Intelligent environment monitoring device for soil health and microbiological analysis

Abstract

The invention discloses a soil health and microbiological analysis intelligent environment monitoring device, and relates to the technical field of soil monitoring, the device comprises the following components: an outer shell, a sensor assembly, a microflora interaction monitoring sensor group, a data acquisition and transmission module, a power supply module, a microprocessor, a control unit and a data analysis module; signal molecule concentration change data and predatory relationship visual data are combined through the data analysis module, deep analysis is carried out by using bioinformatics and mathematical modeling means, and a multi-dimensional regulation network hidden in a microbial community can be comprehensively revealed; according to the soil health assessment device, the interaction relationship between the soil ecological system and the soil ecological system is analyzed accurately, the influence of the interaction relationship on key ecological functions such as material circulation and energy flow in the soil ecological system is analyzed accurately, the device can generate a detailed soil health assessment report based on the analysis results, agricultural scientific researchers, planting farmers and environmental protection monitoring departments can assess the soil health condition accurately, and the soil health assessment efficiency is improved. And targeted soil management measures are taken.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an intelligent environmental monitoring device for soil health and microbial analysis. Background Art

[0002] With the continuous improvement of modern agriculture and environmental protection awareness, soil health monitoring has become an important basis for evaluating ecosystem functions, guiding agricultural production and formulating environmental protection policies. The soil ecosystem is a complex biogeochemical system, in which soil microbial communities, as a key component, play a vital role in soil fertility, nutrient cycling, pollution degradation and ecosystem stability. Therefore, in-depth monitoring and analysis of soil microbial communities and their interactions have become the key to understanding soil health and ecosystem functions.

[0003] Although the existing soil monitoring technology has made certain progress, there are still many shortcomings. Traditional soil monitoring methods mainly focus on the determination of soil physical and chemical properties, such as soil moisture, temperature, pH value and nutrient content. Although these parameters are important, they cannot fully reflect the complexity and diversity of soil microbial communities. At the same time, most of the existing microbial detection methods remain at the simple characterization of microbial species and numbers, lacking in-depth exploration of the complex interactions within microbial communities, especially for the transmission of signal molecules between microbial cells, predator-prey relationships and other multi-dimensional linkage analysis. This makes it difficult for us to accurately reveal how microbial interactions affect key processes such as material circulation and energy flow in soil ecosystems, and thus difficult to conduct a comprehensive and dynamic assessment of soil health.

[0004] In view of the above problems, it is necessary to optimize the existing intelligent environmental monitoring device for soil health and microbial analysis. By integrating soil physical and chemical property sensors and microbial community interaction monitoring sensor groups, comprehensive and dynamic monitoring of soil health status and microbial community interactions can be achieved. Therefore, it is of great significance to develop an intelligent environmental monitoring device for soil health and microbial analysis that can comprehensively realize the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide an intelligent environmental monitoring device for soil health and microbial analysis. It can realize comprehensive and in-depth monitoring and analysis of soil health status and soil microbial communities by integrating soil physical and chemical property sensors and microbial community interaction monitoring sensor groups. It can also capture the concentration changes of signal molecules between soil microbial cells in real time through highly sensitive biosensor arrays and advanced fluorescence microscopy imaging technology, and record the dynamic changes of microbial predation relationships in detail. At the same time, the data analysis module based on bioinformatics and mathematical modeling methods can deeply explore the interactions within the microbial community and its impact mechanism on the soil ecosystem, so as to realize a comprehensive and dynamic assessment of soil health.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent environmental monitoring device for soil health and microbial analysis, the device comprising the following components: an outer shell, a sensor assembly, a microbial community interaction monitoring sensor group, a data acquisition and transmission module, a power module, a microprocessor and a control unit, and a data analysis module;

[0007] The outer shell is made of a material that is corrosion-resistant, strong and has good sealing properties. The bottom of the outer shell is provided with a pointed end that is easy to insert into the soil, so that the device can be firmly buried in the soil area to be monitored. The surface of the outer shell is provided with heat dissipation holes to ensure the heat dissipation of the internal electronic components.

[0008] The sensor assembly includes a soil physical property sensor group and a soil chemical property sensor group, wherein the soil physical property sensor group includes a humidity sensor, a temperature sensor and a soil compaction sensor, each sensor is arranged at a different detection position of the device, and can be extended to different soil depths to carry out multi-point measurement work, and the soil chemical property sensor assembly is equipped with a pH sensor and a nutrient sensor, which are in contact with the soil through a protective structure;

[0009] The microbial community interaction monitoring sensor group includes a biosensor array, a microscopic observation chamber and supporting components, wherein the biosensor array uses biosensor technology to capture subtle changes in the concentration of signal molecules in the soil environment and transmit relevant data to the data analysis module in real time. The microscopic observation chamber and supporting components simulate the native soil environment, collect soil samples at preset time intervals, and use a fluorescent microscopic imaging system and image analysis software to record the predation process, predation frequency and dynamic changes between predatory microorganisms and prey microorganisms under different soil health conditions, and can transmit the obtained visual data to the data analysis module in real time;

[0010] The data acquisition and transmission module is connected to each sensor component and is responsible for collecting, aggregating and preprocessing various types of data obtained from different sensors, and sending the processed data to the cloud server or local monitoring terminal through wired or wireless communication;

