CN112013910B - Drainage pipe network flow detection method and device, server and storage medium - Google Patents
Drainage pipe network flow detection method and device, server and storage medium Download PDFInfo
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Abstract
The invention provides a drainage pipe network flow detection method, which comprises the following steps: acquiring original flow speed data of a drainage pipe network; performing phase space reconstruction on the original flow velocity data to generate a track matrix; performing singular value decomposition on the orbit matrix to generate n singular value points; performing signal-noise separation on the n singular value points to generate de-noising flow speed data; performing smooth fitting on the de-noising flow velocity data to obtain processed flow velocity data; and determining the flow of the drainage pipe according to the processed flow speed data. In the embodiment, by adopting the improved singular value point algorithm, the noise signal interference can be removed, so that the flow detection of the drain pipe is more accurate.
Description
Technical Field
The embodiment of the invention relates to the field of urban water system monitoring, in particular to a drainage pipe network flow detection method, a drainage pipe network flow detection device, a drainage pipe network flow detection server and a storage medium.
Background
Along with the acceleration of the urbanization process, the length of the urban drainage pipe network is rapidly increased, the influence of the discharge of the rainwater and sewage on the urban water environment is increasingly serious, and the accurate measurement of the drainage flow rate has important significance on the discharge control of the rainwater and sewage, the water quantity and quality scheduling of a sewage treatment plant, the hydrodynamic simulation of the pipe network and the simulation and forecast of flood. How to perform error and feature analysis on the measured data of the drainage flow becomes one of the current research hotspots, and the key point is that a mathematical model reflecting the dynamic change process of the drainage flow can be verified or obtained through data processing such as nonlinear fitting of the measured data. Because the drainage pipeline is in the bad working conditions of non-full pipe, high turbidity, siltation at the bottom of the pipe, more oil stain and floating materials on the water surface and filling of inflammable/explosive gas, the flow rate data obtained by measurement is easily interfered by noise, and the flow rate data obtained by fitting needs to be fitted and predicted by the existing flow rate data.
The processing of the traditional drainage pipe network measured flow velocity data is mostly carried out through manual processing, namely, abnormal data are manually judged, and bad values are removed and repaired one by one. The data volume of the flow dynamic process measurement is large, the manual processing efficiency is low, and time and labor are wasted. On the other hand, the process quantity has volatility, so that the accuracy of artificially judging the bad value is not high, and the normal value is easy to eliminate.
Disclosure of Invention
The invention provides a drainage pipe network flow detection method, a drainage pipe network flow detection device, a drainage pipe network flow detection server and a storage medium.
In a first aspect, a method for detecting the flow of a drainage pipe network comprises the following steps:
acquiring original flow speed data of a drainage pipe network;
performing phase space reconstruction on the original flow velocity data to generate a track matrix;
performing singular value decomposition on the orbit matrix to generate n singular value points;
performing signal-noise separation on the n singular value points to generate de-noising flow speed data;
performing smooth fitting on the de-noising flow velocity data to obtain processed flow velocity data;
and determining the flow of the drainage pipe according to the processed flow speed data.
Further, the performing phase space reconstruction on the raw flow velocity data to generate an orbit matrix includes:
acquiring a corresponding time series based on the raw flow rate data;
and embedding the time sequence into a reconstructed track matrix according to a preset delay.
Further, the performing singular value decomposition on the orbit matrix to generate n singular value points includes:
performing orthogonal expansion on the orbit matrix to generate a covariance matrix;
and performing singular value decomposition on the covariance matrix to generate n singular value points.
Further, the performing signal-to-noise separation on the n singular value points to generate denoised flow velocity data includes:
setting k singular value points in the n singular value points as flow velocity signals, and setting n-k singular value points which are not selected as noise signals, wherein k is more than 0 and less than n;
performing phase space reconstruction on the flow velocity signal based on a first preset formula to generate a first singular spectrum curve;
performing phase space reconstruction on the noise signal based on a second preset formula to generate a second singular spectrum curve;
determining the value of k based on the variation trend of the first singular spectrum curve and the second singular spectrum curve;
and taking the k singular value points as the de-noising flow velocity data.
