Method and device for determining layout number of nodes based on water quality monitoring and medium

Abstract

The invention discloses a method and equipment for determining the layout number of water quality monitoring nodes and a medium, and belongs to the technical field of water quality monitoring node layout. Through three-dimensional modeling and uniform cubic lattice deployment, the sensing radius and the communication distance of monitoring nodes are optimized, the coverage continuity of a monitoring network and the integrity of data are improved, the comprehensiveness and the deepness of water quality monitoring are ensured, and the monitoring efficiency and the accuracy of the data are improved. Under the condition that obstacles exist in the water area, the node deployment is optimized through the ant lion optimization algorithm, and the problem that coping strategies are lacked in the prior art is effectively solved. According to the method, the optimal deployment scheme is found through the Pareto optimal solution set, the coverage rate and connectivity of a monitoring area are improved, the number of nodes is reduced, optimization of cost benefits is achieved, it is ensured that the optimal balance point is found among multiple targets in monitoring network deployment, the optimal deployment scheme is found through the Pareto optimal solution set, and meanwhile the number of the nodes is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality monitoring node deployment, and in particular relates to a method, device and medium for determining the number of water quality monitoring node deployments. Background Art

[0002] Water quality monitoring technology is a key link in the field of environmental protection. With the acceleration of industrialization and urbanization, water pollution and water shortage are becoming increasingly serious. Effective water quality monitoring can not only detect water quality problems in a timely manner, but also provide a scientific basis for the rational use and protection of water resources. In recent years, water quality monitoring technology has developed rapidly, including real-time monitoring of pollution sources, water source protection, industrial wastewater treatment and urban sewage treatment.

[0003] Although water quality monitoring technology has made some progress, it still faces some challenges in practical applications. For example, when deploying sensor nodes in a continuous three-dimensional space, traditional monitoring methods are difficult to achieve good coverage, especially in a typical continuous three-dimensional space such as underwater space. Water quality monitoring variables are different in the vertical direction, requiring a more efficient deployment strategy. In addition, the existing technology also lacks deployment strategies when there are obstacles in the water, resulting in a waste of node deployment. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention optimizes the perception radius and communication distance of the monitoring node through three-dimensional modeling and uniform cubic grid deployment, and optimizes the node deployment by using the ant lion optimization algorithm, which effectively solves the problem of lack of response strategies in the prior art, and finds the optimal deployment plan through the Pareto optimal solution set.

[0005] In order to achieve the above purpose, the following technical solution is adopted: The present invention provides a method for determining the number of water quality monitoring nodes, comprising the following steps:

[0006] A rectangular parallelepiped model is built for the monitored water area, and the three-dimensional coordinate system and origin O(0,0,0) of the monitored water area are determined. The size of the water area is set to L×W×H, and the lowest node underwater has a maximum sensing radius R. max Perform a uniform simple cubic grid deployment, where the side length of each cubic grid is a, satisfying Determine the coordinates and number of deployed nodes. The coordinates of the nodes are based on the origin as the reference point.

[0007] The nodes of the even-numbered layers are deployed according to the previous odd-numbered layers, and the nodes of the odd-numbered layers are deployed according to R i =R i-1 -ΔR reduces the communication distance, where R i-1is the sensing radius of the previous layer of nodes, ΔR is the reduction in communication distance dynamically calculated according to the water depth and monitoring requirements. Whether a certain number of nodes need to be added at the same time is determined according to the coverage requirements and the communication distance of the nodes. The uniform cubic grid form is still used during deployment;

[0008] Define a node's minimum perception radius R min , when the perception radius R calculated by the i-th layer i Smaller than the defined minimum perception radius R min When R min is the sensing radius of the i-th layer node to ensure the coverage continuity of the monitoring network and the integrity of the node data;

[0009] According to the above, nodes are deployed layer by layer until the water surface. The sink nodes on the water surface are evenly deployed, with a deployment density of n = A / a 2 , ensuring that all underwater nodes in the first layer below the water surface can communicate directly with the Sink nodes;

[0010] When there are obstacles in the monitored waters, the node deployment is optimized based on the antlion optimization algorithm, where antlions represent sensor nodes and ants represent potential water quality monitoring points. A certain number of sensor nodes are randomly deployed in the waters through the antlion algorithm, and their fitness is evaluated. By modifying the fitness function of the antlion algorithm, an obstacle influence factor is introduced, which will dynamically adjust the deployment of sensor nodes according to the material, size and location of the obstacles.

