CN118612258A - A distributed hydrological communication perception solution and equipment system based on edge computing - Google Patents

Abstract

The present invention discloses a distributed hydrological communication perception scheme and equipment system based on edge computing, which relates to the field of water conservancy survey technology and includes the following steps: S1, perception node; S2, intelligent selection of optimal communication path; S3, edge computing node; In the present invention, by integrating multi-parameter and multi-modal perception technology, edge intelligent processing, intelligent communication network, high-performance computing and distributed learning and other technologies, comprehensive perception, efficient processing and intelligent analysis of the hydrological environment are achieved, which provides strong support for hydrological disaster warning and decision-making, and at the same time, brings new breakthroughs to hydrological research and application.

Description

A distributed hydrological communication perception solution and equipment system based on edge computing

Technical Field

The present invention relates to the field of water conservancy survey technology, and specifically to a distributed hydrological communication perception solution and equipment system based on edge computing.

Background Art

With the rapid development of science and technology, the field of hydrological monitoring is facing challenges such as large data volume, high real-time requirements, and uneven distribution of computing resources. Traditional hydrological monitoring systems mostly rely on centralized data centers for processing and analysis. This approach not only has high data processing latency, but also often fails to meet real-time requirements when faced with large amounts of data. At the same time, with the rapid development of the Internet of Things, big data, and artificial intelligence technologies, distributed computing and edge computing have gradually become important directions for solving these problems. In distributed hydrological communication perception solutions, edge computing nodes play a vital role. Edge computing nodes are located near the data source and can process and analyze data in real time, reduce data transmission latency and bandwidth requirements, and improve the real-time and efficiency of data processing. At the same time, edge computing nodes can also dynamically adjust resource allocation according to system load and data processing requirements, optimize resource utilization, and ensure that critical tasks can obtain sufficient computing resources.

However, the existing edge computing nodes still have some deficiencies in terms of function and performance. First, traditional edge computing nodes often only have a single data processing capability, lack the ability of distributed learning and collaborative training, and are difficult to meet complex hydrological monitoring needs. Secondly, the existing edge computing nodes lack flexibility in resource management and cannot dynamically adjust resource allocation according to system load and data processing requirements, resulting in low resource utilization. In traditional hydrological monitoring systems, a large amount of sensor data is usually first transmitted to the central server for unified processing. However, this centralized data processing method not only increases the delay in data transmission, but may also cause the server to be overloaded, affecting data processing efficiency. In addition, due to the limitations of network bandwidth and transmission stability, remote data transmission often has the risk of loss and delay, which is unacceptable for hydrological monitoring tasks that require real-time response.

In order to solve the above problems, a distributed hydrological communication perception solution and equipment system based on edge computing is provided to overcome the above problems.

Summary of the invention

The purpose of the present invention is to provide a distributed hydrological communication perception solution and equipment system based on edge computing to solve the problems raised in the above background technology.

In order to solve the above technical problems, the present invention provides a distributed hydrological communication perception solution based on edge computing, comprising the following steps:

S1, perception node:

S2, intelligently select the optimal communication path;

S3, edge computing node; specifically includes the following steps:

S3.1, High-performance computing: Deploy the stream processing framework on the edge computing nodes; use cluster technology to manage multiple edge computing nodes and dynamically allocate resources according to computing needs;

S3.2, Distributed learning and collaborative training: Deploy a distributed deep learning framework to collaboratively train a deep learning model for predicting water quality changes on multiple edge computing nodes;

Introduce and use a collaborative training mechanism and a federated learning framework to allow each edge computing node to train the model locally and upload and aggregate the model parameters regularly;

The strategy of task offloading is introduced. According to the load and computing resources of edge computing nodes, it is dynamically determined whether tasks need to be offloaded to the cloud. When the edge node load is too high or the computing resources are insufficient, some computing tasks are sent to the cloud for calculation. After the cloud computing results are returned, the edge node continues the subsequent processing or directly feeds back the results to the user.

S3.3, real-time data analysis and feedback;

S3.4, dynamic resource management;

S4. Build a central processing system.

Furthermore, in S1, low-power hardware and energy-saving algorithms are used to optimize data processing and transmission processes and reduce power consumption.

Furthermore, in S2, during data transmission, data compression technology is introduced to reduce the amount of transmitted data and reduce network latency.

Furthermore, in S2, redundant design is adopted on key equipment and links, including dual power supplies and dual communication modules, to improve the fault tolerance and reliability of the system; and a dynamic network selection function is introduced to select the optimal communication network according to network conditions and data transmission requirements, thereby improving data transmission efficiency and reducing latency.

Furthermore, in S3.1, containerization technology is used to package the hydrological data processing application into an image, which is quickly deployed and migrated on the edge computing node, and the container is orchestrated and managed through Docker Compose, a tool in Kubernetes.

Furthermore, in S3.2, in the federated learning framework, noise is added to the model training process so that the original data cannot be inferred from the model parameters.

Furthermore, in S3.2, a hybrid cloud architecture is built to connect edge computing nodes with virtual machines and container clusters in the cloud, and a cloud-edge collaboration mechanism is used to achieve flexible task scheduling and data transmission between the edge and the cloud.

Furthermore, in S3.4, a resource reservation mechanism is introduced, including:

Task classification and priority setting: Set the real-time water level monitoring task as the highest priority, the water quality analysis task as the medium priority, and other non-critical tasks as the low priority;

Resource reservation settings: reserve two CPU cores and 50% of the memory for the real-time water level monitoring task; this means that at any time, these two CPU cores and this part of the memory can only be used by the real-time water level monitoring task;

Resource scheduling strategy: When a new task arrives, the resource scheduler first checks whether there are enough resources to meet the needs of the task. If there are enough resources, they are allocated to the task; if there are not enough resources, the task is queued or rejected according to its priority. For the real-time water level monitoring task, it can always get the required resources immediately because it has high priority and reserved resources.

Monitoring and adjustment: Use a tool such as Prometheus or Grafana to monitor the resources of edge computing nodes in real time. If the load of the real-time water level monitoring task suddenly increases, temporarily increase its reserved resources to ensure that it can process data in a timely manner. At the same time, limit or suspend low-priority tasks to release more resources for critical tasks.

A distributed hydrological communication sensing device system based on edge computing, comprising:

Sensing devices:

Multi-parameter sensors: including water level sensors, flow meters, water quality analyzers, weather stations, and soil moisture sensors; these sensors can directly measure and transmit data on environmental parameters;

Multimodal sensing devices: including acoustic sensors, optical sensors, and chemical sensors; these devices perceive environmental information through different physical principles;

Edge computing node equipment:

One of the high-performance servers and edge computing devices: including industrial-grade or enterprise-grade servers with built-in high-performance processors, large-capacity memory and storage space; running complex stream processing frameworks and deep learning algorithms;

Container orchestration management system: software running on the server, including Docker Compose and Kubernetes, used to manage the life cycle of containerized applications;

Intelligent communication network equipment:

Advanced wireless communication equipment: including 5G wireless communication modules, Wi-Fi modules, LoRa gateways, Zigbee coordinators, supporting different communication protocols and network topologies;

Data compression equipment and software: software functions embedded in communication modules and edge computing devices to compress data before transmission to reduce bandwidth usage;

Redundant design equipment: including backup power supply, redundant communication modules, and network interface cards to ensure continuous operation of the system in the event of equipment failure;

Central processing system equipment:

Big data analysis and prediction server: a high-performance server cluster with powerful data processing and computing capabilities, equipped with a distributed file system and big data processing framework;

Data cache system: a type of memory database or disk cache system used to cache hot data to reduce access to backend storage.

Hybrid cloud architecture equipment:

Network equipment and systems: including routers, switches, VPN gateways, APIs and SDKs provided by cloud service providers, used to connect edge computing nodes and cloud resources.

