An environmental pollution assessment system and method for volatile environmentally friendly coatings

Abstract

This invention relates to the field of electronic digital data processing technology, and particularly to a system and method for assessing the environmental pollution of volatile environmentally friendly coatings. It addresses the problem of standardizing and fusing heterogeneous multi-source data in the environmental pollution assessment of volatile environmentally friendly coatings to improve the accuracy of quantitative environmental impact analysis. The system includes an intelligent data acquisition unit, a standardization processing unit, a feature extraction unit, an environmental assessment unit, and a result output unit. Based on this system, the invention realizes the entire process of data acquisition, standardization processing, feature extraction, risk assessment, and result output, improving the quality of heterogeneous data fusion and the accuracy of quantitative environmental impact analysis through multi-stage collaboration.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and in particular to an environmental pollution assessment system and method for volatile environmentally friendly coatings. Background Technology

[0002] The Volatile Environmentally Friendly Coating Environmental Pollution Assessment System is an environmental impact analysis tool built on computer data processing technology. This system integrates a multi-source data acquisition module, a pollutant feature extraction module, and an environmental impact assessment module to quantitatively analyze the environmental impact of volatile organic compounds and other pollutants released during the production, construction, and use of volatile environmentally friendly coatings. Its operational logic is as follows: First, the multi-source data acquisition module acquires data on coating composition, environmental background parameters (such as temperature, humidity, and wind speed), and the distribution information of sensitive environmental points in the region through IoT sensors or database interfaces. Second, the pollutant feature extraction module cleans and filters the acquired data using machine learning algorithms to identify the release patterns of major volatile pollutants in the coating. Finally, the environmental impact assessment module combines atmospheric diffusion models and regional environmental capacity data to simulate the migration paths and concentration distribution of pollutants in the atmosphere. By comparing these data with environmental quality standard thresholds, the system determines the environmental pollution risk level under the coating's application scenario.

[0003] The core technical challenge in data processing for the volatile environmental pollution assessment system for coatings lies in the standardized fusion of heterogeneous multi-source data. During system operation, it needs to integrate real-time environmental parameters collected by IoT sensors with coating component data from a structured database. These two types of data exhibit significant heterogeneity in data format, sampling frequency, and dimensional definitions. Without standardization, the machine learning algorithm in the pollutant feature extraction module may misjudge or omit key features during data cleaning and feature selection due to incompatible data formats. This affects the accuracy of simulating pollutant migration paths and concentration distributions, reducing the reliability of the system's assessment of environmental pollution risk levels. For example, when sensors collect ambient temperature data at a frequency of 1 second, if the coating component data only records the daily average temperature, the two types of data cannot be precisely aligned in the time dimension. The algorithm may correlate temperatures from non-corresponding periods with VOC releases, leading to errors in identifying pollutant release patterns. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a system and method for assessing the environmental pollution of volatile environmentally friendly coatings, solving the problem of electronic digital data processing in the standardization and fusion of heterogeneous multi-source data to improve the accuracy of quantitative analysis of environmental impact in the assessment of environmental pollution of volatile environmentally friendly coatings.

[0005] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention provides an environmental pollution assessment system for volatile environmentally friendly coatings, comprising: an intelligent acquisition unit, a standardized processing unit, a feature extraction unit, an environmental assessment unit, and a result output unit;

[0007] The intelligent acquisition unit is equipped with an IoT sensor interface, a structured database interface, and a geographic information system interface. It collects environmental parameters, composition data, and sensitive point data of volatile environmentally friendly coatings. The data quality pre-assessment module dynamically adjusts the acquisition strategy based on the historical error rate of the sensors and the missing field rate of the database, and outputs valid data to the unstructured and structured data pools.

[0008] The standardized processing unit receives environmental parameters, component data, and sensitive point data output by the intelligent acquisition unit, and processes heterogeneity through a dynamic format conversion module, an intelligent time alignment module, and a semantic dimension calibration module to output data in a unified format.

[0009] The feature extraction unit receives uniform format data output by the standardization processing unit, and selects key features after fusion through an attention mechanism using a two-branch machine learning framework.

[0010] The environmental assessment unit receives key features output by the feature extraction unit, simulates pollutant migration paths and concentration distributions using an improved atmospheric diffusion model, combines them with a regional environmental capacity dynamic database, quantifies feature contribution and identifies driving factors through causal inference using the SHAP algorithm, and generates a four-dimensional assessment result including simulated paths, concentration distributions, risk levels, and driving factors.

[0011] The result output unit receives the four-dimensional assessment results output by the environmental assessment unit, distinguishes between R&D personnel, construction parties, or regulatory departments through the user role recognition module, and outputs matching visual animations, structured reports, and decision-making suggestions.

[0012] Furthermore, in the volatile environmentally friendly coating environmental pollution assessment system of the present invention, the intelligent acquisition unit includes an Internet of Things sensor interface, a structured database interface, and a geographic information system interface;

[0013] The IoT sensor interface collects environmental parameters at adjustable frequencies on a second or minute level in real time; the structured database interface periodically acquires coating composition data and sensitive point data at daily or weekly frequencies; and the geographic information system interface dynamically updates sensitive point data synchronously.

[0014] The data quality pre-assessment module receives raw environmental parameters from the IoT sensor interface, raw component data from the structured database interface, and sensitive point data. It obtains the sensor's error rate over the past 30 days and the database field's missing rate over the past 10 days. If the sensor's error rate exceeds 10%, the sampling frequency of the sensor is increased to 0.5 seconds / time to increase data density. If the database field's missing rate exceeds 5%, a manual verification process is triggered to re-transmit the missing component data or sensitive point data. The calibrated environmental parameters are stored in the unstructured data pool, and the calibrated component data and sensitive point data are stored in the structured data pool.

[0015] Furthermore, in the volatile environmentally friendly coating environmental pollution assessment system of the present invention, the standardized processing unit includes a dynamic format conversion module, an intelligent time alignment module, and a semantic dimension calibration module;

[0016] The dynamic format conversion module receives the raw environmental parameters output from the unstructured data pool of the intelligent acquisition unit and the component data output from the structured data pool. It identifies the field correspondence between "T" and "temperature" and "H" and "humidity" through a metadata matching algorithm, generates mapping rules, and converts them into a unified table format.

[0017] The intelligent time alignment module receives unified table data output by the dynamic format conversion module, analyzes the continuous time series of environmental parameters and the discrete time points of component data, calculates the hourly average value of environmental parameters using sliding window aggregation, and calculates the missing time period value of component data using Lagrange interpolation, thus unifying the time granularity of environmental parameters and component data to the hourly level.

[0018] The semantic dimension calibration module receives hourly aligned data output by the intelligent time alignment module, and outputs standardized data with unified field names, time granularity, and dimension definitions based on the domain ontology library and through semantic reasoning.

[0019] Furthermore, in the volatile environmentally friendly coating environmental pollution assessment system of the present invention, the feature extraction unit deploys a dual-branch machine learning framework, and the feature extraction unit includes a basic feature extraction model and a transfer learning module;

[0020] The basic feature extraction model is the XGBoost model, which receives standardized data with unified field names, time granularity, and dimension definitions from the standardized processing unit, and outputs the importance value of each feature in the current data through a feature importance calculation algorithm.

[0021] The transfer learning module accesses the historical task knowledge graph, calculates the feature vector similarity between the current coating and historical coatings based on the cosine similarity algorithm, retrieves historical coatings with a similarity of more than 80%, extracts the importance of their historical features, and transfers the historical weights of the historical features to the current model.

[0022] The attention mechanism weights and fuses the current importance value output by the basic feature extraction model with the historical weights output by the transfer learning module to output dynamic feature importance, and then selects the top 5 key features in descending order of importance value.

[0023] Furthermore, the volatile environmentally friendly coating environmental pollution assessment system of the present invention includes an environmental assessment unit comprising an improved atmospheric diffusion model, a regional environmental capacity dynamic database, and a risk tracing module.

[0024] The improved atmospheric diffusion model receives the top 5 key features selected by the feature extraction unit, the latitude and longitude coordinates of the construction point, and the location data of sensitive points. Based on the atmospheric turbulent diffusion equation and terrain correction parameters, it simulates the migration path of pollutants from the construction point to the sensitive point and the VOCs concentration distribution at each sensitive point.

[0025] The regional environmental capacity dynamic database synchronizes in real time with the environmental quality standard thresholds released by the ecological and environmental departments through an API interface, and provides a threshold comparison basis for the simulation results of the improved atmospheric diffusion model.

