A standardized discovery method and system for clues of ecological environmental problems

Abstract

This invention relates to a standardized method and system for discovering clues to ecological and environmental problems. It addresses the technical problems of the lack of standardization and low processing efficiency in existing clue discovery methods. The method includes: ranking and sampling deterioration trends to determine the final monitoring unit; generating standardized comparison data of spatial constraints and prohibited issues based on geographic vector data and prohibited issues in the zoning control requirements list; conducting compliance verification of dynamic map patch resources based on the standardized comparison data, and identifying suspected violation problem patches through semantic matching and spatial relationship verification; and solidifying evidence based on the suspected violation problem patches corresponding to the prohibited issues and the comparison data resources, and generating a problem clue report through standardized output. This establishes standard operating procedures, achieving the organic integration of business flow, data flow, and rule flow, and is applied to ecological and environmental zoning control operations, realizing the process-oriented, standardized, and efficient discovery of problem clues, and improving the consistency and reusability of regulatory results.

Description

Technical Field

[0001] This invention relates to the fields of remote sensing monitoring applications and ecological environment supervision technology, specifically to a standardized method and system for discovering clues to ecological environment problems. Background Technology

[0002] Against the backdrop of my country's comprehensive implementation of ecological and environmental zoning management, routine and comprehensive supervision of various environmental management units has become a fundamental task. Among these tasks, the core and challenge lies in how to quickly and accurately identify potential ecological damage or illegal development activities within these units due to failure to meet ecological and environmental access requirements.

[0003] Currently, the methods for discovering clues to problems mainly suffer from the following shortcomings: (1) The process is not standardized and relies on personal experience. From the selection of target areas and determination of monitoring units to the interpretation of map patches and the writing of reports, there is a lack of unified and clear operating procedures and technical standards in each link. The quality and efficiency of the work vary from person to person, and the format of the results is inconsistent, which is not conducive to cross-regional comparison and business collaboration.

[0004] (2) The proportion of manual operation is high, and the efficiency bottleneck is prominent. In particular, when manually comparing the massive amount of remote sensing dynamic patches with the complex textual control requirements, a lot of manpower is required for reading, understanding and spatial judgment, which is time-consuming and labor-intensive, and it is difficult to meet the needs of high-frequency and large-scale operational monitoring.

[0005] (3) Data and rules are separated, making it difficult to verify the correlation. Remote sensing change detection results (spatial data) and ecological environment access list (text rules) belong to different systems. There is a lack of effective technical bridges to systematically and standardizedly correlate and verify the two, resulting in a large number of suspected problems that cannot be quickly screened and warned. Summary of the Invention

[0006] To address the aforementioned problems, embodiments of the present invention provide a standardized method and system for discovering clues to ecological and environmental issues, thereby solving the technical problem of low processing efficiency caused by the lack of standardization in existing clue discovery processes.

[0007] The standardized discovery method for clues to ecological and environmental problems according to embodiments of the present invention includes: Based on the environmental impact assessment indicators, the ecological environment quality of the candidate areas is ranked according to the trend of deterioration to determine the target areas, and the zoning control units within the target areas are sampled to determine the final monitoring units. Based on the list of zoning control requirements for the final monitoring unit, structured prohibited issues and geographic vector data are generated. Based on the geographic vector data and prohibited issues, standardized comparison data of associated spatial constraints and prohibited issues are generated. Based on the geographic vector data of the final monitoring unit, the dynamic patch resources of remote sensing interpretation are determined. The dynamic patch resources are then checked for compliance based on standardized comparison data. Suspected non-compliant patches are identified through semantic matching and spatial relationship verification. Based on the suspected violation problem patches corresponding to the prohibited problem mapping and the comparison data resources, evidence is solidified, and problem clue reports are formed through standardized output of evidence.

[0008] In one embodiment of the present invention, the final monitoring unit includes: Using districts and counties as candidate regions, target regions are determined within each province based on the annual decline in EQI data. Using prefecture-level cities as candidate regions, the target regions are determined by ranking the annual decline in AQI and WQI data within each province. Within the target area, the overlapping portions with ecological protection red lines and nature reserves are removed, and the boundaries of the zoning control units are updated. The final monitoring unit is determined by randomly sampling the zoning control units in the target area.

[0009] In one embodiment of the present invention, the AQI data uses historical data on the percentage of days with good or excellent air quality per year, and the WQI data uses historical data on the percentage of Class I-III water quality at cross-sectional sections. In another embodiment of the present invention, the standardized control data used to form the associated spatial constraints and prohibition problem includes: The LLM model extracts templates from the input data of the final monitoring unit, including the list of zoning control requirements and geographic vector data. The LLM model is then used to structure prohibited issues, map spatial constraints to prohibited issues, and output standardized comparison data relating spatial constraints to prohibited issues.

