An ecological environment inspection and testing information management system based on industrial Internet identification resolution

Abstract

The present invention relates to the field of ecological environment inspection and testing, and in particular to an ecological environment inspection and testing information management system based on industrial Internet identity resolution. The present invention realizes unified identification of environmental detection tasks, efficient data collection and transmission, intelligent spatiotemporal correlation analysis, and pollution source tracing prediction by introducing industrial Internet identity resolution technology, spatiotemporal graph convolutional network (ST-GCN), and a dynamic multi-path optimization algorithm of reinforcement learning.

Description

Technical Field

[0001] The present invention relates to the field of ecological environment inspection and testing, and in particular to an ecological environment inspection and testing information management system based on industrial Internet identity resolution. Background Art

[0002] With the acceleration of global industrialization and urbanization, ecological and environmental problems are becoming increasingly serious. In particular, air pollution, water pollution, and soil pollution pose a serious threat to human health and ecosystems. To monitor and address these environmental issues, countries have established ecological and environmental inspection and testing systems. However, current inspection systems still have many deficiencies in data collection, transmission, management, and analysis, as shown in the following:

[0003] The problem of data silos is serious

[0004] Currently, ecological environment inspection and testing data are mostly collected separately by different departments or institutions. The standards and protocols of various sensor equipment are not unified, which makes it difficult to integrate and share data, forming "data islands" and making it impossible to achieve unified management and efficient analysis.

[0005] Data identification is not unified and traceability efficiency is low

[0006] Due to the lack of unified identification of environmental monitoring tasks and data, task management and data traceability often rely on manual operations, which are time-consuming, labor-intensive, and prone to errors, seriously restricting the real-time nature and traceability efficiency of environmental monitoring.

[0007] Insufficient transmission efficiency and reliability

[0008] In complex environmental monitoring scenarios, data transmission is susceptible to network delays, node failures, and bandwidth limitations. The existing single transmission path is difficult to meet the data transmission needs in dynamic network environments, resulting in high packet loss rates and delays in monitoring data.

[0009] Lack of spatiotemporal correlation analysis capabilities

[0010] Ecological and environmental data exhibit significant temporal and spatial characteristics, such as the spatial diffusion paths of pollutants and their temporal trends. Existing environmental monitoring systems often rely on simple statistical analysis and are unable to extract deep features from complex spatiotemporal data, making it difficult to accurately analyze pollution diffusion paths and quickly locate pollution sources.

[0011] Imperfect data visualization and user interaction

[0012] The existing system is relatively simple in data display, failing to achieve multi-dimensional and intuitive display of monitoring data analysis results, and is unable to meet the query needs of the government, enterprises and the public for pollution tracing, monitoring results and governance effects.

[0013] Industrial Internet identity resolution technology, a crucial component of the Industrial Internet, enables accurate data tracking and efficient management by assigning unique identifiers to each item or task. Furthermore, algorithms based on artificial intelligence and deep learning (such as spatiotemporal graph convolutional networks and reinforcement learning) have demonstrated powerful capabilities in dynamic data modeling and analysis. However, existing technologies have yet to combine Industrial Internet identity resolution technology with intelligent algorithms to provide a comprehensive information management solution for ecological and environmental inspection and testing tasks. Summary of the Invention

[0014] In order to solve the problems of data dispersion, inconsistent identification, low transmission efficiency, insufficient analysis capabilities and imperfect user interaction in the existing ecological environment inspection and testing systems, the present invention provides an ecological environment inspection and testing information management system based on industrial Internet identification resolution. By introducing industrial Internet identification resolution technology, spatiotemporal graph convolutional network (ST-GCN) and reinforcement learning dynamic multi-path optimization algorithm, unified identification of environmental testing tasks, efficient data collection and transmission, intelligent spatiotemporal correlation analysis and pollution source tracing prediction are achieved.