[0026] The risk tracing module obtains the VOCs concentration values ​​at sensitive points output by the improved atmospheric diffusion model and the dynamic feature importance values ​​output by the feature extraction unit. It calculates the contribution of each feature to the risk level using the SHAP algorithm, and identifies the main causes of risk by combining causal inference technology. It generates a four-dimensional assessment result including pollutant migration path, sensitive point concentration distribution, risk level, and driving factors.

[0027] Furthermore, in the volatile environmentally friendly coating environmental pollution assessment system of the present invention, the result output unit includes a visualization output module, a structured report module, and a decision suggestion module;

[0028] The visualization output module receives the four-dimensional assessment results output by the environmental assessment unit, overlays the dynamic trajectory of the migration path with the sensitive point layer of the geographic information system, and generates a dynamic diffusion animation.

[0029] Simultaneously, a risk heatmap is generated based on the concentration distribution values ​​and risk levels of sensitive points;

[0030] The structured report module receives the four-dimensional assessment results output by the environmental assessment unit and the dynamic feature importance values ​​output by the feature extraction unit, and generates a feature contribution table and driving factor analysis conclusions.

[0031] The decision suggestion module receives the identification results from the user role recognition module and the driving factor information from the four-dimensional evaluation results, and outputs matching information: if the user is a R&D personnel, it outputs historical knowledge transfer suggestions based on the adjustment experience of highly similar coatings in the historical task knowledge graph; if the user is a construction party, it outputs prevention and control suggestions based on the pollutant migration path and the location of sensitive points; if the user is a regulatory department, it outputs a risk level summary table based on the risk level of all coating use scenarios in the region.

[0032] Secondly, the present invention provides a method for assessing the environmental pollution of volatile environmentally friendly coatings, based on the aforementioned environmental pollution assessment system for volatile environmentally friendly coatings, comprising:

[0033] Step 1: Collect environmental parameters, composition data and sensitive point data of volatile environmentally friendly coatings, and dynamically adjust the collection strategy based on the historical error rate of sensors and the missing field rate of database through the data quality pre-assessment module, and output valid data to the unstructured and structured data pools.

[0034] Step 2: Receive environmental parameters, component data, and sensitive point data; process heterogeneity through a dynamic format conversion module, an intelligent time alignment module, and a semantic dimension calibration module; and output data in a unified format.

[0035] Step 3: Receive data in a unified format, and filter key features by fusing them through an attention mechanism using a two-branch machine learning framework.

[0036] Step 4: Receive key features, simulate pollutant migration paths and concentration distribution using an improved atmospheric diffusion model, combine with a regional environmental capacity dynamic database, quantify feature contribution and identify driving factors through causal inference using the SHAP algorithm, and generate a four-dimensional assessment result including simulated path, concentration distribution, risk level and driving factors.

[0037] Step 5: Receive the four-dimensional assessment results, distinguish between R&D personnel, construction parties, or regulatory departments through the user role recognition module, and output matching visual animations, structured reports, and decision-making suggestions.

[0038] Beneficial effects of this invention;

[0039] This invention addresses the issues of scattered data sources and unstable data quality by dynamically calibrating and classifying multi-source data through an intelligent acquisition unit, providing effective input for subsequent processing. The standardization processing unit, through dynamic format conversion, intelligent time alignment, and semantic dimension calibration, resolves compatibility issues in format, time, and dimension definitions for heterogeneous data, improving data consistency and usability. The feature extraction unit employs a dual-branch machine learning framework, combining current data features with historical task knowledge to screen key features strongly correlated with VOCs release risk, resolving feature selection bias in small sample scenarios and improving the accuracy of feature-risk correlation. The environmental assessment unit, based on key features, calls an improved atmospheric diffusion model to simulate pollutant migration paths and sensitive point concentrations. Combined with real-time synchronized environmental capacity thresholds, the SHAP algorithm, and causal inference techniques, it analyzes risk causes, achieving scientific simulation of pollution diffusion and accurate identification of risk-driving factors. The results output unit outputs dynamic diffusion animations, feature contribution tables, and customized suggestions based on user roles, transforming complex assessment data into understandable and actionable information, enhancing the system's decision support value. The synergistic effect of these technical components effectively improves the accuracy and practicality of quantitative environmental impact analysis in the environmental pollution assessment of volatile environmentally friendly coatings. Attached Figure Description

[0040] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0041] Figure 1 A flowchart of an environmental pollution assessment method for volatile environmentally friendly coatings provided in an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of this invention, it will be described in further detail below.

[0043] In a first aspect, the present invention provides an environmental pollution assessment system for volatile environmentally friendly coatings, comprising: an intelligent acquisition unit, a standardized processing unit, a feature extraction unit, an environmental assessment unit, and a result output unit;

[0044] The intelligent acquisition unit is equipped with an IoT sensor interface, a structured database interface, and a geographic information system interface. It collects environmental parameters, composition data, and sensitive point data of volatile environmentally friendly coatings. The data quality pre-assessment module dynamically adjusts the acquisition strategy based on the historical error rate of the sensors and the missing field rate of the database, and outputs valid data to the unstructured and structured data pools.

[0045] The standardized processing unit receives environmental parameters, component data, and sensitive point data output by the intelligent acquisition unit, and processes heterogeneity through a dynamic format conversion module, an intelligent time alignment module, and a semantic dimension calibration module to output data in a unified format.

[0046] The feature extraction unit receives uniform format data output by the standardization processing unit, and selects key features after fusion through an attention mechanism using a two-branch machine learning framework.

[0047] The environmental assessment unit receives key features output by the feature extraction unit, simulates pollutant migration paths and concentration distributions using an improved atmospheric diffusion model, combines them with a regional environmental capacity dynamic database, quantifies feature contribution and identifies driving factors through causal inference using the SHAP algorithm, and generates a four-dimensional assessment result including simulated paths, concentration distributions, risk levels, and driving factors.

[0048] The result output unit receives the four-dimensional assessment results output by the environmental assessment unit, distinguishes between R&D personnel, construction parties, or regulatory departments through the user role recognition module, and outputs matching visual animations, structured reports, and decision-making suggestions.

[0049] This invention provides an environmental pollution assessment system for volatile environmentally friendly coatings. Its technical solution achieves standardized fusion and quantitative analysis of environmental impact from heterogeneous, multi-source data through multi-unit collaboration. The specific implementation logic and subordinate technical solutions of each unit are as follows:

[0050] The intelligent data acquisition unit collects multi-source data and performs dynamic calibration through multiple interfaces. This unit is equipped with IoT sensor interfaces (such as RS485 and LoRa wireless interfaces), structured database interfaces (such as JDBC database connection interfaces and RESTful API interfaces), and Geographic Information System (GIS) interfaces (such as OGC WFS service interfaces). These interfaces are used for real-time acquisition of environmental parameters (second-level / minute-level data such as temperature, humidity, and wind speed), periodic acquisition of paint composition data (daily-level / weekly data such as VOCs types and proportions), and synchronous acquisition of dynamic data from sensitive points (coordinates of schools and residential areas, etc.). The data quality pre-assessment module analyzes the error distribution of sensors over the past 30 days (such as the mean and standard deviation of temperature sensor errors) and the missing frequency of database fields over the past 10 days (such as the number of missing records in the VOCs percentage field / total number of records). For sensors with an error rate exceeding 10% (such as temperature sensors with an error mean > 2℃), the sampling frequency is automatically increased to 0.5 seconds / time to increase data density. For database fields with a missing rate exceeding 5% (such as VOCs percentage field missing frequency > 5%), a manual verification process is triggered (such as sending a re-upload notification to the data administrator). Finally, the calibrated environmental parameters (including high-density sampling data) are stored in an unstructured data pool (such as Hadoop HDFS), while the component data and sensitive point data are stored in a structured data pool (such as a MySQL relational database).

