Database dynamic update construction method and system based on block control and regulation

Abstract

The invention belongs to the technical field of urban planning management and computer information processing, and discloses a block control and regulation-based database dynamic update construction method and system, and the method comprises the following specific steps: S1, data access and preprocessing; s2, code matching and multi-index synchronous extraction; s3, layer extraction and rule drive mapping; s4, parameter verification and forced coverage inspection; and S5, carrying out templated output and layered packaging storage. According to the system, the file analyzer and the database connector are introduced through the data input module, so that the file format, the field structure and the coordinate system of the multi-source heterogeneous data can be automatically identified, and missing fields, naming differences and abnormal values are detected, corrected and standardized; according to the method, the problems that in the prior art, multi-source data access is difficult, and manual adjustment is needed are solved, and it is guaranteed that data received by the downstream module has consistency and identifiability.

Description

Technical Field

[0001] This invention belongs to the field of urban planning management and computer information processing technology, specifically a method and system for dynamically updating a database based on street block control planning. Background Technology

[0002] Existing systems lack fuzzy matching and batch caching mechanisms for street codes, making it impossible to efficiently extract codes and their associated indicators, such as planned population, land area, and building scale, under keyword search and similarity filtering, thus increasing manual workload. For the processing of layer data, most systems only provide single-threaded serial execution and lack dynamic extraction method allocation and multi-threaded scheduling mechanisms based on rule bases, making it difficult to meet the needs of large-scale standardized mapping in high-concurrency scenarios.

[0003] Furthermore, in the output stage, existing solutions generally neglect multi-dimensional verification such as layer integrity, output path validity, storage space, and overwriting risk, which can easily lead to data loss or output interruption. They also lack automatic generation of empty layer templates and layered packaging mechanisms, affecting the integrity of the database structure and subsequent system compatibility. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for dynamically updating a database based on street block planning, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for dynamically updating and constructing a database based on street control planning, the specific steps of which are as follows: S1 Data Access and Preprocessing: The data input module receives land use plan maps, street guide databases, or custom uploaded data. It uses a file parser or database connector to automatically identify file formats, coordinate systems, and field structures, and detects and standardizes missing fields and outliers to generate memory data objects that can be recognized by the system. S2 coding matching and multi-indicator synchronous extraction: The street code selection module is called, and a fuzzy matching algorithm based on Levenshtein edit distance is adopted. Based on the keyword search and similarity calculation of street codes, codes are selected in batches and stored in the cache. The cache is used to temporarily store the batch selection results to avoid repeated queries. Based on the code index, street attribute indicators, including planned population, land area and building scale, are extracted synchronously. S3 Layer Extraction and Rule-Driven Mapping: The rule engine module dynamically assigns extraction methods (land use plan map, street guide, self-upload, no involvement) to each standard layer based on the layer field mapping rule library, generates a layer extraction task queue, and the task queue adopts multi-threaded parallel processing, scheduling execution according to the task data volume and priority, and completes the standardized mapping of areal and point data. S4 parameter validation and forced overwrite check: Call the parameter compliance forced check module to perform multi-level validation of layer selection integrity, output path legality, target database status, remaining storage space, and write permissions. If an overwrite risk or anomaly is found, the user is prompted to confirm or the operation is interrupted. It also has a retry and rollback mechanism. S5 templated output and hierarchical packaging storage: Standardized data is written to a specified database. For layers that are not involved, built-in or user-defined empty layer templates are called to generate structured files with preset coordinate systems, standard field definitions, and metadata (including generation method identifiers). The data is then output to a new database or a blank database according to user settings, ensuring the integrity of the overall database structure.