[0010] In one embodiment of the present invention, it further includes: The standardized comparison data were verified and confirmed by manual comparison with the list of zoning control requirements.

[0011] In one embodiment of the present invention, the compliance verification of dynamic patch resources based on standardized comparison data includes: Based on the geographic vector data of the final monitoring unit, the satellite remote sensing and telemetry data are filtered to obtain dynamic patch resources for remote sensing interpretation within the corresponding range; Prohibited issues are sequentially extracted from standardized comparison data. Based on the semantic logic conditions and spatial constraints of the prohibited issues, dynamic map resources are checked for compliance one by one to identify suspected non-compliant map patches. When spatial constraints on prohibited issues are missing, dynamic map features that meet the semantic and logical conditions of prohibited issues are identified based on geographic vector data for compliance verification, and suspected non-compliant map features are confirmed.

[0012] In one embodiment of the present invention, the step of forming a problem clue report through standardized output of evidence includes: The boundaries of suspected non-compliant patches are reviewed, and the patches are smoothed, their areas and center coordinates are determined to form structured comparison data. Extracting the preceding and following temporal images within the boundary of the patch to form a structured comparison image; The LLM model output data description template is used to process the prohibited issues, comparison data and comparison images corresponding to suspected violation problem patches, forming a standardized problem clue report.

[0013] The standardized discovery system for clues to ecological and environmental problems according to embodiments of the present invention includes: The unit screening device is used to rank the ecological environment quality of candidate areas according to environmental impact assessment indicators to determine the target area, and to sample the zoning control units within the target area to determine the final monitoring unit. The rule parsing device is used to generate structured prohibited issues and geographic vector data based on the zoning control requirements list of the final monitoring unit, and to generate standardized comparison data of associated spatial constraints and prohibited issues based on the geographic vector data and prohibited issues; The problem verification device is used to determine the dynamic patch resources of remote sensing interpretation based on the geographic vector data of the final monitoring unit, to perform compliance verification on the dynamic patch resources based on standardized comparison data, and to identify suspected non-compliant problem patches through semantic matching and spatial relationship verification. The clue fixing device is used to solidify evidence based on the suspected violation problem patches corresponding to the prohibited problem mapping and the comparison data resources, and to form a problem clue report through the standardized output of evidence.

[0014] The standardized method and system for discovering clues to ecological and environmental problems in this invention establishes a standardized, clear, operationally compliant, and human-machine collaborative mechanism for discovering such clues. Applied to ecological and environmental zoning management, it efficiently and systematically identifies clues suggesting non-compliance with ecological and environmental access lists within zoning management units. This overcomes the arbitrariness and inefficiency of traditional methods, achieving the organic integration of business flow, data flow, and rule flow, thereby significantly improving the overall efficiency of ecological and environmental supervision. It streamlines, standardizes, and expedits the discovery of clues, enhancing the consistency and reusability of supervisory results. Attached Figure Description

[0015] Figure 1 The diagram shown is a flowchart illustrating a standardized clue discovery method for ecological and environmental problems according to an embodiment of the present invention.

[0016] Figure 2The diagram shown is a schematic representation of a standardized clue discovery method for ecological and environmental problems according to an embodiment of the present invention, which matches dynamic patches based on semantics and geographic vector data (without spatial constraints).

[0017] Figure 3 The diagram shown is a schematic representation of the smoothing process for suspected violation patches in a standardized clue discovery method for ecological and environmental problems according to an embodiment of the present invention.

[0018] Figure 4 The diagram shown is a schematic representation of the area measurement of suspected violation patches in a standardized clue discovery method for ecological and environmental problems according to an embodiment of the present invention.

[0019] Figure 5 The image shown is a schematic diagram of image capture of the preceding and following phases in a standardized clue discovery method for ecological and environmental problems according to an embodiment of the present invention.

[0020] Figure 6 The diagram shown is an architectural schematic of a standardized clue discovery system for ecological and environmental problems according to an embodiment of the present invention.

[0021] Figure 7 The diagram shown is a schematic diagram of the architecture of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0023] An embodiment of the present invention provides a standardized method for discovering clues to ecological and environmental problems, as follows: Figure 1 As shown. In Figure 1 In this embodiment, the following are included: Step 100: Based on the environmental impact assessment indicators, rank the ecological environment quality of the candidate areas according to the deterioration trend to determine the target areas, and sample the zoning control units within the target areas to determine the final monitoring units.

[0024] Those skilled in the art will understand that environmental impact assessment (EIA) indicators include, but are not limited to, the Air Quality Index (AQI), the Water Quality Index (WQI), and the Ecological Quality Index (EQI). Historical statistical data from the national-level environmental monitoring network can be used to obtain specific EIA evaluation indicators for administrative divisions at various levels. Annual data from different EIA indicators are used to form a comprehensive evaluation and ranking of the ecological environment deterioration trends of candidate areas at different levels, identifying target areas requiring key monitoring. Subsequently, key regulatory objects within the target areas (e.g., priority protection units, key control units, and general control units) are randomly sampled to determine the final set of monitoring units, reflecting the representativeness of the samples within the target areas.

