Multi-parameter cooperative control system for autoclaved aerated concrete block preparation process

Abstract

This invention discloses a multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete (AAC) blocks, relating to the field of concrete block preparation technology. It includes a central controller, a data acquisition and monitoring module, a multi-parameter collaborative relationship analysis module, a multi-objective optimization PID parameter decision module, a distributed collaborative controller group, and a process knowledge base, all communicatively connected to the central controller. The advantages of this invention are: the multi-parameter collaborative relationship analysis module dynamically constructs and updates a quantitative correlation model between control equipment and process parameters online, accurately characterizing multi-variable coupling relationships; furthermore, the multi-objective optimization PID parameter decision module, based on real-time operating conditions and collaborative control objectives, solves for the optimal PID parameter set for each control device online, achieving adaptive tuning and multi-objective trade-offs of controller parameters, and realizing adaptive and intelligent collaborative control of the preparation process.

Description

Technical Field

[0001] This invention relates to the field of concrete block preparation technology, specifically to a multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete blocks. Background Technology

[0002] In the traditional preparation process of autoclaved aerated concrete (AAC) blocks, the control of key process parameters typically employs independent or simple cascaded PID control methods. The parameters of each controller are often fixed and determined manually based on experience. However, the preparation process encompasses multiple stages, including batching and mixing, pouring and gas generation, static curing, cutting and grouping, and autoclaving. Furthermore, there are complex coupling and time-varying relationships between equipment actions and process parameters within each stage. Adjustments to a single piece of equipment often simultaneously affect multiple controlled parameters. Existing control methods struggle to dynamically describe and quantify these multivariate coupling relationships, resulting in insufficient control accuracy, slow system response, and an inability to perform coordinated optimization based on different production objectives. This hinders further improvements in product quality and production efficiency.

[0003] To overcome the aforementioned shortcomings, some existing technologies have attempted to introduce advanced control strategies. However, most of these solutions focus on optimizing single loops or rely on offline, fixed coupling models, making it difficult to adapt to the real-time changes in operating conditions and the need for multi-objective trade-offs in actual production. Therefore, there is an urgent need for a multi-parameter collaborative control system capable of identifying the dynamic coupling relationships between process parameters online and autonomously optimizing and tuning the controller parameters of each loop based on real-time control objectives. This system would enable high-precision, adaptive, and intelligent control of the entire autoclaved aerated concrete (AAC) block manufacturing process. Summary of the Invention

[0004] To solve the above-mentioned technical problems, a multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete blocks is provided. This technical solution solves at least one of the technical problems mentioned in the background.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A multi-parameter collaborative control system for the autoclaved aerated concrete (AAC) block preparation process includes a central controller, a data acquisition and monitoring module, a multi-parameter collaborative relationship analysis module, a multi-objective optimization PID parameter decision module, a distributed collaborative controller group, and a process knowledge base, all connected in communication with the central controller. The data acquisition and monitoring module is used to collect the operating status data of multiple key process equipment and the real-time measurement values ​​of multiple controlled process parameters in the entire process of autoclaved aerated concrete block preparation, and transmit the operating status data and real-time measurement values ​​to the central controller. The multi-parameter collaborative relationship analysis module is used to dynamically construct and update the correlation model between the actions of the control equipment and the controlled process parameters under different preparation stages based on the process mechanism model and historical data stored in the process knowledge base. The correlation model quantitatively characterizes the degree of influence and coupling relationship of the adjustment action of a single control equipment on multiple controlled process parameters. The multi-objective optimization PID parameter decision module is used to receive the collaborative control objective of the current preparation stage, the real-time correlation model from the multi-parameter collaborative relationship analysis module, and the real-time parameter deviation from the data acquisition and monitoring module. Based on the multi-objective optimization algorithm, it calculates a set of optimized PID control parameters online for each control device involved in the adjustment. This set of parameters minimizes the overall performance cost function that satisfies the collaborative control objective when adjusting multiple process parameters associated with it simultaneously. The distributed collaborative controller group includes multiple independent collaborative controllers, each of which is connected to one or more key process devices. The collaborative controller is used to dynamically load the PID control parameters optimized for its corresponding device from the multi-objective optimization PID parameter decision module, and use the loaded PID control parameters to perform closed-loop control on the device, so as to achieve coordinated adjustment of multiple process parameters associated with it. The central controller coordinates the data interaction and instruction synchronization between the multi-parameter collaborative relationship analysis module, the multi-objective optimization PID parameter decision module, and the distributed collaborative controller group, and switches the control mode according to the process stage.

