A wireless sensor network node deployment method for facility agriculture

Abstract

The present invention relates to the technical field of facility agriculture monitoring, and specifically to a wireless sensor network node deployment method for facility agriculture. The method comprises the following steps: obtaining the size of a target area and the number of wireless sensor network nodes that need to be deployed in the target area, and randomly pre-deploying all wireless sensor network nodes in the target area; constructing a coordinate system in the target area, obtaining the coordinates of any wireless sensor network node after pre-deployment, constructing a perception node set and a communication node set of wireless sensor network nodes adjacent to the wireless sensor network node with the coordinates of the wireless sensor network node as the center, and obtaining the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes; determining whether there are redundant nodes in the wireless sensor network node according to the coverage rate, and optimizing the redundant nodes by using a particle swarm algorithm, thereby solving the problem of optimizing wireless sensor network nodes in facility agriculture.

Description

Technical Field

[0001] The present invention relates to the technical field of facility agriculture monitoring, and in particular to a wireless sensor network node deployment method for facility agriculture. Background Art

[0002] As an important part of modern agriculture, facility agriculture has achieved precise regulation of the agricultural production environment and scientific management of the crop growth process through the integrated application of modern engineering technology, biotechnology and information technology.

[0003] However, in the practical application of facility agriculture, a key issue currently lies in how to efficiently deploy wireless sensor network nodes to achieve comprehensive, real-time monitoring of the agricultural production environment. Traditional node deployment methods often rely on manual experience or simple rules, which are difficult to adapt to the complex and changing agricultural production environment. This results in incomplete and inaccurate monitoring data, and is unable to provide effective decision support for agricultural production.

[0004] Therefore, the present invention provides a wireless sensor network node deployment method for facility agriculture to solve the problem of existing wireless sensor network node coverage optimization. Summary of the Invention

[0005] In view of the above situation, in order to overcome the defects of the existing technology, the present invention provides a wireless sensor network node deployment method for facility agriculture to solve the problem of existing wireless sensor network node coverage optimization.

[0006] In order to achieve the above-mentioned object, the technical solution adopted by the present invention is: a wireless sensor network node deployment method for facility agriculture, the method comprising the following steps:

[0007] S101, obtaining the size of a target area and the number of wireless sensor network nodes to be deployed in the target area, and randomly pre-deploying all wireless sensor network nodes in the target area, and the deployed wireless sensor network nodes are not mobile;

[0008] S102: Construct a coordinate system in the target area, obtain the coordinates of any pre-deployed wireless sensor network node, construct a sensing node set and a communication node set of the wireless sensor network nodes adjacent to the wireless sensor network node with the coordinates of the wireless sensor network node as the center, and obtain the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes;

[0009] S103. Determine whether there are redundant nodes in the wireless sensor network based on the coverage rate of any wireless sensor network node after pre-deployment. If there are redundant nodes, construct an optimized coverage model and optimize the optimized coverage model using a particle swarm algorithm until all wireless sensor network nodes reach optimal deployment.

[0010] A further improvement of the present application is that the initial energy of each sensor node in the wireless sensor network for facility agriculture is the same, and the communication radius of all sensor nodes is twice their perception radius.

[0011] A further improvement of the present application is that the expression of the sensing node set of the adjacent wireless sensor network nodes of any wireless sensor network node after the pre-deployment is:

[0012]

[0013] In expression (1), Represents any wireless sensor network node after pre-deployment, Representation node On the x-axis, Representation node On the y-axis, Representation node The maximum perception radius, Indicates that the node Any node other than Representation node On the x-axis, Representation node On the y-axis, Representation node The set of sensing nodes of all adjacent nodes;

[0014] The expression of the communication node set of the adjacent wireless sensor network nodes of any wireless sensor network node after pre-deployment is:

[0015]

[0016] In expression (2), Represents any wireless sensor network node after pre-deployment, Representation node On the x-axis, Representation node On the y-axis, Representation node Any node other than Representation node On the x-axis, Representation node On the y-axis, Representation node The maximum communication radius of of, Representation node The communication node set of all neighboring nodes.

