Standard address pipeline data association management method and system based on multistage collaborative coding

Abstract

The invention discloses a standard address pipeline data association management method and system based on multi-level collaborative coding, belongs to the technical field of government affair digitization, and aims to solve the technical problem of how to generate and manage standard address data in government affair business, realize automatic triggering and standardized circulation of cross-department business data and improve the management efficiency. According to the technical scheme, the method comprises the steps of cross-department business triggering: automatically triggering a road and courtyard address code generation process through construction project land pre-examination and construction project planning permission business events; a standard road name is generated based on road pre-examination data, a road unified identification code is synthesized according to a 4 + 6 + 9 segmentation rule, a house number is generated according to planning permission data, and a courtyard unified identification code is generated in combination with a naming system; the house prediction data triggers the generation of a house unified identification code; and data dynamic association: constructing a reverse index based on the address coding feature vector, and realizing millisecond-level accurate matching of the address and population information through a distributed query engine.

Description

Technical Field

[0001] The present invention relates to the field of government digital technology, and specifically to a standard address pipeline data association management method and system based on multi-level collaborative coding. Background Art

[0002] Government data refers to the data resources generated, collected, stored, and used by government departments in the course of performing their duties. It covers various forms such as text, images, audio, and video, and is a fundamental element in the construction of the digital economy and digital government. Current government data has the following flaws:

[0003] ① Data silos: Different government departments independently generate their own address codes, and the standards are not unified. For example, the road coding rules are not related to the house coding.

[0004] ② Update lag: Address information needs to be manually entered twice, resulting in insufficient data consistency. For example, newly named roads are not synchronized to the gate sign system in a timely manner.

[0005] ③ Lack of association: There is a lack of dynamic binding between addresses and population and housing data. For example, it is impossible to reversely associate household information through addresses.

[0006] Therefore, how to generate and manage standard address data in government affairs and realize the automated triggering and standardized flow of cross-departmental business data is a technical problem that needs to be solved urgently. Summary of the Invention

[0007] The technical task of the present invention is to provide a standard address pipeline data association management method and system based on multi-level collaborative coding to solve the problem of how to generate and manage standard address data in government affairs and realize the automatic triggering and standardized flow of cross-departmental business data.

[0008] The technical task of the present invention is achieved in the following manner: a standard address pipeline data association management method based on multi-stage collaborative coding, the method is specifically as follows:

[0009] Cross-departmental business triggering: Automatically trigger the generation process of road and courtyard address codes through construction project land pre-examination and construction project planning permit business events;

[0010] Hierarchical code generation: Generates standard road names based on road pre-qualification data, synthesizes unified road identification codes according to the "4+6+9" segmentation rule, generates house numbers based on planning permit data, and generates unified courtyard identification codes based on the naming system; and triggers the generation of unified household identification codes based on housing prediction data;

[0011] Dynamic data association: Build an inverted index based on address encoding feature vectors, and achieve millisecond-level precise matching of addresses and population information through a distributed query engine.

[0012] As a preference, cross-departmental business triggers are as follows:

[0013] Event monitoring and triggering, specifically: ① Define event types: pre-defined trigger events at key nodes in the business system; ② Define monitoring methods: database triggers and API interceptors; database triggers capture business table changes (such as new records in the land_approval table) through database logs (such as MySQL Binlog); API interceptors embed interception logic in the business system interface layer to monitor specific interface calls (such as / api / address / approve); ③ Event data extraction: extract business code fields (such as land pre-approval number HYD-2023-001), spatial data fields (such as land coordinate range POLYGON ((X1 Y1, X2 Y2,...)), and timestamp fields; the timestamp field uses ISO 8601 format and is accurate to milliseconds;

[0014] Data standardization and encapsulation: Encapsulate the original data into a standardized JSON Schema and define data routing rules; the data routing rules are to dynamically route to the next processing node based on event_type: ROAD_APPROVAL → Place Name Collaboration System; BUILDING_PERMIT → House Number Management System.

[0015] More preferably, the "4+6+9" segmentation rule is as follows: the first four digits are the administrative division codes at or above the county level according to the GB / T 2260 standard; the middle six digits contain the address type identifier and subclass code; the address type identifier is the 5th to 6th digits, i.e., 40 / 50 / 70, identifying the road / courtyard / household respectively; the subclass code is the 7th to 9th digits; and the last 9 digits are a unique serial number generated based on the spatiotemporal double hashing algorithm;

[0016] The logic for generating the middle 6-digit classification code is as follows: digits 7-8 of the road code identify the road grade (40 = main road, 41 = secondary road); digits 7-8 of the courtyard code identify the community type (50 = residential community, 51 = commercial park); digits 7-8 of the room code identify the use of the house (70 = residential, 71 = shop);

[0017] The specific spatiotemporal double hashing algorithm is as follows: perform SHA-256 hash calculation on the business event timestamp, and take the modulus to generate the first 5-digit timestamp hash segment; perform hash calculation on the geographic coordinate sequence, and take the modulus to generate the last 4-digit coordinate hash segment; and concatenate the timestamp and coordinate hash segments to form a 9-digit unique serial number.

[0018] More preferably, the hierarchical coding is generated as follows:

[0019] Matching rules: Query the rule base based on event_type, for example, ROAD_APPROVAL matches RULE_001;

[0020] Parameter filling, specifically: ① Administrative division code: extract the administrative division code from data_payload.region_code; ② Classification code: implement the classification code according to the rule prefix assignment (road = 40); ③ Custom serial number: use "timestamp hash + coordinate hash" to generate a unique value: apply a hash function (such as SHA-256) to the timestamp of the business event (accurate to milliseconds), generate the initial hash value, and then map it to an integer in the range of 0 to 999,999,999 through a modulo operation to generate a timestamp hash segment; perform hash calculation on geographic coordinate data (such as the vertex sequence of the land boundary polygon), and also use a modulo operation to constrain it to the range of 0 to 999,999,999 to generate a coordinate hash segment; truncate the first 5 valid digits of the timestamp hash segment and the first 4 valid digits of the coordinate hash segment, and splice them in the order of "first 5 digits + last 4 digits" to form a 9-digit unique serial number;

[0021] Code synthesis: Concatenate according to the 4+6+9 rule: Administrative division (421127) + road / main road (4050) + custom serial number (000000420) → Administrative division + road / main road + custom serial number (4211274050000000420).

