An environmental impact assessment intelligent review method and system based on segment inspection and cross-segment verification

Abstract

The application discloses an environmental impact assessment intelligent review method and system based on segmented checking and cross-segment verification, and the method steps comprise the following steps: performing document analysis on a professional technical report to be reviewed and cutting the professional technical report into independent review segments according to chapters; isolated reasoning is adopted for each paragraph, and only a simplified structured abstract is transmitted between segments; after each segment is completed, a structured checkpoint snapshot is persisted, and breakpoint continuous review is supported; after the review of all segments is completed, cross-segment reverse verification is automatically carried out on multi-chapter parameter data sets, cross-chapter data contradictions are identified through deviation rate calculation; multi-dimensional access control is set to force verification, and only after all review dimensions are completed, the severity judgment process can be entered; before review output, outgoing quality detection is performed to identify model attention attenuation and regulation standard version conflicts; finally, the review results are output through a three-partition visual workbench, and one-key tracing of original texts and regulation fragments is supported.

Description

Technical Field

[0001] This invention relates to the field of intelligent document review technology, specifically to an intelligent environmental impact assessment review method and system based on segmented inspection and cross-segment verification. Background Technology

[0002] Technical compliance review of construction projects is a legally mandated core step in the implementation of projects in the fields of engineering construction, ecological environment, and work safety. Among these, professional technical reports such as environmental impact assessment reports and safety assessment reports are essential legally required technical documents for construction project management, serving as crucial bases for project initiation, approval, and operation. These reports are characterized by their large size, complex content, and stringent compliance requirements. They typically range from 80,000 to 300,000 words, covering more than ten professional chapters. The review process requires comparing the report content against hundreds of national and industry regulations and standards, conducting a comprehensive, clause-by-clause compliance check. This multi-dimensional review demands high precision and has an extremely low tolerance for compliance errors.

[0003] Currently, compliance reviews of the aforementioned statutory technical reports within the industry primarily rely on manual review. Manual review requires reviewers to meticulously study a vast amount of regulations and standards, verify each page of the report text, and check the matching of various technical parameters with compliance clauses. This approach suffers from low efficiency, high labor costs, inconsistent review standards, and significant subjective errors. Furthermore, the multi-dimensional review of long texts demands extremely high levels of professional competence, meticulousness, and experience from reviewers, making it prone to omissions, errors, and duplicate reviews. This compromises the consistency and reliability of the review process and fails to meet the demands of large-scale, efficient, and precise compliance reviews required for current construction projects.

[0004] To address the numerous drawbacks of manual review, existing technologies are gradually attempting to apply large language models to the automated compliance review of professional technical reports. Leveraging the powerful text understanding, semantic matching, and clause comparison capabilities of these models, they aim to replace some manual review work, thereby improving review efficiency and reducing the cost of human intervention. However, in serious compliance application scenarios such as construction project compliance review, which demand high rigor, precision, and strict adherence to regulations, existing automated review technologies using large language models suffer from several unavoidable systemic flaws, making it difficult to meet the compliance review standards for legally mandated technical documents.

[0005] First, existing technologies lack a comprehensive, end-to-end systemic guarantee for review quality. Large language models inherently suffer from attention decay in long text processing. For compliance reports of tens of thousands of words, the model's review of the first half of the text is relatively detailed and accurate, but the accuracy of semantic recognition, clause matching, and compliance determination drops significantly in the second half, resulting in extremely poor review uniformity and stability. Furthermore, existing technologies lack a comprehensive end-to-end guarantee system adapted to long text compliance reviews, including segmented isolation processing, persistent storage of review status, and closed-loop quality detection for final outbound submissions. This fails to technically mitigate the inconsistent review quality caused by the flaws in long-chain inference.

[0006] Secondly, existing technologies cannot automatically verify cross-chapter data consistency, making it highly susceptible to oversights in compliance reviews. In project technical reports, the same core technical data (including pollution source strength, pollutant emissions, process parameters, risk levels, etc.) are often scattered across multiple chapters such as engineering analysis, current status monitoring, impact prediction, prevention and control measures, and review conclusions. This type of cross-chapter data consistency verification is a core aspect of compliance review and a frequent point of oversight in manual reviews. However, existing large-scale model review technologies only support independent review of single paragraphs and chapters, lacking a cross-chapter data correlation comparison and consistency verification mechanism. This makes it impossible to automatically identify contradictions, deviations, and inconsistencies in similar data across different chapters, resulting in insufficient review completeness.

