Control method and device of bulk grain pneumatic conveying system and electronic equipment

Abstract

The invention relates to the technical field of bulk grain pneumatic conveying, discloses a control method and device of a bulk grain pneumatic conveying system and electronic equipment, and aims to solve the problem that an existing control scheme is poor in effect of reducing the breakage rate of bulk grains. According to the scheme, the method mainly comprises the steps that a sparse nonlinear dynamics recognition model is trained according to control parameters of the bulk grain pneumatic conveying system and corresponding response parameters, wherein the response parameters at least comprise the bulk grain breakage rate; when the bulk grain pneumatic conveying system is used for conveying bulk grains, model prediction is used for controlling at each moment, and response parameters of future moments are predicted based on a sparse nonlinear dynamics identification model; solving by using an optimization algorithm to obtain an optimal control parameter sequence at the current moment, wherein the optimization target is that the broken rate of the bulk grains is minimum; and the control parameters of the bulk grain pneumatic conveying system are adjusted to be the first set of optimal control parameters in the optimal control parameter sequence. The control precision is improved, and the bulk grain breakage rate in the pneumatic conveying process is remarkably reduced. The device is suitable for bulk grain conveying.

Description

Technical Field

[0001] The present invention relates to the technical field of pneumatic conveying of bulk grains, and in particular to a control method and device, and electronic equipment of a pneumatic conveying system for bulk grains. Background Art

[0002] The bulk grain pneumatic conveying system is a system that uses the energy of airflow to convey granular bulk grain in the direction of airflow in a closed pipeline. Its basic principle is to use compressed air as the conveying medium, and use the pressure and speed of the airflow to move the bulk grain from one place to another through the pipeline. During the conveying process, the airflow suspends the material and pushes it forward, achieving continuous and uniform conveying, which is suitable for conveying in horizontal, vertical or inclined directions. The system is mainly composed of a feeding device, a conveying pipeline, an air source device and a discharge device.

[0003] In order to reduce the breakage of bulk grain particles during pneumatic conveying, the existing bulk grain pneumatic conveying system mainly adjusts the fan frequency and valve opening through PID control, thereby adjusting the airflow rate, pressure and other variables in the pneumatic conveying system to achieve control of the bulk grain particle conveying process. Specifically, the PID controller adjusts the system error (i.e., the difference between the actual bulk grain speed in the airflow and the set value) through feedback, and through the three control functions of proportion, integration and differentiation, it adjusts the air intake by changing the fan frequency and the feed amount by adjusting the valve opening to try to stabilize the airflow speed and pressure, thereby avoiding excessive impact of bulk grain particles or excessive airflow force causing breakage.

[0004] However, the existing PID control methods have the following limitations when solving the problem of bulk grain particle breakage: First, in the bulk grain pneumatic conveying system, the behavior of bulk grain particles and the interaction between the airflow are highly nonlinear and change all the time with the change of system state. The PID controller cannot accurately capture this nonlinear and time-varying effect and can only be adjusted linearly according to the system error, which makes it limited in effect when dealing with the complex interaction between airflow and bulk grain particles. Second, in the bulk grain pneumatic conveying system, the velocity, pressure of the airflow and the flow state of the bulk grain particles are mutually coupled multiple-input multiple-output (MIMO) systems. PID control does not adequately handle this coupling effect because it is usually adjusted for a single variable and lacks comprehensive consideration of global optimization and coupling effects. Summary of the invention

[0005] The present invention aims to solve the problem that the control scheme of the existing bulk grain pneumatic conveying system has poor effect in reducing the bulk grain breakage rate, and proposes a control method and device, and electronic equipment for the bulk grain pneumatic conveying system.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] In a first aspect, the present invention provides a control method for a bulk grain pneumatic conveying system, the method comprising:

[0008] S1. Acquire multiple groups of control parameters and corresponding response parameters of a bulk grain pneumatic conveying system, and train a sparse nonlinear dynamic identification model based on the control parameters and the corresponding response parameters, wherein the response parameters at least include a bulk grain breakage rate;

