An ecological environment monitoring material intelligent management and pollution source collaborative tracing method and system

Abstract

The application provides an ecological environment monitoring material intelligent management and pollution source collaborative tracing method and system. The ecological environment monitoring material intelligent management and pollution source collaborative tracing method comprises the following steps: S1: intelligent consumable cabinet full life cycle management; S2: multi-source environmental data acquisition and preprocessing; S3: pollution diffusion modeling and tracing analysis; S4: material management and collaborative linkage of tracing results; S5: intelligent early warning and closed-loop feedback, the cloud platform predicts the future pollution trend according to the pollution diffusion model, combines the remaining life of the material, and generates a dynamic scheduling suggestion; the operation and maintenance client receives the early warning information and executes material scheduling, equipment maintenance or pollution control operation. The application realizes automatic warehousing, permission control, environmental regulation, use tracking and intelligent early warning of monitoring materials, greatly improves the management efficiency and safety, and realizes accurate identification and responsibility quantification of the pollution source by combining machine learning and Bayesian regression.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology for ecological and environmental monitoring, specifically to a method and system for intelligent management of ecological and environmental monitoring materials and collaborative tracing of pollution sources that combines intelligent consumable management, multi-source environmental data fusion, and pollution diffusion modeling. Background Technology

[0002] Intelligent monitoring is a technological system in ecological and environmental monitoring that utilizes digital technologies such as artificial intelligence, the Internet of Things, big data, and satellite remote sensing to achieve automated and intelligent monitoring of environmental elements such as water and air. Its core objective is to upgrade the monitoring network from "environmental quality assessment" to "pollution source tracing and analysis," thereby enabling precise early warning and source tracing of environmental problems.

[0003] However, in current ecological and environmental monitoring, the management of monitoring materials (such as sensors, reagents, and sampling bottles) largely relies on manual registration, which suffers from low efficiency, error-proneness, and inability to track in real time. Meanwhile, pollution source tracing methods often depend on a single data source, lacking the linkage analysis between the usage status of monitoring materials and pollution diffusion data, resulting in insufficient source tracing accuracy and difficulties in determining responsibility. Furthermore, existing systems lack the ability to intelligently correlate and provide early warnings with the shelf life and usage frequency of monitoring materials and pollution events. Specific manifestations are as follows:

[0004] Inefficient material management: The processes of warehousing, issuing, returning, and scrapping of monitoring materials (such as sampling bottles, sensors, reagents, etc.) rely heavily on manual registration, lacking real-time video recording and environmental control. This can easily lead to materials being lost, expired, contaminated, or used in violation of regulations, affecting the accuracy and reliability of monitoring data.

[0005] The methods for tracing pollution sources are limited: Traditional source tracing mainly relies on manual investigation and simple statistical models, which makes it difficult to accurately simulate the diffusion of pollution in multiple media (air, water, and soil). Furthermore, it lacks efficient algorithms for identifying pollution source types and quantifying contribution rates, resulting in highly subjective source tracing results and incomplete evidence chains.

[0006] Material management and traceability are disconnected: There is a lack of correlation analysis between the usage status of materials (such as the number of times they are used and the remaining shelf life) and the occurrence of pollution incidents. It is impossible to dynamically assess whether the materials are affected by expiration or pollution, and it is also impossible to provide a scientific basis for material allocation.

[0007] Insufficient data storage and closed-loop feedback: Environmental data is easily tampered with and lacks judicial evidence storage capabilities; after the early warning information is issued, there is no closed-loop tracking mechanism, making it impossible to continuously optimize model parameters.

[0008] Therefore, there is an urgent need for an intelligent solution that integrates intelligent material management, multi-source data fusion, precise pollution source tracing, collaborative linkage, and closed-loop feedback. Summary of the Invention

[0009] To address the problems existing in the prior art, the purpose of this invention is to provide an intelligent management system and method for ecological environment monitoring materials and collaborative source tracing of pollution sources. This system and method offer full-process visibility and traceability, high source tracing accuracy, and the ability to provide judicial evidence and continuous optimization.

[0010] The present invention is achieved through the following technical solution.

[0011] A method for intelligent management of ecological environment monitoring materials and collaborative source tracing of pollution sources includes the following steps:

[0012] S1: The intelligent consumable cabinet manages the entire lifecycle of monitored materials, including real-time video recording of the entire process of warehousing, outbound, return, and disposal, which is then uploaded to the intelligent management cloud platform. Based on user permission verification, the cabinet door is controlled to open and close and the internal environment is adjusted. Consistency verification is performed through RFID / QR code scanning, and early warning reminders are automatically generated based on the quantity of materials, usage time, and remaining shelf life.

[0013] S2: Real-time acquisition of multi-source environmental data, including pollutant concentrations and meteorological and hydrological parameters in the atmosphere, water bodies, and soil, and preprocessing of the raw data by format standardization, time synchronization, outlier removal, and missing value filling;

[0014] S3: Construct a pollution diffusion model based on preprocessed data, use machine learning algorithms to identify pollution source types, and calculate the contribution rate and confidence interval of each pollution source through Bayesian regression, and store key data on the blockchain.

[0015] S4: Conduct correlation analysis between material usage records and pollution source tracing results to construct a "material-pollution" mapping relationship, dynamically assess material status, and generate comprehensive early warning information including pollution diffusion paths, hotspot areas, and material status;

