Flow measurement method and system for water quality safety control

Abstract

The invention provides a flow measurement method and system for water quality safety control. The flow measurement method comprises the following steps: measuring water flow at each monitoring point in real time through an ultrasonic flowmeter; determining attenuation interference of ultrasonic signals in each ultrasonic flowmeter according to the turbidity information of the water body at each monitoring point; performing association analysis on the historical water flow of each monitoring point to obtain an association relationship of the water flow among all the monitoring points; determining the mutual support degree between each monitoring point and other monitoring points in flow measurement according to the confidence distance between the monitoring points and the attenuation interference and incidence relation of the ultrasonic signals in the ultrasonic flowmeters; and when the real-time measured water body flow state is judged to be abnormal, confidence correction is performed on the real-time measured water body flow based on the mutual support degree, and the corrected water body flow is obtained. According to the scheme, relevance correction of the water flow among different monitoring points can be achieved, and therefore the reliability of flow measurement is improved.

Description

Technical Field

[0001] The present application relates to the technical field of flow measurement, and more specifically, to a flow measurement method and system for water quality safety control. Background Art

[0002] Flow measurement plays a vital role in water quality safety control. It can monitor the flow of water in real time, thereby helping to determine whether the water flow is affected by pollution sources and whether flow fluctuations are related to abnormal water quality events. The flow of water directly affects the propagation and dilution process of water quality, especially in situations such as water pollution and sewage discharge. Flow changes can reflect the diffusion and concentration changes of pollutants.

[0003] In the prior art, a single-point flow measurement method is usually used to monitor the water flow in the water quality safety control area. However, it is difficult for single-point flow measurement to effectively deal with the unevenness of water flow. The water flow is usually spatially uneven, and the water flow velocity varies greatly at different locations. The measurement of a single monitoring point cannot capture the overall trend and local fluctuations of the water flow, thereby affecting the accuracy of the overall water quality assessment. At the same time, although the ultrasonic flow meter can provide accurate flow data under ideal conditions, in actual applications, suspended particles, bubbles and dissolved gases in the water will attenuate the ultrasonic signal, resulting in reduced measurement accuracy, especially in an environment with complex or large changes in water quality. The interference is more significant. Therefore, single-point flow measurement cannot fully reflect the actual situation of the water flow. In contrast, combining the flow data of multiple monitoring points to perform correlation measurement of water flow can more accurately reflect the changes in water flow, thereby improving the reliability of flow measurement. Therefore, how to achieve correlation correction of water flow between different monitoring points, thereby improving the reliability of flow measurement has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a flow measurement method and system for water quality safety control, which can realize the correlation correction of water flow between different monitoring points, thereby improving the reliability of flow measurement.

[0005] In a first aspect, the present application provides a flow measurement method for water quality safety control, comprising the following steps: Set up multiple monitoring points in the target water quality safety monitoring area, and then use ultrasonic flow meters to measure the water flow at each monitoring point in real time; Obtaining turbidity information of water bodies at all monitoring points, and then determining the attenuation interference of ultrasonic signals in each ultrasonic flow meter based on the turbidity information of the water bodies; Collect the historical water flow at each monitoring point within a specified time period, and then conduct correlation analysis on all historical water flow to obtain the correlation relationship between water flow at all monitoring points; Determine the confidence distance between each monitoring point, and then determine the mutual support between each monitoring point and all other monitoring points in flow measurement based on all confidence distances, attenuation interference of ultrasonic signals in each ultrasonic flow meter and the correlation relationship; For each monitoring point, the state of the water flow measured in real time at the monitoring point is judged. When the water flow state is judged to be abnormal, a confidence correction is performed on the water flow measured in real time at the monitoring point based on all mutual supports and the water flow measured in real time at other monitoring points to obtain the corrected water flow.

[0006] Preferably, determining the attenuation interference of ultrasonic signals in each ultrasonic flow meter according to the turbidity information of the water body specifically includes: Construct an empirical model for analyzing the effect of water turbidity on ultrasonic signal attenuation; Extracting the turbidity value of the water medium at each monitoring point from the turbidity information of the water body; The turbidity value of the water medium at each monitoring point is used as the initialization parameter of the empirical model; The attenuation disturbance of the ultrasonic signal in each ultrasonic flow meter is determined by means of the empirical model.

