An agricultural irrigation head intelligent measurement and control charging method and terminal

Abstract

The application belongs to the technical field of data processing, and particularly relates to an intelligent measurement and control charging method and a terminal for an agricultural irrigation head, which comprises the following steps: obtaining a median value and a range in a sliding window, and determining a limit change rate according to a pipeline parameter; when a sampling point deviation degree exceeds a deviation limit and an adjacent change rate exceeds the limit change rate, the sampling point is marked as untrusted, otherwise, the sampling point is marked as trusted; for an untrusted point, an initial value is obtained by linear interpolation of front and rear trusted points, a pipeline model containing a pipe diameter, a friction coefficient and a synchronous pressure is used to calculate an allowable interval, if the initial value is in the interval, the initial value is adopted, otherwise, a value closest to the initial value is selected from historical similar working conditions as a final value; when settlement is performed, if an untrusted point proportion is lower than a first tolerance, direct charging is performed, if the untrusted point proportion is between the first tolerance and a second tolerance, accumulated flow is reduced for charging, and if the untrusted point proportion exceeds the second tolerance, manual review is performed. The application improves the accuracy of agricultural irrigation flow measurement through hierarchical data reconstruction and an adaptive charging strategy.

Description

Technical Field

[0001] This invention relates to the field of data processing technology. More specifically, this invention relates to an intelligent measurement, control, and billing method and terminal for agricultural irrigation systems. Background Technology

[0002] Agricultural irrigation systems often employ a multi-pump parallel main pipeline water supply mode, relying on flow sensors on the main pipeline for real-time flow monitoring to complete water metering and water billing. In actual operation, due to sudden changes in water load in the irrigation area, hydraulic hammer effect in the pipeline, and electromagnetic interference from sensors, the real-time collected flow signals often contain abnormal extreme values ​​such as short-term spikes or sudden drops. If these are directly included in the cumulative metering, it will significantly affect the accuracy of the cumulative flow and the fairness of billing.

[0003] Currently, the identification of abnormal flow sampling points generally adopts detection methods based on fixed thresholds, moving averages, or simple statistical rules. These methods only start from the statistical distribution characteristics of the data itself and fail to utilize the limit constraint of the flow rate change rate by the hydraulic inertia of the pipeline. This makes it easy to misjudge normal flow fluctuations as abnormal under normal operating conditions such as rapid opening and closing of valves. At the same time, it is difficult to effectively identify the real outliers caused by sensor failure, resulting in a high false positive and false negative rate.

[0004] In handling abnormal data, the conventional approach is to directly remove outliers or use mathematical methods such as neighborhood averaging or linear interpolation to fill them in. Removal will cause discontinuities in the flow sequence on the time axis, and the reconstructed values ​​obtained by simple interpolation lack mapping and verification with the pressure signals of the pipeline at the same time. The reconstruction results may deviate from the actual state, introducing deviations for subsequent cumulative measurement.

[0005] In addition, most existing billing schemes do not consider the proportion of outliers during the billing period and cannot implement adaptive billing discounts for accumulated traffic based on the degree of data quality degradation. When the proportion of outliers is high during a certain period, the results of automatic billing will lose their due credibility and are prone to metering disputes.

[0006] Based on the above shortcomings, there is a need for an intelligent measurement and control billing method that can integrate pipeline constraints for real-time anomaly identification, reconstruct unreliable points while taking into account temporal continuity and consistency, and adaptively correct the settlement results based on the proportion of anomalies, so as to improve the accuracy of measurement, data integrity and the objectivity and fairness of billing results. Summary of the Invention

[0007] To address the aforementioned technical problems, the present invention provides solutions in the following aspects.

[0008] In a first aspect, the present invention provides an intelligent measurement, control, and billing method for agricultural irrigation heads, characterized in that it includes: Collect flow rate and pressure, calculate the median and range within a sliding window, and determine the limit rate of variation based on pipeline parameters; When the flow rate deviation of a sampling point exceeds the deviation limit and the rate of change between adjacent points exceeds the limit rate, it is marked as unreliable; otherwise, it is marked as reliable. For unreliable points, take the initial value of the linear interpolation of the previous and subsequent reliable points, and obtain the flow allowable range from the pressure at the same time through the pipeline model; If the initial value is within the allowable range, it is taken as the final value; otherwise, the flow rate closest to the initial value is selected from the historical similar operating conditions as the final value. At the end of the settlement period, the percentage of unreliable points is calculated. If the percentage is lower than the first tolerance level, the accumulated traffic including the final value will be charged directly. If the percentage is between the first and second tolerance levels, the accumulated traffic will be multiplied by a discount factor of less than one before charging. If the percentage exceeds the second tolerance level, manual review will be triggered.

