Water level monitoring method and system based on two-way data checking and mutual checking mechanism

Abstract

The invention discloses a water level monitoring method and system based on a double-path data checking and mutual checking mechanism, and the method comprises the steps: checking a system clock with Beijing time, and carrying out the manual observation and comparison measurement of a water level, and obtaining manual timing checking data; acquiring double-path real-time data monitored by the terminal node through which the manual timing check data passes, synchronously acquiring a sampled arithmetic mean value, and carrying out abnormity identification on the double-path data; storing and checking the monitored real-time data and the arithmetic mean value to form final data, and recording the final data as a time sequence monitoring data set; and the server side obtains a monitoring section water level time sequence monitoring data set and stores the data set in a database. The system is composed of a double-water-level monitoring node, a hydrological edge computing node, a hydrological station data convergence point and a hydrological forecasting cloud platform. According to the method, the water level data of the hydrological monitoring section of the same river channel station is acquired through double data, and a perfect checking and mutual checking mechanism is established to improve the accuracy of water level data measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological monitoring, and in particular to a water level monitoring method based on a dual-path data verification and mutual validation mechanism. Background Art

[0002] Hydrology is the "eyes and ears" of water conservancy. Timely, comprehensive, accurate and reliable hydrological monitoring data and forecast products are important bases for flood control command departments at all levels to grasp and predict rain conditions. Water level is one of the basic hydrological elements of rivers, lakes and reservoirs, and is also key data that needs to be collected for national flood control and drought relief, water conservancy project scheduling, and water resources management and protection. Therefore, stable and reliable water level monitoring data is of great significance for accurate prevention of flood and drought disasters and water resources management.

[0003] At present, single water level monitoring technologies such as radar automatic water level gauges and image water level gauges have disadvantages in practical applications, such as low automation, high construction and operation and maintenance costs, and poor adaptability, which leads to a certain gap between the measurement results and actual requirements when encountering complex and changeable hydrological monitoring environmental conditions. In view of the shortcomings of current water level automatic monitoring technology, the present invention provides a new solution for river and lake water level monitoring, which has important practical significance for water supply, flood control scheduling, water resources management, etc.

[0004] Conventional radar automatic water level gauges are easily disturbed by on-site factors such as temperature, pressure, and water vapor; intelligent image recognition water level monitoring technology is easily disturbed by factors such as strong light differences, visibility, and floating objects. The specific environmental disturbances of both are shown in Table 1. Whether using conventional radar electronic water level automatic monitoring technology or image-based water level recognition technology, a single monitoring method cannot effectively solve the large monitoring errors caused by complex environmental factors.

[0005] Table 1 The influence of environmental disturbance factors in the actual application of conventional radar automatic water level monitoring and intelligent image recognition water level monitoring technology

[0006]

[0007] Note: Degree of impact: 1 means easy; 2 means relatively easy; 3 means average; 4 means

[0008] 5 means it is not easy; 5 means it has no impact.

[0009] Disturbance factor ① is low ambient light illumination, mainly including backlight, sun shadow and overexposure at night. When the area of ​​the water gauge is small relative to the image, the absorption of fill light by the water body can easily lead to overexposure of the water gauge fill light, resulting in the loss of texture features of the water level scale, which cannot be well identified by image method. Disturbance factor ② Strong light will cause glare and reflection on the water surface. These phenomena will interfere with the accurate identification of the water level by the image processing algorithm, thus affecting the measurement accuracy. Disturbance factor ③ is affected by the water quality of the river, which is specifically reflected in the low transparency of the water body, making it difficult to identify the water surface, and the reflection of the water gauge makes it difficult to distinguish between the water gauge part above the water surface and the water. Disturbance factors ⑤ to ⑦, under heavy rainfall, high temperature and thunderstorm weather conditions, the measurement accuracy of automatic monitoring such as conventional radar automatic water level gauges will be greatly affected.

[0010] The study found that: whether using conventional radar electronic water level automatic monitoring technology or intelligent image water level recognition technology, a single water level monitoring method cannot effectively solve the large monitoring errors caused by complex environmental factors; however, conventional radar water level automatic monitoring technology and image water level recognition technology have certain complementary characteristics. For example, except for disturbance factors ⑧ and ⑨ in Table 1, the impact of other single disturbance factors on water level monitoring can be improved by using two monitoring technologies at the same time; disturbance factor ⑧ can be solved by image recognition technology (such as obstacle recognition) + manual obstacle removal, and disturbance factor ⑨ can be eliminated by the sliding average method to eliminate water body fluctuation factors.

