A remote monitoring system for intelligent information of facility agriculture

Abstract

The invention discloses a remote monitoring system for intelligent information of facility agriculture, which specifically relates to the technical field of intelligent agricultural monitoring, and comprises a microclimate monitoring module, a microclimate comprehensive analysis module, a soil microorganism monitoring module, a microorganism evaluation module and a remote management module; the microclimate monitoring module collects microclimate data in a greenhouse and forms a data set; the comprehensive analysis module performs deviation analysis on the microclimate data and determines whether to trigger an evaluation mechanism; the soil microorganism monitoring module collects microbial activity data in real time; the microorganism evaluation module performs quality evaluation when the evaluation mechanism is triggered and generates a quality evaluation value; the remote management module accesses data through a network and determines whether to issue an abnormal warning according to the evaluation value; the invention can issue an alarm in time at an early stage when the microbial activity is too high or too low, so that managers can take appropriate intervention measures to ensure the balance and stability of the soil environment, thereby improving the overall production efficiency of crops.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural intelligent monitoring, and more specifically, to a facility agriculture intelligent information remote monitoring system. Background Art

[0002] In controlled environments such as greenhouses, soil microbial activity and microclimate conditions play a vital role in crop growth. Microorganisms in the soil are not only responsible for decomposing organic matter and releasing nutrients, but also maintain the ecological balance of the soil, ensuring that crops can obtain a stable source of nutrients from the soil. However, microbial activity is affected by microclimate factors such as temperature, humidity, and soil respiration rate. Abnormal changes in microclimate may cause microbial activity to be too low or too high, thereby affecting crop growth.

[0003] Traditional facility agriculture monitoring systems mostly focus on environmental parameters such as temperature, humidity, and light, and rarely consider the impact of microbial activity on soil and crop growth. At the same time, existing monitoring methods are usually passive, lack intelligent early warning and analysis functions, and cannot intervene in the early stages of imbalanced microbial activity. This situation may lead to an imbalance in the microbial community, insufficient nutrient supply, and even a decline in soil quality, which is not conducive to the sustainable development of crops.

[0004] In order to solve the above problems, the present invention proposes a facility agriculture intelligent information remote monitoring system to solve the above problems. Summary of the invention

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A facility agriculture intelligent information remote monitoring system, including a microclimate monitoring module, a microclimate comprehensive analysis module, a soil microorganism monitoring module, a microorganism evaluation module, and a remote management module;

[0007] The microclimate monitoring module is used to monitor the preset microclimate data in the agricultural environment where the facility is located in real time, and summarize them to form a microclimate data set;

[0008] The microclimate comprehensive analysis module performs deviation analysis on each preset microclimate data based on the microclimate data set, and performs comprehensive analysis based on the results of all deviation analyses, and determines whether to trigger the evaluation mechanism based on the comprehensive analysis results;

[0009] The soil microorganism monitoring module is used to collect soil microorganism activity information in the agricultural environment where the facility is located in real time to obtain a soil microorganism activity information set;

[0010] When the evaluation mechanism is triggered, the microbial evaluation module performs a quality evaluation on soil microorganisms based on the soil microbial activity information set to obtain a quality evaluation value;

[0011] The remote management module realizes remote data access of the microclimate monitoring module, microclimate comprehensive analysis module, soil microorganism monitoring module, and microbial assessment module through network transmission, and determines whether to issue an abnormal warning based on the quality assessment value.

[0012] In a preferred embodiment, deviation analysis refers to:

[0013] In a fixed time window, the preset microclimate data of each type are arranged in chronological order to form a time series of microclimate data of this type. Then, the corresponding data of two adjacent time points in the time series are obtained and marked as , , t represents the sequence number in the time series, and then the following calculation is performed:

[0014] ; Indicates the time point corresponding to sequence number t, represents the deviation rate at time point t;

[0015] The adaptive interval standardization method is used to measure the deviation of the current data. The formula is:

[0016] ; Indicates the deviation amplitude value, Q1 and Q3 are the 25th percentile and 75th percentile of the deviation rate in the historical data respectively;

[0017] For the microclimate data of each type i, the absolute values ​​of the deviation amplitude values ​​calculated from the microclimate data of this type are summarized, and then the average value pj and standard deviation bz are calculated to obtain the data combination (i, pj, bz).

