A method, system, medium and program product for high-precision monitoring of water flow velocity

Abstract

The present application provides a method, system, medium and program product for high-precision monitoring of water flow velocity, and relates to the field of water flow monitoring technology. In this method, multiple edge devices collect side-scan radar monitoring data of multiple target sub-areas, and multiple sensors collect terrain information and water flow disturbance information at the same time. Then, the side-scan radar monitoring data, terrain information and water flow disturbance information are input into a water flow velocity model constructed by processing the side-scan radar historical monitoring data using a high-order polynomial fitting algorithm to determine the initial water flow velocity data. Then, based on the terrain and water flow disturbance information, the velocity correction parameters are determined through a parameter correction model trained with multiple groups of relevant information and flow velocity influencing data. Finally, the initial flow velocity data and the correction parameters are fused to obtain the final water flow velocity data. The implementation of this method can accurately reflect the true flow velocity of the water flow and provide reliable data support for fields such as water resources management.

Description

Technical Field

[0001] The present application relates to the field of water flow monitoring technology, and in particular to a method, system, medium and program product for high-precision monitoring of water flow velocity. Background Art

[0002] At present, water resources management and flood prevention and disaster reduction work play a vital role in protecting people's lives and property and promoting sustainable social and economic development. In these key areas, accurate acquisition of water flow velocity and flow data is the core and cornerstone for scientific decision-making, effective management and accurate disaster warning.

[0003] Traditional flow velocity monitoring methods, such as acoustic Doppler flowmeter, suspended sand method, floating object method, etc., have problems such as limited measurement range, high cost, and significant impact from weather. They are difficult to meet the needs of large-scale, continuous, and real-time monitoring. Flow velocity monitoring is performed using traditional side-scan radar combined with a low-order fitting algorithm.

[0004] However, when facing a wide river, the limitations of the flow velocity data monitored by the side-scan radar become apparent. The increase in river width will make the water body more complicated. The flow velocity and direction of the water flow in different areas may vary greatly, and may also be affected by a variety of factors such as topography and water depth changes. Therefore, when monitoring such a complex water body, the side-scan radar is difficult to fully and carefully capture the true state of the water flow, and thus it is difficult to accurately reflect the actual water flow velocity. Summary of the invention

[0005] The present application provides a method, system, medium and program product for high-precision monitoring of water flow velocity, which are used to achieve accurate water flow velocity monitoring by fusing multi-source data and building an adaptive model.

[0006] In a first aspect, the present application provides a method for high-precision monitoring of water flow velocity, which is applied to a monitoring system, the method comprising: acquiring side-scan radar monitoring data of multiple target sub-areas sent by multiple edge devices; The terrain information and water flow disturbance information are acquired through a plurality of sensors; initial water flow velocity data are determined through a water flow velocity model in combination with the side-scan radar monitoring data, the terrain information and the water flow disturbance information, and the water flow velocity model is obtained by processing a plurality of side-scan radar historical monitoring data in advance through a high-order polynomial fitting algorithm; the water flow velocity correction parameter is determined through a parameter correction model in combination with the terrain information and the water flow disturbance information, and the parameter correction model is trained in advance based on a plurality of terrain information, water flow disturbance information and corresponding water flow velocity influencing data; the final water flow velocity data are determined by combining the initial water flow velocity data and the water flow velocity correction parameter.

[0007] By adopting the above technical solution, multiple edge devices collect side-scan radar monitoring data of the target sub-area, and combine it with terrain information and water flow disturbance information to provide multi-dimensional data for the water flow velocity model. The water flow velocity model processes the historical monitoring data of the side-scan radar based on a high-order polynomial fitting algorithm, which can capture the inherent laws of the data. Combined with the correction parameters determined by the parameter correction model based on the terrain, water flow disturbance information and the corresponding flow velocity influencing data, the initial flow velocity data is corrected. The fusion of multi-source data and the combination of dual models complement each other and improve each other, which greatly improves the accuracy of the final water flow velocity data, can accurately reflect the true flow velocity of the water flow, and provide reliable data support for fields such as water resources management.

[0008] In combination with some embodiments of the first aspect, in some embodiments, before the step of obtaining side-scan radar monitoring data of multiple target sub-areas sent by multiple edge devices, it also includes: obtaining image information of the target sub-area; inputting the image information into a water flow parameter model to determine the side-scan radar monitoring data corresponding to the target sub-area, the water flow parameter model is constructed in advance based on multiple historical image information and corresponding annotation sets of side-scan radar monitoring data, and the side-scan radar monitoring data includes the side-scan radar scanning frequency and resolution; controlling the side-scan radar to send the side-scan radar monitoring data to multiple edge devices; using multiple edge devices to perform preprocessing operations on the preliminary side-scan radar monitoring data to obtain final side-scan radar monitoring data, and the preprocessing operation at least includes noise removal, data calibration and data format conversion.

