Water-salt ratio dynamic monitoring method and system for farmland

Abstract

The invention relates to the technical field of environment monitoring, in particular to a water-salt ratio dynamic monitoring method and system for farmland, and the method comprises the following steps: collecting multi-point tension and conductivity data, carrying out the ratio judgment, delaying the collection if a threshold value is exceeded, synchronously updating a data set, converting salt, calculating the change of the water-salt ratio, and fitting a trend to obtain change information. Evaluating water stress in combination with crop parameters, judging a water demand period, superposing predicted rainfall to generate an interference result, and calculating a water-salt change acceleration recognition intervention point. According to the method, through the water stress index constructed by fusing the transpiration potential base value of the crop and the water content of the root layer, accurate identification of the water demand period of the crop is realized, comparison with the predicted rainfall time interval is carried out, and trend key points influenced by rainwater disturbance are screened; a continuous acceleration change section is identified through a water-salt ratio acceleration sequence formed by a change rate difference value, and an intervention demand point is marked in time under the condition that a continuous trend is enhanced.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method and system for dynamic monitoring of water-salt ratio in farmland. Background Technology

[0002] The field of environmental monitoring technology involves acquiring, analyzing, and evaluating various physical, chemical, and biological factors in the natural environment to understand environmental change trends, identify environmental problems, and provide a basis for environmental governance and decision-making. This field encompasses pollutant monitoring and ecological indicator assessment across multiple media, including the atmosphere, water bodies, and soil, and is widely applied in environmental protection, agricultural ecology, resource management, and sustainable development. In agricultural ecosystems, environmental monitoring technology requires continuous observation and data analysis of multidimensional environmental factors affecting crop growth and soil quality, such as moisture, salinity, pH, and temperature. Through multi-source sensing technology, data fusion algorithms, and spatial information processing methods, it provides real-time and accurate information on environmental parameter changes, supporting precise and green management of agricultural production.

[0003] Among them, the dynamic monitoring method for water-salt ratio in farmland is a technical solution for continuous observation and analysis of changes in the ratio of water to salt in the farmland environment. It aims to assess the risk of soil salinization and guide irrigation regulation and crop planting management. This method can simultaneously measure the water content and salt concentration in farmland soil profiles, construct water-salt ratio change curves using dynamic monitoring data, and thus reflect the soil water and salt movement process. It is suitable for applications such as farmland water conservancy regulation, saline-alkali land improvement, and agricultural environmental information management.

[0004] Traditional monitoring methods, when continuously observing soil water and salt dynamics, rely solely on the synchronous measurement of moisture content and salt concentration to generate change curves. They fail to dynamically assess the timing of data collection, leading to data bias during periods of intense tension fluctuations. This is particularly true during short-term changes before and after rainfall, where data disturbances are common. Furthermore, traditional methods only generate curves in soil profile trend analysis, without tracking the rate of change or trend direction. This makes it difficult to promptly capture acceleration phases and provide intervention signals, resulting in delayed or misjudged control points. Consequently, the coordination and timeliness of irrigation scheduling and soil salinity management are compromised. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and to propose a method and system for dynamic monitoring of water-salt ratio in farmland.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for dynamic monitoring of water-salt ratio in farmland, comprising the following steps: S1: Obtain the rate of change of soil tension and the magnitude of change of soil conductivity at multiple points in the farmland, and compare them with the set threshold for the ratio of tension and conductivity response. If the ratio exceeds the threshold, delay the data acquisition trigger time, and perform synchronous acquisition according to the updated trigger sequence to generate a multi-point farmland acquisition dataset. S2: Based on the multi-point data collection dataset of the farmland, calculate the water-salt ratio change rate sequence of the monitoring points, perform least squares linear fitting on the change rate sequence in the spatial dimension to obtain the change trend line of the profile section, and generate water-salt ratio change trend information. S3: Call the leaf area index, total solar radiation and root soil moisture content of crops in the farmland area, use the product of total solar radiation and leaf area index as the base value of transpiration potential, calculate the index value of crop water stress, mark the water demand marker time interval, and construct the transpiration stress state marker set. S4: Obtain the cumulative rainfall forecast value and the expected rainfall start time interval within the regional forecast period, compare the time interval with the water demand marker time interval in the evapotranspiration stress state marker set, calculate the water-salt ratio trend influence value in combination with the water-salt ratio change trend information, and generate water-salt ratio trend interference results.

[0007] As a further aspect of the present invention, the farmland multi-point data collection dataset includes soil tension response synchronization status, unified time nodes for profile moisture content, and a balanced collection window for conductivity values. The water-salt ratio change trend information includes profile hierarchical response rate distribution, spatial continuity change tendency, and water-salt ratio horizontal trend classification labels. The transpiration stress state label set specifically includes crop physiological stress identifiers, light-driven water deficit status, and root zone water potential change indicators. The water-salt ratio trend interference results specifically include rainfall-driven disturbance intensity parameters, trend direction shift degree, and response factors in the overlapping areas of rainfall and transpiration periods.

[0008] As a further aspect of the present invention, the steps for obtaining the multi-point data collection dataset from farmland are specifically as follows: S101: Obtain the rate of change of soil tension and the magnitude of change of soil conductivity at multiple points in farmland. Extract the difference between the maximum gradient change intervals for the changes of the two indicators in the same monitoring point within the corresponding time period. Perform ratio calculation on the rate of change of soil tension and the magnitude of change of soil conductivity to generate a sequence of ratio of soil tension and soil conductivity response. S102: Based on the tension and conductivity response ratio sequence, call the set tension and conductivity response ratio threshold, compare the interval size of the two, identify the monitoring point where the ratio is greater than the threshold, delay the acquisition trigger time of the monitoring point until the time node of the stable change interval when the tension change rate value first approaches the initial value change segment, and generate a trigger timing adjustment value group. S103: Call the trigger timing adjustment value group, execute the trigger delay control for each monitoring point acquisition command according to the adjustment value, and extract the tension value and conductivity value record at the trigger time after the delay, assemble them into a multi-point profile soil parameter set under a unified time reference, and generate a farmland multi-point acquisition dataset.

[0009] As a further aspect of the present invention, the step of obtaining the water-salt ratio change trend information specifically includes: S201: Based on the multi-point data collection dataset of the farmland, extract the volumetric water content data and conductivity-converted salt concentration data of each monitoring point in two adjacent collection cycles. Perform ratio calculation on the volumetric water content and salt concentration values ​​of the two cycles within the same monitoring point, and perform difference calculation to generate a water-salt ratio change rate sequence. S202: Based on the water-salt ratio change rate sequence, extract the rate values ​​of the monitoring points, arrange them in spatial distribution order to construct a rate space array, call the least squares fitting criterion to perform a linear function fitting process on the array, and generate water-salt ratio change fitting information. S203: Based on the water-salt ratio change fitting information, perform trend line direction consistency judgment on continuous points in the profile segment, extract the change direction identifier, change rate change range and corresponding profile position information, and establish water-salt ratio change trend information.

[0010] As a further aspect of the present invention, the step of obtaining the evaporation stress state marker set specifically includes: S301: Call the leaf area index, total solar radiation and root soil moisture content of crops in the farmland area, combine wind speed and temperature values, perform ratio and difference compound calculation on the combined data, calculate the crop transpiration stress response index, and establish a transpiration potential response value sequence based on the index value; S302: Based on the transpiration potential response value sequence, call the preset crop transpiration water demand response threshold, judge the difference between the index value and the threshold at each time point, filter the time points where the index value is lower than the threshold, extract the corresponding timestamps and establish a continuous interval sequence to generate a list of transpiration water demand intervals. S303: Based on the list of evapotranspiration water demand intervals, mark the start and end times of continuous intervals, identify the monitoring period markers where water demand pressure exists, extract the time periods and record and integrate them according to the corresponding time axis, and establish a set of evapotranspiration stress state markers.

