Five-pin soil multi-parameter sensor

Abstract

The invention relates to the technical field of soil component analysis, in particular to a five-pin soil multi-parameter sensor, which comprises a soil parameter measurement and calibration module, a measurement data integrity guarantee module, a measurement frequency dynamic adjustment module, a soil state change monitoring module and a regional state distribution identification module, and the measurement frequency is dynamically adjusted. According to the method, energy consumption of data is reduced when the environment is stable, meanwhile, quick response is achieved when conditions change, the real-time performance and accuracy of monitoring are improved, data integrity is enhanced through dual storage and a re-measurement mechanism when necessary, information completeness is guaranteed, and in addition, through accurate time point and regional anomaly analysis, the accuracy of data analysis is improved. According to the scheme, the risk area is effectively identified and positioned, decision support is optimized, and soil monitoring is more scientific and efficient.

Description

Technical Field

[0001] The invention relates to the technical field of soil component analysis, and in particular to a five-pin soil multi-parameter sensor. Background Art

[0002] The technical field of soil composition analysis includes the measurement and analysis of various physical, chemical and biological properties in the soil. The core content involves the use of measuring devices or sensors to detect soil moisture, conductivity, pH value, organic matter content, nitrogen, phosphorus, potassium and other nutrient elements, and obtain soil status information through data collection, analysis and storage. The overall technical field covers technical means such as electrochemical sensing, spectral analysis, electromagnetic induction and physical penetration, which are suitable for multiple application scenarios such as agriculture, environmental monitoring, and land resource assessment. During the measurement process, fixed or portable equipment is often used, combined with data transmission and storage methods to achieve real-time or regular monitoring of soil parameters.

[0003] Among them, the five-pin soil multi-parameter sensor refers to a sensing device that uses five metal pins as electrodes to measure different physical and chemical parameters in the soil. The sensor measures soil conductivity through the electrochemical properties of the metal pins, uses a temperature compensation circuit to correct the effect of ambient temperature on the measurement results, and calculates soil moisture through impedance changes between pins. pH measurement is based on the potential response of ion-selective electrodes, and the pin spacing and arrangement are optimized to improve measurement accuracy and stability. The sensor uses an integrated packaging method to reduce the interference of environmental factors on measurement accuracy, and converts the collected analog signals into digital signals through a signal conversion circuit for subsequent data processing and storage.

[0004] Existing technologies lack a complete integrity check and compensation mechanism for measurement data storage. Missing data may lead to incomplete monitoring results and affect subsequent analysis. The frequency of data collection is often fixed and fails to be dynamically adjusted according to changes in the environment and soil state, resulting in increased energy consumption when measurements are too frequent and difficulty in capturing mutations when measurements are too spaced, affecting the accuracy of monitoring. The detection of abnormal changes mainly relies on single or intermittent measurements, and lacks rate analysis based on multiple time points, resulting in insufficient recognition of the temporal nature of state changes, resulting in inaccurate delineation of abnormal areas, which may affect subsequent management and decision-making. Soil state distribution analysis is mostly based on numerical changes at a single measurement point, and does not fully utilize trend data for spatial division, resulting in a lack of accuracy in the delineation of risk areas and difficulty in providing high-precision soil state monitoring and early warning support. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a five-pin soil multi-parameter sensor.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a five-pin soil multi-parameter sensor comprises:

[0007] The soil parameter measurement and calibration module obtains the measurement data of the five-pin soil multi-parameter sensor, and stores and processes the set reference value and the screened value to obtain the soil parameter data;

[0008] The measurement data integrity assurance module checks the integrity of the main storage unit based on the soil parameter data, and transmits it if it is complete; if it is missing, the backup storage unit is called to supplement it; if both are missing, it is re-measured and stored in the main storage unit and the backup storage unit to obtain the measurement data results;

[0009] The measurement frequency dynamic adjustment module obtains the ambient temperature and humidity based on the measurement data results, analyzes the change amplitude, and extends the measurement interval if it is less than the threshold, shortens the interval if it is greater than the threshold, extends the interval if it tends to be stable, shortens the interval and increases the number of measurements if it changes suddenly, and obtains the dynamically adjusted measurement interval result;

[0010] The soil state change monitoring module analyzes the measurement interval results dynamically adjusted according to the measurement sequence, screens the drastic change points, and obtains the abnormal state change area;

[0011] The regional state distribution identification module obtains the soil parameter change trend based on the abnormal state change area, screens the area where the humidity decrease amplitude and rate exceed the threshold, delineates the risk area, and obtains the soil state distribution result.

[0012] As a further solution of the present invention, the soil parameter data includes conductivity data, humidity data, pH value data, ambient temperature data, GPS positioning data, and measurement time data; the measurement data results include complete data, missing data, and supplementary data; the dynamically adjusted measurement interval results include extending the interval duration, shortening the interval duration, and increasing the number of measurements; the abnormal state change area includes the abnormal change time point, the corresponding GPS positioning information, and the change rate data; the soil state distribution results include humidity decrease areas, rate exceeding threshold areas, and drought risk areas.

[0013] As a further solution of the present invention, the soil parameter measurement and calibration module includes:

[0014] The data acquisition submodule obtains the soil conductivity, humidity, and pH value of the five-pin soil multi-parameter sensor, and collects the ambient temperature, ambient humidity, GPS positioning information, and measurement time. It calls the collected data for format conversion, standardizes the differentiated numerical data, and calculates the spatial distribution between data points based on the GPS positioning information to obtain a standardized measurement data set.