[0007] Preferably, the correlation analysis of all historical water flow rates is performed to obtain the correlation relationship of water flow rates between all monitoring points, specifically including: Perform data standardization on the historical water flow at each monitoring point, and then convert all the standardized historical water flow into the flow time series of each monitoring point; Extract the temporal relationship between every two traffic time series based on long short-term memory network; Determine the correlation between water flow between every two monitoring points through all the time series relationships and the location information of each monitoring point; A correlation matrix is ​​constructed based on all correlation degrees, and the correlation matrix further describes the correlation relationship between the water flow rates of all monitoring points.

[0008] Preferably, determining the confidence distance between each monitoring point specifically includes: For every two monitoring points, the physical distance between the two monitoring points is collected; Obtain the historical water flow at two monitoring points, and then determine the similarity of the historical water flow between the two monitoring points; The confidence distance between two monitoring points is determined by the physical distance and the similarity, and then the confidence distance between every two monitoring points is obtained.

[0009] Preferably, confidence correction is performed on the water flow measured in real time at the monitoring point based on all mutual supports and the water flow measured in real time at other monitoring points, and the corrected water flow is obtained specifically including: Determine the correction weight of water flow based on all mutual supports; Determine the confidence correction amount of the real-time measured water flow at the monitoring point through the real-time measured water flow at other monitoring points and the correction weight; The water flow rate measured in real time at the monitoring point is corrected according to the confidence correction amount to obtain a corrected water flow rate.

[0010] Preferably, the specified time period is a time period corresponding to the current moment to a specified time in the past.

[0011] Preferably, the ultrasonic flowmeter is a time-difference ultrasonic flowmeter.

[0012] In a second aspect, the present application provides a flow measurement system for water quality safety control, comprising: A measurement module is used to set up multiple monitoring points in the target water quality safety monitoring area, and then measure the water flow at each monitoring point in real time through an ultrasonic flow meter; A processing module, used to obtain turbidity information of water bodies at all monitoring points, and then determine the attenuation interference of ultrasonic signals in each ultrasonic flow meter according to the turbidity information of the water bodies; The processing module is also used to collect the historical water flow at each monitoring point within a specified time period, and then perform correlation analysis on all the historical water flow to obtain the correlation relationship between the water flow of all monitoring points; The processing module is further used to determine the confidence distance between each monitoring point, and then determine the mutual support between each monitoring point and all other monitoring points in the flow measurement according to all the confidence distances, the attenuation interference of the ultrasonic signal in each ultrasonic flow meter and the association relationship; The correction module is used to judge the water flow state measured in real time at each monitoring point. When the water flow state is judged to be abnormal, the water flow measured in real time at the monitoring point is confidence corrected based on all mutual supports and the water flow measured in real time at other monitoring points to obtain the corrected water flow.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned flow measurement method for water quality safety control.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the flow measurement method for water quality safety control is implemented.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In an embodiment of the present application, a plurality of monitoring points are set in a target water quality safety monitoring area, and then the water flow at each monitoring point is measured in real time by an ultrasonic flow meter; the turbidity information of the water at all monitoring points is obtained, and then the attenuation interference of the ultrasonic signal in each ultrasonic flow meter is determined based on the turbidity information of the water; the historical water flow at each monitoring point within a specified time period is collected, and then all the historical water flow rates are correlated and analyzed to obtain the correlation relationship between the water flow rates of all monitoring points; the confidence distance between each monitoring point is determined, and then the mutual support between each monitoring point and all other monitoring points in the flow measurement is determined based on all the confidence distances, the attenuation interference of the ultrasonic signal in each ultrasonic flow meter and the correlation relationship; for each monitoring point, the state of the water flow measured in real time at the monitoring point is determined, and when the water flow state is determined to be abnormal, the water flow measured in real time at the monitoring point is confidence corrected based on all the mutual supports and the water flow measured in real time at other monitoring points to obtain the corrected water flow.

