A linear cross-administrative region policy data collaborative analysis and consistency evaluation method and system
By constructing policy data objects containing administrative region identifiers and linear spatial segment identifiers, and performing consistency calculations on policy texts, collaborative events, and implementation, a segment-level policy synergy index is generated. This solves the problems of structured alignment and heterogeneous data in cross-administrative region policy evaluation, and realizes quantitative evaluation and spatial representation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
- Filing Date
- 2026-03-26
- Publication Date
- 2026-06-26
AI Technical Summary
Existing cross-administrative region policy coordination assessment methods fail to effectively identify the hierarchical alignment of policy provisions, do not consider event time decay and source credibility, have heterogeneous data sources leading to incomparable assessment results, and are unable to reflect differences in linear spatial objects.
By constructing policy data objects containing administrative region identifiers, linear spatial segment identifiers, and timestamps, we can calculate policy text consistency, collaborative event consistency, and policy implementation consistency, generate a segment-level policy coordination index, and map it to linear cross-administrative region spatial objects.
It enables automated processing of cross-administrative region policy data, generates quantitative assessment results, supports segment-level spatial expression of policy synergy, identifies synergy differences between different segments, and is applicable to multiple types of linear cross-administrative region spatial objects.
Smart Images
Figure CN122288104A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of computer information processing and spatial information technology, specifically to a method and system for linear cross-administrative region policy data collaborative analysis and consistency assessment. Background Technology
[0002] Existing methods for evaluating cross-administrative region policy coordination mostly rely on qualitative analysis or statistical summarization, which have the following shortcomings: First, policy texts only perform overall similarity calculations without structuring alignment of policy clauses at different levels, making it difficult to identify specific coordinated or conflicting clauses.
[0003] Second, cross-regional collaborative events are mostly represented by simple frequency statistics, without considering event decay over time and the credibility of the source;
[0004] Third, the data sources for the implementation results are heterogeneous, and the statistical standards for the indicators are inconsistent, resulting in incomparable evaluation results;
[0005] Fourth, existing methods do not construct segment-level mapping mechanisms for linear spatial objects, such as watersheds, channels, and corridors, making it difficult to reflect differences along the route. Therefore, a method and system for collaborative analysis and consistency assessment of policy data across administrative regions is proposed. Summary of the Invention
[0006] The purpose of this invention is to provide a method and system for linear cross-administrative region policy data collaborative analysis and consistency assessment, so as to solve the problems mentioned in the background art.
[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for linear cross-administrative region policy data collaborative analysis and consistency assessment, comprising the following steps:
[0008] Step 1: Obtain policy text data, cross-administrative region collaboration event data, and policy implementation result data from multiple administrative regions;
[0009] Step 2: Perform structured processing on policy text data, collaborative event data, and policy implementation result data to construct policy data objects containing administrative region identifiers, linear spatial segment identifiers, and timestamps;
[0010] Step 3: Within the same linear spatial segment, perform consistency calculations on policy text data from different administrative regions to generate segment-level policy text consistency indicators;
[0011] Step 4: Within the same linear spatial segment, process the cross-administrative region collaborative event data to generate segment-level collaborative event consistency indicators;
[0012] Step 5: Within the same linear spatial segment, perform consistency calculations on the policy implementation result data to generate segment-level policy implementation consistency indicators;
[0013] Step 6: Generate a segment-level policy coordination index based on policy text consistency indicators, collaborative event consistency indicators, and policy implementation consistency indicators;
[0014] Step 7: Map the policy coordination index to the corresponding segment of the linear cross-administrative region spatial object to realize the segment-level spatial expression of the degree of cross-administrative region policy coordination.
[0015] Preferably, in step two, the policy data object includes at least an administrative region identifier, a linear spatial segment identifier, a policy release timestamp, policy text content, a set of collaborative event records, and a set of policy implementation result indicators, wherein the linear spatial segment identifier is determined according to the segmentation rules of linear cross-administrative region spatial objects.
[0016] Preferably, in step three, the policy text consistency calculation includes segmenting the policy text at the clause level, performing feature vectorization on the segmented clauses, and calculating the segment-level policy text consistency index based on the similarity between the clause feature vectors.
[0017] Preferably, in step four, the generation of the collaborative event consistency index includes: classifying cross-administrative region collaborative events by event type, and within the same linear spatial segment, calculating the segment-level collaborative event consistency index based on the occurrence frequency of each type of collaborative event and a preset weight.
[0018] Preferably, in step five, the generation of the policy implementation consistency index includes mapping and normalizing the policy implementation result data of different administrative regions, and calculating the consistency of policy implementation results within the same linear spatial segment.