[0016] S5: Based on the pollution diffusion model, predict future pollution trends, and generate dynamic scheduling suggestions based on the remaining lifespan of materials. Issue early warning tasks through the operation and maintenance client and record the execution status. Feedback the closed-loop processing results to the cloud platform for model optimization. Further, in step S1, the full lifecycle management of the intelligent consumable cabinet includes: S1.1: Real-time video recording of material operations using a high-definition camera, with the video stream encrypted and uploaded to the cloud platform; S1.2: Operation and maintenance personnel submit applications for collection, return, or disposal through the client. The application information includes the material number, quantity, operator identity, and timestamp; S1.3: After user authorization verification, the cloud platform issues an unlocking command to the intelligent door lock. Simultaneously, the environmental control unit automatically adjusts the temperature, humidity, and lighting inside the cabinet according to a preset storage threshold; S1.4: The cloud platform performs consistency verification between RFID / QR code scanning data and the application information. If a mismatch is found, an anomaly alert is sent and the operation is locked; S1.5: The cloud platform periodically calculates the material quantity, cumulative usage time, and remaining shelf life. When the data falls below a threshold, it automatically issues reminders for replenishment, replacement, or overdue allocation. Furthermore, in step S2, the multi-source environmental data acquisition and preprocessing further includes: S2.1: Selecting and deploying atmospheric, water, and soil monitoring equipment according to the monitoring targets and deployment areas; S2.2: Accessing multi-source data in real time through an IoT gateway to construct a multi-dimensional environmental dataset; S2.3: Standardizing the format of the raw data, synchronizing the time, removing outliers based on the 3σ principle, and filling missing values ​​based on KNN interpolation. Furthermore, in step S3, the pollution diffusion modeling and source tracing analysis further includes: S3.1: Constructing a multi-media pollution diffusion path simulation by combining Gaussian plume models, fluid dynamics models, and Fick diffusion models; S3.2: Identifying pollution source types using support vector machines and random forests, with input features including pollutant composition, time series, and meteorological and hydrological conditions; S3.3: Calculating the contribution rate and confidence interval of each pollution source through Bayesian regression; S3.4: Calculating the hash value of key data in the pollution source tracing process and uploading it to the blockchain. Furthermore, in step S4, the collaborative linkage between material management and traceability results further includes: S4.1: performing correlation analysis between material usage records and pollution traceability results to construct a "material-pollution" mapping relationship; S4.2: if regional pollution events occur frequently or traceability results are abnormal, automatically determining whether the monitoring materials are expired, contaminated, or need to be replaced; S4.3: generating comprehensive early warning information including contour maps of pollution diffusion paths, GIS markers for hotspot areas, and suggested disposal measures for material status, and displaying it through a GIS visualization platform.Furthermore, in step S5, the intelligent early warning and closed-loop feedback further includes: S5.1: predicting the pollution trend in the next 72 hours based on the pollution diffusion model, and generating dynamic scheduling suggestions in combination with the remaining life of the materials; S5.2: the operation and maintenance client receives the early warning information and displays the specific tasks, and records the execution status; S5.3: feeding back the closed-loop processing results to the cloud platform for model parameter optimization. In addition, this invention also discloses an intelligent management system for ecological environment monitoring materials and a collaborative source tracing system for pollution sources, comprising: a perception and execution layer, including an intelligent consumable cabinet module and a multi-source environmental monitoring equipment module, for material lifecycle management and environmental data collection; a data processing and support layer, including a hybrid networked data communication network for data transmission; a core platform layer, which is an intelligent management cloud platform, including a data receiving and storage unit, a lifecycle management unit, a multi-source data access and preprocessing unit, a pollution diffusion modeling unit, a machine learning source tracing unit, a blockchain evidence storage unit, a collaborative linkage analysis unit, and a GIS visualization unit, for executing the method described in any one of claims 1 to 6; and a user interaction layer, which is an operation and maintenance client module, including an application and operation unit, an early warning and feedback unit, and a video playback unit, for interacting with the cloud platform. Furthermore, the intelligent consumables cabinet module includes: an intelligent door lock unit, employing an electromagnetic lock combined with fingerprint or facial recognition, supporting remote authorization and temporary permission issuance; an environmental control unit, integrating a temperature and humidity sensor, a semiconductor cooling / heating element, and a dehumidifier, automatically adjusting according to material storage requirements; a high-definition camera unit, used to automatically trigger recording and capture images for each operation, with video stored locally and uploaded synchronously; and an RFID / QR code identification unit, assigning a unique EPC code to each material, supporting rapid batch inventory. Furthermore, the multi-source environmental monitoring equipment module includes: atmospheric monitoring equipment, including an electrochemical gas sensor and an ultrasonic weather station; water monitoring equipment, including an optical water quality sensor and a Doppler current meter; and soil monitoring equipment, including an FDR-type temperature and humidity sensor and an XRF heavy metal detector. These devices communicate via an IoT gateway and have a built-in GPS module to record their deployment location. Furthermore, the core platform layer adopts a microservice architecture deployed in the cloud, wherein: the lifecycle management unit collects material information through scheduled tasks and issues reminders when trigger conditions are met; the collaborative analysis unit cross-compares material usage records with traceability results through a correlation analysis engine and outputs comprehensive early warnings; the GIS visualization unit constructs interactive maps based on WebGIS to display pollution diffusion contour lines, hotspot heat maps, and material distribution information.

[0017] In the user interaction layer, the application and operation unit provides a form interface for submitting material applications and displays the cabinet door status and environmental parameters in real time; the early warning and feedback unit receives early warnings via pop-ups, SMS, or emails, and supports one-click confirmation and uploading of processing results; the video playback unit retrieves videos by timeline or operation event, and supports fast forward, slow motion, and screenshot marking.

[0018] Based on the above technical solution, the beneficial effects of the present invention can be described as follows:

[0019] 1) Full-process visibility and traceability: Through high-definition video, RFID / QR code identification and blockchain evidence storage, the entire process of monitoring materials from warehousing to disposal is digitally managed, preventing material loss, illegal operation and data tampering, and meeting the requirements for judicial evidence storage of environmental data.

[0020] 2) Intelligent control of storage conditions: The environmental control unit automatically adjusts parameters such as temperature, humidity, and light according to the material characteristics to extend the service life of the material and ensure monitoring activity.

[0021] 3) Multi-media precise source tracing: Integrating multiple diffusion models and machine learning algorithms, the accuracy rate of identifying the types of air, water and soil pollution sources reaches over 92%, and the contribution rate is quantified to output confidence intervals, providing a scientific basis for environmental law enforcement.

[0022] 4) Collaborative Linkage Between Materials and Traceability: Dynamically assess the status of materials through correlation analysis to avoid misjudgments in monitoring due to expired or contaminated materials, thereby achieving preventive management.

[0023] 5) Closed-loop continuous optimization: The results of the early warning task are fed back to the model to update the Bayesian prior and SVM parameters, forming a data-driven self-improvement mechanism to enhance the long-term reliability of the system. Attached Figure Description

[0024] Figure 1 The diagram shows the intelligent management of ecological and environmental monitoring materials and the collaborative source tracing method for pollution sources.

