Ecological environment data intelligent analysis method and device based on artificial intelligence

Abstract

This application provides an artificial intelligence-based intelligent analysis method and apparatus for ecological and environmental data. Through multi-source fusion and feature extraction, it achieves accurate environmental assessment. An analysis mechanism is constructed, combining knowledge graphs and propagation models to establish a reliable source tracing strategy. Early warning optimization is introduced, ensuring continuous improvement of the analysis through tiered response and feedback updates. This method effectively addresses the shortcomings of traditional technologies in data fusion, pollution source tracing, and early warning response, providing technical support for ecological and environmental monitoring.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to an intelligent analysis method and device for ecological and environmental data based on artificial intelligence. Background Technology

[0002] Existing methods for analyzing ecological and environmental data have significant shortcomings. Traditional systems perform poorly in multi-source data fusion and feature extraction, failing to effectively and accurately assess the environmental status and thus affecting the analysis results.

[0003] Furthermore, existing technologies face bottlenecks in pollution source tracing and propagation analysis. Most systems lack robust knowledge graph construction mechanisms and diffusion simulation strategies, resulting in suboptimal source tracing accuracy.

[0004] Existing systems have technical shortcomings in early warning response. They lack in-depth analysis of pollution transmission and struggle to achieve efficient model updates through feedback optimization, thus affecting the accuracy of early warnings. Solving these problems is crucial for improving ecological and environmental monitoring capabilities. Summary of the Invention

[0005] To address the problems in existing technologies, this application provides an intelligent analysis method and device for ecological and environmental data based on artificial intelligence, which can effectively solve the shortcomings of traditional technologies in data fusion, pollution source tracing, and early warning response, and provide technical support for ecological and environmental monitoring.

[0006] To solve at least one of the above problems, this application provides the following technical solution: Firstly, this application provides an intelligent analysis method for ecological and environmental data based on artificial intelligence, including: A water quality dataset is generated by collecting water quality data streams from IoT sensors, an image dataset is generated by acquiring image data from remote sensing satellites, an environmental dataset is generated by acquiring environmental data from meteorological monitoring stations, and an emission dataset is generated by acquiring enterprise emission data from a sewage monitoring system. The water quality dataset, the image dataset, the environmental dataset, and the emission dataset are aggregated according to a geographic grid to generate a data package. The data package is subjected to quality verification to obtain valid data groups. A feature extractor is constructed based on the valid data groups to generate a feature matrix. The feature matrix is ​​then fused to obtain a fused feature vector. The fused feature vector is mapped to generate a feature sequence according to a grid. An evaluation calculation is performed on the feature sequence to obtain a health index. An indicator analyzer is constructed based on the health index to generate an abnormal pattern group. The abnormal pattern group is processed through a knowledge graph to obtain a pollution knowledge chain. Association mining is performed on the pollution knowledge chain to obtain an impact factor set. A propagation model is constructed based on the impact factor set to generate a diffusion path graph. The diffusion path graph is spatially mapped to obtain the source tracing result. The source tracing results are mapped to generate early warning information, the early warning information is processed in a hierarchical manner to obtain response instructions, and feedback data is collected based on the response instructions to update the propagation model.

[0007] Furthermore, it also includes: constructing a collection template group according to the data source type to generate an access rule set; constructing a data receiver based on the access rule set to generate a multi-source data stream; processing the multi-source data stream through protocol parsing to obtain a parsed data group; performing spatiotemporal annotation on the parsed data group to obtain an annotated dataset; constructing a data cleaner based on the annotated dataset to generate a cleaning rule chain; and processing the cleaning rule chain through rule matching to obtain an initial data packet. The initial data packet is divided into grid data groups according to grid boundaries. A quality score is performed on the grid data groups to obtain a quality score table. A threshold determiner is constructed based on the quality score table to generate a set of verification rules. The set of verification rules is processed through multi-dimensional verification to obtain a set of valid data.

[0008] Furthermore, it also includes: grouping the effective data set according to data type to generate a data subset chain, performing feature extraction on the data subset chain to obtain a feature item set, constructing a feature selector based on the feature item set to generate an importance scoring table, performing threshold filtering on the importance scoring table to obtain a key feature group, performing dimensionality reduction transformation on the key feature group to obtain a feature matrix, and constructing a normalization processor based on the feature matrix to generate a normalized parameter set; The normalized parameter set is mapped according to the feature dimension to generate a feature weight reorganization. Attention calculation is performed on the feature weight reorganization to obtain a fusion weight table. A feature fusion fusion builder is constructed based on the fusion weight table to generate a fusion rule chain. The fusion rule chain is processed by weighted combination to obtain a fusion feature vector.

[0009] Furthermore, it also includes: mapping the fused feature vectors according to the geographic grid boundary to generate a grid feature group, performing temporal encoding on the grid feature group to obtain a sequence dataset, constructing an evaluation engine based on the sequence dataset to generate an evaluation rule family, processing the evaluation rule family through multidimensional calculation to obtain a health index table, performing threshold analysis on the health index table to obtain an abnormal indicator set, and constructing a pattern recognizer based on the abnormal indicator set to generate an abnormal pattern group; The abnormal pattern group is mapped according to the pollution type to generate a pollution entity chain. Relationship extraction is performed on the pollution entity chain to obtain an entity relationship graph. A knowledge inference engine is constructed based on the entity relationship graph to generate a set of inference rules. The set of inference rules is processed by graph computation to obtain a pollution knowledge chain.

[0010] Furthermore, it also includes: grouping the pollution knowledge chain according to the correlation strength to generate a knowledge combination chain; performing factor decomposition on the knowledge combination chain to obtain a factor matrix; constructing a correlation analyzer based on the factor matrix to generate a correlation table; sorting the correlation table by importance to obtain a key factor group; performing conditional probability calculation on the key factor group to obtain an impact factor set; and constructing a propagation rule generator based on the impact factor set to generate a propagation rule base. The propagation rule base is mapped according to the physical characteristics of pollution to generate a diffusion pattern group. Path simulation is performed on the diffusion pattern group to obtain a diffusion path map. A spatial analyzer is constructed based on the diffusion path map to generate a location probability set. The location probability set is processed by cluster analysis to obtain the source tracing result.

[0011] Furthermore, it also includes: grouping the source tracing results according to pollution type and impact range to generate a risk rating table, performing threshold grading on the risk rating table to obtain a warning level set, constructing a warning template generator based on the warning level set to generate a warning template group, and processing the warning template group through rule filling to obtain warning information; The warning information is mapped to a task decomposition table according to the handling strategy. The task decomposition table is sorted by priority to obtain a task sequence. An instruction generator is constructed based on the task sequence to generate an instruction rule set. The instruction rule set is processed by template conversion to obtain a response instruction.

[0012] Furthermore, it also includes: grouping response instructions by executing department to generate execution task books, pushing the execution task books to mobile devices to obtain a set of disposal records, constructing a data collector based on the set of disposal records to generate a set of collection rules, processing the set of collection rules through real-time monitoring to obtain a field data stream, and cleaning and labeling the field data stream to obtain a feedback dataset; The feedback dataset is mapped according to the verification rules to generate a training sample group. The parameters of the training sample group are optimized to obtain an updated parameter set. A model iterator is constructed based on the updated parameter set to generate an optimization policy chain. The optimization policy chain is then updated through incremental learning to update the propagation model.

[0013] Secondly, this application provides an intelligent ecological environment data analysis device based on artificial intelligence, comprising: The feature fusion module is used to generate a water quality dataset from water quality data streams collected from IoT sensors, an image dataset from image data acquired from remote sensing satellites, an environmental dataset from environmental data acquired from meteorological monitoring stations, and an emission dataset from enterprise emission data acquired from a sewage monitoring system. The water quality dataset, the image dataset, the environmental dataset, and the emission dataset are aggregated according to a geographic grid to generate a data package. The data package is subjected to quality verification to obtain valid data groups. A feature extractor is constructed based on the valid data groups to generate a feature matrix. The feature matrix is ​​then fused to obtain a fused feature vector. The propagation tracing module is used to generate a feature sequence by mapping the fused feature vector according to a grid, perform evaluation calculation on the feature sequence to obtain a health index, construct an indicator analyzer based on the health index to generate an abnormal pattern group, process the abnormal pattern group through a knowledge graph to obtain a pollution knowledge chain, perform association mining on the pollution knowledge chain to obtain an impact factor set, construct a propagation model based on the impact factor set to generate a diffusion path graph, and obtain the tracing result by spatial mapping the diffusion path graph. The feedback and early warning module is used to map the source tracing results to generate early warning information, perform hierarchical processing on the early warning information to obtain response instructions, and collect feedback data based on the response instructions to update the propagation model.

