Low-efficiency industrial land diagnosis system and method based on knowledge graph and ai large model

Abstract

This application provides a diagnostic system and method for inefficient industrial land use based on knowledge graphs and large AI models. It includes: a multi-source data semantic foundation module for generating a standardized dataset; a spatiotemporal consistency module for generating a computational input set based on the standardized dataset; an indicator orchestration and calculation module for performing automatic calculations and incremental recalculation on the computational input set, outputting park assessment results; a diagnostic knowledge graph module for writing the park assessment results and basic park information into a relational graph and a causal graph, performing joint reasoning, and outputting problem diagnostic results; a countermeasure knowledge graph module for outputting strategy recommendations associated with policy provisions; an evidence chain report generation module for generating a park diagnostic report; and a retrieval-enhanced question-answering module for outputting diagnostic information, recommendation information, and / or policy information. This application can improve the level of diagnostic automation, enhance the accuracy of strategy matching, and improve the consistency between reports and question-answering.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a diagnostic system and method for inefficient industrial land use based on knowledge graphs and large AI models. Background Technology

[0002] Redeveloping inefficient industrial land is crucial for revitalizing existing land resources, improving land use efficiency, and supporting high-quality urban development. With increasing demands for refined land use management, the identification and redevelopment decisions regarding inefficient land have shifted from being primarily based on experience to being data-driven. There is an urgent need to collaboratively process geospatial information, industrial economic information, park operation information, and policy and regulatory information to form a sustainable and updated diagnostic and decision-making support capability.

[0003] In existing technologies, inefficient land use assessments typically employ a processing model of "data aggregation + indicator calculation + manual judgment": on the one hand, land use efficiency indicators are calculated using multi-source data; on the other hand, business personnel combine policy documents and case experience to form diagnostic conclusions and remediation recommendations, which are then output in the form of reports. This type of method can achieve basic assessments in small-scale object analyses, but in scenarios involving multiple parks, cross-cycles, and dynamic updates, it generally suffers from problems of process fragmentation and insufficient knowledge reuse.

[0004] Specifically, existing technologies have at least the following drawbacks: multi-source heterogeneous data lacks unified semantics and spatiotemporal caliber, resulting in insufficient consistency in indicator calculation; there is a lack of structured correlation between assessment results and the causes of problems and policy provisions, leading to weak traceability of the diagnostic process; strategy generation highly relies on human experience, making it difficult to achieve standardized screening and prioritization; the ability to link report generation with knowledge updates is insufficient, making it difficult to meet the needs of continuous iteration; and intelligent question answering for queries and applications lacks an evidence constraint mechanism, making it difficult to balance response efficiency and result credibility. Summary of the Invention

[0005] In view of this, embodiments of this application provide an inefficient industrial land diagnosis system and method based on knowledge graphs and AI large models to solve the problems of inconsistent multi-source data, insufficient correlation between diagnosis and countermeasures, and difficulty in reliable linkage and updating of reports and Q&A in the existing technology.

[0006] The first aspect of this application provides a diagnostic system for inefficient industrial land use based on knowledge graphs and AI large models, comprising: a multi-source data semantic foundation module for acquiring geospatial data, industrial economic data, basic information of industrial parks, and policy texts, performing field alignment, semantic mapping, and version marking to form a standardized dataset; a spatiotemporal consistency module for performing spatial isotope encoding, temporal resampling, conflict resolution, and quality scoring on the standardized dataset to generate a computational input set with uncertainty markings; an indicator arrangement and calculation module for performing automatic calculation and incremental recalculation on the computational input set based on preset indicator templates, operator dependency graphs, and execution plans to output industrial park evaluation results; and a diagnostic knowledge graph module for integrating the industrial park evaluation results with the industrial park evaluation results. The system writes basic information about the district into a relationship graph and a causal graph, performs joint reasoning, and outputs problem diagnosis results. The countermeasures knowledge graph module is used to recall candidate countermeasures based on the problem diagnosis results, and performs feasibility screening, conflict resolution, and priority ranking, outputting strategy recommendations associated with policy clauses. The evidence chain report generation module is used to integrate park assessment results, problem diagnosis results, and strategy recommendation results according to a preset report template, generating a park diagnosis report containing a clause source index and a graph node index. The enhanced retrieval question-answering module is used to perform hybrid retrieval and constrained generation based on a local knowledge base constructed from park assessment results, problem diagnosis results, strategy recommendation results, and policy texts, outputting diagnostic information, recommendation information, and / or policy information.

[0007] The second aspect of this application provides a method for diagnosing inefficient industrial land use based on knowledge graphs and AI large models, using the system of the first aspect. The method includes: acquiring geospatial data, industrial economic data, basic information about the industrial park, and policy text; performing field alignment, semantic mapping, and version marking to form a standardized dataset; performing spatial isotopic encoding, temporal resampling, conflict resolution, and quality scoring on the standardized dataset to generate a computational input set with uncertainty markings; performing automatic calculation and incremental recalculation on the computational input set based on a preset indicator template, operator dependency graph, and execution plan to output the industrial park evaluation results; and comparing the industrial park evaluation results with the industrial park evaluation results. Basic information is written into the relationship graph and causal graph, joint reasoning is performed, and problem diagnosis results are output. Based on the problem diagnosis results, candidate countermeasures are recalled, and feasibility screening, conflict resolution, and priority ranking are performed, outputting strategy recommendations associated with policy clauses. According to the preset report template, the park assessment results, problem diagnosis results, and strategy recommendation results are integrated to generate a park diagnosis report containing clause source index and graph node index. Based on the local knowledge base constructed from the park assessment results, problem diagnosis results, strategy recommendation results, and policy text, hybrid retrieval and constrained generation are performed to output diagnostic information, recommendation information, and / or policy information.

[0008] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: The multi-source data semantic foundation module acquires geospatial data, industrial economic data, basic park information, and policy texts, performing field alignment, semantic mapping, and version marking to form a standardized dataset. The spatiotemporal consistency module performs spatial isotopic encoding, temporal resampling, conflict resolution, and quality scoring on the standardized dataset, generating a computational input set with uncertainty labels. The indicator orchestration and calculation module automatically calculates and incrementally recalculates the computational input set based on preset indicator templates, operator dependency graphs, and execution plans, outputting park evaluation results. The diagnostic knowledge graph module writes the park evaluation results and basic park information into relational and causal graphs, performing joint... The system comprises three modules: a problem diagnosis module, a policy knowledge graph module, and an evidence chain report generation module. The former integrates park assessment results, problem diagnosis results, and policy recommendation results based on the problem diagnosis results, and performs feasibility screening, conflict resolution, and priority ranking, outputting policy recommendations associated with policy clauses. The latter integrates park assessment results, problem diagnosis results, and policy recommendation results according to a preset report template, generating a park diagnosis report containing a clause source index and a knowledge graph node index. The former enhances the retrieval and question-answering module, which performs hybrid retrieval and constrained generation based on a local knowledge base constructed from park assessment results, problem diagnosis results, policy recommendation results, and policy texts, outputting diagnostic information, recommendation information, and / or policy information. This application improves the level of diagnostic automation, enhances the accuracy of policy matching, and strengthens the consistency between reports and question-answering. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of the structural composition of the inefficient industrial land diagnosis system based on knowledge graphs and AI large models provided in this application embodiment; Figure 2 This is a flowchart illustrating the inefficient industrial land diagnosis method based on knowledge graphs and AI large models provided in this application embodiment. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] Redevelopment of inefficient land is a crucial national initiative to promote high-quality urban and rural development, and a vital support for tapping into urban space potential and building globally benchmark cities. To enhance the digital and intelligent research and management of existing land, the Municipal Planning and Land Development Research Center, based on its business research findings, integrates geospatial data, industrial economic data, and internet big data related to the governance and utilization efficiency of industrial land. It explores the use of computing engines to build standardized computing processes, enabling automatic evaluation of inefficient industrial land. Furthermore, by constructing a policy base knowledge graph, it supports the invocation of AI large-scale models and the generation of intelligent reports. The application of digital and intelligent methods helps to accurately identify, automatically evaluate, and intelligently govern inefficient industrial land, contributing to the formation of new productivity in land space governance.

[0013] Inefficient land use assessment requires multi-source data support and long-term monitoring and feedback. Currently, data management, calculation, and analysis rely heavily on manual labor, resulting in low levels of automation and intelligence, and room for improvement in identification accuracy and efficiency. Furthermore, inefficient land use assessment strategies need to incorporate continuously updated land use policies, plans, standards, and case studies, developing corresponding solutions by analyzing the specific conditions of each land use. This process heavily depends on the knowledge and experience of individual planners and other professionals. Finally, in terms of application, traditional methods present core research findings on land use efficiency analysis through overall characteristic analysis and typical land use case studies. However, as the total amount of land involved in the assessment increases, the depth of manual characteristic analysis in supporting decision-making for individual land uses becomes insufficient. Against this backdrop, the Nanshan District Inefficient Land Use Management Platform in Shenzhen has constructed a full-chain digital and intelligent system encompassing "data-driven, intelligent computing, and scenario application," achieving an automated decision-making process from "indicator calculation to result diagnosis to improvement strategies," providing support for the identification and application of inefficient industrial land.

[0014] In view of the problems existing in the prior art, this application provides a diagnostic system and method for inefficient industrial land use based on knowledge graphs and AI large models. The components and functions of the system will be described in detail below with reference to embodiments in a real-world scenario. The system may include the following: I. Automatic metric calculation driven by the calculation engine Based on existing evaluation standards, an automated data calculation is achieved through a computational engine-based workflow. Building upon the inefficient land use assessment system for industrial parks in Nanshan District, a comprehensive evaluation is conducted from dimensions such as spatial construction, spatial efficiency, innovation capability, and supporting infrastructure level, resulting in underlying indicators covering park building types, park revenue, R&D investment, transportation convenience, and public facility maturity. Relying on the computational engine, the process of manually calculating indicators is aggregated onto a unified computational platform, forming standardized calculation processes and rules. After importing and updating data, rapid calculation of results for 435 parks in Nanshan District is achieved. The visualized architecture allows for the maintenance, adjustment, and reuse of indicator calculation models from a unified interface.