[0025] Step 200: Based on the list of zoning control requirements for the final monitoring unit, structured prohibited issues and geographic vector data are generated. Based on the geographic vector data and prohibited issues, standardized comparison data of associated spatial constraints and prohibited issues are generated.

[0026] Those skilled in the art will understand that the list of zoning control requirements records the core location of the environmental control unit and the geographic vector data of the included types of areas in a formatted text. This includes spatial layout constraints for prohibited, restricted, and strictly prohibited areas; pollutant emission control data for zero new emissions, existing emissions, and agricultural and rural areas; environmental risk prevention and control data for strictly prohibited, emergency, and monitored areas; and resource utilization efficiency data for water resources, land resources, and energy. Using text processing techniques such as Large Language Modeling (LLM), a data extraction template is set to extract the logical relationships of constraints and limitations in the formatted text and convert them into prohibited question items (i.e., standardized rule clauses forming clues), thus obtaining the hierarchical organizational structure of the items. Semantic understanding using Large Language Modeling (LLM) determines the geographic vector data for constraints on the types of areas.

[0027] Spatially mapping attribute and spatial information from geographic vector data with regional information from prohibited issue entries creates comparative data based on spatial constraints and prohibited issues. Standardized comparative data on spatial constraints and prohibited issues can be generated using template tools or general data storage structures. Structured prohibited issue entries from different zoning control requirement lists can be merged, split, and expanded using a general data storage structure to form standardized comparative data sets and targeted subsets of prohibited issue entries, maintaining the standardization of the data storage structure. This allows each final monitoring unit to establish corresponding standardized comparative data (tables) on spatial constraints and prohibited issues based on the issue data and geographic vector data in the pre-set zoning control requirement list, clearly defining the spatial quantitative boundaries of prohibited issues.

[0028] Step 300: Determine the dynamic patch resources for remote sensing interpretation based on the geographic vector data of the final monitoring unit, conduct compliance verification of the dynamic patch resources based on standardized comparison data, and identify suspected non-compliant patches through semantic matching and spatial relationship verification.

[0029] Based on the basic coordinate range of the final monitoring unit and the coordinates and resource distribution in the national-level GIS (Geographic Information System) data, geographic vector data of coordinates, topography, terrain, land feature names, resource distribution, and special protection markers within the final monitoring unit's range are obtained. Simultaneously, based on the range boundaries formed by the vector data in the national-level ecological and environmental satellite monitoring dataset, time-series extraction and interpretation of remote sensing data within the final monitoring unit's range are performed to form corresponding dynamic patch resources. These dynamic patches include time-series information on natural resources quantified by different land types and ecological attributes.

[0030] Dynamic map features include, but are not limited to, newly added construction land features, farmland outflow / non-agriculturalization features, vegetation destruction and forest reduction features, illegal excavation, mining, and soil extraction features, land reclamation and encroachment on river and lake shorelines features, ecological restoration and newly added green land features, and changes in the area surrounding pollution sources and sewage outlets. Using an LLM model, the semantics of prohibited issues in the standardized comparison data are semantically matched with the temporal attribute changes of features in the dynamic map resources to determine whether dynamic map features conform to the semantics of prohibited issues. Furthermore, geographic vector data from the standardized comparison data is used to verify whether dynamic map features meet spatial constraints, forming a standardized dual-check logic. This balances the accuracy of policy understanding with the rigor of spatial relationship judgment to improve the accuracy and reliability of problem clue discovery. To address the ambiguity in spatial relationship judgment caused by missing geographic vector data, human-machine collaborative verification is used to improve the process.

[0031] Step 400: Based on the suspected violation problem patches corresponding to the prohibited problem mapping and the comparison data, solidify the evidence and form a problem clue report through standardized output of the evidence.

[0032] The suspected violation patches are compared with the corresponding violation entries, violation judgment parameters, and comparison data resources in the standardized comparison data. The suspected violation patches include parsable multi-dimensional violation information and / or extractable diverse data resources. A standard process for solidifying evidence is established by mapping the violation information and comparison data resources corresponding to the suspected violations to relational data, forming problem clues through relational data. For example, based on violations in the standardized comparison data, graphic or related evidence is extracted from the suspected violation patches; geographic vector data is parsed from the comparison data resources based on violations in the standardized comparison data. Relational data structures are used to form structured storage of evidence related to suspected violations through key-value pairs or indexes. A standardized output template based on the LLM model is used to standardize the output of relevant relational data for suspected violation patches, forming a fixed-format problem clue report, enabling rapid identification and early warning of regional ecological problems. The problem clue report includes textual and graphic descriptions of the suspected violations after the evidence is transformed by the LLM model, textual and graphic descriptions of the corresponding geographic vector data, and data links to the corresponding evidence resources. A problem description template, combining patch attributes and violated rules, generates a standardized and complete problem description. Customized data output templates ensure standardization of standardized comparison data, suspected violations, relational data, and text-based output formats during the conversion process.