Compared with the prior art, the present invention has the following beneficial effects:

The multi-parameter and multi-modal integration of sensing nodes improves the comprehensiveness and accuracy of data collection, and provides a rich data source for the comprehensive evaluation of the hydrological environment. The integration of multiple sensing technologies can more comprehensively capture changes in the hydrological environment and provide a more accurate basis for prediction and decision-making. It reveals hydrological phenomena or patterns that cannot be observed by a single parameter, and provides a new perspective for scientific research. By implementing energy-saving scheduling algorithms, the energy consumption of sensing nodes can be minimized while ensuring data accuracy and real-time performance, thereby extending their service life.

Edge intelligent processing reduces data transmission delays and bandwidth requirements, and improves the real-time and efficiency of data processing. Edge intelligent processing can promptly detect and process abnormal data, improve the robustness of the system, discover previously unnoticed hydrological anomaly patterns, and provide new clues for hydrological disaster warnings.

Intelligent communication networks improve the reliability and stability of communication networks and reduce the risk of data loss due to network failures. Intelligent selection of the optimal communication path can improve the efficiency and reliability of data transmission. In an emergency, the intelligent communication network may automatically switch to a backup path to ensure real-time transmission of critical data. Through the implementation of redundant design and dynamic network selection functions, the fault tolerance and reliability of distributed hydrological communication perception systems can be greatly improved, while improving data transmission efficiency and reducing latency.

The high-performance computing of edge computing nodes improves the speed and efficiency of data processing and supports more complex analysis and prediction models; cluster technology can make full use of computing resources, improve overall computing performance, and reveal complex hydrological phenomena that could not be processed before due to insufficient computing resources; and through the cloud-edge collaborative mechanism, while maintaining real-time and local processing capabilities, it can fully utilize the powerful computing and analysis capabilities of the cloud to achieve a more flexible and efficient hydrological communication perception solution.

Distributed learning and collaborative training improve the efficiency and accuracy of model training. Through collaborative training of multiple nodes, it can converge to the optimal solution more quickly. The federated learning framework protects data privacy while realizing collaborative training of models. Through collaborative training, hydrological data from different locations may reveal global hydrological laws and trends.

Dynamic resource management improves resource utilization and ensures that critical tasks can obtain sufficient computing resources. Dynamic adjustment of resource allocation can adapt to changes in different tasks and workloads. In the case of resource constraints, dynamic management may optimize resource allocation to ensure that critical tasks are not affected while maintaining the stability of the overall system performance. By introducing a resource reservation mechanism, it can ensure that critical tasks can always obtain sufficient computing resources on edge computing nodes, thereby improving the reliability and response speed of the system.

The big data analysis and prediction of the central processing system, and the in-depth mining and analysis of the data uploaded by the edge nodes can reveal the changing trends and laws of the hydrological environment, and the prediction model can provide support for hydrological disaster warning and decision-making; through the comprehensive analysis of large amounts of data, hydrological phenomena or trends that have not been noticed before can be discovered, providing new perspectives for scientific research and decision-making; on the basis of data analysis and prediction, data cache is set up in the central processing system to reduce the amount of data transmission between the edge computing nodes and reduce network latency; through the above integration, not only can the problems of distributed hydrological communication perception solutions and equipment systems in terms of network latency and bandwidth, computing power, environmental adaptability and customization requirements be solved, but the entire system can also be made more efficient, reliable and flexible.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a distributed hydrological communication perception solution and equipment system based on edge computing in the present invention.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Please refer to Figure 1, the present invention provides a technical solution:

Referring to FIG. 1 , a distributed hydrological communication perception solution based on edge computing includes the following steps:

S1. Sensing nodes: multi-parameter and multi-modal integration: integrated multi-parameter sensors include water level, flow, water quality, meteorology, and soil moisture. These sensors can measure parameters such as water level, flow, water quality, meteorology, and soil moisture respectively. At the same time, acoustic, optical, and chemical multi-modal sensing technologies are introduced. For example, a spectrometer is used for water quality analysis (optical sensing), or sonar technology is used to measure water depth (acoustic sensing), or chemical sensors are used to detect specific chemicals in water quality. The data of all sensors and multi-modal sensing devices need to be centralized into a data acquisition system, which is responsible for collecting data regularly or in real time, and performing preliminary integration and formatting.

Examples:

In a river monitoring project, a sensing node that integrates water level sensors, flow meters, and water quality analyzers was used. At the same time, in order to have a more comprehensive understanding of the hydrological environment, a spectrometer (optical sensing) was introduced to monitor the growth of algae in the water body, and a sonar device (acoustic sensing) was used to measure the depth of the river. All sensors and devices are connected to a data collector, which sends data to the edge computing node for processing through wireless communication technologies (such as LoRa, 4G/5G, etc.);

It also includes edge intelligent processing: preliminary data analysis, anomaly detection and trend prediction through one of the embedded machine learning and deep learning algorithms, reducing the back-end processing pressure; specifically:

Embedded algorithms: Machine learning or deep learning algorithms are embedded in the sensing nodes or edge computing nodes close to the sensing nodes. These algorithms can perform preliminary analysis, anomaly detection, and trend prediction on the data collected in real time.

Data processing: After receiving data from the sensing node, the edge computing node will immediately process it using the embedded algorithm. The processing results can be used for real-time decision-making or as the basis for subsequent in-depth analysis;

Abnormal detection and warning: If abnormal data is detected (such as a sudden increase in water level, a sharp drop in water quality, etc.), the edge computing node will immediately trigger the warning mechanism and notify relevant personnel to handle it;

Examples:

In the above river monitoring project, after the edge computing node receives the data from each sensor, it will use the embedded deep learning algorithm to analyze the data. For example, by analyzing the historical water level data and the current rainfall, the algorithm can predict the trend of water level changes in the future. If the prediction results show that the water level may exceed the warning line, the edge computing node will immediately trigger the early warning system and send an alarm notification to the relevant personnel so that they can take timely measures. At the same time, the edge computing node will also send the processed data to the central processing system for further analysis and decision support;

S2. Intelligent communication network: It uses advanced wireless communication technology to intelligently select the optimal communication path according to network conditions and data transmission requirements, has self-organization and self-healing capabilities, automatically repairs faulty nodes and links, and ensures network stability; specifically:

Intelligent selection of optimal communication path:

Technical methods:

Use network protocols such as SDN (Software Defined Network) or Network Function Virtualization (NFV) to dynamically adjust network traffic; use network optimization algorithms (such as the shortest path algorithm, load balancing algorithm, etc.) to evaluate network status and determine the optimal path; combine network perception technology, such as real-time measurement of network bandwidth, delay, packet loss rate and other parameters, to dynamically select the best transmission path;

Examples:

When an edge computing node in the system needs to send a large amount of data to the central processing system, the intelligent communication network can detect the congestion in the current network and automatically select a path with higher bandwidth and lower latency for data transmission;

Self-organization and self-healing capabilities:

Technical methods:

AdHoc network technology is used to enable network nodes to automatically discover each other and establish connections. A network topology control algorithm is introduced to enable the network to automatically adjust the connection relationship between nodes to adapt to changes in the network environment. Fault detection and recovery mechanisms are introduced, such as heartbeat detection and redundant paths, to quickly discover and repair faults in the network.

Examples:

When a node in the network fails, the self-organizing network can automatically detect the failure and re-establish the connection through communication between other nodes to ensure the normal transmission of data; for example, in a wireless sensor network, if a sensor node fails due to battery exhaustion, other nodes can automatically adjust their communication strategies and send data to the central processing system through other paths;

Automatically repair failed nodes and links:

Technical methods:

Use redundant design and backup mechanisms, such as hot standby and link redundancy, to ensure that the network can quickly switch to the backup device or path when a node or link fails; introduce fault detection and diagnosis technologies, such as network diagnostic tools and log analysis, to quickly locate faults and take appropriate repair measures; combine intelligent algorithms and artificial intelligence technologies, such as machine learning and neural networks, to predict and avoid potential network failures;

Examples:

In an intelligent communication network, dual communication modules can be deployed. When one module fails, the other module can immediately take over the data transmission task. At the same time, the system can regularly collect and analyze network log data to discover potential network problems and repair them in advance.