[0022] More optimally, data dynamic association is constructed by combining address coding level features with multi-source data weight distribution strategies, as follows:

[0023] Hierarchical matching rules; specifically: ① Administrative division hierarchical matching: extract the first 4 digits of the administrative division code of the address code (in accordance with the GB / T 2260 standard), and accurately match it with the administrative division field of the household registration address in the population database to filter out population data within the same administrative division; ② Address type hierarchical matching: based on the middle 6-digit classification code (5th to 10th digits) of the address code, limit the associated address type hierarchy: if the classification code starts with "40" (road type), only the legal person information of street businesses is associated; if the classification code starts with "50" or "70" (courtyard or household type), the resident population information is associated; ③ Unique identifier precise matching: when accurate association with household-level data is required, extract the custom serial number after the 9 digits of the address code and perform a one-to-one binding with the house property certificate number or the household head's ID number;

[0024] Weight allocation rules; specifically: ① Static weight: Set a static weight coefficient based on the authority and timeliness of the data source; ② Dynamic weight: Give a dynamic weight bonus (increase by 0.2) to population data updated within the last 30 days, giving priority to matching records with greater timeliness;

[0025] Conflict resolution rules: If the same address code is associated with multiple conflicting population information, such as different heads of households, the household registration data will be used as the benchmark value. If the authoritative data is missing or conflicting, a verification task will be automatically generated and pushed to the grid worker's mobile terminal for on-site verification. The verification results will be written back to the database after manual review, and the association rules will be updated.

[0026] More optimally, an inverted index is constructed based on the address encoding feature vector, and a distributed query engine is used to achieve millisecond-level precision matching of addresses and population information. The details are as follows:

[0027] Feature vectorization processing; specifically: ① Address code feature vector generation: convert the 21-bit address code into a multidimensional feature vector according to the segmentation rule: V = [administrative division (4), major category (2), medium category (2), minor category (2), serial number (9)]; ② Population feature vector expansion: extract the associated fields (ID number, household registration address code, and insurance unit code) from the population database and construct the vector: P = [ID number (18), household registration code (21), insurance unit code (15)];

[0028] Construct a reverse index; the details are as follows: ① Design the index structure: Use each level of the address code feature vector as the key and the population data ID list as the value to build a multi-level inverted index; the first-level index is administrative division code → population ID list; the second-level index is administrative division code + classification code → population ID list; the third-level index is complete address code → population ID list; ② Index update mechanism: When an address code is added or modified, trigger the index update through a message queue (such as Kafka) to ensure that the index is synchronized with the source data, and the latency is controlled within 100 milliseconds;

[0029] Distributed query optimization, specifically: ① Query decomposition and parallel execution: Split related requests into multi-level sub-queries and distribute them to distributed computing nodes for parallel processing; ② Cache acceleration mechanism: Cache high-frequency query results (such as popular cell address codes) and manage cached data using an LRU (least recently used) strategy, ensuring a response time of less than 10 milliseconds when a cache hit occurs;

[0030] Real-time association interface: Provides a RESTful API interface based on HTTP / HTTPS protocol, supporting JSON format requests and responses.

[0031] Preferably, the method also includes a conflict resolution mechanism: when the same address code is associated with multiple conflicting population information, the household registration data is prioritized; verification tasks are automatically generated and pushed to the grid worker terminal, and the database is updated after manual verification;

[0032] This method also provides a real-time association interface through a RESTful API. The interface definition includes: request parameters: address code, query level (road / courtyard / household); response parameters: matching population list, matching weight, response time; cross-departmental data synchronization delay is less than 1 second, and code generation and write-back are fully automated.

[0033] A standard address pipeline data association management system based on multi-level collaborative coding, the system comprising:

[0034] The coding rule formulation module is used to generate standard road names based on road pre-qualification data, synthesize unified road identification codes according to the "4+6+9" segmentation rule, generate house numbers based on planning permit data, and generate unified courtyard identification codes based on the naming system; the generation of unified household identification codes is triggered by housing prediction data;

[0035] Event monitoring and triggering module, used to define event types, monitoring methods and extract event data;

[0036] The data standardization and encapsulation module is used to encapsulate the original data into a standardized JSON Schema and dynamically route it to the next processing node based on the event_type: ROAD_APPROVAL → Place Name Collaboration System; BUILDING_PERMIT → House Number Management System;

[0037] The dynamic coding rule engine module is used to compile the rule base and store it in a relational database. It then queries the rule base and fills in parameters based on event_type, and finally splices and synthesizes the code according to the 4+6+9 rule.

[0038] The data synchronization and verification module is used to write a write-back mechanism based on the HTTP / HTTPS protocol using the JSON-RPC standard, and then perform exception handling for uniqueness conflict detection and format verification. Among them, uniqueness conflict detection refers to comparing the historical records of the encoding library. If there is a duplicate, the serial number regeneration (serial_code+=1) is triggered; format verification verifies the legitimacy of the encoding using a regular expression: ^\d{4}(40|50|70)\d{6}\d{9}$;

[0039] Association rule building module, used to build association rules based on address coding hierarchical features and multi-source data weight distribution strategy;

[0040] The association path establishment module is used to implement association path establishment based on inverted index optimization and distributed query engine.

[0041] As a preferred method, the “4+6+9” segmentation rule is: the first 4 digits are GB / T 2260 standard administrative division code for county level and above; the middle 6 digits contain address type identifier and subclass code; among them, the address type identifier is the 5th and 6th digits, i.e. 40 / 50 / 70, which respectively identify road / courtyard / household; the subclass code is the 7th to 9th digits; the last 9 digits are a unique serial number generated based on the time-space double hash algorithm; among them, the generation logic of the middle 6-digit classification code is as follows: the 7th and 8th digits in the road code identify the road grade (40 = main road, 41 = secondary road); the 7th and 8th digits in the courtyard code identify the community type (50 = residential community, 51 = commercial park); the 7th and 8th digits in the household code identify the use of the house (70 = residential, 71 = shop); the time-space double hash algorithm is as follows: perform SHA-256 hash calculation on the business event timestamp, and take the modulus to generate the first 5-digit timestamp hash segment; perform hash calculation on the geographic coordinate sequence, and take the modulus to generate the last 4-digit coordinate hash segment; splice the timestamp and coordinate hash segment to form a 9-digit unique serial number;

[0042] The event monitoring and triggering modules include:

[0043] The event type definition submodule is used to predefine trigger events at key nodes of the business system;

[0044] The monitoring mode definition submodule is used to monitor events using database triggers and API interceptors. Database triggers capture business table changes (such as new records in the land_approval table) through database logs (such as MySQL Binlog). API interceptors embed interception logic at the business system interface layer to monitor specific interface calls (such as / api / address / approve).

[0045] The event data extraction submodule is used to extract the business code field (such as the land pre-approval number HYD-2023-001), spatial data fields (such as the land coordinate range POLYGON ((X1 Y1, X2 Y2, ...)) and timestamp field; the timestamp field uses the ISO 8601 format and is accurate to milliseconds;

[0046] The dynamic coding rule engine module includes:

[0047] The rule matching submodule is used to query the rule base based on event_type, for example, ROAD_APPROVAL matches RULE_001;

[0048] The parameter filling submodule is used to extract the administrative division diamante from data_payload.region_code, assign a value to the prefix according to the rule (road = 40) to obtain the classification code, and then use "timestamp hash + coordinate hash" to generate a unique value to implement a custom serial number; among them, the "timestamp hash + coordinate hash" generates a unique value specifically as follows: apply a hash function (such as SHA-256) to the timestamp of the business event (accurate to the millisecond level) to generate an initial hash value, then map it to an integer in the range of 0 to 999,999,999 through a modulo operation to generate a timestamp hash segment; perform hash calculation on the geographic coordinate data (such as the vertex sequence of the land boundary polygon) and also use a modulo operation to constrain it to the range of 0 to 999,999,999 to generate a coordinate hash segment; truncate the first 5 valid digits of the timestamp hash segment and the first 4 valid digits of the coordinate hash segment, and concatenate them in the order of "first 5 digits + last 4 digits" to form a 9-digit unique serial number;

[0049] The encoding synthesis submodule is used to splice according to the 4+6+9 rule, specifically: administrative division (421127) + road / main road (4050) + custom serial number (000000420) → administrative division + road / main road + custom serial number (4211274050000000420).

