Method for calibrating water level elevation of water level gauge cluster of Internet of Things system

Abstract

The invention discloses an internet of things system water level gauge cluster water level elevation calibration method which comprises the following steps: S1, forming an internet of things system by all water level gauges in a water area, selecting one water level gauge as a reference water level gauge, and taking the rest water level gauges as detection water level gauges; s2, calibrating the reference water level gauge to calibrate a reference zero value of the reference water level gauge; and S3, calibrating each detection water level gauge according to the calibrated reference water level gauge so as to calibrate the reference zero value of each detection water level gauge. The method is suitable for calibration of large-scale water level gauge clusters installed in the same water area, and has the advantages of high calibration efficiency, high consistency, high precision and low cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water conservancy monitoring, in particular to a water level gauge cluster water level elevation calibration method of an Internet of Things system. BACKGROUND

[0002] In the intelligent water network system, the Internet of Things water level gauge cluster is widely used in real-time monitoring of water level elevation. The water level gauge converts the detected water depth into water level elevation readings of the water surface relative to the elevation reference surface based on the reference zero point value. Therefore, whether the water level gauge obtains accurate reference zero point value through calibration determines the accuracy of the output elevation.

[0003] The traditional calibration method needs to manually calibrate each water level gauge of the water level gauge cluster, which has the following problems:

[0004] Firstly, the efficiency is low: the time-consuming of individual calibration is long, which is difficult to meet the demand of large-scale cluster;

[0005] Secondly, the cost is high: it depends on professional personnel and high-precision equipment, and the labor cost is high;

[0006] Thirdly, the precision is inconsistent: the water level fluctuation at different calibration times leads to different calibration results.

[0007] In the prior art, a single water level gauge calibration method based on GPS is also proposed, but the synchronization and consistency of cluster calibration are not solved. Therefore, there is an urgent need for a cluster calibration method with high efficiency, high consistency, high precision and low cost. SUMMARY

[0008] The technical problem to be solved by the present application is to provide a water level gauge cluster water level elevation calibration method of an Internet of Things system.

[0009] To solve the above technical problems, the technical solution adopted by the present application is as follows:

[0010] A water level gauge cluster water level elevation calibration method of an Internet of Things system, which is suitable for a multi-point water level monitoring scene in which multiple water level gauges are installed in the same water area such as rivers and lakes, and the method is characterized in that it comprises:

[0011] Step S1, all water level gauges in the water area are grouped into an Internet of Things system, and one of the water level gauges is selected as a reference water level gauge, and the remaining water level gauges are selected as detection water level gauges;

[0012] Step S2, calibrating the reference water level gauge to calibrate the reference zero point value of the reference water level gauge, so that the reference water level gauge can convert the detected water depth into water level elevation readings of the water surface relative to the elevation reference surface based on the reference zero point value calibrated in step S2, and ensure that accurate water level elevation readings are output;

[0013] Step S3, according to the calibrated reference water level meter, calibrate each detection water level meter respectively to calibrate the reference zero point value of each detection water level meter, so that the detection water level meter can convert the detected water depth into water level elevation reading output of the water surface relative to the height reference surface based on the calibrated reference zero point value in step S3, and ensure accurate water level elevation reading output.

[0014] The step S2 comprises:

[0015] Step S2-1, the RTK technology is used to measure the water surface elevation C of the water surface where the reference water level meter is located relative to the height reference surface, and the water level elevation reading D output by the reference water level meter when the RTK technology is measured is recorded; wherein the positioning accuracy of the RTK technology is better than ±2cm.

[0016] Step S2-2, calculate the reference zero point calibration value O of the reference water level meter = C-D;

[0017] Step S2-3, calibrate the reference zero point value of the reference water level meter, that is, add the reference zero point calibration value O to the original reference zero point value of the reference water level meter as the calibrated reference zero point value.

[0018] The step S3 comprises:

[0019] Step S3-1, collect the water level elevation readings output by the calibrated reference water level meter in a day, denoted as daily water level elevation curve A;

[0020] Step S3-2, while performing step S3-1, synchronously collect the water level elevation readings output by each detection water level meter in the same day, denoted as daily water level elevation curve B;

[0021] Step S3-3, calibrate the reference zero point value of each detection water level meter respectively, that is, for any detection water level meter, take the difference between the root mean square of the daily water level elevation curve B of the detection water level meter and the root mean square of the daily water level elevation curve A as the judgment standard, adjust the reference zero point value of the detection water level meter gradually by dichotomy, and update the daily water level elevation curve B with the adjusted reference zero point value at each step, until the updated daily water level elevation curve B meets the judgment standard, and the reference zero point value obtained by the last adjustment is taken as the calibrated reference zero point value of the detection water level meter.

