Traditional Chinese medicinal material growth real-time monitoring system and method based on Internet of Things

Abstract

The invention discloses a traditional Chinese medicine growth real-time monitoring system and method based on the Internet of Things. The system comprises a laser radar module, an environment acquisition module, a soil acquisition module, an image acquisition module, an insect pest acquisition module, a data processing module and an information sending module. The laser radar module is used for performing 3D modeling on traditional Chinese medicinal materials through laser radar to obtain real-time model information of the traditional Chinese medicinal materials; the environment acquisition module is used for acquiring growth environment information of traditional Chinese medicinal materials; the soil acquisition module is used for acquiring growth soil information of traditional Chinese medicinal materials; the image acquisition module is used for acquiring image information of a traditional Chinese medicinal material growth area; the insect pest collection module is used for collecting insect pest information; and the data processing module is used for processing the real-time model information of the traditional Chinese medicinal materials to obtain growth warning information. According to the invention, the growth of traditional Chinese medicinal materials can be monitored in real time more comprehensively and intelligently.

Description

Technical Field

[0001] The present invention relates to the field of monitoring systems, and in particular to a real-time monitoring system and method for the growth of traditional Chinese medicinal materials based on the Internet of Things. Background Art

[0002] The Chinese medicinal materials market has increasingly stringent requirements on the quality and yield of medicinal materials. Precision planting management has become the key to ensuring the quality of Chinese medicinal materials and meeting market demand. A real-time monitoring system is needed to achieve refined management in order to improve yield and quality.

[0003] Standardized development of the industry: The standardized development of the Chinese medicinal materials industry is inseparable from the strict control of the planting process. The establishment of a real-time monitoring system will help achieve standardization and traceability of the planting process and meet industry standards and regulatory requirements.

[0004] The existing real-time monitoring system has a single monitoring type, resulting in poor monitoring effect and unable to effectively improve the quality of Chinese medicinal materials planting. Therefore, a real-time monitoring system and method for Chinese medicinal materials growth based on the Internet of Things is proposed. Summary of the invention

[0005] The technical problem to be solved by the present invention is: how to solve the problem that the existing real-time monitoring system has a single monitoring type, resulting in poor monitoring effect and inability to effectively improve the quality of Chinese medicinal materials planting. A real-time monitoring system and method for the growth of Chinese medicinal materials based on the Internet of Things is provided.

[0006] The present invention solves the above technical problems through the following technical solutions. The present invention includes a laser radar module, an environment acquisition module, a soil acquisition module, an image acquisition module, a pest acquisition module, a data processing module and an information sending module; The laser radar module is used to perform 3D modeling of Chinese medicinal materials through laser radar to obtain real-time model information of Chinese medicinal materials; The environment collection module is used to collect information about the growth environment of Chinese medicinal materials; The soil collection module is used to collect information about the soil in which Chinese medicinal materials grow; The image acquisition module is used to collect image information of the Chinese medicinal material growth area; The pest collection module is used to collect pest information; The data processing module is used to process the real-time model information of Chinese medicinal materials and obtain growth warning information; Process the information on the growth environment of Chinese medicinal materials and obtain environmental warning information; Process the information of the soil where Chinese medicinal materials grow and generate soil warning information; Process the image information of the Chinese medicinal materials growth area and generate regional abnormal warning information; Processing pest information to generate pest warning information; The information sending module is used to send growth warning information, environmental warning information, soil warning information, regional abnormality warning information and pest warning information to a preset receiving terminal.

[0007] Furthermore, the specific process of the growth warning information is as follows: Process the real-time model information of Chinese medicinal materials, and extract from the real-time model information that the volume of the Chinese medicinal material plant at the current time t1 is V1, and the volume at the previous monitoring time t0 is V0. According to the formula Vv=(V1-V0) / V0*100%, the volume change rate Vv is obtained; Then extract the standard volume change rate V at this stage 标 , when the volume change rate Vv is equal to the standard volume change rate V 标 When the difference is less than the preset value, a growth warning message is generated; Traverse the data of the real-time model information of Chinese medicinal materials, find the coordinate value H1 of the highest point of the plant, and compare it with the height H0 at the previous monitoring moment. Set the monitoring period to Gt, and obtain the height growth rate Vh through the formula Vh=(H1-H0) / Gt. Monitor two monitoring periods continuously. When the height growth rate Vh of the two monitoring periods is less than the preset value, a growth warning message is generated; Furthermore, the environmental information collection module collects environmental information in the following process: Temperature and humidity collection based on optical fiber sensing: A fiber Bragg grating (FBG) sensor array is used to measure temperature and humidity simultaneously; Using satellite remote sensing images to assist in light collection: In addition to local light sensors placed in the planting area, satellite remote sensing data is also connected to obtain more macro light information; Using wireless surface acoustic wave sensors to collect atmospheric pressure: Wireless surface acoustic wave (SAW) sensors use the characteristics of piezoelectric materials. When the external atmospheric pressure acts on the sensor surface, the propagation speed and frequency of the surface acoustic wave will change, thus obtaining atmospheric pressure information.

[0008] Furthermore, the environmental warning information includes abnormal temperature warning, abnormal humidity warning, and light intensity and atmospheric pressure warning; The process of obtaining abnormal temperature warning is as follows: a dynamic threshold model is established based on the historical temperature data of Chinese medicinal materials growth; The fiber Bragg grating (FBG) sensor is used to collect temperature data at different times of the day within a preset period of time, and the average temperature W1 and standard deviation W2 of each time period are calculated using statistical methods; During real-time monitoring, if the actual temperature Ww in the current period satisfies Ww<W1-kW2, or Ww>W1+kW2 (k is a reasonable temperature fluctuation tolerance interval, generally 2 - 3), the temperature is considered abnormal and a temperature abnormality warning is generated; The specific past process of humidity abnormality warning is as follows: the humidity information collected by the fiber Bragg grating (FBG) sensor within a preset time period is analyzed by trend analysis method, and the humidity change curve over time is obtained by fitting. If the slope of the curve changes sharply in a short period of time, and the change amplitude exceeds the preset threshold, and the absolute value of the slope change is greater than the preset value (relative humidity change rate / day), it indicates that the humidity environment may be abnormal, and a humidity abnormality warning is generated; The process of obtaining light intensity and atmospheric pressure warning is as follows: Combined abnormality determination is performed on the light intensity obtained by local light sensors and satellite remote sensing data and the atmospheric pressure collected by wireless surface acoustic wave sensors; Establish a light intensity-atmospheric pressure correlation model to determine the normal range of variation and synergistic relationship based on a large amount of historical data and meteorological knowledge; During real-time monitoring, when the light intensity deviates from the normal range and the atmospheric pressure change does not conform to the model prediction trend, that is, the light suddenly weakens and the atmospheric pressure rises sharply, exceeding the joint anomaly judgment threshold range, the comprehensive environmental anomaly is judged and light intensity and atmospheric pressure warnings are generated.