[0051] The standardization processing unit performs triple calibration of multi-source data in terms of format, time, and semantics. The dynamic format conversion module receives raw environmental parameters (such as sensor messages "T:25,H:60") from the unstructured data pool and component data (such as database table "Temperature:25℃, Humidity:60%) from the structured data pool. It identifies the correspondence between "T" and "temperature" and "H" and "humidity" through a metadata matching algorithm (based on field name similarity and data type consistency calculation), generates mapping rules, and converts them into a unified table format (fields: time, temperature, humidity). The intelligent time alignment module receives the data from the table, analyzes the continuous time series of environmental parameters (e.g., 60 second-level data points from 08:00:00 to 08:00:59) and the discrete time points of component data (e.g., one daily-level data point at 08:00:00), uses sliding window aggregation (60-second window size, calculating hourly averages) for environmental parameters, and uses Lagrange interpolation (calculating missing time period values ​​based on data from 08:00 the previous day and 09:00 the current day) for component data, unifying the time granularity of the two types of data to the hourly level. The semantic dimension calibration module receives hourly aligned data and, based on the domain ontology library (predefined as "VOCs" associated with "alkanes" and "aromatics", and "temperature" associated with concepts such as "℃" and "℉"), converts "temperature: 77℉" to "temperature: 25℃" through semantic reasoning (unit calibration) and calibrates "VOCs: benzene series" to "aromatic VOCs" (classification calibration). It outputs standardized data with unified field names, time granularity, and dimension definitions (e.g., fields are time, temperature (℃), and percentage of aromatic VOCs (%)).

[0052] The feature extraction unit filters key features using a two-branch machine learning framework. The basic feature extraction model employs the XGBoost algorithm, receiving hourly standardized data (such as time, temperature, and aromatic VOCs percentage of a batch of water-based coatings) from the standardization processing unit. It calculates the importance value of each feature using Gini impurity (e.g., temperature importance 0.35, aromatic VOCs percentage 0.42). The transfer learning module accesses the historical task knowledge graph (stores feature-risk association experience from 500 past coating evaluations, including coating type, feature set, and corresponding importance weights). Based on the cosine similarity algorithm, it calculates the feature vector similarity between the current coating and historical coatings (e.g., the similarity between the current acrylic coating and historical acrylic coatings is 85%). It retrieves historical coatings with similarity exceeding 80%, extracts their historical feature importance (e.g., the aromatic VOCs percentage importance in historical data is 0.45), and transfers this weight to the current model using knowledge distillation techniques (e.g., teacher-student model training, transferring knowledge from the historical model to the current model). The attention mechanism performs a weighted fusion of the current importance value of the base model and the historical weights of the transfer module (the weight coefficient is dynamically adjusted by similarity, such as 0.6 for a similarity of 85%), outputs the importance of dynamic features (such as the importance of the proportion of aromatic VOCs increases to 0.38), and selects the top 5 key features in descending order of importance value (such as the proportion of aromatic VOCs, humidity, temperature, distance to sensitive points, and wind speed).

[0053] The environmental assessment unit simulates pollution diffusion and traces source risks based on key features. The improved atmospheric diffusion model receives the top 5 key features selected by the feature extraction unit (such as 35% aromatic VOCs, 60% humidity, 25℃ temperature, 800m distance to sensitive points, and 2m / s wind speed), as well as the latitude and longitude coordinates of the construction site (such as 116.4°E, 39.9°N) and the location data of sensitive points (such as the school's coordinates 116.41°E, 39.92°N). Based on atmospheric turbulent diffusion equations (such as the Gaussian plume model) and terrain correction parameters (such as building height and surface roughness), it simulates the migration path of pollutants from the construction site to sensitive points (such as the trajectory of diffusion to the upwind direction of the school within 1 hour) and the VOCs concentration distribution at each sensitive point (such as 0.9mg / m³ at the school and 0.7mg / m³ at the residential area). The regional environmental capacity dynamic database synchronizes environmental quality standard thresholds (such as the hourly average VOCs limit of 1.0 mg / m³) in real time through API interfaces (such as the REST API of the ecological and environmental department's data platform), providing threshold comparison basis for simulation results (such as determining that the 0.9 mg / m³ at the school does not exceed the standard). The risk tracing module obtains the sensitive point concentration values ​​(such as 0.9 mg / m³ at the school) and the dynamic feature importance values ​​of the feature extraction unit (such as the importance of the proportion of aromatic VOCs being 0.38) from the simulation output. It calculates the contribution value of each feature to the risk level through the SHAP algorithm (such as the contribution of the proportion of aromatic VOCs being +0.3 level and the contribution of humidity being -0.1 level). Combined with causal inference technology (such as Do-Calculus), it identifies the main causes of risk (such as determining that "the excessive proportion of aromatic VOCs is the core driving factor of the current low risk"). It generates a four-dimensional assessment result that includes pollutant migration path (dynamic trajectory), sensitive point concentration distribution (concentration value of each sensitive point), risk level (low / medium / high), and driving factors (such as "proportion of aromatic VOCs").

[0054] The results output unit outputs customized information based on the user's role. The visualization output module receives the four-dimensional assessment results, overlays the dynamic trajectory of pollutant migration paths onto the GIS sensitive point layer (overlaying school and residential area locations), and generates a dynamic diffusion animation (e.g., showing the process of pollutants spreading from the construction site to the upwind direction of the school within 1 hour); simultaneously, based on the sensitive point concentration distribution values ​​and risk levels (low / medium / high), it generates a risk heatmap (color gradients represent risk levels, such as green for low risk and red for high risk). The structured report module receives the four-dimensional assessment results and dynamic feature importance values, and generates a feature contribution table (listing the SHAP values ​​of each key feature, such as aromatic VOCs proportion at level 0.3 and humidity at level -0.1) and driving factor analysis conclusions (e.g., "For every 5% increase in the proportion of aromatic VOCs, the risk level increases by 0.15 levels"). The decision-making suggestion module receives user role identification results (R&D personnel / construction party / regulatory department) and driving factor information, and outputs matching information: If the user is an R&D personnel, based on the adjustment experience of highly similar coatings in the historical task knowledge graph (such as the risk level of acrylic coatings decreasing by 0.3 after the proportion of aromatics is reduced to 25%), it outputs historical knowledge transfer suggestions (such as "It is recommended to reduce the proportion of aromatic VOCs from 35% to 25%)); if the user is a construction party, based on the pollutant migration path and the location of sensitive points (such as the spread of pollutants to schools), it outputs prevention and control suggestions (such as "It is recommended to maintain a wind speed > 3m / s during construction to accelerate the spread of pollutants"); if the user is a regulatory department, based on the risk level of all coating use scenarios in the region, it outputs a risk level summary table (listing the proportion of high / medium / low risk in each region).

[0055] Specifically, the volatile environmentally friendly coating environmental pollution assessment system of the present invention includes an intelligent acquisition unit comprising an Internet of Things sensor interface, a structured database interface, and a geographic information system interface.

[0056] The IoT sensor interface collects environmental parameters at adjustable frequencies on a second or minute level in real time; the structured database interface periodically acquires coating composition data and sensitive point data at daily or weekly frequencies; and the geographic information system interface dynamically updates sensitive point data synchronously.

[0057] The data quality pre-assessment module receives raw environmental parameters from the IoT sensor interface, raw component data from the structured database interface, and sensitive point data. It obtains the sensor's error rate over the past 30 days and the database field's missing rate over the past 10 days. If the sensor's error rate exceeds 10%, the sampling frequency of the sensor is increased to 0.5 seconds / time to increase data density. If the database field's missing rate exceeds 5%, a manual verification process is triggered to re-transmit the missing component data or sensitive point data. The calibrated environmental parameters are stored in the unstructured data pool, and the calibrated component data and sensitive point data are stored in the structured data pool.

[0058] The intelligent acquisition unit described in this invention achieves dynamic acquisition and calibration of multi-source data through multi-interface collaboration. Its specific implementation logic and lower-level technical solutions are as follows:

[0059] The intelligent acquisition unit is equipped with three types of data interfaces to cover data from different sources. The IoT sensor interface uses industrial-grade communication protocols (such as RS485 and LoRa wireless interfaces) to collect environmental parameters (including temperature, humidity, and wind speed) in real time. It supports adjustable sampling frequencies at the second level (e.g., 1 second / time) or the minute level (e.g., 1 minute / time) to meet the high-frequency change requirements of environmental parameters in paint production and application scenarios. The structured database interface uses standardized data connection protocols (such as JDBC and RESTful API interfaces) to periodically acquire paint composition data (e.g., VOC types, percentages, and solvent types) and sensitive point data (e.g., latitude and longitude coordinates of schools and residential areas). The data acquisition frequency is set to daily (e.g., daily synchronization at 08:00) or weekly (e.g., weekly synchronization at 08:00 every Monday) according to business needs, ensuring the timeliness of composition data and sensitive point information. The Geographic Information System (GIS) interface adopts the Open Geospatial Consortium (OGC) standard service interface (such as the WFS Network Feature Service Interface) to synchronize the dynamic update data of sensitive points (such as the location change information of newly added schools and residential areas), so as to enable the system to obtain the latest distribution of sensitive points in the region in real time.