[0022] Therefore, the present application is suitable for the calibration of large-scale water level meter clusters installed in the same water area, and can simultaneously consider the advantages of high calibration efficiency, high consistency, high precision and low cost, specifically:

[0023] High efficiency: the present application only needs to calibrate the reference water level meter based on the RTK technology in step S2, and the rest of the detection water level meter can collect data and calculate the reference zero point value in step S3 through the Internet of Things system, which can automatically complete the calibration, compared with calibrating each water level meter, it can reduce more than 80% of the calibration time to meet the demand of large-scale water level meter cluster;

[0024] High consistency: the present application calibrates each detection water level meter based on the calibrated reference water level meter and the synchronously collected daily water level elevation curve, avoiding the problem of difficult unification of elevation reference surface when calibrating each water level meter by RTK technology, ensuring the consistency of all water level meter calibration, and the consistency error of the present application is within ±1cm through test;

[0025] High precision: firstly, the present application uses bisection method to make the daily water level elevation curve B of the detection water level meter gradually approach the daily water level elevation curve A of the reference water level meter, so as to realize the calibration of the detection water level meter, effectively improving the precision of the calibration; secondly, the present application takes the difference between the root mean square of the daily water level elevation curve B and the root mean square of the daily water level elevation curve A less than the preset root mean square error value as the judgment standard of bisection method, eliminating the influence of short-term water level fluctuation of water surface and ensuring the time synchronization of daily water level elevation curve collection, effectively improving the precision of the calibration; thirdly, each detection water level meter is only calibrated based on the calibrated reference water level meter, which only introduces an error when measured by RTK technology, avoiding the cumulative error caused by multiple RTK technology measurements, effectively improving the precision of the calibration; through test, the standard deviation of the calibration of the present application is reduced from ±3cm of the traditional method to ±0.8cm;

[0026] Low cost: the present application only involves manual intervention and high-precision measurement equipment in the measurement of the reference water level meter by RTK technology, and the rest of the steps can be calculated in the cloud service center; through test, compared with manual calibration of each water level meter, the calibration cost is reduced by 60%-70%.

[0027] Preferred: in steps S3-1 and S3-2, the daily water level elevation curve A and the daily water level elevation curve B are respectively composed of the water level elevation readings output by the reference water level meter and the corresponding detection water level meter within 24 hours.

[0028] Preferred: in step S3-3, the root mean square error value is set to 10mm.

[0029] Preferred: in step S1, the Internet of Things system further comprises a cloud service center, and the water level elevation readings output by each water level meter are transmitted to the cloud service center in real time.

[0030] Preferably, the cloud service center performs the calculation process of steps S2 and S3, and sends the reference zero point value calculated in step S2 to the reference water level gauge for calibration, and sends the reference zero point value calculated in step S3 to the detection water level gauge for calibration.

[0031] Compared with the prior art, the present application has the following beneficial effects:

[0032] The present application is suitable for calibration of a large-scale water level gauge cluster installed in the same water area, and can simultaneously achieve high calibration efficiency, high consistency, high precision and low cost.

[0033] High efficiency: the present application only needs to calibrate the reference water level gauge based on the RTK technology in step S2, and the remaining detection water level gauges can automatically complete calibration by collecting data and calculating the reference zero point value through the Internet of Things system in step S3, which can reduce more than 80% of the calibration time compared with calibrating each water level gauge one by one, thereby meeting the needs of a large-scale water level gauge cluster.

[0034] High consistency: the present application calibrates each detection water level gauge based on the reference water level gauge that has been calibrated and the synchronously collected daily water level elevation curve, thereby avoiding the problem of difficulty in unifying the elevation reference surface when calibrating each water level gauge using the RTK technology, and ensuring the consistency of calibration of all water level gauges.

[0035] High precision: first, the present application uses the bisection method to gradually approach the daily water level elevation curve B of the detection water level gauge to the daily water level elevation curve A of the reference water level gauge, thereby realizing calibration of the detection water level gauge and effectively improving the precision of calibration; second, the present application uses the difference between the root mean square of the daily water level elevation curve B and the root mean square of the daily water level elevation curve A as the judgment standard of the bisection method, thereby eliminating the influence of short-term water level fluctuation of the water surface and ensuring the time synchronization of collection of the daily water level elevation curve, and effectively improving the precision of calibration; third, each detection water level gauge is only calibrated based on the reference water level gauge that has been calibrated, thereby only introducing an error when measuring by the RTK technology, avoiding cumulative error caused by multiple measurements by the RTK technology, and effectively improving the precision of calibration; experiments show that the standard deviation of calibration of the present application is reduced to ±0.8 cm from ±3 cm of the traditional method.