[0011] Therefore, in view of the complex and changeable natural hydrological environment and the limited accuracy of single water level automatic monitoring technology, this paper proposes a "dual-path" water level data verification and mutual validation mechanism, that is, through conventional water level automatic monitoring technology and image-based water level recognition technology, to improve the accuracy of water level monitoring data. Summary of the invention

[0012] The present invention proposes a water level monitoring method and system based on a dual-channel data verification mechanism. The method analyzes and summarizes the disturbance factors that affect the water level monitoring results in the monitoring environment, uses radar electronic water level meters and image methods to collect water level data of the hydrological monitoring section of the same river station, and establishes a complete verification mechanism to improve the accuracy of water level data measurement.

[0013] In a first aspect of the present application, a water level monitoring method based on a dual-path data verification mechanism is provided, the method comprising:

[0014] The system clock is calibrated with Beijing time, and the water level is manually observed and compared to obtain manual timing calibration data;

[0015] Obtain dual-channel real-time data monitored by the terminal node through which the artificial timed verification data passes, synchronously obtain the arithmetic mean of the samples, and identify abnormalities in the dual-channel data;

[0016] The real-time data and arithmetic mean of the monitoring are stored, the final data is formed after verification, and recorded as a time series monitoring data set;

[0017] Perform water level data analysis and mining on the water level time series monitoring data set obtained from the server;

[0018] Data analysis and early warning are performed based on the time series monitoring data set.

[0019] The second aspect of the present application provides a system consisting of dual water level monitoring nodes, hydrological edge computing nodes, hydrological site data aggregation points and a hydrological monitoring and reporting cloud platform, and the system is used to execute the water level monitoring method based on a dual-path data verification and mutual verification mechanism.

[0020] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0021] The present application provides a water level monitoring method based on a dual-channel data verification and mutual verification mechanism, which analyzes and summarizes the disturbance factors that affect the water level monitoring results in the monitoring environment, uses radar electronic water level gauges and image methods to collect water level data of the hydrological monitoring section of the same river station, and establishes a complete verification and mutual verification mechanism to improve the accuracy of water level data measurement. It can realize the remote verification of water level monitoring data by computer terminals, effectively overcoming the shortcomings of a single water level monitoring method. The water level monitoring value has a high accuracy rate, strong environmental adaptability, and is easy to expand and integrate, which can provide data support for water and rainfall information monitoring, flood disaster prevention and control, and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic flow chart of a water level monitoring method based on a dual-path data verification mechanism provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of a workflow diagram of a dual-channel water level data verification and mutual verification mechanism provided in an embodiment of the present application;

[0025] Figure 3 An overall block diagram of a dual-channel water level monitoring system based on a radar meter and an imaging method is provided in an embodiment of the present application.

[0026] Figure 4A schematic diagram of the hydrological edge computing node structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The present application provides a water level monitoring method and system based on a dual-path data verification mechanism, which is used to solve the technical problems in the prior art that the current single type of water level automatic monitoring technology has poor stability, is difficult to cope with complex and changing environments, has poor real-time monitoring, and has low accuracy of monitoring results.

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0029] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0030] Embodiment 1

[0031] like Figure 1 As shown, the present application provides a water level monitoring method based on a dual-path data verification mechanism, the method comprising:

[0032] P10, calibrate the system clock with Beijing time, and conduct manual observation and comparison of water level to obtain manual timing calibration data;

[0033] P20, obtains the dual-channel real-time data monitored by the terminal node through which the manual timing verification data passes, synchronously obtains the arithmetic mean of the samples, and identifies abnormal data;

[0034] P30, the real-time data and arithmetic mean of the monitoring are stored, the final data is formed after verification, and recorded as a time series monitoring data set;

[0035] P40, analyze and mine water level data on the water level time series monitoring data set obtained from the server;

[0036] P50, issuing an early warning based on the time series monitoring data set.

[0037] like Figure 2 As shown, the process of entering the workflow diagram of the dual-channel water level data verification and mutual verification mechanism.