[0017] A further improvement of the present application is that the method for obtaining the coverage rate of the wireless sensor network node by adjacent wireless sensor network nodes comprises the following steps:

[0018] S201, obtaining the sensing overlap center angle of the wireless sensor network node, the sensing overlap center angle is the node With node When the perception ranges overlap, the central angle of the fan-shaped area formed by the overlapping area is recorded as , the calculation formula of the perceived overlapping center angle is:

[0019]

[0020] S202. Obtain the area of ​​the overlapping coverage area of ​​the wireless sensor network nodes according to expression (3), which is:

[0021]

[0022] In expression (4), Represents the area of ​​overlapping coverage of wireless sensor network nodes;

[0023] S203. According to expressions (3) and (4), obtain the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes. The expression of the coverage rate of the wireless sensor network node is:

[0024]

[0025] In expression (5), represents the sensing area of ​​the wireless sensor network node, Through expression (6), we can obtain:

[0026] .

[0027] A further improvement of the present application is that the condition for determining whether there is a redundant node in the wireless sensor network is:

[0028] If the coverage rate of any wireless sensor network node is lower than the set threshold, it is determined to be a redundant node; otherwise, it is determined to be a valid node.

[0029] A further improvement of the present application is that the method of constructing an optimized coverage model and optimizing the optimized coverage model using a particle swarm algorithm until all wireless sensor network nodes are optimally deployed comprises the following steps:

[0030] S301, constructing a wireless sensor network node coverage model;

[0031] S302, using the coverage rate of wireless sensor network nodes and node redundancy information as optimization targets of the particle swarm algorithm to optimize the constructed coverage model;

[0032] A further improvement of the present application is that the method for implementing step S302 includes the following steps:

[0033] S401, initializing the relevant parameters of the particle swarm algorithm, including the particle swarm size, number of iterations, initial position and speed of the particles, etc.;

[0034] S402. Calculate the fitness value of each particle based on the constructed coverage model. The fitness value is calculated by comprehensively evaluating the coverage rate of each particle and the node redundancy.

[0035] S403, updating the speed and position of the particles. According to the rules of the particle swarm algorithm, each particle will adjust its speed and position according to its own historical optimal position and the global optimal position;

[0036] S404, iteratively calculating the fitness value and updating the position of the particle until a predetermined number of iterations is reached;

[0037] S405: Outputting the optimized wireless sensor network node deployment solution, that is, the location information of all wireless sensor network nodes when they reach the optimal deployment state.

[0038] A further improvement of the present application is that, in step S402, the smaller the coverage rate of each particle, the more uniform the coverage of the wireless sensor network nodes, and vice versa; the smaller the number of node redundancies, the better the coverage of the wireless sensor network nodes, and vice versa.

[0039] A further improvement of the present application is that, in step S403, the position update and velocity update of the particle are replaced by the mutation operator in the genetic algorithm, including:

[0040] Set a mutation probability. For each particle, before updating its velocity and position, decide whether to perform the mutation operation based on the mutation probability.

[0041] If mutation is decided, one or more components of the particle velocity or position vector are randomly selected and adjusted randomly by a small amount.

[0042] A further improvement of the present application is that in step S404, during the iteration process, if the current number of iterations is less than the preset maximum number of iterations, steps S402 to S403 are continued to be executed until the maximum number of iterations is reached.

[0043] The present invention achieves more uniform coverage by randomly pre-deploying all wireless sensor network nodes and iteratively optimizing their positions using a particle swarm optimization algorithm. By calculating each particle's coverage rate and node redundancy, the present invention accurately assesses the quality of the current node deployment and uses this information to guide subsequent optimization processes. This provides an efficient and cost-effective solution for wireless sensor network node deployment in facility agriculture, demonstrating significant technological advancement and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic flow chart of a wireless sensor network node deployment method for facility agriculture according to the present invention;

[0045] Figure 2 This is an optimization comparison diagram of a wireless sensor network node deployment method for facility agriculture according to the present invention. DETAILED DESCRIPTION

[0046] The following will describe various embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] Facility agriculture refers to a method of agricultural production that uses facilities such as greenhouses, cold sheds, and seedling beds. It can effectively regulate environmental conditions and improve crop growth efficiency and yield. Wireless sensors commonly used in facility agriculture are mainly used for environmental monitoring and data collection, helping farmers better manage crop growth. The following are some common types of wireless sensors:

[0048] Temperature sensors: Monitor soil and air temperature to help regulate the climate within the greenhouse.