[0050] More preferably, the association rule building module includes:

[0051] The hierarchical matching submodule is used to extract the first 4 digits of the administrative division code of the address code (in accordance with the GB / T2260 standard) and accurately match it with the administrative division field of the household registration address in the population database, filter out the population data within the same administrative division, and achieve administrative division hierarchical matching. It also limits the associated address type hierarchy based on the middle 6-digit classification code (5th to 10th digits) of the address code: if the classification code starts with "40" (road type), only the legal person information of street merchants is associated; if the classification code starts with "50" or "70" (courtyard or household type), the resident population information is associated to achieve address type hierarchical matching. At the same time, when it is necessary to accurately associate with household-level data, the 9-digit custom serial number after the address code is extracted and bound one-to-one with the house property certificate number or the household head's ID number to achieve accurate matching of unique identifiers.

[0052] The weight allocation submodule is used to set static weight coefficients based on the authority and timeliness of the data source to achieve static weights, and to give a dynamic weight bonus (increase 0.2) to population data updated in the last 30 days, giving priority to matching records with greater timeliness to achieve dynamic weights:

[0053] The conflict resolution submodule is used to prioritize household registration data as the benchmark value when the same address code is associated with multiple conflicting population information, such as different household heads. If authoritative data is missing or conflicting, a verification task is automatically generated and pushed to the grid worker's mobile terminal for on-site verification. The verification results are manually reviewed and written back to the database, and the association rules are updated.

[0054] The association pathway building module includes:

[0055] The feature vector quantization processing submodule is used to generate the address code feature vector and expand the population feature vector; wherein, the address code feature vector is generated by converting the 21-bit address code into a multi-dimensional feature vector according to the segmentation rule: V = [administrative division (4), major category (2), medium category (2), minor category (2), serial number (9)]; the population feature vector expansion is to extract the associated fields (ID number, household registration address code, and a certain insurance unit code) from the population database to construct the vector: P = [ID number (18), household registration code (21), a certain insurance unit code (15)];

[0056] The inverted index construction submodule is used to build a multi-level inverted index using each level of the address code feature vector as the key and the population data ID list as the value. When the address code is added or modified, the index update is triggered through a message queue (such as Kafka) to ensure that the index is synchronized with the source data and the latency is controlled within 100 milliseconds. Among them, the first-level index is administrative division code → population ID list; the second-level index is administrative division code + classification code → population ID list; the third-level index is complete address code → population ID list;

[0057] The distributed query optimization submodule is used to split association requests into multiple levels of subqueries, distribute them to distributed computing nodes for parallel processing, and cache high-frequency query results (such as popular cell address codes). It uses an LRU (least recently used) strategy to manage cached data, and the response time for cache hits is less than 10 milliseconds.

[0058] The real-time association interface submodule is used to provide a RESTful API interface based on the HTTP / HTTPS protocol and supports JSON format requests and responses.

[0059] The standard address pipeline data association management method and system based on multi-stage collaborative coding of the present invention has the following advantages:

[0060] (1) This invention addresses the problems of inconsistent coding rules for existing government address data, low efficiency in cross-departmental collaboration, and lack of correlation between address and population data. It constructs a three-level standardized address production pipeline (road → courtyard → household), and achieves hierarchical coding collaboration among different government departments through a cross-business data trigger mechanism.

[0061] (2) This invention shortens the address coding production cycle from 15 manual days to 2 days automatically, and improves data consistency to 99.7%. It solves the technical bottleneck of dynamic updating of standard addresses and multi-source data integration in the "One Standard, Three Realities" business of government affairs, and is suitable for smart city, public security and population management scenarios;

[0062] (3) The present invention achieves the automated triggering and standardized flow of cross-departmental business data by building a multi-level collaborative coding pipeline for different government departments, eliminating coding rule conflicts and data redundancy caused by traditional manual transmission, and breaking the problem of departmental data silos;

[0063] (4) The present invention designs a hierarchical and segmented 21-digit unified identification code (administrative division + classification identifier + custom serial number), covering three levels of address: road, courtyard, and household. This solves the data association problem caused by the fragmentation of the existing coding system (such as the independent coding of house numbers and real estate tables), and supports dynamic expansion of new address types, realizing unified coding rules and dynamic updates.

[0064] (5) The present invention constructs a reverse index based on the address coding feature vector, achieving millisecond-level precise association of address information with population and housing data (e.g., directly obtaining householder ID information through household and room coding), replacing the inefficient traditional manual cross-verification model, thereby improving data association efficiency;

[0065] (6) This invention uses a standardized pipeline mechanism to ensure that address data is traceable throughout the entire process, from planning approval, naming and filing to actual use, providing a highly consistent data base for scenarios such as smart city governance and public safety warnings, and supporting full lifecycle management;

[0066] (VII) The coding update time of the present invention is shortened from 3 days manually to 5 minutes automatically by the system, and the cross-departmental data matching rate is >99.7%, which greatly improves data consistency:

[0067] (8) The present invention shortens the period from land use pre-examination to house number compilation from 15 days to 2 days, thereby improving business collaboration efficiency;

[0068] (9) The present invention realizes millisecond-level population association query (measured response time <50ms) through coding prefix matching, thereby improving association retrieval performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention will be further described below with reference to the accompanying drawings.

[0070] Attachment Figure 1 Schematic diagram of the three-level collaborative encoding production line. DETAILED DESCRIPTION

[0071] The standard address pipeline data association management method and system based on multi-stage collaborative coding of the present invention are described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] Example 1:

[0073] This embodiment provides a standard address pipeline data association management method based on multi-stage collaborative coding, which is specifically as follows:

[0074] S1. Cross-departmental business triggering: Automatically trigger the generation process of road and courtyard address codes through construction project land pre-examination and construction project planning permit business events;

[0075] S2. Hierarchical code generation: Generate standard road names based on road pre-approval data, synthesize unified road identification codes according to the "4+6+9" segmentation rule, generate house numbers based on planning permit data, and generate unified courtyard identification codes based on the naming system; trigger the generation of unified household identification codes based on housing prediction data;

[0076] S3. Dynamic data association: Build an inverted index based on the address encoding feature vector, and achieve millisecond-level precise matching of addresses and population information through a distributed query engine.

