal records in the geographic information database. S32. Perform a self-join based on the coordinate attribution table and the target cell number. Keep the join rows where the binary value of the left coordinate number is less than the binary value of the right coordinate number. Subtract the two query attribution values ​​in the join row and take the absolute value. Output the attribution difference table.

[0009] In a preferred embodiment, S3 further includes: S33. Based on the attribution difference table, sum all the absolute values ​​according to the target cell number to obtain the attribution residual, and reverse the two coordinate numbers in the connected row with non-zero absolute values ​​to the cell coordinates, and output the residual record. S34. Using the residual record as the writing object, when the assigned residual is zero, write the target grid number, assigned residual, and coordinate sequence number into the defined grid table; when the assigned residual is not zero, write the target grid number, assigned residual, and grid coordinates into the ambiguous grid table.

[0010] In a preferred embodiment, S4 includes: S41. Using the grid number in the ambiguous grid table as input, read the grid number boundary, grid coordinates, query the assignment value and assignment residual from the geographic information database. For the two grid coordinates that produce non-zero differences, calculate the absolute value of the horizontal difference and the absolute value of the vertical difference respectively. When the absolute value of the horizontal difference minus the absolute value of the vertical difference is non-negative, take the average of the two horizontal coordinates to generate the vertical sub-grid number boundary. When the absolute value of the horizontal difference minus the absolute value of the vertical difference is negative, take the average of the two vertical coordinates to generate the horizontal sub-grid number boundary. Output the boundary initial value table. The grid boundary refers to the closed spatial range corresponding to the target grid in the basic grid of the geographic information database. It is calculated from the database origin, grid side length, horizontal grid number and vertical grid number, and is used to limit the endpoint coordinates of the sub-grid boundary and the clipping range of the basic grid. S42. Generate a boundary variable table based on the initial boundary value table. Write each sub-grid boundary as endpoint coordinates. When the endpoint coordinates fall outside the grid boundary, project them to the grid boundary. When the two endpoint coordinates coincide, delete the corresponding sub-grid boundary. Output a boundary variable table that satisfies the grid boundary constraints. The process of projecting to the grid boundary is as follows: the x-coordinate of the endpoint coordinate is compared with the lower and upper horizontal boundaries of the grid boundary. If the x-coordinate is lower than the lower horizontal boundary, it is rewritten as the lower horizontal boundary; if it is higher than the upper horizontal boundary, it is rewritten as the upper horizontal boundary. The y-coordinate of the endpoint coordinate is compared with the lower and upper vertical boundaries of the grid boundary. If the y-coordinate is lower than the lower vertical boundary, it is rewritten as the lower vertical boundary; if it is higher than the upper vertical boundary, it is rewritten as the upper vertical boundary. When both the x-coordinate and y-coordinate are within the corresponding boundaries, the endpoint coordinate remains unchanged.

[0011] In a preferred embodiment, S4 further includes: S43. Divide the target grid number according to the boundary variable table, write the coordinates in each grid into the subgrid coordinate table according to the subgrid number, subtract the query belonging value of any two grid coordinates in the same subgrid number and take the absolute value, accumulate the absolute value to get the subgrid residual, and then accumulate the subgrid residuals according to the subgrid number to get the boundary target value. S44. Using the boundary target value as the objective function of the Riemann gradient descent algorithm, add a unit coordinate perturbation to the endpoint coordinates of each sub-grid boundary and recalculate the boundary target value. Subtract the boundary target value before perturbation from the perturbation boundary target value to obtain the endpoint gradient. Project the endpoint gradient onto the tangential direction of the grid boundary to obtain the Riemann gradient, and update the endpoint coordinates in reverse according to the Riemann gradient. The process of projecting the endpoint gradient onto the tangential direction of the lattice boundary to obtain the Riemann gradient is as follows: when the endpoint coordinates are inside the lattice boundary, the endpoint gradient is directly used as the Riemann gradient; when the endpoint coordinates are on the vertical boundary of the lattice boundary, the horizontal component of the endpoint gradient is deleted and the vertical component is retained as the Riemann gradient; when the endpoint coordinates are on the horizontal boundary of the lattice boundary, the vertical component of the endpoint gradient is deleted and the horizontal component is retained as the Riemann gradient; when the endpoint coordinates are at the corner of the lattice boundary, the endpoint gradient is set to zero and used as the Riemann gradient. S45. Re-execute S43 to S44 based on the updated endpoint coordinates. Stop updating when the boundary target values ​​of two adjacent rounds are equal. Write the subgrid boundary at the time of stopping the update into the geographic information database, and cut the base grid according to the subgrid boundary to output a set of subgrids containing high-resolution reconstructed blocks.

[0012] In a preferred embodiment, S5 includes: S51. Taking the set of subgrid numbers as input, read the subgrid number boundaries and high-resolution reconstruction blocks in the geographic information database according to the subgrid number, write the coordinates of the cells within the high-resolution reconstruction blocks that fall into the subgrid number boundaries into the verification coordinate table, and read the query belonging value according to the subgrid number, cell coordinates and source code, and output the subgrid verification table. The high-resolution reconstruction block refers to the subdivided spatial data block obtained by cutting the subgrid boundary from the base grid. It contains the coordinates within the subgrid, source code, grid value and query belonging value, and is used to generate fusion attributes and perform digital twin query simulation verification. S52. Perform a self-join based on the subcell validation table by subcell number, retain the join rows where the binary value of the coordinate in the left cell is less than the binary value of the coordinate in the right cell, subtract the two query belonging values ​​in the join row and take the absolute value, accumulate all the absolute values ​​by subcell number to obtain the subcell belonging residual, and output the subcell residual table.

[0013] In a preferred embodiment, S5 includes: S53. For the cell number in the cell residual table where the cell belonging to the residual is zero, shift the binary value of the parent cell number to the left by the cell bit width to obtain the parent cell code. Write the binary value of the cell number in sequence into the low bit of the parent cell code to obtain the spatial index code. Then, sum the cell values ​​under the same spatial index code according to the source code and divide by the number of source codes to obtain the fusion attribute. Output the index record. S54. For the cell number in the cell residual table where the cell attribution residual is not zero, the coordinates of the two cells in the connection row with non-zero absolute values, the cell number and the cell attribution residual will be written back to the ambiguous grid table, and the index records will be written into the geographic information database according to the spatial index encoding to generate the digital twin model of the target city.