[0015] II. Knowledge Graph Support for Automatic Data Analysis and Diagnosis This system constructs a policy knowledge graph of inefficient land use guidelines to automatically link policy recommendations to inefficient land use assessment results, generating automated reports with a single click and integrating the entire process of "automated and precise evaluation - comprehensive and in-depth diagnosis - intelligent improvement guidance." By organizing and graphing existing knowledge related to inefficient land use policies, plans, and guidelines, the system matches relevant policy knowledge to inefficient land use assessments. Through the establishment of rule templates, it automatically outputs basic information, assessment conclusions, and diagnostic recommendations for inefficient land use in an intelligent reporting format. Furthermore, if modifications are made to the park's basic information, knowledge base, report templates, or assessment scores, the park's intelligent report will be updated accordingly, further improving the timeliness of the results.

[0016] III. Enriching Application Scenarios of Large Language Models By building a smart visualization platform and introducing a large language model, the system enables multi-dimensional display and rapid retrieval of land use assessment conclusions. A unified platform allows for quick viewing and drill-down of key data, aggregating and displaying overall situation, zoning, current status overview of individual parks, and inefficient assessment data. A local knowledge base is formed using basic system data and assessment data, and the large language model enables intelligent knowledge question answering and map queries, driving data statistics, conclusion analysis, and spatial analysis map generation through dialogue and prompt word settings. The system also supports the automatic generation and optimization of intelligent reports through large model technology.

[0017] The technical solution of this application includes the following technical innovations: This system utilizes new technologies to construct a "data-assessment-diagnosis-strategy" technical path for inefficient land use evaluation. Based on solid business research, the system leverages new technologies across multiple processes in the evaluation of inefficient industrial land. A computing engine enables automated calculation and management of large-scale data; knowledge graphs facilitate automated diagnosis and analysis of evaluation conclusions; and a large language model integrates research findings to support user-independent queries and applications, thus advancing the exploration of digital and intelligent enhancement throughout the entire process.

[0018] Guided by business needs and leveraging large-scale model capabilities, this system balances the standardization and flexibility of output. It achieves automated intelligent analysis report generation through a combination of report templates, knowledge graphs, and large-scale language models. Its core objective is to provide relatively standardized and flexible technical support for land use reuse. In specific land use analysis reports, indicator-driven analysis and evaluation highlight the horizontal performance of each plot. To ensure the comprehensiveness of individual land use results, data and map analysis of various current-state factors, such as the existing land use foundation, planning guidance, and restrictive elements, are incorporated, forming a detailed basic understanding before land use evaluation. Furthermore, at the strategic level, knowledge graphs and large-scale language models are introduced. Based on knowledge base updates, the system automatically extracts relevant focus areas and policy recommendations for report generation.

[0019] A large language model drives element queries, assisting users in conveniently maintaining their results. This project uses user-uploaded documents to form a policy database and automatically generates an intelligent report database through a large model. It automatically segments text into knowledge units and assigns corresponding knowledge tags based on semantics. Dialogue-driven policy queries, park information queries, and evaluation result queries achieve more efficient knowledge retrieval and recall. Subsequent user-driven self-updates and maintenance of the knowledge database support continuous improvement in question-and-answer quality. Furthermore, this project can also use dialogue to drive large-scale model queries on data, enabling the search, filtering, and display of spatial elements within the system, supporting spatial analysis and decision-making regarding land use.

[0020] The specific modules and functions of the inefficient industrial land diagnosis system based on knowledge graphs and AI large models provided in this application will be described in detail below with reference to the accompanying drawings and specific embodiments. Figure 1 This is a schematic diagram illustrating the structural composition of the inefficient industrial land diagnosis system based on knowledge graphs and AI large models provided in this application embodiment, as shown below. Figure 1 As shown, the system may specifically include the following components: The multi-source data semantic base module 101 is used to acquire geospatial data, industrial economic data, park basic information and policy text, and perform field alignment, semantic mapping and version marking to form a standardized dataset. The spatiotemporal consistency module 102 is used to perform spatial isotopic encoding, temporal resampling, conflict resolution, and quality scoring on the standardized dataset to generate a computational input set with uncertainty labels; The indicator arrangement and calculation module 103 is used to perform automatic calculation and incremental recalculation on the calculation input set based on the preset indicator template, operator dependency graph and execution plan, and output the park evaluation results. The diagnostic knowledge graph module 104 is used to write the park assessment results and basic park information into the relationship graph and causal graph, perform joint reasoning, and output the problem diagnosis results. The countermeasures knowledge graph module 105 is used to recall candidate countermeasures based on the problem diagnosis results, and to perform feasibility screening, conflict resolution and priority ranking, and output strategy recommendations associated with policy provisions. The evidence chain report generation module 106 is used to generate a park diagnostic report containing a clause source index and a graph node index by integrating the park assessment results, problem diagnosis results and strategy recommendation results according to a preset report template. The enhanced question-answering module 107 is used to perform hybrid retrieval and constrained generation based on a local knowledge base constructed from park assessment results, problem diagnosis results, strategy recommendation results, and policy texts, and output diagnostic information, recommendation information, and / or policy information.

[0021] In some embodiments, the multi-source data semantic base module is specifically used for: Source data description information is constructed for geospatial data, industrial economic data, basic information of industrial parks, and policy texts, and entity alignment relationships across data sources are established based on unified object identification rules; Based on the pre-defined domain terminology ontology and indicator semantic lexicon, each source data field is mapped to a unified semantic pattern, forming a semantic mapping result associated with park objects, indicator objects, and policy clause objects; Based on entity alignment relationships and semantic mapping results, priority merging of conflicting fields and rule-based completion of missing fields are performed on geospatial data, industrial economic data, park basic information and policy texts to generate structured candidate datasets. Add source identifiers, collection time identifiers, spatial unit identifiers, and caliber version identifiers to the data records in the structured candidate dataset to form a traceable standardized dataset and write it into the semantic base storage area.

[0022] Specifically, source data description information refers to the set of metadata used to characterize the source attributes and structural attributes of the original data, including but not limited to: data source unit, data update time, field list, field type, spatial reference information, statistical caliber description, and data quality status. Unified object identification rules refer to the unique identifier generation and resolution rules formulated for core objects such as parks, land parcels, indicator items, and policy clauses, used to achieve consistent referencing across systems and time periods.

[0023] Domain terminology ontology refers to the conceptual hierarchy and relational constraint model established for the assessment scenario of inefficient industrial land, used to constrain synonyms, hierarchical terms, and object relationships. Indicator semantic lexicon refers to a mapping dictionary that establishes a correspondence between business field names and indicator semantic labels. Unified semantic schema refers to the target data schema after merging multi-source heterogeneous fields into a unified object model. Semantic base storage area refers to the underlying storage area that stores standardized datasets and their version indexes, source indexes, and caliber indexes.

[0024] In a feasible example, the system accesses four types of data. Geospatial data includes park boundaries, land parcel boundaries, road accessibility, and public service facility coverage information; industrial economic data includes park revenue, tax revenue, R&D investment, and enterprise structure; basic park information includes park name, leading industries, construction intensity, and land use status; and policy texts include guidelines for the redevelopment of inefficient land, planning control clauses, and industry access guidelines. The module generates source data description information for each type of data. For example, it records the coordinate system, scale, and layer version for geospatial data; the statistical period, unit of measurement, and definition for industrial economic data; and the issuing authority, effective date, clause level, and revision version for policy texts. Through this step, subsequent processing no longer directly relies on the original file format but instead relies on a unified metadata framework for scheduling.

[0025] Furthermore, when establishing cross-data source entity alignment relationships, the module performs primary key normalization on park objects according to unified object identification rules. For cases where the same park has different names in different sources, a hard match is first performed based on the park code, land parcel code, and administrative division code, followed by a soft match combining an alias dictionary and spatial overlap relationships to form a single park object identifier. For indicator objects, indicator identifiers are generated using "indicator name + statistical caliber + statistical period"; for policy clause objects, clause identifiers are generated using "policy document identifier + clause level number + version number". Through unified object identification, the correspondence between the same park in spatial data, economic data, and policy constraints is explicitly solidified, avoiding duplicate or mismatched entities in subsequent calculations.

[0026] Furthermore, in the semantic mapping stage, the module invokes a pre-defined domain terminology ontology and indicator semantic lexicon to map each source data field to a unified semantic pattern. For example, "total output value of the park" and "total industrial output" are merged into the same semantic tag "park revenue"; "R&D expenditure" and "R&D investment" are merged into "R&D investment"; and "floor area ratio control requirements" and "construction intensity upper limit" in policy texts are mapped to the unified constraint semantic "construction intensity constraint". For terms with hierarchical relationships, the module performs constraint verification according to the ontology level to ensure that the association relationship between the higher-level concept and the lower-level concept in the semantic pattern is consistent. After the mapping is completed, a semantic mapping result associated with the park object, indicator object, and policy clause object is formed, providing a unified reference for subsequent rule completion and conflict resolution.

[0027] Furthermore, during the conflict field priority merging phase, the module establishes a priority chain for duplicate fields under the same object identifier. Priorities can be determined comprehensively based on the data source's authority level, update time freshness, field completeness, and historical stability. For example, if the revenue field for the same industrial park comes from both statistical ledgers and enterprise reports, the system prioritizes the authoritative statistical ledgers; if the ledgers lack data for the current period, it reverts to using enterprise reports and records any differences in reporting standards. For policy clause conflicts, the version with the more recent effective date and higher level is prioritized, and the replaced clause is retained as a historical reference entry. Through this mechanism, the module outputs single-valued currently valid fields without losing historical information.

