Water-saving irrigation method and system for traditional Chinese medicinal materials

Abstract

The invention discloses a water-saving irrigation method and system for traditional Chinese medicinal materials, and relates to the technical field of water-saving irrigation, by deploying sensors, collecting soil data and traditional Chinese medicinal plant growth data, and utilizing a source sensor to realize accurate positioning of the positions of other sensors; by constructing a sensor ad hoc network, collected soil data are intelligently analyzed to judge the accuracy and effectiveness of the soil data; based on the obtained effective sensor data, designing a water-saving irrigation control scheme by using a neural network model, and optimizing a sensor ad hoc network and a corresponding irrigation control scheme according to the growth condition of the traditional Chinese medicine plants; by analyzing the effectiveness of sensor data, accurate environment data of the traditional Chinese medicine plants can be obtained, and intelligent and accurate water-saving irrigation for the traditional Chinese medicine plants is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of water-saving irrigation, and in particular to a water-saving irrigation method and system for traditional Chinese medicine. Background Art

[0002] As an important part of traditional Chinese medicine, Chinese medicinal materials play an irreplaceable role in preventing and treating diseases and promoting health. However, the cultivation of Chinese medicinal materials has strict requirements on the growth environment, especially in the utilization and management of water resources. It is necessary to accurately control the irrigation amount and irrigation time to ensure the stability of the quality and yield of medicinal materials. The existing technology has the following shortcomings: Traditional irrigation methods mainly rely on experience or timed and quantitative irrigation, lacking real-time monitoring of soil environment and plant growth conditions. Since the irrigation volume cannot be adjusted according to actual needs, it often leads to over-irrigation, wasting precious water resources. At the same time, the irrigation area cannot be accurately controlled, resulting in excessive or insufficient water in some areas, affecting the normal growth of plants. In addition, traditional methods are difficult to adjust in time according to environmental changes, and cannot achieve intelligent and precise management of irrigation. Therefore, it is of great significance to develop an intelligent water-saving irrigation method for Chinese medicinal materials. Summary of the invention

[0003] The purpose of the present invention is to provide a water-saving irrigation method and system for Chinese medicinal materials to solve the shortcomings of the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a water-saving irrigation method for Chinese medicinal materials, comprising: Deploy sensors to collect sensor data including soil data and Chinese medicinal plant growth data; Locate the sensor location and build a sensor ad hoc network to analyze the accuracy and effectiveness of soil sensor data; Analyze sensor data to obtain water-saving irrigation control plans, including water supply areas, water supply amounts, and water supply times, and control irrigation units to irrigate using the water-saving irrigation control plans; Optimize the sensor self-organizing network and water-saving irrigation control scheme according to the growth conditions of Chinese medicinal plants.

[0005] In a preferred embodiment, the step of collecting sensor data including soil data and Chinese medicinal plant growth data is: Soil sensors are evenly deployed in the surface layer, root layer and deep layer of the soil in the planting area to collect soil data, where the sensors deployed in the root layer and deep layer are used as deep soil sensors. The soil sensors include humidity sensors, temperature sensors, conductivity sensors, pH sensors and nutrient sensors; Evenly deploy photosynthetic performance sensors in the planting area to collect growth data of Chinese medicinal plants, where the photosynthetic performance sensors include: photosynthetically active radiation sensors, leaf gas exchange sensors, and plant physiological parameter sensors; The soil sensors are located using source sensors and a sensor ad hoc network is constructed.

[0006] In a preferred embodiment, the step of locating the sensor position is: Deploy movable source sensors, which are equipped with GPS modules; The control source sensor moves along a predetermined path, stops and records the current position after each movement of a preset distance, and sends a positioning signal at the same time; After receiving the positioning signal, the soil sensor transmits back the soil sensor information, wherein the soil sensor information includes the soil sensor ID, soil sensor status and soil sensor type; When the source sensor receives the returned soil sensor information, it records the time difference and signal strength of the soil sensor information as positioning data; Multilateration is used to calculate positioning data to obtain the precise geographic location of the soil sensor.

[0007] In a preferred embodiment, the steps of constructing a sensor ad hoc network are: Divide the deep soil sensors into multiple node networks according to a preset number, select one sensor from each of the multiple node networks as a node gateway to connect to the second-layer network, the second-layer network is composed of surface soil sensors, and the second-layer network is connected to the source sensor; The soil sensor in the node network is connected to the corresponding node gateway through the data transmission channel and the data detection channel, and the node gateway is connected to the second-layer network through the data transmission channel; The data transmission channel is used to transmit soil sensor data, and the data detection channel consists of a ring channel and a linear channel. The ring channel surrounds the node gateway and deploys detection nodes in the ring channel that are the same number as the soil sensors in the node network. An observation window is opened on the detection node. The observation window has the same number of sub-windows as the deep soil sensors in the node network. The sub-window is used to input detection data and has different transmission protocols for different detection data. It can also be used to observe the correlation calculation results. The sensor self-organizing network is composed of node network, layer 2 network and source sensors.

