Soil quality improvement and microbiological analysis integrated system

Abstract

The invention discloses a soil quality improvement and microbiological analysis integrated system, and relates to the technical field of soil improvement and microbiological analysis, the system comprises a microbial agent adding module, an organic acid adjusting module, a soil improvement effect monitoring module, a data analysis and processing module and a control and feedback module. According to the invention, high integration of soil improvement and microbiological analysis is realized, the efficiency and effect of soil improvement are improved through real-time monitoring and accurate regulation and control, and the microbial community is comprehensively and dynamically analyzed, so that deep understanding of a microbial action mechanism in the soil improvement process is facilitated, and the soil improvement efficiency and effect are improved. By monitoring succession of microbial community structures, expression changes of functional genes and diversity of carbon source utilization functions, the effects of microorganisms on straw degradation, soil structure improvement and the like are accurately grasped, problems are found in time through an early warning function, improvement measures are adjusted in time, and the stability and sustainability of a soil ecosystem can be effectively maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of soil improvement and microbial analysis, and in particular to an integrated system for soil quality improvement and microbial analysis. Background Art

[0002] Soil salinization is a serious global environmental and agricultural problem. Due to its special geographical location and geological conditions, coastal saline-alkali land is affected by both sea tides and groundwater, resulting in soil salt content as high as 2%-6%, pH value greater than 8, low soil organic matter content, viscous structure, poor air permeability and water permeability, and slow nutrient release. Under the action of evaporation, the salinization of the soil surface is intensified, and hardening and compaction occur, which poses a great threat to crop growth and sustainable agricultural development.

[0003] At present, there are various methods for improving saline-alkali land, including hydraulic methods, physical methods, chemical methods and biological methods. Although hydraulic methods can leach salt, they consume a lot of water resources and the improvement effect is difficult to last; physical methods such as deep plowing and soil addition are costly and obviously restricted by geographical regions; although the improvers in chemical methods can adjust the pH of the soil, they are costly and may cause secondary pollution; although biological methods are environmentally friendly, they are highly targeted and have a long improvement cycle. As a potential improvement method, returning straw to the field has been studied a lot for its effects on the physical and chemical properties of the soil, but the research on the mechanism of microbial degradation of straw in the improvement of coastal saline-alkali land and its effects on the soil carbon conversion function is not in-depth enough.

[0004] In agricultural production, soil quality is closely related to microbial communities. Microorganisms play a key role in soil nutrient cycling, organic matter decomposition, soil structure improvement, etc. However, existing technologies are difficult to monitor and regulate the changes in microbial communities in real time and accurately during soil improvement, and are unable to adjust improvement measures in a timely manner according to the dynamics of microbial communities, resulting in unstable and inaccurate soil improvement effects, making it difficult to achieve efficient improvement and sustainable improvement of coastal saline-alkali land soil quality. Therefore, it is necessary to propose an integrated system for soil quality improvement and microbial analysis to solve the problems in existing technologies. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide an integrated system for soil quality improvement and microbial analysis, which can effectively improve the soil structure of coastal saline-alkali land, reduce salt, increase organic matter content, enhance soil fertility, create favorable conditions for crop growth, and monitor and analyze microbial communities in real time, which helps to deeply understand the microbial action mechanism in the soil improvement process and provide a scientific basis for optimizing improvement plans. Accurate control strategies can respond to changes in soil quality and microbial communities in a timely manner, maintain the stability of the soil ecosystem, and ensure the sustainable development of agriculture.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an integrated system for soil quality improvement and microbial analysis, the system comprising: a microbial agent addition module, an organic acid regulation module, a soil improvement effect monitoring module, a data analysis and processing module, and a control and feedback module;

[0007] The microbial agent adding module is used to add the selected cellulose degrading bacteria agent that can efficiently degrade straw into the soil. The amount of the agent added is determined according to the soil type, salinity and crop planting requirements. The calculation formula is M = k × S × ( C1+C2×T+C3×N ) , where M is the amount of microbial agent added, k is the adjustment coefficient, the value range is 0.5-1.5, S is the soil area, C1 is the basic addition coefficient, the value range is 5-10, C2 is the salinity influence coefficient, which depends on the soil salt content. For every 1% increase in salt content, C2 increases by 0.5-1.0, T is the soil salt content, C3 is the crop demand coefficient, which depends on the soil fertility and microbial environment requirements of the planted crops, and the value range is 0.5-2.0, N is the crop type influencing factor, different crops correspond to different values, wheat takes 0.8, corn takes 1.2, and rice takes 1.5;

[0008] The organic acid regulating module includes an organic acid reserve unit, which reserves a variety of organic acids and automatically selects and mixes appropriate types and concentrations of organic acids to be added to the soil according to the pH value of the soil monitored in real time. The formula for adjusting the organic acid concentration is: Among them C a is the concentration of organic acid added, pH0 is the target soil pH value, which is determined by the suitable range for crop growth. For wheat, it is 6.0-7.5, for corn, it is 6.5-7.5, for rice, it is 6.0-7.0, and pH t To monitor soil pH in real time, K a is the organic acid dissociation constant, among which oxalic acid K a1 =5.9×10 -2 ,K a2 =6.4×10 -5 , K citrate a1 =7.4×10 -4 ,K a2 =1.7×10 -5 ,K a3 =4.0×10 -7 , V s is the unit soil volume;

[0009] The soil improvement effect monitoring module includes a soil physical property monitoring unit, a soil chemical property monitoring unit and a microbial characteristic monitoring unit, which monitors various physical and chemical properties and microbial related parameters of the soil in real time through a variety of sensors and detection equipment, covering soil salinity, pH value, water content, temperature, nutrient content, microbial community structure, carbon source substrate utilization functional diversity and changes in major carbon conversion functional microorganisms;

[0010] The data analysis and processing module includes a data receiving and integration unit and a data analysis unit, which is used to receive data from the monitoring module and analyze and process it using a specific algorithm. The algorithm includes building a soil quality-microorganism relationship model Q=f ( M s ,M m ,C s ,C m ,T,t ) , where Q is the comprehensive assessment value of soil quality, M s is the characteristic vector of soil microbial community structure, which is obtained by normalizing the data of microbial community diversity index and dominant species abundance. m is the microbial functional gene expression matrix, which was constructed using fluorescence quantitative PCR data. s is the soil chemical property vector, including soil salinity, pH value, nutrient content, and organic matter content. m is the soil physical property vector, including soil moisture content, temperature and porosity data, T is the time variable, and t is the implementation time of the improvement measures. The model is trained by machine learning of a large amount of historical data and real-time monitoring data, and is used to evaluate the effect of soil quality improvement and predict the dynamic changes of microbial communities;

[0011] The control and feedback module is used to automatically adjust the working parameters of the microbial agent addition module and the organic acid regulation module according to the results of the data analysis and processing module, and has an early warning function to issue an alarm when the changes in soil quality and microbial community exceed preset thresholds.

