Intelligent monitoring control method and system for spraying equipment

Abstract

The invention discloses an intelligent monitoring control method and system for spraying equipment, and relates to the technical field of intelligent monitoring and pressure control, and the method comprises the following steps: collecting compressed air branch flow data and pipe network node pressure data in real time, obtaining a pipe network topological parameter set, extracting a characteristic frequency band energy value, and calculating a characteristic frequency band energy value; establishing a branch impedance model in combination with the pipe network topological parameters, and outputting an impedance factor of each branch through the model; if the energy value of the characteristic frequency band is greater than the set threshold value, obtaining the position of a leakage point through a leakage calculation algorithm, and outputting the leakage amount; and constructing a pipe network transfer function at the output of the impedance model, predicting a pressure fluctuation envelope, and dynamically generating a pressure control instruction according to a prediction result. According to the invention, the PID control parameters of the buffer tank are dynamically adjusted according to the comparison of the predicted value and the actual pressure fluctuation, so that the rapid pressure recovery control of self-adaptive correction according to the severity of leakage is realized, and the steady state and dynamic performance of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring and pressure control, and particularly to an intelligent monitoring and control method and system for a spraying device. Background Art

[0002] Spraying devices are widely used in industries such as automobile manufacturing, rail transit, and ship painting. To ensure the coating quality and spraying efficiency, the internal spraying system has extremely high stability requirements for pressure fluctuations. However, the existing spraying pipeline systems generally have the following technical problems:

[0003] Firstly, due to the complex pipe network, numerous branches, and long-distance flexible hoses connected, the spraying system is prone to small-scale leaks. Traditional monitoring methods (such as flow difference analysis and abnormal end pressure detection) have problems of low sensitivity, poor positioning accuracy, and response lag, making it difficult to detect subtle leaks in a timely manner and conduct quantitative analysis.

[0004] Secondly, in the face of sudden leaks, the parameters of the conventional PID controller are fixed and cannot be adaptively adjusted according to the leak degree and pressure change characteristics, resulting in a slow pressure recovery process or excessive oscillation, further affecting the spraying quality. Summary of the Invention

[0005] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0007] To solve the above technical problems, the present invention provides the following technical solution: An intelligent monitoring and control method for a spraying device, comprising:

[0008] Step 1: Real-time collect the flow data of the compressed air branch and the pressure data of the pipe network nodes, obtain the pipe network topology parameter set {L, D}, where L is the branch length and D is the pipe diameter. Then perform Fourier transform on the flow data to obtain the flow spectrum matrix, extract the energy values of the characteristic frequency bands, establish a branch impedance model in combination with the pipe network topology parameters, and output the impedance factors of each branch through this model;

[0009] Step 2: If the energy value of the characteristic frequency band is greater than the set threshold T1, calculate the leak point location through the leak calculation algorithm and output the leak amount;

[0010] Step 3: Construct a pipeline network transfer function based on the output of the impedance model, predict the pressure fluctuation envelope, and dynamically adjust the PID parameters of the buffer tank according to the prediction results to generate a pressure control command.

[0011] As a preferred solution of the intelligent monitoring and control method for a spraying device according to the present invention, wherein: the method for extracting the characteristic frequency band energy value is as follows: First, determine the lower and upper limits [f low , f high of the characteristic frequency band for capturing the leakage disturbance excitation, and reflect the system dynamic disturbance energy intensity E f through the energy integration of the flow rate in a specific frequency band. The calculation formula is:

[0012]

[0013] where FFT(Q(t)) represents the result of the fast Fourier transform of the flow rate data Q(t).

[0014] As a preferred solution of the intelligent monitoring and control method for a spraying device according to the present invention, wherein: the leakage calculation algorithm includes:

[0015] Obtain the leakage pressure difference ΔP of the branch where the leakage hole is located through the pipeline network node pressure data;

[0016] Based on the leakage pressure difference ΔP and the dynamic disturbance energy intensity E f derive the position of the leakage point on the branch;

[0017] Finally, based on the leakage pressure difference ΔP and the dynamic disturbance energy intensity E f back-calculate the gas flow rate loss caused by the leakage to obtain the leakage flow rate Q leak .