[0015] The technical solution of the ecological environment inspection and testing information management system based on industrial Internet identity resolution of the present invention is as follows, which includes the following modules:

[0016] Identification resolution module: used to assign a unique identifier to each ecological environment inspection and testing task through industrial Internet identification resolution technology, supporting rapid query and traceability of tasks;

[0017] Data acquisition module: used to collect real-time ecological environment monitoring data through a distributed sensor network, including but not limited to air quality, water quality, and soil quality;

[0018] Data transmission module: used to achieve secure transmission and dynamic load balancing of collected data based on the Industrial Internet Protocol. The data transmission module adopts a dynamic path optimization algorithm based on reinforcement learning to achieve dynamic adjustment of data traffic between nodes;

[0019] Data management module: used to store, classify and analyze collected data, and use distributed storage technology to achieve efficient data retrieval;

[0020] Spatiotemporal correlation analysis module: used to model the spatiotemporal characteristics of detection data and analyze the diffusion path and source of pollutants. The spatiotemporal correlation analysis module performs spatiotemporal data analysis based on the spatiotemporal graph convolutional network (ST-GCN) technology.

[0021] User interface module: used to provide user interaction functions based on Web and mobile terminals, supporting multi-role access and permission management.

[0022] Preferably, the spatiotemporal correlation analysis module specifically includes:

[0023] Spatiotemporal graph construction unit: used to construct a spatiotemporal correlation graph containing monitoring point locations, time series, and environmental parameters;

[0024] Spatiotemporal feature extraction unit: Based on the ST-GCN model, feature extraction is performed on the spatiotemporal correlation graph. The core formula of the ST-GCN model is:

[0025] Graph convolution part:

[0026]

[0027] Temporal convolution part:

[0028] H t+1 =Conv1D(H t ,W t )

[0029] in:

[0030] H (l+1) is the node feature matrix of the l+1th layer, which represents the output features of the node after the current layer graph convolution. The node feature matrix represents the pollutant concentration data collected by the sensor at each moment;

[0031] H (l) is the input feature matrix of the lth layer, which is the node feature output by the convolution of the previous layer. The input data is the pollutant concentration and other node features monitored by the sensor in real time, including the statistical value of historical data or the identification information of the associated task;

[0032] A k is the kth adjacency matrix of the graph, which represents the connection relationship between nodes in the graph. The edges represent the pollution diffusion paths, and the weights are determined by the river flow rate, the geographical distance between sensors, or the historical pollution diffusion intensity.

[0033] D k A k The degree matrix is ​​defined as D k [i][i]=∑ j A k [i][j]; The degree matrix is ​​used to normalize the adjacency matrix to ensure the numerical stability of node features in the convolution operation;

[0034] W k (l) The learnable weight matrix at the lth layer corresponds to the kth adjacency matrix and captures the characteristics of different relationships in the graph. In pollution diffusion prediction, the weight matrix is ​​used to learn the influence weight of each sensor node feature on the downstream node.

[0035] σ is a nonlinear activation function. In pollution diffusion prediction, the activation function enables complex spatiotemporal characteristics to be captured;

[0036] To sum up the contributions of K adjacency matrices;

[0037] W t is the learnable weight matrix of time convolution, and the model learns the law of pollutant concentration changes based on historical time step data;

[0038] Conv1D is a one-dimensional convolution operation used to capture the temporal changes in node features. In pollution diffusion prediction, temporal convolution extracts the time series characteristics of pollutant concentrations and predicts future trends.

[0039] H t is the input feature matrix at time step t, the features of the node at the current time step, including but not limited to the pollutant concentration and the identification information of the sensor location;

[0040] H t+1 It is the output feature matrix of time step t+1, which represents the features of the node after time convolution and combines the changing trend in time series.

[0041] Preferably, the dynamic multi-path optimization algorithm based on reinforcement learning of the data transmission module includes:

[0042] State space definition: Based on the network spatiotemporal characteristics submitted by ST-GCN, the network state s is defined, including but not limited to path delay, bandwidth utilization, and data flow.

[0043] Action space definition: the set A of possible transmission paths between nodes;

[0044] Strategy optimization: Using a deep reinforcement learning algorithm, we select the optimal action a transmission path based on the state s. The optimization goal is to maximize the cumulative reward R. The technical reward function is:

[0045] R(s,a) = -(α × delay + β × packet loss rate + γ × load imbalance)

[0046] Among them, α, β, and γ are weight coefficients, indicating the importance of different optimization objectives;

[0047] s: state, which indicates the current system environment information. In data transmission path optimization, the state includes but is not limited to: current network delay, packet loss rate, and path load;

[0048] a: Action, indicating the transmission path adjustment strategy adopted by the system based on the status, adjusting the path weight (for example, path 1 is reduced from 80% to 60%, and path 2 is increased from 20% to 40%)

[0049] R(s,a): Reward value, which indicates the impact of the current action on system performance. The higher the reward value, the better the current path adjustment strategy. The ultimate goal is to maximize the cumulative reward.