[0077] The cross-departmental business triggering in step S1 of this embodiment is specifically as follows:

[0078] S101. Event monitoring and triggering, specifically: ① Define event types: pre-defined trigger events at key nodes of the business system, such as "land pre-approval completed" for Department 1 and "housing pre-sale license generated" for Department 2; ② Define monitoring methods: database triggers and API interceptors; database triggers capture business table changes (such as new records in the land_approval table) through database logs (such as MySQL Binlog); API interceptors embed interception logic at the business system interface layer to monitor specific interface calls (such as / api / address / approve); ③ Event data extraction: extract business code fields (such as land pre-approval number HYD-2023-001), spatial data fields (such as land coordinate range POLYGON ((X1 Y1, X2Y2, ...)) and timestamp fields; the timestamp field uses ISO 8601 format and is accurate to milliseconds;

[0079] S102. Data Standardization Encapsulation: Encapsulate the raw data into a standardized JSON Schema and define data routing rules. The data routing rules dynamically route to the next processing node based on event_type: ROAD_APPROVAL → Place Name Collaboration System; BUILDING_PERMIT → House Number Management System. The raw data is encapsulated into a standardized JSON Schema in the following format:

[0080]

[0081] As attached Figure 1 As shown, the "4+6+9" segmentation rule in step S2 of this embodiment is: the first 4 digits are the administrative division code above the county level in the GB / T2260 standard; the middle 6 digits contain the address type identifier and the subclass code; the address type identifier is the 5th to 6th digits, i.e., 40 / 50 / 70, which respectively identify the road / courtyard / household; the subclass code is the 7th to 9th digits; the last 9 digits are a unique serial number generated based on the spatiotemporal double hash algorithm;

[0082] The logic for generating the middle 6-digit classification code is as follows: digits 7-8 of the road code identify the road grade (40 = main road, 41 = secondary road); digits 7-8 of the courtyard code identify the community type (50 = residential community, 51 = commercial park); digits 7-8 of the room code identify the use of the house (70 = residential, 71 = shop);

[0083] The specific spatiotemporal double hashing algorithm is as follows: perform SHA-256 hash calculation on the business event timestamp, and take the modulus to generate the first 5-digit timestamp hash segment; perform hash calculation on the geographic coordinate sequence, and take the modulus to generate the last 4-digit coordinate hash segment; and concatenate the timestamp and coordinate hash segments to form a 9-digit unique serial number.

[0084] The three-level coding production process is as follows:

[0085]

[0086] The hierarchical coding generation in step S2 of this embodiment is specifically as follows:

[0087] S201, matching rules: query the rule base according to event_type, for example, ROAD_APPROVAL matches RULE_001; wherein, the compiled rule base is stored in a relational database (such as MySQL), and the structure is as follows:

[0088] rule_id address_type segment_rules RULE_001 the way {"prefix":"40","lengths":[4,6,9]} RULE_002 courtyard {"prefix":"50","lengths":[4,6,9]}

[0089] S202, parameter filling, specifically: ① Administrative division code: extract the administrative division code from data_payload.region_code; ② Classification code: implement the classification code according to the rule prefix assignment (road = 40); ③ Custom serial number: use "timestamp hash + coordinate hash" to generate a unique value: apply a hash function (such as SHA-256) to the timestamp of the business event (accurate to milliseconds), generate the initial hash value, and then map it to an integer in the range of 0 to 999,999,999 through modular operation to generate a custom serial number. Timestamp hash segment: Perform a hash calculation on geographic coordinate data (e.g., a vertex sequence of a land boundary polygon), also using a modulo operation to constrain the range to 0 to 999,999,999, to generate a coordinate hash segment. The first five significant digits of the timestamp hash segment and the first four significant digits of the coordinate hash segment are truncated, and the two are concatenated in the order of "first five digits + last four digits" to form a 9-digit unique serial number. For example: timestamp hash segment: 00823 (derived from 00823456), coordinate hash segment: 0456 (derived from 04567891), the result is: 008230456.

[0090] S203. Code synthesis: Concatenate according to the 4+6+9 rule: Administrative division (421127) + road / main road (4050) + custom serial number (000000420) → Administrative division + road / main road + custom serial number (4211274050000000420).

[0091] This implementation also includes data synchronization and verification, as follows:

[0092] (1) Encoded write-back mechanism: Based on HTTP / HTTPS protocol, using JSON-RPC standard. Its data format example:

[0093]

[0094]

[0095] (2) Exception handling logic

[0096] 1) Uniqueness conflict detection: Compare the historical records of the code library, and if there is a duplicate, trigger the serial number regeneration (serial_code += 1).

[0097] 2) Format verification: Use regular expression to verify the legality of the encoding: ^\d{4}(40|50|70)\d{6}\d{9}$.

[0098] The dynamic data association in step S3 of this embodiment is to build the address coding level feature and the multi-source data weight distribution strategy, which is as follows:

[0099] S3-101. Hierarchical matching rules; specifically: ① Administrative division hierarchical matching: extract the first 4 digits of the administrative division code of the address code (in accordance with GB / T 2260 standard), and accurately match it with the administrative division field of the household registration address in the population database to filter out population data within the same administrative division; for example, when the first 4 digits of the address code are "4211", only population records with household registration address codes starting with "4211" are matched to ensure that the data retrieval range is accurate to the municipal administrative division; ② Address type hierarchical matching: limit the associated address type hierarchy based on the middle 6 digits of the classification code (5th to 10th digits) of the address code: if the classification code starts with "40" (road type), only the street-side businesses are associated. ③ Unique identifier precise matching: When precise association with household-level data is required, the 9-digit custom serial number after the address code is extracted and bound one-to-one with the property ownership certificate number or the household head's ID number; for example, the custom serial number "000000223" of the household code "421127405007000000223" is directly mapped to the household head's ID number "421127XXXXXXXXXXXX";

[0100] S3-102. Weight allocation rules; specifically: ① Static weight: A static weight coefficient is set based on the authority and real-time nature of the data source; household registration address matching weight: 0.6 (based on its legal authority); insurance enrollment address weight: 0.3 (reflecting actual residence relevance); water and electricity bill payment address weight: 0.1 (assisting in verifying residence status); ② Dynamic weight: A dynamic weight bonus (increase of 0.2) is assigned to population data updated within the last 30 days, giving priority to matching records with greater timeliness; for example, if a population record updated a certain insurance address 10 days ago, its final weight is:

[0101] Final weight = basic weight × dynamic bonus factor = 0.3 × (1 + 0.2) = 0.36;

[0102] S3-103. Conflict resolution rules: If the same address code is associated with multiple conflicting population information, such as different heads of households, the household registration data will be used as the benchmark value. If the authoritative data is missing or conflicting, a verification task will be automatically generated and pushed to the grid worker's mobile terminal for on-site verification. The verification results will be manually reviewed and written back to the database, and the association rules will be updated.