[0018] As a preferred solution of the intelligent monitoring and control method for a spraying device according to the present invention, wherein: the expression form of the pipeline network transfer function is:

[0019]

[0020] where H(s) represents the transfer function of the pipeline system in the Laplace domain, Z i represents the impedance factor of the i-th branch, τ i represents the delay time for the pressure to propagate to the i-th branch; s represents the Laplace complex frequency domain variable; N represents the number of branches in the official website that participate in the leakage impact;

[0021] The prediction formula for the pressure fluctuation envelope is:

[0022] ΔP predicted = H(s)·Q leak ·sin(2πft);

[0023] Among them, ΔP predicted represents the predicted value of the pressure fluctuation caused by leakage, f represents the leakage characteristic frequency, t represents the time variable, and the amplitude of the leakage influence at the next moment is predicted in real time through this formula.

[0024] As a preferred solution of the intelligent monitoring and control method for a spraying device according to the present invention, wherein: the method further includes introducing an update mechanism to adapt to the non-linear disturbance existing in the actual pressure change to update the impedance factor of the branch, and through the predicted value of the pressure fluctuation ΔP predicted and the actual observed fluctuation value ΔP actual for adjustment;

[0025] If: ΔP actual <ΔP predicted It indicates that the actual response is weaker than expected, and the true resistance of the branch may be greater, so the impedance factor of this branch should be increased;

[0026] If: ΔP actual >ΔP predicted It indicates that the actual response is stronger than expected, and the true resistance of the branch is smaller, so the impedance factor of this branch should be decreased.

[0027] As a preferred solution of the intelligent monitoring and control method for a spraying device according to the present invention, wherein: by calculating the dynamic leakage rate and the impedance change rate in real time, different working modes are switched according to different conditions:

[0028] Condition 1: Q leak <threshold T2 and ΔP actual is less than the set threshold T3, the working mode is switched to the precise mode, and the impedance model of the branch is updated every 30 seconds;

[0029] Condition 2: The dynamic leakage rate > the set threshold T4, the working mode is switched to the anti-disturbance mode, and in this mode, the analysis window is shortened and the feedforward compensation ΔP comp is superimposed, and it is superimposed on the conventional current pressure command

[0030] Condition 3: The impedance change rate > the set threshold T5, the working mode is switched to the disaster tolerance mode, and in the disaster tolerance mode, the pressure command of the central control is cut off, and each branch calculates the pressure set value independently according to the impedance factor and the leakage amount Q of the branch leak of the branch.

[0031] An intelligent control system applied to the intelligent monitoring and control method for a spraying device as described above, the system includes the following working modules:

[0032] A pressure monitoring module for collecting the pressure sensor signals of each branch of the spraying system in real time and monitoring the real-time pressure fluctuation;

[0033] A vibration feature extraction module for detecting the vibration signal of a pipeline, extracting energy features, and judging whether there is abnormal fluctuation by setting a threshold value.

[0034] A leakage location and quantification module that triggers leakage location when the energy feature exceeds the set threshold value, and calculates the leakage location and leakage amount through a leakage calculation algorithm.

[0035] A transfer function modeling module that constructs a pipeline network transfer function based on the leakage amount and branch impedance to predict the system pressure fluctuation envelope.

[0036] An adaptive parameter correction module that dynamically corrects the model impedance parameters according to the predicted pressure fluctuation and the actual pressure fluctuation.

[0037] And a pressure control and PID dynamic regulation module that dynamically adjusts the PID control parameters of the buffer tank according to the predicted pressure fluctuation.

[0038] The present invention also discloses a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the intelligent monitoring and control method of a spraying device described above are implemented.

[0039] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the intelligent monitoring and control method of a spraying device described above are implemented.

[0040] Advantages of the present invention:

[0041] 1. By extracting the change of pressure fluctuation energy in the characteristic frequency band, when the characteristic energy exceeds the dynamic threshold value, the present invention uses the leakage location estimation formula to accurately calculate the leakage point location according to the local pressure difference, pipe diameter and medium density, and outputs the leakage amount at the same time, improving the spatial resolution and quantification accuracy of leakage detection.

