Electricity-to-water agricultural water metering method

Abstract

The invention discloses an agricultural water metering method for converting electricity into water. The method comprises the following steps: collecting electricity consumption data during operation of a water pump of a pump station in real time; constructing a dynamic association model in a mode of performing self-adaptive correction optimization on model parameters based on multi-dimensional data fitting of water pump operation parameters and environmental parameters and combining with periodic calibration, and converting electricity consumption data into water yield data through the dynamic association model; selecting a corresponding water quantity verification method according to the irrigation scene type, and acquiring actual water yield data to calibrate the dynamic association model; and outputting and storing the metered water yield through the calibrated dynamic correlation model. According to the method, water pump operation parameters (lift and efficiency attenuation) and environment parameters (temperature and humidity) are fused in real time through a dynamic correlation model and a periodic calibration mechanism, the problem of long-term metering deviation caused by a fixed coefficient in a traditional method for converting electricity into water is solved, and the metering precision is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural water-saving irrigation, and in particular to a method for measuring agricultural water consumption by converting electricity into water. Background Art

[0002] Agricultural water metering is a crucial support for promoting water conservation and improving water resource utilization efficiency. It plays a positive role in promoting optimal water resource allocation, protecting the ecological environment, and advancing the development of modern agriculture. A Ministry of Water Resources document states that for agricultural irrigation water intakes where objective conditions preclude the installation of water metering facilities, water volume can be measured using methods such as electricity-to-water conversion. In the plains of southern my country, numerous irrigation pumping stations have been built to draw water from surrounding rivers and canals, making full use of this electricity-to-water conversion method to measure agricultural water consumption.

[0003] However, the current low accuracy of electricity-to-water metering has seriously impacted agricultural water statistics. This is primarily due to the following issues: First, the metering of water pump electricity consumption is inaccurate. The meter reading at the pumping station is often used as the pump's operating power consumption, accounting for temporary electricity consumption for lighting and management. This meter reading is greater than the actual pump's operating power consumption. Second, the efficiency of a water pump varies with factors such as operating hours and maintenance. Directly estimating water volume from the pump's performance curve can lead to significant errors. Furthermore, pumping stations already have the basic requirements for electricity-to-water metering. However, the high construction and operating costs of installing a large number of traditional metering facilities make this difficult to implement in actual production. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method for measuring agricultural water consumption by converting electricity into water.

[0005] The present invention solves the above technical problems with the following technical solutions: A method for measuring agricultural water consumption by converting electricity into water, comprising the following steps:

[0006] S1: Real-time collection of power consumption data during operation of pumps in pumping stations;

[0007] S2: A dynamic correlation model is constructed based on multi-dimensional data fitting of pump operating parameters and environmental parameters and adaptively correcting and optimizing model parameters in combination with periodic calibration. The power consumption data is converted into water output data through the dynamic correlation model.

[0008] S3: Selecting a corresponding water volume verification method according to the irrigation scenario type, obtaining actual water volume data to calibrate the dynamic correlation model;

[0009] S4: Output and store the measured water output through the calibrated dynamic correlation model.

[0010] Furthermore, the multi-dimensional data fitting based on the water pump operating parameters and environmental parameters described in step S2 is implemented based on the following conversion equation of power consumption W and water consumption Q:

[0011]

[0012] Q=3600·q·t

[0013] Among them, W is the power consumption, Q is the water output, ρ is the density of water, g is the acceleration of gravity, H is the head of the pump, q is the water flow rate, t is the operating time, and η is the comprehensive efficiency of the pump station.

[0014] Furthermore, the adaptive optimization of the model parameters in combination with the periodic calibration described in step S2 is achieved by the following correction function:

[0015] Q=aW b +c

[0016] Among them, a, b, c are correction coefficients, W is the power consumption, and Q is the water output.

[0017] Furthermore, in step S3, the irrigation scenario types include a pumping station pumping water into a canal and a pumping station pumping water into a field.