[0007] Secondly, existing technologies pose a risk of systematic omissions in review dimensions, failing to achieve comprehensive compliance verification. Compliance review of construction project technical reports encompasses over ten core review dimensions and hundreds of detailed inspection points, requiring standardized verification across all dimensions without blind spots. Existing large-scale model review technologies lack a mandatory, comprehensive verification mechanism, a standardized list of review dimensions, and mandatory traversal logic. The review process is highly random, easily leading to omissions of certain compliance dimensions and niche review points, thus failing to meet the comprehensive coverage requirements for legal document compliance review.

[0008] Finally, existing technologies do not support the resumption of the review process after interruption or full-process traceability, resulting in poor practicality for iterative reviews. Compliance reviews of professional technical reports require multiple rounds of revisions, reviews, and iterative optimizations, leading to long review cycles and numerous iterations. Existing large-scale model review technologies operate on a one-time closed-loop model, failing to support pausing or interrupting the review process at any point or resuming from historical checkpoints, thus lacking review flexibility. Furthermore, existing technologies cannot fully retain process logs, modification records, and version iteration history for each round of review, lacking a complete review traceability chain. This fails to meet industry management requirements for traceable, reviewable, and verifiable compliance reviews, hindering the definition of review responsibilities and the iterative optimization of reports.

[0009] From a technical perspective, the root cause of the aforementioned shortcomings of existing technologies lies in the fact that the computational complexity of the Transformer self-attention mechanism in Large Language Models (LLM) is quadratic with the length of the input sequence when processing long text sequences. This leads to a uniform distribution of attention weights in the latter half of long documents, resulting in a significant decrease in semantic representation capabilities. This phenomenon is academically known as "Attention Decay." Existing technical solutions have not addressed this technical bottleneck in long document reasoning at the architectural level. Specifically, they are: (1) lacking a segmented isolation reasoning mechanism, making it impossible to decompose long documents into multiple independent reasoning contexts to avoid the impact of attention decay; (2) lacking a cross-segment state transfer and verification mechanism, making it impossible to guarantee the consistency of data between segments after segmentation; and (3) lacking a recoverability mechanism for the reasoning process, making it impossible for long-chain reasoning tasks to resume execution from an intermediate state after interruption. Summary of the Invention

[0010] To address the aforementioned technical problems, the present invention aims to provide an intelligent environmental impact assessment review method and system based on segmented inspection and cross-segment verification, ensuring the consistency, comprehensive coverage, and full traceability of review quality throughout the entire process.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] An intelligent environmental impact assessment review method based on segmented inspection and cross-segment verification includes the following steps:

[0013] S1. Receive the technical reports to be reviewed, parse the documents to extract text and table data, and divide the reports into multiple review sections by chapter;

[0014] S2. Perform segmented isolation review for each review segment, where the reasoning context of each review segment only contains the original report of the current segment, the structured summary of the previous segment, and the corresponding regulatory and standard search results. Each review segment constructs an independent reasoning context instance, and the context windows are not shared between segments.

[0015] S3. After each section of review is completed, the review status is persisted as structured checkpoint data to support resuming the review at the breakpoint. The structured checkpoint data is stored in JSON format and includes section identifier, timestamp, conclusion statistics, problem summary array, scope of reviewed sections, and list of cited regulations and standards.

[0016] S4. After all review sections are completed, extract the structured data of each section and perform cross-section reverse verification. Perform fuzzy matching by parameter name and object identifier, compare the numerical consistency of the same key parameter in different review sections, and calculate the deviation rate based on the principle of relative error.

[0017] S5. Perform access control and perform matrix verification based on multiple dimensions to check whether all review dimensions have been completed. If there are any incomplete dimensions, block the subsequent process and return for supplementary review.

[0018] S6. Perform key decision isolation review and severity determination on the issues and non-compliance items identified during the review process. The isolation review is performed in an independent reasoning context, loading only the original regulatory text and report fragments of the corresponding issues.

[0019] S7. Perform outbound quality inspection before final output. Identify attention decay signals during the review process by calculating the ratio of the average problem detection density of the subsequent review segment to the previous review segment. Detect standard reference conflicts by traversing the set of standard version numbers referenced by each review segment.

[0020] S8. Output the review results and present the interactive data on the workbench of the display interface.

[0021] Furthermore, in step S2:

[0022] S2-1. Each review segment forms an independent reasoning context. The full report text is not passed between segments. Only a structured summary of no more than 500 words is passed to the next review segment.

[0023] S2-2, The structured summary includes the number of items that passed in the current segment, the number of items that failed, and a core overview of the problem.

[0024] Furthermore, in step S3:

[0025] S3-1, The structured checkpoint data includes segment identifiers, timestamps, conclusion statistics (pass / fail / inapplicable counts), problem summary arrays (including problem numbers, severity markers, and problem summaries of no more than 80 characters), the scope of reviewed segments, and a list of cited regulations and standards;

[0026] S3-2. When a new review session starts, restore the review state by reading the latest checkpoint data and skip completed segments.