[0042] 2. By establishing a pipeline network transfer function based on the leakage amount, predicting the pressure fluctuation envelope, and comparing the predicted value with the actual pressure fluctuation, the present invention dynamically adjusts the PID control parameters of the buffer tank, so as to realize the fast pressure recovery control with adaptive correction according to the leakage severity, and improve the steady-state and dynamic performance of the system. Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts. Among them:

[0044] Figure 1 This is a schematic diagram of the overall structure of an intelligent monitoring and control method for a spraying device proposed by the present invention. Specific embodiments

[0045] To make the above objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings of the specification.

[0046] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0047] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that mutually excludes other embodiments.

[0048] Refer to Figure 1 , which is an embodiment of the present invention, and provides an intelligent monitoring and control method for a spraying device. This method includes the following steps:

[0049] Step 1: Real-time collect the flow data of the compressed air branch and the pressure data of the pipe network nodes, obtain the pipe network topology parameter set {L, D}, where L is the branch length and D is the pipe diameter. Then perform Fourier transform on the flow data to obtain the flow spectrum matrix, and extract the energy value of the characteristic frequency band. The extraction method of the energy value of the characteristic frequency band is as follows: First, determine the lower and upper limits [f low , f high for capturing the characteristic frequency band excited by the leakage disturbance. Through the energy integration of the flow in a specific frequency band, reflect the dynamic disturbance energy intensity E f of the system. The calculation formula is:

[0050]

[0051] Among them, FFT(Q(t)) represents the result of the fast Fourier transform of the flow data Q(t).

[0052] Establish a branch impedance model in combination with the pipe network topology parameters, and output the impedance factors of each branch through this model;

[0053] Step 2: If the energy value of the characteristic frequency band is greater than the set threshold T1, then obtain the leakage point position through the leakage calculation algorithm and output the leakage amount.

[0054] Specifically, the leakage calculation algorithm includes:

[0055] Obtain the leakage pressure difference ΔP of the branch where the leakage hole is located through the pressure data of the pipeline network nodes;

[0056] Derive the position of the leakage point on the branch according to the expression:

[0057]

[0058] where L leak represents the estimated distance from the leakage point to the starting point of the branch; ΔP represents the leakage pressure difference at both ends of the leakage branch; D represents the inner diameter of the pipeline; ρ represents the density of compressed air, and v represents the air flow rate.

[0059] Finally, based on the leakage pressure difference ΔP and the dynamic disturbance energy intensity E f inversely deduce the leakage flow rate Q leak ;

[0060] The inverse deduction process is as follows:

[0061] 1. Generally, the leakage hole is a small-hole outlet flow. Applying the compressed gas outflow formula (based on Bernoulli and continuity equations), the leakage flow rate is related to the square root of the pressure difference and the leakage area.

[0062]

[0063] where C d is the flow coefficient (generally taken as 0.6 - 0.8, corrected according to the shape of the leakage hole); A leak is the leakage hole area;

[0064] 2. Since the leakage hole area A leak is not convenient to measure directly, and the spectral energy E f is squared with the amplitude of the instantaneous flow velocity disturbance, it can be regarded as a direct measure of the leakage intensity. According to energy conservation and experimental verification, the leakage hole area and the spectral energy approximately satisfy the following relationship:

[0065] where k E is the system calibration coefficient, obtained by experimental fitting in the no-leakage / small-leakage state; A leak is the leakage hole area; Combining these parameters, finally calculate the leakage flow rate Q leak .

[0066] Step 3: Based on the output of the impedance model, construct the pipeline network transfer function to predict the pressure fluctuation envelope line,

[0067] The expression form of the pipeline network transfer function is:

[0068]

[0069] Among them, H(s) represents the transfer function of the pipeline system in the Laplace domain, and Z i represents the impedance factor of the i-th branch, and τ i represents the delay time for the pressure to propagate to the i-th branch; s represents the Laplace complex frequency domain variable; N represents the number of branches in the official website that participate in the leakage impact; this transfer function model comprehensively considers the propagation delay and damping effect of the leakage impact in each branch.