[0050] Preferably, the reinforcement learning algorithm adopts a deep deterministic policy gradient DDPG algorithm to achieve policy optimization in a continuous action space.

[0051] Preferably, the identification resolution module marks all task data by assigning a unique identification, and supports identification-based full-process tracking and responsibility tracing.

[0052] Preferably, the data management module adopts a distributed database storage structure to support unified management and retrieval of multimodal data.

[0053] Preferably, the user interface module includes:

[0054] Data visualization unit, used to display test results, pollution paths and traceability analysis;

[0055] Operation control unit, used to create, manage and modify monitoring tasks.

[0056] Preferably, it also includes an anomaly detection module for detecting abnormal changes in environmental indicators based on historical data and real-time data and generating alarms.

[0057] Preferably, the spatiotemporal correlation analysis module realizes dynamic prediction of pollutant diffusion paths by combining graph convolution and time convolution formulas of the spatiotemporal graph convolution network.

[0058] This invention significantly improves the efficiency and intelligence level of ecological environment inspection and testing by introducing industrial Internet identity resolution technology, spatiotemporal graph convolutional network (ST-GCN) and dynamic multi-path optimization algorithm of reinforcement learning.

[0059] First, this invention uses Industrial Internet identity resolution technology to assign a unique identifier to each ecological and environmental inspection and testing task, resolving the issues of inconsistent task identification and inefficient traceability in existing systems. This unique identifier enables rapid query, full-process tracking, and accountability for inspection tasks, ensuring standardized data management and traceability of task execution.

[0060] Secondly, based on a distributed sensor network, this invention enables the real-time collection and dynamic integration of multi-source data, such as air quality, water quality, and soil quality, ensuring the comprehensiveness and accuracy of monitoring data. Combined with the application of Internet of Things technology, this enables efficient monitoring tasks in complex environments, providing technical support for comprehensive ecological monitoring.

[0061] In terms of data transmission, this invention uses a dynamic multipath optimization algorithm based on reinforcement learning. This algorithm dynamically adjusts transmission paths based on the real-time status of the sensor network, thereby reducing data latency and packet loss, ensuring efficient and reliable transmission. This path optimization algorithm demonstrates significant advantages, particularly in complex network environments with uneven node loads, meeting the requirements of real-time data transmission.

[0062] Furthermore, the present invention uses a spatiotemporal graph convolutional network (ST-GCN) to deeply analyze the spatiotemporal characteristics of ecological and environmental data, effectively extracting pollutant diffusion path characteristics and rapidly locating pollution sources, while also achieving accurate predictions of pollution diffusion trends. This analysis method based on spatiotemporal correlations provides a scientific basis for environmental governance and emergency response, filling a gap in existing technologies for modeling complex spatiotemporal characteristics.

[0063] The present invention also combines historical and real-time monitoring data to provide intelligent anomaly detection. When environmental indicators exhibit unusual changes, the system automatically generates early warnings, enabling users to quickly implement countermeasures. Furthermore, trend prediction capabilities can proactively identify potential environmental risks, improving the ability to prevent and control ecological changes.

[0064] Finally, the present invention incorporates an intuitive user interface and multi-role access mechanism to support the visualization of environmental monitoring results, including pollution source tracing, monitoring data analysis, and diffusion trend forecasts. Through modular design and distributed storage technology, the system is highly scalable and practical, adapting to ecological and environmental monitoring needs of varying scales and scenarios, providing an innovative solution for the deep integration of the Industrial Internet and environmental monitoring.

[0065] This technical solution uses a variety of algorithms, which brings many technical effects to this technical solution:

[0066] 1. Technical Effects of Combining Reinforcement Learning with Spatiotemporal Correlation Analysis

[0067] Use reinforcement learning algorithms to dynamically adjust the edge weights of the spatiotemporal correlation graph (such as pollution diffusion intensity and river flow direction weight) to optimize the spatiotemporal correlation graph in real time.

[0068] Adaptive association graph update: When the pollutant diffusion path changes dynamically, the reinforcement learning algorithm can adjust the structure and weight of the association graph in real time, thereby improving the accuracy and response speed of diffusion prediction.

[0069] For example, when a pollution source at a certain node suddenly increases, the edge weights of the association graph are dynamically adjusted to highlight the affected downstream paths, making the diffusion trend more accurately predicted.