[0018] Furthermore, in the scenario where the pump station pumps water into the canal, the following steps are included:

[0019] S3.1: After the pump is running stably, obtain the actual water output Q1 by direct measurement using a flow meter or using the flow measurement method based on hydraulic structures, and simultaneously record the power consumption W1;

[0020] S3.2: Create a scatter plot of power consumption and water output using multiple sets of measured actual water output Q1 and simultaneously recorded power consumption W1, and derive specific parameters of the correction coefficient according to the correction function;

[0021] S3.3: Input the obtained correction coefficient into the dynamic correlation model to calculate the water output, thereby obtaining the water output Q2;

[0022] S3.3: Compare Q1 and Q2. If the relative error exceeds a preset threshold, refit the parameters a, b, and c until the water output Q2 output by the dynamic correlation model and the actual measured water output Q1 satisfy the following formula, which is considered to be satisfied:

[0023]

[0024] S3.5: Update the optimized parameters to the dynamic correlation model to complete the calibration;

[0025] Furthermore, in the scenario where the pump station pumps water into the fields, the following steps are included:

[0026] S4.1: Obtain water balance parameters during the crop growth period, including the depth of the water layer on the surface of the field at the beginning of the period H1, the depth of the water layer on the surface of the field at the end of the period H2, the rainfall P during the period, the amount of paddy field seepage S during the period, and the drainage volume d;

[0027] S4.2: Formulate the following water balance equation and calculate the irrigation volume m during the period based on the water balance equation.

[0028] H2=H1+P+m-ET c -Sd

[0029] ET c =k c ET0

[0030] Among them, ET0 is crop evapotranspiration, k c is the crop coefficient;

[0031] S4.3: Dynamically correct the volumetric water output Q corresponding to the irrigation amount by measuring the crop stem and leaf cross-sectional area Δ in the experimental plots;

[0032] Q=m(A-Δ)×10 -3

[0033] Where A is the field area, Δ is the area occupied by rice stems and leaves in the field;

[0034] S4.4: The calculated volumetric water output Q is used as the actual water output Q1 and input into the dynamic correlation model for calibration to obtain the water output Q2;

[0035] S4.5: Compare Q1 and Q2. If the relative error exceeds a preset threshold, refit the parameters a, b, and c until the water output Q2 output by the dynamic correlation model and the actual measured water output Q1 satisfy the following formula, which is considered to be satisfied:

[0036]

[0037] S4.6: Update the optimized parameters to the dynamic correlation model to complete the calibration;

[0038] Furthermore, in step S4.3, the area Δ occupied by the rice stems and leaves in the field is obtained by actual measurement in the test field, including the following steps:

[0039] S4.30: During each crop growth period, select at least one experimental plot with uniform growth, with an area of 1m 2 ;

[0040] S4.31: Measure the cross-sectional area a of the crop stems and leaves in the test plot i and the number of crop plants in the experimental plot n i , where i represents the growth period number;

[0041] S4.32: Calculate the cross-sectional area of the stem and leaves of a single crop plant:

[0042]

[0043] S4.33: Based on the actual irrigated field area A and the actual number of crop plants N in the experimental field, calculate the area Δ occupied by the rice stems and leaves in the field:

[0044] Δ=N·A·Δ i (i=1,2,3……)

[0045] Among them, Δ i is the cross-sectional area of stems and leaves of a single crop plant, i is the cross-sectional area of the stems and leaves of the crops in the experimental plots, and N is the actual number of crops in the experimental plots.

[0046] Furthermore, the depth of the water layer on the surface of the field at the beginning of the period H1, the depth of the water layer on the surface of the field at the end of the period H2, the rainfall P during the period, the leakage of the paddy field S during the period, the drainage volume d, and the field area can be obtained from the water gauge, rainfall and empirical data. ET0 can be calculated based on the meteorological parameters of temperature, wind speed, relative humidity, and sunshine hours. The crop coefficient k c The value can be obtained by referring to the relevant irrigation water quota results.

[0047] Furthermore, the collection of electricity consumption data in step S1 is achieved through an independently deployed programmable logic controller, the circuit module of which is directly connected to the water pump power supply cable and eliminates interference from non-water pump electricity consumption.

[0048] Furthermore, when the relative error between the measured water output Q1 and the water output Q2 calculated by the dynamic correlation model exceeds a preset threshold, the periodic calibration mentioned in step S2 is triggered.