[0011] As a further aspect of the present invention, the formula for calculating the crop transpiration stress response index is specifically as follows: ; in, Indicating the crop transpiration stress response index, This represents the normalized value of the leaf area index. This represents the normalized value of total solar radiation. This represents the normalized value of root zone soil moisture content. This represents the normalized value of wind speed. This represents the normalized value of temperature.

[0012] As a further aspect of the present invention, the step of obtaining the water-salt ratio trend interference result specifically includes: S401: Obtain the cumulative rainfall forecast value and the expected rainfall start time distance within the regional forecast period, compare the start time distance data with the time periods in the evapotranspiration stress state marker set one by one, identify whether there are overlapping segments on the time axis, filter the monitoring time points corresponding to the overlapping segments, and generate a list of overlapping water demand and rainfall segments. S402: Call the list of overlapping water demand and rainfall sections, calculate the water-salinity ratio trend rate value according to the water-salinity ratio trend rate value corresponding to the time period of each monitoring point, and combine it with the cumulative rainfall at the corresponding time point. Establish a sorting sequence based on the disturbance value, merge the data sections of each point, and generate a water-salinity trend disturbance index sequence. The specific formula for calculating the water-salt ratio trend disturbance value is as follows: ; in, This indicates the magnitude of the trend disturbance in the water-to-salt ratio. This represents the normalized value of cumulative rainfall. The normalized rate of change of the water-salt ratio trend. This represents the normalized value of surface infiltration capacity. This represents the normalized value of the evaporation potential. This represents the normalized value of the root capillary suction. S403: Based on the water-salinity trend disturbance index sequence, extract high-amplitude jump segments according to the disturbance value amplitude, trend direction and spatial location, construct the point correspondence relationship, integrate the disturbance segments according to the time series and profile distribution index, and establish the water-salinity ratio trend disturbance result.

[0013] As a further aspect of the present invention, the method further includes the following steps: S5: Based on the water-salt ratio trend interference results, perform change rate difference calculation on the values ​​of two adjacent time periods, determine whether there are continuous changes in the same direction that exceed the set water-salt change acceleration threshold. If the condition is met, record the corresponding profile monitoring point and time point as intervention demand marker points, and generate water-salt ratio intervention demand identification results. The water-salt ratio intervention requirement identification results specifically refer to the index of abrupt changes in the water-salt ratio evolution, the record of trend jump time nodes, and the information on monitoring priority labeling.

[0014] As a further aspect of the present invention, the step of obtaining the water-salt ratio intervention demand identification result specifically includes: S501: Based on the water-salt ratio trend disturbance results, according to the water-salt ratio trend disturbance values ​​of the monitoring point for three consecutive time periods, perform difference calculation on the disturbance values ​​of any two adjacent time periods, divide the difference results by the time interval, and sequentially construct the change rate difference group of consecutive segments to generate a water-salt ratio acceleration sequence. S502: Based on the water-salt ratio acceleration sequence, determine whether the sign direction is continuous and consistent according to the change trend of three consecutive time periods, calculate the corresponding acceleration amplitude, compare each set of acceleration values ​​with the set water-salt ratio change acceleration threshold, filter the acceleration point set with continuous change direction and amplitude exceeding the threshold, and generate a water-salt jump trend segment index set. S503: Based on the water-salinity jump trend segment index set, extract the corresponding profile monitoring point number and time period index, construct the horizontal comparison results under the same spatial level, add intervention markers to the points that meet the jump conditions, and establish the water-salinity ratio intervention requirement identification results.

[0015] A dynamic water-salinity ratio monitoring system for farmland, wherein the system implements the aforementioned dynamic water-salinity ratio monitoring method for farmland, the system comprising: The data synchronization processing module acquires the rate of change of soil tension and the magnitude of change of soil conductivity at multiple points in the farmland, and compares them with the set threshold for the ratio of tension and conductivity response. If the ratio exceeds the threshold, the data acquisition trigger time is delayed, and synchronous acquisition is performed according to the updated trigger sequence to generate a multi-point data collection dataset of the farmland. The trend analysis module, based on the multi-point data collection dataset of the farmland, calculates the water-salt ratio change rate sequence of the monitoring points, performs least squares linear fitting on the change rate sequence in the spatial dimension, obtains the change trend line of the profile segment, and generates water-salt ratio change trend information. The transpiration stress labeling module calls the leaf area index, total solar radiation and root soil moisture content of crops in the farmland area, uses the product of total solar radiation and leaf area index as the base value of transpiration potential, calculates the index value of crop water stress, identifies the water demand labeling time interval, and constructs a transpiration stress state labeling set. The trend interference identification module obtains the cumulative rainfall forecast value and the expected rainfall start time interval within the regional forecast period, compares whether the time interval overlaps with the water demand mark time interval in the evapotranspiration stress state mark set, and calculates the water-salinity ratio trend impact value by combining the water-salinity ratio change trend information, and generates water-salinity ratio trend interference results. The intervention demand identification module calculates the difference in the rate of change of values ​​between two adjacent time periods based on the water-salt ratio trend interference results. It determines whether there are continuous changes in the same direction that exceed the set water-salt change acceleration threshold. If the condition is met, the corresponding profile monitoring point and time point are recorded as intervention demand marker points, and the water-salt ratio intervention demand identification result is generated.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by introducing a mechanism to determine the ratio of the rate of change of tension to the amplitude of change of electrical conductivity, dynamic delay control of data acquisition timing is achieved, avoiding the impact of non-steady-state response interference on data accuracy. Combined with linear fitting of the rate of change of water-salt ratio in the spatial dimension, the evolution trend of water and salt in the soil profile is effectively captured. The water stress index constructed by integrating the base value of crop transpiration potential and root water content enables accurate identification of crop water demand periods. By comparing with the predicted rainfall time interval, key trend points affected by rainwater disturbance are screened. The continuously accelerating change segment is identified by the water-salt ratio acceleration sequence formed by the difference in the rate of change. When the continuous trend is strengthening, the intervention demand point is marked in a timely manner, ensuring that water and salt regulation decisions have stronger foresight, accuracy and crop water demand response coupling, and improving the accuracy and timeliness of water and salt dynamic management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flowchart of S1 of the present invention; Figure 3 This is a detailed flowchart of the S2 process of the present invention; Figure 4 This is a detailed flowchart of the S3 process of the present invention; Figure 5 This is a detailed flowchart of the S4 process of the present invention; Figure 6 This is a detailed flowchart of S5 of the present invention; Figure 7 This is a system flowchart of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0021] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0022] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] Please see Figure 1 This invention provides a technical solution: a method for dynamic monitoring of water-salt ratio in farmland, comprising the following steps: S1: Obtain the rate of change of soil tension and the magnitude of change of soil conductivity at multiple points in the farmland. Perform ratio calculation for each monitoring point and compare the ratio result with the set threshold for the ratio of tension and conductivity response. If the ratio exceeds the threshold, delay the data acquisition trigger time of the corresponding point until the rate of change of tension falls back and approaches the stable range of the initial rising stage. Perform synchronous acquisition for each monitoring point according to the updated trigger time sequence to generate a multi-point data collection dataset of the farmland. S2: Based on the multi-point data collection dataset of farmland, the salt concentration value is converted according to the volumetric water content and conductivity under two adjacent collection cycles. The ratio is calculated for the two time periods of the same monitoring point, and the difference is calculated to obtain the water-salt ratio change rate sequence of the monitoring point. The change rate sequence is subjected to least squares linear fitting in the spatial dimension to obtain the change trend line of the profile section and generate water-salt ratio change trend information. S3: Call the leaf area index, total solar radiation and root soil moisture content of crops in the farmland area, use the product of total solar radiation and leaf area index as the transpiration potential base value, perform ratio calculation on the transpiration potential base value and root soil moisture content, calculate the crop water stress index value, judge the difference with the set crop transpiration water demand response threshold, mark the water demand marker time interval, and construct the transpiration stress state marker set; S4: Obtain the cumulative rainfall forecast value and the expected rainfall start time interval within the regional forecast period, compare the time interval with the water demand mark time interval in the evapotranspiration stress state mark set, and if there is an overlapping period, calculate the water salinity trend impact value based on the rainfall forecast value and the rate of change of the corresponding point in the water salinity change trend information, and generate the water salinity trend interference result. S5: Based on the water-salt ratio trend interference results, the water-salt ratio trend influence values ​​for three consecutive time periods are used to calculate the difference in the rate of change between two adjacent time periods, resulting in a water-salt ratio change acceleration sequence. It is then determined whether the sequence contains continuous changes in the same direction that exceed the set water-salt change acceleration threshold. If the condition is met, the corresponding profile monitoring point and time point are recorded as intervention demand markers, generating a water-salt ratio intervention demand identification result.