[0070] Improved robustness in complex environments: Reinforcement learning can automatically learn patterns based on historical spatiotemporal data (such as seasonal water flow changes or sudden pollution events) and optimize association graphs to adapt to different ecological environments and pollution source distributions.

[0071] 2. The technical effect of combining reinforcement learning with identity resolution

[0072] The reinforcement learning algorithm is combined with identity resolution technology to dynamically optimize the transmission path of data traffic based on the real-time data of task identification.

[0073] Dynamic management of task priorities: The system uses a reinforcement learning algorithm to learn the data flow characteristics bound to different task identifiers (EIM-20241127001, etc.) and automatically adjusts the task transmission priority.

[0074] For example, monitoring data of high-pollution events will be transmitted first, while ordinary tasks will be delayed to improve the overall efficiency of the system.

[0075] Global optimization of data transmission and management: Reinforcement learning adjusts the transmission path and storage method according to the data flow status of the entire system, realizing efficient distributed management of data between the central server and edge nodes.

[0076] 3. Technical Effects of Combining ST-GCN with Identity Resolution

[0077] The ST-GCN model directly uses the data binding information of identity resolution as feature input and optimizes diffusion prediction by combining spatiotemporal correlation characteristics.

[0078] Data identification enhances prediction capabilities: Identification resolution technology provides full-process binding information of task data (such as time, location, and task type), which serves as the input feature of ST-GCN and can significantly improve the accuracy of pollution diffusion prediction.

[0079] Example: Predicting pollution diffusion trends in a specific mission area based on identification information is more focused and efficient than global predictions.

[0080] Cross-regional correlation analysis: Identity resolution technology can provide multi-level information of cross-regional task data. Combined with the graph modeling capabilities of ST-GCN, it can achieve global modeling and prediction of multi-regional pollution diffusion.

[0081] 4. Deep integration of reinforcement learning and ST-GCN

[0082] The reinforcement learning algorithm dynamically adjusts the structure of ST-GCN (such as edge weights or node feature selection) to achieve adaptive optimization of spatiotemporal correlation analysis.

[0083] Dynamically adjust the association graph weights: Reinforcement learning dynamically adjusts the input graph structure of ST-GCN based on real-time pollution data, making the modeling of pollution diffusion paths more consistent with real-time conditions.

[0084] For example, if a pollution incident occurs in a certain section of a river, the reinforcement learning algorithm adjusts the flow weights of the association graph to focus on the affected areas and avoid bias in model predictions.

[0085] Optimizing model training efficiency: Reinforcement learning reduces the computational complexity of the ST-GCN model through adaptive strategies, such as dynamically selecting important node features or simplifying edge connection relationships.

[0086] 5. Global collaborative optimization of the three

[0087] Reinforcement learning, ST-GCN, and identity resolution technologies work together to optimize the entire process from data collection and transmission to analysis and prediction.

[0088] Real-time dynamic optimization of multi-task management: Reinforcement learning combines task data with identity resolution to optimize transmission paths in real time. ST-GCN prioritizes high-priority tasks when predicting pollution diffusion trends.

[0089] Example: Data from tasks with high pollution risks are prioritized for analysis, allowing for more reasonable allocation of system resources.

[0090] Multi-level intelligent decision support: The data binding provided by identity resolution technology supports full-process tracking, ST-GCN provides prediction capabilities, and reinforcement learning optimizes system performance, thereby providing fast and intelligent decision support for downstream governance.

[0091] Through the deep integration of algorithms, this technical solution not only improves the performance of individual modules (such as transmission optimization and diffusion prediction), but also creates unexpected synergistic effects in full-process system optimization. This effect is particularly prominent in terms of robustness, adaptability, and global optimization performance in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a structural block diagram of the present invention; DETAILED DESCRIPTION

[0093] Example 1:

[0094] The technical solution of an ecological environment inspection and testing information management system based on industrial Internet identity resolution in this embodiment is as follows, which includes the following modules:

[0095] Identification resolution module: used to assign a unique identifier to each ecological environment inspection and testing task through industrial Internet identification resolution technology, supporting rapid query and traceability of tasks;

[0096] Data acquisition module: used to collect real-time ecological environment monitoring data through a distributed sensor network, including but not limited to air quality, water quality, and soil quality;

[0097] Data transmission module: used to achieve secure transmission and dynamic load balancing of collected data based on the Industrial Internet Protocol. The data transmission module adopts a dynamic path optimization algorithm based on reinforcement learning to achieve dynamic adjustment of data traffic between nodes;

[0098] Data management module: used to store, classify and analyze collected data, and use distributed storage technology to achieve efficient data retrieval;

[0099] Spatiotemporal correlation analysis module: used to model the spatiotemporal characteristics of detection data and analyze the diffusion path and source of pollutants. The spatiotemporal correlation analysis module performs spatiotemporal data analysis based on the spatiotemporal graph convolutional network (ST-GCN) technology.