[0049] Humidity sensor: measures soil and air humidity for proper irrigation management.

[0050] Light sensor: monitors light intensity to help optimize lighting conditions for plants.

[0051] CO2 sensor: measures carbon dioxide concentration to ensure plants get enough CO2 for photosynthesis.

[0052] Soil pH sensor: Monitors soil acidity and alkalinity, helping to assess soil conditions and adjust fertilization strategies.

[0053] Nutrient sensor: Detects the nitrogen, phosphorus, potassium and other nutrient contents in the soil to help with precise fertilization.

[0054] Water level sensor: monitors the water level of water sources or irrigation systems to ensure the rational use of water resources.

[0055] Meteorological sensors: collect meteorological data such as wind speed and precipitation, and provide comprehensive climate information for facility agriculture.

[0056] These sensors are usually combined with wireless network technology to form a wireless sensor network. By transmitting data to the management system in real time, it facilitates farmers to conduct remote monitoring and management, thereby improving the production efficiency and sustainability of facility agriculture.

[0057] However, in the practical application of facility agriculture, a key issue currently lies in how to efficiently deploy wireless sensor network nodes to achieve comprehensive, real-time monitoring of the agricultural production environment. Traditional node deployment methods often rely on manual experience or simple rules, which are difficult to adapt to the complex and changing agricultural production environment. This results in incomplete and inaccurate monitoring data, and is unable to provide effective decision support for agricultural production. Example

[0058] like Figure 1 As shown, the present invention discloses a wireless sensor network node deployment method for facility agriculture, the method comprising the following steps:

[0059] S101, obtaining the size of a target area and the number of wireless sensor network nodes to be deployed in the target area, and randomly pre-deploying all wireless sensor network nodes in the target area, and the deployed wireless sensor network nodes are not mobile;

[0060] S102: Construct a coordinate system in the target area, obtain the coordinates of any pre-deployed wireless sensor network node, construct a sensing node set and a communication node set of the wireless sensor network nodes adjacent to the wireless sensor network node with the coordinates of the wireless sensor network node as the center, and obtain the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes;

[0061] S103. Determine whether there are redundant nodes in the wireless sensor network based on the coverage rate of any wireless sensor network node after pre-deployment. If there are redundant nodes, construct an optimized coverage model and optimize the optimized coverage model using a particle swarm algorithm until all wireless sensor network nodes reach optimal deployment.

[0062] In the above steps, we first analyzed the node's perception radius and communication radius. The perception radius determines the area a node can monitor, while the communication radius determines the distance over which nodes can effectively communicate. By properly setting these two parameters, we can ensure that every node in the network can effectively cover its monitoring area and maintain stable communication with other nodes.

[0063] Secondly, we also considered node deployment density. Too high a density can lead to mutual interference and redundant coverage between nodes, while too low a density may not meet monitoring requirements. Therefore, in the optimized coverage model, we appropriately determined the node deployment density based on the specific conditions and monitoring requirements of the target area to ensure the integrity and effectiveness of network coverage.

[0064] Finally, we employed the particle swarm optimization algorithm (PSO) to optimize the coverage model. PSO is an optimization algorithm based on swarm intelligence that mimics the foraging behavior of flocks of birds to find the optimal solution. During the optimization process, we treat each wireless sensor network node as a particle and continuously adjust the particle's position and velocity to find a node deployment solution that maximizes network coverage, maximizes communication efficiency, and minimizes energy consumption.

[0065] By implementing the above steps and methods, we can achieve optimized deployment of wireless sensor network nodes for facility agriculture. This deployment method not only improves network coverage and reliability, but also reduces node energy consumption and deployment costs, providing strong support for intelligent management of facility agriculture.

[0066] Specifically, each sensor node in the wireless sensor network for facility agriculture has the same initial energy, and the communication radius of all sensor nodes is twice their perception radius. This uniform initial energy ensures fair competition among all nodes at the start of deployment, avoiding uneven network performance due to differences in initial energy. This facilitates more accurate evaluation and optimization of the overall network performance, as any node's performance bottlenecks are neither masked nor exaggerated by differences in initial energy.

