Sponge city greening and irrigation scheduling system based on multi-dimensional data fusion

Abstract

The invention relates to the technical field of sponge city greening and irrigation, in particular to a multi-dimensional data fusion sponge city greening and irrigation scheduling system, which comprises a data acquisition module for acquiring soil humidity, meteorological data and water source conditions, analyzing the influence of precipitation and temperature on the soil humidity and generating a soil humidity record; the water source evaluation module analyzes rainwater accumulation and underground water level fluctuation and calculates water supply capacity, the greening demand evaluation module extracts water demand indexes and generates a water demand evaluation result, and the irrigation scheduling optimization module calculates irrigation frequency and time interval, adjusts scheduling risk and generates an optimization result. The method comprises the steps of constructing a soil humidity difference chart, deducing an evaporation trend, identifying a water loss rate, measuring and calculating supply capacity and response delay, constructing a water demand model, implementing irrigation sequencing according to priority, distributing irrigation frequency and interval, avoiding cross scheduling, comparing soil humidity with a target interval after execution, adjusting the irrigation amount and time period in real time, and forming a closed-loop mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sponge city greening and irrigation, and particularly relates to a multi-dimensional data fusion sponge city greening and irrigation scheduling system. BACKGROUND

[0002] The technical field of sponge city greening and irrigation includes technologies for effectively managing and utilizing urban rainwater through natural processes and artificial measures. The core content is to alleviate the hydrological environmental problems caused by urbanization by constructing urban infrastructure with permeability, water storage and purification functions. It mainly involves multiple technical directions such as rainwater collection and management, urban greening and ecological construction, rainwater permeable pavement, wetland restoration and irrigation system optimization. The sponge city technology aims to improve the city's ability to cope with extreme weather events such as floods and droughts through comprehensive design and layout of urban greening, roads, buildings and other elements, and to optimize water resource utilization.

[0003] Among them, the sponge city greening and irrigation scheduling system refers to the data fusion and scheduling problem in the process of urban greening and irrigation management. Through the integration and analysis of multi-dimensional data, precise scheduling is realized. It covers real-time monitoring of multi-dimensional data such as soil moisture, meteorological data and green coverage rate. Based on different data sources, the technology of effective fusion is realized. Through automatic means, the green demand and water supply conditions of different regions are obtained. Combined with rainwater recovery and soil moisture changes, the irrigation scheme and timing are dynamically adjusted to realize water saving and efficient management. The scheduling strategy based on data driving is adopted. Through real-time feedback and adjustment, the irrigation management is more accurate and efficient.

[0004] The existing scheme does not construct a regional humidity difference distribution framework, cannot quantify the dynamic difference of evaporation intensity between localities, and leads to insufficient accuracy of water loss feature recognition. In addition, it lacks a time period distribution model to depict the demand change rhythm, making it difficult to support on-demand sequencing and scheduling. The supply-demand mapping relationship in the scheduling logic is insufficient, and irrigation overlap or frequency imbalance may occur between regions. After execution, there is no feedback parameter correction mechanism, the water replenishment efficiency is reduced, the response delay is lengthened, and the green water state is imbalanced and resources are wasted. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and to provide a multi-dimensional data fusion sponge city greening and irrigation scheduling system.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a multi-dimensional data fusion sponge city greening and irrigation scheduling system, the system comprises:

[0007] The data acquisition module collects soil humidity, meteorological data and water supply conditions of each green area, analyzes the influence of precipitation and temperature on soil humidity, compares the regional soil humidity difference, combines the air humidity and soil humidity difference, deduces the influence of evaporation rate on soil humidity, and generates a soil humidity data record;

[0008] The water source evaluation module calls the soil humidity data in the soil humidity data record and the water supply condition, analyzes the difference between the regional rainwater accumulation and the underground water level fluctuation, calculates the soil water replenishment and absorption capacity, and generates a water supply capacity evaluation result;

[0009] The green demand evaluation module divides the regional vegetation type and land function according to the water supply stability and replenishment capacity in the water supply capacity evaluation result, extracts the water demand index, adjusts the response time of the water supply stability level, deduces the theoretical total water demand and period demand, and generates a water demand evaluation result;

[0010] The irrigation scheduling optimization module sorts the regional water demand based on the total water demand and period demand in the water demand evaluation result, determines the irrigation priority, calculates the irrigation frequency and period interval, adjusts the adjacent period cross-scheduling risk area, and generates an irrigation scheduling optimization result.

[0011] As a further scheme of the application, the soil humidity data record includes regional humidity difference distribution, evaporation rate change characteristics and humidity response correlation parameters, the water supply capacity evaluation result includes rainwater supply capacity index, underground water level fluctuation amplitude and soil water replenishment capacity coefficient, the water demand evaluation result includes vegetation water demand level, land water demand type and period response index, and the irrigation scheduling optimization result includes irrigation priority parameter, irrigation frequency configuration value and period water supply limit.

[0012] As a further scheme of the application, the data acquisition module includes:

[0013] The environment monitoring submodule collects the output signal of the soil humidity sensor of each green area, records the cumulative precipitation of the rain gauge, obtains the temperature sensor reading, reads the air humidity detection value, counts the flow meter data of the rainwater recovery device, monitors the pressure value of the underground water level probe, and implements time synchronization calibration and outlier rejection processing on the six types of data, to generate a multi-source environment data set;

[0014] The fluctuation influence analysis submodule extracts the precipitation sequence and temperature sequence based on the multi-source environment data set, respectively performs sliding window correlation coefficient calculation on the soil humidity sequence, evaluates the contribution degree of the two factors to the humidity fluctuation by using a weighted fusion method, quantifies the discrete characteristics of the regional soil humidity distribution difference, and generates a humidity fluctuation correlation degree;

[0015] The evaporation trend derivation submodule calls the air humidity value and the soil humidity value in the multi-source environment data set, calculates a time-by-time difference value sequence thereof, establishes a difference value change and soil humidity attenuation correlation mapping in combination with a humidity fluctuation correlation degree, derives an actual influence trend of the evaporation effect on the soil humidity per unit time, and generates a soil humidity data record.