[0025] Figure 2 The diagram shows the architecture of the intelligent management system for ecological and environmental monitoring materials and the collaborative tracing system for pollution sources. Detailed Implementation

[0026] To better illustrate the present invention, the following detailed description is provided in conjunction with specific embodiments. However, these specific embodiments are merely for illustrative purposes and are not intended to limit the scope of the invention.

[0027] Figure 1 The diagram displayed is a flowchart of intelligent management of ecological environment monitoring materials and collaborative source tracing methods for pollution sources; such as Figure 1As shown, the present invention provides an intelligent management method for ecological environment monitoring materials and a collaborative source tracing method for pollution sources, comprising the following steps:

[0028] Step S1: Full Lifecycle Management of Intelligent Consumables Cabinets

[0029] S1.1: The entire process of monitoring materials entering, leaving, returning, and being scrapped is recorded by camera and uploaded to the intelligent management cloud platform;

[0030] S1.2: The operation and maintenance client submits a request to the intelligent management cloud platform, and the cloud platform issues an unlocking command according to the user's permissions;

[0031] S1.3: The smart door lock controls unlocking based on access permissions, and the environmental control device automatically adjusts environmental parameters according to material storage requirements (such as temperature, humidity, and light).

[0032] S1.4: If the act of receiving or returning does not match the application, the cloud platform will send an abnormality reminder to the operation and maintenance client;

[0033] S1.5: The cloud platform periodically collects statistics on the quantity, usage time, and shelf life of the monitored materials in each smart consumable cabinet. When the quantity is below the threshold, the usage time exceeds the limit, or the shelf life is close to the expiration date, it issues a replenishment reminder, a replacement reminder, or an overdue allocation reminder, respectively.

[0034] Step S2: Multi-source environmental data acquisition and preprocessing

[0035] S2.1: Based on the type and location of the monitoring materials, select and deploy multi-source environmental monitoring equipment for the atmosphere, water bodies, and soil;

[0036] S2.2: Access multi-source data, including pollutant concentrations, meteorological parameters, hydrological data, etc.

[0037] S2.3: Standardize the format of the raw data, synchronize the time, detect anomalies, and fill in missing values ​​to ensure data consistency and integrity.

[0038] Step S3: Pollution diffusion modeling and source tracing analysis

[0039] S3.1: Based on the preprocessed data, and combining the Gaussian plume model, fluid dynamics model and Fick diffusion model, we construct an accurate pollution diffusion path simulation for the atmosphere, water body and soil;

[0040] S3.2: Use machine learning classification algorithms (such as support vector machine, random forest) to identify pollution source types;

[0041] S3.3: Calculate the contribution rate of each pollution source through Bayesian regression to achieve quantitative tracing of pollution responsibility;

[0042] S3.4: Combine blockchain technology to encrypt and store traceability data to ensure the immutability and traceability of the data.

[0043] Step S4: Synergistic Linkage of Material Management and Traceability Results

[0044] S4.1: The intelligent management cloud platform will perform correlation analysis between the usage records of monitoring materials (such as sampling time, sampling location, and number of uses) and the pollution source tracing results;

[0045] S4.2: If pollution incidents occur frequently in a certain area or the source tracing results are abnormal, the cloud platform will automatically determine whether the monitoring materials used in the area have expired, whether they have been contaminated, and whether they need to be replaced or replenished.

[0046] S4.3: The cloud platform generates comprehensive early warning information, including pollution diffusion paths, hotspot areas, material status, and recommended treatment measures, and displays it through a GIS visualization platform.

[0047] Step S5: Intelligent Early Warning and Closed-Loop Feedback

[0048] S5.1: The cloud platform predicts future pollution trends based on pollution diffusion models and generates dynamic scheduling suggestions based on the remaining lifespan of materials;

[0049] S5.2: The operation and maintenance client receives early warning information and executes material scheduling, equipment maintenance, or pollution control operations;

[0050] S5.3: The system records the closed-loop processing results and feeds them back to the cloud platform for model optimization.

[0051] like Figure 2 The diagram shows the architecture of an intelligent management system for ecological environment monitoring materials and a collaborative source tracing system for pollution sources. Furthermore, to implement the above method, this invention discloses an intelligent management system for ecological environment monitoring materials and a collaborative source tracing system for pollution sources, comprising: an intelligent consumables cabinet module 10, an intelligent management cloud platform module 20, an operation and maintenance client module 30, and a multi-source environmental monitoring equipment module 40.

[0052] The intelligent consumable cabinet module 10 includes: an intelligent door lock unit 11, an environmental control unit 12, a high-definition camera unit 13, and an RFID / QR code recognition unit 14.

[0053] The smart door lock unit 11 is designed to control the opening and closing of the cabinet door based on user permissions, and supports remote authorization and temporary permission issuance.

[0054] The environmental control unit 12 includes a temperature and humidity sensor, a heating / cooling device, a dehumidification device, etc., and automatically adjusts the storage environment according to a preset threshold.

[0055] The high-definition camera unit 13 records and captures the entire process of material warehousing, retrieval, return, and disposal in real time, and uploads the video stream to the cloud platform.

[0056] The RFID / QR code identification unit 14 uniquely identifies each piece of monitored material, enabling precise tracking.

[0057] The intelligent management cloud platform module 20 includes: a data receiving and storage unit 21, a lifecycle management unit 22, a multi-source data access and preprocessing unit 23, a pollution diffusion modeling unit 24, a machine learning source tracing unit 25, a blockchain evidence storage unit 26, a GIS visualization unit 27, and a collaborative analysis unit 28.

[0058] The data receiving and storage unit 21 receives environmental data, operation videos, material status, etc. uploaded by the intelligent consumable cabinet and stores them in a distributed database;

[0059] The life cycle management unit 22 is used to statistically analyze the quantity of materials, usage frequency, and remaining shelf life, and automatically generate reminders for replenishment, replacement, and allocation.

[0060] The multi-source data access and preprocessing unit 23 connects to the raw data from atmospheric, water, and soil monitoring equipment, and performs format standardization, time synchronization, anomaly detection, and missing value filling.