[0009] S2. When the bulk grain pneumatic conveying system is used to convey bulk grain, the following operations are performed at each moment using model predictive control:

[0010] S21, predicting the response parameters at a future time according to the control parameters at the current time and based on the sparse nonlinear dynamics identification model;

[0011] S22, according to the response parameters at the future moment and the set optimization objectives and constraints, using an optimization algorithm to solve and obtain the optimal control parameter sequence at the current moment, wherein the optimization objective is to minimize the bulk grain breakage rate;

[0012] S23. Adjust the control parameters of the bulk grain pneumatic conveying system to the first set of optimal control parameters in the optimal control parameter sequence.

[0013] Further, the control parameters include the fan frequency and valve opening of the bulk grain pneumatic conveying system;

[0014] The constraint conditions include the variable range of fan frequency and the variable range of valve opening of the bulk grain pneumatic conveying system.

[0015] Furthermore, the control parameters also include ambient temperature and ambient humidity;

[0016] The constraint conditions also include the variable range of ambient temperature and the variable range of ambient humidity of the bulk grain pneumatic conveying system.

[0017] Furthermore, the response parameters also include momentum parameters of bulk grain particles, pressure drop of the conveying pipeline, and density of bulk grain particles in the pipeline, and the momentum parameters include velocity and acceleration.

[0018] Furthermore, a sparse nonlinear dynamics identification model is obtained by training according to the control parameters and their corresponding response parameters, including:

[0019] A training data set is constructed by taking the control parameters as input data and the corresponding response parameters as output data, and a sparse nonlinear dynamics identification model is trained according to the training data set until the sparse nonlinear dynamics identification model converges.

[0020] Furthermore, the method further comprises:

[0021] The validation data set is used regularly to determine the error of the sparse nonlinear dynamics identification model, and it is determined whether the error is greater than a preset error. If so, the sparse nonlinear dynamics identification model is retrained.

[0022] In a second aspect, the present invention provides a control device for a bulk grain pneumatic conveying system, the device comprising:

[0023] A training unit is used to obtain multiple groups of control parameters and corresponding response parameters of the bulk grain pneumatic conveying system, and to obtain a sparse nonlinear dynamic identification model through training according to the control parameters and the corresponding response parameters, wherein the response parameters at least include a bulk grain breakage rate;

[0024] A model prediction control unit is used for predicting the response parameters at a future moment according to the control parameters at a current moment and based on the sparse nonlinear dynamic identification model when the bulk grain pneumatic conveying system is used to convey bulk grain; and for using an optimization algorithm to solve and obtain an optimal control parameter sequence at a current moment according to the response parameters at a future moment and the set optimization objectives and constraints, wherein the optimization objective is to minimize the bulk grain breakage rate;

[0025] The parameter adjustment unit is used to adjust the control parameters of the bulk grain pneumatic conveying system to the first group of optimal control parameters in the optimal control parameter sequence.

[0026] In a third aspect, the present invention provides an electronic device, characterized in that the electronic device includes a processor, a memory and a communication bus;

[0027] The communication bus is used to realize the connection and communication between the processor and the memory;

[0028] The processor is used to execute one or more programs in the memory to implement the steps of the control method of the bulk grain pneumatic conveying system as described in the first aspect.

[0029] The beneficial effects of the present invention are as follows: the control method and device, and electronic equipment for the bulk grain pneumatic conveying system provided by the present invention perform rolling prediction and rolling optimization of the bulk grain pneumatic conveying system based on SINDY-MPC (Sparse Identification of Nonlinear Dynamics for Model Predictive Control), that is, at each moment, the model predictive control (MPC) predicts the response parameters of the bulk grain pneumatic conveying system at future moments based on the sparse nonlinear dynamics identification model (SINDYc), solves the control parameters based on the prediction results and takes the minimum bulk grain breakage rate as the optimization target, and then optimizes and controls the bulk grain pneumatic conveying system using the solved control parameters. SINDY-MPC can capture complex nonlinear dynamics, maintain efficient control performance under multi-input and multi-output conditions, and can adapt to different working conditions in real time, thereby improving the response speed of the control and the robustness of the system. Through precise model predictive control, the precise optimization of the control parameters of the bulk grain pneumatic conveying system is achieved, and the bulk grain breakage rate during pneumatic conveying is significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic flow chart of a control method for a bulk grain pneumatic conveying system provided in an embodiment;