[0019] Preferably, in step six, the policy coordination index is obtained by weighted fusion calculation of policy text consistency index, collaborative event consistency index, and policy implementation consistency index.
[0020] Preferably, in step seven, the linear cross-administrative-region spatial objects include heritage corridors, watersheds, or transportation corridors.
[0021] The present invention also provides a system for a linear cross-administrative region policy data collaborative analysis and consistency assessment method. The system includes a processor and a memory, wherein the memory stores a computer program, which, when executed by the processor, is used to implement the method.
[0022] Preferably, the system interacts with a geographic information system to achieve spatial display of the policy synergy index.
[0023] Preferably, the system further includes a data acquisition and object construction module, a policy text consistency calculation module, a collaborative event consistency calculation module, a policy implementation consistency assessment module, and a policy coordination index generation and spatial mapping module;
[0024] The data acquisition and object construction module is connected to the policy text consistency calculation module, the policy text consistency calculation module is connected to the collaborative event consistency calculation module, the collaborative event consistency calculation module is connected to the policy implementation consistency assessment module, and the policy implementation consistency assessment module is connected to the policy coordination index generation and spatial mapping module.
[0025] The data acquisition and object construction module is used to acquire cross-administrative region policy data and construct policy data objects;
[0026] The policy text consistency calculation module is used to generate policy text consistency indicators within the same linear spatial segment.
[0027] The collaborative event consistency calculation module is used to generate segment-level collaborative event consistency indicators;
[0028] The policy implementation consistency assessment module is used to generate policy implementation consistency indicators;
[0029] The policy coordination index generation and spatial mapping module is used to generate policy coordination indices and achieve linear spatial mapping.
[0030] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:
[0031] I. This invention constructs a policy data object containing linear spatial segment identifiers, transforming cross-administrative region policy data, which originally existed in the form of unstructured text, into a data object that can be processed by a computer, thereby achieving automated processing of cross-administrative region policy data and reducing the involvement of manual analysis.
[0032] Second, under the constraint of the same linear spatial segment, this invention jointly calculates the consistency of policy text, the consistency of collaborative events, and the consistency of policy implementation to generate a segment-level policy coordination index, thereby achieving a quantitative assessment of the degree of policy coordination across administrative regions.
[0033] Third, by mapping the segment-level policy coordination index to the corresponding segment of a linear cross-administrative-region spatial object, the segment-level spatial expression of the degree of policy coordination is realized, which facilitates the identification of coordination differences between different segments.
[0034] Fourth, this invention does not depend on specific spatial types and can be applied to various linear cross-administrative-region spatial objects such as heritage corridors, watersheds, and transportation corridors. It is applicable to various linear cross-administrative-region spatial objects and has good versatility and promotional application value. Attached Figure Description
[0035] Figure 1 This is a flowchart of the present invention;
[0036] Figure 2 This is a schematic diagram of the method of the present invention;
[0037] Figure 3 This is a schematic diagram of the system modules of the present invention. Detailed Implementation
[0038] Example
[0039] Please see Figure 1-3 This invention provides a technical solution: a method for linear cross-administrative region policy data collaborative analysis and consistency assessment, comprising the following steps:
[0040] Step 1: Obtain policy text data, cross-administrative region collaboration event data, and policy implementation result data from multiple administrative regions;
[0041] Policy data acquisition and dataset construction: acquiring policy text data, cross-administrative region collaborative event data, and policy implementation result data from multiple administrative regions using computer equipment;
[0042] The system acquires policy text data from multiple administrative regions, including laws and regulations, special plans, and policy documents, as well as cross-administrative region collaboration event data and policy implementation result data. The system performs unified structured processing on the above data to construct policy data objects containing administrative region identifiers, linear spatial segment identifiers, timestamps, and data content, forming a policy dataset for subsequent consistency calculations and spatial mapping.
[0043] The data undergoes unified structuring processing to construct a PolicyRecord data object. A PolicyRecord data object includes at least the following:
[0044] The administrative region identifier is admin_id; the linear spatial segment identifier is segment_id; the policy release timestamp is t_issue; the policy text content is text_raw; the collaborative event record set is event_list; and the execution result indicator set is outcome_list.
[0045] Among them, segment_id is determined according to the segmentation rules of linear cross-administrative region spatial objects and is used to constrain the spatial range of subsequent consistency calculations.
[0046] The above methods transform cross-administrative region policy information, which was originally in an unstructured form, into policy data objects that can be processed by computers.