[0012] Furthermore, the microbial agent screening method in the microbial agent adding module comprises:

[0013] Collect multiple soil samples from coastal saline-alkali land and surrounding ecological and environmental areas, with a collection depth of 0-30 cm. The collection points are distributed according to different vegetation types, soil textures and topography;

[0014] The collected soil samples were mixed and soil suspensions of different concentrations were prepared by gradient dilution method, and then inoculated on selective culture media containing straw as the only carbon source and different concentrations of saline and alkali components;

[0015] Enrichment culture was performed under different temperature gradients and pH gradients for 24-72 hours;

[0016] Select the bacterial colonies that grow well under high salinity, suitable temperature and pH conditions and have obvious straw degradation effects for separation and purification, and repeat the screening many times to obtain cellulose-degrading bacterial strains that have the ability to efficiently degrade straw and adapt to the coastal saline-alkali land environment;

[0017] The screened strains are preserved and expanded to be prepared into a bacterial agent for soil improvement. The preservation condition is -80°C, glycerol is used as a protective agent, and the final concentration of glycerol is 15%-20%.

[0018] Furthermore, the organic acid reserve unit in the organic acid regulating module includes the following organic acid types and characteristics:

[0019] Oxalic acid is highly acidic and has a significant effect on regulating soil pH. It is suitable for coastal saline-alkali soils with high initial pH values. It has stable chemical properties and is not easily decomposed by microorganisms in the soil. It can maintain the regulating effect on soil pH for a long time. It is packed in sealed plastic barrels when stored at an ambient temperature of 5℃-30℃ and avoid direct sunlight.

[0020] Citric acid, in addition to adjusting pH, can chelate with metal ions in the soil to promote the release and effectiveness of nutrients in the soil. It is a food-grade organic acid, environmentally friendly, and does not affect the soil ecosystem. It should be stored in a glass container in a cool and dry place at a temperature of 10°C-25°C and a relative humidity below 70%.

[0021] Malic acid has good biocompatibility, promotes the growth and metabolic activities of beneficial microorganisms in the soil, and maintains good activity under low temperature conditions. It is suitable for cold coastal saline-alkali areas. It is packaged in stainless steel cans for storage. The cans are filled with nitrogen to prevent oxidation. The storage temperature is 0℃-20℃.

[0022] Furthermore, the soil physical property monitoring unit in the soil improvement effect monitoring module adopts the following sensors and installation methods:

[0023] The soil salt sensor uses a four-electrode sensor with graphite and stainless steel electrode materials. It is inserted vertically into the soil 15-20 cm and is covered with a clay tube to keep the soil in good contact with the electrode and prevent soil particles from entering and affecting the measurement accuracy. The measurement data is transmitted to the monitoring center in real time through a wireless transmission module based on LoRa communication technology, with a transmission frequency of once every 30 minutes.

[0024] The soil pH sensor uses a glass electrode sensor with a special ceramic diaphragm on the electrode head. It is inserted into the soil at an angle of 45° to the horizontal plane with an insertion depth of 10-15 cm. It is connected to the data collector using RS485 bus wired transmission. The data collector collects data every 1 hour and uploads it to the data analysis and processing module.

[0025] The soil moisture sensor is designed based on the principle of high-frequency oscillation. The probe is made of stainless steel with an anti-corrosion coating on the surface. It is buried horizontally in the soil 20-25 cm, 5-10 cm away from the crop roots. It communicates with nearby relay nodes through Bluetooth technology, and the relay nodes transmit the data to the monitoring center. The data is updated every 20 minutes.

[0026] The soil temperature sensor uses a thermistor sensor, which is encapsulated in a stainless steel protective tube. The outside of the tube is filled with thermal conductive silicone. It is vertically inserted into the soil 15-20 cm and installed adjacent to the soil salinity sensor. The temperature data is transmitted in real time through the ZigBee wireless communication module, with a transmission interval of once every 15 minutes.

[0027] Furthermore, the detection method and frequency of the soil chemical property monitoring unit in the soil improvement effect monitoring module are as follows:

[0028] For soil nutrient content testing, a portable soil nutrient rapid tester is used. Based on the principle of colorimetry, five soil samples are collected around each monitoring point using the quincunx sampling method, with a depth of 0-20 cm. After mixing, the samples are tested on site. The testing frequency is once a week, and the instrument is calibrated with a standard soil sample before each test.

[0029] For the detection of soil organic matter content, potassium dichromate oxidation-external heating method is used. Three repeated samples are collected at the monitoring point every month, with a depth of 0-30 cm. The samples are naturally air-dried indoors and ground and sieved. A certain amount of soil sample is weighed and placed in a digestion tube. Quantitative potassium dichromate standard solution and concentrated sulfuric acid are added. The samples are heated to 170℃-180℃ in an oil bath and kept boiling for 5-10 minutes to fully oxidize the organic matter in the soil. After cooling, the remaining potassium dichromate is titrated with ferrous sulfate standard solution. The soil organic matter content is calculated based on the amount of ferrous sulfate consumed in the titration. Each sample is measured in parallel, and the relative deviation is controlled within 5%.