[0033] The standardized method for discovering clues to ecological and environmental problems in this invention integrates traditionally fragmented operations relying on individual experience into a unified and repeatable process by constructing a standardized workflow for target unit screening, rule parsing of the zoning control requirement list, verification of target unit map resources, and solidification of clue evidence. This ensures consistency in working methods and the quality of results across different implementing entities and time and space conditions. The rule parsing step, which transforms unstructured text rules (zoning control requirement list) into a structured rule base (standardized comparison data), is a crucial step in efficient automated comparison, effectively solving the efficiency bottleneck of manual reading and comparison. The entire process accurately delineates the task boundaries between manual review and machine processing, achieving complementary advantages of human-machine collaboration. While ensuring verification accuracy, it also significantly improves overall work efficiency. This method can be applied to ecological and environmental zoning control operations to efficiently and systematically discover clues suggesting non-compliance with the ecological and environmental access list within zoning control units.

[0034] like Figure 1 As shown, in one embodiment of the present invention, step 100 includes: Step 110: Using districts and counties as candidate regions, sort the provinces according to the annual decline in EQI data to determine the target regions.

[0035] In one embodiment of the invention, each county is assigned a different functional positioning based on the standard division of urban built-up areas, water conservation functional areas, soil and water conservation functional areas, windbreak and sand fixation functional areas, and other areas. Urban built-up areas are excluded from the county-level administrative divisions, focusing instead on the natural ecological functional areas. The county-level administrative divisions are ranked according to the magnitude of the decline in EQI data between the assessment year and the previous year, using this as an indicator of ecological environment quality deterioration, to determine the candidate areas for the county-level administrative divisions with the largest decline. In one embodiment of the invention, the candidate areas are prioritized based on important ecological functional areas (such as water conservation, soil and water conservation functional areas, windbreak and sand fixation functional areas, etc.). In one embodiment of the invention, five important ecological functional areas are selected from each province as target areas, with any remaining areas supplemented by candidate areas for the county-level administrative divisions with the largest decline.

[0036] Step 120: Using prefecture-level cities as candidate regions, sort the provinces according to the annual decrease in AQI and WQI data to determine the target regions.

[0037] In one embodiment of the invention, urban built-up areas within prefecture-level cities are excluded, focusing instead on the natural ecological areas of those cities. The cities are ranked according to the magnitude of their AQI and WQI data decline between the assessment year and the previous year, using these as indicators of ecological environment quality deterioration. The candidate areas with the largest declines are then identified. Specifically, historical data on the percentage of days with good air quality can be used as AQI data, and historical data on the percentage of Class I-III water quality at cross-sectional sections can be used as WQI data. AQI and WQI data can be weighted according to the climate type of the province to form measurement parameters. In one embodiment of the invention, five candidate areas from each province are selected as target areas. Each target area typically includes at least one zoning control unit.

[0038] Step 130: In the target area, remove the overlapping parts with the ecological protection red line and nature reserves, and update the boundaries of the zoning control units.

[0039] Based on the spatial vector data of the zoning control units, ecological protection red lines, and nature reserves, their respective outlines are determined, spatially overlaid, and the intersecting parts are removed to form new outline boundaries for the zoning control units. Standardized spatial processing is used to redefine the accurate scope of the monitoring area, enhance boundary features within the target region, and improve the accuracy of monitoring and resolving environmental degradation.

[0040] Step 140: Randomly sample the zone control units in the target area to determine the final monitoring units.

[0041] The number of zone control units included in all target areas is determined. Zone control units are then selected using a determined random sampling ratio and rules to determine the final monitoring units. In one embodiment of the invention, an equal-probability, no-replacement random sampling algorithm is applied to select zone control units by province according to a set ratio.

[0042] The standardized method for discovering clues to ecological and environmental problems in this invention ensures that monitoring focus is concentrated on priority protection units through standardized spatial processing, meeting actual regulatory needs. For administrative divisions at different scales, a set of objective and fixed screening rules enables rapid and accurate identification of key regulatory areas with significant ecological quality decline at the macro level, avoiding subjective arbitrariness and providing a scientific basis for subsequent targeted monitoring.

[0043] like Figure 1 As shown, in one embodiment of the present invention, step 200 includes: Step 210: Extract template input data from the LLM model to the zoning control requirements list and geographic vector data corresponding to the final monitoring unit. Use the LLM model to structure prohibited issues, map spatial constraints to prohibited issues, and output standardized comparison data of associated spatial constraints and prohibited issues.