[0025] The farmland multi-point data collection dataset includes soil tension response synchronization status, unified time nodes for profile moisture content, and balanced acquisition windows for electrical conductivity values. The water-salt ratio change trend information includes profile-level response rate distribution, spatial continuity change tendency, and water-salt ratio horizontal trend classification labels. The transpiration stress state label set specifically includes crop physiological stress identifiers, light-driven water deficit status, and root zone water potential change indicators. The water-salt ratio trend interference results specifically include rainfall-driven disturbance intensity parameters, trend direction shift degree, and response factors in the overlapping areas of rainfall and transpiration periods. The water-salt ratio intervention demand identification results specifically refer to the water-salt ratio evolution mutation location index, trend jump time node records, and monitoring priority labeling information.

[0026] Please see Figure 2 The specific steps for obtaining the multi-point data collection dataset from farmland are as follows: S101: Obtain the rate of change of soil tension and the magnitude of change of soil conductivity at multiple points in farmland. Extract the difference between the maximum gradient change intervals for the changes of the two indicators in the same monitoring point within the corresponding time period. Perform ratio calculation on the rate of change of soil tension and the magnitude of change of soil conductivity to generate a sequence of ratio of soil tension and soil conductivity response. To obtain the tension sensor measurement sequences and soil conductivity sampling records from various profile monitoring points in a farmland area, the raw measurement sequences collected by the deployed tension sensors at multiple profile monitoring points within the farmland area are first read. These sequences represent raw tension readings over a specific time span, with a time step set to 30 minutes per sampling point. Simultaneously, the soil conductivity sensor sampling records at the corresponding time points are read, forming a one-to-one pair of tension and conductivity data. For example, if a profile monitoring point numbered P1 is deployed in a certain area, a continuous 6-hour data sequence is obtained, where the hourly tension measurements are as follows: , , , , , The soil electrical conductivity values ​​are as follows: , , , , , Then, in each data sequence, the differences between adjacent tension values ​​and conductivity values ​​are calculated in chronological order to form a sequence of changes, i.e., the tension difference sequence. for Conductivity difference sequence for Next, the maximum gradient change interval is extracted from the two sets of change sequences. The maximum gradient change interval is defined as the interval with the largest cumulative absolute value of change over three consecutive time periods. (Tension sequence) middle For this type of interval, the cumulative change is conductivity sequence middle For the segment corresponding to the largest change range, the amplitude is... The maximum tension change value is obtained by taking the difference between the first and last values ​​of the two maximum change intervals mentioned above. The maximum change in conductivity is Then, the ratio between the change in tension and the change in conductivity is calculated using the following formula: Substituting the values ​​into the equations... This ratio is used as the tension-to-conductivity response ratio for monitoring point P1. The above process is repeated point by point in all profile monitoring points to generate a complete sequence of tension-to-conductivity response ratios.

[0027] S102: Based on the tension and conductivity response ratio sequence, call the set tension and conductivity response ratio threshold, compare the interval size of the two, identify the monitoring point where the ratio is greater than the threshold, delay the acquisition trigger time of the monitoring point until the time node of the stable change interval when the tension change rate value first approaches the initial value change segment, and generate the trigger timing adjustment value group. Based on the ratio of each monitoring point in the tension-to-conductivity response ratio sequence, the system-set tension-to-conductivity response ratio threshold is invoked, and the two ratios are compared within intervals to identify all monitoring points with ratios greater than the threshold. This is achieved by first setting the tension-to-conductivity response ratio threshold pre-defined in the management system. The threshold was set based on a cross-comparison of the root salinity adaptation and stress response capabilities of the target crops in the cultivated area. In medium-cohesive loam, with maize as the main crop, the critical stress change response measured in the previous field was... The boundary of conductivity variation is Take the ratio value To ensure coverage of both gradual and abrupt changes, the threshold was reasonably set after field testing. The data is then fixedly written into the system configuration file for subsequent judgment reference. Subsequently, the response ratio sequence generated by all profile monitoring points is traversed, and the ratio for each point is... and If a comparison is made, The point is then considered the excitation point and included in the delayed acquisition point set. Taking point P1 as an example, its response ratio is 4.17, which meets the condition and is identified as the point that needs adjustment. Then, the tension change rate sequence of this point is re-analyzed to locate the stable change interval time node where it falls back to the starting point of the initial change segment. This interval is defined as the absolute value of the change in tension rate within two consecutive time steps being less than 1. Furthermore, for continuous segments where the difference in tension change rate from the initial stage is less than 20%, taking P1 as an example, the initial stage tension change rate is approximately... To find the absolute value of the rate of change of tension in the subsequent time period that is close to And the change is gradual, for example, the tension value from the 5th to the 6th hour. Rise to The corresponding rate is It does not meet the standard, and the tension value from the 4th to the 5th hour is lower than that. Rise to The rate is The difference before and after the change is If it meets the standard, set this point as the delayed acquisition time point of P1, and finally construct the delayed acquisition trigger time adjustment value corresponding to each monitoring point, and collect them to form a trigger timing adjustment value group.

[0028] S103: Call the trigger timing adjustment value group, execute the trigger delay control for each monitoring point acquisition command according to the adjustment value, and extract the tension value and conductivity value record at the trigger time after the delay, assemble them into a multi-point profile soil parameter set under a unified time reference, and generate a farmland multi-point acquisition dataset. The trigger timing adjustment value group is invoked, and the trigger delay control is executed for each monitoring point acquisition command according to the adjustment value. At the same time, the tension value and conductivity value record at the trigger time after the delay are extracted and assembled into a multi-point profile soil parameter set under a unified time reference, generating a farmland multi-point acquisition dataset. First, the acquisition time delay value of each monitoring point in the adjustment value group is set. Write the acquisition control command queue, and combine it with the sampling interval set in the sensor data acquisition module. For each location, modify its sampling trigger time to the original trigger time. The system triggers delayed data acquisition operations at the corresponding sensor nodes via the docked time synchronization module. For example, if the original acquisition time for point P1 is 07:00 and the delay value is set to 90 minutes, the sampling time is modified to 08:30. Subsequently, the tension and conductivity measurements at 08:30 are retrieved for data reading. The tension value is... Electrical conductivity is The soil parameter values ​​at the synchronous sampling time are respectively used as the soil parameter values ​​at that point. The tension values ​​and electrical conductivity values ​​collected at all points are bound to their coordinate information to form a multi-parameter data matrix of soil profile with a unified time reference. Finally, a multi-point data set of farmland at the current sampling time is generated.