[0015] The measurement value analysis submodule calls the measured values ​​of soil conductivity, humidity, and pH value based on the standardized measurement data set, compares them with the set reference values, calculates the parameter offset rate, screens the data points exceeding the threshold value, and corrects the offset rate in combination with the influence coefficients of ambient temperature and ambient humidity to obtain the screened parameter offset rate;

[0016] The data storage submodule calls the parameter offset rate after screening, sorts by measurement time, stores in partitions according to GPS positioning information, calculates the partition parameter change trend, establishes a partition soil parameter database, and obtains soil parameter data.

[0017] As a further solution of the present invention, the parameter shift rate calculation formula is specifically:

[0018]

[0019] Among them, δ i represents the parameter deviation rate of the i-th measurement point, M i represents the measured value of soil conductivity, moisture or pH at the i-th measuring point, R i represents the set reference value of soil conductivity, moisture or pH value at the i-th measuring point, T i Represents the temperature value of the environment where the measurement point is located, H i Represents the humidity value of the environment where the measurement point is located.

[0020] As a further solution of the present invention, the measurement data integrity assurance module includes:

[0021] The data archiving submodule obtains the soil parameter data, classifies and archives them according to the GPS positioning information, calls the data to check the timestamp, and obtains the classified archived data;

[0022] The data integrity check submodule checks the data integrity of the main storage unit based on the classified archived data, compares the time stamp sequence, screens the abnormal data points in the time interval, determines the missing data, and if it is complete, marks it as transmittable; if it is missing, marks the missing data point to obtain the missing data mark;

[0023] The missing data supplement submodule calls the missing data mark, queries the spare storage unit to obtain the corresponding data, and updates the main storage unit if the data can be supplemented, otherwise re-measures and stores the data to obtain the measurement data result.

[0024] As a further solution of the present invention, the measurement frequency dynamic adjustment module includes:

[0025] The environmental change analysis submodule obtains the measurement data results, calls the ambient temperature and ambient humidity values, calculates the measurement value change amplitude, compares the set threshold, filters the data points below and above the threshold, marks them as low change intervals and high change intervals respectively, monitors the change trend of the data points, identifies the stable and sudden change states, and obtains the environmental change interval mark;

[0026] The measurement interval adjustment submodule calls the environmental change interval mark, monitors the change rate of soil conductivity, humidity, and pH value, analyzes the rate change trend, determines the stable and mutation intervals, adjusts the measurement interval, and obtains the dynamically adjusted measurement interval result.

[0027] As a further solution of the present invention, the calculation formula of the measurement value change amplitude is specifically:

[0028]

[0029] Among them, ΔS j Represents the change amplitude of the measured value at the jth measurement point, M j represents the measured value of the jth measurement point, M j-1 represents the measured value of the j-1th measurement point, T j represents the measurement time of the jth measurement point, T j-1 represents the measurement time of the j-1th measurement point, E j represents the ambient temperature of the jth measurement point, E j-1 represents the ambient temperature of the j-1th measurement point, U j represents the ambient humidity at the jth measurement point, U j-1 represents the ambient humidity at the j-1th measurement point, As an environmental change correction item, it reflects the impact of temperature and humidity on the rate of change of the measured value.

[0030] As a further solution of the present invention, the soil state change monitoring module includes:

[0031] The multi-time point measurement submodule obtains the dynamically adjusted measurement interval results, calls soil conductivity, humidity, and pH value data, sorts them by measurement interval, constructs a time series, and obtains measurement time series data;

[0032] The change rate calculation submodule calculates the change rates of soil conductivity, humidity, and pH value between adjacent time points based on the measurement time series data, compares the rate increments within adjacent measurement intervals, selects the time points with prominent changes, extracts the rate mutation area, and obtains the rate mutation time points;

[0033] The abnormal area identification submodule calls the rate mutation time point, extracts the corresponding GPS information, aggregates the data according to the spatial distribution, calculates the abnormal area range, and obtains the abnormal state change area.

[0034] As a further solution of the present invention, the regional state distribution identification module includes:

[0035] The soil trend calculation submodule obtains the abnormal state change area, calls the time series data of soil conductivity, humidity, and pH value, analyzes the change trend, extracts the trend characteristics in the area, and obtains the soil change trend;

[0036] The humidity drop screening submodule screens the areas with large humidity drop based on the soil change trend, compares the humidity drop rate threshold, extracts the super-threshold area, calculates the humidity change rate gradient value, marks the spatial distribution with faster rate, identifies the rate mutation area, and obtains the humidity drop rate area;

[0037] The drought risk delineation submodule calls the humidity drop rate area, divides the spatial range according to the GPS information, calculates the boundary of the drought risk area, generates a distribution zoning map, and obtains the soil state distribution result.