[0038] P10:

[0039] The system clock is calibrated with Beijing time, and manual observation and comparison of water levels are carried out to obtain manual timing calibration data.

[0040] Specifically, the basic regular manual observation time of water level is 8:00 Beijing standard time. The staff of the hydrological station calibrates the system clock with Beijing time and then conducts manual observation of the water level. They then conduct comparative water level measurements, comparing the water level data collected by the radar electronic water level meter and the imaging method during the same period.

[0041] Furthermore, the data verification program is entered to realize manual timed verification by manual observation and comparative measurement of water level. If the manual observation and comparative measurement of water level fail, abnormal data needs to be identified. If the manual observation and comparative measurement of water level pass, the water level automatic measurement and reporting program is entered.

[0042] P10-1: When the water level is manually observed and compared, and the water level automatic measurement and reporting program is used, the abnormal data is further identified by calculating the difference between the dual-channel synchronous data.

[0043] Specifically, after the manual timing check is passed, the information collected by the dual-channel data is uploaded synchronously, including the radar electronic meter and the image method water level data. That is, data collection can be adopted, and usually the radar electronic meter and the image method can be used for data collection.

[0044] After the manual timing check is passed:

[0045] P10-1-1: Calculate the difference of dual-channel synchronous data. When there is no abnormality in the data, take the average value of the dual channels that do not exceed the set difference as the verified data and report it. The information collected by the dual-channel data uploaded synchronously is the average value of the dual channels.

[0046] Specifically, if there is no abnormality in the data, the difference of the dual-channel synchronous data (set as diff) is calculated. The diff should be less than or equal to 2 times the comprehensive error of the self-counting water level meter. The formula is as follows:

[0047]

[0048] TCE: Comprehensive error (cm) ΔZ: Water level range (m)

[0049] Specifically, taking the water level range △Z≤10m as an example, the allowable comprehensive error of a single automatic water level gauge is 2cm, so the dual-channel difference is ≤4cm, and the arithmetic average of the two channels is taken and saved.

[0050] P10-1-2: Calculate the difference of dual-channel synchronous data. If the data is abnormal, or the difference of dual-channel synchronous data exceeds the set difference, further identify the abnormal dual-channel data.

[0051] P20,

[0052] Obtain dual-channel real-time data from the terminal node monitoring through which the artificial timed verification data passes, synchronously obtain the arithmetic mean of the samples, and identify abnormal data.

[0053] Specifically, abnormal data includes: abnormal data that has not passed the manual timed verification, and abnormal data that has passed the manual timed verification but the difference between the two-way synchronous data exceeds the preset difference. The system identifies the type of water level monitoring disturbance factor based on real-time water environment data, completes abnormal analysis and processing, and determines the best way to measure and report data.

[0054] P20-1-1, for abnormal data that fails the manual timed calibration, synchronously retrieve the real-time image of the monitoring section to locate the source of error, and set the better method for data measurement and reporting.

[0055] Furthermore, for those that fail the manual timed calibration, the system will identify the type of water level monitoring disturbance factor based on real-time water environment data, conduct abnormal analysis and processing based on real-time images of the monitoring section, and determine the better method for data reporting.

[0056] Specifically, when there is an abnormal difference between the manual observation of water level and the measured water level, the real-time image of the monitoring section is synchronously retrieved to locate the source of the error, and the better method is set for data measurement and reporting.

[0057] Furthermore, the data measurement and reporting of the abnormal water level difference includes: the system identifies the type of water level monitoring disturbance factor based on real-time water environment data, and determines the better data in combination with the real-time video image of the monitoring section.

[0058] Furthermore, the system identifies the types of water level monitoring disturbance factors based on real-time water environment data, completes abnormal analysis and processing, and determines the best way to conduct data monitoring and reporting.

[0059] Specifically, when the measured water level difference does not exceed 2cm, the water level automatic measurement and reporting program is entered. When the measured water level difference exceeds 2cm, the system identifies the type of water level monitoring disturbance factor based on real-time water environment data, and the station staff simultaneously retrieves the real-time image of the monitoring section to further locate the source of the error and sets the better method for data measurement and reporting.