[0029] Please see Figure 3 The specific steps for obtaining information on the trend of water-salt ratio changes are as follows: S201: Based on the multi-point data collection dataset of farmland, extract the volumetric water content data and conductivity-converted salt concentration data of each monitoring point in two adjacent collection cycles. Perform ratio calculation on the volumetric water content and salt concentration values ​​of the two cycles within the same monitoring point, and perform difference calculation to generate a water-salt ratio change rate sequence. Based on a multi-point data collection dataset from farmland, volumetric water content data and conductivity-converted salinity data for each monitoring point in two adjacent data collection cycles were extracted. First, monitoring points with consistent numbers were obtained from the dataset. In two consecutive sampling periods and The sampling data, assuming the volumetric water content is respectively , The salt concentration conversion is based on the conductivity value using an empirical coefficient. Let the conductivity values ​​be respectively... , Using conversion formulas Conversion factor for This coefficient is derived from the fitting regression results of field measurements comparing salt extraction experiments and sensor outputs. The average value was obtained within a 30cm tillage depth to determine the steady-state operating value. Based on this, the salt concentrations were calculated as follows: , Then, the ratios of volumetric water content and salt concentration values ​​for the two time periods were calculated, and the water-salt ratios were respectively... Taking monitoring point P3 as an example, let's assume... , Electrical conductivity is , The corresponding salt concentration is , The corresponding water-to-salt ratio is , Perform difference calculation This value is recorded as the rate of change of water-salt ratio at point P3. This process is repeated at all monitoring points to extract and calculate the rate of change of water-salt ratio, generating a sequence in the form of... Each of these values ​​is derived from the ratio difference between two sampling periods at the same monitoring point.

[0030] S202: Based on the water-salt ratio change rate sequence, extract the rate values ​​of the monitoring points, arrange them in spatial distribution order to construct a rate space array, and use the least squares fitting criterion to perform a linear function fitting process on the array to generate water-salt ratio change fitting information. Based on the water-salinity ratio change rate sequence, the rate values ​​of all monitoring points in the profile are extracted and arranged in spatial order to construct a rate space array. The least squares fitting criterion is then used to perform a linear function fitting on this array to generate water-salinity ratio change fitting information. First, the depth information of the monitoring points in the profile coordinate system is read. With the corresponding rate of change value , forming a point set Suppose there are five profile points, with depth values ​​as follows: The corresponding rate of change in the water-salt ratio is Perform a linear function fit According to the least squares criterion, the objective is to minimize the sum of squared errors. The fitting parameters are obtained by solving. , That is, the spatial trend line equation for the change in water-salt ratio is obtained as follows: In this fitting process, the least squares method is not used in an abstract or generalized way, but is explicitly defined as a computational operation that performs linear function fitting with profile depth as the independent variable and rate as the dependent variable. Reflecting the degree of change in direction and rate, this fitting information is subsequently used as a reference for identifying the trend direction and abnormal change areas in the profile.

[0031] S203: Based on the water-salt ratio change fitting information, perform trend line direction consistency judgment on continuous points in the profile segment, extract change direction identifier, change rate change range and corresponding profile position information, and establish water-salt ratio change trend information. Based on the fitting information of water-salt ratio variation, the consistency of trend line direction is judged for continuous points in the profile segment. The change direction identifier, the change rate range and the corresponding profile position information are extracted to establish the water-salt ratio variation trend information. First, the derivative direction parameter is extracted from the fitting equation. ,by Determined to be in an upward trend The current instance is characterized by a downward trend. If the trend is upward, then the actual rate of change of three consecutive points in the profile is judged to see if it is consistent with the overall trend. For example, if the monitoring points are at depths of 30, 40, and 50 cm, the corresponding rates are +0.01, +0.05, and +0.09, respectively. Since the direction of change is consistent, the consistency condition is met. Then, the span of the rate interval is calculated. Mark this segment as the consistent trend interval, with the profile position being 30–50cm. If other segments, such as 10–20cm, have change rates of -0.08 and -0.04 respectively, then the direction is downward, inconsistent with the overall fitted direction. When recording the change trend information, this segment should be marked as the reverse trend segment. The trend direction is determined based on the fitted parameters. As a baseline value, the value is greater than or equal to Considered a significant upward trend, less than or equal to The trend is downward; if it falls between these two extremes, it is considered a slow-change range. This classification criterion is derived from the range of the maximum rate of change of water-salinity ratio in the diurnal variation of the profile observed in field experiments. Therefore, a difference of 10% is set as the threshold for determining a significant trend. Rounding to the nearest whole number is set to 0. To identify the trend threshold, the consistent trend segment and the opposite trend segment in all profiles are finally extracted to form a trend marker array. Combined with the depth record location interval and the change rate value interval, the water-salt ratio change trend information result is generated.

[0032] Please see Figure 4 The specific steps for obtaining the evaporative stress state label set are as follows: S301: Call the leaf area index, total solar radiation and root soil moisture content of crops in the farmland area, combine wind speed and temperature values, perform ratio and difference compound calculation on the combined data, calculate the crop transpiration stress response index, and establish a transpiration potential response value sequence based on the index value; The specific formula for calculating the crop transpiration stress response index is as follows: ; in, Indicating the crop transpiration stress response index, This represents the normalized leaf area index, calculated from vegetation remote sensing images or measured crop leaf area, and obtained through maximum and minimum range normalization. This represents the normalized value of total solar radiation, obtained by normalizing the data (0-1) after collection by an all-weather radiation sensor. This represents the normalized value of root zone soil moisture content, derived from data measured by a tensiometer or TDR sensor, and calculated using maximum and minimum range normalization. This represents the normalized wind speed value, derived from wind speed records from automatic weather stations, and normalized through regional wind speed range conversion. This represents the normalized temperature value, which is derived from temperature sensor observation data and obtained after linear normalization within a set temperature range. The leaf area index and normalized total solar radiation values ​​of each monitoring point within the farmland area were obtained. These two values ​​were then multiplied within the same time period, and the product value was extracted as the basic data for crop transpiration potential. Simultaneously, root soil moisture content was collected for the corresponding time period. Wind speed and air temperature values ​​were also incorporated. The combined data were then subjected to a ratio and difference calculation using the following formula: ; Explanation of formula operation logic: The meanings of each variable and the operational logic are as follows: Molecular part Used to assess the potential transpiration capacity of crops under their current physiological state. The normalized leaf area index reflects the transpiration surface area potential. Solar radiation reflects the intensity of light energy input; their product constitutes the basic transpiration driver for crops; this product is then multiplied by... ( A temperature-regulating factor for transpiration rate is introduced (as normalized to the air temperature). Considering the logarithmic growth effect of temperature changes on stomatal opening and water evaporation rate, a logarithmic function is used to reflect the marginal amplification effect of air temperature changes on transpiration. denominator Used to correct the degree to which root zone moisture conditions and atmospheric resistance inhibit transpiration. It represents the current root zone water status. High water content has a greater impact on the denominator, reducing the transpiration index. This represents the absolute value of the difference between wind speed and air temperature, reflecting the limiting pressure of differences in aerodynamic and thermal conditions on transpiration. The larger the difference, the more severe the micro-meteorological disturbance, which inhibits transpiration, and thus constitutes part of the denominator's influence factor. Final Index The meaning lies in characterizing the intensity of crop transpiration response under hydrothermal conditions and microclimate effects during the current period. When the value approaches 0, it indicates that factors such as strong environmental stress, insufficient water supply, or insufficient radiation suppress transpiration behavior. A value close to or above 1 indicates that transpiration potential has been fully released and environmental conditions are favorable. Therefore, this index is used to compare the current state with the water demand threshold, providing input for subsequent water stress identification.