[0027] The technical effect of the segmented isolation review mechanism is that by dividing long documents (80,000-300,000 words) into multiple independent inference contexts (each segment not exceeding 20,000 words), the length of the inference context of each review segment is controlled within the effective range of the model's attention mechanism, fundamentally avoiding the attention decay problem of the Transformer self-attention mechanism in long sequence processing; at the same time, only a structured summary of no more than 500 words is passed between segments instead of the complete original text, avoiding the linear expansion of the context window.

[0028] Furthermore, step S4 specifically includes:

[0029] S4-1. Extract key parameter datasets from the tabular data output by the preceding review stage. Extract the referenced parameter dataset from the subsequent review section. ;

[0030] S4-2. Perform fuzzy matching based on parameter name and object identifier. For the same parameter k that matches successfully, calculate the cross-segment consistency deviation rate. ;

[0031] S4-3, when the deviation rate When the deviation exceeds the preset threshold, the parameter is marked as a data contradiction, and an independent review of the data contradiction is triggered.

[0032] The technical advantage of the cross-segment reverse verification mechanism lies in its ability to automatically identify numerical differences of the same key parameter across different chapters of a document by constructing a cross-segment parameter correlation graph. This solves the technical problem of isolated data in each segment after segmented review, making global consistency verification impossible. The deviation rate calculation formula is based on the principle of relative error, which can eliminate the interference of absolute magnitude differences in parameters on consistency judgment.

[0033] Furthermore, step S5 specifically includes:

[0034] S5-1. Track the execution status of each review dimension row by row in the multi-dimensional execution matrix. The status includes "completed", "not completed" and "not applicable".

[0035] S5-2. Before entering the severity determination stage, it is mandatory to verify whether all review dimensions are marked as "completed" or "not applicable (with reasonable explanation)".

[0036] S5-3. If any dimension has an "incomplete" status, the severity determination entry will be automatically blocked, and the process will be forced to return to the corresponding review section for supplementary review.

[0037] Furthermore, step S7 specifically includes:

[0038] S7-1, Tail Attenuation Detection: Calculate the ratio D of the average problem detection density of the subsequent review segment to the average problem detection density of the preceding review segment. When D is lower than the preset attenuation threshold, trigger an alarm and return to supplementary review.

[0039] S7-2, Standard Conflict Detection: Traverse the set of standard version numbers referenced in each review section and detect whether there is a conflict in different review sections where the same standard number references different versions;

[0040] S7-3, Coverage Detection: Verify whether the status flags for all review dimensions are all "completed".

[0041] The technical effect of the outbound quality inspection mechanism is as follows: by detecting tail decay (calculating the ratio of problem detection density of the subsequent segment to that of the preceding segment), a quantitative assessment of the degree of model attention decay is achieved. When a decay signal is detected, a supplementary review is automatically triggered to form a closed-loop quality control. By detecting standard conflict, technical conflicts of using different version numbers of the same standard number in different review segments are automatically identified, avoiding errors in review conclusions caused by inconsistent standard versions.

[0042] Furthermore, step S8 specifically includes:

[0043] S8-1. On the left side of the workbench, the review status monitoring panel displays the segmented review progress, checkpoint snapshot save status, access control matrix full-dimensional verification status, and outbound quality inspection results in real time.

[0044] S8-2. In the intermediate review results display area of ​​the workbench, display a review card containing the severity determination conclusion and a summary of the problem;

[0045] S8-3. In the original text tracing and highlighting area on the right side of the workbench, in response to the user's trigger operation on the review card, the corresponding checkpoint data is retrieved, and the original report text and the cited regulatory and standard excerpts are highlighted in the right area.

[0046] This invention also provides an intelligent environmental impact assessment review system based on segmented inspection and cross-segment verification, comprising:

[0047] The parsing and segmentation scheduling module is used to receive professional technical reports to be reviewed, parse the documents, and divide them into multiple review segments by chapter;

[0048] The segmented isolation review module is used to perform segmented isolation review for each review segment. The reasoning context of each review segment only includes the original report of the current segment, the structured summary of the previous segment, and the regulatory and standard search results.

[0049] The checkpoint persistence module is used to persist the review status as structured checkpoint data after each review is completed to support resuming reviews from breakpoints.

[0050] The cross-segment reverse verification module is used to extract structured data from each segment, compare the numerical consistency of the same key parameter in different review segments, and calculate the deviation rate.