[0009] By adopting the above technical solution, the image information of the target sub-area is obtained and the water flow parameter model is input. The model is built based on the historical images and the side-scan radar monitoring data annotation set, which can specifically determine the parameters such as the side-scan radar scanning frequency and resolution suitable for the target sub-area. The side-scan radar sends data accordingly, and the edge device performs preprocessing such as denoising, calibration, and format conversion. This series of operations makes the side-scan radar data more in line with the characteristics of the target area, and the accuracy and availability of the preprocessed data are greatly improved, providing a better data foundation for the subsequent water flow velocity calculation based on these data, thereby improving the reliability of the entire monitoring process.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the final water flow velocity data by combining the initial water flow velocity data and the water flow velocity correction parameter, it also includes: if the final water flow velocity data is greater than the set water flow threshold, a warning message is sent to the user end.

[0011] By adopting the above technical solution, this mechanism can respond to abnormal water flow speed in a timely manner, so that relevant personnel can quickly know when the water flow speed exceeds the safe range.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the final water flow velocity data in combination with the initial water flow velocity data and the water flow velocity correction parameter, it also includes: obtaining seasonal information, and adjusting the scanning parameters of the side scan radar and the set water flow threshold according to a preset strategy library, the strategy library including: if the seasonal information is in a preset key monitoring period, then increasing the scanning parameters by a preset amplitude, and lowering the set water flow threshold; if the seasonal information is in a preset basic monitoring period, then lowering the scanning parameters by a preset amplitude, and increasing the set water flow threshold.

[0013] By adopting the above technical solution, improving scanning parameters during the key monitoring period can capture more detailed water flow information, and lowering the threshold can detect potential dangers earlier; on the contrary, during the basic monitoring period, resources can be saved.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the final water flow velocity data by combining the initial water flow velocity data and the water flow velocity correction parameter, it also includes: obtaining key area information after receiving a special monitoring instruction sent by the user end; obtaining location information of multiple edge devices; if the distance between the edge device and the key area information is within a set range, adjusting the computing power according to the set key adjustment strategy.

[0015] By adopting the above technical solution, we can receive special monitoring instructions from users and adjust the computing power of edge devices within the set range of key areas. This dynamic adjustment based on different scenarios can not only accurately and efficiently monitor water flow in key areas and key periods, but also reasonably allocate system resources, improving the adaptability and resource utilization efficiency of the monitoring system.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after obtaining seasonal information and adjusting the scanning parameters of the side-scan radar and setting the water flow threshold according to a preset strategy library, it also includes: after receiving a viewing operation instruction sent by the user terminal, sending corresponding data to the user terminal for display according to the viewing requirements in the viewing operation instruction.

[0017] By adopting the above technical solution, the system can send corresponding data for display according to the viewing requirements, so that users can understand the operating status of the monitoring system and water flow related data in real time.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the final water flow velocity data by combining the initial water flow velocity data and the water flow velocity correction parameter, it also includes: receiving actual measurement results sent by a professional team through a user terminal at set time intervals; comparing the actual measurement results with the final water flow velocity data to obtain result differences; and correcting the data in the parameter correction model according to the result differences.

[0019] By adopting the above technical solution, the parameter correction model data is corrected according to the difference, forming a feedback optimization closed loop. The actual measurement results reflect the real water flow situation, and the model deviation can be found by comparing with the monitoring data. After correcting the model data, the parameter correction model can more accurately determine the flow rate correction parameters, further improving the accuracy of the final water flow rate data.

[0020] In a second aspect, the present application provides a monitoring system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the monitoring system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a monitoring system, causes the monitoring system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, the present application provides a computer program product. When the computer program product runs on a monitoring system, the monitoring system executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Due to the adoption of technical means that integrate the side-scan radar monitoring data, terrain information and water flow disturbance information of multiple edge devices, and use of high-order polynomial fitting algorithm to build a water flow velocity model and a parameter correction model based on multi-information training to determine the water flow velocity data, it effectively solves the technical problem that the water flow velocity monitoring data in the existing technology is poor in accuracy and it is difficult to meet the demand for accurate data in fields such as water resources management, thereby achieving the technical effect of accurately obtaining water flow velocity data and providing reliable data support for water conservancy planning, flood prevention and disaster reduction, and other work.

[0024] 2. The technical means of obtaining the image information of the target sub-area and determining the side-scan radar monitoring parameters through the water flow parameter model, and then pre-processing the side-scan radar data by the edge device, effectively solves the technical problems of poor adaptability of the side-scan radar monitoring parameters to the target area and low data quality in the existing technology, thereby achieving the technical effect of improving the accuracy and applicability of the side-scan radar monitoring data, and laying a good data foundation for the subsequent precise calculation of the water flow velocity.