[0070] The prediction formula for the pressure fluctuation envelope is:

[0071] ΔP predicted = H(s)·Q leak ·sin(2πft);

[0072] Among them, ΔP predicted represents the predicted value of the pressure fluctuation caused by leakage, f represents the leakage characteristic frequency, t represents the time variable, and the amplitude of the leakage impact at the next moment is predicted in real time through this formula.

[0073] Dynamically adjust the PID parameters of the buffer tank according to the prediction results to generate a pressure control command.

[0074] For the spraying system, the buffer tank is used to store compressed air, offset the supply pressure fluctuation through volume buffering (such as the pressure pulsation caused by the start and stop of the compressor and the action of the branch valve), and the adjustment of the buffer tank can make the spraying equipment obtain a stable output pressure, ensure the constant atomization air pressure, and avoid uneven coating thickness.

[0075] To be exact, in control theory:

[0076] The PID parameters of the buffer tank include:

[0077] Proportional coefficient K p : Reflects the direct response speed of the system to the pressure deviation. K p Large → The system responds quickly, but it is easy to oscillate if it is too large. K p Small → The system responds slowly and is prone to lag.

[0078] Integral time T i : Reflects the cumulative ability of the system to eliminate the pressure deviation.

[0079] T i Short → Can quickly eliminate the steady-state error, but it is easy to accumulate excessively, resulting in overshoot.

[0080] T i Long → The elimination is slow, but the system is more stable.

[0081] That is to say, in the pressure regulation of the buffer tank: K p Controls the response of the buffer tank to the instantaneous pressure fluctuation during spraying; Ti Control the correction of the long-term pressure offset of the buffer tank. By dynamically adjusting these two parameters, the buffer tank can intelligently change the air outlet / air intake speed and amplitude according to the current leakage scale and system status, thereby weakening the pressure fluctuation at the source and ensuring that the pressure at the spraying equipment end is always within the ideal working range.

[0082] The specific adjustment calculation method includes the proportionality coefficient K p The calculation of is as follows:

[0083]

[0084] Among them, α represents the proportional adjustment coefficient, and ΔP max represents the maximum pressure fluctuation limit allowed by the spraying system. When the predicted fluctuation increases, K p also increases accordingly, thereby increasing the adjustment response speed;

[0085] The integral time T i The calculation of:

[0086]

[0087] Among them, β represents the integral adjustment factor; Q max represents the maximum flow rate designed by the spraying system.

[0088] In addition, this method also includes introducing an update mechanism to adapt to the non-linear disturbances existing in the actual pressure change to update the impedance factor of the branch. Through the predicted value of pressure fluctuation ΔP predicted and the actual observed fluctuation value ΔP actual to adjust;

[0089] The adjustment formula is: Z new = Z old ·(ΔP predicted / ΔP actual .

[0090] If: ΔP actual < ΔP predicted It indicates that the actual response is weaker than expected. The true resistance of the branch may be greater, and the impedance factor of this branch should be increased;

[0091] If: ΔP actual > ΔP predicted It indicates that the actual response is stronger than expected. The true resistance of the branch is smaller, and the impedance factor of this branch should be decreased.

[0092] By calculating the dynamic leakage rate (the time differential of the leakage amount in the set time window, [Q(t) - Q(t - Δt)] / Δt) and the impedance change rate (the change amplitude of the impedance factor before and after) in real time, switch different working modes according to different conditions:

[0093] Condition 1: Q leak <threshold T2 and ΔP actual is less than the set threshold T3, the working mode is switched to the precise mode (applicable scenario: stable working conditions for minor leaks), and the branch impedance model is updated every 30 seconds. In this mode, the adjustment step size of the PID parameters is smaller than that in the basic default mode (the PID adjustment step size refers to the percentage of the adjustment range of the control parameters (such as valve opening, motor speed) in each adjustment accounting for the maximum adjustable range);

[0094] Condition 2: Dynamic leakage rate > set threshold T4, the working mode is switched to the anti-interference mode (applicable scenario: sudden large leaks), and in this mode, the analysis window is shortened and the feedforward compensation ΔP comp is superimposed on the conventional current pressure command.

[0095] ΔP comp = K f ·Dynamic leakage rate;

[0096] K f represents the feedforward compensation coefficient, which is used to quantify the direct influence intensity of the dynamic leakage rate on the pressure fluctuation and is generally obtained through experimental calibration.