[0016] It can be seen that the present application determines the mutual support between the monitoring point and all other monitoring points in the flow measurement through the confidence distance between each monitoring point, the attenuation interference of the ultrasonic signal in the ultrasonic flowmeter and the correlation relationship between the water flow between the monitoring points, and then performs confidence correction on the water flow measured in real time at the monitoring point based on the mutual support to obtain the corrected water flow; firstly, by analyzing the attenuation interference of the ultrasonic signal in each ultrasonic flowmeter through the water turbidity information, it is possible to reduce the impact of the decreased measurement accuracy of the ultrasonic flowmeter in a complex water quality environment, thereby improving the initial accuracy and stability of the measurement data; secondly, historical water flow data within a specified time period is collected, and the correlation relationship between the flow rates between the monitoring points is extracted by correlation analysis, and the spatiotemporal characteristics of the flow changes are explored, thereby making up for the single-point flow data that cannot be reflected. The problem of overall trend and local fluctuation is revealed; then, by determining the confidence distance between each monitoring point, and then combining the attenuation interference and correlation relationship of the ultrasonic signal to determine the mutual support between each monitoring point and other monitoring points, the correlation quantitative analysis between the water flow data can be realized, thereby providing mutual verification and support for the water flow data of each monitoring point; finally, in real-time flow measurement, through the flow state judgment and confidence correction steps of the monitoring point, the abnormal water flow data is dynamically corrected, and the mutual support between each monitoring point and other monitoring points and the real-time flow data of other monitoring points are used to perform flow correlation correction, thereby improving the accuracy and anti-interference ability of flow measurement; in summary, the present application scheme can realize the correlation correction of water flow between different monitoring points, thereby improving the reliability of flow measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a flow measurement method for water quality safety control according to some embodiments of the present application; Figure 2 It is a schematic diagram showing the structure of the monitoring point distribution according to some embodiments of the present application; Figure 3 is a schematic diagram of a process for determining attenuated interference according to some embodiments of the present application; Figure 4 is a schematic structural diagram of a flow measurement system for water quality safety control according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing a flow measurement method for water quality safety control according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a flow measurement method for water quality safety control according to some embodiments of the present application. The flow measurement method 100 for water quality safety control mainly includes the following steps: In step 101, a plurality of monitoring points are set in a target water quality safety monitoring area, and then the water flow at each monitoring point is measured in real time by an ultrasonic flow meter.

[0020] For specific implementation, refer to Figure 2 As shown, this figure is a schematic diagram of the structure of the distribution of monitoring points in some embodiments of the present application. A number of monitoring points are deployed in the target water quality safety monitoring area, and the number of monitoring points is at least 4. An ultrasonic flowmeter is equipped at each monitoring point, and the sampling frequency of the ultrasonic flowmeter is set. The sampling frequency is usually set between 10 and 100 Hz. In other embodiments, the sampling frequency and can be set in other sampling frequency ranges, which are not specifically limited here. The water flow data at each monitoring point is measured in real time by the ultrasonic flowmeter at each monitoring point. The water flow data is numerical data, wherein the ultrasonic flowmeter calculates the flow velocity of the water body by the propagation speed of the ultrasonic signal, and then obtains the water flow at the monitoring point; it should be noted that the ultrasonic flowmeter in the present application is a time difference ultrasonic flowmeter.

[0021] In step 102, turbidity information of water bodies at all monitoring points is obtained, and then the attenuation interference of ultrasonic signals in each ultrasonic flow meter is determined according to the turbidity information of the water bodies.

[0022] It should be noted that the turbidity information of the water body in the present application reflects the influence of suspended particles, microorganisms and organic matter in the water on light scattering and absorption. The turbidity information usually reflects the turbidity of the water body by measuring the light transmittance of the water sample. In specific implementation, the turbidity information of the water body at all monitoring points can be obtained in the following way, namely: turbidity is an important indicator for water quality monitoring. The commonly used measurement method is to use an optical turbidity meter to quantify the turbidity of the water body by detecting the change in the transmittance of light of a specific wavelength in water. The optical turbidity meter can be arranged at each monitoring point, and then the turbidity of the water body at the monitoring point is measured by the optical turbidity meter.

[0023] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of the process of determining attenuation interference in some embodiments of the present application. In this embodiment, the attenuation interference of the ultrasonic signal in each ultrasonic flow meter is determined according to the turbidity information of the water body, which can be achieved by the following steps: In step 1021, an empirical model for analyzing the effect of water turbidity on ultrasonic signal attenuation is constructed; In step 1022, the turbidity value of the water medium at each monitoring point is extracted from the turbidity information of the water body; In step 1023, the turbidity value of the water medium at each monitoring point is used as an initialization parameter of the empirical model; In step 1024, the attenuation interference of the ultrasonic signal in each ultrasonic flow meter is determined by using the empirical model.