[0011] The power module is equipped with a rechargeable lithium battery pack as the main power source, and is combined with a solar charging panel and a power management circuit to achieve reasonable power supply to different modules and power monitoring and early warning functions, and can remind charging or battery replacement when the power is lower than the set threshold;

[0012] The microprocessor and control unit control the sampling frequency, microbial sample collection time and data transmission interval of the sensors in each sensor assembly according to preset parameters, and perform preliminary calculation, analysis and judgment on the data collected by the sensors, and compare the data with the preset soil health standard threshold range. Once abnormal data appears, an alarm mechanism is triggered;

[0013] The data analysis module receives data from the sensor, performs similarity analysis on different microbial communities based on signal molecule related characteristics, compares and analyzes the differences in the microbial community structure under different environmental conditions in the soil affected by signal molecules, and uses correlation analysis methods to measure the linear correlation between different microorganisms based on changes in signal molecule concentrations, determines whether there is a linear correlation between two microorganisms at the signal molecule level and the strength and direction of the correlation, and explores potential interactions between microorganisms. At the same time, a dynamic model is used to describe the changes in the number of microbial populations over time, simulates the evolution process of different microbial populations in the soil, and uses information theory related methods to analyze the correlation between predation behavior and changes in signal molecule concentrations to measure the degree of correlation between the two. Based on the constructed equation group about the predator population number, the prey population number and the related variables affected by the signal molecules, by considering the interaction between predators and prey and the influence of signal molecules on their respective growth, the influence of the microbial community caused by this on the decomposition of organic matter and nutrient cycling functions in the soil is analyzed, thereby achieving a comprehensive dynamic assessment of the health status of the soil.

[0014] Furthermore, in the sensor assembly, the humidity sensor adopts a high-precision capacitive humidity sensor, the temperature sensor adopts a thermistor temperature sensor, and the soil compaction sensor uses a pressure sensitive element. When the soil exerts pressure on the sensor probe, the pressure sensitive element deforms, causing its resistance or capacitance parameters to change, and the pressure signal is converted into an electrical signal output to reflect the compaction degree of the soil. The pH sensor adopts a glass electrode pH sensor, and the nutrient sensor adopts an ion selective electrode sensor to detect the nitrogen, phosphorus and potassium nutrient content in the soil.

[0015] Furthermore, in the power module, the lithium battery pack is composed of multiple lithium battery cells connected in series and in parallel, and is equipped with a battery protection circuit to prevent the battery from overcharging, over-discharging and short circuit. The solar charging panel adopts a crystalline silicon solar panel or a thin-film solar panel, which is installed in a position on the top of the outer shell where there is sufficient light and is easy to fix. It is connected to the lithium battery pack through a charging controller, and can automatically adjust the charging current and voltage according to the power status of the lithium battery pack and its own power generation. The power management circuit monitors the power status of the lithium battery pack in real time, obtains the battery power information through the power detection circuit, triggers an alarm prompt signal when the power is lower than a preset threshold, and dynamically adjusts the power supply current and voltage according to the working status of each module to distribute the power to each module.

[0016] Furthermore, the data analysis module performs similarity analysis on different microbial communities based on the signal molecule related characteristics, and the analysis formula is: Among them, A and B are the sets corresponding to two different microbial communities, |A∩B| represents the number of elements in the intersection of the two sets, that is, the number of common signal molecule features, and |A∪B| represents the number of elements in the union of the two sets, that is, the sum of the number of signal molecule features of the two communities.

[0017] Furthermore, the data analysis module uses a correlation analysis method to measure the linear correlation between different microorganisms based on the changes in signal molecule concentrations. Specifically, the signal molecule concentration change sequences corresponding to two different microorganisms are X = {x1, x2, ..., x n} and Y = {y1, y2, ..., y n}, x i ,y i are the measured values ​​in the sequence, n is the number of measurements, and their average values ​​are and The correlation coefficient calculation formula is: Among them, r XY The value range of is [-1, 1]. A value close to 1 indicates a positive correlation, a value close to -1 indicates a negative correlation, and a value close to 0 indicates a weak correlation.

[0018] Furthermore, the data analysis module uses a dynamic model to describe the change of microbial population over time, and its model parameters are: Among them, N m represents the number of the mth microbial population, t is the time, r m is the intrinsic growth rate of the microbial population, K m is the environmental carrying capacity, α mi is the influence coefficient of the i-th signal molecule on the growth of the m-th microbial population, C i is the concentration of the ith signal molecule.

[0019] Furthermore, the data analysis module uses information theory related methods to analyze the correlation between predation behavior and changes in signal molecule concentration to measure the degree of association between the two. Specifically, the predation behavior related variables X = {x1, x2, ..., x n} information entropy H(X), that is, H(X) = -∑ i p(x i )log p (x i ) where p(x i ) is x i The probability of appearing in the sequence X is calculated by the same method. The signal molecule concentration change sequence Y = {y1, y2, ..., y n} information entropy H(Y), that is, H(Y) = -∑ j p(y j )log2p(y j ) calculates its joint information entropy H(X, Y), that is, H(X, Y) = -∑ i ∑ j p(x i ,y j )log2p(x i ,y j ) where p(x i ,y j ) is x i and j The joint probability of simultaneous occurrence is used to calculate the mutual information I(X;Y) between the predation behavior and the change in signal molecule concentration, that is, H(X;Y)=H(X)+H(Y)-H(X,Y). The larger the value of the mutual information I(X;Y), the stronger the correlation between the predation behavior and the change in signal molecule concentration.

