Soil and microorganism comprehensive monitoring system for controlled environment
By integrating high-precision sensors and advanced data analysis technology, the time-consuming and data accuracy problems of traditional soil and microbial monitoring are solved, real-time, accurate monitoring and in-depth analysis of soil and microbial status are achieved, scientific decision-making is supported, and agricultural production and ecological environment improvement is promoted.
Patent Information
- Application Number
- CN202510604763.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-12
- Publication Date
- 2025-08-22
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional soil and microbial monitoring methods are time-consuming and labor-intensive, difficult to achieve comprehensive and continuous monitoring, and cannot promptly reflect dynamic changes, affecting data accuracy and management effect, unable to deeply analyze microbial community structure and its functional changes, and lack of assessment of complex environmental factors.
It adopts integrated multiple high-precision sensors, combined with adaptive data acquisition frequency adjustment algorithm and wavelet transform denoising algorithm, in-depth analysis is performed through the data analysis and processing module, and uses real-time quantitative PCR, PCR-DGGE and BIOLOG-ECO technologies to provide scientific decision-making support.
Real-time and accurate collection of soil and environmental information has been achieved, data collection efficiency and accuracy have been improved, scientific decision-making basis is provided, potential problems are discovered in a timely manner, and sustainable development of agricultural production and ecological environment are promoted.
Smart Images
Figure CN120525338A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a soil and microorganism integrated monitoring system for a controlled environment. Background Art
[0002] In agricultural production and ecological environment management, the status of soil and microorganisms has a vital impact on crop growth, ecological balance and soil fertility. Especially in controlled environments such as saline-alkali land improvement and straw return projects, real-time and accurate understanding of soil physical and chemical properties, microbial community structure and its functional changes is the key to optimizing management measures, improving production efficiency and protecting the ecological environment.
[0003] Traditional technologies have shortcomings. On the one hand, traditional monitoring methods usually rely on manual sampling and laboratory analysis, which is not only time-consuming and labor-intensive, but also difficult to achieve comprehensive and continuous monitoring of soil and microbial status. In addition, manual sampling may introduce errors and affect the accuracy of the data. On the other hand, due to the cumbersome data collection and analysis process, traditional methods are often unable to reflect the dynamic changes in soil and microbial status in a timely manner, resulting in delayed management measures, affecting improvement effects and production efficiency. At the same time, traditional analysis methods are often limited to simple physical and chemical indicator measurements, making it difficult to deeply analyze the structure of microbial communities and their functional changes, and unable to provide sufficient basis for scientific decision-making. There is also a lack of effective evaluation methods for the comprehensive impact of complex environmental factors.
[0004] In view of the above shortcomings of traditional soil and microorganism monitoring technologies, the present invention proposes a comprehensive soil and microorganism monitoring system for a controlled environment, which is particularly important. Summary of the Invention
[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a comprehensive soil and microbial monitoring system for controlled environments. It can realize real-time and accurate collection of soil and environmental information by integrating multiple high-precision sensors. It uses an adaptive data acquisition frequency adjustment algorithm and a wavelet transform-based denoising algorithm to improve the efficiency and accuracy of data acquisition. Through the data analysis and processing module, it uses advanced data algorithms and models to conduct in-depth analysis of the collected data, providing strong support for scientific decision-making.
[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a soil and microorganism integrated monitoring system for a controlled environment, the system comprising the following components: a sensor module, a data acquisition and transmission module, a data analysis and processing module, and a user terminal;
[0007] The sensor module integrates multiple high-precision sensors, including soil physical and chemical property sensors, microbial sensors, and environmental sensors, to collect soil and environmental information in real time. In saline-alkali land monitoring scenarios, the soil salinity sensor can accurately measure soil salt content, providing key data for evaluating saline-alkali land improvement progress. The microbial sensor can monitor specific microorganisms involved in straw degradation and saline-alkali land improvement, and obtain information on changes in their number and activity.
