Multi-sensor fusion agricultural precise irrigation system

Abstract

The invention relates to the technical field of automatic irrigation control, in particular to a multi-sensor fusion agricultural precise irrigation system which comprises a leaf area analysis module, an environment evaluation module, a task arrangement module, an execution regulation and control module and a feedback regulation module. According to the method, the crop leaf area index is introduced to be compared with the growth stage target value, the dynamic expression of the group moisture demand is realized, the environmental evapotranspiration influence factor is generated in combination with illumination and temperature disturbance parameters, the timeliness of water demand driving force identification is enhanced, and the task execution sequence is judged by adopting regional water pressure disturbance data; a multi-region irrigation resource allocation process is optimized, a water quantity and pressure difference double-factor adjustment mechanism is utilized, the rationality and response accuracy of time length allocation are improved, water supply state analysis and task target deviation comparison are performed, dynamic supplementary irrigation regulation and control are realized, the closed-loop capability and supplementary irrigation accuracy of irrigation period control are improved, and the irrigation period control efficiency is improved. And the stable allocation capability and the irrigation response efficiency of the irrigation system under the influence of multiple variables are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic irrigation control, and in particular to a multi-sensor fusion agricultural precision irrigation system. Background Art

[0002] The field of automatic irrigation control technology includes related technologies for automated management of agricultural irrigation processes through sensor monitoring, data collection and control execution. The core content of this technical field is to comprehensively formulate irrigation control strategies based on crop water demand, soil moisture changes, meteorological factors and environmental parameter information, and use actuators such as solenoid valves, drip irrigation systems or sprinkler systems to achieve precise irrigation operations. This field involves multiple technical links such as sensor layout, data acquisition terminals, signal transmission methods, control logic settings, and control node layout, aiming to improve the efficiency of water resource utilization, ensure timely satisfaction of crop water needs, and realize irrigation automation, informatization and intelligence.

[0003] Among them, the multi-sensor fusion agricultural precision irrigation system refers to a composite perception network built based on multiple types of sensor devices such as soil moisture sensors, ambient temperature and humidity sensors, and light sensors. It continuously monitors the crop growth environment and soil conditions through multi-source sensor nodes deployed in the field, and uses the terminal acquisition module to integrate the acquired parameters in time series and make data judgments. Combined with the control module, it realizes the control operation of irrigation equipment such as electromagnetic valves or water pumps. The system mainly covers soil moisture perception, climate factor acquisition, data processing module judgment output, and actuator response. Specifically, it obtains representative environmental information in the area through multi-point deployment of sensor nodes, collects and integrates multi-source data through edge computing devices, and triggers irrigation instructions according to set thresholds or judgment rules to build an irrigation response system consisting of perception, transmission, analysis and control.

[0004] Traditional agricultural precision irrigation control systems rely on single-point monitoring and static threshold triggering of soil moisture and environmental data, lacking a structural expression of the water carrying capacity of crop groups, resulting in a lag in the identification of actual dynamic water demand. Under drastic environmental fluctuations, false triggering and supply-demand mismatch problems are prone to occur. Most irrigation task execution sorting is based on rotation or static priority configuration, and the execution order cannot be dynamically adjusted according to real-time pressure supply capacity, which can easily cause high-load nodes to overlap and cause abnormal pressure drop in local water supply systems, resulting in flow imbalance and uneven crop water supply. In the task completion assessment, a single valve control execution time is relied on as the completion standard, and the comparison process between pressure supply fluctuations, regional task targets and actual water supply is not introduced, resulting in difficulty in identifying biased irrigation and missed irrigation, affecting the accuracy of supplementary irrigation management and the efficiency of water resource utilization. Summary of the invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose an agricultural precision irrigation system with multi-sensor fusion.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An agricultural precision irrigation system with multi-sensor fusion includes:

[0007] The leaf area analysis module acquires the crop canopy image, extracts the current leaf area estimation value, calls the target leaf area parameters corresponding to the crop type and growth stage, analyzes the influence degree of the actual leaf area on the canopy transpiration intensity, evaluates the response degree to the group water consumption ability, and generates the leaf area index response coefficient;

[0008] The environment assessment module utilizes the leaf area index response coefficient, calls the data sequences of the light sensor and the temperature sensor, and evaluates the influence of the real-time environmental factors on the water demand by analyzing the influence of the light intensity and temperature level on the crop transpiration rate, and obtains the environmental evapotranspiration influence factor;

[0009] The task arrangement module calls the environmental evapotranspiration influence factor, acquires the water pressure data at multiple positions of the water supply pipe network in real time, adjusts the execution order of the irrigation tasks in multiple regions by analyzing the influence of the irrigation tasks executed in multiple regions on the water supply pipe network pressure, and generates the task execution list;

[0010] The execution control module calls the leaf area index response coefficient, the environmental evapotranspiration influence factor and the task execution list, calculates the irrigation water volume required for each region, combines the difference between the real-time water pressure and the preset water supply pressure standard, calculates the irrigation duration, and obtains the irrigation duration control parameter.

[0011] As a further solution of the present invention, the leaf area index response coefficient includes the leaf area index difference, the transpiration area ratio, and the stage response level, the environmental evapotranspiration influence factor includes the light fluctuation amplitude coefficient, the temperature rise rate value, and the environmental lag response factor, the task execution list is specifically the partition priority level, the water supply interference intensity, and the task sorting number, and the irrigation duration control parameter specifically refers to the dynamic duration correction value, the water pressure correction coefficient, and the valve opening and closing matching value.

[0012] As a further solution of the present invention, the leaf area analysis module includes:

[0013] The image acquisition sub-module acquires the crop canopy image, analyzes the crop leaf area of the target irrigation area through the proportion of green pixels in each region of the image, and obtains the leaf area estimation value;

[0014] The leaf area comparison sub-module calls the leaf area estimation value, and establishes the leaf area index difference according to the leaf area parameters corresponding to the target crop type at each growth stage;

[0015] The response degree calculation sub-module calls the leaf area index difference and uses the formula:

[0016]

[0017] Calculate the leaf area index response coefficient;

[0018] Among them, C LAI represents the leaf area index response coefficient, LAI e represents the leaf area estimation proportion value, dimensionless, LAI t represents the leaf area proportion parameter corresponding to the target growth stage, dimensionless, S c represents the crop stage transpiration sensitivity coefficient, dimensionless, A r represents the transpiration reference area, in square meters, N p represents the floor area per plant, in square meters.

