Environment monitoring report assessment method and system based on artificial intelligence and mechanism model

Abstract

The invention belongs to the field of report assessment, and provides an environmental monitoring report assessment method and system based on artificial intelligence and a mechanism model.The method comprises the steps that corresponding original multi-source data are obtained according to a to-be-checked third-party monitoring report and preprocessed, and structured monitoring data are obtained; performing time sequence association on the data in the third-party monitoring report and the corresponding original multi-source data to obtain associated time sequence monitoring data; evaluating the associated time sequence monitoring data to obtain a multi-dimensional risk factor of the third-party monitoring report; based on the multi-dimensional risk factors of the third-party monitoring report, a comprehensive risk index is calculated in a fusion mode; performing risk grading on the third-party monitoring report according to the comprehensive risk index, and obtaining corresponding evidence data based on the risk grade of the third-party monitoring report to generate an evidence association graph; and generating a monitoring report problem assessment report according to the comprehensive risk index, the risk level, the risk factor of each dimension and the evidence association graph of the third-party monitoring report.

Description

Technical Field

[0001] This invention belongs to the field of report evaluation technology, specifically relating to an environmental monitoring report evaluation method and system based on artificial intelligence and mechanism models. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With continuous economic development, people's demands for quality of life and the environment are increasing, and the importance of environmental protection has been widely recognized. Environmental protection acceptance upon completion of construction projects is a crucial step and plays a vital role in environmental protection. The environmental protection acceptance process for completed construction projects has shifted from an approval system by the ecological and environmental departments to a system of self-acceptance by the construction unit, with the quality of the acceptance directly relying on the monitoring reports prepared by a third party commissioned by the company.

[0004] Existing technologies for detecting problems in environmental protection self-acceptance monitoring reports for completed construction projects, whether using pure artificial intelligence methods or single-mechanism models, have significant limitations. Although artificial intelligence algorithms have been applied to anomaly detection in the environmental protection field, purely data-driven models lack the ability to explain the mechanisms of pollutant migration, transformation, and removal, resulting in high false alarm rates and poor interpretability. While single-mechanism models can reflect physicochemical processes, they are difficult to adapt to the uncertainties and complex boundary disturbances in actual operation, often causing significant deviations between theoretical and measured values. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes an environmental monitoring report evaluation method and system based on artificial intelligence and mechanistic models. This invention employs artificial intelligence and mechanistic models to construct a method for detecting falsified monitoring reports, fully leveraging the advantages of each to automate and refine the environmental protection acceptance process for completed construction projects. This improves the accuracy of third-party monitoring report evaluations, thereby enhancing acceptance quality and reducing regulatory risks.

[0006] According to some embodiments, the first aspect of the present invention provides an environmental monitoring report evaluation method based on artificial intelligence and mechanism models, employing the following technical solution: Environmental monitoring report evaluation methods based on artificial intelligence and mechanistic models include: Based on the third-party monitoring report to be verified, obtain the corresponding raw multi-source data and preprocess it to obtain structured monitoring data; By performing time-series correlation between the data in the third-party monitoring report and the corresponding original multi-source data, the correlated time-series monitoring data is obtained. The relevant time-series monitoring data is evaluated from four dimensions: logical contradictions, data anomalies, process anomalies, and data conflicts, resulting in multi-dimensional risk factors in the third-party monitoring report. A comprehensive risk index is calculated by integrating multiple risk factors based on third-party monitoring reports. The risk level of third-party monitoring reports is classified according to the comprehensive risk index, and corresponding evidence data is obtained based on the risk level of the third-party monitoring reports to generate an evidence association map. A problem assessment report is generated based on the comprehensive risk index, risk level, risk factors of various dimensions, and evidence correlation diagrams from the third-party monitoring report.

[0007] Furthermore, based on the third-party monitoring report to be verified, the corresponding raw multi-source data is obtained and preprocessed to obtain structured monitoring data, including: Obtain the third-party monitoring report to be verified; Based on information from third-party monitoring reports, trigger the collection task of related data to obtain the corresponding raw multi-source data; By combining third-party monitoring reports with corresponding raw multi-source data and standardizing the process, structured monitoring data is obtained.