[0038] As a further solution of the present invention, the humidity change rate calculation formula is specifically:

[0039]

[0040] Among them, Rate humidity Represents the rate of change of humidity, ΔH current Represents the humidity value of the current measurement point, ΔH previous represents the humidity value of the previous measurement point, Δt represents the time interval between two measurements, H threshold is the threshold of humidity change.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are:

[0042] In the present invention, energy consumption is reduced when the environment is stable through data, and a rapid response is made when conditions change, thereby improving the real-time and accuracy of monitoring. Data integrity is enhanced through dual storage and a re-measurement mechanism when necessary, thereby ensuring the completeness of information. In addition, through precise time point and regional anomaly analysis, the scheme effectively identifies and locates risk areas, optimizes decision support, and makes soil monitoring more scientific and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 is a system flow chart of the present invention;

[0045] Figure 2 It is a submodule flow chart of the present invention;

[0046] Figure 3 It is a flow chart of the soil parameter measurement and calibration module of the present invention;

[0047] Figure 4 This is a flow chart of the measurement data integrity assurance module of the present invention;

[0048] Figure 5 This is a flow chart of the measurement frequency dynamic adjustment module of the present invention;

[0049] Figure 6 It is a flow chart of the soil state change monitoring module of the present invention;

[0050] Figure 7 This is a flow chart of the regional state distribution identification module of the present invention. DETAILED DESCRIPTION

[0051] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0052] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0053] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0054] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0055] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0056] See also Figure 1 , the five-pin soil multi-parameter sensor includes:

[0057] The soil parameter measurement and calibration module obtains the measurement data of the five-pin soil multi-parameter sensor, including soil conductivity, humidity, and pH value, and simultaneously records the ambient temperature, ambient humidity, GPS positioning information, and measurement time. It performs multiple measurements, obtains the measurement values, compares and analyzes the measurement values ​​with the set reference values, filters the data, and stores them to obtain soil parameter data.

[0058] The measurement data integrity assurance module compares data based on soil parameter data, archives data according to GPS positioning information, and checks the data integrity in the main storage unit. If it is complete, the data is directly transmitted. If there is a missing data, the backup storage unit is called to supplement the data. If both storage units are missing, the data is re-measured and stored in the main storage unit and the backup storage unit to obtain the measurement data results.

[0059] The measurement frequency dynamic adjustment module obtains the values ​​of ambient temperature and ambient humidity based on the measurement data results, analyzes the measurement change amplitude, and compares it with the set threshold. If the change amplitude is less than the threshold, the measurement interval is extended; if the change amplitude is greater than the threshold, the measurement interval is shortened. At the same time, the change rate of soil conductivity, humidity, and pH value is monitored. If the rate change tends to be stable, the measurement interval is extended; if a sudden change occurs, the measurement interval is shortened and the number of measurements is increased to obtain the dynamically adjusted measurement interval result;

[0060] The soil state change monitoring module obtains soil conductivity, humidity, and pH values ​​at multiple time points based on the dynamically adjusted measurement interval results, arranges them in order of measurement intervals, analyzes the change rates between adjacent time points, and screens the time points with drastic rate changes, and extracts the GPS information corresponding to the time points to obtain the abnormal state change areas;

[0061] The regional state distribution identification module obtains the changing trends of soil conductivity, humidity, and pH value based on the abnormal state change areas, divides the space according to the GPS information, marks the areas where the soil humidity decreases significantly, and screens the areas where the humidity decrease rate exceeds the set threshold, delineates the drought risk areas, and obtains the soil state distribution results.

[0062] Soil parameter data include conductivity data, humidity data, pH value data, ambient temperature data, GPS positioning data, and measurement time data. The measurement data results include complete data, missing data, and supplementary data. The dynamically adjusted measurement interval results include extending the interval duration, shortening the interval duration, and increasing the number of measurements. The abnormal state change area includes the abnormal change time point, the corresponding GPS positioning information, and the change rate data. The soil state distribution results include the humidity decrease area, the rate exceeding the threshold area, and the drought risk area.

[0063] See also Figure 3 and Figure 2 , soil parameter measurement and calibration module includes:

[0064] The data acquisition submodule obtains the soil conductivity, humidity, and pH value of the five-pin soil multi-parameter sensor, and collects the ambient temperature, ambient humidity, GPS positioning information, and measurement time. It calls the collected data for format conversion, standardizes the differentiated numerical data, and calculates the spatial distribution between data points based on the GPS positioning information to obtain a standardized measurement data set.

[0065] Call the five-pin soil multi-parameter sensor to obtain soil conductivity, humidity, and pH value. Read the conductivity value, water content value, and pH potential difference value returned by the sensor respectively. The conductivity is calculated by measuring the current of the soil solution. The specific process is to collect the current value I and applied voltage V of the soil solution at a known electrode spacing, and calculate the conductivity according to the conductivity calculation formula Calculate the soil conductivity, where k is the electrode constant, which is determined by experimental calibration. Assuming the current measured is 2.5 mA, the voltage applied is 1 V, and the electrode constant k is 0.8, the conductivity is calculated to be The humidity is measured by capacitance method. After reading the capacitance value returned by the sensor, it is converted into soil volume moisture content based on the calibration curve. Assuming the measured capacitance value C = 45pF, the soil moisture is calculated to be 30% based on the calibration curve. The pH value is measured by glass electrode method, and the pH potential difference is collected and converted into pH value in combination with the known Nernst equation. Assuming the measured potential difference is 120mV and the temperature is 25℃, the converted pH value is 6.8. At the same time, the ambient temperature and humidity sensor measures the ambient air temperature T a and relative humidity H a The GPS module obtains the current longitude and latitude lat, lon and measurement time t. All acquired data are converted according to the set format. The numerical data are standardized. The conductivity, humidity and pH values ​​are subtracted from the mean and divided by the standard deviation to normalize the data. Assuming that the mean conductivity is 1.5mS / cm and the standard deviation is 0.5mS / cm, the standardized conductivity is calculated as Humidity and pH values ​​are standardized in the same way. GPS positioning data are used to calculate the spatial distribution of adjacent measurement points. The Haversine formula is used to calculate the distance d between the current point and the adjacent point. Assuming that the longitude and latitude of the adjacent points are lat1=34.0522, lon1=-118.2437 and lat2=34.0525, lon2=-118.2439, the distance between the two points is calculated to be d≈38.9m, and finally a standardized measurement data set is obtained.