[0049] The present invention has the following beneficial effects: the present invention provides a method for measuring agricultural water consumption by converting electricity into water, which integrates the operating parameters of the water pump (head, efficiency attenuation) and the environmental parameters (temperature, humidity) in real time through a dynamic correlation model and a periodic calibration mechanism, solves the problem of long-term measurement deviation caused by a fixed coefficient in the traditional method of converting electricity into water, and improves the measurement accuracy; and adopts a sub-mode metering logic to accurately measure water pumping into canals and water pumping into fields. Specifically, for the water pumping into canals mode of a pump station, the actual electricity consumption and the use of a portable flow meter, The water output of the pumping station is measured by means of flow measurement of hydraulic structures, and a functional relationship between power consumption W and water output Q is established. The influence of conditions such as pump type, measured flow rate, measured power consumption, and actual head is taken into account, and it has high accuracy. For the water-to-field mode of the pumping station, the functional relationship between power consumption W and water output Q is established through the actual power consumption and the measured water output of the pumping station in rice fields at different growth stages. In addition to considering the influence of conditions such as pump type, measured flow rate, measured power consumption, and actual head, it also considers the influence of the cross-sectional area of rice stems and leaves at different growth stages, and it has high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flow chart of the method provided by the present invention;

[0051] Figure 2 This is a flow chart of the method for pumping water into a canal in the present invention;

[0052] Figure 3 This is a flow chart of the method for the scenario in which a pump station pumps water into fields in the present invention. DETAILED DESCRIPTION

[0053] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0054] like Figure 1 As shown, a method for measuring agricultural water consumption by converting electricity into water comprises the following steps:

[0055] S1: Real-time collection of electricity consumption data during the operation of the pump station water pump. The collection of its electricity consumption data is achieved through an independently deployed programmable logic controller (PLC). The circuit module of the programmable logic controller (PLC) is directly connected to the water pump power cable and eliminates interference from non-water pump electricity consumption. In other words, it effectively avoids electricity consumption that is not related to electricity-to-water conversion, such as lighting, cooling, heating, and temporary electricity management, and effectively improves metering accuracy. In addition, the programmable logic controller (PLC) can be directly connected to the existing pump station as an independent device and can be installed on the water pump cable. It is convenient and simple, and there is no need to modify the main structure of the pump station. In addition, the programmable logic controller (PLC) can also be upgraded by connecting to the remote terminal unit RTU to achieve remote control, remote supervision, remote viewing and other functions.

[0056] S2: A dynamic correlation model is constructed based on multi-dimensional data fitting of pump operating parameters and environmental parameters, and adaptively corrects and optimizes model parameters through periodic calibration. This dynamic correlation model converts power consumption data into water output data. This dynamic correlation model is integrated into a programmable logic controller (PLC).

[0057] Among them, the multi-dimensional data fitting based on the pump operating parameters and environmental parameters is realized based on the following conversion equation of electricity consumption W and water consumption Q:

[0058]

[0059] Q=3600·q·t

[0060] Among them, W is the power consumption (KW·h), Q is the water output (m 3 ), ρ is the density of water (kg / m 3 ), g is the acceleration due to gravity (m / s 2 ), H is the head of the pump (m), q is the water flow rate (m 3 / s), t is the operating time (h), and η is the comprehensive efficiency of the pumping station.

[0061] Adaptive optimization of model parameters combined with periodic calibration is achieved through the following correction function:

[0062] Q=aW b +c

[0063] Among them, a, b, c are correction coefficients, W is the power consumption, and Q is the water output.

[0064] In this solution, the water pump operating parameters (head, efficiency attenuation) and environmental parameters (temperature, humidity) are integrated in real time to solve the long-term metering deviation problem caused by the fixed coefficient in the traditional electricity-to-water method and improve the metering accuracy.

[0065] S3: Selecting a corresponding water volume verification method according to the irrigation scenario type, obtaining actual water volume data to calibrate the dynamic correlation model;

[0066] Irrigation scenarios include pumping water into canals and pumping water into fields. Depending on the irrigation scenario type, the verification method for measured water volume varies. For the pumping water into canals model, the water output of the pumping station is measured using actual power consumption and portable flow meters and hydraulic structure flow measurement. A functional relationship between power consumption (W) and water output (Q) is established, taking into account the influence of factors such as pump type, measured flow rate, measured power consumption, and actual head, achieving high accuracy.

[0067] For the pump station water pumping mode, the functional relationship between power consumption W and water output Q is established through actual power consumption and the measured water pumping volume of rice fields at different growth stages. In addition to considering the influence of conditions such as pump type, measured flow rate, measured power consumption, and actual head, the influence of the cross-sectional area of rice stems and leaves at different growth stages is also considered, with high accuracy.