[0008] Furthermore, the data in the third-party monitoring report is correlated with the corresponding original multi-source data in a time series to obtain correlated time series monitoring data, including: Using sample number and monitoring item as indexes, the data in the third-party monitoring report is linked with the corresponding original multi-source data; By aligning the original multi-source data with the timestamps in the third-party monitoring reports on the same timeline in chronological order, we obtain associated time-series monitoring data.

[0009] Furthermore, the relevant time-series monitoring data is evaluated from four dimensions: logical contradictions, data anomalies, process anomalies, and data conflicts, resulting in multi-dimensional risk factors in the third-party monitoring report, including: Calculate the logical error risk score in the associated time-series monitoring data to obtain the logical contradiction risk factor; Calculate the data anomaly risk score in the associated time-series monitoring data to obtain the data anomaly risk factor; Calculate the process anomaly risk score in the associated time-series monitoring data to obtain the process compliance risk factor; Calculate the conflict risk score between the associated time-series monitoring data and other data sources to obtain the cross-source consistency risk factor; Logical contradiction risk factors, data anomaly risk factors, process compliance risk factors, and cross-source consistency risk factors are used as multi-dimensional risk factors in third-party monitoring reports.

[0010] Furthermore, based on the multi-dimensional risk factors in third-party monitoring reports, a comprehensive risk index is calculated, including: The logical contradiction risk factor, data anomaly risk factor, process compliance risk factor, and cross-source consistency risk factor are integrated into a comprehensive risk index (CFRI), calculated as follows:

[0011] in, These are the dimensional weights, representing the relative importance of the four risk factors, satisfying... Logical contradiction risk factor Data anomaly risk factors Process compliance risk factors Cross-source consistency risk factor , It is a norm parameter. .

[0012] Furthermore, the third-party monitoring reports are risk-classified based on a comprehensive risk index, and corresponding evidentiary data is obtained based on the risk levels of the third-party monitoring reports to generate an evidence correlation map, including: Risk classification is performed on third-party monitoring reports based on a comprehensive risk index; For third-party monitoring reports with a risk level of medium or higher, establish a chain of evidence; Generate an evidence association graph based on the evidence chain.

[0013] According to some embodiments, the second aspect of the present invention provides an environmental monitoring report evaluation system based on artificial intelligence and mechanism models, employing the following technical solution: An environmental monitoring report evaluation system based on artificial intelligence and mechanistic models includes: The data acquisition and processing module is configured to acquire the corresponding raw multi-source data based on the third-party monitoring report to be verified and perform preprocessing to obtain structured monitoring data; The data association module is configured to perform time-series association between data in third-party monitoring reports and corresponding original multi-source data to obtain associated time-series monitoring data. The risk assessment module is configured to evaluate the associated time-series monitoring data from four dimensions: logical contradictions, data anomalies, process anomalies, and data conflicts, and obtain multi-dimensional risk factors in the third-party monitoring report; The comprehensive risk index calculation module is configured to calculate the comprehensive risk index based on multi-dimensional risk factors from third-party monitoring reports. The risk classification module is configured to classify the risk of third-party monitoring reports based on a comprehensive risk index, and obtain corresponding evidence data based on the risk level of the third-party monitoring reports to generate an evidence association map. The assessment report generation module is configured to generate an assessment report on issues raised in the monitoring report based on the comprehensive risk index, risk level, risk factors of each dimension, and evidence correlation graph of the third-party monitoring report.

[0014] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the environmental monitoring report evaluation method based on artificial intelligence and mechanistic models as described in the first embodiment above.

[0016] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0017] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the environmental monitoring report evaluation method based on artificial intelligence and mechanism models as described in the first embodiment above.

[0018] According to some embodiments, a fifth aspect of the present invention provides a computer program product or computer program.

[0019] A computer program product or computer program includes computer instructions stored in a computer-readable storage medium, wherein a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the environmental monitoring report assessment method based on artificial intelligence and mechanism models as described in the first embodiment above.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention utilizes artificial intelligence technology to automatically collect, analyze, and correlate multi-source heterogeneous data, completely freeing regulatory personnel from repetitive and inefficient tasks such as manually collecting massive amounts of reports, original records, and instrument data, visually comparing them, and conducting formal reviews. The system can achieve 24 / 7 uninterrupted automatic verification of batch reports, with processing speed and consistency far exceeding manual methods, enabling regulatory resources to be precisely focused on high-risk targets; achieving highly efficient automated verification, and greatly improving regulatory efficiency and coverage.