[0006] Preferably, the multi-parameter collaborative relationship analysis module specifically includes: The process stage deconstruction unit is used to break down the preparation process of autoclaved aerated concrete blocks into several key control stages, including batching and mixing, pouring and gas generation, static curing, cutting and grouping, and autoclaving, and defines a set of dominant controlled process parameters for each stage. The equipment-parameter influence matrix construction unit is used to identify the control equipment to be regulated in each of the key control stages, and to establish the equipment-parameter influence matrix based on the process mechanism model and data analysis. Where A is the device-parameter influence matrix, Let n be the weight of the influence of the i-th control device on the j-th parameter, n be the total number of control devices, and m be the total number of controlled process parameters. The dynamic fine-tuning unit is used to fine-tune and update the influence coefficients in the device-parameter influence matrix online based on real-time operating data and historical cases.

[0007] Preferably, the multi-objective optimization PID parameter decision module specifically includes: The control target parsing unit is used to receive preset or coordinated control target instructions issued by the upper-level system, and parse the instructions into specific and quantifiable multi-objective optimization problems. The coordinated control target includes at least one of the following: quality priority mode, energy efficiency priority mode, or steady-speed production mode. A multi-objective cost function construction unit is used to construct a multi-objective cost function for each control device. The cost function includes multiple sub-objective items. Each sub-objective item corresponds to a weighted sum of the tracking deviation of the controlled process parameter affected by the device, as well as a penalty term for constraining the amplitude or rate of change of the control action. The weight of each sub-objective item is dynamically configured according to the cooperative control objective. An online optimization solver is used to solve the multi-objective cost function online in each control cycle, using the PID parameters of the current control device as decision variables and minimizing the multi-objective cost function as the objective. The solver outputs the optimal combination of PID parameters for the device in that cycle, which includes proportional coefficient, integral coefficient, and derivative coefficient.

[0008] Preferably, each collaborative controller in the distributed collaborative controller group includes: The parameter dynamic loading interface is used to receive the optimized PID parameter values ​​assigned to it from the multi-objective optimized PID parameter decision module at the beginning of the control cycle. A reconfigurable PID control unit is used to load the received optimized PID parameter values ​​into its internal PID control algorithm, replace the PID parameters of the previous cycle, and thus dynamically reconstruct its control law. The closed-loop control execution unit is used to calculate and output control signals to the connected key process equipment based on the reconstructed control law and the deviation between the set value and the real-time measured value of the corresponding controlled process parameter, thereby driving the equipment to operate and reduce the deviation.

[0009] Preferably, the process knowledge base storage includes: The basic process database stores the nominal process curves, standard process parameter setpoint ranges, and equipment characteristic parameters for each preparation stage. The collaborative relationship model library stores historical device-parameter association models and influence matrices generated and updated by the multi-parameter collaborative relationship analysis module; The case library is optimized to store the optimized PID parameter combinations and corresponding operating conditions that are calculated by the multi-objective optimized PID parameter decision module and have been verified to have excellent control effects under different operating conditions and collaborative control objectives during historical control processes. The rule base is optimized to store expert rules that configure cost function weights for different collaborative control objectives in the multi-objective optimization PID parameter decision module, as well as the configuration parameters of the optimization algorithm.

[0010] Preferably, the system operates as follows: Based on the process mechanism and real-time operation data, the preparation process is divided into multiple stages, and a quantitative correlation model between the adjustment action of the control equipment and multiple controlled process parameters is dynamically established and updated in each stage. The data acquisition and monitoring module acquires real-time process parameter data for the entire process, determines the current preparation stage, and receives or parses the collaborative control target for the current stage. Based on the obtained current quantitative correlation model, real-time parameter data, and collaborative control objectives, for each relevant control device, an optimized set of PID control parameters is calculated online by solving an optimization problem with PID parameters as decision variables and minimizing a multi-objective cost function as the objective. The optimized PID parameter sets calculated for different devices are dynamically distributed and loaded into the corresponding distributed collaborative controllers. Each controller uses its own optimized PID parameter set to independently execute closed-loop control of its associated devices, thereby achieving coordinated adjustment of multiple controlled process parameters. Continuously monitor the control effect and store cases where the control performance is significantly better than the historical benchmark, including their operating conditions, the optimized PID parameter set used, and the control results, in the process knowledge base for subsequent iterative updates to the collaborative relationship model and optimization decision rules.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention dynamically constructs and updates a quantitative correlation model between control equipment and process parameters online through a multi-parameter collaborative relationship analysis module, accurately characterizing multi-variable coupling relationships. Furthermore, a multi-objective optimization PID parameter decision module, based on real-time operating conditions and collaborative control objectives, solves for the optimal PID parameter set for each control device online, achieving adaptive tuning of controller parameters and multi-objective trade-offs. The distributed collaborative controller group then dynamically adjusts the control law according to the optimized parameters, performing coordinated and precise regulation of each process parameter. This system effectively overcomes the shortcomings of traditional fixed-parameter PID control in multi-variable coupling processes, significantly improving control accuracy, response speed, and system stability. Simultaneously, it can optimize control strategies according to different production objectives such as quality and energy efficiency, achieving adaptive and intelligent collaborative control of the manufacturing process. This has significant effects on improving product consistency, reducing energy consumption, and enhancing production line flexibility. Attached Figure Description