[0019] As a further solution of the present invention, the environmental assessment module includes:

[0020] The environmental parameter acquisition sub-module uses the leaf area index response coefficient to call the light sensor and the temperature sensor, collects the light intensity sequence and the soil surface temperature value in real time, analyzes the fluctuation characteristics of the data, and establishes the environmental fluctuation characteristic value;

[0021] The transpiration association sub-module analyzes the influence of light intensity and temperature level on the crop transpiration rate according to the environmental fluctuation characteristic value, evaluates the influence of various environmental conditions on the crop water demand, and obtains the transpiration association dynamic value;

[0022] The environmental impact analysis sub-module calls the transpiration association dynamic value, evaluates the influence of real-time environmental factors on the water demand, and uses the formula:

[0023]

[0024] Calculate the environmental evapotranspiration impact factor;

[0025] Among them, E f is the environmental evapotranspiration impact factor, dimensionless, I m is the maximum light intensity of the day, in watts per square meter, I a is the actual light intensity in the current period, in watts per square meter, |I m -I a | is the absolute value of the light intensity deviation, in watts per square meter, T s is the current soil surface temperature, in degrees Celsius, T b is the evapotranspiration physiological starting temperature threshold, in degrees Celsius, W j is the disturbance weight factor in the j-th period, dimensionless, Ir is the reference value of solar radiation intensity set within the area, with the unit of watt per square meter, T r is the reference value of average daily temperature set within the area, with the unit of degree Celsius, and j is the index number of the recording point, with the unit of dimensionless.

[0026] As a further solution of the present invention, the task arrangement module includes:

[0027] The pressure data acquisition sub-module calls the environmental evapotranspiration impact factor, uses a pressure sensor to collect the water pressure data at multiple positions in the water supply pipe network before, during, and after irrigation, analyzes the decline range of the pipe network pressure and the pressure recovery duration, and generates the regional water pressure disturbance characteristic value;

[0028] The pipe network load determination sub-module calls the regional water pressure disturbance characteristic value and uses the formula:

[0029]

[0030] to calculate the pipe network pressure impact coefficient;

[0031] where C p is the pipe network pressure impact coefficient, dimensionless, P min is the lowest water pressure in the current task area, with the unit of kilopascal, P b is the water supply bottom limit pressure, with the unit of kilopascal, ΔP is the water pressure increment during the recovery stage, with the unit of kilopascal, T r is the water pressure recovery time, with the unit of second, P j is the instantaneous water pressure at the j-th moment, with the unit of kilopascal, is the average water pressure value of this irrigation section, with the unit of kilopascal, P s is the standard water supply pressure value, with the unit of kilopascal, A c is the irrigation area of the current region, with the unit of square meter, j is the sampling time index number, dimensionless, and m is the total number of sampling periods, dimensionless;

[0032] The task sequence optimization sub-module calls the pipe network pressure impact coefficient, adjusts the execution order of the irrigation tasks in multiple regions according to the impact of the irrigation tasks in multiple irrigation regions on the water supply pipe network pressure, and obtains the task execution list.

[0033] As a further solution of the present invention, the execution control module includes:

[0034] The water volume calculation sub-module calls the leaf area index response coefficient, the environmental evapotranspiration impact factor, and the task execution list, and evaluates the irrigation water volume requirements of each irrigation region according to the area, leaf area, light intensity, and temperature level of each irrigation region, and generates the regional irrigation water volume value;

[0035] The water pressure extraction sub-module calls the regional irrigation water volume value, collects the real-time water pressure monitoring data of each irrigation area in the water supply pipe network, combines the preset water supply standard value, calculates the differential pressure data corresponding to each irrigation area, and obtains the water supply deviation amplitude value;

[0036] The duration control sub-module calls the water supply deviation amplitude value, calculates the irrigation time required for each area, and adjusts the control parameters of the irrigation solenoid valve in each irrigation area to obtain the irrigation duration control parameters.

[0037] As a further solution of the present invention, the system further includes:

[0038] The feedback adjustment module, based on the irrigation duration control parameters, obtains the solenoid valve opening duration and water pressure fluctuation data of each irrigation area in each irrigation task, calculates the actual water supply completion status of each area, compares it with the target water supply plan, calculates the deviation degree and configures the supplementary irrigation task, and generates the precise irrigation configuration task;

[0039] The precise irrigation configuration task includes the water supply deviation ratio, the supplementary irrigation period, and the water volume replenishment target.

[0040] As a further solution of the present invention, the feedback adjustment module includes:

[0041] The water supply status extraction sub-module, based on the irrigation duration control parameters, calculates the actual water supply completion status of each task area by collecting the solenoid valve opening duration and water pressure fluctuation data in each irrigation task, and obtains the actual water supply completion value;

[0042] The execution deviation determination sub-module calls the actual water supply completion value, combines the water volume value required by the task target, calculates the water supply deviation amount of each area, and generates the water supply execution deviation amount value;

[0043] The supplementary irrigation configuration generation sub-module calls the water supply execution deviation amount value, evaluates the water supply deviation levels of multiple areas according to the water supply deviation amount, configures the supplementary irrigation task, and generates the precise irrigation configuration task.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by introducing the comparison between the crop leaf area index and the target value of the growth stage, the dynamic expression of the population water demand is realized. The environmental evapotranspiration influence factor is generated by combining the light and temperature disturbance parameters, enhancing the timeliness of the identification of the water demand driving force. The regional water pressure disturbance data is used to determine the task execution order, optimizing the multi-region irrigation resource allocation process. The water volume and pressure difference dual-factor adjustment mechanism is utilized to improve the rationality of the duration allocation and the response accuracy. Through the deviation comparison between the water supply state analysis and the task objective, the dynamic supplementary irrigation control is realized, enhancing the closed-loop ability of the irrigation cycle control and the supplementary irrigation accuracy, and strengthening the stable allocation ability and irrigation response efficiency of the irrigation system under the influence of multiple variables. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is the system flow chart of the present invention;

[0047] Figure 2 is the flow chart of the leaf area analysis module of the present invention;

[0048] Figure 3 is the flow chart of the environmental assessment module of the present invention;

[0049] Figure 4 is the flow chart of the task arrangement module of the present invention;

[0050] Figure 5 is the flow chart of the execution control module of the present invention;

[0051] Figure 6 is the flow chart of the feedback adjustment module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. In addition, in the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0054] Please refer to Figure 1 , a multi-sensor fusion agricultural precision irrigation system includes:

[0055] The leaf area analysis module acquires the crop canopy image, extracts the current leaf area estimated value, calls the target leaf area parameters corresponding to the crop type and growth stage, analyzes the influence degree of the actual leaf area on the canopy transpiration intensity, evaluates the response degree to the group water consumption ability, and generates the leaf area index response coefficient;

[0056] The environment assessment module uses the leaf area index response coefficient, calls the data sequences of the light sensor and the temperature sensor, evaluates the influence of the real-time environmental factors on the water use demand by analyzing the influence of the light intensity and temperature level on the crop transpiration rate, and obtains the environmental evapotranspiration influence factor;