[0060] The data quality pre-assessment module calibrates data through statistical analysis. This module receives raw environmental parameters from IoT sensor interfaces (e.g., the raw message "T:25.3" from a temperature sensor), raw component data from structured database interfaces (e.g., the "VOCs percentage: 35%" field in the database), and sensitive point data (e.g., "school coordinates: 116.4°E, 39.9°N"). It then calculates the sensor error rate over the past 30 days and the database field missing rate over the past 10 days. The sensor error rate is calculated by statistically analyzing the distribution of deviations between historical sensor measurements and standard values ​​(e.g., the average difference between temperature sensor measurements and standard thermometer measurements over the past 30 days is 2.1℃, with a standard deviation of 0.5℃). The database field missing rate is calculated by statistically analyzing the ratio of the number of missing records to the total number of records (e.g., the VOCs percentage field has 100 records in the past 10 days, with 5 missing records, resulting in a missing rate of 5%). If the sensor error rate exceeds 10% (e.g., the average error of a temperature sensor is >2℃), the sampling frequency of the sensor will be automatically increased from 1 second / time to 0.5 seconds / time to reduce the impact of error on data quality by increasing the sampling density. If the database field missing rate exceeds 5% (e.g., the missing rate of the VOCs percentage field is 6%), the manual verification process will be triggered (e.g., a field missing notification will be sent to the data administrator, prompting them to re-upload the missing component data or sensitive point data).

[0061] The calibrated data is stored according to its type. The calibrated environmental parameters (such as high-density temperature and humidity data collected after increasing the sampling frequency) are stored in an unstructured data pool (such as the Hadoop HDFS distributed file system) to support efficient storage and subsequent processing of unstructured raw data; the calibrated component data (such as the VOCs percentage field after retransmission) and sensitive point data (such as the updated school coordinates) are stored in a structured data pool (such as a MySQL relational database) to support fast querying and correlation analysis of structured data.

[0062] Through the above-mentioned technical solutions of multi-interface collaborative acquisition, dynamic data quality assessment and classified storage, the intelligent acquisition unit has realized the effective acquisition and calibration of multi-source data, providing basic data with complete format and time alignment for the subsequent standardized processing unit, and realizing the prerequisite for heterogeneous data fusion.

[0063] Specifically, the volatile environmentally friendly coating environmental pollution assessment system of the present invention includes a standardized processing unit comprising a dynamic format conversion module, an intelligent time alignment module, and a semantic dimension calibration module.

[0064] The dynamic format conversion module receives the raw environmental parameters output from the unstructured data pool of the intelligent acquisition unit and the component data output from the structured data pool. It identifies the field correspondence between "T" and "temperature" and "H" and "humidity" through a metadata matching algorithm, generates mapping rules, and converts them into a unified table format.

[0065] The intelligent time alignment module receives unified table data output by the dynamic format conversion module, analyzes the continuous time series of environmental parameters and the discrete time points of component data, calculates the hourly average value of environmental parameters using sliding window aggregation, and calculates the missing time period value of component data using Lagrange interpolation, thus unifying the time granularity of environmental parameters and component data to the hourly level.

[0066] The semantic dimension calibration module receives hourly aligned data output by the intelligent time alignment module, and outputs standardized data with unified field names, time granularity, and dimension definitions based on the domain ontology library and through semantic reasoning.

[0067] The standardization processing unit described in this invention achieves standardized fusion of heterogeneous multi-source data through the collaborative efforts of three modules: dynamic format conversion, intelligent time alignment, and semantic dimension calibration. Its specific implementation logic and underlying technical solutions are as follows:

[0068] The dynamic format conversion module is responsible for resolving the issue of heterogeneous formats in multi-source data. This module receives raw environmental parameters (such as the message "T:25,H:60" sent by an RS485 sensor, where "T" represents temperature and "H" represents humidity) from the unstructured data pool of the intelligent acquisition unit (such as raw environmental parameters stored in Hadoop HDFS), and component data (such as structured records like "Temperature:25℃, Humidity:60%" in a database table) from the structured data pool (such as component data stored in a MySQL database). The module identifies the field correspondence between unstructured and structured data using a metadata matching algorithm (based on semantic similarity of field names and consistency of data types). For example, it analyzes the "T" field in the unstructured message and the "Temperature" field in the structured data, determining that "T" corresponds to "Temperature" through string similarity matching (e.g., the initial letter match rate of "T" and "Temperature" reaches 80%) and data type verification (both are numerical); similarly, it identifies "H" as "Humidity". Based on this mapping rule, the module converts unstructured environmental parameters and structured component data into a unified table format (field examples: timestamp, temperature, humidity), solving the problem of data format incompatibility.

[0069] The intelligent time alignment module calibrates against differences in time granularity among multi-source data. This module receives unified tabular data (such as a mixed time granularity table containing second-level environmental parameters and daily-level component data) output by the dynamic format conversion module, and analyzes the continuous time series of environmental parameters (such as 60 second-level temperature data points from 08:00:00 to 08:00:59) and discrete time points of component data (such as only one daily-level temperature data point at 08:00:00). For continuous time series environmental parameters, the module employs a sliding window aggregation method (window size of 3600 seconds, step size of 3600 seconds) to calculate the average value within each hourly window (e.g., the average value of 60 second-level temperature data points within the hour of 08:00 is 25.3℃). For discrete time point component data, the module uses Lagrange interpolation (based on temperature data from 08:00 the previous day and 09:00 the current day) to estimate the missing temperature value at 08:00 the current day (e.g., based on 24℃ at 08:00 the previous day and 26℃ at 09:00 the current day, the estimated temperature at 08:00 is 25℃). Through these processes, the module unifies the time granularity of environmental parameters and component data to the hourly level (e.g., 08:00, 09:00, etc.), resolving the time dimension mismatch issue.

[0070] The semantic dimension calibration module achieves data dimension unification through domain knowledge. This module receives hourly aligned data (e.g., records with timestamps of 08:00, temperatures of 25.3℃, and humidity of 60%) from the intelligent time alignment module. Based on a pre-built domain ontology (containing a semantic relationship graph of "VOCs" associated with sub-concepts such as "alkanes," "aromatics," and "benzene compounds," and "temperature" associated with unit concepts such as "℃" and "℉"), it performs semantic reasoning. For example, for the input record "temperature: 77℉," the module converts it to "temperature: 25℃" using the conversion relationship between "temperature" and "℃" and "℉" in the ontology (1℉≈-17.22℃+0.555×℉); for the record "VOCs: benzene compounds," the module calibrates it to "aromatic VOCs: benzene compounds" based on the classification relationship of "benzene compounds" belonging to "aromatic VOCs" in the ontology. Ultimately, the module outputs standardized data with consistent field names (such as "Time", "Temperature (°C)", "Percentage of Aromatic VOCs (%)"), time granularity (hourly), and dimension definitions (such as units uniformly set to °C and categories uniformly set to aromatics), thus resolving the issue of inconsistent data dimension definitions.

[0071] The standardized processing unit achieves the standardized fusion of multi-source heterogeneous data through a collaborative approach: a dynamic format conversion module to address format heterogeneity, an intelligent time alignment module to address time granularity differences, and a semantic dimension calibration module to resolve dimension definition conflicts. This provides high-quality input data with unified format, time alignment, and consistent dimensions for the subsequent feature extraction unit.

[0072] Specifically, in the volatile environmentally friendly coating environmental pollution assessment system of the present invention, the feature extraction unit deploys a dual-branch machine learning framework, and the feature extraction unit includes a basic feature extraction model and a transfer learning module.

[0073] The basic feature extraction model is the XGBoost model, which receives standardized data with unified field names, time granularity, and dimension definitions from the standardized processing unit, and outputs the importance value of each feature in the current data through a feature importance calculation algorithm.

[0074] The transfer learning module accesses the historical task knowledge graph, calculates the feature vector similarity between the current coating and historical coatings based on the cosine similarity algorithm, retrieves historical coatings with a similarity of more than 80%, extracts the importance of their historical features, and transfers the historical weights of the historical features to the current model.