[0009] Preferably, the method for obtaining the median and range within the sliding window includes: forming a local flow set by the current sampling point and multiple previous sampling points; calculating the median of the local flow set as the median value; and calculating the difference between the maximum and minimum values ​​of the local flow set as the range.

[0010] Preferably, determining the limiting rate of change from pipeline parameters includes: obtaining the cross-sectional area, characteristic length, short-term maximum allowable pressure difference, and water density of the main pipeline; calculating the ratio of the cross-sectional area to the characteristic length; calculating the ratio of the maximum allowable pressure difference to the water density; and multiplying the two ratios to obtain the limiting rate of change.

[0011] Preferably, the deviation is calculated by: calculating the absolute value of the difference between the flow rate value of the current sampling point and the median value; calculating the sum of the range and a preset micro-amplitude constant; and dividing the absolute value by the sum to obtain the deviation.

[0012] Preferably, the rate of change between adjacent points is calculated as follows: the absolute value of the difference between the flow rate value of the current sampling point and the flow rate value of the previous sampling point is calculated; the absolute value of the difference is divided by a preset sampling time interval to obtain the rate of change between adjacent points.

[0013] Preferably, the initial value for obtaining the linear interpolation between the preceding and following trust points includes: determining the nearest trust point in time before the untrusted point as the preceding trust point, and the nearest trust point after the untrusted point as the following trust point; calculating the difference between the flow value of the following trust point and the flow value of the preceding trust point, dividing it by the time difference between the following trust point and the preceding trust point to obtain the local gradient; and adding the flow value of the preceding trust point to the product of the local gradient and the time difference between the untrusted point and the preceding trust point to obtain the initial value.

[0014] Preferably, the step of obtaining the flow allowable zone from the synchronous pressure via the pipeline model includes: obtaining the synchronous pressure at the unreliable point; calculating the theoretical lower limit and theoretical upper limit of flow rate based on the pipe diameter, friction coefficient, and synchronous pressure of the main pipeline using a preset pressure-flow rate theoretical model; the flow allowable zone is the interval between the theoretical lower limit and the theoretical upper limit of flow rate.

[0015] Preferably, the step of selecting the flow rate closest to the initial value from historical similar operating conditions as the final value includes: when the initial value is not within the allowable flow range, querying historical flow records from historical flow records for operating conditions similar to the current pressure value, time period, and pump unit operating combination; and selecting the flow rate value with the smallest absolute difference from the initial value from the queried historical flow records as the final value.

[0016] Secondly, an intelligent monitoring, control, and billing terminal for agricultural irrigation is characterized by comprising a flow verification and calculation submodule integrated into the main control module, wherein the flow verification and calculation submodule is electrically linked to a multi-channel data acquisition module, a local storage module, a human-computer interaction module, and a communication module. The multi-channel data acquisition module collects raw sampling data of pipeline flow and water pressure in real time and transmits them to the flow verification and calculation submodule. The flow verification calculation submodule is configured with data discrimination, anomaly interpolation, pipeline model calculation, historical operating condition matching, and hierarchical billing determination subroutines, and completes flow credibility marking, abnormal flow correction and settlement hierarchical processing according to any one of the measurement and control billing methods in claims 1 to 8. The local storage module is partitioned to store raw sampling data, data verification marks, corrected traffic data, and anomaly statistics log. The human-computer interaction module displays traffic verification results and billing tier information, while the communication module remotely uploads work orders that exceed the limits and require manual review to the smart agriculture platform.

[0017] The present invention has the following beneficial effects: 1. By extracting the median and range through a sliding window and deriving the limit allowable flow rate change rate using the main pipeline parameters, and using the fluctuation deviation and the rate of change of adjacent points as dual criteria, a point is judged as unreliable only when both exceed the limit. This judgment logic, which integrates statistical characteristics and hydraulic feasibility, effectively filters out false anomalies caused by hydraulic transients or sensor noise, retains normal fluctuations, thereby reducing the false judgment rate and improving the accuracy and robustness of anomaly detection.