[0006] Preferably, the specific steps of data access and preprocessing in S1 are as follows: S11 Multi-Source Data Access and Format Recognition: The system receives data from land use plans, street guide databases, or user-uploaded data through the data input module. The system uses a file parser or database connector to automatically identify the file format, storage structure, and coordinate system of the data source, ensuring that multi-source heterogeneous data can be accessed in a unified manner, providing structured input for subsequent processing. S12 Field Validation and Standardization Preprocessing: After basic identification is completed, the system automatically detects missing fields, inconsistent naming, or outliers in the data. By comparing with the built-in standard field library, it completes field standardization, default value filling, and anomaly correction, and finally generates a consistent memory data object that can be recognized by the system, ensuring the stable operation of subsequent modules.

[0007] Preferably, the specific steps of encoding matching and multi-indicator synchronous extraction in S2 are as follows: S21 Code Filtering and Fuzzy Matching: The system calls the street code selection module and uses a fuzzy matching algorithm based on edit distance, combined with keyword search and similarity calculation, to quickly filter street codes. During the matching process, similar codes can be identified to avoid omissions due to naming differences and ensure that multi-street data can be fully included in the processing scope. S22 Cache Management and Indicator Extraction: The system stores the selected street codes in batches into the cache area for temporary storage and task invocation, reducing repeated queries to the source database. Subsequently, the system automatically extracts the associated street attribute indicators based on the code index, such as planned permanent resident population, urban and rural construction land area, and total above-ground building scale, providing accurate input for subsequent layer mapping.

[0008] Preferably, the specific steps of layer extraction and rule-driven mapping in S3 are as follows: S31 Dynamic Extraction Method Allocation: The rule engine module automatically matches the extraction method for 19 standard layers based on the layer field mapping rule library, including land use plan map, street guide, self-uploaded or not involved. The system dynamically constrains the selectable method according to the layer type to ensure that all types of data have a standardized and suitable extraction path. S32 Task Queue Parallel Scheduling: The system generates a layer extraction task queue based on the allocation results, sorts the data by volume, data type and priority, and uses multi-threaded scheduling to prioritize the processing of areal data such as green spaces and roads, followed by point data such as public service facilities, ensuring efficient and stable completion of data extraction.

[0009] Preferably, the specific steps of parameter verification and forced coverage check in S4 are as follows: S41 Multi-level Parameter Verification: The system call parameter compliance mandatory check module verifies item by item whether the layers selected by the user are complete, whether the output path is valid, whether the target database status is empty or can be overwritten, and checks the remaining storage space and write permissions to ensure that the output conditions meet the technical specifications and avoid potential operational errors. S42 Exception Handling and Rollback Mechanism: During the verification process, if an overwrite risk, path anomaly, or insufficient permissions are detected, the system will prompt the user to confirm whether to continue or interrupt the operation. If a task exception occurs during the process, the module has an automatic retry and rollback mechanism to ensure data consistency and overall task recoverability.

[0010] Preferably, the specific steps of templated output and hierarchical packaging and storage in S5 are as follows: S51 Standardized Data Output: The system writes the standardized data after mapping and verification into the user-specified database. Users can choose to create a new empty database or use an existing blank database. The system automatically ensures that the output data is consistent with the database structure, field definitions and coordinate system to avoid writing failure due to format or metadata mismatch. S52 Empty Layer Template Generation and Layered Storage: For layers that are not involved, the system calls the built-in or user-defined empty layer template to generate a structured file containing metadata such as standard fields, preset coordinate systems, and source identifiers. The output process is carried out according to the layered strategy set by the user to ensure the integrity of the database in terms of physical and logical structure.

[0011] Preferably, a database dynamic update construction system based on street control planning is provided, which is based on the above-described method, and the system includes: Data input and encoding selection module: used to receive multi-source heterogeneous data, identify file format, field structure and coordinate system through file parser or database connector, perform outlier processing and standardization, and generate a street code list based on fuzzy matching algorithm and caching mechanism, supporting batch selection and simultaneous extraction of multiple indicators; The rules engine module is used to dynamically allocate extraction methods for standard layers based on the layer field mapping rule library, generate task queues, and process data in parallel according to task priority and data volume through a multi-threaded scheduling unit to achieve standardized data mapping. Verification and Output Module: Before output, perform multi-dimensional verification of layer selection, path validity, storage space, and overwrite risk, prompt the user for confirmation or interruption, and generate empty layers with standard fields, coordinate systems, and metadata through the empty layer template generation unit to achieve layered and packaged output.