[0012] Figure 1 This is a diagram of the multi-parameter collaborative control system architecture for the autoclaved aerated concrete block preparation process proposed in Embodiment 1 of the present invention. Figure 2 This is a flowchart of the method for constructing a multi-objective cost function proposed in Embodiment 3 of the present invention; Figure 3 This is a flowchart of the method for calculating the weight of sub-objective items proposed in Embodiment 3 of the present invention; Figure 4 This is a flowchart of the method for online solving of the optimal PID parameter combination for a device, as proposed in Embodiment 3 of the present invention. Detailed Implementation

[0013] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0014] Example 1: Reference Figure 1 As shown in this embodiment, a multi-parameter collaborative control system for the autoclaved aerated concrete (AAC) block preparation process is proposed, including a central controller, a data acquisition and monitoring module, a multi-parameter collaborative relationship analysis module, a multi-objective optimization PID parameter decision module, a distributed collaborative controller group, and a process knowledge base connected to the central controller. The process knowledge base stores: The basic process database stores the nominal process curves, standard process parameter setpoint ranges, and equipment characteristic parameters for each preparation stage. The collaborative relationship model library stores historical device-parameter association models and influence matrices generated and updated by the multi-parameter collaborative relationship analysis module; The case library is optimized to store the optimized PID parameter combinations and corresponding operating conditions that have been verified to have excellent control effects by the multi-objective optimization PID parameter decision module under different operating conditions and collaborative control objectives during historical control processes. The rule base is optimized to store expert rules for configuring cost function weights for different cooperative control objectives in the multi-objective optimization PID parameter decision module, as well as configuration parameters for the optimization algorithm. The data acquisition and monitoring module is used to collect real-time operating status data of multiple key process equipment and real-time measured values ​​of multiple controlled process parameters in the entire process of autoclaved aerated concrete block preparation, and transmit the operating status data and real-time measured values ​​to the central controller. The multi-parameter collaborative relationship analysis module is used to dynamically construct and update the correlation model between the actions of the control equipment and the controlled process parameters under different preparation stages based on the process mechanism model and historical data stored in the process knowledge base. The correlation model quantitatively characterizes the degree of influence and coupling relationship of the adjustment action of a single control equipment on multiple controlled process parameters. The multi-parameter collaborative relationship analysis module specifically includes: The process stage deconstruction unit is used to break down the preparation process of autoclaved aerated concrete blocks into several key control stages, including batching and mixing, pouring and gas generation, static curing, cutting and grouping, and autoclaving, and defines a set of dominant controlled process parameters for each stage. The equipment-parameter influence matrix construction unit is used to identify the control equipment being regulated at each critical control stage, and to establish the equipment-parameter influence matrix based on the process mechanism model and data analysis. Where A is the device-parameter influence matrix, Let n be the weight of the influence of the i-th control device on the j-th parameter, n be the total number of control devices, and m be the total number of controlled process parameters. The dynamic fine-tuning unit is used to fine-tune and update the influence coefficients in the equipment-parameter influence matrix online based on real-time operating data and historical cases; The multi-objective optimization PID parameter decision module is used to receive the collaborative control objective of the current preparation stage, the real-time correlation model from the multi-parameter collaborative relationship analysis module, and the real-time parameter deviation from the data acquisition and monitoring module. Based on the multi-objective optimization algorithm, it calculates a set of optimized PID control parameters online for each control device involved in the adjustment. This set of parameters minimizes the overall performance cost function that satisfies the collaborative control objective when adjusting multiple process parameters associated with it simultaneously. The multi-objective optimization PID parameter decision module specifically includes: The control target parsing unit is used to receive preset or coordinated control target instructions issued by the upper-level system, and parse the instructions into specific and quantifiable multi-objective optimization problems. The coordinated control target includes at least one of the following: quality priority mode, energy efficiency priority mode, or steady-speed production mode. The multi-objective cost function construction unit is used to construct a multi-objective cost function for each control device. The cost function contains multiple sub-objective items. Each sub-objective item corresponds to a weighted sum of the tracking deviation of the controlled process parameter affected by the device, as well as a penalty term used to constrain the amplitude or rate of change of the control action. The weights of each sub-objective item are dynamically configured according to the collaborative control objective. The online optimization solver is used to solve the multi-objective cost function online in each control cycle, using the PID parameters of the current control device as decision variables and minimizing the multi-objective cost function as the objective. It outputs the optimal combination of PID parameters for the device in that cycle, including the proportional coefficient, integral coefficient, and derivative coefficient. The distributed collaborative controller group includes multiple independent collaborative controllers, each of which is connected to one or more key process devices. The collaborative controller is used to dynamically load PID control parameters optimized for its corresponding device from the multi-objective optimization PID parameter decision module, and use the loaded PID control parameters to perform closed-loop control on the device to achieve coordinated adjustment of multiple process parameters associated with it. Each collaborative controller in the distributed collaborative controller group contains: The parameter dynamic loading interface is used to receive the optimized PID parameter values ​​assigned to it from the multi-objective optimization PID parameter decision module at the beginning of the control cycle. A reconfigurable PID control unit is used to load the received optimized PID parameter values ​​into its internal PID control algorithm, replace the PID parameters of the previous cycle, and thus dynamically reconstruct its control law. The closed-loop control execution unit is used to calculate and output control signals to the connected key process equipment based on the reconstructed control law and the deviation between the set value and the real-time measured value of the corresponding controlled process parameter, thereby driving the equipment to operate and reduce the deviation. The central controller coordinates the data interaction and command synchronization between the multi-parameter collaborative relationship analysis module, the multi-objective optimization PID parameter decision module, and the distributed collaborative controller group, and switches the control mode according to the process stage.