[0018] 2. For unreliable points, initial values ​​are first obtained through linear interpolation based on the nearest reliable points, maintaining the continuity of the flow sequence. Then, the initial values ​​are compared with the theoretically permissible flow range calculated based on the pressure and pipeline model of the same period. If the flow is within the range, it is directly adopted; otherwise, the flow rate closest to the initial value is selected from historical records of similar operating conditions as the final value. This hierarchical approach, utilizing a pressure-flow model including the friction coefficient and historical operating condition constraints, avoids distortions that may be caused by interpolation, helping to ensure that the reconstructed values ​​are both continuous and consistent with actual hydraulic conditions. 3. At the end of the settlement cycle, the percentage of unreliable points is calculated. When the percentage is lower than the tolerance level, the cumulative traffic including the reconstructed value is billed directly. When the percentage is moderate, a discount factor is applied to the cumulative traffic to automatically compensate for the overestimation that may be caused by data uncertainty. When the percentage is too high, automatic settlement is suspended and an anomaly log is generated, and the process is transferred to manual review. This adaptive billing mechanism dynamically adjusts the settlement rules according to the data quality, effectively preventing billing unfairness caused by abnormally high percentages, and enhancing the fault tolerance and operational transparency under complex operating conditions. Attached Figure Description

[0019] Figure 1 schematically illustrates the steps of an intelligent measurement, control, and billing method for agricultural irrigation heads according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0022] Step S1: Collect flow rate and pressure, calculate the median and range within the sliding window, and determine the limit rate based on the pipeline parameters.

[0023] Because the instantaneous flow rate of the main pipeline is often affected by sudden changes in water usage and hydraulic disturbances, resulting in short-term spikes and drops, and the cumulative measurement is easily affected by outliers, this invention uses a flow sensor on the main pipeline with a fixed sampling interval. Continuously collect instantaneous flow rate.

[0024] Simultaneously, a pressure sensor is installed on the main pipeline to synchronously collect pressure signals with the flow sensor, and the first... The pressure value at each sampling time is recorded as follows: The unit is Pa, which serves as the input for obtaining the flow allowance zone through the pipeline model.

[0025] And the first The flow rate value at each sampling time is denoted as And for the current moment Take the nearest point that includes that point. A set of continuous flow values ​​constitutes a local flow set. ,Right now , where window length Take an empirical value of 30, and use the sampling interval... The empirical value of 1 second is used. Both are preset parameters and can be dynamically adjusted by the implementer according to the actual situation.

[0026] For each local flow set Find the median of all traffic flows as the median value. ,Right now Simultaneously, the difference between the maximum and minimum flow rates within the window is calculated as the range. ,Right now The median value represents the mainstream trend of local flow within the window, while the range characterizes the fluctuation range of flow within the window. Moreover, the median value is more robust to accidental shocks than the arithmetic mean and can stably reflect the normal flow level.

[0027] However, relying solely on local statistical characteristics is insufficient to distinguish between normal hydraulic transients and actual sensor anomalies, as sudden changes in normal water usage can also lead to increased deviations. Therefore, it is necessary to introduce a rate-of-change constraint based on pipeline characteristics: as can be seen from the hydraulic inertia of the pipeline, there is a theoretical limit to the rate of change of flow per unit time, and changes exceeding this limit are necessarily unreliable.

[0028] This limit value is the rate of change, the rate of change. Based on the cross-sectional area of ​​the main pipeline Characteristic length Short-time maximum permissible pressure difference and water density The formula for calculating the limiting rate is jointly determined as follows:

[0029] In the formula, The cross-sectional area is calculated based on the pipe's inner diameter; an empirical value of 0.0314 m² is used. 2 This corresponds to an inner diameter of 200mm; The characteristic length represents the length of the main pipe section affected by hydraulic inertia, and is taken as an empirical value of 10 m. The maximum permissible pressure difference in short time is determined based on empirical values ​​derived from the pipe's pressure rating and water hammer analysis. Pa, all empirical values ​​are preset parameters that can be adjusted by the implementer according to pipeline safety limits.

[0030] The density of the water is taken as a typical value of 1000 kg / m³. 3 .

[0031] For example, substituting the above empirical values ​​yields the result. This limit rate gives the theoretical upper limit of the flow rate change per unit time under the hydraulic inertia constraint of the pipeline.

[0032] Step S2: When the flow deviation of the sampling point exceeds the deviation limit and the change rate between adjacent points exceeds the limit change rate, it is marked as unreliable; otherwise, it is marked as reliable.

[0033] Deviation can reflect the degree of abnormality of a sampling point relative to the local mainstream trend. However, sudden changes in normal water use, such as the rapid opening of a valve, may also cause a temporary increase in deviation. If a deviation exceeding the standard is used to determine that a sample is unreliable, it is easy to misjudge normal hydraulic transients as abnormalities.

[0034] On the other hand, the rate of change between adjacent points is constrained by the hydraulic inertia of the pipeline, and its theoretical upper limit is the rate of change. However, instantaneous vibration of the sensor or electromagnetic interference may also cause the rate of change to exceed the limit temporarily. If it is judged as unreliable based solely on the rate of change exceeding the limit, noise may be easily misjudged as a real sudden change.