[0011] Considering coverage, connectivity and number of nodes at the same time, the optimal deployment scheme is found through the Pareto optimal solution set to improve the coverage and connectivity of the monitoring area while reducing the number of nodes.

[0012] Furthermore, the method requires the deployment of sensor nodes and at least one Sink node, as well as a control center, and the control center is used to receive the data transmitted by the sensor nodes and perform analysis and processing.

[0013] Furthermore, the node deployment optimization algorithm includes:

[0014] a. Determine the location and characteristics of obstacles in the water area, including but not limited to the material, size, shape and distribution of obstacles, and their possible impact on water quality monitoring;

[0015] b. Use the antlion optimization algorithm to randomly deploy a certain number of sensor nodes in the water area, where antlions represent sensor nodes and ants represent potential water quality monitoring points. The deployment location of the nodes is optimized by simulating the behavior of antlions preying on ants;

[0016] c. Calculate the fitness of each node. The fitness function considers the following factors:

[0017] i. The node's ability to monitor water quality parameters;

[0018] ii. Communication efficiency between nodes;

[0019] iii. The impact of obstacles on monitoring results, by introducing obstacle impact factors to adjust the fitness value;

[0020] iv. Energy consumption and lifespan of nodes;

[0021] d. Based on the fitness evaluation results, fine-tune the node positions to optimize the deployment, including:

[0022] i. Increase or decrease the number of nodes to meet monitoring needs;

[0023] ii. Adjust the node position to avoid obstacles or cover monitoring blind spots;

[0024] iii. Optimize the sensing radius of the node to balance the monitoring range and energy consumption;

[0025] e. Repeat steps b to d until a predetermined number of iterations is reached or a specific stopping condition is met to ensure that the optimal node deployment solution is found;

[0026] f. Output the final node deployment plan, including the location, perception radius and communication connection relationship of each node, as well as the corresponding fitness evaluation results.

[0027] Furthermore, each sensor node has the following functions:

[0028] sensing water quality parameters;

[0029] Communicate with neighboring nodes;

[0030] Communicate with the Sink node;

[0031] Adjust the perception radius according to the instructions of the control center.

[0032] Furthermore, the method for dynamically adjusting the perception radius includes the following steps:

[0033] Monitor changes in water environment;

[0034] Dynamically adjust the perception radius R according to environmental changes i ;

[0035] Ensure the coverage continuity of the monitoring network and the integrity of node data.

[0036] Furthermore, when the node’s perception radius R i Less than R min Continuity of coverage is ensured by:

[0037] Increase the perception radius of neighboring nodes;

[0038] Deploy additional nodes to fill coverage gaps.

[0039] Furthermore, the integrity of node data is ensured by:

[0040] Real-time monitoring of data transmission status;

[0041] When data loss is detected, neighboring nodes are triggered to supplement or re-collect data.

[0042] Furthermore, network connectivity is ensured by:

[0043] Regularly check the connectivity status between nodes;

[0044] When a connectivity interruption is detected, node positions are automatically adjusted or spare nodes are activated to restore connectivity.

[0045] The present invention also provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, when the instructions are executed by one or more processors, the processor is used to execute the method for determining the number of water quality monitoring node deployment.

[0046] The present invention also provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed on an electronic device, the electronic device implements the method for determining the number of water quality monitoring node deployment.

[0047] The beneficial effects of the present invention are:

[0048] (1) Through three-dimensional modeling and uniform cubic grid deployment, the present invention optimizes the perception radius and communication distance of the monitoring nodes, significantly improving the coverage continuity and data integrity of the monitoring network, which not only ensures the comprehensiveness and in-depth nature of water quality monitoring, but also improves the monitoring efficiency and data accuracy.