[0024] In the specific implementation, first, an empirical model is constructed based on the experimental data of the analysis of the influence of water turbidity on ultrasonic signal attenuation. The empirical model can describe the relationship between water turbidity and ultrasonic signal attenuation. It should be noted that in the process of constructing the empirical model in the present application, regression analysis (such as polynomial regression) can be used to establish the mathematical relationship between turbidity and ultrasonic signal attenuation; secondly, the turbidity value of the water medium at each monitoring point is extracted from the turbidity information of the water body, and the turbidity value is the turbidity of the water body measured by the optical turbidity meter; then, the turbidity value of the water medium at each monitoring point is input into the empirical model for initialization; finally, the attenuation degree of the ultrasonic signal of each ultrasonic flowmeter in the process of measuring the water flow is calculated by the empirical model, wherein the empirical model will combine the flow velocity, turbidity and propagation path of the water body to calculate the attenuation of the ultrasonic signal at each monitoring point, and the attenuation is used as the attenuation interference of the ultrasonic signal in the ultrasonic flowmeter at the corresponding monitoring point, and the magnitude of the attenuation interference can be expressed by the relative signal strength (ratio to the ideal signal).

[0025] It should be noted that the attenuation interference of the ultrasonic signal in this application represents the strength of the resistance of suspended particles, dissolved gases, and microorganisms in the water to the propagation of ultrasonic waves; it should also be noted that when the turbidity of the water body is high, the suspended matter will absorb and scatter the ultrasonic signal, resulting in weakened or distorted signal intensity, thereby affecting the measurement accuracy of the ultrasonic flow meter.

[0026] In step 103, the historical water flow at each monitoring point within a specified time period is collected, and then all the historical water flow is subjected to correlation analysis to obtain the correlation relationship between the water flow between all the monitoring points.

[0027] In specific implementation, the collection of historical water flow at each monitoring point within a specified time period can be achieved in the following manner, namely: first, a range of the specified time period is preset, and the specified time period is the time period corresponding to the current moment to the specified time in the past. In an embodiment of the present application, the range of the specified time period can be set to the time period corresponding to the current moment to the past 1 year. In other embodiments, the range of the specified time period can also be set within other ranges, which are not specifically limited here; then, the water flow measured in real time by the ultrasonic flowmeter at each monitoring point within the specified time period is used as the historical water flow at the corresponding monitoring point.

[0028] In some embodiments, correlation analysis is performed on all historical water flow rates to obtain the correlation relationship between water flow rates at all monitoring points, which can be achieved by using the following steps: Perform data standardization on the historical water flow at each monitoring point, and then convert all the standardized historical water flow into the flow time series of each monitoring point; Extract the temporal relationship between every two traffic time series based on long short-term memory network; Determine the correlation between water flow between every two monitoring points through all the time series relationships and the location information of each monitoring point; A correlation matrix is ​​constructed based on all correlation degrees, and the correlation matrix further describes the correlation relationship between the water flow rates of all monitoring points.

[0029] In the specific implementation, firstly, the historical water flow data of each monitoring point is standardized, and the Z-score standardization method is used to unify the historical water flow data of each monitoring point into the same dimension and range, and then the standardized historical water flow is arranged into a sequence according to the chronological order of collection time, and the obtained sequence is used as the flow time series of each monitoring point; secondly, the standardized flow time series is modeled using a long short-term memory network to extract the time series relationship between every two monitoring points, where the long short-term memory network is a neural network that is particularly suitable for time series data, which can effectively capture the correlation of long time spans; then, the physical distance between every two monitoring points is extracted from the location information of each monitoring point. The natural exponential function value of the inverse of the physical distance is used as the adjustment factor of the distance between each two monitoring points. For each two monitoring points, the product of the temporal relationship between the corresponding flow time series of the two monitoring points and the adjustment factor of the distance between the two monitoring points is used as the correlation of the water flow between the two monitoring points, and then the correlation of the water flow between each two monitoring points is obtained. The correlation measures the dynamic correlation strength of the water flow time series between the two monitoring points, and can reflect the degree of their coordinated changes in the time dimension. Finally, all the correlations are arranged into a symmetric matrix according to the positions between the monitoring points, and the symmetric matrix is ​​used as the correlation matrix, and then the correlation matrix can be used to describe the correlation relationship between the water flow of all monitoring points.