[0018] In a preferred embodiment, performing a comprehensive analysis based on the results of all deviation analyses refers to:

[0019] According to the data combination (i, pj, bz) of all categories of microclimate data, the average value pj of each category of microclimate data is weighted averaged to obtain the comprehensive deviation value one, and the standard deviation bz of each category of microclimate data is weighted averaged to obtain the comprehensive deviation value two.

[0020] In a preferred embodiment, judging whether to trigger the evaluation mechanism according to the comprehensive analysis result means:

[0021] The comprehensive deviation value 1 is compared with the preset evaluation trigger threshold 1, and the comprehensive deviation value 2 is compared with the preset evaluation trigger threshold 2. If the comprehensive deviation value 1 is less than or equal to the preset evaluation trigger threshold 1 and the comprehensive deviation value 2 is less than or equal to the preset evaluation trigger threshold 2, the evaluation mechanism will not be triggered. If the comprehensive deviation value 1 is not less than or equal to the preset evaluation trigger threshold 1 and the comprehensive deviation value 2 is not less than or equal to the preset evaluation trigger threshold 2, the evaluation mechanism will be triggered.

[0022] In a preferred embodiment, the logic for obtaining the quality assessment value is:

[0023] The microbial activity coefficient is calculated based on the decomposition rate of organic matter in the soil by microorganisms and the soil respiration rate during microbial activity.

[0024] The microbial stability coefficient was calculated based on microbial biomass carbon and microbial community diversity;

[0025] The microbial activity coefficient is marked as HD, and the microbial stability coefficient is marked as WD, and they are substituted into the following calculation formula:

[0026] ; , are preset proportional factors. Represents the quality assessment value.

[0027] In a preferred embodiment, the microbial activity coefficient calculated based on the decomposition rate of organic matter in the soil by microorganisms and the soil respiration rate during microbial activity refers to:

[0028] Calculation of organic matter decomposition rate index based on the decomposition rate of organic matter in soil by microorganisms :

[0029] ; Indicates the current rate of decomposition of organic matter in the soil by microorganisms. represents the geometric mean decomposition rate based on historical data, represents the decomposition rate of organic matter in soil by microorganisms in the i-th historical sample, n is the total number of historical samples, is the preset adjustment parameter;

[0030] Calculation of activity respiration index based on soil respiration rate in microbial activities :

[0031] ; When indicating microbial activity, the preset soil respiration rate standard value, It indicates the current value of soil respiration rate when microbial activity occurs;

[0032] The formula for calculating the microbial activity coefficient is: .

[0033] In a preferred embodiment, the microbial stability coefficient calculated based on microbial biomass carbon and microbial community diversity refers to:

[0034] Calculation of biomass carbon index based on microbial biomass carbon :

[0035] ; represents the historical average value of biomass carbon corresponding to the i-th historical sample, represents the current value of biomass carbon;

[0036] Calculation of diversity index based on microbial community diversity :

[0037] ; represents the relative abundance of the jth microbial community, and S is the total number of microbial populations;

[0038] The formula for calculating the microbial stability coefficient is: ; A preset constant.

[0039] In a preferred embodiment, judging whether to issue an abnormal warning according to the quality evaluation value refers to:

[0040] The quality assessment value is compared with the preset health threshold range. The upper limit of the health threshold range is health threshold 2, and the lower limit of the health threshold range is health threshold 1. If the quality assessment value is within the preset health threshold range, no abnormal warning is issued. If the quality assessment value is less than health threshold 1, a low activity abnormal warning is issued. If the quality assessment value is greater than health threshold 2, a high activity abnormal warning is issued.