[0036] Low cost: the present application only involves manual intervention and high-precision measurement equipment when measuring the reference water level gauge by the RTK technology, and the remaining steps can be calculated in the cloud service center; experiments show that the calibration cost is reduced by 60%-70% compared with manually calibrating each water level gauge one by one. BRIEF DESCRIPTION OF DRAWINGS

[0037] The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] Figure 1 The flow chart of the application;

[0039] Figure 2 The schematic diagram of the installation position of each water level gauge in a certain water area in step S1 of the application, wherein the star symbol represents the water level gauge. DETAILED DESCRIPTION

[0040] The application will be further described in detail below with reference to the accompanying drawings and specific embodiments, to help the skilled in the art better understand the inventive concept of the application, but the protection scope of the claims of the application is not limited to the following embodiments. All other embodiments obtained by the skilled in the art without creative labor on the premise of not departing from the inventive concept of the application belong to the protection scope of the application.

[0041] As shown in Figure 1 and Figure 2 , the application discloses a water level gauge cluster elevation calibration method of an Internet of Things system, which is suitable for a multi-point water level monitoring scene in which multiple water level gauges are installed in a same water area such as a river or a lake, comprising:

[0042] Step S1, grouping all the water level gauges in the water area into an Internet of Things system, and selecting one of the water level gauges as a reference water level gauge, and the rest of the water level gauges as detection water level gauges;

[0043] Step S2, calibrating the reference water level gauge to calibrate the reference zero point value of the reference water level gauge, so that the reference water level gauge can convert the detected water depth into a water level elevation reading of the water surface relative to the elevation reference surface based on the reference zero point value obtained in step S2, and ensure the output of accurate water level elevation readings;

[0044] Wherein, the step S2 comprises:

[0045] Step S2-1, measuring the water surface elevation C of the water surface where the reference water level gauge is located relative to the elevation reference surface by using RTK technology, and recording the water level elevation reading D output by the reference water level gauge when the RTK technology is measured; wherein the positioning accuracy of the RTK technology is better than ±2cm.

[0046] Step S2-2, calculating the reference zero point calibration value O of the reference water level gauge = C-D;

[0047] Step S2-3, calibrating the reference zero point value of the reference water level gauge, that is, adding the reference zero point calibration value O to the original reference zero point value of the reference water level gauge as the calibrated reference zero point value of the reference water level gauge.

[0048] Step S3, according to the calibrated reference water level meter, calibrate each detection water level meter to calibrate the reference zero point value of each detection water level meter, so that the detection water level meter can convert the detected water depth into the water level elevation reading of the water surface relative to the height reference surface based on the reference zero point value calibrated in step S3, and ensure the output of accurate water level elevation reading.

[0049] The step S3 comprises:

[0050] Step S3-1, collect the water level elevation readings output by the calibrated reference water level meter in a day, denoted as daily water level elevation curve A;

[0051] Step S3-2, while performing step S3-1, synchronously collect the water level elevation readings output by each detection water level meter in the same day, denoted as daily water level elevation curve B;

[0052] Step S3-3, calibrate the reference zero point value of each detection water level meter respectively, that is, for any detection water level meter, take the difference between the root mean square of the daily water level elevation curve B and the root mean square of the daily water level elevation curve A as the judgment standard, gradually adjust the reference zero point value of the detection water level meter by dichotomy, and update the daily water level elevation curve B with the adjusted reference zero point value at each step, until the updated daily water level elevation curve B meets the judgment standard, and the last adjusted reference zero point value is taken as the calibrated reference zero point value of the detection water level meter.

[0053] Therefore, the present application is suitable for the calibration of large-scale water level meter clusters installed in the same water area, and can simultaneously consider the advantages of high calibration efficiency, high consistency, high precision and low cost, specifically:

[0054] High efficiency: the present application only needs to calibrate the reference water level meter based on the RTK technology in step S2, and the remaining detection water level meters can automatically complete the calibration by collecting data and calculating the reference zero point value through the Internet of Things system in step S3, which can reduce more than 80% of the calibration time compared with calibrating each water level meter one by one, to meet the needs of large-scale water level meter clusters;

[0055] High consistency: the present application calibrates each detection water level meter based on the calibrated reference water level meter and the synchronously collected daily water level elevation curve, avoiding the problem of difficulty in unifying the height reference surface when calibrating each water level meter by RTK technology, ensuring the consistency of the calibration of all water level meters, and the consistency error of the calibration of the present application is within ±1cm according to experiments;

[0056] High precision: firstly, the daily water level elevation curve B of the detection water level gauge is gradually approached to the daily water level elevation curve A of the reference water level gauge by using the dichotomy, so that the calibration of the detection water level gauge is realized, and the precision of the calibration is effectively improved; secondly, the difference between the root mean square of the daily water level elevation curve B and the root mean square of the daily water level elevation curve A is less than the preset root mean square error value as the judgment standard of the dichotomy, the influence of the short-term water level fluctuation of the water surface is eliminated, and the collection time synchronization of the daily water level elevation curve is ensured, so that the precision of the calibration is effectively improved; thirdly, each detection water level gauge is calibrated based on the reference water level gauge which has completed the calibration, and only one error is introduced in the RTK technology measurement, so that the cumulative error caused by the multiple RTK technology measurements is avoided, and the precision of the calibration is effectively improved; according to experiments, the standard deviation of the calibration of the present application is reduced from ±3cm of the traditional method to ±0.8cm.

