Wireless sensor network node deployment method for facility agriculture

Abstract

The invention relates to the technical field of facility agriculture monitoring, in particular to a wireless sensor network node deployment method for facility agriculture, and the method comprises the steps: obtaining the range size of a target region and the number of wireless sensor network nodes needing to be deployed in the target region; randomly pre-deploying all wireless sensor network nodes in the target area; constructing a coordinate system in the target area, obtaining the coordinate of any wireless sensor network node after pre-deployment, and constructing a sensing node set and a communication node set of the wireless sensor network node adjacent to the wireless sensor network node by taking the coordinate of the wireless sensor network node as the center; the coverage rate of the wireless sensor network node covered by the adjacent wireless sensor network node is obtained; according to the coverage rate, whether redundant nodes exist in the wireless sensor network nodes or not is judged, the redundant nodes are optimized through the particle swarm optimization, and the problem of optimization of the wireless sensor network nodes in the facility agriculture is solved.

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 actual application of facility agriculture, how to efficiently deploy wireless sensor network nodes to achieve comprehensive and real-time monitoring of the agricultural production environment is a key issue currently faced. 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, resulting in incomplete and inaccurate monitoring data, and 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 prior art, the present invention provides a wireless sensor network node deployment method for facility agriculture to solve the problem of optimizing the coverage of existing wireless sensor network nodes.

[0006] In order to achieve the above 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: S101, obtaining the size of the 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 do not have mobility; S102, constructing a coordinate system in the target area, obtaining the coordinates of any wireless sensor network node after pre-deployment, constructing 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 obtaining the coverage rate of the wireless sensor network node covered by the adjacent wireless sensor network nodes; S103, according to the coverage rate of any wireless sensor network node after pre-deployment, determine whether there are redundant nodes in the wireless sensor network nodes, if there are redundant nodes, build an optimized coverage model, and use a particle swarm algorithm to optimize the optimized coverage model until all wireless sensor network nodes reach optimal deployment.

[0007] 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.

[0008] 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: In expression (1), Represents any wireless sensor network node after pre-deployment, Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node The maximum perception radius, Indicates that the node Any node other than Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node The set of sensing nodes of all neighboring nodes; The expression of the communication node set of the adjacent wireless sensor network nodes of any wireless sensor network node after the pre-deployment is: In expression (2), Represents any wireless sensor network node after pre-deployment, Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node Any node other than Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node The maximum communication radius of of, Representation Node The communication node set of all neighboring nodes.

[0009] A further improvement of the present application is that the method for obtaining the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes comprises the following steps: S201, obtaining the sensed overlapping center angle of the wireless sensor network node, wherein the sensed overlapping center angle is the center angle of 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:

[0010] S202. According to expression (3), the area of ​​the overlapping coverage area of ​​the wireless sensor network nodes is obtained. The expression is: In expression (4), 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 covered by the adjacent wireless sensor network nodes, the expression of the coverage rate of the wireless sensor network node is: In expression (5), Represents the sensing area of ​​the wireless sensor network node, Through expression (6), we can obtain: .

[0011] 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: 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.

[0012] 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: S301, constructing a wireless sensor network node coverage model; S302, using the coverage rate of the wireless sensor network nodes and the node redundancy information as the particle swarm algorithm optimization target, and optimizing the constructed coverage model; A further improvement of the present application is that the method for implementing step S302 includes the following steps: S401, initializing relevant parameters of the particle swarm algorithm, including particle swarm size, number of iterations, initial position and speed of particles, etc.; S402, calculating the fitness value of each particle according to the constructed coverage model, where the fitness value is calculated by comprehensively evaluating the coverage rate of each particle and the node redundancy; 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; 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.

[0013] 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.

[0014] A further improvement of the present application is that, in step S403, the position update and speed update of the particle are replaced by the mutation operator in the genetic algorithm, including: Set a mutation probability, and for each particle, decide whether to perform a mutation operation based on the mutation probability before updating its velocity and position; 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.

[0015] 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 a preset maximum number of iterations, steps S402 to S403 are continued to be executed until the maximum number of iterations is reached.