[0012] Preferably, the data input and encoding selection module includes: (1) Multi-source data identification and standardization: The system receives multi-source heterogeneous data from land use plan maps, street guide databases or custom uploads, and uses a file parser or database connector to automatically identify file format, field structure and coordinate system, and detects, corrects and standardizes missing fields and outliers to ensure that subsequent processing modules can consistently identify data; (2) Code matching and batch indicator extraction: The system generates a list of street codes based on the fuzzy matching algorithm. It supports batch selection of codes through keyword search and similarity calculation. The caching mechanism is used to store the filtering results and reduce repeated access. Subsequently, the system synchronously extracts the main indicators associated with the codes, including planned population, construction land area and above-ground building scale.

[0013] Preferably, the rule engine module includes: (1) Extraction method allocation and task generation: The system dynamically allocates extraction methods for each standard layer based on the layer field mapping rule library, including land use plan map, street guide, self-upload or no involvement; the allocation process is constrained by the layer type, for example, the dominant functional area can only be self-uploaded, and then the system generates a layer extraction task queue; (2) Multi-threaded scheduling and standardized mapping: The generated task queue is processed in parallel by a multi-threaded scheduling unit. The scheduling order is sorted according to the volume and priority of the task data. For example, large-volume area data such as green space is processed first, followed by point data such as facility points. Parallel execution ensures that the data of each layer is efficiently and standardizedly mapped to the standard structure.

[0014] The verification and output module includes: (1) Multi-dimensional pre-output verification: Before output, the system performs multi-dimensional verifications such as layer selection integrity, output path legality, target database storage space, and overwrite risk. When an anomaly or conflict is detected, the system will prompt the user to confirm or interrupt the operation to ensure that the data output process is safe, stable and controllable. (2) Empty layer generation and layered output: The system automatically generates structured empty layers with standard fields, preset coordinate system and source identifiers for layers that are not involved through the empty layer template generation unit. During the output process, the data is packaged and stored according to the layered strategy set by the user to ensure the integrity of the database structure and the compatibility of subsequent calls.

[0015] The beneficial effects of the present invention are as follows: 1. This invention introduces a file parser and a database connector through a data input module, which can automatically identify the file format, field structure, and coordinate system of multi-source heterogeneous data, and detect, correct, and standardize missing fields, naming differences, and outliers. This not only solves the pain points of difficulty in accessing multi-source data and the need for manual adjustment in the prior art, but also ensures that the data received by the downstream module has consistency and identifiability, providing a solid technical foundation for subsequent encoding matching, layer extraction, and other steps.

[0016] 2. This invention employs a fuzzy matching algorithm based on edit distance, combined with keyword search, batch selection, and caching mechanisms, which can significantly improve the efficiency of batch screening of multiple codes in the street code selection process. At the same time, the rule engine module dynamically assigns extraction methods to each standard layer based on the layer field mapping rule library, and generates a task queue based on task priority and data volume. Through multi-threaded scheduling, it achieves efficient standardized mapping of area and point data. The synergistic effect of these modules improves the overall data processing speed and accuracy.

[0017] 3. In the output stage, this invention sets up a parameter compliance mandatory check module to cover multi-dimensional verification such as layer selection, path validity, storage space, write permissions, and overwrite risk, reducing the risk of data loss due to operational errors. At the same time, the empty layer template generation unit can generate empty layers with standard fields, coordinate systems, and metadata for layers that are not involved, and package them for output according to user settings. This not only ensures the integrity of the database in terms of physical and logical structure, but also improves the compatibility and stability of the subsequent system. Attached Figure Description

