Autoclaved aerated concrete production line digital management system based on MES system

Abstract

The invention relates to the technical field of production line digital management, and particularly discloses an autoclaved aerated concrete production line digital management system based on an MES system, which comprises a temperature data correction module, a temperature trend prediction module and an autoclave parameter control module, the method comprises the following steps: acquiring the temperature of each monitoring time point corresponding to each target position point in a still kettle in the steam curing process of autoclaved aerated concrete bricks, and performing autonomous correction calculation on the temperature to obtain the corrected uniform temperature of each monitoring time point in the still kettle; and the working parameters of the still kettle at each future time point are adjusted based on the predicted temperature corresponding to each future time point in the still kettle, so that the uniformity of the temperature in the still kettle can be improved, the influence of local overheating or supercooling on the brick quality is avoided, the energy consumption can be optimized, and the steam waste is reduced. And meanwhile, the consistency and the stability of product performance are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of production line digital management, and relates to a digital management system for an autoclaved aerated concrete production line based on a MES system. Background Art

[0002] During the curing process of autoclaved aerated concrete bricks, the adjustment of the steam injection volume is important and necessary, because the steam injection volume directly determines the temperature and pressure changes in the autoclave, and these process parameters have a decisive influence on the physical properties of the bricks (such as strength, density and porosity). During the curing process, the changes in temperature and pressure need to be highly matched with the chemical reaction process inside the bricks, especially the hydration reaction and calcium-silicon reaction, which need to occur at appropriate temperature and pressure to form a uniform microstructure and excellent mechanical properties. If the steam injection volume is insufficient, the temperature may rise slowly or fail to meet the process requirements, resulting in insufficient reaction inside the bricks and insufficient product strength; if the steam injection volume is too large, it may cause the temperature and pressure to rise too quickly, resulting in internal stress inside the bricks due to uneven heating, and even cracks, affecting product quality.

[0003] However, the existing technology still has many technical defects and drawbacks in adjusting the amount of steam injection during the curing process of autoclaved aerated concrete bricks, which are mainly reflected in the following aspects: First, most existing autoclave control systems are based on fixed steam injection curves or preset parameters, lacking the ability to accurately perceive and dynamically adjust the real-time temperature distribution, and cannot flexibly adjust the steam injection amount according to the actual temperature changes in the autoclave, resulting in insufficient temperature control accuracy, especially when the temperature distribution in the autoclave is uneven, it is difficult to achieve temperature correction in local areas. In addition, existing steam injection control systems usually lack the ability to predict future temperature changes, and cannot accurately predict the temperature at future time points based on current temperature and historical data, resulting in a lag in steam injection adjustment, which makes it difficult to meet the needs of refined control. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention provides a digital management system for autoclaved aerated concrete production line based on the MES system, which is used to solve the above technical problems.

[0005] In order to achieve the above purpose and other purposes, the technical solution adopted by the present invention is as follows: The first aspect of the present invention provides a digital management system for an autoclaved aerated concrete production line based on a MES system, the system comprising a temperature data correction module, a temperature trend prediction module and an autoclave parameter control module, wherein the above modules are connected by wired and / or wireless connection to achieve data transmission between the modules; Temperature data correction module: This module collects the temperature of each target position point in the autoclave corresponding to each monitoring time point during the curing process of autoclaved aerated concrete bricks, and performs independent correction calculations on it to obtain the average temperature of each monitoring time point in the autoclave after correction; Temperature trend prediction module: predict the predicted temperature in the autoclave at each future time point; Autoclave parameter control module: Based on the predicted temperature in the autoclave at each future time point, the working parameters of the autoclave at each future time point are adjusted.

[0006] The logic for autonomous correction calculation is: The temperature of each target position point in the autoclave corresponding to each monitoring time point is recorded as , a is the number of each target location point, c is the number of each monitoring time point; The humidity, pressure and carbon dioxide gas concentration of each target position point in the autoclave corresponding to each monitoring time point are collected synchronously, and the humidity of each target position point in the autoclave corresponding to the last monitoring time point is subtracted from the humidity of the first monitoring time point to obtain the humidity change of each target position point in the autoclave; at the same time, the pressure change and carbon dioxide gas concentration change of each target position point in the autoclave are obtained in the same way; thus, the environmental factor correction value corresponding to each target position point in the autoclave is calculated , β1 is the set pressure correction coefficient, β2 is the set humidity correction coefficient, and β3 is the set gas concentration correction coefficient; are the humidity change, pressure change and carbon dioxide gas concentration change at the ath target position in the autoclave respectively; Considering the influence of heat conduction and heat convection in the autoclave, correction is performed based on the heat conduction equation. The correction value of the heat conduction factor corresponding to each monitoring time point at each target position point in the autoclave is obtained by calculation. , MD, κ, ρ are the specific heat capacity, thermal conductivity and density of the corresponding materials in the autoclave, is the Laplace operator of the temperature corresponding to the cth monitoring time point at the ath target position in the autoclave, which is used to describe the change of temperature distribution in space; Thus, the temperature of each target position point in the autoclave corresponding to each monitoring time point after correction is obtained. ,in is the sensor error correction value corresponding to the cth monitoring time point at the ath target position in the autoclave, , They respectively represent the linear error coefficient and fixed offset of the sensor at the ath target position point in the autoclave.