[0028] Furthermore, in the missing field rule-based completion stage, the module performs structured completion based on the semantic mapping results and preset completion rules. The completion rules include three categories: same-source derivation completion, cross-source association completion, and policy constraint completion. For example, if a park lacks the "public facility maturity" field, the module can derive rules based on facility coverage and accessibility indicators in the spatial data; if it lacks the "industry orientation matching label," it can generate a completion value based on the mapping relationship between the dominant industry field and the industry directory in the policy clause object. For fields that cannot meet the completion conditions, the module writes a missing status and retains it for subsequent updates, without performing forced filling. After merging and completion, a structured candidate dataset is generated.

[0029] Furthermore, the module attaches four types of traceability identifiers to each record in the structured candidate dataset. The source identifier indicates the field's source chain; the collection time identifier indicates the time the data entered the system; the spatial unit identifier binds the record to a unified spatial unit; and the statistical version identifier identifies the statistical caliber and rule version. These four identifiers, together with the object identifier, constitute the record-level traceability key. The module writes the record containing the traceability key to the semantic base storage area and simultaneously writes it to the version index and change log. Thus, any evaluation result can be traced back to its data source, time batch, and statistical version.

[0030] For example, taking a certain industrial park in Nanshan District as an example, the spatial platform records the park's boundary number as A-NS-015, while the economic system records a discrepancy between the park's abbreviation and its statistical number. The policy database shows two updated versions of guidelines for the area where the park is located. The module aligns these records to the same park object using a unified object identification rule, adopts the latest effective guideline clause as the current version clause object, and retains historical clauses as traceable versions. For missing R&D investment fields, cross-source completion is performed based on the relationship between enterprise reports and park summaries within the same period. Finally, a standardized dataset containing source identifiers, collection time identifiers, spatial unit identifiers, and caliber version identifiers is output and written to the semantic base storage area for direct use by the subsequent spatiotemporal consistency module.

[0031] Through the above implementation, this embodiment completes object unification, semantic unification, conflict merging, rule completion, and version tracking before multi-source heterogeneous data enters the diagnostic process, enabling the standardized dataset to have a consistent reference basis and traceable attributes, providing a stable data foundation for subsequent park assessment, map diagnosis, strategy generation, and report Q&A.

[0032] In some embodiments, the spatiotemporal consistency module is specifically used for: Based on preset spatial unit coding rules, geographic objects, statistical objects, and park objects in the standardized dataset are mapped to the same spatial index system to form spatial co-position coding results; Based on the evaluation period and indicator calculation window, time axis alignment and multi-granularity time resampling are performed on the standardized dataset to form a set of time series segments corresponding to the indicator template; For conflicting records under the same spatial unit identifier and the same time window identifier, conflict resolution is performed according to source trust level, version priority and time freshness, and a consistent record set is output. A quality scoring vector is constructed based on data integrity, cross-source consistency, spatiotemporal continuity, and caliber matching degree, and a quality score is applied to the consistent record set. Based on the quality score vector, perform regularization correction, substitution value filling, or weight reduction on low-confidence records to generate a computational input set containing uncertainty labels.

[0033] Specifically, spatial unit coding rules refer to a set of rules that map objects such as parks, plots, road network service areas, and public facility service areas to a unified spatial index. These rules include spatial master unit coding, hierarchical mapping relationships, and adjacency relationship coding. Spatial co-location coding results refer to the coding results of data from different sources after location normalization under a unified spatial index. The indicator calculation window refers to the time range and sampling granularity constraints used in the calculation of a certain indicator. Multi-granularity time resampling refers to the process of uniformly converting data of different granularities such as daily, weekly, monthly, and quarterly data according to the target granularity. The quality score vector refers to a multi-dimensional quality measurement result composed of completeness, cross-source consistency, spatiotemporal continuity, and caliber matching. Uncertainty markers refer to identification information used to characterize the reliability of records and the availability of strategies.

[0034] During the spatial unification phase, the module reads geographic objects, statistical objects, and park objects from the standardized dataset and maps them to the same spatial indexing system according to preset spatial unit coding rules. For geographic objects, the module generates spatial master unit codes based on park boundaries, parcel boundaries, and administrative grids; for statistical objects, the module maps them to corresponding spatial master units through object identifiers and address resolution results; for park objects, the module establishes a one-to-one or one-to-many relationship between park object identifiers and spatial master unit codes. For cross-boundary objects, the module generates allocation mapping relationships according to coverage area ratios and business rules. This results in spatial co-location coding, enabling direct comparison and aggregation of data under the same spatial unit identifier.

[0035] During the time unification phase, the module performs timeline alignment and multi-granularity time resampling based on the assessment period and indicator calculation window. The assessment period can be set to annual assessment, and the indicator calculation window can be set to a rolling window of the past 12 months, the past 4 quarters, or the past 3 years, depending on the indicator type. For monthly industry data, the module performs aggregation by quarterly window; for annual park census data, the module performs forward holding or rule interpolation by monthly window; for event-based policy implementation data, the module maps the implementation time to the corresponding time window and generates state transition segments. Through the above processing, a set of time-series segments corresponding to each indicator template is formed, ensuring that data from different sources participate in the calculation under the same time benchmark.

[0036] During the conflict resolution phase, the module performs multi-criteria adjudication on conflict records under the same spatial unit identifier and the same time window identifier. Conflict criteria include source credibility level, version priority, and time freshness. Source credibility level is used to distinguish between government statistical standards, park reporting standards, and third-party collection standards; version priority is used to distinguish between current and historical versions of data from the same source; time freshness is used to prioritize data closer to the target time window. The module first sorts by source credibility level, then filters by version priority, and finally adjudicates by time freshness. If undeterminable conflicts still exist, candidate values ​​are retained and proceed to the quality scoring and low-credibility handling stage. After this step, a consistent record set is output.

[0037] During the quality quantification phase, the module constructs a quality score vector for the consistent record set. The completeness dimension measures the completeness of fields; the cross-source consistency dimension measures the numerical deviation of the same object across sources; the spatiotemporal continuity dimension measures the smoothness of changes between adjacent time windows and adjacent spatial units; and the caliber matching dimension measures the consistency between the record's caliber and the current indicator template's caliber version. The scores for each dimension are normalized to form a record-level quality score vector, and record-level quality grade labels are generated. For records involving key indicators, the module can increase the weight of caliber matching; for high-frequency monitoring data, the module can increase the weight of spatiotemporal continuity.

[0038] During the low-confidence handling phase, the module performs tiered processing on low-confidence records based on the quality scoring vector. The first type is rule-based correction, applicable to data with clearly defined business correction rules, such as misplaced units of measurement or missing conversion values. The second type is substitute value filling, applicable to data with short-term missing values ​​or local anomalies; substitute values ​​can be generated from historically stable segments within the same spatial unit, similar objects in adjacent spatial units, or consistent values ​​across sources for the same indicator. The third type is weight reduction processing, applicable to data that cannot be reliably corrected but still needs to participate in calculations; this involves reducing its contribution coefficient in subsequent operators and adding an uncertainty flag. After processing, the module outputs a calculation input set containing uncertainty flags and writes it to the intermediate storage area for use by the indicator orchestration and calculation module.

[0039] For example, taking the annual assessment scenario of an industrial park in Nanshan District as an example, the park's revenue data comes from two sources: statistical ledgers and park reports; R&D investment comes from quarterly reports; and accessibility of supporting facilities comes from monthly spatial analysis results. The module first maps these three types of data to the same spatial master unit code for the park. Then, it constructs quarterly windows according to the annual assessment requirements, resamples the monthly accessibility data to the quarterly mean, maintains the original granularity of quarterly R&D investment, and breaks down the annual statistical data into window benchmark values. For revenue conflicts in the second quarter, the module prioritizes using statistical ledgers based on the source's reliability level. For missing R&D investment in the third quarter, the module fills in the missing values ​​using data from adjacent quarters within the same park and the same quarter within the same industrial park, and adds a medium uncertainty marker. For sudden anomalies in the spatial analysis, the module performs corrections according to spatiotemporal continuity rules. Finally, a computational input set that can be directly used for park assessment calculations is formed.

[0040] Through the above implementation methods, this embodiment enables multi-source heterogeneous data to enter the index calculation process under a unified spatiotemporal benchmark and unified quality constraints by using spatial co-location coding, time axis alignment, multi-criteria conflict resolution, quality score vector evaluation, and low-confidence hierarchical handling. This reduces calculation bias caused by mixed calibers and spatiotemporal misalignment, and improves the data consistency and reusability among subsequent evaluation results, diagnostic conclusions, and strategy recommendations.

[0041] In some embodiments, the indicator arrangement and calculation module is specifically used for: Based on the preset indicator templates, the indicator dimensions, indicator definitions, operator types, and input / output constraints are analyzed, and an operator dependency graph corresponding to each indicator template is constructed. A hierarchical execution plan is generated based on the topological relationships of the operator dependency graph, operator resource requirements, and data arrival status, and the computation input set is divided into parallel computation fragments according to spatial unit identifiers and time window identifiers; Operator chaining computation is performed on each parallel computation slice according to the hierarchical execution plan. Uncertainty flags are passed and operator-level error propagation is performed during operator execution to obtain intermediate index results and dimension aggregation results. Based on data change events, indicator template change events, or operator parameter change events, determine the affected operator subgraphs, trigger incremental recalculation based on the affected operator subgraphs, and reuse the results of unaffected operators; Versioned encapsulation is performed on the dimensional aggregation results and incremental recalculation results to generate park evaluation results associated with the park object.

[0042] Specifically, an indicator template refers to a standardized description of the calculation of a single indicator or a group of indicators, including but not limited to: indicator dimensions, indicator definitions, input field constraints, output field constraints, calculation period, and dependencies. An operator type refers to a standardized processing unit type used to perform data processing or indicator calculations, including normalization operators, aggregation operators, ratio operators, segmentation decision operators, and weight fusion operators. An operator dependency graph refers to a computation graph structure with operators as nodes and preceding / following dependencies as directed edges.

[0043] A hierarchical execution plan refers to a batch execution sequence generated based on operator dependency topology, resource requirements, and data arrival status. Uncertainty labeling refers to a reliability identifier passed from upstream data quality processing to the operator execution chain. Operator-level error propagation refers to the process of combining and calculating input uncertainties during operator execution and then passing them to the output. Versioned encapsulation refers to the encapsulation process of attaching version identifiers, caliber identifiers, template identifiers, and time identifiers to the calculation results.

