Metering method of polyurethane stock solution, storage medium and execution equipment

Abstract

The invention discloses a metering method of a polyurethane stock solution, a storage medium and execution equipment. According to the method, a dynamic coupling differential equation is constructed to fuse a temperature distortion function and a viscosity mutation inhibition mechanism, and parameters alpha, beta and gamma are optimized in real time to match nonlinear characteristics. A recursive least square method is adopted to calibrate parameters online, and the final flow is output through self-adaptive fusion in combination with dual-channel Kalman filtering (main channel sensor data and virtual channel model prediction). In terms of hardware, millisecond-level calculation is achieved through an FPGA, and a three-level signal filtering and encryption storage strategy is integrated. According to the method, the metering error is reduced to 0.12% or below, the response time is shortened to 80 ms, the stability under the temperature change working condition is remarkably improved, and the method is suitable for efficient and precise production of polyurethane in the fields of automobiles, furniture and the like.

Description

Technical Field

[0001] The invention relates to the technical field of metering devices, and in particular to a metering method, a storage medium and an execution device for a polyurethane stock solution. Background Art

[0002] In the industrial production of polyurethane stock solution, accurate metering is the key to achieving product quality consistency. Traditional metering methods mostly rely on static viscosity compensation or linear control models, such as PID control based on a fixed temperature-viscosity relationship. However, polyurethane stock solution has significant non-Newtonian fluid properties, and its viscosity changes nonlinearly with temperature and shear rate. Especially in rapid temperature changes (such as ±10℃ / min) or near phase change points, traditional methods are difficult to accurately compensate for dynamic characteristic differences.

[0003] In the existing technology, the static compensation model cannot respond to sudden changes in viscosity in real time, resulting in the accumulation of metering errors (usually 0.5%-1.2%), and the response delay (200-300ms) affects the efficiency of high-speed production lines. In addition, the existing parameter calibration methods mostly use offline methods, which cannot adapt to differences in raw material batches or environmental fluctuations, resulting in frequent model mismatch problems. Therefore, there is an urgent need for a metering method that can integrate dynamic coupling of multiple physical fields and real-time self-adaptation to solve core problems such as temperature sensitivity, nonlinear disturbances and parameter drift. Summary of the invention

[0004] 1. Technical issues to be resolved In order to solve the above problems, the present invention proposes a metering method, storage medium and execution equipment for polyurethane stock solution, aiming to solve the problem that the static compensation model in the prior art cannot respond to viscosity mutation in real time, resulting in accumulation of metering errors, and the response delay (200-300ms) affects the efficiency of high-speed production lines.

[0005] (II) Technical solution A method for measuring a polyurethane stock solution of the present invention comprises: Get the initial viscosity , thermal conductivity of the material And the characteristic temperature range of the equipment , the characteristic temperature range of the equipment is determined based on the historical statistical data of the production line, thereby obtaining a dynamic coupled differential equation framework model; Preset unit time , linearly increasing the dynamic pressure difference of the flow device per unit time Collect multiple sets of data ,in, To observe the flow in real time, The real-time temperature of the polyurethane stock solution in the metering device is Online optimization of core parameters using recursive least squares method , , ,in, is the temperature response coefficient , is the viscosity mutation inhibitor, is the distortion amplitude coefficient, and constructs the Jacobian matrix , the parameters are iteratively updated by damped least squares method until the residual converges; The Kalman gain matrix is ​​used to balance the confidence of sensor measurement and framework model prediction to obtain deviation characteristics. , , Perform virtual prediction and adaptively adjust weights based on the deviation characteristics , get the final flow of real-time optimization .

[0006] In the present invention, the dynamic coupled differential equation framework model is: ,in is the initial flow coefficient, For real-time traffic, is the temperature compensated viscosity, is the temperature distortion function; The temperature response coefficient Can reflect the rate of temperature change The sensitivity to flow fluctuations ranges from 0.5 to 1.8; The viscosity mutation inhibitor Ability to suppress the second derivative of viscosity caused by drastic temperature changes The nonlinear disturbance caused by is forced to be limited to a value range between 0.8 and 1.2 to prevent overcompensation.