[0041] Technical effects and advantages of the present invention:

[0042] This system can monitor multiple microclimate parameters (such as temperature, humidity, carbon dioxide concentration, etc.) and soil microbial activity data (such as organic matter decomposition rate, soil respiration rate, biomass carbon and microbial diversity, etc.) in the facility agricultural environment in real time, generating a comprehensive data set. This all-round monitoring can provide agricultural managers with rich environmental and microbial information, making the management of greenhouse and other facility environments more scientific and accurate.

[0043] Through the microclimate comprehensive analysis module, the system analyzes the degree of deviation of various microclimate parameters and determines whether the microbial assessment mechanism needs to be triggered based on the comprehensive evaluation results of all deviation data. In this way, when the microclimate parameters deviate from the normal range, it can quickly evaluate whether the ecological state of the microorganisms is stable, thereby identifying potential abnormal risks in a timely manner.

[0044] The microbial assessment module calculates the quality assessment value based on the real-time collected microbial activity information through multiple indicators (including microbial activity and stability) to accurately determine the health status of soil microorganisms. When the quality assessment value exceeds the preset health threshold range, the system will automatically trigger a low activity or high activity abnormal warning. This can promptly issue an alarm at the early stage of excessive or low microbial activity, allowing managers to take appropriate intervention measures to ensure the balance and stability of the soil environment.

[0045] By properly controlling microbial activity and soil ecological stability, this system can provide crops with a more optimized growth environment, which helps to increase crop growth rate and yield. At the same time, healthy microbial communities can effectively decompose organic matter in the soil, continuously supply the nutrients needed by crops, improve crop quality and disease resistance, and thus improve the overall production efficiency of crops.

[0046] This system uses the remote management module to achieve remote data access and management of each monitoring module. Agricultural managers can grasp the microclimate and soil microbial conditions in the greenhouse at any time through the network. This convenient remote management function not only improves the management efficiency of agricultural production, but also reduces the workload of manual inspections, reduces human errors, and improves the timeliness and accuracy of management.

[0047] The system of the present invention integrates multiple functions such as real-time monitoring, intelligent analysis, automatic early warning, and remote management, bringing a higher level of intelligent management to agriculture, which can greatly reduce production risks, improve crop quality, and reduce dependence on the environment. It meets the needs of modern agriculture for precise management and sustainable development, and promotes the development of agriculture towards automation and intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0049] Figure 1 The schematic diagram of a remote monitoring system for intelligent information of facility agriculture in the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Example 1

[0052] The present invention mainly focuses on the impact of microbial changes on the crop growth environment. In the crop growth environment under a fixed combination of environmental parameters, such as greenhouse crops, light, temperature, and humidity regulation are all fixed parameter adjustments. Therefore, changes in microclimate are more a reflection of microbial activity rather than a direct object of artificial adjustment. The core of the present invention is to detect abnormal microbial activity, thereby judging the degree of impact on the health status of crops, which is conducive to taking timely measures to ensure the health of microorganisms and ultimately stabilize the crop growth environment.

[0053] Reference Figure 1 , a facility agriculture intelligent information remote monitoring system, including a microclimate monitoring module, a microclimate comprehensive analysis module, a soil microorganism monitoring module, a microorganism evaluation module, and a remote management module;

[0054] The microclimate monitoring module is used to monitor the preset microclimate data in the agricultural environment where the facility is located in real time, and summarize them to form a microclimate data set; the function of the microclimate monitoring module is to monitor various microclimate parameters in the facility agricultural environment in real time, such as temperature, humidity, light intensity, carbon dioxide concentration, etc. The real-time collection of these data helps to timely understand and analyze the environmental conditions in the facility to ensure that they are suitable for crop growth. By summarizing these data to form a microclimate data set, it provides a basis for subsequent deviation analysis and comprehensive analysis.

