A population level label fine identification method based on urban governance block data

Abstract

The application discloses a population hierarchical label fine identification method based on city governance block data and belongs to the technical field of data fine management. The steps of the application are as follows: 1, through a data aggregation method, original data is aggregated into a special topic library; 2, the related fields of the city governance block data involved in the special topic library are normalized; 3, hierarchical label identification; 4, according to the business source constraint, the subject to be labeled is identified from the special topic library; 5, according to the end-level labeling rule constraint and the block hierarchical attribution constraint, the end-level label attribution party of the current label labeling is identified; 6, according to the block hierarchical attribution constraint, the upper block hierarchical attribution to be labeled is identified; 7, according to the labeling mode and frequency constraint, the label result is continuously and dynamically updated. The application provides more efficient block hierarchical label fine identification services for city governance, and is particularly suitable for label accurate identification in the field of grassroots governance population.

Description

Technical Field

[0001] This invention relates to the field of data refinement governance technology, and more specifically, to a method for hierarchical labeling of urban governance block data. Background Technology

[0002] In urban governance, it is common practice to label the population with various types of government data, social data, community data, property data, and sensor data to assist management and service departments in more accurately identifying residents and thus providing more efficient services. However, as urban governance operations gradually penetrate from the city and district levels to townships, streets, administrative villages, and communities, the same entity may have multiple different, or even completely opposite, label identities depending on the location of various operations and at different spatial levels (community, grid, village, town / street, district / county, etc.).

[0003] When grid or community-level spatial blocks need to manage dog owners within their jurisdiction, they must mark residents as dog owners. A resident may reside in two grids (A and B) simultaneously (multiple properties), owning a dog in grid A but not in grid B. In this case, grid A should be marked as "dog owned," grid B as "no dog owned," while the community level should mark it as "dog owned."

[0004] Currently, there are two main methods for handling label annotation:

[0005] Method 1: A single set of tags for the entire system

[0006] In this approach, the entire system shares a single tagging system and results. If a subject is tagged, any user can see the same tag whenever they query. Similarly, if a user updates a subject's tag, anyone within the system will see the updated tag information.

[0007] Method 2: Each operator receives a set of labels.

[0008] In this method, the tags assigned by operators are independent of each other. Operator B cannot see the tags assigned by operator A to a certain entity. When operator B searches for the details of the entities tagged with a certain tag, he can only see the results of his own tagging and cannot see the results of operator A's tagging.

[0009] Neither of the above two methods can meet the demand for more efficient and refined labeling services for geographic and administrative block data at all levels as urban governance operations gradually penetrate from the city and district / county levels to the town / street, village / community, and grid levels. Summary of the Invention

[0010] 1. The technical problem that the invention aims to solve

[0011] In view of the problems existing in the prior art, the present invention provides a hierarchical labeling method based on urban governance block data. The present invention combines multi-level spatial block data in urban governance with traditional portrait labeling and marking technology, thereby helping urban governance business to gradually penetrate from the city and district / county levels to the town / street, village / community, and grid levels, providing more efficient block-level labeling services for urban governance, and is particularly suitable for accurate labeling and marking in the population field of grassroots governance.

[0012] 2. Technical Solution

[0013] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0014] The present invention provides a method for fine-grained identification of population hierarchical labels based on urban governance block data, the steps of which are as follows:

[0015] Step 1: Data Aggregation: The raw data is aggregated into the thematic database using data aggregation methods.

[0016] Step 2, Data Governance: Organize the relevant fields for identifying urban governance block data in the thematic database;

[0017] Step 3: Hierarchical Label Identification: Identify hierarchical label constraints, and clarify the label name, business source constraints, labeling rule constraints, block level attribution constraints, and labeling method and frequency constraints;

[0018] Step 4: Identification of the subject to be annotated: Based on the constraints of business source, identify the subjects to be annotated from the topic database;

[0019] Step 5: Identify the owner of the last-level label: Based on the constraints of the last-level labeling rules and the block-level ownership constraints, identify the owner of the last-level label for the current labeling.

[0020] Step 6: Identify the parent block level label owner: Based on the block level ownership constraints, identify the parent block level owner that needs to be tagged;

[0021] Step 7: Update the labeling results: Based on the labeling method and frequency constraints, continuously and dynamically update the labeling results.

[0022] Furthermore, the data aggregation method described in step one includes database exchange, interface connection, spreadsheet data import, and business system entry; the information aggregated into the thematic database in step one must include specific business data fields and urban governance block data identifier fields, and the identifier fields must include the block data level to which the current data belongs.