[0016] As a further scheme of the present application, the water source evaluation module comprises:

[0017] The water source feature analysis submodule calls the soil humidity average value, the humidity change rate, the rainwater recovery amount and the underground water level data in the soil humidity data record, calculates a rainwater cumulative value total sum in a continuous time period, determines an upper limit and a lower limit of an underground water level fluctuation amplitude interval, performs a time-by-time difference value operation on the rainwater cumulative value and the fluctuation amplitude interval, and generates a water source balance feature.

[0018] The water division dynamic calculation submodule, based on the water source balance feature, in combination with the greening area water supply capacity data and the soil humidity variation data, calculates a difference ratio of the water supply capacity and the humidity variation, derives a corresponding relationship between the soil water division supply strength and the absorption rate, and obtains a water division dynamic capacity.

[0019] The supply capacity generation submodule calls the water division dynamic capacity, integrates the soil humidity change curve morphological feature and the water supply capacity distribution state, integrates the water division supply strength and the absorption rate corresponding relationship, establishes a supply capacity evaluation matrix, and generates a water source supply capacity evaluation result.

[0020] As a further scheme of the present application, the greening demand evaluation module comprises:

[0021] The greening feature classification submodule calls the area water supply stability grade and the supply capacity distribution in the water source supply capacity evaluation result, obtains a greening area vegetation type database and a land function planning data, classifies according to the tree, shrub, lawn vegetation types and the park green land, the protection green land and the land function, establishes a corresponding mapping relationship between the vegetation type and the functional area, and generates a vegetation water demand feature.

[0022] The water demand configuration adjustment submodule, based on the vegetation water demand feature, extracts a unit area water demand quota of different types of greening areas under standard meteorological conditions, compares the stability coefficient in the water supply stability grade, linearly compensates and corrects the supply response time of each greening area, and obtains a water demand configuration scheme.

[0023] The demand distribution derivation submodule calls the water demand configuration scheme, combines the greening area area data and the meteorological time period division, calculates a total amount of theoretical water demand of each area, allocates a water demand proportion according to the day and night time period, determines a peak water demand time period according to the supply response time, and generates a water demand evaluation result.

[0024] As a further scheme of the present application, the irrigation scheduling optimization module comprises:

[0025] a water demand ordering sub-module, which calculates the water demand intensity per unit time of each green area based on the total water demand and the time period demand value in the water demand evaluation result, arranges the water demand intensity values in descending order, determines the irrigation sequence of the areas according to the ordering sequence, and generates an irrigation priority sequence;

[0026] a scheduling calculation sub-module, which calls the irrigation priority sequence, combines the available water data in the water source supply capacity evaluation result, calculates the difference between the total water demand and the available water of each area, determines the irrigation frequency according to the difference, determines the irrigation interval length according to the time period demand distribution, generates a scheduling configuration scheme;

[0027] a conflict coordination sub-module, which detects the areas with irrigation equipment use conflicts in adjacent time periods based on the scheduling configuration scheme, reallocates the time schedule for the conflict areas, adjusts the upper limit of the water supply limit in the overlapping time period, optimizes the overall irrigation time sequence arrangement, and generates an irrigation scheduling optimization result.

[0028] As a further scheme of the present application, the system further comprises:

[0029] a feedback and adjustment module, which collects the soil humidity values of each green area after execution according to the water supply frequency and irrigation time period configuration parameters in the irrigation scheduling optimization result, compares the humidity values with the target humidity interval, adjusts the irrigation frequency and time period configuration, corrects the irrigation scheme, and generates a feedback scheduling result;

[0030] The feedback scheduling result comprises a humidity comparison result set, a frequency correction parameter, and a water quantity adjustment upper limit value.

[0031] As a further scheme of the present application, the feedback and adjustment module comprises:

[0032] an execution monitoring sub-module, which collects the current signal values output by the soil humidity sensors of each green area in real time according to the water supply frequency parameters and the irrigation time period configuration parameters in the irrigation scheduling optimization result, synchronously records the opening time point and the closing time point of the electromagnetic valve, aligns the humidity measurement values and the time node data in time sequence, and generates a humidity monitoring data set with time sequence markers;

[0033] a humidity comparison sub-module, which calls the humidity monitoring data set, reads the upper threshold value and the lower threshold value of the preset target humidity range, calculates the numerical difference between the current humidity measurement value and the upper threshold value and the lower threshold value for each area, judges whether the humidity state of each area is above or below the target interval according to the difference, and generates a humidity deviation indication;

[0034] The scheme correction sub-module, based on the humidity deviation indication, increases irrigation frequency per unit time on the basis of original irrigation frequency for the area where the humidity value is continuously lower than the target interval, reduces single irrigation operation time and adjusts the upper limit value of water supply flow for the area where the humidity value is continuously higher than the target interval, updates time parameters and water quantity parameters in the irrigation scheme, and generates feedback scheduling results.

[0035] Compared with the prior art, the application has the advantages and positive effects that:

[0036] In the application, by constructing a cross-regional soil humidity difference distribution map, combining air and soil humidity difference comparison, deducing local evaporation trend, dynamically identifying green land water loss rate, and then taking water supply and demand variation as the basis, calculating supply capacity and response delay, constructing time period water demand model, implementing irrigation sequencing according to regional response priority, distributing irrigation frequency and interval through supply and demand matching difference, avoiding cross-scheduling overlap, comparing recovered soil humidity data with target interval after execution, and adjusting irrigation amount and time period configuration in the next period in real time, the closed-loop control mechanism is formed through iteration, and the scheduling accuracy, water saving efficiency and regional coordination degree are improved. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The system flowchart of the application is shown in the figure;

[0038] Figure 2 The acquisition flowchart of the data acquisition module of the application is shown in the figure;

[0039] Figure 3 The acquisition flowchart of the water source evaluation module of the application is shown in the figure;

[0040] Figure 4 The acquisition flowchart of the green demand evaluation module of the application is shown in the figure;

[0041] Figure 5 The acquisition flowchart of the irrigation scheduling optimization module of the application is shown in the figure;

[0042] Figure 6 The acquisition flowchart of the feedback and adjustment module of the application is shown in the figure. DETAILED DESCRIPTION

[0043] The technical solutions in the application will be described below with reference to the drawings.

[0044] In the embodiments of the application, the words such as "example", "for example" and the like are used to represent an example, illustration or description. Any embodiment or design scheme described as "example" in the application should not be interpreted as more preferred or more advantageous than the embodiments or design schemes. In fact, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0045] In the embodiments of the present application, "image" and "picture" can be used interchangeably, and it should be pointed out that their meanings are consistent when their differences are not emphasized.