[0014] The technical effects and advantages of this invention are as follows: This scheme uses attribution residuals as the basis for dynamic subdivision, shifting mesh generation from texture enhancement to query ambiguity response, thereby reducing POI, road and traffic activity attribution errors in the spatial index of densely populated areas. By uniformly writing the source code, time position, geographic grid number and grid coordinates into the grid record table, multi-source spatiotemporal data are included in the same geographic information database caliber, which relatively improves the data consistency of grid value field construction. By performing implicit neural representation mapping on the cell coordinates and source code, a cell value field of the source code index is formed and a query attribution value is generated, which relatively alleviates the cell query break caused by sparse cell spacing. In ambiguous grids, the subgrid boundary is used as the Riemannian manifold optimization variable and iteratively updated according to the boundary objective value, which relatively improves the computability and verifiability of dynamic mesh subdivision. The digital twin query simulation verification is performed based on the subgrid number set. After the verification is successful, the spatial index code and fusion attribute are written, which reduces the risk of erroneous spatial indexes entering the model. Attached Figure Description

[0015] Figure 1 This is a flowchart outlining the method steps of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Refer to the instruction manual appendix Figure 1 The present invention provides a method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data, comprising: S1. Obtain urban spatiotemporal records. Read the time observation value corresponding to the collection location from the geographic information database according to the source code. Calculate the geographic grid number and grid coordinates from the collection location. Call the grid operator according to the source code to convert the time observation value into grid value and write it into the basic grid. Output the grid record table. Before urban spatiotemporal records are written into the basic grid, it is necessary to complete the unified processing of spatiotemporal caliber, spatial index and grid value caliber. This implementation uses the record number as the sole entry point for urban spatiotemporal records. The source code, collection location, collection time, and time observation value are read from the geographic information database. First, the collection location is converted to the target coordinate reference registered in the database, and the collection time is converted into time units. Then, geographic grid numbers and grid coordinates are generated based on the database origin and grid side length. Finally, the time observation value is converted into grid values ​​according to the grid-based operator corresponding to the source code and written into the basic grid, thus forming a grid record table for subsequent implicit neural representation algorithms to read. This implementation process includes the following steps: When standardizing urban spatiotemporal records for database entry, the processing action starts with the unique index of the record number. S11 uses the record number as the key to read the source code, collection location, collection time and time observation value from the geographic information database, and reads the source coordinate benchmark based on the source code. At the same time, it reads the target coordinate benchmark, database start time and database time granularity registered in the geographic information database. The coordinate transformation of the acquisition position is performed sequentially by origin correction, axial correction, scale correction and projection conversion. Origin correction rewrites the acquisition position according to the translation amount from the source coordinate reference to the target coordinate reference. Axial correction rewrites the coordinate axis direction of the acquisition position according to the rotation amount. Scale correction rewrites the distance ratio of the acquisition position according to the scale factor. Projection conversion obtains the acquisition position under the target coordinate reference according to the projection parameters. The time position calculation for the acquisition time uses the database start time as the time zero point. First, the acquisition time is subtracted from the database start time to obtain the time difference. Then, the time difference is divided by the database time granularity and the integer quotient is used to obtain the time position. The geographic information database writes the record number, source code, converted acquisition location, time position, and time observation value into a standard spatiotemporal record for S12 to calculate the geographic grid number and grid coordinates. When the collection location is missing, the record number is written into the location missing table and the corresponding city spatiotemporal record filling process is stopped. When the collection time is missing, the record number is written into the time missing table and the time bit calculation is stopped. When the source code cannot match the source coordinate reference, the record number is written into the source code exception table. After the standard spatiotemporal record enters the basic grid positioning stage, S12 converts the collection location into a searchable geographic grid number and grid coordinates. The geographic information database reads the collection location, time position, source code and time observation value from the standard spatiotemporal record, and reads the database origin and grid side length. When performing lateral positioning, the lateral offset is obtained by subtracting the lateral coordinate of the origin from the lateral coordinate of the acquisition location. The lateral offset is divided by the grid side length and the integer quotient is used to obtain the lateral grid number. The remainder after dividing the lateral offset by the grid side length is used as the lateral coordinate within the grid. During vertical positioning, the vertical offset is obtained by subtracting the vertical coordinate of the origin from the vertical coordinate of the acquisition location. The vertical offset is divided by the grid side length and the integer quotient is used to obtain the vertical grid number. The remainder after dividing the vertical offset by the grid side length is used as the vertical coordinate within the grid. When encoding geographic grid numbers, the horizontal and vertical grid numbers are converted into binary grid numbers of the same width, and the curve order is recursively calculated bit by bit according to the quadrant rotation and quadrant flipping rules of the Hilbert curve. The one-dimensional order number obtained by recursion is written as the geographic grid number. The geographic information database writes the geographic grid number, the horizontal coordinate within the grid, the vertical coordinate within the grid, the time position, the source code, and the time observation value into a location record, which is then used by S13 to perform grid value conversion. When a horizontal or vertical grid number exceeds the registration range of the basic grid, the record number and the direction of the boundary violation are written into the boundary violation record table, and the generation of positioning records is stopped. After the location record specifies the geographic grid number into which the urban spatiotemporal record falls, S13 converts the time observation value into grid value according to the spatial carrying form corresponding to the source code. The geographic information database reads the source code, collection location, geographic grid number, grid coordinates, time position and time observation value from the location record, and reads the grid-falling operator with the source code. The grid operator first determines the recording geometry. When the source code points to a point-type record, the collection position is used as the recording geometry. When the source code points to a line-type record, the line segment connecting adjacent collection positions is used as the recording geometry. When the source code points to a surface-type record, the surface boundary corresponding to the collection position is used as the recording geometry. When the grid operator calculates the intersection measure, it first reads the horizontal lower bound, horizontal upper bound, vertical lower bound, and vertical upper bound of the grid cell corresponding to the current geographic grid number, and adopts the boundary assignment rule of assigning the horizontal lower bound and vertical lower bound to the current grid cell, and assigning the horizontal upper bound and vertical upper bound to the adjacent grid cell, so as to avoid the position located on the common edge being calculated repeatedly. For point-based records, read the x-coordinate and y-coordinate of the collection location, compare the x-coordinate with the lower and upper horizontal boundaries of the grid cell in sequence, and compare the y-coordinate with the lower and upper vertical boundaries of the grid cell in sequence. When the x-coordinate is between the lower and upper horizontal boundaries and the y-coordinate is between the lower and upper vertical boundaries, write the intersection measure as one; otherwise, write the intersection measure as zero and directly use the intersection measure as the grid percentage. For linear records, two adjacent acquisition positions are designated as the start and end points of the line segment, respectively. The coordinates of the start point are marked as the first x-coordinate and the first y-coordinate, and the coordinates of the end point are marked as the second x-coordinate and the second y-coordinate. The position between the start and end points is represented by a line segment position parameter that varies from zero to one. The first x-coordinate is added to the product of the line segment position parameter and the difference between the second and first x-coordinates to obtain the parameter x-coordinate. The first y-coordinate is added to the product of the line segment position parameter and the difference between the second and first y-coordinates to obtain the parameter y-coordinate. The position parameter intervals that place the parameter x-coordinate between the lower and upper horizontal boundaries of the grid cell and the parameter y-coordinate between the lower and upper vertical boundaries of the grid cell are calculated. The common interval between the two position parameter intervals and the parameter range from zero to one is taken. If there is no common interval, the intersection length is written as zero. If there is a common interval, the start and end parameters of the common interval are read. The intersection length is obtained by multiplying the parameter difference (the end parameter minus the start parameter) by the line segment length. The grid proportion of the linear record is then obtained by dividing the intersection length by the record geometry length. When the two x-coordinates of a line segment are the same, the position parameter range is no longer calculated using the x-coordinates. When the x-coordinate is within the horizontal boundary of the grid cell, the parameter range of zero to one is retained. When it is outside the horizontal boundary of the grid cell, the intersection length is written as zero. When the two y-coordinates of a line segment are the same, the parameter range is determined according to the vertical boundary in the same way. For facet records, the vertices of the facet boundary are arranged into a vertex sequence according to the boundary order. The vertex sequence is then trimmed using the lower horizontal bound, upper horizontal bound, lower vertical bound, and upper vertical bound of the lattice cell. When two adjacent vertices are both inside the current boundary, the next vertex is retained. When the previous vertex is inside and the next vertex is outside, the intersection of the line connecting the two vertices with the current boundary is written. When the previous vertex is outside and the next vertex is inside, the intersection of the line connecting the two vertices with the current boundary and the next vertex are written in sequence. When both vertices are outside, the corresponding vertices are not written. After processing the four boundaries, the intersection vertex sequence formed by the intersection of the record geometry and the lattice cell is obtained. When the intersection vertex sequence has fewer than three vertices, the intersection area is written as zero; when the intersection vertex sequence has more than three vertices, the x-coordinate of each vertex is multiplied by the y-coordinate of the next vertex in the order of the vertices, and the results are accumulated. At the same time, the y-coordinate of each vertex is multiplied by the x-coordinate of the next vertex, and the results are accumulated. The difference between the two accumulated results is taken, the absolute value is taken, and then divided by two to obtain the intersection area. The vertex after the last vertex is read from the first vertex. Then, the intersection area is divided by the recorded geometric area to obtain the grid ratio of the face shape record. After completing the above calculations, the grid operator outputs the grid percentage corresponding to the current recorded geometry and the current grid cell. The time observation value is multiplied by the grid percentage to obtain the grid value under the current geographic grid number. Then, the grid cell corresponding to the geographic grid number is read, the intersection measure of the record geometry and the grid cell is calculated, and the grid ratio is generated according to the spatial carrying form of the record geometry. When the collection position falls into the grid cell, the grid ratio is written as one, and when the collection position does not fall into the grid cell, the grid ratio is written as zero. For linear records, the grid ratio is obtained by dividing the intersection length by the record geometry length. For area records, the grid ratio is obtained by dividing the intersection area by the record geometry area. After multiplying the time observation value by the grid ratio to obtain the grid value, the geographic information database generates a write key according to the geographic grid number, time position, source code and grid coordinates, writes the grid value into the basic grid and outputs the grid record table; If the write key already exists, the newly added cell value will be added to the total cell value field of the same write key, and the write count will be incremented by one to retain multiple observation results under the same geographic cell number, the same time position, the same source code, and the same cell coordinates; Through the above processing, the urban spatiotemporal record is converted into a standard spatiotemporal record, a location record, and a grid record table. The collection location, collection time, and time observation value respectively form the location field under the target coordinate reference, the time position field in the geographic information database, and the grid value field in the basic grid. Subsequent steps can directly read the extended grid value according to the target grid number and construct the grid value field. In practical applications: After the trajectory records near the entrance of urban roads are entered into the geographic information database, the source code, collection location, collection time and time observation value are read from the record number. The collection location is converted to the target coordinate reference, and the collection time is converted into time bits. Then, the geographic grid number and grid coordinates are obtained according to the origin and grid side length; the intersection length of the trajectory line segment and the grid cell is converted into the grid ratio according to the source code, and the time observation value is converted into grid value and written into the basic grid. When there is already a grid value for the same write key, the cumulative write is performed so that subsequent query simulations can read the complete spatial activity intensity.