[0057] The task arrangement module calls the environmental evapotranspiration influence factor, acquires the water pressure data at multiple positions of the water supply pipe network in real time, adjusts the execution order of the irrigation tasks in multiple regions by analyzing the influence of the irrigation tasks executed in multiple regions on the water supply pipe network pressure, and generates the task execution list;

[0058] The execution regulation module calls the leaf area index response coefficient, the environmental evapotranspiration influence factor and the task execution list, calculates the irrigation water volume required for each region, combines the difference between the real-time water pressure and the preset water supply pressure standard, calculates the irrigation duration, and obtains the irrigation duration control parameter;

[0059] The feedback regulation module, based on the irrigation duration control parameter, acquires the solenoid valve opening duration and the water pressure fluctuation data of each irrigation region in each irrigation task, calculates the actual water supply completion status of each region, compares it with the target water supply plan, calculates the deviation degree and configures the supplementary irrigation task, and generates the precise irrigation configuration task;

[0060] The leaf area index response coefficient includes the leaf area index difference, the transpiration area proportion, and the stage response level. The environmental evapotranspiration influence factor includes the light fluctuation amplitude coefficient, the temperature rise rate value, and the environmental lag response factor. The task execution list is specifically the partition priority level, the water supply interference intensity, and the task sorting number. The irrigation duration control parameter specifically refers to the dynamic duration correction value, the water pressure correction coefficient, and the valve opening and closing matching value. The precise irrigation configuration task includes the water supply deviation ratio, the supplementary irrigation period, and the water volume supplement target.

[0061] Please refer to Figure 2 , the leaf area analysis module includes:

[0062] The image acquisition sub-module acquires the crop canopy image, analyzes the crop leaf area of the target irrigation region through the proportion of green pixels in each region of the image, and obtains the leaf area estimated value;

[0063] The image acquisition sub-module is used to obtain the crop canopy images of the target area. This process takes the multi-spectral images collected by a high-resolution optical sensor as the input data source. The image is divided into multiple sub-areas through the preset regional contour coordinates. The number of green pixel points in each sub-area is extracted respectively and the ratio is calculated with the total number of pixel points. This ratio is the green pixel occupancy ratio. Within a single area, this occupancy ratio reflects the green coverage degree of the surface of this area. Combining the crop types in the irrigated plot and the expected value of green coverage preset for the current day-age stage, the image samples within the range of the green occupancy ratio threshold that meet the target plot are screened. If the set green pixel threshold is 0.65, when the number of green pixel points in a certain sub-area is 26000 and the total number of pixel points is 40000, the green occupancy ratio of this area is 0.65, meeting the screening criteria. Further, traverse the green occupancy ratios of all sub-areas in the target area to form a green coverage matrix array. For example, if the green occupancy ratio array is G = [0.65, 0.63, 0.68, 0.70, 0.66], after calculating the mean value of this array, the average green coverage of the crop canopy of the current plot is obtained, denoted as: Obtain the linear conversion relationship between the green occupancy ratio and the leaf area index (LAI) at this crop stage by looking up the table: LAI = a·G avg +b, where LAI is the leaf area index, a is the green coverage conversion coefficient set for different crop stages, G avg is the average green pixel occupancy ratio of the target area, and b is the fixed offset value in the conversion relationship. Set a = 4.5, b = 0.2, and substitute the values to get: LAI = 4.5·0.664 + 0.2 = 3.188, thus obtaining the leaf area estimation value of this area.

[0064] The leaf area ratio comparison sub-module calls the leaf area estimation value and establishes a leaf area index difference according to the leaf area parameters corresponding to each growth stage of the target crop type;

[0065] Based on obtaining the leaf area estimation value, the leaf area ratio comparison sub-module refers to the type of crop planted and the current growth stage, and calls the corresponding target leaf area parameters in the preset database. For example, the target leaf area parameter of corn at the jointing stage is 3.00. According to the preset value division standard for different crop stages, LAI is set as a group of threshold paragraphs. For example, the reasonable range at the jointing stage is (2.80, 3.20). If the actual estimated value is 3.188, it falls within this interval and is recorded as valid data. Further, an exponential difference calculation formula is established for the estimated value and the target parameter: LAI diff = LAI e - LAI t = 3.188 - 3.00 = 0.188, where LAI diff is the leaf area index difference, LAI e is the leaf area estimation value obtained through image analysis, LAI tLet \(\overline{LAI}\) be the preset target leaf area index parameter, both being dimensionless. If the difference exceeds ±0.3, it is marked as an abnormal area and requires re - image sampling. In addition, to clarify the influence degree of this difference on the subsequent transpiration response coefficient, the response levels are divided according to the amplitude of the difference. If the difference is in the interval [-0.1, 0.1], it is level I; in the interval [-0.2, -0.1) ∪ (0.1, 0.2], it is level II; beyond the above intervals, it is level III. The current difference is 0.188, so it is in level II, providing a basis for calculating the subsequent response degree.

[0066] The response degree calculation sub - module calls the leaf area index difference and uses the formula:

[0067]

[0068] Calculate the leaf area index response coefficient;

[0069] Among them, C LAI represents the leaf area index response coefficient, LAI e represents the leaf area estimation proportion value, dimensionless, LAI t represents the leaf area proportion parameter corresponding to the target growth stage, dimensionless, S c represents the crop - stage transpiration sensitivity coefficient, dimensionless, A r represents the transpiration reference area, with the unit of square meters, N p represents the floor area per plant, with the unit of square meters;

[0070] Based on the completion of the leaf area index difference calculation, the response degree calculation sub - module calls each participating parameter and performs operations such as parameter normalization, formula substitution, and multiple multiplication and division operations. First, read the leaf area estimation proportion value LAI e = 0.85, the target leaf area parameter LAI t = 0.75, and the square of the difference between the two is calculated as (0.85 - 0.75) 2 = 0.01. Then read the crop - stage transpiration sensitivity coefficient S c = 1.2, read the transpiration reference area A r = 3.5 m 2 and read the floor area per plant N p = 0.25 m 2 , and substitute them into the calculation formula:

[0071]

