A method for measuring agricultural water by electrically folding water

Abstract

The application discloses a kind of electric water metering method for agricultural water, comprising the following steps: real-time acquisition of the power consumption data of pump station water pump when running;Based on the multidimensional data fitting of water pump operating parameters and environmental parameters and the adaptive correction optimization mode of model parameters combined with periodic calibration to construct a dynamic correlation model, convert the power consumption data into water output data through the dynamic correlation model;According to the irrigation scene type selection corresponding water quantity verification method, obtain the actual water output data to calibrate the dynamic correlation model;Through the calibrated dynamic correlation model, output and store the measured water output.The method fuses pump operating parameters (head, efficiency attenuation) and environmental parameters (temperature, humidity) in real time through dynamic correlation model and periodic calibration mechanism, solves the long-term measurement deviation problem caused by fixed coefficient in traditional electric water metering method, effectively improves the measurement accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural water-saving irrigation technology, in particular to an electricity-to-water conversion method for agricultural water metering. BACKGROUND

[0002] Agricultural water metering is an important support for promoting agricultural water conservation and improving water resource utilization efficiency, and has a positive effect on promoting water resource optimization allocation and ecological environment protection and promoting modern agricultural development. The relevant documents of the Ministry of Water Resources point out that for the agricultural irrigation water intake that cannot install water intake metering facilities due to objective conditions, the electricity-to-water conversion method can be used to meter water quantity. In the plain areas of southern China, a large number of water pumping irrigation pumping stations have been built to take water from surrounding canals for irrigation, and the electricity-to-water conversion method can be fully utilized to meter agricultural water quantity.

[0003] However, the current electricity-to-water conversion metering has low precision, which seriously affects the agricultural water statistics results, mainly in the following aspects: first, the water pump power consumption metering is not accurate, and the power meter reading of the pumping station is generally used as the power consumption of the water pump, and the lighting, management and temporary power consumption are included, so the power meter reading is greater than the actual power consumption of the water pump; second, the working efficiency of the water pump will be affected by many factors such as working time and maintenance degree, and directly calculating the water quantity by checking the performance curve of the water pump will cause large error. At the same time, the pumping station adopts the electricity-to-water conversion method to meter, which has the metering basis condition, and the construction cost and operation cost of a large number of additional metering facilities are high, which is difficult to popularize in actual production. SUMMARY

[0004] In order to solve the above technical problems, the present application provides an electricity-to-water conversion method for agricultural water metering.

[0005] The technical scheme for solving the above technical problems is as follows: an electricity-to-water conversion method for agricultural water metering, comprising the following steps:

[0006] S1: collecting the power consumption data of the water pump of the pumping station in real time;

[0007] S2: constructing a dynamic correlation model based on the multi-dimensional data fitting of the water pump operation parameters and environmental parameters and the self-adaptive correction and optimization of the model parameters in a periodic calibration mode, and converting the power consumption data into water discharge data through the dynamic correlation model;

[0008] S3: selecting a corresponding water quantity verification method according to the irrigation scene type, and obtaining actual water discharge data to calibrate the dynamic correlation model;

[0009] S4: outputting and storing the metered water discharge through the calibrated dynamic correlation model.

[0010] Further, the multi-dimensional data fitting based on the water pump operating parameters and the environmental parameters in step S2 is based on the conversion equation of the electricity consumption and the water discharge :

[0011]

[0012] wherein, is the electricity consumption, is the water discharge, is the density of water, is the acceleration of gravity, is the lift of the water pump, is the water discharge flow rate, is the operating time, is the comprehensive efficiency of the pump station.

[0013] Further, the adaptive correction optimization of the model parameters combined with the periodic calibration in step S2 is achieved by the following correction function:

[0014]

[0015] wherein, a, b, c are correction coefficients respectively, is the electricity consumption, is the water discharge.

[0016] Further, in step S3, the irrigation scene types include the water pumping into the canal by the pump station and the water pumping into the field by the pump station.