[0018] S2. Using the target cell number in the grid record table as the read key, read the extended cell value from the basic grid. Based on the implicit neural representation algorithm, map the cell coordinates to the cell value field of the source code index. Calculate the object attribution function from the cell value field, generate the query attribution value, and output the query simulation result. The key to S2's processing is to transform the discrete cell values ​​in the cell record table into a continuously queryable cell value field, and to use the cell value field to deduce the query belonging value within the target cell number. This implementation first reads the cell records corresponding to the outermost ring cell number based on the target cell number, and organizes the cell coordinates, source code, and cell value into a training sample table; then, it fits the cell value field of the source code index using an implicit neural representation algorithm; subsequently, it constructs an intra-cell query graph based on the cell value field, calculates the belonging edge cost, path cost, and conflict value, so that the query simulation results can reflect the spatial belonging differences within the target cell number; this implementation process includes the following steps: The target grid number serves as the data reading entry point for S2. S21 uses the target grid number as the reading key to read the horizontal and vertical grid numbers corresponding to the target grid number from the geographic information database. The horizontal grid number is incremented by one, remains unchanged, and is decremented by one, respectively. The vertical grid number is incremented by one, remains unchanged, and is decremented by one, respectively. The combined results are used to obtain the outer ring grid number. After removing grid numbers that exceed the boundary of the basic grid, read the grid records corresponding to the target grid number and the outermost ring grid number. The grid records include the geographic grid number, grid coordinates, source code, grid value, and time position. The query point is generated by deduplicating the coordinates within the grid record. When the same coordinate within the grid exists for multiple source codes, the same query point is retained and multiple training samples are generated according to the source code. When generating training samples, the query point and the source code are concatenated as the sample input, and the grid value is written as the sample output. When the grid value corresponding to a certain source code is missing at the same query point, an empty value is written after the sample input corresponding to that source code at that query point. The training sample table is written into the geographic information database for S22 to calculate grid value reconstruction error, empty value backfilling error and common edge consistency error; When neither the target cell number nor the outermost ring cell number has a record, write the target cell number into an empty sample table and stop the query simulation for that target cell number. After the query points and source codes in the training sample table enter the implicit neural representation algorithm, S22 calculates the coordinate network output value with the query points as coordinate input, the source codes as conditional input, and the grid values ​​as supervision values. The calculation process of the coordinate network output value is as follows: the horizontal and vertical coordinates of the query point are converted into coordinate encoded values ​​according to the granularity of the grid coordinates, and the coordinate encoded values ​​are written into the coordinate features; the source code embedding weights are read using the source code as an index, and the encoded values ​​corresponding to the source code embedding weights are written into the source condition features; the coordinate features and source condition features are concatenated into the network input vector in the order of the fields; the network mapping layer reads the intermediate mapping weights in the network input vector and the network mapping weights; assuming that the network input vector contains multiple input components in sequence, and that the network mapping layer contains multiple intermediate mapping positions, for each intermediate mapping position, each input component is multiplied by the intermediate mapping weight corresponding to the intermediate mapping position, and all multiplications are accumulated to obtain the intermediate mapping value of the intermediate mapping position, and the intermediate mapping value sequence is formed according to the order of the intermediate mapping positions; The output layer reads the intermediate mapping value sequence and the output weights in the network mapping weights. Each intermediate mapping position corresponds to an output weight. Each intermediate mapping value is multiplied by the corresponding output weight, and all multiplications are summed to obtain the output total value. The output total value is written as the predicted grid value under the current query point and the current source code according to the numerical precision registered in the grid value field of the training sample table, and the predicted grid value is written as the coordinate network output value. The number of intermediate mapping positions and the corresponding intermediate mapping weights and output weights are read from the training parameter table before the start of this round of grid field training, and the correspondence remains unchanged during the execution of S22; when the field loss is updated in reverse, the intermediate mapping weights and output weights are used as network mapping weights to participate in the partial derivative calculation and parameter update; The output value of the coordinate network is the predicted grid value obtained by the coordinate network after a given query point and source code. It is used to subtract and square the grid value in the training sample to form the grid value reconstruction error. For sample inputs with empty positions, the coordinate network calculates the empty position backfill value and reads the cell value of the first and second position of the query point coordinate sequence under the same source code. If both sides exist, the average of the two is taken as the adjacent cell value. If one side exists, the cell value of one side is taken. If both sides are missing, the empty position backfill error is not calculated. Then, the difference between the empty position backfill value and the adjacent cell value is squared to form the backfill error. For query points on both sides of the common edge of adjacent grid numbers, calculate the square of the difference between the output values ​​on both sides of the common edge to form the common edge consistency error; The grid reconstruction error, backfilling error, and common edge consistency error are accumulated to form the field loss. The geographic information database reads the learning step size from the training parameter table, calculates the partial derivatives of the coordinate encoding weight, source code embedding weight, and network mapping weight with respect to the field loss, and subtracts the product of the learning step size and the corresponding partial derivative from each parameter to obtain the updated coordinate network parameters. The updated coordinate network parameters are used to recalculate the field loss. The field loss is written into the loss field according to the number of decimal places registered in the geographic information database. The update stops when the loss fields of two adjacent rounds are equal. The grid value field of the source code index is output. The object attribution function is a mapping relationship for calculating the query attribution value from the lattice value field, and it does not belong to a new model that is trained separately. The object attribution function takes the query point in the target lattice number, the lattice value field of the source code index, the attribution edge cost, the path cost value, and the conflict value as input, and the query attribution value as output. During the calculation, the same source cost is first generated based on the difference in the lattice value fields of two query points under the same source code, and then the different source cost is generated based on the difference in the lattice value fields of two query points under different source codes. The attribution edge cost is obtained by subtracting the different source cost from the same source cost, and the path cost value is formed by accumulating the attribution edge costs, thereby generating the attribution number, conflict value, and query attribution value. After the grid value field is formed, S23 does not directly output the query attribution value, but first converts the grid value difference between query points into searchable graph edge cost; During execution, the geographic information database reads the query point and source code in the grid value field according to the target grid number, takes the absolute value of the difference between the grid value fields of two query points under the same source code, and accumulates them according to the source code to obtain the same source cost; Then, take the absolute value of the difference between the grid values ​​of the two query points under different source codes, and sum them up according to the source code combination to obtain the heterogeneous cost; The cost of belonging edge is obtained by subtracting the cost of dissimilar origin from the cost of same origin, and the coordinates and cost of belonging edge of the two query points are written into the query graph within the cell. To avoid the impact of loop paths on path value, the intra-cell query graph only retains adjacent edges in the direction of increasing coordinate indices. Adjacent edges are generated by query points with adjacent coordinate indices within the same target cell. When two query points lack comparable source codes, the corresponding adjacent edges are written to the missing edge table and are not included in the path cost calculation. After the cost of the belonging edge is entered into the query graph within the cell, S24 takes each query point in the target cell as the starting point and accumulates the cost of the belonging edge edge along the direction of increasing coordinate number to obtain the path cost from the starting point to the subsequent query point. The path value is written into the path field according to the number of decimal places registered in the geographic information database, and query points with the same path field are written into the same attribution number; When the same query point obtains the attribution number under different source codes, the difference between the attribution numbers under different source codes and the absolute value are taken to obtain the conflict value; Simultaneously, the number of times the source code participates in forming a conflict value is accumulated to obtain the source code conflict count, which is then used by S25 to select the source code to be deleted. When a query point has no reachable adjacent edges, a unique ownership number is written to the query point, and an orphan mark is written to the path field; The conflict value is used to determine whether the query simulation result has reached the consistency of the source code. S25 reads the conflict value, the attribution number and the source code conflict count. When the conflict value is zero, the attribution number is used as the query attribution value and the query simulation result is output. When the conflict value is not zero, select the source code input in descending order of source code conflict count. When the source code conflict count is the same, select the source code input in ascending order of source code binary value. Delete the selected source code input and re-execute S22 to S24. The difference between the newly obtained conflict value and the conflict value before deletion is taken as the absolute value to obtain the correction difference. When the correction difference is zero, the query simulation result is output. If the correction difference is not zero, continue to delete the next source code input and re-execute S22 to S24 until the correction difference is zero or the source code input is completely deleted. If conflicting values ​​still exist after the source code input has been deleted, output the query simulation results with conflict markers for S3 to continue calculating the attribution residuals; Through the above processing, S2 converts the cell records within the target cell number and the outer ring cell number into a training sample table, cell value field, cell query graph and query simulation results. This preserves the cell value differences between source codes and marks query points that are difficult to be assigned to in a consistent manner through conflict values ​​and correction differences, providing verifiable input for subsequent calculation of assignment residuals. In practical applications: After reading the grid records from different source codes near the entrance of the commercial street in the old city, the system first deduplicates the grid coordinates to find the query point, and writes the position of the grid value corresponding to the missing source code into the null value position. Subsequently, the implicit neural representation algorithm fits the lattice value field with the query point and the source code, and maintains the boundary coherence between the outer ring lattice numbers through the common edge consistency error; the lattice value field then generates the intra-lattice query graph, and the system calculates the attribution number according to the attribution edge cost. If the same query point has different attribution numbers under different source codes, the conflict source is deleted according to the source code conflict count and recalculated. Finally, the query simulation results that can enter S3 are output.