[0009] Furthermore, the soil information collection process is as follows: Use ground penetrating radar and other professional equipment to regularly detect soil thickness in the Chinese medicinal material planting area to obtain soil thickness information; Soil samples are collected regularly, and the composition and content of impurities in the soil are determined through a combination of spectral analysis and chemical analysis to obtain soil impurity information; By setting traps, combining regular manual observation with video monitoring, the types and numbers of common beneficial insects in the soil are counted, thus obtaining information on soil beneficial insects.

[0010] Furthermore, the abnormal soil warning information includes abnormal soil thickness, abnormal soil impurities and abnormal soil beneficial insects; The process of obtaining soil thickness anomaly is as follows: Extract soil thickness information. When the soil thickness value measured at a certain point deviates from the historical average value for the same period by more than a preset ratio, it is judged as abnormal and a soil thickness anomaly is generated. The process of obtaining soil impurity anomalies is as follows: When the content of any impurity component in the soil exceeds the set safety upper limit, or the combined effect of multiple impurities causes the physical and chemical properties of the soil to significantly deviate from the range suitable for the growth of Chinese medicinal materials, the impurities in the soil are judged to be abnormal; The process of obtaining abnormal beneficial insects in soil is as follows: When the number of one or more beneficial insects in the soil beneficial insect information is found to have continued to decrease by more than 50% compared with the normal range, and there is no sign of recovery after continuous monitoring for multiple cycles, or a certain key beneficial insect suddenly disappears completely in a certain area, it is determined that the beneficial insect information in the soil is abnormal.

[0011] Furthermore, the process of obtaining the regional abnormal warning information is as follows: Establish a personnel database: During the system initialization phase, collect image information of all authorized staff in the Chinese medicinal materials planting area, including front and side photos and photos in different working postures, to form a personnel database; Real-time image acquisition and preprocessing: The image acquisition module uses a high-definition smart camera to shoot the Chinese medicinal material growth area at preset time intervals (such as shooting every 10 minutes) or based on event triggers (such as when an object moves). The collected images are the image information of the Chinese medicinal material growth area, that is, the original images. The collected original images are first subjected to noise reduction processing; the preprocessed images are obtained; Then, the target detection algorithm in deep learning is used to detect human targets in the preprocessed images to obtain task feature information; The extracted character features are then compared with the information in the personnel database, and a feature matching algorithm is used to calculate the similarity score between the real-time collected character features and the features stored in the database. A reasonable similarity threshold is set, and when the similarity score is lower than the threshold, it is determined to be a non-staff member. At the same time, combined with the character's behavioral trajectory analysis, when it is detected that non-staff members linger in the planting area for a long time and frequently enter and exit specific sensitive areas, it is further confirmed that abnormal behavior exists and regional warning information is generated.

[0012] Furthermore, the specific process of the pest collection module collecting pest information is as follows: Use traps for trapping: Set up traps in the Chinese medicinal materials planting area, arrange special personnel to check the traps at fixed time intervals (such as every morning) and record the capture data, that is, the trap data.

[0013] Image monitoring and acquisition: Install cameras at high altitudes in the planting area and at key monitoring locations, and transmit the images collected by the cameras in real time to the receiving end via a wired or wireless network to obtain image monitoring data.

[0014] Through sensor-assisted data collection, some pests will release specific volatile organic compounds (VOCs) when attacking Chinese medicinal materials. Sensors that can detect the corresponding substances are deployed to collect data and obtain the first sensor data.

[0015] Vibration sensor: For some pests that eat inside plants or move in the soil, a vibration sensor is installed on the plant stem or in the soil near the roots to obtain the second sensor data.

[0016] Furthermore, the process of obtaining the pest warning information is as follows: The trapping device data, image monitoring data, first sensor data and second sensor data collected from physical trapping, image monitoring and sensor assistance are collected on a regular basis every day. Analyze the data of the trapping device to obtain the number of the first pest; Processing the image monitoring data to obtain the number of second pests; Processing the first sensor data to obtain third pest data; Processing the second sensor data to obtain fourth pest data; When any one of the first pest quantity, the second pest data, the third pest data and the fourth pest quantity exceeds its corresponding warning threshold, pest warning information is generated; The first pest quantity is marked as F1, the second pest data is marked as F2, the third pest data is marked as F3, and the fourth pest quantity is marked as F4; Assign F1 weight P1, F2 weight P2, F3 weight P3, F4 weight P4; P1+P2+P3+P4=1, P1=P2<P3<P4; The comprehensive evaluation parameter Ff is obtained through the formula F1*P1+F2*P2+F3*P3+F4*P4=Ff; When the comprehensive evaluation parameter Ff is greater than the preset value, pest warning information is generated.

[0017] A method for real-time monitoring of the growth of Chinese medicinal materials based on the Internet of Things, the method comprising the following steps: Step 1: Use laser radar to perform 3D modeling of Chinese medicinal materials and obtain real-time model information of Chinese medicinal materials; Step 2: Collect information about the growth environment and soil of Chinese medicinal materials; Step 3: Collect image information of the Chinese medicinal materials growing area; Step 4: The collection module collects pest information; Step 5: Process the real-time model information of Chinese medicinal materials to obtain growth warning information; Step 6: Process the information on the growth environment of Chinese medicinal materials to obtain environmental warning information; Step 7: Process the information of the soil where the Chinese medicinal materials grow and generate soil warning information; Step 8: Process the image information of the Chinese medicinal material growth area to generate regional abnormality warning information; Step nine: Process the pest information and generate pest warning information; Step 10: Send the growth warning information, environment warning information, soil warning information, regional abnormality warning information and pest warning information to a preset receiving terminal.

[0018] Compared with the prior art, the present invention has the following advantages: the real-time monitoring system and method of Chinese medicinal material growth based on the Internet of Things can accurately monitor and warn of growth, and can accurately capture the volume, height and other three-dimensional morphological information of Chinese medicinal material plants and their dynamic changes. By comparing the volume change rate and height growth rate of plants at different times, the growth trend can be accurately judged, and growth warning information can be generated in time, so that growers can detect growth abnormalities in advance, such as nutrient deficiency or root diseases, so as to quickly take targeted measures to ensure the healthy growth of Chinese medicinal materials and improve yield and quality.

[0019] Temperature and humidity acquisition based on optical fiber sensing is not only highly accurate, but also resistant to electromagnetic interference, and can operate stably in complex planting environments; satellite remote sensing images assist in light acquisition, breaking through local limitations, providing a macroscopic light field of view, and facilitating the rational planning of planting layouts; wireless surface acoustic wave sensors collect atmospheric pressure, which is convenient and efficient. With the help of dynamic threshold models, trend analysis, and joint anomaly determination, the abnormalities of temperature, humidity, light, and atmospheric pressure can be keenly perceived, and growers can accurately control the environment and create optimal growth conditions.