[0020] Furthermore, the data analysis module considers the interaction between predators and prey and the influence of signal molecules on their respective growth, and then analyzes the impact of the microbial community on the ecological functions such as organic matter decomposition and nutrient cycling in the soil. Specifically, the microbial community is divided into the predator population number P and the prey population number B. For the prey population, the model formula is: Among them, r B is the intrinsic growth rate of the prey population, K B is the carrying capacity of the environment, β is the predation coefficient of the predator to the prey, D j is the concentration of the jth signal molecule related to predation or prey, γ Bj is the influence coefficient of the signal molecule on the growth of the prey population. For the predator population, the model formula is: Among them, α is the growth benefit coefficient obtained by the predator from preying on the prey, rP is the intrinsic growth rate of the predator population, K P is its environmental carrying capacity, E k is the concentration of the kth signal molecule related to the regulation of the growth of the predator itself by signal molecules, δ Pk is the influence coefficient. Through the simultaneous equations and combined with the actual observation data, the values ​​of each parameter are determined to simulate and analyze the dynamic changes in the structure and function of the microbial community under the interaction of predation and signal molecules over time, and then analyze the impact mechanism on the soil ecosystem and soil health.

[0021] Compared with the existing technology, the soil health and microbial analysis intelligent environmental monitoring device has the following beneficial effects:

[0022] 1. The present invention combines the signal molecule concentration change data with the predator-prey relationship visualization data through a data analysis module, and uses bioinformatics and mathematical modeling methods for in-depth analysis, which can fully reveal the hidden, multi-dimensional regulatory network within the microbial community, and accurately analyze the impact of these interactions on key ecological functions such as material circulation and energy flow in the soil ecosystem. Based on these analysis results, the device can generate a detailed soil health assessment report, which will help agricultural researchers, farmers and environmental monitoring departments to accurately assess the soil health status and take targeted soil management measures.

[0023] 2. The present invention integrates soil physical and chemical property sensors and microbial community interaction monitoring sensor groups, which can not only monitor physical and chemical indicators such as soil moisture, temperature, compactness, pH, and main nutrient content in real time, but also capture the concentration changes of signal molecules between soil microbial cells in real time through a highly sensitive biosensor array, and visualize the microbial predator-prey relationship through a microscopic observation chamber. This comprehensive and in-depth monitoring method provides scientific researchers with rich data support, enabling them to have a deeper understanding of the interactions between soil microbial communities and their impact on soil health.

[0024] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 Process operation diagram of intelligent environmental monitoring device for soil health and microbial analysis;

[0027] Figure 2 Flowchart of the smart environmental monitoring device for soil health and microbial analysis. DETAILED DESCRIPTION

[0028] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0029] Embodiment 1

[0030] Orchard planting involves a variety of fruit tree varieties, such as apple trees, pear trees, etc. Different fruit trees have specific requirements for soil conditions. Soil fertility level, air permeability, microbial environment, etc. will affect the growth and development of fruit trees, fruit quality and yield. Fruit farmers need to accurately understand the health of the orchard soil, so as to scientifically carry out management work such as fertilization, irrigation, pruning, and pest and disease control to ensure that the orchard produces high-quality fruit products and meet the market demand for high-quality fruits.

[0031] An orchard with an area of ​​50 mu was selected as the soil monitoring area, and the soil health and microbial analysis intelligent environmental monitoring device of the present invention was installed. According to factors such as the topography of the orchard, the spacing between fruit tree planting rows, and the layout of the irrigation system, a number of representative monitoring points (a total of 10 points) were selected, and a pit with a depth of about 60 cm was dug at each point. The device was vertically inserted into the soil so that the tip of the outer shell was firmly embedded in the soil, and then buried and fixed to ensure that each sensor could fully contact the soil.

[0032] The parameters of each module of the device are set through the supporting upper computer software. For the soil physical property sensor group, the humidity sensor is set to collect soil moisture data at different depths (probes are set at three depths of 10 cm, 30 cm, and 50 cm respectively) every 6 hours, and the temperature sensor also collects temperature data at each depth every 6 hours. The soil compaction sensor collects soil compaction data at different positions every 4 days (5 probes are evenly distributed around the device). In terms of the soil chemical property sensor group, the pH sensor and nutrient sensor are set to collect corresponding data every 7 days. For the microbial community interaction monitoring sensor group, the biosensor array collects the concentration change data of signal molecules between soil microbial cells in real time. The microscopic observation cabin is set to collect soil samples every 10 days for visual observation of microbial predation relationships. At the same time, the data transmission unit is configured with a Bluetooth module (to facilitate fruit farmers to use mobile devices to connect to view data at close range in the orchard) and a 5G communication module (to ensure rapid transmission of remote data to the cloud server), and the collected and preprocessed data is sent to the cloud server every 12 hours.