[0015] The working process of the above system is as follows: Based on the process mechanism and real-time operation data, the preparation process is divided into multiple stages, and a quantitative correlation model between the adjustment action of the control equipment and multiple controlled process parameters is dynamically established and updated in each stage. The data acquisition and monitoring module acquires real-time process parameter data for the entire process, determines the current preparation stage, and receives or parses the collaborative control target for the current stage. Based on the obtained current quantitative correlation model, real-time parameter data, and collaborative control objectives, for each relevant control device, an optimized set of PID control parameters is calculated online by solving an optimization problem with PID parameters as decision variables and minimizing a multi-objective cost function as the objective. The optimized PID parameter sets calculated for different devices are dynamically distributed and loaded into the corresponding distributed collaborative controllers. Each controller uses its own optimized PID parameter set to independently execute closed-loop control of its associated devices, thereby achieving coordinated adjustment of multiple controlled process parameters. Continuously monitor the control effect and store cases where the control performance is significantly better than the historical benchmark, including their operating conditions, the optimized PID parameter set used, and the control results, in the process knowledge base for subsequent iterative updates to the collaborative relationship model and optimization decision rules.

[0016] The system proposed in this embodiment constructs a complete intelligent collaborative control closed loop involving multiple devices and parameters. First, the system perceives the production status in real time through process stage deconstruction and data acquisition. Then, using a multi-parameter collaborative relationship analysis module, it dynamically establishes and fine-tunes a quantitative coupling model between devices and multiple parameters, accurately characterizing the dynamic characteristics of the process. Based on this, a multi-objective optimization PID parameter decision module transforms specific production objectives into mathematical multi-objective cost functions. Using these functions as criteria, it solves for a set of collaboratively optimal PID parameters for each controlled device online and in real time, thereby achieving multi-variable decoupling and multi-objective trade-offs at the controller level. A distributed collaborative controller group ensures that optimized parameters can be loaded and executed agilely and reliably, ensuring that each device's action serves the overall collaborative objective. Finally, the process knowledge base continuously accumulates optimization cases and rules, enabling the system's model and decision-making capabilities to iteratively evolve through historical data. This embodiment, from architecture to process, fully demonstrates how to upgrade traditional independent PID control into an adaptive, strongly coupled, and goal-differentiable intelligent collaborative control system.