[0030] It should be noted that the correlation relationship in the present application represents the temporal correlation of water flow between different monitoring points; it should also be noted that the correlation relationship can reveal the dynamic relationship between the water flow data of different monitoring points changing over time. By analyzing this dynamic relationship, the future flow change trend can be predicted, thereby identifying abnormal fluctuations in water flow and providing a quantitative basis for the mutual influence of water flow data between different monitoring points.

[0031] In step 104, the confidence distance between each monitoring point is determined, and then the mutual support between each monitoring point and all other monitoring points in flow measurement is determined based on all the confidence distances, the attenuation interference of the ultrasonic signal in each ultrasonic flow meter and the association relationship.

[0032] In some embodiments, determining the confidence distance between each monitoring point may be achieved by using the following steps: For every two monitoring points, the physical distance between the two monitoring points is collected; Obtain the historical water flow at two monitoring points, and then determine the similarity of the historical water flow between the two monitoring points; The confidence distance between two monitoring points is determined by the physical distance and the similarity, and then the confidence distance between every two monitoring points is obtained.

[0033] In the specific implementation, for every two monitoring points, first, use the geographic information system software to import the coordinate data of the two monitoring points, and use the built-in distance calculation function of the geographic information system software to directly obtain the physical distance between the two monitoring points; then, calculate the Euclidean distance of the historical water flow between the two monitoring points, and use the Euclidean distance as the similarity of the historical water flow between the two monitoring points; finally, use the product of the physical distance between the two monitoring points and the similarity as the confidence distance between the two monitoring points, repeat the above steps, and you can get the confidence distance between every two monitoring points; it should be noted that the confidence distance in this application measures the distance metric of the measurement intensity between the monitoring points.

[0034] In some embodiments, the mutual support between each monitoring point and all other monitoring points in flow measurement can be determined based on all confidence distances, attenuation interference of ultrasonic signals in each ultrasonic flow meter and the association relationship, which can be achieved by the following steps: A monitoring point is selected as a selected monitoring point, and the interference amount of the selected monitoring point is determined by the confidence distance between the selected monitoring point and each other monitoring point and the attenuation interference of the ultrasonic signal in the ultrasonic flowmeter corresponding to the selected monitoring point; The mutual support between the selected monitoring point and all other monitoring points is determined according to the interference amount and the correlation between the selected monitoring point and each other monitoring point in the correlation relationship, and the mutual support between the remaining monitoring points and all other monitoring points is further determined.

[0035] In the specific implementation, first, a monitoring point is selected from all monitoring points as the selected monitoring point, and the confidence distance between the selected monitoring point and each other monitoring point is obtained, and the average value of all confidence distances is used as the average confidence distance, and then the product between the natural exponential function of the inverse of the average confidence distance and the attenuation interference of the ultrasonic signal in the ultrasonic flowmeter corresponding to the selected monitoring point is used as the interference amount of the selected monitoring point; then, the correlation between the selected monitoring point and each other monitoring point is obtained from the description matrix of the association relationship (i.e., the association matrix), and the average value of all correlations is used as the average correlation between the selected monitoring point and all other monitoring points, and further the product between the average correlation and the interference amount is used as the mutual support between the selected monitoring point and all other monitoring points, and the above steps are repeated to determine the mutual support between the remaining monitoring points and all other monitoring points.

[0036] It should be noted that the mutual support in the present application indicates the degree of credibility of the measurement results of the ultrasonic flowmeter in flow measurement. The higher the mutual support, the higher the credibility of the measurement results of the ultrasonic flowmeter in flow measurement. The specific manifestation is that in flow measurement, the higher the consistency between the results of the selected monitoring point and all other monitoring points measured by the ultrasonic flowmeter, the more reliable the measurement results of the ultrasonic flowmeter at the selected monitoring point are.

[0037] In step 105, for each monitoring point, the state of the water flow measured in real time at the monitoring point is determined. When the water flow state is determined to be abnormal, a confidence correction is performed on the water flow measured in real time at the monitoring point based on all mutual supports and the water flow measured in real time at other monitoring points to obtain a corrected water flow.