[0060] P20-1-2: For the anomaly where the difference between the two-way synchronous data exceeds the preset difference, the anomaly is also analyzed and processed, and the better method is set for data measurement and reporting.

[0061] Specifically, since the water level values ​​of rivers and lakes are continuous random variables, the continuous variable outlier monitoring method and outlier analysis method are used to analyze the water level data.

[0062] Furthermore, the system identifies the type of disturbance factors in water level monitoring based on real-time water environment data, and determines the better data in combination with real-time video images of the monitoring section.

[0063] If there is no abnormality in the data, calculate the difference of the dual-channel synchronous data (set as diff). The diff should be less than or equal to 2 times the comprehensive error of the self-counting water level meter. The formula is as follows:

[0064]

[0065] TCE: Comprehensive error (cm) ΔZ: Water level range (m)

[0066] Specifically, taking the water level range △Z≤10m as an example, the allowable comprehensive error of a single automatic water level meter is 2cm. Therefore, if the dual-channel difference is ≤4cm, the arithmetic mean of the two channels is taken and saved; if the difference is greater than 4cm, the abnormal processing and analysis stage is entered.

[0067] Furthermore, the system identifies the types of disturbance factors in water level monitoring based on real-time water environment data. Station staff simultaneously retrieve real-time images of the monitoring section to further locate the source of errors and set the best method for data monitoring and reporting.

[0068] P30: The real-time data and arithmetic mean of monitoring are stored, and the final data is formed after verification and recorded as a time series monitoring data set, that is, the server obtains the time series monitoring data set of the water level of the monitoring section and saves it to the database.

[0069] Specifically, the monitored real-time data and arithmetic mean are stored, and the final data is formed after verification, and recorded as a time-series monitoring data set and stored in the database. The water level data is stored on the edge computing terminal and the server at the same time. Among them, the edge computing terminal stores 1 year of radar electronic meter water level data and image method water level data, and verifies the water level data after mutual verification, as well as abnormal data and abnormal cross-sectional images. The cloud server only needs to save the water level data after mutual verification, abnormal data, and cross-sectional images, and form the final data after verification and report it to the hydrological monitoring center.

[0070] Furthermore, the stored data is the data that has passed the manual timing calibration without any abnormality. It includes the data of dual-channel synchronization, and the calibrated data obtained by taking the average value of the dual-channel, that is, the arithmetic mean of the sampling. It also includes the data that failed the calibration but was determined to be better after comparison.

[0071] Furthermore, the recorded time-series monitoring data set includes: the data stored in the system includes radar electronic meter water level data, image method water level data, water level data after verification and mutual verification, abnormal water level data and real-time cross-sectional image data.

[0072] Specifically, water level data storage and reporting use edge computing nodes to reduce data pressure on the server side. The data stored in the system include: radar electronic meter water level data, image method water level data, water level data after calibration and mutual verification, abnormal water level data, and real-time cross-sectional image data.

[0073] P40: Perform water level data analysis and mining on the water level time series monitoring data set obtained from the server.

[0074] Specifically, the server obtains the water level data of the monitoring section and saves it to the database, and analyzes and mines the water level data: real-time water level display and warning, surface water level prediction, and other extended applications.

[0075] P50: Produce an early warning based on the time series monitoring data set.

[0076] Specifically, the server obtains the water level time series monitoring data set of the monitoring section and uploads it to computers, mobile phones and other terminals synchronously for early warning.

[0077] In summary, the embodiments of the present application have at least the following technical effects:

[0078] The present application provides a water level monitoring method based on a dual-channel data calibration and mutual verification mechanism, which analyzes and summarizes the disturbance factors that affect the water level monitoring results in the monitoring environment, uses radar electronic water level meters and imaging methods to collect water level data of the hydrological monitoring section of the same river station, and establishes a complete calibration and mutual verification mechanism to improve the accuracy of water level data measurement.

[0079] Embodiment 2

[0080] Based on the same inventive concept as the water level monitoring method based on a dual-path data verification and mutual verification mechanism in the aforementioned embodiment one, the present application provides a system, which is composed of dual water level monitoring nodes, hydrological edge computing nodes, hydrological site data aggregation points and hydrological monitoring and reporting cloud platform. The system is used to execute a water level monitoring method based on a dual-path data verification and mutual verification mechanism as described in embodiment one.