[0030] Furthermore, the analysis technology and operation process of the microbial characteristic monitoring unit in the soil improvement effect monitoring module are as follows:

[0031] PCR-DGGE technology was used to analyze the microbial community structure: soil samples were collected at the monitoring points every month, DNA was extracted and then amplified by PCR, denaturing gradient gel electrophoresis was prepared, and the bands in the map were analyzed to evaluate the diversity of community structure and succession law;

[0032] Fluorescence quantitative PCR technology is used to detect the expression of microbial functional genes: primers and probes are designed for specific functional genes, standard curves are established, soil samples are collected monthly to detect gene expression, and changes in microbial functional activity are analyzed;

[0033] BIOLOG-ECO technology analyzes the functional diversity of microbial carbon source substrate utilization: prepare inoculation solution, inoculate it into ECO plates for culture, read the light absorption value to calculate the average absorbance index, and analyze the functional diversity of microbial community carbon source utilization and changes in metabolic activity.

[0034] Furthermore, the data processing flow of the data receiving and integrating unit in the data analysis and processing module is as follows:

[0035] Data reception: Receive monitoring module data through a combination of wireless and wired communications. Set up a data buffer for real-time monitoring data. When communication is interrupted, buffer data within 24 hours. Automatically upload after communication is restored, and verify data format at the same time.

[0036] Data verification: range verification is performed on numerical data. Data beyond a reasonable range is marked as abnormal data. The abnormal time and monitoring point location are recorded. The integrity of the microbial community structure data is verified to check whether the clarity and quantity of the bands meet expectations. If not, it is marked as abnormal. Data redundancy verification is also used to compare the same type of data collected at the same monitoring point at different times and with different equipment. The accuracy is verified if the difference exceeds the preset threshold.

[0037] Data cleaning, repairing abnormal data, using data interpolation algorithm to estimate reasonable values ​​for repairable data, deleting serious abnormal data that cannot be repaired and recording missing information, smoothing data noise with sliding average filter algorithm, and taking the average value of data within a certain time window as processed data;

[0038] Data classification and storage: sort and organize data by data type, monitoring time, and monitoring point location, store it in a relational database, design a reasonable data table structure, and back it up regularly.

[0039] Furthermore, the algorithm implementation details of the data analysis unit in the data analysis and processing module are as follows:

[0040] Soil quality-microorganism relationship model training: collect data on various types of soil in various improvement processes, including soil physical properties, chemical properties, microbial community composition, microbial functional gene expression levels and soil quality evaluation indicators, pre-process the data, divide the data set into 70% training set and 30% test set, use machine learning algorithm to train and build the model, adjust model parameters during training to optimize performance, use cross-validation to evaluate model accuracy and generalization ability, and ensure that the prediction accuracy of the test set is above 80%;

[0041] Microbial community structure analysis algorithm: For PCR-DGGE map data, grayscale processing is first performed to reduce the amount of data and highlight the strip information. The edge detection algorithm is used to identify the strip edge, and the strip position and width are determined by threshold segmentation. The characteristic parameters of the strip gray value, area, and shape factor are calculated to construct a feature vector to characterize the microbial community structure. The cluster analysis algorithm is used to cluster the feature vectors of different samples, evaluate the similarity and difference of community structure, analyze the law of community structure succession, and combine the principal component analysis algorithm to reduce the high-dimensional microbial community structure data to two-dimensional and three-dimensional space, draw a PCA diagram, and reveal the potential relationship between microbial community structure and soil environmental factors;

[0042] Microbial functional gene expression analysis algorithm: For the microbial functional gene expression data detected by fluorescent quantitative PCR, the differential expression analysis algorithm is used to determine the functional genes with significant differential expression under different soil improvement conditions, calculate the fold change and difference significance of gene expression, perform functional annotation and enrichment analysis on the differentially expressed genes, construct a gene regulatory network, analyze the interaction relationship between functional genes, explore key regulatory genes and gene modules, and deeply understand the functional regulatory mechanism of microorganisms in soil improvement.

[0043] Furthermore, the specific control strategy and early warning mechanism of the control and feedback module are as follows:

[0044] Control strategy: When the data analysis and processing module determines that the soil quality improvement has not reached expectations and the microbial community has changed abnormally, the control and feedback module initiates the corresponding strategy. If the soil salinity decreases too slowly, the amount of microbial agent added is increased by 20%-50% of the original amount according to the microbial agent addition amount calculation formula of the microbial agent addition module. At the same time, the organic acid addition effect in the organic acid adjustment module is checked. If the pH value is not well adjusted, the organic acid concentration is recalculated and adjusted according to the organic acid concentration adjustment formula. The adjustment range is ±1-3mmol / L. The number of beneficial microorganisms in the microbial community is reduced. When adding microbial agents, this type of beneficial microbial strains are supplemented first. The supplementary amount is determined according to the degree of reduction, which is generally 1.5-2.5 times the original number of beneficial microorganisms.

[0045] Early warning mechanism: Set multiple types of early warning indicators and thresholds. For soil quality indicators, including sudden increase in soil salinity, sharp changes in pH value, and low nutrient content, a first-level early warning is issued. The early warning methods are sound and light alarms, text message notifications to relevant management personnel and technical personnel, and detailed early warning information is displayed on the system interface. For microbial community indicators, including a significant decrease in the diversity of beneficial microbial communities and a large number of harmful microorganisms, a second-level early warning is issued. In addition to the above-mentioned early warning methods, an early warning report is automatically generated. The report contains detailed data on changes in the structure of microbial communities, possible impacts on soil ecosystems and crop growth, and preliminary response suggestions. The early warning threshold is dynamically adjusted according to different regions, soil types, and crop needs. The adjustment is based on the statistical analysis results of long-term accumulated historical data and real-time monitoring data. At the same time, a warning event record database is established to record detailed information on each warning event for subsequent analysis and experience summary, and to optimize the early warning mechanism and control strategy.

[0046] Compared with the existing technology, the integrated system for soil quality improvement and microbial analysis has the following beneficial effects:

[0047] The present invention achieves a high degree of integration of soil improvement and microbial analysis, improves the efficiency and effect of soil improvement through real-time monitoring and precise regulation, and conducts a comprehensive and dynamic analysis of microbial communities, which helps to deeply understand the microbial action mechanism in the soil improvement process. By monitoring the succession of microbial community structure, the expression changes of functional genes and the functional diversity of carbon source utilization, the role of microorganisms in straw degradation, soil structure improvement and other aspects can be accurately grasped. The early warning function can detect problems in a timely manner and adjust improvement measures in a timely manner, which can effectively maintain the stability and sustainability of the soil ecosystem.

