CN113757566A - Control method for intelligent instrument of urban gas pipe network - Google Patents
Control method for intelligent instrument of urban gas pipe network Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D3/00—Arrangements for supervising or controlling working operations
- F17D3/01—Arrangements for supervising or controlling working operations for controlling, signalling, or supervising the conveyance of a product
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D3/00—Arrangements for supervising or controlling working operations
- F17D3/18—Arrangements for supervising or controlling working operations for measuring the quantity of conveyed product
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Abstract
The invention relates to a control method for an intelligent instrument of an urban gas pipe network, which comprises the following steps: the edge layer calculation part drives the additional equipment to apply gas additional pulse between the pressure regulating box and the branch node of each branch pipeline of the branch pipeline part, the edge node of the edge layer receives the output flow-time function of the pressure regulating box and the flow-time pressing function of each branch pipeline part module, and the pipeline blockage condition is judged by comparing the output flow-time function of the pressure regulating box and the flow-time pressing function of each branch pipeline part module.
Description
Technical Field
The invention relates to a control method for an intelligent instrument of an urban gas pipe network.
Background
With the advance of urbanization process, the arrangement of gas pipe networks is popularized, and gas pipeline systems of all levels of cities are generally built. With the advancement of gas pipe network layout, the judgment and diagnosis of the urban gas pipe network blockage situation are very important for the management of the gas pipe network. If the blockage condition of the pipeline cannot be detected and solved in time, the normal life of a user can be influenced, and potential safety hazards can be brought. The urban gas pipe network comprises high-pressure and medium-pressure pipelines, wherein the high-pressure pipelines are usually used as urban peripheral annular main lines, the medium-pressure and low-pressure pipelines are usually used as yard pipelines, and the high-pressure pipelines and the medium-pressure pipelines are generally distributed in a branch shape from a community pressure regulating box or a pressure regulating box outlet of a building to a user introducing pipe. The low-pressure pipeline is more complex in layout and the outlet is closer to a user, so that accurate diagnosis and positioning of medium and low-pressure pipe network blockage are very important for pipeline management and user use.
With the development of the internet of things, more intelligent instruments are used in the current medium and low voltage pipe network, an edge calculation mode is adopted, and edge calculation means that an open platform integrating network, calculation, storage and application core capabilities is adopted on one side close to an object or a data source to provide nearest-end service nearby. The method is initiated at the edge side, generates faster network service response, and meets the basic requirements of the industry in the aspects of real-time business, application intelligence, safety, privacy protection and the like. The edge computation is between the physical entity and the industrial connection, or on top of the physical entity. While the cloud computing (i.e. the central control system) still has access to the historical data of the edge computing. On one hand, the load pressure of a central control system is relieved, and on the other hand, the response can be realized more quickly when the calculation is carried out at the inlet end.
According to the urban gas medium-low pressure pipe network blockage diagnosis and positioning method in the prior art, a total additional pulse signal is applied before branch of a branch pipe, and a plurality of flow functions are collected at the tail end of the branch pipe for analysis. The problem of all set up pressure pulse's detection procedure to each pipeline, increase equipment complexity and calculated amount is solved. However, when the above calculation is implemented by the edge calculation network, the edge node may collect a process in which flow data fed back by the flow meters at the ends of a plurality of pipelines may exist, but since lengths of each end pipeline in the urban low-pressure pipeline are different, when an additional pulse signal is applied at the branch node, times at which the additional pulse signal flows and affects flow at the ends of the pipelines are different, and thus lengths of time at which flow data at the ends of each pipeline returns to the edge node are different. Due to the above-mentioned time difference of data feedback, the synchronicity at the time of data collection of each edge node is reduced, and a complicated compensation calculation is required to offset the above-mentioned time difference. The design length of each branch pipeline is often used as a compensation basis in compensation calculation, but the design length and the construction length are often different in the actual process, so that the compensation calculation may be inaccurate. Therefore, the calculation amount and the data transmission amount are increased, and the number of branch pipelines in the actual urban gas pipeline is large, so that how to realize the synchronization of data among large data groups is also a technical problem to be solved.
Therefore, it is desirable to provide a control method for an intelligent instrument of an urban gas pipe network, which can avoid the above problems in the prior art, achieve synchronization of data among huge data sets, and reduce excessive compensation calculation caused by different lengths of pipelines.