[0072] This value represents the leaf area index response coefficient of the current plot at the current crop stage. To facilitate differential calculations for different crop plots at different times, it is necessary to set parameter collection and quantization standards, S cThrough the experimental value determination of crop water sensitivity, the general range is set to [1.0, 2.0], A r Set according to the field transpiration measurement standard in the range of 3.0 - 4.5 square meters, N p Collected according to the crop planting density. For example, the corn planting density is 4 plants / m 2 , then the floor area per plant is 0.25 m 2 . Among them, the leaf area index response coefficient is a comprehensive index used to quantify the relationship between the current leaf area of the crop and the target leaf area at its growth stage, reflecting the response degree of the leaf area per unit area of the current crop population to water transpiration. The calculation of this coefficient takes into account factors such as the estimated leaf area of the crop, the target leaf area parameters during the growth period, the crop transpiration sensitivity coefficient, the transpiration reference area, and the floor area per plant, reflecting whether the actual leaf area of the crop meets its physiological water demand, thus becoming an important basis for determining the irrigation priority. If the coefficient is low, it indicates that the crop water absorption capacity in this area is low and water replenishment is required first; if the coefficient is high, it means that its leaf coverage and water use efficiency are at a better level. The formula is constructed based on the physiological response principle of the relationship between the crop leaf area index and transpiration water demand. In agricultural irrigation control, the leaf area index of the crop canopy reflects the structural basis of photosynthesis and transpiration. The larger the leaf area, the more transpiration area it has and the stronger its water consumption ability. Therefore, when there is a deviation between the actual leaf area and the leaf area required at the target growth stage, it will lead to an increase or decrease in the water demand per unit area. To reflect the influence of this deviation on the reaction intensity of transpiration water volume, the system takes the leaf area index deviation as the basic input, uses the transpiration sensitivity of the current crop growth stage as a regulatory factor, introduces a phased regulatory factor to strengthen the reaction difference of different crop growth periods to the deviation, and further combines the planting density and the reference transpiration area to normalize the transpiration demand per unit area of the population, making the irrigation water demand response intensity comparable under different regions, different varieties, and different configuration methods. Therefore, this response coefficient becomes an important basis for judging the priority of regional water regulation requirements.

[0073] Table 1 Leaf Area Index Response Parameter Setting Table

[0074] Crop type Growth stage Transpiration sensitivity coefficient <![CDATA[Transpiration reference area (m 2 )]]> <![CDATA[Occupied area per single plant (m 2 )]]> Maize Jointing stage 1.2 3.5 0.25 Rice Tillering stage 1.4 3.0 0.20 Wheat Jointing stage 1.1 3.8 0.22

[0075] As shown in Table 1, through parameter configuration, different response models can be set for different crop types and growth stages. In the current embodiment, substituting the corn parameter combination for calculation, the response coefficient is 0.01371. This result indicates that the current deviation of the leaf area from the target parameter is not high, and the water consumption ability is in a response state that is moderately above average.

[0076] Please refer to Figure 3 , the environmental assessment module includes:

[0077] The environmental parameter acquisition sub-module uses the leaf area index response coefficient to call the light sensor and temperature sensor, collect the light intensity sequence and soil surface temperature value in real time, analyze the fluctuation characteristics of the data, and establish the environmental fluctuation characteristic value.

[0078] Based on obtaining the leaf area index response coefficient, the environmental parameter acquisition sub-module first calls the leaf area index response coefficient value calculated for the corresponding area from the previous module. This value is 0.01371 in the currently set irrigation plan for the jointing stage of corn. Subsequently, it uses the light sensor to collect the light intensity value of the current period and stores it in the light intensity sequence, with a sampling interval of 10 minutes. If it is collected from 10:00 to 11:00 in the morning, data such as I = [690, 720, 735, 710, 700, 680] (unit: W / m 2 ) can be obtained. At the same time, it uses the temperature sensor to collect the soil surface temperature value of the corresponding area on the ground. For example, the sampling result at 10:30 is 34°C. Perform a sliding window volatility evaluation operation on the above light data sequence, that is, calculate the difference between the maximum and minimum values in a continuous time period divided by the average value to evaluate the degree of fluctuation. For example, max(I) = 735, min(I) = 680, avg(I) ≈ 706, then the volatility is (735 - 680) / 706 ≈ 0.078. In the preset volatility evaluation interval [0, 0.05] is stable, [0.05, 0.1] is medium, [above 0.1 is high volatility], and the current light fluctuation is at a medium level; further compare the soil temperature value with the daily temperature sequence, calculate the degree of deviation of the current temperature from the mean value, form the temperature fluctuation characteristic value, and form the current environmental fluctuation characteristic value through the balanced combination of the above light volatility and temperature fluctuation degree. For example, the light fluctuation is 0.078, the temperature fluctuation is 0.11, and the average of the two gives the environmental fluctuation characteristic value of 0.094.

[0079] The transpiration correlation sub-module analyzes the influence of light intensity and temperature level on the crop transpiration rate according to the environmental fluctuation characteristic value, evaluates the influence of various environmental conditions on the crop water demand, and obtains the transpiration correlation dynamic value.

[0080] The transpiration correlation sub-module analyzes according to the obtained environmental fluctuation characteristic value. First, read the light intensity value of 700 W / m of the current period 2 and the soil surface temperature value of 34°C, and look up the table to obtain the transpiration sensitive range of the crop stage. If it is currently the jointing stage of corn, the transpiration rate is limited when the light intensity is less than 600 W / m 2 , and tends to be saturated when it exceeds 800 W / m 2 . The current light is 700 W / m 2In the sensitive growth range, the transpiration activity is determined to be at a medium rate. At the same time, the current temperature of 34°C is 6°C higher than the transpiration starting temperature threshold of 28°C. Looking up the table shows that this temperature difference amplitude will increase the stomatal aperture by 8%, indirectly promoting the increase of the transpiration rate. The transpiration impact degree score is obtained by multiplying and normalizing the combined score of the light sensitivity range determination and the temperature offset amplitude. If the light sensitivity score is 0.7 and the temperature amplitude score is 0.8, then the transpiration impact degree score is 0.7·0.8 = 0.56. Then, the disturbance weight factors in different time periods are weighted and summed. The weight values of three sub-time periods are set to 1.1, 1.2, and 1.0 respectively, and the total dynamic weight value formed by each parameter combination within 3 hours is 3.3. Multiplying this weight value by the transpiration score gives the current comprehensive transpiration value of 3.3·0.56 = 1.848, denoted as the transpiration correlation dynamic value.

[0081] The environmental impact analysis sub-module calls the transpiration correlation dynamic value to evaluate the impact of real-time environmental factors on water demand, using the formula:

[0082]

[0083] Calculate the environmental evapotranspiration impact factor;

[0084] Among them, E f is the environmental evapotranspiration impact factor, with the unit of dimensionless, I m is the maximum light intensity of the day, with the unit of watts per square meter, I a is the actual light intensity of the current period, with the unit of watts per square meter, |I m -I a | is the absolute value of the light intensity offset, with the unit of watts per square meter, T s is the current surface temperature of the soil, with the unit of degrees Celsius, T b is the evapotranspiration physiological starting temperature threshold, with the unit of degrees Celsius, W j is the disturbance weight factor of the j-th period, with the unit of dimensionless, I r is the reference value of the sunshine intensity set within the region, with the unit of watts per square meter, T r is the reference value of the average daily temperature set within the region, with the unit of degrees Celsius, and j is the index number of the recording point, with the unit of dimensionless;