[0031] Figure 2 A schematic flow chart of another control method for a bulk grain pneumatic conveying system provided in an embodiment;

[0032] Figure 3 A schematic structural diagram of a control device for a bulk grain pneumatic conveying system provided in an embodiment;

[0033] Figure 4 A schematic structural diagram of an electronic device provided in an embodiment. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution in this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment.

[0035] In some of the processes described in the specification of the present invention and the above-mentioned figures, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The sequence numbers of the operations are only used to distinguish different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0036] The technical solution of the present invention is applicable to the application scenario of using a bulk grain pneumatic conveying system to convey bulk grain particles, such as rice, wheat, corn, etc. At present, the control scheme of the bulk grain pneumatic conveying system usually adopts PID control, that is, the PID controller adjusts the error of the system through feedback, and based on the three control functions of proportion, integration and differentiation, adjusts the air intake by changing the fan frequency, and adjusts the feed amount by adjusting the valve opening to try to stabilize the speed and pressure of the airflow, so as to avoid excessive impact of the bulk grain particles or breakage caused by excessive airflow force. The inventor found through research that this scheme has limited effect in dealing with the complex interaction between airflow and bulk grain particles, and lacks comprehensive consideration of global optimization and coupling effects, resulting in poor effect in reducing the breakage rate of bulk grain particles.

[0037] Based on this, the technical solution of the present invention is proposed. In the present invention, multiple groups of control parameters of a bulk grain pneumatic conveying system and their corresponding response parameters are obtained, and a sparse nonlinear dynamic identification model is trained according to the control parameters and their corresponding response parameters, wherein the response parameters at least include the bulk grain breakage rate; when the bulk grain pneumatic conveying system is used to convey bulk grain, the following operations are performed at each moment using model predictive control: the response parameters at future moments are predicted according to the control parameters at the current moment and based on the sparse nonlinear dynamic identification model; the optimal control parameter sequence at the current moment is solved using an optimization algorithm according to the response parameters at the future moment and the set optimization objectives and constraints, wherein the optimization objective is to minimize the bulk grain breakage rate; and the control parameters of the bulk grain pneumatic conveying system are adjusted to the first group of optimal control parameters in the optimal control parameter sequence.

[0038] Specifically, the present invention controls the bulk grain pneumatic conveying system based on SINDY-MPC, and first constructs a sparse nonlinear dynamic identification model. Then, the model predictive control uses the sparse nonlinear dynamic identification model in the closed-loop control mode to predict the response parameters of the bulk grain pneumatic conveying system at future moments, and based on the set optimization goal of the minimum bulk grain breakage rate and related constraints, a set of optimal control parameter sequences corresponding to the prediction results are solved at each moment by rolling optimization. The optimization process will find a balance point to ensure that the bulk grain particle breakage rate is minimized while not violating the constraints. Finally, the first set of control parameters of the optimal control parameter sequence is input into the pneumatic conveying system. Then re-predict and optimize to achieve closed-loop control. By combining sparse nonlinear dynamic identification and model predictive control technology, the present invention can effectively learn and control complex bulk grain pneumatic conveying systems in limited data and noise environments, and has the advantages of high performance, low data dependence, high computational efficiency and strong interpretability. Through precise model predictive control, the precise optimization of the control parameters of the bulk grain pneumatic conveying system is achieved, and the bulk grain breakage rate during pneumatic conveying is significantly reduced.

[0039] The technical solution in this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment. Obviously, the described embodiment is only a part of the embodiments of the present invention, rather than all the embodiments.

