Flow control system and method for filter element flow resistance performance test

Abstract

According to the flow control system and method for the filter element flow resistance performance test provided by the invention, the self-adaptive PID control closed loop taking the real-time flow resistance coefficient as the core feedback quantity is constructed, and the high-precision sensing network and the intelligent decision algorithm are integrated; the defect that traditional manual or simple PID control is difficult to adapt to filter element characteristic change and environmental interference is successfully overcome; specifically, the system dynamically collects flow, pressure difference and temperature signals and calculates the flow resistance coefficient of a filter element in real time, PID control parameters (proportion, integration and differential terms) are adjusted in a self-adaptive mode according to the flow resistance coefficient, meanwhile, the reference value of a sensor is periodically calibrated so as to eliminate drift errors, and an electric control valve is driven to rapidly and accurately respond; according to the method, the dynamic stability and static precision of flow control are remarkably improved, multi-source interference such as pipeline pressure pulsation and fluid temperature change is effectively compensated, the steady-state error of the test flow is greatly reduced, and finally the efficient and high-precision automatic test of the flow resistance performance of the filter element is achieved.

Description

Technical Field

[0001] This invention relates to the field of water treatment, and in particular to a flow control system and method for testing the flow resistance performance of filter elements. Background Technology

[0002] Filter elements are widely used in water treatment. Flow resistance performance is one of the selection criteria for filter elements, and accurate measurement of the rated flow rate of filter elements is crucial. However, current flow control methods for filter element flow resistance performance testing rely on manual adjustment or simple PID control, which is difficult to adapt to different filter element characteristics, leading to large test errors. The flow adjustment speed slows down as the flow setpoint increases, and simply controlling the frequency of the centrifugal pump motor is insufficient to quickly and accurately adjust the test flow rate to the setpoint, resulting in low testing efficiency. Furthermore, flow fluctuations are significantly affected by pipeline pressure pulsations and temperature changes. The lack of dynamic calibration and multi-parameter feedback mechanisms, low system integration, and weak anti-interference capabilities fail to meet the demands of high-precision industrial testing.

[0003] To address the aforementioned problems, this invention discloses a flow control system and method for testing the flow resistance performance of filter elements. By integrating high-precision sensors, adaptive PID control algorithms, and modular hardware design, dynamic and stable flow control is achieved. The system can automatically compensate for temperature and pressure disturbances, significantly improving test accuracy and solving the problems of low accuracy and slow response in traditional testing. Summary of the Invention

[0004] A first aspect of this disclosure provides a flow control system for testing the flow resistance performance of a filter element, comprising:

[0005] Data acquisition module: used to acquire in real time the flow rate signal of the fluid flowing through the filter element under test, the pressure difference signal across the filter element, and the fluid temperature signal;

[0006] Data processing module: connected to the data acquisition module, used to receive the flow signal, differential pressure signal and temperature signal. The data processing module has an adaptive PID control algorithm unit, used to calculate the real-time flow resistance coefficient of the filter element according to the received signal. The parameter setting value of the adaptive PID control algorithm unit is dynamically adjusted according to the change of the real-time flow resistance coefficient relative to the reference flow resistance coefficient. The adaptive PID control algorithm unit calculates the output control quantity based on the set target flow value, the real-time acquired flow signal and the dynamically adjusted parameter setting value.

[0007] Control execution module: connected to the data processing module, used to receive the output control quantity, and adjust the opening of the flow regulating mechanism in the pipeline based on the output control quantity, so as to control the actual flow rate through the filter element;

[0008] Communication module: Connects to the data processing module and is used for internal data exchange and / or communication with external devices.

[0009] In conjunction with the first aspect, the rule for dynamically adjusting the parameter settings of the adaptive PID control algorithm module is as follows:

[0010] The adjustment value of the proportional coefficient is directly proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

[0011] The adjustment value of the integral time is proportional to its reference value and proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient;

[0012] The adjustment value of the differential time is proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

[0013] In conjunction with the first aspect, the data processing module is also configured with a dynamic calibration program for automatically correcting the reference value of the sensor signal of the data acquisition module at preset time intervals.

[0014] In conjunction with the first aspect, when the reference flow resistance coefficient is unknown, the data processing module can inversely calculate and store the reference flow resistance coefficient based on the flow rate, pressure difference, temperature signals and the geometric parameters of the filter element collected in the first test, for use in setting parameters for subsequent tests.