[0017] Example 2: This embodiment provides specific implementation steps for constructing the device-parameter influence matrix in Embodiment 1, based on Embodiment 1. The specific steps for establishing the device-parameter influence matrix are as follows: Conduct steady-state simulation experiments for each critical control stage. In the steady-state simulation experiments, only a step signal with a known amplitude is applied to a single device i, while keeping the control quantities of all other devices unchanged. Record the transient response curves of all controlled parameters at a high frequency until a new steady state is reached; For the j-th parameter, calculate its numerical deviation before and after steady state; The weight of the influence of the i-th control device on the j-th parameter is: ; The amplitude of the step signal applied to a single device i. This represents the numerical deviation of the j-th parameter before and after it reaches steady state.

[0018] This embodiment is based on the step response test method in classical control theory. In a specific process stage, such as the isothermal autoclaving stage, when the system is in steady state, changing the setpoint of a single control device while keeping other inputs constant will cause the system to transition from the original steady state to a new steady state. This method assumes that near typical operating points, the influence of the device on the parameters is approximately linear within the main operating range; therefore, its steady-state gain can be approximated as a constant and obtained by calculating the ratio of the output change to the input change. This coefficient has a clear physical meaning: the steady-state deviation of the controlled parameter caused by a unit change in the control quantity. Implementation must be carried out under safe and controlled trial operation or dedicated commissioning mode. Taking the autoclaving stage as an example, to obtain the influence coefficients of the steam regulating valve on the pressure and temperature inside the vessel, during the stabilization of autoclaving, only a small step increase in the valve opening can be made, while closely monitoring the pressure and temperature sensor data inside the vessel and recording its complete dynamic response until it stabilizes again. Calculate the steady-state changes in pressure and temperature, and divide them by the change in valve opening, respectively, to obtain the two independent influence coefficients of the valve on pressure and temperature. Repeat this process sequentially for all key equipment such as the mixer speed, exhaust valve, and water pump, and finally complete the complete equipment-parameter influence matrix for this stage. To ensure accuracy, forward and reverse step tests can be performed on the same equipment to verify linearity and the average value can be taken.

[0019] The initial device-parameter influence matrix K obtained by this method provides a reliable and accurate initial baseline, ensuring that the cooperative control system has basic and reliable coupling cognition capabilities from the start of operation.

[0020] Example 3: This embodiment, based on Embodiment 1 or Embodiment 2, further provides an optimized design for the operation process of the multi-objective optimization PID parameter decision module. Specifically, refer to... Figure 2 As shown, constructing a multi-objective cost function in this embodiment specifically includes: By using experimental calibration, and considering only single-parameter control, the optimal combination of PID parameters for each parameter of the control device is determined. Based on the current focus of control over each parameter, determine the global control weight of each parameter; The weight of the sub-objective item of the device for the parameter at the current stage is calculated by comprehensively considering the global control weight of each parameter and the device-parameter influence weight of the device's regulation of the parameter in the device-parameter influence matrix. The multi-objective cost function is as follows:

[0021] For the multi-objective cost function value, Let be the weight of the sub-objective item of the j-th parameter for the device at the current stage. The function is used to solve for the deviation. To determine the optimal combination of PID parameters for the j-th parameter of the control device, The optimal combination of PID parameters is to be determined.

[0022] Among them, reference Figure 3 As shown, the weights of sub-objectives are calculated as follows: The global control weight of each parameter is multiplied by the influence weight of the device on the parameter in the device-parameter influence matrix to obtain the control emphasis value of the device on the parameter. The ratio of the device's emphasis value for this parameter to the sum of the device's emphasis values ​​for all parameters is used as the weight of the device's sub-objective item for this parameter in the current stage.

[0023] Reference Figure 4 As shown, using the PID parameters of the current control device as decision variables and minimizing the multi-objective cost function as the objective, an online solution is performed, outputting the optimal PID parameter combination for the device within this cycle, specifically including: The solution range for each coefficient is determined by using the optimal combination of PID parameters for each parameter of the control device. The goal is to minimize the multi-objective cost function value. The optimal combination of PID parameters for the device is obtained by combining the values ​​of each coefficient within the solution interval.