Comprehensive application:

In practical applications, these technical methods are used in combination to achieve efficient, stable and reliable operation of intelligent communication networks. For example, in a distributed hydrological communication perception system based on edge computing, the intelligent communication network can dynamically adjust the transmission path and resource allocation according to the network status and data transmission requirements, while using self-organization and self-healing capabilities to quickly recover from network failures and ensure real-time and accurate data transmission.

S3, edge computing nodes:

S3.1, High-performance computing: Deploy a stream processing framework on the edge computing node to process the data streams generated by hydrological sensors in real time, including water level and flow; specifically:

Choose a stream processing framework: Choose a framework suitable for real-time data stream processing, such as Apache Flink, Apache Storm, or Kafka Streams;

Deployment framework: Install and configure the selected stream processing framework on the edge computing nodes;

Integrate sensor data: connect the data streams generated by hydrological sensors (such as water level and flow sensors) to the stream processing framework;

Write processing logic: Use the API or DSL (domain-specific language) of the stream processing framework to write data processing logic, including data cleaning, conversion, aggregation, analysis and other operations;

Examples:

Apache Flink is selected as the stream processing framework. The data streams of the water level and flow sensors have been connected to Flink. A Flink job is written to process these data streams. The job includes the following steps:

Data source: read the real-time data stream of water level and flow from message queues such as Kafka;

Data processing: clean and convert data streams to ensure data validity and format consistency;

Data aggregation: Aggregate water level and flow data according to time windows (such as 1 minute, 5 minutes, etc.) and calculate indicators such as average, maximum, and minimum values;

Result output: The processed data is output to a database, message queue or other storage system for subsequent analysis and query;

It also includes using cluster technology to manage multiple edge computing nodes and dynamically allocate resources according to computing needs; specifically:

Select a cluster management framework: Choose a cluster management framework suitable for edge computing node management, such as Kubernetes, Docker Swarm, or Apache Mesos;

Deploy cluster management framework: Install and configure the selected cluster management framework on multiple edge computing nodes;

Define services: Use the cluster management framework’s API or configuration files to define stream processing services (such as Flink jobs) and specify the service’s operating requirements and resource limits.

Service scheduling and resource allocation: The cluster management framework will automatically schedule and allocate services to each node based on the node load and resource constraints;

Examples:

Kubernetes was selected as the cluster management framework, and Kubernetes clusters have been deployed on multiple edge computing nodes. A Flink job service is defined using the Kubernetes YAML configuration file, and resource limits such as CPU and memory required by the service are specified. The configuration file is then submitted to the Kubernetes cluster, and Kubernetes will automatically schedule and allocate resources to run the service. If a node is overloaded or has insufficient resources, Kubernetes will automatically migrate the service to other nodes to ensure service stability and performance.

S3.2, Distributed learning and collaborative training: Deploy a distributed deep learning framework to collaboratively train a deep learning model for predicting water quality changes on multiple edge computing nodes; specifically:

Choose a deep learning framework: First, choose a deep learning framework suitable for distributed training, such as TensorFlow, PyTorch, etc.

Deploy a distributed deep learning framework: Deploy the selected deep learning framework on multiple edge computing nodes and configure a distributed training environment;

Design a water quality prediction model: Based on the characteristics of hydrological data, design a deep learning model suitable for water quality prediction, such as long short-term memory network (LSTM), convolutional neural network (CNN) or their combination;

Dataset division: historical water quality data is divided into training set, validation set and test set. Since the data is distributed on multiple edge nodes, data partitioning technology can be used to distribute the data set to each node;

Distributed training: Use the distributed training function of the deep learning framework to collaboratively train the water quality prediction model on multiple edge computing nodes; each node uses local data for model training and regularly aggregates and synchronizes model parameters;

It also includes introducing and implementing a collaborative training mechanism and using a federated learning framework to allow each edge computing node to train models locally and regularly upload and aggregate model parameters to protect data privacy and improve model performance. Specifically:

Introducing Federated Learning: To protect data privacy, a federated learning framework is introduced, in which each edge node performs model training locally and only uploads model parameters or updates instead of raw data;

Model parameter aggregation: Use a central server (or selected edge nodes) to aggregate model parameters or updates uploaded by each node, implemented through a secure multi-party computing protocol to ensure the security of the parameter aggregation process;

Update distribution: Distribute the aggregated model parameters or updates to individual edge nodes so that they can continue local training;

It also includes the introduction of task offloading strategies, which dynamically determine whether to offload tasks to the cloud based on the load and computing resources of edge computing nodes; when the edge node load is too high or the computing resources are insufficient, some computing tasks are sent to the cloud for computing; after the cloud computing results are returned, the edge node continues the subsequent processing or directly feeds back the results to the user; specifically:

Monitor load and computing resources: Use resource monitoring tools (such as Prometheus, Grafana, etc.) to monitor the system load and computing resource usage of edge computing nodes;

Dynamic decision-making: Based on the monitoring results, when the load of an edge node is too high or the computing resources are insufficient, the task offloading mechanism is triggered;

Task offloading: Sending some computing tasks (such as a certain training phase of a model or data processing tasks) to the cloud for computing; this can be achieved through cloud-edge collaboration technologies, such as using container orchestration tools such as Kubernetes to manage computing resources in the cloud and on the edge;

Result return: After the cloud completes the computing task, it returns the result to the corresponding edge node; the edge node can continue the subsequent processing or directly feedback the result to the user;

Examples:

TensorFlow was selected as the deep learning framework, and a distributed training environment was deployed on 10 edge computing nodes. Each node has a local water quality dataset, and a water quality prediction model based on LSTM was designed;

Initial stage: load the local dataset on each edge node and initialize the LSTM model;

Distributed training: Each node uses a local data set to train the model and regularly uploads the model parameters to the central server for aggregation. The aggregated model parameters are then distributed to each node for the next round of training.

Federated learning: During the entire training process, a federated learning framework is used to protect data privacy. Only model parameters or updates are uploaded to the central server, and the original data is always kept locally.

Task offloading: Assume that during the fifth round of training, the load of an edge node is too high, triggering the task offloading mechanism, sending the model training task of the node to the cloud for calculation. After the cloud completes the calculation, it returns the result to the node, and the node continues to participate in the next round of training;

Model evaluation: Use the test set to evaluate the trained model and verify its performance in water quality prediction. If the performance meets the requirements, the model can be deployed to the production environment for real-time prediction.

S3.3, Real-time data analysis and feedback; Introduce a real-time stream processing framework to process the real-time data stream generated by sensors;

Specifically:

Deploy the real-time stream processing framework: Deploy the real-time stream processing framework on the edge computing node and configure related resources (such as CPU, memory, and network bandwidth);

Configure data streams: connect the real-time data streams (such as water level, flow, water quality, etc.) generated by hydrological sensors to the real-time stream processing framework;

Define data flow processing logic: Define data flow processing logic according to business needs, including data cleaning, filtering, conversion, etc.;

The real-time data can be processed and analyzed through data analysis algorithms to extract useful information. Once the data stream is connected to the real-time stream processing framework, the real-time data can be processed and analyzed using data analysis algorithms. Specifically:

Select appropriate data analysis algorithms: Select appropriate data analysis algorithms based on business needs, such as anomaly detection algorithms, trend prediction algorithms, etc.

Implement data analysis algorithms: Implement the selected data analysis algorithms using programming languages (such as Java, Python, etc.) in the real-time stream processing framework;

Integrate data analysis algorithms: Integrate the implemented data analysis algorithms into the real-time stream processing framework, so that it can process and analyze data streams in real time;

Examples and data:

A real-time water level data stream, the data format is {"timestamp":"2023-10-23T10:00:00Z","water_level":5.2};

Data cleaning: remove data with incorrect timestamp format or abnormal water level value;

Anomaly detection: Use algorithms such as the Z-score algorithm to detect abnormal changes in water level values;

Trend prediction: Use simple linear regression or more complex machine learning models (such as LSTM) to predict water level trends over a period of time in the future;

At the same time, a flexible feedback mechanism is provided according to user needs, including real-time charts and alarm notifications; specifically:

Define feedback content: Based on business needs, define the content that needs to be fed back to users, such as real-time charts, alarm notifications, etc.