[0014] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the artificial intelligence-based intelligent analysis method for ecological environment data.

[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the artificial intelligence-based intelligent analysis method for ecological and environmental data.

[0016] Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the artificial intelligence-based intelligent analysis method for ecological environment data.

[0017] As can be seen from the above technical solution, this application provides an intelligent analysis method and device for ecological and environmental data based on artificial intelligence. Through multi-source fusion and feature extraction, it achieves accurate environmental assessment. An analysis mechanism is constructed, combining knowledge graphs and propagation models to establish a reliable source tracing strategy. Early warning optimization is introduced, ensuring continuous improvement of the analysis through tiered response and feedback updates. This method effectively addresses the shortcomings of traditional technologies in data fusion, pollution source tracing, and early warning response, providing technical support for ecological and environmental monitoring. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the intelligent analysis method for ecological and environmental data based on artificial intelligence in the embodiments of this application; Figure 2 This is a structural diagram of the artificial intelligence-based intelligent analysis device for ecological and environmental data in the embodiments of this application; Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0022] In view of the problems existing in the prior art, this application provides an intelligent analysis method and device for ecological and environmental data based on artificial intelligence. Through multi-source fusion and feature extraction, it achieves accurate environmental assessment. An analysis mechanism is constructed, combining knowledge graphs and propagation models to establish a reliable source tracing strategy. Early warning optimization is introduced, ensuring continuous improvement of the analysis through hierarchical response and feedback updates. This method effectively solves the shortcomings of traditional technologies in data fusion, pollution source tracing, and early warning response, providing technical support for ecological and environmental monitoring.

[0023] To effectively address the shortcomings of traditional technologies in data fusion, pollution source tracing, and early warning response, and to provide technical support for ecological and environmental monitoring, this application provides an embodiment of an artificial intelligence-based intelligent analysis method for ecological and environmental data. See [link to embodiment]. Figure 1 The artificial intelligence-based intelligent analysis method for ecological and environmental data specifically includes the following: Step S101: Collect water quality data streams from IoT sensors to generate a water quality dataset, acquire image data from remote sensing satellites to generate an image dataset, acquire environmental data from meteorological monitoring stations to generate an environmental dataset, acquire enterprise emission data from a sewage monitoring system to generate an emission dataset, aggregate the water quality dataset, the image dataset, the environmental dataset, and the emission dataset according to a geographic grid to generate a data package, perform quality verification on the data package to obtain valid data groups, construct a feature extractor based on the valid data groups to generate a feature matrix, and obtain a fused feature vector by fusion processing of the feature matrix; First, the water quality data streams generated by IoT sensors are time-aligned and source-verified. Specifically, each sensor record is labeled with its sampling time and latitude / longitude grid number. Records missing key fields are removed, and abnormal jumps within adjacent time windows of the same grid are smoothed using a moving median method to form a water quality dataset. This water quality dataset records the measuring point identifier, grid number, and water quality element fields, providing a consistent structure for subsequent aggregation and feature extraction.

[0024] After the water quality dataset is prepared, images transmitted from remote sensing satellites are acquired and their projection is unified. Based on a known geographic coordinate system, the images are divided into slices with grid numbers consistent with the grid numbers. Cloud and fog masks are extracted, and disturbed pixels are marked to generate an image dataset. The image dataset uses slice numbers to index spectral vectors and registers grid numbers shared with the water quality dataset to ensure direct comparison of cross-source mappings.

[0025] Based on the prepared image dataset, environmental observations from meteorological monitoring stations are received, and spatial interpolation from stations to grids is performed to obtain an environmental dataset. This interpolation preferentially uses a weighted average of adjacent stations combined with topographic influence factors, outputting estimated values ​​of temperature, wind field, and precipitation at the grid scale. Subsequently, enterprise emission records from the wastewater monitoring system are retrieved, mapped to grids according to the location of enterprise discharge outlets, and the integrity of the time periods is verified to form an emission dataset. Both the environmental dataset and the emission dataset carry the same grid number and timestamp as the previous two types of data.

[0026] Based on the aforementioned four types of datasets, aggregation is performed according to geographic grids and time windows to generate data packets. During aggregation, keyed stitching is established for multi-source records within the same grid and the same time window, and fingerprint comparison is performed on duplicate records to eliminate redundancy. Subsequently, quality verification is performed on the data packets, and the verification rules include time continuity checks, outlier containment checks, and cross-source constraint comparisons. Records that pass the verification are marked as valid data groups, and the reasons for records that fail are recorded for future reference.

[0027] Based on the valid data set, a feature extractor is constructed and a feature matrix is ​​output. The feature extractor includes a time-series statistical operator for water quality elements, an image spectral index mapping operator, a hysteresis coding operator for meteorological driving factors, and a threshold counting operator for emission intensity. Each operator produces a fixed-length vector according to the grid and time window, and performs scale standardization and missing data imputation before stitching, ultimately forming a feature matrix organized by sample number and feature dimension. The feature matrix retains a mapping table from fields to their source for easy subsequent interpretation.

[0028] After the feature matrix is ​​generated, a fusion process is performed to obtain a fused feature vector. The fusion process is completed by a "multi-source balancer," which assigns differentiated weights to the stability and confidence levels of different sources and suppresses components with excessively high correlation. To avoid the weights being influenced by a single anomaly, a smoothing constraint is set on the weights within a window, while retaining audit traces of the source proportions to support subsequent source tracing.

[0029] In this embodiment, the following unique fusion constraint formula is used to determine the scalar components of the fused output, and is applied component-by-component at the vector level: R = u·P + v·Q - w·H, Wherein, R is the output of a certain component after fusion, P is the standardized component from water quality elements, Q is the image spectral index component, H is the penalty component composed of meteorological driving force and emission intensity, and u, v, and w are non-negative weights within the current time window, satisfying that the sum of weights is subject to smoothing constraints rather than fixed constants. The aforementioned penalty component is obtained by combining wind field and emission intensity from the same grid, and its weight is increased during abnormal periods to suppress the amplification of random noise.

[0030] The component-level outputs obtained from the formula are stacked component by component to form a complete fused feature vector, which is then backfilled into the sample index. This fusion result is registered in the grid and time window dimensions as input for subsequent steps to construct the feature sequence. Simultaneously, its source mapping and weight records are read by the downstream index analyzer to interpret the composition of the health index.

[0031] Finally, the fused feature vector is exposed to subsequent processing via an interface. Specifically, subsequent steps will generate feature sequences according to the grid mapping and perform evaluation calculations. The reference keys of the effective data set and feature matrix will continue to be used in this stage to refer back to the original source and quality verification records during anomaly detection, ensuring that the link from acquisition to fusion is verifiable.

[0032] Step S102: Generate a feature sequence by mapping the fused feature vector to a grid, perform evaluation calculation on the feature sequence to obtain a health index, construct an indicator analyzer based on the health index to generate an abnormal pattern group, process the abnormal pattern group through a knowledge graph to obtain a pollution knowledge chain, perform association mining on the pollution knowledge chain to obtain an impact factor set, construct a propagation model based on the impact factor set to generate a diffusion path graph, and obtain the source tracing result through spatial mapping of the diffusion path graph; First, the fused feature vector obtained in step S101 is read and mapped according to the geographic grid number to generate a feature sequence. Specifically, using the grid and time window as keys, gaps between adjacent time windows are filled using nearest-neighbor interpolation, and after performing correlation tests on cross-source homonymous components, stable components are retained, forming a sequence of items arranged in ascending order of time. This feature sequence retains source weights and quality labels during generation, serving as constraint inputs for subsequent evaluation calculations.

[0033] The health index is calculated based on the aforementioned characteristic sequence. The assessment is performed by a "grid health assessor," which establishes a family of rules around three components: water physicochemical characteristics, spectral characterization, and external driving forces. The calculation process first separates short-term fluctuations from seasonal cycles, then compares the deviations of adjacent windows within the same grid, and introduces cross-grid spatial contrast to suppress systematic drift. The output health index is a time series, with each time point accompanied by its contribution and confidence interval for subsequent analysis.