[0008] The data acquisition and transmission module is responsible for collecting data acquired by the sensor and transmitting the data to the data analysis and processing module through wireless transmission technology. The module also has a data pre-processing function, which can filter and remove noise from the original data to improve data quality. During the transmission process, encryption technology is used to ensure data security and prevent data leakage;
[0009] The data analysis and processing module uses data algorithms and models to conduct in-depth analysis of the collected data, combines relevant research results and theories on saline-alkali land improvement and straw return to the field, predicts the trend of changes in soil physical and chemical properties, analyzes the succession rules of microbial community structure and changes in carbon conversion function, uses real-time quantitative PCR and PCR-DGGE modern molecular biology techniques to deeply analyze the dynamic changes in microbial community structure, accurately determine the types and quantity changes of dominant bacteria, uses BIOLOG-ECO technology to comprehensively evaluate the functional diversity of microbial carbon source substrate utilization, accurately grasps the changes in microbial metabolic function after straw return to the field, uses machine learning algorithms to predict the effect of saline-alkali land improvement based on soil salinity, pH and microbial data, and reveals the impact of straw return on microbial metabolic function through analysis of microbial carbon source utilization data at different periods;
[0010] The user terminal is presented in the form of a web page or mobile phone application. Users can view monitoring data, analysis reports and early warning information in real time through the terminal. The system also provides interactive functions. Users can adjust the controlled environmental parameters according to the monitoring results and add appropriate amounts of organic acids to adjust the pH value of coastal saline-alkali land, so that the environmental pH is more conducive to the full activity of cellulose-degrading microorganisms, further improve the degradation efficiency of returned straw, improve the physical and chemical properties of saline-alkali land, increase the organic matter content of saline-alkali land, and thus improve the fertility of saline-alkali land.
[0011] Furthermore, the sensor module includes multiple types of sensors. The soil physical and chemical property sensor uses ion selective electrode technology and spectral analysis technology. The soil salt sensor selectively detects salt ions in the soil based on a specific ion exchange membrane with an accuracy of ±0.01mS / cm. The pH sensor uses the glass electrode method to accurately measure the soil pH value with an error control of ±0.05pH units. The microbial sensor uses immunoassay and bioelectrochemical principles to target microorganisms, binds to microbial surface antigens by labeling specific antibodies, and then uses electrochemical signal conversion to detect their quantity and Among the active environmental sensors, the temperature and humidity sensors use capacitive and thermistor technology, which can monitor the ambient temperature and humidity in real time. The temperature measurement range is -20℃-80℃ with an accuracy of ±0.5℃ and the humidity measurement accuracy is ±3%RH. The light sensor uses a photodiode to accurately measure light intensity with a measurement range of 0-2000μmol·m-2·s-1. These sensors are distributed in different locations according to the characteristics of the controlled environment and monitoring needs. In saline-alkali land monitoring, sensors are arranged at different depths of soil and close to plant root areas to ensure comprehensive and accurate data collection.
[0012] Furthermore, the data acquisition and transmission module adopts a unique adaptive data acquisition frequency adjustment algorithm, which dynamically adjusts the acquisition frequency according to the rate of change of sensor data. The formula is: where f n is the current acquisition frequency, f n+1 is the frequency of the next acquisition, k is the frequency adjustment coefficient, which is determined according to different sensor types and controlled environments, and the value range is 0.1-0.5. For example, in saline-alkali land where the salinity of the soil changes rapidly, k is 0.3, ΔS n is the difference between the current collected data and the last collected data, S max This is the maximum value of the sensor's measurement range. In terms of data preprocessing, a denoising algorithm based on wavelet transform is used to remove noise signals by performing multi-scale decomposition on the original signal, retaining valid data features, improving data accuracy and stability, and reducing data transmission volume and errors in subsequent analysis.