[0019] S3. Based on the query simulation results, subtract the query assignment values ​​of any two coordinates within the target grid number and take the absolute value. Accumulate the absolute values ​​to obtain the assignment residual. If the assignment residual is zero, write it into the definite grid table; if it is not zero, write it into the ambiguous grid table and record the coordinates of the grid that produced the non-zero difference. The calculation of the attribution residual plays a filtering role: keeping the target grid numbers that have been assigned to the same grid in the query simulation results in the definite grid table, and sending the target grid numbers that still have spatial attribution disagreements to the ambiguous grid table. This implementation method revolves around "coordinate number generation, pairwise assignment comparison, residual convergence, and grid splitting," enabling S4 to process only truly ambiguous grid numbers and avoiding sub-grid boundary optimization for all target grid numbers. The implementation process includes the following steps: The generation of coordinate sequence numbers first solves a retrieval problem, namely, the coordinates within the same target cell need to be converted into a sortable, concatenable, and reverse-searchable database key; S31 reads the target grid number, grid coordinates, time position, and query attribute value from the query simulation results. The time position is obtained by subtracting the database start time from the collection time and dividing by the database time granularity. The grid coordinates are quantized according to the grid coordinate granularity registered in the geographic information database. The grid horizontal coordinate is divided by the grid coordinate granularity and the integer quotient is obtained to get the grid horizontal position. The grid vertical coordinate is divided by the grid coordinate granularity and the integer quotient is obtained to get the grid vertical position. Then, the field widths of the target cell number, time position, horizontal position and vertical position within the cell are read and registered. Each field is padded with zeros according to the registered width, and then concatenated into a coordinate sequence number in the order of target cell number, time position, horizontal position within the cell and vertical position within the cell. The geographic information database writes the target grid number, coordinate sequence number, grid coordinates, time position, and query attribute value into the coordinate attribute table for subsequent self-join comparison and reading. When the cell coordinates cannot be quantized at the cell coordinate granularity, the target cell number and cell coordinates are written into the coordinate quantization anomaly table, and the cell coordinates are stopped from participating in the attribution residual calculation. Pairwise comparisons do not use traversal repetitive enumeration, but instead rely on the self-join of the coordinate attribution table to retain only unidirectional connection relationships; S32 copies the coordinate assignment table under the same target cell number into a left table and a right table, uses the same target cell number as the connection condition, and compares the binary values ​​of the left coordinate number and the right coordinate number. Connecting rows whose left coordinate index is less than their right coordinate index are retained, while connecting rows whose left coordinate index is equal to or greater than their right coordinate index are discarded. For the retained join rows, read the left query attribution value and the right query attribution value, subtract the right query attribution value from the left query attribution value and take the absolute value to obtain the attribution difference item; The attribution difference item, along with the target cell number, left coordinate number, right coordinate number, left query attribution value, and right query attribution value, is written into the attribution difference table for residual accumulation and reading. When there is only one coordinate number under the same target grid number, the attribution difference table is written with an empty connection marker so that the attribution residual can be written to zero in the next step. The attribution difference term already provides the pairwise differences between the queried attribution values. S33 further compresses the pairwise differences into attribution residuals at the target cell number level and retains the in-cell coordinates that cause ambiguity. The geographic information database reads the attribution difference table according to the target cell number. When the attribution difference table contains attribution difference items, all attribution difference items under the same target cell number are summed to obtain the attribution residual. When the attribution difference table contains an empty join mark, the attribution residual is written as zero. Then, filter the connected rows where the difference in attribution is not zero, look up the left and right coordinate numbers in the coordinate attribution table, read the corresponding cell coordinates, and write the cell coordinates obtained from the lookup into the ambiguous coordinates field. The target grid number, the associated residual, the coordinate sequence number, and the ambiguous coordinate field are collectively written into a residual record for grid splitting and reading. When the coordinate sequence number cannot be found in the cell, the coordinate sequence number is written to the reverse lookup anomaly table, and the corresponding attribution difference item is retained to participate in the attribution residual accumulation. Grid splitting relies on the attribution residuals in the residual records and no longer rereads the grid field or training sample table; S34 reads the target cell number, attribution residual, coordinate sequence number and ambiguous coordinate field from the residual record. When the attribution residual is zero, it writes the target cell number, attribution residual and coordinate sequence number into the defined grid table and marks the target cell number as an accessible spatial index code. When the attribution residual is not zero, write the target cell number, attribution residual, and ambiguous coordinate fields into the ambiguous grid table, and mark the target cell number as entering the sub-cell number boundary optimization. When a historical write record already exists for the same target grid number, an append key is generated using the time position and coordinate sequence number to append the residual record of this round, thus avoiding the overwriting of the ownership status under different time positions; At this point, the query simulation results have been converted from coordinate-level query attribution values ​​to grid-level attribution residuals, and the grid table and ambiguous grid table have been determined to take unambiguous grid numbers and ambiguous grid numbers respectively; the subsequent steps can read the ambiguous grid table to obtain the target grid number, attribution residual and ambiguous coordinate field that need to be optimized. In practical applications: When there are multiple query points in the target grid number where the entrance of a city road is located, the system first quantizes the coordinates in the grid and concatenates them into coordinate numbers, and then compares the query belonging values ​​corresponding to any two coordinate numbers through self-connection. If the sum of all the attribution differences is zero, the target grid number is entered into the defined grid table; if there are non-zero attribution differences, the system reverses the lookup to generate the grid coordinates of the non-zero difference and writes them into the ambiguous grid table for the subsequent Riemann gradient descent algorithm to generate the sub-grid boundary.