[0103] In step S3 of this embodiment, an inverted index is constructed based on the address encoding feature vector, and millisecond-level precise matching of addresses and population information is achieved through a distributed query engine as follows:

[0104] S3-201, feature vector quantization processing; specifically: ① address code feature vector generation: convert the 21-bit address code into a multidimensional feature vector according to the segmentation rule: V = [administrative division (4), major category (2), medium category (2), minor category (2), serial number (9)]; example: 421127405007000000223 → [4211, 40, 50, 07, 000000223]; ② population feature vector expansion: extract related fields (ID number, household registration address code, certain insurance unit code) from the population database and construct a vector: P = [ID number (18), household registration code (21), certain insurance unit code (15)]; example: P = [421127XXXXXXXXXXXX, 421127405007000000223, 91440101MA5XXXXXXX];

[0105] S3-202. Construct a reverse index. The details are as follows: ① Design the index structure: Use each level of the address code feature vector as the key and the population data ID list as the value to construct a multi-level inverted index. The first-level index is administrative division code → population ID list; the second-level index is administrative division code + classification code → population ID list; the third-level index is complete address code → population ID list. Example:

[0106] First-level index: 4211→[P001, P002, P003] / / Administrative division level;

[0107] Secondary index: 4211405007→[P005,P008] / / Administrative division + major category + medium category + minor category;

[0108] Level 3 index: 421127405007000000223→[P015] / / complete address code;

[0109] ② Index update mechanism: When an address code is added or modified, an index update is triggered through a message queue (such as Kafka) to ensure that the index is synchronized with the source data, with latency controlled within 100 milliseconds;

[0110] S3-203, distributed query optimization, specifically: ① Query decomposition and parallel execution: Split the associated request into multi-level sub-queries and distribute them to distributed computing nodes for parallel processing;

[0111] For example, query: associated address code 421127405007000000223;

[0112] Subquery 1: Administrative division = 4211 → returns 1000 records;

[0113] Subquery 2: Category = 40 → returns 300 items;

[0114] Subquery 3: Medium class = 50 → returns 50 records;

[0115] Subquery 4: Subcategory = 70 → returns 30 results;

[0116] Final intersection: 30 → sorted by weight;

[0117] ② Cache acceleration mechanism: Cache high-frequency query results (such as popular cell address codes) and use an LRU (least recently used) strategy to manage cached data. The response time for a cache hit is less than 10 milliseconds.

[0118] S3-204, Real-time Association Interface: Provides a RESTful API interface based on HTTP / HTTPS protocol, supporting JSON format requests and responses.

[0119] Example request:

[0120]

[0121] Example response:

[0122]

[0123]

[0124] This embodiment also includes a conflict resolution mechanism: when the same address code is associated with multiple conflicting population information, the household registration data is prioritized; verification tasks are automatically generated and pushed to the grid worker terminal, and the database is updated after manual verification;

[0125] This embodiment also provides a real-time association interface through a RESTful API. The interface definition includes: request parameters: address code, query level (road / courtyard / household); response parameters: matching population list, matching weight, response time; cross-department data synchronization delay is less than 1 second, and code generation and write-back are fully automated.

[0126] Example 2:

[0127] This embodiment provides a standard address pipeline data association management system based on multi-stage collaborative coding, the system comprising:

[0128] The coding rule formulation module is used to generate standard road names based on road pre-qualification data, synthesize unified road identification codes according to the "4+6+9" segmentation rule, generate house numbers based on planning permit data, and generate unified courtyard identification codes based on the naming system; the generation of unified household identification codes is triggered by housing prediction data;

[0129] Event monitoring and triggering module, used to define event types, monitoring methods and extract event data;

[0130] The data standardization and encapsulation module is used to encapsulate the original data into a standardized JSON Schema and dynamically route it to the next processing node based on the event_type: ROAD_APPROVAL → Place Name Collaboration System; BUILDING_PERMIT → House Number Management System;

[0131] The dynamic coding rule engine module is used to compile the rule base and store it in a relational database. It then queries the rule base and fills in parameters based on event_type, and finally splices and synthesizes the code according to the 4+6+9 rule.

[0132] The data synchronization and verification module is used to write a write-back mechanism based on the HTTP / HTTPS protocol using the JSON-RPC standard, and then perform exception handling for uniqueness conflict detection and format verification. Among them, uniqueness conflict detection refers to comparing the historical records of the encoding library. If there is a duplicate, the serial number regeneration (serial_code+=1) is triggered; format verification verifies the legitimacy of the encoding using a regular expression: ^\d{4}(40|50|70)\d{6}\d{9}$;

[0133] Association rule building module, used to build association rules based on address coding hierarchical features and multi-source data weight distribution strategy;

[0134] The association path establishment module is used to implement association path establishment based on inverted index optimization and distributed query engine.

[0135] As attached Figure 1 As shown, the "4+6+9" segmentation rule in this embodiment is: the first 4 digits are GB / T 2260 standard administrative division code for county level and above; the middle 6 digits contain address type identifier and subclass code; among them, the address type identifier is the 5th to 6th digits, i.e. 40 / 50 / 70, which respectively identify road / courtyard / household; the subclass code is the 7th to 9th digits; the last 9 digits are a unique serial number generated based on the time-space double hash algorithm; among them, the generation logic of the middle 6-digit classification code is as follows: the 7th to 8th digits in the road code identify the road grade (40 = main road, 41 = secondary road); the 7th to 8th digits in the courtyard code identify the community type (50 = residential community, 51 = commercial park); the 7th to 8th digits in the household code identify the purpose of the house (70 = residential, 71 = shop); the time-space double hash algorithm is specifically as follows: perform SHA-256 hash calculation on the business event timestamp, and take the modulus to generate the first 5-digit timestamp hash segment; perform hash calculation on the geographic coordinate sequence, and take the modulus to generate the last 4-digit coordinate hash segment; splice the timestamp and coordinate hash segment to form a 9-digit unique serial number.

[0136] The event monitoring and triggering module in this embodiment includes:

[0137] The event type definition submodule is used to predefine trigger events at key nodes of the business system;

[0138] The monitoring mode definition submodule is used to monitor events using database triggers and API interceptors. Database triggers capture business table changes (such as new records in the land_approval table) through database logs (such as MySQL Binlog). API interceptors embed interception logic at the business system interface layer to monitor specific interface calls (such as / api / address / approve).

[0139] The event data extraction submodule is used to extract business code fields (such as land use pre-qualification number HYD-2023-001; 2) spatial data), spatial data fields (such as land use coordinate range POLYGON ((X1Y1, X2 Y2, ...)) and timestamp fields; among them, the timestamp field uses ISO 8601 format and is accurate to milliseconds.

[0140] The dynamic coding rule engine module in this embodiment includes:

[0141] The rule matching submodule is used to query the rule base based on event_type, for example, ROAD_APPROVAL matches RULE_001;

[0142] The parameter filling submodule is used to extract the administrative division diamante from data_payload.region_code, assign a value to the prefix according to the rule (road = 40) to obtain the classification code, and then use "timestamp hash + coordinate hash" to generate a unique value to implement a custom serial number; among them, the "timestamp hash + coordinate hash" generates a unique value specifically as follows: apply a hash function (such as SHA-256) to the timestamp of the business event (accurate to the millisecond level) to generate an initial hash value, then map it to an integer in the range of 0 to 999,999,999 through a modulo operation to generate a timestamp hash segment; perform hash calculation on the geographic coordinate data (such as the vertex sequence of the land boundary polygon) and also use a modulo operation to constrain it to the range of 0 to 999,999,999 to generate a coordinate hash segment; truncate the first 5 valid digits of the timestamp hash segment and the first 4 valid digits of the coordinate hash segment, and concatenate them in the order of "first 5 digits + last 4 digits" to form a 9-digit unique serial number;

[0143] The encoding synthesis submodule is used to splice according to the 4+6+9 rule, specifically: administrative division (421127) + road / main road (4050) + custom serial number (000000420) → administrative division + road / main road + custom serial number (4211274050000000420).