[0017] Further, in the scene of the water pumping into the canal by the pump station, the following steps are included:

[0018] S3.1: After the water pump is stably operated, the actual water discharge is directly measured by the flow meter or is obtained based on the water structure flow measurement method, and the electricity consumption is recorded synchronously;

[0019] S3.2: A scatter plot of the electricity consumption and the water discharge is established by the measured multiple sets of actual water discharge and the synchronously recorded electricity consumption , and the specific parameters of the correction coefficients are obtained according to the correction function;

[0020] S3.3: The obtained correction coefficients are input into the dynamic correlation model to calculate the water discharge, and then the water discharge is obtained;

[0021] S3.4: The and are compared, and if the relative error exceeds the preset threshold, the parameters a, b, c are refitted until the water discharge output by the dynamic correlation model the actual measured water output It is considered to meet the condition if the following formula is met:

[0022]

[0023] S3.5: Update the optimized parameters to the dynamic correlation model to complete the calibration;

[0024] Further, in the scenario of pumping water into the field, the following steps are included:

[0025] S4.1: Obtain the water balance parameters of the crop growth period, including the initial depth of the field water layer , the final depth of the field water layer , the rainfall in the period , the rice field seepage in the period , and the drainage ;

[0026] S4.2: Formulate the following water balance equation and calculate the irrigation amount in the period according to the water balance equation ,

[0027]

[0028] wherein, is the crop evapotranspiration, is the crop coefficient;

[0029] S4.3: Measure the crop stem and leaf cross-sectional area in the test field block and dynamically correct the volumetric water output corresponding to the irrigation amount ;

[0030]

[0031] wherein, is the field area, is the area occupied by the rice stem and leaves in the test field block;

[0032] S4.4: Take the calculated volumetric water output as the actual water output , input the dynamic correlation model for calibration to obtain the water output ;

[0033] S4.5: Compare and , if the relative error exceeds the preset threshold, re-fit the parameters a, b, and c until the water output output by the dynamic correlation model meets the condition that the actual measured water output meets the following formula:

[0034]

[0035] S4.6: update the optimized parameters to the dynamic correlation model, and complete the calibration;

[0036] Further, in step S4.3, the area occupied by the rice stems and leaves in the test plot is obtained by actual measurement, including the following steps:

[0037] S4.30: select at least one test plot with uniform growth in each growth period of the crop, and the area is 1 m2; 2 ;

[0038] S4.31: measure the stem and leaf cross-sectional area of the crop in the test plot and the number of crop plants in the test plot , wherein i represents the growth period number;

[0039] S4.32: calculate the stem and leaf cross-sectional area of a single plant:

[0040]

[0041] S4.33: calculate the area occupied by the rice stems and leaves in the test plot according to the actual irrigation plot area and the actual number of crop plants in the test plot :

[0042]

[0043] wherein, is the stem and leaf cross-sectional area of a single plant, is the stem and leaf cross-sectional area of the crop in the test plot, is the actual number of crop plants in the test plot.

[0044] Further, the initial depth of the water layer on the field surface , the final depth of the water layer on the field surface , the rainfall in the period , the rice field seepage in the period , the drainage , the area of the plot can be obtained by the water gauge, rainfall and experience data, can be calculated according to the meteorological parameters of air temperature, wind speed, relative humidity and sunshine duration, and the crop coefficient value can be obtained by referring to the relevant irrigation water quota results.

[0045] Further, the collection of power consumption data in step S1 is realized by an independently deployed programmable logic controller, and the circuit module of the programmable logic controller is directly connected to the power cable of the water pump, and excludes non-water pump power consumption interference.

[0046] Further, when the measured water yield and the water yield calculated by the dynamic correlation model exceeds the preset threshold, the periodic calibration mentioned in step S2 is triggered.

[0047] The present application has the following beneficial effects: The water metering method provided by the present application solves the long-term metering deviation problem caused by fixed coefficients in the traditional electricity-to-water method by real-time fusion of pump operation parameters (lift, efficiency attenuation) and environmental parameters (temperature, humidity) through a dynamic correlation model and a periodic calibration mechanism, thereby improving the metering accuracy; and the water metering method adopts a split-mode metering logic to accurately meter water pumping into a canal and water pumping into a field. Specifically, for the pump station water pumping into a canal mode, the function relationship between the electricity consumption W and the water yield Q is established by actual electricity consumption and by means of portable flow meters, water structure flow measurement, etc. to measure the pump station water yield, and the influences of pump type, measured flow, measured electricity consumption, actual lift, etc. are considered, thereby achieving high accuracy; for the pump station water pumping into a field mode, the function relationship between the electricity consumption W and the water yield Q is established by actual electricity consumption and by different growth period rice field measured pump water yield, and the influences of pump type, measured flow, measured electricity consumption, actual lift, etc. are considered in addition to the influences of different growth period rice stem leaf cross-sectional area, thereby achieving high accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The method flowchart provided by the present application;

[0049] Figure 2 The method flowchart in the pump station water pumping into a canal scenario in the present application;

[0050] Figure 3 The method flowchart in the pump station water pumping into a field scenario in the present application. DETAILED DESCRIPTION

[0051] The principles and features of the present application are described below in conjunction with the accompanying drawings, and the examples are only used to explain the present application and are not used to limit the scope of the present application.