[0066] The measurement value analysis submodule calls the measured values ​​of soil conductivity, humidity, and pH value based on the standardized measurement data set, compares them with the set reference values, calculates the parameter offset rate, screens the data points exceeding the threshold value, and corrects the offset rate by combining the influence coefficients of ambient temperature and ambient humidity to obtain the offset rate of the screened parameters;

[0067] The parameter deviation rate calculation formula is as follows:

[0068]

[0069] Among them, δ i represents the parameter deviation rate of the i-th measurement point, M i represents the measured value of soil conductivity, moisture or pH at the i-th measuring point, R i represents the set reference value of soil conductivity, moisture or pH value at the i-th measuring point, T i Represents the temperature value of the environment where the measurement point is located, H i Represents the humidity value of the environment where the measurement point is located.

[0070] This formula is used to calculate the deviation rate of soil parameters (such as conductivity, moisture, pH value) and consider the influence of ambient temperature and humidity. i represents the corrected offset rate of the i-th measurement point, M i is the actual measured value of the i-th measuring point, R i is the reference value of the corresponding parameter, α and β are the influence coefficients of temperature and humidity respectively, T i and H i are the ambient temperature and humidity of the i-th measurement point, T ref and H ref For reference temperature and humidity.

[0071] Assume that at a certain measurement point, the actual measured value of soil conductivity M i is 150μS / cm, reference value R i 100μS / cm, ambient temperature T i The reference temperature is 25°C. ref The ambient temperature is 20℃ and the humidity is H i The reference humidity is 60%. ref is 50%, the temperature influence coefficient α is 0.02 / ℃, and the humidity influence coefficient β is 0.01 / %.

[0072] First, calculate the uncorrected drift rate:

[0073]

[0074] Then, calculate the correction factors for temperature and humidity:

[0075] 1+α×(Ti -T ref )+β×(H i -H ref )=1+0.02×(25-20)+0.01×(60-50)=1+0.1+0.1=1.2;

[0076] Finally, calculate the corrected offset rate:

[0077] δ i =0.5×1.2=0.6;

[0078] The results show that after considering the influence of ambient temperature and humidity, the deviation rate of soil conductivity is 0.6, that is, the measured value is 60% higher than the reference value.

[0079] The data storage submodule calls the parameter offset rate after screening, sorts by measurement time, stores the partitions according to GPS positioning information, calculates the partition parameter change trend, establishes the partition soil parameter database, and obtains the soil parameter data;

[0080] According to the parameter offset rate after screening, the data are sorted by measurement time, divided into regions according to GPS positioning information and stored. The parameter change trend is calculated after the data stored in each region is sorted by time. The linear regression method is used for calculation. The regression model σ=at+b is set to fit the stored data points. It is assumed that the historical conductivity measurement values ​​of a certain area are 1.6, 1.7, 1.9, and 2.0 mS / cm, corresponding to the measurement time t=1, 2, 3, and 4 days. The least squares method is used to calculate the regression coefficients a=0.133 and b=1.467, and the regression equation σ=0.133t+1.467 is obtained, indicating that the conductivity trend of the area is rising. The trend data of each area is stored in the partitioned soil parameter database to form soil parameter data.

[0081] See also Figure 4 and Figure 2 , the measurement data integrity assurance module includes:

[0082] The data archiving submodule obtains soil parameter data, classifies and archives them according to GPS positioning information, calls data to check timestamps, and obtains classified archived data;

[0083] Soil parameter data is called and classified and archived according to GPS positioning information. First, the stored soil parameter data set is read, the longitude and latitude coordinates of each measuring point are extracted, and classified according to the preset area range. The classification method is based on the grid division of longitude and latitude coordinates. Assuming that the area division grid size is 0.001° (i.e., a range of about 111m×111m), each measuring point is calculated according to its coordinate value. The grid number to which it belongs is calculated by dividing the longitude and latitude by the grid step size and then rounding. For example, the grid number to which the measuring point lat=34.0522, lon=-118.2437 belongs is calculated as Then the point belongs to the area numbered (34052, -118243). After archiving is completed, check the timestamp of the data, extract the time information of all measurement points, and sort them in chronological order. Set the timestamp format to "YYYY-MM-DDHH:MM:SS", check the time interval of adjacent data points, and if there are duplications or abnormal jumps in the timestamp, mark the data point as abnormal. Assuming that the timestamps of the measured data in a certain area are 2025-02-1612:00:00, 2025-02-1612:05:00, 2025-02-1612:20:00, and 2025-02-1612:10:00, it can be found that the timestamp of the fourth data point is earlier than the previous point, which is judged as an abnormal point, and finally the classified archived data is obtained.