[0061] The pollution diffusion modeling unit 24 integrates the Gaussian plume model, the fluid dynamics model, and the Fick diffusion model, and supports model selection based on pollutant type and environmental medium.

[0062] The machine learning source tracing unit 25 uses algorithms such as support vector machine and random forest to identify pollution source types and combines Bayesian regression to calculate the contribution rate.

[0063] The blockchain evidence storage unit 26 hashes and uploads key data (such as model inputs, outputs, and user operation logs) during the pollution source tracing process to ensure that the data cannot be tampered with.

[0064] The GIS visualization unit 27 is used to construct a GIS map to display pollution diffusion paths, pollution hotspots, material distribution, and management status.

[0065] The collaborative analysis unit 28 is a record of material usage and pollution source tracing results that generates comprehensive early warning and scheduling recommendations.

[0066] The operation and maintenance client module 30 includes: an application and operation unit 31, an early warning and feedback unit 32, and a video playback unit 33.

[0067] The application and operation unit 31: Maintenance personnel submit applications for material collection, return, and disposal through the client, and receive unlocking permissions;

[0068] The early warning and feedback unit 32 receives early warning information such as supplementation, replacement, expiration, and pollution source tracing issued by the cloud platform, and provides feedback on the processing results.

[0069] The video playback unit 33 supports querying operation videos inside the consumable cabinet for tracing abnormal events.

[0070] The multi-source environmental monitoring equipment module 40 includes: atmospheric monitoring equipment 41, water monitoring equipment 42, and soil monitoring equipment 43.

[0071] Atmospheric monitoring equipment 41: including gas sensors, weather stations, etc.;

[0072] Water monitoring equipment 42: including water quality sensors, flow meters, etc.;

[0073] Soil monitoring equipment 43: including soil temperature and humidity, heavy metal detectors, etc.;

[0074] The aforementioned devices communicate with the cloud platform via an IoT gateway, supporting access via multiple protocols such as 4G / 5G / LoRa.

[0075] Example 1

[0076] The complete execution process of the intelligent management of ecological environment monitoring materials and collaborative source tracing method in this invention aims to achieve integrated intelligent control from the entire life cycle management of materials to the precise source tracing of pollution sources, and then to collaborative linkage and closed-loop feedback. The specific steps are as follows:

[0077] Step S1: Full Lifecycle Management of Intelligent Consumables Cabinets

[0078] S1.1 A high-definition camera (resolution no less than 1080P, supporting night vision) records the entire process of material warehousing, warehousing, return, and disposal in real time. The video stream is encrypted and uploaded to the intelligent management cloud platform. Technical benefits: Enables full-process visibility and traceability, preventing material loss or unauthorized operations.

[0079] S1.2 Maintenance personnel submit applications for receiving, returning, or scrapping materials to the cloud platform via a maintenance client (PC or APP). Application information includes material number, quantity, operator identity, and timestamp. Technical benefit: Standardizes operating procedures and prevents unauthorized requisition.

[0080] The S1.3 cloud platform verifies user permissions (role, department, validity period). Upon successful verification, it sends an unlocking command to the smart lock. Simultaneously, the environmental control unit (temperature and humidity sensors, heating / cooling / dehumidifying devices) automatically adjusts the cabinet environment according to preset storage thresholds (e.g., temperature 4~8℃, humidity 30%~50%, light intensity <100 lux). Technical benefits: Ensures optimal material storage conditions and extends material lifespan.

[0081] The S1.4 cloud platform verifies the consistency between RFID / QR code scanning data and application information (e.g., whether material numbers and quantities match). If a discrepancy is found, an anomaly alert (including a screenshot of the violation) is immediately sent to the operations and maintenance client, and the operation is locked. Technical benefit: Instantly detects and blocks violations, preventing incorrect or missed material requisitions.

[0082] The S1.5 cloud platform periodically (hourly / daily) tracks the quantity, cumulative usage time, and remaining shelf life of materials in each smart consumable cabinet. When the quantity falls below a threshold (e.g., <10%), usage time exceeds limits (e.g., exceeding 80% of the design life), or the shelf life is nearing expiration (e.g., <30 days), it automatically sends replenishment, replacement, or overdue allocation reminders to the maintenance client. Technical benefit: Enables intelligent early warning of material inventory, preventing monitoring failure due to material shortages or expiration.

[0083] Step S2: Multi-source environmental data acquisition and preprocessing

[0084] S2.1 Select and deploy atmospheric monitoring equipment (gas sensors, weather stations), water monitoring equipment (water quality sensors, flow meters), and soil monitoring equipment (temperature, humidity, and heavy metal detectors) based on the monitoring targets (e.g., PM2.5 in the atmosphere, COD in water bodies, and heavy metals in soil) and the deployment areas (e.g., industrial areas, river sections, and farmland). Ensure the relevance and comprehensiveness of data collection.

[0085] S2.2 Real-time access to multi-source data, including pollutant concentrations (µg / m³, mg / L), meteorological parameters (wind speed, wind direction, temperature, humidity), and hydrological data (water level, flow velocity), is achieved through an IoT gateway (supporting 4G / 5G / LoRa). This allows for the construction of a multi-dimensional environmental dataset.

[0086] S2.3 Standardizes the format of the raw data (unifying it to JSON format), synchronizes the time (NTP server calibration to millisecond level), detects anomalies (outliers are removed using the 3σ principle), and fills in missing values ​​(KNN interpolation method). This ensures data consistency and integrity, providing high-quality input for subsequent modeling.

[0087] Step S3: Pollution diffusion modeling and source tracing analysis

[0088] S3.1 Based on preprocessed data, and combining the Gaussian plume model (suitable for steady-state atmospheric diffusion), the fluid dynamics model (CFD, suitable for water / atmospheric diffusion under complex terrain), and the Fick diffusion model (suitable for molecular diffusion of pollutants in soil), an accurate pollution diffusion path simulation is constructed. This enables pollution propagation prediction under multiple media and scenarios.

[0089] S3.2 employs Support Vector Machine (SVM, kernel function RBF) and Random Forest (100 trees, maximum depth 10) to identify pollution source types (such as industrial emissions, agricultural non-point sources, and domestic sewage). Input features include pollutant composition, time series, and meteorological / hydrological conditions. This improves the accuracy of source type identification (experiments achieved over 92%).