[0067] Secondly, setting the communication radius to twice the perception radius significantly improves network connectivity and reliability. The perception radius determines the range of environmental information a node can perceive, while the communication radius determines the distance a node can exchange information with other nodes. When the communication radius is twice the perception radius, even if two nodes don't directly perceive the same environmental information, they can still exchange information indirectly through other nodes, thereby enhancing network connectivity and information sharing capabilities. This setting also helps reduce communication failures and energy waste caused by long distances between nodes, improving network reliability and energy efficiency.

[0068] Furthermore, this setup facilitates network topology optimization and routing protocol design. Knowing the node communication and perception radius makes it easier to predict and evaluate communication links and transmission paths within the network, enabling the design of more efficient and reliable routing protocols. This setup also helps optimize network topology, adjusting the location and number of nodes to optimize network coverage and connectivity, further improving network performance.

[0069] The expression of the sensing node set of the adjacent wireless sensor network nodes of any wireless sensor network node after pre-deployment is:

[0070]

[0071] In expression (1), Represents any wireless sensor network node after pre-deployment, Representation node On the x-axis, Representation node On the y-axis, Representation node The maximum perception radius, Indicates that the node Any node other than Representation node On the x-axis, Representation node On the y-axis, Representation node The set of sensing nodes of all adjacent nodes;

[0072] The expression of the communication node set of the adjacent wireless sensor network nodes of any wireless sensor network node after pre-deployment is:

[0073]

[0074] In expression (2), Represents any wireless sensor network node after pre-deployment, Representation node On the x-axis, Representation node On the y-axis, Representation node Any node other than Representation node On the x-axis, Representation node On the y-axis, Representation node The maximum communication radius of of, Representation node The communication node set of all neighboring nodes.

[0075] In one embodiment of the present invention, the method for obtaining the coverage rate of the wireless sensor network node by adjacent wireless sensor network nodes includes the following steps:

[0076] S201, obtaining the sensing overlap center angle of the wireless sensor network node, the sensing overlap center angle is the node With node When the perception ranges overlap, the central angle of the fan-shaped area formed by the overlapping area is recorded as , the calculation formula of the perceived overlapping center angle is:

[0077]

[0078] S202. Obtain the area of ​​the overlapping coverage area of ​​the wireless sensor network nodes according to expression (3), which is:

[0079] ,

[0080] In expression (4), Represents the area of ​​overlapping coverage of wireless sensor network nodes;

[0081] S203. According to expressions (3) and (4), obtain the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes. The expression of the coverage rate of the wireless sensor network node is:

[0082] ,

[0083] In expression (5), represents the sensing area of ​​the wireless sensor network node, Through expression (6), we can obtain:

[0084] .

[0085] Specifically, the conditions for determining whether there is a redundant node in the wireless sensor network are:

[0086] If the coverage rate of any wireless sensor network node is lower than the set threshold, it is determined to be a redundant node; otherwise, it is determined to be a valid node.

[0087] In the above embodiment, by obtaining the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes, it is determined which network node is the redundant node.

[0088] In order to improve the coverage rate of pre-deployed wireless sensor network nodes, an optimized coverage model is constructed and a particle swarm algorithm is used to optimize the optimized coverage model until all wireless sensor network nodes are optimally deployed. The method includes the following steps:

[0089] S301, constructing a wireless sensor network node coverage model;

[0090] S302, using the coverage rate of wireless sensor network nodes and node redundancy information as optimization targets of the particle swarm algorithm to optimize the constructed coverage model;

[0091] Specifically, the method for implementing step S302 includes the following steps:

[0092] S401, initializing the relevant parameters of the particle swarm algorithm, including the particle swarm size, number of iterations, initial position and speed of the particles, etc.;

[0093] S402. Calculate the fitness value of each particle based on the constructed coverage model. The fitness value is calculated by comprehensively evaluating the coverage rate of each particle and the node redundancy.

[0094] S403, updating the speed and position of the particles. According to the rules of the particle swarm algorithm, each particle will adjust its speed and position according to its own historical optimal position and the global optimal position;

[0095] S404, iteratively calculating the fitness value and updating the position of the particle until a predetermined number of iterations is reached;

[0096] S405: Outputting the optimized wireless sensor network node deployment solution, that is, the location information of all wireless sensor network nodes when they reach the optimal deployment state.