[0144] The association rule establishment module in this embodiment includes:

[0145] The hierarchical matching submodule is used to extract the first 4 digits of the administrative division code of the address code (in accordance with the GB / T2260 standard) and accurately match it with the administrative division field of the household registration address in the population database, filter out the population data within the same administrative division, and achieve administrative division hierarchical matching. It also limits the associated address type hierarchy based on the middle 6-digit classification code (5th to 10th digits) of the address code: if the classification code starts with "40" (road type), only the legal person information of street merchants is associated; if the classification code starts with "50" or "70" (courtyard or household type), the resident population information is associated to achieve address type hierarchical matching. At the same time, when it is necessary to accurately associate with household-level data, the 9-digit custom serial number after the address code is extracted and bound one-to-one with the house property certificate number or the household head's ID number to achieve accurate matching of unique identifiers.

[0146] The weight allocation submodule is used to set static weight coefficients based on the authority and timeliness of the data source to achieve static weights, and to give a dynamic weight bonus (increase 0.2) to population data updated in the last 30 days, giving priority to matching records with greater timeliness to achieve dynamic weights:

[0147] The conflict resolution submodule is used to prioritize household registration data as the benchmark value when the same address code is associated with multiple conflicting population information, such as different household heads. If the authoritative data is missing or conflicting, a verification task is automatically generated and pushed to the grid worker's mobile terminal for on-site verification. The verification results are manually reviewed and written back to the database, and the association rules are updated.

[0148] The association pathway establishment module in this embodiment includes:

[0149] The feature vector quantization processing submodule is used to generate the address code feature vector and expand the population feature vector; wherein, the address code feature vector is generated by converting the 21-bit address code into a multi-dimensional feature vector according to the segmentation rule: V = [administrative division (4), major category (2), medium category (2), minor category (2), serial number (9)]; the population feature vector expansion is to extract the associated fields (ID number, household registration address code, and a certain insurance unit code) from the population database to construct the vector: P = [ID number (18), household registration code (21), a certain insurance unit code (15)];

[0150] The inverted index construction submodule is used to build a multi-level inverted index using each level of the address code feature vector as the key and the population data ID list as the value. When the address code is added or modified, the index update is triggered through a message queue (such as Kafka) to ensure that the index is synchronized with the source data and the latency is controlled within 100 milliseconds. Among them, the first-level index is administrative division code → population ID list; the second-level index is administrative division code + classification code → population ID list; the third-level index is complete address code → population ID list;

[0151] The distributed query optimization submodule is used to split association requests into multiple levels of subqueries, distribute them to distributed computing nodes for parallel processing, and cache high-frequency query results (such as popular cell address codes). It uses an LRU (least recently used) strategy to manage cached data, and the response time for cache hits is less than 10 milliseconds.

[0152] The real-time association interface submodule is used to provide a RESTful API interface based on the HTTP / HTTPS protocol and supports JSON format requests and responses.

[0153] Example 3:

[0154] Taking road coding as an example, the details are as follows:

[0155] (1) Production of unified road identification codes, as follows:

[0156] (1) Data triggering and acquisition, as follows:

[0157] ① Trigger condition: The department completes the approval of a road land use project (project number: HYD-2023-001) in the "Construction Project Land Use Preliminary Review System", and the system automatically captures the approval completion event;

[0158] ②Data encapsulation: Extract the following fields and encapsulate them into JSON data packets:

[0159]

[0160] (2) Inter-departmental data transfer, as follows:

[0161] ①Route push: Push data packets to the "geographical name collaboration system" of participating departments through the government data sharing platform;

[0162] ②Generate standard road names: The department matches the historical place name database according to the coordinate range and generates a standard road name "xx Road, xx County, xx City, xx Province";

[0163] (3) Code generation and write-back: The details are as follows:

[0164] ①Rule matching: The department calls the coding rule engine to match the road coding rule (RULE_001);

[0165] ② Parameter filling: Administrative division code: 421127 (Huang County); Classification code: 4050 (Road / Main Road); Custom serial number: 000000420 generated using the time-space double hash algorithm;

[0166] ③ Coding synthesis: Generate a 21-digit unified identification code 4211274050000000420 and write it back to the business system of the department responsible for land planning through the API;

[0167] (2) Production of a unified identification code for courtyard addresses, as follows:

[0168] (1) Planning permission triggers are as follows:

[0169] ① Trigger condition: Approving a residential complex in the "Construction Project Planning Permit System" to generate land redline coordinate data;

[0170] (2) House number compilation: After receiving the land use data, the department generates the house number "No. xx, xx Road" and pushes it to the local municipal big data sharing platform;

[0171] (3) Code generation and association: The department generates the courtyard code 42112750004000000112 according to the rules, where: 421127: xx County; 500040: courtyard / residential community; 00000112: unique serial number generated by the hash algorithm. The code is then simultaneously associated with the community name "xx community" in the place name database;

[0172] (3) Production of unified identification codes for household addresses, as follows:

[0173] (1) Access to housing forecast data: Generate a forecast property table in the "housing pre-sale permit system", including household information: "xx community x building x unit xx room";

[0174] (2) Household and room code generation: extract household and room information and generate code 421127405007000000223, where: 4050: road level classification code (xx road); 07: household and room subclass identifier; 000000223: unique serial number;

[0175] (3) Inverted index construction: bind the household code to the household head’s ID number 421127XXXXXXXXXXXX and write it into the inverted index library;

[0176] (IV) Address-population dynamics correlation, as follows:

[0177] (1) Association request trigger: The user submits a query request through the API: address_code = 421127405007000000223, requesting to associate household-level population information;

[0178] (2) Distributed query execution: First-level subquery: extract the first 4 digits "4211", and hit 100,000 registered residents of xx city in the population database; second-level subquery: extract the first 12 digits "421127405007", and filter out 2,000 residents along xx Road; third-level subquery: complete code "421127405007000000223", accurately matching the household head "xx";

[0179] (3) Weight sorting and return, as follows:

[0180] Sort the results by weight (household registration data weight 0.6), and return:

[0181]

[0182] (V) Exception Handling Example

[0183] (1) Scenario: The same household code 421127405007000000223 is associated with two different household heads (ID card number conflict);

[0184] (2) The processing flow is as follows:

[0185] ① Conflict detection: The system detects that the match_score difference in the matching results is less than 0.1 and determines it as a conflict;

[0186] ② Authoritative data priority: Prioritize the use of household registration data (weight 0.6) as the benchmark value;

[0187] ③ Automatic verification trigger: Push verification tasks to grid workers, requiring on-site verification of property ownership certificates and resident information;

[0188] ④ Manual confirmation and update: After verifying and confirming that the head of household is "xx", update the population database and reset the association rules.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A standard address pipeline data association management method based on multi-level collaborative coding, characterized in that: The method is as follows: Cross-departmental business triggering: Automatically trigger the generation process of road and courtyard address codes through construction project land pre-examination and construction project planning permit business events; Hierarchical code generation: Generates standard road names based on road pre-approval data, synthesizes unified road identification codes according to the "4+6+9" segmentation rule, generates house numbers based on planning permit data, and generates unified courtyard identification codes based on the naming system; and triggers the generation of unified household identification codes based on housing prediction data. Dynamic data association: Build an inverted index based on address encoding feature vectors, and achieve millisecond-level precise matching of addresses and population information through a distributed query engine.