Further, the setting k singular value points of the n singular value points as flow velocity signals includes:
and sequencing the n singular value points from large to small in sequence, and setting the first k singular value points as the flow velocity signal.
Further, the performing smooth fitting on the de-noised flow velocity data to obtain processed flow velocity data includes:
to the aboveThe de-noised flow velocity data performs window filtering to determine a minimum value v of the in-window flow velocityminWindow median value of flow velocity vmedMaximum value of flow velocity v in windowmaxWindow median value of flow velocity vmed；
Judging the vmin、vmedAnd vmaxWhether or not the magnitude relation of (1) is in accordance with vmin＜vmed＜vmax；
If not, increasing the size of a filtering window, and executing window filtering on the denoising flow speed data;
judging the v againmin、vmedAnd vmaxWhether or not the magnitude relation of (1) is in accordance with vmin＜vmed＜vmax；
Repeating the above steps until vmin、vmedAnd vmaxIs in accordance with vmin＜vmed＜vmax；
Determining | vi-vmedWhether or not | is less than or equal to a preset threshold, wherein vi denotes the ith flow rate data within the window;
if the current flow rate is less than or equal to a preset threshold value, outputting the vi as processed flow rate data;
if the v is larger than a preset threshold value, the v is addedmedThe output is the processed flow rate data.
In a second aspect, the present invention provides a device for detecting the flow rate of a drainage pipe network, comprising:
the first acquisition module is used for acquiring original flow speed data of the drainage pipe network;
the matrix establishing module is used for executing phase space reconstruction on the original flow velocity data to generate a track matrix;
the singular value decomposition module is used for performing singular value decomposition on the orbit matrix to generate n singular value points;
the signal-noise separation module is used for performing signal-noise separation on the n singular value points to generate de-noising flow speed data;
the smooth fitting module is used for performing smooth fitting on the de-noising flow speed data to obtain processed flow speed data;
and the flow determining module is used for determining the flow of the drainage pipe according to the processed flow speed data.
Further, the signal-to-noise separation module includes:
a k value setting unit, configured to set k singular value points of the n singular value points as flow velocity signals, and n-k singular value points that are not selected as noise signals, where 0 < k < n;
the first reconstruction unit is used for performing phase space reconstruction on the flow velocity signal based on a first preset formula to generate a first singular spectrum curve;
the second reconstruction unit is used for performing phase space reconstruction on the noise signal based on a second preset formula to generate a second singular spectrum curve;
a k value determining unit, configured to determine a value of k based on a variation trend of the first singular spectral curve and the second singular spectral curve;
and the signal-noise separation unit is used for taking the k singular value points as the de-noising flow speed data.
In a third aspect, the present invention provides a server, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor executes the program to implement a method for detecting a flow rate of a drainage pipe network as described in any one of the above.
In a fourth aspect, the present invention provides a terminal readable storage medium, on which a program is stored, which when executed by a processor is capable of implementing a method for drain network flow detection according to any one of claims 1 to 6.
By adopting the improved singular value point algorithm, the noise signal interference can be removed, and the flow detection of the drain pipe is more accurate.
Drawings
Fig. 1 is a flowchart of a method for detecting a flow rate of a drainage pipe network according to a first embodiment of the present invention.
Fig. 2 is a plot of raw flow rate data measured by an acoustic doppler ultrasonic flow meter.
Fig. 3 is a flow rate data point diagram based on a three-order sliding smoothing low-pass filtering process.
Fig. 4 is a flow velocity data point diagram after the singular spectrum analysis of the first embodiment.
Fig. 5 is a flowchart of a flow detection method for a drainage pipe network according to the second embodiment.
Fig. 6 is a flow velocity data point diagram after processing of the improved singular spectrum analysis of the second embodiment.
Fig. 7 is a functional block diagram of a third embodiment.