[0046] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and their meanings are consistent when their differences are not emphasized.

[0047] In order to make the technical problems, technical solutions and advantages of the present application clearer, specific embodiments will be described in detail below with reference to the drawings.

[0048] Please refer to Figure 1 The present application provides a technical solution: a multi-dimensional data fusion sponge city greening and irrigation scheduling system, which comprises:

[0049] A data acquisition module acquires soil moisture, precipitation, temperature, air humidity, rainwater recovery amount and underground water level data in each greening area, analyzes the influence of precipitation and temperature on soil moisture fluctuation in each greening area, compares the difference distribution of soil moisture in each greening area, and combines the difference between air humidity and soil humidity to deduce the influence trend of evaporation rate on soil moisture, and generates soil moisture data record;

[0050] A water source evaluation module calls the average soil moisture, moisture change rate, rainwater recovery amount and underground water level data in the soil moisture data record, and performs difference analysis on the rainwater cumulative value and underground water level fluctuation amplitude interval in the continuous period of the greening area, calculates the soil moisture supply and absorption capacity through the difference relationship between the water supply capacity of the greening area and the soil moisture change, and generates water source supply capacity evaluation result according to the soil moisture change curve and the water supply capacity distribution;

[0051] A greening demand evaluation module calls the area water supply stability grade and supply capacity distribution in the water source supply capacity evaluation result, classifies the vegetation types and land functions in the greening area, extracts the standard water demand index corresponding to different types of greening areas, adjusts the supply response time of each greening area according to the water supply stability grade, deduces the theoretical total water demand and period demand distribution, and generates water demand evaluation result;

[0052] The irrigation scheduling optimization module sorts the water demand of each green area based on the total water demand and the time period demand value in the water demand evaluation result, determines the irrigation priority according to the sorting result, calculates the irrigation frequency and time period interval through the water gap, splits the time schedule and adjusts the water supply limit for the areas with cross scheduling risk in adjacent time periods, and generates the irrigation scheduling optimization result;

[0053] The feedback and adjustment module collects the latest soil humidity value of each green area after execution according to the water supply frequency and irrigation time period configuration parameters in the irrigation scheduling optimization result, and compares the value with the target humidity range area by area, if it is lower than the target interval, the irrigation frequency is increased in the next cycle, if it is higher than the target interval, the irrigation duration is compressed and the water quantity upper limit is reduced, the irrigation scheme parameters are corrected, and the feedback scheduling result is generated.

[0054] The soil humidity data record includes regional humidity difference distribution, evaporation rate change characteristics, humidity response correlation parameters, water source supply capacity evaluation results include rainwater supply capacity index, groundwater level fluctuation amplitude, soil water replenishment capacity coefficient, water demand evaluation results include vegetation water demand grade, land use water demand type, time period response index, irrigation scheduling optimization results include irrigation priority parameters, irrigation frequency configuration value, time period water supply limit, feedback scheduling results include humidity comparison result set, frequency correction parameter, water quantity adjustment upper limit value.

[0055] Please refer to Figure 2 , the data acquisition module includes:

[0056] The environmental monitoring sub-module collects the output signal of the soil humidity sensor of each green area, records the cumulative rainfall of the rain gauge, obtains the temperature sensor reading, reads the air humidity detection value, counts the flow meter data of the rainwater recovery device, monitors the pressure value of the groundwater level probe, and implements time synchronization calibration and outlier rejection processing on the six types of data, to generate a multi-source environmental data set;

[0057] The six types of data of soil humidity, precipitation, temperature, air humidity, rainwater recovery amount and underground water level in each green area are collected, through the multi-type sensor array arranged in the green area G-01, including soil humidity sensors, rain gauges, temperature sensors, air humidity sensors, rainwater recovery flowmeters and underground water level probes, real-time collection of various data signals, for example, the soil humidity sensor outputs a current signal of 4 mA to 20 mA at a frequency of once per minute, the rainfall sensor records the cumulative precipitation at a frequency of once per hour, the temperature sensor reads the number at a frequency of once per hour, the air humidity sensor reads the detection value at a frequency of once per hour, the flowmeter of the rainwater recovery device counts the flow at a frequency of once per hour, and the underground water level probe monitors the pressure at a frequency of once per hour. These data streams are uniformly sent to the central controller, the data timestamps of all sensors are synchronized and calibrated using GPS or Network Time Protocol (NTP), ensuring that the time reference of all data points is consistent, and outlier rejection processing is performed on each type of data sequence. First, for a type of data collected in the past 24 hours, such as temperature data, calculate its average value μ and standard deviation σ, then compare the latest collected temperature value T new with the average value μ, if |T new - μ | > 3σ, then the value is determined to be an outlier and is removed from the data set, for example, if the average temperature of a certain area in the past 24 hours is 28°C, and the standard deviation is 2°C, the determination interval of the outlier rejection is 28 ± 3 × 2 = [22°C, 34°C], if the newly collected temperature value is 35°C, the value is determined to be abnormal and is removed. Through this series of processing, a multi-source environmental data set containing time stamp, geographic coordinates and various types of environmental parameters is generated.

[0058] The fluctuation influence analysis submodule extracts the precipitation sequence and the temperature sequence, respectively, and performs sliding window correlation coefficient calculation with the soil humidity sequence, evaluates the contribution degree of the two factors to the humidity fluctuation in a weighted fusion manner, quantifies the discrete characteristics of the soil humidity distribution difference between regions, and generates the humidity fluctuation correlation degree based on the multi-source environmental data set;

[0059] The soil humidity sequence S H , the precipitation sequence R P and the temperature sequence T E of the green area G-01 are extracted from the multi-source environmental data set, wherein the time sampling interval of S H is 10 minutes, the time sampling interval of R P and T E is 1 hour, the S H sequence is resampled at an interval of 1 hour before correlation calculation, a new S H ' sequence is obtained, and the size of the sliding window W is set to 24 hours with a step of 1 hour in S Hsliding on the sequence, in each sliding window, the Pearson correlation coefficient r between precipitation and soil moisture is calculated R and the Pearson correlation coefficient r between temperature and soil moisture T ;

[0060]