[0048] 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

[0049] 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.

[0050] Figure 1 This is a schematic diagram of the integrated system for soil quality improvement and microbial analysis;

[0051] Figure 2Schematic diagram of the workflow of the integrated system for soil quality improvement and microbial analysis. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Embodiment 1

[0054] A soil improvement experiment was conducted on a piece of coastal saline-alkali land. The area of ​​the experimental field is 5,000 square meters. The initial salt content of the soil is 4.5%, the pH value is 8.8, and the organic matter content is 0.6%. It is planned to plant wheat.

[0055] According to the initial soil conditions and wheat planting needs, relevant parameters are set in the data analysis and processing module. The target soil pH value is set to 7.0 (suitable for wheat growth), and the soil salinity is expected to drop to below 2% within 3 months. For the microbial agent addition module, the initial agent addition amount is calculated according to the soil area and initial conditions. It is assumed that the adjustment coefficient k=1, the basic addition coefficient C1=8, the salinity influence coefficient C2 (soil salt content is 4.5%, and for every 1% increase, the C2 value increases by 0.8, here C2=3.6), the wheat crop demand coefficient C3=1.2, and the crop type influence factor N=0.8, then the agent addition amount M=1×5000×(8+3.6×4.5+1.2×0.8)=103600 grams, that is, 103.6 kilograms, and the initial addition amount is set to 50 kilograms.

[0056] The organic acid adjustment module selects oxalic acid for adjustment based on the initial pH value of 8.8. It is known that oxalic acid K a1 =5.9×10 -2 , K a2 =6.4×10 -5 , assuming that the unit soil volume V s = 1 cubic meter, according to the formula The initial concentration of oxalic acid added was calculated to be (The absolute value is taken here, and appropriate adjustments should be made according to the equipment accuracy and regulation effect during actual addition).

[0057] The microbial agent addition module selects strains suitable for local soil conditions from the pre-screened and preserved cellulose-degrading bacteria strain library, mixes 50 kg of the agent with an appropriate amount of water to make a bacterial solution, and uses an electric spreader equipped with an intelligent control system to evenly spread the bacterial solution on the soil surface of the test field. The spreading width is set to 5 meters, and the spreading speed is automatically adjusted according to the calculated dosage to ensure even distribution of the agent.

[0058] The organic acid regulation module extracts oxalic acid solution from a sealed plastic barrel and evenly adds the oxalic acid solution to the soil at a rate of 0.5 liters per minute through a precision metering pump. The metering pump automatically adjusts the flow rate according to the set concentration. During the addition process, the stirring device continues to work to ensure that the organic acid is evenly mixed in the storage tank. At the same time, the temperature sensor and liquid level sensor monitor the temperature and liquid level in the storage tank in real time to ensure a safe and stable addition process.

[0059] The soil improvement effect monitoring module starts working. The soil salt sensor (four-electrode type, graphite and stainless steel electrodes, inserted vertically into the soil 18 cm), pH sensor (glass electrode type, inserted into the soil 12 cm at an angle of 45°), soil moisture sensor (high-frequency oscillation principle, buried horizontally 22 cm in the soil, 8 cm away from the crop roots) and soil temperature sensor (thermistor type, inserted vertically 18 cm into the soil) collect data in real time. The wireless transmission module (LoRa communication technology) transmits soil salinity, humidity and temperature data once every 30 minutes. The pH sensor data collector with wired transmission mode (RS485 bus) collects and uploads data every 1 hour.

[0060] Every week, soil samples are collected in the experimental field according to the predetermined sampling points and the plum blossom sampling method is used to collect soil samples for chemical property testing. The portable soil nutrient rapid tester is used to conduct on-site testing of the nitrogen, phosphorus, potassium and other nutrient contents in the soil. Before each test, standard soil samples are used for calibration to ensure measurement accuracy. At the same time, soil samples are collected monthly for soil organic matter content testing. The potassium dichromate oxidation-external heating method is used. The collected soil samples are naturally air-dried, ground and sieved, and a certain amount is accurately weighed and placed in a digestion tube for processing. The organic matter content is calculated and each sample is measured in parallel to control the relative deviation within 5%.

[0061] In terms of microbial characteristic monitoring, soil samples are collected at the monitoring points every month for microbial community structure analysis. PCR-DGGE technology is used to extract soil DNA first, then PCR amplification is performed, and universal primers for the bacterial 16SrDNAV3-V5 region are used. The reaction system and conditions are strictly in accordance with standard operations. The amplified products are subjected to DGGE electrophoresis, and the number, position and brightness of the bands in the map are analyzed to evaluate the diversity and succession of the microbial community structure. At the same time, specific primers and TaqMan probes are designed for functional genes such as amoA genes and cellulase genes of ammonia oxidizing bacteria. After the standard curve is established, soil samples are collected every month for fluorescent quantitative PCR detection to analyze the changes in the expression of microbial functional genes and reflect the changes in microbial functional activity. In addition, BIOLOG-ECO technology is used to prepare the inoculum and inoculate it into the ECO plate for culture. Starting from 24 hours, the light absorption values ​​of each well at wavelengths of 750nm and 590nm are read every 12 hours, and the average absorbance AWCD and other indicators are calculated to analyze the functional diversity and metabolic activity changes of the microbial community carbon source utilization.

[0062] The data receiving and integration unit receives various types of data from the monitoring module. The data cache effectively stores data during communication interruption and automatically uploads the data after communication is restored. During the data verification process, the range check ensures that numerical data such as soil salinity and pH value are within a reasonable range. Data exceeding the 0-10% salinity range and the 1-14 pH range are marked as abnormal and recorded. The integrity check checks the clarity and number of PCR-DGGE spectrum bands. The data redundancy check compares the same type of data collected at different times and equipment, and verifies the accuracy when the difference exceeds 10%. The abnormal data is processed by interpolation algorithm or deletion, and then stored in a relational database together with normal data by type, time and location.