[0013] Furthermore, the microbial community structure analysis algorithm used by the data analysis and processing module is a community similarity analysis algorithm based on feature vectors. First, the microbial community data is standardized, and the formula is: where X ij is the number of the jth microbial species in the i-th sample, is the average value of the jth microbial species in all samples, S j is the standard deviation of the jth microbial species in all samples, and then the similarity matrix between samples is calculated using the formula: Among them Simij is the similarity index between sample i and sample j, w k is the weight of the kth microbial species, which is determined based on the functional importance of microorganisms in saline-alkali land improvement or straw return. In the process of promoting soil carbon conversion by straw return, microorganisms related to cellulose degradation have a higher weight. The weight range is determined through expert evaluation and experimental data statistics. and are the standardized numbers of the kth microbial species in the ith sample and the jth sample, respectively, and n is the total number of microbial species. Through this algorithm, the similarities and differences in the microbial community structures between different samples can be accurately analyzed, providing strong support for the study of the laws of microbial community succession.
[0014] Furthermore, the soil physical and chemical property prediction algorithm used by the data analysis and processing module is a prediction algorithm based on weighted grey correlation analysis. First, a reference sequence and a comparison sequence are determined. The reference sequence is the soil physical and chemical property index to be predicted, and the comparison sequence is the relevant factors affecting the index. Then, the grey correlation coefficient is calculated. The formula is: where ξ i (k) is the grey correlation coefficient between the ith comparison sequence and the reference sequence at the kth moment, X0(k) is the value of the reference sequence at the kth moment, X i (k) is the value of the i-th comparison sequence at the k-th moment, ρ is the resolution coefficient, which ranges from 0 to 1 and is usually 0.5. Then calculate the grey relational degree, the formula is: where r i is the grey relational degree between the ith comparison sequence and the reference sequence, w k is the weight at the kth moment, which is determined according to the degree of influence of data at different moments on the prediction results, and is obtained through analysis of historical data and actual monitoring conditions. m is the number of time points of the data. Finally, the influencing factors are ranked according to the grey correlation degree, and a prediction model is constructed to predict the changing trend of soil physical and chemical properties, providing a scientific basis for saline-alkali land improvement and soil fertility management.
[0015] Furthermore, the user terminal adopts an intelligent interactive interface design, and the interface is divided into a data display area, an analysis report area and a control operation area. The data display area presents the monitoring data in the form of intuitive charts and real-time data tables. Users can select different time periods and monitoring indicators for viewing according to their needs. The analysis report area provides detailed data analysis results, including assessment of soil and microbial conditions, change trend analysis and comparison with historical data. The control operation area is equipped with various control buttons and parameter input boxes. In the saline-alkali land improvement scenario, users can input the amount of organic acid added in the control operation area according to the soil pH monitoring data. The system automatically calculates and sends control instructions to related equipment to achieve precise adjustment of soil pH value, promote straw degradation and microbial activity, and enhance the saline-alkali land improvement effect. At the same time, the user terminal supports simultaneous access to multiple devices, which is convenient for different personnel to view and operate data in different locations.
[0016] Furthermore, the system also has an intelligent early warning function. The algorithm for setting the early warning threshold is a dynamic threshold setting algorithm based on risk assessment, and the formula is: Where T ij is the warning threshold of the i-th monitoring indicator in the j-th monitoring area, T 0ij is the initial threshold, determined according to relevant industry standards and historical experience. α is the risk adjustment coefficient, determined according to the risk level of the controlled environment. The risk level is determined by evaluating multiple factors such as soil quality, crop growth conditions, and ecosystem stability. The value range is 0.5-2. In high-risk areas, α is 1.5, and σ ij is the standard deviation of the i-th monitoring indicator in the j-th monitoring area, μ ij is the average value of the i-th monitoring indicator in the j-th monitoring area. When the monitoring data exceeds the warning threshold, the system will send a warning message to the user through SMS or pop-up window to remind the user to take timely measures. During the process of returning straw to the field, if the soil microbial community structure changes abnormally, the returning strategy should be adjusted in time to ensure the stability of agricultural production and the ecological environment.
