Ecological environment inspection and detection information management system based on industrial internet identifier analysis

Abstract

The invention relates to the field of ecological environment inspection and detection, in particular to an ecological environment inspection and detection information management system based on industrial internet identification analysis, which introduces an industrial internet identification analysis technology, a space-time diagram convolutional network (ST-GCN) and a dynamic multi-path optimization algorithm of reinforcement learning. Uniform identification of environment detection tasks, efficient data acquisition and transmission, intelligent space-time correlation analysis and pollution traceability prediction are realized.

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, especially air pollution, water pollution, soil pollution and other phenomena, which pose a serious threat to human health and the ecosystem. In order to monitor and control these environmental problems, countries have established ecological and environmental inspection and testing systems. However, the current detection system still has many deficiencies in data collection, transmission, management and analysis, which are as follows:

[0003] Data silos are a serious problem

[0004] Currently, ecological environment inspection and testing data are mostly collected by different departments or institutions in a decentralized manner. 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 is 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 requirements in a dynamic network environment, resulting in high packet loss rates and delays in monitoring data.

[0009] Lack of spatial and temporal correlation analysis capabilities

[0010] Ecological and environmental data have significant temporal and spatial characteristics, such as the diffusion path of pollutants in space and the changing trend over time. Existing environmental monitoring systems often rely on simple statistical analysis and are unable to extract deep features from complex spatiotemporal correlation data, making it difficult to accurately analyze the pollution diffusion path and quickly locate the pollution source.

[0011] Data visualization and user interaction are not perfect

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

[0013] As an important part of the Industrial Internet, the Industrial Internet identification resolution technology can achieve accurate tracking and efficient management of data by assigning a unique identifier to each item or task. At the same time, algorithms based on artificial intelligence and deep learning (such as spatiotemporal graph convolutional networks, reinforcement learning, etc.) have demonstrated strong capabilities in dynamic data modeling and analysis. However, existing technologies have not yet combined the Industrial Internet identification resolution technology with intelligent algorithms to propose a complete information management solution for ecological environment 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 detection systems, the present invention provides an ecological environment inspection and detection information management system based on industrial Internet identification resolution, which 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 identification resolution technology, spatiotemporal graph convolutional network (ST-GCN) and dynamic multi-path optimization algorithm of reinforcement learning.

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

[0016] Identification resolution module: used to assign a unique identification 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 ecological environment monitoring data in real time through a distributed sensor network, including but not limited to air quality, water quality, and soil;

[0018] Data transmission module: used to realize secure transmission and dynamic load balancing of collected data based on the industrial Internet protocol, wherein the data transmission module adopts a dynamic path optimization algorithm based on reinforcement learning to realize dynamic adjustment of data traffic between nodes;

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

[0020] Spatiotemporal correlation analysis module: used to model the spatiotemporal characteristics of the detection data, analyze the diffusion path and source of pollutants, wherein 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, and support 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 of 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 of the convolution output 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 of 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 of the lth layer corresponds to the kth adjacency matrix, capturing the characteristics of different relationships in the graph. In the 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 the prediction of pollution diffusion, the activation function enables the complex spatiotemporal characteristics to be captured;

[0036] To sum 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, which is used to capture the temporal changes of 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 deep reinforcement learning algorithm, the optimal action a transmission path is selected according to the state s. The optimization goal is to maximize the cumulative reward R, where 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, which means the transmission path adjustment strategy adopted by the system according to the status, adjusting the path weight (such as path 1 from 80% to 60%, path 2 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 strategy optimization in a continuous action space.

[0051] Preferably, the identification resolution module marks all data of the task 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 comprises:

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

[0059] First, the present invention uses the industrial Internet identification resolution technology to assign a unique identification to each ecological environment inspection and testing task, solving the problems of inconsistent task identification and low traceability efficiency in the existing system. Through the unique identification, it is possible to quickly query the inspection task, track the entire process, and trace the responsibility, ensuring the standardization of data management and the traceability of task execution.

[0060] Secondly, based on the distributed sensor network, the present invention realizes the real-time collection and dynamic fusion of multi-source data such as air quality, water quality and soil, ensuring the comprehensiveness and accuracy of the monitoring data. Combined with the application of Internet of Things technology, the monitoring tasks in complex environments can be carried out efficiently, providing technical support for the comprehensive monitoring of the ecological environment.