[0034] After the health index is output, an index analyzer is constructed and an anomaly pattern group is generated. The index analyzer simultaneously reads the trend term and component contribution of the health index, and uses in-window change point detection and multi-scale threshold conditions to jointly determine anomalies. Anomalies are classified according to pollution type candidate labels, such as eutrophication, increased suspended solids, and specific absorption peak anomalies. The output anomaly pattern group records the triggering basis, involved components, and time position for each pattern, and maintains index consistency with the previous feature sequence.

[0035] Based on the aforementioned abnormal pattern group, knowledge graph processing is performed to obtain a contaminated knowledge chain. The knowledge graph uses abnormal types as entities and co-occurrence, sequence, and causal orientation as relationships, aligning entries from historical cases and rule bases with current observations. The processing steps first perform entity disambiguation, then retrieve comparable paths under the same watershed and similar seasonal conditions. The output contaminated knowledge chain contains entity nodes, relationship strength, and evidence references, which can be directly read for subsequent mining.

[0036] After the pollution knowledge chain is ready, association mining is performed to obtain the set of influencing factors. The association mining process jointly evaluates the strength, time lag, and directionality of entity relationships, and eliminates fragile associations using leave-one-out validation. The set of influencing factors is represented by factor name, direction of action, and time lag interval, and points back to the triggered anomalous pattern to ensure that subsequent model inputs have explanatory constraints.

[0037] A propagation model is constructed based on the aforementioned set of influencing factors to generate a diffusion path diagram. The propagation model employs a "waterway diffusion inference engine," using river network connectivity, wind field, and emission timing as driving factors, and injecting influencing factors as conditional terms into the transition probability. During simulation, the process progresses over time at the grid level, generating several feasible paths and ranking them by confidence level, while also recording supporting evidence at key bifurcation points. The diffusion path diagram uses nodes as grids and edges as diffusion directions, with the progression sequence labeled over time.

[0038] After the diffusion path map is generated, spatial mapping is performed to obtain the source tracing results. Spatial mapping projects the path map onto the real water system and administrative boundaries, merges duplicate paths, and prunes low-confidence sub-paths, outputting the suspected source region and its time window. This source tracing result is written back into the same index system as the feature sequence for subsequent early warning and disposal, and serves as the verification entry point for weight review and model update in step S101.

[0039] Step S103: Map the source tracing results to generate early warning information, perform hierarchical processing on the early warning information to obtain response instructions, and collect feedback data based on the response instructions to update the propagation model.

[0040] First, the source tracing results generated in step S102 are read and mapped according to geographic grids and administrative boundaries to form early warning candidate records. Specifically, the suspected source areas and corresponding time windows are aligned to a unified event timeline, and key branching nodes on the diffusion path are referenced back as supporting evidence. Based on this, a list of source evidence and a triggered abnormal pattern label are attached to each candidate record as input fields for subsequent hierarchical judgment.

[0041] Warning information is generated based on the candidate warning records. During generation, the source region, time window, and path confidence rank from the source tracing results are used as core fields, and historical similar events are supplemented with comparison entries to characterize the risk of recurrence. Subsequently, based on population exposure, water intake distribution, and the location of ecologically sensitive points within the grid, a reference value for treatment priority is calculated, and the calculation basis and data source index are explicitly recorded in the information body, allowing the warning information to be traced upstream. The warning information is organized by event number and maintains consistency with the feature matrix index formed in step S101, facilitating cross-stage tracking.

[0042] Based on the aforementioned early warning information, a tiered processing approach is implemented to obtain response instructions. The tiered processing first reads the risk reference value and anomaly pattern label, and then, combined with administrative threshold conditions and the current seasonal context, classifies them into multiple response levels. Early warnings from adjacent grids within the same water system are spatially merged to avoid duplicate issuance. Each entry clearly specifies the type of response action, target location, and time requirements, and includes task templates for on-site sampling and mobile monitoring, forming a structured response instruction. The response instruction retains bidirectional references between the early warning information and the source tracing results for matching during data transmission from the execution phase.

[0043] After the response command is generated, a field data acquisition process is constructed and feedback data is collected. The acquisition process unfolds according to the task template in the command, including three types of actions: fixed-point sampling, aerial survey image verification, and portable sensor tracking. To ensure a one-to-one correspondence between data and commands, event numbers and grid labels are embedded in the feedback data at the acquisition end, and it is archived using the same time window rules as in step S101. Subsequently, basic cleaning and spatiotemporal annotation are completed to form a feedback dataset, and suspicious anomalies and equipment status are recorded to reduce the impact of false alarms on subsequent training.

[0044] Based on the prepared feedback dataset, training samples are constructed and reinjected into the propagation model. The training samples use the node sequence along the diffusion path as the skeleton, with measured concentration changes and wind field deviations as observations, the source tracing time window as constraints, and edge weight correction records aligned chronologically. To avoid instability in the path structure caused by new data impacts, an incremental learning strategy is introduced, updating parameters only for subgraphs of regions strongly correlated with the current event, and deferring low-confidence edges to subsequent batches.

[0045] Accordingly, the propagation model was updated and its parameters were solidified. During the update, the path transition probability and time lag parameters were jointly corrected. Edges that contradicted field observations were weighted less, while edges consistent with multi-source feedback were weighted more, and the uncovered areas were maintained at a conservative estimate. The updated model re-outputs local verification values ​​of the propagation path and generates a summary of parameter changes for interpretation, which can be used for subsequent review of the warning level.

[0046] Finally, the updated results are written back into the closed-loop process. Specifically, the new propagation model is read by the diffusion deduction in step S102 to correct the path ordering of subsequent events; the execution status of the response instructions and the reference keys of the feedback data are recorded and saved for easy review when generating the next round of warning information. At the same time, the alignment relationship between the warning information and the response instructions is exposed as an interface for cross-departmental platform retrieval and auditing, achieving a coherent transition from tracing results to model updates.

[0047] As described above, the AI-based intelligent analysis method for ecological and environmental data provided in this application can achieve accurate environmental assessment through multi-source fusion and feature extraction. It constructs an analysis mechanism, combining knowledge graphs and propagation models to establish a reliable source tracing strategy. Early warning optimization is introduced, ensuring continuous improvement of the analysis through tiered response and feedback updates. This method effectively addresses the shortcomings of traditional technologies in data fusion, pollution source tracing, and early warning response, providing technical support for ecological and environmental monitoring.

[0048] In one embodiment of the AI-based intelligent analysis method for ecological and environmental data in this application, the method may further include the following: Step S201: Construct a collection template group according to the data source type to generate an access rule set; construct a data receiver based on the access rule set to generate a multi-source data stream; process the multi-source data stream through protocol parsing to obtain a parsed data group; perform spatiotemporal annotation on the parsed data group to obtain an annotated dataset; construct a data cleaner based on the annotated dataset to generate a cleaning rule chain; and process the cleaning rule chain through rule matching to obtain an initial data packet. Step S202: Divide the initial data packet into grid data groups according to grid boundaries, perform quality scoring on the grid data groups to obtain a quality score table, construct a threshold determiner based on the quality score table to generate a verification rule group, and process the verification rule group through multi-dimensional verification to obtain a valid data group.

[0049] First, the access characteristics were analyzed based on four data sources: water quality, imagery, environment, and emissions. Data acquisition template groups were constructed according to data source type, and an access rule set was generated. Specifically, for water quality sources, sampling period fields, equipment identifiers, and anomaly code mappings were defined; for imagery sources, projection coordinates, slice scales, and cloud mask markers were defined; for environment sources, site numbers, observation lists, and interpolation strategy instructions were defined; and for emissions sources, discharge outlet locations, emission media, and calibration markers were defined. These rules were encoded into verifiable field constraints and temporal constraints, serving as a unified entry point for subsequent reception and parsing.

[0050] A data receiver is constructed based on the access rule set, generating a multi-source data stream. The data receiver establishes an independent listening channel for each source and queues arrival events after timestamping. To handle sudden congestion, the receiver enables a brief buffering of high-frequency batches from the same source and segments them by time windows, ensuring that the cross-source arrival order is reproducible within the window. This multi-source data stream retains arrival times and source pointers as external indexes for protocol parsing.