[0044] Those skilled in the art will understand that LLM models define parsing objectives, table structures, parsing rules, and output requirements through templates. Extracting templates from input data enhances the LLM model's understanding of the core logic of the text, eliminating logical inconsistencies and version differences in text descriptions. It allows for rapid location of the information to be extracted, and the output data tables are clearly structured and categorized, avoiding vague expressions. Establishing specific parsing rules prevents redundant or deviating LLM outputs from the original text. Based on business terminology prompts, it creates a professionally adapted data processing scenario, standardizing data processing and ensuring that the data table structure aligns with the business scenario.

[0045] In one embodiment of the present invention, the textualized list of zoning control requirements is parsed using the standard interactive process of the China National Environmental Monitoring Centre's Ecological Language Model (Environmental Sentinel), and combined with geographic vector data to be parsed into a structured spatial constraint-prohibition problem comparison table.

[0046] The spatial constraint-prohibited issue matching table extracts and generates a pairing list of spatial constraints and prohibited issues based on predefined input data and template constraint instructions. For example, the template expression for "such as vegetation protection zones or steep slopes above 25 degrees - cultivating crops, etc." is "Please extract spatial layout constraints and corresponding prohibited issues from the above zoning control requirements document." The input data extraction templates correspond one-to-one between spatial layout constraints and prohibited issues. For example, "Yangtze River Basin sand mining prohibited area" corresponds to "sand mining activities," "soil pollution control area" corresponds to "new construction, reconstruction, or expansion of buildings and facilities unrelated to soil pollution control or remediation," and "soil pollution control area" corresponds to "other land use behaviors that may harm public health and the living environment"; "vegetation protection zone" corresponds to "cultivating crops," and "steep slopes above 25 degrees" corresponds to "cultivating crops," etc.

[0047] In one embodiment of the present invention, the spatial constraint-prohibition problem comparison table can be stored in JSON format to form standardized comparison data.

[0048] The standardized discovery method for ecological and environmental problem clues in this invention automatically transforms policy clauses and standard parameters described in natural language into machine-readable and comparable structured rules through a standardized “instruction input-model processing-result verification” process. This enables the standardized extraction and normalization of key semantics in the unified rule basis for subsequent automated verification.

[0049] like Figure 1 As shown, in one embodiment of the present invention, step 200 further includes: Step 220: The standardized comparison data is verified and confirmed by manual comparison with the list of zoning control requirements.

[0050] Manual verification targets the low-probability key semantic illusions to ensure the accuracy of prohibited questions, prohibited conditions, and data mappings.

[0051] like Figure 1 As shown, in one embodiment of the present invention, step 300 includes: Step 310: Based on the geographic vector data of the final monitoring unit, filter the satellite remote sensing and telemetry data to obtain dynamic patch resources for remote sensing interpretation within the corresponding range.

[0052] By filling out the "Ecological Environment Zoning Control Data Sharing Service Filing Form," an application is made to access the zoning control data of the Ministry of Ecology and Environment, obtaining the geographic vector data and zoning control requirements list of the zoning control unit corresponding to the final monitoring unit. The boundaries and regional characteristics of the final monitoring unit are determined based on the geographic vector data. Dynamic patch data of the final monitoring unit's regional remote sensing interpretation is then obtained from the national ecological environment monitoring dataset based on the geographic vector data. Satellite remote sensing data within the spatial range of the final monitoring unit is filtered using spatial information overlay and temporal information analysis to form dynamic patch resources corresponding to the final monitoring unit. The dynamic patch attributes can reflect the temporal changes of quantifiable ecological characteristics within a defined spatial range.

[0053] Step 320: Sequentially extract prohibited issues from the standardized comparison data, and conduct compliance checks on the dynamic map patch resources one by one according to the semantic logic conditions and spatial constraints of the prohibited issues to confirm the map patches suspected of violating regulations.

[0054] Semantic matching of dynamic map patch attributes is performed based on the comparison object types and comparison logic relationships included in the prohibited issues. For cases with spatial constraints and geographic vector data, spatial overlay analysis is used to filter map patches that do not meet control requirements, identifying suspected violation problem patches based on the vector data with spatial layout constraints. In one embodiment of the invention, a suspected violation threshold is formed by combining geographic attributes, vegetation attributes, or hydrological attributes with the coordinates in the spatial constraints of the prohibited issues, and the map patch attributes in the corresponding dynamic map patch resources are compared to obtain suspected violation problem patches. In one embodiment of the invention, comparison units can be formed by gridding the coordinate space of the dynamic map patch resources. In one embodiment of the invention, comparison units can be formed by combining attributes in the attribute space of the dynamic map patch resources. In one embodiment of the invention, a composite comparison unit is formed by combining comparison units, and a comprehensive comparison is performed with the suspected violation threshold to determine the compliance verification result for complex environmental states. The judgment result is formed by combining the attribute temporality of the comparison unit with the suspected violation threshold.