[0068] S4: The measured water output is output and stored through the calibrated dynamic correlation model. The water output is the water output Q measured by the electricity-to-water method.

[0069] Two embodiments are provided for the above two types of irrigation scenarios. Embodiment 1 is a scenario where a pump station pumps water into a canal, and embodiment 2 is a scenario where a pump station pumps water into a field. The main difference between embodiment 1 and embodiment 2 is that in embodiment 1, after the pump station pumps water into the canal, the initial flow rate Q1 can be measured by using a portable flow meter for the canal outlet, or the pump station outlet pool has gates, weirs, culverts and other structures, and the initial flow rate Q1 can be measured by using a hydraulic structure flow measurement method; while in embodiment 2, the water is directly discharged into the rice field after the pump station discharges water, and the above conditions are not met to measure the initial flow rate Q1. A specifically designed calculation method is used to solve the measurement of the initial flow rate Q1.

[0070] First, in the first embodiment, in the scenario of pumping water into a canal at a pump station, the following steps are included:

[0071] S3.1: Start the pump. Once the pump is running smoothly, measure the actual water output Q1 using a flow meter or a standard flow measurement method based on the channel structure. Simultaneously record the power consumption W1. Specifically, use a portable flow meter to measure the pumping station water output Q1 at a location with a regular cross-section and smooth flow (no water diversion or confluence between the pumping station and the monitoring point) in the outlet channel adjacent to the pumping station. If the pumping station outlet pool has structures such as gates, weirs, or culverts, the pumping station water output Q1 can also be measured using flow measurement methods based on hydraulic structures, as per the "Specifications for Water Measurement in Irrigation Channel Systems" (GB / T21303).

[0072] S3.2: Based on the programmable logic controller (PLC), the power consumption W1 and the measured water output Q1 of the pump station are recorded. A scatter plot of the power consumption W1 and the water output Q1 is established by measuring multiple sets of actual water output Q1 and the synchronously recorded power consumption W1. According to the correction function, the specific parameters of the correction coefficient are obtained.

[0073] S3.3: Input the obtained correction coefficient into the conversion equation of the dynamic correlation model to calculate the water output, thereby obtaining the water output Q2;

[0074] S3.3: Compare the actual water output Q1 with the water output Q2 output by the model. If the relative error exceeds a preset threshold, refit the correction coefficients a, b, and c until the water output Q2 output by the dynamic correlation model and the actually measured water output Q1 satisfy the following formula, which is considered to be satisfied:

[0075]

[0076] S3.5: The optimized parameters are updated to the dynamic correlation model to complete the calibration. The water output Q2 output by the dynamic correlation model is the water output Q measured by the electricity-to-water method.

[0077] In the second embodiment, in the scenario where a pump station pumps water into a field, the method for measuring the water output Q1 is completely different from that in the first embodiment. In the second embodiment, the following steps are included:

[0078] S4.1: Obtain water balance parameters during the crop growth period, including the depth of the water layer on the surface of the field at the beginning of the period (H1) (mm), the depth of the water layer on the surface of the field at the end of the period (H2) (mm), the rainfall P (mm) during the period, the amount of seepage from the paddy field (S) (mm) during the period, and the drainage volume d (mm). The depth of the water layer on the surface of the field at the beginning of the period (H1) (H2), the depth of the water layer on the surface of the field at the end of the period (H2), the rainfall P (mm), the amount of seepage from the paddy field (S) during the period, the drainage volume d (mm), and the field area can be obtained from the water gauge, rainfall, and empirical data.

[0079] S4.2: Develop a water balance equation and calculate the irrigation volume m during the period based on the water balance equation.

[0080] H2=H1+P+m-ET c -Sd

[0081] ET c =k c ET0

[0082] Among them, ET0 is crop evapotranspiration (mm / d), k c is the crop coefficient; ET0 can be calculated based on meteorological parameters such as temperature, wind speed, relative humidity, and sunshine hours. The crop coefficient k c The value can be obtained by referring to the relevant irrigation water quota results.