[0085] Based on obtaining the transpiration correlation dynamic value of 1.848, the environmental water demand assessment sub-module calls the current light and temperature parameters and executes the calculation of the environmental evapotranspiration impact factor. The input parameters are: the maximum light intensity of the day I m = 950 W / m 2 , the current light intensity I a = 700 W / m 2 , and calculate its absolute offset as |950 - 700| = 250 W / m2 , the current soil temperature is T s = 34 °C, and the starting temperature threshold for evapotranspiration is T b = 28 °C, then the absolute value of the temperature difference is 6, and the square root is The three-section disturbance factors are set to 1.1, 1.2, and 1.0, and the sum is:

[0086]

[0087] Substitute into the formula to calculate the environmental evapotranspiration impact factor:

[0088]

[0089] Among them, the environmental evapotranspiration impact factor is a dimensionless parameter used to measure the driving ability of the current environmental conditions (mainly including light intensity and soil surface temperature) on the transpiration behavior of crops, comprehensively evaluating the excitation intensity of environmental disturbances on crop water evaporation. Specifically, this factor reflects whether environmental factors enhance crop transpiration during a specific period. For example, under high temperature and strong light, the transpiration rate of crops increases, and the water demand increases, thereby increasing the value of this factor. As an important reference for judging irrigation timing, it helps the system dynamically adjust the irrigation plan. The formula first reflects the light disturbance intensity through the difference between the maximum light intensity of the day and the current actual light value, then evaluates the evaporation trend caused by temperature fluctuations through the deviation between the current soil temperature and the physiological starting temperature. Subsequently, the two disturbance terms are combined and superimposed with the disturbance weights within a continuous period to express the cumulative excitation intensity of environmental disturbances on the transpiration system over a certain time scale. Finally, the above excitation value is normalized with respect to the set reference light intensity and average daily temperature to ensure that the output result is a dimensionless relative disturbance ratio, which is used to measure the simple environmental driving ability of the current environmental state on crop evapotranspiration behavior and serves as an important input coefficient for judging irrigation water demand.

[0090] Table 2 Key parameter settings table for the environmental assessment module

[0091] Parameter name Value Unit Source description Maximum light intensity 950 <![CDATA[W / m 2 > Sensor maximum value acquisition Actual light intensity 700 <![CDATA[W / m 2 > Measured value at sampling moment Soil surface temperature 34 ℃ Temperature probe acquisition Initial temperature threshold 28 ℃ Actual measurement setting for transpiration physiology Sunshine reference intensity 900 <![CDATA[W / m 2 > Historical statistical mean Temperature reference value 30 ℃ Historical statistical mean Disturbance weight sequence [1.1,1.2,1.0] Dimensionless Set by time period

[0092] As shown in Table 2, a complete evapotranspiration impact factor is formed through the above calculation process, and this factor will be used as an important reference index for judging the irrigation sequence of each region in the subsequent task arrangement module.

[0093] Please refer to Figure 4 , the task arrangement module includes:

[0094] The pressure data acquisition sub-module calls the environmental evapotranspiration impact factor, uses pressure sensors to collect the water pressure data at multiple positions in the water supply pipe network before, during, and after irrigation, analyzes the decline range of the pipe network pressure and the pressure recovery duration, and generates the regional water pressure disturbance characteristic value;

[0095] On the basis of obtaining the environmental evapotranspiration impact factor E output by the previous module and E f = 0.03085, the pressure data acquisition sub-module starts to call the pressure sensors deployed at each regional node, and selects three time periods in the current scheduling cycle for pressure sampling, namely 1 minute before irrigation, within a 5-minute cycle during irrigation, and the pressure recovery process is continuously monitored for 10 minutes after irrigation. The recorded time points are in the intervals of t1, t2, and t3. The monitoring point data of a certain regional number A in the example settings are as follows: the water pressure before irrigation is 188 kPa, the lowest water pressure during irrigation drops to 180 kPa, it recovers to 185 kPa at the 6th minute after irrigation, and to 187 kPa at the 10th minute, and finally recovers to 188 kPa. The starting recovery point water pressure is set to the minimum value of 180 kPa, and the maximum recovery value is 188 kPa. Then the water pressure recovery increment ΔP = 188 - 180 = 8 kPa, and the recovery time is set to take 120 seconds from 180 kPa to 188 kPa. At the same time, the instantaneous water pressure sequence during irrigation is P = [182, 188, 185, 183, 186] kPa, and the average value of this sequence is calculated as After performing the absolute value operation of the deviation of each moment from the mean value, a fluctuation set is obtained The final water pressure disturbance characteristics are calculated by synthesizing the decline amplitude, water pressure increment, and deviation amplitude.

[0096] The pipe network load determination sub-module calls the regional water pressure disturbance characteristic value and uses the formula:

[0097]

[0098] Calculate the pipe network pressure influence coefficient;

[0099] Among them, C p is the pipe network pressure influence coefficient, dimensionless, P min is the lowest water pressure in the current task area, in kPa, P b is the water supply bottom limit pressure, in kPa, ΔP is the water pressure increment in the recovery stage, in kPa, T r is the water pressure recovery time, in seconds, P j is the instantaneous water pressure at the jth moment, in kPa, is the average water pressure value of this irrigation section, in kPa, P s is the standard supply pressure value, in kPa, A c is the irrigation area of the current region, in square meters, j is the sampling time index number, dimensionless, m is the total number of sampling time periods, dimensionless;

[0100] After reading the key parameters obtained from the aforementioned sampling calculation, the pipe network load determination sub-module executes the pipe network pressure influence coefficient C p Calculation process, substituting the parameter values as follows: The minimum water pressure is P min = 180 kPa, and the water supply bottom limit pressure is P b = 150 kPa, so the difference square term is (180 - 150) 2 = 900, the water pressure recovery increment is ΔP = 70 kPa, and the recovery time is T r = 120 s, calculate the square of the increment term as The instantaneous water pressures sampled at five moments are P1 = 182, P2 = 188, P3 = 185, P4 = 183, P5 = 186 kPa respectively, and the average value is The sum of the absolute values of the offset values at each moment is The overall numerator is 900 + 0.34028 + 9.2 = 909.54028, and the standard supply pressure value is P s = 200 kPa, and the irrigation area is A c = 500 m 2 , and the product score denominator is P s ·A c = 100000, and the final calculation result is:

[0101]