[0061] where n is the number of data points in the window, and are the precipitation and the resampled soil moisture data in the window, respectively, and are the mean of the precipitation and the resampled soil moisture in the window, respectively, r T is calculated in the same way, the contribution of precipitation and temperature factors to the soil moisture fluctuation is evaluated by weighted fusion, the calculation formula of weighted fusion is C SH = a · |r R | + β · |r T |, where C SH represents the soil moisture fluctuation correlation degree, a and β are the weights of precipitation and temperature, and a + β = 1, the weights a and β are set in reference to the long-term statistical law of historical meteorological data and soil moisture change, for example, in humid areas, the influence of precipitation on soil moisture is more significant, therefore a is set to 0.7 and β is set to 0.3, in this embodiment, the region is located in South China, which belongs to a humid region, therefore a = 0.7 and β = 0.3 are set, for the data in a certain 24-hour window, assuming that r R = 0.82 and r T = -0.65 are calculated, the calculation process is C SH = 0.7 · |0.82| + 0.3 · |-0.65| = 0.7 · 0.82 + 0.3 · 0.65 = 0.574 + 0.195 = 0.769, which indicates that precipitation and temperature have a high impact on the soil moisture fluctuation in this period, the standard deviation is used as an index to quantify the discrete characteristics of the soil moisture distribution difference of multiple green areas, the standard deviation of the soil moisture value of each region is calculated throughout the day, as shown in Table 1:

[0062] Table 1 Comparison of soil moisture discrete characteristics of green areas

[0063]

[0064]

[0065] As shown in Table 1, by comparing the numerical values 5.2% and 7.8%, it is concluded that the soil moisture fluctuation of region G-02 is more severe, and the humidity fluctuation correlation degree of this region is 0.769.

[0066] The evaporation trend derivation sub-module calls the air humidity value and the soil humidity value in the multi-source environment data set, calculates the hourly difference value sequence thereof, establishes a difference value change and soil humidity attenuation correlation mapping in combination with the humidity fluctuation correlation degree, derives the actual influence trend of the evaporation effect on the soil humidity per unit time, and generates the soil humidity data record;

[0067] The hourly air humidity sequence H air and the soil humidity sequence S soil are extracted from the multi-source environment data set. h The hourly difference value sequence D air is obtained by performing a difference value operation on the two sequences. soil On a typical sunny summer day, at a certain time, the air humidity value of the region G-01 is 65%, and the soil humidity value is 35%. The difference value is calculated as 65%-35%=30%. This difference value indicates that there is a large humidity gradient between the environment and the soil. In combination with the humidity fluctuation correlation degree C SH =0.769 calculated in the previous step, a difference value change and soil humidity attenuation correlation mapping model is established. The model is realized by linear mapping, and the calculation formula is V evap =δ·(S soil -H air )·C SH , where V evap is the evaporation rate per unit time, δ is the evaporation coefficient, which is set in reference to field experiments on specific vegetation types and soil texture types. The experimental verification process of the coefficient is as follows: in a controlled laboratory environment, the actual vegetation and soil types are simulated, different air humidity and soil humidity difference values are set, a high-precision weighing sensor is used to measure the water loss per unit time, which is the evaporation rate, and the slope in the linear mapping relationship is obtained by fitting the experimental data. The slope is the evaporation coefficient δ. In this example, the vegetation type of the region is lawn, and the soil type is loam, so δ is set to 0.02. The above data is substituted into the formula to calculate V evap =0.02·(35%-65%)·0.769=0.02·(-30)·0.769=-0.6·0.769=-0.4614 percentage points / hour. The calculation result shows that due to the evaporation effect, the soil humidity in this period decays at a rate of about 0.46% per hour. This result quantifies the difference between air humidity and soil humidity, as well as the comprehensive influence of precipitation and temperature on the evaporation rate. The evaporation rate is integrated with the real-time collected soil humidity value to derive the soil humidity data record, which includes real-time monitoring values, historical trends, and decay trends caused by evaporation.

[0068] Please refer toFigure 3 The water source evaluation module comprises:

[0069] The water source feature analysis submodule calls the average soil moisture value, moisture change rate, rainwater recovery amount, and groundwater level data in the soil moisture data record, calculates the total sum of rainwater accumulation values in consecutive time periods, determines the upper and lower limits of the groundwater level fluctuation range, performs a time-period-by-time-period difference operation on the rainwater accumulation values and the fluctuation range, and generates water source balance features;

[0070] The soil moisture data record is called to extract the average soil moisture value, moisture change rate, rainwater recovery amount, and groundwater level data for a consecutive 7-day period, wherein the total sum of rainwater accumulation values R total The daily rainfall amounts in these 7 days are calculated by accumulation, as shown in Table 2:

[0071] Table 2: Consecutive 7-day rainfall and rainwater recovery data table

[0072]

[0073]

[0074] As shown in Table 2, the daily rainfall amounts in these 7 days are:

[0075] 5 mm, 10 mm, 0 mm, 0 mm, 15 mm, 5 mm, 0 mm;

[0076] The R total = 5 + 10 + 0 + 0 + 15 + 5 + 0 = 35 mm, and the cumulative recovery amount of rainwater in the same period is 150 m 3 Secondly, the groundwater level fluctuation range in these 7 days is determined through the groundwater level probe monitoring data, for example, the highest water level is -1.5 m, and the lowest water level is -2.8 m (the negative value indicates being lower than the ground surface), the upper limit of the fluctuation range is -1.5 m, and the lower limit is -2.8 m, then a time-period-by-time-period difference operation is performed on the rainwater accumulation values and the groundwater level fluctuation range, the difference operation is realized by calculating D W = V rain -V ground , wherein V rain represents the rainwater accumulation value (including rainfall and recovery), V ground represents the groundwater level change value, and the calculation of the rainwater accumulation value (unit: m 3 ) is A × R total × η eff , wherein A is the area of the green area (m 2 ), R total is the cumulative rainfall amount (m), and η effFor the effective runoff coefficient, the setting of the coefficient is based on the comprehensive evaluation of local soil type, vegetation coverage, surface slope and other factors, and is verified by runoff experiment. In this case, the area of the park green area G-01 is 5000m 2 , the soil type is loam, the vegetation coverage is 80%, and the slope is gentle. According to relevant research, the runoff coefficient η eff is set to 0.75, then:

[0077] V rain = 5000m 2 ×(0.035m)×0.75+150m 3 = 131.25m 3 +150m 3 = 281.25m 3 ;

[0078] The change value V ground of groundwater level is calculated by the product of the water level fluctuation range and the area of groundwater penetration. In this case, the change of groundwater level is-1.5m-(-2.8m)=1.3m, and the area of groundwater penetration is A sub , then V ground = A sub ×1.3m, since the area of groundwater penetration is approximately equal to the surface area, then V ground = 5000m 2 ×1.3m=6500m 3 , finally the difference D W = 281.25m 3 -6500m 3 = -6218.75m 3 , the negative value indicates that the decline of groundwater level is much larger than the cumulative amount of rainwater in these 7 days, and the 7-day water balance feature of the area is -6218.75m 3 .