[0018] Figure 1 This is a flowchart of the database dynamic update construction method based on street block control planning of the present invention; Figure 2This is a flowchart of the database dynamic update construction system based on street block control planning of the present invention; Figure 3 This is a block diagram of the overall structure of the present invention; Figure 4 This is a diagram of the user interface for generating a street block control planning database based on land use schemes according to the present invention. Figure 5 This diagram illustrates the intelligent street block coding selection method for generating a street block control planning database based on land use schemes, as described in this invention. Detailed Implementation

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] like Figures 1 to 5 As shown, this embodiment of the invention provides a method for dynamically updating and constructing a database based on street control planning. The specific steps of this method are as follows: S1 Data Access and Preprocessing: The data input module receives land use plan maps, street guide databases, or custom uploaded data. It uses a file parser or database connector to automatically identify file formats, coordinate systems, and field structures, and detects and standardizes missing fields and outliers to generate memory data objects that can be recognized by the system. S2 coding matching and multi-indicator synchronous extraction: The street coding selection module is called, and a fuzzy matching algorithm based on Levenshtein edit distance is used. Based on keyword search and similarity calculation, codes are selected in batches and stored in the cache. The cache is used to temporarily store the batch selection results to avoid repeated queries. Street attribute indicators, including planned population, land area and building scale, are extracted synchronously based on the coding index. S3 Layer Extraction and Rule-Driven Mapping: The rule engine module dynamically assigns extraction methods (land use plan map, street guide, self-upload, no involvement) to each standard layer based on the layer field mapping rule library, generates a layer extraction task queue, and the task queue adopts multi-threaded parallel processing, scheduling execution according to the task data volume and priority, and completes the standardized mapping of areal and point data. S4 parameter validation and forced overwrite check: Call the parameter compliance forced check module to perform multi-level validation of layer selection integrity, output path legality, target database status, remaining storage space, and write permissions. If an overwrite risk or anomaly is found, the user is prompted to confirm or the operation is interrupted. It also has a retry and rollback mechanism. S5 templated output and hierarchical packaging storage: Standardized data is written to a specified database. For layers that are not involved, built-in or user-defined empty layer templates are called to generate structured files with preset coordinate systems, standard field definitions, and generation method identifiers. The data is then output to a new database or a blank database according to user settings, ensuring the integrity of the overall database structure.

[0021] Example 1: Automatic Construction of Street Block Control Planning Database Based on Land Use Scheme Map In a planning control database construction project in a new district of Beijing, the planning unit needs to convert the latest land use plan map (*.shp or *.gdb) into a database that conforms to the street planning control standards. The system first imports the land use plan map through the data interface layer, and uses a file parser to identify the coordinate system (such as CGCS2000), fields (such as land use nature and plot ratio) and standardize the naming.

[0022] Upon entering the rules engine layer, the system automatically calls the land use scheme map conversion rules. For example, it extracts the areal data with the land use nature field of A33 to the public service facilities layer, automatically calculates the identification code, land area, and building scale, and converts them according to the plot ratio field. For S3 category data (bus station), it extracts it to the transportation facilities layer and generates a point data copy.

[0023] Finally, the output layer undergoes multi-dimensional verification to ensure that the user has selected the output path and checked the complete layers, and performs writability checks on the target database (such as .gdb files). In the output stage, standardized data is written to the relevant layers, and empty templates with "_not involved" are automatically generated for the layers that are not involved. Finally, the results are packaged to generate the street control planning database.

[0024] Example 2: Incremental Update Driven by Block Guide Database In existing urban areas, to optimize the timeliness of the control planning database, planning units use the street block guidance database (*.gdb or *.mdb) for incremental updates; the system directly connects to the server's standard data source through the data interface layer and calls the street block code selection module; users can search for street block code keywords (such as 01, 02) through the interface, select street block codes in batches (such as BJ001, BJ002), and cache them to the temporary storage area.

[0025] The rule engine layer extracts the planning scope, street attributes, population size, land area, and building scale of the corresponding street based on the street guide conversion rules, and extracts three major facility points (such as public service facility points and transportation facility points). These data are named according to standardized fields, and the area, scale, and facility type are calculated according to the rules.