Fig. 8 is a functional block diagram of three alternative embodiments of the present embodiment.
Fig. 9 is a block diagram of a server in the fourth embodiment.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first feature information may be the second feature information or the third feature information, and similarly, the second feature information and the third feature information may be the first feature information without departing from the scope of the present application. The first characteristic information, the second characteristic information and the third characteristic information are characteristic information of the distributed file system, but are not the same characteristic information. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "plurality", "batch" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
The embodiment provides a method for detecting the flow of a drainage pipe network, which describes in detail the removal of noise clutter signals in flow rate data on the basis of the above embodiment, so that the flow measurement is more accurate, as shown in fig. 1, the steps are as follows:
s101, acquiring original flow speed data of a drainage pipe network.
In this step, raw flow rate data is obtained by a flow meter provided in the drainage pipe network, such as an ultrasonic flow meter, a mechanical flow meter, etc., for describing the flow rate of water in the drainage pipe network, i.e., the displacement of water in the drainage pipe per unit time, in meters per second (m/s). The flow rate of the water flow per unit time can be calculated through the flow speed. Because the water flow contains impurities and suspended particles, the original flow speed data detected by the flow meter contains a water flow speed signal and a noise signal, and operations such as denoising and smoothing are required.
And S102, performing phase space reconstruction on the original flow velocity data to generate a track matrix.
The step of obtaining a corresponding time sequence based on the original flow velocity data, and embedding the time sequence into a reconstructed orbit matrix according to a preset delay. Illustratively, by obtaining a time series { Xi }, i 1, 2.. N of N flow rate signals collected in the drainage official network, the reconstructed orbit matrix X of dimension M is embedded with a delay of 1, as follows:
s103, performing singular value decomposition on the orbit matrix to generate n singular value points.
In this step, singular value decomposition is performed on X to obtain M singular values e arranged in an increasing order1≥e2≥…≥emAnd (5) more than or equal to 0, arranging singular value points in sequence and drawing to obtain a singular spectrum of the signal. Specifically, the singular value decomposition process is as follows:
a. and performing orthogonal expansion on the orbit matrix to generate a covariance matrix.
b. And performing singular value decomposition on the covariance matrix to generate n singular value points.
Specifically, taking the above steps as an example, the orbit matrix X in the above step S102 is subjected to empirical orthogonal expansion, and let C be mxm dimensional covariance matrix of the matrix X, so that the covariance matrix C is XXT/n。
ekThe corresponding feature vector is EkThe k-th principal component of the signal is defined as the orthogonal projection coefficient of the original sequence { Xi } on Ek:
and S104, performing signal-noise separation on the n singular value points to generate de-noising flow speed data.
And setting k singular value points in the n singular value points as flow velocity signals, and setting n-k singular value points which are not selected as noise signals, wherein k is more than 0 and less than n.
Specifically, the step may be: and sequencing the n singular value points from large to small in sequence, setting the first k singular value points as the flow velocity signal, and performing phase space reconstruction on the flow velocity signal based on a first preset formula to generate a first singular spectrum curve. And performing phase space reconstruction on the noise signal based on a second preset formula to generate a second singular spectrum curve, determining the value of k based on the variation trend of the first singular spectrum curve and the second singular spectrum curve, and taking the k singular value points as the de-noising flow velocity data.
Specifically, the points with the k finite singular values in the front are selected as the principal components of the signal, and k is expressed as the following formula
Computing a reconstructed signal x and a residual principal component reconstructed noise signal nk：
In the step, as the energy of the noise-containing flow velocity signal is greater than the energy of the noise signal, the first k flow velocity signals are set, and the rest smaller singular value energy is lower and is used as the noise signal. By comparing the change trend of the singular spectrum curve and multiple iteration attempts, a proper k value is determined, after the k value is determined, the signal and the noise of a singular value point are separated, and the denoising of the original flow velocity data is realized.