[0051] The access control module is used to verify whether all review dimensions have been completed. If there are any incomplete dimensions, it will block the subsequent process and return to supplementary review.

[0052] The severity assessment and isolation module is used to perform critical decision isolation review and severity assessment on verified issues;

[0053] The outbound quality inspection module is used to identify conflicts between attention decay signals and standard references before the final output;

[0054] The interaction and visualization module is used to present the workbench interface on the display interface and output the review results and multi-stage status data to the workbench interface.

[0055] Furthermore, the cross-segment reverse verification module is configured as follows:

[0056] Extract the tabular dataset from the output of the preceding segment. and the parameter dataset referenced in the subsequent section Perform fuzzy matching based on parameter name and object identifier;

[0057] Calculate the deviation rate for the same parameter k that is successfully matched:

[0058]

[0059] Parameters with deviation rates exceeding a preset deviation threshold are marked as data contradictions, and cross-segment contradiction markers are generated.

[0060] Furthermore, the interaction and visualization module includes:

[0061] The left-side status monitoring and rendering unit is used to render the segmented review progress, checkpoint saving status, access control matrix verification status, and outbound quality inspection results in real time on the left-side review status monitoring panel of the display interface.

[0062] The intermediate results display unit is used to display review cards containing severity determination conclusions and problem summaries in the intermediate review results display area of ​​the display interface;

[0063] The right-side source tracing and highlighting unit is used to retrieve checkpoint data and highlight the original report text and cited regulatory and standard excerpts in response to the user's trigger operation on the review card in the original source tracing and highlighting area on the right side of the display interface.

[0064] Compared with existing technologies, this invention constructs a closed-loop quality assurance system across the entire process, solving the attention decay defect in long document inference using large language models, avoiding systematic omissions in review dimensions, automatically detecting cross-chapter data inconsistencies, and ensuring that the entire review process is interruptible, recoverable, and fully traceable, thus possessing significant engineering application value. The specific beneficial effects of this invention include:

[0065] 1. Closed-loop quality assurance system: A closed-loop assurance system with six links, including segmented isolation, checkpoint snapshot, cross-segment reverse verification, access control verification, critical decision isolation, and outbound quality inspection, was constructed, which solved the technical problem of attention decay defect in reasoning of large models and long documents.

[0066] 2. Automated cross-chapter data consistency verification: The L3-level cross-segment reverse verification mechanism is adopted to automatically extract the same parameters from multiple chapters for fuzzy matching and quantify the deviation rate. It automatically identifies cross-chapter data contradictions, which solves the technical problems of long time consumption and high difficulty of manual review.

[0067] 3. Forced full-coverage access control verification mechanism: Based on a multi-dimensional access control execution matrix, a forced full-dimensional verification is performed before severity determination, which solves the technical problem of easy omission of review dimensions.

[0068] 4. Resume review from interruption and full-process traceability: Adopting a dedicated structured checkpoint data structure, it supports accurate resumption of the review task after any paragraph is interrupted, and fully retains the review history of each stage, solving the technical problem of long time consumption caused by repeated review of the whole text.

[0069] 5. Three-zone visual interactive workbench: The interactive interface adopts a three-zone layout with status monitoring on the left, result cards in the middle, and original text tracing on the right. It provides real-time visualization of the entire review process status and supports one-click location of original legal fragments for review issues, improving the practicality of implementation. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of the main flow of the method provided in an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram of the segmented isolation reasoning and checkpoint snapshot mechanism provided in an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram of cross-segment reverse verification and access control logic provided in an embodiment of the present invention;

[0073] Figure 4 This is a schematic diagram of the outbound quality detection logic provided in an embodiment of the present invention;

[0074] Figure 5 This is a schematic diagram of the system architecture provided in an embodiment of the present invention;

[0075] Figure 6 This is a schematic diagram of the user interface workbench interaction provided in an embodiment of the present invention. Detailed Implementation

[0076] The technical solution of the present invention will be fully described in conjunction with the accompanying drawings and specific embodiments. The application scenario of the present invention is defined as "intelligent review of professional technical reports". The core typical embodiment is based on the Environmental Impact Assessment (EIA) report, while also expanding to general implementation cases of safety assessment reports.

[0077] Example 1: Intelligent Review of Environmental Impact Assessment Report for Guixi Sanyu Wastewater Treatment Plant Project

[0078] Please see Figures 1 to 6 This embodiment uses the review of a real "Environmental Impact Report for the Renovation and Expansion Project of Guixi Sanyu Wastewater Treatment Plant" as an example, and provides a detailed description in conjunction with the system workbench interface described in this invention. The workbench interface is divided into a left-side review status monitoring panel, a middle review results display area, and a right-side original text tracing and highlighting area.