[0025] 3. Due to the technical means of adjusting the side-scan radar scanning parameters and setting the water flow threshold according to the preset strategy library based on seasonal information, and adjusting the computing power of the edge devices in key areas according to the user's special monitoring instructions, the technical problems of the existing technology that the monitoring system cannot flexibly adapt to the needs of different scenarios and the unreasonable resource allocation are effectively solved, thereby achieving the technical effect of improving the monitoring accuracy of the monitoring system in different seasons and key areas, while optimizing resource allocation and improving monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method for high-precision monitoring of water flow velocity in an embodiment of the present application; Figure 2 is another flow chart of the method for high-precision monitoring of water flow velocity in an embodiment of the present application; Figure 3 (a) is a schematic diagram of a query operation display scene of a method for high-precision monitoring of water flow velocity in an embodiment of the present application; Figure 3 (b) is a schematic diagram showing a display scene of adding operation of the method for high-precision monitoring of water flow velocity in an embodiment of the present application; Figure 3 (c) is a schematic diagram of the editing operation display scene of the method for high-precision monitoring of water flow velocity in an embodiment of the present application; Figure 3 (d) is a schematic diagram showing details of the method for high-precision monitoring of water flow velocity in an embodiment of the present application, and a flow recalculation and deletion operation display scene; Figure 3 (e) is a schematic diagram of a display scene of a batch deletion operation of the method for high-precision monitoring of water flow velocity in an embodiment of the present application; Figure 3 (f) is a schematic diagram of a statistical graph display scene of the method for high-precision monitoring of water flow velocity in an embodiment of the present application; Figure 3 (g) is a schematic diagram of a process profile display scene of a method for high-precision monitoring of water flow velocity in an embodiment of the present application; Figure 3 (h) is a schematic diagram of a flow comparison display scene of a method for high-precision monitoring of water flow velocity in an embodiment of the present application; Figure 4 It is a schematic diagram of the structure of a physical device of the monitoring system in the embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] For ease of understanding, the following is a description of the process of the method provided by this implementation. Figure 1 , is a flow chart of a method for high-precision monitoring of water flow velocity in an embodiment of the present application.

[0030] S101, acquiring side-scan radar monitoring data of multiple target sub-areas sent by multiple edge devices; In actual water flow monitoring scenarios, the water area is usually quite large, and the water flow conditions are complex and changeable. In order to comprehensively and meticulously grasp the water flow information in different areas, the entire monitored water area is divided into multiple target sub-areas. Each sub-area has its own unique water flow characteristics, which are affected by multiple factors such as terrain, water depth, and surrounding environment.

[0031] Edge devices play the role of data acquisition terminals in the entire monitoring system. They are deployed at key locations in each target sub-area and can collect side-scan radar monitoring data in close proximity and in real time. These edge devices have certain computing and communication capabilities, can perform preliminary processing on the collected data, and send the data back to the monitoring system in a timely manner. The reason for using multiple edge devices is that the monitoring range and accuracy of a single device are limited, and it is difficult to cover the entire monitoring area and obtain comprehensive data. Multiple edge devices work together to form a distributed data acquisition network, which can greatly improve the efficiency and accuracy of data acquisition. As an important monitoring device, side-scan radar obtains information on the surface of the water body by emitting electromagnetic waves to the water body and receiving the reflected signals, and then infers the relevant parameters of the water flow. The edge device works closely with the side-scan radar to receive the monitoring data sent by the side-scan radar in real time. After the edge device collects the side-scan radar monitoring data, it will send the data back to the monitoring system according to the predetermined communication protocol. The monitoring system receives this data through a dedicated communication interface and stores it in a database for subsequent processing and analysis.

[0032] In some embodiments, before this step, a high-definition image of the target sub-area can be taken with the help of satellite remote sensing technology and input into the water flow parameter model, which is constructed based on a large amount of historical image information and the corresponding side-scan radar monitoring data annotation set. During the construction process, the researchers collected historical images of many different regions and different water flow conditions, as well as the corresponding monitoring data such as the side-scan radar scanning frequency and resolution, and annotated them in detail. The machine learning algorithm is used to deeply analyze and train these annotated data, so that the model can learn the association between image features and side-scan radar monitoring parameters. When the image information of the target sub-area is input, the water flow parameter model will analyze the topography, water flow characteristics and other information in the image according to the learned rules, so as to determine the monitoring data such as the side-scan radar scanning frequency and resolution applicable to the target sub-area. After determining the side-scan radar monitoring data, the monitoring system will send a control instruction to the side-scan radar to monitor the target sub-area according to the determined scanning frequency and resolution, and send the monitoring data to multiple edge devices. After receiving the instruction, the side-scan radar adjusts its own working parameters and starts to emit electromagnetic waves to scan the target waters. After receiving the preliminary side-scan radar monitoring data, the edge device will perform preprocessing operations on it. Noise removal is to remove abnormal data points caused by electromagnetic interference, environmental noise and other factors through filtering algorithms, such as mean filtering and median filtering, to improve the stability and accuracy of the data.

[0033] S102, acquiring terrain information and water flow disturbance information through multiple sensors; In addition to side-scan radar monitoring data, terrain information and water flow disturbance information are also crucial for accurately calculating water flow velocity. The monitoring system obtains these two types of information by deploying multiple sensors.