[0044] During the template parsing phase, the module reads the preset indicator template and performs structured decomposition. Taking the assessment scenario of inefficient industrial land as an example, it can include spatial construction dimension, spatial efficiency dimension, innovation capability dimension, and supporting infrastructure level dimension. For each dimension, the template defines the underlying indicators and their definitions. For example, the park revenue intensity indicator can be defined as the ratio of revenue value to construction area, the R&D investment intensity indicator can be defined as the ratio of R&D investment to revenue value, and the transportation accessibility indicator can be defined as the time segment score for reaching major transportation nodes. The module extracts input field constraints and output field constraints based on the template, determines the upstream fields and target output fields required for each indicator, and generates the template parsing results.

[0045] During the dependency graph construction phase, the module builds an operator dependency graph based on the template parsing results. For each indicator, it first generates field cleaning and caliber conversion operators, then generates core calculation operators and decision operators, and finally generates dimension aggregation operators and comprehensive fusion operators. If an indicator depends on the output of other indicators, cross-indicator dependency edges are established. For indicators that share intermediate results, the module establishes shared nodes to avoid duplicate calculations. In this way, an operator dependency graph corresponding to each indicator template is formed, which can be visualized, maintained, and reused at the graph level.

[0046] During the hierarchical scheduling phase, the module generates a hierarchical execution plan based on the topological relationships of the operator dependency graph, operator resource requirements, and data arrival status. The first layer typically involves field standardization and caliber conversion; the second layer calculates underlying metrics; the third layer aggregates dimensions; and the fourth layer performs comprehensive evaluation and fusion. The module also partitions parallel computing into shards based on spatial unit identifiers and time window identifiers. For example, it partitions the various parks in Nanshan District by park object, and further partitions each park object by quarterly window. For operators with high resource consumption, the module can set upper limits on parallelism and priorities to ensure execution stability.

[0047] During the chained computation phase, the module executes operator chained computation on each parallel computation slice according to the hierarchical execution plan. Each operator, in addition to producing a numerical result, also transmits an uncertainty flag in its output and performs operator-level error propagation. For example, if a certain underlying field has medium uncertainty, the ratio operator outputs the original uncertainty plus the denominator fluctuation effect, the aggregation operator transmits uncertainty according to sample weights, and the fusion operator outputs the comprehensive uncertainty according to dimensional weights. After the chained computation is completed, intermediate index results and dimensional aggregation results are obtained. The dimensional aggregation results are used to generate the comprehensive evaluation value and multi-dimensional evaluation values ​​for the park.

[0048] During the incremental recalculation phase, the module monitors three types of trigger events: data change events, indicator template change events, and operator parameter change events. Upon triggering, the module first locates the affected fields and indicators, then traces the affected operator subgraphs backward in the operator dependency graph. Incremental recalculation is performed only on the affected operator subgraphs; results from unaffected operators are directly reused. For example, if R&D investment data is updated for a quarter, only the innovation capability-related subgraph is recalculated, not the corresponding horizontal subgraph. If the definition of a transportation accessibility indicator is adjusted, only the corresponding operator chain is recalculated, and the upstream unchanged intermediate results are reused. This mechanism shortens the recalculation path and maintains result consistency.

[0049] During the version encapsulation phase, the module performs unified versioning encapsulation on the dimensional aggregation results and incremental recalculation results. The encapsulated content includes, but is not limited to: park object identifier, spatial unit identifier, time window identifier, indicator template identifier, caliber version identifier, calculation batch identifier, and result version identifier. The encapsulated results are written to the evaluation result storage area and output to the diagnostic knowledge graph module as the input basis for generating problem diagnostic results. For historical versions of the same park object, the module maintains a version chain for traceability and comparison.

[0050] The following example illustrates this: a certain industrial park's annual assessment involves 12 underlying indicators. The system first parses the template, constructing an operator dependency graph that includes field conversion, indicator calculation, dimension aggregation, and comprehensive fusion. During execution, it partitions the data by park object and quarterly window, performing parallel calculations. After the third quarter's industry data is updated, the system identifies the affected innovation capability subgraph and spatial benefit subgraph, triggering local incremental recalculation and reusing intermediate results from the remaining subgraphs, ultimately outputting a new version of the park assessment result. This assessment result also carries uncertainty and version information, providing stable input for subsequent joint inference using relational and causal graphs.

[0051] Through the above implementation method, this embodiment transforms the indicator calculation process from a static, full-scale, and manual serial mode to a templated, graphical, parallel, and incremental mode. While maintaining the consistency of object identifiers, caliber identifiers, and version identifiers, it outputs traceable evaluation results associated with park objects and provides a continuous and reusable computational foundation for subsequent diagnosis and strategy generation, thereby improving the processing efficiency and result consistency of multi-park, multi-cycle evaluation tasks.

[0052] In some embodiments, the diagnostic knowledge graph module is specifically used for: Based on the unified entity identifier, the indicator entities and status entities in the park evaluation results are aligned with the attribute entities in the park's basic information to construct entity nodes and associated edges in the relationship graph; Based on the pre-defined causal rule base, restrictive elements, abnormal indicators, and problem representations are mapped into causal triples, a causal graph is constructed, and a cross-graph anchor point mapping is established between the graph and the relation graph. Starting with the park object as the query starting point, perform relation path retrieval in the relation graph to generate a set of candidate issues, and perform causal chain expansion in the causal graph based on cross-graph anchor point mapping; The relationship path score and causal chain confidence score corresponding to the candidate problem set are jointly calculated to obtain the problem type determination result and the severity level corresponding to the problem type. For the identified problem types, the system outputs a causal path consisting of abnormal indicator nodes, constraint element nodes, and problem nodes, forming a problem diagnosis result associated with the park object.

[0053] Specifically, unified entity identification refers to the unique identification rules for objects throughout the assessment, mapping, and reporting process, covering park objects, indicator objects, status objects, attribute objects, and problem objects. Indicator entities are indicator objects output by the indicator compilation and calculation module and referenced by the mapping. Status entities are status objects obtained through indicator threshold determination or interval mapping, such as below the threshold, abnormal fluctuations, or limited growth. Attribute entities are structured attribute objects in the park's basic information, such as dominant industry type, development intensity category, and location condition category.

[0054] A relationship graph is a graph structure describing the relationships between entities. A causal graph is a graph structure describing the causal relationships between limiting factors, anomaly indicators, and problem representations. Cross-graph anchor mapping refers to the one-to-one or one-to-many mapping relationship established between nodes in the relationship graph and nodes in the causal graph. Relationship path score refers to the relevance score of the path from park objects to problem candidate nodes in the relationship graph. Causal chain confidence refers to the confidence score of the validity of the causal chain in the causal graph. Problem type refers to the pre-defined problem category in the diagnosis of inefficient industrial land. Severity level refers to the classification result of the degree of impact of the problem.

[0055] In the relationship graph construction phase, the module first reads the park assessment results and basic park information, and performs entity alignment based on a unified entity identifier. Specifically, it maps indicator entities and status entities from the park assessment results to park objects, and associates attribute entities from the park's basic information with the same park object, forming a relational skeleton of "park object - indicator entity - status entity - attribute entity". Then, it writes the associated edges according to a preset relationship pattern, including attribution relationships, representation relationships, constraint relationships, and similarity relationships. Taking a park in Nanshan District as an example, the system can form relationships such as "Park A - has indicator - R&D investment intensity", "R&D investment intensity - in status - low", and "Park A - has attribute - leading industry is advanced manufacturing". Through this step, the assessment results and basic attributes can be linked and looked up in the unified graph structure.

[0056] During the causal graph construction phase, the module maps restrictive elements, abnormal indicators, and problem representations into causal triples based on a pre-defined causal rule base. The causal rule base can be formed by combining historical diagnostic knowledge, policy constraint rules, and business expert rules. For example, sample rules can be represented as "Insufficient innovation investment + Insufficient high-end carriers → Inefficient innovation capabilities," or "Weak transportation accessibility + Uneven supporting facilities → Inefficient supporting facilities." The module instantiates the rules into "causal node-relationship-outcome node" triples and writes them into the causal graph according to the rule version. Simultaneously, the module establishes cross-graph anchor mappings between the relationship graph and the causal graph. For example, it anchors the "low R&D investment intensity" state entity in the relationship graph to the "insufficient innovation investment" causal node in the causal graph, and anchors "low supporting facility accessibility" to the "uneven supporting facilities" causal node. This mapping ensures that the two types of graphs can be mutually invoked during inference.

[0057] In the relationship path retrieval and causal chain expansion phases, the module performs relationship path retrieval in the relationship graph, starting with park objects, to generate a candidate problem set. Relationship path retrieval prioritizes paths containing entities with abnormal states and key attributes, and allows setting upper limits for path length and filtering conditions for relationship types. After obtaining the candidate problem set, the module performs causal chain expansion in the causal graph based on cross-graph anchor mapping, forming candidate causal chains along the direction of "restrictive elements → abnormal indicators → problem representation." For the same candidate problem, the module can expand multiple causal chains and record parameters such as the number of nodes covered by the chain, rule matching degree, and historical consistency.

[0058] During the joint computation phase, the module jointly calculates the relational path score and causal chain confidence score corresponding to the candidate problem set. The relational path score reflects the degree of structural correlation between the candidate problem and the current park objects, while the causal chain confidence score reflects the reliability of the candidate problem in causal explanation. The joint computation can output a problem type determination score using a weighted fusion method, and map the severity level according to the score range. In the example, if a park has a high relational path score and a high corresponding causal chain confidence score for the "inefficient innovation capability" problem, then this problem type is determined to be a valid problem and assigned a high severity level; if the path relevance is high but the causal evidence is insufficient, it is downgraded or marked as a problem to be verified.