[0100] User interface module: used to provide user interaction functions based on Web and mobile terminals, supporting multi-role access and permission management.

[0101] Example 2:

[0102] The difference between this embodiment and embodiment 1 is that this embodiment further includes:

[0103] The spatiotemporal correlation analysis module specifically includes:

[0104] Spatiotemporal graph construction unit: used to construct a spatiotemporal correlation graph containing monitoring point locations, time series, and environmental parameters;

[0105] Spatiotemporal feature extraction unit: Based on the ST-GCN model, feature extraction is performed on the spatiotemporal correlation graph. The core formula of the ST-GCN model is:

[0106] Graph convolution part:

[0107]

[0108] Temporal convolution part:

[0109] H t+1 =Conv1D(H t ,W t )

[0110] in:

[0111] H (l+1) is the node feature matrix of the l+1th layer, which represents the output features of the node after the current layer graph convolution. The node feature matrix represents the pollutant concentration data collected by the sensor at each moment;

[0112] H (l) is the input feature matrix of the lth layer, which is the node feature output by the convolution of the previous layer. The input data is the pollutant concentration and other node features monitored by the sensor in real time, including the statistical value of historical data or the identification information of the associated task;

[0113] A k is the kth adjacency matrix of the graph, which represents the connection relationship between nodes in the graph. The edges represent the pollution diffusion paths, and the weights are determined by the river flow rate, the geographical distance between sensors, or the historical pollution diffusion intensity.

[0114] D k A k The degree matrix is ​​defined as D k [i][i]=∑ j A k [i][j]; The degree matrix is ​​used to normalize the adjacency matrix to ensure the numerical stability of node features in the convolution operation;

[0115] W k (l) The learnable weight matrix at the lth layer corresponds to the kth adjacency matrix and captures the characteristics of different relationships in the graph. In pollution diffusion prediction, the weight matrix is ​​used to learn the influence weight of each sensor node feature on the downstream node.

[0116] σ is a nonlinear activation function. In pollution diffusion prediction, the activation function enables complex spatiotemporal characteristics to be captured;

[0117] To sum up the contributions of K adjacency matrices;

[0118] W t is the learnable weight matrix of time convolution, and the model learns the law of pollutant concentration changes based on historical time step data;

[0119] Conv1D is a one-dimensional convolution operation used to capture the temporal changes in node features. In pollution diffusion prediction, temporal convolution extracts the time series characteristics of pollutant concentrations and predicts future trends.

[0120] H t is the input feature matrix at time step t, the features of the node at the current time step, including but not limited to the pollutant concentration and the identification information of the sensor location;

[0121] H t+1 It is the output feature matrix of time step t+1, which represents the features of the node after time convolution and combines the changing trend in time series.

[0122] Preferably, the dynamic multi-path optimization algorithm based on reinforcement learning of the data transmission module includes:

[0123] State space definition: Based on the network spatiotemporal characteristics submitted by ST-GCN, the network state s is defined, including path delay, bandwidth utilization, data flow, and other information.

[0124] Action space definition: the set A of possible transmission paths between nodes;

[0125] Strategy optimization: Using a deep reinforcement learning algorithm, we select the optimal action a transmission path based on the state s. The optimization goal is to maximize the cumulative reward R. The technical reward function is:

[0126] R(s,a) = -(α × delay + β × packet loss rate + γ × load imbalance)

[0127] Among them, α, β, and γ are weight coefficients, indicating the importance of different optimization objectives;

[0128] s: state, which indicates the current system environment information. In data transmission path optimization, the state includes but is not limited to: current network delay, packet loss rate, and path load;

[0129] a: Action, indicating the transmission path adjustment strategy adopted by the system based on the status, adjusting the path weight (for example, path 1 is reduced from 80% to 60%, and path 2 is increased from 20% to 40%)

[0130] R(s,a): Reward value, which indicates the impact of the current action on system performance. The higher the reward value, the better the current path adjustment strategy. The ultimate goal is to maximize the cumulative reward.