[0075] The attention mechanism weights and fuses the current importance value output by the basic feature extraction model with the historical weights output by the transfer learning module to output dynamic feature importance, and then selects the top 5 key features in descending order of importance value.

[0076] The feature extraction unit of this invention achieves accurate selection of key features through a dual-branch machine learning framework. Its specific implementation logic and underlying technical solutions are as follows:

[0077] The basic feature extraction model processes the current task data based on the XGBoost algorithm. This model receives standardized data (e.g., hourly records containing fields such as time, temperature (°C), percentage of aromatic VOCs (%), humidity (%), distance to sensitive points (meters), and wind speed (m / s)) from the standardized processing unit, where field names, time granularity, and dimensional definitions are all consistent. It then analyzes the contribution of each feature to the target variable (e.g., VOCs emission or risk level) using a feature importance calculation algorithm (e.g., the Gini impurity calculation method built into XGBoost). During model training, the input data is first imputed (e.g., filling missing temperature values ​​with the mean) and normalized (e.g., normalizing humidity values ​​from 0-100% to the 0-1 range). Then, iterative training (e.g., setting a maximum depth of 6 and a learning rate of 0.1) optimizes model performance, ultimately outputting the importance values ​​of each feature in the current data (e.g., temperature importance 0.35, aromatic VOCs percentage importance 0.42, humidity importance 0.28).

[0078] The transfer learning module supplements the feature weights for small-sample scenarios with a historical task knowledge graph. This module accesses a pre-built historical task knowledge graph (stores feature-risk correlation experience from 500 past coating evaluations; the data structure includes coating type (e.g., acrylic, alkyd), feature set (e.g., temperature, percentage of aromatic VOCs), corresponding feature importance weights, and risk level results), and calculates the feature vector similarity between the current coating and historical coatings based on a cosine similarity algorithm. The feature vector construction selects features strongly correlated with VOC release (e.g., percentage of aromatic VOCs, temperature, humidity, distance to sensitive points, wind speed), and vectorizes the feature values ​​of the current coating and historical coatings respectively (e.g., the feature vector of the current acrylic coating is [35%, 25℃, 60%, 800m, 2m / s], and the feature vector of historical acrylic coatings is [30%, 24℃, 55%, 750m, 2.5m / s]), calculating the cosine similarity between the two vectors (e.g., a similarity of 0.85). If the similarity exceeds 80%, the feature importance weight of the historical coating is retrieved (e.g., the importance of aromatic VOCs in historical data is 0.45), and the weight is transferred to the current model through knowledge distillation technology (e.g., using the historical model as the teacher model and the current model as the student model, and transferring knowledge through soft labels).

[0079] The attention mechanism improves feature selection accuracy through dynamic weighted fusion. This mechanism receives the current importance value (e.g., temperature 0.35, aromatic VOCs percentage 0.42) from the basic feature extraction model and the historical weights (e.g., aromatic VOCs percentage 0.45) from the transfer learning module. It dynamically adjusts the fusion weights based on the similarity between the current and historical tasks (e.g., a similarity of 0.85 corresponds to a historical weight coefficient of 0.6 and a current weight coefficient of 0.4). The fusion calculation uses a weighted average method (e.g., the dynamic importance of aromatic VOCs percentage = 0.42 × 0.4 + 0.45 × 0.6 = 0.438), ultimately outputting the dynamic importance values ​​of each feature (e.g., aromatic VOCs percentage 0.438, temperature 0.32, humidity 0.25). The module sorts features in descending order of dynamic importance (e.g., percentage of aromatic VOCs > temperature > humidity > distance to sensitive point > wind speed), and selects the top 5 key features (e.g., percentage of aromatic VOCs, temperature, humidity, distance to sensitive point, wind speed) as inputs for subsequent environmental assessment units.

[0080] By leveraging the collaborative efforts of extracting current data features from the base model, supplementing historical experience through the transfer module, and dynamically integrating the attention mechanism, the feature extraction unit addresses the issue of feature selection bias in small sample scenarios. This improves the accuracy of the correlation between key features and VOCs release risk, providing the environmental assessment unit with input features strongly correlated with risk levels.

[0081] Specifically, the volatile environmentally friendly coating environmental pollution assessment system of the present invention includes an environmental assessment unit comprising an improved atmospheric diffusion model, a regional environmental capacity dynamic database, and a risk tracing module.

[0082] The improved atmospheric diffusion model receives the top 5 key features selected by the feature extraction unit, the latitude and longitude coordinates of the construction point, and the location data of sensitive points. Based on the atmospheric turbulent diffusion equation and terrain correction parameters, it simulates the migration path of pollutants from the construction point to the sensitive point and the VOCs concentration distribution at each sensitive point.

[0083] The regional environmental capacity dynamic database synchronizes in real time with the environmental quality standard thresholds released by the ecological and environmental departments through an API interface, and provides a threshold comparison basis for the simulation results of the improved atmospheric diffusion model.

[0084] The risk tracing module obtains the VOCs concentration values ​​at sensitive points output by the improved atmospheric diffusion model and the dynamic feature importance values ​​output by the feature extraction unit. It calculates the contribution of each feature to the risk level using the SHAP algorithm, and identifies the main causes of risk by combining causal inference technology. It generates a four-dimensional assessment result including pollutant migration path, sensitive point concentration distribution, risk level, and driving factors.

[0085] The environmental assessment unit described in this invention, through the collaboration of an improved atmospheric diffusion model, a regional environmental capacity dynamic database, and a risk tracing module, achieves pollutant diffusion simulation and risk cause analysis. Its specific implementation logic and subordinate technical solutions are as follows:

[0086] An improved atmospheric diffusion model simulates pollutant migration and concentration distribution based on multi-source input data. The model receives the top five key features selected by the feature extraction unit (e.g., 35% aromatic VOCs, temperature 25°C, humidity 60%, sensitive point distance 800 meters, wind speed 2 m / s), as well as the latitude and longitude coordinates of the construction site (e.g., 116.4°E, 39.9°N) and the location data of sensitive points (e.g., school coordinates 116.41°E, 39.92°N, residential area coordinates 116.39°E, 39.91°N). The model employs an improved atmospheric turbulent diffusion equation (e.g., adding a terrain correction term to the classic Gaussian plume model), combined with terrain correction parameters (e.g., average building height around the construction site 15 meters, surface roughness 0.5 meters) to simulate the migration path of pollutants from the construction site to each sensitive point (e.g., the trajectory of diffusion to 200 meters upwind of the school within 1 hour) and the VOCs concentration distribution at each sensitive point (e.g., 0.9 mg / m³ at the school, 0.7 mg / m³ at the residential area). During the simulation, the model considers meteorological parameters such as atmospheric stability (e.g., neutral stability level D) and mixing layer height (e.g., 800 meters) to ensure that the simulation results conform to the actual atmospheric diffusion patterns.

[0087] The regional environmental capacity dynamic database provides threshold comparison criteria through real-time data synchronization. This database connects to external data sources via standardized API interfaces (such as the REST API of the ecological and environmental department's data platform) to synchronize environmental quality standard thresholds in real time (such as the hourly average VOCs limit of 1.0 mg / m³ specified in the "Ambient Air Quality Standard" GB 3095-2012). The database stores thresholds for different regional types, including but not limited to Level I standards (applicable to nature reserves) and Level II standards (applicable to residential areas). When the improved atmospheric diffusion model outputs VOCs concentration values ​​at various sensitive points (e.g., 0.9 mg / m³ at a school), the database provides threshold comparison services for the simulation results (e.g., if the school is in a residential area, the corresponding Level II standard limit is 1.0 mg / m³, and 0.9 mg / m³ is considered within the limit), providing data support for subsequent risk level determination.

[0088] The risk tracing module identifies the main causes of risk and generates a four-dimensional assessment result through the SHAP algorithm and causal inference technology. This module obtains the VOCs concentration values ​​at sensitive points (e.g., 0.9 mg / m³ for schools, 0.7 mg / m³ for residential areas) output by the improved atmospheric diffusion model and the dynamic feature importance values ​​(e.g., importance of aromatic VOCs proportion 0.38, importance of temperature 0.30) output by the feature extraction unit. It then calculates the contribution of each feature to the risk level using the SHAP algorithm (e.g., a 5% increase in aromatic VOCs proportion raises the risk level by 0.15 levels; a 1°C increase in temperature raises the risk level by 0.1 levels). Combined with causal inference technology (e.g., the Do-Calculus method), the module analyzes the causal relationship between features and risk levels (e.g., determining that "excessively high aromatic VOCs proportion" is a direct driving factor for the risk level, while "increased temperature" is an indirect influencing factor). Ultimately, the module generates a four-dimensional assessment result that includes pollutant migration paths (such as a 1-hour diffusion trajectory map), concentration distribution at sensitive points (a list of concentration values ​​at each sensitive point), risk level (low / medium / high, such as low risk for schools), and driving factors (such as "the proportion of aromatic VOCs").