[0024] The weights of sub-objectives employ a two-layer weighting mechanism. The global control weight represents the control priority of different controlled parameters, such as pressure, temperature, and consistency, at the current stage from a production process perspective. For example, in the initial stage of autoclaving, the pressure weight might be set > temperature weight to ensure rapid pressurization, while in the heat preservation stage, the temperature weight might be set ≈ pressure weight to pursue stability. This weight can be determined by preset process formulas, upper-level optimizers, or operator instructions. The control influence weight, i.e., the element in the equipment-parameter influence matrix, quantifies the actual physical influence capability of a specific piece of equipment i on a specific parameter j. It is an objective physical coupling metric. The sub-objective weights do not directly adopt the global weights but are determined through a formula. The calculation shows that, The global control weight for the j-th parameter. This represents the influence weight of a device on the j-th parameter in the device-parameter influence matrix. This calculation achieves a key transformation: it precisely allocates a process objective according to the physical influence of each device. This means that even if a parameter has a high global weight, it will have a low weight in the cost function of a device that has little influence on it; conversely, for a device that can effectively influence the parameter, this parameter will occupy the absolute core of its optimization objective. The constructed cost function is a weighted sum of the deviations of all associated parameters of the device. The deviation function... The difference in control performance of parameter j under the PID parameter combination to be optimized and the ideal parameter combination was measured. The task is to find a set of PID parameters (Kp, Ki, Kd) that minimizes the total weighted overall deviation. Since the weights of the sub-objectives of each device are different, the optimized PID parameter combination will inevitably be personalized, specifically designed to coordinate the controlled parameters that the device excels at. This mechanism is dynamic. When the process stage changes and the global control weights change, the sub-item weights of all devices will be automatically adjusted accordingly, thereby changing the optimization objective and enabling the entire control system to seamlessly adapt to the new process requirements. Simultaneously, if the online learning module updates the device-parameter influence matrix, it will also be updated synchronously, ensuring that the optimization objective is always based on the latest understanding of system coupling.

[0025] The multi-objective cost function construction method provided in this embodiment brings the following outstanding benefits to the entire cooperative control system: It achieves precise decision-making by matching objectives with capabilities: by multiplying and normalizing the weights of process objectives with physical influence coefficients. This ensures that the optimization objectives of each device are closely aligned with its actual control influence. For example, valves with a strong impact on pressure will focus on optimizing pressure response, while valves with a greater impact on temperature will focus on temperature control. This achieves a precise match between control responsibility and control capability, improving collaborative efficiency from the source. It dynamically resolves inherent conflicts between multiple objectives: In multivariate coupled systems, optimizing multiple objectives simultaneously often leads to conflicts. This method decomposes the global multi-objective optimization problem into a series of personalized optimization sub-problems at the device level with different focuses by dynamically calculating differentiated weight combinations for each device. During the solution process, the optimizer intelligently weighs conflicting objectives based on their respective weights. This is equivalent to a distributed, differentiated trade-off strategy, which achieves better overall performance than seeking a globally unified compromise solution, improving the overall control quality of complex process systems. The system's interpretability and adaptability have been enhanced: Since the weights can be automatically adjusted according to changes in the understanding of process stages and coupling relationships, the control system can not only optimize under fixed operating conditions, but also actively adapt to changes in production targets and slow changes in process characteristics, demonstrating strong environmental adaptability and process flexibility, and providing core algorithm support for coping with multi-variety and multi-formula production.

[0026] In summary, the advantages of this invention are as follows: A multi-parameter collaborative relationship analysis module dynamically constructs and updates a quantitative correlation model between control equipment and process parameters online, accurately depicting multi-variable coupling relationships. Furthermore, a multi-objective optimization PID parameter decision module, based on real-time operating conditions and collaborative control objectives, solves for the optimal PID parameter set for each control device online, achieving adaptive tuning of controller parameters and multi-objective trade-offs. The distributed collaborative controller group dynamically adjusts the control law according to the optimized parameters, performing coordinated and precise regulation of each process parameter. This system effectively overcomes the shortcomings of traditional fixed-parameter PID control in multi-variable coupling processes, significantly improving control accuracy, response speed, and system stability. Simultaneously, it can optimize control strategies according to different production objectives such as quality and energy efficiency, achieving adaptive and intelligent collaborative control of the manufacturing process. This has significant effects on improving product consistency, reducing energy consumption, and enhancing production line flexibility.