2. The standard address pipeline data association management method based on multi-stage collaborative coding according to claim 1 is characterized in that: The cross-department business triggers are as follows: Event monitoring and triggering, specifically: ① Define event types: pre-defined trigger events at key nodes in the business system; ② Define monitoring methods: database triggers and API interceptors; database triggers capture business table changes through database logs; API interceptors embed interception logic at the business system interface layer to monitor specific interface calls; ③ Event data extraction: extract business code fields, spatial data fields, and timestamp fields; the timestamp field uses the ISO 8601 format and is accurate to milliseconds; Data standardization and encapsulation: Encapsulate the original data into a standardized JSON Schema and define data routing rules; the data routing rules are to dynamically route to the next processing node based on event_type: ROAD_APPROVAL → Place Name Collaboration System; BUILDING_PERMIT → House Number Management System.

3. The standard address pipeline data association management method based on multi-stage collaborative coding according to claim 1 or 2, characterized in that: The "4+6+9" segmentation rule is as follows: the first four digits are the administrative division code at or above the county level according to the GB / T 2260 standard; the middle six digits contain the address type identifier and subclass code; the address type identifier is digits 5-6, i.e., 40 / 50 / 70, identifying a road / courtyard / household, respectively; the subclass code is digits 7-9; and the last nine digits are a unique serial number generated using the spatiotemporal double hashing algorithm. The generation logic of the middle 6-digit classification code is as follows: the 7th and 8th digits of the road code identify the road grade; the 7th and 8th digits of the courtyard code identify the community type; the 7th and 8th digits of the household code identify the purpose of the house; The specific spatiotemporal double hashing algorithm is as follows: perform SHA-256 hash calculation on the business event timestamp, and take the modulus to generate the first 5-digit timestamp hash segment; perform hash calculation on the geographic coordinate sequence, and take the modulus to generate the last 4-digit coordinate hash segment; and concatenate the timestamp and coordinate hash segments to form a 9-digit unique serial number.

4. The standard address pipeline data association management method based on multi-stage collaborative coding according to claim 3 is characterized in that: The hierarchical coding is generated as follows: Matching rules: query the rule base based on event_type; Parameter filling, specifically: ① Administrative division code: Extract the administrative division code from data_payload.region_code; ② Classification code: Implement the classification code according to the prefix assignment rule; ③ Custom serial number: Generate a unique value using "timestamp hash + coordinate hash": Apply a hash function to the timestamp of the business event to generate an initial hash value, then use a modulo operation to map it to an integer in the range of 0 to 999,999,999 to generate the timestamp hash segment; perform a hash calculation on the geographic coordinate data, also using a modulo operation to constrain it to the range of 0 to 999,999,999 to generate the coordinate hash segment; truncate the first 5 significant digits of the timestamp hash segment and the first 4 significant digits of the coordinate hash segment, concatenating them in the order of "first 5 digits + last 4 digits" to form a 9-digit unique serial number; Code synthesis: splicing according to the 4+6+9 rule: administrative division + road / main road + custom serial number → administrative division + road / main road + custom serial number.

5. The standard address pipeline data association management method based on multi-stage collaborative coding according to claim 4 is characterized in that: Dynamic data association is based on the address coding level features and multi-source data weight distribution strategy, as follows: Hierarchical matching rules; specifically: ① Administrative division hierarchical matching: Extract the first 4 digits of the administrative division code of the address code and perform an exact match with the administrative division field of the household registration address in the population database to filter out population data within the same administrative division; ② Address type hierarchical matching: Based on the middle 6 digits of the classification code of the address code, limit the associated address type hierarchy: If the classification code begins with "40", that is, the road type, only the legal person information of street-side businesses is associated; if the classification code begins with "50" or "70", the resident population information is associated; ③ Unique identifier precise matching: When precise association with household-level data is required, extract the custom serial number after the 9 digits of the address code and perform a one-to-one binding with the house property certificate number or the household owner's ID number; Weight allocation rules; specifically: ① Static weight: Set a static weight coefficient based on the authority and timeliness of the data source; ② Dynamic weight: Give dynamic weight bonus to population data updated within the last 30 days, giving priority to matching records with greater timeliness; Conflict resolution rules: If the same address code is associated with multiple conflicting population information, the household registration data will be used as the benchmark value. If the authoritative data is missing or conflicting, a verification task will be automatically generated and pushed to the grid worker's mobile terminal for on-site verification. The verification results will be written back to the database after manual review, and the association rules will be updated.

6. The standard address pipeline data association management method based on multi-stage collaborative coding according to claim 5 is characterized in that: An inverted index is constructed based on the address encoding feature vector, and a distributed query engine is used to achieve millisecond-level precision matching of addresses and population information. The details are as follows: Feature vectorization processing; specifically: ① Address code feature vector generation: convert the 21-bit address code into a multi-dimensional feature vector according to the segmentation rule: V = [administrative division (4), major category (2), medium category (2), minor category (2), serial number (9)]; ② Population feature vector expansion: extract related fields from the population database and construct a vector: P = [ID number (18), household registration code (21), insurance unit code (15)]; Construct a reverse index; the details are as follows: ① Design the index structure: Use each level of the address code feature vector as the key and the population data ID list as the value to build a multi-level inverted index; the first-level index is administrative division code → population ID list; the second-level index is administrative division code + classification code → population ID list; the third-level index is complete address code → population ID list; ② Index update mechanism: When an address code is added or modified, the index update is triggered through the message queue to ensure that the index is synchronized with the source data, and the latency is controlled within 100 milliseconds; Distributed query optimization, specifically: ① Query decomposition and parallel execution: Split related requests into multi-level sub-queries and distribute them to distributed computing nodes for parallel processing; ② Cache acceleration mechanism: Cache high-frequency query results and use the LRU strategy to manage cached data, ensuring a response time of less than 10 milliseconds when a cache hit occurs; Real-time association interface: Provides a RESTful API interface based on HTTP / HTTPS protocol, supporting JSON format requests and responses.