[0033] In the daily management of the orchard, the device continuously collects data according to the set parameters. During the budding period of fruit trees in spring, from early March to mid-April, the soil temperature at a depth of 10 cm gradually increases from 10°C to 18°C, the soil temperature at a depth of 30 cm increases from 8°C to 15°C, and the soil temperature at a depth of 50 cm increases from 7°C to 12°C. The temperature changes at different depths can assist fruit farmers in determining whether the environment for the growth of the fruit tree roots is suitable. Changes in water content can also guide reasonable irrigation to avoid drought or waterlogging that affects the growth of fruit trees. The soil compaction data is maintained between 1.2-1.8MPa (this range is suitable for the extension of the fruit tree roots). If the compaction changes abnormally due to farming operations and other reasons, fruit farmers can take measures such as tillage in time to improve soil permeability.

[0034] With the fertilization at different growth stages of fruit trees and the changes in the soil itself, the soil pH and nutrient content will fluctuate. For example, before the fruit trees bloom, the average pH value is 6.2. After the flowering period, due to the influence of fertilization, the pH value drops slightly to about 6.0. By comparing with the preset soil health threshold suitable for the growth of corresponding fruit trees (the suitable pH range is 5.5-7.5), fruit farmers can take timely improvement measures when the soil is too acidic or alkaline based on the data of the pH sensor. The consumption and surplus of nutrients such as nitrogen, phosphorus and potassium fed back by the nutrient sensor show that before the flowering period, the content of available nitrogen is about 80 mg, the content of available phosphorus is about 20 mg, and the content of available potassium is about 120 mg per kilogram of soil. After the flowering period, the content of available nitrogen is reduced to about 60 mg, the content of available phosphorus is reduced to about 15 mg, and the content of available potassium is reduced to about 100 mg. Fruit farmers can accurately grasp the timing and amount of fertilization based on these data to ensure that the fruit trees have sufficient and balanced nutrient supply and improve the quality and yield of the fruit.

[0035] The data analysis module deeply analyzed the data and found that before and after the flowering period of fruit trees, the signal molecules between the beneficial microorganisms in the soil that coexist with the fruit trees frequently communicated. Using the similarity coefficient formula The microbial communities in different regions (such as soil microbial communities corresponding to different fruit tree varieties, set as sets A and B) were similarly analyzed based on the signal molecule-related characteristics. For example, taking the soil microbial communities in two fruit tree planting areas as an example, it was found through analysis that there were 5 common specific signal molecule types (such as quorum sensing signal molecules acyl homoserine lactone substances, oligopeptide signal molecules, etc.) (i.e., |A∩B|=5), and the total number of all signal molecule types detected in each of the two regions was 10 (i.e., |A∪B|=10), so it was calculated that This coefficient value can intuitively measure the similarity of different microbial communities based on the signal molecule level, helping fruit farmers understand the differences in the influence of signal molecules on the soil microbial community structure in different fruit tree planting areas, and providing a basis for subsequent judgment on whether it is necessary to specifically adjust the microbial community to adapt to the growth of different fruit trees.

[0036] Using the correlation coefficient formula To measure the linear correlation between different microorganisms based on the changes in signal molecule concentrations, assume that the signal molecule concentration change sequences corresponding to two microorganisms closely related to nutrient absorption of fruit trees (referred to as microorganisms M1 and M2) are X = {x1, x2, …, x n} and Y={y1,y2,…, y n}(x i ,y i is the measured value in the sequence collected at different time points before and after the flowering period of the fruit tree, n is the number of measurements, here we assume n = 15 measurements), and the average value of the X sequence is obtained by statistical calculation The mean of the Y series By substituting each measured value and the average value for detailed calculation, it is assumed that r XY =0.8 (the value range of rXY is [-1, 1]), and a value close to 1 indicates a positive correlation, which means that the concentrations of signal molecules of microorganisms M1 and M2 change in the same direction, suggesting that there may be promotional interactions between the two microorganisms. For example, the signal molecules produced by one microorganism can activate certain physiological functions of another microorganism. This helps fruit farmers to clarify the interaction network relationship mediated by signal molecules within the microbial community, and then analyze its impact mechanism on soil ecological functions such as nutrient absorption of fruit trees.

[0037] Using the first-order ordinary differential equation model To describe the change of microbial population over time, take a microorganism that has an important impact on the growth of fruit trees (recorded as microorganism M3) as an example, Nm represents the population size of microorganism M3, t is the time (in days), and the intrinsic growth rate r of the microbial population is estimated through long-term monitoring and related experiments. m =0.05, environmental carrying capacity K m =1000, assuming that there are two key signal molecules (referred to as signal molecules S1 and S2) that affect its growth, the influence coefficient α of signal molecule S1 on the growth of microbial M3 population is determined by experimental data fitting and other methods. m1 =0.2, the influence coefficient of signal molecule S2 α m2 =0.1, corresponding to the average concentration C1 of signal molecule S1 monitored at different times before and after the flowering period is 5, and the average concentration C2 of signal molecule S2 is 3. By substituting these parameters into the equation, the change of the population of microorganism M3 over time can be simulated, and the changing trend of the microbial community structure can be predicted in advance. For example, during the flowering period, as the concentration of signal molecules changes, by solving the equation, it can be seen that the population of microorganism M3 shows an upward trend. Further analysis of the impact of this change on key functions of the soil ecosystem, such as material circulation and energy flow (for example, an increase in the number of microorganisms M3 may accelerate the decomposition and transformation of certain organic nutrients in the soil, which is more conducive to the absorption and utilization of fruit trees), can be achieved. Prospective assessment of changes in soil health status can be achieved.