[0061] In terms of data transmission, the present invention adopts a dynamic multi-path optimization algorithm based on reinforcement learning, which can dynamically adjust the transmission path according to the real-time status of the sensor network, thereby reducing data delay and packet loss rate, and ensuring the efficiency and reliability of transmission. Especially in the case of complex network environment and uneven node load, the path optimization algorithm shows significant advantages and meets the needs of real-time data transmission.

[0062] In addition, the present invention uses a spatiotemporal graph convolutional network (ST-GCN) to deeply analyze the spatiotemporal characteristics of ecological environment data, effectively extract the characteristics of pollutant diffusion paths and quickly locate pollution sources, while achieving accurate prediction of pollution diffusion trends. The analysis method based on spatiotemporal correlation provides a scientific basis for environmental governance and emergency response, filling the gap in existing technologies in modeling complex spatiotemporal characteristics.

[0063] At the same time, the present invention also combines historical data and real-time monitoring data to provide an intelligent anomaly detection function. When environmental indicators show abnormal changes, the system can automatically generate early warning information to assist users in taking quick countermeasures. In addition, based on the trend prediction function, potential environmental risks can be identified in advance, improving the ability to prevent and control changes in the ecological environment.

[0064] Finally, the present invention designs an intuitive user interface and multi-role access mechanism to support the visualization of environmental monitoring results, including pollution source tracing paths, monitoring data analysis results, and diffusion trend predictions. Through modular design and distributed storage technology, the system has high scalability and practicality, and is suitable for ecological and environmental monitoring needs of different scales and scenarios, providing an innovative solution for the deep integration of the industrial Internet and the field of environmental monitoring.

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

[0066] 1. Technical effect 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 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] Example: When the 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 prediction of diffusion trends more accurate.

[0070] Improved robustness in complex environments: Reinforcement learning can automatically learn rules 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. Technical effects of combining reinforcement learning with identity resolution

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

[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] 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 the spatiotemporal correlation characteristics.

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

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

[0080] Cross-regional association analysis: Identity resolution technology can provide multi-level information of cross-regional task data, combined with the graph modeling capability of ST-GCN, to 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 according to real-time pollution data, making the modeling of pollution diffusion paths more in line with real-time status.

[0084] Example: When a pollution incident occurs in a section of a river, the reinforcement learning algorithm adjusts the flow weights of the association graph to focus on the affected areas and avoid model prediction bias.

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

[0086] 5. Global synergistic optimization of the three

[0087] Reinforcement learning, ST-GCN, and identity resolution technologies work together to optimize the entire process from data collection, 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, and ST-GCN prioritizes high-priority tasks when predicting pollution diffusion trends.

[0089] Example: Data from tasks with high contamination risk are analyzed first, and system resources are allocated more reasonably.

[0090] Multi-level intelligent decision support: The data binding provided by the 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 can not only improve the performance of a single module (such as transmission optimization and diffusion prediction), but also produce unexpected synergistic effects in the optimization of the entire process system. 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

[0094] Embodiment 1:

[0095] 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:

[0096] Identification resolution module: used to assign a unique identification to each ecological environment inspection and testing task through industrial Internet identification resolution technology, supporting rapid query and traceability of tasks;

[0097] Data acquisition module: used to collect ecological environment monitoring data in real time through a distributed sensor network, including but not limited to air quality, water quality, and soil;

[0098] Data transmission module: used to realize secure transmission and dynamic load balancing of collected data based on the industrial Internet protocol, wherein the data transmission module adopts a dynamic path optimization algorithm based on reinforcement learning to realize dynamic adjustment of data traffic between nodes;

[0099] Data management module: used to store, classify and analyze the collected data, and use distributed storage technology to achieve efficient data retrieval;

[0100] Spatiotemporal correlation analysis module: used to model the spatiotemporal characteristics of the detection data, analyze the diffusion path and source of pollutants, wherein the spatiotemporal correlation analysis module performs spatiotemporal data analysis based on the spatiotemporal graph convolutional network (ST-GCN) technology;

[0101] User interface module: used to provide user interaction functions based on Web and mobile terminals, and support multi-role access and permission management.

[0102] Embodiment 2:

[0103] The difference between this embodiment and embodiment 1 is that this embodiment further includes:

[0104] The spatiotemporal correlation analysis module specifically includes:

[0105] Spatiotemporal graph construction unit: used to construct a spatiotemporal correlation graph containing monitoring point locations, time series and environmental parameters;

[0106] 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:

[0107] Graph convolution part:

[0108]

[0109] Temporal convolution part:

[0110] H t+1 =Conv1D(H t ,W t )

[0111] in:

[0112] 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 of graph convolution. The node feature matrix represents the pollutant concentration data collected by the sensor at each moment;

[0113] H (l) is the input feature matrix of the lth layer, which is the node feature of the convolution output 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;

[0114] 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.