[0052] As shown in Figure 1 , a water metering method for agricultural use includes the following steps:

[0053] S1: Real-time collection of power consumption data of the water pump during operation of the pump station. The collection of power 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 power cable of the water pump, and non-water pump power interference is excluded, that is, lighting, refrigeration, heating, management of temporary power consumption, and other power consumption unrelated to electricity-to-water conversion are effectively avoided, effectively improving the measurement accuracy. Moreover, the programmable logic controller (PLC) can be directly connected to the existing pump station as an independent device, and can be installed on the cable of the water pump, which is convenient and simple, and does not need to modify the main structure of the pump station. In addition, the programmable logic controller (PLC) can be upgraded by connecting a remote terminal unit (RTU), and can realize remote control, remote supervision, remote viewing, and other functions.

[0054] S2: A dynamic correlation model is constructed by a multi-dimensional data fitting based on water pump operation parameters and environmental parameters, and a self-adaptive correction and optimization of model parameters through periodic calibration. The power consumption data is converted into water output data through the dynamic correlation model. The dynamic correlation model is integrated on the programmable logic controller (PLC).

[0055] Among them, the multi-dimensional data fitting based on water pump operation parameters and environmental parameters is based on the following power consumption and the conversion equation of water output

[0056]

[0057] Among them, is the power consumption ( ), is the water output ( ), is the density of water (kg / m ), is the acceleration of gravity (m / s ), is the lift of the water pump (m), is the water flow rate ( / s), is the running time (h), is the comprehensive efficiency of the pump station.

[0058] The self-adaptive correction and optimization of model parameters through periodic calibration is achieved through the following correction function:

[0059]

[0060] Among them, a, b, and c are correction coefficients, is the power consumption, is the water output.

[0061] ​This solution integrates pump operating parameters (head, efficiency decay) with environmental parameters (temperature, humidity) in real time, solving the long-term metering deviation problem caused by fixed coefficients in the traditional electricity-to-water conversion method and improving metering accuracy.

[0062] S3: Select the corresponding water volume verification method according to the irrigation scenario type, and obtain actual water output data to calibrate the dynamic correlation model;

[0063] The irrigation scenarios include pumping water into canals and pumping water into fields. The verification methods for measured water volume vary depending on the type of irrigation scenario. For the pumping water into canals mode, the pumping water output is measured by actual power consumption and by using portable flow meters and hydraulic structures to measure the flow. A functional relationship between power consumption W and water output Q is established, taking into account the influence of pump type, measured flow rate, measured power consumption, and actual head, which has high accuracy.

[0064] For the pumping station water delivery mode, a functional relationship between power consumption W and water output Q was established by using actual power consumption and measured water delivery volume of the pumping station in paddy fields at different growth stages. In addition to considering the influence of 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 was also considered, which has high accuracy.

[0065] S4: Output and store the measured water output through the calibrated dynamic correlation model. This water output is the water output Q measured by the electro-water conversion method.

[0066] Two implementation examples are provided for the two irrigation scenarios mentioned above. Example 1 is a scenario where water is pumped from a pumping station into a canal, and Example 2 is a scenario where water is pumped from a pumping station into a field. The main difference between Example 1 and Example 2 is that in Example 1, after water is pumped from the pumping station into the canal, the initial flow rate can be measured using a portable flow meter at the canal's outlet. Alternatively, the initial flow rate can be measured using hydraulic structure flow measurement methods, where the pumping station's outlet pool contains structures such as sluice gates, weirs, or culverts. Example 2 involves pumping water directly into paddy fields after it exits the pumping station, which does not meet the above conditions for measuring the initial flow rate. A specially designed calculation method is used to solve the initial flow. Measurement.

[0067] First, in the first embodiment, in the scenario of pumping water from a pumping station into a canal, the following steps are included:

[0068] S3.1: Start the water pump. After the water pump is running stably, obtain the actual water output by directly measuring the flow rate with a flow meter or by using a standard flow measurement method based on the channel structure. And record electricity consumption simultaneously. Specifically: on the water outlet channel near the pumping station, the cross section is regular, and the portable flowmeter is used to measure the water quantity of the pumping station If there are gate, weir body, culvert and other buildings in the pumping station outlet pool, the water quantity of the pumping station can also be measured by the hydraulic structure flow measurement method according to the requirements of "Irrigation Channel System Water Measurement Specification" (GB / T21303) .