[0017] Furthermore, the system adopts a modular design concept. The sensor module, data acquisition and transmission module, data analysis and processing module and user terminal are all independent functional modules. The modules communicate and exchange data through standardized interfaces, which facilitates the expansion and upgrade of the system. When new monitoring indicators need to be added, it is only necessary to add the corresponding sensors to the sensor module and make appropriate adjustments to the algorithm of the data analysis and processing module. There is no need to make large-scale changes to the entire system. At the same time, the modular design improves the reliability and maintainability of the system. When a module fails, the module can be quickly replaced or repaired, reducing system downtime, reducing maintenance costs, ensuring long-term stable operation of the system, and meeting the ever-changing monitoring needs under different controlled environments.
[0018] Compared with existing technologies, this soil and microbial integrated monitoring system for controlled environments has the following benefits:
[0019] 1. The system uses advanced data algorithms and models through the data analysis and processing module to conduct in-depth analysis of the collected data. Combined with the relevant research results on saline-alkali land improvement and straw return to the field, it predicts the changing trends of soil physical and chemical properties, analyzes the evolution of microbial community structure and changes in carbon conversion function, and through modern molecular biology technology and BIOLOG-ECO technology, the system can comprehensively evaluate the functional diversity of microbial carbon source substrate utilization and accurately grasp the changes in microbial metabolic function after straw return to the field. In addition, the system also has an intelligent early warning function. The dynamic threshold setting algorithm based on risk assessment can timely detect potential problems and send early warning information to users through user terminals, providing strong support for scientific decision-making and promoting the sustainable development of agricultural production.
[0020] 2. The system integrates a variety of high-precision sensors to achieve real-time and accurate collection of soil and environmental information. The data acquisition and transmission module adopts an adaptive data acquisition frequency adjustment algorithm to dynamically adjust the acquisition frequency according to the data change rate, which not only ensures the timeliness of the data, but also effectively reduces data redundancy and transmission burden. At the same time, the denoising algorithm based on wavelet transform ensures data quality, provides a reliable foundation for subsequent data analysis, and significantly improves monitoring efficiency and data accuracy.
[0021] Other advantages, objects and features of the present invention will be described 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 learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.
[0023] Figure 1 Provide a flow chart for the functional implementation of a soil and microbial integrated monitoring system for controlled environments;
[0024] Figure 2 The flowchart of the overall architecture of the soil and microorganism integrated monitoring system for controlled environments. DETAILED DESCRIPTION
[0025] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.
[0026] Example 1
[0027] This example describes how the soil and microbial integrated monitoring system played a key role in a coastal saline-alkali land improvement project, fully assisting in the saline-alkali land improvement work. The specific implementation process is as follows:
[0028] In order to achieve all-round monitoring of saline-alkali soil and environment, the sensor modules have been carefully arranged. A variety of soil physical and chemical property sensors are evenly distributed in soil layers at different depths. Among them, the soil salt sensor based on ion selective electrode technology can accurately sense the concentration changes of salt ions in the soil. Its working principle is to use a specific ion exchange membrane to selectively adsorb salt ions, and then measure the ion concentration through changes in electrode potential; the pH sensor using spectral analysis technology can monitor soil pH in real time and accurately. This technology detects the absorption or emission characteristics of light by soil samples under a specific spectrum. To determine the pH value, the microbial sensor uses immunoassay and bioelectrochemical principles to target microorganisms with special functions in saline-alkali land, such as salt- and alkali-tolerant cellulose-degrading bacteria. By labeling specific antibodies and binding to microbial surface antigens, the binding reaction is converted into a detectable electrochemical signal to accurately detect the number and activity of microorganisms. In terms of environmental sensors, the temperature and humidity sensor uses capacitive and thermistor technology to monitor changes in temperature and humidity of the soil and atmosphere in real time; the light sensor uses a photodiode, which can sensitively capture fluctuations in light intensity, providing data support for analyzing the impact of light on saline-alkali soil and microorganisms.