[0033] Table 1 lists the core input parameters for the five monitoring points in the current monitoring cycle. These parameters, after normalization, correspond to the variable terms required in the formula.

[0034] Table 1. Parameters of Evapotranspiration Stress Response First, raw leaf area index values ​​were collected from each monitoring point. Compared with the original value of total solar radiation The leaf area index was then normalized, and the normalized leaf area index was set as follows: The normalization formula is ,in , For example, point P1 Substituting into Similarly, the total radiation is normalized, and the maximum value is set to... The minimum value is P1 point ,have to Temperature Within the set range , Normalization The maximum and minimum wind speed range is set to , P1 point ,have to ; Root layer soil moisture content The normalization range is , After normalization, we get Substitute the values ​​into the formula: ; This value is the crop transpiration stress response index of monitoring point P1 in the current cycle. It is then compared with the threshold to determine the crop's water stress status.

[0035] This result indicates that the transpiration stress response index calculated at point P1 is... This indicates that its transpiration capacity is suppressed to a certain extent during the current cycle, indicating moderate water stress. This is suitable for determining whether it falls into the water demand response zone and for promoting threshold judgment and stress state identification in subsequent steps.

[0036] S302: Based on the transpiration potential response value sequence, call the preset crop transpiration water demand response threshold, judge the difference between the index value and the threshold at each time point, filter the time points where the index value is lower than the threshold, extract the corresponding timestamps and establish a continuous interval sequence to generate a list of transpiration water demand intervals. Based on the transpiration potential response value sequence, the system's preset crop transpiration water demand response threshold is invoked. The difference between the index value at each time point and the threshold is compared, and all time points with index values ​​below the threshold are selected. Their corresponding timestamps are extracted and a continuous interval sequence is established to generate a list of transpiration water demand intervals. First, the transpiration stress response index obtained in paragraph 1 is used... The sequence is arranged in chronological order, and the corresponding time period is set as follows: Set the response value to be 1. The system's preset evapotranspiration water demand response threshold is set to... This threshold is derived from the corresponding index conversion values ​​defined by observational indicators such as a significant decrease in transpiration rate when the average root layer moisture content of the target crop (e.g., maize) is below 0.20 during the jointing stage, and a more than 10% increase in leaf temperature difference when solar radiation exceeds 550 W / m². Combined with experimental results, the typical value range is 0.40–0.50, and we assume the median value is taken. Used for determination, performing difference calculation at each time point. ,like Then it is recorded as the start or end point of the water demand interval. If two or more consecutive time points meet the requirements... If they are consecutive, they are divided into the same interval, and their start and end times are recorded. For example, three consecutive time points of a certain point. If all values ​​are below the threshold, it is marked as an evapotranspiration water demand range. The final generated interval list has the following format: .

[0037] S303: Based on the list of evapotranspiration water demand intervals, mark the start and end times of continuous intervals, identify the monitoring period markers where water demand pressure exists, extract the time periods and record and integrate them on the corresponding time axis, and establish a set of evapotranspiration stress state markers.

[0038] Based on the list of evapotranspiration water demand intervals, mark the start and end times of all consecutive intervals, identify the monitoring periods with water demand pressure, extract all time periods and record and integrate them according to the time axis, and establish a set of evapotranspiration stress state markers. First, traverse the interval list obtained in section 2, and for each group of intervals... Extract the first and last timestamps and mark them as "Water Demand Start" and "Water Demand End". Then, set a consistent sampling step size for the entire timeline for each monitoring point. The system scans step by step to determine whether the time falls within the specified interval. If it does, the corresponding time point is assigned the state flag "S1"; otherwise, it is assigned "S0", thus forming a state vector. This set of markers indicates the presence of evapotranspiration stress at a given point throughout the entire monitoring period. It ultimately serves as the basis for determining water demand superposition in subsequent water-salinity ratio trend disturbance analysis. Furthermore, by comparing this set with predicted meteorological data over time, high-risk water-salinity ratio disturbance time windows can be extracted. This status marker set will be categorized and summarized according to monitoring point number and then integrated and stored uniformly within the timeline data of each point.

[0039] Please see Figure 5 The specific steps for obtaining the water-salt ratio trend interference results are as follows: S401: Obtain the cumulative rainfall forecast value and the expected rainfall start time distance within the regional forecast period, compare the start time distance data with the time periods in the evapotranspiration stress state marker set one by one, identify whether there are overlapping segments on the time axis, filter the monitoring time points corresponding to the overlapping segments, and generate a list of overlapping water demand and rainfall segments. The cumulative rainfall and the estimated rainfall start time interval within the regional forecast period are obtained. The start time interval data are compared one by one with each time period in the evapotranspiration stress state marker set to identify whether there are overlapping segments on the time axis. All monitoring time points corresponding to the overlapping segments are screened out to generate a list of overlapping water demand and rainfall segments. First, the hourly rainfall data within the forecast period are extracted from the agricultural meteorological forecast module, and the total cumulative rainfall is calculated. Then, the rainfall start time interval is obtained based on the forecast model. The unit is minutes, and this value is set to 90 minutes, indicating that rainfall is expected to begin 90 minutes from the current time. Simultaneously, the evapotranspiration water demand time periods for each monitoring point are read from the evapotranspiration stress state marker set constructed in the preceding paragraphs. The structure is a list of several time intervals, for example, the water demand interval for a certain monitoring point is... Using the current system time as a reference, search within the water demand interval for any overlap segments falling within the predicted rainfall start time window. Corresponding time point Falling into a certain water demand range, such as If the time period overlaps with the rainfall, it is determined that the monitoring point is a point where rainfall and water demand overlap. After traversing all monitoring points, all recorded points with overlapping times and their corresponding time periods are combined to form a list of overlapping water demand and rainfall sections, in the form of a list structure as follows: .