[0051] After the multi-source data streams are ready, protocol parsing is performed to obtain parsed data sets. The parsing process, based on the protocol type registered in the access rules, unpacks fields and converts units, and performs consistency checks on hash fields to identify duplicate frames. For records still missing key fields after parsing, the missing type and location are recorded and marked as objects to be supplemented. The parsed data sets exist as grid-independent atomic records, allowing direct reading by spatiotemporal annotation.

[0052] Spatiotemporal annotation is performed on the parsed data set to obtain the annotated dataset. The annotation process first maps latitude and longitude or outlet coordinates to a unified grid number, then aligns sampling times with a fixed time window, and establishes a bidirectional mapping between satellite slice indices and grid numbers. Interpolation coefficients from the site to the grid are simultaneously written to the environmental source to allow for review of weighted sources during subsequent source tracing. The annotated dataset carries a grid number, time window number, and source label on each record, achieving cross-source alignment.

[0053] After the labeled dataset is prepared, a data cleaner is constructed based on the integrity and source characteristics of the records, generating a cleaning rule chain. The cleaning rules cover three types of operations: first, boundary detection, used to remove out-of-bounds coordinates and physically impossible values; second, temporal smoothing, used to mitigate false anomalies caused by single jumps; and third, cross-source consistency comparison, used to constrain comparable indicators from different sources within the same grid and window to prevent structural conflicts. The cleaning rule chain is bound to source labels during generation to ensure that threshold conditions from different sources do not interfere with each other.

[0054] Based on the cleaning rule chain, rule matching processing is performed to obtain the initial data packet. Rule matching records are aggregated by grid and time window, and reasons and review pointers are recorded for both passed and failed entries. If mutually corroborating anomaly markers appear in the image and water quality data within the same grid, they are retained as candidate corroborating evidence instead of being directly eliminated, for use in subsequent feature extraction stages. The initial data packet is the direct input for subsequent gridded quality scoring, and structurally, intra-source denoising and cross-source alignment have already been completed.

[0055] Based on the initial data packet, grid data groups are generated by dividing the data according to grid boundaries. During the division, time window slices from different sources within the same grid are merged to form a consistent structure that can be directly used for scoring. To avoid information fragmentation at boundary grids, adjacency relationships are marked for cross-boundary records so that continuity evidence from adjacent grids can be referenced during quality scoring. The grid data groups use grid number as the primary key and source components as column sets to meet the column-by-column reading requirements for subsequent scoring.

[0056] A quality score is performed on the gridded data set to obtain a quality score table. The score calculates three indicators: missing rate, cross-source consistency, and temporal continuity, and reduces the penalty weight for outliers with supporting evidence. The score results are output as multiple scores and a comprehensive label indexed by the grid and time window. The comprehensive label is not directly used as a rejection criterion but is provided to the threshold decision maker for secondary decision-making. The quality score table also records the adjacency relationships referenced during the scoring process, facilitating the interpretation of boundary judgments.

[0057] Once the quality score table is ready, a threshold determiner is constructed and a set of verification rules is generated. The threshold determiner reads each score and source label, sets condition combinations according to source differences, such as allowing environmental continuity compensation when images are obscured by clouds, and forms executable decision rules by combining threshold conditions. The set of verification rules is bound to the time window length during generation to adapt to differences in sampling density in different regions.

[0058] Finally, multi-dimensional verification processing is performed based on the verification rule set to obtain a valid data set. Multi-dimensional verification sequentially completes single-item threshold condition checks, cross-item logical consistency checks, and continuity confirmation of adjacent grids. Only grid slices that pass all checks are written into the valid data set. This valid data set is directly read by the feature extractor in step S101 to generate a feature matrix. During anomaly review, the original analysis record and cleaning decision are traced back through the registered review pointer, achieving a closed-loop connection between data acquisition and modeling.

[0059] In one embodiment of the AI-based intelligent analysis method for ecological and environmental data in this application, the method may further include the following: Step S301: Group the effective data sets according to data types to generate a data subset chain, perform feature extraction on the data subset chain to obtain a feature item set, construct a feature selector based on the feature item set to generate an importance scoring table, process the importance scoring table through threshold filtering to obtain key feature groups, perform dimensionality reduction transformation on the key feature groups to obtain a feature matrix, and construct a normalization processor based on the feature matrix to generate a normalized parameter set; Step S302: Map the normalized parameter set according to the feature dimension to generate a feature weight reorganization, perform attention calculation on the feature weight reorganization to obtain a fusion weight table, construct a feature fusion fusion rule chain based on the fusion weight table, and obtain a fusion feature vector by weighted combination processing of the fusion rule chain.

[0060] First, after reading the valid data sets output in step S202, the data is grouped according to data type to generate a data subset chain. Specifically, water quality elements are grouped into the physicochemical group, image spectra and textures into the remote sensing group, meteorological drivers into the environmental group, and pollution discharge intensity and outlet status into the discharge group. During grouping, the grid number and time window number remain unchanged, and a mapping table is established for fields with the same name across groups for subsequent feature alignment. The above data subset chain is arranged in ascending order of time to ensure that subsequent time-series operators can read continuous windows at once.

[0061] Feature extraction is performed on the aforementioned data subset chain to obtain a set of feature terms. For the water quality group, the mean, quantiles, and abrupt change rate within the time window are calculated; for the remote sensing group, typical spectral indices and local texture statistics are calculated; for the environmental group, wind direction coding, precipitation accumulation, and lag terms are calculated; and for the emission group, threshold counts and duration of intensity are calculated. The extracted features are organized using a ternary index of "sample key, source label, and metric name." Missing data items are only masked and their source is retained to avoid premature filling and introducing bias.

[0062] Once the feature set is ready, a feature selector is constructed to generate an importance scoring table. The feature selector is implemented using a "multi-source contribution evaluator," which first establishes initial screening scores based on univariate discriminative power and temporal stability, then introduces cross-group redundancy penalties to suppress highly correlated duplicates. The scoring table outputs three quantitative indicators for each feature: contribution, stability, and redundancy, while simultaneously recording the position in the mapping table for easy review of feature sources and dependencies.

[0063] Based on the importance scoring table, a threshold filtering process is performed to obtain key feature groups. During the filtering process, both contribution and stability are read simultaneously, a joint lower limit is set, and features with redundancy exceeding the threshold are merged into similar features, retaining only representative features. The key feature groups are output with the same mask as the original feature items. Unselected features are no longer involved in subsequent dimensionality reduction, but their source information can still be referenced in the anomaly interpretation stage.

[0064] After the key feature groups are generated, a dimensionality reduction transformation is performed to obtain the feature matrix. Dimensionality reduction employs a "structure-preserving mapping," first performing a linear mapping within each source to align the scale, and then performing low-rank embeddings across sources to compress the dimension, while simultaneously constraining the local distances between adjacent samples in the embedding space to avoid abrupt changes. The dimensionality reduction output is a feature matrix arranged by sample keys, with columns representing the compressed, unified feature dimensions, and rows corresponding one-to-one with the grid time windows. This matrix retains the retracement index from the columns to the original features, providing an interpretable entry point for subsequent fusion.

[0065] Based on the feature matrix, a standardization processor is constructed to generate a normalized parameter set. The standardization processor records the statistical center and scale of each column, as well as the quantile boundaries for outlier pruning, forming parameter entries for online inference. The normalized parameter set shares column indices with the feature matrix, ensuring consistent normalization process reproduction in both batch and incremental scenarios, and avoiding weight bias caused by data drift.

[0066] After the normalized parameter set is prepared, feature weight reassemblies are generated by mapping according to feature dimensions. The mapping process reads the source labels and adjacency consistency, assigning higher initial weights to dimensions from stable sources with good continuity on the adjacent grid, and assigning conservative weights to dimensions significantly affected by cloud occlusion or sampling fluctuations. The reassemblies are aligned with the columns of the normalized matrix, and smoothing coefficients are recorded for gradual variation constraints within the time window.