[0038] After visual observation of the microbial predation relationship of the collected soil samples in the microscopic observation chamber, the total number of predation events n was counted by image analysis software, combined with the set total observation time T (each observation time is set to 2 hours, and observation is once every 10 days), and the predation frequency calculation formula was used to calculate the number of predation events. The predation frequency F is calculated. For example, in a certain observation, the total number of predation events n = 12 times is statistically obtained, so the predation frequency (Unit: times / hour), the predation frequency directly reflects the predation activity of predator microorganisms on prey microorganisms in the soil within a certain time range. Then, the mutual information entropy method is used to further analyze the correlation between predation behavior and changes in signal molecule concentration. First, the time series composed of predation behavior-related variables (such as predation frequency and other quantitative indicators) is calculated, set as X = {x1, x2, ..., x n})’s information entropy H(X), calculated as H(X)=-∑ i p(x i )log2p(x i )where p(x i ) is x i The probability of appearing in the sequence X is similarly calculated by calculating the concentration change sequence of the signal molecule (assuming that the concentration change sequence of a key signal molecule related to the above-mentioned predation behavior is Y = {y1, y2, ..., y n The information entropy H(Y) formula is: H(Y) = -∑j p(y j )log2p(y j ) and then calculate their joint information entropy H(X,Y), the formula is H(X,Y) = -∑ i ∑ j p(x i ,y j )log2p(x i ,y j )where p(x i ,y j ) is x i and j The joint probability of simultaneous occurrence is calculated. Finally, the mutual information I(X;Y) between the predation behavior and the change in signal molecule concentration is calculated. The formula is I(X;Y)=H(X)+H(Y)-H(X,Y). Assuming that H(X)=2.5, H(Y)=2.0, H(X,Y)=3.0 are obtained through actual data statistics and calculations, then I(X;Y)=2.5+2.0-3.0=1.5. The larger the value of mutual information I(X;Y), the stronger the correlation between the predation behavior and the change in signal molecule concentration, that is, the more common information they contain. Through this analysis method based on information theory, Compared with traditional correlation analysis, it can better capture complex correlations such as nonlinearity, which helps to more accurately analyze the complex mechanisms of coordination or restriction between predation behavior and interactions mediated by signal molecules, and provide strong data support for further clarifying the regulatory network within the entire microbial community and its impact on the function of the soil ecosystem. For example, when the mutual information value between the concentration change of a certain signal molecule and the predation frequency is found to be high, it means that the signal molecule may play an important role in regulating the microbial predator relationship. Fruit farmers can pay attention to the dynamic changes of related microbial communities in the soil based on this, and ensure the stability of the orchard soil ecology to facilitate the growth of fruit trees.

[0039] Assuming that the microbial community is divided into the predator population P (taking a certain predatory protozoa as an example), the prey population B (taking the bacteria preyed by the protozoa as an example), and related variables affected by signal molecules, the following set of equations is constructed to describe its dynamic change process for the prey population: Among them, r B is the intrinsic growth rate of the prey population (estimated through long-term monitoring and related experiments) B =0.1, K B is its environmental carrying capacity, K B =5000, β is the predation coefficient of the predator to the prey (which can be estimated from data such as predation frequency, assuming that β = 0.02 at present), D j is the concentration of the jth signal molecule related to predation or prey (assuming there are two related signal molecules, D Iis a certain quorum sensing signal molecule, with a concentration of 4, D2 is a certain oligopeptide signal molecule, with a concentration of 2, γ Bj is the effect coefficient of the signal molecule on the growth of the prey population. For the predator population: Among them, α is the growth benefit coefficient obtained by the predator from preying on the prey (assuming α = 0.3), r P is the endogenous growth rate of the predator population (r P =0.08, unit: 1 / day), K P is its environmental carrying capacity (K P =800), E k is the concentration of the kth signal molecule related to the regulation of the growth of the predator itself by the signal molecule (assuming that there is a related signal molecule with a concentration of 3, δ Pk is the corresponding influence coefficient (assuming δ P1 =0.1), and through the simultaneous equations and combining the actual observation data to determine the parameter values, it is possible to simulate how the microbial community changes in quantity, structure and function under the combined influence of the predator-prey relationship and the interaction of signal molecules in the actual soil environment. For example, when the concentration of a certain signal molecule changes due to external interference (such as soil compaction, pest and disease invasion, etc.), the model can predict the dynamic trend of the predator and prey populations, as well as the impact of the microbial community on the ecological functions of soil organic matter decomposition, nutrient cycling, etc., and ultimately achieve a more comprehensive and dynamic assessment of soil health, helping fruit farmers to formulate reasonable orchard soil management measures based on the simulation results, such as increasing the application of microbial fertilizers, adjusting the orchard ecological environment, etc., to maintain a good soil ecology in the orchard and ensure the healthy growth of fruit trees and the output of high-quality fruits.