[0023] Furthermore, the specific method of regularization in step two is as follows:

[0024] ① Identify the block data identifier field and determine whether its field type belongs to a block data independent identifier field or a block data combined identifier field; the block data independent identifier field contains two or more block data levels, and each field in the block data combined identifier field stores only one block data level identifier, and the block data level is reflected through multiple fields;

[0025] ②According to the city governance block hierarchy, the block data is standardized, and the block data is independently identified by fields. The fields are then broken down according to the block data hierarchy to make them consistent with the block data combination identifier fields; the block data combination identifier fields are not processed.

[0026] ③ Determine the validity of the split data blocks;

[0027] ④ Supplement the missing levels in the block data.

[0028] Furthermore, the labeling rule constraints described in step three include both the final-level labeling rule constraints and the higher-level convergence rule constraints, wherein:

[0029] Label Name: The name of the label;

[0030] Business source constraints: Specify the scope of the topic library to participate in tag calculation;

[0031] Last-level tagging rule constraints: Define the tagging rules for the last-level business fields in the block data hierarchy;

[0032] Upper-level aggregation rule constraints: Define the rules for generating labels at the lowest level and above in the block data hierarchy;

[0033] Block hierarchy attribution constraint: Defines whether tagging operations are performed at the block data hierarchy level;

[0034] Tagging method and frequency constraints: Define the tag update rules.

[0035] Furthermore, the block data hierarchy is as follows: district / county level, town / street level, village / community level, grid level, community level, and building level.

[0036] Furthermore, the label update rules include one-time labeling, cyclical labeling by time interval, and cyclical labeling by data governance update frequency.

[0037] 3. Beneficial effects

[0038] Compared with existing known technologies, the technical solution provided by this invention has the following significant advantages:

[0039] (1) The present invention provides a hierarchical labeling method based on urban governance block data, which solves the problem of inflexible labeling results in the existing technology where the whole system has a set of label data. It also avoids the problem of excessive computational consumption of the system and the inability to share label results among multiple people in the method where each operator has a set of label data.

[0040] (2) The present invention provides a hierarchical labeling method based on urban governance block data. The labeling results can be selected and labeled according to business needs, and the labeled subjects are optimized and screened, thereby improving the overall labeling efficiency.

[0041] (3) The present invention provides a hierarchical labeling method for fine identification based on urban governance block data, which perfectly combines the application scenarios of urban governance block data and provides complete support for the fine labeling of multi-level geographic administrative spatial block data, from basic housing units to buildings, communities, grids, villages, towns and streets. Attached Figure Description

[0042] Figure 1 This is a flowchart of a hierarchical labeling method for fine-grained identification based on urban governance block data according to the present invention;

[0043] Figure 2 This is a schematic diagram of the preset block hierarchy for urban governance block data. Detailed Implementation

[0044] To further understand the content of this invention, a detailed description of the invention will be provided in conjunction with the accompanying drawings and embodiments.

[0045] Example 1

[0046] In the context of urban governance being decentralized to towns, streets, villages, and grid management, traditional labeling of people is no longer applicable. A large number of labels are generated after people interact with other entities (such as houses or communities) or services (such as vaccination services or urban dog management services).

[0047] Because people behave differently at different times and places, and each street, community, and grid has a different perspective on people.

[0048] For example, resident A owns houses in two communities, but keeps a dog in his house in community A. In community A, where the dog is kept, this person will be considered a dog owner and labeled as such; however, in community B, where no dog is kept, this label will not be applied. However, from the perspective of the town / street, since he keeps a dog in community A but not in community B, he should be labeled as a dog owner by the street administration.

[0049] Based on the above problem analysis, it can be concluded that:

[0050] ① A single set of tags for the entire system cannot meet the requirements: Community B cannot delete resident A's dog ownership tag because once deleted, the tag results for Community A will be inaccurate.

[0051] ② Displaying a single set of tags for each operator is insufficient to meet the needs: Villages and neighborhoods may have multiple operators. These operators may require information on specific dog owners in a particular village or town / street, making it impossible to respond using an independent tagging system and data for each operator.

[0052] The above business requirements mean that the labels cannot be a single set of results shared across the entire system; nor can a single set of labels be displayed for each operator.

[0053] Combination Figure 1 and Figure 2 This embodiment provides a method for fine-grained hierarchical labeling based on urban governance block data, which includes the following steps:

[0054] Step 1: Data Aggregation. Using various data aggregation methods, such as database exchange, interface integration, spreadsheet data import, and business system entry, the raw data is aggregated into the respective thematic databases. The exchanged information must include specific business data fields and urban governance block data identifier fields. Identifier fields typically include the building, residential area, grid, community, town / street, or district / county to which the current data belongs.