[0029] The data acquisition and transmission module adopts an adaptive data acquisition frequency adjustment algorithm (formula: ), dynamically adjust the collection frequency according to the change rate of sensor data. In the early stage of saline-alkali land improvement, due to the implementation of various improvement measures, data such as soil salinity and pH change dramatically. For example, within one week after the application of the amendment, the soil salinity data change rate is large. At this time, the collection frequency will automatically increase to obtain more intensive data, providing a basis for timely adjustment of the improvement strategy. As the improvement work progresses, the soil condition gradually stabilizes, the data change rate decreases, and the collection frequency decreases accordingly, avoiding unnecessary data redundancy. The collected data is sent to the data analysis and processing module in an encrypted manner through wireless transmission technology to ensure the security and integrity of the data during transmission. At the same time, in the data preprocessing link, the denoising algorithm based on wavelet transform decomposes the original signal at multiple scales, effectively removes the noise signal, and improves the data quality.
[0030] The data analysis and processing module uses a variety of advanced technologies and algorithms to conduct in-depth analysis of the collected data, and the prediction algorithm based on weighted grey correlation analysis (determine the reference sequence and comparison sequence, calculate the grey correlation coefficient formula: Formula for calculating grey relational degree: By determining the reference sequence (such as the soil pH index to be predicted) and the comparison sequence (such as the salt content affecting pH, the amount of amendment added, and other related factors), the grey correlation coefficient and correlation degree are calculated, so as to accurately predict the changing trends of soil physical and chemical properties. Combined with the relevant research results of straw return to the field, real-time quantitative PCR and PCR-DGGE modern molecular biology techniques are used to deeply analyze the dynamic changes in the structure of microbial communities, and to understand in detail the growth and decline patterns of different microbial populations in the process of straw return to the field. Using BIOLOG-ECO technology, the functional diversity of microbial carbon source substrate utilization is comprehensively evaluated, and the changes in microbial metabolic functions after straw return to the field are accurately grasped, providing a scientific basis for optimizing straw return strategies. In addition, machine learning algorithms are used to predict the improvement effect of saline-alkali land based on soil salinity, pH and microbial data, providing strong support for the adjustment of subsequent improvement work.
[0031] The user terminal is presented in the form of a mobile phone application, providing users with a convenient and intuitive operation experience. The interface is clearly divided into a data display area, an analysis report area, and a control operation area. In the data display area, users can use charts (such as line charts, bar charts) and real-time data tables to intuitively view key monitoring data such as soil salinity, pH, and microbial counts in different time periods. By selecting different time ranges and monitoring indicators, users can quickly obtain the required information, such as viewing the changing trend of soil salinity in the past month. The analysis report area provides detailed data analysis results, including a comprehensive assessment of soil and microbial conditions, analysis of changing trends, and comparison with historical data. For example, in the report It will point out the significant changes in the current soil microbial community structure compared to before improvement, as well as the impact of these changes on the soil ecosystem. In the control operation area, the user enters the amount of organic acid to be added based on the soil pH monitoring data. The system automatically calculates and sends control instructions to related equipment to achieve precise adjustment of the soil pH value. When the system detects that the soil pH value is too high, which is not conducive to the activity of cellulose-degrading microorganisms, the user can enter the appropriate amount of organic acid to be added in the control operation area. The system will quickly convey the instructions to the irrigation equipment or fertilization equipment to ensure that the organic acid is evenly applied to the soil. The system also has an intelligent early warning function and adopts a dynamic threshold setting algorithm based on risk assessment (Formula: ) Set the warning threshold. When the soil salinity exceeds the warning threshold, the system will immediately send a warning message to the user via SMS and pop-up window, reminding the user to take timely measures, such as increasing the number of leaching times or adjusting the formula of the improver, to ensure the smooth progress of saline-alkali land improvement work.