[0055] The microclimate comprehensive analysis module performs deviation analysis on each preset microclimate data based on the microclimate data set, and performs comprehensive analysis based on the results of all deviation analyses, and determines whether to trigger the evaluation mechanism based on the comprehensive analysis results; this module uses the microclimate data set provided by the microclimate monitoring module to perform deviation analysis on various parameters and calculate the degree of deviation of each parameter relative to the normal value. The comprehensive analysis module generates a comprehensive analysis result by calculating the deviation amplitude and volatility of different microclimate data to determine whether there is a significant abnormality in the current environment. When the degree of deviation exceeds the preset threshold, the evaluation mechanism is triggered to further analyze the potential impact of environmental changes, that is, the living environment of microorganisms in the soil, on crops.

[0056] The soil microorganism monitoring module is used to collect soil microbial activity information in the agricultural environment where the facility is located in real time to obtain a soil microbial activity information set; the soil microbial monitoring module focuses on collecting soil microbial activity data in the facility agricultural environment, including the decomposition rate of soil organic matter by microorganisms, soil respiration rate, biomass carbon and community diversity. Through the real-time collection of these data, the activity of soil microorganisms can be obtained, the impact of microorganisms on soil health can be understood, and it helps to determine whether the soil environment is suitable for crop growth. These data form a soil microbial activity information set, which provides a basis for subsequent quality assessment.

[0057] When the assessment mechanism is triggered, the microbial assessment module conducts a quality assessment on soil microorganisms based on the soil microbial activity information set to obtain a quality assessment value; the microbial assessment module is activated when the assessment mechanism is triggered, and conducts a quality assessment based on the soil microbial activity information set. By calculating the microbial activity coefficient (based on the organic matter decomposition rate and respiration rate) and the microbial stability coefficient (based on biomass carbon and community diversity), a comprehensive quality assessment value is obtained. The quality assessment value is used to measure the activity and stability of soil microorganisms, thereby judging the health of the soil microbial ecology and providing a basis for facility management.

[0058] The remote management module transmits data through the network to achieve remote data access to the microclimate monitoring module, microclimate comprehensive analysis module, soil microorganism monitoring module, and microbial assessment module, and determines whether to issue an abnormal warning based on the quality assessment value. The remote management module is used to achieve remote access to the data of each module and abnormal warning management. Users can use this module to monitor the microclimate and microbial data in the greenhouse, focusing on changes in microbial quality assessment values. When the quality assessment value exceeds the set health threshold range, the system will trigger an abnormal warning to notify the user of the possible risk of microbial ecological imbalance. Users can use this warning information to take targeted measures, such as supplementing soil nutrients and adjusting organic matter input, to stabilize the state of microorganisms without directly adjusting fixed environmental parameters such as temperature and humidity.

[0059] Deviation analysis refers to:

[0060] In a fixed time window, the preset microclimate data of each type are arranged in chronological order to form a time series of microclimate data of this type. Then, the corresponding data of two adjacent time points in the time series are obtained and marked as , , t represents the sequence number in the time series, and then the following calculation is performed:

[0061] ; Indicates the time point corresponding to sequence number t, Represents the deviation rate at time point t; the rate of change of microclimate data at each time point is obtained by calculating the difference between adjacent time points. This rate analysis can capture the fluctuation of microclimate parameters and help detect the dynamic changes of microclimate. The calculation of rate introduces the time difference , to avoid excessive amplification of fluctuations with smaller time intervals. This processing method can ensure the relative stability of the rate and better reflect the natural fluctuations of microclimate changes.

[0062] The adaptive interval standardization method is used to measure the deviation of the current data. The formula is:

[0063] ; It represents the deviation amplitude value, Q1 and Q3 are the 25th percentile and 75th percentile of the deviation rate in the historical data respectively; the data is normalized by the 25th percentile (Q1) and 75th percentile (Q3) in the historical data using the quartile method. This method can reduce the impact of extreme values ​​on the calculation, making the calculation of deviation more representative. The deviation amplitude value S(t) measures the degree to which the current data deviates from the historical trend, which can help identify significant abnormal data points and reflect the overall stability of the microclimate in this time window.