[0069] S3.2: According to the programmable logic controller (PLC) record power consumption And the measured water quantity of the pumping station , through the measured multiple sets of actual water quantity And the synchronous recorded power consumption , the scatter diagram of power consumption And water quantity is established, and the specific parameters of the correction coefficient are obtained according to the correction function;

[0070] S3.3: The obtained correction coefficient is input into the conversion equation of the dynamic correlation model to calculate the water quantity, and then the water quantity is obtained;

[0071] S3.4: Compare the actual water quantity With the water quantity output by the model , if the relative error exceeds the preset threshold, the correction coefficients a, b and c are refitted until the water quantity output by the dynamic correlation model And the actually measured water quantity satisfy the following formula, that is, it is considered to meet the conditions:

[0072]

[0073] S3.5: Update the optimized parameters to the dynamic correlation model to complete the calibration, and the water quantity Output by the dynamic correlation model is the water quantity Q measured by the electricity-to-water method.

[0074] In the pumping station water lifting into the field scenario, the measured water quantity is completely different from the method of example one, and in example two, the following steps are included:

[0075] S4.1: Obtain the water balance parameters of the crop growth period, including the initial field water layer depth (mm), the final field water layer depth (mm), the rainfall in the period (mm), the rice field seepage in the period (mm), and the drainage (mm); where the initial water depth of the field during the time period , depth of water layer on the field surface at the end of the period Rainfall during the period Paddy field seepage volume during the specified time period Discharge The area of ​​a field can be obtained from water level gauges, rainfall data, and empirical data.

[0076] S4.2: Draft the water balance equation and calculate the irrigation volume for the specified time period based on the water balance equation. ,

[0077]

[0078] in, This represents crop evapotranspiration (mm / d). For crop coefficients; The crop coefficient can be calculated based on meteorological parameters such as temperature, wind speed, relative humidity, and sunshine duration. The value can be obtained by referring to the relevant irrigation water quota results.

[0079] S4.3: Measured cross-sectional area of ​​crop stems and leaves in experimental fields. Dynamically adjust the volumetric water output corresponding to the irrigation amount. ( );

[0080]

[0081] in, For the area of ​​the field ( ), The area occupied by rice stems and leaves in the experimental field ( );

[0082] It should be noted that the area occupied by rice stems and leaves in the experimental field... The data was obtained through field measurements in experimental plots, including the following steps:

[0083] S4.30: During each growth stage of the crop, select at least one experimental plot with uniform growth, with an area of ​​1m². 2 ;

[0084] S4.31: Measure the cross-sectional area of ​​crop stems and leaves in the experimental field. and the number of crop plants in the experimental plot , where i represents the reproductive period number;

[0085] S4.32: Calculate the cross-sectional area of ​​the stem and leaves of a single crop plant:

[0086]

[0087] S4.33: Based on the actual irrigated field area and the actual number of crop plants in the experimental field Calculate the area occupied by rice stems and leaves in the experimental field. :

[0088]

[0089] in, This refers to the cross-sectional area of ​​the stem and leaves of a single crop. The cross-sectional area of ​​the crop stems and leaves in the experimental field. This represents the actual number of crop plants in the experimental field.

[0090] Specifically, in the experimental field, based on different growth stages of rice, such as the greening stage, early tillering stage, early tillering stage (with water layer), late tillering stage (drying), booting stage, heading and flowering stage, milk stage, and yellowing stage, areas with uniform growth were selected as... Typical fields, statistics Number of rice plants in the field , calculation Rice stem and leaf cross-sectional area in the field The statistical results are shown in the table below;

[0091]

[0092] Note: Rice plants need to be dried in the field at the end of tillering and during the ripening stage. These stages can be excluded from the statistics based on actual irrigation conditions.

[0093] The cross-sectional area of ​​the stem and leaves of each rice plant at different growth stages is △ i for:

[0094]

[0095] According to actual measurements, the area irrigated by electric water pumping is A, and the area with uniform growth is 1m². 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... for:

[0096]

[0097] By substituting the parameters determined above into the water balance equation, the volumetric water output during the time period can be obtained. ;

[0098] S4.4: Calculate the volumetric water output As the actual water output The output water volume is obtained by calibrating the dynamic correlation model. ;

[0099] S4.5: Comparison and If the relative error exceeds a preset threshold, the parameters a, b, and c are refitted until the water output of the dynamic correlation model satisfies the following formula:

[0100]

[0101] S4.6: Update the optimized parameters to the dynamic correlation model, and complete the calibration.