[0102] Among them, the pipeline network pressure influence coefficient is used to evaluate the disturbance intensity caused to the water supply pipeline network during the task execution in a specific irrigation area. The coefficient comprehensively considers factors such as the offset between the minimum water pressure and the system bottom limit pressure during the irrigation period in this area, the increment and time of the water pressure recovering from the minimum value to the stable state, the water pressure fluctuation amplitude, etc., and is normalized in combination with the regional irrigation area to quantify the pressure disturbance risk of the water supply system per unit area. A high coefficient indicates that the irrigation task in this area has a significant impact on the pipeline network pressure, and its execution priority needs to be reduced in the task sorting to avoid system instability caused by local pressure deficiency. The formula takes the disturbance behavior generated to the water supply pipeline network during the irrigation task execution as the evaluation object. First, the extreme pressure loss risk is reflected by the offset between the minimum water pressure value monitored during the task execution period in the current irrigation area and the set bottom limit of the water supply pressure. Then, the pressure recovery rate is obtained by dividing the pressure change amount generated when the pressure recovers from the lowest point to the stable state by the recovery time, and the disturbance determination of the slow recovery area is strengthened through squaring. Next, the offset amplitude between the instantaneous water pressure at multiple time points during the task execution and the average water pressure value of this section is extracted, and the absolute value summation is performed to reflect the fluctuation frequency and amplitude. Finally, the sum of the three disturbance characteristics is divided by the product of the system standard water supply pressure and the area of the current irrigation area, and the pressure disturbance intensity borne per unit area is output under the unified dimension scale to construct a dimensionless pipeline network pressure influence coefficient for task sorting.

[0103] Table 3 Example Table of Calculation Parameters for Pipeline Network Pressure Influence

[0104] Parameter name Value Unit Source description Minimum water pressure 180 kPa Minimum monitored value during irrigation Bottom limit pressure 150 kPa System preset water supply bottom limit Water pressure increment 70 kPa Return to initial value after irrigation Recovery time 120 Seconds Duration from minimum to initial water pressure Standard water supply pressure 200 kPa Pipe network design water supply pressure standard Irrigation area 500 <![CDATA[m 2 > Obtained from regional plane surveying and mapping Instantaneous water pressure sequence [182,188,185,183,186] kPa Measured record during sampling period

[0105] As shown in Table 3, the parameters collected can all be obtained through sensor layout in the conventional field environment and constitute a complete quantitative description of the pressure disturbance.

[0106] The task sequence optimization sub-module calls the pipeline network pressure influence coefficient, adjusts the execution order of the irrigation tasks in multiple areas according to the influence of the irrigation tasks executed in multiple irrigation areas on the water supply pipeline network pressure, and obtains the task execution list;

[0107] After the task sequence optimization sub-module obtains the pressure influence coefficients calculated for all irrigation areas, it takes them as the irrigation task priority judgment factors to sort the priorities of multiple areas. The system reads that the numbers of the areas to be irrigated are A, B, C, D, E respectively, and the corresponding C p values are C p=[0.0091, 0.0132, 0.0075, 0.0153, 0.0111]. The system sorts the above values in ascending order to form the task priorities as C, A, E, B, D. At the same time, it calculates the overlapping area weights of the water pressure transmission paths downstream in combination with the upstream pipe network topology map, and sets the critical pressure interference threshold for the overlapping area pairs. If the difference in the pressure influence coefficients of any pair of areas is less than 0.003 and more than 90% of the nodes on the paths coincide, they are classified into mutually exclusive irrigation groups and the intermittent irrigation strategy is executed. Finally, the irrigation task execution list is comprehensively formed as C, A, (E, B), D, where E and B execute intermittent irrigation with a 15-minute time difference. This result indicates that the system has completed the task sorting for all irrigation areas in the current cycle and solved the problem of the conflict in the stability of the pipe network water supply pressure.

[0108] Please refer to Figure 5 , and the execution control module includes:

[0109] The water volume calculation sub-module calls the leaf area index response coefficient, the environmental evapotranspiration influence factor, and the task execution list, and evaluates the irrigation water volume requirements for each irrigation area based on the area, leaf area, light intensity, and temperature level of each irrigation area, generating the regional irrigation water volume value;

[0110] After receiving the leaf area index response coefficient, the environmental evapotranspiration influence factor, and the task execution list data, the water volume calculation sub-module first calls the basic parameters of each irrigation area in turn. It reads that the area of area A is 500 square meters, its leaf area index response coefficient is 0.0137, the environmental evapotranspiration influence factor is 0.03085, accesses the light intensity sensor to obtain the current value of 720 W / m 2 , and the temperature sensor reads a value of 33 °C. The system normalizes the light intensity to 0.72 based on the set empirical adjustment factor model, calculates the adjustment coefficient as 1.8 in the temperature adjustment model with 25 °C as the reference for the temperature, and then performs a combined calculation. It uses the combined coefficient product model to solve the product of each factor, and obtains that the theoretical irrigation water volume requirement for area A is 273.87 liters. This water volume value will participate in the water supply pressure comparison and irrigation duration setting in the subsequent steps.

[0111] The water pressure extraction sub-module calls the regional irrigation water volume value, collects the real-time water pressure monitoring data of each irrigation area in the water supply pipe network, and combines the preset water supply standard value to calculate the differential pressure data corresponding to each irrigation area, obtaining the water supply pressure deviation amplitude value;

[0112] After the water pressure extraction sub-module generates the regional irrigation water volume value, it immediately activates the pipe network pressure monitoring interface to obtain the real-time water pressure data corresponding to the starting period of irrigation in Area A. The recorded value is 180 kPa. At the same time, it retrieves the set supply pressure standard value of 200 kPa from the system. By directly subtracting, the supply pressure offset of the current area is obtained as 20 kPa, that is: ΔP = 200 - 180 = 20 kPa. The supply pressure offset amplitude value is used to judge the actual flow deviation degree of the current area. The flow benchmark set by the system is the water supply rate of 10 L / min per unit time at 200 kPa. Under the condition that the actual water pressure is 180 kPa, it needs to be adjusted proportionally. The flow velocity correction factor is 180 / 200 = 0.9, and the actual available flow velocity is: q real = 10·0.9 = 9 L / min, and this data will be used for the next step of calculating the irrigation duration.

[0113] The duration control sub-module calls the supply pressure offset amplitude value, calculates the irrigation time required for each area, and adjusts the irrigation solenoid valve control parameters of each irrigation area to obtain the irrigation duration control parameters;

[0114] The duration control sub-module receives the actual flow velocity q real = 9 L / min obtained above and the target irrigation water volume Q = 273.87 L. By calculating the ratio of the two, the irrigation time required is obtained. After converting the unit to minutes, it is: minutes. According to the system control logic, after converting 30.43 minutes to seconds and adjusting it to an integer time, the solenoid valve opening duration is set. Finally, the irrigation duration control parameter is set to 1826 seconds and written into the task table of the solenoid valve controller for timed irrigation start. This duration parameter will be used as the input signal of the execution control module to execute the precise control command.