[0055] Step Two: Data Governance: Organize the data identifier fields related to urban governance blocks in the thematic database. The specific method is as follows:

[0056] ① Identify block data identifier fields: Identify the block data identifier fields and determine their field type. Types include: independent block data identifier fields and combined block data identifier fields.

[0057] Block data independent identifier field definition: contains two or more block data levels, such as a certain community / a certain grid, a certain community / a certain neighborhood / a certain building.

[0058] Block data composition identifier field definition: Each field stores only one block data level identifier, and the block data level is represented by multiple fields. These multiple fields constitute the block data composition identifier field.

[0059] ② Organize block data according to the city governance block level:

[0060] Independent identifier fields for block data: Decompose the field according to the block data hierarchy. Make it consistent with the combined identifier fields of the block data.

[0061] No processing is performed on the block data combination identifier field.

[0062] ③ Perform validity checks on the split block data: Check each field to ensure that the stored information is a single, specific block-level data; based on the built-in block-level data list (district / county, town / street, village / residential area, grid, community, building), determine that the specific block level of each field value exists and is correct. Remove data rows corresponding to fields with abnormal block level identifiers.

[0063] ④ Supplement missing levels in block data: Check the block data combination identifier field and, based on the built-in block data list, supplement the missing level data upwards. For example, if the block data combination identifier field only contains grid, community, and building information, then it is necessary to supplement the information upwards to district / county, town / street, and village / residential information.

[0064] Step 3: Hierarchical label identification:

[0065] The hierarchical tags include the following information: tag name, business source constraints, tagging rule constraints, block level attribution constraints, and tagging method and frequency constraints.

[0066] ①Label Name: Text data indicating the name of the label;

[0067] ② Business source constraints: Specify the scope of the topic library participating in the tag calculation. The topic library must contain the business data participating in the tag rule calculation and the identifier field of the block data after normalization.

[0068] ③ Last-level tagging rule constraints: Define the tagging rules for the last-level business fields in the block data hierarchy. Specific rules include the following:

[0069] String: equality, inequality, contain, do not contain, null value comparison;

[0070] Numbers: greater than, greater than or equal to, less than, less than or equal to, equal to, not equal to, and empty condition checks;

[0071] Dictionary entries: equal to, not equal to, empty condition;

[0072] Date: Range (inclusive of start and end, inclusive of start but exclusive of end, exclusive of start but inclusive of end, exclusive of start and exclusive of start and end), start of range, end of range;

[0073] ④ Upper-level aggregation rule constraints: Define the rules for generating labels at the lowest level and above in the block data hierarchy, as follows:

[0074] Inclusion rule: If each last-level tag contains a specified tag, the parent tag is considered to need to be tagged;

[0075] Exclusion rule: If the specified tag is not included in any of the last-level tags, the higher-level tag is considered to need to be tagged;

[0076] Combination rule: If each last-level tag contains the specified tag A but does not contain the specified tag B, the parent tag is considered to need to be tagged;

[0077] ⑤ Block Hierarchy Constraints: Define whether the block data hierarchy should be tagged. The block data hierarchies that can be configurable for tagging are: district / county level, town / street level, village / community level, grid level, community level, and building level.

[0078] ⑥ Tagging method and frequency constraints: Define tag update rules, including one-time tagging, cyclic tagging by time interval, and cyclic tagging by data governance update frequency.

[0079] Step 4: Identification of the Subject to be Annotated: Based on the constraints of business source, identify the subjects to be annotated from the business topic database;

[0080] Step 5: Identify the owner of the last-level label: Based on the constraints of the last-level labeling rules and the block-level ownership constraints, identify the owner of the last-level label for the current labeling.

[0081] Step 6: Identify the parent block level label owner: Based on the block level ownership constraints, identify the parent block level owner that needs to be tagged.

[0082] Step 7: Update the labeling results: Based on the labeling method and frequency constraints, continuously and dynamically update the labeling results.

[0083] Specific implementation examples are as follows:

[0084] Tag metadata:

[0085] Tag name: Dog owners

[0086] Business source constraints: Dog ownership ledger for XX residential community

[0087] Final-level tagging rule constraint: Number of dogs owned > 0

[0088] Superior aggregation rule constraint: If this tag appears in the last level, the superior level must inherit the tag.

[0089] Block-level hierarchical constraints: building level, community level

[0090] Marking method and frequency constraints: one-time marking

[0091] Example data:

[0092] Because Mr. Zhang owns two properties in the current residential complex, there are two entries for him in the ledger database.