[0007] In the present invention, the temperature distortion function The definition of is: ,in is the critical distortion temperature, is the distortion frequency, and , used to match the operating temperature range of the device.

[0008] In the present invention, the Jacobian matrix ,in ; ; ,in Critical distortion temperature , is the distortion frequency, and .

[0009] In the present invention, the formula for the damped least squares iteration is: ,in is the parameter vector to be optimized, composed of , contains three key dynamic parameters of the dynamic coupled differential equation framework model, is the damping coefficient, used to prevent the matrix Singularity leads to numerical instability, the damping coefficient The value range is 0.001-0.1.

[0010] In the present invention, a Kalman filter is used to update the flow estimation value through the state equation , the state equation is: , where A is the state transfer matrix, B is the control input matrix, is the Kalman gain matrix; A matrix contains the viscosity function The partial derivative of the B matrix integrates the valve control quantity , is the reference temperature, usually the calibration temperature when the system is initialized; The estimated flow rate The predicted flow rate is obtained by virtual prediction based on the state equation .

[0011] In the present invention, The predicted flow , and the final flow is , Weight .

[0012] Another computer-readable storage medium of the present invention stores a computer program thereon, characterized in that when the program is executed by a processor, the method for measuring the polyurethane stock solution described in the above technical solution is implemented.

[0013] Another execution device of the present invention includes an execution device body and a controller, and is characterized in that the controller includes a processor and a computer program stored in a memory and executable on the processor, and when the processor executes the program, the metering method of the polyurethane stock solution described in the above technical solution is implemented.

[0014] (III) Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: In the present invention, the metering error is reduced to ≤0.12% (0.5%-1.2% in the traditional method) through the coordinated optimization of the dynamic coupled differential equation and the recursive least squares method. For example, the standard deviation in the MDI system production line is reduced from 0.43% to 0.09%.

[0015] The dual-channel Kalman filter in the present invention is combined with FPGA hardware acceleration, and the control cycle is shortened to 80ms, which is more than 2.5 times faster than the traditional PID control and meets the needs of high-speed continuous production. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 The figure is a schematic diagram of the logical structure of the method for measuring the polyurethane stock solution; Figure 2 Schematic diagram of the framework structure of the execution equipment.

[0018] 10. Processor, 11. Memory, 12. Communication interface, 13. Communication bus. DETAILED DESCRIPTION Example

[0019] like Figure 1 The method for measuring a polyurethane stock solution shown in the figure specifically comprises the following steps: S100, parameter acquisition and basic parameter presetting: obtaining initial viscosity , thermal conductivity of the material And the characteristic temperature range of the equipment It should be noted that the means of obtaining data can be to manually input calibration values ​​and ranges through the machine, or to obtain data through sensors. Among them, the characteristic temperature range of the equipment is determined based on the historical statistical data of the production line, and the historical statistical data of the production line mainly includes real-time temperature series, process parameter correlation data, quality inspection data and abnormal event records.

[0020] Real-time temperature sequences are mainly recorded by embedded temperature sensors, such as PT100 and thermocouples, which are usually collected at a frequency of 1Hz-10Hz. The process parameter-related data are heating or cooling system logs, production batch information, and equipment status tags. The heating or cooling system logs include heating power setting values, cooling water flow, heat exchange efficiency, etc.; production batch information includes formula type, raw material batch number, production rate (kg / h), etc.; equipment status tags include equipment start and stop time, maintenance records, abnormal shutdown events, etc.