[0115] Table 4 Regional Irrigation Calculation and Control Parameter Table

[0116] Parameter name Region A Region B Region C <![CDATA[Irrigation area (m 2 )]]> 500 650 420 Leaf area response coefficient 0.0137 0.0152 0.0129 Evapotranspiration impact factor 0.03085 0.03410 0.02975 <![CDATA[Illumination intensity (W / m 2 )]]> 720 680 750 Temperature (°C) 33 32 31 Irrigation water volume (L) 273.87 389.47 193.42 Current water pressure (kPa) 180 175 185 Standard water pressure (kPa) 200 200 200 Actual flow rate (L / min) 9.00 8.75 9.25 Irrigation duration (minutes) 30.43 44.51 20.91

[0117] As shown in Table 4, the theoretical irrigation water volumes calculated by calling the parameters such as the irrigation area, leaf area response coefficient, environmental evapotranspiration impact factor, light intensity, and temperature of Areas A, B, and C under the same control logic are listed. By comparing the current water pressure with the system standard supply pressure value, the actual flow rate is obtained. Finally, the irrigation duration control results of each area are formed to ensure that the water volume allocation and time distribution of each area are reasonably controllable under different water supply states and environmental conditions.

[0118] Please refer to Figure 6 , the feedback adjustment module includes:

[0119] Based on the irrigation duration control parameter, the water supply status extraction sub-module calculates the actual water supply completion status of each task area and obtains the actual water supply completion value by collecting the solenoid valve opening duration and water pressure fluctuation data in each irrigation task.

[0120] After receiving the irrigation duration control parameter, the water supply status extraction sub-module monitors the actual task execution according to the solenoid valve opening control parameter set for each irrigation area and in combination with the real-time monitoring value. The specific operation is to read the set irrigation opening duration of 1830 seconds for Area A, and at the same time, the pressure sensor records the average working water pressure during its execution as 180 kPa. According to the proportional relationship between the system supply pressure standard of 200 kPa and the reference flow rate of 10 L / min, first convert the current pressure into the actual flow rate, that is, convert 180 kPa into the relative supply pressure percentage 180 / 200 = 0.9, and accordingly adjust the flow rate to 10·0.9 = 9 L / min. Calculate the actual water supply volume for this task cycle as (9·1830) / 60 = 274.5 L in combination with time, and compare it with the target water supply volume of 273.87 L in the task plan. The synchronously collected water pressure fluctuation values are recorded as: peak value 190 kPa, valley value 175 kPa, and standard deviation 5.4 kPa. This fluctuation data is recorded as the water supply stability background parameter. At this stage, the actual water supply completion value of Area A is finally output as 274.5 L. Subsequently, the water pressure sampling, flow rate conversion, and total water supply calculation of Areas B and C are performed in the same way. For Area B, the solenoid valve is opened for 2600 seconds, the water pressure is 175 kPa, the converted flow rate is 8.75 L / min, and the total water supply is 379.17 L. For Area C, the opening duration is 1250 seconds, the water pressure is 185 kPa, the converted flow rate is 9.25 L / min, and the water supply volume is 192.71 L. Independent water supply completion values are formed for all three areas to await subsequent deviation comparison processing.

[0121] The deviation determination execution sub-module calls the actual water supply completion value, combines it with the water volume value required by the task target, calculates the water supply deviation amount for each area, and generates the water supply execution deviation amount value.

[0122] After receiving the water supply completion values of all regions, the execution deviation determination sub-module first retrieves the target water volume parameters from the configuration table for corresponding comparison. For region A, its target water volume is 273.87 L, the actual completion volume is 274.5 L, and the direct difference is 274.5 - 273.87 = 0.63 L. Since it is a positive number, it means the water supply is slightly more. The system further calculates the deviation ratio as (0.63 / 273.87) · 100% = 0.23%. The system sets the deviation ratio level division standard as follows: when the deviation ratio is within the range of 0–5%, it is calibrated as level I, belonging to the allowable accuracy range; 5–10% is level II; more than 10% is level III, entering the supplementary irrigation control range. Region A is 0.23%, belonging to level I; for region B, the target water volume is 389.47 L, the actual water supply is 379.17 L, the deviation is -10.30 L, and the deviation ratio is (10.30 / 389.47) · 100% = 2.65%, also in level I; for region C, the target water volume is 193.42 L, the actual water supply is 192.71 L, the deviation is -0.71 L, and the deviation ratio is (0.71 / 193.42) · 100 = 0.37%, also classified as level I. The system writes the above deviation levels into the task execution status database to form the water supply execution deviation volume value and the deviation level determination result.

[0123] The supplementary irrigation configuration generation sub-module calls the water supply execution deviation volume value, evaluates the water supply deviation levels of multiple regions according to the water supply deviation, and configures the supplementary irrigation tasks to generate accurate irrigation configuration tasks;

[0124] After the supplementary irrigation configuration generation sub-module completes the identification of the execution deviation volume level, it takes the deviation level information of all regions as the scheduling configuration basis. According to the current setting, only when the deviation level is II or III will it execute the immediate supplementary irrigation task configuration. Currently, regions A, B, and C are all at level I. The system marks it as "no supplementary irrigation required" and does not register new tasks in the supplementary irrigation task queue. However, this process does not skip the calculation. The system still completes the configuration judgment of the deviation level and deviation volume for each region and writes the results into the accurate irrigation configuration report to facilitate the formation of a complete cycle irrigation feedback data chain. If a certain region shows a continuous positive deviation trend at level I in multiple subsequent cycles, it will also be used to dynamically correct the future water supply setting reference value. In the current task cycle, the system does not generate new supplementary irrigation configuration tasks and only outputs the configuration suggestion report form.

[0125] Table 5 Water Supply Status and Deviation Level Judgment Table

[0126] Parameter name Region A Region B Region C Target water volume (L) 273.87 389.47 193.42 Actual water pressure (kPa) 180 175 185 Valve opening duration (s) 1830 2600 1250 Actual water supply (L) 274.50 379.17 192.71 Water supply deviation (L) 0.63 -10.30 -0.71 Deviation ratio 0.23％ 2.65％ 0.37％ Supplementary irrigation level Ⅰ Ⅰ Ⅰ

[0127] As shown in Table 5, the system calculates the deviation and the corresponding ratio based on the difference between the water supply targets and the actual completion values in each region during the current execution cycle, and then conducts grade classification. Since all deviation ratios are within the tolerance range of 0–5%, all three regions are marked as Grade I, and the trigger conditions for supplementary irrigation configuration are not met.