[0047] Step 2: Perform structured processing on policy text data, collaborative event data, and policy implementation result data to construct policy data objects containing administrative region identifiers, linear spatial segment identifiers, and timestamps;
[0048] Policy data objects include at least administrative region identifiers, linear spatial segment identifiers, policy release timestamps, policy text content, a set of collaborative event records, and a set of policy implementation result indicators. The linear spatial segment identifiers are determined according to the segmentation rules of linear cross-administrative region spatial objects.
[0049] Policy text data processing and consistency calculation: After obtaining policy text data through computer equipment, the processor executes the computer program stored in memory to preprocess the policy text data, including word segmentation, noise reduction and keyword extraction.
[0050] The policy text data is processed by word segmentation, noise reduction and clause-level segmentation, and clause feature vectors are generated. Based on the clause feature vectors, the consistency between policy texts of different administrative regions is calculated within the same linear space segment, and segment-level policy text consistency data is generated.
[0051] The policy text is segmented at the clause level to generate a set of clauses:
[0052] ;
[0053] Each policy text is represented as a feature vector:
[0054] ;
[0055] in, This indicates that the k-th keyword is in clause C. ij In the TF-IDF weights of each keyword.
[0056] Within the same linear segment, clause sets from different administrative regions are matched one-to-one to generate clause alignment sets. And calculate the policy text consistency within segment m:
[0057] ;
[0058] Step 3: Within the same linear spatial segment, perform consistency calculations on policy text data from different administrative regions to generate segment-level policy text consistency indicators;
[0059] The policy text consistency calculation includes segmenting the policy text at the clause level, performing feature vectorization on the segmented clauses, and calculating the segment-level policy text consistency index based on the similarity between the clause feature vectors.
[0060] Cross-administrative region collaborative event data are classified according to event type, and within the same linear spatial segment, segment-level collaborative event consistency data is calculated based on the frequency of occurrence of each type of event and its corresponding weight.
[0061] The segment constraint consistency modeling for cross-administrative region collaborative events involves classifying these events by event type and pre-setting a weight coefficient 'a' for each event type. k Within the same linear segment m, the intensity C of cooperative events m Defined as:
[0062] ;
[0063] Among them, a k f is the weight coefficient for the k-th type of collaborative event. km Let K be the number of times the k-th type of collaborative event occurs in segment m, and K be the total number of collaborative event types.
[0064] Step 4: Within the same linear spatial segment, process the cross-administrative region collaborative event data to generate segment-level collaborative event consistency indicators; perform indicator caliber mapping and normalization processing on the policy implementation result data, calculate the consistency between policy implementation results of different administrative regions within the same linear spatial segment, and generate segment-level policy implementation consistency data.
[0065] The generation of the consistency index for collaborative events includes: classifying cross-administrative region collaborative events by event type, and calculating the segment-level consistency index for collaborative events within the same linear spatial segment based on the frequency of occurrence of each type of collaborative event and the preset weight.
[0066] Cross-regional mapping and consistency calculation of policy implementation results: mapping the indicator definitions of policy implementation result data from different administrative regions, and unifying heterogeneous indicators into a set of standard indicators.
[0067] The mapped execution result metrics are normalized to generate execution result feature vectors;
[0068] Within the same linear segment, based on the normalized feature vector of the execution results, the degree of difference between the execution results of different administrative regions is calculated, and the segment-level execution consistency index E is generated through reverse mapping of the degree of difference. m ;
[0069] The following is the normalized execution result vector:
[0070] Let the linear segment be m, and the set of administrative regions participating in this segment be m. The set of standard indicators for execution results is as follows The normalized result of the execution of administrative region a in segment m is given as a vector:
[0071] ;
[0072] Intra-segment variation (Average pairwise distance), using the average pairwise L1 distance:
[0073] ;
[0074] but ;
[0075] Inverse mapping of differences yields execution consistency. :
[0076] ;
[0077] thereby Furthermore, the smaller the difference, the higher the consistency.
[0078] Step 5: Within the same linear spatial segment, perform consistency calculations on policy implementation result data to generate segment-level policy implementation consistency indicators; the processor then performs fusion calculations on policy text consistency data, collaborative event consistency data, and policy implementation consistency data to generate segment-level policy coordination indexes. These policy coordination indexes are then mapped to the corresponding segments of linear cross-administrative region spatial objects to achieve segment-level spatial expression of the degree of policy coordination.
[0079] The generation of policy implementation consistency indicators involves mapping and normalizing the policy implementation results data of different administrative regions, and calculating the consistency of policy implementation results within the same linear spatial segment.