Disclosure of Invention
The technical problem to be solved by the invention is the following defects in the prior art: because the lengths of all tail end pipelines in the urban low-pressure pipelines are different, when the additional pulse signal is applied to the branch node, the time for the additional pulse signal to flow and influence the tail end flow of the pipeline is different, and therefore the time lengths for the flow data at the tail end of each pipeline to return to the edge node are different. Due to the above-mentioned time difference of data feedback, the synchronicity at the time of data collection of each edge node is reduced, and a complicated compensation calculation is required to offset the above-mentioned time difference. The design length of each branch pipeline is often used as a compensation basis in compensation calculation, but the design length and the construction length are often different in the actual process, so that the compensation calculation may be inaccurate. Therefore, the calculation amount and the data transmission amount are increased, and the number of branch pipelines in the actual urban gas pipeline is large, so that how to realize the synchronization of data among large data groups is also a technical problem to be solved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a control method for an intelligent instrument of an urban gas pipe network comprises the following steps: the edge layer calculation part drives the additional equipment to apply gas additional pulses between the pressure regulating box and the branch node of each branch pipeline of the branch pipeline part, the edge node of the edge layer receives the output flow-time function of the pressure regulating box and the flow-time pressing function of each branch pipeline part module, and the pipeline blockage condition is judged by comparing the output flow-time function of the pressure regulating box and the flow-time pressing function of each branch pipeline part module; the method also includes time delay adjusting the flow-time of each branch conduit portion module as a function.
Specifically, an intelligent flow meter is installed at the tail end of the branch pipeline.
Specifically, an intelligent flow meter installed at the tail end of the branch pipeline uploads data to the edge layer in real time.
Specifically, the intelligent flow meter installed at the tail end of the branch pipeline uploads data to edge nodes in the edge layer, which correspond to the edge nodes one by one, in real time.
In particular, the amplitude u of the additional pulse is expressed as a function u ═ f (t0), where t0 represents the time variable of the application of the additional pulse of gas.
Specifically, historical data of a flow meter at the outlet of the pressure regulating box is extracted from the (i + 1) th edge node, and a flow-time function phi (t) of the pressure regulating box is formed.
Specifically, historical data of the end flow meters of the branch pipes 3-1, 3-2, … … and 3-i extracted from the 1-i th edge node form i branch flow-time functions β 1(t) … … β i (t).
The control method for the urban gas pipe network intelligent instrument has the advantages that synchronization of data among huge data sets is achieved, and excessive compensation calculation caused by different lengths of pipelines is reduced.
Drawings
Fig. 1 is a schematic structural diagram of an edge computing system for an intelligent instrument of an urban gas pipe network according to the present application.
Fig. 2 is a schematic diagram of a step of extracting time delay in the control method for the intelligent instrument of the urban gas pipe network provided by the application.
Detailed Description
The present invention will now be described in more detail with reference to the accompanying drawings, in which preferred embodiments of the invention are shown, it being understood that one skilled in the art may modify the invention herein described while still achieving the beneficial results of the present invention. Accordingly, the following description should be construed as broadly as possible to those skilled in the art and not as limiting the invention.
In the interest of clarity, not all features of an actual implementation are described. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific details must be set forth in order to achieve the developer's specific goals.
In order to make the objects and features of the present invention more comprehensible, embodiments of the present invention are described in detail below with reference to the accompanying drawings. It is to be noted that the drawings are in a very simplified form and are intended to use non-precision ratios for the purpose of facilitating and clearly facilitating the description of the embodiments of the invention.
The control method of the urban gas pipe network intelligent instrument is based on the edge computing system of the urban gas pipe network intelligent instrument. The edge computing system of the urban gas pipe network intelligent instrument comprises an equipment layer, an edge layer and a cloud computing layer.
The equipment layer comprises a gas station 1, a pressure regulating box 2 and a branch pipeline part 3. Wherein, gas station 1, pressure regulating box 2, branch pipeline portion 3 are connected through the gas pipeline between.