Implement feedback mechanism: Use visualization libraries (such as D3.js, ECharts, etc.) and notification services (such as email notifications, SMS notifications, etc.) to implement feedback mechanism;

Integrated feedback mechanism: Integrate the implemented feedback mechanism into the real-time stream processing framework so that it can send feedback to users in real time;

Examples:

Real-time chart: When users need to view real-time water level changes, the system can generate a real-time updated water level change chart and display it to users through the Web interface;

Alarm notification: When the water level exceeds the warning line or changes abnormally, the system can immediately send an alarm notification to the user so that the user can handle it in time; the alarm notification can be sent to the user via email, SMS, etc.;

S3.4. Dynamic resource management:

Monitor the system load and data processing requirements of edge computing nodes; involve real-time monitoring of key indicators such as CPU usage, memory occupancy, disk I/O, network bandwidth utilization, etc.; implementation steps:

Deploy monitoring tools: Deploy monitoring tools (such as Prometheus, Zabbix, etc.) on edge computing nodes to collect real-time data of key indicators;

Configure monitoring strategies: set monitoring frequency, alarm thresholds, etc. to ensure timely response when resources are tight or abnormal;

Integrate monitoring data: Integrate monitoring data into resource management systems or edge computing platforms for unified management and analysis;

Dynamically adjust the resource allocation of computing resources, memory, and network bandwidth based on monitoring results; specifically:

Analyze monitoring data: Analyze the collected monitoring data to determine the current system load and data processing requirements;

Formulate resource adjustment strategies: Based on the analysis results, formulate appropriate resource adjustment strategies, such as increasing or decreasing the number of CPU cores, adjusting memory size, optimizing network bandwidth allocation, etc.

Perform resource adjustments: Perform resource adjustment operations through the edge computing platform or resource management system to ensure that resource allocation matches current needs;

In order to further improve resource utilization and system performance, the resource scheduling algorithm is used to optimize resource utilization and ensure efficient operation of the system; specifically:

Select or develop resource scheduling algorithms: Select or develop appropriate resource scheduling algorithms based on business requirements and technical characteristics, such as priority-based scheduling algorithms, load balancing-based scheduling algorithms, etc.

Integrated resource scheduling algorithm: Integrate the resource scheduling algorithm into the edge computing platform or resource management system so that it can automatically schedule resources according to system status and demand;

Monitoring and tuning: Continuously monitor the system's operating status and resource usage, and tune and optimize the resource scheduling algorithm as needed to ensure efficient operation of the system;

Examples and data

An edge computing node cluster, each of which is equipped with water level and flow sensors and generates data streams in real time. As the amount of data increases, the CPU usage and memory usage of some nodes gradually increase, and the network bandwidth tends to be saturated;

Dynamic resource management example:

Monitoring data: The monitoring tool found that the CPU usage of node A exceeded 80%, and the memory usage reached 70%, while the resource utilization of node B was relatively low;

Resource adjustment strategy: According to the monitoring data, formulate a resource adjustment strategy, migrate some data flows from node A to node B, and appropriately increase the number of CPU cores and memory size of node B;

Execute resource adjustment: Execute resource adjustment operations through the edge computing platform or resource management system, migrate part of the data flow from node A to node B, and adjust the resource configuration of node B;

Monitoring and tuning: Continuously monitor the system's operating status and resource usage. It is found that after resource adjustment, the resource utilization of nodes A and B remains within a reasonable range, and the system performance is improved. Further tune and optimize the resource scheduling algorithm as needed to adapt to changes in demand in different scenarios.

Data example:

Through dynamic resource management, the resource load between different nodes can be effectively balanced to improve the overall performance and stability of the system;

S4. Build the central processing system:

Big data analysis and prediction: Based on the data uploaded by edge nodes, deep mining and analysis are carried out to establish prediction models and predict the changing trend of the hydrological environment;

Set up data cache to reduce the amount of data transmission between edge computing nodes and reduce network latency;

Implementation: Set up a data cache layer in the central processing system, such as using in-memory database technologies such as Redis or Memcached to store hydrological data recently uploaded by edge computing nodes;

Function: When an edge computing node uploads new data, the central processing system first checks whether there is data with the same or similar timestamp in the cache. If so, the data in the cache is directly read for analysis, reducing the number of times data is pulled from the edge computing node, thereby reducing network latency.

Examples:

Assume that the edge computing node uploads water level data every 5 minutes, and the central processing system stores this data in the cache and sets the cache expiration time to 10 minutes;

When it is necessary to analyze the water level change trend in the past hour, the central processing system will first check whether there is such data in the cache. If so, it will read it directly from the cache; if not, it will pull it from the edge computing node;

data:

Cache data: contains information such as timestamp, water level value, source edge computing node identifier, etc.

Cache strategy: Least Recently Used (LRU) strategy or First In First Out (FIFO) strategy to ensure that the latest or most frequently used data is always stored in the cache;

Establish a multi-source data fusion mechanism to integrate data from different sensing nodes and improve the comprehensive utilization of data;

Implementation: Use data fusion algorithms (such as weighted average, Kalman filter, etc.) to integrate data from different sensing nodes; these data may vary due to factors such as device accuracy and environmental conditions;

Function: Through data fusion, these differences can be eliminated, the accuracy and reliability of data can be improved, and higher quality data can be provided for subsequent prediction models;

Examples:

Suppose there are two different water level sensors installed in the upstream and downstream of a river. Due to factors such as geographical location and water flow velocity, the water level data collected by these two sensors may be different.

After receiving these data, the central processing system will first perform data cleaning and quality checks to ensure the validity and reliability of the data;

Then, the data from these two sensors is integrated using a data fusion algorithm (such as Kalman filtering) to obtain a more accurate water level estimate;

data:

Raw data: including water level data, timestamps, device identification and other information from different sensing nodes;

Fusion data: contains fused water level estimation value, timestamp, fusion algorithm identifier and other information;

Intelligent decision support: automatic decision making and recommendations based on data analysis;

Implementation:

Based on data analysis: Use data mining, machine learning and other technologies to conduct in-depth analysis on the fused data to extract useful information and features;

Automatic decision-making and recommendation: Based on the analysis results, combined with preset decision-making rules and recommendation algorithms, automatic decision-making and recommendation services are provided to users;

Examples:

Assume that the central processing system has established a prediction model for predicting flood risk. When new hydrological data is received, the model will be updated in real time and output the flood risk level;

If the flood risk level exceeds the preset threshold, the central processing system will automatically trigger an alarm notification and display the flood risk map and corresponding emergency measures to the user through a visual interface;

data:

Prediction model output: flood risk level, prediction time, etc.;

Alarm notification: contains information such as alarm type, time, location, and recommended measures;

Visualization interface: displays flood risk maps, real-time hydrological data, alarm notifications, etc.

Visualization and interactive interface: Provide an intuitive and easy-to-use visualization interface to facilitate users to view data and perform interactive operations;

Implementation:

In order to achieve an intuitive and easy-to-use visualization interface and facilitate users to view data and perform interactive operations, the following technology stack and steps are adopted:

Front-end technology stack: Use HTML, CSS, JavaScript and other front-end technologies to build the user interface. In order to enhance the user experience and interactivity, introduce modern front-end frameworks such as React, Vue.js or Angular.

Data visualization library: Use data visualization libraries such as ECharts, D3.js, Highcharts, etc. to display hydrological data in the form of charts, maps, etc.;

Backend API interface: The central processing system needs to provide interfaces such as RESTful API or GraphQL for the front end to call to obtain data and send instructions;

User authentication and permission management: To ensure data security, user authentication and permission management functions need to be implemented to ensure that only authorized users can access and operate data;

Real-time update mechanism: Real-time data update is achieved through technologies such as WebSocket, ensuring that users can see changes in the hydrological environment in real time;

Examples:

Assume that a system called “Hydrological Monitoring Platform” is being developed, which includes the “Visualization and Interactive Interface” function;

Function 1: Real-time water level monitoring:

Implementation: Display a real-time water level monitoring chart on the interface, with the horizontal axis representing time and the vertical axis representing water level value. Use the ECharts library to draw the chart and update the data in real time.