[0020] The soil collection module detects soil conditions from multiple dimensions. Ground penetrating radar detects soil thickness, spectral and chemical analysis determine the content of impurities, and multiple methods are used to count soil beneficial insect information. The module fully understands the soil status and immediately triggers an alarm if abnormal thickness, excessive impurities, or imbalanced beneficial insect communities are found, guiding growers to improve the soil and restore the ecology to ensure that the soil fertility and ecology are suitable for Chinese medicinal materials to take root and absorb nutrients.

[0021] The image acquisition module combines deep learning algorithms to accurately identify people and organisms in the planting area. By establishing a personnel database, real-time image processing and intelligent comparison, it can effectively identify intrusions and abnormal behaviors of non-staff members and protect the safety of the planting area; at the same time, it can quickly identify abnormal organisms to prevent them from harming Chinese medicinal materials, maintain planting order, and reduce interference from human and biological factors.

[0022] The pest collection module integrates a variety of advanced methods. Trapping devices, image monitoring, volatile substance sensors and vibration sensors work together to collect pest information in all directions, from the number and type of pests to signs of activity. Comprehensively assess the pest risk and generate accurate warnings based on the weights of different data sources. Growers can formulate scientific prevention and control strategies based on this, and choose biological or chemical prevention and control methods to curb the spread of pests in the early stages and reduce losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0024] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0025] like Figure 1 As shown, this embodiment provides a technical solution: a real-time monitoring system for the growth of Chinese medicinal materials based on the Internet of Things, including a laser radar module, an environment acquisition module, a soil acquisition module, an image acquisition module, a pest acquisition module, a data processing module and an information sending module; The laser radar module is used to perform 3D modeling of Chinese medicinal materials through laser radar to obtain real-time model information of Chinese medicinal materials; The environment collection module is used to collect information about the growth environment of Chinese medicinal materials; The soil collection module is used to collect information about the soil in which Chinese medicinal materials grow; The image acquisition module is used to collect image information of the Chinese medicinal material growth area; The pest collection module is used to collect pest information; The data processing module is used to process the real-time model information of Chinese medicinal materials and obtain growth warning information; Process the information on the growth environment of Chinese medicinal materials and obtain environmental warning information; Process the information of the soil where Chinese medicinal materials grow and generate soil warning information; Process the image information of the Chinese medicinal materials growth area and generate regional abnormal warning information; Processing pest information to generate pest warning information; The information sending module is used to send growth warning information, environmental warning information, soil warning information, regional abnormality warning information and pest warning information to a preset receiving terminal.

[0026] The specific process of growing warning information is as follows: Process the real-time model information of Chinese medicinal materials, and extract from the real-time model information that the volume of the Chinese medicinal material plant at the current time t1 is V1, and the volume at the previous monitoring time t0 is V0. According to the formula Vv=(V1-V0) / V0*100%, the volume change rate Vv is obtained; Then extract the standard volume change rate V at this stage 标 , when the volume change rate Vv is equal to the standard volume change rate V 标 When the difference is less than the preset value, a growth warning message is generated; Traverse the data of the real-time model information of Chinese medicinal materials, find the coordinate value H1 of the highest point of the plant, and compare it with the height H0 at the previous monitoring time. Set the monitoring period to Gt (in days), and obtain the height growth rate Vh through the formula Vh= (H1-H0) / Gt. Monitor two monitoring periods continuously. When the height growth rate Vh of the two monitoring periods is less than the preset value, a growth warning message is generated; There are two consecutive monitoring cycles Gt, each cycle has a day Gt=7, the initial plant height H0=18 cm in the previous cycle t0, and the initial plant height h1=19 cm in this cycle t1, then the average height growth rate in these two cycles is Vh=(19-18) / 7=1 / 7 cm / day. Compared with the lower limit of the normal height growth rate of this Chinese herbal medicine in the current suitable growth stage, if Vh is less than this lower limit, it also means that there may be problems with the growth; The above process not only monitors the growth of Chinese medicinal materials from the perspective of volume change, but also from the perspective of height growth rate. The comprehensive analysis of multi-dimensional data can reflect the growth status of Chinese medicinal materials more comprehensively and accurately, avoiding the one-sidedness that may be caused by single indicator monitoring.

[0027] By calculating the volume change rate and height growth rate through specific formulas, the growth conditions of Chinese medicinal materials can be quantified, making the assessment of growth conditions more scientific and accurate, and able to accurately capture subtle changes in the growth process.

[0028] The calculated volume change rate is compared with the standard volume change rate, and the height growth rate is compared with the lower limit of the normal height growth rate. With clear reference standards, it is possible to more intuitively judge whether the growth of Chinese medicinal materials is normal.

[0029] By processing the real-time model information of Chinese medicinal materials, it is possible to timely obtain relevant data at the current moment and the previous monitoring moment, quickly calculate growth indicators and compare them with standard values, and immediately generate growth warning information in case of abnormalities, so that timely measures can be taken.

[0030] When the difference between the volume change rate and the standard value is close to the preset value, or the height growth rate is less than the preset value for two consecutive monitoring cycles, an alarm will be issued. This can provide early warning before obvious problems occur in the growth of Chinese medicinal materials, giving growers more time to adjust their planting strategies and avoid serious losses.

[0031] The environmental information collection module performs the following process to collect environmental information: Temperature and humidity collection based on optical fiber sensing: A special fiber Bragg grating (FBG) sensor array is used to measure temperature and humidity simultaneously. For temperature measurement, the Bragg wavelength of the FBG changes linearly with the ambient temperature. When the ambient temperature rises or falls, by injecting broadband light into the optical fiber, the Bragg wavelength of the reflected light will shift accordingly. The wavelength shift is accurately measured using a spectrum analyzer, and then the precise ambient temperature value can be converted based on the pre-calibrated temperature-wavelength relationship curve. In terms of humidity measurement, the surface of the optical fiber is coated with a humidity-sensitive polymer material. Humidity changes will cause the refractive index of the polymer to change, which in turn affects the transmission characteristics of light in the optical fiber, such as the degree of light attenuation. By detecting the attenuation change of the optical signal and combining the calibration model of humidity and light attenuation, the relative humidity of the environment can be calculated. This fiber optic sensing method has the advantages of strong resistance to electromagnetic interference and high sensitivity, and is particularly suitable for situations where there may be complex electromagnetic environments around the Chinese medicinal materials planting area.

[0032] Using satellite remote sensing images to assist in light collection: In addition to the local light sensors arranged in the planting area, satellite remote sensing data is also connected to obtain more macroscopic light information; the multispectral imager carried by the satellite can regularly photograph the ground area, and by analyzing the reflected light intensity of specific bands (such as the visible light band), the average light intensity of the planting area can be calculated. In addition, combined with the satellite's positioning information and the sun angle model, the actual light conditions of the Chinese medicinal materials planting areas with different slopes and different time periods can be further calculated to make up for the deficiency that a single ground light sensor can only reflect local light. For example, when planting Chinese medicinal materials in mountainous areas, the light conditions on different slopes vary greatly. Satellite remote sensing images can provide planting personnel with comprehensive light distribution information to facilitate the rational planning of planting layout.