[0079] S101. Reception and Parsing: The system receives the report (.docx file) and parses it using both MinerU and WPSAPI engines to extract paragraph text and table data. This report includes basic project information (D4620 wastewater treatment and reuse, total investment of 50 million RMB, total plant capacity of 10,000 m³ / d). After locating key metadata, the system divides the report into five review sections according to chapters.

[0080] S102, Segmented Isolation Review (L1): Segmented isolation review is performed for each review segment. The reasoning context of segment 1 (General Principles → Engineering Analysis) only includes the original report of segment 1 (no more than 20,000 words), the structured summary of the preceding segment (empty here), and the search results of the EIA_00 unified knowledge base. After the review of segment 1 is completed, only a structured summary of no more than 500 words is passed to segment 2, including the number of items that passed, the number of items that failed, and a core overview of the issues in the current segment. The review status monitoring panel on the left displays the status in real time, such as "Segment 1 is under review..." and "Segment 1 checkpoint seg1 has been saved".

[0081] S103, Checkpoint Snapshot (L2): After Segment 1 is completed, the review status is serialized into structured JSON data (including segment identifier seg1, pass / fail count, issue summary array, etc.) and written to disk. If interrupted due to unforeseen circumstances, the review status is restored by reading the latest checkpoint data when a new session starts, skipping completed segments.

[0082] S104, Cross-Segment Reverse Verification (L3): After all 5 segments are completed, the system extracts the tabular data output from each segment for cross-segment reverse verification. For example, the system extracts analogous data on the source strength of odorous gases from the engineering analysis section of segment 1 (analogous to "Phase I of the Urban Sewage Treatment Renovation and Expansion Project in a High-tech Zone") and performs fuzzy matching with the source strength data referenced in the impact prediction section of segment 3. The system finds that the analogous project in segment 1 is "Urban Sewage Treatment" (influent mainly consists of domestic sewage), while this project is "Industrial Wastewater Centralized Treatment Plant" (influent contains sulfides, volatile organic compounds, etc.), and there is a significant difference in the composition of the influent water. The system calculates the deviation rate of parameter consistency. Due to insufficient feasibility demonstration of the analogy, the deviation rate of the context logic exceeds the preset threshold. The system automatically marks this item as "Data Contradiction (A26-05)" and triggers independent review. In the left-hand review status monitoring panel, the corresponding cross-segment verification item is marked as "Contradiction Found".

[0083] S105, Access Control (GATE): The system maintains a multi-dimensional execution matrix, covering three major modules: A. Special Inspection Checklist (130 items), B. Quality Audit of Items 26 / 27 (18 items), and C. Standard Timeliness + Text Quality (42 items). Before entering the severity determination, the status of all dimensions is forcibly verified. If there are still unverified items in the "Standard Timeliness Verification" dimension (such as whether the 2025 amendment to the "Discharge Standard of Pollutants for Urban Wastewater Treatment Plants" GB18918-2002 has been cited), the system automatically blocks the severity determination entry and forces a return to the corresponding section for supplementary review. In the review status monitoring panel on the left, the status of each dimension is rendered in matrix form, and incomplete items are displayed with a red blocking indicator.

[0084] S106. Severity Determination and Isolation Review (L4): For issues that may trigger serious quality problems, a new independent reasoning context is created for isolation determination. For example, for issue A26-02 (the soil environmental impact assessment level is incorrectly determined, misclassifying project D4620 as a Category I project under "Hazardous Waste Utilization and Disposal" in Appendix A of HJ964-2018), the system only loads the original text and specific paragraphs of Appendix A of HJ964-2018 corresponding to this issue, and determines its severity level as "important".

[0085] S107, Outbound Quality Inspection (L5): Performs tail-end attenuation detection, calculating the ratio D of the problem detection density in the latter half to that in the first half. If D is lower than the preset attenuation threshold, an alarm is triggered. Simultaneously, it scans the standard version numbers referenced in each segment, detecting conflicts between different versions with the same number (e.g., finding a report that references both GB12523-2011 and its replacement GB12523-2025). The left-hand review status monitoring panel displays either "Outbound Inspection Passed" or "Standard Conflict Alarm".

[0086] S108. Output and Source Tracing Interaction: The system displays structured review opinions in the intermediate review results display area, including a review statistics overview (e.g., 85 items passed, 15 failed, and 30 inapplicable in Module A) and review cards (e.g., issue number A26-05, severity "important," issue summary "insufficient analogy basis for exhaust gas source strength"). When the user clicks on the card, an interactive command is triggered, and the original text source tracing and highlighted area on the right retrieves the data from checkpoint seg1, highlights the relevant paragraphs in the original report, and simultaneously displays the original text of Article 5.2 of the "Technical Guidelines for Pollution Source Strength Accounting" (HJ884-2018), achieving second-level source tracing of review opinions.