[0035] Therefore, two conditions need to be combined: only when both the deviation exceeds the deviation limit and the rate of change between adjacent points exceeds the limit rate of change, can it be concluded that the sampling point deviates significantly in both statistical trend and physical rate exceeds the pipeline limit. Only such an anomaly has a truly unreliable characteristic. Based on this, the following discrimination procedure is executed.

[0036] Current sampling point The degree of fluctuation deviation within the local window is determined by the deviation degree. calculate, The specific calculation is performed using the following formula:

[0037] In the formula, This reflects the deviation of the flow rate at that point relative to the current mainstream trend of the window; The absolute difference between the current flow rate and the midpoint value within the window. It is the sum of the window range and a minimal constant.

[0038] Therefore, the deviation reflects the magnitude of the deviation of the current sampling point's flow value from the mainstream trend of the window. The larger the value, the less the point resembles the normal fluctuation within the window.

[0039] To determine whether the deviation is too large, a deviation limit is introduced. It is dimensionless, and the empirical value of 1.75 is taken. It can also be adjusted by the implementer based on historical results.

[0040] It is a preset threshold, representing an upper bound on the degree of deviation from normalization: when At that time, it was considered that the sampling point significantly deviated from the recent main trend of traffic in a statistical sense.

[0041] However, the deviation alone is not enough to determine that the data is unreliable, because normal rapid water use, such as short-term large changes in valve opening, can also cause a temporary increase in the deviation.

[0042] Therefore, it is necessary to examine the time-varying rate of flow and define the rate of change between adjacent points. for:

[0043] in, For a fixed sampling interval, The unit is m 3 / s 2 , which represents the actual rate of change of flow rate per unit time.

[0044] Limiting rate Derived from the pipeline parameters, it represents the theoretical maximum allowable rate of flow change per unit time under the pipe segment structure and hydraulic conditions of this embodiment.

[0045] Only when Exceeding and Exceeding Only when both conditions are met does it indicate that the sampling point not only seriously deviates from the recent mainstream trend of flow statistics, but also that its mutation rate has exceeded the limit of change that the pipeline can provide.

[0046] This dual overshoot phenomenon of local statistical characteristics and constraints jointly rules out the possibility that a single indicator exceeding the limit is caused by normal rapid water usage or slight fluctuations in the sensor. Therefore, this sampling point is marked as an unreliable point for flow observation.

[0047] like Not exceeding ,or Not exceeding If either of the two conditions is not met, then the sampling point does not possess dual anomaly characteristics and is within the range of local statistical fluctuations or is permissible by hydraulic limit changes.

[0048] At this point, the sampling point is marked as a reliable point for flow observation, and its measured value is retained and directly entered into subsequent cumulative measurement and water level analysis without triggering the abnormal reconstruction process.

[0049] Step S3: For unreliable points, take the initial value of the linear interpolation of the previous and subsequent reliable points, and obtain the flow allowable range from the synchronous pressure through the pipeline model.

[0050] For sampling points marked as unreliable flow observations through the aforementioned steps, their corresponding measured flow values ​​are deemed inaccurate at that moment, and directly including them in cumulative measurement would introduce errors.

[0051] Therefore, for each untrusted point, the nearest trusted sampling point in time sequence is taken as the previous trusted point, and the nearest trusted sampling point after it is taken as the next trusted point. Their flow values ​​are recorded as follows. and The corresponding timestamp is and .

[0052] These two reliable points provide effective traffic boundary information within the local neighborhood of the unreliable points, forming the basis for subsequent trend estimation.

[0053] Based on the flow measurement values ​​from the previous and next confidence points, the average rate of change between the two is calculated to obtain the local gradient. Its calculation method is as follows

[0054] in, The unit is m 3 / s 2 The gradient It represents the average rate of change of flow over time within a reliable observation period, i.e., the local trend slope.

[0055] If the gradient is positive, it indicates that the flow rate is increasing; if it is negative, it indicates that the flow rate is decreasing; if it is zero, it indicates that the flow rate is basically stable during this period.

[0056] This gradient is used to perform linear interpolation at the time of the untrusted point: using the earlier trust point... Using this as a baseline, adding the product of the gradient and the time offset to the flow rate value yields the expected flow rate value extrapolated from the original trend at the unreliable point, which serves as the corresponding initial value. The specific calculation formula is as follows:

[0057] in, The unit is m 3 / s, This represents the time interval between an untrusted point and the previous trustworthy point.