[0097] In step S402, the fitness function is expressed as:

[0098]

[0099] In expression (7), and Represents the weight coefficient, which is used to adjust the impact of coverage rate and node redundancy on fitness. Represents a constant used to avoid the denominator being zero and ensure stable calculation. Indicates the number of node redundancy, represents the coverage rate of wireless sensor network nodes, which is obtained by expression (5);

[0100] In one embodiment of the present invention, in step S402, the smaller the coverage rate of each particle, the more uniform the coverage of the wireless sensor network nodes, and vice versa; the smaller the number of node redundancies, the better the coverage of the wireless sensor network nodes, and vice versa.

[0101] In one embodiment of the present invention, in step S403, the position update and velocity update of the particle are replaced by the mutation operator in the genetic algorithm, including:

[0102] Set a mutation probability. For each particle, before updating its velocity and position, decide whether to perform the mutation operation based on this mutation probability.

[0103] If mutation is decided, one or more components of the particle velocity or position vector are randomly selected and adjusted randomly by a small amount.

[0104] Specifically, the mutation probability can be set to, for example, 0.1, which means that for each particle, there is a 10% chance of performing a mutation operation.

[0105] In one embodiment of the present invention, in step S404, during the iteration process, if the current number of iterations is less than the preset maximum number of iterations, steps S402 to S403 are continued to be executed until the maximum number of iterations is reached.

[0106] By optimizing the optimal coverage model using a particle swarm algorithm, a rational node deployment in a wireless sensor network is achieved. Each particle represents a possible node deployment scheme, and its fitness is evaluated based on network coverage and node redundancy. Specifically, a high particle coverage rate and low node redundancy indicate that the deployment scheme effectively covers the target area while reducing the number of unnecessary nodes, thereby improving the network's energy efficiency and cost-effectiveness.

[0107] During the optimization process, the mutation operator in the genetic algorithm is used to improve the particle swarm algorithm, increasing the diversity of particles and preventing the algorithm from falling into the local optimal solution. By setting the mutation probability, each particle has a certain probability of mutation when updating its speed and position, thus jumping out of the current local optimal area and exploring a wider solution space.

[0108] As the number of iterations increases, the algorithm gradually converges to the optimal solution. When the maximum number of iterations is reached, the algorithm stops and outputs the currently optimal node deployment plan. This solution not only achieves high network coverage but also minimizes node redundancy, providing reliable and efficient wireless sensor network support for facility agriculture.

[0109] Specifically, the comparison results of wireless sensor network nodes after optimizing the coverage model using particle swarm optimization are as follows: Figure 2 As shown in the figure, after optimization, the coverage of wireless sensor network nodes is significantly improved and redundant nodes are reduced.