[0087] Example 2: Intelligent Review and Interface Interaction of Environmental Impact Assessment Report for Copper Processing Project

[0088] This embodiment focuses on the environmental impact assessment report for a copper processing project, which contains approximately 120,000 words and 52 tables representing process and emission source strength. It is accompanied by the visual workbench interactive interface of this invention, which is divided into three main sections: a review status monitoring panel on the left, a review results display area in the middle, and a source tracing and highlighting area on the right.

[0089] The complete execution process steps S101~S108 are as follows:

[0090] S101, Receiving and Document Parsing

[0091] The system receives environmental impact assessment reports in docx format, calls the python-docx tool library to extract full-text paragraph text and table structured data, and automatically captures project metadata (industry category C3216, atmospheric environment level 1 assessment, etc.); it automatically divides the report into 5 independent review sections according to the standard chapters of the report.

[0092] S102, Segmented Isolation Review (Level 1)

[0093] Follow the segmented isolation and context isolation rules: Segment 1 (General Principles + Engineering Analysis) reasoning context only loads the original text of this segment (maximum of 20,000 characters), without a preceding summary, and matches the search terms of the EIA regulatory standard library; after the review of this segment is completed, only a structured summary of no more than 500 characters is transmitted to the next review segment, without transmitting the complete original text of the preceding text.

[0094] The monitoring panel on the left side of the workbench refreshes the status text in real time: "Segment 1 under review" and "Segment isolation context construction completed".

[0095] S103, Checkpoint Snapshot Persistence (L2 Level)

[0096] After a single segment review is completed, all review statuses for that segment are serialized into structured JSON snapshot data and stored on disk. The snapshot fields include segment identifier seg1, timestamp, pass / fail / inapplicable statistics, issue summary array (number + severity level + brief description within 80 characters), scope of reviewed segments, and list of cited regulations.

[0097] The left panel of the workbench displays a checkpoint save icon, marking that a seg1 snapshot has been generated; when a new session starts, the snapshot is read and the completed paragraphs are automatically skipped, enabling breakpoint continuation of the review.

[0098] S104, Cross-segment reverse verification (L3 level)

[0099] After all five review sections are completed, the system automatically extracts the source strength table dataset for section 1 engineering analysis. Section 3, Atmospheric Prediction Chapter, References Parameter Dataset .

[0100] Example parameters: Section 1 source strength SO2 emission rate Section 3 predicts that the chapter cites the SO2 source code.

[0101] Substitute into the deviation rate formula to calculate:

[0102]

[0103] The system has a preset deviation threshold of 20%. The result of this calculation exceeds the limit, and the parameter is automatically marked as a cross-segment data contradiction. The corresponding verification item on the left monitoring panel is highlighted in red with the message "Data contradiction found".

[0104] S105, Access Control Verification (GATE Access Control Mechanism)

[0105] The system has a built-in matrix of 12 environmental impact assessment dimensions (general outline, air, surface water, groundwater, noise, soil, solid waste, risk, planning, environmental protection measures, total quantity, and attached figures and appendices); mandatory full-dimensional verification is required before entering the severity determination process.

[0106] If the soil dimension status is marked as "incomplete", the system will automatically block subsequent judgment entries, mark the soil row in the workbench matrix interface with a red blocking mark, and force a jump to the corresponding paragraph for supplementary review.

[0107] S106. Severity Assessment and Isolation Review (Level 4)

[0108] To address high-risk issues related to cross-segment verification and segmented review, a lightweight, isolated reasoning context is constructed separately. Only the original legal text (≤2000 words) and the corresponding report segment (≤2000 words) for this issue are loaded for independent review, and the severity level of the issue (minor / moderate / major) is determined.

[0109] S107, Outbound Quality Inspection (Level L5)

[0110] Tail attention decay detection: Calculate the ratio D of the average problem detection density in the second half (segment 4 and segment 5) to the detection density in the first half (segment 1 to segment 3); preset decay threshold is 0.5, if D < 0.5, a quality alarm is triggered and the second half is returned for review.

[0111] Standard conflict detection: Traverse all referenced standard numbers and versions to identify conflict issues where multiple versions of the same standard number appear.

[0112] Dimension coverage re-check: Secondary verification of all reviewed dimensions is complete.

[0113] The left-side monitoring panel uniformly outputs the final conclusion of the outbound test: "Outbound test passed" or "Attenuation alarm / standard conflict".