The pressure regulating box 2 comprises a pressure regulator 2-1 and additional equipment 2-2, wherein the pressure regulator 2-1 realizes pressure control of medium and low pressure fuel gas in a medium and low pressure pipe network, for example, the pressure of the medium and low pressure fuel gas can be 0.01-0.4MPa, such as 0.01MPa, 0.2MPa, 0.3MPa and 0.4 MPa. The additional equipment 2-2 is in communication connection with the edge layer calculating part, and the additional equipment 2-2 receives the instruction sent by the edge layer calculating part, so that the additional impulse of the gas with the pressure value formed by the specific function which takes time as a variable is applied to the gas network.
In particular, the additional device 2-2 can apply the additional pulses of gas to the gas network for a specific detection period, for example once every 24 hours, or once every 12 hours. Alternatively, the additional device 2-2 may apply the additional gas pulse to the gas pipe network by driving the additional device 2-2 to the gas pipe network by the edge layer calculation unit when the edge layer calculation unit detects that the flow value detected by the electromagnetic valve with flow detection function provided in each of the branch pipes 3-1, 3-2, … …, 3-i of the branch pipe unit 3 is lower than a specific threshold value (for example, lower than 60% of the normal or average flow).
The additional device 2-2 applies additional pulses of gas to the gas pipeline for a total duration of time T, preferably 3-5 seconds, which is likely to cause repeated oscillations of the fluid in the pipeline if T is too large and to cause too little sampled data and errors if T is too small.
The branch pipe section 3 includes a plurality of branch pipes 3-1, 3-2, 3-3, 3-4, … …, 3-i. The length of each branch pipe is shown as the same length in fig. 1 for convenience of illustration, while the length of each branch pipe in actual connection is different according to the needs of the household, and the length of each branch pipe is L1, L2, L3, L4, … …, Li. A gas meter is arranged at the end of each branch pipe 3-1, 3-2, 3-3, 3-4, … …, 3-i of the branch pipe part 3, a valve with a flow detection function, such as an electromagnetic valve with a flow detection function, is arranged between the gas meter and the position where the additional device 2-2 applies additional gas pulses to the gas pipe network, on the one hand, the valve can play a role of controlling the opening and closing of the branch pipes,
the valve with the flow detection function and the gas meter are intelligent meters, and can realize the functions of data acquisition and transmission and simple data processing.
And the edge layer comprises i +1 edge nodes and an edge layer calculation part, the 1 st edge node to the i th edge node in the i +1 edge nodes in the edge layer are in communication connection with the valves and the gas meters with the flow detection functions of all the branch pipelines, and the i +1 th edge node is connected to the flow meter at the outlet of the pressure regulating box 2, so that the real-time flow transmission data of the pressure regulating box 2 are obtained. The connection means may be wired or wireless. The valve with the flow detection function and the gas meter are connected through communication, and data collected by the valve and the gas meter can be uploaded to the edge node in real time.
The boundary layer calculation unit is configured to control the additional device 2-2 to apply the additional gas pulse and collect flow rate information measured by the valve having the flow rate detection function, at regular time (a specific detection period, for example, once every 24 hours or once every 12 hours) or in accordance with a preset threshold excitation pattern (when a flow rate value detected by an electromagnetic valve having the flow rate detection function provided in each of the branch pipes 3-1, 3-2, … …, 3-i in the boundary layer detection branch pipe unit 3 is lower than a specific threshold, the additional device 2-2 is driven by the boundary layer calculation unit to apply the additional gas pulse to the gas pipe network).
The cloud computing layer is remotely connected to the edge layer, the cloud computing layer can read data of the edge layer, and diagnose and position pipeline blockage according to the data of the edge layer, the data diagnose and position pipeline blockage comprises coarse positioning (namely positioning at least one branch pipeline with blockage possibility) and re-positioning (namely confirming the pipeline with blockage and judging the blockage position), the flow information is relation information of gas flow passing through a valve with a flow detection function and time, namely waveform information of the gas flow-time, and the waveform information comprises real-time flow amplitude.
Specifically, the cloud computing layer further has a preset deep learning pattern recognition function capable of obtaining a delay time between the response fluctuation signal of each branch pipe and the additional pulse signal by using a deep learning model by using the additional pulse signal of the additional device 2-2 and the response fluctuation signal of each branch pipe opposite thereto and by using a large amount of historical data for time compensation.
