A method and system for monitoring pollution discharge risks based on enterprise pollution discharge rights

Abstract

This invention provides a method and system for monitoring pollution risks based on enterprise pollution discharge rights, comprising: generating an initial pollutant combination type for the target enterprise based on its pollution discharge rights information and actual operating equipment; formulating a basic detection strategy for the target enterprise based on the initial pollutant combination type, and obtaining real-time detection data according to the initial detection strategy; generating a pollutant combination type to be tested based on the real-time detection data; retrieving the corresponding pollutant type to be tested from the pollutant combination according to the enterprise's actual operating equipment, and comparing the initial declared pollutant type of the target enterprise with the pollutant type to be tested through a comparison and verification strategy to identify and mark potential risk items; and generating an environmental monitoring strategy based on the potential risk items and the consistency verification results of the enterprise's actual operating equipment and the front-end treatment equipment list, combined with the distribution of environmentally sensitive points in the pollution discharge area.

Description

Technical Field

[0001] This invention relates to the technical field of pollution discharge rights regulation, and in particular to a method and system for monitoring pollution discharge risks based on enterprise pollution discharge rights. Background Technology

[0002] Discharge rights management is one of the core means of environmental pollution control. Its core lies in achieving precise control and total quantity control of pollutant emissions by clarifying enterprises' discharge rights and standardizing pollutant declaration and discharge behavior. Currently, enterprise discharge risk monitoring mainly relies on manual verification of enterprises' discharge rights declaration materials, on-site inspection of the operating status of treatment equipment, and subsequent comparison of environmental monitoring data. This aims to identify potential risks such as omissions in declarations, illegal discharges, and substandard treatment equipment during the discharge process, thus providing support for environmental management.

[0003] However, existing methods and systems for monitoring pollution discharge risks have many technical shortcomings, making it difficult to meet the needs for precise, systematic, and end-to-end pollution discharge risk management. Specific deficiencies are as follows:

[0004] First, existing pollution risk monitoring lacks an in-depth verification mechanism for the actual implementation of enterprises' pollution discharge rights. Current verifications mostly focus on a simple comparison between the actual operating equipment and the declared treatment equipment, without establishing a comprehensive rule system to link pollutant attributes, treatment technologies, and hardware parameters. This makes it impossible to accurately match the optimal treatment technology and necessary front-end treatment equipment for different pollutants, and also fails to comprehensively verify whether the equipment operating data is within the normal range and whether the equipment's processing capacity meets the declared emission requirements. As a result, it is difficult to accurately judge the actual implementation effect of pollution discharge rights, and risks such as treatment equipment being ineffective and pollution discharge exceeding standards are easily overlooked.

[0005] Secondly, existing technologies lack scientific and systematic methods for deriving the pollutants to be tested, making it impossible to accurately predict the type and content of pollutants corresponding to a company's discharge rights. During the pollutant declaration process, companies often rely solely on manual reporting of directly generated pollutants based on experience, neglecting indirect pollutants generated during raw material conversion and the operation of treatment equipment. Furthermore, they fail to incorporate the law of conservation of mass to calculate and calibrate the types and contents of pollutants, resulting in significant discrepancies between the pollutants to be tested and the actual pollutants discharged. This makes it impossible to provide a reliable theoretical basis for verifying discharge declarations, and consequently, it is difficult to identify omissions in the discharge declarations. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for monitoring pollution discharge risks based on enterprise pollution discharge rights, so as to overcome the above-mentioned defects in the existing technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for monitoring pollution risk based on enterprise pollution discharge rights, comprising: The theoretical pollutant adjustment steps, the target enterprise's pollution discharge rights information and actual operating equipment, through a pre-built association database and a derivation strategy, generate the target enterprise's initial pollutant combination type. The pollution discharge rights information includes industry type, process flow, pollution discharge area and initial declared pollutant type. The basic detection strategy execution steps are as follows: Based on the initial pollutant combination type of the target enterprise, a basic detection strategy is formulated for the target enterprise through general standards and industry experience. The basic detection strategy includes the initial detection location, initial detection cycle and initial detection factor. The target enterprise is preliminarily detected according to the initial detection strategy to obtain real-time detection data. The detection data calibration and feedback step involves adjusting and optimizing the initial combination of pollutants to be tested based on the real-time detection data to generate a combination type of pollutants to be tested. The comparative verification step involves retrieving the corresponding pollutant type to be tested from the pollutant combination based on the actual operating equipment of the enterprise, and comparing the initial declared pollutant type of the target enterprise with the pollutant type to be tested through the comparative verification strategy to identify and mark potential risk items. The environmental monitoring strategy generation step involves constructing a risk assessment model based on the potential risk items and the consistency verification results between the enterprise's actual operating equipment and the front-end treatment equipment list, combined with the distribution of environmentally sensitive points in the sewage discharge area. The risk level of the target enterprise is calculated based on the risk assessment model, and an environmental monitoring strategy is generated based on the risk level. The dynamic detection thresholds for pollutants of the target enterprise are then adjusted.

[0008] Preferably, the pollution discharge rights information includes material information, which includes a raw material list and a discharge list, and the derivation strategy includes: The forward derivation sub-step establishes a mapping relationship between raw material information and pollutants, and extracts characteristic elements or compounds contained in the raw materials as direct pollutants. The indirect derivation sub-step has a pre-set pollutant conversion rule base, which has a pre-set mapping relationship between treatment equipment and by-products. Based on the direct pollutants and the front-end treatment equipment used by the target enterprise, the intermediate products that are bound to exist are generated by chemical reactions, thus constituting indirect pollutants. The derivation sub-steps are combined to obtain direct and indirect pollutants as a pollutant set. Based on the raw material list and the output list, the mass conservation law is used for calculation. Based on the calculation results, the content of each pollutant in the pollutant set is dynamically adjusted to generate an initial combination of pollutant types to be tested.