[0090] S3.3 The contribution rate of each pollution source is calculated using Bayesian regression (with a normal prior distribution and MCMC sampling), and the percentage contribution and confidence interval of each source are output. This enables quantitative tracing of pollution liability and provides a scientific basis for environmental law enforcement.

[0091] S3.4 Calculate the hash value of key data (model inputs, outputs, intermediate parameters, user operation logs) during the pollution source tracing process and upload it to the blockchain (Hyperledger Fabric) to ensure data immutability and traceability. This meets the requirements for judicial evidence preservation of environmental data and enhances public trust.

[0092] Step S4: Synergistic Linkage of Material Management and Traceability Results

[0093] The S4.1 intelligent management cloud platform performs correlation analysis between material usage records (sampling time, sampling location, number of uses, and operators) and pollution source tracing results (pollution source type, contribution rate, and diffusion path) to construct a "material-pollution" mapping relationship. This reveals the potential correlation between material deployment and pollution events.

[0094] S4.2 If pollution incidents occur frequently in a certain area (e.g., more than 3 times within a month) or the source tracing results are abnormal (e.g., a sudden change in contribution rate > 50%), the cloud platform automatically determines whether the monitoring materials used in that area are expired, contaminated (e.g., pollutants exceeding standards are detected on the material surface), and whether they need to be replaced or replenished. This enables dynamic assessment of material status and avoids misjudgments due to material failure.

[0095] The S4.3 cloud platform generates comprehensive early warning information, including pollution diffusion paths (contour maps), hotspot areas (GIS markers), material status (normal / alarm / expired), and recommended handling measures (such as emergency dispatch of new consumables and activation of emergency monitoring), which are displayed through a GIS visualization platform (based on Leaflet / OpenLayers). This provides decision-makers with an intuitive and comprehensive situational map.

[0096] Step S5: Intelligent Early Warning and Closed-Loop Feedback

[0097] The S5.1 cloud platform predicts pollution trends over the next 72 hours based on a pollution diffusion model, and generates dynamic scheduling suggestions (such as pre-deploying materials to high-risk areas) by combining the remaining lifespan of materials (calculated based on usage frequency and design life). This enables preventative management and reduces losses from sudden pollution events.

[0098] The S5.2 maintenance client (PC / mobile APP) receives alert information, displays specific tasks (such as "Immediately replace the water sampling bottle on the 5th layer of cabinet 3"), and records the execution status (executor, time, and result photo). This ensures the closed-loop execution of alert tasks.

[0099] The S5.3 system feeds back closed-loop processing results (such as material replacement records and pollution control reports) to the cloud platform for model optimization (updating Bayesian priors and adjusting SVM parameters), thus forming a data-driven continuous improvement mechanism.

[0100] Example 2

[0101] This embodiment describes the physical and logical composition of the intelligent management system for ecological environment monitoring materials and the collaborative source tracing system for pollution sources according to the present invention, based on a layered architecture. Each layer interacts with the other through standardized interfaces to achieve high cohesion and low coupling.

[0102] The specific composition and technical effects are explained below:

[0103] First layer: Perception and execution layer

[0104] This layer interacts directly with the physical environment, responsible for data acquisition, material management, and execution control. It contains two modules:

[0105] Intelligent consumable cabinet module 10;

[0106] Smart door lock unit 11: Employs an electromagnetic lock + fingerprint / facial recognition system, supporting remote authorization (validity period accurate to the minute), temporary permission issuance (such as visitor scanning a code to open the door), and real-time reporting of unlocking records. This enables refined access control and prevents unauthorized access.

[0107] Environmental control unit 12: integrates temperature and humidity sensors (accuracy ±0.3℃, ±2% RH), semiconductor cooling / heating elements, and a dehumidifier, automatically adjusting according to material storage requirements (e.g., reagent cabinet constant temperature 4℃±1℃). This extends the shelf life of materials and ensures the activity of monitored materials.

[0108] High-definition camera unit 13: A 2-megapixel wide-angle camera supporting H.265 encoding. It automatically triggers recording and captures images for each operation (opening / closing a door, taking / placing materials), storing the video on a local SD card and simultaneously uploading it to the cloud platform. It provides irrefutable evidence of the operation.

[0109] RFID / QR code identification unit 14: High-frequency RFID reader (ISO 15693 protocol) and QR code scanner, assigning a unique ID (EPC code) to each material, achieving "one item, one code" refined tracking. Supports batch rapid inventory (100 items / second), reducing human error.

[0110] Multi-source environmental monitoring equipment module 40;

[0111] Atmospheric monitoring equipment 41: Electrochemical gas sensor (detects NOx, SO2, O3, etc.), ultrasonic weather station (wind speed ±0.3m / s, wind direction ±3°), sampling frequency 1 time / minute.

[0112] Water monitoring equipment 42: Optical water quality sensors (COD, ammonia nitrogen, total phosphorus), Doppler current meter, sampling frequency 1 time / 5 minutes.

[0113] Soil monitoring equipment 43: FDR type temperature and humidity sensor, XRF heavy metal detector (Pb, Cd, As), supporting timed or remotely triggered sampling.

[0114] The above devices communicate with the upper layer via an IoT gateway (supporting the MQTT protocol for encrypted transmission) and have built-in GPS modules to record their deployment locations. This enables multi-media, high-frequency environmental data collection, covering major pollution indicators.

[0115] Second layer: Data processing and support layer

[0116] This includes a data communication network that employs a hybrid networking approach combining 5G / 4G (mobile network), LoRa (low-power wide area network), and wired Ethernet (for industrial scenarios), capable of automatic switching based on site conditions. This ensures reliable, real-time, and low-power data transmission.

[0117] Third layer: Core platform layer (Intelligent management cloud platform module 20)

[0118] Deployed in the cloud (Alibaba Cloud / Private Cloud), it adopts a microservice architecture, with each unit deployed independently and scaled horizontally. Specific components:

[0119] Data receiving and storage unit 21: Receives device data, operation logs, and video streams based on a Kafka message queue, and stores them in a distributed database (InfluxDB time-series database + MinIO object storage). Supports high-concurrency writing and fast querying of massive amounts of data.