[0040] Once the concentration of key signal molecules becomes abnormal (such as concentration fluctuations exceeding 50% of the normal range) or the predation frequency changes (such as the predation frequency suddenly increases or decreases outside the normal range) due to factors such as soil compaction, pest and disease invasion, the device will issue an alarm. After receiving the information pushed by the mobile phone APP, fruit farmers can take timely measures such as increasing the application of microbial fertilizers, adjusting the orchard ecological environment, etc. to regulate the soil microbial community and maintain a good soil ecology in the orchard.

[0041] Embodiment 2

[0042] As an important part of the urban ecosystem, urban green space plays a key role in improving the urban environment, regulating the climate, and beautifying the city. However, the soil in urban green space is often affected by human activities and environmental pollution, resulting in problems such as deterioration of the soil physical structure, imbalance of chemical properties, and reduction of microbial diversity. Urban garden management departments need to understand the health of green space soil in detail in order to formulate reasonable maintenance plans, including soil improvement, vegetation replanting, pest and disease control and other measures, to ensure the normal functioning of the ecological functions of urban green space and improve the quality of living environment for urban residents.

[0043] For different types of green areas in the city (such as park green spaces, street green spaces, etc., with a total area of ​​80 mu), multiple monitoring points are selected (12 points in total, taking into account factors such as green area, frequency of use, and degree of pollution), and the soil health and microbial analysis intelligent environmental monitoring device of the present invention is installed. At each selected monitoring point, the device is carefully inserted into the soil to a depth of about 40 cm, buried and fixed to ensure that the device is stable and the sensor can have good contact with the soil. At the same time, protective measures are taken to prevent interference from surrounding pedestrian activities, municipal construction and other factors.

[0044] The host computer software is used to set the parameters of each module of the device. For the soil physical property sensor group, the humidity sensor and temperature sensor are set to collect data every 4 hours, the soil compaction sensor collects data every 5 days, and in the soil chemical property sensor group, the pH sensor collects data every 3 days to timely grasp the changes in soil acidity and alkalinity. The nutrient sensor collects data every 10 days to understand the soil fertility status. In the microbial community interaction monitoring sensor group, the biosensor array captures the changes in the concentration of signal molecules between soil microbial cells in real time. The microscopic observation cabin is set to collect soil samples every 15 days for visual observation of microbial predation relationships. The data transmission unit is configured to send data to the cloud server every 8 hours through the 4G communication module, which is convenient for urban garden management departments to remotely view and analyze data.

[0045] During the daily maintenance of urban green spaces, the device continuously collects data, providing comprehensive and valuable information for green space soil management. The data from the soil physical property sensor group shows that in park green spaces with large traffic, the soil compactness is relatively high, with an average compactness of about 2.0MPa (the normal range suitable for vegetation growth is 1.0-1.5MPa). The water content fluctuates greatly due to rainfall and irrigation. In a rainless week in summer, the soil water content gradually decreases from 30% to about 15%. The temperature change is also closely related to the surrounding environment. The surface temperature can be as high as 35°C on sunny days in summer, and the soil temperature at a depth of 10 cm can reach about 30°C. Based on these data, the garden management department can reasonably arrange soil loosening operations and adjust the irrigation frequency to ensure that the green space soil maintains suitable physical properties and is conducive to vegetation growth. For example, during the hot and dry periods in summer, the number of irrigation times can be increased in time according to the water content data to avoid vegetation from being damaged. Withering due to lack of water. The soil chemical property sensor group found that some street green spaces are close to traffic roads and are affected by automobile exhaust emissions, so the soil pH is acidic, with an average pH value of about 5.0 (the appropriate pH value range of normal urban green space soil is 6.5-7.5), and there is a risk of heavy metal pollution. The soil fertility in some areas of the park green space is insufficient, with only about 30 mg of available nitrogen, about 8 mg of available phosphorus, and about 60 mg of available potassium per kilogram of soil (generally healthy urban green space soil has a fast-acting nitrogen content of 60-100 mg, a fast-acting phosphorus content of 15-30 mg, and a fast-acting potassium content of 80-120 mg). According to the data from pH sensors and nutrient sensors, the management department has taken targeted measures such as spreading lime to neutralize acidity, adding organic fertilizers to improve fertility, and planting plants that absorb heavy metals, so as to gradually improve the chemical properties of the soil, ensure the healthy growth of green vegetation, and maintain the ecological functions of green spaces.