[0009] Preferably, the potential risk items include omissions in the declaration and logical conflicts, and the comparison and verification strategy includes: The difference retrieval sub-step is used to obtain the initial declared pollutant type and the pollutant type to be tested, and compare them one by one to obtain the difference items between them. The pollutant conversion rule base in the indirect derivation sub-step is called to analyze whether the difference item belongs to the pollutant that can be completely converted. If the difference item can be completely converted, it is judged as reasonable. If the difference item cannot be completely converted, it is defined as the declared omission item. The cross-validation sub-step involves obtaining the initial declared pollutant types and any missing declared items as a set of pollutants to be verified, and obtaining the content of the pollutants to be verified. Based on the actual operating equipment and operating data, the corresponding pollutant type to be tested is retrieved. The pollutant type to be tested includes the range of pollutant content to be tested. The pollutant content to be verified and the range of pollutant content to be tested are compared and verified. If the verification fails, a logical conflict item is generated.

[0010] Preferably, the environmental detection strategy generation step includes: The multi-dimensional risk assessment sub-steps obtain potential risk items, consistency verification results, and distribution data of sensitive points in the surrounding environment of the target enterprise, and construct a multi-dimensional risk assessment model. When there are omissions in the declaration among the potential risk items, the dynamic expansion mechanism of the detection factor is triggered to include the omissions in the declaration into the detection factor. The alternative factor screening sub-step analyzes the chemical properties and migration and change patterns of the pollutants that are difficult to detect among the pollutants to be declared by the target enterprise, screens out alternative detection factors that are strongly correlated with the pollutants that are difficult to detect and are easy to detect, and establishes a correlation model between alternative detection factors and pollutants that are difficult to detect. The detection optimization sub-step involves incorporating alternative detection factors into the detection factor list and, based on the correlation model, converting the alternative detection factors into the estimated content of pollutants that are difficult to detect, thereby generating an environmental detection strategy. The environmental detection strategy also includes a judgment threshold.

[0011] Preferably, the alternative factor screening sub-step also includes establishing a feature library of difficult-to-detect pollutants, which includes the chemical properties, migration and change patterns, and detection methods of pollutants; constructing a database of potential alternative factors, which includes the detection costs, detection sensitivity, and chemical correlations of various pollutants; and using a correlation analysis algorithm to calculate the correlation coefficient between difficult-to-detect pollutants and potential alternative factors, and obtaining alternative detection factors with correlation coefficients greater than a preset threshold.

[0012] Preferably, the method also includes an emission effect analysis sub-step, wherein the detection locations include upstream and downstream points of the enterprise's emission outlet, the initial pollutant content in the water at the upstream point of the emission outlet is obtained and the content of pollutants to be reported is obtained, the chemical reaction between the pollutants to be reported and the initial pollutants is analyzed, and the detection strategy at the downstream point of the emission outlet is dynamically adjusted according to the type of chemical reaction, wherein the detection strategy includes a judgment threshold.

[0013] Preferably, the environmental detection strategy includes a dynamic threshold adjustment sub-step, which is used to acquire historical detection data and geographic feature data of the target enterprise's detection location, construct a historical data model based on the historical detection data, construct a dynamic threshold correction model, calculate the dynamic threshold of the target enterprise under different environmental conditions, acquire detection data of the detection location, use anomaly detection algorithms to identify unnatural fluctuations in the detection data, and generate encrypted detection suggestions when unnatural fluctuations are identified.

[0014] As an alternative, a pollution discharge matching step is also included. Based on the initial pollutant type and production process of the target enterprise, a pollution discharge matching algorithm is used to obtain the production node and corresponding front-end treatment process corresponding to each initially declared pollutant. The algorithm then reverse-engineers a list of the necessary front-end treatment equipment for the enterprise and obtains the actual operating equipment and operating data of the enterprise. The consistency between the actual operating equipment and the list of front-end treatment equipment is verified to verify the actual implementation of the pollution discharge right.

[0015] Preferably, the discharge matching step includes a three-dimensional rule base, which includes pollutant attributes, treatment technologies, and hardware equipment parameters. For each pollutant attribute, there are multiple treatment technologies and their corresponding mapped equipment parameter ranges. Based on the physicochemical properties and emission standards of each pollutant in the initial declaration, recommended treatment technologies are generated, and a list of necessary front-end treatment equipment for each treatment technology is obtained. The consistency verification includes analyzing whether the actual operating equipment of the enterprise meets the list of front-end treatment equipment; obtaining the dynamic operating parameter threshold table of the corresponding equipment in the three-dimensional rule base, and dynamically adjusting the normal operating range of the current equipment based on the calibration values ​​of the equipment operating parameters uploaded by the enterprise in real time; verifying whether the operating data of the actual operating equipment is within the dynamically adjusted normal operating range; and verifying whether the processing capacity of the equipment meets the requirements of the declared discharge volume.

[0016] A pollution risk monitoring system based on enterprise pollution discharge rights includes: The theoretical pollutant adjustment module uses the target enterprise's pollution discharge rights information and actual operating equipment, through a pre-built association database and a derivation strategy, to generate the target enterprise's initial pollutant combination type. The pollution discharge rights information includes industry type, process flow, pollution discharge area, and initial declared pollutant type. The basic detection strategy execution module, based on the initial pollutant combination type of the target enterprise, formulates a basic detection strategy for the target enterprise through general standards and industry experience. The basic detection strategy includes the initial detection location, initial detection cycle and initial detection factor. The module performs preliminary detection on the target enterprise according to the initial detection strategy to obtain real-time detection data. The detection data calibration and feedback module adjusts and optimizes the initial combination of pollutants to be tested based on the real-time detection data to generate a combination type of pollutants to be tested. The comparison and verification module retrieves the corresponding pollutant type to be tested from the pollutant combination based on the actual operating equipment of the enterprise, and compares the initial declared pollutant type of the target enterprise with the pollutant type to be tested through the comparison and verification strategy to identify and mark potential risk items. The environmental monitoring strategy generation module constructs a risk assessment model based on the potential risk items and the consistency verification results of the enterprise's actual operating equipment and front-end processing equipment list, combined with the distribution of environmentally sensitive points in the sewage discharge area. It calculates the risk level of the target enterprise based on the risk assessment model, generates an environmental monitoring strategy based on the risk level, and adjusts the dynamic detection threshold of pollutants for the target enterprise.