[0027] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete blocks, characterized in that, It includes a central controller, a data acquisition and monitoring module, a multi-parameter collaborative relationship analysis module, a multi-objective optimization PID parameter decision module, a distributed collaborative controller group, and a process knowledge base that are communicatively connected to the central controller. The data acquisition and monitoring module is used to collect the operating status data of multiple key process equipment and the real-time measurement values ​​of multiple controlled process parameters in the entire process of autoclaved aerated concrete block preparation, and transmit the operating status data and real-time measurement values ​​to the central controller. The multi-parameter collaborative relationship analysis module is used to dynamically construct and update the correlation model between the actions of the control equipment and the controlled process parameters under different preparation stages based on the process mechanism model and historical data stored in the process knowledge base. The correlation model quantitatively characterizes the degree of influence and coupling relationship of the adjustment action of a single control equipment on multiple controlled process parameters. The multi-objective optimization PID parameter decision module is used to receive the collaborative control objective of the current preparation stage, the real-time correlation model from the multi-parameter collaborative relationship analysis module, and the real-time parameter deviation from the data acquisition and monitoring module. Based on the multi-objective optimization algorithm, it calculates a set of optimized PID control parameters online for each control device involved in the adjustment. This set of parameters minimizes the overall performance cost function that satisfies the collaborative control objective when adjusting multiple process parameters associated with it simultaneously. The distributed collaborative controller group includes multiple independent collaborative controllers, each of which is connected to one or more key process devices. The collaborative controller is used to dynamically load the PID control parameters optimized for its corresponding device from the multi-objective optimization PID parameter decision module, and use the loaded PID control parameters to perform closed-loop control on the device, so as to achieve coordinated adjustment of multiple process parameters associated with it. The central controller coordinates the data interaction and instruction synchronization between the multi-parameter collaborative relationship analysis module, the multi-objective optimization PID parameter decision module, and the distributed collaborative controller group, and switches the control mode according to the process stage.

2. The multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete blocks according to claim 1, characterized in that, The multi-parameter collaborative relationship analysis module specifically includes: The process stage deconstruction unit is used to break down the preparation process of autoclaved aerated concrete blocks into several key control stages, including batching and mixing, pouring and gas generation, static curing, cutting and grouping, and autoclaving, and defines a set of dominant controlled process parameters for each stage. The equipment-parameter influence matrix construction unit is used to identify the control equipment being regulated in each of the key control stages, and to establish the equipment-parameter influence matrix based on the process mechanism model and data analysis. Where A is the device-parameter influence matrix, Let n be the weight of the influence of the i-th control device on the j-th parameter, n be the total number of control devices, and m be the total number of controlled process parameters. The dynamic fine-tuning unit is used to fine-tune and update the influence coefficients in the device-parameter influence matrix online based on real-time operating data and historical cases.

3. The multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete blocks according to claim 2, characterized in that, The specific steps for establishing the device-parameter influence matrix are as follows: Conduct steady-state simulation experiments for each critical control stage. In the steady-state simulation experiments, only a step signal with a known amplitude is applied to a single device i, while keeping the control quantities of all other devices unchanged. Record the transient response curves of all controlled parameters at a high frequency until a new steady state is reached; For the j-th parameter, calculate its numerical deviation before and after steady state; The weight of the influence of the i-th control device on the j-th parameter is: ； The amplitude of the step signal applied to a single device i. This represents the numerical deviation of the j-th parameter before and after it reaches steady state.

4. The multi-parameter collaborative control system for the autoclaved aerated concrete block preparation process according to claim 3, characterized in that, The multi-objective optimization PID parameter decision module specifically includes: The control target parsing unit is used to receive preset or coordinated control target instructions issued by the upper-level system, and parse the instructions into specific and quantifiable multi-objective optimization problems. The coordinated control target includes at least one of the following: quality priority mode, energy efficiency priority mode, or steady-speed production mode. A multi-objective cost function construction unit is used to construct a multi-objective cost function for each control device. The cost function includes multiple sub-objective items. Each sub-objective item corresponds to a weighted sum of the tracking deviation of the controlled process parameter affected by the device, as well as a penalty term for constraining the amplitude or rate of change of the control action. The weight of each sub-objective item is dynamically configured according to the cooperative control objective. An online optimization solver is used to solve the multi-objective cost function online in each control cycle, using the PID parameters of the current control device as decision variables and minimizing the multi-objective cost function as the objective. The solver outputs the optimal combination of PID parameters for the device in that cycle, which includes proportional coefficient, integral coefficient, and derivative coefficient.