[0089] By employing an improved atmospheric diffusion model to simulate pollution diffusion, a regional environmental capacity dynamic database to provide threshold comparisons, and a risk tracing module to analyze driving factors, the environmental assessment unit achieves a full-process analysis from pollution diffusion simulation to risk cause analysis. This provides the results output unit with multi-dimensional assessment data including pathways, concentrations, levels, and driving factors, supporting the decision-making needs of different user roles.

[0090] Specifically, the volatile environmentally friendly coating environmental pollution assessment system of the present invention includes a result output unit comprising a visualization output module, a structured report module, and a decision suggestion module.

[0091] The visualization output module receives the four-dimensional assessment results output by the environmental assessment unit, overlays the dynamic trajectory of the migration path with the sensitive point layer of the geographic information system, and generates a dynamic diffusion animation.

[0092] Simultaneously, a risk heatmap is generated based on the concentration distribution values ​​and risk levels of sensitive points;

[0093] The structured report module receives the four-dimensional assessment results output by the environmental assessment unit and the dynamic feature importance values ​​output by the feature extraction unit, and generates a feature contribution table and driving factor analysis conclusions.

[0094] The decision suggestion module receives the identification results from the user role recognition module and the driving factor information from the four-dimensional evaluation results, and outputs matching information: if the user is a R&D personnel, it outputs historical knowledge transfer suggestions based on the adjustment experience of highly similar coatings in the historical task knowledge graph; if the user is a construction party, it outputs prevention and control suggestions based on the pollutant migration path and the location of sensitive points; if the user is a regulatory department, it outputs a risk level summary table based on the risk level of all coating use scenarios in the region.

[0095] The result output unit described in this invention achieves multi-dimensional presentation and customized output of evaluation results through the collaborative efforts of three modules: visualization output, structured report, and decision suggestions. Its specific implementation logic and underlying technical solutions are as follows:

[0096] The visualization output module generates intuitive spatial-risk visualization information based on the four-dimensional assessment results. This module receives the four-dimensional assessment results (including dynamic trajectories of pollutant migration paths, concentration distribution values ​​at sensitive points, risk levels, and driving factors) from the environmental assessment unit. It then calls a Geographic Information System (GIS) service interface (such as the ArcGIS API) to obtain sensitive point layer data (including the location, name, and area type of sensitive points such as schools and residential areas). The module overlays the dynamic trajectory of pollutant migration paths (e.g., animation frames showing the diffusion from the construction site (116.4°E, 39.9°N) to the school (116.41°E, 39.92°N) within one hour onto the sensitive point layer to generate a dynamic diffusion animation (supporting dragging the timeline to view the diffusion status at different times). Simultaneously, based on the concentration distribution values ​​at sensitive points (e.g., 0.9 mg / m³ at schools, 0.7 mg / m³ in residential areas) and risk levels (low / medium / high), the module generates a risk heatmap using color gradient encoding (e.g., green for low risk, yellow for medium risk, and red for high risk), overlaying sensitive point location information (e.g., school icon markings) to intuitively display the spatial distribution characteristics of pollution risk within the area.

[0097] The structured report module generates traceable analysis documents through data integration. This module receives the four-dimensional assessment results output by the environmental assessment unit (such as pollutant migration path data, sensitive point concentration value lists, and risk level determination results) and the dynamic feature importance values ​​output by the feature extraction unit (such as the importance of aromatic VOCs proportion being 0.38 and temperature importance being 0.30), and integrates them to generate a structured report. The report contains two parts: the first is a feature contribution table, listing the names of each key feature (such as aromatic VOCs proportion and temperature), SHAP contribution values ​​(such as +0.3 level and +0.1 level), and importance ranking (such as 1st and 2nd); the second is the driving factor analysis conclusion, based on SHAP values ​​and causal inference results (such as "for every 5% increase in aromatic VOCs proportion, the risk level increases by 0.15 levels" and "the impact of temperature increase on risk level depends on aromatic VOCs proportion"), summarizing the main driving factors of the risk level (such as "aromatic VOCs proportion is the core driving factor for the current low risk").

[0098] The decision-making suggestion module outputs customized decision support information based on user roles. This module obtains user role information through the user role recognition module (such as the role tag field when the user logs in, with values ​​such as "R&D personnel", "construction party", "regulatory department"), and generates matching suggestions by combining the driving factor information in the four-dimensional assessment results (such as "the proportion of aromatic VOCs is too high"). If the user is a research and development personnel, the module accesses the historical task knowledge graph (stores adjustment experience for highly similar coatings, such as "after reducing the proportion of aromatics in acrylic coatings from 35% to 25%, the risk level decreases by 0.3") and outputs historical knowledge transfer suggestions (such as "it is recommended to reduce the proportion of aromatic VOCs from the current 35% to 25%, and referring to historical data, the risk level can be reduced by 0.3"). If the user is a construction party, the module outputs prevention and control suggestions based on the pollutant migration path (such as the trajectory of diffusion to schools) and the location of sensitive points (such as the school being 800 meters away from the construction site) (such as "it is recommended to adjust the operation time during construction to a period with wind speed > 3m / s to accelerate the diffusion of pollutants away from schools"). If the user is a regulatory department, the module summarizes the risk levels of all coating use scenarios in the region (such as 5% high risk, 20% medium risk, and 75% low risk in area A) and outputs a risk level summary table (including the region name, the proportion of high / medium / low risk, and the marking of key areas of concern).

[0099] The system's collaborative work, which uses a visualization module to intuitively display spatial risks, a structured reporting module to provide traceable analysis, and a decision-making suggestion module to output customized information, transforms complex environmental assessment data into information that is understandable and actionable for different user roles, thereby enhancing the system's practicality and decision support value.

[0100] Secondly, please refer to Figure 1This invention provides a method for assessing the environmental pollution of volatile environmentally friendly coatings, based on the aforementioned system for assessing the environmental pollution of volatile environmentally friendly coatings, comprising:

[0101] Step 1: Collect environmental parameters, composition data and sensitive point data of volatile environmentally friendly coatings, and dynamically adjust the collection strategy based on the historical error rate of sensors and the missing field rate of database through the data quality pre-assessment module, and output valid data to the unstructured and structured data pools.

[0102] Step 2: Receive environmental parameters, component data, and sensitive point data; process heterogeneity through a dynamic format conversion module, an intelligent time alignment module, and a semantic dimension calibration module; and output data in a unified format.

[0103] Step 3: Receive data in a unified format, and filter key features by fusing them through an attention mechanism using a two-branch machine learning framework.

[0104] Step 4: Receive key features, simulate pollutant migration paths and concentration distribution using an improved atmospheric diffusion model, combine with a regional environmental capacity dynamic database, quantify feature contribution and identify driving factors through causal inference using the SHAP algorithm, and generate a four-dimensional assessment result including simulated path, concentration distribution, risk level and driving factors.

[0105] Step 5: Receive the four-dimensional assessment results, distinguish between R&D personnel, construction parties, or regulatory departments through the user role recognition module, and output matching visual animations, structured reports, and decision-making suggestions.

[0106] The environmental pollution assessment method for volatile environmentally friendly coatings provided by this invention is based on the aforementioned system, solving the problem of heterogeneous multi-source data fusion and improving the accuracy of environmental impact analysis. The specific implementation logic and subordinate technical solutions for each step are as follows:

[0107] Step 1: Collect environmental parameters, composition data, and sensitive point data of volatile environmentally friendly coatings, and dynamically adjust the collection strategy through data quality pre-assessment. In this step, the system collects environmental parameters (temperature, humidity, wind speed) in real time at the second / minute level through IoT sensor interfaces (such as RS485, LoRa interface), periodically obtains daily / weekly coating composition data (VOCs types, percentages) and sensitive point data (coordinates of schools, residential areas) through structured database interfaces (such as JDBC, RESTful API), and synchronously updates sensitive point data (such as adding school locations) through GIS interfaces (such as OGC WFS service). The data quality pre-assessment module calculates the error rate of sensors over the past 30 days (e.g., the average error of temperature sensors > 2℃) and the missing rate of database fields over the past 10 days (e.g., the missing rate of VOCs percentage field > 5%). For sensors with excessive error rates (e.g., temperature sensors), the sampling frequency is increased to 0.5 seconds / time to increase data density. For database fields with excessive missing rates, a manual verification process is triggered (e.g., notifying the administrator to re-upload data). Finally, the calibrated environmental parameters are stored in an unstructured data pool (e.g., Hadoop HDFS), while the component data and sensitive point data are stored in a structured data pool (e.g., MySQL database).