[0032] Example 2
[0033] This example describes a large greenhouse vegetable planting base. To ensure that vegetables grow in a suitable soil environment and achieve high yield and quality, the soil and microbial integrated monitoring system is widely used. The specific implementation is as follows:
[0034] In different areas of the greenhouse, various sensors are reasonably installed according to the layout and growth needs of vegetable planting. Soil physical and chemical property sensors are responsible for monitoring soil nutrient status. For example, sensors using ion-selective electrode technology and spectral analysis technology can accurately detect the content of large elements such as nitrogen, phosphorus, potassium, and trace elements such as iron and zinc in the soil. These sensors collect data in real time, providing an important basis for judging soil fertility and formulating fertilization plans. At the same time, pH sensors and conductivity sensors closely monitor changes in soil pH and conductivity to ensure that the soil environment does not have an adverse effect on vegetable growth. Microbial sensors focus on detecting and controlling plant growth. The number and activity of relevant beneficial and harmful microorganisms, for example, rhizobia that promote plant root growth and pathogens that may cause diseases, can be monitored in a timely manner through specific detection technologies. Environmental sensors comprehensively monitor environmental parameters in the greenhouse. Temperature and humidity sensors use capacitive and thermistor technologies to accurately measure the temperature and humidity in the greenhouse; light sensors use photodiodes to monitor light intensity in real time; carbon dioxide sensors use specific gas detection technology to monitor carbon dioxide concentrations in the greenhouse. Monitoring these environmental parameters is crucial for regulating the greenhouse environment and creating optimal conditions for vegetable growth.
[0035] The data acquisition and transmission module flexibly adjusts the collection frequency according to the changes in sensor data. During the vigorous growth period of vegetables, soil nutrients, microbial communities and environmental parameters change frequently. For example, during the flowering and fruiting period of tomatoes, nitrogen, phosphorus and other nutrients in the soil are absorbed in large quantities, resulting in rapid changes in nutrient content. At this time, the data acquisition and transmission module will increase the collection frequency to ensure timely acquisition of accurate data. In the later stage of vegetable growth, the data changes relatively slowly, and the collection frequency is reduced accordingly. The collected data is quickly transmitted to the data analysis and processing module through a stable wireless transmission network. During the transmission process, advanced encryption technology is used to ensure data security and prevent data leakage or tampering. At the same time, the collected raw data is preprocessed to remove noise and outliers to improve data reliability and availability.
[0036] The data analysis and processing module conducts in-depth analysis and mining of the collected data. Through long-term monitoring and analysis of soil nutrient data, combined with the fertilizer requirements of vegetables at different growth stages, it predicts the changing trend of soil nutrients. For example, based on the current nitrogen content in the soil and the growth progress of tomatoes, it predicts the nitrogen consumption in the next week, providing a scientific basis for precise fertilization. It uses professional microbial analysis technology to analyze the changes in the structure of microbial communities. By comparing the microbial community data of different planting areas and different growth stages, it judges the health status of the soil microbial ecosystem. For example, if the number of beneficial microorganisms in a certain area decreases and the number of harmful microorganisms increases, it promptly reminds growers to take corresponding measures, such as adding microbial agents to improve the soil microbial environment. Combined with environmental parameters and soil data, it comprehensively evaluates the impact of the greenhouse environment on vegetable growth, analyzes the relationship between environmental factors such as light intensity, temperature, humidity and carbon dioxide concentration and soil nutrients and microorganisms, and provides a reference for optimizing greenhouse environment control strategies.