[0026] Before output, the system automatically verifies the integrity of the layers, the database status, and write permissions. If the target database is not empty, the system prompts the user to confirm whether to overwrite. Finally, the output module writes the data into the existing database and uses a standard empty template for layers that are not involved, ensuring the integrity of the database structure and that it can be rolled back.

[0027] Example 3: Full-process construction under multi-source data access In comprehensive projects, planning units need to integrate land use plan maps, street guidance databases, and planning unit-specific planning layer data; the system simultaneously accesses three types of data sources at the data interface layer, respectively identifying .shp files, .gdb files, and .mdb files, and standardizing coordinate systems, fields, and naming.

[0028] At the rule engine layer, the system assigns the optimal extraction method to 19 standard layers based on layer field mapping rules, for example: River and lake systems, green spaces, and transportation facilities are derived from the land use plan. Rail transit micro-centers and public service facility locations are sourced from the neighborhood guidance database; The main functional areas and resource organization are derived from custom-uploaded data.

[0029] The multi-threaded task queue is prioritized for scheduling based on whether the data is surface-based or point-based, and attribute calculations (such as land area, building scale, facility category, and facility sub-category) are performed during the extraction process.

[0030] During the verification and execution phase, the system checks various output conditions, such as database storage space, path validity, and coverage risk. The output module stores the merged standardized data into a newly created blank database according to a layered strategy. Layers not involved use automatically generated empty templates to ensure the database results are complete and standardized.

[0031] The specific steps for data access and preprocessing in S1 are as follows: S11 Multi-Source Data Access and Format Recognition: The system receives data from land use plans, street guide databases, or user-uploaded data through the data input module. The system uses a file parser or database connector to automatically identify the file format, storage structure, and coordinate system of the data source, ensuring that multi-source heterogeneous data can be accessed in a unified manner, providing structured input for subsequent processing. S12 Field Validation and Standardization Preprocessing: After basic identification is completed, the system automatically detects missing fields, inconsistent naming, or outliers in the data. By comparing with the built-in standard field library, it completes field standardization, default value filling, and anomaly correction, and finally generates a consistent memory data object that can be recognized by the system, ensuring the stable operation of subsequent modules.

[0032] The specific steps for encoding matching and simultaneous extraction of multiple indicators in S2 are as follows: S21 Code Filtering and Fuzzy Matching: The system calls the street code selection module and uses a fuzzy matching algorithm based on edit distance, combined with keyword search and similarity calculation, to quickly filter street codes. During the matching process, similar codes can be identified to avoid omissions due to naming differences and ensure that multi-street data can be fully included in the processing scope. S22 Cache Management and Indicator Extraction: The system stores the selected street codes in batches into the cache area for temporary storage and task invocation, reducing repeated queries to the source database. Subsequently, the system automatically extracts the associated street attribute indicators based on the code index, such as planned permanent resident population, urban and rural construction land area, and total above-ground building scale, providing accurate input for subsequent layer mapping.

[0033] The specific steps of layer extraction and rule-driven mapping in S3 are as follows: S31 Dynamic Extraction Method Allocation: The rule engine module automatically matches the extraction method for 19 standard layers based on the layer field mapping rule library, including land use plan map, street guide, self-uploaded or not involved. The system dynamically constrains the selectable method according to the layer type to ensure that all types of data have a standardized and suitable extraction path. S32 Task Queue Parallel Scheduling: The system generates a layer extraction task queue based on the allocation results, sorts the data by volume, data type and priority, and uses multi-threaded scheduling to prioritize the processing of areal data such as green spaces and roads, followed by point data such as public service facilities, ensuring efficient and stable completion of data extraction.