[0083] S4.3: The volumetric water output Q (m 3 );

[0084] Q=m(A-Δ)×10 -3

[0085] Where A is the field area (m 2 ), Δ is the area occupied by rice stems and leaves in the field (m2 );

[0086] It should be noted that the area Δ occupied by the rice stems and leaves in the field is obtained through actual measurement of the test field, including the following steps:

[0087] S4.30: During each crop growth period, select at least one experimental plot with uniform growth, with an area of 1m 2 ;

[0088] S4.31: Measure the cross-sectional area a of the crop stems and leaves in the test plot i and the number of crop plants in the experimental plot n i , where i represents the growth period number;

[0089] S4.32: Calculate the cross-sectional area of the stem and leaves of a single crop plant:

[0090]

[0091] S4.33: Based on the actual irrigated field area A and the actual number of crop plants N in the experimental field, calculate the area Δ occupied by the rice stems and leaves in the field:

[0092] Δ=N·A·Δ i (i=1,2,3……)

[0093] Among them, Δ i is the cross-sectional area of stems and leaves of a single crop plant, i is the cross-sectional area of the stems and leaves of the crops in the experimental plots, and N is the actual number of crops in the experimental plots.

[0094] Specifically, in the experimental field, according to the different growth stages of rice, such as the greening stage, early tillering stage, early tillering stage (with water layer), late tillering stage (sun drying), booting stage, heading and flowering stage, milky stage, and yellow stage, an area of 1m2 with uniform growth was selected. 2 Typical plots, statistics 1m 2 Number of rice plants in the field n i , measure 1m 2 The cross-sectional area of rice stems and leaves in the field a i , the statistical results are shown in the table below;

[0095]

[0096] Note: Rice needs to be sun-dried in the fields at the end of tillering and yellow maturity stages. This stage may not be counted based on actual irrigation conditions.

[0097] The cross-sectional area of stem and leaf of each rice plant at different growth stages is i for:

[0098]

[0099] According to the actual measurement, the area of irrigation actually irrigated by electricity is A, and the area with uniform growth is 1m 2 The actual number of rice plants in the field is N, and the area occupied by the rice stems and leaves in the field is Δ:

[0100] Δ=N·A·Δ i (i=1,2,3……8)

[0101] By substituting the above-determined parameters into the water balance equation for calculation, the volumetric water output Q within the period can be obtained;

[0102] S4.4: The calculated volumetric water output Q is used as the actual water output Q1 and input into the dynamic correlation model for calibration to obtain the water output Q2;

[0103] S4.5: Compare Q1 and Q2. If the relative error exceeds a preset threshold, refit the parameters a, b, and c until the water output Q2 output by the dynamic correlation model and the actual measured water output Q1 satisfy the following formula, which is considered to be satisfied:

[0104]

[0105] S4.6: Update the optimized parameters to the dynamic correlation model to complete the calibration;

[0106] Furthermore, when the relative error between the measured water output Q1 and the water output Q2 calculated by the dynamic correlation model exceeds a preset threshold, the periodic calibration mentioned in step S2 is triggered. In order to maintain the measurement accuracy, calibration can also be performed regularly.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for measuring agricultural water consumption by converting electricity into water, characterized in that: The following steps are involved: S1: Real-time collection of power consumption data during operation of pumps in pumping stations; S2: A dynamic correlation model is constructed based on multi-dimensional data fitting of pump operating parameters and environmental parameters and adaptively correcting and optimizing model parameters in combination with periodic calibration. The power consumption data is converted into water output data through the dynamic correlation model. S3: Selecting a corresponding water volume verification method according to the irrigation scenario type, obtaining actual water volume data, and comparing the actual water volume data with the water volume data calculated by the dynamic correlation model to calibrate the dynamic correlation model; S4: Output and store the measured water output through the calibrated dynamic correlation model.

2. The method for measuring agricultural water consumption by converting electricity into water according to claim 1, characterized in that: The multi-dimensional data fitting based on the water pump operating parameters and environmental parameters described in step S2 is implemented based on the following conversion equation of power consumption W and water consumption Q: Q=3600·q·t Among them, W is the power consumption, Q is the water output, ρ is the density of water, g is the acceleration of gravity, H is the head of the pump, q is the water flow rate, t is the operating time, and η is the comprehensive efficiency of the pump station.