[0040] Figure 1 A flow chart showing a control method for a bulk grain pneumatic conveying system is shown in FIG. Figure 1 , the method comprises the following steps:

[0041] S1. Acquire multiple groups of control parameters and corresponding response parameters of a bulk grain pneumatic conveying system, and train a sparse nonlinear dynamic identification model based on the control parameters and the corresponding response parameters, wherein the response parameters at least include a bulk grain breakage rate.

[0042] See also Figure 2 In practical applications, in the open-loop control mode, the control parameters and their corresponding response parameters can be collected from the bulk grain pneumatic conveying system through the corresponding sensors, and the control parameters and response parameters can be preprocessed by removing noise and standardizing them to provide accurate basic data for subsequent modeling.

[0043] In the embodiment of the present application, the control parameters include the fan frequency and valve opening of the bulk grain pneumatic conveying system, and the control parameters are used to affect the momentum parameters of the bulk grain particles, thereby affecting the breakage rate of the bulk grain particles. In practical applications, the control parameters may also include external disturbance factors, such as ambient temperature and ambient humidity, which may also affect the response parameters during the bulk grain conveying process.

[0044] In the embodiment of the present application, the response parameters also include momentum parameters of bulk grain particles, pressure drop of the conveying pipeline, and density of bulk grain particles in the pipeline. The response parameters reflect the dynamic behavior of the bulk grain pneumatic conveying system, especially the force and motion state of the bulk grain particles during the conveying process.

[0045] In an embodiment of the present application, a sparse nonlinear dynamics identification model is obtained by training according to the control parameters and their corresponding response parameters, including: taking the control parameters as input data, taking the corresponding response parameters as output data, constructing a training data set, and training the sparse nonlinear dynamics identification model according to the training data set until the sparse nonlinear dynamics identification model converges.

[0046] In practical applications, nonlinear dynamic features are extracted from control parameters and response parameters respectively to construct a training data set, and then training data is obtained from the training data set and a suitable training data window is selected to train the sparse nonlinear dynamic identification model. During the training process, the model parameters are adjusted so that the model can accurately predict the response parameters of the bulk grain pneumatic conveying system. After the training is completed, it is stored in the model library to provide model support for closed-loop control.

[0047] S2. When the bulk grain pneumatic conveying system is used to convey bulk grain, steps S21 to S23 are executed at each moment using model predictive control.

[0048] S21. Predicting response parameters at future times according to the control parameters at the current time and based on the sparse nonlinear dynamics identification model.

[0049] S22. According to the response parameters at the future moment and the set optimization objectives and constraints, an optimization algorithm is used to solve and obtain the optimal control parameter sequence at the current moment, wherein the optimization objective is to minimize the bulk grain breakage rate.

[0050] S23. Adjust the control parameters of the bulk grain pneumatic conveying system to the first set of optimal control parameters in the optimal control parameter sequence.

[0051] It can be understood that the embodiment of the present application is based on model predictive control (MPC) to optimize the control parameters of the bulk grain pneumatic conveying system. The core idea of ​​MPC is to use the model to predict the system state in the future, and then calculate the control output at the current moment based on these prediction results. This method not only takes into account the dynamic characteristics of the system, but also introduces various constraints in the control process, such as input and output restrictions.

[0052] In the embodiment of the present application, first, MPC predicts the response parameters of the bulk grain pneumatic conveying system at multiple moments in the future based on the sparse nonlinear dynamic identification model (SINDYc). In the bulk grain pneumatic conveying system, the control goal is to reduce the bulk grain particle breakage rate by adjusting the airflow rate, system pressure and other parameters of the bulk grain pneumatic conveying system. Therefore, MPC will set an optimization goal and related constraints, where the optimization goal is to minimize the bulk grain breakage rate.