[0064] For each type of microclimate data i, the absolute values ​​of the deviation amplitude values ​​calculated for the microclimate data of this type are summarized, and then the average value pj and standard deviation bz are calculated to obtain the data combination (i, pj, bz). This combination can clearly show the deviation from the average value and volatility of each microclimate parameter, which helps to identify whether the microclimate parameter deviates from the expected stable state. The average deviation value indicates the overall degree of deviation, while the standard deviation reflects the volatility of the deviation. The combination of the two provides a clear indicator of microclimate stability and abnormality.

[0065] Comprehensive analysis based on the results of all deviation analyses means: according to the data combination (i, pj, bz) of all categories of microclimate data, the average value pj of each category of microclimate data is weighted averaged to obtain a comprehensive deviation value 1, and the standard deviation bz of each category of microclimate data is weighted averaged to obtain a comprehensive deviation value 2. The comprehensive deviation value 1 is compared with the preset evaluation trigger threshold 1, and the comprehensive deviation value 2 is compared with the preset evaluation trigger threshold 2. If the comprehensive deviation value 1 is less than or equal to the preset evaluation trigger threshold 1 and the comprehensive deviation value 2 is less than or equal to the preset evaluation trigger threshold 2, the evaluation mechanism is not triggered. If the comprehensive deviation value 1 is less than or equal to the preset evaluation trigger threshold 1 and the comprehensive deviation value 2 is less than or equal to the preset evaluation trigger threshold 2, the evaluation mechanism is triggered.

[0066] The comprehensive deviation value 1 represents the average deviation of all microclimate data categories, which is obtained by weighted calculation of the deviation from the average value of each microclimate data. When calculating, the average deviation value of each microclimate data category is multiplied by its corresponding weight, and then the weighted results of all categories are added. In this way, the microclimate data with more significant impact on the environment can be highlighted, so that they occupy a higher proportion in the comprehensive deviation value 1. The comprehensive deviation value 2 represents the fluctuation of various types of microclimate data, which is obtained by weighted calculation of the volatility (i.e., standard deviation) of each microclimate data category. When calculating, the standard deviation of each microclimate data category is multiplied by the corresponding weight, and then all weighted results are added. In this way, the microclimate data with a greater impact on the overall volatility can be reflected, so that it has a higher contribution in the comprehensive deviation value 2. The comprehensive deviation value 1 and the comprehensive deviation value 2 are compared with the preset trigger threshold to determine whether the evaluation mechanism is triggered, and further evaluation operations can be performed if necessary, that is, subsequent evaluation of microorganisms.

[0067] The logic for obtaining the quality assessment value is:

[0068] The microbial activity coefficient is calculated based on the decomposition rate of organic matter in the soil by microorganisms and the soil respiration rate during microbial activity; the activity level of microorganisms (such as the decomposition rate of organic matter and the respiration rate) reflects the efficiency and activity intensity of microorganisms in utilizing soil organic matter. Active microbial activity usually means a healthy soil environment, but if the activity is too high or too low, it may affect the balance of soil nutrients. The microbial activity coefficient helps measure the activity level of microorganisms.

[0069] The microbial stability coefficient was calculated based on microbial biomass carbon and microbial community diversity; the stability of the microbial community (biomass carbon and diversity) represents the balance of the microbial population structure and the resilience of the ecosystem. A diverse and stable microbial community can enhance the anti-disturbance ability of the soil ecosystem.