[0015] A second aspect of this disclosure provides a flow control method for testing the flow resistance performance of a filter element, comprising the following steps:

[0016] S201: System initialization, setting target flow rate and filter element related parameters, and real-time acquisition of the actual flow rate of the fluid flowing through the filter element, the pressure difference across the filter element, and the fluid temperature.

[0017] S202: Calculate the real-time flow resistance coefficient of the filter element based on the relevant parameters of the filter element, the actual flow rate, the pressure difference across the filter element, and the fluid temperature.

[0018] S203: Based on the change of the real-time flow resistance coefficient relative to the reference flow resistance coefficient, dynamically adjust the parameter setting value of the adaptive PID control algorithm;

[0019] S204: Based on the target flow rate, the actual flow rate, and the parameter setting value, the output control quantity is calculated using an adaptive PID control algorithm, and the opening degree of the flow regulation mechanism is adjusted according to the output control quantity;

[0020] S205: After a predetermined delay, repeat steps S202 to S204 until the deviation between the actual flow rate and the target flow rate value meets the preset accuracy requirement.

[0021] In conjunction with the second aspect, the rule for dynamically adjusting the parameter setting value is as follows:

[0022] The adjustment value of the proportional coefficient is directly proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

[0023] The adjustment value of the integral time is proportional to its reference value and proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient;

[0024] The adjustment value of the differential time is proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

[0025] In conjunction with the second aspect, in step S202, the collected actual flow signal is subjected to moving average filtering.

[0026] In conjunction with the second aspect, in step S205, when the deviation meets the preset accuracy requirements, the current control parameters are locked and the test results are recorded.

[0027] A third aspect of this disclosure provides an electronic device, characterized in that it comprises:

[0028] one or more processors;

[0029] A storage unit is used to store one or more programs that, when executed by one or more processors, enable the one or more processors to implement the flow control method for the filter element flow resistance performance test.

[0030] A fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it can implement a flow control method for testing the flow resistance performance of the filter element.

[0031] Beneficial Effects: This disclosure provides a flow control system and method for testing the flow resistance performance of filter elements. By constructing an adaptive PID control closed loop with the real-time flow resistance coefficient as the core feedback quantity, and integrating a high-precision sensor network and intelligent decision-making algorithm, it successfully overcomes the shortcomings of traditional manual or simple PID control, which is difficult to adapt to changes in filter element characteristics and environmental interference. Specifically, the system dynamically acquires flow rate, differential pressure, and temperature signals and calculates the filter element flow resistance coefficient in real time. Based on this, it adaptively adjusts the PID control parameters (proportional, integral, and derivative terms), while periodically calibrating the sensor reference value to eliminate drift error, driving the electric regulating valve to respond quickly and accurately. This method significantly improves the dynamic stability and static accuracy of flow control, effectively compensates for multi-source interference such as pipeline pressure pulsation and fluid temperature changes, greatly reduces the steady-state error of the test flow rate, and ultimately achieves efficient and high-precision automated testing of filter element flow resistance performance. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the structure of a flow control system for testing the flow resistance performance of a filter element according to an embodiment of the present disclosure;

[0033] Figure 2 This is a schematic flowchart of a flow control method for testing the flow resistance performance of a filter element according to an embodiment of the present disclosure.

[0034] Figure 3 An electronic device according to an embodiment of this disclosure. Detailed Implementation

[0035] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those disclosed herein.

[0036] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0037] like Figure 1 The diagram shown is a structural schematic of a flow control system for testing the flow resistance performance of a filter element according to an embodiment of this disclosure, comprising:

[0038] Data acquisition module 110: used to acquire in real time the flow rate signal of the fluid flowing through the filter element under test, the pressure difference signal across the filter element, and the fluid temperature signal;

[0039] Data processing module 120: connected to the data acquisition module, used to receive the flow signal, differential pressure signal and temperature signal. The data processing module has an adaptive PID control algorithm unit, used to calculate the real-time flow resistance coefficient of the filter element according to the received signal. The parameter setting value of the adaptive PID control algorithm unit is dynamically adjusted according to the change of the real-time flow resistance coefficient relative to the reference flow resistance coefficient. The adaptive PID control algorithm unit calculates the output control quantity based on the set target flow value, the real-time acquired flow signal and the dynamically adjusted parameter setting value.

[0040] Control execution module 130: connected to the data processing module, used to receive the output control quantity, and adjust the opening of the flow regulating mechanism in the pipeline based on the output control quantity, so as to control the actual flow rate through the filter element;

[0041] Communication module 140: Connects to the data processing module and is used for internal data exchange and / or communication with external devices.