[0033] Using wireless surface acoustic wave sensors to collect atmospheric pressure: The wireless surface acoustic wave (SAW) sensor uses the characteristics of piezoelectric materials. When the external atmospheric pressure acts on the sensor surface, it will cause the propagation speed and frequency of the surface acoustic wave to change, that is, to obtain atmospheric pressure information; the wireless RF module transmits an inquiry signal to the SAW sensor, and the sensor returns a response signal with pressure information. The receiving end processes the received signal and calculates the atmospheric pressure value based on the change in frequency or wave speed and the pre-calibrated pressure-frequency / wave speed relationship. This sensor does not require wiring, is easy to deploy quickly in large areas of Chinese medicinal materials planting, and has good stability and accuracy, and can reflect the dynamic changes of atmospheric pressure in real time and accurately.

[0034] Environmental warning information includes abnormal temperature warning, abnormal humidity warning, and light intensity and atmospheric pressure warning; The process of obtaining abnormal temperature warning is as follows: a dynamic threshold model is established based on the historical temperature data of Chinese medicinal materials growth; The fiber Bragg grating (FBG) sensor is used to collect temperature data at different times of the day within a preset period of time (such as the past month), and the average temperature W1 and standard deviation W2 of each time period are calculated using statistical methods; During real-time monitoring, if the actual measured temperature Ww in the current period satisfies Ww<W1-kW2, or Ww>W1+kW2 (k is a reasonable temperature fluctuation tolerance range, generally 2 - 3), the temperature is judged to be abnormal and a temperature abnormality alert is generated. For example, for a certain Chinese medicinal material from 10 am to 11 am, the average temperature W1=25℃ in the past month and the standard deviation W2=2℃, if k=2, and the real-time temperature is lower than 21℃ or higher than 29℃, a temperature abnormality alert is triggered to remind the growers to pay attention to the impact of temperature changes.

[0035] The specific past process of humidity abnormality warning is as follows: The humidity information collected by the fiber Bragg grating (FBG) sensor within a preset time period is analyzed by trend analysis, and a humidity change curve over time is obtained by fitting. If the slope of the curve changes sharply in a short period of time (3-5 consecutive days), and the change amplitude exceeds the preset threshold, and the absolute value of the slope change is greater than the preset value (relative humidity change rate / day), it indicates that the humidity environment may be abnormal, which may be caused by recent rainfall, irrigation or ventilation system problems. A humidity abnormality warning is generated to remind the planting staff to check the humidity factors and adjust the planting environment management measures; The process of obtaining light intensity and atmospheric pressure warning is as follows: Combined abnormality determination is performed on the light intensity obtained by local light sensors and satellite remote sensing data and the atmospheric pressure collected by wireless surface acoustic wave sensors; Establish a light intensity-atmospheric pressure correlation model to determine the normal range of variation and synergistic relationship based on a large amount of historical data and meteorological knowledge; During real-time monitoring, when the light intensity deviates from the normal range and the atmospheric pressure changes do not conform to the model prediction trend, that is, the light suddenly weakens and the atmospheric pressure rises sharply, exceeding the combined abnormality judgment threshold range, the environment is judged to be abnormal, and light intensity and atmospheric pressure warnings are generated to remind the growers that they may encounter sudden weather changes and make preparations in advance, such as strengthening windproof facilities and adjusting irrigation strategies. The above process establishes a dynamic threshold model based on historical temperature data, and uses statistical methods to calculate the average temperature and standard deviation to determine the temperature anomaly range. It can fully consider the natural fluctuation characteristics of the temperature in the growth environment of Chinese medicinal materials, making the anomaly judgment more scientific and reasonable.

[0036] Through real-time monitoring and comparison with dynamic thresholds, abnormal temperature changes can be accurately captured. Once the current measured temperature exceeds the normal fluctuation range, an alarm can be generated in time to provide accurate temperature abnormality information to growers.

[0037] The trend analysis method is used to analyze the humidity information and fit the humidity change curve over time. By paying attention to the sharp change in the slope of the curve to judge the humidity anomaly, it can effectively predict the sudden change of the humidity environment instead of just paying attention to the current humidity value, thereby improving the foresight of humidity anomaly detection.

[0038] Clearly pointing out the possible causes of abnormal humidity, such as rainfall, irrigation or ventilation system problems, makes it easier for growers to quickly locate and troubleshoot problems, adjust planting environment management measures in a targeted manner, and improve the efficiency of problem solving.

[0039] A joint abnormal judgment is made on light intensity and atmospheric pressure, and a correlation model is established to determine the normal range of changes and synergistic relationships based on historical data and meteorological knowledge. This fully considers the interrelationships between environmental factors and avoids misjudgments or missed judgments that may result from single-factor judgments.

[0040] When there are combined anomalies in light intensity and atmospheric pressure, it can remind growers that they may encounter sudden weather changes, and give specific response suggestions such as strengthening wind-proof facilities and adjusting irrigation strategies. It provides clear guidance for growers to take preventive measures in advance, which helps reduce the adverse effects of sudden weather changes on the growth of Chinese medicinal materials.

[0041] Through a comprehensive environmental warning information acquisition process, covering key environmental factors such as temperature, humidity, light intensity and atmospheric pressure, it can provide all-round environmental monitoring and early warning for the growth of Chinese medicinal materials, timely discover and solve environmental problems, help ensure the normal growth of Chinese medicinal materials, and improve the yield and quality of Chinese medicinal materials.

[0042] Timely and accurate warning information can enable planting personnel to take corresponding measures quickly when the environment is abnormal, avoiding serious impacts on the growth of Chinese medicinal materials due to environmental problems, thereby effectively reducing planting losses caused by environmental factors and improving planting benefits.

[0043] It provides planting personnel with rich environmental information and clear response guidance, which helps planting personnel better understand and grasp the changes in the planting environment, improve the scientific and refined level of planting environment management, and promote the sustainable development of the Chinese medicinal materials planting industry.

[0044] The soil information collection process is as follows: Use ground penetrating radar and other professional equipment to regularly detect soil thickness in the Chinese medicinal material planting area to obtain soil thickness information. Ground penetrating radar emits high-frequency electromagnetic waves underground, and infers the interface position of soil layers at different depths based on the time delay and intensity change of the reflected echo of the electromagnetic waves, thereby accurately measuring the soil thickness. Collect soil thickness data from multiple points in the planting area over the past few years, establish a soil thickness database, and analyze the normal range of soil thickness changes in different seasons and different planting stages.