3. The method for measuring agricultural water consumption by converting electricity into water according to claim 2, characterized in that: The adaptive optimization of the model parameters in combination with periodic calibration described in step S2 is achieved by the following correction function: Q=aW b +c Among them, a, b, c are correction coefficients, W is the power consumption, and Q is the water output.

4. The method for measuring agricultural water consumption by converting electricity into water according to claim 1, characterized in that: In step S3, the irrigation scenario types include a pumping station pumping water into a canal and a pumping station pumping water into a field.

5. The method for measuring agricultural water consumption by converting electricity into water according to claim 4, characterized in that: In the scenario where the pump station pumps water into the canal, the following steps are included: S3.1: After the pump has stabilized, obtain the actual water output Q1 by direct measurement using a portable flow meter or by using the flow measurement method based on hydraulic structures, and simultaneously record the power consumption W1; S3.2: Create a scatter plot of power consumption and water output using multiple sets of measured actual water output Q1 and simultaneously recorded power consumption W1, and derive specific parameters of the correction coefficient according to the correction function; S3.3: Input the obtained correction coefficient into the dynamic correlation model to calculate the water output, thereby obtaining the water output Q2; S3.4: Compare Q1 and Q2. If the relative error exceeds a preset threshold, refit the correction coefficients a, b, and c until the water output Q2 output by the dynamic correlation model and the actual measured water output Q1 satisfy the following formula, which is considered to be satisfied: S3.5: Update the optimized parameters to the dynamic correlation model to complete the calibration.

6. The method for measuring agricultural water consumption by converting electricity into water according to claim 4, characterized in that: In the scenario where a pump station pumps water into the fields, the following steps are included: S4.1: Obtain water balance parameters during the crop growth period, including the depth of the water layer on the surface of the field at the beginning of the period H1, the depth of the water layer on the surface of the field at the end of the period H2, the rainfall P during the period, the amount of paddy field seepage S during the period, and the drainage volume d; S4.2: Formulate the following water balance equation and calculate the irrigation volume m during the period based on the water balance equation. H2=H1+P+m-ET c -Sd AND c =k c ·ET0 Among them, ET0 is crop evapotranspiration, k c is the crop coefficient; S4.3: Dynamically correct the volumetric water output Q corresponding to the irrigation amount by measuring the crop stem and leaf cross-sectional area Δ in the experimental plots; Q=m(A-Δ)×10 -3 Where A is the field area, Δ is the area occupied by rice stems and leaves in the field; S4.4: The calculated volumetric water output Q is used as the actual water output Q1 and input into the dynamic correlation model for calibration to obtain the water output Q2; S4.5: Compare Q1 and Q2. If the relative error exceeds a preset threshold, refit the parameters a, b, and c until the water output Q2 output by the dynamic correlation model and the actual measured water output Q1 satisfy the following formula, which is considered to be satisfied: S4.6: Update the optimized parameters to the dynamic correlation model to complete the calibration.

7. The method for measuring agricultural water consumption by converting electricity into water according to claim 6, characterized in that: In step S4.3, the area Δ occupied by the rice stems and leaves in the experimental plot is obtained by the following steps: S4.30: During each crop growth period, select at least one experimental plot with uniform growth, with an area of 1m 2 ; S4.31: Measure the cross-sectional area a of the crop stems and leaves in the test plot i and the number of crop plants in the experimental plot n i , where i represents the growth period number; S4.32: Calculate the cross-sectional area of the stem and leaves of a single crop plant: S4.33: Based on the actual irrigated field area A and the actual number of crop plants N in the experimental field, calculate the area Δ occupied by the rice stems and leaves in the field: Δ=N·A·Δ i (i=1,2,3……) Among them, Δ i is the cross-sectional area of stems and leaves of a single crop plant, i is the cross-sectional area of the stems and leaves of the crops in the experimental plots, and N is the actual number of crops in the experimental plots.

8. The method for measuring agricultural water consumption by converting electricity into water according to claim 1, characterized in that: The collection of electricity consumption data in step S1 is achieved through an independently deployed programmable logic controller, the circuit module of which is directly connected to the water pump power supply cable and eliminates interference from non-water pump electricity consumption.

9. The method for measuring agricultural water consumption by converting electricity into water according to claim 5 or 6, characterized in that: When the relative error between the measured value Q1 of the water output and the model predicted value Q2 exceeds a preset threshold, the periodic calibration mentioned in step S2 is triggered.

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