[0084] The data integrity check submodule checks the data integrity of the primary storage unit based on the classified archived data, compares the timestamp sequence, filters the abnormal data points in the time interval, and determines the missing data. If it is complete, it is marked as transferable. If it is missing, it marks the missing data point to obtain the missing data mark;

[0085] Call the classified archive data to check the data integrity of the main storage unit. First, read all the timestamp data in the main storage unit and sort them according to the measurement time. Check the time interval between the data and set the normal collection interval threshold D. min and D max , assuming that D min =5min,D max=15min, then check whether the time intervals of all adjacent data points are within the range. If the time interval of a measurement point is less than 5 minutes or greater than 15 minutes, the time interval of the data point is judged to be abnormal, and the data points with abnormal time intervals are screened. Assuming that the timestamps of the measurement points in a certain area are 2025-02-1612:00:00, 2025-02-1612:05:00, 2025-02-1612:25:00, and 2025-02-1612:30:00, the time interval of the third data point relative to the previous point is 20 minutes, which exceeds the upper limit of 15 minutes and is judged to be abnormal. Further check the missing data and calculate the timestamp sequence of the theoretically required data points. Assuming that the standard collection frequency is set to once every 5 minutes, there should be data at 12:10:00, 12:15:00, and 12:20:00, but the actual data is missing, so the missing data points are marked. If the data is complete, the mark can be transmitted, otherwise a missing data mark is generated.

[0086] The missing data supplement submodule calls the missing data mark, queries the spare storage unit to obtain the corresponding data, and updates the main storage unit if it can be supplemented, otherwise re-measures and stores to obtain the measurement data result;

[0087] Call the missing data mark and query the backup storage unit. First, read the data index list in the backup storage unit to find the data at the corresponding time point. If the data at the corresponding time point exists in the backup storage unit, directly call the backup storage data to update the main storage unit. Assuming that the timestamp of the missing data point is 2025-02-1612:10:00, and the data at this time point exists in the backup storage unit, write the data to the main storage unit and update the index information. If the data at the missing time point does not exist in the backup storage unit, determine whether the time point can be supplemented for measurement. If the current measuring device is still online and located in the original measuring area, trigger the supplementary measurement function and call the measuring sensor to re-acquire the soil parameter data at this time point. If the measuring device has left the measuring area or is offline, record the data point as permanently missing and no longer supplement the measurement to finally obtain the measurement data result.

[0088] See also Figure 5 and Figure 2 , the measurement frequency dynamic adjustment module includes:

[0089] The environmental change analysis submodule obtains the measurement data results, calls the ambient temperature and humidity values, calculates the measurement value change amplitude, compares the set threshold, filters the data points below and above the threshold, marks them as low change intervals and high change intervals respectively, monitors the change trend of the data points, identifies the stable and sudden change states, and obtains the environmental change interval mark;

[0090] The specific calculation formula for the measurement value change amplitude is:

[0091]

[0092] Among them, ΔS j Represents the change amplitude of the measured value at the jth measurement point, M j represents the measured value of the jth measurement point, M j-1 represents the measured value of the j-1th measurement point, T j represents the measurement time of the jth measurement point, T j-1 represents the measurement time of the j-1th measurement point, E j represents the ambient temperature of the jth measurement point, E j-1 represents the ambient temperature of the j-1th measurement point, U j represents the ambient humidity at the jth measurement point, U j-1 represents the ambient humidity at the j-1th measurement point, As an environmental change correction item, it reflects the impact of temperature and humidity on the rate of change of the measured value.

[0093] Formula ΔS j It is used to calculate the rate of change of environmental measurements, including changes in soil parameters and environmental conditions such as temperature and humidity. j and M j-1 Represents the soil parameter values ​​measured twice in a row, such as conductivity, T j and T j-1 Indicates the time corresponding to these two measurements. Parameter E j and E j-1 represents the ambient temperature at these two time points, and U j and U j-1 Represents the corresponding ambient humidity.

[0094] The specific calculation example is as follows:

[0095] Set M j =105 units, M j-1 =100 units, measuring time T j =11:00, T j-1 =10:00, so the time difference is 1 hour. Ambient temperature E j =25℃, E j-1 =23℃, ambient humidity U j =80%, U j-1 =75%.

[0096] First, calculate the parameter change rate:

[0097]

[0098] Calculate the composite value of the rate of change of temperature and humidity:

[0099]

[0100] Therefore, the total rate of change is:

[0101] ΔS j =5+5.385=10.385 units / h;

[0102] This result shows that within one hour of monitoring, considering the comprehensive influence of environmental factors, the total change rate of the measured value is 10.385 units per hour. This result is used to identify whether it exceeds the preset threshold and then decide whether to mark it as a stable or mutation state, providing accurate environmental change monitoring.

[0103] The measurement interval adjustment submodule calls the environmental change interval mark, monitors the change rate of soil conductivity, humidity, and pH value, analyzes the rate change trend, determines the stable and mutation intervals, adjusts the measurement interval, and obtains the dynamically adjusted measurement interval results;