[0020] S4. For grid numbers in the ambiguous grid table, the subgrid number boundary is used as the Riemann manifold optimization variable with the meaning of dynamic grid design. The Riemann gradient descent algorithm is executed with the assignment residual as the objective function. The subgrid number boundary is updated according to the Riemann gradient. The algorithm stops when the assignment residuals of two adjacent rounds are equal. The output is a set of subgrid numbers containing high-resolution reconstruction blocks. The ambiguous grid table has exposed the coordinates within the target grid that produce non-zero differences. Implementation S4 generates subgrid boundaries around these ambiguous coordinates and iteratively updates these subgrid boundaries as Riemannian manifold optimization variables, thus dividing the target grid into a set of subgrids whose attribution residuals can be re-examined. This implementation process includes the following steps: The grid numbers given in the ambiguous grid table are first restored to spatial ranges with boundaries. S41 takes the grid numbers in the ambiguous grid table as input and reads the grid number boundaries, grid coordinates, query assignment values ​​and assignment residuals from the geographic information database. The grid boundary is calculated from the origin, grid side length, horizontal grid number and vertical grid number. The horizontal lower boundary is the horizontal coordinate of the origin plus the horizontal grid number multiplied by the grid side length. The horizontal upper boundary is the horizontal lower boundary plus the grid side length. The vertical lower boundary is the vertical coordinate of the origin plus the vertical grid number multiplied by the grid side length. The vertical upper boundary is the vertical lower boundary plus the grid side length. For two cell coordinates that produce a non-zero difference, calculate the absolute value of the difference between the two x-coordinates and the absolute value of the difference between the two y-coordinates respectively; When the absolute value of the difference between the horizontal and vertical sides is not negative, the mean of the two horizontal coordinates is taken as the horizontal coordinate of the vertical subgrid boundary, and the lower and upper vertical boundaries of the grid boundary are taken as the vertical coordinates of the two endpoints. When the absolute value of the horizontal difference minus the absolute value of the vertical difference is negative, the mean of the two vertical coordinates is taken as the vertical coordinate of the horizontal sub-cell boundary, and the lower and upper horizontal boundaries of the cell boundary are taken as the horizontal coordinates of the two endpoints; the sub-cell boundary generated in this way is written into the boundary initial value table. When the query attribute value cannot be read for the same ambiguous coordinate pair, the corresponding cell coordinates are written to the boundary initial value exception table and the ambiguous coordinate pair is skipped. Before the initial boundary values ​​are optimized, the grid number boundary constraints must be met. S42 reads the initial boundary value table, writes each sub-grid number boundary as two endpoint coordinates, and checks whether the endpoint coordinates fall into the grid number boundary. When the x-coordinate of the endpoint is lower than the lower horizontal bound, it is rewritten as the lower horizontal bound; when it is higher than the upper horizontal bound, it is rewritten as the upper horizontal bound. When the y-coordinate of the endpoint is lower than the lower vertical bound, it is rewritten as the lower vertical bound; when it is higher than the upper vertical bound, it is rewritten as the upper vertical bound. When both the x-coordinate and y-coordinate are within the corresponding boundaries, the endpoint coordinates remain unchanged. When the coordinates of two endpoints coincide, delete the corresponding sub-cell boundary; when the endpoint coordinates of two sub-cell boundaries are exactly the same, merge them into one sub-cell boundary. When the vertical subgrid boundary intersects with the horizontal subgrid boundary, the two subgrid boundaries are cut off at the intersection point, and the coordinates of the cut endpoints are written into the boundary variable table. The endpoint coordinates in the boundary variable table constitute the Riemannian manifold optimization variables, the lattice boundary constitutes the feasible region, and the sum of squared differences of endpoint coordinates serves as the manifold metric. After the target grid number is divided by the boundary variable table, S43 recalculates the assigned residual according to the sub-grid number. In specific execution, the target grid number is divided into multiple sub-grid numbers according to each sub-grid number boundary. The geographic information database reads the grid coordinates within the target grid number one by one and writes them into the sub-grid coordinate table according to the sub-grid number where the grid coordinates are located. For any two coordinates within the same sub-cell number in the sub-cell coordinate table, read the corresponding query value, subtract the two query values ​​and take the absolute value to obtain the sub-cell difference term, and then sum all sub-cell difference terms within the sub-cell number to obtain the sub-cell residual. The sum of the subcell residuals of all subcell numbers is written as the boundary target value; if there is only one cell coordinate in a certain subcell number, the subcell residual of that subcell number is written as zero. If the coordinates within a cell fall on the boundary of a sub-cell, then write the sub-cell number that comes first in ascending order of sub-cell number to avoid the same coordinates within a cell being counted repeatedly. The update amount of the Riemann gradient descent algorithm is given by the perturbation change of the boundary target value to the endpoint coordinates. S44 uses the boundary target value as the objective function and reads the grid coordinate granularity registered in the geographic information database as a unit coordinate perturbation. For the endpoint coordinates of each subgrid boundary, first add a one-unit coordinate perturbation to the horizontal coordinate of the endpoint and re-execute S43 to obtain the boundary target value after horizontal perturbation. Subtract the boundary target value before perturbation from the boundary target value after horizontal perturbation to obtain the horizontal endpoint gradient. Then add a one-unit coordinate perturbation to the vertical coordinate of the endpoint and re-execute S43 to obtain the boundary target value after vertical perturbation. Subtract the boundary target value before perturbation from the boundary target value after vertical perturbation to obtain the vertical endpoint gradient. When the endpoint coordinates are inside the grid boundary, the horizontal endpoint gradient and the vertical endpoint gradient together serve as the Riemann gradient. When the endpoint coordinates are on the vertical boundary, delete the horizontal component and retain the vertical component; when the endpoint coordinates are on the horizontal boundary, delete the vertical component and retain the horizontal component. When the endpoint coordinates are located at the corner of the grid boundary, the endpoint gradient is set to zero; then the endpoint coordinates are updated in reverse along the Riemann gradient using the granularity of the grid coordinates as the update step size, and the endpoint projection is performed again after the update. Whether the iteration has ended is determined by the boundary target value field. S45 re-executes S43 to S44 based on the updated endpoint coordinates and writes the boundary target value of each round into the target value field according to the number of decimal places registered in the geographic information database. When the target value fields of two adjacent rounds are equal, the update stops and the sub-cell boundary at the time of stopping the update is written to the geographic information database. The subcell numbers are generated in ascending order of the x-coordinate of the centroid of the subcell number; if the x-coordinates are the same, they are generated in ascending order of the y-coordinate of the centroid. Then, the base grid is clipped according to the subgrid number boundary. When the base grid cell intersects with the subgrid number boundary, the ratio of the intersection area to the area of ​​the base grid cell is calculated, and the clipping grid value is obtained by multiplying the grid value by this ratio. The subgrid number, subgrid number boundary, clipping grid value, grid coordinates, source code and query belonging value are written into the high-resolution reconstruction block. Finally, the set of subgrid numbers containing the high-resolution reconstruction block is output. If a subcell boundary cannot form a closed subcell, the corresponding boundary variable is written to the boundary closure exception table, and the boundary variables that could form a closed subcell in the previous round are retained. The above process transforms the non-zero difference coordinates in the ambiguous grid table into subgrid boundaries, and continuously reduces the boundary target value formed by the accumulation of subgrid residuals through the Riemann gradient descent algorithm, so that the subgrid set has the spatial boundaries and high-resolution reconstruction blocks required for subsequent digital twin query simulation verification. In practical applications: When the entrance to a city road and the boundary of a building fall on the same target grid number, the coordinates of the two cells recorded in the ambiguous grid table will generate an initial sub-grid boundary. The system updates the endpoint coordinates with Riemann gradients based on the assigned residuals until the boundary target value fields of two adjacent rounds are equal. The subgrid boundaries after the update is stopped are written into the geographic information database, and high-resolution reconstructed blocks are cut from the base grid for S5 to continue performing spatial index coding and digital twin query simulation verification.