[0034] The specific steps for parameter verification and forced coverage check in S4 are as follows: S41 Multi-level Parameter Verification: The system call parameter compliance mandatory check module verifies item by item whether the layers selected by the user are complete, whether the output path is valid, whether the target database status is empty or can be overwritten, and checks the remaining storage space and write permissions to ensure that the output conditions meet the technical specifications and avoid potential operational errors. S42 Exception Handling and Rollback Mechanism: During the verification process, if an overwrite risk, path anomaly, or insufficient permissions are detected, the system will prompt the user to confirm whether to continue or interrupt the operation. If a task exception occurs during the process, the module has an automatic retry and rollback mechanism to ensure data consistency and overall task recoverability.

[0035] The specific steps for templated output and hierarchical packaging and storage in S5 are as follows: S51 Standardized Data Output: The system writes the standardized data after mapping and verification into the user-specified database. Users can choose to create a new empty database or use an existing blank database. The system automatically ensures that the output data is consistent with the database structure, field definitions and coordinate system to avoid writing failure due to format or metadata mismatch. S52 Empty Layer Template Generation and Layered Storage: For layers that are not involved, the system calls the built-in or user-defined empty layer template to generate a structured file containing metadata such as standard fields, preset coordinate systems, and source identifiers. The output process is carried out according to the layered strategy set by the user to ensure the integrity of the database in terms of physical and logical structure.

[0036] This embodiment provides a database dynamic update construction system based on street block control planning, which includes the following: Data input and encoding selection module: used to receive multi-source heterogeneous data, identify file format, field structure and coordinate system through file parser or database connector, perform outlier processing and standardization, and generate a street code list based on fuzzy matching algorithm and caching mechanism, supporting batch selection and simultaneous extraction of multiple indicators; The rules engine module is used to dynamically allocate extraction methods for standard layers based on the layer field mapping rule library, generate task queues, and process data in parallel according to task priority and data volume through a multi-threaded scheduling unit to achieve standardized data mapping. Verification and Output Module: Before output, perform multi-dimensional verification of layer selection, path validity, storage space, and overwrite risk, prompt the user for confirmation or interruption, and generate empty layers with standard fields, coordinate systems, and metadata through the empty layer template generation unit to achieve layered and packaged output.

[0037] The data input and encoding selection module includes: (1) Multi-source data identification and standardization: The system receives multi-source heterogeneous data from land use plan maps, street guide databases or custom uploads, and uses a file parser or database connector to automatically identify file format, field structure and coordinate system, and detects, corrects and standardizes missing fields and outliers to ensure that subsequent processing modules can consistently identify data; (2) Code matching and batch indicator extraction: The system generates a list of street codes based on the fuzzy matching algorithm. It supports batch selection of codes through keyword search and similarity calculation. The caching mechanism is used to store the filtering results and reduce repeated access. Subsequently, the system synchronously extracts the main indicators associated with the codes, including planned population, construction land area and above-ground building scale.

[0038] The rules engine module includes: (1) Extraction method allocation and task generation: The system dynamically allocates extraction methods for each standard layer based on the layer field mapping rule library, including land use plan map, street guide, self-upload or no involvement; the allocation process is constrained by the layer type, for example, the dominant functional area can only be self-uploaded, and then the system generates a layer extraction task queue; (2) Multi-threaded scheduling and standardized mapping: The generated task queue is processed in parallel by a multi-threaded scheduling unit. The scheduling order is sorted according to the volume and priority of the task data. For example, large-volume area data such as green space is processed first, followed by point data such as facility points. Parallel execution ensures that the data of each layer is efficiently and standardizedly mapped to the standard structure.

[0039] The verification and output module includes: (1) Multi-dimensional pre-output verification: Before output, the system performs multi-dimensional verifications such as layer selection integrity, output path legality, target database storage space, and overwrite risk. When an anomaly or conflict is detected, the system will prompt the user to confirm or interrupt the operation to ensure that the data output process is safe, stable and controllable. (2) Empty layer generation and layered output: The system automatically generates structured empty layers with standard fields, preset coordinate system and source identifiers for layers that are not involved through the empty layer template generation unit. During the output process, the data is packaged and stored according to the layered strategy set by the user to ensure the integrity of the database structure and the compatibility of subsequent calls.