[0067] Attention calculation is performed based on the reorganized feature weights to obtain a fusion weight table. The attention calculation is performed by a "multi-scale attention unit," which simultaneously considers the trend term within the temporal neighborhood and cross-source mutual information, and introduces a smoothing coefficient during weight updates to limit rapid drift. The fusion weight table outputs the adaptive weights for each column of features within the current time window, corresponding one-to-one with the reorganized feature weights, serving as direct input for the next fusion step.

[0068] After the fusion weight table is ready, a feature fusion processor is constructed and a fusion rule chain is generated. The fusion processor reads the standardized feature matrix and the fusion weight table, and forms a hierarchical weighting and residual preservation rule order according to source priority and correlation suppression strategy. The rule chain clearly defines the field selection order, weight reading position, and residual accumulation method to ensure consistent processing across different time windows within the same grid.

[0069] Finally, the fusion rule chain is processed through weighted combination to obtain the fusion feature vector. The fusion outputs a fixed-length vector on each sample key, with vector elements being the comprehensive components after hierarchical weighting, and retaining the weight snapshot and source back-index. This fusion feature vector is directly read by the grid health evaluator in step S102 to generate feature sequences and health indices; simultaneously, the normalized parameter set and fusion weight table are reviewed during the anomaly interpretation and model update stages to support path tracing and parameter correction.

[0070] In one embodiment of the AI-based intelligent analysis method for ecological and environmental data in this application, the method may further include the following: Step S401: Map the fused feature vectors according to the geographic grid boundary to generate a grid feature group, perform temporal encoding on the grid feature group to obtain a sequence dataset, build an evaluation engine based on the sequence dataset to generate an evaluation rule family, process the evaluation rule family through multidimensional calculation to obtain a health index table, perform threshold analysis on the health index table to obtain an abnormal indicator set, and build a pattern recognizer based on the abnormal indicator set to generate an abnormal pattern group. Step S402: Map the abnormal pattern group according to the pollution type to generate a pollution entity chain, perform relation extraction on the pollution entity chain to obtain an entity relation graph, construct a knowledge inference engine based on the entity relation graph to generate a set of inference rules, and process the set of inference rules through graph computation to obtain a pollution knowledge chain.

[0071] First, after reading the fused feature vector obtained in step S302, grid feature groups are generated by mapping according to the geographic grid boundaries. During mapping, the grid number and time window number are used as keys to arrange the vectors of the same grid within a continuous time window in chronological order, and the gap windows are interpolated based on the consistency of the source of neighboring windows. To ensure that the data can be reviewed across stages, the grid feature group saves a weight snapshot and source backreference at each time point, and registers a consistency flag with the valid data group as a constraint input for subsequent encoding and evaluation.

[0072] Temporal encoding is performed on the aforementioned grid feature set to obtain a sequence dataset. Temporal encoding simultaneously handles trend terms and short-term disturbances, calculating the difference, movement statistics, and rhythmic location index within a window for each timeline, and concatenating them in a fixed order to the end of the vector to form a fixed-length sequence segment. During the encoding process, weak constraint labels of adjacent grids are referenced for boundary grids to reduce evaluation bias of isolated grids during seasonal transitions. The sequence dataset, along with its time index, is written to a unified directory for direct reading by the evaluation engine.

[0073] Once the sequence dataset is ready, an evaluation engine is built and a family of evaluation rules is generated. The evaluation engine is implemented by a "grid health evaluator," and the rule family covers three types of computational paths: first, a baseline-based deviation path, which gives the current deviation according to the distribution of historical seasonal windows; second, a spatial comparison-based relative path, which compares the differences between adjacent and upstream grids; and third, a drive-suppression-based correction path, which uses the weights of wind field and emission intensity to suppress anomalous expansion. These three paths share the same time index during execution and output comparable scores, which are ultimately summarized into a health index table. The health index table is organized using grid and time window indices and retains the composition and evidence citations for each score, facilitating downstream interpretation.

[0074] After the health index table is generated, threshold analysis is performed to obtain an anomaly indicator set. The threshold analysis first detects change points within the same timeline, then sets a double threshold condition based on robust quantile ranges across the grid. Points outside the range and unexplained by the driven suppression path are marked as anomaly candidates. Subsequently, the candidate points are merged according to their constituent components, outputting the anomaly indicator set, which includes the time and location of the anomaly, the spatial adjacency information of the dominant component and the reference. Each entry in this set carries a lookback pointer, pointing to a specific segment of the sequence dataset.

[0075] A pattern recognizer is constructed based on the aforementioned set of abnormal indicators to generate anomaly pattern groups. The pattern recognizer reads the temporal form and component combinations of the anomaly candidates and, referring to the source weights in the fused feature vector, uses a combination of template matching and clustering to provide pattern labels. The label system corresponds one-to-one with the pollution types in the claims, covering common scenarios such as eutrophication, suspended solids rise, and abnormal specific absorption peaks. The output anomaly pattern groups are aligned with the health index table in the grid and time window dimensions, and triggering rules, evidence fragments, and trustworthy markers are recorded as input for the mapping stage.

[0076] After the anomalous pattern group is prepared, a contaminated entity chain is generated by mapping according to the contamination type. The nodes of the entity chain are mapped from the anomalous pattern entries, and the initial relationships of the edges are determined by the sequential relationships within the same grid and the path directions of adjacent grids, with a source confidence hint attached. To maintain consistency with the upstream, each node in the entity chain retains an evidence reference from the pattern recognizer for verification during graph calculation.

[0077] Based on the pollution entity chain, relation extraction is performed to obtain an entity relation graph. Relationship extraction checks the connectivity of nodes within the same watershed over time and filters out unreasonable edges by considering upstream and downstream hydrological connectivity. For relationships crossing administrative boundaries, boundary types are labeled to distinguish management responsibility paths. After the entity relation graph is formed, nodes and edges are associated with three attributes: strength, time delay, and directionality, serving as direct input for knowledge reasoning.

[0078] After the entity relationship graph is ready, a knowledge inference engine is constructed and a set of inference rules is generated. The knowledge inference engine uses rule entries to represent "under what driving force and seasonal background, what type of anomaly combination suggests what type of pollution factor," and sets conditional domains for time lag and intensity range. The rule set simultaneously reads the edge attributes and node labels of the entity relationship graph, employing alternating top-down matching and bottom-up merging to eliminate paths that conflict with driving conditions, retaining only candidates with closed evidence chains. The inference output, after graph computation processing, yields a pollution knowledge chain. The knowledge chain is arranged by grid and time window, containing pollution entities, relationship types, and evidence references. This knowledge chain is used in subsequent steps for association mining to determine influencing factors and serves as a conditional input for the propagation model; simultaneously, its lookback pointer can trace back to the anomaly pattern group and health index table, supporting transparency in anomaly interpretation and subsequent handling.

[0079] In one embodiment of the AI-based intelligent analysis method for ecological and environmental data in this application, the method may further include the following: Step S501: Group the pollution knowledge chain according to the correlation strength to generate a knowledge combination chain, perform factor decomposition on the knowledge combination chain to obtain a factor matrix, construct an association analyzer based on the factor matrix to generate a correlation table, sort the correlation table by importance to obtain a key factor group, perform conditional probability calculation on the key factor group to obtain an impact factor set, and construct a propagation rule generator based on the impact factor set to generate a propagation rule base. Step S502: The propagation rule base is mapped according to the physical characteristics of pollution to generate a diffusion pattern group. Path simulation is performed on the diffusion pattern group to obtain a diffusion path map. A spatial analyzer is constructed based on the diffusion path map to generate a location probability set. The location probability set is processed by cluster analysis to obtain the source tracing result.

[0080] First, after reading the pollution knowledge chain output in step S402, it is grouped according to the strength of association to generate a knowledge combination chain. Grouping uses the strength, directionality, and time lag of entity relationships as keys, aggregating segments with similar time sequences and relationship strengths within the same watershed, and retaining the evidence references and time window indexes for each combination. During the generation of this knowledge combination chain, entries crossing administrative boundaries are labeled with boundary types, serving as constraints for subsequent factor decomposition.

[0081] Factor decomposition is performed based on the aforementioned knowledge combination chain to obtain a factor matrix. Factor decomposition uses contaminating entities and relation types as observation axes, and relation strength and time-lag normalized values ​​as observations. The goal is to extract latent factors that explain most of the covariance. To avoid noise dominance, sample weights are adjusted according to the completeness of evidence, and missing time lags are imputed to preserve uncertainty. The factor matrix is ​​organized according to the "latent factor—observation axis" and includes the explanatory contribution percentage of each factor for downstream retrieval.