Example data: water level data every 5 minutes in the last 24 hours, such as [{"time":"2023-10-2300:00:00","level":10.5},{"time":"2023-10-2300:05:00","level":10.7},...];

User operation: Users can drag the time axis of the chart to view water level data for different time periods, or click on a data point on the chart to view detailed information;

Function 2: Water quality prediction and analysis:

Implementation: Display a forecast trend chart based on the water quality data predicted by the distributed deep learning model; users can select different time periods and forecast models to view the forecast results;

Example data: Water quality forecast data for the next 7 days, including indicators such as dissolved oxygen and pH value;

User operation: Users can select different time periods (such as today, tomorrow, next week, etc.) and forecast models (such as model A, model B, etc.) to view forecast results; at the same time, the system also provides a "download report" function, allowing users to export forecast results as PDF or Excel files;

Function 3: Alarm notification:

Implementation: When hydrological data is abnormal or reaches a certain threshold, the system automatically triggers an alarm notification; the alarm notification can be sent to users via email, SMS, APP push, etc.

Example: When the water level exceeds the warning level, the system automatically sends a text message to the administrator, saying "The water level has exceeded the warning level, please handle it in time!";

User operation: Users can set the alarm trigger conditions and notification methods. At the same time, after receiving the alarm notification, users can quickly respond and handle it through the "emergency processing" function provided by the system.

Furthermore, in S1, low-power hardware and energy-saving algorithms are used to optimize data processing and transmission processes and reduce power consumption;

Low power hardware implementation:

Sensor selection:

Choose sensors with low power consumption characteristics, such as low power water level sensors, flow sensors, etc.

Such sensors can significantly reduce energy consumption when collecting data;

Hardware Platform:

Use low-power processors (such as ARM Cortex-M series) and microcontrollers (MCUs) as the core hardware of the sensing node;

Choose memory with low-power operating modes, such as low-power DRAM (LPDDR) or low-power flash memory (eMMC);

Power Management:

Introducing an efficient power management unit (PMU) that supports multiple power modes and voltage regulation;

Implement intelligent sleep and wake-up mechanisms to put the hardware into low-power mode when the sensor has no data to collect or process;

Energy-saving algorithm implementation:

Data Compression:

After data collection, the data size is reduced through lossless or lossy compression algorithms to reduce transmission energy consumption;

For example, compression algorithms such as LZ77 and LZ78 are used to compress hydrological data;

Transmission Optimization:

Introducing an adaptive transmission strategy to dynamically adjust the transmission frequency and rate based on network conditions and data importance;

Realize data aggregation, combine the data of multiple sensors into one data packet and send it, reducing the number of transmissions;

Energy-saving scheduling:

Traffic analysis:

Real-time monitoring: The system first monitors the changes in hydrological data (such as water level, flow, etc.) in real time;

Traffic pattern recognition: Through machine learning or statistical methods, the system recognizes different traffic patterns, such as high traffic periods, low traffic periods, stable traffic periods, etc.

Sampling frequency adjustment:

Dynamic sampling: Based on the identified traffic patterns, the system dynamically adjusts the sampling frequency of the sensor. For example, during low traffic periods, the sampling frequency can be reduced to reduce energy consumption;

Threshold setting: The system can also set thresholds for parameters such as water level and flow. When the data value is lower than these thresholds, it enters low power mode, reduces the sampling frequency or stops sampling completely.

Sleep and wake-up mechanism:

Sleep strategy: During non-critical time periods (such as late at night or early in the morning), the system can put the sensing node into sleep mode to further reduce energy consumption;

Wake-up condition: When the system detects that there is important data to be collected (such as exceeding the preset threshold) or receives a wake-up command, the node will be awakened and resume normal working state;

Real-time adjustments:

Adaptive learning: The system can continuously learn and adapt to changes in the environment, dynamically adjusting the sampling frequency and sleep strategy based on historical data and real-time data;

User configuration: Users can also configure and adjust relevant parameters of the energy-saving scheduling algorithm through the system interface or API interface according to actual needs;

Examples:

Application scenario: River water level monitoring

Low flow period: In the late night or early morning hours, the river flow is usually low and stable. At this time, the system can reduce the sampling frequency of the water level sensor from once per minute to once every half hour and put the sensing node into sleep mode to save energy.

High flow period: During the rainy season or flood period, the river flow may increase dramatically. At this time, the system needs to monitor the water level changes in real time to issue an alarm in time. Therefore, the system will increase the sampling frequency to once per second and keep the sensing nodes active.

Adaptive adjustment: The system can also automatically adjust the sampling frequency and sleep strategy based on historical data and real-time data. For example, if the system finds that the water level changes frequently during a period of time, even if the period is usually a low flow period, the system will increase the sampling frequency to ensure the accuracy of the data;

By implementing this energy-saving scheduling algorithm, the energy consumption of the sensing nodes can be minimized and their service life can be extended while ensuring data accuracy and real-time performance.

Low power hardware examples:

A low-power ARM Cortex-M7 processor is used as the core hardware of the perception node, with a power consumption of only 0.5mW/MHz; it is equipped with low-power LPDDR4 memory, which reduces power consumption by 30% compared with traditional DRAM;

Energy-saving algorithm example:

In terms of data compression, the LZ77 algorithm is used to compress hydrological data, with an average compression ratio of 2:1, which significantly reduces the amount of data transmitted;

In terms of transmission optimization, an adaptive transmission strategy is implemented to dynamically adjust the transmission rate according to network bandwidth and data importance, reducing transmission energy consumption by 20% on average;

Energy saving effect data:

By adopting low-power hardware and energy-saving algorithms, the overall power consumption of the sensing node is reduced by about 40%;

In actual deployment, the average battery life of a single sensing node has been increased from 3 months to more than 6 months;

These examples and data demonstrate how to optimize data processing and transmission processes through low-power hardware and energy-saving algorithms, reduce power consumption, and improve the energy efficiency of hydrological communication perception solutions.

Furthermore, in S2, during data transmission, data compression technology is introduced to reduce the amount of data transmitted and reduce network latency;

Implementation of data compression technology:

Select a compression algorithm:

Choose a lossless or lossy compression algorithm suitable for hydrological data. For hydrological data, especially data that requires high-precision processing (such as water level and flow), you may prefer to use a lossless compression algorithm (such as LZ77, LZ78, LZ4, etc.) to ensure data integrity. However, in scenarios where a certain amount of data loss can be tolerated, you can also consider using a lossy compression algorithm (such as JPEG, MPEG, etc.) to obtain a higher compression ratio;

Data preprocessing:

Before compression, the hydrological data is preprocessed, such as removing redundant information, processing outliers, and normalizing data, to improve compression efficiency;

Real-time compression:

Implement real-time compression on sensing nodes or edge computing nodes. When data is ready to be transmitted, it is first compressed using a compression algorithm before being transmitted.

Compression ratio and transmission efficiency:

Evaluate the compression ratio and transmission efficiency of different compression algorithms and select the most suitable algorithm or algorithm combination. The higher the compression ratio, the less data is transmitted, but the time overhead of compression and decompression will also increase. Therefore, it is necessary to find a balance between compression ratio and transmission efficiency.

Examples and data:

The LZ77 compression algorithm is used to compress the hydrological data. The following is a specific example and data:

Examples:

In a distributed hydrological communication perception system, the perception node is responsible for collecting hydrological data such as water level and flow, and sending the data to the edge computing node through wireless communication technology. In order to reduce the amount of transmitted data and reduce network latency, we introduced the LZ77 compression algorithm before data transmission;

data:

Raw data volume: Assume that each sensing node generates 10KB of hydrological data (including water level, flow, water quality and other parameters) per minute;

Compression ratio: The original data is compressed using the LZ77 algorithm, with an average compression ratio of 2:1 (i.e. the original data is compressed to half of its original size);

Data volume transferred: After compression, the amount of data that needs to be transferred per minute is reduced to 5KB;

Network latency: Since the amount of data transmitted is reduced by half, the network latency is also reduced accordingly. Assuming the original network latency is 100ms (including data transmission and processing time), after the introduction of compression technology, the network latency is reduced to about 50ms (assuming that the compression and decompression time overhead is small);

Energy-saving effect: In addition to reducing network latency, the amount of data transmitted is reduced by half, which also reduces the energy consumption of the wireless communication module, thereby further extending the service life of the sensing node.