[0059] In the causal path output phase, the module outputs structured causal paths for the identified problem types. Each causal path contains at least anomaly nodes, constraint element nodes, and problem nodes, along with path order, node identifiers, relationship identifiers, and rule version identifiers. The output results form a problem diagnosis result associated with the park object and are transmitted to the countermeasure knowledge graph module for candidate countermeasure retrieval and constraint screening. Taking Park A as an example, the output may include two causal paths: "Low R&D investment intensity → Insufficient supply of innovation carriers → Inefficient innovation capabilities" and "Low transportation accessibility → Insufficient coverage of supporting facilities → Inefficient level of supporting facilities," with severity levels and confidence parameters attached to each.

[0060] For example, in a cross-cycle update example, when the quarterly assessment results of Park A cause the "R&D investment intensity" status to change from low to medium, the diagnostic knowledge graph module only recalculates the relationship paths and causal chains related to the entities in this status, retaining the historical inference results of unaffected problem types. After the recalculation, the joint score of the "inefficient innovation capability" problem decreases, and the severity level drops from high to medium. The corresponding problem diagnostic result version is updated synchronously and written to the diagnostic result storage area. This mechanism ensures that the graph inference is consistent with changes in assessment data.

[0061] Through the above implementation, this embodiment transforms the evaluation results into problem diagnosis results with structural associations and causal explanations by using unified entity identifier alignment, dual-graph modeling of relation graph and causal graph, cross-graph anchor mapping, and joint reasoning computation. This forms a standardized output that includes problem type, severity level, and causal path, providing a directly callable diagnostic basis for subsequent countermeasure matching and evidence chain report generation.

[0062] In some embodiments, the strategy knowledge graph module is specifically used for: Based on the problem diagnosis results, multi-hop association retrieval is performed in the countermeasure knowledge graph to recall the set of candidate countermeasures and policy clauses corresponding to the problem type; The predetermined planning constraints, ownership constraints, development boundary constraints, and industry orientation constraints are uniformly encoded into constraint vectors. Based on the constraint vectors, rule verification and graph constraint reasoning are performed on the candidate strategy set to eliminate candidate strategies that do not meet the constraints. For candidate strategies that pass the constraint verification, a strategy conflict relationship graph is constructed and target conflicts, resource conflicts, and temporal conflicts are identified. Based on the conflict relationship graph, mutual exclusion resolution and combination modification are performed to generate executable strategy combinations. Based on severity level, coverage of causal paths, constraint matching degree and policy clause suitability, the feasible countermeasure combinations are jointly scored to form a priority ranking result; The strategy recommendations are output in order of priority, and each strategy recommendation is accompanied by a corresponding policy clause identifier, clause version identifier, and graph association path identifier.

[0063] Specifically, the candidate policy set refers to the set of policy nodes in the policy knowledge graph that have a reachable relationship with the target problem type. The policy clause set refers to the set of clause nodes that have a basis or constraint relationship with the candidate policy nodes. The constraint vector refers to the structured constraint expression after encoding planning constraints, ownership constraints, development boundary constraints, and industry orientation constraints. Graph constraint reasoning refers to the reasoning process of determining the feasibility of candidate policies based on constraint relationship edges in a graph structure.

[0064] A policy conflict graph is a graph structure built with candidate policies as nodes and conflict relationships as edges. Goal conflict refers to conflicting governance objectives of different policies; resource conflict refers to different policies competing for the same limited resource; and temporal conflict refers to the implementation order not meeting prerequisite dependencies. Causal path coverage refers to the proportion of causal path nodes in the problem diagnosis results covered by a policy or combination of policies. Policy clause fit refers to the degree of matching between policies and clauses in terms of applicable objects, applicable conditions, and effective versions. Graph association path identifiers are unique identifiers of the association paths from problem nodes to policy node and then to clause nodes.

[0065] During the candidate recall phase, the module reads the problem type, severity level, and causal path from the problem diagnosis results. Using the problem type node as the starting point, it performs a multi-hop association search in the policy knowledge graph. Search relationships include targeting relationships, supporting relationships, restrictive relationships, and basis relationships. For each problem type, the module recalls a set of candidate policies and their corresponding policy clause sets, and records the path length, relationship type sequence, and historical application tags between the candidate policies and the problem node. Taking the problem of "inefficient innovation capability" as an example, it can recall candidate policies such as "innovation carrier renewal strategy," "R&D space reconstruction strategy," and "industrial structure adjustment strategy," while simultaneously recalling the associated policy clause nodes.

[0066] During the constraint screening phase, the module uniformly encodes predefined constraints into constraint vectors. Planning constraints can be encoded as land use, floor area ratio ranges, and urban renewal unit rules; ownership constraints can be encoded as ownership integrity, number of rights holders, and clarity of rights boundaries; development boundary constraints can be encoded as development intensity boundaries, ecological control boundaries, and traffic capacity boundaries; and industry guidance constraints can be encoded as encouraged industry catalogs, restricted industry catalogs, and access conditions. Based on the constraint vectors, the module first performs rule verification, then performs graph constraint reasoning. Rule verification is used to quickly eliminate candidate strategies that explicitly do not meet the conditions, while graph constraint reasoning is used to identify candidate strategies that implicitly do not meet the conditions. This step yields a subset of candidate strategies that pass the constraint verification.

[0067] During the conflict resolution phase, the module constructs a conflict relationship graph for candidate countermeasures that have passed constraint verification. The module first identifies target conflicts, such as one countermeasure focusing on short-term output improvement while another focuses on long-term spatial preparation; then it identifies resource conflicts, such as both countermeasures occupying the same update funding window or construction resources within the same construction period; and finally, it identifies temporal conflicts, such as one countermeasure relying on infrastructure development while another is scheduled for a phase where preconditions are not met. For the identified conflict edges, the module performs mutual exclusion resolution and combination correction according to mutual exclusion and sequential dependency rules, forming executable countermeasure combinations that meet constraint and temporal requirements. If a conflict cannot be resolved, the module retains the high-priority path and marks the low-priority countermeasure as an alternative.

[0068] During the joint scoring phase, the module performs multi-factor joint scoring on feasible policy combinations. Severity level reflects the urgency of problem resolution, causal path coverage measures the scope of the policy's impact on the core causal chain, constraint fit measures the consistency between the policy and the current constraint vector, and policy provision adaptation measures the normative consistency between the policy and existing provisions. The module calculates the combination score according to preset weights and generates a priority ranking. The weights can be adjusted according to the business phase; the weight of constraint fit can be increased during the pilot phase, and the weight of causal path coverage can be increased during the implementation phase. After ranking, a primary and alternative combination is generated.

[0069] In the results output phase, the module prioritizes the output of strategy recommendations and adds a policy clause identifier, clause version identifier, and graph association path identifier to each recommendation. The strategy recommendation results include the strategy identifier, implementation conditions, prerequisite dependencies, recommended order, and related evidence. For outputs across multiple parks of the same problem type, the module can retain park-specific parameters, ensuring a one-to-one correspondence between strategy recommendations and park objects. The output results are written to the strategy recommendation storage area and then transferred to the evidence chain report generation module.

[0070] The following example illustrates this: A park's problem diagnosis results show "inefficient innovation capability" as high severity and "inefficient supporting infrastructure" as medium severity, with causal paths including "insufficient R&D investment," "insufficient high-quality carriers," and "weak transportation connections." The module first recalls eight candidate countermeasures and corresponding policy clauses. After constraint vector filtering, two countermeasures that do not meet development boundary constraints are eliminated. A conflict relationship diagram is constructed for the remaining six items, identifying a set of resource conflicts and a set of temporal conflicts. After mutual exclusion resolution, two executable countermeasure combinations are formed. After joint scoring, the first combination scores higher and is determined as the primary strategy combination, with corresponding clause identifiers, clause version identifiers, and graph association path identifiers from problem to countermeasure to clause, for direct reference during automatic report generation.

[0071] In another update scenario, when the policy text version is updated and some clauses become invalid, the module triggers a partial re-evaluation by indexing the clause version identifier, recalculates the policy clause suitability and adjusts the priority order, without changing the scoring weight of unaffected countermeasures, thus maintaining the continuity and traceability of the strategy recommendation results.

[0072] Through the above implementation, this embodiment transforms the problem diagnosis results into executable, interpretable, and traceable strategy recommendations by using multi-hop recall, constraint vector filtering, conflict relationship graph resolution, joint scoring and ranking, and identifier output. This improves the standardization and consistency of the countermeasure generation process and provides a structured strategy basis for subsequent report preparation and question-and-answer invocation.

[0073] In some embodiments, the evidence chain report generation module is specifically used for: Based on the preset report template, the chapter skeleton, field placeholders and evidence citation rules are parsed to construct the report generation syntax tree; Align and map the park assessment results, problem diagnosis results, and strategy recommendation results according to a unified object identifier to generate report content fragments corresponding to field placeholders; Based on the evidence citation rules, policy clause identifiers and clause version identifiers are extracted from the strategy recommendation results to form a clause source index. Problem node, indicator abnormal node, and constraint element node identifiers are extracted from the problem diagnosis results to form a graph node index. Based on the report, a syntax tree is generated, and segmented assembly and consistency checks are performed on the report content fragments, clause source indexes, and graph node indexes to generate a structured park diagnostic report.

[0074] Specifically, the pre-configured report template refers to the pre-configured report structure specifications for inefficient industrial land diagnosis, which includes chapter hierarchy, field placeholders, paragraph constraints, and output format constraints. Field placeholders are replaceable markers in the template used to carry dynamic data fragments. Evidence citation rules refer to the set of rules that stipulate how the report text should cite policy clauses and map node evidence, including citation granularity, citation location, and version consistency requirements.

[0075] A report generation syntax tree is an arrangement model that expresses the chapter hierarchy, paragraph relationships, and placeholder dependencies of a template as a tree structure. A clause source index is a set of citation indexes composed of policy clause identifiers and clause version identifiers. A graph node index is a set of evidence indexes composed of issue nodes, indicator anomaly nodes, and constraint element node identifiers. A structured park diagnostic report is a report entity with a fixed chapter structure, dynamic data fragments, and a traceable evidence index.