[0082] Once the factor matrix is ​​ready, an association analyzer is constructed to generate a correlation table. The association analyzer calculates the strength and direction of the correlation between each latent factor and entity attribute, and introduces a penalty for multiple comparisons to reduce random correlations. The output correlation table includes factor identifiers, associated entities, directional and robustness labels, while retaining lookback pointers from the knowledge combination chain to support subsequent source attribution interpretation.

[0083] Based on the aforementioned relevance table, importance ranking is performed to obtain the key factor group. The ranking considers both association strength and robustness, appropriately raising the threshold condition for entries marked as cross-administrative boundaries to ensure the executability of the conclusions. The key factor group uses the event time window as the primary key, recording the contextual conditions and reference entity set required to trigger each factor, serving as input for conditional probability calculations.

[0084] Conditional probability calculations are performed on the key factor set to obtain the influencing factor set. During the calculation, the anomalous pattern is used as the outcome variable, and the key factors are used as conditional terms to estimate the probability interval of the outcome under given time lag and directional constraints. A conservative lower bound is introduced for the evidence gap. The influencing factor set is organized as "factor name, direction of action, time constraint, and probability interval," and refers back to the corresponding anomalous pattern and entity relationship to ensure that subsequent propagation modeling has verifiable conditions.

[0085] A propagation rule generator is constructed based on the aforementioned set of influencing factors to generate a propagation rule base. Rule entries describe diffusion and transfer conditions and boundary weight correction methods under specific hydrological connectivity, meteorological driving forces, and emission backgrounds, and specify the minimum set of factors and evidence required for triggering. The propagation rule base is aligned with the watershed topology table, serving as a direct reading object for path simulation.

[0086] Based on the aforementioned propagation rule base, diffusion pattern groups are generated by mapping according to the physical characteristics of the pollutants. The mapping uses solubility, particle size, and buoyancy / sinking characteristics as classification keys, merging rule entries into several patterns and injecting matching time-delay windows and edge weight initialization methods. The diffusion pattern groups are bound to the grid-based water system connectivity graph, forming a deducible structured configuration.

[0087] Path simulations are performed based on the aforementioned diffusion pattern group to obtain a diffusion path graph. The simulation progresses along the time axis, updating the transition probability for each node based on the condition satisfaction of incoming and outgoing edges, and retaining parallel branches at critical forks. To prevent path explosion, a pruning criterion based on evidence support is introduced, retaining only candidate paths exceeding a threshold. The diffusion path graph uses nodes as a grid and edges as transitions, recording the triggering rules and evidence references for each step.

[0088] After the diffusion path map is generated, a spatial analyzer is constructed to output a set of location probabilities. The spatial analyzer overlays the path probabilities onto a spatial grid, redistributes the probabilities by incorporating terrain obstruction and river network hierarchy, and applies adjacency smoothing to the boundary grid to suppress isolated spikes. The set of location probabilities, indexed by the grid, provides a temporally layered probability distribution.

[0089] Finally, the location probability set is processed through cluster analysis to obtain the source tracing results. Clustering uses spatial adjacency and temporal continuity as dual constraints, merging high-probability contiguous areas and marking the time window and confidence label of suspected source areas. This source tracing result is written back to the index system of step S102 as direct input for early warning information generation and response instructions, while retaining references to the propagation rule base and influence factor set for evidence verification during the handling phase and subsequent model correction.

[0090] In one embodiment of the AI-based intelligent analysis method for ecological and environmental data in this application, the method may further include the following: Step S601: Group the source tracing results according to pollution type and impact range to generate a risk rating table, perform threshold classification on the risk rating table to obtain a warning level set, construct a warning template generator based on the warning level set to generate a warning template group, and obtain warning information by filling the warning template group with rules; Step S602: Map the warning information according to the handling strategy to generate a task decomposition table, perform priority sorting on the task decomposition table to obtain a task sequence, construct an instruction generator based on the task sequence to generate an instruction rule set, and process the instruction rule set through template conversion to obtain a response instruction.

[0091] First, after reading the source tracing results output in step S502, the data is grouped according to pollution type and impact range to generate a risk rating table. Specifically, using pollution type, suspected source area area, and exposed population as primary keys, entries within the same water system with similar timeframes are merged into candidate events, and key bifurcations and evidence citations in the diffusion path are backfilled. During the generation process, boundary attributes are marked for entries crossing administrative boundaries as additional conditions for subsequent threshold classification.

[0092] Based on the risk rating table, threshold grading is performed to obtain a set of warning levels. Threshold grading simultaneously reads three indicators: propagation intensity, duration, and overlap of ecologically sensitive points. Change points are first detected on the timeline, and then level labels are assigned according to the grading rules. For entries with incomplete evidence chains but high spatial probability concentration, temporary observation levels are set and directed to the supplementary sampling task entry point to avoid misjudgments and omissions. The warning level set is organized by event number, retaining the basis for level generation and review pointers for direct use by downstream template filling.

[0093] Once the warning level set is ready, a warning template generator is constructed and outputs a set of warning templates. The warning templates are laid out with subdivided fields according to pollution type, clearly specifying the source area location, time window, triggering anomaly mode, and necessary handling recommendations to be displayed, and registering the administrative notification path in the template header. During template generation, communication and disclosure scope strategies are automatically loaded for different levels to ensure consistency between information disclosure and handling pace. The warning template set corresponds one-to-one with the event numbers in the risk rating table, forming a stable population framework.

[0094] Based on the aforementioned warning template group, rule-based population processing is performed to obtain warning information. The population process extracts core evidence from the source tracing results and diffusion paths, writes it in the order of the template fields, and attaches a data source key and quality marker. Special explanations are added to entries with boundary conflicts to indicate the need for cross-departmental coordination. The warning information is finally output in structured message format, maintaining consistency with the indexes obtained in steps S101 to S502.

[0095] Based on the aforementioned early warning information, a task breakdown table is generated according to the response strategy. The mapping is primarily controlled by level labels, breaking down the response actions into four categories: fixed-point sampling, mobile monitoring, outlet inspection, and temporary control. Each category is assigned spatial objectives, time limits, and a list of required equipment. The task breakdown table retains the event number and grid index of the early warning information in each row, ensuring that the execution end can directly reconstruct the spatial location and time requirements.

[0096] Based on the task breakdown table, priority sorting is performed to obtain the task sequence. The sorting is based on the forward risk of population exposure, proximity to water supply facilities, and diffusion paths, using a tiered scoring system, and then prioritizing tasks within the same tier according to the shortest route distance. For tasks requiring cross-departmental collaboration, a coordination marker and suggested lead unit are added to the priority list to reduce the risk of execution delays. The task sequence is divided into several batches for easy rolling distribution within different execution windows.

[0097] Once the task sequence is ready, an instruction generator is constructed and outputs an instruction rule set. The instruction generator reads the spatial target and time limit of each task, binds it to the minimum field set for on-site acquisition, sampling frequency, and feedback format, and inserts safety prompts for anomaly handling and personnel protection requirements. The instruction rule set corresponds one-to-one with the task sequence and records the communication channel and feedback verification method, providing clear constraints for rapid decoding and accurate feedback from the terminal.

[0098] Based on the instruction rule set, template transformation is performed to obtain response instructions. Template transformation renders rule entries into terminal-executable instruction messages, including task summary, coordinates and route, time window and frequency, and hash signature location for upload verification. For continuous monitoring tasks, the instruction additionally loads the scrolling window length and trigger feedback threshold conditions to reduce unnecessary communication overhead. The final response instruction is sent through the existing transmission channel and maintains a bidirectional reference relationship with the warning information.

[0099] Finally, the response command is read by the execution end and drives on-site data collection, forming feedback data which is then fed back to update the propagation model and health assessment. During the feedback phase, the event numbers in the task sequence and response command are used to align the grid and time window, ensuring that the data can be directly read by the evaluator and propagation deduction module in step S102. Thus, steps S601 and S602 establish a closed-loop connection between early warning generation, task issuance, and feedback collection.