[0063] The data analysis unit conducts regular analysis and collects multiple types of data including soil texture, porosity, microbial community structure at different stages, and functional gene expression when constructing a soil quality-microorganism relationship model. After preprocessing, the data are divided into 70% training set and 30% test set, and trained using the random forest algorithm. Parameters such as the number of decision trees are adjusted. After 10-fold cross-validation, the prediction accuracy of the test set reached 82%. The microbial community structure was analyzed, PCR-DGGE map data were processed, band characteristic parameters were calculated to construct feature vectors, cluster analysis was used to evaluate community similarities and differences, PCA analysis revealed potential connections with soil environmental factors, and microbial functional gene expression was analyzed. The DESeq2 algorithm was used to determine differentially expressed genes, annotation enrichment analysis clarified gene functions and involved pathways, and a gene regulatory network was constructed to understand the functional regulatory mechanism.

[0064] After one month of implementation of improvement measures, the data analysis and processing module found that the soil salinity only dropped to 4.2%, which did not reach the expected decline, and the number of key microorganisms involved in straw degradation in the microbial community increased slowly. According to the control strategy, the control and feedback module calculated the new amount of microbial agent addition. Due to the slow decline in soil salinity, according to the microbial agent addition amount calculation formula, the new adjustment coefficient k = 1.2 (adjusted according to experience and model prediction), the new microbial agent addition amount M = 1.2 × 5000 × (8 + 3.6 × 4.5 + 1.2 × 0.8) = 124320 grams, that is, 124.32 kilograms, and the increase this time was 74.32 kilograms (124.32-50). At the same time, the organic acid regulation effect was checked and it was found that the pH value dropped to 8.3, which was still out of the target range. The oxalic acid addition concentration was recalculated and adjusted to (Adjust by taking the absolute value), the metering pump adjusts the flow rate according to the new concentration.

[0065] In subsequent monitoring, if the soil moisture is found to be lower than 30% during certain periods (affecting microbial activity and straw degradation), the early warning unit will issue a first-level warning, the sound and light alarm will sound an alarm in the monitoring center and on-site equipment, and text messages will be sent to managers and technicians. The system interface will display the early warning information (time, location, abnormal humidity and possible causes). Based on the early warning information, the managers will start the irrigation system to increase the soil moisture to an appropriate range (40%-60%).

[0066] The effects brought by this embodiment are as follows: after three months of improvement, the soil salinity was reduced to 1.8%, reaching the expected target, the pH value was stabilized at about 7.0, the soil organic matter content was increased to 1.5%, the soil structure was significantly improved, the air permeability and water permeability were enhanced, and after wheat was planted, the emergence rate was increased by 30% compared with the unimproved soil, the seedlings grew strong, and the root system was well developed. Finally, the wheat yield was increased by 45% compared with the unimproved coastal saline-alkali land, effectively achieving efficient improvement of coastal saline-alkali land and significant increase in crop yields. At the same time, the system's precise monitoring and regulation maintained the stability of the soil ecosystem, providing strong support for sustainable agricultural development.

[0067] Embodiment 2

[0068] The experiment was carried out in a coastal saline-alkali land with an area of ​​8,000 square meters. The initial soil salt content was 5%, the pH value was 9.0, and the organic matter content was only 0.4%. The plan was to plant corn.

[0069] Based on the initial soil conditions and corn growth requirements, parameters are set in the data analysis and processing module, and the target soil pH is set to 6.8 (suitable for corn growth). It is expected that the soil salinity will be reduced to below 2.5% within 4 months. For the microbial agent addition module, it is assumed that the adjustment coefficient k=1.1, the basic addition coefficient C1=7, the salinity influence coefficient C2 (soil salt content is 5%, and for every 1% increase, the C2 value increases by 0.7, here C2=3.5), the corn crop demand coefficient C3=1.5, and the crop type influence factor N=1.2, then the amount of microbial agent added M=1.1×8000×(7+3.5×5+1.5×1.2)=172480 grams, i.e. 172.48 kilograms, and the initial addition amount is set to 80 kilograms.

[0070] The organic acid adjustment module selects citric acid for adjustment based on the initial pH value of 9.0. It is known that citric acid K a1 =7.4×10 -4 , K a2 =1.7×10 -5 , K a3 =4.0×10 -7 , assuming that the unit soil volume V s = 1 cubic meter, according to the formula Calculate the initial concentration of citric acid added (Take the absolute value and adjust according to the actual situation).

[0071] The microbial agent addition module selects cellulose-degrading bacteria strains suitable for the local environment, prepares 80 kg of the agent into a bacterial solution, and evenly spreads it on the soil surface through an intelligently controlled electric spreader. The spreading width is set to 6 meters. The spreader automatically adjusts the spreading speed according to the preset dosage to ensure uniform distribution of the agent.

[0072] The organic acid regulation module extracts citric acid solution from a glass container and adds it to the soil at a rate of 0.6 liters per minute by a precision metering pump. The metering pump automatically adjusts the flow rate according to the set concentration. The liquid level sensor and temperature sensor in the storage tank monitor in real time. The stirring device continuously stirs to ensure that the citric acid is evenly mixed, maintaining the storage environment temperature at 10°C-25°C and the relative humidity below 70%.

[0073] The soil improvement effect monitoring module is started, and the soil salt sensor (four-electrode type, electrode materials graphite and stainless steel, vertically inserted into the soil 20 cm), pH sensor (glass electrode type, inserted into the soil 13 cm at an angle of 45°), soil moisture sensor (high-frequency oscillation principle, horizontally buried 23 cm in the soil, 7 cm away from the crop roots) and soil temperature sensor (thermistor type, vertically inserted into the soil 19 cm) continuously collect data. The wireless transmission module (LoRa communication technology) transmits soil salinity, humidity and temperature data every 30 minutes, and the pH sensor data collector with wired transmission (RS485 bus) collects and uploads data every 1 hour.