Based on the edge computing system of the urban gas pipe network intelligent instrument, the application also provides a control method of the urban gas pipe network intelligent instrument. The method specifically comprises the following steps:
Step 2, judging whether a specific condition is reached by the edge layer calculating section, if the specific condition is reached, sending a driving signal to the additional equipment 2-2 of the pressure regulating tank 2 by the edge layer calculating section, thereby driving the additional equipment 2-2 to apply a gas additional pulse between the pressure regulating tank 2 and the branch node o of each branch pipe 3-1, 3-2, 3-3, 3-4, … …, 3-i of the branch pipe section 3, while the pressure regulator 2-1 of the pressure regulating tank 2 maintains a basic gas pressure of a middle or low pressure between the pressure regulating tank 2 and the branch node o of each branch pipe 3-1, 3-2, 3-3, 3-4, … …, 3-i of the branch pipe section 3, the pressure of the middle or low pressure gas may be 0.01 to 0.4MPa, for example, 0.01MPa, 0.2MPa, 0.3MPa, 0.4 MPa; if no specific condition is reached, the wait continues.
Wherein, the specific condition may be a fixed period/frequency condition, that is, a detection is performed once within a specific time period, for example: every 24 hours, or every 12 hours. The specific condition may be that the predetermined threshold triggering condition is reached, that is, the time for the additional device 2-2 to apply the additional gas pulse to the gas pipe network may be detected by the boundary layer calculating unit that the flow value detected by the electromagnetic valve with flow detecting function provided in each of the branch pipes 3-1, 3-2, … …, 3-i of the branch pipe unit 3 is lower than a specific threshold (for example, lower than 60% of the normal or average flow), and the boundary layer calculating unit drives the additional device 2-2 to apply the additional gas pulse to the gas pipe network.
The amplitude u of the gas supplementary pulse can be expressed as a function u-f (t0), where t0 represents the time variable of the gas supplementary pulse application, preferably u-f (t0) ksin (ω t0), where k is an amplitude parameter and ω is a frequency parameter. the time span of T0 is T, T is 3-5 seconds, that is, the time length of the pulse wave emission does not exceed T, T is 3-5 seconds. If T is too large, it tends to cause repeated oscillation of the fluid in the pipe, and if T is too small, it tends to cause too little sampling data, which tends to cause errors. And k and omega are set, so that 5-8 sine pulse periods are formed in the time span T, and the amplitude of the sine pulse is 3-4 times of the basic gas pressure.
Step 4, the cloud computing layer reads the data of the edge layer to fit the i fitting functions vj＝gj(t) delay adjustment is performed separately.
Referring to fig. 2, the method specifically includes:
1. and the cloud computing layer extracts historical data of the flow meter at the outlet of the pressure regulating box 2 from the (i + 1) th edge node to form a pressure regulating box flow-time function phi (t).
2. Historical data of the branch pipe 3-1, 3-2, … … and 3-i end flow meters extracted by the cloud computing layer from the 1 st edge node to the i edge nodes of the ith edge node form i branch flow-time functions beta 1(t) … … beta i (t).
3. Establishing a deep learning model, and identifying characteristic points (see fig. 2) of each of the pressure regulating tank flow-time function phi (t) and the i branch flow-time functions beta 1(t) … … beta i (t) with response relations through a deep learning neural network, namely identifying the characteristic points 1-1, … …, 1-i of the characteristic points 1 in phi (t) with response relations in each of the beta 1(t) … … beta i (t) curves. And determining a compensation time delay at (j) for each pipe. Where j denotes a branch pipe number, j 1 to i, k denotes a feature number, and k 1 to s.
The steps specifically include:
1) respectively forming phi (t) curves and beta 1(t) … … beta i (t) curves;
2) by constructing a deep learning model, s feature points (feature 1, feature 2, … …, feature s) in phi (t) curves are extracted, and a plurality of feature points, such as feature 1-1, … …, feature 1-i, feature 2-1, … …, feature 2-i, feature s-1, … …, feature s-i, in each beta 1(t) … … beta i (t) curve are extracted.