[0017] The beneficial effects of this invention are as follows: This method, through the acquisition step of the pollutant to be tested, combines three derivation sub-steps—forward, indirect, and merging—to construct a systematic strategy for deducing the pollutant to be tested. Forward derivation accurately extracts characteristic elements or compounds from raw materials as direct pollutants; indirect derivation predicts intermediate products generated during the operation of treatment equipment based on a pollutant conversion rule base; and merging derivation uses the law of conservation of mass to calculate and calibrate the pollutant set, ultimately generating an accurate combination of pollutant types to be tested. This design solves the problems of neglecting indirect pollutants and large deviations between theory and reality in existing technologies, achieving accurate prediction of the types and contents of pollutants to be tested corresponding to an enterprise's discharge rights, and providing a scientific and reliable theoretical basis for discharge declaration verification. Attached Figure Description

[0018] Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a flowchart of the sewage matching steps of the present invention; Figure 3 This is a flowchart of the steps for obtaining the pollutant to be tested according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0021] Unless otherwise defined, 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. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings: like Figures 1-3 As shown, the present invention provides a method for monitoring pollution risk based on enterprise pollution discharge rights, comprising: The theoretical pollutant adjustment steps, based on the target enterprise's pollution discharge rights information and actual operating equipment, utilize a pre-built correlation database and a deduction strategy to generate the initial pollutant combination type for the target enterprise. The pollution discharge rights information includes industry type, production process flow, pollution discharge area, and initially declared pollutant type. The pollutant acquisition steps rely on the target enterprise's pollution discharge rights information and actual operating equipment as the core basis, and conduct systematic deduction based on the pre-built correlation database. The actual operating equipment is the equipment list submitted by the enterprise. Based on the enterprise's declared equipment list and the target enterprise's pollution discharge rights information, the initial pollutant combination type of possible products is deduced. The correlation database integrates multi-dimensional information such as the correspondence between production processes and pollutant generation in various industries, the reaction mechanism of treatment equipment operation, and the correlation data between material composition and pollutant transformation, providing comprehensive data support for the deduction process and ensuring that the generation of the pollutant type combination is based on scientific evidence and data foundation.

[0023] Discharge rights information includes material information, which includes a raw material list and a discharge list. The derivation strategy includes: The forward derivation sub-step establishes a mapping relationship between raw material information and pollutants, extracting characteristic elements or compounds contained in the raw materials as direct pollutants. This sub-step focuses on the precise correlation between raw materials and direct pollutants. By analyzing the chemical composition and material structure of various raw materials in the raw material list, a stable mapping relationship between raw material information and pollutants is established. This mapping relationship is determined based on the properties of characteristic elements or compounds in the raw materials, which refer to substances that easily detach from the main raw material and form polluting components under production process conditions. Through qualitative analysis and quantitative calculation of the raw material components, these characteristic components are extracted as direct pollutants, clarifying the types and initial content ranges of direct pollutants, providing basic data for subsequent derivation.

[0024] The indirect derivation sub-step pre-defines a pollutant conversion rule base, which includes a mapping relationship between treatment equipment and byproducts. Based on the direct pollutants and the front-end treatment equipment used by the target company, intermediate products inevitably generated by chemical reactions constitute indirect pollutants. This sub-step focuses on identifying indirect pollutants generated during the treatment process. The pollutant conversion rule base stores data on reaction paths and product generation patterns of different types of treatment equipment when treating specific pollutants, clarifying the fixed mapping relationship between treatment equipment and byproducts. Combining the physicochemical properties of direct pollutants with the type and technology of the front-end treatment equipment used by the target company, and based on the basic principles of chemical reactions, the sub-step analyzes the reaction process between direct pollutants and treatment media and other substances under operating conditions such as temperature and pressure, predicting the inevitable intermediate products. These intermediate products, possessing pollution properties and not covered by the direct derivation step, are defined as indirect pollutants, further improving the coverage of pollutant types. Furthermore, the derivation results of indirect pollutants are dynamically adjusted based on the intermediate product mapping relationship.

[0025] The merged derivation sub-step obtains direct and indirect pollutants as a pollutant set. Based on the raw material and output lists, it performs calculations using real-time production data and the law of conservation of mass. Based on the calculation results, the content of each pollutant in the pollutant set is dynamically adjusted to generate an initial combination of pollutant types to be tested. During the law of conservation of mass calculation, the raw material and output lists, as well as factors such as material loss and recycling during production, are considered to calculate the value ranges for each pollutant. The merged derivation sub-step integrates and calibrates the pollutant set. First, the direct pollutants obtained from the forward derivation and the indirect pollutants obtained from the indirect derivation are summarized to form a preliminary pollutant set. Then, based on the law of conservation of mass, combined with the input quantities of various raw materials in the raw material list and the generation quantities of products and by-products in the output list, the difference between the total input and total output of substances during the production process is calculated. This accounting process verifies whether the content of various pollutants in the pollutant pool conforms to the law of conservation of matter. Based on the accounting results, the content data of each pollutant are dynamically adjusted, unreasonable pollutant items are eliminated, and content values ​​with large deviations are corrected. Finally, a complete and accurate combination of pollutant types to be tested is generated.