[0102] When the measured water output and the water output calculated by the dynamic correlation model exceed a preset threshold, the periodic calibration mentioned in step S2 is triggered. In order to maintain the measurement accuracy, periodic calibration can also be performed.

[0103] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.​

Claims

1. An electrically folding water agricultural water metering method, characterized by, The method comprises the following steps: S1: collecting power consumption data of the water pump in real time when the pump station is running; S2: constructing a dynamic correlation model based on multi-dimensional data fitting of the water pump operation parameters and environmental parameters and self-adaptive correction and optimization of model parameters in combination with periodic calibration, converting the power consumption data into water output data through the dynamic correlation model; S3: selecting a corresponding water quantity verification method according to the irrigation scene type, obtaining actual water output data, and comparing the actual water output data with the water output data calculated by the dynamic correlation model to calibrate the dynamic correlation model; S4: outputting and storing the metered water output through the calibrated dynamic correlation model; In step S3, the irrigation scene types include pumping water into a canal and pumping water into a field at the pump station; In the pumping water into a canal scene at the pump station, the following steps are included: S3.1: After the water pump is stable, the actual water output is directly measured by a portable flowmeter or obtained based on the water structure flow measurement method , and the power consumption is recorded simultaneously ; S3.2: multiple sets of actual water output measured and the simultaneously recorded electricity consumption , to establish a scatter plot of electricity consumption and water output, and to obtain 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 quantity, thereby obtaining the water quantity ; S3.4: Contrast and If the relative error exceeds the preset threshold, the correction coefficients a, b, and c are refitted until the water output of the dynamic correlation model satisfies the following formula: is considered to satisfy the condition. S3.5: updating the optimized parameters to the dynamic correlation model to complete the calibration; In the pumping water into a field scene at the pump station, the following steps are included: S4.1: Obtain water balance parameters of the crop growth period, including initial field water layer depth , final field water layer depth , rainfall during the period , rice field leakage during the period , drainage ; S4.2: Formulate the following water balance equation, and calculate the irrigation amount within the period according to the water balance equation , wherein, Evapotranspiration for the crop, Crop coefficient; S4.3: Measuring the crop stem leaf area by the test field plot , dynamically correcting the volume of water corresponding to the irrigation amount ; wherein, is the area of the field, is the area of the field occupied by the rice stems and leaves; S4.4: The calculated volume of water outflow As the actual water outflow , input the dynamic correlation model for calibration to obtain the water outflow ; S4.5: Contrast and If the relative error exceeds a preset threshold, the parameters a, b, and c are refitted until the water output of the dynamic correlation model satisfies the following formula: is considered to satisfy the condition. S4.6: updating the optimized parameters to the dynamic correlation model to complete the calibration.

2. The method of claim 1, wherein the water is agricultural water. The multi-dimensional data fitting based on the water pump operating parameters and the environmental parameters in step S2 is implemented based on the conversion equation of the electricity consumption and the water discharge ​ wherein, is the power consumption, is the water output, is the density of water, is the acceleration of gravity, is the head of the water pump, is the water flow rate, is the running time, is the comprehensive efficiency of the pumping station.

3. The method of claim 2, wherein the water is agricultural water. The self-adaptive correction and optimization of model parameters in combination with periodic calibration in step S2 is realized through the following correction function: Wherein, a, b, c are correction coefficients respectively, is the power consumption, is the water output.

4. The method of claim 1, wherein the water is agricultural water. In step S4.3, the area occupied by the rice stems and leaves in the test plot By the following steps: S4.30: In each growth period of the crop, select at least one test plot with uniform growth, with an area of 1 m 2 ; S4.31: measuring the crop leaf area index in the test plot and the number of crop plants in the test plot where i denotes the growth stage number; S4.32: calculating the stem and leaf cross-sectional area of a single crop plant: S4.33: Calculate the area occupied by rice stems and leaves in the test plot based on the actual irrigated plot area and the actual number of plants in the test plot S4.34: Calculate the area occupied by rice stems and leaves in the test plot based on the actual irrigated plot area : wherein, is the leaf area of a single plant, is the leaf area of a single plant in a plot, is the actual number of plants in a plot.

5. The method of claim 1, wherein the water is agricultural water. The collection of power consumption data in step S1 is realized through an independently deployed programmable logic controller, and a circuit module of the programmable logic controller is directly connected to a water pump power supply cable and excludes non-water pump power consumption interference.

6. The method of claim 1, wherein the water is agricultural water. When the relative error of the measured value of the water output and the model predicted value exceeds the preset threshold, the periodic calibration mentioned in step S2 is triggered.

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