[0040] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0041] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamically updating a database based on street block planning, characterized in that: The specific steps of this database dynamic update construction method based on street block control planning are as follows: S1 Data Access and Preprocessing: Receives land use plan maps, street guide databases, and custom-specified uploaded data; parses file formats, coordinate systems, and fields; detects missing and outlier values; and standardizes and generates system-recognizable memory objects. S2 coding matching and multi-indicator synchronous extraction: Based on the edit distance algorithm, fuzzy coding matching is performed, batch selection results are cached, and block attribute indicators such as planned population, land area, and building scale are extracted synchronously. S3 Layer Extraction and Rule-Driven Mapping: A task queue is generated based on the layer field mapping rule library, and multi-threaded parallel processing is used to complete the standardized mapping of area and point data according to volume and priority. S4 parameter verification and forced overwrite check: Multi-layer verification of layer integrity, path validity, storage status, and write permissions; detection of overwrite risks and prompts to the user; operation confirmation, retry, and rollback mechanisms. S5 templated output and hierarchical packaging storage: Standardized data is written to the database, empty template files are generated for those not involving layers, metadata is attached, and output is carried out in layers according to user settings to ensure the integrity of the database structure.

2. The method for dynamically updating a database based on street block planning as described in claim 1, characterized in that: The specific steps for data access and preprocessing in S1 are as follows: S11 Multi-Source Data Access and Format Recognition: The system receives data from land use plans, street guide databases, or user-uploaded data through the data input module. The system uses a file parser or database connector to automatically identify the file format, storage structure, and coordinate system of the data source, ensuring that multi-source heterogeneous data can be accessed in a unified manner, providing structured input for subsequent processing. S12 Field Validation and Standardization Preprocessing: After basic identification is completed, the system automatically detects missing fields, inconsistent naming, or outliers in the data. By comparing with the built-in standard field library, it completes field standardization, default value filling, and anomaly correction, and finally generates a consistent memory data object that can be recognized by the system, ensuring the stable operation of subsequent modules.

3. The method for dynamically updating a database based on street block planning as described in claim 2, characterized in that: The specific steps for encoding matching and simultaneous extraction of multiple indicators in S2 are as follows: S21 Code Filtering and Fuzzy Matching: The system calls the street code selection module and uses a fuzzy matching algorithm based on edit distance, combined with keyword search and similarity calculation, to quickly filter street codes. Similar codes can be identified during the matching process. S22 Cache Management and Indicator Extraction: The system stores the selected street codes in batches into the cache area for temporary storage and task invocation, reducing repeated queries to the source database. Subsequently, the system automatically extracts the associated street attribute indicators based on the code index.

4. The method for dynamically updating a database based on street block planning as described in claim 3, characterized in that: The specific steps of layer extraction and rule-driven mapping in S3 are as follows: S31 Dynamic Extraction Method Allocation: The rule engine module automatically matches the extraction method for 19 standard layers based on the layer field mapping rule library, including land use plan map, street guide, self-uploaded or not involved. The system dynamically constrains the selectable method according to the layer type to ensure that all types of data have a standardized and suitable extraction path. S32 Task Queue Parallel Scheduling: The system generates a layer extraction task queue based on the allocation results, sorts it by data volume, data type and priority, and the task queue adopts multi-threaded scheduling, giving priority to processing isometric data.

5. The method for dynamically updating a database based on street block planning as described in claim 4, characterized in that: The specific steps for parameter verification and forced coverage check in S4 are as follows: S41 Multi-level Parameter Verification: The system call parameter compliance mandatory check module verifies item by item whether the layers selected by the user are complete, whether the output path is valid, whether the target database status is empty or can be overwritten, and checks the remaining storage space and write permissions to ensure that the output conditions meet the technical specifications. S42 Exception Handling and Rollback Mechanism: During the verification process, if an overwrite risk, path anomaly, or insufficient permissions are detected, the system will prompt the user to confirm whether to continue or interrupt the operation. If a task exception occurs during the process, the module has an automatic retry and rollback mechanism to ensure data consistency and overall task recoverability.