[0081] Specifically, the dual water level monitoring nodes are used to monitor the water level monitoring sections through radar meters and image methods to obtain water level monitoring data; among them, the image method water level monitoring is used to transmit and process image data; the hydrological edge computing node is used to collect and store dual-channel real-time data, and calculate the arithmetic mean of the samples; the hydrological site data aggregation point is used to record the time series monitoring data set; the hydrological monitoring and reporting cloud platform is used to issue early warnings for the monitoring data set.

[0082] like Figure 3 As shown in the figure, the system consists of dual-technology water level monitoring nodes (radar meter and imaging method), hydrological edge computing nodes, hydrological site data aggregation points and prefecture-level city hydrological monitoring and reporting cloud platform.

[0083] Specifically, at the water level monitoring node, each monitoring section is equipped with a digital radar water level meter and an outdoor water level gauge image acquisition device, which is powered by 12V DC via POE. The video image acquisition device is aimed at the water level gauge section, while ensuring that the video signal can cover the radar water level meter.

[0084] In this application, image-based water level monitoring requires the transmission and processing of image data collected by the image acquisition device, and uploading all image data to the cloud server for unified processing.

[0085] In this application, image-based water level monitoring requires the transmission and processing of image data collected by the scale image acquisition device. It is also possible to implement localized computing services by designing edge computing terminal nodes near the data end to complete the simultaneous acquisition of water level data from the radar water level meter and the water scale image.

[0086] This application uses image recognition algorithms to identify water gauge data, verify two-way data, upload water level data, and realize cloud service program interaction and other functions.

[0087] Furthermore, the present application adopts the method of edge computing nodes to obtain dual-channel real-time data monitored by terminal nodes through which the data is manually timed and verified, stores the monitored real-time data and the arithmetic mean, synchronously obtains the sampled arithmetic mean, identifies anomalies in the dual-channel data, and forms the final data after verification.

[0088] Specifically, the arithmetic mean of the hydrological edge computing nodes is obtained through comparative difference analysis.

[0089] This application uses edge computing nodes to save network bandwidth costs, reduce transmission delays, and greatly reduce the pressure on the cloud service platform.

[0090] Specifically, Figure 4As shown in the figure, the hydrological edge computing node consists of a microprocessor module, an Ethernet network module, a wireless communication module, a data storage module, a digital input module, a water gauge image recognition module, and a main and backup power supply. The terminal node needs to be powered by DC and installed in the hydrological station room. It is connected to the front-end POE switch through the Ethernet network module, and the network interface chip uses W5500. The water gauge image acquisition device uses an outdoor wide dynamic camera with infrared night vision function and a fill light. The angle should be adjusted to aim at the water gauge. The POE switch uses a cat5 cable to pass through a steel pipe and is buried to the water gauge image acquisition camera set at the monitoring point. A standard water gauge with a sampling size of 1 000mm×80mm is used, and the water level reading accuracy is 1cm; the water gauge is made of stainless steel, with a standard white background and pure blue and pure red fonts and logos. The scale is clearly identifiable in the visible light band and has a high contrast in the near-infrared band at night. The electronic water level meter uses a digital radar water level meter to directly upload the water level data to the edge terminal node through the digital input module.

[0091] The microcontroller of the hydrological edge computing node adopts a low-power ARM core microcontroller, and the storage capacity can meet the requirements of water level image storage and real-time transmission. It has the function of storing water level data for more than one year on site; the stored data can be downloaded on site; clock module; supports data collection methods such as timed collection and event collection. Remote clock calibration function. The water level information transmission method can adopt two different transmission channels, which can automatically switch between each other as backup. The wireless communication interface module adopts a 4G communication interface module, connects to the microcontroller through the serial port, and transmits the water level data to the data aggregation point through traffic. The wireless communication module adopts W5500 and connects to the microprocessor through SPI. It adopts dual power supply mode, the main power supply is connected to 220V AC power, and is converted into DC power through the switching power supply to supply the edge computing terminal node. The backup power supply uses a rechargeable polymer lithium battery to support long standby and sleep modes.

[0092] The hydrological monitoring and reporting cloud platform consists of a cloud server and a storage database. The server is used to analyze and mine water level data for the water level time series monitoring data set of the monitoring section. The server-side service program receives the collected data transmitted by the monitoring module to form historical data. At the same time, it can mine and analyze historical data, such as dynamic changes in water levels and information on super-warning water levels.