5. The multi-parameter collaborative control system for the autoclaved aerated concrete block preparation process according to claim 4, characterized in that, The construction of a multi-objective cost function specifically includes: By using experimental calibration, and considering only single-parameter control, the optimal combination of PID parameters for each parameter of the control device is determined. Based on the current focus of control over each parameter, determine the global control weight of each parameter; The weight of the sub-objective item of the device for the parameter at the current stage is calculated by comprehensively calculating the global control weight of each parameter and the device-parameter influence weight of the device's regulation of the parameter in the device-parameter influence matrix. The multi-objective cost function is specifically as follows: ； For the multi-objective cost function value, Let be the weight of the sub-objective item of the j-th parameter for the device at the current stage. The function is used to solve for the deviation. To determine the optimal combination of PID parameters for the j-th parameter of the control device, The optimal combination of PID parameters is to be determined.

6. The multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete blocks according to claim 5, characterized in that, The weight of the sub-objective item of the device for that parameter at the current stage is calculated by comprehensively combining the global control weight of each parameter with the device-parameter influence weight of the device's regulation of that parameter in the device-parameter influence matrix. The global control weight of each parameter is multiplied by the influence weight of the device on the parameter in the device-parameter influence matrix to obtain the device's emphasis value for the parameter. The ratio of the device's emphasis value for this parameter to the sum of the device's emphasis values ​​for all parameters is used as the weight of the device's sub-objective item for this parameter in the current stage.

7. The multi-parameter collaborative control system for the autoclaved aerated concrete block preparation process according to claim 6, characterized in that, The process of using the PID parameters of the current control device as decision variables, minimizing the multi-objective cost function as the objective, performing online solution, and outputting the optimal PID parameter combination for the device within the current period specifically includes: The solution range for each coefficient is determined by using the optimal combination of PID parameters for each parameter of the control device. The goal is to minimize the multi-objective cost function value. The optimal combination of PID parameters for the device is obtained by combining the values ​​of each coefficient within the solution interval.

8. The multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete blocks according to claim 1, characterized in that, Each collaborative controller in the distributed collaborative controller group contains: The parameter dynamic loading interface is used to receive the optimized PID parameter values ​​assigned to it from the multi-objective optimized PID parameter decision module at the beginning of the control cycle. A reconfigurable PID control unit is used to load the received optimized PID parameter values ​​into its internal PID control algorithm, replace the PID parameters of the previous cycle, and thus dynamically reconstruct its control law. The closed-loop control execution unit is used to calculate and output control signals to the connected key process equipment based on the reconstructed control law and the deviation between the set value and the real-time measured value of the corresponding controlled process parameter, thereby driving the equipment to operate and reduce the deviation.

9. The multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete blocks according to claim 1, characterized in that, The process knowledge base stores: The basic process database stores the nominal process curves, standard process parameter setpoint ranges, and equipment characteristic parameters for each preparation stage. The collaborative relationship model library stores historical device-parameter association models and influence matrices generated and updated by the multi-parameter collaborative relationship analysis module; The case library is optimized to store the optimized PID parameter combinations and corresponding operating conditions that are calculated by the multi-objective optimized PID parameter decision module and have been verified to have excellent control effects under different operating conditions and collaborative control objectives during historical control processes. The rule base is optimized to store expert rules that configure cost function weights for different collaborative control objectives in the multi-objective optimization PID parameter decision module, as well as the configuration parameters of the optimization algorithm.

10. The multi-parameter collaborative control system for the preparation process of autoclaved aerated concrete blocks according to any one of claims 1-9, characterized in that, The system operates as follows: Based on the process mechanism and real-time operation data, the preparation process is divided into multiple stages, and a quantitative correlation model between the adjustment action of the control equipment and multiple controlled process parameters is dynamically established and updated in each stage. The data acquisition and monitoring module acquires real-time process parameter data for the entire process, determines the current preparation stage, and receives or parses the collaborative control target for the current stage. Based on the obtained current quantitative correlation model, real-time parameter data, and collaborative control objectives, for each relevant control device, an optimized set of PID control parameters is calculated online by solving an optimization problem with PID parameters as decision variables and minimizing a multi-objective cost function as the objective. The optimized PID parameter sets calculated for different devices are dynamically distributed and loaded into the corresponding distributed collaborative controllers. Each controller uses its own optimized PID parameter set to independently execute closed-loop control of its associated devices, thereby achieving coordinated adjustment of multiple controlled process parameters. Continuously monitor the control effect and store cases where the control performance is significantly better than the historical benchmark, including their operating conditions, the optimized PID parameter set used, and the control results, in the process knowledge base for subsequent iterative updates to the collaborative relationship model and optimization decision rules.

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