[0070] The microbial activity coefficient is marked as HD, and the microbial stability coefficient is marked as WD, and they are substituted into the following calculation formula:

[0071] ; , These are preset proportional coefficients used to balance the effects of microbial activity and stability on the quality assessment value ZL. Represents the quality assessment value. Logarithmic transformation can compress the numerical range of HD and WD, thereby reducing the impact of extreme values ​​and making the calculated ZL more stable and representative. After logarithmic transformation, the proportional relationship between the activity coefficient HD and the stability coefficient WD is retained, and can more sensitively reflect the impact of lower values. The final exponential function exp is to remap the logarithmic transformed results back to the original numerical range, making ZL more intuitive and easy to interpret. The quality assessment value ZL increases with the increase of HD and WD. If the ZL value is high, it usually means that the microorganisms are active and the ecology is stable, and the soil health is good; but there will be problems when the ZL value is too high. If the ZL value is low, it may indicate insufficient microbial activity or unstable ecosystem, suggesting that the soil microbial environment needs to be paid attention to and improved. This evaluation method can provide a reliable reference for soil health in agriculture and help decision-making to optimize the growth environment of microorganisms and crops.

[0072] The microbial activity coefficient is calculated based on the decomposition rate of organic matter in the soil by microorganisms and the soil respiration rate during microbial activity. It refers to:

[0073] Calculation of organic matter decomposition rate index based on the decomposition rate of organic matter in soil by microorganisms :

[0074] ; Indicates the current rate of decomposition of organic matter in the soil by microorganisms. It represents the geometric mean decomposition rate based on historical data, and the average decomposition rate of soil organic matter by microorganisms under multiple historical samples. represents the decomposition rate of organic matter in soil by microorganisms in the i-th historical sample, n is the total number of historical samples, It is a preset adjustment parameter used to adjust the sensitivity of the index; by comparing the geometric mean of the current decomposition rate with the historical decomposition rate, it reflects whether the current organic matter decomposition is within the normal range. If the current decomposition rate is much higher than the geometric mean, the H1 value will be larger, which means that the microbial activity is higher; conversely, if the decomposition rate is lower, the H1 value will be smaller. This calculation method can avoid the influence of extreme values, because the geometric mean is more suitable for data with a wider distribution range than the arithmetic mean.

[0075] Calculation of activity respiration index based on soil respiration rate in microbial activities :

[0076] ; When indicating microbial activity, the preset soil respiration rate standard value, When representing microbial activity, the corresponding current value of soil respiration rate is the activity intensity of microorganisms in the soil; the degree of change in microbial activity is measured by the square root of the difference between the current respiration rate and the standard respiration rate. If the respiration rate deviates significantly from the standard value, it means that the microbial activity is abnormal (too high or too low). Square root processing can smooth extreme deviations and is suitable for observing the volatility of microbial activity.

[0077] The formula for calculating the microbial activity coefficient is: The microbial activity coefficient HD combines H1 and H2 through a harmonic mean. The harmonic mean is used in a way that smaller values ​​have a greater impact on the result, so if either the decomposition rate index or the respiration rate index is low, the overall activity coefficient will also be low. This approach helps balance the two indices and ensures that the overall situation of microbial activity is accurately assessed.

[0078] The microbial stability coefficient calculated based on microbial biomass carbon and microbial community diversity refers to:

[0079] Calculation of biomass carbon index based on microbial biomass carbon :

[0080] ; represents the historical average value of biomass carbon corresponding to the i-th historical sample, Indicates the current value of biomass carbon, i.e. the carbon content of microorganisms in the soil; the biomass carbon index W1 measures the relative level of current microbial biomass by comparing the current biomass carbon with the historical average biomass carbon. The comparison results are logarithmically transformed to smooth the impact of extreme values ​​and make the index more stable. If the current biomass carbon is significantly higher than the historical average, W1 will be larger, indicating that the microbial biomass is relatively sufficient.

[0081] Calculation of diversity index based on microbial community diversity :

[0082] ; represents the relative abundance of the jth microbial community, indicating the proportion of this community in the overall microorganisms, S is the total number of microbial populations, indicating the diversity of the microbial population; the diversity index W2 weights the relative abundance by introducing a power (1.5), which enhances the weight of rare communities and makes the community diversity more significant. If the distribution of the microbial community is relatively uniform and there are multiple microbial communities, the value of W2 will be larger, indicating that the community diversity is higher. This index structure can help capture the diversity and stability of microbial ecosystems.