[0021] This invention overcomes the limitations of traditional manual review or simple rule checks, which can only detect obvious omissions and formatting errors. It uses four risk factors to conduct quantitative and penetrating analysis from four dimensions: logical contradictions, data anomalies, process compliance, and cross-source consistency. In particular, it integrates mechanistic models (such as pollutant correlation and mass conservation) to verify the physical feasibility of data rationality, which can discover deeper logical fallacies, abnormal data that violates scientific laws, and potential traces of forgery and tampering. It breaks through the traditional depth of verification and achieves a deep diagnosis that integrates form, logic, and mechanism.

[0022] The weights (W) and norm parameters (p) in the Comprehensive Risk Index (CFRI) model described in this invention can be flexibly adjusted according to regulatory priorities. For example, a veto can be set for logical flaws (increasing the p value), or different data anomaly indicators can be emphasized in different industries (adjusting the W value). This makes the system no longer a rigid tool, but an intelligent decision support system that can internalize regulatory experience and strategies, continuously optimize, and is highly adaptable. It provides configurable intelligent decision support to adapt to diverse regulatory scenarios and strategies.

[0023] This invention automatically generates structured monitoring reports and problem assessment reports. The content, format, and evidence presentation are highly standardized, effectively avoiding the problem of inconsistent regulatory standards caused by differences in the experience and capabilities of different inspectors. It improves the standardization and professionalism of the entire regulatory work, standardizes the inspection output, and enhances the standardization and consistency of regulatory work. Attached Figure Description

[0024] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0025] Figure 1 This is a flowchart of an environmental monitoring report evaluation method based on artificial intelligence and mechanism model in an embodiment of the present invention. Detailed Implementation

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0027] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0028] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0029] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0030] Terminology Explanation: The mechanism model, based on the environmental impact assessment documents and environmental protection acceptance data of industrial enterprise construction projects, is a technical tool for discovering falsification of third-party monitoring reports by industrial enterprises in the environmental protection acceptance process of construction projects.

[0031] Self-acceptance of environmental protection upon completion of a construction project refers to the activity in which, after the completion of a construction project, the construction unit, in accordance with relevant environmental protection laws and regulations, technical specifications, environmental impact assessment approvals, and other requirements, organizes the acceptance of the supporting environmental protection facilities through on-site inspections, on-site sampling and monitoring of emission standards compliance, and other means, compiles an acceptance report, discloses relevant information, accepts social supervision, and independently assesses whether the construction project meets environmental protection requirements.

[0032] A monitoring report refers to the use of modern scientific and technological methods such as physics, chemistry, and biology to conduct on-site monitoring and measurement of environmental chemical pollutants and physical and biological pollution factors, and to make an accurate environmental quality assessment.

[0033] Example 1 like Figure 1As shown, this embodiment provides an environmental monitoring report evaluation method based on artificial intelligence and mechanism models. This embodiment uses the application of this method to a server as an example for illustration. It is understood that this method can also be applied to terminals, and can also be applied to systems including terminals, servers, and other components, and can be implemented through interaction between the terminal and the server. The server can be an independent physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, CDN security services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. In this embodiment, the method includes the following steps: Step S1: Obtain the corresponding raw multi-source data based on the third-party monitoring report to be verified and preprocess it to obtain structured monitoring data; Step S2: Perform time-series correlation between the data in the third-party monitoring report and the corresponding original multi-source data to obtain correlated time-series monitoring data; Step S3: Evaluate the associated time-series monitoring data from four dimensions: logical contradictions, data anomalies, process anomalies, and data conflicts, to obtain multi-dimensional risk factors in the third-party monitoring report; Step S4: Calculate the comprehensive risk index based on the multi-dimensional risk factors in the third-party monitoring report; Step S5: Classify the risk of the third-party monitoring report according to the comprehensive risk index, and obtain the corresponding evidence data based on the risk level of the third-party monitoring report to generate an evidence association map; Step S6: Generate a problem assessment report based on the comprehensive risk index, risk level, risk factors of each dimension, and evidence correlation graph of the third-party monitoring report.