[0008] In a preferred embodiment, the step of analyzing the accuracy and validity of the sensor data is: After collecting the soil sensor data, the deep soil sensor copies the soil sensor data into two copies and marks the digital watermark, one copy of the soil sensor data is marked as detection data, and the other copy of the soil sensor data is marked as transmission data; The transmission data is transmitted to the corresponding node gateway through the data transmission channel, and the node gateway arranges the transmission data in the detection node in order of receiving time to form a detection loop; After the detection loop is formed, the detection data enters the detection loop through the data detection channel in sequence; The kernel canonical correlation analysis algorithm is used to calculate the correlation between the soil sensor data and the transmission data in the detection nodes in the order of the detection nodes; A correlation threshold is preset. If the correlation between the transmission data of a detection node and the detection data exceeds the correlation threshold, the sub-windows of all detection nodes on the detection loop corresponding to the detection data are closed and the deep sensor to which the detection data in the detection node belongs is marked as an abnormal sensor. The sensor information of the abnormal sensor and the collected soil sensor data are packaged and transmitted to the source sensor based on the data transmission channel, and then sent to the administrator through the source sensor; The detected data are labeled with valid data and transmitted to the source sensor based on the data transmission channel.

[0009] In a preferred embodiment, the step of analyzing sensor data to obtain a water-saving irrigation control scheme is: The source sensor transmits the acquired valid data to the cloud server; The soil data and the growth data of Chinese medicinal plants are clustered and analyzed. Based on the cluster analysis results, the planting area is divided into several irrigation areas. Each irrigation area has the same soil environment and the same growth conditions of Chinese medicinal plants. At the same time, meteorological data is obtained, and a water-saving irrigation control plan is generated using a particle swarm optimization algorithm based on soil data, growth conditions of Chinese medicinal plants, and meteorological data in different irrigation areas; The water-saving irrigation control plan includes water supply area, water supply amount and water supply time; The water-saving irrigation control plan is sent to each irrigation execution unit to implement irrigation.

[0010] In a preferred embodiment, the steps of optimizing the sensor ad hoc network and the water-saving irrigation control system according to the growth conditions of the Chinese medicinal plants are: The growth data of Chinese medicinal plants are collected through cloud servers, and a normal growth model of Chinese medicinal plants is constructed using neural networks; The growth of Chinese medicinal plants is obtained at a preset period, and compared with the normal model to extract the planting area with a growth rate lower than the preset rate as the poor growth area; Optimize the sensor self-organizing network and water-saving irrigation control scheme in poor growth areas, and collect soil data and Chinese medicinal plant growth data in poor growth areas in real time.