Furthermore, in S2, redundant design is adopted on key devices and links, including dual power supplies and dual communication modules, to improve the fault tolerance and reliability of the system; and the function of dynamic network selection is introduced to select the optimal communication network according to the network status and data transmission requirements, so as to improve data transmission efficiency and reduce latency;

Redundant design implementation:

Dual power supply design:

Implementation method: Install two independent power systems, such as UPS (uninterruptible power supply) or backup battery packs, on each key device and link. When one power system fails, the other power system can immediately take over to ensure continuous power supply to the equipment.

Example: Install dual UPS power systems on edge computing nodes and sensing nodes. When the main UPS power fails, the backup UPS power starts immediately to ensure the continuous operation of the node.

Dual communication module design:

Implementation method: Two communication modules are configured on each device and link. Each module supports different wireless communication technologies (such as 4G, 5G, Wi-Fi, LoRa, etc.). When one communication module fails or the signal is poor, the other communication module can take over the data transmission task.

Example: Install a 4G communication module and a LoRa communication module on the sensing node. When the 4G network signal is unstable, the system can automatically switch to the LoRa network for data transmission;

Dynamic network selection function implementation:

Real-time monitoring of network status:

Implementation method: Use built-in network monitoring tools or third-party network monitoring services to monitor key indicators such as signal strength, bandwidth, and latency of each communication network in real time;

Example: Use network monitoring software or API interfaces to obtain real-time status data of 4G, 5G, Wi-Fi and other networks;

Dynamically select the optimal communication network:

Implementation method: According to the preset algorithms and strategies, combined with network status data and data transmission requirements, the optimal communication network is dynamically selected for data transmission. The algorithm can consider multiple factors such as network bandwidth, delay, and stability;

Example: When a large amount of data needs to be transmitted, 4G or 5G networks with larger bandwidth are preferred; when higher network stability is required, Wi-Fi networks with stronger signals can be selected. The system can be adjusted dynamically according to actual conditions;

Automatic switching and fault recovery:

Implementation method: When the currently used communication network fails or its performance degrades, the system can automatically switch to other available communication networks and try to restore data transmission. At the same time, the system can also record the failure information and notify the administrator for further processing;

Example: When the 4G network is suddenly interrupted, the system can automatically switch to the LoRa network to continue transmitting data and send a fault notification to the administrator. After receiving the notification, the administrator can check the 4G network status and troubleshoot the problem.

Through the implementation of the above redundant design and dynamic network selection function, the fault tolerance and reliability of the distributed hydrological communication perception system can be greatly improved, while improving data transmission efficiency and reducing latency.

Furthermore, in S3.1, the hydrological data processing application is packaged into an image using containerization technology, which is quickly deployed and migrated on edge computing nodes, and the container is orchestrated and managed through a tool in Docker Compose and Kubernetes; specifically:

Step 1: Application Development:

First, the development of hydrological data processing applications should be designed to run in a containerized environment and have good portability and scalability;

Step 2: Build the Docker image:

Write a Dockerfile: A Dockerfile is a text file that contains the instructions and configurations needed to build a Docker image. You need to specify the base image, install dependencies, set environment variables, copy files to the container, define the commands to run when the container starts, etc.

Build the image: Run the dockerbuild command in the directory containing the Dockerfile to build the Docker image according to the instructions in the Dockerfile;

Step 3: Deploy to edge computing nodes:

Push the image to the image repository (optional): If you plan to deploy the same application on multiple edge computing nodes, you can push the built Docker image to a central image repository (such as DockerHub, Harbor, etc.);

Pull and run the image on the edge computing node: Install the Docker runtime environment on each edge computing node, then use the dockerpull command to pull the image from the image repository, or use the dockerload command to load the image from a local file. Then, use the dockerrun command to start the container instance and run the hydrological data processing application.

Step 4: Container orchestration and management:

Using tools such as Docker Compose or Kubernetes for container orchestration and management can further improve deployment and management efficiency;

DockerCompose example:

Write a docker-compose.yml file: define services (i.e. containers), networks, volumes and other configurations in the docker-compose.yml file. For example, specify the ports, environment variables, and other dependent services that the hydrological data processing application needs to use;

Start the service: Run the docker-composeup command in the directory containing the docker-compose.yml file to start the service according to the configuration file. Docker Compose will automatically handle the container dependencies, network configuration, etc.

Kubernetes instance:

Write Kubernetes configuration files: Use YAML or JSON format to write Kubernetes configuration files (such as Deployment, Service, etc.), define Pod templates, replica sets, service discovery and other configurations;

Deploy to Kubernetes cluster: Apply the configuration file to the Kubernetes cluster. Kubernetes will automatically create Pod, Service and other resources according to the configuration and manage their lifecycle. Use the kubectl command-line tool or Kubernetes API for deployment and management.

It is necessary to add that:

When choosing a container orchestration tool, you need to consider factors such as the resource limitations, network conditions, and operation and maintenance capabilities of edge computing nodes. For edge computing nodes with limited resources, Docker Compose may be a lighter choice; while for scenarios that require managing a large number of nodes and complex applications, Kubernetes may be more suitable.

Before deploying applications, you need to ensure that the edge computing nodes have installed the corresponding container runtime environment (such as Docker) and container orchestration tools (such as DockerCompose, Kubernetes, etc.);

During the actual deployment process, you also need to consider application configuration management, log collection, monitoring and alarm issues to ensure the stability and maintainability of the application.

Furthermore, in S3.2, in the federated learning framework, noise is added to the model training process so that the original data cannot be inferred from the model parameters; specifically:

Local training and differential privacy:

When each edge computing node performs model training locally, in addition to normal gradient updates, it also adds differential privacy noise to the calculated gradients. The addition of noise usually follows a Laplace distribution or a Gaussian distribution, depending on the privacy budget and the sensitivity of the data.

Privacy budget (ε):

The privacy budget is a parameter that measures the degree of privacy protection, which determines the scale of noise and the timing of adding it; a smaller privacy budget means stronger privacy protection, but may also lead to a decrease in model performance.

Aggregation and global model update:

After all edge computing nodes complete local training and upload noisy gradients, the central processing system aggregates these gradients; the aggregated gradients are used to update the global model.

Example implementation:

The above steps are implemented in a federated learning framework such as TensorFlowFederated (TFF). The following is a simplified pseudocode and conceptual description:

#A local training function in a federated learning framework (such as TFF)

deflocal_training(model,data,noise_scale,epochs):

#...Train the model on a local dataset...

gradients=compute_gradients(model,data)

#Add differential privacy noise

noisy_gradients=add_differential_privacy_noise(gradients,noise_scale)

# Apply gradients and update the model

update_model(model,noisy_gradients)

#Return the updated model or gradient

returnmodel#or return noisy_gradients for aggregation

#Central Processing System Aggregation Function

defaggregate_updates(noisy_gradients_from_all_clients):

#...Aggregate the noisy gradients of all edge nodes...

aggregated_gradients=sum(noisy_gradients_from_all_clients)

#Update the global model using the aggregated gradients

update_global_model(aggregated_gradients)

#Function to add differential privacy noise (pseudocode)

defadd_differential_privacy_noise(gradients,noise_scale):

#Add noise according to differential privacy algorithms (such as Laplace mechanism)

noisy_gradients=gradients+laplace_noise(scale=noise_scale)

returnnoisy_gradients

#In actual deployment, it is also necessary to consider how to choose an appropriate privacy budget (ε) and noise scale.