[0046] The data analysis module receives data from sensors, conducts similarity analysis on different microbial communities based on signal molecule-related characteristics, compares and analyzes the differences in the structure of microbial communities under different environmental conditions in the soil affected by signal molecules, and uses correlation analysis methods to measure the linear correlation between different microorganisms based on changes in signal molecule concentrations. It determines whether there is a linear correlation between two microorganisms at the signal molecule level and the strength and direction of the correlation, and explores potential interactions between microorganisms. At the same time, a dynamic model is used to describe the changes in the number of microbial populations over time, and the evolution process of different microbial populations in the soil is simulated. The information theory-related methods are used to analyze the correlation between predation behavior and changes in signal molecule concentrations to measure the degree of correlation between the two. Based on the constructed set of equations for the predator population, the prey population, and related variables affected by signal molecules, by considering the interaction between predators and prey and the signal The data are analyzed based on the influence of various factors on the growth of each of them, and then the influence of the microbial community caused by this on the ecological functions such as the decomposition of organic matter and nutrient circulation in the soil is analyzed, so as to achieve a more comprehensive and dynamic assessment of the health of the soil. After the data are analyzed, the changes of the soil microbial community in urban green spaces after human interference are presented. For example, in areas that are frequently trampled, the structure of the microbial community is relatively simple, the signal molecule exchange between microorganisms is weak, and the concentration of some key oligopeptide signal molecules is about 40% lower than that in normal undisturbed areas. The predator-prey relationship is unstable, and the predation frequency is reduced by about 0.1-0.2 times per hour compared with normal conditions. When changes in the concentration of key signal molecules or abnormal predation frequencies are found through monitoring, the garden management department can take timely measures such as restricting the entry of personnel, replanting vegetation, adding microbial agents, etc. with the help of the alarm prompts of the data analysis module to repair the soil microbial community and improve the ecological health level of urban green space soil.

[0047] By applying the monitoring device of the present invention to the soil monitoring of urban green spaces, urban garden management departments can comprehensively and dynamically grasp the soil health status and microbial community changes in each green space area, timely optimize maintenance strategies based on accurate data, effectively improve the management efficiency and ecological quality of urban green spaces, and play a positive role in improving the urban ecological environment and improving the quality of life of urban residents, highlighting the important application value of the device in the field of urban green space management.

[0048] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. Soil health and microbial analysis intelligent environmental monitoring device, characterized in that: The device includes the following components: an outer shell, a sensor assembly, a sensor group for monitoring microbial community interactions, a data acquisition and transmission module, a power module, a microprocessor and a control unit, and a data analysis module; The outer shell is made of a material that is corrosion-resistant, strong and has good sealing properties. The bottom of the outer shell is provided with a pointed end that is easy to insert into the soil, so that the device can be firmly buried in the soil area to be monitored. The surface of the outer shell is provided with heat dissipation holes to ensure the heat dissipation of the internal electronic components. The sensor assembly includes a soil physical property sensor group and a soil chemical property sensor group, wherein the soil physical property sensor group includes a humidity sensor, a temperature sensor and a soil compaction sensor, each sensor is arranged at a different detection position of the device, and can be extended to different soil depths to carry out multi-point measurement work, and the soil chemical property sensor assembly is equipped with a pH sensor and a nutrient sensor, which are in contact with the soil through a protective structure; The microbial community interaction monitoring sensor group includes a biosensor array, a microscopic observation chamber and supporting components, wherein the biosensor array uses biosensor technology to capture subtle changes in the concentration of signal molecules in the soil environment and transmit relevant data to the data analysis module in real time. The microscopic observation chamber and supporting components simulate the native soil environment, collect soil samples at preset time intervals, and use a fluorescent microscopic imaging system and image analysis software to record the predation process, predation frequency and dynamic changes between predatory microorganisms and prey microorganisms under different soil health conditions, and can transmit the obtained visual data to the data analysis module in real time; The data acquisition and transmission module is connected to each sensor component and is responsible for collecting, aggregating and preprocessing various types of data obtained from different sensors, and sending the processed data to the cloud server or local monitoring terminal through wired or wireless communication; The power module is equipped with a rechargeable lithium battery pack as the main power source, and is combined with a solar charging panel and a power management circuit to achieve reasonable power supply to different modules and power monitoring and early warning functions, and can remind charging or battery replacement when the power is lower than the set threshold; The microprocessor and control unit control the sampling frequency, microbial sample collection time and data transmission interval of the sensors in each sensor assembly according to preset parameters, and perform preliminary calculation, analysis and judgment on the data collected by the sensors, and compare the data with the preset soil health standard threshold range. Once abnormal data appears, an alarm mechanism is triggered; The data analysis module receives data from the sensor, performs similarity analysis on different microbial communities based on signal molecule related characteristics, compares and analyzes the differences in the microbial community structure under different environmental conditions in the soil affected by signal molecules, and uses correlation analysis methods to measure the linear correlation between different microorganisms based on changes in signal molecule concentrations, determines whether there is a linear correlation between two microorganisms at the signal molecule level and the strength and direction of the correlation, and explores potential interactions between microorganisms. At the same time, a dynamic model is used to describe the changes in the number of microbial populations over time, simulates the evolution process of different microbial populations in the soil, and uses information theory related methods to analyze the correlation between predation behavior and changes in signal molecule concentrations to measure the degree of correlation between the two. Based on the constructed equation group about the predator population number, the prey population number and the related variables affected by the signal molecules, by considering the interaction between predators and prey and the influence of signal molecules on their respective growth, the influence of the microbial community caused by this on the decomposition of organic matter and nutrient cycling functions in the soil is analyzed, thereby achieving a comprehensive dynamic assessment of the health status of the soil.