[0053] Then, MPC calculates an optimal control parameter sequence at each moment based on the output of the sparse nonlinear dynamic identification model through rolling optimization. Specifically, MPC performs the following operations at each moment: using the sparse nonlinear dynamic identification model, based on the current control parameters and response parameters, it simulates the response parameters at several moments in the future to predict the momentum change and breakage rate change of the bulk grain particles. Based on the prediction results, under the preset optimization objectives and constraints, an optimization algorithm (such as quadratic programming, gradient descent, etc.) is used to calculate a control parameter sequence that can achieve the optimal goal. The optimization process will look for a balance point to ensure that the breakage rate of the bulk grain particles is minimized while not violating the constraints.

[0054] Finally, through the above optimization process, MPC generates an optimal control parameter sequence, and inputs the first element of the optimal control parameter sequence, i.e., the first set of optimal control parameters, into the bulk grain pneumatic conveying system, so that the bulk grain pneumatic conveying system is adjusted to the optimal control parameters, thereby achieving optimization of the control parameters.

[0055] At each moment, the response parameters of the bulk grain pneumatic conveying system are recollected, the model input is updated, and the prediction and optimization are repeated to achieve closed-loop control.

[0056] In the embodiment of the present application, the constraint conditions include the variable range of fan frequency and valve opening of the bulk grain pneumatic conveying system, and also include the variable range of ambient temperature and ambient humidity of the bulk grain pneumatic conveying system.

[0057] In an embodiment of the present application, the method further includes: regularly using a validation data set to determine an error of the sparse nonlinear dynamics identification model, and determining whether the error is greater than a preset error, and if so, retraining the sparse nonlinear dynamics identification model.

[0058] Specifically, in order to ensure the adaptability of the system under different operating conditions, the system will regularly check the error of the sparse nonlinear dynamic identification model. If the error is greater than the preset error, a model that better meets the current conditions will be selected from the model library or a new model will be trained to ensure the control accuracy of the system under changing conditions.

[0059] In summary, the control method of the bulk grain pneumatic conveying system provided in this embodiment performs rolling prediction and rolling optimization of the bulk grain pneumatic conveying system based on SINDY-MPC, that is, at each moment, the model predictive control predicts the response parameters of the bulk grain pneumatic conveying system at the future moment based on the sparse nonlinear dynamic identification model, and solves the control parameters according to the prediction results and with the minimum bulk grain breakage rate as the optimization goal, and then optimizes the bulk grain pneumatic conveying system using the solved control parameters. SINDY-MPC can capture complex nonlinear dynamics, maintain efficient control performance under multi-input and multi-output conditions, and can adapt to different working conditions in real time, thereby improving the response speed of the control and the robustness of the system. Through precise model predictive control, the precise optimization of the control parameters of the bulk grain pneumatic conveying system is achieved, and the bulk grain breakage rate during pneumatic conveying is significantly reduced.

[0060] Figure 3 A schematic diagram of the control device of a bulk grain pneumatic conveying system is shown in FIG. Figure 3 , the device comprises:

[0061] A training unit is used to obtain multiple groups of control parameters and corresponding response parameters of the bulk grain pneumatic conveying system, and to obtain a sparse nonlinear dynamic identification model through training according to the control parameters and the corresponding response parameters, wherein the response parameters at least include a bulk grain breakage rate;

[0062] A model prediction control unit is used for predicting the response parameters at a future moment according to the control parameters at a current moment and based on the sparse nonlinear dynamic identification model when the bulk grain pneumatic conveying system is used to convey bulk grain; and for using an optimization algorithm to solve and obtain an optimal control parameter sequence at a current moment according to the response parameters at a future moment and the set optimization objectives and constraints, wherein the optimization objective is to minimize the bulk grain breakage rate;

[0063] The parameter adjustment unit is used to adjust the control parameters of the bulk grain pneumatic conveying system to the first group of optimal control parameters in the optimal control parameter sequence.

[0064] Figure 4 A schematic diagram of the structure of an electronic device is shown. Figure 4 , the electronic device includes a processor, a memory and a communication bus;

[0065] The communication bus is used to realize the connection and communication between the processor and the memory;

[0066] The processor is used to execute one or more programs in the memory to implement the steps of the control method of the bulk grain pneumatic conveying system as described in this embodiment.