3) And constructing s two types of classifiers, wherein the s two types of classifiers are respectively used for sequentially judging whether a plurality of characteristic points in each beta 1(t) … … beta i (t) curve have a response relation with one of the s characteristic points in the phi (t) curve, namely the characteristic points are caused by one of the s characteristic points in the phi (t) curve. The s classifiers respectively correspond to s feature points in phi (t) curves, and a plurality of feature points (such as features 1-1, … …, features 1-i, features 2-1, … …, features 2-i, features s-1, … … and features s-i) extracted from each beta 1(t) … … beta i (t) curve are classified into s two classes of classifiers through the operation of the s classifiers, namely, are classified into the response relations of the s feature points. Therefore, response relations between s characteristic points in phi (t) curves and a plurality of characteristic points (such as characteristic 1-1, … …, characteristic 1-i, characteristic 2-1, … …, characteristic 2-i, characteristic s-1, … … and characteristic s-i) extracted from each phi (t) curve and beta 1(t) … … beta i (t) curves are established, namely, which characteristic points of beta 1(t) … … beta i (t) are responded to one of the s characteristic points on phi (t) are judged. For example: feature points 2-1 at β 1(t), feature points 2-i at β i (t), are responsive to feature 2 at φ (t).
The training samples of the classifier are from historical data, and the classifier can be constructed by using two types of classifiers known in the art, such as a linear classifier and the like.
4) According to the response relation, the cloud computing layer calculates the time delay delta t from each type of feature point of each branch pipeline to the feature point with the response relation phi (t)(j,B)＝t(j，B)-t(B)Wherein j represents a branch pipe number, j is 1 to i, B represents a feature number which has a corresponding relationship with phi (t) after classification, and B is 1 to s; t is t(j，B)Time of a feature representing the presence of a response to the Bth feature on the jth pipe, t(B)Indicating the time of the B-th feature at phi (t) that has a response relationship with the feature. S Δ t values for each branch pipe will then be calculated(j,B)Obtaining the compensation time delay delta t of the branch pipeline(j)And j is 1 to i.
For example, the feature points 1-1, 2-1, … …, s-1 on β 1(t) are respectively associated with the feature points 1, 2, … …, s on φ (t), the difference between the feature point 1-1 time and the feature 1 time, the difference between the feature point 2-1 time and the feature 2 time, … …, the difference between the feature point s-1 time and the feature s time are calculated, and the harmonic mean of the above-mentioned s differences is calculated as Δ t of the branch pipe(1)Where j is 1.
4. Fitting function v to i cloud computing layersj＝gj(t) delay adjustment is performed, respectively, to adjust vj＝gj(t) is adjusted to vjc＝gj(t-Δt(j))。
Step 5, fitting function v after time delay adjustment is carried out through cloud computing layerjc＝gj(t-Δt(j)) Performing a fourier FFT to obtain a frequency domain function Fj ═ FFT (v)jc)。
Step 6, utilizing the cloud computing layer, and detecting the frequency domain function Fj ═ FFT (v)jc) Whether or not there is a high intensity frequency f exceeding a thresholdhighAnd (4) a signal is used for judging whether the branch pipeline has the blockage risk or not. The method specifically comprises the following steps:
1) computing an average frequency domain function using a cloud computing layer
And determining the high intensity frequency fhigh：
F1, F2, … … are carried out on the frequency domain functions Fj of the i branch pipelines, and the average frequency domain function is obtained by taking the arithmetic mean value of Fi
The average frequency domain function
Points on the curve can all beExpressed in X-Y coordinates of frequency-signal intensity composition based on average frequency domain function
Calculating the frequency of all frequency intensities exceeding the maximum intensity 1/2 of the frequency domain function in the average frequency domain function, i.e. the high intensity frequency fhighHigh intensity frequency f in frequency domain functionhighThe partial signal with a relatively large rate of change of the flow in the frequency domain function signal, i.e. the signal which causes a significant change at the valve at the end of the pipeline after the application of the additional pulse of gas, is shown.
2) Calculating a frequency domain function Fj ═ FFT (v) of the i branches by using a cloud computing layerjc) At a corresponding high intensity frequency fhighWhether there is a super-threshold condition on the frequency:
calculating the frequency domain function Fj of all i branches as FFT (v)jc) At a corresponding high intensity frequency fhighAverage value of loudness of signal in whole frequency range, i.e. all high intensity frequencies f on all brancheshighIs arithmetically averaged to obtain
When the frequency domain function of each branch has the following condition,
The time domain function of the flow is converted into the frequency domain function through Fourier transform, and signals which obviously change at a valve at the tail end of a pipeline after additional gas pulses are applied can be filtered out through screening of high-intensity signals in the average frequency domain function, so that more complex calculation is avoided, and the calculation efficiency can be improved.