[0100] In one embodiment of the AI-based intelligent analysis method for ecological and environmental data in this application, the method may further include the following: Step S701: Group the response instructions by the executing department to generate an execution task book, push the execution task book to the mobile terminal to obtain a disposal record set, build a data collector based on the disposal record set to generate a collection rule group, process the collection rule group through real-time monitoring to obtain a field data stream, and perform cleaning and labeling on the field data stream to obtain a feedback dataset; Step S702: Map the feedback dataset according to the verification rules to generate a training sample group, perform parameter optimization on the training sample group to obtain an updated parameter set, construct a model iterator based on the updated parameter set to generate an optimization policy chain, and update the propagation model through incremental learning processing of the optimization policy chain.

[0101] First, after reading the response command generated in step S602, the tasks are grouped by the executing department to generate execution task sheets. During grouping, tasks of the same type within adjacent time windows from the same department are merged using task type, target grid, and time window as keys. Each task sheet is then bound with an event number and coordinate route. The task sheet clearly specifies the sampling location, sampling frequency, required instrument model, and data transmission format, serving as operational constraints for subsequent data push and acquisition.

[0102] Based on the task execution plan, mobile push notifications are executed to obtain a handling record set. During the push process, terminal identifiers are registered according to departmental communication channels, and messages are sent in batches, with delivery times and confirmation receipts recorded. For terminals that fail to confirm on time, retry and alternative terminal switching are triggered. The handling record set includes task reception status, start and end times, and executor identifiers, and corresponds one-to-one with the event numbers in the task plan for alignment during subsequent data collection.

[0103] Once the disposal record set is ready, a data acquisition device is constructed, generating a set of acquisition rules. The acquisition rules set up reading items around three types of actions: fixed-point sampling reads water quality indicator fields and sampling container numbers; mobile monitoring reads continuous readings from portable sensors; and aerial survey verification reads low-altitude image slices and positioning errors. The rules encode the time granularity, minimum field, and anomaly code for each acquisition item to ensure data structure consistency across different terminals for the same task. The field layout of the acquisition rule set is aligned with the execution task sheet.

[0104] Based on the aforementioned data collection rule set, real-time monitoring is conducted to obtain the field data stream. During the monitoring process, the equipment is activated according to the rules, and the data stream is standardized by arrival timestamps. High-frequency data is windowed and written to a buffer. To reduce the impact of communication interruptions, short-term buffering and breakpoint resumption are enabled on the terminal side, and the task number and grid label are written to the data packet header. The field data stream is divided into three channels—water quality, image, and environment—based on source labels for subsequent cleaning and labeling reading.

[0105] Once the field data stream is ready, cleaning and annotation are performed to obtain the feedback dataset. Cleaning first involves boundary detection to remove out-of-bounds coordinates and physically impossible values, followed by noise suppression. For the moving monitoring channel, sliding statistics are used to suppress instantaneous peak values. Subsequently, spatiotemporal annotation is completed, mapping latitude and longitude to grid numbers and aligning them to the time window specified in the task description. Event numbers and terminal identifiers are written as dual indices. For missing fields in the feedback dataset, only masking is applied and the reason is recorded for subsequent sample selection.

[0106] Based on the aforementioned feedback dataset, training sample groups were generated according to the verification rules. During mapping, the node sequence on the diffusion path was used as the skeleton, the concentration changes and wind field deviations observed on-site were used as supervisory variables, and the discharge outlet control status was used as a condition term, which were combined into sample entries. Path segments with gaps were temporarily recorded as samples to be supplemented, and the correspondence between the sample header and the execution time sequence in the disposal record set was retained for easy review.

[0107] Based on the training sample set, parameter optimization is performed to obtain an updated parameter set. Parameter optimization is only performed within subgraphs of regions strongly correlated with events, and three types of parameters are read: path transition probability, time lag, and external driving weights. A constrained minimization objective is used to solve the problem. To ensure convergence stability, a window-based smoothing limit is set on the parameter update magnitude, and conservative values ​​are maintained for edges with insufficient evidence. The updated parameter set records the parameter name, the affected edge or node, and the difference before and after the update, which serves as input for subsequent iterators.

[0108] After the updated parameter set is prepared, a model iterator is constructed to generate an optimization policy chain. The model iterator divides parameter changes into sequentially executed optimization steps, including subgraph locking, edge weight correction, time delay reestimation, and driver term recalibration, and sets rollback conditions and consistency checks for each step. The optimization policy chain is aligned with the topology table of the propagation model, indicating the processing order and verification points for specific edges and nodes.

[0109] Based on the optimized policy chain, the propagation model is updated through incremental learning. Incremental learning is executed step by step according to the policy chain. After each parameter is written, a re-simulation is performed on the local subgraph, and the deviation between the simulation output and the training samples is compared. If the deviation exceeds the limit, the process is undone according to the rollback condition and the step size is reduced. After the update is completed, a lightweight consistency check is performed on the entire graph to ensure the continuity of the boundary probabilities of adjacent subgraphs.

[0110] Finally, the updated propagation model, along with the updated parameter set and sample references, is registered as a new version. This version is read by the path simulation component in step S502 for subsequent event propagation simulation; the index keys of the feedback dataset and the disposal record set are continued in the early warning generation stage for evidence reference and result verification. Through the above processing, the sequential connection between response execution, data feedback, and model adjustment is achieved, while maintaining consistency of indexes at the grid and time window levels.

[0111] To effectively address the shortcomings of traditional technologies in data fusion, pollution source tracing, and early warning response, and to provide technical support for ecological and environmental monitoring, this application provides an embodiment of an AI-based intelligent ecological and environmental data analysis device for implementing all or part of the aforementioned AI-based intelligent ecological and environmental data analysis method. See [link to embodiment]. Figure 2 The artificial intelligence-based intelligent analysis device for ecological and environmental data specifically includes the following components: The feature fusion module 10 is used to generate a water quality dataset from water quality data streams collected from IoT sensors, an image dataset from image data acquired from remote sensing satellites, an environmental dataset from environmental data acquired from meteorological monitoring stations, and an emission dataset from enterprise emission data acquired from a sewage monitoring system. The water quality dataset, the image dataset, the environmental dataset, and the emission dataset are aggregated according to a geographic grid to generate a data package. The data package is subjected to quality verification to obtain a valid data group. A feature extractor is constructed based on the valid data group to generate a feature matrix. The feature matrix is ​​then fused to obtain a fused feature vector. The propagation tracing module 20 is used to generate a feature sequence by mapping the fused feature vector according to a grid, perform evaluation calculation on the feature sequence to obtain a health index, construct an indicator analyzer based on the health index to generate an abnormal pattern group, process the abnormal pattern group through a knowledge graph to obtain a pollution knowledge chain, perform association mining on the pollution knowledge chain to obtain an impact factor set, construct a propagation model based on the impact factor set to generate a diffusion path graph, and obtain the tracing result by spatial mapping the diffusion path graph. The feedback and early warning module 30 is used to map the source tracing results to generate early warning information, perform hierarchical processing on the early warning information to obtain response instructions, and collect feedback data based on the response instructions to update the propagation model.

[0112] As described above, the AI-based intelligent ecological environment data analysis device provided in this application can achieve accurate environmental assessment through multi-source fusion and feature extraction. It constructs an analysis mechanism, combining knowledge graphs and propagation models to establish a reliable source tracing strategy. Early warning optimization is introduced, ensuring continuous improvement of the analysis through tiered response and feedback updates. This method effectively addresses the shortcomings of traditional technologies in data fusion, pollution source tracing, and early warning response, providing technical support for ecological environment monitoring.

[0113] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the artificial intelligence-based intelligent analysis method for ecological and environmental data.

[0114] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned artificial intelligence-based intelligent analysis method for ecological and environmental data.

[0115] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described intelligent analysis method for ecological and environmental data based on artificial intelligence.