[0131] Preferably, the reinforcement learning algorithm adopts a deep deterministic policy gradient DDPG algorithm to achieve policy optimization in a continuous action space.

[0132] Preferably, the identification resolution module marks all task data by assigning a unique identification, and supports identification-based full-process tracking and responsibility tracing.

[0133] Preferably, the data management module adopts a distributed database storage structure to support unified management and retrieval of multimodal data.

[0134] Preferably, the user interface module includes:

[0135] Data visualization unit, used to display test results, pollution paths and traceability analysis;

[0136] Operation control unit, used to create, manage and modify monitoring tasks.

[0137] Preferably, it also includes an anomaly detection module for detecting abnormal changes in environmental indicators based on historical data and real-time data and generating alarms.

[0138] Preferably, the spatiotemporal correlation analysis module realizes dynamic prediction of pollutant diffusion paths by combining graph convolution and time convolution formulas of the spatiotemporal graph convolution network.

[0139] Example 3:

[0140] The following is a data example of "An Ecological Environment Inspection and Testing Information Management System Based on Industrial Internet Identifier Resolution", which demonstrates the specific operation and effect of the system in actual application:

[0141] Scene Background

[0142] Abnormal water quality appeared in a river near an industrial park. In order to monitor the pollution source and predict the pollution spread trend, the ecological environment inspection and detection information management system of the present invention was used to carry out monitoring and analysis.

[0143] System Configuration:

[0144] Sensor network: 20 water quality monitoring sensor nodes are deployed in and around the industrial park. Each node collects river pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH3-N) and other indicators.

[0145] Data collection frequency: Data is collected every 10 minutes.

[0146] Data transmission protocol: Adopt the industrial Internet protocol and combine it with the reinforcement learning dynamic multi-path optimization algorithm to optimize the data transmission path.

[0147] Analysis algorithm: Spatiotemporal graph convolutional network (ST-GCN) is used for spatiotemporal correlation analysis and pollution diffusion prediction.

[0148] Implementation steps:

[0149] Identity allocation:

[0150] The system generates a unique identifier EIM-20241127001 for this task through the identity resolution module, and binds the identifier to all task-related data to ensure full-process tracking of the task.

[0151] Data collection:

[0152] 20 sensor nodes monitor water quality data in real time. The sample data is as follows:

[0153] Collection time Node number pH DO (mg / L) COD (mg / L) <![CDATA[NH3-N(mg / L)]]> 2024-11-27 10：00 Node 1 6.8 5.2 15 04 2024-11-27 10：00 Node 2 6.5 4.8 20 0.6 2024-11-27 10：00 Node 3 67 5.0 18 0.5 2024-11-27 10：00 Node 4 6.6 4.6 22 0.7 2024-11-27 10：00 Node 5 6.9 5.5 14 0.3 2024-11-27 10：00 Node 6 6.8 5.3 16 0.4 2024-11-27 10：00 Node 7 6.5 4.9 19 0.6 2024-11-27 10：00 Node 8 67 54 17 0.5 2024-11-27 10：00 Node 9 6.4 47 21 0.8 2024-11-27 10：00 Node 10 6.8 5.6 13 03 2024-11-27 10：00 Node 20 6.9 6.0 12 0.3

[0154] Data transmission:

[0155] The data acquisition module transmits real-time data to the system center server through the transmission module. The transmission module uses a reinforcement learning dynamic multi-path optimization algorithm to dynamically adjust the transmission priority when selecting a path. The example adjustment is as follows:

[0156] Initial path weights: path 1 (80%), path 2 (20%).

[0157] Real-time adjustment: Dynamically adjusts to path 1 (60%) and path 2 (40%) based on transmission delay and packet loss rate, effectively reducing data delay to below 100ms and packet loss rate to 0.2%.

[0158] Data Management:

[0159] After data is transmitted to the central server, it is categorized and stored in the data management module. Data is indexed and stored by node number and collection time, facilitating subsequent retrieval and analysis.

[0160] Spatiotemporal correlation analysis:

[0161] The collected data is analyzed using the ST-GCN model. During the analysis:

[0162] Construct a spatiotemporal correlation graph: nodes represent sensor locations, and edges represent river flow directions and pollution diffusion paths.