[0021] In this way, the dynamic coupled differential equation framework model is obtained: ,in is the initial flow coefficient, It is the real-time flow rate, which is measured by the flow sensor in real time. is the temperature compensated viscosity, is the temperature distortion function, is the dynamic pressure difference, specifically the pressure difference between the inlet and outlet of the metering pump; the temperature distortion function The definition of is: ,in is the critical distortion temperature, is the distortion frequency, and , used to match the operating temperature range of the device. , is the reference temperature, which is usually the calibration temperature when the system is initialized. In this embodiment, ,Right now The value is obtained by measuring with a rotational viscometer at a reference temperature of 25°C.

[0022] When measuring the initial viscosity, use a rotational viscometer (such as Brookfield DV2T) to perform repeated measurements to ensure that the measurement error is less than ±0.5%. For example, for a certain type of MDI system polyurethane stock solution, the typical initial viscosity is, and the data needs to be recorded after stabilization in a constant temperature bath for 30 minutes. The thermal conductivity of the material It is obtained by differential scanning calorimetry (DSC, such as TAInstruments Q200). During the test, the temperature is increased at a rate of 2°C / min, and the viscosity changes at different temperature points (such as 20°C, 30°C, and 40°C) are recorded. The temperature response coefficient Can reflect the rate of temperature change The sensitivity to flow fluctuations ranges from 0.5 to 1.8; The viscosity mutation inhibitor Ability to suppress the second derivative of viscosity caused by drastic temperature changes The nonlinear disturbance caused by is forced to be limited to a value range between 0.8 and 1.2 to prevent overcompensation. In this embodiment, , , .

[0023] S200, dynamic parameter online calibration and real-time compensation: preset unit time , linearly increasing the dynamic pressure difference of the flow device per unit time Collect multiple sets of data In this embodiment, the unit time is 5 seconds, that is, the dynamic pressure difference is linearly adjusted from 0 to 2.5 MPa within 5 seconds, and 20 sets of data pairs are collected at a sampling rate of 1 kHz.

[0024] In order to observe the flow in real time, a sliding average filter needs to be applied to eliminate high-frequency noise after obtaining the real-time observed flow; It is the real-time temperature of the polyurethane stock solution in the metering device.

[0025] According to the collected data Online optimization of core parameters using recursive least squares method , , .in, is the temperature response coefficient, is the viscosity mutation inhibitor, is the distortion amplitude coefficient, and constructs the Jacobian matrix The Jacobian matrix ,in ; ; ,in is the critical distortion temperature, preset to 35°C, is the distortion frequency, and .

[0026] The core goal of the S200 phase is to calibrate the key parameters in the dynamic model online through experimental data-driven methods to ensure that the mathematical model can accurately match the dynamic characteristics of the actual physical system. Specifically, it includes: optimizing the undetermined parameters in the nonlinear coupled differential equations , , , solve the model mismatch problem caused by differences in material properties and environmental fluctuations; establish a mapping relationship between parameters and working conditions so that the model can adapt to different production conditions (such as temperature changes, pressure fluctuations); verify the initial assumptions of the model (such as the viscosity-temperature exponential relationship, the temperature distortion function form), and trigger model structure adjustments when necessary.

[0027] The parameters are updated by iterative damped least squares method until the residual converges. The formula of the damped least squares method iteration is: ,in is the parameter vector to be optimized, composed of , contains three key dynamic parameters of the dynamic coupled differential equation framework model, is the damping coefficient, which is used to prevent the matrix Singularity leads to numerical instability, the damping coefficient The value range is 0.001-0.1.

[0028] In this embodiment, the damping coefficient Used to prevent matrix ill-conditioning, the iteration termination condition is 10 consecutive residuals After calibration, the core parameters , , Write to non-volatile memory and synchronize to the virtual prediction channel. S200 calibration completed , , Directly write into the virtual channel model of S300 as the basic parameters.

[0029] S300, dual-channel fusion data: In the main channel, the Kalman gain matrix is ​​used to balance the confidence of sensor measurement and framework model prediction to obtain deviation characteristics, and at the same time, in the virtual channel, based on the core parameters , , Make a virtual forecast.