[0034] Specifically, the detailed process of this embodiment is as follows: Step S1: Obtain the corresponding raw multi-source data based on the third-party monitoring report to be verified and preprocess it to obtain structured monitoring data, including: Step S1.1: Obtain the third-party monitoring report to be verified; Step S1.2: Based on the information in the third-party monitoring report, trigger the data collection task of related data to obtain the corresponding raw multi-source data; Specifically, the original records, electronic data, and process records corresponding to the third-party monitoring reports are retrieved from the testing organization's LIMS system, instrument workstation, and archive system through a secure interface.

[0035] Step S1.3: Combine the third-party monitoring report with the corresponding raw multi-source data and standardize the data to obtain structured monitoring data; Specifically, analyze third-party monitoring reports and corresponding original records, electronic data, and process records; All extracted information (such as monitoring items, concentration values, sampling time, analysis time, location information, instrument serial number, operator, etc.) is stored in a structured data table.

[0036] Step S1.4: Based on the locations in the third-party monitoring report, obtain historical data and concurrent background monitoring data for the same locations, and correlate them with the structured monitoring data.

[0037] Specifically, historical data from the same location and background monitoring data from the same period are obtained from external databases as analytical benchmarks to obtain historical data of wastewater or exhaust gas emission outlets in the monitoring report, including monitoring data from the same period of the previous year, as well as quarterly and monthly monitoring data.

[0038] Step S2: Perform time-series correlation between the data in the third-party monitoring report and the corresponding original multi-source data to obtain correlated time-series monitoring data, including: S2.1: Using sample number and monitoring item as indexes, associate the data in the third-party monitoring report with the corresponding original multi-source data; Using sample number and monitoring item as key indexes, the data in the third-party monitoring report is accurately linked with the original records and instrument data.

[0039] S2.2: Align the timestamps of the original multi-source data with those in the third-party monitoring report on the same timeline according to the time sequence to obtain the associated time-series monitoring data; Establish a timeline and align the timestamps of all stages, including sampling, handover, analysis, and report preparation, to the same timeline to obtain correlated time-series monitoring data.

[0040] It is important to note that the purpose of establishing a timeline is to ensure that the timelines of each work stage are sequential and logical, and to match the data according to the time order.

[0041] Step S3: Evaluate the associated time-series monitoring data from four dimensions: logical contradictions, data anomalies, process anomalies, and data conflicts, to obtain multi-dimensional risk factors in the third-party monitoring report, including: Calculate the logical error risk score in the associated time-series monitoring data to obtain the logical contradiction risk factor; Calculate the data anomaly risk score in the associated time-series monitoring data to obtain the data anomaly risk factor; Calculate the process anomaly risk score in the associated time-series monitoring data to obtain the process compliance risk factor; Calculate the conflict risk score between the associated time-series monitoring data and other data sources to obtain the cross-source consistency risk factor; Logical contradiction risk factors, data anomaly risk factors, process compliance risk factors, and cross-source consistency risk factors are used as multi-dimensional risk factors in third-party monitoring reports.

[0042] Specifically, multi-dimensional risk factors include: 1. Logical contradiction risk factor The logical flaws in this factor assessment report and process are as follows:

[0043] in, It is an indicator function, when the first one is violated. When a predefined logical rule is used, = 1, otherwise 0, totaling A predefined logical rule; This is the rule weight, predefined by the expert knowledge base, reflecting the importance of the rule. For example: Rule 1 (Very High Weight): The analysis time in the report is earlier than the sampling end time recorded in LIMS. Rule 2 (high weight): The sample analysis time exceeds the maximum retention time specified in the method standard.

[0044] Rule 3 (Medium Weight): The relative deviation of parallel samples of the same project is much greater than the allowable range specified by the method.

[0045] 2. Data Anomaly Risk Factors This factor uses statistical models and environmental science knowledge to assess the anomalies in the data itself. The calculation formula is as follows:

[0046] In the formula, It is a harmonic weight that satisfies This is used to balance the contributions of the three sub-factors and can be adjusted according to different detection types; The standardized anomaly scores are as follows:

[0047] in, This is the current reported value. and The mean and standard deviation of historical data for the same location. This is the adjustment factor (usually 2 or 3); this factor measures the degree to which the current value deviates from the historical normal range.