[0055] In one embodiment of the present invention, firstly, it is determined whether the temporal attributes of the patch before and after the change semantically match the prohibited problem. If they match, it is determined whether a corresponding vector data layer exists based on spatial constraints, and spatial overlay analysis or coordinate picking and distance calculation operations are performed respectively to verify whether the patch conforms to the spatial constraints. For example, if the "prohibited problem" is "logging", then the temporal attribute of the patch before the change is "forest land", and the temporal attribute after the change is "non-forest land". If the "prohibited problem" is "sand mining activities", then the temporal attribute of the patch after the change is "mining land".

[0056] Step 330: When the spatial constraints of prohibited issues are missing, determine the dynamic patches that meet the semantic logic conditions of prohibited issues based on geographic vector data, conduct compliance checks, and confirm the suspected non-compliant patches.

[0057] For cases where spatial layout constraints lack vector data, a coordinate picking system with feature names is used in conjunction with GIS software measurement tools to measure whether spatial layout constraints exist within the distance range corresponding to a feature, thus finding dynamic features that meet the requirements. In one embodiment of the present invention, the standardized clue discovery method for ecological and environmental problems performs dynamic feature matching based on semantics and geographic vector data, as follows: Figure 2 As shown. In Figure 2 In the process, based on the spatial constraint-prohibition problem correspondence, the target area is "1 km range of primary water source protection area - logged forest land". Since there is no reservoir vector data, the reservoir location is found through the coordinate picking system. Dynamic patches that may meet the distance requirements are roughly screened. Then, GIS software measurement tools are used to measure whether a reservoir exists within 1 km of the patch.

[0058] The standardized clue discovery method for ecological and environmental issues in this invention utilizes standardized comparative data to establish a dual standardized verification logic of semantic initial screening and spatial verification, which takes into account both the accuracy of policy understanding and the rigor of spatial relationship judgment, and significantly improves the accuracy and reliability of problem clue discovery.

[0059] like Figure 1 As shown, in one embodiment of the present invention, step 400 includes: Step 410: Review the boundaries of suspected non-compliant patches, smooth the patch boundaries, determine the patch area and center coordinates, and form structured comparison data.

[0060] Map boundary verification, area, and center determination are used to correct the connectivity and integrity between fitted adjacent map patch division units. This can be accomplished through computer image processing techniques and human-controlled processing techniques.

[0061] In one embodiment of the invention, boundary verification is performed through manual, refined vector editing. Smoothing of suspected violation patches is as follows: Figure 3 As shown. In Figure 3In the monitoring, the lines of the patches are smooth, match the edges of ground features on the image, have a deviation of less than 2 pixels, and have a polygonal topological relationship with no topological errors.

[0062] In one embodiment of the present invention, the area and center coordinates are calculated using the geometric calculation function of the attribute table in GIS software. The area measurement of suspected violation-prone patches is as follows: Figure 4 As shown. In Figure 4 In the process, after verifying the boundary, the area of ​​the map patch (unit: mu) and the coordinates of the center point (unit: decimal system) are automatically generated.

[0063] By determining the precise boundaries of suspected non-compliance issues, the results of compliance verification are collected. Using methods such as common data clustering, the collected compliance verification data is structured and stored to form a fixed evidence.

[0064] Step 420: Extract the preceding and following temporal images within the boundary of the patch to form a structured comparison image.

[0065] Based on the boundaries, area, and center of the suspected violation patch, the changed areas are marked on the preceding and following temporal images of the suspected violation patch. In one embodiment of the invention, the image of the suspected violation patch is cropped as follows: Figure 5 As shown. In Figure 5 In this process, image processing tools are used to capture screenshots of remote sensing patches, extracting temporal images before and after the patches, marking the changed areas, and creating comparison images. The comparison images and related patch attributes are then stored in a structured manner, thus establishing evidence.

[0066] Step 430: Process the prohibited issues, comparison data and comparison images corresponding to suspected violation problem patches using the output data description template of the LLM model to form a standardized problem clue report.

[0067] By standardizing the unit name, problem type, area range, and violated zoning control requirements (prohibited problems) in the output data description template, the prohibited problems, comparison data, and comparison images are output in a readable, standardized, and complete problem description format using an LLM model, thus forming a standardized problem clue report.

[0068] In one embodiment of the present invention, the generated problem clue report is as follows: According to remote sensing satellite imagery from the monitoring center, [Priority Protection Unit 4, Maojian District, Shiyan City, Hubei Province] is suspected of undergoing other construction activities around the reservoir, resulting in a reduction of 14.13 mu (approximately 9,333 hectares) of forest land. This violates Hubei Province's overall regional control requirements, which prohibit deforestation for cultivation, quarrying, sand mining, soil extraction, and other acts that damage trees and forest land. This demonstrates the readability and logic of the prohibited issues, comparative data, and comparative resources.