[0037] Growers can quickly and easily obtain monitoring data and analysis reports through the web-based user terminal. In the data display area, growers can intuitively see various monitoring data presented in the form of charts and tables. By selecting different vegetable planting areas, time periods and monitoring indicators, they can view in detail the changes in soil nutrient content, microbial counts, environmental parameters and other data. For example, by viewing the soil moisture change curve of a greenhouse area in the past week, abnormal humidity fluctuations can be discovered in time to adjust irrigation strategies. The analysis report area provides a wealth of information, including soil fertility assessment reports, microbial community health status analysis, vegetable growth status forecasts, etc. These reports are based on professional data analysis and algorithms. The system generates a scientific model and provides growers with a scientific basis for decision-making. In the control operation area, growers input the amount of fertilizer to be added according to soil nutrient data and vegetable growth needs. The system will automatically send the instructions to the fertilization equipment to achieve precise fertilization. For example, when the system shows that the phosphorus content in the soil of a certain area is lower than the standard value, the grower inputs the appropriate amount of phosphorus fertilizer to be added in the control operation area, and the fertilization equipment will accurately perform fertilization operations according to the instructions. The system also has an intelligent early warning function. When the soil pH exceeds the appropriate range, the number of harmful microorganisms exceeds the standard, or the environmental parameters are abnormal, the system will send an early warning message to the grower through a pop-up window, reminding the grower to take timely measures to ensure the healthy growth of vegetables.
[0038] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. 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. A soil and microorganism integrated monitoring system for a controlled environment, characterized in that: The system includes the following components: sensor module, data acquisition and transmission module, data analysis and processing module and user terminal; The sensor module integrates a variety of high-precision sensors, including soil physical and chemical property sensors, microbial sensors, and environmental sensors, to collect soil and environmental information in real time in saline-alkali land monitoring scenarios; The data acquisition and transmission module is responsible for collecting data acquired by the sensor and transmitting the data to the data analysis and processing module through wireless transmission technology. The module also has a data pre-processing function and can filter and remove noise from the original data. During the transmission process, encryption technology is used to ensure data security. The data analysis and processing module uses data algorithms and models to conduct in-depth analysis of the collected data, combines relevant research results and theories on saline-alkali land improvement and straw return to the field, predicts the trend of changes in soil physical and chemical properties, analyzes the evolution of microbial community structure and changes in carbon conversion function, uses real-time quantitative PCR and PCR-DGGE modern molecular biology techniques to deeply analyze the dynamic changes in microbial community structure, adopts BIOLOG-ECO technology to comprehensively evaluate the functional diversity of microbial carbon source substrate utilization, accurately grasps the changes in microbial metabolic function after straw return to the field, uses machine learning algorithms to predict the effect of saline-alkali land improvement based on soil salinity, pH and microbial data, and reveals the impact of straw return on microbial metabolic function through analysis of microbial carbon source utilization data at different periods; The user terminal is presented in the form of a web page or mobile phone application. Users can view monitoring data, analysis reports and early warning information in real time through the terminal. The system also provides interactive functions. Users can adjust the controlled environmental parameters according to the monitoring results and add appropriate amounts of organic acids to adjust the pH value of coastal saline-alkali land, so that the environmental pH is more conducive to the full activity of cellulose-degrading microorganisms.
2. The soil and microorganism integrated monitoring system for a controlled environment according to claim 1, characterized in that: The sensor module includes various types of sensors. The soil physical and chemical property sensor adopts ion selective electrode technology and spectral analysis technology. The soil salt sensor selectively detects salt ions in the soil based on a specific ion exchange membrane. The pH sensor uses the glass electrode method. The microbial sensor uses immunoassay and bioelectrochemical principles. It targets microorganisms, binds to microbial surface antigens by labeling specific antibodies, and then uses electrochemical signal conversion to detect their number and activity. Among environmental sensors, the temperature and humidity sensor adopts capacitive and thermistor technology, and the light sensor adopts a photodiode.
3. The soil and microorganism integrated monitoring system for a controlled environment according to claim 1, characterized in that: The data acquisition and transmission module adopts an adaptive data acquisition frequency adjustment algorithm, which dynamically adjusts the acquisition frequency according to the rate of change of sensor data. The formula is: where f n is the current acquisition frequency, f n+1 is the next acquisition frequency, k is the frequency adjustment coefficient, ΔS n is the difference between the current collected data and the last collected data, S max is the maximum value of the sensor's measurement range. In terms of data preprocessing, a denoising algorithm based on wavelet transform is used to remove noise signals by performing multi-scale decomposition on the original signal.