[0057] Low cost: the present application only involves manual intervention and high-precision measurement equipment in the measurement of the reference water level gauge by using the RTK technology, and the rest steps can be calculated in the cloud service center; according to experiments, compared with the manual calibration of the water level gauges one by one, the calibration cost is reduced by 60%-70%.

[0058] The above is the basic embodiment of the present application, which can be further optimized, improved and limited on the basis of the basic embodiment:

[0059] Preferably, in the step S3-1 and the step S3-2, the daily water level elevation curve A and the daily water level elevation curve B are respectively composed of the water level elevation readings output by the reference water level gauge and the corresponding detection water level gauge within 24 hours.

[0060] Preferably, in the step S3-3, the root mean square error value is set to 10mm.

[0061] Preferably, in the step S1, the Internet of Things system further comprises a cloud service center, and the water level elevation readings output by each water level gauge are transmitted to the cloud service center in real time.

[0062] Preferably, the calculation process of the step S2 and the step S3 is executed by the cloud service center, and the reference zero point value calculated by the step S2 is sent to the reference water level gauge for calibration, and the reference zero point value calculated by the step S3 is sent to the detection water level gauge for calibration.

[0063] The present application is not limited to the above specific embodiments, according to the above content, according to the ordinary technical knowledge and conventional means in the art, other various forms of equivalent modifications, replacements or changes can be made without departing from the above basic technical idea of the present application, which all fall within the protection scope of the present application.

Claims

1. A water level elevation calibration method for a water level gauge cluster of an Internet of Things system, suitable for a multi-point water level monitoring scenario in which multiple water level gauges are installed in the same water area, characterized in that, The application relates to a method for calibrating water level gauges in a water area. The method comprises the following steps: S1, all water level gauges in the water area are combined into an Internet of Things system, and one water level gauge is selected as a reference water level gauge, and the rest are detection water level gauges; S2, the reference water level gauge is calibrated to calibrate the reference zero point value of the reference water level gauge; 2. The method of claim 1, wherein: S3, each detection water level gauge is calibrated according to the calibrated reference water level gauge to calibrate the reference zero point value of each detection water level gauge. The step S2 comprises the following steps: S2-1, the water surface height C of the water surface where the reference water level gauge is located relative to the height reference surface is measured by using the RTK technology, and the water level height reading D output by the reference water level gauge when the RTK technology is measured is recorded; S2-2, the reference zero point calibration value O of the reference water level gauge is calculated as C-D; 3. The method of claim 1, wherein: S2-3, the reference zero point value of the reference water level gauge is calibrated, that is, the reference zero point value of the reference water level gauge is added to the reference zero point calibration value O to serve as the calibrated reference zero point value. The step S3 comprises the following steps: S3-1, the water level height readings output by the calibrated reference water level gauge in one day are collected and recorded as a daily water level height curve A; S3-2, the water level height readings output by each detection water level gauge in the same day are synchronously collected and recorded as a daily water level height curve B while the step S3-1 is performed; 4. The method of claim 3, wherein: S3-3, the reference zero point value of each detection water level gauge is calibrated, that is, for any detection water level gauge, the difference between the root mean square of the daily water level height curve B of the detection water level gauge and the root mean square of the daily water level height curve A is less than a preset root mean square error value as a judgment standard, the reference zero point value of the detection water level gauge is gradually adjusted by using the dichotomy method, and the reference zero point value is updated in each step, and the daily water level height curve B is updated until the updated daily water level height curve B meets the judgment standard, and the reference zero point value obtained through the last adjustment is taken as the calibrated reference zero point value of the detection water level gauge.

5. The method of claim 3, wherein: In the steps S3-1 and S3-2, the daily water level height curve A and the daily water level height curve B are respectively composed of the water level height readings output by the reference water level gauge and the corresponding detection water level gauge in 24 hours.

6. The method according to any one of claims 1 to 5, wherein: In the step S3-3, the root mean square error value is set to 10 mm.

7. The method of claim 6, wherein: In the step S1, the Internet of Things system further comprises a cloud service center, and the water level height readings output by the water level gauges are transmitted to the cloud service center in real time. The cloud service center performs the calculation processes of the steps S2 and S3, and sends the reference zero point value calculated in the step S2 to the reference water level gauge for calibration and sends the reference zero point value calculated in the step S3 to the detection water level gauges for calibration.

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