[0074] Soil samples are collected at predetermined sampling points every week using the plum blossom sampling method for chemical property testing. A portable soil nutrient rapid tester is used to test the soil nitrogen, phosphorus, potassium and other nutrient contents. The instrument is calibrated before each test. Soil samples are collected monthly and the organic matter content is determined by the potassium dichromate oxidation-external heating method. Soil samples are naturally air-dried, ground and sieved, then accurately weighed and processed, and parallel measurements are performed to control the relative deviation within 5%.

[0075] In terms of microbial characteristic monitoring, soil samples are collected every month, and PCR-DGGE technology is used to extract soil DNA and then perform PCR amplification. Specific primers are used, and the reaction system and conditions are strictly controlled. The amplified products are subjected to DGGE electrophoresis, and the map is analyzed to evaluate the microbial community structure. Primers and probes are designed for key functional genes involved in carbon and nitrogen cycles. After the standard curve is established, the changes in gene expression levels are detected every month. At the same time, BIOLOG-ECO technology is used to prepare inoculation solution and inoculate it into ECO plates for cultivation. The light absorption values ​​are read on time to calculate relevant indicators, and the functional diversity of microbial carbon source utilization and changes in metabolic activity are analyzed.

[0076] The data receiving and integration unit receives monitoring data, caches communication interruption data and uploads it after recovery. During data verification, range verification ensures data rationality, marks and records abnormal data, checks PCR-DGGE spectra for integrity verification, and compares data for data redundancy verification. After processing, abnormal data is classified with normal data and stored in a relational database (MySQL).

[0077] The data analysis unit regularly conducts analysis and builds a soil quality-microorganism relationship model. It collects multi-category data and divides the data set after preprocessing. It uses the support vector machine algorithm for training and adjusts the kernel function parameters. After 10-fold cross validation, the prediction accuracy of the test set reaches 83%. It also analyzes the microbial community structure, processes the PCR-DGGE map data, performs clustering and PCA analysis, analyzes the expression of microbial functional genes, uses the edgeR algorithm to determine differentially expressed genes, annotates enrichment analysis, and constructs a regulatory network.

[0078] Two months later, data analysis found that the soil salinity dropped to 4%, which was not as expected, and the diversity of beneficial microbial communities decreased (Shannon index decreased by 23%). The control and feedback module adjusted the amount of microbial agent added, and the new adjustment coefficient k = 1.3. The new amount of microbial agent added was calculated as M = 1.3 × 8000 × (7 + 3.5 × 5 + 1.5 × 1.2) = 200160 grams, that is, 200.16 kilograms. The increase this time was 120.16 kilograms (200.16-80). At the same time, the organic acid adjustment effect was checked, and the pH value dropped to 8.0. The citric acid addition concentration was recalculated as (Adjust by taking absolute value), and adjust the flow rate by metering pump.

[0079] If it is found that the nitrogen content in the soil nutrient content is lower than 80% of the minimum requirement for corn growth for two consecutive weeks, the early warning unit will issue a second-level warning, use sound and light alarms, and text messages to notify relevant personnel and generate an early warning report (including changes in the structure of microbial communities, impacts on crops and response suggestions). Based on the report, technicians will add nitrogen fertilizer and adjust the proportion of microorganisms that promote nitrogen conversion in the bacterial agent.

[0080] The effects brought by this embodiment are as follows: after four months of improvement, the soil salinity dropped to 2.2%, which was in line with the expected target, the pH value was stabilized at around 6.8, the organic matter content was increased to 1.2%, the soil structure was significantly optimized, and after corn was sown, the germination rate was 28% higher than that of the unimproved soil, the plants grew vigorously, and the stems were thick. Finally, the corn yield was increased by 50% compared with the unimproved coastal saline-alkali land, which strongly proved the effectiveness of the integrated system in different coastal saline-alkali land scenarios, achieved the multiple goals of improving soil quality, increasing crop yields, and stabilizing the soil ecosystem, and provided reliable technical support for the development of coastal saline-alkali land agriculture.

[0081] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the same elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An integrated system for soil quality improvement and microbial analysis, characterized in that: The system includes: a microbial agent addition module, an organic acid regulation module, a soil improvement effect monitoring module, a data analysis and processing module, and a control and feedback module; The microbial agent adding module is used to add the screened cellulose degrading bacteria agent that efficiently degrades straw into the soil. The amount of the agent added is determined according to the soil type, salinity and crop planting requirements. The calculation formula is M=k×S×(C1+C2×T+C3×N), where M is the amount of the agent added, k is the adjustment coefficient, and the value range is 0.5-1.5, S is the soil area, C1 is the basic addition coefficient, and the value range is 5-10, C2 is the salinity influence coefficient, which depends on the soil salt content. For every 1% increase in salt content, C2 increases by 0.5-1.0, T is the soil salt content, C3 is the crop demand coefficient, which depends on the soil fertility and microbial environment requirements of the planted crops, and the value range is 0.5-2.0, and N is the crop type influence factor. Different crops correspond to different values, and the value of wheat is 0.8, the value of corn is 1.2, and the value of rice is 1.5; The organic acid regulating module includes an organic acid reserve unit, which reserves a variety of organic acids and automatically selects and mixes appropriate types and concentrations of organic acids to be added to the soil according to the pH value of the soil monitored in real time. The formula for adjusting the organic acid concentration is: Among them C a is the concentration of organic acid added, pH0 is the target soil pH value, which is determined by the suitable range for crop growth. For wheat, it is 6.0-7.5, for corn, it is 6.5-7.5, for rice, it is 6.0-7.0, and pH t To monitor soil pH in real time, K a is the organic acid dissociation constant, among which oxalic acid K a1 =5.9×10 -2 ,K a2 =6.4×10 -5 , K citrate a1 =7.4×10 -4 ,K a2 =1.7×10 -5 ,K a3 =4.0×10 -7 , V s is the unit soil volume; The soil improvement effect monitoring module includes a soil physical property monitoring unit, a soil chemical property monitoring unit and a microbial characteristic monitoring unit, which monitors various physical and chemical properties and microbial related parameters of the soil in real time through a variety of sensors and detection equipment, covering soil salinity, pH value, water content, temperature, nutrient content, microbial community structure, carbon source substrate utilization functional diversity and changes in major carbon conversion functional microorganisms; The data analysis and processing module includes a data receiving and integration unit and a data analysis unit, which is used to receive data from the monitoring module and use a specific algorithm to analyze and process the data. The algorithm includes building a soil quality-microorganism relationship model Q=f(M s ,M m ,C s ,C m ,T,t), where Q is the comprehensive assessment value of soil quality, M s is the characteristic vector of soil microbial community structure, which is obtained by normalizing the data of microbial community diversity index and dominant species abundance. n is the microbial functional gene expression matrix, which was constructed using fluorescence quantitative PCR data. s is the soil chemical property vector, including soil salinity, pH value, nutrient content, and organic matter content. m is the soil physical property vector, including soil moisture content, temperature and porosity data, T is the time variable, and t is the implementation time of the improvement measures. The model is trained by machine learning of a large amount of historical data and real-time monitoring data, and is used to evaluate the effect of soil quality improvement and predict the dynamic changes of microbial communities; The control and feedback module is used to automatically adjust the working parameters of the microbial agent addition module and the organic acid regulation module according to the results of the data analysis and processing module, and has an early warning function to issue an alarm when the changes in soil quality and microbial community exceed preset thresholds.