[0104] Call the environmental change interval mark and monitor the change rate of soil conductivity, humidity and pH value. First, extract the soil conductivity, humidity and pH value at each time point, and calculate the change rate of adjacent time points. The calculation method is to subtract the measurement value of the previous time point from the current measurement value and divide it by the time interval. Assuming that the conductivity measurement values ​​are 1.5, 1.8, 2.1, and 2.3 mS / cm, respectively, and the adjacent time interval is 10 minutes, the conductivity change rate is calculated to be 0.03, 0.03, and 0.02 mS / cm / min, respectively. The humidity and pH value change rates are calculated similarly. Analyze the rate change trend, calculate the mean and standard deviation of the change rate within a certain time window, assuming that the set time window is 1 hour, calculate the mean and standard deviation of the change rate of all measurement points in the time window, if the mean is less than the set threshold and the standard deviation is less than the set If the current time window is within a certain range, it is judged as a stable interval, otherwise it is judged as a mutation interval. Assuming that the mean conductivity change rate in a certain time window is 0.02mS / cm / min, which is less than the stable threshold value of 0.05mS / cm / min, and the standard deviation is 0.005mS / cm / min, which is less than the set standard deviation range of 0.01mS / cm / min, then the time window is judged to be a stable interval. The measurement interval is adjusted according to the judgment result. If the current time window is a stable interval, the measurement interval is extended, and the adjustment coefficient is set to 1.5. The original measurement interval is 10 minutes, and the adjusted measurement interval is calculated to be 10×1.5=15 minutes. If the current time window is a mutation interval, the measurement interval is shortened, and the shortening coefficient is set to 0.5, then the adjusted measurement interval is calculated to be 10×0.5=5 minutes, and finally the dynamically adjusted measurement interval result is obtained.

[0105] See also Figure 6 and Figure 2, the soil state change monitoring module includes:

[0106] The multi-time point measurement submodule obtains the dynamically adjusted measurement interval results, calls the soil conductivity, humidity, and pH value data, sorts them by measurement interval, constructs a time series, and obtains the measurement time series data;

[0107] Call the dynamically adjusted measurement interval results and obtain soil conductivity, humidity, and pH value data. First, extract all time points in the measurement time series and sort them in chronological order. Set the storage structure of the time series, including timestamp, measurement data, and measurement interval. Read the soil conductivity, humidity, and pH value corresponding to each measurement point, reorder them according to the timestamp, construct the time series, and calculate the time intervals between adjacent data points. Assuming that the measurement point timestamps are 2025-02-1612:00:00, 2025-02-1612:05:00, 2025-02-1612:20:00, and 2025-02-1612:30:00, the adjacent time intervals are calculated to be 5 minutes, 15 minutes, and 10 minutes, respectively, and store this information. At the same time, to ensure the integrity of the time series, check whether the time interval conforms to the dynamic adjustment results. If the measurement interval is abnormal, mark the point as abnormal, and finally obtain the measurement time series data.

[0108] The change rate calculation submodule calculates the change rate of soil conductivity, humidity, and pH value between adjacent time points based on the measured time series data, compares the rate increments within adjacent measurement intervals, screens the time points with prominent changes, extracts the rate mutation area, and obtains the rate mutation time points;

[0109] Call the measurement time series data and calculate the soil conductivity, humidity and pH value change rate between adjacent time points. First, extract the soil conductivity, humidity and pH value of adjacent measurement points, and calculate the change rate of each parameter. The calculation method is to subtract the measurement value of the previous time point from the measurement value of the current time point and divide it by the time interval. Assuming that the soil conductivity measurement values ​​of a certain area are 1.5, 1.8, 2.1, and 2.3 mS / cm, respectively, and the corresponding time intervals are 5 minutes, 15 minutes, and 10 minutes, respectively, then the calculated conductivity change rates are 0.06, 0.02, and 0.02 mS / cm / min, respectively. The humidity and pH value change rates are calculated in the same way. After the calculation is completed, compare the rate increments in adjacent measurement intervals and calculate the absolute value of the rate increment. Assuming that the mutation threshold is set to 0.03 mS / cm / min, screen the time points with prominent changes and extract the rate mutation area. If the change rate of a certain measurement point is greater than the threshold, the point is judged to be a mutation point, and finally the rate mutation time point is obtained.

[0110] The abnormal area identification submodule calls the rate mutation time point, extracts the corresponding GPS information, aggregates the data according to the spatial distribution, calculates the abnormal area range, and obtains the abnormal state change area;

[0111] Call the rate mutation time point and extract the corresponding GPS information. First, read the GPS positioning data of the mutation time point, extract the longitude and latitude coordinates of all mutation points, and aggregate the data according to the spatial distribution. Set the spatial radius threshold r. Assume that r = 50m is set. Calculate the distance between all mutation points and determine whether adjacent mutation points are within the radius. If the conditions are met, merge them into the same area. Calculate the boundary range of the area. Set the area boundary calculation method to the minimum circumscribed rectangle. Extract the minimum longitude and latitude and maximum longitude and latitude of all mutation points in the area. Assume that the distribution range of mutation points is longitude -118.2445 to -118.2435 and latitude 34.0518 to 34.0528, then this range is the boundary of the abnormal area, and finally obtain the abnormal state change area.

[0112] See also Figure 7 and Figure 2 , the regional state distribution identification module includes:

[0113] The soil trend calculation submodule obtains the abnormal state change area, calls the time series data of soil conductivity, humidity, and pH value, analyzes the change trend, extracts the trend characteristics in the area, and obtains the soil change trend;

[0114] The abnormal state change area is called, and the time series data of soil conductivity, humidity, and pH value are obtained. First, the soil parameter data of all abnormal areas are read and arranged in chronological order. The time change trend of each parameter is calculated, and the time window for trend calculation is set. Assuming that the time window is set to 1 day, all measurement values ​​within the time range are extracted, and the linear fitting slope is calculated. The slope represents the rate of change of each parameter. If the slope is positive, it means that the parameter increases over time, and if it is negative, it means that it decreases. Assuming that the conductivity of a certain area increases from 1.5mS / cm to 2.1mS / cm within 1 day, and the time span is 24 hours, the calculated slope is (2.1-1.5) / 24=0.025mS / cm / h. The humidity and pH value are calculated in the same way. At the same time, the standard deviation of the slope is calculated, and the standard deviation threshold is set. If the standard deviation exceeds the threshold, it is judged that the trend is unstable, and the trend calculation window is readjusted. Finally, the trend characteristics of each abnormal area are extracted to obtain the soil change trend.