[0011] The present invention also provides a water-saving irrigation system for Chinese medicinal materials, comprising: Sensor module: deploy sensors to collect sensor data including soil data and Chinese medicinal plant growth data; locate sensors and build a sensor self-organizing network to analyze the accuracy and effectiveness of soil sensor data; Water-saving irrigation module: connected with the sensor module, analyzes the sensor data to obtain the water-saving irrigation control plan, including the water supply area, water supply amount and water supply time, and controls the irrigation unit to irrigate through the water-saving irrigation control plan; Control scheme optimization: Connect with the irrigation scheme design module to optimize the sensor self-organizing network and water-saving irrigation control scheme according to the growth of Chinese medicinal plants.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention deploys a variety of sensors to monitor the soil and plant growth conditions in real time, and intelligently generates water-saving irrigation control plans based on sensor data through particle swarm optimization algorithm to achieve precise control of irrigation amount and time. This active monitoring and intelligent decision-making significantly improves the utilization efficiency of water resources and avoids the waste of water resources caused by inaccurate experience in traditional irrigation methods. By analyzing data such as soil moisture, temperature, and pH value, the system can dynamically adjust the water supply according to actual needs to ensure that each irrigation area can obtain appropriate moisture and avoid drought or waterlogging. At the same time, by dividing the planting area into several irrigation areas, different regions can receive customized irrigation plans according to their soil conditions and plant needs; 2. The present invention improves the reliability and autonomy of the entire water-saving irrigation system by constructing a sensor self-organizing network and utilizing intelligent data analysis and transmission mechanisms. The sensor self-organizing network can effectively integrate the data of multiple sensors, making data collection more comprehensive and accurate. Through the self-organizing structure, the sensor can transmit and share information in real time, thereby avoiding the problem of isolated data of a single sensor. During the data transmission process, the system can apply a variety of data verification and correction methods to ensure that the data provided by each sensor is repeatedly verified, thereby improving the accuracy of the overall data. In addition, the ring channel is used to set the detection nodes and observation windows, and abnormal sensors are promptly eliminated to avoid repeated detection, thereby improving the efficiency of data detection. 3. The present invention effectively promotes the growth of Chinese medicinal materials and improves their quality through precise irrigation control methods. By real-time collection and analysis of the photosynthetic performance, gas exchange and physiological parameters of the plants, the system can optimize the irrigation plan in a targeted manner to ensure that Chinese medicinal plants can obtain the best water and nutrition in different growth periods. Based on the precise environmental data obtained by the sensor data detection mechanism, this refined management and data can not only significantly increase the growth rate of the plants, but also increase the content of their active ingredients, which helps to improve the market competitiveness of Chinese medicinal materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example 1, please refer to Figure 1 As shown, the water-saving irrigation method for Chinese medicinal materials described in this embodiment includes: S1. Deploy sensors to collect sensor data including soil data and Chinese medicinal plant growth data; S2, locate the sensor position, build a sensor self-organizing network to analyze the accuracy and effectiveness of soil sensor data; S3, analyzing the sensor data to obtain a water-saving irrigation control plan, including water supply areas, water supply amounts, and water supply time, and controlling the irrigation units to irrigate through the water-saving irrigation control plan; S4, optimize the sensor self-organizing network and water-saving irrigation control scheme according to the growth of Chinese medicinal plants; As described in the above steps S1-S4, Chinese medicinal materials, as an important part of traditional Chinese medicine, play an irreplaceable role in preventing and treating diseases and promoting health. However, the cultivation of Chinese medicinal materials has strict requirements on the growth environment, especially in the utilization and management of water resources. It is necessary to accurately control the irrigation amount and irrigation time to ensure the stability of the quality and yield of medicinal materials. The existing technology has the following shortcomings: the traditional irrigation method mainly relies on experience or timed and quantitative irrigation, lacks real-time monitoring of the soil environment and plant growth conditions, and cannot adjust the irrigation amount according to actual needs, which often leads to over-irrigation and waste of precious water resources. At the same time, the irrigation area cannot be accurately controlled, resulting in excessive or insufficient water in some areas. Insufficient water resources affect the normal growth of plants, and traditional methods are difficult to adjust in time according to environmental changes, and cannot achieve intelligent and precise management of irrigation. The present invention deploys a variety of sensors to monitor the soil and plant growth conditions in real time, and intelligently generates water-saving irrigation control plans based on sensor data through particle swarm optimization algorithm to achieve precise control of irrigation amount and time. This active monitoring and intelligent decision-making significantly improves the utilization efficiency of water resources and avoids the waste of water resources caused by inaccurate experience in traditional irrigation methods. By analyzing soil moisture, temperature, pH value and other data, the system can dynamically adjust the water supply according to actual needs to ensure that each irrigation area can obtain appropriate moisture and avoid Drought or waterlogging. At the same time, by dividing the planting area into several irrigation areas, different regions can receive customized irrigation plans according to their soil conditions and plant needs. By building a sensor self-organizing network and using intelligent data analysis and transmission mechanisms, the reliability and autonomy of the entire water-saving irrigation system are improved. The sensor self-organizing network can effectively integrate the data of multiple sensors to make data collection more comprehensive and accurate. Through the self-organizing structure, sensors can transmit and share information in real time, thereby avoiding the problem of isolated data from a single sensor. During the data transmission process, the system can apply a variety of data verification and correction methods to ensure that the data provided by each sensor is repeatedly verified, thereby Improve the accuracy of the overall data, and use a ring channel to set up detection nodes and observation windows, timely eliminate abnormal sensors to avoid repeated detection, and improve the efficiency of data detection; at the same time, through precise irrigation control methods, effectively promote the growth of Chinese medicinal materials and improve their quality. By real-time collection and analysis of the photosynthetic performance, gas exchange and physiological parameters of the plants, the system can optimize the irrigation plan in a targeted manner to ensure that Chinese medicinal plants can obtain the best water and nutrition in different growth periods. Based on the precise environmental data obtained by the sensor data detection mechanism, this refined management and data can not only significantly increase the growth rate of the plants, but also increase the content of their active ingredients, which helps to improve the market competitiveness of Chinese medicinal materials.