[0042] Specifically, the data acquisition module 110 integrates a high-precision flow sensor, differential pressure sensor, and temperature sensor to collect in real time the fluid flow rate signal, the differential pressure signal across the filter element, and the fluid temperature signal. The sensor signals are transmitted via shielded cables and equipped with hardware filtering circuits to effectively suppress electromagnetic interference. At the same time, the control actuator is equipped with a vibration isolation base to reduce the impact of mechanical vibration and ensure the reliability of the original data.

[0043] The data processing module 120, as the core of the system, receives multi-dimensional signals from the data acquisition module and performs real-time analysis. Its built-in adaptive PID control algorithm unit first calculates the real-time flow resistance coefficient R (unit: Pa·s / m²) based on the pressure difference signal ΔP, temperature signal T, and flow rate signal Q, combined with preset filter element geometric parameters (thickness L, cross-sectional area A) and permeability k, using the flow resistance coefficient formula. 3 If the permeability is unknown due to the filter element being tested for the first time, the initial flow resistance coefficient R0 is calculated by inverting the pressure-flow relationship and stored in the database. The algorithm unit further compares the real-time flow resistance coefficient R with the reference flow resistance coefficient R0 (factory parameter or historical inversion value), and dynamically adjusts the PID control parameters according to the ratio (R / R0): the proportional coefficient Kp is adjusted inversely to (R0 / R) to suppress overshoot, the integral time Ti is adjusted directly to (R / R0) to smoothly eliminate steady-state error, and the derivative time Td is adjusted inversely to (R0 / R) to reduce flow pulsation interference. Based on the dynamically adjusted PID parameters, the target flow setpoint Qtarget, and the real-time flow feedback Qactua l, the algorithm unit generates a precise output control quantity u(t).

[0044] The control execution module 130 receives the control quantity u(t) output by the data processing module and drives the flow regulating mechanism (such as an electric regulating valve or a variable frequency pump) in the pipeline. The electric regulating valve has a millisecond-level response speed (<50ms) and quickly adjusts the valve opening according to the control quantity to achieve closed-loop regulation of the flow through the filter element. After the actuator is activated, the system delays for a predetermined time (e.g., τ = 2s) to wait for the flow to stabilize, and then triggers a new round of data acquisition and parameter adjustment iteration.

[0045] The communication module 140 connects each hardware unit via an industrial bus (such as RS485 / CAN) to achieve real-time data synchronization between sensors, controllers and actuators. It also supports remote communication with external monitoring equipment (such as an industrial PC) to facilitate test parameter configuration, process monitoring and result recording.

[0046] Furthermore, the rule for dynamically adjusting the parameter settings of the adaptive PID control algorithm module is as follows:

[0047] The adjustment value of the proportional coefficient is directly proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

[0048] The adjustment value of the integral time is proportional to its reference value and proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient;

[0049] The adjustment value of the differential time is proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

[0050] Specifically, the dynamic parameter adjustment mechanism of the adaptive PID control algorithm module constitutes the core of the system's intelligent response. The proportional gain is set as a function of its reference value, specifically maintaining a direct proportionality to the reference value while being inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient. This strategy ensures that when the flow resistance increases due to filter blockage, the proportional gain intensity is automatically reduced, effectively suppressing the risk of flow overshoot. The integral time is also related to its reference value and is positively correlated with the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient, slowing down the integral action when the flow resistance increases, avoiding system oscillations caused by excessive accumulation of errors, and smoothly eliminating steady-state deviations. The derivative time follows a similar adjustment logic to the proportional gain—it is directly proportional to its reference value but inversely proportional to the ratio of the flow resistance coefficient, thereby weakening the sensitivity of the derivative term to high-frequency flow fluctuations when the flow resistance increases, significantly reducing control jitter. This parameter linkage mechanism based on the flow resistance ratio enables the control system to dynamically adapt to complex operating conditions such as filter performance degradation and fluid viscosity changes.

[0051] Furthermore, the data processing module is also equipped with a dynamic calibration program for automatically correcting the reference value of the sensor signal of the data acquisition module at preset time intervals.