[0114] S108, Results Output and Inter-regional Traceability

[0115] The intermediate results display area generates standardized review cards, which include the issue number, severity level, and concise issue summary. When the user clicks on any review card, the system triggers the interaction logic, retrieves the corresponding paragraph checkpoint snapshot, and the source tracing section on the right automatically loads the original report, highlighting conflicting paragraphs and corresponding excerpts from regulations and standards such as HJ2.2-2018.

[0116] Example 3: General Adaptation Example for Safety Evaluation Report Review

[0117] This invention also applies to the automated and intelligent review of safety assessment reports. The system splits safety assessments into independent review sections based on their chapters, relying on a segmented isolation context mechanism to avoid full-text context bloat and model inference distortion. The cross-segment reverse verification module automatically matches the chemical storage data in the hazardous source chapter and the storage data cited in the quantitative risk assessment chapter, batch calculating parameter deviations and identifying data contradictions. The access control matrix covers all safety assessment review dimensions, including overall layout, process safety, fire protection, and hazardous chemical storage, eliminating any omissions in the review. Finally, the workbench interface outputs a complete review report with highlighted original text for traceability.

[0118] Example 4: System Architecture and Electronic Equipment

[0119] Corresponding to the method and process described above, the intelligent review system of this invention is built (architecture as follows). Figure 5 The system comprises eight core functional modules: parsing and segmented scheduling module, segmented isolation review module, checkpoint persistence module, cross-segment reverse verification module, access control module, severity determination and isolation module, outbound quality inspection module, and interaction and visualization module. These modules work together in a sequential and coordinated manner to complete the entire review process.

[0120] This system can be deployed on standard electronic devices. The device hardware includes at least one processor and a memory that is communicatively connected to the processor. The memory stores executable program instructions, and the processor executes all of the aforementioned review methods by running the instructions.

[0121] It also provides a computer-readable storage medium that stores computer-executable instructions, allowing any computer to run the intelligent review method of this invention after reading the instructions on the medium.

[0122] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this invention should be included within the protection scope of this invention. Parts of this invention not described in detail belong to well-known technology to those skilled in the art.

Claims

1. An intelligent environmental impact assessment review method based on segmented inspection and cross-segment verification, characterized in that, Includes the following steps: S1. Receive the technical reports to be reviewed, parse the documents to extract text and table data, and divide the reports into multiple review sections by chapter; S2. Perform segmented isolation review for each review segment, where the reasoning context of each review segment only contains the original report of the current segment, the structured summary of the previous segment, and the corresponding regulatory and standard search results. Each review segment constructs an independent reasoning context instance, and the context windows are not shared between segments. S3. After each section of review is completed, the review status is persisted as structured checkpoint data to support resuming the review at the breakpoint. The structured checkpoint data is stored in JSON format and includes section identifier, timestamp, conclusion statistics, problem summary array, scope of reviewed sections, and list of cited regulations and standards. S4. After all review sections are completed, extract the structured data of each section and perform cross-section reverse verification. Perform fuzzy matching by parameter name and object identifier, compare the numerical consistency of the same key parameter in different review sections, and calculate the deviation rate based on the principle of relative error. S5. Perform access control and perform matrix verification based on multiple dimensions to check whether all review dimensions have been completed. If there are any incomplete dimensions, block the subsequent process and return for supplementary review. S6. Perform key decision isolation review and severity determination on the issues and non-compliance items identified during the review process. The isolation review is performed in an independent reasoning context, loading only the original regulatory text and report fragments of the corresponding issues. S7. Perform outbound quality inspection before final output. Identify attention decay signals during the review process by calculating the ratio of the average problem detection density of the subsequent review segment to the previous review segment. Detect standard reference conflicts by traversing the set of standard version numbers referenced by each review segment. S8. Output the review results and present the interactive data on the workbench of the display interface.

2. The intelligent environmental impact assessment review method based on segmented inspection and cross-segment verification according to claim 1, characterized in that, In step S2: S2-1. Each review segment forms an independent reasoning context. The full report text is not passed between segments. Only a structured summary of no more than 500 words is passed to the next review segment. S2-2, The structured summary includes the number of items that passed in the current segment, the number of items that failed, and a core overview of the problem.

3. The intelligent environmental impact assessment review method based on segmented inspection and cross-segment verification according to claim 1, characterized in that, In step S3: S3-1, The structured checkpoint data includes segment identifiers, timestamps, conclusion statistics, problem summary arrays, scope of reviewed segments, and a list of cited regulations and standards; S3-2. When a new review session starts, restore the review state by reading the latest checkpoint data and skip completed segments.