[0017] In one embodiment, the step S1 of collecting sensor data including soil data and Chinese medicinal plant growth data includes: S11, evenly deploying soil sensors in the surface layer, root layer and deep layer of the soil in the planting area to collect soil data, wherein the sensors deployed in the root layer and deep layer are used as deep soil sensors, and the soil sensors include humidity sensors, temperature sensors, conductivity sensors, pH sensors and nutrient sensors; S12. Evenly deploy photosynthetic performance sensors in the planting area to collect growth data of Chinese medicinal plants, including photosynthetic active radiation sensors, leaf gas exchange sensors, and plant physiological parameter sensors; S13, using source sensors to locate soil sensor positions and constructing a sensor self-organizing network; As described in the above steps S11-S13, in the planting area, different types of soil sensors are deployed according to the layer characteristics of the soil to collect data on the entire soil layer, including surface soil sensors, which are deployed in the surface layer of the planting area and mainly monitor the surface moisture, temperature and nutritional status of the soil to quickly reflect changes in the soil state. Root layer and deep soil sensors are deployed at the depth of plant root growth and below, mainly including humidity sensors, temperature sensors, conductivity sensors, pH sensors and nutritional sensors, among which the conductivity sensor is used to analyze the dissolved salts in the soil, thereby inferring the fertility of the soil; at the same time, photosynthetic performance sensors are evenly arranged at appropriate locations in the planting area to collect growth data of Chinese medicinal plants. The specific sensors include: photosynthetic active radiation sensors are used to measure the light energy absorbed by plants and evaluate lighting conditions; leaf gas exchange sensors are used to detect the photosynthesis in the photosynthesis. Carbon dioxide absorption and water evaporation help evaluate the physiological state of plants. Plant physiological parameter sensors are used to monitor plant physiological indicators such as leaf temperature and water content to analyze the health of plant growth. In the actual deployment of deep soil sensors, deep soil sensors may be displaced due to changes in the environment and the growth of the root system of Chinese medicinal plants. For example, after heavy rainfall and irrigation, the soil structure becomes loose, causing the position of the deep soil sensor to change, resulting in the inability to accurately collect soil data. At the same time, for economic cost considerations, configuring a GPS module for each sensor will incur a lot of expenses. Therefore, using a source sensor equipped with a GPS module to locate the remaining deep soil sensors can reduce economic pressure and obtain more accurate soil data. At the same time, the acquired deep soil sensor information is used to build a sensor self-organizing network to process the collected soil data.

[0018] In one embodiment, the step S2 of locating the position of the sensor includes: S21, deploying a movable source sensor, wherein the source sensor is equipped with a GPS module; S22, controlling the source sensor to move along a predetermined path, stopping and recording the current position each time it moves a preset distance, and sending a positioning signal at the same time; S23, after receiving the positioning signal, the soil sensor transmits back soil sensor information, wherein the soil sensor information includes soil sensor ID, soil sensor status and soil sensor type; S24, when the source sensor receives the returned soil sensor information, it records the time difference and signal strength of the returned soil sensor information as positioning data; S25, using multilateration to calculate positioning data to obtain the precise geographic location of the soil sensor; As described in the above steps S21-S25, a movable source sensor with a GPS module is selected to ensure that it can still work stably under different weather conditions and has good signal receiving capabilities. The source sensor should have a wireless communication module such as LoRa or Zigbee to enable stable data exchange with soil sensors and cloud servers. A reasonable path is designed for the source sensor to ensure that all soil sensors in the planting area are covered to avoid blind spots. The path can be adjusted according to the distribution of soil and Chinese medicinal plants. A preset distance is set for each movement. For example, a stop is set every 10 meters to ensure that the positioning signal of each position can be clearly received by the surrounding soil sensors. The source sensor automatically sends a positioning signal each time it stops. The positioning signal instruction includes the GPS coordinates and timestamp of the current position. The soil sensors deployed underground receive the positioning signal sent by the source sensor. After receiving the signal, the soil sensor information is immediately transmitted back. Each soil sensor must have the ability to receive and process signal instructions. The transmitted soil sensor information includes the soil sensor ID used to uniquely identify the sensor, the soil sensor status such as normal operation, failure or maintenance status, and the soil sensor type used to describe the sensor function such as humidity, temperature, etc. After receiving the feedback information from the soil sensor, the source sensor will record two important parameters including time difference and signal strength, calculate the time difference between the positioning signal sending and return to evaluate the signal transmission delay, and record the received signal strength to evaluate the effective communication distance and signal quality between the source sensor and each soil sensor. Through the known position of the source sensor and the received soil sensor feedback information, the multilateral measurement method is used to calculate the precise position of the soil sensor based on the signal transmission time difference and signal strength information.