[0080] The policy coordination index is generated under linear segment constraints. This index is a segment-level consistency fusion indicator used to characterize the coordination status of a linear cross-administrative region policy system. Within the same linear segment m, it is based on the consistency S of policy texts. m Collaborative event intensity C m and the consistency index E m Generate a policy coordination index:
[0081] The policy coordination index (PCI) within segment m m Defined as:
[0082] ;
[0083] in, These are the weight parameters.
[0084] Step 6: Generate a segment-level policy coordination index based on policy text consistency indicators, collaborative event consistency indicators, and policy implementation consistency indicators;
[0085] The policy coordination index is obtained by weighted fusion calculation of policy text consistency index, collaborative event consistency index, and policy implementation consistency index.
[0086] The policy coordination index spatial mapping is obtained by weighted fusion calculation of policy text consistency indicators, collaborative event consistency indicators, and policy implementation consistency indicators.
[0087] Step 7: Map the policy coordination index to the corresponding segments of linear cross-administrative region spatial objects to achieve segment-level spatial expression of the degree of cross-administrative region policy coordination. Linear cross-administrative region spatial objects include heritage corridors, watersheds, or transportation corridors.
[0088] Example 2
[0089] The difference between this embodiment and Embodiment 1 is that:
[0090] The present invention also provides a system for a linear cross-administrative region policy data collaborative analysis and consistency assessment method. The system includes a processor and a memory. The memory stores a computer program, which, when executed by the processor, is used to implement the above-mentioned method steps.
[0091] The system interacts with a geographic information system to display the spatial representation of the policy coordination index; the system is deployed on a server or local terminal and can interact with a geographic information system.
[0092] In this embodiment, the calculation of policy text consistency, the calculation of collaborative event consistency, and the calculation of policy synergy index are all completed by the processor in the computer device executing the computer program stored in the memory.
[0093] Policy text feature vectors are stored in memory in vector or matrix form, while collaborative event consistency data and policy implementation consistency data are stored in structured record form. Through the above methods, automated processing and segment-level collaborative analysis of cross-administrative region policy data are realized, and linear spatial mapping and visualization output of policy collaboration results are supported.
[0094] The system also includes a data acquisition and object construction module, a policy text consistency calculation module, a collaborative event consistency calculation module, a policy implementation consistency assessment module, and a policy coordination index generation and spatial mapping module;
[0095] The data acquisition and object construction module is connected to the policy text consistency calculation module; the policy text consistency calculation module is connected to the collaborative event consistency calculation module; the collaborative event consistency calculation module is connected to the policy implementation consistency assessment module; and the policy implementation consistency assessment module is connected to the policy coordination index generation and spatial mapping module.
[0096] The data acquisition and object construction module is used to acquire cross-administrative region policy data and construct policy data objects; it acquires policy text data, cross-administrative region collaborative event data, and policy implementation result data issued by multiple administrative regions, and performs structured processing on the data to construct policy data objects containing administrative region identifiers, linear spatial segment identifiers, and timestamps.
[0097] The policy text consistency calculation module is used to generate policy text consistency indicators within the same linear space segment; within the same linear space segment, the policy text is segmented at the clause level and processed into feature vectors, and the segment-level policy text consistency indicators are calculated based on the clause feature vectors.
[0098] The collaborative event consistency calculation module is used to generate segment-level collaborative event consistency indicators; it classifies cross-administrative region collaborative events by event type and calculates segment-level collaborative event consistency indicators within the same linear spatial segment.
[0099] The policy implementation consistency assessment module is used to generate policy implementation consistency indicators; it performs indicator mapping and normalization on policy implementation result data from different administrative regions, and generates policy implementation consistency indicators within the same linear spatial segment.
[0100] The policy coordination index generation and spatial mapping module is used to generate policy coordination indices and realize linear spatial mapping. It generates segment-level policy coordination indices based on policy text consistency indicators, collaborative event consistency indicators, and policy implementation consistency indicators, and maps the policy coordination indices to the corresponding segments of linear cross-administrative region spatial objects.
[0101] In summary, this invention transforms cross-administrative region policy data, which originally existed as unstructured text, into a computer-processable data object by constructing a policy data object containing linear spatial segment identifiers. This achieves automated processing of cross-administrative region policy data and reduces the need for manual analysis.
[0102] This invention, under the constraint of the same linear spatial segment, jointly calculates the consistency of policy text, the consistency of collaborative events, and the consistency of policy implementation to generate a segment-level policy coordination index, thereby achieving a quantitative assessment of the degree of policy coordination across administrative regions.
[0103] By mapping the segment-level policy coordination index to the corresponding segment of a linear cross-administrative-region spatial object, a segment-level spatial expression of the degree of policy coordination can be achieved, making it easier to identify coordination differences between different segments.