[0110] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0113] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0114] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0115] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0116] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0117] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A wireless sensor network node deployment method for facility agriculture, characterized in that: The method comprises the following steps: S101, obtaining the size of a target area and the number of wireless sensor network nodes to be deployed in the target area, and randomly pre-deploying all wireless sensor network nodes in the target area, and the deployed wireless sensor network nodes are not mobile; S102: Construct a coordinate system in the target area, obtain the coordinates of any pre-deployed wireless sensor network node, construct a sensing node set and a communication node set of the wireless sensor network nodes adjacent to the wireless sensor network node with the coordinates of the wireless sensor network node as the center, and obtain the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes; S103, determining whether there are redundant nodes in the wireless sensor network based on the coverage rate of any wireless sensor network node after pre-deployment; if there are redundant nodes, constructing an optimized coverage model and optimizing the optimized coverage model using a particle swarm algorithm until all wireless sensor network nodes reach optimal deployment; The method for obtaining the coverage rate of a wireless sensor network node by adjacent wireless sensor network nodes comprises the following steps: S201. Obtain the sensing overlap center angle of the wireless sensor network node. The sensing overlap center angle is the center angle corresponding to the fan-shaped area formed by the overlapping area when the sensing ranges of node i and node j overlap. The sensing overlap center angle is denoted as α. The sensing overlap center angle is calculated as follows: S202. Obtain the area of ​​the overlapping coverage area of ​​the wireless sensor network nodes according to expression (3), which is: In expression (4), A overlap Represents the area of ​​overlapping coverage of wireless sensor network nodes; S203. According to expressions (3) and (4), obtain the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes. The expression of the coverage rate of the wireless sensor network node is: In expression (5), A total Represents the sensing area of ​​the wireless sensor network node, A total By expression (6), we can obtain: The condition for determining whether there is a redundant node in the wireless sensor network: If the coverage rate of any wireless sensor network node is lower than the set threshold, it is determined to be a redundant node, otherwise it is determined to be a valid node; The method of constructing an optimized coverage model and optimizing the optimized coverage model using a particle swarm algorithm until all wireless sensor network nodes are optimally deployed comprises the following steps: S301, constructing a wireless sensor network node coverage model; S302, using the coverage rate of wireless sensor network nodes and node redundancy information as optimization targets of the particle swarm algorithm to optimize the constructed coverage model; The method for implementing step S302 includes the following steps: S401, initializing the relevant parameters of the particle swarm algorithm, including the particle swarm size, number of iterations, initial position and speed of the particles, etc.; S402. Calculate the fitness value of each particle based on the constructed coverage model. The fitness value is calculated by comprehensively evaluating the coverage rate of each particle and the node redundancy. The expression of the fitness function is: In expression (7), w1 and w2 represent weight coefficients, which are used to adjust the impact of coverage rate and node redundancy on fitness, ò represents a constant, which is used to avoid the denominator being zero and ensure calculation stability, d represents the number of node redundancy, and η represents the coverage rate of wireless sensor network nodes, which is obtained from expression (5); S403, updating the speed and position of the particles. According to the rules of the particle swarm algorithm, each particle will adjust its speed and position based on its own historical optimal position and the global optimal position; S404, iteratively calculating the fitness value and updating the position of the particle until a predetermined number of iterations is reached; S405: Outputting the optimized wireless sensor network node deployment solution, that is, the location information of all wireless sensor network nodes when they reach the optimal deployment state.

2. The wireless sensor network node deployment method for facility agriculture according to claim 1, characterized in that: The initial energy of each sensor node in the wireless sensor network for facility agriculture is the same, and the communication radius of all sensor nodes is twice their perception radius.

3. The wireless sensor network node deployment method for facility agriculture according to claim 2, characterized in that: The expression of the sensing node set of the adjacent wireless sensor network nodes of any wireless sensor network node after pre-deployment is: In expression (1), i represents any wireless sensor network node after pre-deployment, i x Indicates the coordinate of node i on the x-axis, i y represents the coordinate of node i on the y-axis, R s represents the maximum perception radius of node i, j represents any node except node i, j x Indicates the coordinate of node j on the x-axis, j y A represents the coordinate of node j on the y-axis, i Represents the set of sensing nodes of all adjacent nodes of node i; The expression of the communication node set of the adjacent wireless sensor network nodes of any wireless sensor network node after pre-deployment is: In expression (2), i represents any wireless sensor network node after pre-deployment, i x Indicates the coordinate of node i on the x-axis, i y represents the coordinate of node i on the y-axis, j represents any node other than node i, x Indicates the coordinate of node j on the x-axis, j y represents the coordinate of node j on the y-axis, R c represents the maximum communication radius of node i, and satisfies R c =2R s Yes, B i Represents the communication node set of all adjacent nodes of node i.

4. The wireless sensor network node deployment method for facility agriculture according to claim 1, characterized in that: In step S402, the smaller the coverage rate of each particle, the more uniform the wireless sensor network node coverage, and vice versa; the smaller the number of node redundancies, the better the wireless sensor network node coverage, and vice versa.

5. The wireless sensor network node deployment method for facility agriculture according to claim 4, characterized in that: In step S403, the position update and velocity update of the particle are replaced by the mutation operator in the genetic algorithm, including: Set a mutation probability. For each particle, before updating its velocity and position, decide whether to perform the mutation operation based on this probability. If mutation is decided, one or more components of the particle velocity or position vector are randomly selected and adjusted randomly by a small amount.

6. The wireless sensor network node deployment method for facility agriculture according to claim 5, characterized in that: In step S404, during the iteration process, if the current number of iterations is less than the preset maximum number of iterations, steps S402 to S403 are continued to be executed until the maximum number of iterations is reached.

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