[0019] In one embodiment, step S2 of building a sensor ad hoc network includes: S26, dividing the deep soil sensors according to a preset number to obtain multiple node networks, selecting one sensor from each of the multiple node networks as a node gateway to connect to the second-layer network, the second-layer network is composed of surface soil sensors, and the second-layer network is connected to the source sensor; S27, the soil sensor in the node network is connected to the corresponding node gateway through the data transmission channel and the data detection channel, and the node gateway is connected to the second layer network through the data transmission channel; S28, wherein the data transmission channel is used to transmit soil sensor data, and the data detection channel is composed of a ring channel and a linear channel, the ring channel surrounds the node gateway and the same number of detection nodes as the soil sensors in the node network are deployed in the ring channel; S29, an observation window is opened on the detection node, and the observation window has the same number of sub-windows as the deep soil sensors in the node network. The sub-window is used to input detection data and has different transmission protocols for different detection data, and can also be used to observe the correlation calculation results; S210, forming a sensor self-organizing network through a node network, a layer 2 network and a source sensor; As described in the above steps S26-S210, the required number of deep soil sensors is determined according to the size of the planting area and the soil characteristics to ensure coverage of each important soil layer. The deep soil sensors are grouped to form multiple node networks. Each node network should include at least 5 soil sensors to achieve data redundancy and reliability. In each node network, a soil sensor is selected as a node gateway. The sensor will be responsible for collecting soil data in its own node network and for connecting to the second-layer network. The node gateway should have strong communication capabilities and edge computing capabilities to ensure that it can stably transmit and analyze data. Each soil sensor is connected to the node gateway of its node network through a wireless channel such as LoRa, Zigbee or NB-IoT, where the wireless channel includes a data transmission channel and a data detection channel. The data transmission channel is used to transmit deep soil sensor data, and the data detection channel consists of a ring channel and a straight channel. The ring channel surrounds the node network and The ring channel deploys the same number of detection nodes as the deep soil sensors in the node network except the node gateway to realize multiple verification and detection of the deep soil sensor data. The detection node is a database with edge computing and has an observation window. The observation window has the same number of sub-windows as the deep soil sensors in the node network. The sub-windows are used to input detection data and have different transmission protocols for different detection data, that is, each detection data has a specific channel to enter the detection node, and the detection node can block the entry of a certain detection data by closing the sub-window. At the same time, the sub-window can be used to observe the correlation calculation results, and the linear channel connects the deep soil sensors; the second-layer network is composed of surface soil sensors, because the surface sensors are located on the surface for easy adjustment and modification, and the second-layer network is adopted to avoid a large amount of data from pouring into the source sensor, reduce the pressure of data transmission and disperse the calculation pressure. The second-layer network is connected to the source sensor through the data transmission channel, and the sensor self-organizing network is composed of the node network, the second-layer network and the source sensor.

[0020] In one embodiment, the step S2 of analyzing the accuracy and validity of the sensor data includes: S211, after collecting the soil sensor data, the deep soil sensor copies the soil sensor data into two copies and marks the digital watermark, wherein one copy of the soil sensor data is marked as detection data, and the other copy of the soil sensor data is marked as transmission data; S212, transmitting the transmission data to the corresponding node gateway through the data transmission channel, and the node gateway arranges the transmission data in the detection node in order of receiving time to form a detection loop; S213, after the detection loop is formed, the detection data enters the detection loop through the data detection channel in sequence; S214, using a kernel canonical correlation analysis algorithm to sequentially calculate the correlation between the soil sensor data and the transmission data in the detection nodes in the order of the detection nodes; S215, preset a correlation threshold, if the correlation between the transmission data of a certain detection node and the detection data exceeds the correlation threshold, then close the sub-windows of all detection nodes on the detection loop corresponding to the detection data and standardize the deep sensor to which the detection data in the detection node belongs as an abnormal sensor, encapsulate the sensor information of the abnormal sensor and the collected soil sensor data, transmit them to the source sensor based on the data transmission channel, and send them to the administrator through the source sensor; S216, marking the detected data with valid data, and transmitting the data to the source sensor based on the data transmission channel; As described in the above steps S211-S216, the deep soil sensor copies the collected deep soil sensor data into two copies to ensure data redundancy and security, and adds a digital watermark to each deep soil sensor data to mark the type of the deep soil sensor data, one marked as transmission data and the other marked as detection data. The deep soil sensor transmits the transmission data to the node gateway through the data transmission channel, and the node gateway places the received transmission data in the detection node. The order of placement is first come first served. For example, if a deep soil sensor in the node network first transmits the transmission data to the node gateway, it is placed in the first detection node. After all the detection nodes are filled, a detection loop is generated. Then the deep soil sensor transmits the detection data to the node network. Similarly, the detection data is first come first served, that is, the detection data that arrives first is detected first. When all the detection data are received, a detection data chain is generated based on the arrival time, and the detection data chain is input into the detection loop. The kernel canonical correlation analysis algorithm is used to perform detection in each detection node. The algorithm can effectively identify the potential relationship between variables to determine the accuracy of the data. The correlation threshold is preset according to physical common sense, soil characteristics and physiological habits of Chinese medicinal plants. The detection results are observed through the observation window during the detection process. If the correlation between the transmission data and the detection data of a detection node exceeds the preset threshold, the sub-windows of all detection nodes on the detection loop corresponding to the detection data are closed for troubleshooting and data processing to prevent further propagation of erroneous data. The deep sensor to which the detection data belongs is marked as an abnormal sensor, which indicates that there may be problems with the deep soil sensor data of the sensor. The sensor information of the abnormal sensor is encapsulated with the collected soil sensor data to ensure the comprehensiveness and accuracy of the information. The encapsulated data is transmitted to the source sensor through the data transmission channel and then transmitted to the administrator through the source sensor to remind the administrator of the sensor failure. At the same time, the data passing through the detection loop is transmitted to the source sensor through the second layer network as valid data.