[0093] Name Number of dogs residential housing Building of Belonging Belonging to the community Zhang 1 Room 101, Building 1 Building 1 Community A Zhang 0 Room 203, Building 3 3 buildings Community A

[0094] The label calculation result according to this embodiment is as follows:

[0095] Building hierarchy:

[0096] Building 1 is marked for dog owners

[0097] 3 buildings were not marked

[0098] Community level:

[0099] Community A is marked as a place for dog owners

[0100] The results of existing technical annotations:

[0101] 1. A single label for the entire system: Zhang is labeled as a dog owner.

[0102] Question: When screening dog owners in Building 3, Mr. Zhang should not be marked, because Mr. Zhang does not own a dog in Building 3.

[0103] 2. Each operator displays a set of tags: Operator Xiao Wang tags Zhang as a dog owner, but other staff cannot see this tagging result and cannot share Xiao Wang's data information.

[0104] In summary, this embodiment solves the problem of inflexibility in the existing technology of using a single set of label data for the entire system. It also avoids the excessive computational burden on the system and the inability to share label results among multiple users, which is problematic with each operator having their own set of label data. This embodiment perfectly integrates with the application scenario of urban governance block data, providing comprehensive support for refined label recognition of multi-level geographic administrative spatial block data, from basic housing units to buildings, communities, grids, villages, towns, and streets.

[0105] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the figures shown are only one embodiment of the present invention; the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for fine-grained identification of population hierarchy labels based on urban governance block data, characterized in that, The steps are as follows: Step 1: Data Aggregation: The raw data is aggregated into the thematic database using data aggregation methods. The information aggregated into the thematic database includes specific business data fields and urban governance block data identifier fields. The identifier fields include the block data level to which the current data belongs. Step 2, Data Governance: The data identifier fields of the urban governance blocks involved in the thematic database are standardized; the specific standardization method is as follows: ① Identify the block data identifier field and determine whether its field type belongs to the block data independent identifier field or the block data combined identifier field; the block data independent identifier field contains two or more block data levels, and each field in the block data combined identifier field stores only one block data level identifier, and the block data level is reflected through multiple fields; ②According to the city governance block hierarchy, the block data is standardized, and the block data is independently identified by fields. The fields are then broken down according to the block data hierarchy to make them consistent with the block data combination identifier fields; the block data combination identifier fields are not processed. ③ Determine the validity of the split block data: Check each field to see if the stored information is a single specific block-level data; Based on the built-in block-level data list, determine whether the specific block level of each field value exists and is correct; Remove the data rows corresponding to the abnormal block level identifier field; ④ Supplement missing levels in block data: Check the block data combination identifier field and, based on the built-in block data list, supplement the missing level data upwards; Step 3: Hierarchical label identification: Hierarchical tags include the following information: tag name, business source constraints, tagging rule constraints, block hierarchy belonging constraints, and tagging method and frequency constraints; ①Label Name: Text data indicating the name of the label; ② Business Source Constraints: Specify the scope of the topic library participating in the tag calculation. The topic library includes business data participating in the tag rule calculation and the identifier field of the block data after normalization. ③ Last-level labeling rule constraints: Define the labeling rules for the last-level business fields in the block data hierarchy; ④ Upper-level aggregation rule constraints: Define the rules for generating labels at the lowest level and above in the block data hierarchy, as follows: Inclusion rule: If each last-level tag contains a specified tag, the parent tag is considered to need to be tagged; Exclusion rule: If the specified tag is not included in any of the last-level tags, the higher-level tag is considered to need to be tagged; Combination rule: If each last-level tag contains the specified tag A but does not contain the specified tag B, the parent tag is considered to need to be tagged; ⑤ Block hierarchy ownership constraint: Defines whether the tagging operation is performed at the block data hierarchy level; ⑥ Tagging method and frequency constraints: Define tag update rules, including one-time tagging, cyclic tagging by time interval, and cyclic tagging by data governance update frequency; Step 4: Identification of the subject to be annotated: Based on the constraints of business source, identify the subjects to be annotated from the topic database; Step 5: Identify the owner of the last-level label: Based on the constraints of the last-level labeling rules and the block-level ownership constraints, identify the owner of the last-level label for the current labeling. Step 6: Identify the parent block level label owner: Based on the block level ownership constraints, identify the parent block level owner that needs to be tagged; Step 7: Update the labeling results: Based on the labeling method and frequency constraints, continuously and dynamically update the labeling results.

2. The method for fine-grained identification of population hierarchy labels based on urban governance block data according to claim 1, characterized in that: The data aggregation methods in step one include database exchange, interface integration, spreadsheet data import, and business system data entry.

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