[0021] S5. Based on the subgrid set, perform digital twin query simulation verification in the geographic information database. If the attribution residual is zero, write it into the spatial index code with fusion attribute. If it is not zero, write it back into the ambiguous grid table to generate the digital twin model of the target city. After the subgrid set enters S5, the focus of processing shifts to digital twin query simulation verification and spatial index encoding generation; This implementation method uses subgrid boundaries and high-resolution reconstructed blocks as the verification basis. First, it recalculates the query attribution value within the subgrid in the geographic information database. Then, it calculates the subgrid attribution residual using a self-join method. Subsequently, it generates spatial index codes and fusion attributes for unambiguous subgrids, and writes back the ambiguous grid table for subgrids that still have ambiguity, ensuring that the target city's digital twin model only receives spatial index records verified through query simulation. This implementation process includes the following steps: The high-resolution reconstruction block provides detailed spatial data within the subgrid number. S51 takes the set of subgrid numbers as input and reads the subgrid number boundaries and high-resolution reconstruction blocks by subgrid number from the geographic information database. For each cell coordinate within the high-resolution reconstruction block, determine whether the x-coordinate of the cell coordinate is between the lower and upper horizontal boundaries of the sub-cell boundary, and determine whether the y-coordinate of the cell coordinate is between the lower and upper vertical boundaries of the sub-cell boundary. If both determinations are true, write the cell coordinate into the verification coordinate table. Then, using the subcell number, cell coordinates, and source code as the read keys, the query attribution value is recalculated from the object attribution function, and the subcell number, cell coordinates, source code, cell value, and query attribution value are written into the subcell verification table; If the object attribution function lacks the calculation result of the corresponding source code, then read the query attribution value from the query simulation result output by S2 according to the coordinate sequence and write it into the inheritance flag. If the query attribution value still cannot be read, then write the sub-cell number, cell coordinates and source code into the verification missing table. The subcell validation table needs to be converted into a residual result that can determine the consistency of the subcell number's internal belonging. S52 uses the subcell number as a self-join condition, copies the subcell validation table under the same subcell number into a left table and a right table, reads the binary values ​​corresponding to the coordinates in the left cell and the right cell, and retains the join rows where the binary value of the coordinate in the left cell is less than the binary value of the coordinate in the right cell. For each retained join row, read the left query belonging value and the right query belonging value, subtract the right query belonging value from the left query belonging value and take the absolute value to obtain the subcell difference term; Then, all subcell difference terms are summed according to the subcell number to obtain the subcell belonging residual, and the subcell number, connecting row, subcell difference term and subcell belonging residual are written into the subcell residual table; When there is only one coordinate within a cell under the same subcell number, the subcell assignment residual of that subcell number is written as zero, and a single-point verification mark is written in the subcell residual table; Spatial index encoding is only generated for subcell numbers that have passed the subcell attribution residual verification. S53 reads the subcell numbers in the subcell residual table whose subcell attribution residual is zero, and reads the parent cell number, subcell number order and high-resolution reconstruction block corresponding to the subcell number. The sub-cell width is determined by the number of sub-cells under the same parent cell number. First, subtract one from the number of sub-cells to obtain the upper bound of the sequence. Then, convert the upper bound of the sequence to binary and read the number of bits to use as the sub-cell width. Then, the binary value of the parent cell number is shifted left by the width of the child cell to obtain the parent cell code. The binary value of the child cell number is padded with zeros according to the width of the child cell and written into the lower bits of the parent cell code to obtain the spatial index code. When calculating the fusion attribute, non-space values ​​in the high-resolution reconstructed block are read according to the same spatial index encoding. First, the source code grid values ​​are accumulated according to the source code. Then, the average value is calculated using the number of source codes corresponding to the non-space values ​​as the denominator to obtain the fusion attribute. Spatial index encoding, subgrid number boundaries, high-resolution reconstruction blocks, fusion attributes, and query attribution values ​​are collectively written into index records, which are then written into the target city's digital twin model by S54. Subcell numbers with remaining subcell assignment residuals are not entered into the spatial index encoding. S54 reads the subcell numbers in the subcell residual table where the subcell assignment residual is not zero, and reads the coordinates of the two cells that produce non-zero absolute values, the subcell number, and the subcell assignment residual from the corresponding connection row. These fields are written back to the ambiguous grid table for S4 to continue performing subcell number boundary optimization. For the index records output by S53, the geographic information database establishes a write key according to the spatial index code, and writes the spatial index code, subgrid boundary, high-resolution reconstruction block, fusion attribute, query attribution value and ambiguous write-back record into the target city digital twin model. If a historical index record already exists for the same spatial index code, then the version record is appended according to the time position, and the writing order of the old and new versions is retained to avoid overwriting the city digital twin status under different time positions; After the above processing is completed, the set of subgrid numbers is placed in the geographic information database again for digital twin query simulation verification. Subgrid numbers with zero subgrid attribution residuals generate spatial index codes and fusion attributes, while subgrid numbers with non-zero subgrid attribution residuals are written back to the ambiguous grid table, so that the target city digital twin model only receives spatial index records with consistent query attribution. In practical applications: After the target grid near the entrance of an urban road is divided into multiple sub-grids by S4, the system first checks whether the grid coordinates within the high-resolution reconstruction block fall within the sub-grid boundary, then recalculates the query value and generates a sub-grid verification table. If the query attribution value within a certain sub-cell number is consistent, the system concatenates the parent and sub-cell numbers in order to form a spatial index code, calculates the fusion attribute according to the source code, and writes it into the target city digital twin model; if a certain sub-cell number still has a non-zero sub-cell attribution residual, the system writes the corresponding cell coordinates and sub-cell attribution residual back to the ambiguous grid table, and continues to hand it over to S4 to optimize the sub-cell number boundary.