[0052] Specifically, based on this, the system continuously ensures measurement accuracy through a built-in dynamic calibration program. This program is automatically triggered at preset time intervals (e.g., every 2 seconds) to perform baseline value correction on the flow, differential pressure, and temperature sensor signals in the data acquisition module. By compensating for systematic errors such as sensor zero drift and temperature drift in real time, the system eliminates the long-term drift effect in the data acquisition process from the source, ensuring that the raw data on which the flow resistance coefficient calculation and PID parameter adjustment depend always maintain high reliability, making it particularly suitable for long-term continuous testing scenarios.

[0053] Furthermore, when the reference flow resistance coefficient is unknown, the data processing module can inversely calculate and store the reference flow resistance coefficient based on the flow rate, pressure difference, temperature signals and the geometric parameters of the filter element collected in the first test, for use in setting parameters for subsequent tests.

[0054] Specifically, for testing compatibility issues of new filter elements or filter cartridges with unknown permeability, the system employs an intelligent inversion calculation strategy to overcome initial parameter limitations. When the baseline flow resistance coefficient is unknown, the data processing module automatically invokes the inversion calculation engine: based on the real-time flow-pressure difference data, temperature data, and filter element geometric parameters (thickness, cross-sectional area) collected during the initial test, it inversely calculates the equivalent baseline flow resistance coefficient of the filter element. The calculation results are automatically stored in the system database, providing not only an initial parameter anchor point for adaptive PID control in the current test, but also establishing a reference benchmark for subsequent tests of similar filter elements, forming a closed-loop knowledge accumulation system of "testing-learning-optimization".

[0055] like Figure 2 The diagram shown is a flowchart illustrating a flow control method for testing the flow resistance performance of a filter element according to an embodiment of this disclosure, including:

[0056] S201: System initialization, setting target flow rate and filter element related parameters, and real-time acquisition of the actual flow rate of the fluid flowing through the filter element, the pressure difference across the filter element, and the fluid temperature.

[0057] Specifically, the operator sets the target flow rate Qtarget and key filter element parameters (thickness L, cross-sectional area A, permeability k). If this is the first test of the filter element, the permeability k can be left blank and automatically filled in by the system. The initialization command triggers the data acquisition module to start synchronously, capturing the actual flow rate Qactual through the filter element in real time through a high-precision flow sensor, obtaining the pressure difference ΔP across the filter element through a differential pressure sensor, and monitoring the fluid temperature T through a temperature sensor. The raw signal is then input to the core processing unit after hardware filtering and moving average processing (e.g., 5-point smoothing).

[0058] S202: Calculate the real-time flow resistance coefficient of the filter element based on the relevant parameters of the filter element, the actual flow rate, the pressure difference across the filter element, and the fluid temperature.

[0059] Specifically, the data processing module calculates the filter element resistance coefficient R in real time using a flow resistance physical model based on the collected ΔP, T, Qactual, and preset filter element geometric parameters. Specifically, it calculates the real-time flow resistance value using the formula R = (ΔP·A) / (Qactual·μ(T)·L) by combining the fluid dynamic viscosity μ(T) obtained from temperature interpolation. When the permeability k is unknown, the system automatically substitutes the current ΔP, Qactual, and geometric parameters into the formula to deduce the initial reference flow resistance coefficient R0 and stores it, establishing a reference benchmark for subsequent parameter adjustments.

[0060] S203: Based on the change of the real-time flow resistance coefficient relative to the reference flow resistance coefficient, dynamically adjust the parameter setting value of the adaptive PID control algorithm;

[0061] Specifically, the system compares the real-time flow resistance coefficient R with the reference value R0, and dynamically reconstructs the PID control parameters based on the ratio (R / R0). The proportional coefficient Kp is adjusted downward according to the rule Kp0·(R0 / R) to suppress the risk of overshoot under high flow resistance conditions caused by filter blockage; the integral time Ti is extended proportionally to Ti 0·(R / R0) to slow down the integral action speed and thus smoothly eliminate the steady-state error caused by high flow resistance; the derivative time Td is shortened according to Td0·(R0 / R) to reduce the sensitivity of the control system to high-frequency flow pulsation. This parameter linkage mechanism ensures that the control algorithm always matches the dynamic flow resistance characteristics of the filter element.

[0062] S204: Based on the target flow rate, the actual flow rate, and the parameter setting value, the output control quantity is calculated using an adaptive PID control algorithm, and the opening degree of the flow regulation mechanism is adjusted according to the output control quantity;

[0063] Specifically, the adaptive PID algorithm calculates the output control quantity u(t) based on the target flow rate Qtarget, the real-time feedback flow rate Qactual, and the reconstructed parameter set (Kp, Ti, Td). This control quantity is transmitted to the electric regulating valve via the communication bus, driving the valve core to adjust the opening within milliseconds (<50ms) to precisely regulate the flow rate through the filter element. Simultaneously, the dynamic calibration program automatically corrects the sensor reference value at fixed intervals (e.g., Δt = 2s) to eliminate the long-term impact of environmental temperature drift on the measurement data.