[0097] In this mode, the adjustment step size of the PID parameters is larger than that in the basic default mode.

[0098] Condition 3:: Impedance change rate > set threshold T5, the working mode is switched to the disaster tolerance mode (applicable scenario: serious deformation or rupture of the pipeline). In the disaster tolerance mode, the pressure command of the central control is cut off, and the pressure set value is calculated independently according to the impedance factor and leakage Q of each branch leak ;

[0099] The calculation formula is:

[0100] P i = μ·Z i / (Q leak + ε)

[0101] where μ represents the globally unified proportional constant, and ε represents a very small number to prevent division by zero errors when the leakage is zero.

[0102] It should be noted that in the disaster tolerance mode, each branch needs to survive independently and cannot rely on the central command, that is, each branch can independently calculate the most reasonable pressure setting according to its own impedance and leakage situation. And since each branch has its own local actuator, usually including: electric valves (adjusting flow / pressure), variable frequency pumps. The execution adjustment command can be made according to the calculated pressure.

[0103] Intuitively speaking, according to the above formula, if the leakage rate is high → the denominator is large → P i becomes small → the pressure of this branch is reduced to avoid continuous large-scale loss. If the impedance is high → the numerator is large → P i becomes large → maintain the flow stability and avoid flow interruption caused by impedance.

[0104] If the above conditions are not met, the existing basic mode (default state) is maintained.

[0105] A monitoring and control system for an intelligent monitoring and control method applied to the spraying equipment described above. The system includes the following working modules:

[0106] A pressure monitoring module for collecting the pressure sensor signals of each branch of the spraying system in real time and monitoring the real-time pressure fluctuation; a vibration feature extraction module for detecting the pipeline vibration signal, extracting the energy feature, and judging whether there is an abnormal fluctuation by setting a threshold.

[0107] A leakage location and quantification module that triggers leakage location when the energy feature exceeds the set threshold; calculates the leakage location and the leakage amount through a leakage calculation algorithm; a transfer function modeling module that constructs a pipeline network transfer function based on the leakage amount and the branch impedance to predict the system pressure fluctuation envelope.

[0108] An adaptive parameter correction module that dynamically corrects the model impedance parameters according to the predicted pressure fluctuation and the actual pressure fluctuation; and a pressure control and PID dynamic adjustment module that dynamically adjusts the PID control parameters of the buffer tank according to the predicted pressure fluctuation.

[0109] This embodiment also provides a computer device applicable to an intelligent monitoring and control method for a spraying device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement an intelligent monitoring and control method for a spraying device as proposed in the above embodiment.

[0110] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0111] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the intelligent monitoring and control method of a spraying device as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An intelligent monitoring and control method for a spraying device, characterized in that, Including: Step 1: Real-time collect the flow rate data of the compressed air branch and the pressure data of the pipe network nodes, obtain the pipe network topology parameter set {L, D}, where L is the branch length and D is the pipe diameter. Then perform Fourier transform on the flow rate data to obtain the flow rate spectrum matrix, extract the energy values of the characteristic frequency bands, establish a branch impedance model in combination with the pipe network topology parameters, and output the impedance factors of each branch through this model; Step 2: If the energy value of the characteristic frequency band is greater than the set threshold T1, calculate the leakage point location through the leakage calculation algorithm and output the leakage amount; Step 3: Construct a pipe network transfer function based on the output of the impedance model, predict the pressure fluctuation envelope, and dynamically adjust the PID parameters of the buffer tank according to the prediction result to generate a pressure control command.

2. The intelligent monitoring and control method of a spraying device according to claim 1, characterized in that: The method for extracting the energy value of the characteristic frequency band is as follows: First, determine the lower and upper limits [f low , f high of the characteristic frequency band used to capture the leakage disturbance excitation. The energy intensity E f of the system dynamic disturbance is reflected by the energy integration of the flow rate in a specific frequency band. The calculation formula is as follows: Among them, FFT(Q(t)) represents the result of the fast Fourier transform of the flow rate data Q(t).