[0115] The humidity drop screening submodule screens areas with large humidity drop based on soil change trends, compares humidity drop rate thresholds, extracts areas above the threshold, calculates the humidity change rate gradient, marks the spatial distribution with faster rates, identifies rate mutation areas, and obtains humidity drop rate areas;

[0116] The specific formula for calculating the humidity change rate is:

[0117]

[0118] Among them, Rate humidity Represents the rate of change of humidity, ΔH current Represents the humidity value of the current measurement point, ΔH previous represents the humidity value of the previous measurement point, Δt represents the time interval between two measurements, H threshold is the threshold of humidity change.

[0119] Calculate the gradient value of humidity change rate, mark the spatial distribution with faster rate, identify the rate mutation area, and obtain the humidity decrease rate area;

[0120] The formula first calculates the absolute rate of change of humidity between two consecutive measurements, and then compares this rate with the set threshold value. By adding the ratio of the humidity change value to the threshold value, the sensitivity to small changes is enhanced;

[0121] The specific calculation example is as follows:

[0122] Setting ΔH current =75%, ΔH previous =80%, time interval Δt=1 hour, threshold H threshold =5%.

[0123] First, calculate the rate of change of humidity between two measurements:

[0124]

[0125] Then, calculate the ratio of the humidity change value to the threshold:

[0126]

[0127] The total rate of change is:

[0128] Rate humidity =5%+1=6;

[0129] This result shows that after considering the correction factor of the threshold, the rate of change of humidity exceeds the simple percentage change, indicating possible areas of rapid humidity decline. This result is used to further screen out areas with large humidity declines, corresponding to the key rate mutation identification in environmental monitoring.

[0130] The drought risk delineation submodule calls the humidity drop rate area, divides the spatial range according to GPS information, calculates the boundary of the drought risk area, generates a distribution zoning map, and obtains the soil state distribution result;

[0131] The humidity drop rate area is called and the spatial range is divided according to the GPS information. First, all GPS coordinate points in the humidity drop rate area are extracted and aggregated according to the spatial distribution. The spatial aggregation radius is set, assuming it is set to 500m. The distance between all measurement points is calculated. If the distance between adjacent measurement points is less than 500m, they are merged into the same area and the regional boundary is calculated. The boundary calculation method is set to the minimum circumscribed rectangle, and the minimum longitude and latitude and maximum longitude and latitude of all measurement points in the area are extracted. Assuming that the distribution range of the humidity drop rate area is longitude -118.2450 to -118.2430 and latitude 34.0510 to 34.0530, the center point of the range is calculated, and the drought index in the area is further calculated. The drought index calculation method is set, and the risk level is calculated according to the humidity drop rate and soil moisture content. Assuming that the drought index is calculated as DI=(H s -H t ) / (T s -T t ), where H s is the initial humidity, H t is the current humidity, T s is the initial time, T t For the current time, if the drought index exceeds the set threshold, it is delineated as a high-risk drought area, and finally a distribution zoning map is generated to obtain the soil state distribution results.

[0132] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. Five-pin soil multi-parameter sensor, characterized by: The system comprises: The soil parameter measurement and calibration module obtains the measurement data of the five-pin soil multi-parameter sensor, and stores and processes the set reference value and the screened value to obtain the soil parameter data; The measurement data integrity assurance module checks the integrity of the main storage unit based on the soil parameter data, and transmits it if it is complete; if it is missing, the backup storage unit is called to supplement it; if both are missing, it is re-measured and stored in the main storage unit and the backup storage unit to obtain the measurement data results; The measurement frequency dynamic adjustment module obtains the ambient temperature and humidity based on the measurement data results, analyzes the change amplitude, and extends the measurement interval if it is less than the threshold, shortens the interval if it is greater than the threshold, extends the interval if it tends to be stable, and shortens the interval and increases the number of measurements if it changes suddenly, to obtain a dynamically adjusted measurement interval result; The soil state change monitoring module analyzes the measurement sequence based on the dynamically adjusted measurement interval results, screens the drastic change points, and obtains the abnormal state change area; The regional state distribution identification module obtains the soil parameter change trend based on the abnormal state change area, screens the area where the humidity decrease amplitude and rate exceed the threshold, delineates the risk area, and obtains the soil state distribution result.

2. The five-pin soil multi-parameter sensor system according to claim 1, characterized in that: The soil parameter data includes conductivity data, humidity data, pH value data, ambient temperature data, GPS positioning data, and measurement time data; the measurement data results include complete data, missing data, and supplementary data; the dynamically adjusted measurement interval results include extending the interval duration, shortening the interval duration, and increasing the number of measurements; the abnormal state change area includes the abnormal change time point, corresponding GPS positioning information, and change rate data; the soil state distribution results include humidity drop areas, rate exceeding threshold areas, and drought risk areas.