4. The soil and microorganism integrated monitoring system for a controlled environment according to claim 1, characterized in that: The microbial community structure analysis algorithm used by the data analysis and processing module is a community similarity analysis algorithm based on feature vectors. First, the microbial community data is standardized using the formula: where X ij is the number of the jth microbial species in the i-th sample, is the average value of the jth microbial species in all samples, S j is the standard deviation of the jth microbial species in all samples, and then the similarity matrix between samples is calculated using the formula: Among them Sim ij is the similarity index between sample i and sample j, w k is the weight of the kth microbial species, and are the standardized numbers of the kth microbial species in the ith sample and the jth sample, respectively, and n is the total number of microbial species.
5. The soil and microorganism integrated monitoring system for a controlled environment according to claim 1, characterized in that: The soil physical and chemical property prediction algorithm used by the data analysis and processing module is a prediction algorithm based on weighted grey correlation analysis. First, a reference sequence and a comparison sequence are determined. The reference sequence is the soil physical and chemical property index to be predicted, and the comparison sequence is the relevant factors affecting the index. Then, the grey correlation coefficient is calculated. The formula is: where ξ i (k) is the grey correlation coefficient between the ith comparison sequence and the reference sequence at the kth moment, X0(k) is the value of the reference sequence at the kth moment, X i (k) is the value of the i-th comparison sequence at the k-th moment, ρ is the resolution coefficient, and then the grey relational degree is calculated. The formula is: where r i is the grey relational degree between the ith comparison sequence and the reference sequence, w k is the weight at the kth moment, m is the number of time points of the data, and finally the influencing factors are ranked according to the grey correlation degree to construct a prediction model to predict the changing trend of soil physical and chemical properties.
6. The soil and microorganism integrated monitoring system for a controlled environment according to claim 1, characterized in that: The user terminal adopts an intelligent interactive interface design, and the interface is divided into a data display area, an analysis report area and a control operation area. The data display area presents monitoring data in the form of intuitive charts and real-time data tables. Users can select different time periods and monitoring indicators for viewing according to their needs. The analysis report area provides detailed data analysis results, including assessment of soil and microbial conditions, change trend analysis and comparison with historical data. Various control buttons and parameter input boxes are set in the control operation area. In the saline-alkali land improvement scenario, users can enter the amount of organic acid added in the control operation area based on the soil pH monitoring data. The system automatically calculates and sends control instructions to related equipment. At the same time, the user terminal supports simultaneous access by multiple devices.
7. The soil and microorganism integrated monitoring system for a controlled environment according to claim 1, characterized in that: The system also has an intelligent early warning function. The algorithm for setting the early warning threshold is a dynamic threshold setting algorithm based on risk assessment. The formula is: Where T ij is the warning threshold of the i-th monitoring indicator in the j-th monitoring area, T 0ij is the initial threshold, α is the risk adjustment coefficient, σ ij is the standard deviation of the i-th monitoring indicator in the j-th monitoring area, μ ij is the average value of the i-th monitoring indicator in the j-th monitoring area. When the monitoring data exceeds the warning threshold, the system sends a warning message to the user through SMS or pop-up window to remind the user to take timely measures.
8. The soil and microorganism integrated monitoring system for a controlled environment according to claim 1, characterized in that: The system adopts a modular design concept. The sensor module, data acquisition and transmission module, data analysis and processing module and user terminal are all independent functional modules. The modules communicate and exchange data through standardized interfaces. When new monitoring indicators need to be added, it is only necessary to add the corresponding sensors to the sensor module and make appropriate adjustments to the algorithm of the data analysis and processing module, without the need for large-scale changes to the entire system.