[0064] S205: After a predetermined delay, repeat steps S202 to S204 until the deviation between the actual flow rate and the target flow rate value meets the preset accuracy requirement.

[0065] Specifically, after the actuator operates, the system delays for a preset stabilization time (e.g., τ = 2s) to allow the flow rate to stabilize. Then, data is re-acquired and the S202-S204 process is executed iteratively. Each iteration updates the flow resistance coefficient and PID parameters until the absolute deviation between the actual flow rate Qactual and the target value Qtarget is less than or equal to a set accuracy threshold ε (e.g., 0.01m). 3 / h). Once the required accuracy is achieved, the system locks the current control parameters and records the filter element flow resistance performance data, generating a test report. Through an intelligent iterative cycle of "measurement-calculation-adjustment-execution-verification," the accuracy of steady-state flow control is improved.

[0066] Furthermore, the rules for dynamically adjusting the parameter settings are as follows:

[0067] The adjustment value of the proportional coefficient is directly proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

[0068] The adjustment value of the integral time is proportional to its reference value and proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient;

[0069] The adjustment value of the differential time is proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

[0070] For example, the implementation of the dynamic parameter adjustment rule relies on a real-time mapping mechanism for the flow resistance ratio. After the system calculates the real-time flow resistance coefficient R, it immediately performs a division operation with the pre-stored reference flow resistance coefficient R0 to obtain the ratio factor (R / R0). This factor serves as the core variable input parameter adjustment engine: the proportional coefficient is set to be positively correlated with its reference value Kp0, and inversely correlated with the ratio factor (R / R0). That is, when filter blockage causes R to increase, R / R0>1, causing Kp to automatically decay (Kp=Kp0×R0 / R), thereby weakening the proportional effect and avoiding the risk of overshoot under large flow deviations. The integral time adjustment value is kept proportional to the reference value Ti 0 and changes in the same direction as the ratio factor (R / R0), so that the integral time is extended when R increases (Ti=Ti0×R / R0), slowing down the integral accumulation speed to suppress system oscillation. The derivative time adjustment value follows a negative adjustment law similar to the proportional coefficient—its value is proportional to the reference value Td0, but inversely proportional to the ratio factor (R / R0) (Td=Td0×R0 / R), actively compressing the sensitivity of the derivative element to high-frequency noise when the flow resistance increases. This rule is executed in real time through matrix operations in the embedded controller to ensure that the PID parameters always match the dynamic characteristics of the filter element.

[0071] Furthermore, in step S202, the collected actual flow signal is subjected to moving average filtering.

[0072] Specifically, in the data preprocessing stage before the flow resistance coefficient calculation, the system incorporates a signal smoothing optimization strategy. In step S202, the raw pulse signal acquired by the flow sensor first undergoes moving average filtering: the controller acquires the flow data stream at a fixed sampling frequency (e.g., 100Hz) and maintains a first-in-first-out data queue of length N (e.g., 5 points); each time new data enters, the system automatically removes the earliest data point and calculates the arithmetic mean of the N data points in the queue as the actual flow value Qactual output. This processing effectively filters out instantaneous spike noise caused by pump vibration or fluid turbulence, reducing the fluctuation amplitude of the flow signal by more than 60%, providing a highly stable input source for subsequent flow resistance coefficient calculation.

[0073] Furthermore, in step S205, once the deviation meets the preset accuracy requirements, the current control parameters are locked and the test results are recorded.

[0074] Specifically, an intelligent convergence management mechanism is introduced during the control iteration termination phase. In step S205, when the system detects that the absolute deviation between the actual flow Qactual and the target flow Qtarget remains within a preset accuracy threshold ε (e.g., 0.01m), 3 When the flow rate is within the range of / h, it is determined that the flow rate has entered a steady state. At this time, the system immediately locks the current adaptive PID control parameter group (Kp, Ti, Td) and the opening of the electric regulating valve, stops the dynamic adjustment cycle of parameters, and triggers the data logging program: packaging the steady-state flow rate value, flow resistance coefficient, temperature, and differential pressure data, and uploading them to the monitoring database through the communication module to generate a test report. This mechanism not only avoids energy waste caused by excessive iteration, but also ensures that the test results capture the steady-state operating performance of the filter element, providing reliable data support for flow resistance evaluation.