3. The five-pin soil multi-parameter sensor system according to claim 1, characterized in that: The soil parameter measurement and calibration module includes: The data acquisition submodule obtains the soil conductivity, humidity, and pH value of the five-pin soil multi-parameter sensor, and collects the ambient temperature, ambient humidity, GPS positioning information, and measurement time. It calls the collected data for format conversion, standardizes the differentiated numerical data, calculates the spatial distribution between data points based on the GPS positioning information, and obtains a standardized measurement data set. The measurement value analysis submodule calls the measured values ​​of soil conductivity, humidity, and pH value based on the standardized measurement data set, compares them with the set reference values, calculates the parameter offset rate, screens the data points exceeding the threshold value, and corrects the offset rate in combination with the influence coefficients of ambient temperature and ambient humidity to obtain the screened parameter offset rate; The data storage submodule calls the filtered parameter offset rate, sorts by measurement time, stores in partitions according to GPS positioning information, calculates the partition parameter change trend, establishes a partition soil parameter database, and obtains soil parameter data.

4. The five-pin soil multi-parameter sensor system according to claim 3 is characterized in that: The parameter deviation rate calculation formula is specifically: Among them, δ i represents the parameter deviation rate of the i-th measurement point, M i represents the measured value of soil conductivity, moisture or pH at the i-th measuring point, R i represents the set reference value of soil conductivity, moisture or pH value at the i-th measuring point, T i Represents the temperature value of the environment where the measurement point is located, H i Represents the humidity value of the environment where the measurement point is located.

5. The five-pin soil multi-parameter sensor system according to claim 1, characterized in that: The measurement data integrity assurance module comprises: The data archiving submodule obtains the soil parameter data, classifies and archives them according to the GPS positioning information, calls the data to check the timestamp, and obtains the classified archived data; The data integrity check submodule checks the data integrity of the main storage unit based on the classified archived data, compares the time stamp sequence, screens the abnormal data points in the time interval, determines the missing data, and if it is complete, marks it as transmittable; if it is missing, marks the missing data point to obtain the missing data mark; The missing data supplement submodule calls the missing data mark, queries the spare storage unit to obtain the corresponding data, and updates the main storage unit if the data can be supplemented, otherwise re-measures and stores the data to obtain the measurement data result.

6. The five-pin soil multi-parameter sensor system according to claim 1, characterized in that: The measurement frequency dynamic adjustment module includes: The environmental change analysis submodule obtains the measurement data results, calls the ambient temperature and ambient humidity values, calculates the measurement value change amplitude, compares the set threshold, filters the data points below and above the threshold, marks them as low change intervals and high change intervals respectively, monitors the change trend of the data points, identifies the stable and sudden change states, and obtains the environmental change interval mark; The measurement interval adjustment submodule calls the environmental change interval mark, monitors the change rate of soil conductivity, humidity, and pH value, analyzes the rate change trend, determines the stable and mutation intervals, adjusts the measurement interval, and obtains the dynamically adjusted measurement interval result.

7. The five-pin soil multi-parameter sensor system according to claim 6, characterized in that: The specific calculation formula for the measurement value change amplitude is: Among them, ΔS j Represents the change amplitude of the measured value at the jth measurement point, M j represents the measured value of the jth measurement point, M j-1 represents the measured value of the j-1th measurement point, T j represents the measurement time of the jth measurement point, T j-1 represents the measurement time of the j-1th measurement point, E j represents the ambient temperature of the jth measurement point, E j-1 represents the ambient temperature of the j-1th measurement point, U j represents the ambient humidity at the jth measurement point, U j-1 represents the ambient humidity at the j-1th measurement point, As an environmental change correction item, it reflects the impact of temperature and humidity on the rate of change of the measured value.

8. The five-pin soil multi-parameter sensor system according to claim 1, characterized in that: The soil state change monitoring module comprises: The multi-time point measurement submodule obtains the dynamically adjusted measurement interval results, calls soil conductivity, humidity, and pH value data, sorts them by measurement interval, constructs a time series, and obtains measurement time series data; The change rate calculation submodule calculates the change rates of soil conductivity, humidity, and pH value between adjacent time points based on the measurement time series data, compares the rate increments within adjacent measurement intervals, selects the time points with prominent changes, extracts the rate mutation area, and obtains the rate mutation time points; The abnormal area identification submodule calls the rate mutation time point, extracts the corresponding GPS information, aggregates the data according to the spatial distribution, calculates the abnormal area range, and obtains the abnormal state change area.

9. The five-pin soil multi-parameter sensor system according to claim 1, characterized in that: The regional state distribution identification module includes: The soil trend calculation submodule obtains the abnormal state change area, calls the time series data of soil conductivity, humidity, and pH value, analyzes the change trend, extracts the trend characteristics in the area, and obtains the soil change trend; The humidity drop screening submodule screens the areas with large humidity drop based on the soil change trend, compares the humidity drop rate threshold, extracts the super-threshold area, calculates the humidity change rate gradient value, marks the spatial distribution with faster rate, identifies the rate mutation area, and obtains the humidity drop rate area; The drought risk delineation submodule calls the humidity drop rate area, divides the spatial range according to the GPS information, calculates the boundary of the drought risk area, generates a distribution zoning map, and obtains the soil state distribution result.

10. The five-pin soil multi-parameter sensor system according to claim 9, characterized in that: The humidity change rate calculation formula is specifically: Among them, Rate humidity Represents the rate of change of humidity, ΔH current Represents the humidity value of the current measurement point, ΔH previous represents the humidity value of the previous measurement point, Δt represents the time interval between two measurements, H threshold is the threshold of humidity change.

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