In the prior art, the denoising method is usually selected as follows: moving the median and performing the denoising processing by the three-order sliding smooth low-pass filtering, for example, the three-order smooth low-pass filtering processing method is as follows: if the filter window size is m and m is an odd number, then take 3m data [ x [ ]1,x2,…,x3m-1,x3m]Smoothing was performed 3 times.
Let the first smoothing result be:
for m + 1YKResult of smoothing of value ZKIt can be expressed as:
last pair ZKSimilar smoothing can be done to obtain the result of three-level smoothing low-pass filtering:
and performing linear fitting of a least square polynomial on the L data subjected to filtering processing in the step, performing pre-estimation judgment on the next data by using the linear value of a fitting function, and rejecting the next data if the next data is abnormal.
For example, as shown in fig. 2, which is a plot of raw flow rate data measured by an acoustic doppler ultrasonic flowmeter actually measured by a drainage pipe network at a certain place, the flow rate of water flow is kept stable between about 0.8m/S and about 1.2m/S in a time period of 0S to 100S, and since the flow rate of water flow does not suddenly jump in a continuous time sequence, flow rate data points with a flow rate of more than 1.2m/S or less than 0.8m/S in a time period of 0S to 100S can be considered as abnormal data points represented by noise data.
As shown in FIG. 3, as shown in FIG. 3, the flow velocity data after the three-level smoothing low-pass filtering process, in the flow velocity data points after de-noising in the graph, a plurality of flow velocity data points with a flow velocity of more than 1.2m/S or less than 0.8m/S still appear in a time period of 0-100S, which indicates that the method is easy to have misjudgment and missing judgment, and abnormal values cannot be completely removed.
As shown in fig. 4, the flow rate data after the singular spectrum analysis processing according to this embodiment is obtained by using a large singular value in the singular spectrum corresponding to a signal component with large energy and a small singular value corresponding to a noise component in the signal, and finally, the large preceding singular value corresponding to the orbit matrix is retained, the small following singular value is removed as noise, and then, the inverse transformation is performed on the noise component to obtain a new flow rate data of the time sequence. The abnormal flow velocity data points of the processed flow velocity data within the time period of 0-100S, which is more than 1.2m/S or less than 0.8m/S, can be removed, the effect is better than that of three-level smooth low-pass filtering, clutter signals can be removed, and abnormal value interference can be eliminated.
And S105, performing smooth fitting on the de-noising flow speed data to obtain the processed flow speed data.
And S106, determining the flow of the drainage pipe according to the processed flow rate data.
In the embodiment, by adopting the improved singular value point algorithm, the noise signal interference can be removed, so that the flow detection of the drain pipe is more accurate. Meanwhile, noise information is removed, so that a signal curve is smoother.
Example two
As shown in fig. 2 and 4, the denoising and smoothing process of the singular spectrum analysis can make the curve of the raw data smoother, but the data of the measurement point is changed greatly, the flow velocity of the water flow is fixed at about 1m/S within a time period of 0-100S and is almost kept unchanged, while in the raw data of fig. 2, the flow velocity of the water flow has a certain fluctuation within a normal value range, and if the flow velocity data determined by the denoised and smoothed curve is directly used for calculating the flow, the data is easy to distort. In order to solve the problem, the embodiment adds a step of performing smooth fitting on the denoised signal on the basis of the above embodiment, so that each numerical value of the denoised curve tends to a true value, as shown in fig. 5, the specific steps are as follows:
s201, acquiring original flow speed data of a drainage pipe network.
S202, performing phase space reconstruction on the original flow velocity data to generate a track matrix.
S203, performing singular value decomposition on the orbit matrix to generate n singular value points.
S204, performing signal-noise separation on the n singular value points to generate de-noising flow speed data.
S2051, performing window filtering on the de-noising flow rate data to determine a minimum value v of the flow rate in the windowminWindow median value of flow velocity vmedMaximum value of flow velocity v in windowmaxWindow median value of flow velocity vmed。
S2052, judging vmin、vmedAnd vmaxWhether or not the magnitude relation of (1) is in accordance with vmin＜vmed＜vmax。
And S2053, if the data are not accordant, increasing the size of a filtering window, and executing window filtering on the denoising flow speed data.