[0076] During the template parsing phase, the module reads the preset report template and parses the chapter skeleton, field placeholders, and evidence citation rules. The chapter skeleton can include a basic overview of the park, assessment conclusions, problem diagnoses, strategy recommendations, and key implementation points. Field placeholders correspond to different module outputs; for example, the assessment conclusions section references the park's assessment results, the problem diagnosis section references the problem type and severity level, and the strategy recommendations section references the priority ranking results. The evidence citation rules stipulate that the strategy recommendations section must be bound to a policy clause index, and the problem diagnosis section must be bound to a graph node index. Based on this, the module constructs a report generation syntax tree, clarifying the parent-child dependencies and sequential generation relationships between each chapter and placeholder.

[0077] During the object alignment phase, the module aligns and maps the park assessment results, problem diagnosis results, and strategy recommendation results using a unified object identifier. In the alignment process, the report subject is first determined by the park object identifier, and then results from the same batch are selected based on the time window identifier and version identifier to avoid cross-version splicing. Subsequently, each result is mapped to its corresponding field placeholder, generating report content fragments. For example, the "Assessment Conclusion" placeholder is filled with dimension aggregation results, the "Problem Diagnosis" placeholder is filled with problem type, severity level, and causal path summary, and the "Strategy Recommendation" placeholder is filled with the recommended countermeasure combination, alternative countermeasure combinations, and implementation conditions. If a placeholder lacks available data, the module writes a missing status marker according to template rules and records the task identifier to be supplemented.

[0078] During the index extraction phase, the module extracts policy clause identifiers and clause version identifiers from the strategy recommendation results according to evidence citation rules, forming a clause source index; it extracts problem node, indicator anomaly node, and constraint element node identifiers from the problem diagnosis results, forming a graph node index. When the same strategy recommendation corresponds to multiple clauses, the module retains the main clause index and records the extended clause index according to priority; when the same problem type corresponds to multiple causal paths, the module extracts the core node index according to severity level and path confidence. Through this step, the strategies and diagnostic conclusions in the report text can be traced back to clear evidence nodes.

[0079] During the segmented assembly phase, the module performs content arrangement based on the syntax tree generated from the report. First, it assembles the chapter skeleton, then fills in report content fragments according to placeholder dependencies, inserting clause source indexes and graph node indexes into the corresponding paragraphs. During assembly, the module performs deduplication and number merging on cross-paragraph references to ensure consistency in referencing the same clause identifier or node identifier within the same report. If the report contains a hierarchical strategy presentation, the module can output in a "primary-alternative" order and append a set of related indexes after each strategy paragraph. After assembly, a structured intermediate report is generated.

[0080] During the consistency verification phase, the module performs at least four types of verifications on the structured intermediate report. The first type is object consistency verification, verifying whether all content segments in the report correspond to the same park object identifier. The second type is version consistency verification, verifying whether the evaluation results, diagnostic results, and strategy recommendation results are within a compatible version set. The third type is evidence completeness verification, verifying that each strategy recommendation has at least one clause source index and each issue conclusion has at least one graph node index. The fourth type is terminology consistency verification, verifying that the issue type, constraint name, and strategy name in the report are consistent with the graph definition. If the verification passes, a structured park diagnostic report is output; if the verification fails, a verification failure list is returned, triggering reassembly or upstream data retrieval.

[0081] The following example illustrates this: A certain industrial park has been assessed as having "inefficient innovation capabilities" and "inefficient supporting infrastructure" in the current evaluation batch. After reading the template, the module constructs a syntax tree. The comprehensive evaluation results are written in the evaluation conclusion section, the two types of problems and their severity levels are written in the problem diagnosis section, and the primary and alternative combinations are written in the strategy recommendation section. Subsequently, the policy clause identifiers and version identifiers associated with the primary combination are extracted, along with the abnormal indicator nodes and constraint element nodes for the corresponding problems. These are used to create a clause source index and a graph node index, which are then inserted into the corresponding paragraphs. After consistency verification, a structured industrial park diagnostic report is generated, in which each strategy recommendation can be traced back to a specific clause and graph node.

[0082] For example, in another update scenario, when the policy clause version is adjusted, the module quickly locates the affected report paragraphs based on the clause version identifier, reassembles only the strategy recommendation chapter and its reference index, and retains the content of the unaffected chapters, thus achieving partial updates and shortening the report regeneration path.

[0083] Through the above implementation method, this embodiment realizes the structured automatic generation of park diagnostic reports from data results to evidence citations by using template syntax tree arrangement, unified object alignment mapping, dual index extraction of clauses and graphs, and multi-dimensional consistency verification. This ensures that the report content is consistent with the diagnostic and strategy links, and improves the standardization, traceability and continuous updating capability of the report results.

[0084] In some embodiments, the enhanced question-answering module is specifically used for: A unified knowledge index is constructed based on the park assessment results, problem diagnosis results, strategy recommendation results, and policy texts. The unified knowledge index includes a structured index, a vector index, and a graph index, and each index is cross-linked through a unified object identifier. The query request is subjected to intent parsing and constraint extraction to generate a retrieval plan that includes constraints on park objects, question types, time ranges, and policy scopes. According to the retrieval plan, structured retrieval, semantic vector retrieval and graph path retrieval are performed in parallel to obtain a candidate evidence set, and then reordered based on evidence consistency score, timeliness score and constraint matching score to generate the target evidence set; Input the target set of evidence into the constrained generation unit, and perform evidence binding verification and terminology consistency verification on the output content according to the preset generation constraints; Output diagnostic information, recommendations, and / or policy information corresponding to the query request, along with a corresponding evidence index identifier for result traceability and verification.

[0085] Specifically, a unified knowledge index refers to a multi-index system that unifies the organization of park assessment results, problem diagnosis results, strategy recommendation results, and policy texts. A structured index refers to a key-value index oriented towards object fields and conditional filtering. A vector index refers to a vectorized index oriented towards semantic similarity retrieval. A graph index refers to a graph-structured index oriented towards relational path retrieval. A unified object identifier refers to a unique object identifier that runs through all index layers. Intent parsing refers to the process of identifying the task type to which a query request belongs.

[0086] Constraint extraction refers to the process of extracting constraints such as object, time, policy scope, and question type from the query text. A retrieval plan is an execution plan consisting of retrieval channels, filtering conditions, and ranking strategies. Evidence consistency score is a rating of the semantic and factual consistency between candidate evidence. Timeliness score is a rating of the matching between evidence version and effective date relative to the query time. Constraint matching score is a rating of the degree to which candidate evidence satisfies constraints. A constrained generation unit is a generation processing unit that performs evidence binding and terminology verification during the generation phase. Evidence index identifiers are clause identifiers, node identifiers, or record identifiers used to locate the basis for the answer.

[0087] During the unified index construction phase, the module extracts data from the assessment result repository, diagnostic result repository, strategy recommendation repository, and policy text repository to construct structured indexes, vector indexes, and graph indexes, respectively. The structured index records fields such as park object identifier, time window identifier, problem type identifier, severity level, strategy priority, and clause version identifier. The vector index generates semantic vectors for policy clause texts, diagnostic description texts, and strategy explanation texts and establishes retrieval mappings. The graph index constructs traversable relationship paths using problem nodes, indicator anomaly nodes, constraint element nodes, strategy nodes, and policy clause nodes. The three types of indexes establish cross-links through a unified object identifier, enabling evidence of the same park object to be mapped between different retrieval channels.

[0088] During the query constraint phase, the module performs intent parsing and constraint extraction on the query request. Intent parsing can distinguish between diagnostic explanations, strategy suggestions, policy basis, and comprehensive question-and-answer types. Constraint extraction identifies park-related constraints, question type constraints, time range constraints, and policy scope constraints from the query text. For example, taking the query "Query the reasons for the inefficiency of innovation capacity in a certain park in Nanshan District over the past year and available policy suggestions" as an example, the module extracts the park-related constraints as the target park, the question type as innovation capacity-related issues, the time range as the past year, and the policy scope as current redevelopment policies. Based on this, the module generates a search plan, specifying the three-channel search parameters, filtering conditions, and re-ranking strategies.

[0089] During the parallel retrieval phase, the module simultaneously executes structured retrieval, semantic vector retrieval, and graph path retrieval according to the retrieval plan. Structured retrieval is used to quickly locate assessment results, diagnostic results, and strategy recommendation records that meet the constraints. Semantic vector retrieval is used to recall semantically similar evidence from policy texts and historical report fragments. Graph path retrieval is used to extract the association path of "problem node - causal node - strategy node - clause node" from the graph. The outputs of the three channels are merged to form a candidate evidence set. Each evidence item in the candidate set carries a source channel identifier, object identifier, version identifier, and time identifier to facilitate subsequent unified scoring.

[0090] During the evidence re-ranking phase, the module calculates evidence consistency score, timeliness score, and constraint matching score for the candidate evidence set, and re-ranks them accordingly. The evidence consistency score primarily assesses the consistency of support for the conclusion on the same issue from evidence from different channels. The timeliness score primarily assesses whether the evidence version is within the valid range corresponding to the query time. The constraint matching score primarily assesses the degree of consistency between the evidence and the constraints of the park's target, issue type, time frame, and policy scope. The module calculates a comprehensive score using a weighted fusion method and selects preceding evidence to form the target evidence set. For evidence with similar scores but conflicting versions, the version with the higher validity is retained first, and the conflicting evidence is marked as a backup item.

[0091] During the constrained generation phase, the module inputs the target evidence set into the constrained generation unit. Before generation, a response skeleton is constructed, containing at least a conclusion paragraph, a supporting paragraph, and a recommendation paragraph. During generation, evidence binding checks are performed, requiring each conclusion sentence to be bound to at least one evidence index; terminology consistency checks are performed, requiring the question type, strategy name, constraint name, and the graph definition to be consistent; and scope checks are performed to prevent the response from exceeding the query constraints. If the checks fail, the module triggers a re-retrieval or downgraded generation, outputting only the content that has passed the checks. After passing the checks, the final response text is generated.