[0049] (2) When there are obstacles in the water area, the present invention uses the ant lion optimization algorithm to optimize the node deployment. This strategy effectively solves the problem of lack of response strategies in the prior art and enhances the adaptability and flexibility of the monitoring network to complex environments.

[0050] (3) The present invention finds the optimal deployment plan through the Pareto optimal solution set. This method not only improves the coverage and connectivity of the monitoring area, but also reduces the number of nodes, achieves cost-effectiveness optimization, and ensures that the best balance point is found between multiple objectives in the monitoring network deployment. Considering coverage, connectivity and the number of nodes at the same time, the optimal deployment plan is found through the Pareto optimal solution set, thereby improving the coverage and connectivity of the monitoring area while reducing the number of nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention is a flow chart of a method for determining the number of water quality monitoring nodes to be deployed.

[0052] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] Unless otherwise defined, all professional and scientific terms used herein have the same meanings as those familiar to those skilled in the art. In addition, any methods and materials similar or equivalent to those described herein may be applied to the present invention. The preferred implementation methods and materials described herein are for demonstration purposes only and are not intended to limit the content of this application.

[0055] The experimental methods in the following examples are conventional methods unless otherwise specified, and the experimental materials used in the following examples are purchased from commercial channels unless otherwise specified.

[0056] Example

[0057] Method, equipment and medium for determining the number of water quality monitoring nodes

[0058] The method comprises the following steps:

[0059] A rectangular parallelepiped model is built for the monitored water area, and the three-dimensional coordinate system and origin O(0,0,0) of the monitored water area are determined. The size of the water area is set to L×W×H, and the lowest node underwater has a maximum sensing radius R. max Perform a uniform simple cubic grid deployment, where the side length of each cubic grid is a, satisfying Determine the coordinates and number of deployed nodes. The coordinates of the nodes are based on the origin as the reference point.

[0060] The nodes of the even-numbered layers are deployed according to the previous odd-numbered layers, and the nodes of the odd-numbered layers are deployed according to R i =R i-1 -ΔR reduces the communication distance, where R i-1 is the sensing radius of the previous layer of nodes, ΔR is the reduction in communication distance dynamically calculated according to the water depth and monitoring requirements. Whether a certain number of nodes need to be added at the same time is determined according to the coverage requirements and the communication distance of the nodes. The uniform cubic grid form is still used during deployment;

[0061] Define a node's minimum perception radius R min , when the perception radius R calculated by the i-th layer i Smaller than the defined minimum perception radius R min When R min is the sensing radius of the i-th layer node to ensure the coverage continuity of the monitoring network and the integrity of the node data;

[0062] According to the above, nodes are deployed layer by layer until the water surface. The sink nodes on the water surface are evenly deployed, with a deployment density of n = A / a 2 , ensuring that all underwater nodes in the first layer below the water surface can communicate directly with the Sink nodes;

[0063] When there are obstacles in the monitored waters, the node deployment is optimized based on the antlion optimization algorithm, where antlions represent sensor nodes and ants represent potential water quality monitoring points. A certain number of sensor nodes are randomly deployed in the waters through the antlion algorithm, and their fitness is evaluated. By modifying the fitness function of the antlion algorithm, an obstacle influence factor is introduced, which will dynamically adjust the deployment of sensor nodes according to the material, size and location of the obstacles.

[0064] Considering coverage, connectivity and number of nodes at the same time, the optimal deployment scheme is found through the Pareto optimal solution set to improve the coverage and connectivity of the monitoring area while reducing the number of nodes.

[0065] The method requires the deployment of sensor nodes and at least one Sink node, as well as a control center, which is used to receive data transmitted by the sensor nodes and perform analysis and processing.