[0108] Step 2: Receive multi-source data and handle heterogeneity through dynamic format conversion, time alignment, and semantic calibration. The system receives raw environmental parameters (such as sensor messages "T:25, H:60") and component data (such as database tables "Temperature:25℃, Humidity:60%)) output by the intelligent acquisition unit. First, the metadata matching algorithm of the dynamic format conversion module (based on field name similarity and data type consistency) identifies "T" as "Temperature" and "H" as "Humidity," generating a unified table format (fields: Time, Temperature, Humidity). Second, the intelligent time alignment module uses a sliding window aggregation method to calculate the hourly average value of the continuous time series of environmental parameters (such as second-level data) (e.g., the average temperature of 25.3℃ within the hour from 08:00). For discrete time points of component data (such as daily data), Lagrange interpolation is used to estimate missing time period values ​​(e.g., estimating the temperature of 25℃ at 08:00 on the current day based on data from 08:00 on the previous day and 09:00 on the current day), with a unified time granularity of hour. Finally, the semantic dimension calibration module is used based on the domain ontology library (predefined "VOCs" associated with "aromatics" and "alkanes", and "temperature" associated with "℃" and "℉") to convert "temperature:77℉" to "temperature:25℃" (unit calibration) and "VOCs: benzene series" to "aromatic VOCs" (classification calibration), outputting standardized data with unified field names, time granularity, and dimension definitions.

[0109] Step 3: Receive standardized data and filter key features using a dual-branch machine learning framework. The system inputs standardized data (e.g., hourly records: time, temperature (°C), percentage of aromatic VOCs (%)) into the feature extraction unit. The basic feature extraction model (XGBoost) calculates the importance value of each feature using Gini impurity (e.g., temperature 0.35, percentage of aromatic VOCs 0.42). The transfer learning module accesses the historical task knowledge graph (storing 500 sets of paint evaluation experience), calculates the feature vector similarity between the current paint and historical paints based on the cosine similarity algorithm (e.g., 85% similarity for acrylic paints), extracts the importance of historical features (e.g., percentage of aromatic VOCs 0.45), and transfers it to the current model using knowledge distillation technology. The attention mechanism dynamically weights and fuses the current importance with historical weights based on similarity (e.g., 85% similarity corresponds to a historical weight coefficient of 0.6), outputs dynamic feature importance (e.g., percentage of aromatic VOCs 0.38), and filters the top 5 key features (e.g., percentage of aromatic VOCs, temperature, humidity, distance to sensitive points, wind speed).

[0110] Step 4: Receive key features and simulate pollution diffusion, analyzing risk causes. The system inputs key features (e.g., 35% aromatic VOCs, temperature 25℃) and construction site coordinates (116.4°E, 39.9°N), sensitive point location (school 116.41°E, 39.92°N) into the improved atmospheric diffusion model. Based on atmospheric turbulent diffusion equations (e.g., Gaussian plume model) and terrain correction parameters (building height 15 meters), it simulates pollutant migration paths (e.g., diffusion to 200 meters upwind of the school in 1 hour) and sensitive point concentrations (e.g., 0.9 mg / m³ at the school); the regional environmental capacity dynamic database is accessed. The system synchronizes thresholds from the ecological and environmental departments (such as the hourly average limit of VOCs of 1.0 mg / m³) via API interface, providing a basis for threshold comparison (such as the school's 0.9 mg / m³ not exceeding the standard); the risk tracing module calculates feature contribution values ​​(such as the contribution of aromatic VOCs by +0.3 level) through the SHAP algorithm, and identifies driving factors (such as "the proportion of aromatic VOCs is too high") by combining causal inference technology (Do-Calculus), generating a four-dimensional assessment result that includes migration path, concentration distribution, risk level (low risk) and driving factors.

[0111] Step 5: Receive the four-dimensional assessment results and output customized information. The system distinguishes user types (R&D personnel / construction team / regulatory department) through the user role recognition module. The visualization output module overlays the migration path with the GIS sensitive point layer to generate a dynamic diffusion animation (showing the diffusion process over 1 hour) and generates a heat map based on concentration and risk level (green indicates low risk). The structured report module generates a feature contribution table (e.g., aromatic hydrocarbon VOCs account for 0.3 level) and driving factor conclusions (e.g., "For every 5% increase in the proportion of aromatic hydrocarbons, the risk level increases by 0.15 level"). The decision suggestion module outputs matching information: R&D personnel receive historical migration suggestions (e.g., "Reducing the proportion of aromatic hydrocarbons to 25% can reduce the risk level by 0.3 level"), construction teams receive prevention and control suggestions (e.g., "Adjust the operation time to wind speed > 3m / s"), and regulatory departments receive a risk summary table (e.g., the proportion of low-risk areas is 75%).

[0112] Through the coordinated implementation of the above steps, the method achieves full-process coverage from data collection to decision support, solves the problem of heterogeneous data fusion, and improves the accuracy and practicality of environmental impact analysis.

[0113] This invention achieves dynamic calibration and classified storage of multi-source data through an intelligent acquisition unit, laying the foundation for heterogeneous data fusion. The intelligent acquisition unit is equipped with an IoT sensor interface, a structured database interface, and a geographic information system interface, respectively collecting second-level / minute-level environmental parameters (temperature, humidity, wind speed), daily / weekly paint composition data (VOC types and proportions), and dynamic data of sensitive points (coordinates of schools and residential areas). The data quality pre-assessment module calculates the error rate of sensors over the past 30 days and the missing data rate of database fields over the past 10 days. For sensors with an error rate exceeding 10%, the sampling frequency is increased to increase data density; for database fields with a missing data rate exceeding 5%, manual verification and data retransmission are triggered. Finally, the calibrated environmental parameters are stored in an unstructured data pool, while the composition data and sensitive point data are stored in a structured data pool, solving the problems of scattered data sources, heterogeneous formats, and unstable data quality.

[0114] The standardized processing unit achieves format, time, and semantic uniformity for heterogeneous data through triple calibration. The dynamic format conversion module uses a metadata matching algorithm to identify the field correspondence between unstructured environmental parameters (such as sensor messages "T:25,H:60") and structured component data (such as database tables "Temperature:25℃, Humidity:60%) ("T"→"Temperature", "H"→"Humidity"), and converts them into a unified table format. The intelligent time alignment module uses a sliding window aggregation to calculate the hourly average for continuous time series (second-level data) of environmental parameters, and uses Lagrange interpolation to calculate missing time period values ​​for discrete time points (day-level data) of component data, unifying the time granularity to the hour level. The semantic dimension calibration module is based on a domain ontology library (predefined "VOCs" associated with "aromatics" and "alkanes", "temperature" associated with "℃" and "℉"), and uses semantic reasoning to convert "Temperature:77℉" to "Temperature:25℃" (unit calibration) and "VOCs:benzene series" to "aromatic VOCs" (classification calibration), outputting standardized data with unified field names, time granularity, and dimension definitions, solving the problems of data format incompatibility, time dimension misalignment, and dimension definition conflicts.

[0115] The subsequent analysis and output stages enhance the accuracy of environmental impact quantification analysis through feature selection, risk simulation, and customized output. The feature extraction unit uses a dual-branch machine learning framework (XGBoost model extracts current feature importance, transfer learning module supplements historical task knowledge, and attention mechanism dynamically fuses weights) to select the top 5 key features strongly correlated with VOCs release risk (such as the proportion of aromatic VOCs and temperature). The environmental assessment unit, based on these key features, calls an improved atmospheric diffusion model to simulate pollutant migration paths and sensitive point concentrations, combining real-time synchronized environmental capacity thresholds (such as the hourly average VOCs limit of 1.0 mg / m³) with the SHAP algorithm and causal inference technology to analyze the causes of risk. The results output unit outputs dynamic diffusion animations, feature contribution tables, and customized suggestions (such as historical migration suggestions for R&D personnel and prevention and control suggestions for construction personnel) according to user roles (R&D personnel / construction companies / regulatory departments). Through precise selection of key features, scientific risk simulation, and targeted results output, the accuracy of environmental impact quantification analysis is effectively improved.