[0092] During the results output phase, the module outputs diagnostic information, recommendations, and / or policy information corresponding to the query request, along with corresponding evidence index identifiers. Evidence index identifiers may include issue node identifiers, strategy node identifiers, policy clause identifiers, and clause version identifiers. For comprehensive Q&A, the module organizes the output in the order of "diagnostic conclusion - strategy recommendation - policy basis," and appends a corresponding index at the end of each paragraph for user traceability and review. The output results are also written to the Q&A log area for subsequent quality assessment and index optimization.

[0093] The following example illustrates this: A user initiates a query: "What are the main reasons for the low level of supporting facilities in a certain industrial park in Nanshan District in 2024, and what strategies should be prioritized?" After parsing and obtaining constraints on the park object, time range, and problem type, the module performs a three-channel parallel search to obtain candidate evidence. After reordering, the target evidence shows that the park has two core types of evidence within the target time range: abnormal traffic accessibility and insufficient public facility coverage. These are stably associated with two priority strategies and their corresponding policy clauses. Based on this, the constrained generation unit outputs diagnostic and recommendation information, along with problem node indexes, strategy node indexes, and clause version indexes. Users can then use these indexes to retrieve specific graph paths and policy clause content.

[0094] For example, in another scenario, when policy text updates lead to changes in the version of clauses, the module increases the weight of the current version in the timeliness score, automatically reduces the ranking of evidence from the old version, and only appends the index of the valid version clauses to the output, ensuring that the Q&A results are consistent with the current policy status.

[0095] Through the above implementation methods, this embodiment achieves explicit binding between question-and-answer results and evaluation data, diagnostic links, strategy paths, and policy provisions by using a unified knowledge index, multi-constraint retrieval plan, three-channel parallel retrieval, evidence-level reordering, and constrained generation verification. This improves query response efficiency while enhancing the consistency, interpretability, and traceability of answers.

[0096] In some embodiments, the system further includes a linkage update module, which is used for: Monitor standardized dataset update events, indicator template change events, operator dependency graph change events, policy text change events, knowledge graph change events, and report template change events, and generate change identifiers for each type of change event; Based on the change identifier and the preset module dependency graph, the affected computing nodes, affected graph subgraphs, affected report fragments, and affected knowledge index fragments are identified to form an incremental update task set; The spatiotemporal consistency module, indicator arrangement and calculation module, diagnostic knowledge graph module, countermeasure knowledge graph module, evidence chain report generation module, and retrieval enhancement question answering module are triggered sequentially according to the incremental update task set, and local recalculation, subgraph re-reasoning, fragment reassembly, and index reconstruction are performed. Perform cross-module consistency checks on the updated park assessment results, problem diagnosis results, strategy recommendation results, park diagnosis report, and Q&A results; When the cross-module consistency check passes, submit the updated version; when the cross-module consistency check fails, generate a rollback instruction and write the corresponding incremental update task to the repair queue.

[0097] Specifically, a change event refers to an input or rule change that can cause changes in downstream results, including updates to standardized datasets, changes to indicator templates, changes to operator dependency graphs, changes to policy texts, changes to knowledge graphs, and changes to report templates. A change identifier is an identifiable object that provides a unified description of a change event, including event type, object identifier, time window identifier, version identifier, and scope of impact identifier. A module dependency graph is a directed graph that depicts the input-output dependencies of various functional modules.

[0098] Affected computation nodes refer to operator nodes or subgraph nodes that need to be recalculated in the indicator orchestration calculation module. Affected graph subgraphs refer to local graph structures that need to be re-reasoned in the diagnostic knowledge graph or countermeasure knowledge graph. Affected report fragments refer to chapter fragments that need to be reassembled in the report syntax tree. Affected knowledge index shards refer to index partitions that need to be rebuilt in the unified knowledge index. Incremental update task set refers to the set of executable tasks formed based on the impact analysis results. Cross-module consistency verification refers to the joint verification of object, version, evidence, and terminology consistency in the updated outputs of multiple modules. The repair queue refers to the task queue used to store verification failure tasks awaiting repair processing.

[0099] During the event monitoring phase, the linked update module continuously monitors six types of change events via the event bus. For standardized dataset update events, the monitoring objects can be refined to park objects and time windows; for indicator template and operator dependency graph change events, the monitoring objects can be refined to indicator template identifiers and operator subgraph identifiers; for policy text and knowledge graph change events, the monitoring objects can be refined to clause identifiers, clause version identifiers, and graph node identifiers; for report template change events, the monitoring objects can be refined to chapter identifiers and placeholder identifiers. After receiving an event, the module generates a corresponding change identifier and writes it to the update log area, forming a traceable event chain.

[0100] During the impact analysis phase, the module performs impact scope deduction based on change identifiers and a pre-defined module dependency graph. If the change originates from the data layer, it prioritizes locating affected computing nodes, then propagates this to affected graph subgraphs, report fragments, and index pieces. If the change originates from the policy layer, it prioritizes locating affected countermeasure subgraphs, report strategy sections, and policy-related index pieces. If the change originates from the template layer, it prioritizes locating affected report fragments and backtracks to their referenced versions. The module outputs affected computing nodes, affected graph subgraphs, affected report fragments, and affected knowledge index pieces, summarizing them to form an incremental update task set, and assigning execution priority and dependency preconditions to each task.

[0101] During the task execution phase, the modules sequentially trigger the spatiotemporal consistency module, indicator orchestration and calculation module, diagnostic knowledge graph module, countermeasure knowledge graph module, evidence chain report generation module, and retrieval enhancement question answering module according to the incremental update task set. Local recalculation is performed for data change tasks, subgraph re-reasoning is performed for graph change tasks, fragment reassembly is performed for template change tasks, and index reconstruction is performed for knowledge change tasks. During execution, the modules only process objects and fragments within the affected scope and reuse unaffected result versions. To ensure chain stability, the modules proceed according to the order of prerequisite dependencies, and the next task is triggered only after the output version of the previous stage is confirmed.

[0102] During the consistency verification phase, the module performs cross-module consistency verification on the updated park assessment results, problem diagnosis results, strategy recommendation results, park diagnosis reports, and Q&A results. Verification dimensions include object identifier consistency, version identifier consistency, evidence index consistency, and terminology consistency. Object identifier consistency ensures that the same update task corresponds to the same set of park objects; version identifier consistency ensures that assessments, diagnoses, strategies, reports, and Q&As reference the same batch of valid versions; evidence index consistency ensures that indexes in reports and Q&As can be traced back to the current map nodes and policy clause versions; and terminology consistency ensures that problem types, strategy names, and map definitions are consistent. Verification results are written to the verification log area.

[0103] During the commit and rollback phases, when cross-module consistency verification passes, the module commits the updated version and marks the corresponding task status as completed; when verification fails, the module generates a rollback instruction, restoring the relevant module output to the previous stable version and writing the corresponding incremental update task to the repair queue. Repair queue tasks can carry failure reason codes, such as clause version conflicts, index mismatches, or missing template placeholders, for subsequent automated repair or manual review processes. After repair is completed, the incremental execution of the task can be triggered again.

[0104] The following example illustrates this: A certain industrial park experienced two types of changes during its quarterly update: first, the addition of quarterly revenue records to the industrial economic data; and second, the release of new versions of policy text clauses. The linked update module first generates two change identifiers and performs impact analysis, locating the innovation capability-related operator subgraph, the corresponding problem diagnosis subgraph, the strategy suggestion sorting segment, the report strategy chapter, and the policy vector index fragments. Subsequently, it triggers local recalculation and subgraph re-inference in dependency order, reassembling the affected report fragments and rebuilding the relevant index fragments. During the verification phase, it is discovered that an old version clause index is still being referenced in the report. The module determines that the evidence indexes are inconsistent, automatically rolls back the current report fragment update, and writes the task to the repair queue. After repair, it executes again, passes verification, and submits the new version, ultimately achieving synchronous effectiveness of data updates, strategy updates, and report Q&A updates.

[0105] Through the above implementation method, this embodiment incorporates the multi-module update process into a unified dependency orchestration and consistency control framework through an event-driven linkage update mechanism. It achieves partial recalculation, partial re-inference, and partial reassembly without performing a full recalculation, thereby improving the system's update efficiency, version consistency, and result stability in scenarios of continuous updates of multi-source data and dynamic changes in policies and rules.

[0106] The above embodiments have described in detail the specific components and functions of the inefficient industrial land diagnosis system based on knowledge graphs and AI large models of this application. The implementation process of the inefficient industrial land diagnosis method based on knowledge graphs and AI large models of this application will be described in detail below with reference to specific embodiments. Figure 2 This is a flowchart illustrating the inefficient industrial land diagnosis method based on knowledge graphs and AI large models provided in this application embodiment, such as... Figure 2 As shown, the method may specifically include the following steps: S201: Acquire geospatial data, industrial economic data, basic information of the park and policy texts, perform field alignment, semantic mapping and version marking to form a standardized dataset; S202 performs spatial isotopic coding, temporal resampling, conflict resolution, and quality scoring on the standardized dataset to generate a computational input set with uncertainty labels; S203 automatically calculates and incrementally recalculates the input set of calculations based on preset indicator templates, operator dependency graphs and execution plans, and outputs the park evaluation results. S204: Write the park assessment results and basic park information into the relationship graph and causal graph, perform joint reasoning, and output the problem diagnosis results; S205, based on the problem diagnosis results, recalls candidate countermeasures, performs feasibility screening, conflict resolution and priority ranking, and outputs strategy recommendations related to policy provisions; S206, Based on the preset report template, integrate the park assessment results, problem diagnosis results and strategy recommendation results to generate a park diagnosis report containing a clause source index and a graph node index; S207 performs hybrid retrieval and constrained generation based on a local knowledge base constructed from park assessment results, problem diagnosis results, strategy recommendation results, and policy texts, and outputs diagnostic information, recommendation information, and / or policy information.