[0075] Electronic device 300 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 300 may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 300 and does not constitute a limitation on electronic device 300. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0076] Processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0077] The memory 302 can be an internal storage unit of the electronic device 300, such as a hard disk or RAM of the electronic device 300. The memory 302 can also be an external storage device of the electronic device 300, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 300. Furthermore, the memory 302 can include both internal and external storage units of the electronic device 300. The memory 302 is used to store the computer program 303 and other programs and data required by the electronic device. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0078] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A flow control system for testing the flow resistance performance of filter elements, characterized in that, include: Data acquisition module: used to acquire in real time the flow rate signal of the fluid flowing through the filter element under test, the pressure difference signal across the filter element, and the fluid temperature signal; Data processing module: connected to the data acquisition module, used to receive the flow signal, differential pressure signal and temperature signal. The data processing module has an adaptive PID control algorithm unit, used to calculate the real-time flow resistance coefficient of the filter element according to the received signal. The parameter setting value of the adaptive PID control algorithm unit is dynamically adjusted according to the change of the real-time flow resistance coefficient relative to the reference flow resistance coefficient. The adaptive PID control algorithm unit calculates the output control quantity based on the set target flow value, the real-time acquired flow signal and the dynamically adjusted parameter setting value. Control execution module: connected to the data processing module, used to receive the output control quantity, and adjust the opening of the flow regulating mechanism in the pipeline based on the output control quantity, so as to control the actual flow rate through the filter element; Communication module: Connects to the data processing module and is used for internal data exchange and / or communication with external devices.

2. The system according to claim 1, characterized in that, The rule for dynamically adjusting the parameter settings of the adaptive PID control algorithm module is as follows: The adjustment value of the proportional coefficient is directly proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient. The adjustment value of the integral time is proportional to its reference value and proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient; The adjustment value of the differential time is proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

3. The system according to claim 1, characterized in that, The data processing module is also equipped with a dynamic calibration program, which is used to automatically correct the reference value of the sensor signal of the data acquisition module at preset time intervals.

4. The system according to claim 1, characterized in that, When the reference flow resistance coefficient is unknown, the data processing module can inversely calculate and store the reference flow resistance coefficient based on the flow rate, pressure difference, temperature signals and the geometric parameters of the filter element collected in the first test, for use in setting parameters for subsequent tests.

5. A flow control method for testing the flow resistance performance of a filter element using the system described in any one of claims 1-4, characterized in that, Includes the following steps: S201: System initialization, setting the target flow rate and filter element related parameters, by real-time acquisition of the actual flow rate of the fluid flowing through the filter element, the pressure difference across the filter element, and the fluid temperature; S202: Calculate the real-time flow resistance coefficient of the filter element based on the relevant parameters of the filter element, the actual flow rate, the pressure difference across the filter element, and the fluid temperature. S203: Based on the change of the real-time flow resistance coefficient relative to the reference flow resistance coefficient, dynamically adjust the parameter setting value of the adaptive PID control algorithm; S204: Based on the target flow rate, the actual flow rate, and the parameter setting value, the output control quantity is calculated using an adaptive PID control algorithm, and the opening degree of the flow regulation mechanism is adjusted according to the output control quantity; S205: After a predetermined delay, repeat steps S202 to S204 until the deviation between the actual flow rate and the target flow rate value meets the preset accuracy requirement.

6. The method according to claim 5, characterized in that, The rules for dynamically adjusting the parameter settings are as follows: The adjustment value of the proportional coefficient is directly proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient. The adjustment value of the integral time is proportional to its reference value and proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient; The adjustment value of the differential time is proportional to its reference value and inversely proportional to the ratio of the real-time flow resistance coefficient to the reference flow resistance coefficient.

7. The method according to claim 5, characterized in that, In step S202, the collected actual flow signal is subjected to moving average filtering.

8. The method according to claim 5, characterized in that, In step S205, once the deviation meets the preset accuracy requirements, the current control parameters are locked and the test results are recorded.

9. An electronic device, characterized in that, include: one or more processors; A storage unit is used to store one or more programs that, when executed by one or more processors, enable the one or more processors to implement the flow control method for testing the flow resistance performance of filter elements according to claims 1-7.

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 can implement the flow control method for testing the flow resistance performance of filter elements according to claims 1-7.

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