[0048] It is a correlation score, which calculates the ratio of currently reported values ​​for indicator pairs with strong environmental science correlations (such as COD and BOD, TVOC and benzene series compounds). The score is calculated as follows:

[0049] in, This is the median of the typical ratio. This represents a reasonable fluctuation range. The lower the score, the higher the risk.

[0050] It refers to the trend deviation, using time series models (such as ARIMA) to predict the reasonable range of the current value based on historical data. The calculation is as follows:

[0051] The larger the value, the more severely the data deviates from historical trends.

[0052] 3. Process compliance risk factors The degree of missing and non-standard records in this factor quantification process.

[0053]

[0054] in, This refers to the required number of nodes, which is the total number of key process nodes that must be recorded to complete this test, according to the testing standards and quality manual (such as sampling site records, sample handover forms, instrument calibration records, original spectra, review records, etc.). This is the number of verified nodes, representing the number of nodes that the system actually collected that are in the correct format and have complete information.

[0055] 4. Cross-source consistency risk factors The degree of conflict between the data in this factor assessment report and external reliable data sources or internal multi-source data is calculated by taking the item with the most severe conflict, as follows:

[0056] in, It is the credibility coefficient, representing the first... The reliability of each comparison data source is evaluated, with a value range of [0,1]. For example: continuous online monitoring data V=0.9, data compared with authoritative institutions V=0.8, and data from other projects of the same institution V=0.6.

[0057] It is the conflict level, indicating that the reported data is inconsistent with the first conflict level. The degree of normalization conflict for each data source; it can be a Boolean value (0 / 1) or a normalized value based on concentration differences, as follows:

[0058] This step calculates the risk factor scores for four dimensions in parallel. The value range for each factor is [0, 1], and the larger the value, the higher the corresponding risk.

[0059] Step S4: Based on the multi-dimensional risk factors in the third-party monitoring report, calculate the comprehensive risk index, including: The logical contradiction risk factor, data anomaly risk factor, process compliance risk factor, and cross-source consistency risk factor are integrated into a comprehensive risk index (CFRI), calculated as follows:

[0060] in, These are the dimensional weights, representing the relative importance of the four risk factors, satisfying... ; It is a norm parameter. ;when When it is a linear weighted average, the risk factors can compensate for each other; when At times, such as This would amplify the contribution of high-risk factors, which is more in line with the regulatory logic of veto power.

[0061] Step S5: Classify the risk of the third-party monitoring report based on the comprehensive risk index, and obtain corresponding evidence data based on the risk level of the third-party monitoring report to generate an evidence correlation graph, including: S5.1: Classify the risk of third-party monitoring reports based on the Comprehensive Risk Index (CFRI) value. For example: 0-0.3 (low risk), 0.3-0.6 (medium risk), 0.6-0.8 (high risk), 0.8-1.0 (extremely high risk).

[0062] S5.2: For third-party monitoring reports with a risk level of medium or higher, establish a chain of evidence; Retrieve the original rules and data that trigger high risks from the knowledge base, and automatically extract or link to relevant evidence files (such as contradictory original record pages, instrument log screenshots, and external data comparison charts).

[0063] The automated construction of dynamic evidence chains is as follows: For reports of medium risk or above, the system dynamically assembles the chain of evidence according to the following process: Cause identification: Based on the Comprehensive Risk Index (CFRI) value, determine the risk factors that trigger the risk as potential trigger points; The system first traces back to step S3 (risk factor calculation) to determine which specific sub-rule(s) or calculation triggered the high-risk score. For example: Rule R001 was triggered: the analysis time is earlier than the sampling end time.

[0064] middle Calculations show that the proportion of benzene compounds is significantly inconsistent with the predicted range of typical mechanism models of TVOC.

[0065] There is a significant conflict between the data from the online monitoring system and the actual data.