[0034] Different terrains can cause changes in the speed and direction of water flow. For example, in narrow river channels, the water flow speed usually increases; while in wide river channels, the water flow speed may slow down. In order to obtain accurate terrain information, the monitoring system uses various types of sensors, such as underwater topography surveyors and satellite remote sensing equipment. Underwater topography surveyors can directly measure the depth, slope and other parameters of underwater terrain. It emits sound waves and uses the propagation speed and reflection time of sound waves in water to calculate the shape of underwater terrain. This measurement method can provide high-precision local terrain information, which is very helpful for studying the changes in water flow in specific areas. Satellite remote sensing equipment can obtain large-area terrain information from a macro perspective. It uses sensors carried by satellites to observe the earth's surface and obtain the general outline and elevation information of the terrain by analyzing the terrain features in satellite images.

[0035] Water flow disturbance information reflects the dynamic changes of water flow, including fluctuations in water flow, vortices and other phenomena. This information is crucial for accurately judging the true flow rate of water flow. The monitoring system uses a variety of sensors such as water flow velocity sensors, water level sensors, and water quality sensors to obtain water flow disturbance information. Water flow velocity sensors can directly measure the speed and direction of water flow. It usually works based on the principles of electromagnetic induction, acoustic Doppler, etc., and calculates the flow rate and direction of water flow by sensing the changes in physical quantities in the water flow. Water level sensors are used to monitor changes in water levels. Water level fluctuations are often closely related to the flow rate and flow rate of water flow. By monitoring the changes in water level in real time, the dynamic changes of water flow can be indirectly understood.

[0036] Multiple sensors are deployed at different locations in the monitoring area to obtain comprehensive terrain information and water flow disturbance information. When deploying sensors, the terrain characteristics, water flow distribution and data collection requirements of the monitoring area are fully considered, and the data obtained by the sensors will also be transmitted back to the monitoring system in real time.

[0037] S103, combining the side-scan radar monitoring data, the terrain information and the water flow disturbance information, determining initial water flow velocity data through a water flow velocity model, wherein the water flow velocity model is obtained by processing a plurality of side-scan radar historical monitoring data through a high-order polynomial fitting algorithm in advance; The construction of the water flow velocity model is based on the deep processing of a large amount of historical side-scan radar monitoring data using a high-order polynomial fitting algorithm. Before building the model, the monitoring system needs to collect and organize a wealth of historical side-scan radar monitoring data. These historical data cover monitoring results at different times, under different hydrological conditions, and in different regions, and are an important basis for model training. The monitoring system conducts strict data quality checks on these historical data, removes outliers and erroneous data, and ensures the accuracy and reliability of the data. Through data cleaning, the interference of erroneous data on model training can be avoided, thereby improving the accuracy of the model.

[0038] The high-order polynomial fitting algorithm plays a core role in model construction. The algorithm finds a high-order polynomial function to fit the distribution law of the historical monitoring data of the side-scan radar as accurately as possible. Compared with the low-order fitting algorithm, the high-order polynomial fitting algorithm can capture more complex change trends and potential relationships in the data. For example, when the water flow velocity is affected by a variety of factors, the low-order fitting algorithm may not be able to accurately describe the change in flow velocity, while the high-order polynomial fitting algorithm can fit complex data curves by increasing the degree of the polynomial and introducing more parameters. The monitoring system continuously adjusts the order and parameters of the polynomial and uses optimization methods such as the least squares method to minimize the error between the fitting function and the historical monitoring data. After repeated training and optimization, a water flow velocity model that can accurately reflect the relationship between the side-scan radar monitoring data and the water flow velocity is finally obtained.

[0039] After the monitoring system obtains real-time side-scan radar monitoring data, terrain information and water disturbance information, these multi-source data are input into the constructed water flow velocity model. The side-scan radar monitoring data provides echo information on the surface of the water body. By analyzing this information, the speed and direction of the water flow can be preliminarily inferred. The terrain information reveals the shape, slope, depth and other characteristics of the river channel, which have important constraints and influences on the water flow. The monitoring system organically integrates these three types of information as the input of the water flow velocity model. The model calculates and analyzes the input data based on the pre-trained parameters and algorithms, and outputs the initial water flow velocity data. These initial water flow velocity data are based on multi-source information and trained models, and have higher accuracy and reliability than the velocity calculation results from a single data source. However, due to the complexity of the actual water flow situation, relying solely on the initial water flow velocity data may not be able to fully and accurately reflect the actual water flow velocity, so it is necessary to further optimize it through the parameter correction model.

[0040] S104, combining the terrain information and the water flow disturbance information, determining a water flow velocity correction parameter through a parameter correction model, wherein the parameter correction model is trained in advance based on a plurality of terrain information, water flow disturbance information and corresponding water flow velocity influence data; The parameter correction model is trained by the monitoring system based on a large amount of multi-source information. In the early preparation stage, the monitoring system collected terrain information covering a wide area and under various water flow conditions. This information not only includes the shape, width, and depth of the river channel, but also involves detailed features such as the roughness and slope of the riverbed. There are various means of collection, such as the use of high-precision underwater topographic measurement equipment, which accurately measures the contour and depth of underwater terrain by emitting sound waves and receiving reflected signals; satellite remote sensing technology obtains large-area terrain data from a macro level. The two complement each other to ensure the comprehensiveness and accuracy of terrain information.