[0120] Lifecycle Management Unit 22: Scheduled Task (Cron) tracks material number, usage frequency, and remaining shelf life (calculated based on warehousing time and material type). When trigger conditions are met (e.g., quantity < 20 pieces), a notification is sent via push notification. This enables automatic material alerts and reduces manual management costs.

[0121] Multi-source data access and preprocessing unit 23: Provides standardized APIs for connecting to data from various devices, performing unified formatting (converting to CSV / Parquet), timestamp alignment (linear interpolation to the whole hour), outlier removal (IQR method), and missing value filling (multiple interpolation). This ensures data quality for downstream analysis models.

[0122] Pollution diffusion modeling unit 24: Integrates Gaussian plume model (AERMOD), fluid dynamics model (OpenFOAM), and Fick diffusion model (finite difference method). Users can select and call these models according to the type of pollutant (gaseous / liquid / solid) and the environmental medium (atmosphere / water / soil). It quickly generates high-precision diffusion path maps (spatial resolution 10m).

[0123] Machine Learning Source Tracing Unit 25: This unit employs SVM (sklearn.svm.SVC) and Random Forest (sklearn.ensemble.RandomForestClassifier) ​​for pollution source type identification. Feature engineering includes pollutant concentration ratios, temporal gradients, and spatial correlations. Bayesian regression (PyMC3) outputs the contribution rate of each source and its 95% confidence interval. This improves source tracing accuracy to over 90%.

[0124] Blockchain Evidence Storage Unit 26: Calculates SHA-256 hashes for model input and output parameters, user operation logs, timestamps, etc., writes them to the Hyperledger Fabric chain, and packages one block every 10 minutes. This meets the requirement of data immutability in the "Environmental Data Judicial Appraisal Standard".

[0125] Collaborative Analysis Unit 28: Through the correlation analysis engine (Spark SQL), material usage records are cross-referenced with traceability results to output comprehensive early warnings (such as "This sampling bottle has been used 80 times; replacement is recommended"). This achieves bidirectional optimization of material management and pollution traceability.

[0126] GIS Visualization Unit 27: Based on WebGIS (Mapbox GL), an interactive map is constructed to display pollution diffusion contour lines, hotspot heat maps, and material distribution pie charts, supporting layer switching and spatial queries. This allows managers to intuitively grasp the overall situation.

[0127] User access control unit (implicit in the platform): Based on the RBAC model, it defines roles such as administrators, operations and maintenance personnel, and auditors, and supports fine-grained permissions (e.g., only allowing viewing of data within this jurisdiction). This ensures data security and hierarchical control.

[0128] Fourth layer: User interaction layer (Operations and Maintenance Client Module 30)

[0129] It runs on PCs (Electron framework) or mobile devices (React Native), communicating with cloud platforms via WebSocket / HTTP / HTTPS. Specific components:

[0130] Application and Operation Unit 31: Provides a form interface for submitting material retrieval / return / disposal applications, and displays cabinet door status and environmental parameters in real time. Technical benefits: Simplifies operation processes and improves work efficiency.

[0131] Early Warning and Feedback Unit 32: Receives early warnings from the cloud platform via pop-ups, SMS, and emails regarding supplements, replacements, expired items, and pollution source tracing. Supports one-click confirmation and processing, and uploading of handling results (such as photos and notes). Ensures timely delivery and closed-loop management of early warning information.

[0132] Video playback unit 33: Retrieves videos within the consumables cabinet by timeline or operation event, supporting fast forward / slow motion and screenshot annotation. Facilitates post-event auditing and traceability of abnormal events.

[0133] Application Example 3

[0134] This embodiment takes the scenario of atmospheric VOCs (volatile organic compounds) leakage tracing in an industrial park in Ordos City, Inner Mongolia as an example to further refine the pollution diffusion modeling and tracing analysis in step S3.

[0135] Scene setting:

[0136] Monitoring materials: 200 passive VOCs sampling tubes (numbered VOC-0001 to VOC-0200), placed in smart consumable cabinet A, with each sampling tube having a rated effective sampling time of 720 hours (30 days).

[0137] Monitoring area: Industrial park, covering an area of ​​5km × 5km. Ten atmospheric monitoring stations (numbered M1-M10) were deployed, and the monitoring data included the concentrations (ppm level) of pollutants such as benzene, toluene, and xylene, as well as wind speed, wind direction, temperature, and air pressure.

[0138] Time: 10:00 on June 1, 2024. An abnormally high concentration of benzene series compounds was detected at the M3 monitoring station (coordinates (x=2.0km, y=3.0km)), reaching 12.5 ppm (background value is 0.5 ppm), triggering an alert.

[0139] Detailed steps:

[0140] Step S3.1: Pollution Diffusion Modeling

[0141] Model selection: For atmospheric VOCs point source leakage, a Gaussian plume model is used for forward simulation. The leakage source is assumed to be located at unknown coordinates (x0, y0), and the leakage intensity is Q (g / s).

[0142] Gaussian plume model formula: [

[0144] C(x, y, z) = \frac{Q}{2\pi u \sigma_y \sigma_z} \exp\left( -\frac{(y- y_0)^2}{2\sigma_y^2} \right) \left[ \exp\left( -\frac{(z - H)^2}{2\sigma_z^2} \right) + \exp\left( -\frac{(z + H)^2}{2\sigma_z^2} \right) \right] ]

[0146] in:

[0147] (C) is the pollutant concentration (g / m³) at the downwind direction (x, y, z).

[0148] (u) is the average wind speed (m / s). In this example, the monitoring station data shows a wind speed of 2.5 m / s and a northerly wind (due north is 0°), so the (x) axis points downwind (due south).

[0149] (H) is the effective source height (m). Assuming the leak point is on the ground, H=0.

[0150] (\sigma_y) and (\sigma_z) are the horizontal and vertical diffusion parameters, which are related to the downwind distance (x) and atmospheric stability. Based on environmental monitoring data (such as sunshine and cloud cover), the atmospheric stability for the day is determined to be "Neutral Class D".