[0107] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although the technical solutions of this application have been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A diagnostic system for inefficient industrial land use based on knowledge graphs and large AI models, characterized in that, include: The multi-source data semantic foundation module is used to acquire geospatial data, industrial economic data, basic information of industrial parks and policy texts, and perform field alignment, semantic mapping and version marking to form a standardized dataset. The spatiotemporal consistency module is used to perform spatial isotope encoding, temporal resampling, conflict resolution, and quality scoring on the standardized dataset to generate a computational input set with uncertainty labels. The indicator arrangement and calculation module is used to perform automatic calculation and incremental recalculation on the calculation input set based on the preset indicator template, operator dependency graph and execution plan, and output the park evaluation results. The diagnostic knowledge graph module is used to write the park assessment results and basic park information into the relationship graph and causal graph, perform joint reasoning, and output the problem diagnosis results. The countermeasures knowledge graph module is used to recall candidate countermeasures based on the problem diagnosis results, and to perform feasibility screening, conflict resolution and priority ranking, and output strategy suggestions associated with policy provisions. The evidence chain report generation module is used to integrate the park assessment results, problem diagnosis results and strategy recommendation results according to the preset report template to generate a park diagnosis report containing the clause source index and the graph node index; The enhanced question-answering module is used to perform hybrid retrieval and constrained generation based on a local knowledge base constructed from the park's assessment results, problem diagnosis results, strategy recommendation results, and policy texts, and output diagnostic information, recommendation information, and / or policy information.

2. The system according to claim 1, characterized in that, The multi-source data semantic base module is specifically used for: Source data description information is constructed for the geospatial data, industrial economic data, park basic information and policy text respectively, and entity alignment relationship across data sources is established based on unified object identification rules; Based on the pre-defined domain terminology ontology and indicator semantic lexicon, each source data field is mapped to a unified semantic pattern, forming a semantic mapping result associated with park objects, indicator objects, and policy clause objects; Based on the entity alignment relationship and semantic mapping results, the geospatial data, industrial economic data, park basic information and policy text are subjected to priority merging of conflicting fields and rule-based completion of missing fields to generate a structured candidate dataset. The data records in the structured candidate dataset are appended with source identifiers, collection time identifiers, spatial unit identifiers, and caliber version identifiers to form a traceable standardized dataset, which is then written into the semantic base storage area.

3. The system according to claim 1, characterized in that, The spatiotemporal consistency module is specifically used for: Based on preset spatial unit coding rules, geographic objects, statistical objects, and park objects in the standardized dataset are mapped to the same spatial index system to form spatial co-position coding results; Based on the evaluation period and indicator calculation window, time axis alignment and multi-granularity time resampling are performed on the standardized dataset to form a set of time series segments corresponding to the indicator template; For conflicting records under the same spatial unit identifier and the same time window identifier, conflict resolution is performed according to source trust level, version priority and time freshness, and a consistent record set is output. A quality scoring vector is constructed based on data integrity, cross-source consistency, spatiotemporal continuity, and caliber matching degree, and the quality score is applied to the consistent record set. Based on the quality score vector, perform regularization correction, substitution value filling, or weight reduction on low-confidence records to generate a computational input set containing uncertainty labels.

4. The system according to claim 1, characterized in that, The index arrangement and calculation module is specifically used for: Based on the preset indicator templates, the indicator dimensions, indicator definitions, operator types, and input / output constraints are analyzed, and an operator dependency graph corresponding to each indicator template is constructed. A hierarchical execution plan is generated based on the topological relationships of the operator dependency graph, operator resource requirements, and data arrival status, and the computation input set is divided into parallel computation fragments according to spatial unit identifiers and time window identifiers; According to the hierarchical execution plan, operator chaining is performed on each parallel computing slice. During the operator execution process, uncertainty markers are passed and operator-level error propagation is performed to obtain intermediate index results and dimension aggregation results. Based on data change events, indicator template change events, or operator parameter change events, determine the affected operator subgraphs, trigger incremental recalculation according to the affected operator subgraphs, and reuse the results of unaffected operators; The aggregated results and incremental recalculation results of the dimensions are versioned and encapsulated to generate park evaluation results associated with the park object.

5. The system according to claim 1, characterized in that, The diagnostic knowledge graph module is specifically used for: Based on the unified entity identifier, the indicator entities and status entities in the park evaluation results are aligned with the attribute entities in the park basic information to construct entity nodes and associated edges in the relationship graph; Based on the preset causal rule base, restrictive elements, abnormal indicators, and problem representations are mapped into causal triples, a causal graph is constructed, and a cross-graph anchor point mapping is established between the graph and the relationship graph. Starting with the park object as the query starting point, perform relation path retrieval in the relation graph to generate a candidate set of questions, and perform causal chain expansion in the causal graph based on the cross-graph anchor point mapping; The relationship path score and causal chain confidence score corresponding to the candidate problem set are jointly calculated to obtain the problem type determination result and the severity level corresponding to the problem type. For the identified problem types, the system outputs a causal path consisting of abnormal indicator nodes, constraint element nodes, and problem nodes, forming a problem diagnosis result associated with the park object.

6. The system according to claim 5, characterized in that, The strategy knowledge graph module is specifically used for: Based on the problem diagnosis results, a multi-hop association retrieval is performed in the countermeasure knowledge graph to recall the candidate countermeasure set and policy clause set corresponding to the problem type; The predetermined planning constraints, ownership constraints, development boundary constraints, and industry orientation constraints are uniformly encoded into constraint vectors. Based on the constraint vectors, rule verification and graph constraint reasoning are performed on the candidate strategy set to eliminate candidate strategies that do not meet the constraints. For candidate strategies that pass the constraint verification, a strategy conflict relationship graph is constructed and target conflicts, resource conflicts, and temporal conflicts are identified. Based on the conflict relationship graph, mutual exclusion resolution and combination modification are performed to generate executable strategy combinations. Based on the severity level, causal path coverage, constraint matching degree, and policy clause suitability, the feasible countermeasure combination is jointly scored to form a priority ranking result. The strategy recommendations are output according to the priority ranking results, and each strategy recommendation is appended with a corresponding policy clause identifier, clause version identifier, and graph association path identifier.

7. The system according to claim 1, characterized in that, The evidence chain report generation module is specifically used for: Based on the preset report template, the chapter skeleton, field placeholders and evidence citation rules are parsed to construct the report generation syntax tree; The park assessment results, problem diagnosis results, and strategy recommendation results are aligned and mapped according to a unified object identifier to generate report content fragments corresponding to the field placeholders; Based on the evidence citation rules, policy clause identifiers and clause version identifiers are extracted from the strategy recommendation results to form a clause source index. Problem node, indicator abnormal node, and constraint element node identifiers are extracted from the problem diagnosis results to form a graph node index. Based on the report, a syntax tree is generated to perform segmented assembly and consistency checks on the report content fragments, clause source indexes, and graph node indexes, thereby generating a structured campus diagnostic report.

8. The system according to claim 1, characterized in that, The enhanced search and question answering module is specifically used for: Based on the park assessment results, problem diagnosis results, strategy recommendation results, and policy texts, a unified knowledge index is constructed. The unified knowledge index includes a structured index, a vector index, and a graph index, and each index is cross-linked through a unified object identifier. The query request is subjected to intent parsing and constraint extraction to generate a retrieval plan that includes constraints on park objects, question types, time ranges, and policy scopes. According to the retrieval plan, structured retrieval, semantic vector retrieval and graph path retrieval are performed in parallel to obtain a candidate evidence set, and then reordered based on evidence consistency score, timeliness score and constraint matching score to generate a target evidence set; The target evidence set is input into the constrained generation unit, and the output content is subjected to evidence binding verification and terminology consistency verification according to the preset generation constraints. Output diagnostic information, recommendations, and / or policy information corresponding to the query request, along with a corresponding evidence index identifier for result traceability and verification.

9. The system according to claim 1, characterized in that, The system also includes a linkage update module, which is used for: Monitor standardized dataset update events, indicator template change events, operator dependency graph change events, policy text change events, knowledge graph change events, and report template change events, and generate change identifiers for each type of change event; Based on the change identifier and the preset module dependency graph, the affected computing nodes, affected graph subgraphs, affected report fragments, and affected knowledge index fragments are determined to form an incremental update task set; According to the incremental update task set, the spatiotemporal consistency module, indicator arrangement and calculation module, diagnostic knowledge graph module, countermeasure knowledge graph module, evidence chain report generation module and retrieval enhancement question answering module are triggered in sequence to perform local recalculation, subgraph re-reasoning, fragment reassembly and index reconstruction; Perform cross-module consistency checks on the updated park assessment results, problem diagnosis results, strategy recommendation results, park diagnosis report, and Q&A results; When the cross-module consistency check passes, submit the updated version; when the cross-module consistency check fails, generate a rollback instruction and write the corresponding incremental update task to the repair queue.

10. A method for diagnosing inefficient industrial land use based on knowledge graphs and large AI models, according to any one of claims 1 to 9, characterized in that, include: Acquire geospatial data, industrial economic data, basic information of the park, and policy texts; perform field alignment, semantic mapping, and version marking to form a standardized dataset. Spatial isotope coding, temporal resampling, conflict resolution, and quality scoring are performed on the standardized dataset to generate a computational input set with uncertainty labels; Based on preset indicator templates, operator dependency graphs, and execution plans, the computational input set is automatically calculated and incrementally recalculated to output park evaluation results. The park assessment results and basic park information are written into a relationship graph and a causal graph, joint reasoning is performed, and the problem diagnosis results are output. Based on the problem diagnosis results, candidate countermeasures are recalled, and feasibility screening, conflict resolution, and priority ranking are performed to output strategy recommendations associated with policy provisions. Based on the preset report template, the park assessment results, problem diagnosis results, and strategy recommendation results are integrated to generate a park diagnosis report that includes a clause source index and a graph node index; Based on a local knowledge base constructed from the park's assessment results, problem diagnosis results, strategy recommendation results, and policy texts, hybrid retrieval and constrained generation are performed to output diagnostic information, recommendation information, and / or policy information.

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