[0066] Evidence metadata extraction: Based on the triggering suspicious points, obtain the corresponding raw data elements and the rules violated; In other words, for each triggering point, the system automatically extracts all the original data elements and rule definitions upon which its calculation depends: Data elements: precise down to specific fields and sources. For example, for rule R001, extract the analysis timestamp of benzene in the report (2023-10-27 14:00:00) and the sampling end timestamp of the corresponding sample in LIMS (2023-10-27 14:30:00), and record that they come from page 5 of the report PDF and the sampling_log table in the LIMS database, respectively, with ID#xxx.

[0067] Rule definition: Retrieve the complete description and weight of this rule. (and violation of conditions).

[0068] Original evidence file association and retrieval: Based on the extracted original data elements, the following operations are automatically performed to obtain a snapshot of the original evidence: File location: Locate the physical file or database record containing the original evidence using a predefined index or storage path.

[0069] Evidence consolidation: For electronic documents (such as PDFs and logs): automatically take screenshots or highlight key paragraphs (such as analysis time in reports or sampling records in the LIMS interface).

[0070] For database records: Generate a snapshot of the query results with timestamps and data signatures.

[0071] For external data (such as online monitoring curves): automatically extract the data trend chart for the relevant time period and overlay it with the reported values ​​for comparison and annotation.

[0072] Generate Evidence Unit: Package the above content (triggering doubts, rule violations, original data elements, original evidence snapshots) into a structured Evidence Unit.

[0073] S5.3: Generate an evidence association graph based on the evidence chain to visually display the logical contradictions and breakpoints between report data, raw data, and process records in the form of a graph.

[0074] The generation and visualization of the evidence association graph are as follows: After generating all evidence units, graph database technology is used to automatically construct and render a visual evidence association graph to intuitively reveal complex contradictions.

[0075] Map construction: Node definition: Core entity nodes: such as monitoring reports, sample S001, pollutant benzene, and analytical instrument GC-001.

[0076] Data nodes: such as reported value: 1.2 mg / L, LIMS sampling end time: 14:30.

[0077] Rule / mechanism nodes: such as rule R001: causality, mechanism M001: benzene series compound proportion model.

[0078] Evidence file nodes: such as report PDF_P5, LIMS log screenshots.

[0079] Edge (relation) definition: Support / Source Relationship: The report value node originates from the monitoring report node.

[0080] Violation / Conflict Relationship: The report analysis time node violates rule R001. The report benzene percentage node deviates from the mechanism M001 node.

[0081] Proof of Relationship: The LIMS log screenshot node proves the LIMS sampling end time node.

[0082] Visual presentation: The map is centered around monitoring reports or problematic samples.

[0083] Different colors and shapes are used to distinguish node types (e.g., a red octagon indicates a rule violation, and a green document icon indicates evidence files).

[0084] Use different line styles and colors to indicate relationship types (e.g., red dashed lines indicate violation, green solid lines indicate support).

[0085] Key contradictory paths will be highlighted. For example, a clear path might be shown as: Report benzene concentration — (violation) → Mechanism model prediction range ← (support) — Historical data statistics, while reporting analysis time — (violation) → Causality rule ← (proof) — LIMS raw log.

[0086] Value of the map: Global insight: Enables reviewers to see at a glance whether the problem is isolated or systemic, whether it is a single data error or a break in the entire logical chain.

[0087] Highly explanatory: It transforms the judgment logic of AI and mechanistic models into graphical reasoning stories that conform to human cognition.

[0088] Review Navigation: Reviewers can click on any evidence file node in the graph to directly access the original evidence for in-depth verification.

[0089] Step S6: Based on the comprehensive risk index, risk level, risk factors of each dimension, and evidence correlation diagram of the third-party monitoring report, generate a problem assessment report of the monitoring report, including: S6.1: The system automatically generates the final monitoring report and problem assessment report.

[0090] S6.2: The monitoring report problem assessment report is presented in a structured manner: the comprehensive risk index CFRI value and risk level, radar charts of risk factors in each dimension, a detailed list of suspicious points (each suspicious point is accompanied by the basis for risk level calculation and evidence chain), a visualized evidence chain, and conclusions and verification recommendations.

[0091] This embodiment achieves automated batch processing of massive reports through AI technology, solving the bottleneck problems of low efficiency and difficulty in scaling traditional manual verification. Secondly, it uses a mechanism model to conduct physical simulation verification of the entire process of pollutant generation-treatment-emission, giving the verification conclusions a solid scientific basis and significantly improving the credibility and interpretability of problem discovery. Finally, through an original reverse inference mechanism, the system can not only identify surface anomalies, but also accurately locate the root cause of the problem.