[0066] The node deployment optimization algorithm includes:

[0067] a. Determine the location and characteristics of obstacles in the water area, including but not limited to the material, size, shape and distribution of obstacles, and their possible impact on water quality monitoring;

[0068] b. Use the antlion optimization algorithm to randomly deploy a certain number of sensor nodes in the water area, where antlions represent sensor nodes and ants represent potential water quality monitoring points. The deployment location of the nodes is optimized by simulating the behavior of antlions preying on ants;

[0069] c. Calculate the fitness of each node. The fitness function considers the following factors:

[0070] i. The node's ability to monitor water quality parameters;

[0071] ii. Communication efficiency between nodes;

[0072] iii. The impact of obstacles on monitoring results, by introducing obstacle impact factors to adjust the fitness value;

[0073] iv. Energy consumption and lifespan of nodes;

[0074] d. Based on the fitness evaluation results, fine-tune the node positions to optimize the deployment, including:

[0075] i. Increase or decrease the number of nodes to meet monitoring needs;

[0076] ii. Adjust the node position to avoid obstacles or cover monitoring blind spots;

[0077] iii. Optimize the sensing radius of the node to balance the monitoring range and energy consumption;

[0078] e. Repeat steps b to d until a predetermined number of iterations is reached or a specific stopping condition is met to ensure that the optimal node deployment solution is found;

[0079] f. Output the final node deployment plan, including the location, perception radius and communication connection relationship of each node, as well as the corresponding fitness evaluation results.

[0080] Each sensor node has the following functions:

[0081] sensing water quality parameters;

[0082] Communicate with neighboring nodes;

[0083] Communicate with the Sink node;

[0084] Adjust the perception radius according to the instructions of the control center.

[0085] The method for dynamically adjusting the perception radius includes the following steps:

[0086] Monitor changes in water environment;

[0087] Dynamically adjust the perception radius R according to environmental changes i ;

[0088] Ensure the coverage continuity of the monitoring network and the integrity of node data.

[0089] When the node's perception radius R i Less than R min Continuity of coverage is ensured by:

[0090] Increase the perception radius of neighboring nodes;

[0091] Deploy additional nodes to fill coverage gaps.

[0092] Ensure the integrity of node data by:

[0093] Real-time monitoring of data transmission status;

[0094] When data loss is detected, neighboring nodes are triggered to supplement or re-collect data.

[0095] Ensure network connectivity by:

[0096] Regularly check the connectivity status between nodes;

[0097] When a connectivity interruption is detected, node positions are automatically adjusted or spare nodes are activated to restore connectivity.

[0098] This comparative example also provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, when the instructions are executed by one or more processors, the processor is used to execute the method for determining the number of water quality monitoring node deployment.

[0099] The present comparative example also provides a computer-readable storage medium, on which instructions are stored, and when the instructions are executed on an electronic device, the electronic device implements the method for determining the number of water quality monitoring node deployments.

[0100] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0101] The present invention and its implementation methods are described above, which is not restrictive. The drawings are only one of the implementation methods of the present invention, and the actual application is not limited thereto. In short, if ordinary technicians in the field are inspired by it and design methods and embodiments similar to the technical solution without creativity without departing from the purpose of the invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for determining the number of water quality monitoring nodes, characterized in that: The steps include: A rectangular parallelepiped model is built for the monitored water area, and the three-dimensional coordinate system and origin O(0,0,0) of the monitored water area are determined. The size of the water area is set to L×W×H, and the lowest node underwater has a maximum sensing radius R. max Perform a uniform simple cubic grid deployment, where the side length of each cubic grid is a, satisfying Determine the coordinates and number of deployed nodes. The coordinates of the nodes are based on the origin as the reference point. The nodes of the even-numbered layers are deployed according to the previous odd-numbered layers, and the nodes of the odd-numbered layers are deployed according to R i =R i-1 -ΔR reduces the communication distance, where R i-1 is the sensing radius of the previous layer of nodes, ΔR is the reduction in communication distance dynamically calculated according to the water depth and monitoring requirements. Whether a certain number of nodes need to be added at the same time is determined according to the coverage requirements and the communication distance of the nodes. The uniform cubic grid form is still used during deployment; Define a node's minimum perception radius R min , when the perception radius R calculated by the i-th layer i Smaller than the defined minimum perception radius R min When R min is the sensing radius of the i-th layer node to ensure the coverage continuity of the monitoring network and the integrity of the node data; According to the above, nodes are deployed layer by layer until the water surface. The sink nodes on the water surface are evenly deployed, with a deployment density of n = A / a 2 , ensuring that all underwater nodes in the first layer below the water surface can communicate directly with the Sink nodes; When there are obstacles in the monitored waters, the node deployment is optimized based on the antlion optimization algorithm, where antlions represent sensor nodes and ants represent potential water quality monitoring points. A certain number of sensor nodes are randomly deployed in the waters through the antlion algorithm, and their fitness is evaluated. By modifying the fitness function of the antlion algorithm, an obstacle influence factor is introduced, which will dynamically adjust the deployment of sensor nodes according to the material, size and location of the obstacles. Considering coverage, connectivity and number of nodes at the same time, the optimal deployment scheme is found through the Pareto optimal solution set to improve the coverage and connectivity of the monitoring area while reducing the number of nodes.