[0041] At the same time, the monitoring system uses various sensors to collect water flow disturbance information. For example, water flow velocity sensors can monitor the speed changes and flow direction fluctuations of water flow in real time; water level sensors can keenly capture the fluctuations of water levels; and wave sensors focus on monitoring the height, frequency and other parameters of surface waves. These data comprehensively reflect the dynamic changes of water flow and are an indispensable basis for determining the correction parameters. The monitoring system also records the water flow velocity impact data corresponding to the above-mentioned terrain information and water flow disturbance information. These data are accumulated during the long-term monitoring process. Each set of data reflects the actual impact of water flow velocity under specific terrain and water flow disturbance conditions. For example, in an area where the river channel is narrow and the water flow is turbulent, the monitoring system records the terrain parameters, water flow disturbance data and the actual measured water flow velocity at that time. Through the accumulation of a large amount of similar data, it provides rich materials for the training of the parameter correction model.

[0042] Based on these massive training data, the monitoring system uses machine learning algorithms to train the parameter correction model. Taking the neural network algorithm as an example, the model will use terrain information and water flow disturbance information as nodes of the input layer. After complex calculations and layer-by-layer processing in the hidden layer, it attempts to learn the intrinsic relationship between this information and the impact of water flow velocity, and continuously adjusts the weights and thresholds between the nodes in the hidden layer to make the model output results as close as possible to the actual water flow velocity impact data. When the model training is completed, the monitoring system will input the terrain information and water flow disturbance information obtained in real time into the parameter correction model. The model will quickly identify the influence of this situation on the water flow velocity based on the training memory, and output the corresponding water flow velocity correction parameters. These correction parameters can accurately reflect the adjustment direction and amplitude of the initial water flow velocity data under the current terrain and water flow disturbance conditions.

[0043] S105, determining final water flow velocity data by combining the initial water flow velocity data and the water flow velocity correction parameter.

[0044] When the monitoring system integrates the initial water flow velocity data and the water flow velocity correction parameter, the monitoring system will perform specific mathematical operations on the correction parameter and the initial water flow velocity data according to the nature and meaning of the correction parameter. For example, if the correction parameter indicates the proportion of the water flow velocity that needs to be increased, the monitoring system will multiply the initial water flow velocity data by a coefficient greater than 1 to obtain the corrected water flow velocity data; if the correction parameter indicates a fixed value that needs to be increased or decreased based on the initial flow velocity, the monitoring system will directly perform the corresponding addition and subtraction operations on the initial water flow velocity data, and then determine the corrected final water flow velocity data, which can more accurately reflect the actual flow velocity of the actual water flow and provide a reliable data basis for water resources management departments to formulate scientific and reasonable water resources allocation plans.

[0045] In some embodiments, the monitoring system always maintains a communication connection with the user end and monitors the user's instructions in real time. When the user end sends a special monitoring instruction to the monitoring system due to certain specific circumstances, such as water conservancy project construction, sudden water pollution incidents, key water ecological research, etc., the monitoring system will respond quickly. The instruction clearly contains the regional information that needs to be monitored, which can be a specific geographical coordinate range or an area based on a specific geographical identifier, which is not limited here. After receiving the instruction, the monitoring system parses and extracts the key area information from the instruction, and then starts the process of obtaining the location information of multiple edge devices. When each edge device is deployed, it is equipped with a positioning module, such as a global positioning system (GPS) or a base station-based positioning system. These positioning modules record the location information of the edge device in real time and send it to the monitoring system regularly. The monitoring system collects the location data fed back by each edge device through the communication link with the edge device. Then calculate the distance between each edge device and the key area one by one. If the distance between the edge device and the key area information is within the set range, it means that the edge device is close to the key monitoring area, which plays an important role in accurately monitoring the water flow in the key area. At this time, the monitoring system will adjust the computing power of the edge device according to the set focus adjustment strategy. The set focus adjustment strategy is pre-determined according to monitoring needs, which may include increasing the computing frequency of the edge device so that it can process the collected side-scan radar monitoring data, terrain information, and water disturbance information more quickly. Through this computing power adjustment, the monitoring capability of water flow conditions in key areas can be significantly enhanced, providing users with more detailed and timely water flow information in key areas.

[0046] In the embodiment of the present application, multiple edge devices are used to collect the side-scan radar monitoring data of the target sub-area, and the terrain information and water disturbance information obtained by multiple sensors are combined. At the same time, a high-order polynomial fitting algorithm is used to build a water flow velocity model and a parameter correction model is trained based on multiple sets of relevant information, so that the deep fusion of multi-source data and the collaborative work of the dual models are achieved. It effectively solves a series of problems in the prior art, such as the poor accuracy of water flow velocity monitoring data, the poor adaptability of side-scan radar monitoring parameters and target areas, and the inability of the monitoring system to flexibly adapt to the needs of different scenarios, thereby achieving accurate acquisition of water flow velocity data, improving the quality of side-scan radar monitoring data, and improving the monitoring accuracy and resource utilization efficiency of the monitoring system in different scenarios, providing reliable data support for water resources management, flood prevention and disaster reduction, and other fields, and promoting the goal of scientific decision-making and efficient operation of the water conservancy industry.