[0163] Extracting pollution diffusion characteristics: The ST-GCN model predicts the downstream path of pollutant diffusion through spatiotemporal correlation characteristics.

[0164] Output pollution diffusion trend:

[0165] Time step time Scope of impact Changes in pollutant concentrations Time step 1 2024-11-2710：10 Downstream node (1 node) Concentration increased by 5% Time step 2 2024-11-2710：20 Downstream nodes (spread to 3 nodes) Concentration increased by 10%

[0166] User interface display

[0167] The analysis results are displayed in the form of visual charts through the user interface module:

[0168] Water quality monitoring map: displays water quality data of each monitoring point in real time.

[0169] Pollution diffusion path diagram:

[0170] Schematic diagram of the pollution diffusion path, showing the path of pollutants from a sensor location to downstream nodes and their diffusion intensity.

[0171] node:

[0172] Each node represents a sensor location, marked with a circle and an accompanying label (such as "Sensor1").

[0173] The node locations reflect the geographical distribution of sensors.

[0174] side:

[0175] The arrows on the edges indicate the direction of pollution diffusion (from upstream to downstream).

[0176] The labels on the edges indicate the intensity of the pollution spread (eg, "5%" indicates the percentage of pollution concentration increase).

[0177] Diffusion path:

[0178] Example:

[0179] Sensor 1 → Sensor 2: Pollution concentration increases by 5%.

[0180] Sensor 2 → Sensor 3 and Sensor 5: The diffusion path branches, and the concentration increases by 8% and 6%, respectively.

[0181] Sensor 3→Sensor 4 and Sensor 5→Sensor 4: Concentrated diffusion occurs downstream, with concentration increases of 10% and 7%, respectively.

[0182] Mark the pollutant diffusion trend and downstream affected areas.

[0183] Source tracing analysis: The possible sources of pollution are indicated to be two discharge points in the industrial park.

[0184] Technical effects:

[0185] Technical Solution Transmission delay (ms) Data packet loss rate (%) Pollution tracing accuracy (%) Existing technology 300 1.5 80 The present invention 100 0.2 95

[0186] Data transmission efficiency: Transmission delay is reduced from 300ms in traditional methods to below 100ms, packet loss rate is reduced from 1.5% to 0.2%, and pollution tracing accuracy is improved from 80% to 95%.

[0187] Analysis accuracy: The pollution source tracing results were consistent with the on-site investigation, and two pollution sources were accurately located.

[0188] Predictive capability: The system can predict pollution diffusion trends 20 minutes in advance, providing decision-making support for downstream control measures.

[0189] Experimental results show that the reinforcement learning dynamic multi-path optimization algorithm adopted in the present invention reduces the data transmission delay from 300ms to below 100ms and the packet loss rate from 1.5% to 0.2%, significantly improving the data transmission efficiency.