[0030] The Kalman filter of the main channel uses the Kalman filter to update the flow estimate through the state equation , the state equation is: , where A is the state transfer matrix, B is the control input matrix, is the Kalman gain matrix; A matrix contains the viscosity function The partial derivative of the B matrix integrates the valve control quantity , The reference temperature is usually the calibration temperature when the system is initialized.

[0031] The estimated flow rate The predicted flow rate is obtained by the state equation and predicted through the virtual channel prediction .

[0032] Forecast traffic .

[0033] Adaptively adjust the weight based on the deviation feature ,When the deviation between the main and auxiliary channels increases, the weight of the virtual channel is reduced to prevent abnormal prediction interference. Get the final flow of real-time optimization , .

[0034] When the main and auxiliary channels deviate When the weight It approaches 1 rapidly, giving priority to the main channel data to ensure the robustness of the system.

[0035] The Xilinx Zynq-7020 chip is used to accelerate the algorithm. The programmable logic (PL) part deploys a differential equation solver to complete the integral operation at a clock frequency of 200MHz; the processing system (PS) part runs the Linux real-time kernel to handle parameter updates and anomaly detection tasks. The temperature signal is processed by three levels of filtering: the first level RC filtering (cutoff frequency 1kHz) eliminates high-frequency noise; the second level median filtering (window width 11) suppresses pulse interference; the third level is based on The nonlinear compensation corrects the distortion in the sensitive area. The actuator control cycle is set to 0.8ms to ensure that the viscosity change rate The calculation resolution is 0.1%, and the metering pump is driven by PWM modulation with a duty cycle accuracy of ±0.5%. Example

[0036] An embodiment of the present invention provides a computer-readable storage medium.

[0037] The computer-readable storage medium provided in the embodiment of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for measuring the polyurethane stock solution can be implemented.

[0038] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0039] For an introduction to the computer-readable storage medium provided in an embodiment of the present invention, please refer to the above method embodiment, and the present invention will not elaborate on it here.

[0040] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0041] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0042] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. Example

[0043] An embodiment of the present invention provides an execution device.

[0044] Please refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of an execution device provided by the present invention, and the execution device may include: Memory for storing computer programs; The processor is used to implement the steps of any one of the above-mentioned polyurethane stock solution metering methods when executing the computer program.

[0045] like Figure 2 , which is a schematic diagram of the composition structure of the execution device, the execution device may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11 and the communication interface 12 all communicate with each other through the communication bus 13.

[0046] In the embodiment of the present invention, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0047] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute the operations in the embodiment of the push button switch fault detection method.

[0048] The memory 11 is used to store one or more programs, which may include program codes, and the program codes include computer operation instructions. In the embodiment of the present invention, the memory 11 at least stores programs for implementing the following functions: Get the initial viscosity , thermal conductivity of the material And the characteristic temperature range of the equipment , the characteristic temperature range of the equipment is determined based on the historical statistical data of the production line, thereby obtaining a dynamic coupled differential equation framework model; Preset unit time , linearly increasing the dynamic pressure difference of the flow device per unit time Collect multiple sets of data ,in, To observe the flow in real time, The real-time temperature of the polyurethane stock solution in the metering device is Online optimization of core parameters using recursive least squares method , , ,in, is the temperature response coefficient, is the viscosity mutation inhibitor, is the distortion amplitude coefficient, and constructs the Jacobian matrix , the parameters are iteratively updated by damped least squares method until the residual converges; The Kalman gain matrix is ​​used to balance the confidence of sensor measurement and framework model prediction to obtain deviation characteristics. , , Perform virtual prediction and adaptively adjust weights based on the deviation characteristics , get the final flow of real-time optimization .