[0038] In specific implementation, for each monitoring point, judging the state of water flow measured in real time at the monitoring point can be achieved in the following manner, namely: first, collecting historical water flow data and related environmental variables (such as water turbidity and temperature) as training data sets, and then using supervised learning algorithms (such as support vector machines) to establish a mapping relationship between flow states and environmental factors, and then completing the training of the recognition model. After further inputting the water flow measured in real time at the monitoring point into the recognition model, the recognition model can predict the state of the current water flow based on the existing pattern and determine whether it is abnormal. For example, when the real-time water flow deviates from the historical normal range, the recognition model will identify it as an abnormal state. Furthermore, through the judgment results of the recognition model, it can quickly respond to abnormal water flow conditions and perform corresponding data corrections.

[0039] In some embodiments, confidence correction is performed on the water flow rate measured in real time at the monitoring point based on all mutual supports and the water flow rate measured in real time at other monitoring points, and the corrected water flow rate can be obtained by the following steps: Determine the correction weight of water flow based on all mutual supports; Determine the confidence correction amount of the real-time measured water flow at the monitoring point through the real-time measured water flow at other monitoring points and the correction weight; The water flow rate measured in real time at the monitoring point is corrected according to the confidence correction amount to obtain a corrected water flow rate.

[0040] In the specific implementation, first, the detection point where the water flow state is judged to be abnormal is taken as the target monitoring point, and then the mutual support between the target monitoring point and all other monitoring points is obtained from all the mutual supports, and the natural exponential function value of the inverse of the mutual support is used as the correction weight of the real-time measurement of the water flow at the target monitoring point; then, the average value of the water flow measured in real time at other monitoring points is taken as the reference flow, and the difference between the real-time measurement of the water flow at the target monitoring point and the reference flow is taken as the flow deviation, and the product of the flow deviation and the correction weight is further taken as the confidence correction amount of the real-time measurement of the water flow at the target monitoring point; finally, the sum of the real-time measurement of the water flow at the target monitoring point and the confidence correction amount is taken as the corrected water flow.

[0041] On the other hand, in some embodiments, the present application provides a flow measurement system for water quality safety control, referring to Figure 4 , which is a schematic structural diagram of a flow measurement system for water quality safety control according to some embodiments of the present application. The flow measurement system 400 for water quality safety control includes: a measurement module 401, a processing module 402 and a correction module 403, which are described as follows: Measuring module 401, in this application, the measuring module 401 is mainly used to set up multiple monitoring points in the target water quality safety monitoring area, and then measure the water flow at each monitoring point in real time through an ultrasonic flow meter; Processing module 402, in the present application, the processing module 402 is used to obtain turbidity information of the water body at all monitoring points, and then determine the attenuation interference of the ultrasonic signal in each ultrasonic flow meter according to the turbidity information of the water body; The processing module 402 in the present application is also used to collect the historical water flow at each monitoring point within a specified time period, and then perform correlation analysis on all the historical water flow to obtain the correlation relationship between the water flow of all monitoring points; The processing module 402 in the present application is also used to determine the confidence distance between each monitoring point, and then determine the mutual support between each monitoring point and all other monitoring points in the flow measurement according to all the confidence distances, the attenuation interference of the ultrasonic signal in each ultrasonic flow meter and the association relationship; Correction module 403. In the present application, correction module 403 is mainly used to determine the state of water flow measured in real time at each monitoring point. When the water flow state is judged to be abnormal, confidence correction is performed on the water flow measured in real time at the monitoring point based on all mutual supports and the water flow measured in real time at other monitoring points to obtain the corrected water flow.

[0042] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned flow measurement method for water quality safety control.

[0043] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a flow measurement method for water quality safety control according to some embodiments of the present application. The flow measurement method for water quality safety control in the above embodiments can be achieved by Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0044] The processor 501 may be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0045] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0046] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0047] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The flow measurement method for water quality safety control in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0048] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0049] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0050] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0051] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the flow measurement method for water quality safety control is implemented.