[0016] The beneficial effects of the present invention are as follows: the present invention achieves a more uniform coverage effect by randomly pre-deploying all wireless sensor network nodes and iteratively optimizing the node positions using a particle swarm optimization algorithm. By calculating the coverage rate of each particle and the number of node redundancies, the present invention can accurately evaluate the quality of the current node deployment, and use this as a basis to guide the subsequent optimization process, providing an efficient and economical solution for the deployment of wireless sensor network nodes in facility agriculture, with significant technical progress and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of a wireless sensor network node deployment method for facility agriculture according to the present invention; Figure 2This is an optimization comparison diagram of a wireless sensor network node deployment method for facility agriculture according to the present invention. DETAILED DESCRIPTION

[0018] 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 protection scope of the present invention.

[0019] Facility agriculture refers to a method of agricultural production using greenhouses, cold sheds, nursery beds and other facilities. 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 to help farmers better manage crop growth. The following are some common types of wireless sensors: Temperature Sensors: Monitor soil and air temperature to help regulate the climate within the greenhouse.

[0020] Humidity sensor: measures soil and air humidity for proper irrigation management.

[0021] Light sensor: monitors light intensity to help optimize lighting conditions for plants.

[0022] CO2 sensor: Measures carbon dioxide concentration to ensure plants get enough CO2 for photosynthesis.

[0023] Soil pH Sensor: Monitors soil acidity and alkalinity, helping to assess soil conditions and adjust fertilization strategies.

[0024] Nutrient sensor: detects the content of nitrogen, phosphorus, potassium and other nutrients in the soil to help with precise fertilization.

[0025] Water level sensor: monitors the water level of water sources or irrigation systems to ensure the rational use of water resources.

[0026] Meteorological sensors: collect meteorological data such as wind speed, precipitation, etc., to provide comprehensive climate information for facility agriculture.

[0027] These sensors are usually combined with wireless network technology to form a wireless sensor network, which transmits data to the management system in real time, facilitating farmers to conduct remote monitoring and management, thereby improving the production efficiency and sustainability of facility agriculture.

[0028] However, in the actual application of facility agriculture, how to efficiently deploy wireless sensor network nodes to achieve comprehensive and real-time monitoring of the agricultural production environment is a key issue currently faced. 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, resulting in incomplete and inaccurate monitoring data, and unable to provide effective decision support for agricultural production. Example

[0029] 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: S101, obtaining the size of the 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 do not have mobility; S102, constructing a coordinate system in the target area, obtaining the coordinates of any wireless sensor network node after pre-deployment, constructing 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 obtaining the coverage rate of the wireless sensor network node covered by the adjacent wireless sensor network nodes; S103, according to the coverage rate of any wireless sensor network node after pre-deployment, determine whether there are redundant nodes in the wireless sensor network nodes, if there are redundant nodes, build an optimized coverage model, and use a particle swarm algorithm to optimize the optimized coverage model until all wireless sensor network nodes reach optimal deployment.

[0030] In the above steps, first, we analyzed the node's perception radius and communication radius. The perception radius determines the size of the area that the node can monitor, while the communication radius determines the distance over which nodes can communicate effectively. By setting these two parameters reasonably, we can ensure that each node in the network can effectively cover its monitoring area and maintain a stable communication connection with other nodes.

[0031] Secondly, we also considered the deployment density of nodes. Too high a deployment density may cause mutual interference and redundant coverage between nodes, while too low a deployment density may not meet monitoring needs. Therefore, in the optimized coverage model, we reasonably determined the node deployment density based on the specific conditions and monitoring needs of the target area to ensure the integrity and effectiveness of network coverage.

[0032] Finally, we used the particle swarm algorithm to optimize the optimal coverage model. The particle swarm algorithm is an optimization algorithm based on swarm intelligence. It simulates the foraging behavior of bird flocks to find the optimal solution to the problem. During the optimization process, we regard each wireless sensor network node as a particle, and by continuously adjusting the position and speed of the particles, we find the node deployment solution that maximizes the network coverage area, has the highest communication efficiency, and has the lowest energy consumption.

[0033] Through the implementation of the above steps and methods, we can achieve the optimal deployment of wireless sensor network nodes for facility agriculture. This deployment method not only improves the coverage and reliability of the network, but also reduces the energy consumption and deployment cost of the nodes, providing strong support for the intelligent management of facility agriculture.