2. The method for determining the number of water quality monitoring nodes according to claim 1, characterized in that: The method requires the deployment of sensor nodes and at least one Sink node, as well as a control center, which is used to receive data transmitted by the sensor nodes and perform analysis and processing.

3. The method for determining the number of water quality monitoring nodes according to claim 2 is characterized in that: The node deployment optimization algorithm includes: a. Determine the location and characteristics of obstacles in the water area, including but not limited to the material, size, shape and distribution of obstacles, and their possible impact on water quality monitoring; b. Use the antlion optimization algorithm to randomly deploy a certain number of sensor nodes in the water area, where antlions represent sensor nodes and ants represent potential water quality monitoring points. The deployment location of the nodes is optimized by simulating the behavior of antlions preying on ants; c. Calculate the fitness of each node. The fitness function considers the following factors: i. The node's ability to monitor water quality parameters; ii. Communication efficiency between nodes; iii. The impact of obstacles on monitoring results, by introducing obstacle impact factors to adjust the fitness value; iv. Energy consumption and lifespan of nodes; d. Based on the fitness evaluation results, fine-tune the node positions to optimize the deployment, including: i. Increase or decrease the number of nodes to meet monitoring needs; ii. Adjust the node position to avoid obstacles or cover monitoring blind spots; iii. Optimize the sensing radius of the node to balance the monitoring range and energy consumption; e. Repeat steps b to d until a predetermined number of iterations is reached or a specific stopping condition is met to ensure that the optimal node deployment solution is found; f. Output the final node deployment plan, including the location, perception radius and communication connection relationship of each node, as well as the corresponding fitness evaluation results.

4. The method for determining the number of water quality monitoring nodes according to claim 3 is characterized in that: Each sensor node has the following functions: sensing water quality parameters; Communicate with neighboring nodes; Communicate with the Sink node; Adjust the perception radius according to the instructions of the control center.

5. The method for determining the number of water quality monitoring nodes according to claim 4 is characterized in that: The method for dynamically adjusting the perception radius includes the following steps: Monitor changes in water environment; Dynamically adjust the perception radius R according to environmental changes i ; Ensure the coverage continuity of the monitoring network and the integrity of node data.

6. The method for determining the number of water quality monitoring nodes according to claim 5, characterized in that: When the node's perception radius R i Less than R min Continuity of coverage is ensured by: Increase the perception radius of neighboring nodes; Deploy additional nodes to fill coverage gaps.

7. The method for determining the number of water quality monitoring nodes according to claim 6, characterized in that: Ensure the integrity of node data by: Real-time monitoring of data transmission status; When data loss is detected, neighboring nodes are triggered to supplement or re-collect data.

8. The method for determining the number of water quality monitoring nodes according to claim 7, characterized in that: Ensure network connectivity by: Regularly check the connectivity status between nodes; When a connectivity interruption is detected, node positions are automatically adjusted or spare nodes are activated to restore connectivity.

9. An electronic device, comprising: A memory for storing instructions to be executed by one or more processors of an electronic device, and a processor, when the instructions are executed by one or more processors, the processor is used to execute the method for determining the number of water quality monitoring nodes to be deployed based on any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed on an electronic device, enable the electronic device to execute the method for determining the number of water quality monitoring node deployment based on any one of claims 1-8.

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