[0067] It can be understood that since the control device and equipment of the bulk grain pneumatic conveying system described in this embodiment are devices and equipment for implementing the control method of the bulk grain pneumatic conveying system described in the embodiment, for the devices and equipment disclosed in the embodiment, since they correspond to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method, which will not be repeated here.

Claims

1. A control method for a bulk grain pneumatic conveying system, characterized in that: The method comprises: S1. Acquire multiple groups of control parameters and corresponding response parameters of a bulk grain pneumatic conveying system, and train a sparse nonlinear dynamic identification model based on the control parameters and the corresponding response parameters, wherein the response parameters at least include a bulk grain breakage rate; S2. When the bulk grain pneumatic conveying system is used to convey bulk grain, the following operations are performed at each moment using model predictive control: S21, predicting the response parameters at a future time according to the control parameters at the current time and based on the sparse nonlinear dynamics identification model; S22, according to the response parameters at the future moment and the set optimization objectives and constraints, using an optimization algorithm to solve and obtain the optimal control parameter sequence at the current moment, wherein the optimization objective is to minimize the bulk grain breakage rate; S23. Adjust the control parameters of the bulk grain pneumatic conveying system to the first set of optimal control parameters in the optimal control parameter sequence.

2. The control method of bulk grain pneumatic conveying system according to claim 1, characterized in that: The control parameters include the fan frequency and valve opening of the bulk grain pneumatic conveying system; The constraint conditions include the variable range of fan frequency and the variable range of valve opening of the bulk grain pneumatic conveying system.

3. The control method of bulk grain pneumatic conveying system according to claim 2, characterized in that: The control parameters also include ambient temperature and ambient humidity; The constraint conditions also include the variable range of ambient temperature and the variable range of ambient humidity of the bulk grain pneumatic conveying system.

4. The control method of bulk grain pneumatic conveying system according to claim 1, characterized in that: The response parameters also include momentum parameters of bulk grain particles, pressure drop of the conveying pipeline, and density of bulk grain particles in the pipeline. The momentum parameters include velocity and acceleration.

5. The control method of bulk grain pneumatic conveying system according to claim 1, characterized in that: The sparse nonlinear dynamics identification model is obtained by training according to the control parameters and their corresponding response parameters, including: A training data set is constructed by taking the control parameters as input data and the corresponding response parameters as output data, and a sparse nonlinear dynamics identification model is trained according to the training data set until the sparse nonlinear dynamics identification model converges.

6. The control method of bulk grain pneumatic conveying system according to claim 1, characterized in that: The method further comprises: The validation data set is used regularly to determine the error of the sparse nonlinear dynamics identification model, and it is determined whether the error is greater than a preset error. If so, the sparse nonlinear dynamics identification model is retrained.

7. A control device for a bulk grain pneumatic conveying system, characterized in that: The device comprises: A training unit is used to obtain multiple groups of control parameters and corresponding response parameters of the bulk grain pneumatic conveying system, and to obtain a sparse nonlinear dynamic identification model through training according to the control parameters and the corresponding response parameters, wherein the response parameters at least include a bulk grain breakage rate; A model prediction control unit is used for predicting the response parameters at a future moment according to the control parameters at a current moment and based on the sparse nonlinear dynamic identification model when the bulk grain pneumatic conveying system is used to convey bulk grain; and for using an optimization algorithm to solve and obtain an optimal control parameter sequence at a current moment according to the response parameters at a future moment and the set optimization objectives and constraints, wherein the optimization objective is to minimize the bulk grain breakage rate; The parameter adjustment unit is used to adjust the control parameters of the bulk grain pneumatic conveying system to the first group of optimal control parameters in the optimal control parameter sequence.

8. An electronic device, characterized in that: The electronic device includes a processor, a memory and a communication bus; The communication bus is used to realize the connection and communication between the processor and the memory; The processor is used to execute one or more programs in the memory to implement the steps of the control method of the bulk grain pneumatic conveying system according to any one of claims 1 to 6.

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