3. The intelligent monitoring and control method of a spraying device according to claim 2, characterized in that: The leakage calculation algorithm includes: Obtain the leakage pressure difference ΔP of the branch where the leakage hole is located through the pipe network node pressure data; Based on the differential pressure ΔP caused by leakage and the dynamic disturbance energy intensity E f Derive the position of the leakage point on the branch pipeline; Finally, based on the differential pressure loss ΔP and the dynamic disturbance energy intensity E f back-calculate the gas flow rate loss caused by leakage to obtain the leakage flow rate Q leak .

4. The intelligent monitoring and control method of a spraying device according to claim 3, characterized in that: The expression form of the pipe network transfer function is: Among them, H(s) represents the transfer function of the pipeline system in the Laplace domain, and Z i represents the impedance factor of the i-th branch, and τ i represents the delay time for the pressure to propagate to the i-th branch; s represents the Laplace complex frequency domain variable; N represents the number of branches in the official website that are affected by leakage; The prediction formula of the pressure fluctuation envelope is: ΔP predicted = H(s)·Qlea k ·sin(2πft); Among them, ΔP predicted represents the predicted value of the pressure fluctuation caused by leakage, f represents the leakage characteristic frequency, t represents the time variable, and the amplitude of the leakage influence at the next moment is predicted in real time through this formula.

5. The intelligent monitoring and control method of a spraying device according to claim 4, characterized in that: The method also includes introducing an update mechanism to adapt to the non-linear disturbances existing in the actual pressure change, so as to update the impedance factor of the branch, and predicting the pressure fluctuation value ΔP predicted and the actual observed fluctuation value ΔP actual for adjustment; If: ΔP actual <ΔP predicted It indicates that the actual response is weaker than expected. The true resistance of the branch may be greater, and the impedance factor of this branch should be increased; If: ΔP actual > ΔP predicted It indicates that the actual response is stronger than expected, the true resistance of the branch is smaller, and the impedance factor of this branch should be lowered.

6. The intelligent monitoring and control method of a spraying device according to claim 5, characterized in that: Dynamically calculate the dynamic leakage rate and impedance change rate in real time, and switch different working modes according to different conditions: Condition 1: Q leak is less than threshold T2 and ΔP actual is less than the set threshold T3, the working mode is switched to the precise mode, and the branch impedance model is updated every 30 seconds; Condition 2: The dynamic leakage rate > the set threshold T4, and the working mode is switched to the anti-interference mode. In this mode, the analysis window is shortened and the feedforward compensation ΔP is superimposed. comp , and it is superimposed on the regular current pressure command.

7. The intelligent monitoring and control method of a spraying device according to claim 6, characterized in that: The working mode further includes: a disaster recovery mode, whose triggering condition is: the impedance change rate > the set threshold T5, and the working mode switches to the disaster recovery mode. In the disaster recovery mode, the pressure command of the central control is cut off, and each branch independently calculates the pressure set value according to the impedance factor and the leakage Q of the branch. leak Calculate the pressure set value independently.

8. An intelligent monitoring and control system for a spraying device, which is applied to the intelligent monitoring and control method of the spraying device according to any one of the above claims 1-7, and is characterized in that: The system includes the following working modules: A pressure monitoring module, which is used to collect the pressure sensor signals of each branch of the spraying system in real time and monitor the real-time pressure fluctuation; A vibration feature extraction module, which is used to detect the pipeline vibration signal, extract the energy feature, and judge whether there is abnormal fluctuation by setting a threshold; A leakage location and quantification module, which triggers leakage location when the energy feature exceeds the set threshold; calculates the leakage location and calculates the leakage amount through the leakage calculation algorithm; A transfer function modeling module, which constructs a pipe network transfer function based on the leakage amount and branch impedance, so as to predict the system pressure fluctuation envelope; An adaptive parameter correction module, which dynamically corrects the model impedance parameters according to the predicted pressure fluctuation and the actual pressure fluctuation; And a pressure control and PID dynamic adjustment module, which dynamically adjusts the PID control parameters of the buffer tank according to the predicted pressure fluctuation.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it realizes the steps of the intelligent monitoring and control method of a spraying device according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it realizes the steps of the intelligent monitoring and control method of a spraying device according to any one of claims 1 to 7.

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