[0026] The basic detection strategy implementation steps are as follows: Based on the initial pollutant combination type of the target enterprise, a basic detection strategy is formulated for the target enterprise through general standards and industry experience. The basic detection strategy includes initial detection locations, initial detection cycles, and initial detection factors. Preliminary detection is carried out on the target enterprise according to the initial detection strategy to obtain real-time detection data. The basic detection strategy includes initial detection locations, initial detection cycles, and initial detection factors. Among them, the initial detection locations are selected from the enterprise's production discharge nodes, total discharge outlets, and upstream and downstream points of environmentally sensitive points in the discharge area. The initial detection cycle is determined according to the industry's emission characteristics and risk level. High-emission-risk industries shorten the detection interval, while conventional industries use the standard detection frequency. The initial detection factors are based on the pollutant types initially declared by the enterprise, and typical pollutants and generally controlled pollutants of the corresponding production processes are also included. On-site preliminary detection is carried out on the target enterprise according to the above basic detection strategy, and real-time detection data of each detection point and each detection factor is collected and obtained to provide data support for subsequent risk assessment and detection strategy optimization. Industry type includes the industrial sector and sub-category to which the enterprise belongs; process production flow covers the entire process and operating procedures from raw material input to product output; pollution discharge area is the specific geographical range of the enterprise's pollutant discharge; initial pollutant type declaration refers to the various types of pollutants that the enterprise declares as required; and this pollution discharge right information is accurately obtained by integrating information from multiple channels such as enterprise filing documents and production ledgers.

[0027] The detection data calibration and feedback step involves adjusting and optimizing the initial pollutant combination to be tested based on the real-time detection data, generating a pollutant combination type. This initial pollutant combination type is generated according to a derivation strategy, including a comparison of the types and concentrations of each pollutant. Real-time detection data serves as an auxiliary calibration and verification basis. The pollutant combination type is determined based on the proportion of pollutants in the real-time detection data. The detection data calibration and feedback step involves adjusting and optimizing the initial pollutant combination to be tested based on the real-time detection data, generating a pollutant combination type. First, the real-time data obtained from preliminary testing undergoes anomaly removal, dimensional standardization, and location aggregation preprocessing. Next, each pollutant in the initial test pollutant combination is matched against the test data. For pollutants detected and with matching concentrations, the calibration concentration range is narrowed. Pollutants that are inferred to exist but not actually detected are assessed for conversion effectiveness using a pollutant conversion rule base and are removed or marked. Pollutants not covered by inference but actually detected are supplemented. Subsequently, the theoretical concentration ranges for each pollutant are dynamically corrected based on the company's current production load, raw material usage ratio, and real-time removal efficiency of the front-end treatment equipment. Finally, after material balance verification and rationality checks, a final test pollutant combination type is formed, including pollutant type, precise concentration range, generation node, and confidence level, providing a precise benchmark for subsequent reporting comparisons and risk identification.

[0028] The comparative verification process involves retrieving the corresponding pollutant types from the pollutant type combinations based on the company's actual operating equipment. The initial declared pollutant types are then compared with the target pollutant types using a comparative verification strategy to identify and mark potential risks. This process uses the company's actual operating equipment as the core reference. First, it precisely retrieves the target pollutant types from the generated combinations that match the actual operating equipment's processing capacity and process adaptability, ensuring the comparison is targeted. Then, the comparative verification strategy is activated, comprehensively comparing the initial declared pollutant types with the selected target pollutant types. Through systematic analysis of the compatibility in terms of type, concentration, and other dimensions, it accurately identifies and marks potential omissions and logical conflicts in the declaration, providing core evidence for determining pollution discharge risks.

[0029] Potential risks include omissions in the declaration and logical conflicts. Comparative verification strategies include: The difference retrieval sub-step is used to obtain the initial declared pollutant type and the pollutant type to be tested, and compare them one by one to identify the differences. It then calls the pollutant conversion rule base from the indirect derivation sub-step to analyze whether the difference belongs to a pollutant that can be completely converted. If the difference is completely convertible, it is judged as reasonable; if it is not completely convertible, it is defined as a missing item in the declaration. The difference retrieval sub-step focuses on the difference mining and reasonableness determination of the two types of pollutants. First, through a systematic type comparison mechanism, it comprehensively reviews the initial declared pollutant type and the pollutant type to be tested, and identifies and extracts the differences between them in terms of type. Next, it calls the pollutant conversion rule base built in the indirect derivation sub-step, using the pollutant conversion pathways, reaction conditions, conversion efficiencies, and other data stored in the base to deeply analyze the conversion characteristics of each difference. Pollutants that can be completely converted refer to components that can be completely converted into harmless substances or non-pollutants through chemical reactions under the operating conditions of existing front-end treatment equipment. Such differences are deemed reasonable because they do not cause actual emissions pollution. On the other hand, differences that cannot be completely converted are clearly defined as omissions in the declaration because they pose actual emission risks and have not been declared by the company.

[0030] The cross-validation sub-step involves obtaining the initial declared pollutant types and any omitted declarations as a set of pollutants to be validated, and acquiring the content of these pollutants. Based on the actual operating equipment and operational data, the corresponding pollutant types to be tested are retrieved, including their content ranges. The pollutant content and its range are compared for validation. If validation fails, a logical conflict is generated. The cross-validation sub-step then conducts in-depth content-level verification of the pollutant set. First, the initial declared pollutant types are integrated with the identified omitted declarations to form a complete set of pollutants to be validated. The actual declared or estimated content of each pollutant in the set is then obtained through a data acquisition system. Simultaneously, based on the model and operating parameters of the company's actual operating equipment, the corresponding content range of the pollutants to be tested is retrieved from the pollutant types. This range is a reasonable range calculated by considering multiple factors such as equipment processing capacity, production process standards, and material input. By comparing the actual content of the pollutant to be verified with the theoretical content range, if the actual content exceeds the upper limit of the theoretical range or is lower than the lower limit of the range, and there is no reasonable technical reason to support the deviation, the verification is deemed unsuccessful, and a logical conflict item is generated. Such potential risk items often reflect the contradiction between the data declared by the enterprise and the actual production emissions.