[0115] D k A k The degree matrix of 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;

[0116] 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 the pollution diffusion prediction, the weight matrix is ​​used to learn the influence weight of each sensor node feature on the downstream node;

[0117] σ is a nonlinear activation function. In the prediction of pollution diffusion, the activation function enables the complex spatiotemporal characteristics to be captured;

[0118] To sum the contributions of K adjacency matrices;

[0119] 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;

[0120] Conv1D is a one-dimensional convolution operation, which is used to capture the temporal changes of node features. In pollution diffusion prediction, temporal convolution extracts the time series characteristics of pollutant concentrations and predicts future trends.

[0121] 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;

[0122] 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.

[0123] Preferably, the dynamic multi-path optimization algorithm based on reinforcement learning of the data transmission module includes:

[0124] State space definition: Based on the network spatiotemporal characteristics submitted by ST-GCN, define the network state s, including path delay, bandwidth utilization, data flow, etc.

[0125] Action space definition: the set A of possible transmission paths between nodes;

[0126] Strategy optimization: Using deep reinforcement learning algorithm, the optimal action a transmission path is selected according to the state s. The optimization goal is to maximize the cumulative reward R, where the technical reward function is:

[0127] R(s,a)=-(α×delay+β×packet loss rate+γ×load imbalance)

[0128] Among them, α, β, and γ are weight coefficients, indicating the importance of different optimization objectives;

[0129] 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;

[0130] a: Action, which means the transmission path adjustment strategy adopted by the system according to the status, adjusting the path weight (such as path 1 from 80% to 60%, path 2 from 20% to 40%)

[0131] 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.

[0132] Preferably, the reinforcement learning algorithm adopts a deep deterministic policy gradient DDPG algorithm to achieve strategy optimization in a continuous action space.

[0133] Preferably, the identification resolution module marks all data of the task by assigning a unique identification, and supports identification-based full-process tracking and responsibility tracing.

[0134] Preferably, the data management module adopts a distributed database storage structure to support unified management and retrieval of multimodal data.

[0135] Preferably, the user interface module comprises:

[0136] Data visualization unit, used to display test results, pollution paths and traceability analysis;

[0137] Operation control unit, used to create, manage and modify monitoring tasks.

[0138] 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.

[0139] 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.

[0140] Embodiment 3:

[0141] The following is a data implementation example of "An Ecological Environment Inspection and Testing Information Management System Based on Industrial Internet Identifier Resolution", which shows the specific operation and effect of the system in actual application:

[0142] Scene background

[0143] Abnormal water quality appeared in a river near an industrial park. In order to monitor the pollution source and predict the pollution spreading trend, the ecological environment inspection and detection information management system of the present invention was used to carry out monitoring and analysis.

[0144] System Configuration:

[0145] Sensor network: 20 water quality monitoring sensor nodes are deployed in and near the industrial park. Each node collects indicators such as pH value, dissolved oxygen (DO), chemical oxygen demand (COD), and ammonia nitrogen (NH3-N) of the river.

[0146] Data collection frequency: Data is collected every 10 minutes.

[0147] Data transmission protocol: The industrial Internet protocol is adopted, combined with the reinforcement learning dynamic multi-path optimization algorithm to optimize the data transmission path.

[0148] Analysis algorithm: Spatiotemporal graph convolutional network (ST-GCN) is used for spatiotemporal correlation analysis and pollution diffusion prediction.

[0149] Implementation steps:

[0150] Identity allocation:

[0151] The system generates a unique identifier EIM-20241127001 for this task through the identifier resolution module, and binds the identifier to all task-related data to ensure full-process tracking of the task.

[0152] Data collection:

[0153] 20 sensor nodes monitor water quality data in real time. Sample data is as follows:

[0154] 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

[0155] Data transmission:

[0156] The data collection 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:

[0157] Initial path weights: Path 1 (80%), Path 2 (20%).

[0158] Real-time adjustment: Dynamically adjust to path 1 (60%) and path 2 (40%) based on transmission delay and packet loss rate, effectively reducing data delay to less than 100ms and packet loss rate to 0.2%.

[0159] Data Management:

[0160] After the data is transmitted to the central server, it is classified and stored through the data management module. The data is indexed and stored by node number and collection time to facilitate subsequent retrieval and analysis.