[0045] For example, for some rhizome Chinese medicinal materials, the thickness of the soil surface layer of 0 - 30 cm is more critical in the early stage of planting. Through long-term monitoring, it was found that the average soil thickness in this depth range at this stage was 25 cm, with a standard deviation of 2 cm.

[0046] Soil samples are collected regularly, and the composition and content of impurities in the soil are determined through a combination of spectral analysis and chemical analysis, so as to obtain soil impurity information; spectral analysis can quickly identify some impurities with specific spectral characteristics, such as heavy metal ions that have absorption peaks at specific wavelengths; chemical analysis quantitatively determines impurities such as abnormal pH (possibly from industrial pollution) and excessive salt (caused by seawater intrusion or unreasonable irrigation) through chemical reactions. For organic impurities, such as plastic fragments, undegraded pesticide residue packaging, etc., statistics can be made through manual sorting combined with microscopic observation.

[0047] The upper limit of the safe content of heavy metal lead in the soil for planting Chinese medicinal materials is set at 300ppm, the upper limit of salt content is set at 1.5g / kg, and the upper limit of organic impurities such as plastic fragments is calculated by area ratio, with an upper limit of 0.5%. By combining traps, regular manual observation and video monitoring, the types and numbers of common beneficial insects in the soil (such as earthworms, predatory mites, carabid larvae, etc.) are counted, that is, information on beneficial insects in the soil is obtained. Traps can use specific food or pheromones to attract beneficial insects into the collection device; manual observation is to carefully turn the soil in a sample plot of a specified area to check the distribution of beneficial insects; video monitoring is to place high-definition cameras in the planting area, regularly shoot the dynamics of the soil surface and shallow soil, and use image recognition technology to identify beneficial insects.

[0048] For example, it is known that in a certain Chinese medicinal material growing area, the normal number of earthworms per square meter of soil ranges from 30 to 50, the normal number of predatory mites ranges from 20 to 30, and the normal number of carabid larvae ranges from 10 to 15.

[0049] Soil abnormality warning information includes abnormal soil thickness, abnormal soil impurities and abnormal soil beneficial insects; The process of obtaining soil thickness anomaly is as follows: Extract soil thickness information. When the soil thickness value measured at a certain point deviates from the historical average value for the same period by more than a preset ratio, it is judged as abnormal and a soil thickness anomaly is generated. The process of obtaining soil impurity anomalies is as follows: When the content of any impurity component in the soil exceeds the set safety upper limit, or the combined effect of multiple impurities causes the physical and chemical properties of the soil to significantly deviate from the range suitable for the growth of Chinese medicinal materials, the impurities in the soil are judged to be abnormal; The process of obtaining abnormal beneficial insects in soil is as follows: When the number of one or more beneficial insects in the soil beneficial insect information is found to have continuously decreased by more than 50% compared with the normal range, and there is no sign of recovery after continuous monitoring for multiple cycles (such as 3-5 growth cycles), or a certain key beneficial insect suddenly disappears completely in a certain area, it is determined that the beneficial insect information in the soil is abnormal. This may indicate that the soil ecology is deteriorating, such as soil pH imbalance, pesticide abuse leading to the destruction of beneficial insect habitats or food source shortages, which in turn affects the natural prevention and control capabilities of Chinese medicinal materials for diseases and pests. Growers need to adjust their planting management strategies in a timely manner, such as reducing the use of chemical pesticides, supplementing organic materials to improve soil ecology, etc., to restore the beneficial biological community in the soil; By comparing the soil thickness measured at a certain point with the historical average value for the same period, we can accurately detect abnormal changes in soil thickness at a specific location, and promptly determine whether the soil thickness poses a potential threat to the growth of Chinese medicinal materials. This can provide growers with targeted soil information, helping them to accurately understand the soil conditions.

[0050] Soil thickness is crucial to the growth and development of the roots of Chinese medicinal materials. Timely detection of abnormal soil thickness allows growers to take corresponding measures, such as cultivating too thin soil and improving too thick and poorly aerated soil, to create a good growth space for the roots of Chinese medicinal materials and promote their healthy growth.

[0051] Abnormal soil impurities ensure the quality and safety of medicinal materials: Strictly monitor the content of impurities in the soil, and issue warnings when any impurity exceeds the standard or multiple impurities jointly affect the physical and chemical properties of the soil. This can effectively avoid the quality decline and safety problems of Chinese medicinal materials caused by soil impurity pollution, and ensure the safety of consumers' use of medicines.

[0052] Clarifying the criteria for determining abnormal soil impurities will help growers detect soil pollution problems in a timely manner and take targeted soil remediation measures, such as bioremediation and chemical improvement, according to specific circumstances, to restore the health of the soil and maintain a good planting environment.

[0053] Abnormal beneficial insects in the soil maintain ecological balance: Beneficial insects in the soil play an important role in maintaining the ecological balance of the soil and the natural prevention and control of diseases and pests of Chinese medicinal materials. Through continuous monitoring of the number and types of beneficial insects in the soil, when there is an abnormal decrease or disappearance of beneficial insects, an alarm is issued to remind growers to pay attention to the deterioration of the soil ecology, which helps to take measures to protect the soil ecosystem and promote ecological balance.

[0054] It alerts growers to possible soil ecological problems, such as pH imbalance and pesticide abuse, so as to prompt them to adjust planting management strategies in a timely manner, reduce the use of chemical pesticides, increase the input of organic materials, improve the soil ecological environment, and create suitable living conditions for beneficial insects, thereby improving the natural prevention and control capabilities of Chinese medicinal materials for diseases and pests, and reducing the cost of chemical prevention and control and the negative impact on the environment.

[0055] The process of obtaining the abnormal warning information of the area is as follows: Establish a personnel database: During the system initialization phase, image information of all authorized staff in the Chinese medicinal materials planting area is collected, including front and side photos and photos in different working postures, to form a personnel database; these images are pre-processed, such as cropping, normalization, grayscale processing, etc., to facilitate subsequent feature extraction and comparison. At the same time, basic information such as name, work number, scope of work responsibilities, etc. is associated with each staff member to facilitate accurate identification and management.

[0056] Real-time image acquisition and preprocessing: The image acquisition module uses a high-definition smart camera to shoot the Chinese medicinal material growth area at preset time intervals (such as shooting every 10 minutes) or based on event triggers (such as when an object moves). The collected images are the image information of the Chinese medicinal material growth area, that is, the original images. The collected original images are first subjected to noise reduction processing; the preprocessed images are obtained; image noise caused by light changes, slight disturbances, etc. is removed to improve image clarity. Then image enhancement is performed to make the human features in the image more prominent by adjusting parameters such as contrast and brightness, which is convenient for subsequent recognition.