[0031] The pollution discharge matching step, based on the target enterprise's initial declared pollutant types and production process flow, uses a pollution discharge link matching algorithm to obtain the corresponding production nodes and front-end treatment processes for each initially declared pollutant. It then reverse-engineers a list of essential front-end treatment equipment for the enterprise and obtains the enterprise's actual operating equipment and operational data. The consistency between the actual operating equipment and the front-end treatment equipment list is verified to confirm the actual implementation of pollution discharge rights. The pollution discharge matching step uses the initial declared pollutant types and production process flow as its core basis, achieving precise correlation through a pollution discharge link matching algorithm. This algorithm analyzes the reaction mechanisms and material flow paths of each process step in the production process to locate the specific production nodes that generate each type of initially declared pollutant. Simultaneously, it matches suitable front-end treatment processes based on the pollutant treatment requirements. The front-end treatment processes must be tailored to the operating conditions of the production nodes to ensure targeted and effective treatment of the corresponding pollutants. Based on this, a list of front-end treatment equipment that the enterprise must equip to implement pollution discharge rights is derived, clearly defining the core functions and technical specifications of the equipment.

[0032] The pollution discharge matching step employs a three-dimensional rule base, which includes pollutant attributes, treatment technologies, and hardware equipment parameters. For each pollutant attribute, there are multiple corresponding treatment technologies and their corresponding mapped equipment parameter ranges. Based on the physicochemical properties and emission standards of each pollutant in the initial declaration, recommended treatment technologies are generated, and a list of necessary front-end treatment equipment for each treatment technology is obtained. The consistency verification includes analyzing whether the enterprise's actual operating equipment meets the list of front-end treatment equipment; obtaining a dynamic operating parameter threshold table for the corresponding equipment in the three-dimensional rule base and dynamically adjusting the current normal operating range of the equipment based on the calibration values ​​of the equipment operating parameters uploaded by the enterprise in real time; verifying whether the operating data of the actual operating equipment is within the dynamically adjusted normal operating range; and verifying whether the equipment's processing capacity meets the declared emission requirements. The three-dimensional rule base constructed in the pollution discharge matching step is a key support for achieving accurate matching. The pollutant attributes encompass core characteristics such as the pollutant's physical state, chemical activity, and toxicity level. Treatment technologies include mature processes and technical principles for treating various pollutants. Hardware equipment parameters involve key indicators such as equipment processing capacity, operating power, and operating conditions. A dynamic threshold table of operating parameters related to process load is included to dynamically adjust parameters within the normal operating range of the equipment. Real-time process compliance data is used to dynamically adapt to verification standards. The three-dimensional rule base includes pollutant attributes, treatment technologies, and hardware equipment parameters, establishing a correspondence between them. This flexible correspondence forms a rule system supporting multiple technology path recommendations, aiming to balance treatment effectiveness, equipment feasibility, and the actual selection needs of enterprises. For each type of pollutant, the rule base provides multiple optional treatment technologies and their corresponding front-end treatment equipment parameter ranges, rather than a single fixed solution. Based on pollutant attributes, the maturity of treatment technologies, treatment efficiency, cost, and equipment parameters, this rule base constructs a multi-dimensional recommendation system, aiming to provide different types of enterprises with a flexible and compliant space for choosing treatment technologies. By comprehensively evaluating the pollutant characteristics and production process conditions declared by enterprises, the system can select combinations of treatment technologies with superior treatment effects and reasonable energy consumption and cost from the rule base, and generate recommended treatment technologies accordingly. Consistency verification includes: analyzing whether the enterprise's actual operating equipment falls within the scope of the recommended front-end treatment equipment list in the three-dimensional rule base; obtaining the dynamic operating parameter threshold table for the corresponding equipment in the three-dimensional rule base, and dynamically adjusting the normal operating range of the equipment based on the calibration values ​​of the equipment operating parameters periodically uploaded by the enterprise; verifying whether the operating data of the enterprise's actual operating equipment is within the dynamically adjusted normal operating range; and simultaneously verifying whether the equipment's treatment capacity meets the declared emission requirements.

[0033] The environmental monitoring strategy generation process involves several steps. First, based on potential risk items and the consistency verification results between the enterprise's actual operating equipment and the list of front-end treatment equipment, and considering the distribution of environmentally sensitive points in the discharge area, a risk assessment model is constructed. This model calculates the target enterprise's risk level, generates an environmental monitoring strategy based on the risk level, and adjusts the dynamic detection thresholds for pollutants at the target enterprise. The environmental monitoring strategy generation process relies heavily on potential risk items and consistency verification results, fully considering the distribution density and sensitivity types of environmentally sensitive points in the discharge area to construct a multi-dimensional risk assessment model. This model integrates multiple factors, including pollutant emission risk, equipment operation compliance risk, and environmental impact risk, to achieve a comprehensive assessment of the enterprise's discharge risk. This results in a targeted and operable environmental monitoring strategy, providing technical support for accurate monitoring of discharge behavior and timely detection of environmental hazards.