7. The standard address pipeline data association management method based on multi-stage collaborative coding according to claim 6 is characterized in that: The method also includes a conflict resolution mechanism: when the same address code is associated with multiple conflicting population information, the household registration data is prioritized; verification tasks are automatically generated and pushed to the grid worker terminal, and the database is updated after manual verification; This method also provides a real-time association interface through a RESTful API. The interface definition includes: request parameters: address code, query level; response parameters: matching population list, matching weight, response time; cross-departmental data synchronization delay is less than 1 second, and code generation and write-back are fully automated.

8. A standard address pipeline data association management system based on multi-level collaborative coding, characterized in that: The system includes: The coding rule formulation module is used to generate standard road names based on road pre-approval data, synthesize unified road identification codes according to the "4+6+9" segmentation rule, generate house numbers based on planning permit data, and generate unified courtyard identification codes based on the naming system. The generation of unified household identification codes is triggered by housing prediction data. Event monitoring and triggering module, used to define event types, monitoring methods and extract event data; The data standardization and encapsulation module is used to encapsulate the original data into a standardized JSON Schema and dynamically route it to the next processing node based on the event_type: ROAD_APPROVAL → Place Name Collaboration System; BUILDING_PERMIT → House Number Management System; The dynamic coding rule engine module is used to compile the rule base and store it in a relational database. It then queries the rule base and fills in parameters based on event_type, and finally splices and synthesizes the code according to the 4+6+9 rule. The data synchronization and verification module is used to write a write-back mechanism based on the HTTP / HTTPS protocol using the JSON-RPC standard, and then perform exception handling for uniqueness conflict detection and format verification. Among them, uniqueness conflict detection refers to comparing the historical records of the encoding library. If there is a duplicate, the serial number regeneration (serial_code+=1) is triggered; format verification verifies the legitimacy of the encoding using a regular expression: ^\d{4}(40|50|70)\d{6}\d{9}$; Association rule building module, used to build association rules based on address coding hierarchical features and multi-source data weight distribution strategy; The association path establishment module is used to implement association path establishment based on inverted index optimization and distributed query engine.

9. The standard address pipeline data association management system based on multi-stage collaborative coding according to claim 8, characterized in that: The "4+6+9" segmentation rule is as follows: the first four digits are the administrative division code at or above the county level according to the GB / T 2260 standard; the middle six digits contain the address type identifier and subclass code; the address type identifier is the 5th to 6th digits, i.e., 40 / 50 / 70, which respectively identify the road / courtyard / household; the subclass code is the 7th to 9th digits; the last 9 digits are a unique serial number generated based on the spatiotemporal double hashing algorithm; the generation logic of the middle 6-digit classification code is as follows: the 7th to 8th digits of the road code identify the road grade; the 7th to 8th digits of the courtyard code identify the community type; the 7th to 8th digits of the household code identify the use of the house; the spatiotemporal double hashing algorithm is as follows: the business event timestamp is hashed with SHA-256, and the first 5-digit timestamp hash segment is generated modulo; the geographic coordinate sequence is hashed and the last 4-digit coordinate hash segment is generated modulo; the timestamp and coordinate hash segments are concatenated to form a 9-digit unique serial number; The event monitoring and triggering modules include: The event type definition submodule is used to predefine trigger events at key nodes of the business system; The monitoring mode definition submodule is used to monitor events using database triggers and API interceptors. Database triggers capture business table changes through database logs; API interceptors embed interception logic at the business system interface layer to monitor specific interface calls. The event data extraction submodule is used to extract the business code field, spatial data field, and timestamp field; the timestamp field uses the ISO 8601 format and is accurate to milliseconds; The dynamic coding rule engine module includes: The rule matching submodule is used to query the rule base according to event_type; The parameter filling submodule is used to extract the administrative division diamante from data_payload.region_code, assign a value to the classification code according to the prefix rule, and then use "timestamp hash + coordinate hash" to generate a unique value to implement a custom serial number. The unique value generated by "timestamp hash + coordinate hash" is specifically as follows: a hash function is applied to the timestamp of the business event to generate an initial hash value, which is then mapped to an integer in the range of 0 to 999,999,999 through a modulo operation to generate the timestamp hash segment; a hash calculation is performed on the geographic coordinate data, also using a modulo operation to constrain it to the range of 0 to 999,999,999 to generate the coordinate hash segment; the first five significant digits of the timestamp hash segment and the first four significant digits of the coordinate hash segment are truncated, and the two are concatenated in the order of "first five digits + last four digits" to form a 9-digit unique serial number. The encoding synthesis submodule is used to splice according to the 4+6+9 rule, specifically: administrative division + road / main road + custom serial number → administrative division + road / main road + custom serial number.

10. The standard address pipeline data association management system based on multi-stage collaborative coding according to claim 8 or 9, characterized in that: The association rule building module includes: The hierarchical matching submodule is used to extract the first four digits of the administrative division code of the address code and accurately match it with the administrative division field of the household registration address in the population database, filtering out population data within the same administrative division to achieve administrative division hierarchical matching. It also limits the associated address type hierarchy based on the middle six-digit classification code of the address code: if the classification code begins with "40" (road type), only the legal person information of street-side businesses is associated; if the classification code begins with "50" or "70", the resident population information is associated to achieve address type hierarchical matching. At the same time, when accurate association with household-level data is required, the custom serial number after the nine digits of the address code is extracted and bound one-to-one with the house property certificate number or the household head's ID number to achieve accurate matching of unique identifiers. The weight allocation submodule is used to set static weight coefficients based on the authority and real-time nature of the data source to achieve static weights, and to assign dynamic weight bonuses to population data updated within the last 30 days, giving priority to matching records with greater timeliness to achieve dynamic weights: The conflict resolution submodule is used to prioritize household registration data as the benchmark value when the same address code is associated with multiple conflicting population information. If authoritative data is missing or conflicting, a verification task is automatically generated and pushed to the grid worker's mobile terminal for on-site verification. The verification results are manually reviewed and written back to the database, and the association rules are updated. The association pathway building module includes: The feature vector quantization processing submodule is used to generate the address code feature vector and expand the population feature vector; wherein, the address code feature vector is generated by converting the 21-bit address code into a multi-dimensional feature vector according to the segmentation rule: V = [administrative division (4), major category (2), medium category (2), minor category (2), serial number (9)]; the population feature vector expansion is to extract the associated fields from the population database and construct the vector: P = [ID number (18), household registration code (21), insurance unit code (15)]; The inverted index construction submodule is used to construct a multi-level inverted index using each level of the address code feature vector as the key and the population data ID list as the value. When the address code is added or modified, the index update is triggered through the message queue to ensure that the index is synchronized with the source data, and the latency is controlled within 100 milliseconds. Among them, the first-level index is administrative division code → population ID list; the second-level index is administrative division code + classification code → population ID list; the third-level index is complete address code → population ID list; The distributed query optimization submodule is used to split the associated request into multiple sub-queries, distribute them to distributed computing nodes for parallel processing, and cache the results of high-frequency queries. It uses the LRU strategy to manage cached data, and the response time for cache hits is less than 10 milliseconds. The real-time association interface submodule is used to provide a RESTful API interface based on the HTTP / HTTPS protocol and supports JSON format requests and responses.

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