Furthermore, in S3.2, a hybrid cloud architecture is constructed to connect edge computing nodes with virtual machines and container clusters in the cloud, and use the cloud-edge collaboration mechanism to achieve flexible task scheduling and data transmission between the edge and the cloud; specifically:

Architecture design:

Hybrid cloud architecture: Design an architecture that can manage resources in both edge computing nodes and the cloud, including the edge computing layer, network connection layer, and cloud data center layer;

Edge computing layer: deployed close to the data source for real-time processing and preliminary analysis of data;

Network connection layer: provides communication between the edge layer and the cloud, and can use various network technologies such as 5G, Wi-Fi, optical fiber, etc. Cloud data center layer: provides powerful computing, storage and analysis capabilities to handle tasks that the edge layer cannot handle and store large amounts of data;

Connection between edge computing nodes and the cloud:

VPN or dedicated line: Establish a secure network connection to ensure the security and privacy of data during transmission; API interface: Define a unified API interface so that edge nodes and cloud applications can communicate and call each other;

Virtualization and containerization:

Deploy virtual machines or container clusters in the cloud to manage and run cloud applications; use tools such as Docker and Kubernetes to orchestrate and manage containers to achieve dynamic resource allocation and rapid application deployment;

Cloud-edge collaboration mechanism:

Task scheduling: Dynamically assign tasks to edge nodes or the cloud for execution based on the load and computing resources of edge nodes and the idle resources of the cloud.

Data transmission: For data that needs to be transmitted between the edge and the cloud, use efficient data transmission protocols and compression algorithms to reduce transmission delays and bandwidth consumption;

Examples:

There is a Kubernetes-based cloud container cluster and an edge computing node deployed near the river. When the edge node detects abnormal water quality data, it may not be able to handle this complex analysis task independently. At this time, the following cloud-edge collaboration mechanism can be used:

Task scheduling: The edge node sends a task scheduling request to the cloud through the API interface, indicating the analysis task to be performed and the current node load;

Cloud response: After receiving the request, the cloud decides whether to assign the task to the cloud for execution based on the current cloud resource usage and the importance of the task. If it is decided to execute in the cloud, it selects a suitable container instance and starts the corresponding analysis application.

Data transmission: If data needs to be transmitted between the edge and the cloud, an efficient data transmission protocol (such as HTTP/2) and a compression algorithm (such as Gzip) are used to send data from the edge node to the cloud.

Result return: After the cloud completes the analysis task, it returns the result to the edge node through the API interface. The edge node takes corresponding measures based on the result, such as sending an alarm notification or adjusting the sensor configuration;

Through this cloud-edge collaborative mechanism, while maintaining real-time and local processing capabilities, the powerful computing and analysis capabilities of the cloud can be fully utilized to achieve a more flexible and efficient hydrological communication perception solution.

Furthermore, in S3.4, a resource reservation mechanism is introduced, including:

Task classification and priority setting: Set the real-time water level monitoring task as the highest priority, the water quality analysis task as the medium priority, and other non-critical tasks as the low priority;

Resource reservation settings: reserve two CPU cores (assuming the edge computing node has four cores in total) and 50% of the memory for the real-time water level monitoring task; this means that at any time, these two CPU cores and this part of the memory can only be used by the real-time water level monitoring task;

Resource scheduling strategy: When a new task arrives, the resource scheduler first checks whether there are enough resources to meet the needs of the task. If there are enough resources, they are allocated to the task; if there are not enough resources, the task is queued or rejected according to its priority. For the real-time water level monitoring task, it can always get the required resources immediately because it has high priority and reserved resources.

Monitoring and adjustment: Use a tool such as Prometheus or Grafana to monitor the resources of edge computing nodes in real time. If the load of the real-time water level monitoring task suddenly increases, temporarily increase its reserved resources (for example, reserve another CPU core) to ensure that it can process data in a timely manner. At the same time, limit or suspend low-priority tasks to release more resources for critical tasks.

Implementation method:

Task classification and priority setting:

Identify and define critical tasks (such as real-time water level monitoring) and non-critical tasks (such as water quality analysis, other non-real-time tasks);

Assign a priority to each task category, such as real-time water level monitoring as the highest priority, water quality analysis as a medium priority, and other tasks as a low priority;

Resource reservation settings:

According to the priority of the task and the estimated resource requirements, a certain proportion of computing resources (CPU cores, memory, network bandwidth, etc.) is reserved for each type of task;

For example, two CPU cores and 50% of the memory are reserved for the real-time water level monitoring task, one CPU core and 30% of the memory are reserved for the water quality analysis task, and the remaining resources are allocated to other low-priority tasks;

Resource scheduling strategy:

When a new task arrives, the resource scheduler first checks whether the currently available resources are sufficient to meet the needs of the task;

If there are enough resources, the tasks are assigned to the corresponding resources according to their priorities;

If resources are insufficient, the scheduler will decide whether to wait for resource release (put the task into the queue), reject task execution, or temporarily borrow resources from other tasks based on the task priority;

Monitoring and Adjustment:

Use monitoring tools (such as Prometheus and Grafana) to monitor the resource usage of edge computing nodes in real time;

When it is found that the resource demand of a task exceeds the reserved amount (such as a sudden increase in the load of the real-time water level monitoring task), the scheduler will trigger the resource adjustment mechanism;

The adjustment mechanism may include: temporarily increasing the reserved resources for the task, limiting or pausing the execution of low-priority tasks to free up resources, dynamically acquiring additional resources from the cloud, etc.

Examples:

The following tasks are running on an edge computing node:

Real-time water level monitoring task (highest priority);

Water quality analysis tasks (medium priority);

Data backup tasks (low priority);

The resource reservation settings are as follows:

Real-time water level monitoring task: reserve two CPU cores and 50% of the memory;

Water quality analysis task: reserve one CPU core and 30% of the memory;

The remaining resources are allocated to data backup tasks;

At some point, the real-time water level monitoring task may experience a sudden increase in data traffic due to an emergency, which exceeds the processing capacity of the reserved resources. At this point, the resource scheduler will detect this situation and trigger the resource adjustment mechanism:

First, the scheduler will try to temporarily borrow resources from the water quality analysis task. If the current load of the water quality analysis task is not high, some resources can be released for the real-time water level monitoring task.

If the water quality analysis task also cannot release enough resources, the scheduler will further limit or suspend the execution of the data backup task to release more resources for the real-time water level monitoring task;

If the above measures still cannot meet the needs of the real-time water level monitoring task, the scheduler will consider dynamically obtaining additional resources from the cloud. This can be achieved through the cloud-edge collaboration mechanism, which offloads part of the computing tasks to the cloud for processing and then returns the results to the edge node;

Throughout the process, the monitoring tool will continuously monitor the resource usage of each task to ensure that the system can optimize the overall resource utilization as much as possible while ensuring the performance of key tasks.

A distributed hydrological communication sensing device system based on edge computing, comprising:

Sensing devices:

Multi-parameter sensors: including water level sensors, flow meters, water quality analyzers, weather stations (including temperature, humidity, wind speed, wind direction sensors), soil moisture sensors; these sensors can directly measure and transmit data on environmental parameters;

Multimodal sensing devices: including acoustic sensors (such as microphones or sonar), optical sensors (such as spectrometers or cameras), and chemical sensors (such as ion selective electrodes or gas analyzers); these devices perceive environmental information through different physical principles;

Edge computing node equipment:

One of the high-performance servers and edge computing devices: including industrial-grade or enterprise-grade servers with built-in high-performance processors, large-capacity memory and storage space; running complex stream processing frameworks and deep learning algorithms;

Container orchestration management system: software running on the server, including Docker Compose and Kubernetes, used to manage the life cycle of containerized applications;

Intelligent communication network equipment:

Advanced wireless communication equipment: including 5G wireless communication modules, Wi-Fi modules, LoRa gateways, Zigbee coordinators, supporting different communication protocols and network topologies;

Data compression equipment and software: software functions embedded in communication modules and edge computing devices to compress data before transmission to reduce bandwidth usage;

Redundant design equipment: including backup power supplies (such as UPS or backup batteries), redundant communication modules, and network interface cards to ensure continuous operation of the system in the event of equipment failure;

Central processing system equipment:

Big data analysis and prediction server: a high-performance server cluster with powerful data processing and computing capabilities, equipped with distributed file systems (such as HDFS) and big data processing frameworks (such as Hadoop and Spark);

Data caching system: It is a memory database (such as Redis or Memcached) or a disk cache system, used to cache hot data to reduce access to backend storage;

Hybrid cloud architecture equipment:

Network equipment and systems: including routers, switches, VPN gateways, APIs and SDKs provided by cloud service providers, used to connect edge computing nodes and cloud resources.