[0069] The standardized clue discovery method for ecological and environmental problems in this invention utilizes standardized evidence fixation and integration operations to rapidly transform scattered map information into standardized clue results that are complete in elements, uniform in format, and have a complete chain of evidence, greatly improving the standardization and direct applicability of the output results. By using the problem clue report to output a structured list of problem clues and vector data packages, rapid identification and early warning of regional ecological problems can be achieved.

[0070] An embodiment of the invention provides a standardized clue discovery system for ecological and environmental problems, such as... Figure 6 As shown. In Figure 6 In this embodiment, the following are included: Unit screening device 10 is used to sort the ecological environment quality of candidate areas according to environmental impact assessment indicators to determine the target area, and to sample the zoning control units within the target area to determine the final monitoring unit. The rule parsing device 20 is used to generate structured prohibited issues and geographic vector data based on the list of zoning control requirements of the final monitoring unit, and to generate standardized comparison data of associated spatial constraints and prohibited issues based on the geographic vector data and prohibited issues; Problem verification device 30 is used to determine the dynamic patch resources of remote sensing interpretation based on the geographic vector data of the final monitoring unit, to conduct compliance verification of the dynamic patch resources based on standardized comparison data, and to identify suspected non-compliant problem patches through semantic matching and spatial relationship verification. The clue fixing device 40 is used to solidify evidence based on the suspected violation problem patches corresponding to the prohibited problem mapping and the comparison data resources, and to form a problem clue report through the standardized output of evidence.

[0071] like Figure 6 In one embodiment of the present invention, the unit screening device 10 includes: The first-scale candidate module 11 is used to select target areas by districts and counties as candidate areas, and sort them according to the annual decline of EQI data in each province. The second-scale candidate module 12 is used to select target areas in each province based on the annual decline of AQI and WQI data, with prefecture-level cities as candidate areas. Feature removal enhancement module 13 is used to remove the overlapping parts with ecological protection red lines and nature reserves in the target area and update the boundaries of the zoning control units; The unit balance screening module 14 is used to randomly sample the zoning control units in the target area to determine the final monitoring unit.

[0072] like Figure 6 In one embodiment of the present invention, the rule parsing device 20 includes: The data parsing module 21 is used to extract the template input of the LLM model, the list of zoning control requirements and geographic vector data corresponding to the final monitoring unit, and to perform structuring of prohibited issues, mapping of spatial constraints and prohibited issues through the LLM model, and output standardized comparison data of associated spatial constraints and prohibited issues.

[0073] like Figure 6 In one embodiment of the present invention, the rule parsing device 20 further includes: The data verification module 22 is used to verify and confirm standardized comparison data by manually comparing it with the list of zoning control requirements.

[0074] like Figure 6 In one embodiment of the present invention, the problem verification device 30 includes: The image patch resource filtering module 31 is used to filter satellite remote sensing and telemetry data based on the geographic vector data of the final monitoring unit, and obtain dynamic image patch resources for remote sensing interpretation within the corresponding range; The compliance verification standard module 32 is used to sequentially extract prohibited issues from the standardized comparison data, and to perform compliance verification on the dynamic map patch resources one by one according to the semantic logic conditions and spatial constraints of the prohibited issues to confirm the map patches with suspected violations. The compliance verification alternative module 33 is used to identify dynamic patches that meet the semantic logic conditions of prohibited issues based on geographic vector data when the spatial constraints of prohibited issues are missing, and to verify suspected non-compliant patches.

[0075] like Figure 6 In one embodiment of the present invention, the clue fixing device 40 includes: The data standardization module 41 is used to review the boundaries of suspected non-compliant patches, smooth the patch boundaries, determine the patch area and center coordinates, and form structured comparison data. Image normalization module 42 is used to extract the preceding and following temporal images within the boundary of the patch to form a structured comparison image; The output standardization module 43 is used to process the prohibited issues, comparison data and comparison images corresponding to suspected violation problem patches through the output data description template of the LLM model, and form a standardized problem clue report.

[0076] This application also provides an electronic device, the structure of which is as follows: Figure 7As shown, the electronic device 4000 includes at least one processor 4001, a memory 4002, and a bus 4003. The at least one processor 4001 is electrically connected to the memory 4002. The memory 4002 is configured to store at least one computer-executable instruction, and the processor 4001 is configured to execute the at least one computer-executable instruction, thereby performing the steps of the standardized clue discovery method for ecological and environmental problems provided in any embodiment or optional implementation of this application.

[0077] Furthermore, the processor 4001 can be an FPGA (Field-Programmable Gate Array) or other devices with logic processing capabilities, such as an MCU (Microcontroller Unit) or a CPU (Central Processing Unit).