[0021] In one embodiment, the step S3 of analyzing sensor data to obtain a water-saving irrigation control scheme includes: S31, the source sensor transmits the acquired valid data to the cloud server; S32, performing cluster analysis on the soil data and the growth data of the Chinese medicinal plants, and dividing the planting area into a number of irrigation areas based on the cluster analysis results, each irrigation area having the same soil environment and the same growth conditions of the Chinese medicinal plants; S33, simultaneously acquiring meteorological data, and using a particle swarm optimization algorithm to generate a water-saving irrigation control plan based on soil data of different irrigation areas, growth conditions of Chinese medicinal plants, and meteorological data; S34, wherein the water-saving irrigation control scheme includes water supply area, water supply amount and water supply time; S35, sending the water-saving irrigation control plan to each irrigation execution unit to implement irrigation; As described in the above steps S31-S35, the source sensor regularly collects valid data, including soil sensor data and Chinese medicinal plant growth data, and transmits the valid data to the cloud server through a secure wireless communication network such as LoRa, NB-IoT or Wi-Fi. On the cloud server side, the collected soil data and Chinese medicinal plant growth data are integrated into a unified data set, where each data set should contain time, location and other necessary context information, and an appropriate clustering algorithm such as K-means, DBSCAN or hierarchical clustering is selected. The clustering parameters and the number of categories are determined according to the characteristics of the soil data and the Chinese medicinal plant growth data. The parameters can be optimized through experiments and adjustments. For example, areas with soil moisture of 30%-35% are divided into the same area. The selected clustering algorithm is used to analyze the soil data and the Chinese medicinal plant growth data, and the planting area is divided into several irrigation areas based on similarity. Each irrigation area should have the same soil environment, such as soil type, moisture, nutrients, etc., and the growth conditions of Chinese medicinal plants, such as growth stage, health status, etc.; obtain relevant meteorological data, including rainfall, temperature, humidity, wind speed, sunshine, etc., through weather stations or online meteorological service APIs such as weather data providers, integrate meteorological data with soil and plant data of each irrigation area, and analyze their impact on irrigation demand. For example, the amount of irrigation can be reduced accordingly in areas with heavy rainfall. The particle swarm optimization algorithm is used to generate a water-saving irrigation control plan. In the particle swarm optimization algorithm, each particle represents a different irrigation strategy or parameter combination, such as water supply area, water supply, and water supply time. The optimal irrigation plan is found by iteratively optimizing the particle position and speed. The objective function should be designed to minimize While consuming water resources, the water needs of plants can be met to the greatest extent. The weight of the objective function is set according to the actual situation to adapt to the needs of different irrigation areas. The generated water-saving irrigation control plan should include the following key aspects: determine the specific irrigation area and the plants or plots to be irrigated in each area, determine the reasonable irrigation amount based on soil moisture, meteorological conditions and plant needs, and set the best irrigation time in combination with meteorological forecasts and crop growth laws to improve water utilization efficiency. The generated water-saving irrigation control plan is sent to the irrigation execution unit in the form of instructions, such as water pumps, valves, etc. Furthermore, the irrigation execution unit can be set to a drip irrigation structure, which minimizes water evaporation and leakage by directly delivering water to the roots of plants, and effectively improves the utilization efficiency of water resources.