[0083] The formula for calculating the microbial stability coefficient is: ; The microbial stability coefficient WD is a preset constant. It reflects the overall ecological stability of microorganisms by combining the biomass carbon index and the diversity index. The square root operation can reduce the sensitivity of the index and make the overall stability assessment smoother. This index reflects the health and stability of the microbial ecosystem when both biomass carbon and diversity are high.

[0084] Judging whether to issue an abnormal warning based on the quality assessment value means:

[0085] The quality assessment value is compared with the preset health threshold range. The upper limit of the health threshold range is health threshold 2, and the lower limit of the health threshold range is health threshold 1. If the quality assessment value is within the preset health threshold range, no abnormal warning is issued. If the quality assessment value is less than health threshold 1, a low activity abnormal warning is issued. If the quality assessment value is greater than health threshold 2, a high activity abnormal warning is issued.

[0086] In most cases, the larger the quality assessment value, the better, because it usually means higher microbial activity and ecological stability. However, in practical applications, high thresholds should be combined for abnormal warnings, because excessive microbial activity (too high assessment value) may also lead to ecological imbalance. Therefore, the larger the better is relative, and it still needs to be judged within a reasonable range. By setting the healthy threshold range, the system can clearly define the normal state of microbial ecology. When the quality assessment value ZL is within this range, it means that the activity and stability of soil microorganisms are at a healthy level, so no abnormal warning is required.

[0087] The upper and lower limits of the health threshold range (health threshold 2 and health threshold 1) are used to distinguish between excessively high and low states of microbial activity, respectively.

[0088] Low activity abnormal warning: When the quality assessment value ZL is lower than the health threshold of 1, the system determines that the microbial activity is lower than the expected level, which may indicate that the microbial activity in the soil is insufficient or the stability of the microbial community is poor. Low activity may slow down the soil nutrient cycle and affect crop growth. At this time, the system will trigger a low activity abnormal warning to remind managers to take measures (such as adding organic fertilizers, improving the soil environment, etc.) to improve microbial activity.

[0089] High activity abnormal warning: When the quality assessment value ZL is higher than the health threshold 2, the system determines that the microbial activity is too active, which may lead to excessive consumption of soil organic matter or community imbalance, and may even cause soil ecological imbalance. At this time, the system will trigger a high activity abnormal warning to remind managers to take corresponding measures to control microbial activity and prevent excessive microbial activity from causing adverse effects on soil ecology.

[0090] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0091] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0092] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0093] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0094] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A facility agriculture intelligent information remote monitoring system, characterized in that: Including microclimate monitoring module, microclimate comprehensive analysis module, soil microorganism monitoring module, microorganism assessment module, and remote management module; The microclimate monitoring module is used to monitor the preset microclimate data in the agricultural environment where the facility is located in real time, and summarize them to form a microclimate data set; The microclimate comprehensive analysis module performs deviation analysis on each preset microclimate data based on the microclimate data set, and performs comprehensive analysis based on the results of all deviation analyses, and determines whether to trigger the evaluation mechanism based on the comprehensive analysis results; The soil microorganism monitoring module is used to collect soil microorganism activity information in the agricultural environment where the facility is located in real time to obtain a soil microorganism activity information set; When the evaluation mechanism is triggered, the microbial evaluation module performs a quality evaluation on soil microorganisms based on the soil microbial activity information set to obtain a quality evaluation value; The remote management module transmits data through the network to achieve remote data access to the microclimate monitoring module, microclimate comprehensive analysis module, soil microorganism monitoring module, and microbial assessment module, and determines whether to issue an abnormal warning based on the quality assessment value; The microbial activity coefficient is calculated based on the decomposition rate of organic matter in the soil by microorganisms and the soil respiration rate during microbial activity. The microbial stability coefficient was calculated based on microbial biomass carbon and microbial community diversity; The microbial activity coefficient is marked as HD, and the microbial stability coefficient is marked as WD, and they are substituted into the following calculation formula: ; , are preset proportional factors. represents the quality assessment value; The microbial activity coefficient is calculated based on the decomposition rate of organic matter in the soil by microorganisms and the soil respiration rate during microbial activity. It refers to: Calculation of organic matter decomposition rate index based on the decomposition rate of organic matter in soil by microorganisms : ; Indicates the current rate of decomposition of organic matter in the soil by microorganisms. represents the geometric mean decomposition rate based on historical data, represents the decomposition rate of organic matter in soil by microorganisms in the i-th historical sample, n is the total number of historical samples, is the preset adjustment parameter; Calculation of activity respiration index based on soil respiration rate in microbial activities : ; When indicating microbial activity, the preset soil respiration rate standard value, It indicates the current value of soil respiration rate when microbial activity occurs; The formula for calculating the microbial activity coefficient is: ; The microbial stability coefficient calculated based on microbial biomass carbon and microbial community diversity refers to: Calculation of biomass carbon index based on microbial biomass carbon : ; represents the historical average value of biomass carbon corresponding to the i-th historical sample, represents the current value of biomass carbon; Calculation of diversity index based on microbial community diversity : ; represents the relative abundance of the jth microbial community, and S is the total number of microbial populations; The formula for calculating the microbial stability coefficient is: ; A preset constant.