[0040] S402: Call the list of overlapping water demand and rainfall sections, calculate the water-salinity ratio trend rate value based on the water-salinity ratio trend rate value corresponding to the time period of each monitoring point, and combine it with the cumulative rainfall at the corresponding time point. Establish a sorting sequence based on the disturbance value, merge the data sections of each point, and generate a water-salinity trend disturbance index sequence. The specific formula for calculating the water-salt ratio trend disturbance value is as follows: ; in, This indicates the magnitude of the trend disturbance in the water-to-salt ratio. This represents the normalized cumulative rainfall value, which is the ratio of the hourly rainfall value to the historical maximum rainfall value within the forecast period. The data type is a floating-point ratio value, obtained through normalization processing of meteorological station forecast data. The normalized rate of change of the water-salt ratio trend is obtained by dividing the difference in the water-salt ratio between two adjacent sampling periods by the time interval, followed by normalization. This is achieved by calculating the difference between continuous monitoring data from the profile. This represents the normalized value of surface infiltration capacity, which is the ratio of the infiltration rate at the observation point to the maximum infiltration rate in the region. The data type is a proportional real number, and the method of obtaining it is standard normalization after actual measurement using an infiltration tube. This represents the normalized value of evaporation potential, which is the ratio of the evaporation rate of the evaporation pan to the highest evaporation rate in the region. The data type is a floating-point ratio, obtained through data standardization processing from surface evaporation monitoring devices. This represents the normalized value of the root capillary suction force, which is the ratio of the measured suction force to the maximum suction force. The data type is a positive floating-point number, and the acquisition method is the normalization conversion of tension meter data. The water-salt ratio trend rate value for each monitoring point in the list of overlapping water demand and rainfall areas is used. This value is then combined with the normalized cumulative rainfall value at the corresponding time point for multiplication and division calculations. Surface infiltration capacity, evaporation potential, and root capillary suction are incorporated, and the disturbance index is calculated using the following formula: ; The variables are explained as follows: The normalized value of predicted rainfall is the ratio of the hourly rainfall during the predicted period to the historical maximum hourly rainfall. For example, if the predicted hourly rainfall is 7.8 mm and the historical maximum is 10 mm, then... ; The normalized value of the water-salinity ratio trend rate at the corresponding time point is calculated by dividing the difference in water-salinity ratio over two weeks by the time interval, and then normalizing it. For example, if the difference is 0.12 and the maximum rate is 0.15, then... ; The normalized value of surface infiltration capacity is the ratio of the current infiltration rate at the monitoring point to the maximum value in the region. For example, if the current rate is 2.4 mm / h and the maximum is 3.75 mm / h, then... ; The evaporation potential normalization value is obtained by comparing the observed value on the evaporating dish with the highest evaporation rate. For example, if the current value is 3.2 mm / d and the maximum is 5.71 mm / d, then... ; The normalized value of capillary suction in the root layer was measured to be 72 kPa. The maximum suction was set to 100 kPa and normalized to... ; Taking monitoring point P1 as an example, the calculation is as follows: ; The disturbance index This represents the magnitude of the disturbance effect of rainfall on the water-salinity ratio trend during the current overlapping water demand-rainfall period. A larger value indicates a more significant disturbance. The calculation results of the disturbance index for different monitoring points are listed in Table 2, with parameters and results as follows: Table 2 Calculation Parameters for Disturbance Index Table 2 shows the disturbance values ​​calculated from rainfall and micrometeorological factors at different monitoring points. It can be used to identify key intervention target points and further serve as a basis for ranking trend interference indicators.

[0041] Explanation of formula operation logic: The formula is used to estimate the disturbance intensity of a rainfall event on the water-salinity ratio change trend at a specific monitoring point under conditions of overlapping water demand and rainfall. This disturbance... It is a composite reflection of meteorological input, dynamic changes in water-salt ratio, and surface / root layer environmental factors, and its mathematical structure is constructed in three parts: Molecular part This section comprehensively represents the cumulative effect of dynamic water input and the soil's ability to slow down water release in response: First item The product of rainfall and the rate of change of water-salinity ratio is used to simulate the direct driving force of rainfall on the existing trend of water-salinity ratio change. If both are high, the superimposed trend is enhanced. Second item The square root of the surface infiltration capacity represents its "response release" degree. The larger the value, the faster the surface can respond to rainfall input. Third item Introducing a logarithmic decay term for capillary suction: the stronger the suction, the higher its buffering capacity against trend disturbances, thus playing a weakening role.

[0042] denominator This term represents the degree of dispersion between evaporation potential and trend rate, and is a moderating factor for the amount of perturbation. When the evaporation potential... With trend rate When the values ​​are close, the denominator decreases, and the overall disturbance is amplified; conversely, the disturbance intensity is suppressed when the values ​​are close. This design considers whether the "moisture release capacity" and the "trend driving force" cancel each other out or reinforce each other.

[0043] Overall interaction relationship: When rainfall is abundant ( ), trends change rapidly ( ), good penetration ( ), weak suction ( Evaporation potential and trend are similar ( When ), the disturbance amount This indicates that the trend of water-salt ratio change is significantly disturbed by sudden water input; conversely, it indicates that the trend is not significantly disturbed by sudden water input. This indicates that the trend is relatively stable and has not been disrupted by rainfall.

[0044] Disturbance Explanation of the numerical meaning: like This indicates that the disturbance is weak, the trend remains stable, and no further intervention is needed. like This indicates a moderate risk of disturbance; attention should be paid to the inflection point of the trend change. like If the disturbance is significant and the trend change may be reversed, the point and time period should be recorded as a potential target for water and salt fluctuation intervention.

[0045] This result indicates that the disturbance amount As an important indicator for judging the stability of trend changes, the above-mentioned structured combination can systematically assess the impact of sudden water input and the synergistic effect of the land-atmosphere system on the changes in the water-salt ratio of the profile, and provide a basis for subsequent identification of disturbance segments and formulation of intervention strategies. This indicator does not directly represent salinity concentration, nor is it soil moisture, but rather a summary value of the interaction between cross-dimensional parameters, which is suitable for dynamically judging the degree of system disturbance.

[0046] S403: Based on the water-salinity trend disturbance index sequence, extract high-amplitude jump segments according to the disturbance value amplitude, trend direction and spatial location, and construct the point correspondence relationship. Integrate the disturbance segments according to the time series and profile distribution index to establish the water-salinity ratio trend disturbance result.

[0047] Based on the amplitude, direction, and spatial location of the disturbance values ​​in the water-salinity trend disturbance index sequence, high-amplitude jump segments are extracted and point correspondences are constructed. Disturbance segments are integrated according to time series and profile distribution indexes to establish the water-salinity ratio trend disturbance results. First, based on the disturbance indices of each monitoring point obtained from segment 2... Construct a complete index sequence and set a threshold for perturbation intensity classification. , They were divided into medium-intensity and high-intensity disturbance zones, and all... Perform a traversal, if satisfied If it meets the following conditions, it is marked as a segment requiring attention. If a point is identified as a transition segment, it is then sorted and grouped according to its corresponding profile depth, and the corresponding time axis information is recorded to construct a two-dimensional distribution matrix. Combined with the previous trend direction of water-salt ratio (positive increase / negative decrease), the same direction judgment is made. If the trend direction of the jump segment is consistent with the fitted main trend direction, it is marked as amplified interference; otherwise, it is marked as reverse disturbance. Finally, a complete set of trend interference results is formed for the determination of intervention needs in the next stage.

[0048] Please see Figure 6 The specific steps for obtaining the water-salt ratio intervention demand identification results are as follows: S501: Based on the water-salt ratio trend disturbance results, according to the water-salt ratio trend disturbance values ​​of the monitoring point for three consecutive time periods, perform difference calculation on the disturbance values ​​of any two adjacent time periods, divide the difference results by the time interval, and construct the change rate difference group of consecutive segments in sequence to generate the water-salt ratio acceleration sequence. Based on the water-salinity ratio trend disturbance values ​​of each monitoring point over three consecutive time periods in the water-salinity ratio trend disturbance results, the difference between the disturbance values ​​of any two adjacent time periods is calculated. Then, the difference result is divided by the time interval to sequentially construct the rate of change difference group for all consecutive segments, generating a water-salinity ratio acceleration sequence. This is first performed using the monitoring point number... The disturbance value sequence is represented as This represents the disturbance results within five consecutive time periods, with the sampling time interval uniformly set to [value]. For each pair of adjacent disturbance values, a difference operation is performed to obtain the disturbance change rate as follows: ; Taking a certain point as an example, the disturbance value sequence is as follows: The acceleration is calculated as follows: ; ; ; ; This ultimately forms the acceleration sequence at that point. The above operations are performed one by one at all monitoring points, organized according to profile number and time index, to construct the total sequence matrix of water-salt ratio disturbance acceleration, which will serve as the basic data source for subsequent jump trend analysis.