[0034] The steps for generating an environmental monitoring strategy include: The multi-dimensional risk assessment sub-step involves acquiring potential risk items, consistency verification results, and distribution data of sensitive points in the target company's surrounding environment. A multi-dimensional risk assessment model is then constructed to calculate the target company's risk level. When any potential risk items are omitted from the declaration, a dynamic expansion mechanism for detection factors is triggered to incorporate the omitted items into the detection factors. This sub-step focuses on the scientific determination of risk levels and the initial construction of basic detection strategies. First, detailed information on the target company's potential risk items is systematically collected, including the types of omitted items and the severity of logical conflicts. Simultaneously, verification data from the consistency verification results, covering dimensions such as equipment configuration, operating status, and processing capacity, is organized. Then, data on the distribution location, protection level, and sensitive ecological factors of sensitive points in the company's surrounding environment are collected. Based on this multi-source data, a multi-dimensional risk assessment model is constructed. The model calculates the target company's comprehensive risk level by quantitatively analyzing the weight and impact of each risk factor. Based on the risk level, a preliminary detection strategy is generated. Detection locations prioritize key pollutant emission nodes and areas surrounding environmentally sensitive points. The detection cycle is dynamically adjusted according to the risk level, with shorter cycles for higher risks. Detection factors cover various pollutants and characteristic indicators involved in potential risk items. When an omission in the declaration is identified, a dynamic expansion mechanism for detection factors is triggered. The omitted pollutant type or its key characteristic indicator is added to the initial detection factor list to update the subsequent environmental detection strategy. The omitted pollutant type or its representative chemical characteristic indicators, such as key elements, byproducts, and reaction intermediates, are added as new detection factors to the initial detection factor list. Reasonable detection methods and judgment thresholds are set based on their physicochemical properties to ensure that subsequent environmental detection can cover theoretically derived potential risk pollutants, achieving closed-loop management from risk identification to detection coverage.

[0035] The alternative factor screening sub-step analyzes the chemical properties and migration patterns of difficult-to-detect pollutants among the pollutants to be declared by the target enterprise. It screens out easily detectable alternative detection factors that are strongly correlated with these pollutants and are readily detectable, and establishes a correlation model between these alternative detection factors and the difficult-to-detect pollutants. This sub-step also includes establishing a feature library of difficult-to-detect pollutants, which includes the chemical properties, migration patterns, and detection methods of the pollutants. A potential alternative factor database is constructed, containing the detection costs, detection sensitivity, and chemical correlations of various pollutants. Correlation analysis algorithms are used to calculate the correlation coefficients between difficult-to-detect pollutants and potential alternative factors, and alternative detection factors with correlation coefficients greater than a preset threshold are identified. The alternative factor screening sub-step focuses on effectively monitoring difficult-to-detect pollutants. First, a feature library of difficult-to-detect pollutants is established, systematically recording the chemical stability, solubility, volatility, and other chemical properties of these pollutants; their migration and diffusion paths and transformation rates in water, soil, and atmosphere; and information on the operational difficulty and accuracy limitations of existing detection technologies. Simultaneously, a database of potential alternative factors is constructed, covering the detection costs, sensitivity, chemical bonding relationships, and reaction correlations of various substances that may serve as alternative indicators. Through correlation analysis algorithms, the degree of association between difficult-to-detect pollutants and potential alternative factors is comprehensively calculated. Alternative detection factors with correlation coefficients exceeding preset standards are screened out. These factors must meet the requirements of simple detection operation, controllable cost, and close correlation with the target pollutant. Subsequently, a quantitative correlation model between alternative factors and the target pollutant is established to ensure that the relevant information of the target pollutant can be accurately inferred from the alternative factors.

[0036] The detection optimization sub-step involves incorporating alternative detection factors into the detection factor list and, based on a correlation model, converting these alternative factors into estimated levels of difficult-to-detect pollutants, thus generating an environmental monitoring strategy. This strategy also includes a judgment threshold. The detection optimization sub-step refines and improves the accuracy of the monitoring strategy. Selected alternative detection factors are incorporated into the formal detection factor list, enriching the monitoring indicator system and compensating for the limitations of direct detection of difficult-to-detect pollutants. Utilizing the established correlation model, the alternative detection factor data obtained during the detection process is converted into estimated levels of corresponding difficult-to-detect pollutants, ensuring that the detection results comprehensively reflect the emission status of various pollutants. Simultaneously, the judgment threshold in the environmental monitoring strategy is defined. This threshold is a critical value determined by comprehensively considering factors such as pollutant emission standards, environmental quality targets, and sensitive point protection requirements, used to determine whether pollutant emissions exceed standards and whether environmental risks exist.

[0037] The process also includes an emission effect analysis sub-step. Detection locations include upstream and downstream points of the enterprise's discharge outlet. Initial pollutant concentrations in the water at the upstream point and the concentrations of pollutants to be declared are obtained. The analysis determines whether a chemical reaction occurs between the initial and declared pollutants, and dynamically adjusts the detection strategy at the downstream point based on the type of chemical reaction. The detection strategy includes a judgment threshold. This emission effect analysis sub-step dynamically assesses and adjusts strategies for the environmental impact after pollutant discharge. Detection locations are simultaneously set at upstream and downstream points of the enterprise's discharge outlet. The upstream point detection obtains the types and concentrations of initial pollutants in the water body. Combined with the concentration data of the pollutants to be declared by the enterprise, an in-depth analysis is conducted to determine whether the two types of pollutants will undergo chemical reactions such as oxidation, reduction, and neutralization under natural environmental conditions. Based on the type of chemical reaction, reaction rate, and product toxicity, the detection strategy at the downstream point is dynamically adjusted. This includes optimizing detection factors to increase relevant indicators of reaction products and adjusting judgment thresholds to match the environmental standards of the products, ensuring that downstream detection can accurately capture the actual environmental impact after pollutant discharge.