[0161] Spatiotemporal correlation analysis:

[0162] The collected data is analyzed using the ST-GCN model. During the analysis:

[0163] Construct a spatiotemporal correlation graph: nodes represent sensor locations, and edges represent river flow directions and pollution diffusion paths.

[0164] Extracting pollution diffusion characteristics: The ST-GCN model predicts the downstream path of pollutant diffusion through spatiotemporal correlation characteristics.

[0165] Output pollution diffusion trend:

[0166] Time step time 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%

[0167] User interface display

[0168] The analysis results are displayed in the form of visual charts through the user interface module:

[0169] Water quality monitoring map: real-time display of water quality data at each monitoring point.

[0170] Pollution diffusion path diagram:

[0171] Schematic diagram of the pollution diffusion path diagram, showing the path of pollutants from a sensor location to downstream nodes and their diffusion intensity.

[0172] node:

[0173] Each node represents a sensor location, marked with a circle and an accompanying label (such as "Sensor1").

[0174] The node locations reflect the geographical distribution of sensors.

[0175] side:

[0176] The arrows on the edges indicate the direction of pollution spread (from upstream to downstream).

[0177] The labels on the sides indicate the intensity of the pollution spread (eg, "5%" indicates the percentage increase in pollution concentration).

[0178] Diffusion path:

[0179] Example:

[0180] Sensor 1 → Sensor 2: Pollution concentration increases by 5%.

[0181] Sensor 2 → Sensor 3 and Sensor 5: The diffusion path branches and the concentration increases by 8% and 6%, respectively.

[0182] Sensor 3→Sensor 4 and Sensor 5→Sensor 4: Downstream concentrated diffusion, the concentration increases are 10% and 7% respectively.

[0183] Mark the pollutant diffusion trend and downstream affected areas.

[0184] Source tracing analysis: The possible sources of pollution are marked as two sewage discharge points in the industrial park.

[0185] Technical effect:

[0186] Technical Solution Transmission delay (ms) Data packet loss rate (%) Pollution tracing accuracy (%) Prior art 300 1.5 80 The present invention 100 0.2 95

[0187] Data transmission efficiency: The transmission delay is reduced from 300ms in the traditional method to below 100ms, the packet loss rate is reduced from 1.5% to 0.2%, and the pollution tracing accuracy is improved from 80% to 95%.

[0188] Analysis accuracy: The pollution source tracing results were consistent with the on-site investigation and accurately located two pollution sources.

[0189] Predictive capability: The system predicts pollution diffusion trends 20 minutes in advance, providing decision-making support for downstream governance measures.

[0190] 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 identification 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 ecological environment monitoring data in real time through a distributed sensor network, including but not limited to air quality, water quality, and soil; Data transmission module: used to realize secure transmission and dynamic load balancing of collected data based on the industrial Internet protocol, wherein the data transmission module adopts a dynamic path optimization algorithm based on reinforcement learning to realize dynamic adjustment of data traffic between nodes; Data management module: used to store, classify and analyze the collected data, and use distributed storage technology to achieve efficient data retrieval; Spatiotemporal correlation analysis module: used to model the spatiotemporal characteristics of the detection data, analyze the diffusion path and source of pollutants, wherein the spatiotemporal correlation analysis module performs spatiotemporal data analysis based on the spatiotemporal graph convolutional network (ST-GCN) technology; User interface module: used to provide user interaction functions based on Web and mobile terminals, and support multi-role access and permission management.

2. According to claim 1, an ecological environment inspection and detection information management system based on industrial Internet identification resolution is characterized in that: 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 of 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 of the convolution output 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 of k [i][i]=∑ j A k [i][j]; the degree matrix is ​​used to normalize the adjacency matrix, Ensure the numerical stability of node features in convolution operations; 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 the pollution diffusion prediction, the weight matrix is ​​used to learn the influence weight of each sensor node feature on the downstream node; σ is a nonlinear activation function. In the prediction of pollution diffusion, the activation function enables the complex spatiotemporal characteristics to be captured; To sum 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, which is used to capture the temporal changes of 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 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.

3. According to claim 2, an ecological environment inspection and detection information management system based on industrial Internet identification resolution 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, define the network state s, 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 deep reinforcement learning algorithm, the optimal action a transmission path is selected according to the state s. The optimization goal is to maximize the cumulative reward R, where 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, which indicates the transmission path adjustment strategy adopted by the system according to the status and adjusts 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.

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

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

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

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

8. According to claim 1, an ecological environment inspection and detection information management system based on industrial Internet identity resolution 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.

9. The ecological environment inspection and detection information management system based on industrial Internet identity 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 convolution network.

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