[0034] Specifically, 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. In this way, first, the uniform initial energy ensures that all nodes are in a fair competition state at the beginning of deployment, avoiding the problem of uneven network performance caused by initial energy differences. This helps us to more accurately evaluate and optimize the overall performance of the network, because the performance bottleneck of any node will not be concealed or exaggerated due to different initial energies.

[0035] Secondly, the setting that the communication radius is twice the perception radius greatly improves the connectivity and reliability of the network. The perception radius determines the range of environmental information that a node can perceive, while the communication radius determines the distance at which a node can exchange information with other nodes. When the communication radius is twice the perception radius, even if two nodes do not directly perceive the same environmental information, they may still be able to exchange information indirectly through other nodes, thereby enhancing the connectivity of the network and the ability to share information. At the same time, this setting also helps to reduce communication failures and energy waste caused by long distances between nodes, and improves the reliability and energy efficiency of the network.

[0036] In addition, this setting also makes it easier for us to optimize the network topology and design routing protocols. With the node communication radius and perception radius known, we can more easily predict and evaluate the communication links and transmission paths in the network, thereby designing a more efficient and reliable routing protocol. At the same time, this setting also helps us optimize the network topology, by adjusting the location and number of nodes to optimize the network's coverage and connectivity, and further improve the network's performance.

[0037] 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: In expression (1), Represents any wireless sensor network node after pre-deployment, Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node The maximum perception radius, Indicates that the node Any node other than Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node The set of sensing nodes of all neighboring nodes; The expression of the communication node set of the adjacent wireless sensor network nodes of any wireless sensor network node after the pre-deployment is: In expression (2), Represents any wireless sensor network node after pre-deployment, Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node Any node other than Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node The maximum communication radius of of, Representation Node The communication node set of all neighboring nodes.

[0038] In one embodiment of the present invention, the method for obtaining the coverage rate of the wireless sensor network node by the adjacent wireless sensor network nodes comprises the following steps: S201, obtaining the sensed overlapping center angle of the wireless sensor network node, wherein the sensed overlapping center angle is the center angle of 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:

[0039] S202. According to expression (3), the area of ​​the overlapping coverage area of ​​the wireless sensor network nodes is obtained. The expression is: , In expression (4), 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 covered by the adjacent wireless sensor network nodes, the expression of the coverage rate of the wireless sensor network node is: , In expression (5), Represents the sensing area of ​​the wireless sensor network node, Through expression (6), we can obtain: .

[0040] Specifically, the conditions for determining whether there are redundant nodes in the wireless sensor network are: 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.

[0041] 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 a redundant node.

[0042] 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: S301, constructing a wireless sensor network node coverage model; S302, using the coverage rate of the wireless sensor network nodes and the node redundancy information as the particle swarm algorithm optimization target, and optimizing the constructed coverage model; Specifically, the method for implementing step S302 includes the following steps: S401, initializing relevant parameters of the particle swarm algorithm, including particle swarm size, number of iterations, initial position and speed of particles, etc.; S402, calculating the fitness value of each particle according to the constructed coverage model, where the fitness value is calculated by comprehensively evaluating the coverage rate of each particle and the node redundancy; 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; 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.

[0043] Wherein, in step S402, the expression of the fitness function is:

[0044] 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); 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.

[0045] 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: Set a mutation probability. For each particle, before updating its speed and position, decide whether to perform a mutation operation based on this mutation 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.

[0046] 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.

[0047] 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 continuously executed until the maximum number of iterations is reached.

[0048] The optimal coverage model is optimized by particle swarm algorithm, and the reasonable deployment of nodes in wireless sensor networks is achieved. Each particle represents a possible node deployment scheme, and the fitness value of the particle is evaluated according to the network coverage and node redundancy. Specifically, when the particle coverage rate is high and the node redundancy is low, it indicates that the deployment scheme can effectively cover the target area, while reducing the number of unnecessary nodes and improving the energy efficiency and cost-effectiveness of the network.