[0052] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A flow measurement method for water quality safety control, characterized in that: The steps include: Set up multiple monitoring points in the target water quality safety monitoring area, and then use ultrasonic flow meters to measure the water flow at each monitoring point in real time; Obtaining turbidity information of water bodies at all monitoring points, and then determining the attenuation interference of ultrasonic signals in each ultrasonic flow meter based on the turbidity information of the water bodies; Collect the historical water flow at each monitoring point within a specified time period, and then conduct correlation analysis on all historical water flow to obtain the correlation relationship between water flow at all monitoring points; Determine the confidence distance between each monitoring point, and then determine the mutual support between each monitoring point and all other monitoring points in flow measurement based on all the confidence distances, the attenuation interference of the ultrasonic signal in each ultrasonic flow meter and the correlation relationship; For each monitoring point, the state of the water flow measured in real time at the monitoring point is judged. When the water flow state is judged to be abnormal, a confidence correction is performed on the water flow measured in real time at the monitoring point based on all mutual supports and the water flow measured in real time at other monitoring points to obtain the corrected water flow.

2. The method according to claim 1, characterized in that Determining the attenuation interference of ultrasonic signals in each ultrasonic flow meter according to the turbidity information of the water body specifically includes: Construct an empirical model for analyzing the effect of water turbidity on ultrasonic signal attenuation; Extracting the turbidity value of the water medium at each monitoring point from the turbidity information of the water body; The turbidity value of the water medium at each monitoring point is used as the initialization parameter of the empirical model; The attenuation disturbance of the ultrasonic signal in each ultrasonic flow meter is determined by means of the empirical model.

3. The method according to claim 1, characterized in that The correlation analysis of all historical water flow is carried out to obtain the correlation relationship between water flow of all monitoring points, including: Perform data standardization on the historical water flow at each monitoring point, and then convert all the standardized historical water flow into the flow time series of each monitoring point; Extract the temporal relationship between every two traffic time series based on long short-term memory network; Determine the correlation between water flow between every two monitoring points through all the time series relationships and the location information of each monitoring point; A correlation matrix is ​​constructed based on all correlation degrees, and the correlation matrix further describes the correlation relationship between the water flow rates of all monitoring points.

4. The method according to claim 1, characterized in that Determining the confidence distance between each monitoring point specifically includes: For every two monitoring points, the physical distance between the two monitoring points is collected; Obtain the historical water flow at two monitoring points, and then determine the similarity of the historical water flow between the two monitoring points; The confidence distance between two monitoring points is determined by the physical distance and the similarity, and then the confidence distance between every two monitoring points is obtained.

5. The method according to claim 1, characterized in that Based on all mutual supports and the water flow measured in real time at other monitoring points, the water flow measured in real time at the monitoring point is confidence corrected, and the corrected water flow specifically includes: Determine the correction weight of water flow based on all mutual supports; Determine the confidence correction amount of the real-time measured water flow at the monitoring point through the real-time measured water flow at other monitoring points and the correction weight; The water flow rate measured in real time at the monitoring point is corrected according to the confidence correction amount to obtain a corrected water flow rate.

6. The method according to claim 1, characterized in that The specified time period is the time period corresponding to the current time to the specified time in the past.

7. The method according to claim 1, characterized in that The ultrasonic flowmeter is a time difference ultrasonic flowmeter.

8. A flow measurement system for water quality safety control, characterized in that: include: A measurement module is used to set up multiple monitoring points in the target water quality safety monitoring area, and then measure the water flow at each monitoring point in real time through an ultrasonic flow meter; A processing module, used to obtain turbidity information of water bodies at all monitoring points, and then determine the attenuation interference of ultrasonic signals in each ultrasonic flow meter according to the turbidity information of the water bodies; The processing module is also used to collect the historical water flow at each monitoring point within a specified time period, and then perform correlation analysis on all the historical water flow to obtain the correlation relationship between the water flow of all monitoring points; The processing module is further used to determine the confidence distance between each monitoring point, and then determine the mutual support between each monitoring point and all other monitoring points in the flow measurement according to all the confidence distances, the attenuation interference of the ultrasonic signal in each ultrasonic flow meter and the association relationship; The correction module is used to judge the water flow state measured in real time at each monitoring point. When the water flow state is judged to be abnormal, the water flow measured in real time at the monitoring point is confidence corrected based on all mutual supports and the water flow measured in real time at other monitoring points to obtain the corrected water flow.

9. A computer device, comprising a memory and a processor, wherein the memory stores a code, characterized in that: The processor is configured to obtain the code and execute the flow measurement method for water quality safety control according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the flow measurement method for water quality safety control according to any one of claims 1 to 7 is implemented.

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