4. The intelligent environmental impact assessment review method based on segmented inspection and cross-segment verification according to claim 1, characterized in that, Step S4 specifically includes: S4-1. Extract key parameter datasets from the tabular data output by the preceding review stage. Extract the referenced parameter dataset from the subsequent review section. ; S4-2. Perform fuzzy matching based on parameter name and object identifier. For the same parameter k that matches successfully, calculate the cross-segment consistency deviation rate. ; S4-3, when the deviation rate When the deviation exceeds the preset threshold, the parameter is marked as a data contradiction, and an independent review of the data contradiction is triggered.

5. The intelligent environmental impact assessment review method based on segmented inspection and cross-segment verification according to claim 1, characterized in that, Step S5 specifically includes: S5-1. Track the execution status of each review dimension row by row in the multi-dimensional execution matrix. The status includes "completed", "not completed" and "not applicable". S5-2. Before entering the severity determination stage, it is mandatory to verify whether all review dimensions are marked as "completed" or "not applicable". S5-3. If any dimension has an "incomplete" status, the severity determination entry will be automatically blocked, and the process will be forced to return to the corresponding review section for supplementary review.

6. The intelligent environmental impact assessment review method based on segmented inspection and cross-segment verification according to claim 1, characterized in that, Step S7 specifically includes: S7-1, Tail Attenuation Detection: Calculate the ratio D of the average problem detection density of the subsequent review segment to the average problem detection density of the preceding review segment. When D is lower than the preset attenuation threshold, trigger an alarm and return to supplementary review. S7-2, Standard Conflict Detection: Traverse the set of standard version numbers referenced in each review section and detect whether there is a conflict in different review sections where the same standard number references different versions; S7-3, Coverage Detection: Verify whether the status flags for all review dimensions are all "completed".

7. The intelligent environmental impact assessment review method based on segmented inspection and cross-segment verification according to claim 1, characterized in that, Step S8 specifically includes: S8-1. On the left side of the workbench, the review status monitoring panel displays the segmented review progress, checkpoint snapshot save status, access control matrix full-dimensional verification status, and outbound quality inspection results in real time. S8-2. In the intermediate review results display area of ​​the workbench, display a review card containing the severity determination conclusion and a summary of the problem; S8-3. In the original text tracing and highlighting area on the right side of the workbench, in response to the user's trigger operation on the review card, the corresponding checkpoint data is retrieved, and the original report text and the cited regulatory and standard excerpts are highlighted in the right area.

8. An intelligent environmental impact assessment review system based on segmented inspection and cross-segment verification, characterized in that, include: The parsing and segmentation scheduling module is used to receive professional technical reports to be reviewed, parse the documents, and divide them into multiple review segments by chapter; The segmented isolation review module is used to perform segmented isolation review for each review segment. The reasoning context of each review segment only includes the original report of the current segment, the structured summary of the previous segment, and the regulatory and standard search results. The checkpoint persistence module is used to persist the review status as structured checkpoint data after each review is completed to support resuming reviews from breakpoints. The cross-segment reverse verification module is used to extract structured data from each segment, compare the numerical consistency of the same key parameter in different review segments, and calculate the deviation rate. The access control module is used to verify whether all review dimensions have been completed. If there are any incomplete dimensions, it will block the subsequent process and return to supplementary review. The severity assessment and isolation module is used to perform critical decision isolation review and severity assessment on verified issues; The outbound quality inspection module is used to identify conflicts between attention decay signals and standard references before the final output; The interaction and visualization module is used to present the workbench interface on the display interface and output the review results and multi-stage status data to the workbench interface.

9. The intelligent environmental impact assessment review system based on segmented inspection and cross-segment verification according to claim 8, characterized in that, The cross-segment reverse verification module is configured as follows: Extract the tabular dataset from the output of the preceding segment. and the parameter dataset referenced in the subsequent section Perform fuzzy matching based on parameter name and object identifier; Calculate the deviation rate for the same parameter k that is successfully matched: Parameters with deviation rates exceeding a preset deviation threshold are marked as data contradictions, and cross-segment contradiction markers are generated.

10. The intelligent environmental impact assessment review system based on segmented inspection and cross-segment verification according to claim 8, characterized in that, The interaction and visualization module includes: The left-side status monitoring and rendering unit is used to render the segmented review progress, checkpoint saving status, access control matrix verification status, and outbound quality inspection results in real time on the left-side review status monitoring panel of the display interface. The intermediate results display unit is used to display review cards containing severity determination conclusions and problem summaries in the intermediate review results display area of ​​the display interface; The right-side source tracing and highlighting unit is used to retrieve checkpoint data and highlight the original report text and cited regulatory and standard excerpts in response to the user's trigger operation on the review card in the original source tracing and highlighting area on the right side of the display interface.

Images