[0092] Deep coupling solves the problem of model disconnect: Through dynamic parameter mapping, the actual operating characteristics mined by AI from real-time data are integrated into the mechanism model, making theoretical calculations closer to the field and greatly improving the accuracy and reliability of mechanism model verification.

[0093] Forming a computational closed loop and making decisions more scientific: A complete computational chain has been established, from AI feature extraction -> rule screening -> mechanism quantification -> two-way verification. The output of each step is the input of the next step, making the problem discovery process based on evidence, and moving from feeling something is wrong to calculating that it is unreasonable.

[0094] Efficiency and depth are combined: the expert rule base enables rapid initial screening of problems, ensuring system efficiency; while the mechanism model performs in-depth verification of complex and implicit problems, ensuring the depth of insight of the system.

[0095] It has the ability to evolve on its own: through a closed-loop iterative feedback mechanism, the entire system can continuously learn new fraud methods and adapt to new policies and regulations, thus solving the problem of rigidity and backwardness in traditional systems.

[0096] Example 2 This embodiment provides an environmental monitoring report evaluation system based on artificial intelligence and mechanism models, including: The data acquisition and processing module is configured to acquire the corresponding raw multi-source data based on the third-party monitoring report to be verified and perform preprocessing to obtain structured monitoring data; The data association module is configured to perform time-series association between data in third-party monitoring reports and corresponding original multi-source data to obtain associated time-series monitoring data. The risk assessment module is configured to evaluate the associated time-series monitoring data from four dimensions: logical contradictions, data anomalies, process anomalies, and data conflicts, and obtain multi-dimensional risk factors in the third-party monitoring report. The comprehensive risk index calculation module is configured to calculate the comprehensive risk index based on multi-dimensional risk factors from third-party monitoring reports. The risk classification module is configured to classify the risk of third-party monitoring reports based on a comprehensive risk index, and obtain corresponding evidence data based on the risk level of the third-party monitoring reports to generate an evidence association map. The assessment report generation module is configured to generate an assessment report on issues raised in the monitoring report based on the comprehensive risk index, risk level, risk factors of each dimension, and evidence correlation graph of the third-party monitoring report.

[0097] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1 above. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0098] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0099] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0100] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the environmental monitoring report evaluation method based on artificial intelligence and mechanism models as described in Embodiment 1 above.

[0101] Example 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the environmental monitoring report evaluation method based on artificial intelligence and mechanism model as described in Embodiment 1 above.

[0102] Example 5 This embodiment provides a computer program product or computer program, including computer instructions stored in a computer-readable storage medium. The processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the environmental monitoring report evaluation method based on artificial intelligence and mechanism model described in Embodiment 1 above.

[0103] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments 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 and optical storage) containing computer-usable program code.

[0104] 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, as well as 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. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] 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.

[0106] 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.

[0107] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0108] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. An environmental monitoring report evaluation method based on artificial intelligence and mechanistic models, characterized in that, include: Based on the third-party monitoring report to be verified, obtain the corresponding raw multi-source data and preprocess it to obtain structured monitoring data; By performing time-series correlation between the data in the third-party monitoring report and the corresponding original multi-source data, the correlated time-series monitoring data is obtained. The relevant time-series monitoring data is evaluated from four dimensions: logical contradictions, data anomalies, process anomalies, and data conflicts, resulting in multi-dimensional risk factors in the third-party monitoring report. A comprehensive risk index is calculated by integrating multiple risk factors based on third-party monitoring reports. The risk level of third-party monitoring reports is classified according to the comprehensive risk index, and corresponding evidence data is obtained based on the risk level of the third-party monitoring reports to generate an evidence association map. A problem assessment report is generated based on the comprehensive risk index, risk level, risk factors of various dimensions, and evidence correlation diagrams from the third-party monitoring report.