[0079] The water dynamic calculation submodule calculates the difference ratio of water supply capacity and humidity change based on the water balance feature, combines the water supply capacity data and soil humidity change data of the green area, and deduces the corresponding relationship between soil water replenishment intensity and absorption rate to obtain the water dynamic capacity.

[0080] Based on the water balance feature value -6218.75m 3 , the water supply capacity data and soil humidity change data of the green area G-01 are called. The water supply capacity data represents the maximum water supply per unit time, for example, the water supply capacity of the irrigation system of the area is 10m 3 / h, the soil moisture variation data is expressed as the average moisture change rate per unit time, for example, during a dry period without irrigation, the soil moisture change rate is -0.5% per hour, the difference ratio of water supply capacity and moisture variation is calculated wherein Q supply is the water supply capacity (unit: m 3 / h), ΔH S is the moisture variation rate (unit: % / h), this ratio quantifies the multiple of the water supply system's water supply capacity relative to the natural moisture change rate of the soil by comparing the replenishment capacity of the water supply system with the natural water loss rate of the soil, in this example, This ratio indicates that for every 20 m 3 of water supplied, the soil moisture is increased by 1%, based on this ratio, the corresponding relationship between the soil water replenishment intensity I replenish and the absorption rate S absorb is derived, this relationship is achieved through linear mapping, i.e. S absorb =k·I replenish , wherein k is the absorption rate coefficient, which is set in reference to the soil type (loam) and vegetation type (lawn) of the area, through experimental verification, when the replenishment intensity is 10 m 3 / h, the soil moisture increase rate is 0.5% per hour, then 0.5=k·10, thus k=0.05, this k value indicates the moisture change per unit replenishment intensity of the area, finally, by integrating the water supply capacity, the moisture variation ratio, and the replenishment absorption corresponding relationship, the water dynamic capacity is obtained, the calculation formula of the water dynamic capacity C dynamic is wherein R diff represents the difference ratio of water supply capacity and moisture variation, represents the target average soil moisture, H current represents the current soil moisture, represents the target moisture change rate, this formula combines the water supply capacity with the current and target states of the soil, accurately quantifying the water required to reach the target moisture, providing a dynamic water source capacity evaluation result, in this example, assuming the target average soil moisture is 30%, the current soil moisture is 25%, and the target moisture change rate is 0.2% per hour, then This calculation result indicates that to increase the soil moisture of the G-01 area from 25% to 30%, 500 m 3 of water needs to be replenished, and finally the water dynamic capacity of this area is obtained as 500 m 3 .

[0081] The supply capacity generation submodule calls the water dynamic capacity, integrates the corresponding relationship between the water supply intensity and the absorption rate according to the morphological characteristics of the soil humidity change curve and the distribution state of the water supply capacity, establishes a supply capacity evaluation matrix, and generates a water source supply capacity evaluation result;

[0082] The water dynamic capacity is called 500 m 3 The water source supply capacity is evaluated according to the morphological characteristics of the soil humidity change curve and the distribution state of the water supply capacity. The morphological characteristics of the soil humidity change curve are determined by the slope and the turning point. When the absolute value of the slope is greater than 0.5% / h, it is determined to be a fast drying state. When the absolute value of the slope is less than 0.1% / h, it is determined to be a stable state. The distribution state of the water supply capacity is determined according to the peak water supply flow and the duration. For example, a peak flow of 10 m 3 / h can be determined as high-intensity water supply, and a duration of more than 8 hours can be determined as long-time water supply. Based on these characteristics, the corresponding relationship between the water supply intensity and the absorption rate is integrated. By cross-mapping the humidity curve characteristics (such as fast drying and stable) and the water supply capacity distribution (such as high-intensity water supply and long-time water supply), the water source supply capacity evaluation level is obtained. For example, when the humidity curve presents the fast drying characteristic, high-intensity water supply capacity is needed for rapid replenishment. This combination is evaluated as “high response capacity”. When the curve presents a stable state, long-time low-intensity water supply capacity is needed for maintenance. This combination is evaluated as “high maintenance capacity”. In this example, the soil humidity change curve of the region G-01 presents a fast drying morphology, and its water supply system has a high-intensity water supply capacity of 10 m 3 / h. According to the mapping evaluation, the water source supply capacity evaluation result of this region is “high response capacity”, and its supply stability level is “A level”, and the supply capacity distribution is “sufficient”. The water source supply capacity evaluation result of this region is generated.

[0083] Please refer to Figure 4 The greening demand evaluation module includes:

[0084] The greening feature classification submodule calls the region water supply stability level and the supply capacity distribution in the water source supply capacity evaluation result, obtains the vegetation species database of the green area and the land function planning data, classifies the vegetation types according to the tree, shrub and lawn vegetation types, and classifies the park green land, protection green land and land function, establishes the corresponding mapping relationship between the vegetation type and the functional area, and generates the vegetation water demand feature.

[0085] The water source supply capacity evaluation result is called, wherein the water supply stability level of the region G-01 is "A level", the supply capacity distribution is "sufficient", the vegetation type information of the region G-01 is obtained from the vegetation type database, it is determined that the main vegetation of the region is lawn, the function information of the region G-01 is obtained from the land use function planning data, it is determined that the function of the region is park green land, the vegetation type "lawn" and the land use function "park green land" are combined to form a composite category, and the corresponding mapping relationship of "lawn-park green land" is established, which indicates that the region is a lawn mainly used for leisure and sightseeing, and the water demand and the response requirement of irrigation are different from the trees of the shelter forest belt or the shrubs on the side of the urban road, and the vegetation water demand characteristics of the region are generated as "lawn-park green land".