[0038] The environmental monitoring strategy includes a dynamic threshold adjustment sub-step, which acquires historical monitoring data and geographic feature data for the target company's monitoring locations. Based on this historical data, a historical data model is constructed, along with a dynamic threshold correction model. This calculates the dynamic thresholds for the target company under different environmental conditions. Monitoring data for each location is acquired, and anomaly detection algorithms are used to identify unnatural fluctuations in the monitoring data. When an unnatural fluctuation is detected, encrypted monitoring recommendations are generated. The dynamic threshold adjustment sub-step ensures the adaptability and scientific validity of the monitoring thresholds. Historical monitoring data for each monitoring location of the target company is acquired through a data acquisition system, including pollutant content data under different time periods and environmental conditions. Geographic feature data such as topography, climate conditions, and hydrological characteristics of the monitoring locations are also collected. A historical data model is constructed based on this historical monitoring data to analyze the natural fluctuation patterns and trends of pollutant content. This is then combined with geographic feature data to construct a dynamic threshold correction model. This correction model calculates the dynamic thresholds for the company under different seasons, meteorological conditions, and hydrological conditions, ensuring that the thresholds adapt to natural environmental changes. During actual testing, anomaly detection algorithms are used to monitor the data in real time. When unnatural fluctuations exceeding the natural fluctuation range are detected, encrypted detection suggestions are immediately generated. These suggestions include increasing the detection frequency, refining detection indicators, risk warnings, and identifying anomalies requiring on-site verification, promptly investigating potential risks such as illegal discharge. Natural fluctuations include normal data fluctuations caused by environmental factors that conform to historical statistical patterns and model predictions. Unnatural fluctuations, on the other hand, are those exceeding model predictions, significantly deviating from historical patterns, and potentially caused by abnormal factors such as illegal discharge, equipment malfunction, or data anomalies. The system compares current detection data with dynamic thresholds and uses a machine learning model to identify data points exceeding preset threshold ranges, classifying them as unnatural fluctuations. These unnatural fluctuations indicate potential risks such as illegal emissions, equipment malfunctions, or data distortion, and the system automatically generates encrypted detection suggestions to enhance monitoring and risk assessment.

[0039] A pollution risk monitoring system based on enterprise pollution discharge rights, characterized in that it includes: The basic detection strategy execution module, based on the initial pollutant combination type of the target enterprise, formulates a basic detection strategy for the target enterprise through general standards and industry experience. The basic detection strategy includes the initial detection location, initial detection cycle and initial detection factor. The initial detection strategy is used to conduct preliminary detection on the target enterprise to obtain real-time detection data. The pollution discharge rights information includes industry type, process production flow, pollution discharge area and initial declared pollutant type. The theoretical pollutant adjustment module uses the target enterprise's pollution discharge rights information and actual operating equipment, through a pre-built association database and a derivation strategy, to generate the initial pollutant combination type for the target enterprise. Based on real-time monitoring data, the initial pollutant combination to be tested is adjusted and optimized to generate the pollutant combination type to be tested. The comparison and verification module retrieves the corresponding pollutant type to be tested from the pollutant combination based on the actual operating equipment of the enterprise, and compares the initial declared pollutant type of the target enterprise with the pollutant type to be tested through the comparison and verification strategy to identify and mark potential risk items. The environmental monitoring strategy generation module constructs a risk assessment model based on potential risk items and the consistency verification results of the enterprise's actual operating equipment and front-end treatment equipment list, combined with the distribution of environmentally sensitive points in the sewage discharge area. It calculates the risk level of the target enterprise based on the risk assessment model, generates an environmental monitoring strategy based on the risk level, and adjusts the dynamic detection thresholds of pollutants for the target enterprise.

[0040] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring pollution risk based on enterprise pollution discharge rights, characterized in that, include: The theoretical pollutant adjustment steps, the target company's pollution discharge rights information and actual operating equipment, through a pre-built association database and derivation strategy, generate the initial pollutant combination type for the target company; The pollution discharge rights information includes material information, which includes a raw material list and a discharge list. The derivation strategy includes: a forward derivation sub-step, establishing a mapping relationship between raw material information and pollutants, and extracting characteristic elements or compounds contained in the raw materials as direct pollutants. The indirect derivation sub-step has a pre-set pollutant conversion rule base, which has a pre-set mapping relationship between treatment equipment and by-products. Based on the direct pollutants and the front-end treatment equipment used by the target enterprise, the intermediate products that are bound to exist are generated by chemical reactions, thus constituting indirect pollutants. The derivation sub-steps are merged to obtain direct and indirect pollutants as a pollutant set. Based on the raw material list and the output list, the mass conservation law is used for calculation. Based on the calculation results, the content of each pollutant in the pollutant set is dynamically adjusted to generate the initial pollutant combination type. The basic detection strategy execution steps are as follows: based on the initial pollutant combination type of the target enterprise, a basic detection strategy is formulated for the target enterprise, and real-time detection data is obtained according to the basic detection strategy; The detection data calibration and feedback step involves adjusting and optimizing the initial pollutant combination type based on the real-time detection data to generate the pollutant combination type to be tested. The comparative verification step involves retrieving the corresponding pollutant type from the pollutant combination type to be tested based on the actual operating equipment of the enterprise, and comparing the initial declared pollutant type of the target enterprise with the pollutant type to be tested through the comparative verification strategy to identify and mark potential risk items. The environmental monitoring strategy generation step involves constructing a risk assessment model based on the potential risk items and the consistency verification results of the enterprise's actual operating equipment and front-end treatment equipment list, combined with the distribution of environmentally sensitive points in the discharge area. This model calculates the risk level of the target enterprise and generates an environmental monitoring strategy accordingly, adjusting the dynamic pollutant detection thresholds. The environmental monitoring strategy generation step includes: a multi-dimensional risk assessment sub-step, which obtains the target enterprise's potential risk items, consistency verification results, and distribution data of environmentally sensitive points around the enterprise; constructs a multi-dimensional risk assessment model; and triggers a dynamic expansion mechanism for detection factors when there are omissions in the declaration of potential risk items, incorporating the omissions into the detection factors; and a substitution factor screening sub-step, which analyzes the chemical properties and migration patterns of pollutants that are difficult to detect among the pollutants to be declared by the target enterprise, and screens out those that have similar characteristics to the difficult-to-detect pollutants. The process involves identifying highly correlated and easily detectable alternative detection factors and establishing a correlation model between these factors and difficult-to-detect pollutants. A detection optimization sub-step involves incorporating alternative detection factors into a detection factor list and, based on the correlation model, converting these factors into estimated levels of difficult-to-detect pollutants to generate an environmental detection strategy. This strategy also includes a judgment threshold. A substitute factor screening sub-step further includes establishing a feature library of difficult-to-detect pollutants, which includes the chemical properties, migration and change patterns, and detection methods of the pollutants. A potential substitute factor database is constructed, containing the detection costs, detection sensitivity, and chemical correlations of various pollutants. A correlation analysis algorithm is used to calculate the correlation coefficient between difficult-to-detect pollutants and potential substitute factors, and substitute detection factors with correlation coefficients greater than a preset threshold are identified.