[0047] In some embodiments, in order to continuously optimize the accuracy of monitoring data, the actual measurement results of professional teams can be introduced to optimize the parameter correction model of the monitoring system. With their professional measurement equipment and rich measurement experience, the professional team conducts field measurements of water flow velocity within a set time interval. These professional equipment may include high-precision measuring instruments such as acoustic Doppler current meters, which can directly and accurately measure water flow velocity. The professional team sends the measured results to the monitoring system through the user terminal. After receiving the actual measurement results of the professional team, the monitoring system will compare it with the final water flow velocity data obtained by itself through a series of calculations. During the comparison process, the monitoring system will calculate the difference between the two, which can be measured in a variety of ways, such as calculating the absolute difference between the two, or calculating the relative error. Through this comparison, the monitoring system can clearly understand the deviation between its own monitoring data and the actual measurement results. According to the difference in the results obtained by the comparison, the monitoring system will correct the data in the parameter correction model. The parameter correction model is trained in advance based on multiple terrain information, water flow disturbance information and corresponding water flow velocity influence data. The result difference reflects the deviation of the current model in some aspects. The monitoring system will adjust the relevant parameters in the model based on these differences. For example, if multiple comparison results show that the flow rate data of the monitoring system is generally lower than the measurement results of the professional team, it may mean that the model weights are set unreasonably when considering certain influencing factors. The monitoring system will increase the parameter weights related to these influencing factors so that the model can more accurately reflect the actual situation when subsequently calculating the flow rate correction parameters. This correction process may involve the adjustment of a large number of parameters in the model. Through continuous feedback optimization, the parameter correction model can more accurately determine the flow rate correction parameters, thereby improving the accuracy of the final water flow velocity data, allowing the monitoring system to more accurately reflect the actual flow rate of the water.

[0048] After combining the above content, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the method for high-precision monitoring of water flow velocity in an embodiment of the present application.

[0049] S201, obtaining seasonal information, and adjusting the scanning parameters of the side scan radar and setting the water flow threshold according to the preset strategy library; The monitoring system has the ability to obtain seasonal information. It can establish a data connection with the meteorological data platform to obtain the current seasonal information in real time. The meteorological data platform gathers a large number of observation data from meteorological monitoring stations. Through complex data analysis and processing, it can accurately determine the current season. The monitoring system is connected to these authoritative meteorological data platforms and receives seasonal information updates regularly through standardized data interfaces. At the same time, the monitoring system can also have a built-in seasonal judgment algorithm based on geographic location and time. According to the longitude and latitude information of the monitoring area, combined with the laws of the earth's revolution and the local climate characteristics, the current season is calculated by itself. For example, for the monitoring area in the mid-latitudes of the northern hemisphere, the system can make seasonal judgments based on the general rules that March to May is spring, June to August is summer, September to November is autumn, and December to February of the following year is winter.

[0050] After obtaining the seasonal information, the monitoring system compares it with the preset strategy library. The preset strategy library is established based on long-term research on the laws of water flow changes and monitoring experience. In the strategy library, the side-scan radar scanning parameter adjustment strategy and the water flow threshold adjustment strategy corresponding to different seasons are specified in detail. The scanning parameters of the side-scan radar include scanning frequency, scanning angle, etc. The settings of these parameters directly affect the accuracy and coverage of the monitoring data. Setting the water flow threshold is an important indicator for judging whether the water flow is in an abnormal state. By obtaining seasonal information and adjusting the side-scan radar parameters according to the preset strategy library, the monitoring system can adapt to the changes in water flow in different seasons and improve the quality and reliability of the monitoring data.

[0051] S202, when the seasonal information is within the preset key monitoring period, the scanning parameter is increased according to the preset amplitude, and the set water flow threshold is reduced; The preset key monitoring periods are usually determined based on historical data and actual experience, and these periods are often accompanied by high-risk factors of water flow changes. For example, during the summer flood season, rainfall increases significantly, river water levels rise rapidly, and water flow speeds increase sharply, which may cause disasters such as floods. In this case, accurate and timely grasp of water flow information is crucial for flood prevention and disaster reduction.

[0052] When the monitoring system determines that the current season information is in the preset key monitoring period, the scanning parameters of the side scan radar will be increased according to the preset amplitude. Increasing the scanning frequency allows the side scan radar to scan more times per unit time. This allows echo information on the surface of the water flow to be obtained more frequently, thereby capturing subtle changes in the speed and direction of the water flow more promptly. The scanning angle will also be adjusted accordingly. For example, when the scanning angle is 120° at ordinary times, it may be expanded to 150° during the key monitoring period to expand the monitoring range and ensure that more water areas where danger may occur are covered. By increasing the scanning parameters, the side scan radar can obtain more detailed and real-time water flow information, providing richer data support for accurately judging the state of the water flow.