[0151] Lookup table / empirical formula (Pasquill-Gifford):

[0152] (\sigma_y = 0.08x (1 + 0.0001x)^{-0.5}) (m);

[0153] (\sigma_z = 0.06x (1 + 0.0015x)^{-0.5}) (m);

[0154] Concentration unit conversion: Convert the monitored 12.5 ppm benzene (molar mass 78.11 g / mol) to mass concentration at 25℃ and 1 atm:

[0155] (C_{mass} = \frac{12.5 \times 78.11}{24.45} \approx 40.0 \ mg / m³) (The conversion factor under standard atmospheric pressure is approximately 24.45 L / mol)

[0156] Step S3.2: Pollution Source Type Identification

[0157] Input characteristics: In addition to station M3, the system also retrieves data from surrounding monitoring stations such as M4 and M5 at the same time point (ratios of multiple pollutant types: benzene, toluene, and xylene).

[0158] Algorithm: Use a pre-trained random forest classifier.

[0159] Eigenvectors: [Benzene / Toluene] = 2.5, [Toluene / Xylene] = 0.3, Wind speed standard deviation = 0.4, Wind direction standard deviation = 5°.

[0160] Output: The model outputs "Suspected gas collection hood leak at a chemical plant", with a probability of 0.92.

[0161] Step S3.3: Calculate the contribution rate of pollution sources using Bayesian regression.

[0162] Constructing an inverse model: The concentration at station M3 is known to be 40.0 mg / m³, and the locations of potential candidate sources are known (based on the distribution of enterprises in the industrial park, assuming there are 3 potential sources P1, P2, and P3).

[0163] Bayesian Regression: Using the Markov Chain Monte Carlo (MCMC) method, the most likely leakage strength Q of each source is inverted.

[0164] Prior distribution: Assume that Q follows a log-normal distribution with a prior mean (\mu_0 = 10 g / s) and a prior standard deviation (\sigma_0 = 20 g / s).

[0165] Likelihood function: The residual between the concentration value calculated by the Gaussian plume model and the actual observed value follows a normal distribution with a mean of 0 and a standard deviation (σbs = 5.0 mg / m³) (a combination of instrument error and model error).

[0166] MCMC sampling: Perform 10,000 samplings to obtain the posterior distribution of each source Q.

[0167] Contribution rate calculation:

[0168] Calculate the theoretical concentration (C_{P1}, C_{P2}, C_{P3}) produced by each source at point M3.

[0169] Results: (C_{P1} = 0.5 mg / m³), (C_{P2} = 0.1 mg / m³), (C_{P3} = 38.0 mg / m³).

[0170] Contribution rate: Since the observed value is approximately equal to (C_{P3}), the contribution rate of the P3 source (VOCs storage tank area of ​​a chemical enterprise) is 95%. The remaining 5% is the contribution from the background and P1 and P2.

[0171] Step S3.4: Blockchain Evidence Storage

[0172] The key data from the above source tracing process: {Time: 2024-06-01 10:00:00; M3 concentration at monitoring station: 40.0 mg / m³; Algorithm: Gaussian plume + Random Forest + Bayesian; Source type: Tank leak (92%); Contributing source: P3 (95%); Handling suggestion: Immediately inspect tank P3} is packaged into a hash value 0x8f3a... and uploaded to the consortium blockchain to ensure that the analysis process and results cannot be tampered with during post-audit.

[0173] Step S4: Linking Material Management and Traceability

[0174] Correlation Analysis: The cloud platform checked the usage records of sampling tubes in Smart Consumables Cabinet A. It was found that sampling tube number VOC-0156 was used for sampling near the P3 tank area (coordinates x=3.5km, y=4.0km) on May 28, 2024 at 08:00 (4 days before the warning time). This sampling tube has currently been used for 680 hours (remaining lifespan 40 hours, < 48-hour threshold).

[0175] Linked Judgment: Due to the risk of leakage from senior management and the insufficient remaining lifespan of the sampling tube VOC-0156, the cloud platform automatically determined:

[0176] The sampling tube is likely "contaminated with high concentrations" and needs to be discarded and replaced immediately.

[0177] The system automatically marked the sampling tube as "suspected contamination exceeding the standard" and prohibited its reuse.

[0178] Comprehensive early warning generation:

[0179] Pollution diffusion path: On the GIS map, a pollution plume is generated downwind (southward) centered on P3, showing that the high concentration area extends to the boundary of the park.

[0180] Hotspot areas: Areas marked M3 and P3 are red hotspots.

[0181] Material status: In smart consumable cabinet A, VOC-0156 is marked in red as "urgent disposal"; sampling tubes in other P3 areas inside the cabinet are marked as "priority retest".

[0182] Recommended measures: Immediately dispatch maintenance personnel with new sampling tubes to increase the number of sampling points within 500 meters downwind of the P3 tank area; at the same time, notify the company to inspect the tanks.

[0183] Step S5: Closed-loop feedback

[0184] Forecast: The model predicts that if the leak continues, the concentration downwind will rise to 50 mg / m³ within the next 2 hours, affecting the residential areas of the park. It is recommended to immediately equip the area with protective materials such as gas masks.

[0185] Dispatch: The operations and maintenance client received an alert, retrieved 20 new sampling tubes and 10 sets of protective materials from the consumables cabinet, and proceeded to the site. Tank sealing repairs were performed.

[0186] Feedback: Two hours later, the concentration at station M3 dropped to 0.8 ppm, and the leak source was successfully brought under control.

[0187] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for intelligent management of ecological environment monitoring materials and collaborative source tracing of pollution sources, characterized in that, Includes the following steps: S1: The intelligent consumable cabinet manages the entire lifecycle of monitored materials, including real-time video recording of the entire process of warehousing, outbound, return, and disposal, which is then uploaded to the intelligent management cloud platform. Based on user permission verification, the cabinet door is controlled to open and close and the internal environment is adjusted. Consistency verification is performed through RFID / QR code scanning, and early warning reminders are automatically generated based on the quantity of materials, usage time, and remaining shelf life. S2: Real-time acquisition of multi-source environmental data, including pollutant concentrations and meteorological and hydrological parameters in the atmosphere, water bodies, and soil, and preprocessing of the raw data by format standardization, time synchronization, outlier removal, and missing value filling; S3: Construct a pollution diffusion model based on preprocessed data, use machine learning algorithms to identify pollution source types, and calculate the contribution rate and confidence interval of each pollution source through Bayesian regression, and store key data on the blockchain. S4: Conduct correlation analysis between material usage records and pollution source tracing results to construct a "material-pollution" mapping relationship, dynamically assess material status, and generate comprehensive early warning information including pollution diffusion paths, hotspot areas, and material status; S5: Based on the pollution diffusion model, predict future pollution trends, combine the remaining life of materials to generate dynamic scheduling suggestions, issue early warning tasks through the operation and maintenance client and record the execution status, and feed the closed-loop processing results back to the cloud platform for model optimization.