Step S2052 is executed again to judge the vmin、vmedAnd vmaxSize relationship ofWhether or not it meets vmin＜vmed＜vmax。
Repeating the above steps until vmin、vmedAnd vmaxIs in accordance with vmin＜vmed＜vmax。
S2054, determining | vi－vmedWhether or not | is less than or equal to a predetermined threshold, where viRefers to the ith flow rate data within the window.
S2055, if the v is less than or equal to a preset threshold value, determining the viThe output is the processed flow rate data.
S2056, if the v is larger than a preset threshold value, determining the vmedThe output is the processed flow rate data.
And S206, determining the flow of the drainage pipe according to the processed flow rate data.
As shown in FIG. 6, the processed data of the singular spectral analysis and the smooth fitting improved on the basis of the above embodiment of the present embodiment, the flow rate of the water flow is kept stable between about 0.8m/S and about 1.2m/S in the time period of 0S and 100S, the abnormal value of the flow rate lower than 0.8m/S or higher than 1.2m/S is removed, and meanwhile, the flow rate of the water flow is ensured to reflect the flow rate characteristics of the original data shown in FIG. 2 in the normal value range of 0.8m/S to 1.2m/S, and the flow rate data after denoising is prevented from being distorted. The flow detection is more accurate, and the accurate simulation of rain sewage discharge control, water quantity and quality scheduling of sewage treatment plants, pipe network hydrodynamic simulation, flood simulation and forecasting is facilitated.
In the embodiment, smooth fitting is performed on the denoised signal, so that the numerical values of all the denoised curves tend to be true values, and the flow calculation is more accurate.
EXAMPLE III
As shown in fig. 7, the present embodiment provides a terminal feature collecting device 3, which includes the following modules:
the first acquisition module 301 is used for acquiring original flow rate data of a drainage pipe network;
a matrix establishing module 302, configured to perform phase space reconstruction on the original flow rate data to generate a trajectory matrix;
a singular value decomposition module 303, configured to perform singular value decomposition on the orbit matrix to generate n singular value points;
a signal-to-noise separation module 304, configured to perform signal-to-noise separation on the n singular value points, so as to generate denoising flow rate data;
and a smooth fitting module 305, configured to perform smooth fitting on the denoising flow rate data to obtain processed flow rate data.
And the flow determining module 306 is used for determining the flow of the drainage pipe according to the processed flow rate data.
In an alternative embodiment, as shown in fig. 8, the matrix building module 302 further includes:
a time series determination unit 3021 configured to obtain a corresponding time series based on the raw flow rate data;
a reconstructing unit 3022, configured to embed the time sequence into a reconstructed track matrix according to a preset delay.
In an alternative embodiment, the singular value decomposition module 303 further comprises:
an orthogonal expansion unit 3031, configured to perform orthogonal expansion on the orbit matrix to generate a covariance matrix;
a singular value decomposition unit 3032, configured to perform singular value decomposition on the covariance matrix to generate n singular value points.
In another alternative embodiment, the signal-to-noise separation module 304 further comprises:
a k value setting unit 3041 configured to set k singular value points of the n singular value points as flow rate signals, and n-k singular value points that are not selected as noise signals, where 0 < k < n;
a first reconstructing unit 3042, configured to perform phase space reconstruction on the flow velocity signal based on a first preset formula, and generate a first singular spectrum curve;
a second reconstruction unit 3043, configured to perform phase space reconstruction on the noise signal based on a second preset formula, and generate a second singular spectrum curve;
a k value determining unit 3044, configured to determine a value of k based on the variation trends of the first singular spectral curve and the second singular spectral curve;
a signal-to-noise separating unit 3045 configured to use the k singular value points as the de-noised flow velocity data.