[0022] In one embodiment, the step S4 of optimizing the sensor ad hoc network and the water-saving irrigation control system according to the growth conditions of the Chinese medicinal plants includes: S41, collecting growth data of Chinese medicinal plants through a cloud server, and constructing a normal growth model of the Chinese medicinal plants using a neural network; S42, obtaining the growth condition of the Chinese medicinal plants at a preset period, comparing it with the normal model, and extracting the planting area with a growth rate lower than the preset rate as the poor growth area; S43, optimizing the sensor self-organizing network and water-saving irrigation control scheme in the poor growth area, and collecting soil data and Chinese medicinal plant growth data in the poor growth area in real time; As described in the above steps S41-S43, a growth data set of Chinese medicinal plants is obtained in the network through a cloud server. The data set should include growth data of plants under different environments, different growth stages, and different treatment conditions. The data includes information such as growth height, leaf health, soil moisture, and fertilization conditions. A suitable neural network model is selected to train a normal growth model of Chinese medicinal plants. At the same time, a photosynthetic performance sensor is used to collect the growth conditions of Chinese medicinal plants at a preset period, and the photosynthetic performance sensor is used to compare the growth conditions of the Chinese medicinal plants with the normal model to extract the planting areas with a growth rate lower than the preset growth rate as poor growth areas. After identifying the poor growth areas, it is possible to increase the arrangement of sensors in this area, especially to monitor key parameters such as soil moisture and nutrient content. According to the performance of the sensors and the collected data, the positions and quantities of certain sensors are adjusted to optimize the reliability and accuracy of the overall network. The optimized sensor data is used in combination with meteorological data and growth status prediction to generate a real-time irrigation demand model. According to the newly generated water-saving irrigation control plan, the water supply and irrigation frequency can be flexibly adjusted to meet the actual needs of the Chinese medicinal plants. The optimized water-saving irrigation control plan is sent to the irrigation execution unit to execute the irrigation task.

[0023] Example 2, please refer to Figure 2 As shown, the water-saving irrigation system for Chinese medicinal materials described in this embodiment includes: Sensor module: deploy sensors to collect sensor data including soil data and Chinese medicinal plant growth data; locate sensors and build a sensor self-organizing network to analyze the accuracy and effectiveness of soil sensor data; Water-saving irrigation module: connected with the sensor module, analyzes the sensor data to obtain the water-saving irrigation control plan, including the water supply area, water supply amount and water supply time, and controls the irrigation unit to irrigate through the water-saving irrigation control plan; Control scheme optimization module: connected with the irrigation scheme design module, it optimizes the sensor self-organizing network and water-saving irrigation control scheme according to the growth of Chinese medicinal plants.

[0024] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A water-saving irrigation method for Chinese medicinal materials, characterized in that: Deploy sensors to collect sensor data including soil data and Chinese medicinal plant growth data; Locate the sensor location and build a sensor ad hoc network to analyze the accuracy and effectiveness of soil sensor data; Analyze sensor data to obtain water-saving irrigation control plans, including water supply areas, water supply amounts, and water supply times, and control irrigation units to irrigate using the water-saving irrigation control plans; Optimize the sensor self-organizing network and water-saving irrigation control scheme according to the growth conditions of Chinese medicinal plants.

2. A water-saving irrigation method for Chinese medicinal materials according to claim 1, characterized in that: The steps of collecting sensor data including soil data and Chinese medicinal plant growth data are as follows: Soil sensors are evenly deployed in the surface layer, root layer and deep layer of the soil in the planting area to collect soil data, where the sensors deployed in the root layer and deep layer are used as deep soil sensors. The soil sensors include humidity sensors, temperature sensors, conductivity sensors, pH sensors and nutrient sensors; Evenly deploy photosynthetic performance sensors in the planting area to collect growth data of Chinese medicinal plants, where the photosynthetic performance sensors include: photosynthetically active radiation sensors, leaf gas exchange sensors, and plant physiological parameter sensors; The soil sensors are located using source sensors and a sensor ad hoc network is constructed.

3. A water-saving irrigation method for Chinese medicinal materials according to claim 1, characterized in that: The steps of positioning the sensor position are: Deploy movable source sensors, which are equipped with GPS modules; The control source sensor moves along a predetermined path, stops and records the current position after each movement of a preset distance, and sends a positioning signal at the same time; After receiving the positioning signal, the soil sensor transmits back the soil sensor information, wherein the soil sensor information includes the soil sensor ID, soil sensor status and soil sensor type; When the source sensor receives the returned soil sensor information, it records the time difference and signal strength of the soil sensor information as positioning data; Multilateration is used to calculate positioning data to obtain the precise geographic location of the soil sensor.