[0049] S502: Based on the water-salt ratio acceleration sequence, determine whether the sign direction is continuous and consistent according to the change trend of three consecutive time periods, calculate the corresponding acceleration amplitude, compare each set of acceleration values ​​with the set water-salt ratio change acceleration threshold, filter the acceleration point set with continuous change direction and amplitude exceeding the threshold, and generate a water-salt jump trend segment index set. Based on the changing trends of three consecutive time periods in the water-salt ratio acceleration sequence, it is determined whether their sign direction is continuous and consistent, and the corresponding acceleration amplitude is calculated. Each set of acceleration values ​​is compared with the water-salt ratio change acceleration threshold set by the system. Acceleration points with continuous changes in the same direction and amplitudes exceeding the threshold are selected, generating a water-salt ratio jump trend segment index set. First, each set of three consecutive acceleration values ​​is traversed to determine whether their signs (positive or negative) are consistent, i.e.,... Determine if it satisfies If the signs are the same, then calculate the average magnitude of the absolute values ​​of the three. ; Compare this average value with the water-salt ratio acceleration threshold set by the system. For comparison, this threshold is set based on the range of peak acceleration of water-salinity ratio disturbance within a typical water-salinity jump region. The data, sourced from monitoring data of the topsoil profile in a typical irrigation area in central China, shows that under continuous disturbance, the average acceleration of the maximum water-salt trend jump fluctuates between 0.22 and 0.31. Taking the median as a standard reference, and using the aforementioned acceleration sequence as an example, the first three values ​​are... Consistent symbols and ; If the conditions for identifying a jump trend segment are met, the time series is recorded as a jump segment, and its starting index position and monitoring point number are marked, for example, recorded as (P1, t2–t4). All combinations that meet the conditions are extracted to form a water-salt jump trend segment index set, which serves as a key structural index for subsequent intervention judgment.

[0050] S503: Based on the index set of water-salinity jump trend segments, extract the corresponding profile monitoring point number and time period index, construct the horizontal comparison results under the same spatial level, add intervention markers to the points that meet the jump conditions, and establish the water-salinity ratio intervention demand identification results.

[0051] Based on the index set of water-salinity jump trend segments, the corresponding profile monitoring point numbers and time period indices are extracted. A horizontal comparison result is constructed at the same spatial level. Intervention markers are added to points that meet the jump conditions, establishing a water-salinity ratio intervention demand identification result. First, all markers meeting the jump criteria are obtained from the index set; each marker includes a monitoring point number. Time period The lateral acceleration amplitude is compared at different depths of the profile (i.e., between monitoring points) within the same time period, along with the direction of the jump (positive / negative). This is to determine whether there is an overall trend of co-variation. If there are jumps in the same direction at more than three profile points and the acceleration values ​​all exceed [a certain value], [the following is considered]. If the time period is marked as the synchronous transition period of the profile, the point with the maximum acceleration is marked as "priority intervention". For example, if points P1, P2 and P4 transition simultaneously in a certain time period, and the acceleration of point P2 reaches 0.52, which is the maximum of the entire profile, then it is recorded as (P2, t3) the point that needs intervention. Finally, all points that meet the conditions of transition and lateral consistency are written into the water-salt ratio intervention demand identification result set. The data structure retains four pieces of information: point number, time period, trend direction and acceleration value, so that the subsequent control response module can directly call them.

[0052] Please see Figure 7 A dynamic water-salinity ratio monitoring system for farmland, used to execute the aforementioned dynamic water-salinity ratio monitoring method for farmland, the system comprising: The data synchronization processing module acquires the rate of change of soil tension and the magnitude of change of soil conductivity at multiple points in the farmland, and compares them with the set threshold for the ratio of tension and conductivity response. If the ratio exceeds the threshold, the data acquisition trigger time is delayed, and synchronous acquisition is performed according to the updated trigger sequence to generate a multi-point data collection dataset of the farmland. The trend analysis module, based on a multi-point data collection dataset from farmland, calculates the water-salt ratio change rate sequence at monitoring points, performs least-squares linear fitting on the change rate sequence in the spatial dimension, obtains the trend line of the profile segment, and generates water-salt ratio change trend information. The transpiration stress labeling module calls the leaf area index, total solar radiation and root soil moisture content of crops in the farmland area, uses the product of total solar radiation and leaf area index as the base value of transpiration potential, calculates the index value of crop water stress, identifies the water demand labeling time interval, and constructs a transpiration stress state labeling set. The trend interference identification module obtains the cumulative rainfall forecast value and the expected rainfall start time interval within the regional forecast period, compares whether the time interval overlaps with the water demand marker time interval in the evapotranspiration stress state marker set, and calculates the water-salinity ratio trend impact value by combining the water-salinity ratio change trend information to generate water-salinity ratio trend interference results. The intervention demand identification module calculates the difference in the rate of change between two adjacent time periods based on the water-salt ratio trend interference results. It determines whether there are continuous changes in the same direction that exceed the set water-salt change acceleration threshold. If the condition is met, the corresponding profile monitoring point and time point are recorded as intervention demand marker points, and the water-salt ratio intervention demand identification results are generated.

[0053] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0054] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0055] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply 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 invention.

[0056] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0057] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0058] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0059] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0060] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0061] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamic monitoring of water-salt ratio in farmland, characterized in that, Includes the following steps: S1: Obtain the rate of change of soil tension and the magnitude of change of soil conductivity at multiple points in the farmland, and compare them with the set threshold for the ratio of tension and conductivity response. If the ratio exceeds the threshold, delay the data acquisition trigger time, and perform synchronous acquisition according to the updated trigger sequence to generate a multi-point farmland acquisition dataset. S2: Based on the multi-point data collection dataset of the farmland, calculate the water-salt ratio change rate sequence of the monitoring points, perform least squares linear fitting on the change rate sequence in the spatial dimension to obtain the change trend line of the profile section, and generate water-salt ratio change trend information. S3: Call the leaf area index, total solar radiation and root soil moisture content of crops in the farmland area, use the product of total solar radiation and leaf area index as the base value of transpiration potential, calculate the index value of crop water stress, mark the water demand marker time interval, and construct the transpiration stress state marker set. S4: Obtain the cumulative rainfall forecast value and the expected rainfall start time interval within the regional forecast period, compare the time interval with the water demand marker time interval in the evapotranspiration stress state marker set, calculate the water-salt ratio trend influence value in combination with the water-salt ratio change trend information, and generate water-salt ratio trend interference results.

2. The method for dynamic monitoring of water-salt ratio in farmland according to claim 1, characterized in that, The farmland multi-point data collection dataset includes soil tension response synchronization status, uniform time nodes for profile moisture content, and balanced collection windows for electrical conductivity values. The water-salt ratio change trend information includes profile hierarchical response rate distribution, spatial continuity change tendency, and water-salt ratio horizontal trend classification labels. The transpiration stress state label set specifically includes crop physiological stress identifiers, light-driven water deficit status, and root zone water potential change indicators. The water-salt ratio trend interference results specifically include rainfall-driven disturbance intensity parameters, trend direction shift degree, and response factors in the overlapping areas of rainfall and transpiration periods.

3. The method for dynamic monitoring of water-salt ratio in farmland according to claim 2, characterized in that, The specific steps for obtaining the multi-point data collection dataset from farmland are as follows: S101: Obtain the rate of change of soil tension and the magnitude of change of soil conductivity at multiple points in farmland. Extract the difference between the maximum gradient change intervals for the changes of the two indicators in the same monitoring point within the corresponding time period. Perform ratio calculation on the rate of change of soil tension and the magnitude of change of soil conductivity to generate a sequence of ratio of soil tension and soil conductivity response. S102: Based on the tension and conductivity response ratio sequence, call the set tension and conductivity response ratio threshold, compare the interval size of the two, identify the monitoring point where the ratio is greater than the threshold, delay the acquisition trigger time of the monitoring point until the time node of the stable change interval when the tension change rate value first approaches the initial value change segment, and generate a trigger timing adjustment value group. S103: Call the trigger timing adjustment value group, execute the trigger delay control for each monitoring point acquisition command according to the adjustment value, and extract the tension value and conductivity value record at the trigger time after the delay, assemble them into a multi-point profile soil parameter set under a unified time reference, and generate a farmland multi-point acquisition dataset.