[0058] initial value This indicates the expected flow level at that moment if the flow continues smoothly according to the neighborhood trend, but whether it is consistent with the actual pipeline condition still needs to be further confirmed through pressure constraints.

[0059] Therefore, it is necessary to use the pressure signal collected at the same time to deduce the theoretically permissible flow range under the current pressure through the pipeline hydraulic model, and then compare the initial value with this range.

[0060] Pressure signals synchronously collected on the main pipeline The unit is Pa, which reflects the driving force of the pipeline water supply at that moment.

[0061] Because the relationship between flow rate and pressure is rigidly constrained by the hydraulic characteristics of the pipeline, there is a definite correspondence between pressure and flow rate at the same moment.

[0062] Therefore, starting from the current pressure value, the allowable flow range, namely the theoretical lower and upper limits of flow, can be calculated based on physical parameters such as pipe diameter and friction coefficient, and then the initial value can be checked to see if it falls within this range.

[0063] Obtain the corresponding synchronous pressure value of the unreliable point. This serves as the input condition for calculating the theoretical flow allowable zone, in order to determine the possible flow range within the pipeline under a given pressure through mapping relationships.

[0064] Using the measured parameters of the main pipeline and the concurrent pressure, the theoretical flow rate lower limit is calculated through a pre-set pressure-flow theoretical model. and upper limit The two constitute the traffic allowance zone .

[0065] This pressure-flow theory model uses pipe diameter... coefficient of friction and the pressure of the same period For input, where, Taking an empirical value of 0.2 m, The empirical value of 0.025, which is dimensionless, is used to represent the influence of pipe wall roughness and friction resistance on the pressure-flow relationship. It can be obtained by fitting pipeline design parameters, on-site calibration, or historical operating data. The empirical value can be adjusted by the implementer according to the specific implementation scenario.

[0066] In one alternative implementation, a baseline flow rate is first calculated based on a simplified pipeline energy equation. :

[0067] in The cross-sectional area of ​​the pipe is the largest area. The larger the area, the stronger the flow capacity of the pipe, and the larger the reference flow rate under the same pressure difference. The two are directly proportional.

[0068] This represents the synchronous pressure measurement value corresponding to the unreliable point, i.e., gauge pressure, in Pa. This pressure is the driving force behind the water flow; the higher the pressure, the greater the reference flow rate. In the formula, pressure appears in the numerator within the square root and is related to... Forming net driving force, usually Taking 0 Pa, i.e., atmospheric pressure, the net driving force is approximately... .

[0069] 0 Pa represents the pressure on the outlet side of the pipeline, i.e., gauge pressure, indicating that the outlet is open to the atmosphere. If there is back pressure at the outlet, it can be set to a corresponding positive value. In this case, the net driving force decreases and the reference flow rate decreases accordingly.

[0070] The friction coefficient represents the roughness of the pipe wall and the magnitude of head loss along the pipe. The larger the friction coefficient, the greater the pipe resistance, and the smaller the flow rate under the same pressure difference; the two are inversely proportional to the square root.

[0071] The characteristic length of the pipeline, in meters, represents the length of the pipe section that generates the main friction loss. The longer the pipeline, the greater the resistance and the smaller the reference flow rate, showing an inverse square root relationship.

[0072] This refers to the inner diameter of the pipe, measured in meters (m). The pipe diameter not only... It directly affects the baseline flow rate and also appears in the denominator. In the middle: the larger the pipe diameter, The smaller the diameter, the smaller the denominator, thus increasing the baseline flow rate. Therefore, pipe diameter has a dual amplifying effect on flow rate.

[0073] Baseline flow and and Proportional to, with Inversely proportional, that is, the higher the pressure, the larger the pipe diameter, the shorter the pipe, the smaller the friction coefficient, and the lower the water density, the larger the reference flow rate.

[0074] After calculating the baseline flow rate, a range factor is then introduced. , dimensionless, with an empirical value of 0.15, is used to represent the reasonable range of fluctuation that the actual flow may experience around the baseline value.

[0075] This range factor comprehensively considers the uncertainties brought about by factors such as pressure measurement error, local pipeline resistance, water temperature changes, and the randomness of field water use.

[0076] This leads to the theoretical lower and upper limits of flow:

[0077]

[0078] This flow allowance range represents the theoretical flow fluctuation limit permitted by the pipeline under current pressure. If the reconstructed flow exceeds this range, it indicates a lack of rationality.

[0079] Step S4: If the initial value is within the allowable range, then take it as the final value; otherwise, select the flow rate closest to the initial value from the historical similar operating conditions as the final value.