[0086] The water demand configuration adjustment sub-module extracts the unit area water demand quota of different types of green areas under standard meteorological conditions based on the vegetation water demand characteristics, and linearly compensates and corrects the supply response time of each green area according to the stability coefficient in the water supply stability level, to obtain a water demand configuration scheme;

[0087] Based on the vegetation water demand characteristics of "lawn-park green land", the unit area water demand quota of this type of green area is extracted from the standard water demand index library, for example, the daily average water demand quota of lawn is 2.5 mm / d under standard meteorological conditions, and according to the water supply stability level in the water source supply capacity evaluation result, the level of this region is "A level", and the stability coefficient is γ=0.9. The stability coefficient is set by referring to the probability of the decrease of the water supply capacity of the irrigation system due to equipment failure, pipe network pressure fluctuation and other factors in the actual operation, and verified by long-term operation data. The stability coefficient γ is used for linear compensation correction of the supply response time, and the calculation formula of the corrected response time T adjusted is T adjusted =T base ·(1-γ), wherein T base is the basic response time, for example, the average basic response time of this type of region is 12 hours, then T adjusted =12·(1-0.9)=12·0.1=1.2 hours, which indicates that the system stability of this region is high and can quickly respond to the water supply demand, and the supply response time is corrected to 1.2 hours, and finally the water demand configuration scheme of the region is obtained.

[0088] The demand distribution derivation sub-module calls the water demand configuration scheme, combines the green area area data and the meteorological time period division, calculates the theoretical total water demand of each region, allocates the water demand proportion according to the day and night period, determines the peak water demand period according to the supply response time, and generates a water demand evaluation result;

[0089] The water demand configuration scheme is called, in which the daily average water demand quota of the lawn is 2.5 mm / d, combined with the area data of the G-01 region, the area of the region is 5000 m 2 , the total theoretical water demand V total is calculated 2 = area x water demand quota = 5000 m 2 x (2.5 mm / d ÷ 1000) = 5000 m 3 x 0.0025 m / d = 12.5 m 3 / d, according to the diurnal period distribution of water demand, according to the transpiration law of the lawn and the best irrigation time, the daytime water demand proportion is set to 70%, and the nighttime water demand proportion is set to 30%, then the daytime water demand is 12.5 m 3 x 0.7 = 8.75 m 3 , the nighttime water demand is 12.5 m 3 x 0.3 = 3.75 m 3 , according to the aforementioned modified supply response time of 1.2 hours, the peak water demand period is determined, considering that the response time is 1.2 hours, the peak water demand period is set to the period with the highest temperature and the largest evaporation amount during the day, for example, 11:00 to 15:00, the demand of 8.75 m demand during the day is concentratedly distributed to this period, and the water demand evaluation result of the region is generated.

[0090] Referring to Figure 5 , the irrigation scheduling optimization module comprises:

[0091] A water demand sorting submodule calculates the water demand intensity of each green area unit time based on the water demand total and period demand value in the water demand evaluation result, arranges in descending order according to the water demand intensity value, determines the irrigation sequence of the region according to the sorting sequence, and generates an irrigation priority sequence;

[0092] Based on the water demand evaluation result, the water demand total and period demand value of a plurality of green areas are obtained, as shown in Table 3:

[0093] Table 3 Green area water demand data table

[0094]

[0095]

[0096] As shown in Table 3, the calculation formula of the water demand intensity I demand is wherein V demand is the water demand total, T periodis the total length of the demand period, the formula combines the total water demand with the period demand to quantify the urgency of the region through the water demand per unit time, avoiding the problem of only considering the total water demand and ignoring the timeliness. In this embodiment, the peak water demand period of each region is 4 hours, and the water demand intensity of each region is calculated: the water demand intensity of region G-01 is 12.5 m 3 ÷ 4h = 3.125 m 3 / h, the water demand intensity of region G-02 is 15 m 3 ÷ 4h = 3.75 m 3 / h, and the water demand intensity of region G-03 is 10 m 3 ÷ 4h = 2.5 m 3 / h. The calculated water demand intensity values are arranged in descending order, and the arrangement result is: region G-02 (3.75 m 3 / h), region G-01 (3.125 m 3 / h), and region G-03 (2.5 m 3 / h). According to this sorting sequence, the irrigation sequence is determined, that is, G-02 is preferentially irrigated, followed by G-01, and finally G-03, and the irrigation priority sequence is generated.

[0097] The scheduling calculation submodule calls the irrigation priority sequence, combines the available water data in the water source supply capacity evaluation result, calculates the difference between the total water demand and the available water of each region, determines the irrigation frequency according to the difference, determines the irrigation interval length according to the period demand distribution, and generates a scheduling configuration scheme;

[0098] The irrigation priority sequence is called, and the sequence is G-02, G-01, and G-03. Combined with the water source supply capacity evaluation result, it is assumed that the total available water of the system is 100 m 3 . First, the difference between the total water demand and the available water is calculated. For the first irrigation region G-02, the total water demand is 15 m 3 , the available water is 100 m 3 , and the difference is 15-100 = -85 m 3 . The difference is negative, indicating that the water is sufficient. For the difference V gap between the total water demand and the available water, when V gapThe value of the water quantity gap is greater than zero, indicating that there is a water quantity gap, and the irrigation frequency is determined according to the difference value, when the water quantity gap is greater than 50% of the available water quantity, the irrigation frequency is increased to 2 times per day, when the water quantity gap is less than 50%, the irrigation frequency is 1 time per day, in this example, there is no water quantity gap, so the irrigation frequency is set to 1 time per day, and the irrigation interval length is determined according to the time period demand distribution, since the peak water demand period of the G-02 area is 4 hours, and the water supply stability level is A level, the irrigation duration is set to 2 hours, and the interval is 24 hours, and the scheduling configuration scheme including the irrigation frequency and interval of the G-02 area is generated.