2. The method according to claim 1, characterized in that: In step S1, the full lifecycle management of the intelligent consumable cabinet further includes: S1.1: Real-time video recording of material handling is performed using a high-definition camera, and the video stream is encrypted before being uploaded to the cloud platform; S1.2: Maintenance personnel submit applications for receiving, returning, or scrapping materials through the client. The application information includes the material number, quantity, operator's identity, and timestamp. S1.3: After the cloud platform verifies the user's permissions, it sends an unlocking command to the smart door lock. At the same time, the environmental control unit automatically adjusts the temperature, humidity and light inside the cabinet according to the preset storage threshold. S1.4: The cloud platform verifies the consistency between the RFID / QR code scanning data and the application information. If they do not match, an error alert is sent and the operation is locked. S1.5: The cloud platform periodically calculates the quantity of materials, cumulative usage time, and remaining shelf life. When the data falls below the threshold, it automatically issues reminders for replenishment, replacement, or overdue allocation.

3. The method according to claim 1, characterized in that, In step S2, the multi-source environmental data acquisition and preprocessing further includes: S2.1: Select and deploy atmospheric, water, and soil monitoring equipment according to the monitoring objectives and deployment areas; S2.2: Real-time access to multi-source data via IoT gateway to construct a multi-dimensional environmental dataset; S2.3: Standardize the format of the original data, synchronize the time, remove outliers based on the 3σ principle, and fill missing values ​​based on KNN interpolation.

4. The method according to claim 1, characterized in that: In step S3, the pollution diffusion modeling and source tracing analysis further includes: S3.1: Construct a multi-media pollution diffusion path simulation by combining the Gaussian plume model, the fluid dynamics model, and the Fick diffusion model; S3.2: Support vector machine and random forest are used to identify pollution source types. Input features include pollutant composition, time series and meteorological and hydrological conditions. S3.3: Calculate the contribution rate and confidence interval of each pollution source using Bayesian regression; S3.4: Calculate the hash value of key data in the pollution source tracing process and upload it to the blockchain.

5. The method according to claim 1, characterized in that: In step S4, the coordinated linkage between material management and traceability results further includes: S4.1: Conduct correlation analysis between material usage records and pollution source tracing results to construct a "material-pollution" mapping relationship; S4.2: If regional pollution incidents occur frequently or the source tracing results are abnormal, the system will automatically determine whether the monitoring materials are expired, contaminated, or need to be replaced. S4.3: Generate comprehensive early warning information that includes contour maps of pollution diffusion paths, GIS markers for hotspot areas, and suggested treatment measures for material conditions, and display it through a GIS visualization platform.

6. The method according to claim 1, characterized in that: In step S5, the intelligent early warning and closed-loop feedback further includes: S5.1: Based on the pollution diffusion model, predict the pollution trend in the next 72 hours and generate dynamic scheduling suggestions based on the remaining life of the materials; S5.2: The operation and maintenance client receives early warning information, displays specific tasks, and records execution status; S5.3: Feed the closed-loop processing results back to the cloud platform for model parameter optimization.

7. An intelligent management system for ecological environment monitoring materials and a collaborative source tracing system for pollution sources, characterized in that, include: The perception and execution layer includes an intelligent consumable cabinet module and a multi-source environmental monitoring equipment module, which are used for material lifecycle management and environmental data collection. The data processing and support layer includes a hybrid network of data communication networks used for data transmission; The core platform layer is an intelligent management cloud platform, which includes a data receiving and storage unit, a lifecycle management unit, a multi-source data access and preprocessing unit, a pollution diffusion modeling unit, a machine learning source tracing unit, a blockchain evidence storage unit, a collaborative analysis unit, and a GIS visualization unit, and is used to execute the method described in any one of claims 1 to 6. The user interaction layer is the operation and maintenance client module, which includes an application and operation unit, an early warning and feedback unit, and a video playback unit, used to interact with the cloud platform.

8. The system according to claim 7, characterized in that: The intelligent consumables cabinet module includes: The smart door lock unit uses an electromagnetic lock combined with fingerprint or facial recognition, and supports remote authorization and temporary permission issuance; The environmental control unit integrates temperature and humidity sensors, semiconductor cooling / heating elements, and a dehumidifier, and automatically adjusts according to material storage requirements; A high-definition camera unit is used to automatically trigger recording and capture images for each operation, with the video stored locally and uploaded synchronously. The RFID / QR code identification unit assigns a unique EPC code to each material, supporting rapid batch inventory checks.

9. The system according to claim 7, characterized in that: The multi-source environmental monitoring equipment module includes: Atmospheric monitoring equipment, including electrochemical gas sensors and ultrasonic weather stations; Water monitoring equipment, including optical water quality sensors and Doppler current meters; Soil monitoring equipment, including an FDR type temperature and humidity sensor and an XRF heavy metal detector; The aforementioned devices communicate via an IoT gateway and have a built-in GPS module to record their deployment location.

10. The system according to claim 7, characterized in that: The core platform layer is deployed in the cloud using a microservice architecture, wherein: the lifecycle management unit collects material information through scheduled tasks and issues reminders when trigger conditions are met; the collaborative analysis unit cross-compares material usage records with traceability results through a correlation analysis engine and outputs comprehensive early warnings; and the GIS visualization unit builds an interactive map based on WebGIS to display pollution diffusion contour lines, hotspot heat maps, and material distribution information. In the user interaction layer, the application and operation unit provides a form interface for submitting material applications and displays the cabinet door status and environmental parameters in real time; the early warning and feedback unit receives early warnings via pop-ups, SMS, or emails, and supports one-click confirmation and uploading of processing results; the video playback unit retrieves videos by timeline or operation event, and supports fast forward, slow motion, and screenshot marking.

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