[0057] Then, we use the target detection algorithm in deep learning, such as the model based on YOLO (You Only Look Once) or Faster R-CNN (Region-based Convolutional Neural Network), to detect the human target in the preprocessed image and obtain the task feature information; these models are pre-trained on public data sets containing a large number of human images and human images collected in specific scenes in the planting area, and can quickly and accurately identify the human area in the image. Once the person is detected, the feature extraction of the human area is immediately performed. Common features include facial features (using facial recognition technology to extract features such as facial features layout and facial texture), body features (such as height, body shape, walking posture, etc.) and clothing features (color, style, work clothes logo, etc.).

[0058] The extracted character features are then compared with the information in the personnel database, and a feature matching algorithm is used to calculate the similarity score between the real-time collected character features and the features stored in the database. A reasonable similarity threshold (such as 0.8) is set. When the similarity score is lower than the threshold, it is determined to be a non-staff member. At the same time, combined with the character's behavioral trajectory analysis, when it is detected that non-staff members wander around the planting area for a long time (staying longer than the preset 5 minutes) and frequently enter and exit specific sensitive areas (such as seedling cultivation areas and high-value medicinal material storage areas), it is further confirmed that there is abnormal behavior and regional warning information is generated; By establishing a personnel database containing various types of image information and basic information, and using advanced deep learning target detection algorithms and feature matching algorithms, it is possible to accurately identify the identities of people entering the planting area, distinguish between staff and non-staff, achieve refined management of personnel in the planting area, and improve the safety of the planting area.

[0059] It can not only identify people, but also monitor the behavior of non-staff members in the planting area by combining behavioral trajectory analysis. When it is found that non-staff members have abnormal behaviors such as wandering for a long time or frequently entering and leaving sensitive areas, warning information will be generated in time, which will help growers take timely measures to prevent possible sabotage, theft and other behaviors, and ensure the normal production order and property safety of the planting area.

[0060] Timely detection and handling of unauthorized entry into the planting area can avoid non-staff interference in the growth process of Chinese medicinal materials, such as accidental stepping on or picking, reduce adverse effects on the growth of Chinese medicinal materials, and ensure the smooth progress of planting and production.

[0061] Focused monitoring of sensitive areas such as seedling cultivation areas and high-value medicinal material storage areas can effectively protect important resources and core assets in the planting areas. Once abnormal behavior is discovered, growers can take prompt measures to prevent seedlings from being stolen and high-value medicinal materials from being destroyed, thus protecting the economic interests of growers and the safety of planting resources.

[0062] The specific process of the pest collection module for collecting pest information is as follows: Trapping with traps: Set up traps in the Chinese medicinal material planting area, arrange special personnel to check the traps at fixed time intervals (such as every morning), and record the capture data, that is, the trap data, collect the captured pests, put them into a special sample bag, and record the location of each trap, the type of captured pests (identified by preliminary appearance or with the help of a simple insect illustration), and the number. For pests that are difficult to directly identify, mark them and prepare for further identification. For example, for pests with phototropism, such as moths and beetles, set up a black light trap at a certain distance (such as 20 meters). The black light can emit ultraviolet rays of a specific wavelength (usually between 330-380 nanometers) to attract pests to fly to the light source. There is a collection funnel and a collection bottle at the bottom. Once the pest touches it, it will fall into the bottle. For some pests that rely on their sense of smell to find food or mates, such as fruit flies and fruit flies, a sweet and sour liquid trap is set up. The smell of the sweet and sour liquid made by mixing sugar, vinegar, wine and water in a specific ratio (such as 3:4:1:2) is used to attract the pests. The container is also designed with an anti-escape structure to ensure that the trapped pests can be retained.

[0063] For image monitoring and acquisition, cameras are installed at high altitudes in the planting area and in key monitoring locations (such as areas with high incidence of pests and diseases, and newly planted seedling areas). The images collected by the cameras in real time are transmitted to the receiving end via a wired or wireless network to obtain image monitoring data, ensuring comprehensive coverage and no visual blind spots. The camera should have a day and night switching function, shooting in normal color mode during the day and turning on the infrared function at night to ensure 24-hour uninterrupted monitoring. According to the area of ​​the planting area and the possible range of pest activity, the focal length and resolution of the camera should be reasonably adjusted. For example, for large-scale contiguous planting areas, a telephoto lens is used to capture pest activities at a distance, while in seedling breeding areas, a macro lens is used to focus on the details of small pests.

[0064] Through sensor-assisted collection, some pests will release specific volatile organic compounds (VOCs) when attacking Chinese medicinal materials. Sensors that can detect the corresponding substances are deployed to collect data and obtain the first sensor data. For example, when aphids suck plant sap, they will release specific warning pheromones. Once the sensor detects the signal, combined with meteorological data (such as wind direction and wind speed), it can roughly determine the direction of the pest source and the potential damage area. The sensor collects data every few minutes and compares the detection value with the preset threshold (determined through a large number of preliminary experiments and field monitoring). If the threshold is exceeded, an early warning is triggered, indicating that there may be a risk of an aphid pest outbreak.

[0065] Vibration sensor: For some pests that eat inside plants or move in the soil, such as white grubs and longhorn beetle larvae, vibration sensors are installed on the stems of plants or in the soil near the roots to obtain the second sensor data. The crawling, chewing and other activities of pests will cause slight vibrations. The sensor converts these vibration signals into electrical signals, which are then amplified, filtered and processed before being transmitted to the data processing center. By analyzing the frequency, amplitude, duration and other characteristics of the vibration, combined with the behavioral pattern database of known pests, it is possible to determine whether there is hidden pest activity and assist in accurately locating the site of pest occurrence.

[0066] The process of obtaining the pest warning information is as follows: The trapping device data, image monitoring data, first sensor data and second sensor data collected from physical trapping, image monitoring and sensor assistance are collected on a regular basis every day. Analyze the data of the trapping device to obtain the number of the first pest; Processing the image monitoring data to obtain the number of second pests; Processing the first sensor data to obtain third pest data; Processing the second sensor data to obtain fourth pest data; When any one of the first pest quantity, the second pest data, the third pest data and the fourth pest quantity exceeds its corresponding warning threshold, pest warning information is generated; The first pest quantity is marked as F1, the second pest data is marked as F2, the third pest data is marked as F3, and the fourth pest quantity is marked as F4; Assign F1 weight P1, F2 weight P2, F3 weight P3, F4 weight P4; P1+P2+P3+P4=1, P1=P2<P3<P4; The comprehensive evaluation parameter Ff is obtained through the formula F1*P1+F2*P2+F3*P3+F4*P4=Ff; When the comprehensive evaluation parameter Ff is greater than the preset value, pest warning information is generated.

[0067] Pest data is collected through physical trapping, image monitoring, sensor assistance and other methods, covering the monitoring needs of different types of pests. Physical trapping can directly obtain the number of pest entities; image monitoring can intuitively understand the activity range and distribution of pests; sensor-assisted collection can capture some difficult-to-detect pest information, such as monitoring specific pests through volatile organic compounds (VOCs) and vibration. This multi-channel data collection method ensures a comprehensive grasp of pest information and avoids possible omissions in a single monitoring method.