[0080] For each unreliable sampling point, when the initial value of the first-stage interpolation... satisfy Since this initial value is obtained by linear extrapolation from adjacent reliable points in the time series, maintaining the continuity of the local flow rate change trend, and is verified by the theoretical allowable range derived from the pressure at the same time, confirming that its value is within the range allowed by the pipeline limits, this initial value can be directly determined as the final reconstructed flow rate value of the unreliable sampling point. ,Right now .

[0081] when Below or higher This indicates that at the point of inconsistency, the initial value obtained by simply relying on local interpolation is inconsistent with the actual pipeline fluid capacity under the current pressure conditions. Directly adopting this value will disrupt the consistency of subsequent cumulative metering and may easily lead to overestimation or underestimation of water consumption. Therefore, flow data similar to the current operating conditions are retrieved from the historical flow record database to provide a more consistent alternative final value.

[0082] The search for similar working conditions must meet three conditions simultaneously: the historical pressure value is within the specified range. Within, the pressure deviation tolerance Get experience points Pa is a preset parameter, which can also be adjusted by the implementer. It represents the hourly time period number to which the timestamp of the historical record belongs. Consistent with the hourly time period number of the current unreliable point; historical pump unit operation combination code. Completely consistent with the current actual operating combination; extracting flow values ​​that simultaneously meet the above conditions from the historical flow record database to form a candidate set of flow rates for similar historical operating conditions. .

[0083] in, The day is divided into 24 time periods by the hour and numbered accordingly. These 24 time periods are empirical values ​​and can be adjusted by the implementer according to the specific implementation scenario.

[0084] In the non-empty candidate set Above, for each candidate traffic Calculate its relationship with the initial value. The absolute deviation is selected based on the smallest deviation. As the final value ,Right now The calculation formula is:

[0085] In the formula, The candidate historical flow values ​​are derived from records in the historical flow record database that are similar to the current operating conditions, i.e., records that match in pressure, time period, and pump combination. Each of these is a specific historical measured flow value, representing the actual flow level that occurred under similar conditions in the past.

[0086] This is a candidate set of traffic volumes for historically similar operating conditions, i.e., all traffic volumes that meet the similarity criteria. The set that constitutes the data. The size of this set depends on the richness of the historical data, and it should contain at least one element. If it is an empty set, it should be downgraded, for example, by directly using the initial value.

[0087] The initial value is obtained by linear interpolation of the current untrusted point, in units of m. 3 / s. Although this initial value may not meet the constraints of the allowable zone, it still reflects the best prediction based on the time-series trend and is therefore used as a matching benchmark for historical candidate traffic.

[0088] This represents the absolute deviation, which is the absolute value of the difference between each candidate historical flow value and the initial value. The smaller this value, the closer the historical flow is to the current expected value based on trend extrapolation.

[0089] In the absence of direct and reliable measurements, the initial value is the most reasonable soft estimate for the current moment. Selecting the flow rate that is closest to the initial value from history means maintaining consistency with local trends as much as possible based on historical experience, and avoiding drastic jumps in the reconstructed value.

[0090] This indicates that the independent variable is selected from the candidate set to minimize the objective function. Select the one that minimizes the absolute deviation. As the final value .

[0091] Step S5: Calculate the percentage of unreliable points at the end of the settlement period.

[0092] In a complete water billing cycle At the end, the flow data processing for all sampling times within this period has been completed: the sampling points marked as reliable retain the measured values, while the sampling points marked as unreliable have obtained consistent final values ​​through hierarchical progressive reconstruction. .

[0093] Therefore, it has every sampling moment within this period. A flow equivalent value For credible points, It equals its original measured value; for unreliable points, Equal to its corresponding final reconstruction value .

[0094] by As an element, the cumulative flow including the final value is obtained by accumulating the entire settlement cycle in chronological order. Its unit is cubic meters (m³) 3 This value reflects the total water consumption for the current settlement period based on the existing data processing results.

[0095] Since the identification and reconstruction of untrusted points inevitably introduces a certain degree of uncertainty, if the number of untrusted sampling points is too large within the settlement period, the accumulated traffic will be affected. The credibility of this will decrease significantly.

[0096] Therefore, the number of sampling points marked as unreliable within this period was counted. and the total number of all sampling points within the period. And calculate the percentage of unreliable points. :

[0097] In the formula, The count of unreliable sampling points during the settlement period, dimensionless; This is the count value of all sampling points within the settlement period, and is dimensionless. The percentage of unreliable points is dimensionless, and its value directly represents the degree of data quality degradation within the current settlement period.

[0098] Calculated percentage As the direct basis for determining the billing strategy.

[0099] It has a pre-set first tolerance level for tiered billing processing. Second tolerance .