[0053] At the same time, the monitoring system will lower the set water flow threshold. The set water flow threshold is a key indicator for determining whether the water flow is abnormal. During the key monitoring period, due to the complexity of the water flow conditions and the increase in potential dangers, lowering the set water flow threshold can detect potential dangerous water flow conditions earlier. For example, the water flow threshold set in normal times is 3 meters per second, and during key monitoring periods such as the flood season, the threshold may be lowered to 2.5 meters per second. Once the monitored water flow velocity reaches or exceeds this lowered threshold, the monitoring system will promptly trigger the early warning mechanism, which enables relevant personnel to learn about abnormal water flow conditions earlier, so that they have more time to take countermeasures, such as organizing personnel evacuation and strengthening flood control facilities.

[0054] S203, if the season information is within the preset basic monitoring period, the scanning parameter is reduced according to a preset amplitude, and the set water flow threshold is increased; The preset basic monitoring period is usually a period of time when the water flow is relatively stable and the water flow velocity and flow rate change relatively little. For example, in the winter dry season in some areas, precipitation decreases, the river water volume is stable, and the change range of water flow is within a relatively controllable range. During such a period, the accuracy and frequency requirements for water flow monitoring are relatively reduced. After the monitoring system recognizes that it is in the preset basic monitoring period, it will first reduce the scanning parameters of the side scan radar. The scanning parameters mainly include key elements such as scanning frequency and scanning angle. Reducing the scanning frequency can reduce the amount of data collected by the side scan radar, thereby reducing the operating load of the system, reducing energy consumption, and extending the service life of the equipment. At the same time, the scanning angle may also be adjusted according to actual conditions. At the same time, the monitoring system will increase the set water flow threshold. During the basic monitoring period, the water flow is relatively stable, the flow velocity is usually at a low level and the fluctuation is small. Increasing the set water flow threshold can avoid frequent triggering of warnings due to normal small fluctuations in the water flow and reduce the occurrence of false alarms.

[0055] S204: After receiving the viewing operation instruction sent by the user terminal, send corresponding data to the user terminal for display according to the viewing requirement in the viewing operation instruction.

[0056] The viewing operation instructions may include but are not limited to viewing instructions for real-time data, process profiles and flow comparisons sent by the user through the user terminal.

[0057] Specifically, for real-time data, the real-time data module mainly displays the flow rate, cross-sectional flow rate and other data calculated by the system based on flow rate, area, water level and other data. The specific functions are as follows: Figure 3 As shown in (a), the page displays the data of the "Shijiao" station by default, starting from the 24-hour data of the day. The conditions can be changed according to the user's requirements. Click the query button to display the page.

[0058] It also includes adding operations, such as Figure 3 As shown in (b), click the Add button to enter the Add page. After adding required data and other data, click the Save button at the bottom of the page to save the data.

[0059] It also includes editing operations, such as Figure 3 As shown in (c), click the Edit button to enter the editing page, where you can modify basic data such as water level, flow, cross-sectional flow velocity, process profile flow velocity, etc. The measuring station, time, and radar equipment have been determined to be unmodifiable when they are added. Click the Save button at the bottom of the page to save the data.

[0060] It also includes the details view operation. The details button is the view function of the data, and the data cannot be modified. It also includes the flow recalculation content. When the basic data has been added to the system, but the flow and cross-sectional flow velocity data are not displayed, or after the basic data has been modified, you can click the flow recalculation button to recalculate the flow. Delete: Delete the current data. After deletion, it cannot be restored. For details, Figure 3 (d) shown.

[0061] It also includes bulk deletion of content, such as Figure 3 As shown in (e), check the checkbox in front of the list, and the batch operation button will automatically appear. Click the delete button to perform batch deletion. It also includes export operations. The export function can set conditions according to user needs. Select the start and end time and click the query button. If you want to export all data, you don’t need to check it. If you check it, the checked data will be exported.

[0062] It also includes line chart display operations, such as Figure 3 (f) As shown, below the list is a line graph corresponding to the list data. The results of the line graph will change according to the changes in the list data; as shown in the figure below: the line graph displays two lines of flow and water level in the most recent time period by default. The user can display all the lines by checking the cross-sectional flow rate, index flow rate, and area buttons at position 1. The user can view the line graph of the entire time period by dragging the scroll bar at position 2.

[0063] The process profile column mainly displays the real-time flow velocity data of this station in the last three days. Figure 3 (g) shows that operations such as adding, modifying, and deleting are not allowed. The specific functions are as follows: Query: The top of the page is a conditional component. Users can modify the station name, time range, and other conditions by clicking the query button to display all data under the selected conditions. Export: Click the export button to export all data under the selected conditions. If the check box is checked, all the checked data will be exported. Set columns: There are many columns in this list. The default display shows the main slot value. Users can select the columns to be displayed.

[0064] The traffic comparison mainly shows the comparison list of traffic and other data calculated by radar software and this system, such as Figure 3 As shown in (h), the main data for comparison are water level and flow. The specific functions are as follows: Query: Users can select a time range and click the query button to display all data within the time range. Export: Users can check the box to export data or uncheck the box to export all data within the time range.