[0068] The data obtained from various channels are analyzed regularly every day. Once the number of pests from any source exceeds the corresponding warning threshold, pest warning information is immediately generated. This rapid response mechanism allows growers to be informed of the situation in a timely manner when the number of pests begins to increase and has not yet caused serious damage to Chinese medicinal materials, so that they can quickly take preventive measures to effectively control the spread and harm of pests and reduce the impact on the yield and quality of Chinese medicinal materials.

[0069] By assigning weights to different data sources and calculating the comprehensive evaluation parameter Ff, the actual degree of damage caused by pests can be assessed more comprehensively and accurately. Different data sources have different importance in reflecting pest damage. For example, for some pests hidden in the soil or inside plants, sensor data may more accurately reflect their actual damage, so they are given higher weights. This comprehensive evaluation method avoids the one-sidedness of judging pest damage based on a single piece of data, and provides growers with a more accurate basis for decision-making. Growers can reasonably choose prevention and control methods and input resources based on the size of the comprehensive evaluation parameter Ff, avoid waste of resources caused by excessive prevention and control, and ensure that sufficiently effective prevention and control measures are taken when necessary.

[0070] The weights and preset values ​​of each data source can be flexibly adjusted according to the characteristics of different Chinese medicinal material varieties, growth stages, and planting environments. For example, for some Chinese medicinal material varieties that are particularly sensitive to specific pests, the weights of the corresponding data sources can be appropriately increased; during the critical period of Chinese medicinal material growth, the preset values ​​can be lowered to increase the sensitivity of monitoring. This flexibility enables the system to adapt to diverse planting needs and better meet the requirements of pest monitoring and control in different planting scenarios.

[0071] A method for real-time monitoring of the growth of Chinese medicinal materials based on the Internet of Things, the method comprising the following steps: Step 1: Use laser radar to perform 3D modeling of Chinese medicinal materials and obtain real-time model information of Chinese medicinal materials; Step 2: Collect information about the growth environment and soil of Chinese medicinal materials; Step 3: Collect image information of the Chinese medicinal materials growing area; Step 4: The collection module collects pest information; Step 5: Process the real-time model information of Chinese medicinal materials to obtain growth warning information; Step 6: Process the information on the growth environment of Chinese medicinal materials to obtain environmental warning information; Step 7: Process the information of the soil where the Chinese medicinal materials grow and generate soil warning information; Step 8: Process the image information of the Chinese medicinal material growth area to generate regional abnormality warning information; Step nine: Process the pest information and generate pest warning information; Step 10: Send the growth warning information, environment warning information, soil warning information, regional abnormality warning information and pest warning information to a preset receiving terminal.

[0072] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0073] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0074] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A real-time monitoring system for the growth of Chinese medicinal materials based on the Internet of Things, characterized in that: It includes laser radar module, environment acquisition module, soil acquisition module, image acquisition module, pest acquisition module, data processing module and information sending module; The laser radar module is used to perform 3D modeling of Chinese medicinal materials through laser radar to obtain real-time model information of Chinese medicinal materials; The environment collection module is used to collect information about the growth environment of Chinese medicinal materials; The soil collection module is used to collect information about the soil in which Chinese medicinal materials grow; The image acquisition module is used to collect image information of the Chinese medicinal material growth area; The pest collection module is used to collect pest information; The data processing module is used to process the real-time model information of Chinese medicinal materials and obtain growth warning information; Process the information on the growth environment of Chinese medicinal materials and obtain environmental warning information; Process the information of the soil where Chinese medicinal materials grow and generate soil warning information; Process the image information of the Chinese medicinal materials growth area and generate regional abnormal warning information; Processing pest information to generate pest warning information; The information sending module is used to send growth warning information, environmental warning information, soil warning information, regional abnormality warning information and pest warning information to a preset receiving terminal.

2. The real-time monitoring system for growth of Chinese medicinal materials based on the Internet of Things according to claim 1, characterized in that: The specific process of growing warning information is as follows: Process the real-time model information of Chinese medicinal materials, and extract from the real-time model information that the volume of the Chinese medicinal material plant at the current time t1 is V1, and the volume at the previous monitoring time t0 is V0. According to the formula Vv=(V1-V0) / V0*100%, the volume change rate Vv is obtained; Then extract the standard volume change rate V at this stage 标 , when the volume change rate Vv is equal to the standard volume change rate V 标 When the difference is less than the preset value, a growth warning message is generated; Traverse the data of the real-time model information of Chinese medicinal materials, find the coordinate value H1 of the highest point of the plant, and compare it with the height H0 at the previous monitoring moment. Set the monitoring period to Gt, and obtain the height growth rate Vh through the formula Vh= (H1-H0) / Gt. Monitor two monitoring periods continuously. When the height growth rate Vh of the two monitoring periods is less than the preset value, a growth warning message is generated.

3. The real-time monitoring system for growth of Chinese medicinal materials based on the Internet of Things according to claim 1 is characterized in that: The environmental information collection module performs the following process to collect environmental information: Temperature and humidity collection based on optical fiber sensing: A fiber Bragg grating (FBG) sensor array is used to measure temperature and humidity simultaneously. Using satellite remote sensing images to assist in light collection: In addition to local light sensors placed in the planting area, satellite remote sensing data is also connected to obtain more macro light information; Using wireless surface acoustic wave sensors to collect atmospheric pressure: Wireless surface acoustic wave (SAW) sensors use the characteristics of piezoelectric materials. When the external atmospheric pressure acts on the sensor surface, the propagation speed and frequency of the surface acoustic wave will change, thus obtaining atmospheric pressure information.

4. The real-time monitoring system for growth of Chinese medicinal materials based on the Internet of Things according to claim 3 is characterized in that: Environmental warning information includes abnormal temperature warning, abnormal humidity warning, and light intensity and atmospheric pressure warning; The process of obtaining abnormal temperature warning is as follows: a dynamic threshold model is established based on the historical temperature data of Chinese medicinal materials growth; The fiber Bragg grating (FBG) sensor is used to collect temperature data at different times of the day within a preset period of time, and the average temperature W1 and standard deviation W2 of each time period are calculated using statistical methods; During real-time monitoring, if the actual temperature Ww in the current period satisfies Ww<W1-kW2, or Ww>W1+kW2 (k is a reasonable temperature fluctuation tolerance interval, generally 2 - 3), the temperature is considered abnormal and a temperature abnormality warning is generated; The specific process of abnormal humidity warning is as follows: The humidity information collected by the fiber Bragg grating (FBG) sensor within a preset time period is analyzed by trend analysis, and a humidity change curve over time is obtained by fitting. If the slope of the curve changes sharply in a short period of time, and the change amplitude exceeds the preset threshold, and the absolute value of the slope change is greater than the preset value (relative humidity change rate / day), it indicates that the humidity environment may be abnormal, and a humidity abnormality warning is generated; The process of obtaining light intensity and atmospheric pressure warning is as follows: Combined abnormality determination is performed on the light intensity obtained by local light sensors and satellite remote sensing data and the atmospheric pressure collected by wireless surface acoustic wave sensors; Establish a light intensity-atmospheric pressure correlation model to determine the normal range of variation and synergistic relationship based on a large amount of historical data and meteorological knowledge; During real-time monitoring, when the light intensity deviates from the normal range and the atmospheric pressure change does not conform to the model prediction trend, that is, the light suddenly weakens and the atmospheric pressure rises sharply, exceeding the joint anomaly judgment threshold range, the comprehensive environmental anomaly is judged and light intensity and atmospheric pressure warnings are generated.