[0022] exist Figure 1 It should be noted that the figure illustrates the overall technical route for constructing a digital twin model of a target city based on multi-source heterogeneous spatiotemporal data. The process begins with the spatiotemporal record of the city. The source code, collection location, collection time, and time observation values ​​are converted to coordinates, converted to time positions, encoded in geographic grids, and written to grid values ​​via S1, forming a grid record table. S2 then reads the grid record table, generates a grid value field using implicit neural representation, calculates the query attribution value, and outputs the query simulation result. S3 performs pairwise difference calculations on the query attribution value to obtain the attribution residual. If the attribution residual is zero, the process proceeds to determine the grid table and generate a spatial index code. If the attribution residual is not zero, the process proceeds to S4, where the sub-grid boundaries are updated using Riemann gradient descent and the sub-grid set is output. S5 performs a digital twin query simulation verification on the sub-grid set. After successful verification, the spatial index code and fusion attributes are written to form the digital twin model of the target city. The solid lines in the diagram represent the data transfer and processing order of the main process, such as the grid record table being passed to the query simulation, the query simulation result being passed to the attribution residual determination, and the subgrid number set being passed to the digital twin verification; the dashed lines represent the write-back correction relationship when there are anomalies or the verification fails, that is, the subgrid number whose attribution residual is still not zero in S5 will be written back to the ambiguous grid table and re-enter S4 to continue to perform subgrid number boundary optimization.

[0023] Working Principle: This scheme first writes urban spatiotemporal records from different sources into a unified geographic information database. Through coordinate transformation, time position conversion, geographic grid number encoding, and grid-based operator calculation, the collected location and time observation values ​​are converted into grid records in the basic grid. Then, the extended grid values ​​are read with the target grid number, and a grid value field with source code index is generated through an implicit neural representation algorithm. The query attribution value is then calculated from the grid value field. Next, the query attribution values ​​within the same target grid number are compared in pairs, and the differences are accumulated to obtain the attribution residual. The attribution residual is used to distinguish between the confirmed grid number and the ambiguous grid number. For ambiguous grid numbers, the sub-grid boundary is used as the Riemann manifold optimization variable. The boundary is adjusted through the Riemann gradient descent algorithm so that the ambiguous grid number is divided into sub-grid numbers with more consistent query attribution. Finally, a digital twin query simulation verification is performed on the sub-grid numbers in the geographic information database. The verified sub-grid numbers are written into the spatial index encoding and fusion attributes, while the ambiguous sub-grid numbers are written back for further optimization, thereby generating a city digital twin model that can support accurate spatial queries. For example, in old city commercial streets, near subway entrances and road intersections, building boundaries, shop entrances, road traffic trajectories, and traffic turning positions often fall into the same low-resolution grid number. Ordinary super-resolution can only make the image clearer, but cannot guarantee the correct spatial query attribution. This solution first writes these data into a base grid according to the same coordinates and time caliber, then uses an implicit neural representation algorithm to recover the changes in grid values ​​within the grid number, and checks whether the query attribution of different locations within the same grid number is consistent. If the entrance location and road trajectory are judged to different attributions, the grid number is sent to the Riemann gradient descent process to automatically adjust the sub-grid boundary until the attribution residual within the sub-grid converges, and then the verified sub-grid is written into the spatial index. In this way, when the digital twin platform queries a certain entrance, road, or traffic activity location, it returns a spatial index result verified by the attribution residual, rather than a fine grid result generated solely from image sharpness.

[0024] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data, characterized in that, include: S1. Obtain urban spatiotemporal records. Read the time observation value corresponding to the collection location from the geographic information database according to the source code. Calculate the geographic grid number and grid coordinates from the collection location. Call the grid operator according to the source code to convert the time observation value into grid value and write it into the basic grid. Output the grid record table. S2. Using the target cell number in the grid record table as the read key, read the extended cell value from the basic grid. Based on the implicit neural representation algorithm, map the cell coordinates to the cell value field of the source code index. Calculate the object attribution function from the cell value field, generate the query attribution value, and output the query simulation result. S3. Based on the query simulation results, subtract the query assignment values ​​of any two coordinates within the target grid number and take the absolute value. Accumulate the absolute values ​​to obtain the assignment residual. If the assignment residual is zero, write it into the definite grid table; if it is not zero, write it into the ambiguous grid table and record the coordinates of the grid that produced the non-zero difference. S4. For grid numbers in the ambiguous grid table, the subgrid number boundary is used as the Riemann manifold optimization variable with the meaning of dynamic grid design. The Riemann gradient descent algorithm is executed with the assignment residual as the objective function. The subgrid number boundary is updated according to the Riemann gradient. The algorithm stops when the assignment residuals of two adjacent rounds are equal. The output is a set of subgrid numbers containing high-resolution reconstruction blocks. S5. Based on the subgrid set, perform digital twin query simulation verification in the geographic information database. If the attribution residual is zero, write it into the spatial index code with fusion attributes. If it is not zero, write it back into the ambiguous grid table to generate the digital twin model of the target city.

2. The method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data according to claim 1, characterized in that: S1 includes: S11. Obtain urban spatiotemporal records. Using the record number as the key, read the source code, collection location, collection time, and time observation value from the geographic information database. Perform coordinate transformation on the collection location according to the coordinate reference registered in the geographic information database. Then, subtract the database start time from the collection time and divide by the database time granularity, taking the integer quotient to obtain the standard spatiotemporal record. S12. Based on the collection location in the standard spatiotemporal record, subtract the x-coordinate of the library origin from the x-coordinate of the collection location, divide by the grid side length, and take the integer quotient to obtain the horizontal grid number. Subtract the y-coordinate of the library origin from the y-coordinate of the collection location, divide by the grid side length, and take the integer quotient to obtain the vertical grid number. Encode the horizontal and vertical grid numbers into geographic grid numbers according to the order of the Hilbert curve, and generate the grid coordinates from the remainder of the two divisions. Output the location record. S13. Read the grid-based operator from the source code in the location record, and determine the record geometry according to the spatial carrying form corresponding to the source code. For point-type records, generate the grid percentage based on whether the collection location falls into the grid cell corresponding to the geographic grid number. For line-type records, calculate the intersection length of the record geometry and the grid cell, and divide the intersection length by the record geometry length to obtain the grid percentage. For area-type records, calculate the intersection area of ​​the record geometry and the grid cell, and divide the intersection area by the record geometry area to obtain the grid percentage. Then, multiply the time observation value by the grid percentage to obtain the grid value, write the grid value into the basic grid according to the location record, and output the grid-based record table.