2. The facility agriculture intelligent information remote monitoring system according to claim 1 is characterized in that: Deviation analysis refers to: In a fixed time window, the preset microclimate data of each type are arranged in chronological order to form a time series of microclimate data of this type. Then, the corresponding data of two adjacent time points in the time series are obtained and marked as , , t represents the sequence number in the time series, and then the following calculation is performed: ; Indicates the time point corresponding to sequence number t, represents the deviation rate at time point t; The adaptive interval standardization method is used to measure the deviation of the current data. The formula is: ; Indicates the deviation amplitude value, Q1 and Q3 are the 25th percentile and 75th percentile of the deviation rate in the historical data respectively; For the microclimate data of each type i, the absolute values ​​of the deviation amplitude values ​​calculated from the microclimate data of this type are summarized, and then the average value pj and standard deviation bz are calculated to obtain the data combination (i, pj, bz).

3. The facility agriculture intelligent information remote monitoring system according to claim 2 is characterized in that: A comprehensive analysis based on the results of all deviation analyses refers to: According to the data combination (i, pj, bz) of all categories of microclimate data, the average value pj of each category of microclimate data is weighted averaged to obtain the comprehensive deviation value one, and the standard deviation bz of each category of microclimate data is weighted averaged to obtain the comprehensive deviation value two.

4. The facility agriculture intelligent information remote monitoring system according to claim 3 is characterized in that: Judging whether to trigger the evaluation mechanism based on the comprehensive analysis results means: The comprehensive deviation value 1 is compared with the preset evaluation trigger threshold 1, and the comprehensive deviation value 2 is compared with the preset evaluation trigger threshold 2. If the comprehensive deviation value 1 is less than or equal to the preset evaluation trigger threshold 1 and the comprehensive deviation value 2 is less than or equal to the preset evaluation trigger threshold 2, the evaluation mechanism will not be triggered. If the comprehensive deviation value 1 is not less than or equal to the preset evaluation trigger threshold 1 and the comprehensive deviation value 2 is not less than or equal to the preset evaluation trigger threshold 2, the evaluation mechanism will be triggered.

5. The facility agriculture intelligent information remote monitoring system according to claim 4, characterized in that: Judging whether to issue an abnormal warning based on the quality assessment value means: The quality assessment value is compared with the preset health threshold range. The upper limit of the health threshold range is health threshold 2, and the lower limit of the health threshold range is health threshold 1. If the quality assessment value is within the preset health threshold range, no abnormal warning is issued. If the quality assessment value is less than health threshold 1, a low activity abnormal warning is issued. If the quality assessment value is greater than health threshold 2, a high activity abnormal warning is issued.

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