Summarize:

The multi-parameter and multi-modal integration of sensing nodes improves the comprehensiveness and accuracy of data collection, and provides a rich data source for the comprehensive assessment of the hydrological environment; the integration of multiple sensing technologies can more comprehensively capture changes in the hydrological environment, and provide a more accurate basis for prediction and decision-making; it reveals hydrological phenomena or patterns that cannot be observed by a single parameter, and provides a new perspective for scientific research.

Edge intelligent processing reduces data transmission delays and bandwidth requirements, and improves the real-time and efficiency of data processing. Edge intelligent processing can promptly detect and process abnormal data, improve the robustness of the system, discover previously unnoticed hydrological anomaly patterns, and provide new clues for hydrological disaster warnings.

Intelligent communication networks improve the reliability and stability of communication networks and reduce the risk of data loss due to network failures. Intelligent selection of the optimal communication path can improve the efficiency and reliability of data transmission. In an emergency, the intelligent communication network may automatically switch to an alternative path to ensure real-time transmission of critical data.

The high-performance computing of edge computing nodes improves the speed and efficiency of data processing and supports more complex analysis and prediction models; cluster technology can make full use of computing resources, improve overall computing performance, and reveal complex hydrological phenomena that could not be processed before due to insufficient computing resources.

Distributed learning and collaborative training improve the efficiency and accuracy of model training. Through collaborative training of multiple nodes, it can converge to the optimal solution more quickly. The federated learning framework protects data privacy while realizing collaborative training of models. Through collaborative training, hydrological data from different locations may reveal global hydrological laws and trends.

Dynamic resource management improves resource utilization and ensures that critical tasks have access to sufficient computing resources. Dynamic adjustment of resource allocation can adapt to changes in different tasks and workloads. In the case of resource constraints, dynamic management can optimize resource allocation to ensure that critical tasks are not affected while maintaining the stability of the overall system performance.

The big data analysis and prediction of the central processing system, and the in-depth mining and analysis of data uploaded by edge nodes can reveal the changing trends and patterns of the hydrological environment. The prediction model can provide support for hydrological disaster warning and decision-making. Through the comprehensive analysis of large amounts of data, previously unnoticed hydrological phenomena or trends can be discovered, providing a new perspective for scientific research and decision-making.

In summary, this distributed hydrological communication perception solution realizes comprehensive perception, efficient processing and intelligent analysis of the hydrological environment by integrating multi-parameter and multi-modal perception technology, edge intelligent processing, intelligent communication network, high-performance computing and distributed learning technologies, providing strong support for hydrological disaster warning and decision-making, and at the same time, bringing new breakthroughs to hydrological research and application.

Claims (9)

1. A distributed hydrological communication perception solution based on edge computing, characterized by comprising the following steps: S1, perception node: S2, intelligently select the optimal communication path; S3, edge computing node; specifically includes the following steps: S3.1, High-performance computing: Deploy the stream processing framework on the edge computing nodes; use cluster technology to manage multiple edge computing nodes and dynamically allocate resources according to computing needs; S3.2, Distributed learning and collaborative training: Deploy a distributed deep learning framework to collaboratively train a deep learning model for predicting water quality changes on multiple edge computing nodes; Introduce and use a collaborative training mechanism and a federated learning framework to allow each edge computing node to train the model locally and upload and aggregate the model parameters regularly; The strategy of task offloading is introduced. According to the load and computing resources of edge computing nodes, it is dynamically determined whether tasks need to be offloaded to the cloud. When the edge node load is too high or the computing resources are insufficient, some computing tasks are sent to the cloud for calculation. After the cloud computing results are returned, the edge node continues the subsequent processing or directly feeds back the results to the user. S3.3, real-time data analysis and feedback; S3.4, dynamic resource management; S4. Build a central processing system. 2. A distributed hydrological communication perception solution based on edge computing as described in claim 1, characterized in that: in S1, low-power hardware and energy-saving algorithms are used. 3. A distributed hydrological communication perception solution based on edge computing as described in claim 1, characterized in that: in S2, data compression technology is introduced during data transmission. 4. A distributed hydrological communication perception solution based on edge computing as described in claim 1, characterized in that: in S2, redundant design is adopted on key equipment and links, including dual power supplies and dual communication modules; and a dynamic network selection function is introduced to select the optimal communication network according to network conditions and data transmission requirements. 5. A distributed hydrological communication perception solution based on edge computing as described in claim 1, characterized in that: in S3.1, containerization technology is used to package the hydrological data processing application into an image, which is quickly deployed and migrated on the edge computing node, and the container is orchestrated and managed through a tool in Docker Compose or Kubernetes. 6. A distributed hydrological communication perception solution based on edge computing as described in claim 1, characterized in that: in S3.2, in the federated learning framework, noise is added to the model training process. 7. A distributed hydrological communication perception solution based on edge computing as described in claim 1 is characterized by: in S3.2, a hybrid cloud architecture is constructed to connect the edge computing nodes with the virtual machines and container clusters in the cloud, and a cloud-edge collaboration mechanism is used to achieve flexible task scheduling and data transmission between the edge and the cloud. 8. A distributed hydrological communication perception solution based on edge computing as claimed in claim 1, characterized in that: in S3.4, a resource reservation mechanism is introduced, including: Task classification and priority setting: Set the real-time water level monitoring task as the highest priority, the water quality analysis task as the medium priority, and other non-critical tasks as the low priority; Resource reservation settings: reserve two CPU cores and 50% of the memory for the real-time water level monitoring task; Resource scheduling strategy: When a new task arrives, the resource scheduler first checks whether there are enough resources to meet the needs of the task. If there are enough resources, they are allocated to the task; if there are not enough resources, the task is queued or rejected according to its priority. Monitoring and adjustment: Use a tool such as Prometheus or Grafana to monitor the resources of edge computing nodes in real time. If the load of the real-time water level monitoring task suddenly increases, temporarily increase its reserved resources. At the same time, limit or suspend low-priority tasks to release more resources for critical tasks. 9. A distributed hydrological communication sensing device system based on edge computing, characterized by comprising: Sensing devices: Multi-parameter sensors: including water level sensors, flow meters, water quality analyzers, weather stations, soil moisture sensors; Multimodal sensing devices: including acoustic sensors, optical sensors, and chemical sensors; Edge computing node equipment: One of the high-performance servers and edge computing devices: including industrial-grade or enterprise-grade servers with built-in high-performance processors, large-capacity memory and storage space; running complex stream processing frameworks and deep learning algorithms; Container orchestration management system: software running on the server, including Docker Compose and Kubernetes, used to manage the life cycle of containerized applications; Intelligent communication network equipment: Advanced wireless communication equipment: including 5G wireless communication modules, Wi-Fi modules, LoRa gateways, Zigbee coordinators, supporting different communication protocols and network topologies; Data compression equipment, software: software functions embedded in communication modules and edge computing devices; Redundant design equipment: including backup power supply, redundant communication modules, network interface cards; Central processing system equipment: Big data analysis and prediction server: a high-performance server cluster with powerful data processing and computing capabilities, equipped with a distributed file system and big data processing framework; Data cache system: a type of memory database or disk cache system used to cache hot data to reduce access to backend storage. Hybrid cloud architecture equipment: Network equipment and systems: including routers, switches, VPN gateways, APIs and SDKs provided by cloud service providers, used to connect edge computing nodes and cloud resources.