Claims

1. An ecological environment inspection and testing information management system based on industrial Internet identity resolution, characterized in that: Includes the following modules: Identification resolution module: used to assign a unique identifier to each ecological environment inspection and testing task through industrial Internet identification resolution technology, supporting rapid query and traceability of tasks; Data acquisition module: used to collect real-time ecological environment monitoring data through a distributed sensor network, including but not limited to air quality, water quality, and soil quality; Data transmission module: used to achieve secure transmission and dynamic load balancing of collected data based on the Industrial Internet Protocol. The data transmission module adopts a dynamic path optimization algorithm based on reinforcement learning to achieve dynamic adjustment of data traffic between nodes; Data management module: used to store, classify and analyze collected data, and use distributed storage technology to achieve efficient data retrieval; Spatiotemporal correlation analysis module: used to model the spatiotemporal characteristics of detection data and analyze the diffusion path and source of pollutants. The spatiotemporal correlation analysis module performs spatiotemporal data analysis based on the spatiotemporal graph convolutional network (ST-GCN) technology. The spatiotemporal correlation analysis module specifically includes: Spatiotemporal graph construction unit: used to construct a spatiotemporal correlation graph containing monitoring point locations, time series, and environmental parameters; Spatiotemporal feature extraction unit: Based on the ST-GCN model, feature extraction is performed on the spatiotemporal correlation graph. The core formula of the ST-GCN model is: Graph convolution part: Temporal convolution part: H t+1 =Conv1D(H t ,W t ) in: H (l+1) is the node feature matrix of the l+1th layer, which represents the output features of the node after the current layer graph convolution. The node feature matrix represents the pollutant concentration data collected by the sensor at each moment; H (l) is the input feature matrix of the lth layer, which is the node feature output by the convolution of the previous layer. The input data is the pollutant concentration and other node features monitored by the sensor in real time, including the statistical value of historical data or the identification information of the associated task; A k is the kth adjacency matrix of the graph, which represents the connection relationship between nodes in the graph. The edges represent the pollution diffusion paths, and the weights are determined by the river flow rate, the geographical distance between sensors, or the historical pollution diffusion intensity. D k A k The degree matrix is ​​defined as D k [i][j]=∑ j A k [i][j]; The degree matrix is ​​used to normalize the adjacency matrix to ensure the numerical stability of node features in the convolution operation; W k (l) The learnable weight matrix of the lth layer corresponds to the kth adjacency matrix, capturing the characteristics of different relationships in the graph. In pollution diffusion prediction, the weight matrix is ​​used to learn the influence weight of each sensor node feature on the downstream nodes; σ is a nonlinear activation function. In pollution diffusion prediction, the activation function enables complex spatiotemporal characteristics to be captured; To sum up the contributions of K adjacency matrices; W t is the learnable weight matrix of time convolution, and the model learns the law of pollutant concentration changes based on historical time step data; Conv1D is a one-dimensional convolution operation used to capture the temporal changes in node features. In pollution diffusion prediction, temporal convolution extracts the time series characteristics of pollutant concentrations and predicts future trends. H t is the input feature matrix at time step t, the features of the node at the current time step, including but not limited to the pollutant concentration and the identification information of the sensor location; H t+1 The output feature matrix of time step t+1 represents the characteristics of the node after time convolution, combined with the trend of change in time series User interface module: used to provide user interaction functions based on Web and mobile terminals, supporting multi-role access and permission management.

2. The ecological environment inspection and detection information management system based on industrial Internet identification resolution according to claim 1 is characterized in that: The dynamic multi-path optimization algorithm based on reinforcement learning of the data transmission module includes: State space definition: Based on the network spatiotemporal characteristics submitted by ST-GCN, the network state s is defined, including but not limited to path delay, bandwidth utilization, and data flow; Action space definition: the set A of possible transmission paths between nodes; Strategy optimization: Using a deep reinforcement learning algorithm, we select the optimal action a transmission path based on the state s. The optimization goal is to maximize the cumulative reward R. The technical reward function is: R(s,a) = -(α × delay + β × packet loss rate + γ × load imbalance) Among them, α, β, and γ are weight coefficients, indicating the importance of different optimization objectives; s: state, which indicates the current system environment information. In data transmission path optimization, the state includes but is not limited to: current network delay, packet loss rate, and path load; a: Action, indicating the transmission path adjustment strategy adopted by the system based on the status and adjusting the path weight; R(s,a): Reward value, which indicates the impact of the current action on system performance. The higher the reward value, the better the current path adjustment strategy. The ultimate goal is to maximize the cumulative reward.

3. The ecological environment inspection and detection information management system based on industrial Internet identification resolution according to claim 2 is characterized in that: The reinforcement learning algorithm uses the deep deterministic policy gradient DDPG algorithm to achieve policy optimization in the continuous action space.

4. The ecological environment inspection and detection information management system based on industrial Internet identification resolution according to claim 1 is characterized in that: The identification resolution module marks all task data by assigning a unique identification, and supports full-process tracking and responsibility tracing based on identification.

5. The ecological environment inspection and detection information management system based on industrial Internet identification resolution according to claim 1 is characterized in that: The data management module adopts a distributed database storage structure to support unified management and retrieval of multimodal data.

6. The ecological environment inspection and detection information management system based on industrial Internet identification resolution according to claim 1 is characterized in that: The user interface module includes: Data visualization unit, used to display test results, pollution paths and traceability analysis; Operation control unit, used to create, manage and modify monitoring tasks.

7. The ecological environment inspection and detection information management system based on industrial Internet identification resolution according to claim 1 is characterized in that: It also includes an anomaly detection module for detecting abnormal changes in environmental indicators based on historical data and real-time data and generating alarms.

8. The ecological environment inspection and detection information management system based on industrial Internet identification resolution according to claim 1 is characterized in that: The spatiotemporal correlation analysis module realizes dynamic prediction of pollutant diffusion paths by combining graph convolution and time convolution formulas of the spatiotemporal graph convolutional network.

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