[0093] The present application provides a water level monitoring method system based on a dual-path data verification mechanism, which can realize remote verification of water level monitoring data by computer terminals, effectively overcoming the shortcomings of a single water level monitoring method. The water level monitoring value has high accuracy, strong environmental adaptability, and is easy to expand and integrate, and can provide data support for water and rainfall information monitoring, flood disaster prevention and control, and decision-making.

[0094] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0096] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A water level monitoring method based on a dual-path data verification mechanism, characterized in that: The method comprises: The system clock is calibrated with Beijing time, and the water level is manually observed and compared to obtain manual timing calibration data; Obtain dual-channel real-time data monitored by the terminal node through which the artificial timed verification data passes, synchronously obtain the arithmetic mean of the samples, and identify abnormalities in the dual-channel data; The real-time data and arithmetic mean of the monitoring are stored, the final data is formed after verification, and recorded as a time series monitoring data set; Perform water level data analysis and mining on the water level time series monitoring data set obtained from the server; Data analysis and early warning are performed based on the time series monitoring data set.

2. The method according to claim 1, characterized in that The dual-path data anomaly identification includes: When the manual timed calibration data is compared with the measured water level difference, the dual-channel synchronous data water level automatic measurement and reporting program is adopted; When the manual timed calibration data is abnormal compared with the measured water level, or the difference between the dual-channel synchronous data exceeds the preset difference, the abnormality is identified, the real-time image of the monitoring section is synchronously retrieved to locate the source of the error, and the better method is set for data measurement and reporting.

3. The method according to claim 2, characterized in that The data report of the abnormal water level difference includes: The system identifies the types of water level monitoring disturbance factors based on real-time water environment data, completes abnormal analysis and processing, and determines the best method for data measurement and reporting.

4. The method according to claim 1, characterized in that The abnormal identification of the two-way data includes: Determine whether outlier data appears based on the arithmetic mean of the samples; When abnormal data appears, the system will identify the type of water level monitoring disturbance factor based on real-time water environment data, and determine the better data in combination with the real-time video image of the monitoring section; When the data is normal, the difference between the two-way synchronous data is calculated and processed according to the difference.

5. The method according to claim 4, characterized in that The obtaining of the arithmetic mean of the samples comprises: The arithmetic mean is calculated based on multiple sampling data before and after the storage time.

6. The method according to claim 4, characterized in that The obtaining of the arithmetic mean of the samples comprises: When the data is normal, calculate the difference of the dual-channel synchronous data (set as diff). The diff should be less than or equal to 2 times the comprehensive error of the self-counting water level meter. The formula is as follows: TCE: Comprehensive error (cm) ΔZ: Water level range (m) Taking the water level range △Z≤10m as an example, the allowable comprehensive error of a single automatic water level meter is 2cm. Therefore, if the dual-channel difference is ≤4cm, the arithmetic mean of the two channels is taken and saved; if the difference is greater than 4cm, the abnormal processing and analysis stage is entered.

7. The method according to claim 1, characterized in that The recorded time series monitoring data set includes: The data stored in the system include radar electronic meter water level data, image method water level data, water level data after calibration and mutual verification, abnormal water level data and real-time cross-sectional image data.

8. A system, which consists of dual water level monitoring nodes, hydrological edge computing nodes, hydrological site data aggregation points and a hydrological monitoring and reporting cloud platform, characterized in that: The system is used to execute a water level monitoring method based on a dual-path data verification mechanism as described in any one of claims 1-7.

9. The system according to claim 8, characterized in that The dual water level monitoring node is used to monitor the water level monitoring section through radar meter and image method to obtain water level monitoring data; wherein the image method water level monitoring is used to transmit and process image data; The hydrological edge computing node is used to collect and store dual-channel real-time data and calculate the arithmetic mean of the samples; The hydrological site data aggregation point is used to record the time series monitoring data set; The hydrological monitoring and reporting cloud platform is used to provide early warning for monitoring data sets.

10. The system according to claim 9, characterized in that The arithmetic mean of the hydrological edge computing nodes is obtained through comparative difference analysis.

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