3. The method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data according to claim 2, characterized in that: S2 includes: S21. Using the target grid number as the read key, read the grid records corresponding to the target grid number and the outer ring grid number from the geographic information database. Generate query points according to the grid coordinates in the grid records. Generate training samples according to the query points, source codes and grid values. Write empty value positions when the grid record is missing the grid value corresponding to the source code. Output the training sample table. S22. Execute the implicit neural representation algorithm based on the training sample table. Take the query point as the coordinate input, the source code as the condition input, and the grid value as the supervision value. Calculate the squared difference between the coordinate network output value and the grid value, the squared difference between the empty value backfill value and the adjacent grid value with the same source code, and the squared difference between the output values ​​on both sides of the common edge of adjacent grid numbers. Accumulate these values ​​as the field loss. Calculate the gradient of the coordinate network parameters according to the field loss and update them. Stop when the field loss of two adjacent rounds is equal, and output the grid value field.

4. The method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data according to claim 3, characterized in that: S2 also includes: S23. Calculate the belonging edge cost between query points based on the grid value field. Add the absolute values ​​of the difference between the grid value fields of the same source code of the two query points to obtain the same source cost. Add the absolute values ​​of the difference between the grid value fields of different source codes of the two query points to obtain the different source cost. Subtract the different source cost from the same source cost to obtain the belonging edge cost. Write the belonging edge cost into the intra-grid query graph. S24. In the intra-cell query graph, starting from each query point in the target cell number, the cost of the belonging edge is accumulated to obtain the path cost. Query points with equal path costs are written to the same belonging number. The belonging numbers obtained by the same query point under different source codes are subtracted and the absolute value is taken to obtain the conflict value. S25. When the conflict value is zero, use the attribution number as the query attribution value and output the query simulation result; when the conflict value is not zero, delete the corresponding source code input and re-execute S22 to S24, subtract the conflict value obtained again from the conflict value before deletion and take the absolute value as the correction difference, and output the query simulation result when the correction difference is zero.

5. The method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data according to claim 4, characterized in that: S3 includes: S31. Using the query simulation results as input, read the grid coordinates, time position and query value in the geographic information database according to the target grid number, concatenate the target grid number, time position and grid coordinates in the field order to form a coordinate sequence number, and output the coordinate assignment table. S32. Perform a self-join based on the coordinate attribution table and the target cell number. Keep the join rows where the binary value of the left coordinate number is less than the binary value of the right coordinate number. Subtract the two query attribution values ​​in the join row and take the absolute value. Output the attribution difference table.

6. The method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data according to claim 5, characterized in that: S3 also includes: S33. Based on the attribution difference table, sum all the absolute values ​​according to the target cell number to obtain the attribution residual, and reverse the two coordinate numbers in the connected row with non-zero absolute values ​​to the cell coordinates, and output the residual record. S34. Using the residual record as the writing object, when the assigned residual is zero, write the target grid number, assigned residual, and coordinate sequence number into the defined grid table; when the assigned residual is not zero, write the target grid number, assigned residual, and grid coordinates into the ambiguous grid table.

7. The method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data according to claim 6, characterized in that: S4 includes: S41. Using the grid number in the ambiguous grid table as input, read the grid number boundary, grid coordinates, query the assignment value and assignment residual from the geographic information database. For the two grid coordinates that produce non-zero differences, calculate the absolute value of the horizontal difference and the absolute value of the vertical difference respectively. When the absolute value of the horizontal difference minus the absolute value of the vertical difference is non-negative, take the average of the two horizontal coordinates to generate the vertical sub-grid number boundary. When the absolute value of the horizontal difference minus the absolute value of the vertical difference is negative, take the average of the two vertical coordinates to generate the horizontal sub-grid number boundary. Output the boundary initial value table. S42. Generate a boundary variable table based on the initial boundary value table. Write each sub-grid boundary as endpoint coordinates. When the endpoint coordinates fall outside the grid boundary, project them onto the grid boundary. When the two endpoint coordinates coincide, delete the corresponding sub-grid boundary. Output a boundary variable table that satisfies the grid boundary constraints.

8. The method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data according to claim 7, characterized in that: S4 also includes: S43. Divide the target grid number according to the boundary variable table, write the coordinates in each grid into the subgrid coordinate table according to the subgrid number, subtract the query belonging value of any two grid coordinates in the same subgrid number and take the absolute value, accumulate the absolute value to get the subgrid residual, and then accumulate the subgrid residuals according to the subgrid number to get the boundary target value. S44. Using the boundary target value as the objective function of the Riemann gradient descent algorithm, add a unit coordinate perturbation to the endpoint coordinates of each sub-grid boundary and recalculate the boundary target value. Subtract the boundary target value before perturbation from the perturbation boundary target value to obtain the endpoint gradient. Project the endpoint gradient onto the tangential direction of the grid boundary to obtain the Riemann gradient, and update the endpoint coordinates in reverse according to the Riemann gradient. S45. Re-execute S43 to S44 based on the updated endpoint coordinates. Stop updating when the boundary target values ​​of two adjacent rounds are equal. Write the subgrid boundary at the time of stopping the update into the geographic information database, and cut the base grid according to the subgrid boundary to output the subgrid set.

9. The method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data according to claim 8, characterized in that: S5 includes: S51. Taking the set of subgrid numbers as input, read the subgrid number boundaries and high-resolution reconstruction blocks in the geographic information database according to the subgrid number, write the coordinates of the cells within the high-resolution reconstruction blocks that fall into the subgrid number boundaries into the verification coordinate table, and read the query belonging value according to the subgrid number, cell coordinates and source code, and output the subgrid verification table. S52. Perform a self-join based on the subcell validation table by subcell number, retain the join rows where the binary value of the coordinate in the left cell is less than the binary value of the coordinate in the right cell, subtract the two query belonging values ​​in the join row and take the absolute value, accumulate all the absolute values ​​by subcell number to obtain the subcell belonging residual, and output the subcell residual table.

10. The method for constructing a super-resolution city model based on multi-source heterogeneous spatiotemporal data according to claim 9, characterized in that: S5 includes: S53. For the cell number in the cell residual table where the cell belonging to the residual is zero, shift the binary value of the parent cell number to the left by the cell bit width to obtain the parent cell code. Write the binary value of the cell number in sequence into the low bit of the parent cell code to obtain the spatial index code. Then, sum the cell values ​​under the same spatial index code according to the source code and divide by the number of source codes to obtain the fusion attribute. Output the index record. S54. For the cell number in the cell residual table where the cell attribution residual is not zero, the coordinates of the two cells in the connection row with non-zero absolute values, the cell number and the cell attribution residual will be written back to the ambiguous grid table, and the index records will be written into the geographic information database according to the spatial index encoding to generate the digital twin model of the target city.

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