[0104] This invention is not dependent on specific spatial types and can be applied to various linear cross-administrative-region spatial objects such as heritage corridors, watersheds, and transportation corridors. It is applicable to various linear cross-administrative-region spatial objects and has good versatility and promotional application value.
[0105] Those skilled in the art will understand that the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.
Claims
1. A linear cross-administrative region policy data collaborative analysis and consistency assessment method, characterized in that, Includes the following steps: Step 1: Obtain policy text data, cross-administrative region collaboration event data, and policy implementation result data from multiple administrative regions; Step 2: Perform structured processing on policy text data, collaborative event data, and policy implementation result data to construct policy data objects containing administrative region identifiers, linear spatial segment identifiers, and timestamps; Step 3: Within the same linear spatial segment, perform consistency calculations on policy text data from different administrative regions to generate segment-level policy text consistency indicators; Step 4: Within the same linear spatial segment, process the cross-administrative region collaborative event data to generate segment-level collaborative event consistency indicators; Step 5: Within the same linear spatial segment, perform consistency calculations on the policy implementation result data to generate segment-level policy implementation consistency indicators; Step 6: Generate a segment-level policy coordination index based on policy text consistency indicators, collaborative event consistency indicators, and policy implementation consistency indicators; Step 7: Map the policy coordination index to the corresponding segment of the linear cross-administrative region spatial object to realize the segment-level spatial expression of the degree of cross-administrative region policy coordination.
2. The linear cross-administrative region policy data collaborative analysis and consistency assessment method according to claim 1, characterized in that, In step two, the policy data object includes at least an administrative region identifier, a linear spatial segment identifier, a policy release timestamp, policy text content, a set of collaborative event records, and a set of policy implementation result indicators. The linear spatial segment identifier is determined according to the segmentation rules of linear cross-administrative region spatial objects.
3. The linear cross-administrative region policy data collaborative analysis and consistency assessment method according to claim 1, characterized in that, In step three, the policy text consistency calculation includes segmenting the policy text at the clause level, performing feature vectorization on the segmented clauses, and calculating the segment-level policy text consistency index based on the similarity between the clause feature vectors.
4. The linear cross-administrative region policy data collaborative analysis and consistency assessment method according to claim 1, characterized in that, In step four, the generation of the collaboration event consistency index includes: classifying cross-administrative region collaboration events by event type, and within the same linear spatial segment, calculating the segment-level collaboration event consistency index based on the occurrence frequency of each type of collaboration event and a preset weight.
5. The linear cross-administrative region policy data collaborative analysis and consistency assessment method according to claim 1, characterized in that, In step five, the generation of the policy implementation consistency index includes mapping and normalizing the policy implementation result data of different administrative regions, and calculating the consistency of policy implementation results within the same linear spatial segment.
6. The linear cross-administrative region policy data collaborative analysis and consistency assessment method according to claim 1, characterized in that, In step six, the policy coordination index is obtained by weighted fusion calculation of policy text consistency index, collaborative event consistency index and policy implementation consistency index.
7. The linear cross-administrative region policy data collaborative analysis and consistency assessment method according to claim 1, characterized in that, In step seven, the linear cross-administrative-region spatial objects include heritage corridors, watersheds, or transportation corridors.
8. A system for a linear cross-administrative region policy data collaborative analysis and consistency assessment method according to any one of claims 1-7, characterized in that, The system includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the method according to any one of claims 1 to 7.
9. A linear cross-administrative region policy data collaborative analysis and consistency evaluation system according to claim 8, characterized in that, The system interacts with a geographic information system to achieve spatial display of the policy synergy index.
10. A linear cross-administrative region policy data collaborative analysis and consistency evaluation system according to claim 9, characterized in that, The system also includes a data acquisition and object construction module, a policy text consistency calculation module, a collaborative event consistency calculation module, a policy implementation consistency assessment module, and a policy coordination index generation and spatial mapping module. The data acquisition and object construction module is connected to the policy text consistency calculation module, the policy text consistency calculation module is connected to the collaborative event consistency calculation module, the collaborative event consistency calculation module is connected to the policy implementation consistency assessment module, and the policy implementation consistency assessment module is connected to the policy coordination index generation and spatial mapping module. The data acquisition and object construction module is used to acquire cross-administrative region policy data and construct policy data objects; The policy text consistency calculation module is used to generate policy text consistency indicators within the same linear spatial segment. The collaborative event consistency calculation module is used to generate segment-level collaborative event consistency indicators; The policy implementation consistency assessment module is used to generate policy implementation consistency indicators; The policy coordination index generation and spatial mapping module is used to generate policy coordination indices and achieve linear spatial mapping.