[0100] The empirical value of 0.05 is used. It is dimensionless and is a preset parameter to describe the upper limit of tolerance for low anomaly ratio. It can also be adjusted by the implementer according to the lower limit of data quality allowed by business. The empirical value of 0.20 is dimensionless and is a preset parameter describing the upper limit of the abnormal percentage in billing that can be automatically corrected. It can also be adjusted by the implementer according to their risk tolerance.

[0101] The two tolerance settings allow billing to adaptively select different processing modes when data quality changes.

[0102] when Less than This indicates that the vast majority of sampling points within this period are reliable points, with only sporadic abnormal observations. The overall statistical reliability of the data is sufficient to support water fee settlement directly based on the cumulative metering results.

[0103] In this case, no further cost adjustment will be applied to the accumulated traffic; the accumulated traffic, including its terminal value, will be directly charged. The water price is submitted as the billing benchmark to the billing module, and automatic billing is completed according to the preset water price.

[0104] when satisfy This means that the proportion of untrusted points within the period has reached a certain level, resulting in accumulated traffic. The contribution from the reconstructed values ​​is not negligible, and the overall measurement reliability has declined compared to the normal data cycle.

[0105] To reasonably reflect this uncertainty in the billing process and mitigate the metering risk borne by users due to data quality degradation, the cumulative traffic is multiplied by a billing discount factor less than 1. .

[0106] The discount factor is less than one, and in this embodiment it is fixed at 0.96, which is dimensionless and used to provide concessions for measurement uncertainty under moderate abnormality ratios; 0.96 is the preferred billing discount value in this embodiment, which can be adjusted by the implementer according to the specific implementation scenario.

[0107] Corrected billing flow for:

[0108] In the formula, The adjusted billing flow rate used for final water pricing calculation, in cubic meters (m³). 3 ); The cumulative flow including the final value, in cubic meters (m³). 3 ).

[0109] This correction automatically provides users with a certain percentage of measurement uncertainty relief when data quality deteriorates to a moderate degree, thus sharing the measurement error risk caused by occasional abnormal periods.

[0110] when Exceeding the second tolerance level This indicates that the proportion of unreliable points in this period has reached an unacceptably high level.

[0111] At this point, even with a discount factor applied, the baseline value of the cumulative flow remains. The system already relies too heavily on reconstructed data, and continuing automatic billing could easily lead to metering disputes.

[0112] Therefore, the manual review process is triggered: automatic settlement is suspended, and automatic billing documents are no longer generated; all sampling point information marked as unreliable within this period, and their respective discrimination deviations are collected. With rate of change , corresponding initial values ​​for reconstruction With future value and the final The value is packaged and generated into an exception log that records the reason for the unreliable point, and pushed to the administrator's review terminal for manual processing.

[0113] This invention also discloses an intelligent measurement, control and billing terminal for agricultural irrigation, comprising: a flow verification and calculation submodule integrated within the main control module, wherein the flow verification and calculation submodule is electrically connected to a multi-channel data acquisition module, a local storage module, a human-computer interaction module and a communication module, and the modules cooperate to complete the flow data verification and billing management work; The multi-channel data acquisition module is responsible for collecting real-time flow and pressure signals from the pipeline on-site, and transmitting the collected raw data to the flow verification and calculation submodule as the data source for algorithm calculation. The flow verification calculation submodule has five independent subroutines: data discrimination, anomaly interpolation, pipeline model calculation, historical working condition matching, and graded billing determination. It performs calculations according to the measurement and control billing method defined in any one of claims 1 to 8, and sequentially realizes the whole process of sampling data credibility identification and marking, abnormal flow value correction, and graded calculation of irrigation costs. The local storage module is divided into independent storage partitions to classify and store the original sampling data, data credibility verification mark information, corrected effective flow data, and statistical ledger of sampling anomalies, so as to achieve full data retention and traceability. The human-computer interaction module retrieves and processes the results, and displays the traffic verification details and corresponding billing tier results locally. When the proportion of abnormal settlement data exceeds the second tolerance threshold, the communication module generates a manual review work order and uploads it remotely to the smart agriculture platform to complete the abnormal information reporting.