[0078] This application also provides another computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the standardized clue discovery method for ecological and environmental problems provided in any embodiment or optional implementation of this application.

[0079] The computer-readable storage media provided in this application include, but are not limited to, any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, readable storage media include any medium by which a device (e.g., a computer) stores or transmits information in a readable form.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A standardized method for discovering clues to ecological and environmental problems, characterized in that, include: Based on the environmental impact assessment indicators, the ecological environment quality of the candidate areas is ranked according to the trend of deterioration to determine the target areas, and the zoning control units within the target areas are sampled to determine the final monitoring units. Based on the list of zoning control requirements for the final monitoring unit, structured prohibited issues and geographic vector data are generated. Based on the geographic vector data and prohibited issues, standardized comparison data of associated spatial constraints and prohibited issues are generated. Based on the geographic vector data of the final monitoring unit, the dynamic patch resources of remote sensing interpretation are determined. The dynamic patch resources are then checked for compliance based on standardized comparison data. Suspected non-compliant patches are identified through semantic matching and spatial relationship verification. Based on the suspected violation problem patches corresponding to the prohibited problem mapping and the comparison data resources, evidence is solidified, and problem clue reports are formed through standardized output of evidence.

2. The standardized method for discovering clues to ecological and environmental problems according to claim 1, characterized in that, The determination of the final monitoring unit includes: Using districts and counties as candidate regions, target regions are determined within each province based on the annual decline in EQI data. Using prefecture-level cities as candidate regions, the target regions are determined by ranking the annual decline in AQI and WQI data within each province. Within the target area, the overlapping portions with ecological protection red lines and nature reserves are removed, and the boundaries of the zoning control units are updated. The final monitoring unit is determined by randomly sampling the zoning control units in the target area.

3. The standardized method for discovering clues to ecological and environmental problems according to claim 2, characterized in that, AQI data uses historical data on the percentage of days with good or excellent air quality per year, while WQI data uses historical data on the percentage of Class I-III water quality at cross-sectional sections.

4. The standardized method for discovering clues to ecological and environmental problems as described in claim 1, characterized in that, The standardized comparative data for forming the associated spatial constraints and prohibitions problem includes: The LLM model extracts templates from the input data of the final monitoring unit, including the list of zoning control requirements and geographic vector data. The LLM model is then used to structure prohibited issues, map spatial constraints to prohibited issues, and output standardized comparison data relating spatial constraints to prohibited issues.

5. The standardized method for discovering clues to ecological and environmental problems according to claim 4, characterized in that, Also includes: The standardized comparison data were verified and confirmed by manual comparison with the list of zoning control requirements.

6. The standardized method for discovering clues to ecological and environmental problems as described in claim 1, characterized in that, The compliance verification of dynamic map resources based on standardized comparison data includes: Based on the geographic vector data of the final monitoring unit, the satellite remote sensing and telemetry data are filtered to obtain dynamic patch resources for remote sensing interpretation within the corresponding range; Prohibited issues are sequentially extracted from standardized comparison data. Based on the semantic logic conditions and spatial constraints of the prohibited issues, dynamic map resources are checked for compliance one by one to identify suspected non-compliant map patches. When spatial constraints on prohibited issues are missing, dynamic map features that meet the semantic and logical conditions of prohibited issues are identified based on geographic vector data for compliance verification, and suspected non-compliant map features are confirmed.

7. The standardized method for discovering clues to ecological and environmental problems as described in claim 1, characterized in that, The process of generating problem clue reports through standardized output of evidence includes: The boundaries of suspected non-compliant patches are reviewed, and the patches are smoothed, their areas and center coordinates are determined to form structured comparison data. Extracting the preceding and following temporal images within the boundary of the patch to form a structured comparison image; The LLM model output data description template is used to process the prohibited issues, comparison data and comparison images corresponding to suspected violation problem patches, forming a standardized problem clue report.

8. A standardized system for discovering clues to ecological and environmental problems, characterized in that, include: The unit screening device is used to rank the ecological environment quality of candidate areas according to environmental impact assessment indicators to determine the target area, and to sample the zoning control units within the target area to determine the final monitoring unit. The rule parsing device is used to generate structured prohibited issues and geographic vector data based on the zoning control requirements list of the final monitoring unit, and to generate standardized comparison data of associated spatial constraints and prohibited issues based on the geographic vector data and prohibited issues; The problem verification device is used to determine the dynamic patch resources of remote sensing interpretation based on the geographic vector data of the final monitoring unit, to perform compliance verification on the dynamic patch resources based on standardized comparison data, and to identify suspected non-compliant problem patches through semantic matching and spatial relationship verification. The clue fixing device is used to solidify evidence based on the suspected violation problem patches corresponding to the prohibited problem mapping and the comparison data resources, and to form a problem clue report through the standardized output of evidence.

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