[0065] In the embodiment of the present application, the method of obtaining seasonal information and flexibly adjusting the side-scan radar scanning parameters and setting the water flow threshold according to the preset strategy library is adopted, and an interactive mechanism is constructed to provide diversified data display according to the user's viewing operation instructions, so that the monitoring system can adaptively monitor the water flow conditions in different seasons and interact with users efficiently. It effectively solves the problem that the existing monitoring system has a single monitoring strategy in different seasons and it is difficult to balance monitoring accuracy and resource consumption, thereby achieving the goal of optimizing resource allocation while ensuring the accuracy of monitoring data, improving the operating efficiency of the monitoring system, and helping water resources management and other fields to make scientific decisions based on accurate data.

[0066] The monitoring system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 4 , is a schematic diagram of a physical device structure of a monitoring system in an embodiment of the present application.

[0067] It should be noted that Figure 4 The structure of the monitoring system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0068] like Figure 4As shown, the monitoring system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, the ROM 402 and the RAM 403 are connected to each other through the bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0069] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, a button switch, etc.; an output section 407 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0070] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the present invention are performed.

[0071] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0072] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0073] Specifically, the monitoring system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the high-precision water flow velocity monitoring method provided in the above embodiment is implemented.

[0074] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the monitoring system described in the above embodiment; or may exist independently without being assembled into the monitoring system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the monitoring system, the monitoring system implements the high-precision water flow velocity monitoring method provided in the above embodiment.

[0075] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0076] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0077] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A high-precision water flow velocity monitoring method, applied to a monitoring system, characterized in that: The method comprises: Acquire side-scan radar monitoring data of multiple target sub-areas sent by multiple edge devices; Acquire terrain information and water disturbance information through multiple sensors; In combination with the side-scan radar monitoring data, the terrain information and the water flow disturbance information, initial water flow velocity data is determined by a water flow velocity model, wherein the water flow velocity model is obtained by processing a plurality of side-scan radar historical monitoring data in advance by a high-order polynomial fitting algorithm; Combining the terrain information and the water flow disturbance information, determining the water flow velocity correction parameter through a parameter correction model, wherein the parameter correction model is trained in advance based on a plurality of terrain information, water flow disturbance information and corresponding water flow velocity influence data; The final water flow velocity data is determined by combining the initial water flow velocity data and the water flow velocity correction parameter.

2. The method according to claim 1, characterized in that Before the step of acquiring the side-scan radar monitoring data of multiple target sub-areas sent by multiple edge devices, the step further includes: Obtain image information of the target sub-region; Inputting the image information into a water flow parameter model to determine the side scan radar monitoring data corresponding to the target sub-area, wherein the water flow parameter model is constructed in advance based on a plurality of historical image information and a label set of corresponding side scan radar monitoring data, wherein the side scan radar monitoring data includes a side scan radar scanning frequency and resolution; Control the side-scan radar to send side-scan radar monitoring data to multiple edge devices; The preliminary side-scan radar monitoring data is preprocessed using a plurality of the edge devices to obtain final side-scan radar monitoring data, wherein the preprocessing operation at least includes noise removal, data calibration, and data format conversion.

3. The method according to claim 1, characterized in that: After the step of determining the final water flow velocity data by combining the initial water flow velocity data and the water flow velocity correction parameter, the method further includes: If the final water flow velocity data is greater than the set water flow threshold, an early warning message is sent to the user end.

4. The method according to claim 1, characterized in that: After the step of determining the final water flow velocity data by combining the initial water flow velocity data and the water flow velocity correction parameter, the method further includes: Acquire seasonal information, and adjust the scanning parameters of the side-scan radar and the set water flow threshold according to a preset strategy library, wherein the strategy library includes increasing the scanning parameters by a preset amplitude and reducing the set water flow threshold if the seasonal information is within a preset key monitoring period; If the season information is within the preset basic monitoring period, the scanning parameter is reduced by a preset amplitude and the set water flow threshold is increased.

5. The method according to claim 1, characterized in that After the step of determining the final water flow velocity data by combining the initial water flow velocity data and the water flow velocity correction parameter, the method further includes: After receiving the special monitoring command from the user end, the key area information is obtained; Get location information of multiple edge devices; If the distance between the edge device and the key area information is within a set range, the computing power is adjusted according to the set key adjustment strategy.

6. The method according to claim 4, characterized in that After obtaining the seasonal information and adjusting the scanning parameters of the side scan radar and setting the water flow threshold according to the preset strategy library, it also includes: After receiving the viewing operation instruction sent by the user terminal, the corresponding data is sent to the user terminal for display according to the viewing requirement in the viewing operation instruction.

7. The method according to claim 1, characterized in that After the step of determining the final water flow velocity data by combining the initial water flow velocity data and the water flow velocity correction parameter, the method further includes: Receive actual measurement results sent by professional teams through the user end at set time intervals; Comparing the actual measurement result with the final water flow velocity data to obtain a result difference; The data in the parameter correction model is corrected according to the result difference.

8. A monitoring system, characterized in that: The monitoring system comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the monitoring system executes the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a monitoring system, the monitoring system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is executed on a monitoring system, the monitoring system is caused to execute the method according to any one of claims 1 to 7.

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