And 7, aiming at the branch with the blockage risk determined in the step 6, further determining the blocked pipeline and the blocked position by the cloud computing layer.
And (6) aiming at the determined branch with the blockage risk in the step (6), the cloud computing layer sends a control signal to the edge layer computing part, so that an electromagnetic valve with a flow detection function at the tail end of the corresponding pipeline is driven to perform instantaneous closing-opening action. Due to the continuous steady-state gas supply of the branch pipeline, the instantaneous closing-opening action of the electromagnetic valve with the flow detection function at the tail end of the corresponding pipeline is carried out, so that the energy pressure, namely the pressure wave, is transmitted upstream after the gas flow meets the closed valve. When the pressure wave reaches the downstream end (user end) of the blockage in the pipeline, the kinetic energy is reduced when the flow is blocked, the potential energy is increased to cause the pressure at the position to be increased, and the pressure wave is transmitted to the direction of the valve in the form of positive pressure wave. Thereafter, when the pressure wave reaches the upstream end of the blockage (the supply end) in the pipe, the pressure wave enters the unblocked area and, due to the sudden expansion and reduction in velocity, forms a negative pressure wave that propagates toward the downstream end of the line (the valve direction). When the positive pressure wave is transmitted to the valve direction, an upward transient wave crest can appear in the flow-time function obtained by detecting the valve flowmeter, and when the negative pressure wave is transmitted to the valve direction, a downward transient wave trough can appear in the flow-time function obtained by detecting the valve flowmeter.
And detecting whether the wave crest and the wave trough of the flow-time function appear in each pipeline or not through the cloud computing layer, and if the wave crest and the wave trough of the flow-time function do not appear, judging that the pipeline does not have the blockage phenomenon by the cloud computing layer. And if the wave crest and the wave trough of the flow-time function appear, the cloud computing layer judges that the pipeline has the blockage phenomenon.
Further, the start point and the end point of the blockage are calculated according to the time tp and tv of the appearance of the extreme value of the peak and the trough of the flow-time function, specifically, Xstart is Ctp/2, and Ld is C (tp-tv)/2. Wherein, Xstart is the distance between the blockage and the valve, Ld is the blockage length, and C is the sound velocity.
The control method for the urban gas pipe network intelligent instrument has the advantages that synchronization of data among huge data sets is achieved, and excessive compensation calculation caused by different lengths of pipelines is reduced.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A control method for an intelligent instrument of an urban gas pipe network is characterized by comprising the following steps: the method comprises the following steps: the edge layer calculation part drives the additional equipment to apply gas additional pulses between the pressure regulating box and the branch node of each branch pipeline of the branch pipeline part, the edge node of the edge layer receives the output flow-time function of the pressure regulating box and the flow-time pressing function of each branch pipeline part module, and the pipeline blockage condition is judged by comparing the output flow-time function of the pressure regulating box and the flow-time pressing function of each branch pipeline part module; the method also includes time delay adjusting the flow-time of each branch conduit portion module as a function.
2. The control method for the intelligent instrument of the urban gas pipe network according to claim 1, characterized in that: and an intelligent flowmeter is arranged at the tail end of the branch pipeline.
3. The control method for the intelligent instrument of the urban gas pipe network according to claim 2, characterized in that: and an intelligent flowmeter installed at the tail end of the branch pipeline uploads data to the boundary layer in real time.
4. The control method for the intelligent instrument of the urban gas pipe network according to claim 3, characterized in that: and the intelligent flow meters arranged at the tail ends of the branch pipelines upload data to the edge nodes in the edge layers, which correspond to the edge nodes one by one.
5. The control method for the intelligent instrument of the urban gas pipe network according to claim 1, characterized in that: the amplitude u of the additional pulse is expressed as a function u ═ f (t0), where t0 represents the time variable of the gas additional pulse application.
6. The control method for the intelligent instrument of the urban gas pipe network according to claim 1, characterized in that: extracting historical data of the pressure regulating box outlet flowmeter from the (i + 1) th edge node to form a pressure regulating box flow-time function phi
(t)。
7. The control method for the intelligent instrument of the urban gas pipe network according to claim 1, characterized in that: historical data of the branch pipe 3-1, 3-2, … …, 3-i end flow meters extracted from the 1-i edge nodes form i branch flow-time functions β 1(t) … … β i (t).
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