[0099] The conflict coordination sub-module detects, based on the scheduling configuration scheme, that there is an irrigation equipment use conflict in the adjacent time period in the area, re-allocates the time period for the conflict area, adjusts the upper limit of the water supply limit in the overlapping time period, optimizes the overall irrigation timing arrangement, and generates an irrigation scheduling optimization result;

[0100] Based on the scheduling configuration scheme, it is detected whether there is a conflict in the irrigation period, assuming that the irrigation period of the G-02 area is from 11:00 to 13:00, the irrigation period of the G-01 area is also from 11:00 to 13:00, and the two areas share a main pump, which constitutes a device use conflict, the time period for the conflict area is re-allocated, for example, the irrigation period of the G-01 area is adjusted to the adjacent 13:00-15:00, avoiding the device conflict, at the same time, in order to optimize the irrigation timing, the upper limit of the water supply limit in the overlapping time period is adjusted, if multiple areas need to be irrigated in the same time period, but the water supply capacity is limited, for example, the total water supply capacity is 20m 3 / h, the water demand of the G-01 and G-03 areas in the same time period is 10m 3 and 15m 3 , the total demand exceeds the water supply capacity, then according to the irrigation priority sequence (G-01 is prior to G-03), the water supply limit of the low-priority area G-03 is adjusted, for example, the water supply is reduced to 5m 3 / h, and the remaining water demand is allocated to the non-conflict time period for compensation by splitting the time, through the time period splitting and the water supply limit adjustment, the irrigation timing is optimized, and a conflict-free and optimized irrigation scheduling optimization result is generated.

[0101] Please refer to Figure 6 , the feedback and adjustment module includes:

[0102] The execution monitoring sub-module collects the current signal value output by the soil humidity sensor of each green area in real time according to the water supply frequency parameter and the irrigation period configuration parameter in the irrigation scheduling optimization result, synchronously records the opening time point and the closing time point of the electromagnetic valve, and performs time sequence alignment processing on the humidity measurement value and the time node data, to generate a humidity monitoring data set with time sequence markers;

[0103] The irrigation scheduling optimization result is executed in the park green area G-01, which specifies that the water supply frequency is once a day, and the irrigation period is 13:00-15:00. During this period, the soil humidity sensor located in the G-01 area outputs a 4-20 mA current signal in real time, which is converted to a digital value by an analog-to-digital converter and linearly mapped to a soil humidity value of 0-100%. The opening time point (e.g., 13:00:05) and closing time point (e.g., 14:59:55) of the electromagnetic valve are recorded synchronously, and a precise timestamp is generated for each humidity measurement value, for example, the humidity value 30% is bound with the timestamp 14:00:10, and the humidity value 45% is bound with the timestamp 14:59:50. Comparing these timestamps with the timestamps of the electromagnetic valve opening and closing ensures the precise correspondence of humidity data and irrigation operation, and generates the humidity monitoring data set of the area after irrigation with time sequence markers.

[0104] The humidity comparison submodule calls the humidity monitoring data set, reads the preset upper and lower threshold values of the target humidity range, calculates the numerical difference between the current humidity measurement value and the upper and lower threshold values for each area, and judges whether the humidity state of each area is above or below the target interval according to the positive and negative differences, and generates a humidity deviation indication.

[0105] The humidity monitoring data set is called, which contains the soil humidity values of the G-01 area after irrigation from 13:00 to 15:00, and the preset target humidity range is 35%-40%, with an upper threshold of 40% and a lower threshold of 35%. At the end of irrigation, the latest soil humidity measurement value of the G-01 area is 42.5%. Calculate the numerical difference between the measurement value and the upper and lower threshold values. The difference with the upper threshold is 42.5%-40%=+2.5%, and the difference with the lower threshold is 42.5%-35%=+7.5%. According to the positive and negative differences, it is determined that the humidity state of the G-01 area is above the target interval, and the humidity deviation indication of the area is "humidity value is higher than the target interval".

[0106] The scheme correction submodule, based on the humidity deviation indication, increases the irrigation frequency per unit time for areas where the humidity value is continuously below the target interval, and reduces the single irrigation operation time and adjusts the upper limit value of the water flow for areas where the humidity value is continuously above the target interval, updates the time and water parameters in the irrigation scheme, and generates a feedback scheduling result.

[0107] Based on the humidity deviation indication, which indicates that the humidity value of the G-01 area is higher than the target interval, the irrigation parameters of the area are adjusted. In the original irrigation scheme, the single irrigation time of the area is 120 minutes, and the upper limit of the water flow is 10 m 3 / h, the single irrigation operation time is reduced by 15% because the humidity value is out of the target range, the new irrigation time is calculated as 120*(1-0.15)=102 minutes, and the upper limit of water supply flow is reduced by 10%, the new upper limit of water supply flow is calculated as 10*(1-0.1)=9 m 3 / h, the single irrigation operation time is reduced by 15% because the humidity value is out of the target range, the new irrigation time is calculated as 120*(1-0.15)=102 minutes, and the upper limit of water supply flow is reduced by 10%, the new upper limit of water supply flow is calculated as 10*(1-0.1)=9 m

[0108] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A multi-dimensional data fusion sponge city greening and irrigation scheduling system, characterized in that, The system comprises: a data acquisition module, which acquires the soil humidity, meteorological data and water supply conditions of each green area, analyzes the influence of precipitation and temperature on soil humidity, compares the regional soil humidity difference, combines the air humidity and soil humidity difference, deduces the influence of evaporation rate on soil humidity, and generates soil humidity data records; a water source evaluation module, which calls the soil humidity data and water supply conditions in the soil humidity data records, analyzes the difference between regional rainwater accumulation and underground water level fluctuation, calculates the soil water supply and absorption capacity, and generates water supply capacity evaluation results; a green demand evaluation module, which divides the regional vegetation type and land function according to the water supply stability and supply capacity in the water supply capacity evaluation results, extracts the water demand index, adjusts the response time of the water supply stability level, deduces the theoretical total water demand and period demand, and generates water demand evaluation results; an irrigation scheduling optimization module, which sorts the regional water demand based on the total water demand and period demand in the water demand evaluation results, determines the irrigation priority, calculates the irrigation frequency and period interval, adjusts the adjacent period cross-scheduling risk area, and generates irrigation scheduling optimization results.