2. The environmental monitoring report evaluation method based on artificial intelligence and mechanism models as described in claim 1, characterized in that, Based on the third-party monitoring report to be verified, the corresponding raw multi-source data is obtained and preprocessed to obtain structured monitoring data, including: Obtain the third-party monitoring report to be verified; Based on information from third-party monitoring reports, trigger the collection task of related data to obtain the corresponding raw multi-source data; By combining third-party monitoring reports with corresponding raw multi-source data and standardizing the process, structured monitoring data is obtained.

3. The environmental monitoring report evaluation method based on artificial intelligence and mechanism models as described in claim 1, characterized in that, The data in the third-party monitoring report is correlated with the corresponding original multi-source data in a time series to obtain correlated time series monitoring data, including: Using sample number and monitoring item as indexes, the data in the third-party monitoring report is linked with the corresponding original multi-source data; By aligning the original multi-source data with the timestamps in the third-party monitoring reports on the same timeline in chronological order, we obtain associated time-series monitoring data.

4. The environmental monitoring report evaluation method based on artificial intelligence and mechanism models as described in claim 1, characterized in that, The relevant time-series monitoring data is evaluated from four dimensions: logical contradictions, data anomalies, process anomalies, and data conflicts. This yields a multi-dimensional risk factor report from a third-party monitoring agency, including: Calculate the logical error risk score in the associated time-series monitoring data to obtain the logical contradiction risk factor; Calculate the data anomaly risk score in the associated time-series monitoring data to obtain the data anomaly risk factor; Calculate the process anomaly risk score in the associated time-series monitoring data to obtain the process compliance risk factor; Calculate the conflict risk score between the associated time-series monitoring data and other data sources to obtain the cross-source consistency risk factor; Logical contradiction risk factors, data anomaly risk factors, process compliance risk factors, and cross-source consistency risk factors are used as multi-dimensional risk factors in third-party monitoring reports.

5. The environmental monitoring report evaluation method based on artificial intelligence and mechanism models as described in claim 1, characterized in that, Based on multi-dimensional risk factors from third-party monitoring reports, a comprehensive risk index is calculated, including: The logical contradiction risk factor, data anomaly risk factor, process compliance risk factor, and cross-source consistency risk factor are integrated into a comprehensive risk index (CFRI), calculated as follows: in, These are the dimensional weights, representing the relative importance of the four risk factors, satisfying... Logical contradiction risk factor Data anomaly risk factors Process compliance risk factors Cross-source consistency risk factor , It is a norm parameter. .

6. The environmental monitoring report evaluation method based on artificial intelligence and mechanism models as described in claim 1, characterized in that, The risk level of third-party monitoring reports is classified according to a comprehensive risk index. Based on the risk level of the third-party monitoring reports, corresponding evidence data is obtained to generate an evidence correlation map, including: Risk classification is performed on third-party monitoring reports based on a comprehensive risk index; For third-party monitoring reports with a risk level of medium or higher, establish a chain of evidence; Generate an evidence association graph based on the evidence chain.

7. An environmental monitoring report evaluation system based on artificial intelligence and mechanistic models, characterized in that, include: The data acquisition and processing module is configured to acquire the corresponding raw multi-source data based on the third-party monitoring report to be verified and perform preprocessing to obtain structured monitoring data; The data association module is configured to perform time-series association between data in third-party monitoring reports and corresponding original multi-source data to obtain associated time-series monitoring data. The risk assessment module is configured to evaluate the associated time-series monitoring data from four dimensions: logical contradictions, data anomalies, process anomalies, and data conflicts, and obtain multi-dimensional risk factors in the third-party monitoring report. The comprehensive risk index calculation module is configured to calculate the comprehensive risk index based on multi-dimensional risk factors from third-party monitoring reports. The risk classification module is configured to classify the risk of third-party monitoring reports based on a comprehensive risk index, and obtain corresponding evidence data based on the risk level of the third-party monitoring reports to generate an evidence association map. The assessment report generation module is configured to generate an assessment report on issues raised in the monitoring report based on the comprehensive risk index, risk level, risk factors of each dimension, and evidence correlation graph of the third-party monitoring report.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the environmental monitoring report evaluation method based on artificial intelligence and mechanism model as described in any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the environmental monitoring report evaluation method based on artificial intelligence and mechanism model as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps in the environmental monitoring report evaluation method based on artificial intelligence and mechanism model as described in any one of claims 1-6.

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