6. The method for dynamically updating a database based on street block planning as described in claim 5, characterized in that: The specific steps for templated output and hierarchical packaging and storage in S5 are as follows: S51 Standardized Data Output: The system writes the standardized data after mapping and verification into the user-specified database. Users can choose to create a new empty database or use an existing blank database. The system automatically ensures that the output data is consistent with the database structure, field definitions and coordinate system to avoid writing failure due to format or metadata mismatch. S52 Empty Layer Template Generation and Layered Storage: For layers that are not involved, the system calls the built-in or user-defined empty layer template to generate a metadata structured file containing standard fields, preset coordinate systems, and source identifiers. The output process is carried out according to the user-defined layered strategy to ensure the integrity of the database in terms of physical and logical structure.

7. A database dynamic update construction system based on street block control planning, characterized in that: This database dynamic update construction system based on street block planning is based on the method described in claim 6, and the system includes: Data input and encoding selection module: used to receive multi-source heterogeneous data, identify file format, field structure and coordinate system through file parser or database connector, perform outlier processing and standardization, and generate a street code list based on fuzzy matching algorithm and caching mechanism, supporting batch selection and simultaneous extraction of multiple indicators; The rules engine module is used to dynamically allocate extraction methods for standard layers based on the layer field mapping rule library, generate task queues, and process data in parallel according to task priority and data volume through a multi-threaded scheduling unit to achieve standardized data mapping. Verification and Output Module: Before output, perform multi-dimensional verification of layer selection, path validity, storage space, and overwrite risk, prompt the user for confirmation or interruption, and generate empty layers with standard fields, coordinate systems, and metadata through the empty layer template generation unit to achieve layered and packaged output.

8. A database dynamic update construction system based on street block control planning according to claim 7, characterized in that: The data input and encoding selection module includes: Multi-source data identification and standardization: The system receives multi-source heterogeneous data from land use plan maps, street guide databases or custom uploads, and uses a file parser or database connector to automatically identify file formats, field structures and coordinate systems. It also detects, corrects and standardizes missing fields and outliers to ensure that subsequent processing modules can consistently identify the data. Code matching and batch indicator extraction: The system generates a list of street codes based on a fuzzy matching algorithm. It supports batch selection of codes through keyword search and similarity calculation of street codes. A caching mechanism is used to store the filtering results and reduce repeated access. Subsequently, the system synchronously extracts the main indicators associated with the codes, including planned population, construction land area, and above-ground building scale.

9. A database dynamic update construction system based on street block control planning according to claim 8, characterized in that: The rule engine module includes: Extraction method allocation and task generation: The system dynamically allocates an extraction method for each standard layer based on the layer field mapping rule library, including land use plan map, street guide, self-uploaded or not involved; the allocation process is constrained by the layer type. Multi-threaded scheduling and standardized mapping: The generated task queue is processed in parallel by a multi-threaded scheduling unit. The scheduling order is sorted according to the task data volume and priority. Parallel execution ensures that the data of each layer is efficiently and standardizedly mapped to the standard structure.

10. A database dynamic update construction system based on street block control planning according to claim 9, characterized in that: The verification and output module includes: Multi-dimensional pre-output verification: Before output, the system performs multi-dimensional verification of layer selection integrity, output path validity, target database storage space, and overwrite risk. When an anomaly or conflict is detected, the system will prompt the user to confirm or interrupt the operation to ensure that the data output process is safe, stable, and controllable. Empty layer generation and layered output: The system automatically generates structured empty layers with standard fields, preset coordinate systems and source identifiers for layers that are not involved through the empty layer template generation unit. During the output process, the data is packaged and stored according to the layered strategy set by the user to ensure the integrity of the database structure and the compatibility of subsequent calls.

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