Claims

1. An environmental pollution assessment system for volatile environmentally friendly coatings, characterized in that, include: The system comprises an intelligent data acquisition unit, a standardized processing unit, a feature extraction unit, an environmental assessment unit, and a result output unit. The intelligent acquisition unit is equipped with an IoT sensor interface, a structured database interface, and a geographic information system interface. It collects environmental parameters, composition data, and sensitive point data of volatile environmentally friendly coatings. The data quality pre-assessment module dynamically adjusts the acquisition strategy based on the historical error rate of the sensors and the missing field rate of the database, and outputs valid data to the unstructured and structured data pools. The standardized processing unit receives environmental parameters, component data, and sensitive point data output by the intelligent acquisition unit, and processes heterogeneity through a dynamic format conversion module, an intelligent time alignment module, and a semantic dimension calibration module to output data in a unified format. The feature extraction unit receives uniform format data output by the standardization processing unit, and selects key features after fusion through an attention mechanism using a two-branch machine learning framework. The environmental assessment unit receives key features output by the feature extraction unit, simulates pollutant migration paths and concentration distributions using an improved atmospheric diffusion model, combines them with a regional environmental capacity dynamic database, quantifies feature contribution and identifies driving factors through causal inference using the SHAP algorithm, and generates a four-dimensional assessment result including simulated paths, concentration distributions, risk levels, and driving factors. The improved atmospheric diffusion model receives the top 5 key features selected by the feature extraction unit, the latitude and longitude coordinates of the construction point, and the location data of sensitive points. Based on the atmospheric turbulent diffusion equation and terrain correction parameters, it simulates the migration path of pollutants from the construction point to the sensitive point and the VOCs concentration distribution at each sensitive point. The result output unit receives the four-dimensional assessment results output by the environmental assessment unit, distinguishes between R&D personnel, construction parties, or regulatory departments through the user role recognition module, and outputs matching visual animations, structured reports, and decision-making suggestions.

2. The volatile environmentally friendly coating environmental pollution assessment system according to claim 1, characterized in that, The intelligent data acquisition unit includes an IoT sensor interface, a structured database interface, and a geographic information system interface. The IoT sensor interface collects environmental parameters at adjustable frequencies on a second or minute level in real time; the structured database interface periodically acquires coating composition data and sensitive point data at daily or weekly frequencies; and the geographic information system interface dynamically updates sensitive point data synchronously. The data quality pre-assessment module receives raw environmental parameters from the IoT sensor interface, raw component data from the structured database interface, and sensitive point data. It obtains the sensor's error rate over the past 30 days and the database field's missing rate over the past 10 days. If the sensor's error rate exceeds 10%, the sampling frequency of the sensor is increased to 0.5 seconds / time to increase data density. If the database field's missing rate exceeds 5%, a manual verification process is triggered to re-transmit the missing component data or sensitive point data. The calibrated environmental parameters are stored in the unstructured data pool, and the calibrated component data and sensitive point data are stored in the structured data pool.

3. The environmental pollution assessment system for volatile environmentally friendly coatings according to claim 1, characterized in that, The standardization processing unit includes a dynamic format conversion module, an intelligent time alignment module, and a semantic dimension calibration module. The dynamic format conversion module receives the raw environmental parameters output from the unstructured data pool of the intelligent acquisition unit and the component data output from the structured data pool. It identifies the field correspondence between T and temperature, and H and humidity through a metadata matching algorithm, generates mapping rules, and converts them into a unified table format. The intelligent time alignment module receives unified table data output by the dynamic format conversion module, analyzes the continuous time series of environmental parameters and the discrete time points of component data, calculates the hourly average value of environmental parameters using sliding window aggregation, and calculates the missing time period value of component data using Lagrange interpolation, thus unifying the time granularity of environmental parameters and component data to the hourly level. The semantic dimension calibration module receives hourly aligned data output by the intelligent time alignment module, and outputs standardized data with unified field names, time granularity, and dimension definitions based on the domain ontology library and through semantic reasoning.

4. The volatile environmentally friendly coating environmental pollution assessment system according to claim 1, characterized in that, The feature extraction unit deploys a dual-branch machine learning framework, and the feature extraction unit includes a basic feature extraction model and a transfer learning module. The basic feature extraction model is the XGBoost model, which receives standardized data with unified field names, time granularity, and dimension definitions from the standardized processing unit, and outputs the importance value of each feature in the current data through a feature importance calculation algorithm. The transfer learning module accesses the historical task knowledge graph, calculates the feature vector similarity between the current coating and historical coatings based on the cosine similarity algorithm, retrieves historical coatings with a similarity of more than 80%, extracts the importance of their historical features, and transfers the historical weights of the historical features to the current model. The attention mechanism weights and fuses the current importance value output by the basic feature extraction model with the historical weights output by the transfer learning module to output dynamic feature importance, and then selects the top 5 key features in descending order of importance value.

5. The environmental pollution assessment system for volatile environmentally friendly coatings according to claim 1, characterized in that, The environmental assessment unit includes an improved atmospheric diffusion model, a regional environmental capacity dynamic database, and a risk tracing module. The regional environmental capacity dynamic database synchronizes in real time with the environmental quality standard thresholds released by the ecological and environmental departments through an API interface, and provides a threshold comparison basis for the simulation results of the improved atmospheric diffusion model. The risk tracing module obtains the VOCs concentration values ​​at sensitive points output by the improved atmospheric diffusion model and the dynamic feature importance values ​​output by the feature extraction unit. It calculates the contribution of each feature to the risk level using the SHAP algorithm, and identifies the main causes of risk by combining causal inference technology. It generates a four-dimensional assessment result including pollutant migration path, sensitive point concentration distribution, risk level, and driving factors.

6. The environmental pollution assessment system for volatile environmentally friendly coatings according to claim 1, characterized in that, The result output unit includes a visualization output module, a structured report module, and a decision suggestion module; The visualization output module receives the four-dimensional assessment results output by the environmental assessment unit, overlays the dynamic trajectory of the migration path with the sensitive point layer of the geographic information system, and generates a dynamic diffusion animation. Simultaneously, a risk heatmap is generated based on the concentration distribution values ​​and risk levels of sensitive points; The structured report module receives the four-dimensional assessment results output by the environmental assessment unit and the dynamic feature importance values ​​output by the feature extraction unit, and generates a feature contribution table and driving factor analysis conclusions. The decision suggestion module receives the recognition results from the user role recognition module and the driving factor information from the four-dimensional evaluation results, and outputs matching information: if the user is a researcher, it outputs historical knowledge transfer suggestions based on the adjustment experience of highly similar coatings in the historical task knowledge graph. If the user is the construction company, prevention and control recommendations will be provided based on the pollutant migration path and the location of sensitive points; If the user is a regulatory authority, output a risk level summary table based on the risk level of all paint usage scenarios in the region.

7. A method for assessing the environmental pollution of volatile environmentally friendly coatings, based on the environmental pollution assessment system for volatile environmentally friendly coatings according to any one of claims 1-6, characterized in that, include: Step 1: Collect environmental parameters, composition data and sensitive point data of volatile environmentally friendly coatings, and dynamically adjust the collection strategy based on the historical error rate of sensors and the missing field rate of database through the data quality pre-assessment module, and output valid data to the unstructured and structured data pools. Step 2: Receive environmental parameters, component data, and sensitive point data; process heterogeneity through a dynamic format conversion module, an intelligent time alignment module, and a semantic dimension calibration module; and output data in a unified format. Step 3: Receive data in a unified format, and filter key features by fusing them through an attention mechanism using a two-branch machine learning framework. Step 4: Receive key features, simulate pollutant migration paths and concentration distribution using an improved atmospheric diffusion model, combine with a regional environmental capacity dynamic database, quantify feature contribution and identify driving factors through causal inference using the SHAP algorithm, and generate a four-dimensional assessment result including simulated path, concentration distribution, risk level and driving factors. Step 5: Receive the four-dimensional assessment results, distinguish between R&D personnel, construction parties, or regulatory departments through the user role recognition module, and output matching visual animations, structured reports, and decision-making suggestions.

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