2. The soil quality improvement and microbial analysis integrated system according to claim 1, characterized in that: The microbial agent screening method in the microbial agent adding module comprises: Collect multiple soil samples from coastal saline-alkali land and surrounding ecological and environmental areas, with a collection depth of 0-30 cm. The collection points are distributed according to different vegetation types, soil textures and topography; The collected soil samples were mixed and soil suspensions of different concentrations were prepared by gradient dilution method, and then inoculated on selective culture media containing straw as the only carbon source and different concentrations of saline and alkali components; Enrichment culture was performed under different temperature gradients and pH gradients for 24-72 hours; Select the colonies that grow well under high salinity, suitable temperature and pH conditions and have obvious straw degradation effects for separation and purification, and repeat the screening many times to obtain cellulose-degrading bacterial strains that have the ability to efficiently degrade straw and adapt to the coastal saline-alkali land environment; The screened strains are preserved and expanded to be prepared into a bacterial agent for soil improvement. The preservation condition is -80°C, glycerol is used as a protective agent, and the final concentration of glycerol is 15%-20%.

3. The soil quality improvement and microbial analysis integrated system according to claim 1, characterized in that: The organic acid reserve unit in the organic acid regulation module includes the following organic acid types and characteristics: Oxalic acid is highly acidic and has a significant effect on regulating soil pH. It is suitable for coastal saline-alkali soils with high initial pH values. It has stable chemical properties and is not easily decomposed by microorganisms in the soil. It can maintain the regulating effect on soil pH for a long time. It is packed in sealed plastic barrels when stored at an ambient temperature of 5℃-30℃ and avoid direct sunlight. Citric acid, in addition to adjusting pH, can chelate with metal ions in the soil to promote the release and effectiveness of nutrients in the soil. It is a food-grade organic acid, environmentally friendly, and does not affect the soil ecosystem. It should be stored in a glass container in a cool and dry place at a temperature of 10°C-25°C and a relative humidity below 70%. Malic acid has good biocompatibility, promotes the growth and metabolic activities of beneficial microorganisms in the soil, and maintains good activity under low temperature conditions. It is suitable for cold coastal saline-alkali areas. It is packaged in stainless steel cans for storage. The cans are filled with nitrogen to prevent oxidation. The storage temperature is 0℃-20℃.

4. The soil quality improvement and microbial analysis integrated system according to claim 1, characterized in that: The soil physical property monitoring unit in the soil improvement effect monitoring module adopts the following sensors and installation methods: The soil salt sensor uses a four-electrode sensor with graphite and stainless steel electrode materials. It is inserted vertically into the soil 15-20 cm and is covered with a clay tube to keep the soil in good contact with the electrode and prevent soil particles from entering and affecting the measurement accuracy. The measurement data is transmitted to the monitoring center in real time through a wireless transmission module based on LoRa communication technology, with a transmission frequency of once every 30 minutes. The soil pH sensor uses a glass electrode sensor with a special ceramic diaphragm on the electrode head. It is inserted into the soil at an angle of 45° to the horizontal plane with an insertion depth of 10-15 cm. It is connected to the data collector using RS485 bus wired transmission. The data collector collects data every 1 hour and uploads it to the data analysis and processing module. The soil moisture sensor is designed based on the principle of high-frequency oscillation. The probe is made of stainless steel with an anti-corrosion coating on the surface. It is buried horizontally in the soil 20-25 cm, 5-10 cm away from the crop roots. It communicates with nearby relay nodes through Bluetooth technology, and the relay nodes transmit the data to the monitoring center. The data is updated every 20 minutes. The soil temperature sensor uses a thermistor sensor, which is encapsulated in a stainless steel protective tube. The outside of the tube is filled with thermal conductive silicone. It is vertically inserted into the soil 15-20 cm and installed adjacent to the soil salinity sensor. The temperature data is transmitted in real time through the ZigBee wireless communication module, with a transmission interval of once every 15 minutes.

5. The soil quality improvement and microbial analysis integrated system according to claim 1, characterized in that: The detection method and frequency of the soil chemical property monitoring unit in the soil improvement effect monitoring module are as follows: For soil nutrient content testing, a portable soil nutrient rapid tester is used. Based on the principle of colorimetry, five soil samples are collected around each monitoring point using the quincunx sampling method, with a depth of 0-20 cm. After mixing, the samples are tested on site. The testing frequency is once a week, and the instrument is calibrated with a standard soil sample before each test. For the detection of soil organic matter content, potassium dichromate oxidation-external heating method is used. Three repeated samples are collected at the monitoring point every month, with a depth of 0-30 cm. The samples are naturally air-dried indoors and ground and sieved. A certain amount of soil sample is weighed and placed in a digestion tube. Quantitative potassium dichromate standard solution and concentrated sulfuric acid are added. The samples are heated to 170℃-180℃ in an oil bath and kept boiling for 5-10 minutes to fully oxidize the organic matter in the soil. After cooling, the remaining potassium dichromate is titrated with ferrous sulfate standard solution. The soil organic matter content is calculated based on the amount of ferrous sulfate consumed in the titration. Each sample is measured in parallel, and the relative deviation is controlled within 5%.