4. The method for dynamic monitoring of water-salt ratio in farmland according to claim 3, characterized in that, The specific steps for obtaining the water-salt ratio change trend information are as follows: S201: Based on the multi-point data collection dataset of the farmland, extract the volumetric water content data and conductivity-converted salt concentration data of each monitoring point in two adjacent collection cycles. Perform ratio calculation on the volumetric water content and salt concentration values ​​of the two cycles within the same monitoring point, and perform difference calculation to generate a water-salt ratio change rate sequence. S202: Based on the water-salt ratio change rate sequence, extract the rate values ​​of the monitoring points, arrange them in spatial distribution order to construct a rate space array, call the least squares fitting criterion to perform a linear function fitting process on the array, and generate water-salt ratio change fitting information. S203: Based on the water-salt ratio change fitting information, perform trend line direction consistency judgment on continuous points in the profile segment, extract the change direction identifier, change rate change range and corresponding profile position information, and establish water-salt ratio change trend information.

5. The method for dynamic monitoring of water-salt ratio in farmland according to claim 4, characterized in that, The specific steps for obtaining the transpiration stress state label set are as follows: S301: Call the leaf area index, total solar radiation and root soil moisture content of crops in the farmland area, combine wind speed and temperature values, perform ratio and difference compound calculation on the combined data, calculate the crop transpiration stress response index, and establish a transpiration potential response value sequence based on the index value; S302: Based on the transpiration potential response value sequence, call the preset crop transpiration water demand response threshold, judge the difference between the index value and the threshold at each time point, filter the time points where the index value is lower than the threshold, extract the corresponding timestamps and establish a continuous interval sequence to generate a list of transpiration water demand intervals. S303: Based on the list of evapotranspiration water demand intervals, mark the start and end times of continuous intervals, identify the monitoring period markers where water demand pressure exists, extract the time periods and record and integrate them according to the corresponding time axis, and establish a set of evapotranspiration stress state markers.

6. The method for dynamic monitoring of water-salt ratio in farmland according to claim 5, characterized in that, The specific formula for obtaining the crop transpiration stress response index is as follows: ； in, Indicating the crop transpiration stress response index, This represents the normalized value of the leaf area index. This represents the normalized value of total solar radiation. This represents the normalized value of root zone soil moisture content. This represents the normalized value of wind speed. This represents the normalized value of temperature.

7. The method for dynamic monitoring of water-salt ratio in farmland according to claim 6, characterized in that, The specific steps for obtaining the water-salt ratio trend interference results are as follows: S401: Obtain the cumulative rainfall forecast value and the expected rainfall start time distance within the regional forecast period, compare the start time distance data with the time periods in the evapotranspiration stress state marker set one by one, identify whether there are overlapping segments on the time axis, filter the monitoring time points corresponding to the overlapping segments, and generate a list of overlapping water demand and rainfall segments. S402: Call the list of overlapping water demand and rainfall sections, calculate the water-salinity ratio trend rate value according to the water-salinity ratio trend rate value corresponding to the time period of each monitoring point, and combine it with the cumulative rainfall at the corresponding time point. Establish a sorting sequence based on the disturbance value, merge the data sections of each point, and generate a water-salinity trend disturbance index sequence. The specific formula for calculating the water-salt ratio trend disturbance value is as follows: ； in, This indicates the magnitude of the trend disturbance in the water-to-salt ratio. This represents the normalized value of cumulative rainfall. The normalized rate of change of the water-salt ratio trend. This represents the normalized value of surface infiltration capacity. This represents the normalized value of the evaporation potential. This represents the normalized value of the root capillary suction. S403: Based on the water-salinity trend disturbance index sequence, extract high-amplitude jump segments according to the disturbance value amplitude, trend direction and spatial location, construct the point correspondence relationship, integrate the disturbance segments according to the time series and profile distribution index, and establish the water-salinity ratio trend disturbance result.

8. The method for dynamic monitoring of water-salt ratio in farmland according to claim 7, characterized in that, The method further includes the following steps: S5: Based on the water-salt ratio trend interference results, perform change rate difference calculation on the values ​​of two adjacent time periods, determine whether there are continuous changes in the same direction that exceed the set water-salt change acceleration threshold. If the condition is met, record the corresponding profile monitoring point and time point as intervention demand marker points, and generate water-salt ratio intervention demand identification results. The water-salt ratio intervention requirement identification results specifically refer to the index of abrupt changes in the water-salt ratio evolution, the record of trend jump time nodes, and the information on monitoring priority labeling.

9. The method for dynamic monitoring of water-salt ratio in farmland according to claim 8, characterized in that, The specific steps for obtaining the water-salt ratio intervention demand identification results are as follows: S501: Based on the water-salt ratio trend disturbance results, according to the water-salt ratio trend disturbance values ​​of the monitoring point for three consecutive time periods, perform difference calculation on the disturbance values ​​of any two adjacent time periods, divide the difference results by the time interval, and sequentially construct the change rate difference group of consecutive segments to generate a water-salt ratio acceleration sequence. S502: Based on the water-salt ratio acceleration sequence, determine whether the sign direction is continuous and consistent according to the change trend of three consecutive time periods, calculate the corresponding acceleration amplitude, compare each set of acceleration values ​​with the set water-salt ratio change acceleration threshold, filter the acceleration point set with continuous change direction and amplitude exceeding the threshold, and generate a water-salt jump trend segment index set. S503: Based on the water-salinity jump trend segment index set, extract the corresponding profile monitoring point number and time period index, construct the horizontal comparison results under the same spatial level, add intervention markers to the points that meet the jump conditions, and establish the water-salinity ratio intervention requirement identification results.

10. A dynamic monitoring system for water-salt ratio in farmland, characterized in that, The system is used to implement the method for dynamic monitoring of water-salt ratio in farmland as described in any one of claims 1-9, the system comprising: The data synchronization processing module acquires the rate of change of soil tension and the magnitude of change of soil conductivity at multiple points in the farmland, and compares them with the set threshold for the ratio of tension and conductivity response. If the ratio exceeds the threshold, the data acquisition trigger time is delayed, and synchronous acquisition is performed according to the updated trigger sequence to generate a multi-point data collection dataset of the farmland. The trend analysis module, based on the multi-point data collection dataset of the farmland, calculates the water-salt ratio change rate sequence of the monitoring points, performs least squares linear fitting on the change rate sequence in the spatial dimension, obtains the change trend line of the profile segment, and generates water-salt ratio change trend information. The transpiration stress labeling module calls the leaf area index, total solar radiation and root soil moisture content of crops in the farmland area, uses the product of total solar radiation and leaf area index as the base value of transpiration potential, calculates the index value of crop water stress, identifies the water demand labeling time interval, and constructs a transpiration stress state labeling set. The trend interference identification module obtains the cumulative rainfall forecast value and the expected rainfall start time interval within the regional forecast period, compares whether the time interval overlaps with the water demand mark time interval in the evapotranspiration stress state mark set, and calculates the water-salinity ratio trend impact value by combining the water-salinity ratio change trend information, and generates water-salinity ratio trend interference results. The intervention demand identification module calculates the difference in the rate of change of values ​​between two adjacent time periods based on the water-salt ratio trend interference results. It determines whether there are continuous changes in the same direction that exceed the set water-salt change acceleration threshold. If the condition is met, the corresponding profile monitoring point and time point are recorded as intervention demand marker points, and the water-salt ratio intervention demand identification result is generated.

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