4. A water-saving irrigation method for Chinese medicinal materials according to claim 1, characterized in that: The steps to build a sensor ad hoc network are: Divide the deep soil sensors into multiple node networks according to a preset number, select one sensor from each of the multiple node networks as a node gateway to connect to the second-layer network, the second-layer network is composed of surface soil sensors, and the second-layer network is connected to the source sensor; The soil sensor in the node network is connected to the corresponding node gateway through the data transmission channel and the data detection channel, and the node gateway is connected to the second-layer network through the data transmission channel; The data transmission channel is used to transmit soil sensor data, and the data detection channel consists of a ring channel and a linear channel. The ring channel surrounds the node gateway and deploys detection nodes in the ring channel that are the same number as the soil sensors in the node network. An observation window is opened on the detection node. The observation window has the same number of sub-windows as the deep soil sensors in the node network. The sub-window is used to input detection data and has different transmission protocols for different detection data. It can also be used to observe the correlation calculation results. The sensor self-organizing network is composed of node network, layer 2 network and source sensors.

5. A water-saving irrigation method for Chinese medicinal materials according to claim 1, characterized in that: The steps of analyzing the accuracy and validity of sensor data are: After collecting the soil sensor data, the deep soil sensor copies the soil sensor data into two copies and marks the digital watermark, one copy of the soil sensor data is marked as detection data, and the other copy of the soil sensor data is marked as transmission data; The transmission data is transmitted to the corresponding node gateway through the data transmission channel, and the node gateway arranges the transmission data in the detection node in order of receiving time to form a detection loop; After the detection loop is formed, the detection data enters the detection loop through the data detection channel in sequence; The kernel canonical correlation analysis algorithm is used to calculate the correlation between the soil sensor data and the transmission data in the detection nodes in the order of the detection nodes; A correlation threshold is preset. If the correlation between the transmission data of a detection node and the detection data exceeds the correlation threshold, the sub-windows of all detection nodes on the detection loop corresponding to the detection data are closed and the deep sensor to which the detection data in the detection node belongs is marked as an abnormal sensor. The sensor information of the abnormal sensor and the collected soil sensor data are packaged and transmitted to the source sensor based on the data transmission channel, and then sent to the administrator through the source sensor; The detected data are labeled with valid data and transmitted to the source sensor based on the data transmission channel.

6. A water-saving irrigation method for Chinese medicinal materials according to claim 1, characterized in that: The steps of analyzing sensor data to obtain a water-saving irrigation control solution are: The source sensor transmits the acquired valid data to the cloud server; The soil data and the growth data of Chinese medicinal plants are clustered and analyzed. Based on the cluster analysis results, the planting area is divided into several irrigation areas. Each irrigation area has the same soil environment and the same growth conditions of Chinese medicinal plants. At the same time, meteorological data is obtained, and a water-saving irrigation control plan is generated using a particle swarm optimization algorithm based on soil data, growth conditions of Chinese medicinal plants, and meteorological data in different irrigation areas; The water-saving irrigation control plan includes water supply area, water supply amount and water supply time; The water-saving irrigation control plan is sent to each irrigation execution unit to implement irrigation.

7. A water-saving irrigation method for Chinese medicinal materials according to claim 1, characterized in that: The steps of optimizing the sensor self-organizing network and water-saving irrigation control system according to the growth conditions of Chinese medicinal plants are as follows: The growth data of Chinese medicinal plants are collected through cloud servers, and a normal growth model of Chinese medicinal plants is constructed using neural networks; The growth of Chinese medicinal plants is obtained at a preset period, and compared with the normal model to extract the planting area with a growth rate lower than the preset rate as the poor growth area; Optimize the sensor self-organizing network and water-saving irrigation control scheme in poor growth areas, and collect soil data and Chinese medicinal plant growth data in poor growth areas in real time.

8. A water-saving irrigation system for Chinese medicinal materials, used to implement a water-saving irrigation method for Chinese medicinal materials according to any one of claims 1 to 7, characterized in that: Sensor module: deploy sensors to collect sensor data including soil data and Chinese medicinal plant growth data; locate sensors and build a sensor self-organizing network to analyze the accuracy and effectiveness of soil sensor data; Water-saving irrigation module: connected with the sensor module, analyzes the sensor data to obtain the water-saving irrigation control plan, including the water supply area, water supply amount and water supply time, and controls the irrigation unit to irrigate through the water-saving irrigation control plan; Control scheme optimization module: connected with the irrigation scheme design module, optimizes the sensor self-organizing network and water-saving irrigation control scheme according to the growth of Chinese medicinal plants.

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