2. The soil health and microbial analysis intelligent environmental monitoring device according to claim 1, characterized in that: In the sensor assembly, the humidity sensor adopts a high-precision capacitive humidity sensor, the temperature sensor adopts a thermistor temperature sensor, and the soil compaction sensor uses a pressure sensitive element. When the soil exerts pressure on the sensor probe, the pressure sensitive element deforms, causing its resistance or capacitance parameters to change, and the pressure signal is converted into an electrical signal output to reflect the compaction degree of the soil. The pH sensor adopts a glass electrode pH sensor, and the nutrient sensor adopts an ion selective electrode sensor to detect the nitrogen, phosphorus and potassium nutrient content in the soil.

3. The soil health and microbial analysis intelligent environmental monitoring device according to claim 1, characterized in that: In the power module, the lithium battery pack is composed of multiple lithium battery cells connected in series and in parallel, and is equipped with a battery protection circuit to prevent the battery from overcharging, over-discharging and short circuit. The solar charging panel adopts a crystalline silicon solar panel or a thin-film solar panel, which is installed in a position on the top of the outer shell where there is sufficient light and it is easy to fix. It is connected to the lithium battery pack through a charging controller, and can automatically adjust the charging current and voltage according to the power status of the lithium battery pack and its own power generation. The power management circuit monitors the power status of the lithium battery pack in real time, obtains the battery power information through the power detection circuit, triggers an alarm prompt signal when the power is lower than a preset threshold, and dynamically adjusts the power supply current and voltage according to the working status of each module to distribute the power supply power to each module.

4. The soil health and microbial analysis intelligent environmental monitoring device according to claim 1, characterized in that: The data analysis module performs similarity analysis on different microbial communities based on the signal molecule related characteristics, and the analysis formula is: Among them, A and B are the sets corresponding to two different microbial communities, |A∩B| represents the number of elements in the intersection of the two sets, that is, the number of common signal molecule features, and |A∪B| represents the number of elements in the union of the two sets, that is, the sum of the number of signal molecule features of the two communities.

5. The soil health and microbial analysis intelligent environmental monitoring device according to claim 1, characterized in that: The data analysis module uses a correlation analysis method to measure the linear correlation between different microorganisms based on the changes in signal molecule concentrations. Specifically, the signal molecule concentration change sequences corresponding to two different microorganisms are X={x1, x2, ..., x n } and Y = {y1, y2, ..., y n }, x i ,y i are the measured values ​​in the sequence, n is the number of measurements, and their average values ​​are and The correlation coefficient calculation formula is: Among them, r XY The value range of is [-1, 1]. A value close to 1 indicates a positive correlation, a value close to -1 indicates a negative correlation, and a value close to 0 indicates a weak correlation.

6. The soil health and microbial analysis intelligent environmental monitoring device according to claim 1, characterized in that: The data analysis module uses a dynamic model to describe the change of microbial population over time, and its model parameters are: Among them, N m represents the number of the mth microbial population, t is the time, r m is the intrinsic growth rate of the microbial population, K m is the environmental carrying capacity, α mi is the influence coefficient of the i-th signal molecule on the growth of the m-th microbial population, C i is the concentration of the ith signal molecule.

7. The soil health and microbial analysis intelligent environmental monitoring device according to claim 1, characterized in that: The data analysis module uses information theory related methods to analyze the correlation between predation behavior and changes in signal molecule concentration to measure the degree of association between the two. Specifically, the predation behavior related variables X = {x1, x2, ..., x n } information entropy H(X), that is, H(X) = -Σ i p(x i )log2p(x i ), where p(x i ) is x i The probability of appearing in the sequence X is calculated by the same method. The signal molecule concentration change sequence Y = {y1, y2, ..., y n } information entropy H(Y), that is, H(Y) = -∑ j p(y j )log2p(y j ), calculate its joint information entropy H(X, Y), that is, H(X, Y) = -∑ i ∑ j p(x i ,y j )log2p(x i ,y j ), where p(x i ,y j ) is x i and j The joint probability of simultaneous occurrence is used to calculate the mutual information I(X;Y) between the predation behavior and the change in signal molecule concentration, that is, I(X;Y)=H(X)+H(Y)-H(X,Y). The larger the value of the mutual information I(X;Y), the stronger the correlation between the predation behavior and the change in signal molecule concentration.

8. The soil health and microbial analysis intelligent environmental monitoring device according to claim 1, characterized in that: The data analysis module considers the interaction between predators and prey and the influence of signal molecules on their respective growth, and then analyzes the influence of the microbial community on the decomposition of organic matter and nutrient cycle in the soil. Specifically, the microbial community is divided into the predator population number P and the prey population number B. For the prey population, the model formula is: Among them, r B is the intrinsic growth rate of the prey population, K B is the carrying capacity of the environment, β is the predation coefficient of the predator to the prey, D j is the concentration of the jth signal molecule related to predation or prey, γ Bj is the influence coefficient of the signal molecule on the growth of the prey population. For the predator population, the model formula is: Among them, α is the growth benefit coefficient obtained by the predator from preying on the prey, r P is the intrinsic growth rate of the predator population, K P is its environmental carrying capacity, E k is the concentration of the kth signal molecule related to the regulation of the growth of the predator itself by signal molecules, δ Pk is the influence coefficient. Through the simultaneous equations and combined with the actual observation data, the values ​​of each parameter are determined to simulate and analyze the dynamic changes in the structure and function of the microbial community under the interaction of predation and signal molecules over time, and then analyze the impact mechanism on the soil ecosystem and soil health.

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