6. The soil quality improvement and microbial analysis integrated system according to claim 1, characterized in that: The analysis technology and operation process of the microbial characteristic monitoring unit in the soil improvement effect monitoring module are as follows: PCR-DGGE technology was used to analyze the microbial community structure: soil samples were collected at the monitoring points every month, DNA was extracted and then PCR amplified, denaturing gradient gel electrophoresis was prepared, and the bands in the map were analyzed to evaluate the diversity of community structure and succession law; Fluorescence quantitative PCR technology is used to detect the expression of microbial functional genes: primers and probes are designed for specific functional genes, standard curves are established, soil samples are collected monthly to detect gene expression, and changes in microbial functional activity are analyzed; BIOLOG-ECO technology analyzes the functional diversity of microbial carbon source substrate utilization: prepare inoculation solution, inoculate it into ECO plates for culture, read the light absorption value to calculate the average absorbance index, and analyze the functional diversity of microbial community carbon source utilization and changes in metabolic activity.

7. The integrated soil quality improvement and microbial analysis system according to claim 1, characterized in that: The data processing flow of the data receiving and integrating unit in the data analysis and processing module is as follows: Data reception: Receive monitoring module data through a combination of wireless and wired communications. Set up a data buffer for real-time monitoring data. When communication is interrupted, buffer data within 24 hours. Automatically upload after communication is restored, and verify data format at the same time. Data verification: range verification is performed on numerical data. Data beyond a reasonable range is marked as abnormal data. The abnormal time and monitoring point location are recorded. The integrity of the microbial community structure data is verified to check whether the clarity and quantity of the bands meet expectations. If not, it is marked as abnormal. Data redundancy verification is also used to compare the same type of data collected at the same monitoring point at different times and with different equipment. The accuracy is verified if the difference exceeds the preset threshold. Data cleaning, repairing abnormal data, using data interpolation algorithm to estimate reasonable values ​​for repairable data, deleting serious abnormal data that cannot be repaired and recording missing information, smoothing data noise with sliding average filter algorithm, and taking the average value of data within a certain time window as processed data; Data classification and storage: sort and organize data by data type, monitoring time, and monitoring point location, store it in a relational database, design a reasonable data table structure, and back it up regularly.

8. The soil quality improvement and microbial analysis integrated system according to claim 1, characterized in that: The algorithm implementation details of the data analysis unit in the data analysis and processing module are as follows: Soil quality-microorganism relationship model training: collect data on various types of soil in various improvement processes, including soil physical properties, chemical properties, microbial community composition, microbial functional gene expression levels and soil quality evaluation indicators, pre-process the data, divide the data set into 70% training set and 30% test set, use machine learning algorithm to train and build the model, adjust model parameters during training to optimize performance, use cross-validation to evaluate model accuracy and generalization ability, and ensure that the prediction accuracy of the test set is above 80%; Microbial community structure analysis algorithm: For PCR-DGGE map data, grayscale processing is first performed to reduce the amount of data and highlight the strip information. The edge detection algorithm is used to identify the strip edge, and the strip position and width are determined by threshold segmentation. The characteristic parameters of the strip gray value, area, and shape factor are calculated to construct a feature vector to characterize the microbial community structure. The cluster analysis algorithm is used to cluster the feature vectors of different samples, evaluate the similarity and difference of community structure, analyze the law of community structure succession, and combine the principal component analysis algorithm to reduce the high-dimensional microbial community structure data to two-dimensional and three-dimensional space, draw a PCA diagram, and reveal the potential relationship between microbial community structure and soil environmental factors; Microbial functional gene expression analysis algorithm: For the microbial functional gene expression data detected by fluorescent quantitative PCR, the differential expression analysis algorithm is used to determine the functional genes with significant differential expression under different soil improvement conditions, calculate the fold change and difference significance of gene expression, perform functional annotation and enrichment analysis on the differentially expressed genes, construct a gene regulatory network, analyze the interaction relationship between functional genes, explore key regulatory genes and gene modules, and deeply understand the functional regulatory mechanism of microorganisms in soil improvement.

9. The soil quality improvement and microbial analysis integrated system according to claim 1, characterized in that: The specific control strategy and early warning mechanism of the control and feedback module are as follows: Control strategy: When the data analysis and processing module determines that the soil quality improvement has not reached expectations and the microbial community has changed abnormally, the control and feedback module initiates the corresponding strategy. If the soil salinity decreases too slowly, the amount of microbial agent added is increased by 20%-50% of the original amount according to the microbial agent addition amount calculation formula of the microbial agent addition module. At the same time, the organic acid addition effect in the organic acid adjustment module is checked. If the pH value is not well adjusted, the organic acid concentration is recalculated and adjusted according to the organic acid concentration adjustment formula. The adjustment range is ±1-3mmol / L. The number of beneficial microorganisms in the microbial community is reduced. When adding microbial agents, this type of beneficial microbial strains are supplemented first. The supplementary amount is determined according to the degree of reduction, which is generally 1.5-2.5 times the original number of beneficial microorganisms. Early warning mechanism: Set multiple types of early warning indicators and thresholds. For soil quality indicators, including sudden increase in soil salinity, sharp changes in pH value, and low nutrient content, a first-level early warning is issued. The early warning methods are sound and light alarms, text message notifications to relevant management personnel and technical personnel, and detailed early warning information is displayed on the system interface. For microbial community indicators, including a significant decrease in the diversity of beneficial microbial communities and a large number of harmful microorganisms, a second-level early warning is issued. In addition to the above-mentioned early warning methods, an early warning report is automatically generated. The report contains detailed data on changes in the structure of microbial communities, possible impacts on soil ecosystems and crop growth, and preliminary response suggestions. The early warning threshold is dynamically adjusted according to different regions, soil types, and crop needs. The adjustment is based on the statistical analysis results of long-term accumulated historical data and real-time monitoring data. At the same time, a warning event record database is established to record detailed information on each warning event for subsequent analysis and experience summary, and to optimize the early warning mechanism and control strategy.

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