[0116] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An artificial intelligence-based ecological environment data intelligent analysis method, characterized in that, The method includes: A water quality dataset is generated by collecting water quality data streams from IoT sensors, an image dataset is generated by acquiring image data from remote sensing satellites, an environmental dataset is generated by acquiring environmental data from meteorological monitoring stations, and an emission dataset is generated by acquiring enterprise emission data from a sewage monitoring system. The water quality dataset, the image dataset, the environmental dataset, and the emission dataset are aggregated according to a geographic grid to generate a data package. The data package is subjected to quality verification to obtain valid data groups. A feature extractor is constructed based on the valid data groups to generate a feature matrix. The feature matrix is ​​then fused to obtain a fused feature vector. The fused feature vector is mapped to generate a feature sequence according to a grid. An evaluation calculation is performed on the feature sequence to obtain a health index. An indicator analyzer is constructed based on the health index to generate an abnormal pattern group. The abnormal pattern group is processed through a knowledge graph to obtain a pollution knowledge chain. Association mining is performed on the pollution knowledge chain to obtain an impact factor set. A propagation model is constructed based on the impact factor set to generate a diffusion path graph. The diffusion path graph is spatially mapped to obtain the source tracing result. The source tracing results are mapped to generate early warning information, the early warning information is processed in a hierarchical manner to obtain response instructions, and feedback data is collected based on the response instructions to update the propagation model. 2.The artificial intelligence-based ecological environment data intelligent analysis method according to claim 1, characterized in that, The process involves generating a water quality dataset from water quality data streams collected from IoT sensors, an image dataset from image data acquired from remote sensing satellites, an environmental dataset from environmental data acquired from meteorological monitoring stations, and an emission dataset from enterprise emission data acquired from a wastewater monitoring system. The water quality dataset, the image dataset, the environmental dataset, and the emission dataset are then aggregated according to a geographic grid to generate a data package. A quality check is performed on the data package to obtain valid data groups, including: A collection template group is constructed according to the data source type to generate an access rule set. A data receiver is constructed based on the access rule set to generate a multi-source data stream. The multi-source data stream is processed through protocol parsing to obtain a parsed data group. Spatiotemporal annotation is performed on the parsed data group to obtain an annotated dataset. A data cleaner is constructed based on the annotated dataset to generate a cleaning rule chain. The cleaning rule chain is processed through rule matching to obtain an initial data packet. The initial data packet is divided into grid data groups according to grid boundaries. A quality score is performed on the grid data groups to obtain a quality score table. A threshold determiner is constructed based on the quality score table to generate a set of verification rules. The set of verification rules is processed through multi-dimensional verification to obtain a set of valid data. 3.The AI-based ecological environment data intelligent analysis method of claim 1, wherein, The step of constructing a feature extractor based on the effective data set to generate a feature matrix, and then obtaining a fused feature vector from the feature matrix through fusion processing, includes: The effective data sets are grouped by data type to generate a data subset chain. Feature extraction is performed on the data subset chain to obtain a feature item set. A feature selector is constructed based on the feature item set to generate an importance scoring table. The importance scoring table is filtered by threshold to obtain key feature groups. Dimensionality reduction transformation is performed on the key feature groups to obtain a feature matrix. A normalization processor is constructed based on the feature matrix to generate a normalized parameter set. The normalized parameter set is mapped according to the feature dimension to generate a feature weight reorganization. Attention calculation is performed on the feature weight reorganization to obtain a fusion weight table. A feature fusion fusion builder is constructed based on the fusion weight table to generate a fusion rule chain. The fusion rule chain is processed by weighted combination to obtain a fusion feature vector. 4.The AI-based ecological environment data intelligent analysis method of claim 1, wherein, The process involves generating a feature sequence from the fused feature vectors using a grid mapping method, performing an evaluation calculation on the feature sequence to obtain a health index, constructing an indicator analyzer based on the health index to generate an abnormal pattern group, and processing the abnormal pattern group through a knowledge graph to obtain a contaminated knowledge chain, including: The fused feature vectors are mapped according to the geographic grid boundaries to generate grid feature groups. Temporal encoding is performed on the grid feature groups to obtain a sequence dataset. An evaluation engine is built based on the sequence dataset to generate an evaluation rule family. The evaluation rule family is processed through multidimensional calculation to obtain a health index table. Threshold analysis is performed on the health index table to obtain an abnormal indicator set. A pattern recognizer is built based on the abnormal indicator set to generate an abnormal pattern group. The abnormal pattern group is mapped according to the pollution type to generate a pollution entity chain. Relationship extraction is performed on the pollution entity chain to obtain an entity relationship graph. A knowledge inference engine is constructed based on the entity relationship graph to generate a set of inference rules. The set of inference rules is processed by graph computation to obtain a pollution knowledge chain. 5.The artificial intelligence-based ecological environment data intelligent analysis method according to claim 1, characterized in that, The process involves performing association mining on the pollution knowledge chain to obtain an impact factor set, constructing a propagation model based on the impact factor set to generate a diffusion path graph, and then using spatial mapping to obtain source tracing results, including: The pollution knowledge chain is grouped according to the correlation strength to generate a knowledge combination chain. Factor decomposition is performed on the knowledge combination chain to obtain a factor matrix. Based on the factor matrix, a correlation analyzer is constructed to generate a correlation table. The correlation table is sorted by importance to obtain key factor groups. Conditional probability calculation is performed on the key factor groups to obtain an impact factor set. Based on the impact factor set, a propagation rule generator is constructed to generate a propagation rule base. The propagation rule base is mapped according to the physical characteristics of pollution to generate a diffusion pattern group. Path simulation is performed on the diffusion pattern group to obtain a diffusion path map. A spatial analyzer is constructed based on the diffusion path map to generate a location probability set. The location probability set is processed by cluster analysis to obtain the source tracing result. 6.The artificial intelligence-based ecological environment data intelligent analysis method according to claim 1, characterized in that, The step of mapping the source tracing results to generate early warning information and performing hierarchical processing on the early warning information to obtain response instructions includes: The source tracing results are grouped according to pollution type and impact range to generate a risk rating table. Threshold classification is applied to the risk rating table to obtain a set of warning levels. A warning template generator is constructed based on the set of warning levels to generate a set of warning templates. The warning templates are then processed by rule filling to obtain warning information. The warning information is mapped to a task decomposition table according to the handling strategy. The task decomposition table is sorted by priority to obtain a task sequence. An instruction generator is constructed based on the task sequence to generate an instruction rule set. The instruction rule set is processed by template conversion to obtain a response instruction. 7.The AI-based eco-environment data intelligent analysis method according to claim 1, characterized in that, The step of updating the propagation model based on the feedback data collected from the response command includes: The response instructions are grouped by the executing department to generate an execution task book. The execution task book is pushed to a mobile terminal to obtain a set of disposal records. A data collector is built based on the set of disposal records to generate a set of collection rules. The set of collection rules is processed through real-time monitoring to obtain a field data stream. The field data stream is cleaned and labeled to obtain a feedback dataset. The feedback dataset is mapped according to the verification rules to generate a training sample group. The parameters of the training sample group are optimized to obtain an updated parameter set. A model iterator is constructed based on the updated parameter set to generate an optimization policy chain. The optimization policy chain is then updated through incremental learning to update the propagation model.

8. An artificial intelligence-based ecological environment data intelligent analysis device, characterized in that, The device includes: The feature fusion module is used to generate a water quality dataset from water quality data streams collected from IoT sensors, an image dataset from image data acquired from remote sensing satellites, an environmental dataset from environmental data acquired from meteorological monitoring stations, and an emission dataset from enterprise emission data acquired from a sewage monitoring system. The water quality dataset, the image dataset, the environmental dataset, and the emission dataset are aggregated according to a geographic grid to generate a data package. The data package is subjected to quality verification to obtain valid data groups. A feature extractor is constructed based on the valid data groups to generate a feature matrix. The feature matrix is ​​then fused to obtain a fused feature vector. The propagation tracing module is used to generate a feature sequence by mapping the fused feature vector according to a grid, perform evaluation calculation on the feature sequence to obtain a health index, construct an indicator analyzer based on the health index to generate an abnormal pattern group, process the abnormal pattern group through a knowledge graph to obtain a pollution knowledge chain, perform association mining on the pollution knowledge chain to obtain an impact factor set, construct a propagation model based on the impact factor set to generate a diffusion path graph, and obtain the tracing result by spatial mapping the diffusion path graph. The feedback and early warning module is used to map the source tracing results to generate early warning information, perform hierarchical processing on the early warning information to obtain response instructions, and collect feedback data based on the response instructions to update the propagation model.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the intelligent analysis method for ecological and environmental data based on artificial intelligence as described in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the artificial intelligence-based intelligent analysis method for ecological and environmental data as described in any one of claims 1 to 7.

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