[0049] In the optimization process, the mutation operator in the genetic algorithm is used to improve the particle swarm algorithm, which increases the diversity of particles and avoids the algorithm from falling into the local optimal solution. By setting the mutation probability, each particle has a certain probability of mutation when updating the speed and position, so as to jump out of the current local optimal area and explore a wider solution space.

[0050] As the number of iterations increases, the algorithm gradually converges to the optimal solution. When the preset maximum number of iterations is reached, the algorithm stops iterating and outputs the current optimal node deployment plan. This solution not only has a high network coverage rate, but also minimizes node redundancy, providing reliable and efficient wireless sensor network support for facility agriculture.

[0051] Specifically, the comparison results of wireless sensor network nodes after optimizing the coverage model using particle swarm algorithm 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.

[0052] Various implementations of the systems and techniques described above herein 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), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including 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.

[0053] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0054] 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, device, or equipment. 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, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0055] 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).

[0056] The systems and techniques described herein may 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 may 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.

[0057] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0058] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0059] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present 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 the 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 do not have mobility; S102, constructing a coordinate system in the target area, obtaining the coordinates of any wireless sensor network node after pre-deployment, constructing 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 obtaining the coverage rate of the wireless sensor network node covered by the adjacent wireless sensor network nodes; S103, according to the coverage rate of any wireless sensor network node after pre-deployment, determine whether there are redundant nodes in the wireless sensor network nodes, if there are redundant nodes, build an optimized coverage model, and use a particle swarm algorithm to optimize the optimized coverage model until all wireless sensor network nodes reach optimal deployment.

2. The wireless sensor network node deployment method for facility agriculture according to claim 1 is 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 is characterized in 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: In expression (1), Represents any wireless sensor network node after pre-deployment, Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node The maximum perception radius, Indicates that the node Any node other than Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node The set of sensing nodes of all neighboring nodes; The expression of the communication node set of the adjacent wireless sensor network nodes of any wireless sensor network node after the pre-deployment is: In expression (2), Represents any wireless sensor network node after pre-deployment, Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node Any node other than Representation Node The coordinate on the x-axis, Representation Node The coordinate on the y-axis, Representation Node The maximum communication radius of of, Representation Node The communication node set of all neighboring nodes.

4. The wireless sensor network node deployment method for facility agriculture according to claim 3 is characterized in that: The method for obtaining the coverage rate of the wireless sensor network node by adjacent wireless sensor network nodes comprises the following steps: S201, obtaining the sensed overlapping center angle of the wireless sensor network node, wherein the sensed overlapping center angle is the center angle of 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: S202. According to expression (3), the area of ​​the overlapping coverage area of ​​the wireless sensor network nodes is obtained. The expression is: ， In expression (4), 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 covered by the adjacent wireless sensor network nodes, the expression of the coverage rate of the wireless sensor network node is: ， In expression (5), Represents the sensing area of ​​the wireless sensor network node, Through expression (6), we can obtain: （6）。 5. The wireless sensor network node deployment method for facility agriculture according to claim 4 is characterized in that: The condition for determining whether there are redundant nodes in the wireless sensor network node: 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.

6. The wireless sensor network node deployment method for facility agriculture according to claim 5, characterized in 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: S301, constructing a wireless sensor network node coverage model; S302: Using the coverage rate of the wireless sensor network nodes and the node redundancy information as the optimization target of the particle swarm algorithm to optimize the constructed coverage model.

7. The wireless sensor network node deployment method for facility agriculture according to claim 6, characterized in that: The method for implementing step S302 includes the following steps: S401, initializing relevant parameters of the particle swarm algorithm, including particle swarm size, number of iterations, initial position and speed of particles, etc.; S402, calculating the fitness value of each particle according to the constructed coverage model, where the fitness value is calculated by comprehensively evaluating the coverage rate of each particle and the node redundancy; 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; 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.

8. The wireless sensor network node deployment method for facility agriculture according to claim 7, characterized in 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.

9. The wireless sensor network node deployment method for facility agriculture according to claim 7, 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, and for each particle, decide whether to perform a mutation operation based on the mutation probability before updating its velocity and position; If mutation is decided, one or more components of the particle velocity or position vector are randomly selected and randomly adjusted.

10. The wireless sensor network node deployment method for facility agriculture according to claim 7, 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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