2. The multi-dimensional data fusion sponge city greening and irrigation scheduling system according to claim 1, characterized in that: The soil humidity data records include regional humidity difference distribution, evaporation rate change characteristics and humidity response correlation parameters, the water supply capacity evaluation results include rainwater supply capacity index, underground water level fluctuation amplitude and soil water supply capacity coefficient, the water demand evaluation results include vegetation water demand level, land water demand type and period response index, and the irrigation scheduling optimization results include irrigation priority parameters, irrigation frequency configuration value and period water supply limit. 3.The sponge city greening and irrigation scheduling system of multi-dimensional data fusion of claim 1, wherein: The data acquisition module comprises: an environmental monitoring submodule, which acquires the output signal of the soil humidity sensor of each green area, records the cumulative precipitation of the rain gauge, obtains the temperature sensor reading, reads the air humidity detection value, counts the flow meter data of the rainwater recovery device, monitors the pressure value of the underground water level probe, implements time synchronization calibration and outlier rejection processing on the six types of data, and generates a multi-source environmental data set; a fluctuation influence analysis submodule, which extracts the precipitation sequence and temperature sequence based on the multi-source environmental data set, respectively calculates the sliding window correlation coefficient with the soil humidity sequence, evaluates the contribution degree of the two factors to humidity fluctuation by using a weighted fusion method, quantifies the discrete characteristics of the soil humidity distribution difference between regions, and generates a humidity fluctuation correlation degree; an evaporation trend derivation submodule, which calls the air humidity value and soil humidity value in the multi-source environmental data set, calculates the hour-by-hour difference value sequence, combines the humidity fluctuation correlation degree, establishes the correlation mapping between difference change and soil humidity attenuation, deduces the actual influence trend of evaporation on soil humidity per unit time, and generates soil humidity data records. 4.The sponge city greening and irrigation scheduling system of multi-dimensional data fusion of claim 1, wherein: The water source evaluation module comprises: a water source feature analysis submodule, which calls the soil humidity average value, humidity change rate, rainwater recovery amount and underground water level data in the soil humidity data records, calculates the total sum of rainwater accumulation values in consecutive periods, determines the upper and lower limits of the underground water level fluctuation amplitude interval, and performs hour-by-hour difference value operation on the rainwater accumulation value and the fluctuation amplitude interval to generate water source balance features; The water dynamic calculation submodule calculates a difference ratio of the water supply capacity and the humidity variation based on the water source balance characteristics, combines the water supply capacity data and the soil humidity variation data of the green area, deduces a corresponding relationship between the soil water replenishment intensity and the absorption rate, and obtains a water dynamic capacity; The supply capacity generation submodule calls the water dynamic capacity, integrates the corresponding relationship between the water replenishment intensity and the absorption rate according to the soil humidity variation curve shape characteristics and the water supply capacity distribution state, establishes a supply capacity evaluation matrix, and generates a water source supply capacity evaluation result. 5.The sponge city greening and irrigation scheduling system of multi-dimensional data fusion of claim 1, wherein: The green demand evaluation module comprises: The green feature classification submodule calls the area water supply stability level and the replenishment capacity distribution in the water source supply capacity evaluation result, obtains a vegetation type database of the green area and land function planning data, combines and classifies the vegetation types of trees, shrubs and lawns and the park green land, the protection green land and the land function, establishes a corresponding mapping relationship between the vegetation types and the functional areas, and generates vegetation water demand characteristics; The water demand configuration adjustment submodule extracts a unit area water demand quota of different types of green areas under standard meteorological conditions based on the vegetation water demand characteristics, compares the stability coefficient in the water supply stability level, linearly compensates and corrects the replenishment response time of each green area, and obtains a water demand configuration scheme; The demand distribution derivation submodule calls the water demand configuration scheme, combines the green area area data and the meteorological time period division, calculates the theoretical total water demand of each area, allocates the water demand proportion according to the day and night period, determines the peak water demand period according to the replenishment response time, and generates a water demand evaluation result. 6.The sponge city greening and irrigation scheduling system of multi-dimensional data fusion of claim 1, wherein: The irrigation scheduling optimization module comprises: The water demand sorting submodule calculates the unit time water demand intensity of each green area based on the total water demand and the time period demand value in the water demand evaluation result, arranges the water demand intensity values in descending order, determines the area irrigation sequence according to the sorting sequence, and generates an irrigation priority sequence; The scheduling calculation submodule calls the irrigation priority sequence, combines the available water amount data in the water source supply capacity evaluation result, calculates the difference value between the total water demand and the available water amount of each area, determines the irrigation frequency number according to the difference value, determines the irrigation interval length according to the time period demand distribution, and generates a scheduling configuration scheme; The conflict coordination submodule detects the areas with irrigation equipment use conflicts in adjacent time periods based on the scheduling configuration scheme, re-distributes the time periods of the conflict areas, adjusts the upper limit of the water supply limit in the overlapping time periods, optimizes the overall irrigation time sequence arrangement, and generates an irrigation scheduling optimization result. 7.The sponge city greening and irrigation scheduling system of multi-dimensional data fusion of claim 1, wherein: The system further comprises: The feedback and adjustment module collects the soil humidity values of each green area after the execution according to the water supply frequency and the irrigation time period configuration parameters in the irrigation scheduling optimization result, compares the target humidity interval, adjusts the irrigation frequency and the time period configuration, corrects the irrigation scheme, and generates a feedback scheduling result; The feedback scheduling result comprises a humidity comparison result set, a frequency correction parameter and a water amount adjustment upper limit value. 8.The multi-dimensional data fusion sponge city greening and irrigation scheduling system of claim 7, wherein: The feedback and adjustment module comprises: The execution monitoring submodule collects the current signal values output by the soil humidity sensors of each green area in real time according to the water supply frequency parameter and the irrigation time period configuration parameter in the irrigation scheduling optimization result, synchronously records the opening time point and the closing time point of the electromagnetic valve, performs time sequence alignment processing on the humidity measurement values and the time node data, and generates a humidity monitoring data set with time sequence labels; The humidity comparison submodule calls the humidity monitoring data set, reads the upper limit threshold value and the lower limit threshold value of the preset target humidity range, calculates the numerical difference between the current humidity measurement value and the upper limit threshold value and the lower limit threshold value for each area, judges whether the humidity state of each area is above or below the target interval according to the difference, and generates a humidity deviation indication; The scheme correction submodule increases the irrigation frequency per unit time on the basis of the original irrigation frequency for the area where the humidity value is continuously lower than the target interval, reduces the single irrigation operation time and adjusts the upper limit value of the water supply flow for the area where the humidity value is continuously higher than the target interval, updates the time parameter and the water quantity parameter in the irrigation scheme, and generates a feedback scheduling result.

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