In another alternative implementation, the smooth fitting module 305 further comprises:
a window filtering unit 3051, configured to perform window filtering on the de-noised flow velocity data to determine a minimum value v of a flow velocity in a windowminWindow median value of flow velocity vmedMaximum value of flow velocity v in windowmaxWindow median value of flow velocity vmed；
A first judging unit 3052 for judging said vmin、vmedAnd vmaxWhether or not the magnitude relation of (1) is in accordance with vmin＜vmed＜vmax；
A window size adjusting unit 3053, configured to increase a filtering window size if the denoising flow rate data does not meet the window size, and perform window filtering on the denoising flow rate data;
a looping unit 3054 for judging said v againmin、vmedAnd vmaxWhether or not the magnitude relation of (1) is in accordance with vmin＜vmed＜vmax(ii) a Repeating the above steps until vmin、vmedAnd vmaxIs in accordance with vmin＜vmed＜vmax；
A second determining unit 3055 for determining | vi－vmedWhether or not | is less than or equal to a predetermined threshold, where viThe ith flow rate data in the window;
an output unit 3056, configured to, if the v is less than or equal to a preset threshold, apply the viOutputting the processed flow rate data; if the v is larger than a preset threshold value, the v is addedmedThe output is the processed flow rate data.
The terminal characteristic acquisition device provided by the embodiment of the invention can execute the drainage pipe network data preprocessing method provided by any embodiment of the invention, and has corresponding execution methods and beneficial effects of the functional modules.
Example four
The present embodiment provides a schematic structural diagram of a server, as shown in fig. 9, the server includes a processor 401, a memory 402, an input device 403, and an output device 404; the number of the processors 401 in the server may be one or more, and one processor 401 is taken as an example in the figure; the processor 401, memory 402, input device 403 and output device 404 in the device/terminal/server may be linked by a bus or other means, as exemplified by the linking via a bus in fig. 9.
The memory 402 is a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules (e.g., the first obtaining module 301, the matrix building module 302, etc.) corresponding to the drainage network flow detecting method in the embodiment of the present invention. The processor 401 executes various functional applications of the device/terminal/server and data processing by executing software programs, instructions and modules stored in the memory 402, that is, implements the above-described method.
The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 402 may further include memory located remotely from the processor 401, which may be linked to the device/terminal/server through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 403 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the device/terminal/server. The output device 404 may include a display device such as a display screen.
The embodiment of the invention also provides a server which can execute the drainage pipe network flow detection method provided by any embodiment of the invention and has the corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for detecting a flow rate of a drainage pipe network according to any embodiment of the present invention:
acquiring original flow speed data of a drainage pipe network;
performing phase space reconstruction on the original flow velocity data to generate a track matrix;
performing singular value decomposition on the orbit matrix to generate n singular value points;
performing signal-noise separation on the n singular value points to generate de-noising flow speed data;
performing smooth fitting on the de-noising flow velocity data to obtain processed flow velocity data;
and determining the flow of the drainage pipe according to the processed flow speed data.
The computer-readable storage media of embodiments of the invention may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical link having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a storage medium may be transmitted over any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be linked to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the link may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for detecting the flow of a drainage pipe network is characterized by comprising the following steps:
acquiring original flow speed data of a drainage pipe network;
performing phase space reconstruction on the original flow velocity data to generate a track matrix;
performing singular value decomposition on the orbit matrix to generate n singular value points;
performing signal-noise separation on the n singular value points to generate de-noising flow speed data;
performing smooth fitting on the de-noising flow velocity data to obtain processed flow velocity data;
and determining the flow of the drainage pipe according to the processed flow speed data.
2. The method for detecting the flow of the drainage pipe network according to claim 1, wherein the phase space reconstruction of the original flow velocity data is performed to generate a track matrix, and the method comprises the following steps:
acquiring a corresponding time series based on the raw flow rate data;
and embedding the time sequence into a reconstructed track matrix according to a preset delay.