[0049] In a possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0050] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0051] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0052] Of course, it should be noted that Figure 2 The structure shown does not constitute a limitation on the execution device in the embodiment of the present invention. In actual applications, the execution device may include Figure 2 More or fewer components than shown, or combinations of certain components.

[0053] The above-described embodiments are merely descriptions of preferred implementations of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made by ordinary persons in the art to the technical solution of the present invention should fall within the protection scope of the present invention, and the technical contents for which protection is sought in the present invention have been fully recorded in the claims.

[0054] The above-described embodiments are merely descriptions of preferred implementations of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made by ordinary persons in the art to the technical solution of the present invention should fall within the protection scope of the present invention, and the technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A method for measuring polyurethane stock solution, characterized in that: The metering method of the polyurethane stock solution comprises: Get the initial viscosity , thermal conductivity of the material And the characteristic temperature range of the equipment , the characteristic temperature range of the equipment is determined based on the historical statistical data of the production line, thereby obtaining a dynamic coupled differential equation framework model; Preset unit time , linearly increasing the dynamic pressure difference of the flow device per unit time Collect multiple sets of data ,in, To observe the flow in real time, The real-time temperature of the polyurethane stock solution in the metering device is Online optimization of core parameters using recursive least squares method , , ,in, is the temperature response coefficient, is the viscosity mutation inhibitor, is the distortion amplitude coefficient, and constructs the Jacobian matrix , the parameters are iteratively updated by damped least squares method until the residual converges; The Kalman gain matrix is ​​used to balance the confidence of sensor measurement and framework model prediction to obtain deviation characteristics. , , Perform virtual prediction and adaptively adjust weights based on the deviation characteristics , get the final flow of real-time optimization .

2. The method for measuring the polyurethane stock solution according to claim 1, characterized in that: The dynamic coupled differential equation framework model is: ,in is the initial flow coefficient, For real-time traffic, is the temperature compensated viscosity, is the temperature distortion function; The temperature response coefficient Can reflect the rate of temperature change The sensitivity to flow fluctuations ranges from 0.5 to 1.8; The viscosity mutation inhibitor Ability to suppress the second derivative of viscosity caused by drastic temperature changes The nonlinear disturbance caused by is forced to be limited to a value range between 0.8 and 1.2 to prevent overcompensation.

3. The method for measuring the polyurethane stock solution according to claim 2, characterized in that: The temperature distortion function The definition of is: ,in is the critical distortion temperature, is the distortion frequency, and , used to match the operating temperature range of the device.

4. The method for measuring the polyurethane stock solution according to claim 2 or 3, characterized in that: The Jacobian matrix ,in ; ; ,in is the critical distortion temperature, is the distortion frequency, and .

5. The method for measuring the polyurethane stock solution according to claim 4, characterized in that: The formula for the damped least squares iteration is: ,in is the parameter vector to be optimized, composed of , contains three key dynamic parameters of the dynamic coupled differential equation framework model, is the damping coefficient, which is used to prevent the matrix Singularity leads to numerical instability, the damping coefficient The value range is 0.001-0.

1.

6. The method for measuring the polyurethane stock solution according to claim 5, characterized in that: Using Kalman filter to update flow estimate through state equation , the state equation is: , where A is the state transfer matrix, B is the control input matrix, is the Kalman gain matrix; A matrix contains the viscosity function The partial derivative of the B matrix integrates the valve control quantity , is the reference temperature, usually the calibration temperature when the system is initialized; The estimated flow rate The predicted flow rate is obtained by virtual prediction based on the state equation .

7. The method for measuring the polyurethane stock solution according to claim 6, characterized in that: The predicted flow , and the final flow is , Weight .

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for measuring the polyurethane stock solution according to any one of claims 1 to 7 is implemented.

9. An execution device, comprising an execution device body and a controller, characterized in that: The controller includes a processor and a computer program stored in a memory and executable on the processor. When the processor executes the program, the method for metering the polyurethane stock solution according to any one of claims 1 to 7 is implemented.

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