5. The real-time monitoring system for growth of Chinese medicinal materials based on the Internet of Things according to claim 1 is characterized in that: The soil information collection process is as follows: Use ground penetrating radar and other professional equipment to regularly detect soil thickness in the Chinese medicinal material planting area to obtain soil thickness information; Soil samples are collected regularly, and the composition and content of impurities in the soil are determined through a combination of spectral analysis and chemical analysis to obtain soil impurity information; By setting traps, combining regular manual observation with video monitoring, the types and numbers of common beneficial insects in the soil are counted, thus obtaining information on soil beneficial insects.

6. The real-time monitoring system for growth of Chinese medicinal materials based on the Internet of Things according to claim 5 is characterized in that: Soil abnormality warning information includes abnormal soil thickness, abnormal soil impurities and abnormal soil beneficial insects; The process of obtaining soil thickness anomaly is as follows: Extract soil thickness information. When the soil thickness value measured at a certain point deviates from the historical average value for the same period by more than a preset ratio, it is judged as abnormal and a soil thickness anomaly is generated. The process of obtaining soil impurity anomalies is as follows: When the content of any impurity component in the soil exceeds the set safety upper limit, or the combined effect of multiple impurities causes the physical and chemical properties of the soil to significantly deviate from the range suitable for the growth of Chinese medicinal materials, the impurities in the soil are judged to be abnormal; The process of obtaining abnormal beneficial insects in soil is as follows: When the number of one or more beneficial insects in the soil beneficial insect information is found to have continued to decrease by more than 50% compared with the normal range, and there is no sign of recovery after continuous monitoring for multiple cycles, or a certain key beneficial insect suddenly disappears completely in a certain area, it is determined that the beneficial insect information in the soil is abnormal.

7. The real-time monitoring system for growth of Chinese medicinal materials based on the Internet of Things according to claim 1, characterized in that: The process of obtaining the abnormal warning information of the area is as follows: Establish a personnel database: During the system initialization phase, collect image information of all authorized staff in the Chinese medicinal materials planting area, including front and side photos and photos in different working postures, to form a personnel database; Real-time image acquisition and preprocessing: The image acquisition module uses a high-definition smart camera to shoot the Chinese medicinal material growth area at preset time intervals (such as shooting every 10 minutes) or based on event triggers (such as when an object moves). The collected images are the image information of the Chinese medicinal material growth area, that is, the original images. The collected original images are first subjected to noise reduction processing; the preprocessed images are obtained; Then, the target detection algorithm in deep learning is used to detect human targets in the preprocessed images to obtain the characteristic information of the human. The extracted character features are then compared with the information in the personnel database, and a feature matching algorithm is used to calculate the similarity score between the real-time collected character features and the features stored in the database. A reasonable similarity threshold is set, and when the similarity score is lower than the threshold, it is determined to be a non-staff member. At the same time, combined with the character's behavioral trajectory analysis, when it is detected that non-staff members linger in the planting area for a long time or frequently enter and exit specific sensitive areas, it is further confirmed that there is abnormal behavior and a regional warning information is generated.

8. The real-time monitoring system for growth of Chinese medicinal materials based on the Internet of Things according to claim 1, characterized in that: The specific process of the pest collection module for collecting pest information is as follows: Trapping with traps: Set up traps in the Chinese medicinal material planting area, arrange special personnel to check the traps at fixed time intervals (such as every morning), and record the capture data, i.e., the trap data; Image monitoring and acquisition: Install cameras at high altitudes in the planting area and at key monitoring locations, and transmit the images collected by the cameras in real time to the receiving end via a wired or wireless network to obtain image monitoring data; Through sensor-assisted data collection, some pests release specific volatile organic compounds (VOCs) when attacking Chinese medicinal materials. Sensors that can detect corresponding substances are deployed to collect data and obtain the first sensor data; Vibration sensor: For pests that bore into plants or move in the soil, a vibration sensor is installed on the plant stem or in the soil near the roots to obtain second sensor data.

9. The real-time monitoring system for growth of Chinese medicinal materials based on the Internet of Things according to claim 8, characterized in that: The process of obtaining the pest warning information is as follows: The trapping device data, image monitoring data, first sensor data and second sensor data collected from physical trapping, image monitoring and sensor assistance are collected on a regular basis every day. Analyze the data of the trapping device to obtain the number of the first pest; Processing the image monitoring data to obtain the number of second pests; Processing the first sensor data to obtain third pest data; Processing the second sensor data to obtain fourth pest data; When any one of the first pest quantity, the second pest data, the third pest data and the fourth pest quantity exceeds its corresponding warning threshold, pest warning information is generated; The first pest quantity is marked as F1, the second pest data is marked as F2, the third pest data is marked as F3, and the fourth pest quantity is marked as F4; Assign F1 weight P1, F2 weight P2, F3 weight P3, F4 weight P4; P1+P2+P3+P4=1, P1=P2<P3<P4; The comprehensive evaluation parameter Ff is obtained through the formula F1*P1+F2*P2+F3*P3+F4*P4=Ff; When the comprehensive evaluation parameter Ff is greater than the preset value, pest warning information is generated.

10. A method for real-time monitoring of the growth of Chinese medicinal materials based on the Internet of Things, the method being based on the monitoring system required by any one of claims 1 to 9, characterized in that: The method comprises the following steps: Step 1: Use laser radar to perform 3D modeling of Chinese medicinal materials and obtain real-time model information of Chinese medicinal materials; Step 2: Collect information about the growth environment and soil of Chinese medicinal materials; Step 3: Collect image information of the Chinese medicinal materials growing area; Step 4: The collection module collects pest information; Step 5: Process the real-time model information of Chinese medicinal materials to obtain growth warning information; Step 6: Process the information on the growth environment of Chinese medicinal materials to obtain environmental warning information; Step 7: Process the information of the soil where the Chinese medicinal materials grow and generate soil warning information; Step 8: Process the image information of the Chinese medicinal material growth area to generate regional abnormality warning information; Step nine: Process the pest information and generate pest warning information; Step 10: Send the growth warning information, environment warning information, soil warning information, regional abnormality warning information and pest warning information to a preset receiving terminal.

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