2. The method for monitoring pollution risk based on enterprise pollution discharge rights according to claim 1, characterized in that, The potential risk items include omissions in the declaration and logical conflicts. The comparison and verification strategy includes: The difference retrieval sub-step is used to obtain the initial declared pollutant type and the pollutant type to be tested, and compare them one by one to obtain the difference items between them. The pollutant conversion rule base in the indirect derivation sub-step is called to analyze whether the difference item belongs to the pollutant that can be completely converted. If the difference item can be completely converted, it is judged as reasonable. If the difference item cannot be completely converted, it is defined as the declared omission item. The cross-validation sub-step involves obtaining the initial declared pollutant types and any missing declared items as a set of pollutants to be verified, and obtaining the content of the pollutants to be verified. Based on the actual operating equipment and operating data, the corresponding pollutant type to be tested is retrieved. The pollutant type to be tested includes the range of pollutant content to be tested. The pollutant content to be verified and the range of pollutant content to be tested are compared and verified. If the verification fails, a logical conflict item is generated.

3. The method for monitoring pollution risk based on enterprise pollution discharge rights according to claim 1, characterized in that, It also includes an emission effect analysis sub-step, with detection locations including upstream and downstream points of the enterprise's emission outlet. The initial pollutant content in the water at the upstream point of the emission outlet is obtained, as well as the content of pollutants to be reported. The analysis is performed to determine whether a chemical reaction occurs between the pollutants to be reported and the initial pollutants. The detection strategy at the downstream point of the emission outlet is dynamically adjusted based on the type of chemical reaction. The detection strategy includes a judgment threshold.

4. The method for monitoring pollution risk based on enterprise pollution discharge rights according to claim 1, characterized in that, The environmental detection strategy includes a dynamic threshold adjustment sub-step, which is used to acquire historical detection data and geographic feature data of the target enterprise's detection location, construct a historical data model based on the historical detection data, construct a dynamic threshold correction model, calculate the dynamic threshold of the target enterprise under different environmental conditions, acquire detection data of the detection location, use anomaly detection algorithms to identify unnatural fluctuations in the detection data, and generate encrypted detection suggestions when unnatural fluctuations are identified.

5. The method for monitoring pollution risk based on enterprise pollution discharge rights according to claim 1, characterized in that, This includes a pollution discharge matching step. Based on the initial pollutant types and production processes declared by the target enterprise, a pollution discharge matching algorithm is used to obtain the production nodes and corresponding front-end treatment processes corresponding to each initially declared pollutant. The algorithm then reverse-engineers a list of the necessary front-end treatment equipment for the enterprise and obtains the actual operating equipment and operating data of the enterprise. The consistency between the actual operating equipment and the list of front-end treatment equipment is verified to confirm the actual implementation of the pollution discharge rights.

6. The method for monitoring pollution risk based on enterprise pollution discharge rights according to claim 5, characterized in that, The pollution matching step includes a three-dimensional rule base, which includes pollutant attributes, treatment technologies, and hardware equipment parameters. For each pollutant attribute, there are multiple treatment technologies and their corresponding mapping equipment parameter ranges. Based on the physicochemical properties and emission standards of each pollutant in the initial declaration, recommended treatment technologies are generated, and a list of necessary front-end treatment equipment for the corresponding treatment technologies is obtained. The consistency verification includes analyzing whether the actual operating equipment of the enterprise meets the list of front-end treatment equipment. Obtain the dynamic operating parameter threshold table of the corresponding device in the 3D rule base, and dynamically adjust the normal operating range of the current device based on the calibration values ​​of the device operating parameters uploaded by the enterprise in real time; Verify whether the actual operating data of the equipment is within the dynamically adjusted normal operating range; and verify whether the equipment's processing capacity meets the emission requirements for declaration.

7. A pollution risk monitoring system based on enterprise pollution discharge rights, used to implement the pollution risk monitoring method as described in any one of claims 1 to 6, characterized in that, include: The theoretical pollutant adjustment module uses the target company's pollution discharge rights information and actual operating equipment to generate the initial pollutant combination type for the target company through a pre-built association database and derivation strategy. The basic detection strategy execution module formulates a basic detection strategy for the target enterprise based on the initial pollutant combination type of the target enterprise, and obtains real-time detection data according to the basic detection strategy. The detection data calibration and feedback module adjusts and optimizes the initial pollutant combination type based on the real-time detection data to generate the pollutant combination type to be tested. The comparison and verification module retrieves the corresponding pollutant type from the pollutant combination type to be tested based on the actual operating equipment of the enterprise, and compares the initial declared pollutant type of the target enterprise with the pollutant type to be tested through the comparison and verification strategy to identify and mark potential risk items. The environmental monitoring strategy generation module constructs a risk assessment model based on the potential risk items and the consistency verification results between the enterprise's actual operating equipment and the front-end processing equipment list, combined with the distribution of environmentally sensitive points in the sewage discharge area. It calculates the risk level of the target enterprise, generates an environmental monitoring strategy accordingly, and adjusts the dynamic monitoring thresholds for pollutants.

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