[0114] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent measurement, control, and billing of agricultural irrigation heads, characterized in that, include: Collect flow rate and pressure, calculate the median and range within a sliding window, and determine the limit rate of variation based on pipeline parameters; When the flow rate deviation of a sampling point exceeds the deviation limit and the rate of change between adjacent points exceeds the limit rate, it is marked as unreliable; otherwise, it is marked as reliable. For unreliable points, take the initial value of the linear interpolation of the previous and subsequent reliable points, and obtain the flow allowable range from the pressure at the same time through the pipeline model; If the initial value is within the allowable range, it is taken as the final value; otherwise, the flow rate closest to the initial value is selected from the historical similar operating conditions as the final value. At the end of the settlement period, the percentage of unreliable points is calculated. If the percentage is lower than the first tolerance level, the accumulated traffic including the final value will be charged directly. If the percentage is between the first and second tolerance levels, the accumulated traffic will be multiplied by a discount factor of less than one before charging. If the percentage exceeds the second tolerance level, manual review will be triggered.

2. The intelligent measurement, control, and billing method for agricultural irrigation heads according to claim 1, characterized in that, The calculation of the median and range within the sliding window includes: The current sampling point and its previous multiple sampling points are combined into a local flow set; The median of the local flow set is calculated as the median value; The difference between the maximum and minimum values ​​of the local flow set is calculated as the range.

3. The intelligent measurement, control, and billing method for agricultural irrigation heads according to claim 1, characterized in that, The determination of the limiting rate based on pipeline parameters includes: Obtain the cross-sectional area, characteristic length, short-time maximum allowable pressure difference, and water density of the main pipeline; Calculate the ratio of the cross-sectional area to the feature length; Calculate the ratio of the maximum allowable pressure difference to the density of the water body; Multiplying the two ratios together yields the limiting rate.

4. The intelligent measurement, control, and billing method for agricultural irrigation heads according to claim 1, characterized in that, The deviation is calculated in the following manner: Calculate the absolute value of the difference between the current flow rate value at the sampling point and the median value; Calculate the sum of the range and the preset micro-amplitude constant; The deviation is obtained by dividing the absolute value by the sum.

5. The intelligent measurement, control, and billing method for agricultural irrigation heads according to claim 1, characterized in that, The rate of change between adjacent points is calculated in the following way: Calculate the absolute value of the difference between the flow rate value at the current sampling point and the flow rate value at the previous sampling point; The absolute value of the difference is divided by a preset sampling time interval to obtain the rate of change between adjacent points.

6. The intelligent measurement, control, and billing method for agricultural irrigation heads according to claim 1, characterized in that, The initial values ​​for the linear interpolation of the reliable points before and after the extraction include: The nearest trust point in time that precedes the untrusted point is determined as the previous trust point, and the nearest trust point that follows the untrusted point is determined as the next trust point. Calculate the difference between the flow value of the next credible point and the flow value of the previous credible point, divide it by the time difference between the next credible point and the previous credible point, and obtain the local gradient; The initial value is obtained by adding the flow value of the previous reliable point to the product of the local gradient and the time difference between the unreliable point and the previous reliable point.

7. The intelligent measurement, control, and billing method for agricultural irrigation heads according to claim 1, characterized in that, The flow allowable range obtained from the synchronous pressure via the pipeline model includes: Obtain the synchronous pressure at the untrusted point; Based on the pipe diameter, friction coefficient, and synchronous pressure of the main pipeline, the theoretical flow lower limit and theoretical flow upper limit are calculated using a preset pressure-flow theoretical model. The allowable flow range is the interval between the theoretical lower limit and the theoretical upper limit of flow.

8. The intelligent measurement, control, and billing method for agricultural irrigation heads according to claim 1, characterized in that, The step of selecting the flow rate closest to the initial value from similar historical operating conditions as the final value includes: When the initial value is not within the allowable flow range, query the historical flow records corresponding to the operating conditions similar to the current pressure value, time period and pump set operation combination from the historical flow records; Among the retrieved historical traffic records, the traffic value with the smallest absolute difference from the initial value is selected as the final value.

9. A smart monitoring, control, and billing terminal for agricultural irrigation, characterized in that, This includes a traffic verification and calculation submodule integrated into the main control module, which is electrically linked to the multi-channel data acquisition module, local storage module, human-machine interaction module, and communication module. The multi-channel data acquisition module collects raw sampling data of pipeline flow and water pressure in real time and transmits them to the flow verification and calculation submodule. The flow verification calculation submodule is configured with data discrimination, anomaly interpolation, pipeline model calculation, historical operating condition matching, and hierarchical billing determination subroutines, and completes flow credibility marking, abnormal flow correction and settlement hierarchical processing according to any one of the measurement and control billing methods in claims 1 to 8. The local storage module is partitioned to store raw sampling data, data verification marks, corrected traffic data, and anomaly statistics log. The human-computer interaction module displays traffic verification results and billing tier information, while the communication module remotely uploads work orders that exceed the limits and require manual review to the smart agriculture platform.

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