[0128] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A multi-sensor fusion agricultural precision irrigation system, characterized in that: The system comprises: The leaf area analysis module obtains crop canopy images, extracts the current leaf area estimate, calls the target leaf area parameters corresponding to the crop type and growth stage, analyzes the impact of the actual leaf area on the intensity of canopy transpiration, evaluates the response to the water consumption capacity of the group, and generates the leaf area index response coefficient; The environmental assessment module uses the leaf area index response coefficient to call the data sequence of the light sensor and the temperature sensor, analyzes the influence of light intensity and temperature level on the crop transpiration rate, evaluates the influence of real-time environmental factors on water demand, and obtains the environmental evapotranspiration influencing factor; The task arrangement module calls the environmental evapotranspiration influencing factor to obtain water pressure data of multiple locations of the water supply network in real time, adjusts the execution order of irrigation tasks in multiple areas and generates a task execution list by analyzing the impact of irrigation tasks in multiple areas on the pressure of the water supply network; The execution control module calls the leaf area index response coefficient, environmental evapotranspiration influencing factor and task execution list, calculates the amount of irrigation water required for each area, combines the difference between the real-time water pressure and the preset supply pressure standard, calculates the irrigation time, and obtains the irrigation time control parameter.

2. The multi-sensor fusion agricultural precision irrigation system according to claim 1, characterized in that: The leaf area index response coefficient includes the leaf area index difference, the transpiration area ratio, and the stage response level. The environmental evapotranspiration influencing factors include the light fluctuation amplitude coefficient, the temperature rise rate value, and the environmental lag response factor. The task execution list specifically includes the partition priority level, water supply interference intensity, and task sorting number. The irrigation duration control parameters specifically refer to the dynamic duration correction value, the water pressure correction coefficient, and the valve opening and closing matching value.

3. The multi-sensor fusion agricultural precision irrigation system according to claim 1, characterized in that: The leaf area analysis module comprises: The image acquisition submodule obtains crop canopy images, analyzes the crop leaf area in the target irrigation area based on the proportion of green pixels in each area of ​​the image, and obtains the estimated leaf area value; The leaf area comparison submodule calls the leaf area estimation value and establishes a leaf area index difference value according to the leaf area parameter corresponding to the target crop type at each growth stage; The response degree calculation submodule calls the leaf area index difference and adopts the formula: Calculate the leaf area index response coefficient; Among them, C LAI represents the leaf area index response coefficient, LAI e Represents the estimated leaf area ratio, LAI t Represents the leaf area ratio parameter corresponding to the target growth stage, S c Represents the transpiration sensitivity coefficient of the crop stage, A r represents the transpiration base area, N p Represents the area occupied by a single plant.

4. The multi-sensor fusion agricultural precision irrigation system according to claim 3, characterized in that: The environmental assessment module includes: The environmental parameter acquisition submodule utilizes the leaf area index response coefficient to call the light sensor and the temperature sensor to collect the light intensity sequence and the soil surface temperature value in real time, analyzes the fluctuation characteristics of the data, and establishes the environmental fluctuation characteristic value; The transpiration association submodule analyzes the influence of light intensity and temperature level on crop transpiration rate according to the environmental fluctuation characteristic value, evaluates the influence of various environmental conditions on crop water demand, and obtains transpiration association dynamic value; The environmental impact analysis submodule calls the transpiration-related dynamic value to evaluate the impact of real-time environmental factors on water demand using the formula: Calculate environmental evapotranspiration impact factors; Among them, E f is the environmental evapotranspiration influencing factor, I m is the maximum light intensity of the day, I a is the actual light intensity in the current period, T s is the current soil surface temperature, T b is the physiological starting temperature threshold of evapotranspiration, W j is the disturbance weight factor of the jth period, I r is the reference value of sunshine intensity set in the area, T r is the daily average temperature reference value set in the area, and j is the index number of the recording point.

5. The multi-sensor fusion agricultural precision irrigation system according to claim 4, characterized in that: The task arrangement module comprises: The pressure data acquisition submodule calls the environmental evapotranspiration influencing factor and uses a pressure sensor to collect water pressure data at multiple locations in the water supply network before, during and after irrigation, analyzes the pressure drop amplitude and pressure recovery time of the network, and generates a regional water pressure disturbance characteristic value; The pipe network load determination submodule calls the regional water pressure disturbance characteristic value and adopts the formula: Calculate the pipe network pressure influence coefficient; Among them, C p is the influence coefficient of pipe network pressure, dimensionless, P min is the lowest water pressure in the current mission area, in kPa, P b is the water supply bottom pressure, in kPa, ΔP is the water pressure increment in the recovery phase, in kPa, T r is the water pressure recovery time, in seconds, P j is the instantaneous water pressure at the jth moment, in kPa, is the average water pressure value of the irrigation section, in kPa, P s is the standard supply pressure value, in kPa, A c is the irrigation area of ​​the current region, in square meters, j is the index number of the sampling time, dimensionless, and m is the total number of sampling periods, dimensionless; The task sequence optimization submodule calls the pipe network pressure influence coefficient, adjusts the execution order of the irrigation tasks in multiple irrigation areas according to the influence of the irrigation tasks in multiple irrigation areas on the water supply pipe network pressure, and obtains the task execution list.

6. The multi-sensor fusion agricultural precision irrigation system according to claim 5, characterized in that: The execution control module includes: The water calculation submodule calls the leaf area index response coefficient, environmental evapotranspiration influencing factor and task execution list, evaluates the irrigation water demand of each irrigation area according to the area, leaf area, light intensity and temperature level of each irrigation area, and generates regional irrigation water value; The water pressure extraction submodule calls the regional irrigation water volume value, collects the real-time water pressure monitoring data of each irrigation area in the water supply network, calculates the pressure difference data corresponding to each irrigation area in combination with the preset supply pressure standard value, and obtains the supply pressure deviation amplitude value; The duration control submodule calls the supply pressure offset amplitude value, calculates the irrigation time required for each area, adjusts the irrigation solenoid valve control parameters of each irrigation area, and obtains the irrigation duration control parameters.

7. The agricultural precision irrigation system with multi-sensor fusion according to claim 1, characterized in that: The system further comprises: The feedback regulation module obtains the solenoid valve opening time and water pressure fluctuation data of each irrigation area in each irrigation task based on the irrigation duration control parameter, calculates the actual water supply completion status of each area, and compares it with the target water supply plan, calculates the deviation degree and configures the supplementary irrigation task, and generates a precise irrigation configuration task; The precision irrigation configuration tasks include water supply deviation ratio, supplementary irrigation period, and water replenishment target.

8. The multi-sensor fusion agricultural precision irrigation system according to claim 7, characterized in that: The feedback adjustment module comprises: The water supply status extraction submodule calculates the actual water supply completion status of each task area based on the irrigation duration control parameter and obtains the actual water supply completion value by collecting the solenoid valve opening duration and water pressure fluctuation data in each irrigation task; The execution deviation determination submodule calls the actual water supply completion value, combines the water volume value required by the task target, calculates the water supply deviation of each area, and generates a water supply execution deviation value; The supplementary irrigation configuration generation submodule calls the water supply execution deviation value, evaluates the water supply deviation level of multiple areas according to the water supply deviation, configures supplementary irrigation tasks, and generates precise irrigation configuration tasks.

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