3. The method for detecting the flow of the drainage pipe network according to claim 1, wherein the step of performing singular value decomposition on the orbit matrix to generate n singular value points comprises the following steps:
performing orthogonal expansion on the orbit matrix to generate a covariance matrix;
and performing singular value decomposition on the covariance matrix to generate n singular value points.
4. The method for detecting the flow of the drainage pipe network according to claim 1, wherein the signal-noise separation is performed on the n singular value points to generate de-noised flow velocity data, and the method comprises the following steps:
setting k singular value points in the n singular value points as flow velocity signals, and setting n-k singular value points which are not selected as noise signals, wherein k is more than 0 and less than n;
performing phase space reconstruction on the flow velocity signal based on a first preset formula to generate a first singular spectrum curve;
performing phase space reconstruction on the noise signal based on a second preset formula to generate a second singular spectrum curve;
determining the value of k based on the variation trend of the first singular spectrum curve and the second singular spectrum curve;
and taking the k singular value points as the de-noising flow velocity data.
5. The method for detecting the flow of the drainage pipe network according to claim 4, wherein the step of setting k singular value points of the n singular value points as flow velocity signals comprises the following steps:
and sequencing the n singular value points from large to small in sequence, and setting the first k singular value points as the flow velocity signal.
6. The method for detecting the flow of the drainage pipe network according to claim 1, wherein the step of performing smooth fitting on the de-noised flow velocity data to obtain the processed flow velocity data comprises the following steps:
performing window filtering on the de-noised flow velocity data to determine a minimum value v of the flow velocity within the windowminWindow median value of flow velocity vmedMaximum value of flow velocity v in windowmax；
Judging the vmin、vmedAnd vmaxWhether or not the magnitude relation of (1) is in accordance with vmin＜vmed＜vmax；
If not, increasing the size of a filtering window, and executing window filtering on the denoising flow speed data;
judging the v againmin、vmedAnd vmaxWhether or not the magnitude relation of (1) is in accordance with vmin＜vmed＜vmax；
Repeating the above steps until vmin、vmedAnd vmaxSize relation symbol ofAnd vmin＜vmed＜vmax；
Determining |. vi－vmedWhether or not | is less than or equal to a predetermined threshold, where viThe ith flow rate data in the window;
if the v is less than or equal to a preset threshold value, the v is determinediOutputting the processed flow rate data;
if the v is larger than a preset threshold value, the v is addedmedThe output is the processed flow rate data.
7. The utility model provides a drain pipe network flow detection device which characterized in that includes:
the first acquisition module is used for acquiring original flow speed data of the drainage pipe network;
the matrix establishing module is used for executing phase space reconstruction on the original flow velocity data to generate a track matrix;
the singular value decomposition module is used for performing singular value decomposition on the orbit matrix to generate n singular value points;
the signal-noise separation module is used for performing signal-noise separation on the n singular value points to generate de-noising flow speed data;
the smooth fitting module is used for performing smooth fitting on the de-noising flow speed data to obtain processed flow speed data;
and the flow determining module is used for determining the flow of the drainage pipe according to the processed flow speed data.
8. The device of claim 7, wherein the signal-to-noise separation module comprises:
a k value setting unit, configured to set k singular value points of the n singular value points as flow velocity signals, and n-k singular value points that are not selected as noise signals, where 0 < k < n;
the first reconstruction unit is used for performing phase space reconstruction on the flow velocity signal based on a first preset formula to generate a first singular spectrum curve;
the second reconstruction unit is used for performing phase space reconstruction on the noise signal based on a second preset formula to generate a second singular spectrum curve;
a k value determining unit, configured to determine a value of k based on a variation trend of the first singular spectral curve and the second singular spectral curve;
and the signal-noise separation unit is used for taking the k singular value points as the de-noising flow speed data.
9. A server comprising a memory, a processor and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method of detecting a flow in a drain network according to any one of claims 1 to 6.
10. A terminal readable storage medium, on which a program is stored, wherein the program, when executed by a processor, is capable of implementing a method for drain network flow detection according to any one of claims 1-6.
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