[0007] The logic for obtaining the average temperature at each monitoring time point in the autoclave after correction is: Based on the temperature of each target position point in the autoclave corresponding to each monitoring time point after correction , using the calculation formula , and obtain the average temperature at each monitoring time point in the autoclave after correction , a is the number of each target location point, the value range of a is 1 to A, A is the total number of target location points, is the dynamic weight of the ath target position point in the autoclave.

[0008] Calculating the dynamic weight of each target position point in the autoclave includes the following steps: Step 1: Obtain the spatial coordinates of each target position point in the autoclave, and compare them with the spatial coordinates of each key position point contained in the set key position point area in the autoclave. If the spatial coordinates of a target position point in the autoclave are consistent with the spatial coordinates of a key position point contained in the set key position point area in the autoclave, then the position importance factor of the target position point in the autoclave is recorded as φ1, otherwise, the position importance factor of the target position point in the autoclave is recorded as φ2, thereby comprehensively obtaining the position importance factor of each target position point in the autoclave ,in The value is φ1 or φ2; Step 2: Based on the temperature of each target position point in the autoclave corresponding to each monitoring time point, the temperature change rate of each target position point in the autoclave is calculated, and the absolute value calculation of each target position point in the autoclave is performed simultaneously, and the result after the absolute value calculation is recorded as the thermal field dynamic change factor of each target position point in the autoclave ; Step 3: Calculate the dynamic weight of each target position point in the autoclave .

[0009] Predict the predicted temperature in the autoclave at each future time point, including: Get the ambient temperature at each monitoring time point, calculate the average value, and get the average ambient temperature ; Synchronously obtain the average temperature of the autoclave at the last monitoring time point after correction ; Get the time point of each future time point, calculate the difference between it and the time point of the last monitoring time point, and get the difference between each future time point and the last monitoring time point , j is the number of each future time point; Substituting into the formula: ; Calculate the predicted temperature in the autoclave at each future time point , k is the set thermal conductivity coefficient, and e is the natural constant.

[0010] The specific calculation formula of the set thermal conductivity coefficient k is as follows: A target position point is randomly selected from each target position point in the autoclave corresponding to each monitoring time point as a reference position point, and the temperature of each monitoring time point corresponding to the reference position point is obtained; The temperatures of the reference position points corresponding to the monitoring time points are combined in pairs according to the order of the monitoring time points to obtain the temperatures of the reference position points corresponding to the monitoring time points in each combination; Sum the ambient temperatures of each monitoring time point in each combination corresponding to the reference position point and divide it by 2 to get the average ambient temperature of each combination corresponding to the reference position point. ; Substituting into the formula: ; Get the thermal conductivity coefficient of each combination corresponding to the reference position point , u is the number of each combination; They represent the time points corresponding to the second monitoring time point and the first monitoring time point in the u-th combination of the reference position point, respectively. are the temperatures of the first monitoring time point and the second monitoring time point in the u-th combination corresponding to the reference position point respectively; The heat conductivity coefficients of the reference position point corresponding to each combination are summed and averaged to obtain the heat conductivity coefficient of the reference position point; The heat conductivity coefficients of other target positions in the autoclave except the reference position are calculated similarly based on the heat conductivity coefficient of the reference position; The thermal conductivity coefficients of the target positions other than the reference position in the autoclave and the thermal conductivity coefficient of the reference position are arranged in ascending order, and the thermal conductivity coefficient whose median is selected is used as the set thermal conductivity coefficient k.

[0011] The operating parameter of the autoclave at each future time point is the steam injection rate.

[0012] The process of adjusting the steam injection rate of the autoclave at various future time points includes: Based on analytical model , calculate the steam injection amount of the autoclave at each future time point , is the average temperature of the environment in the autoclave corresponding to the jth future time point, is the expected temperature in the autoclave corresponding to the jth future time point; λ is the heat loss coefficient, which indicates the speed at which the temperature tends to the ambient temperature, reflecting the heat loss of the autoclave. is an equivalent thermal efficiency parameter, indicating the average thermal efficiency at all locations; , is the average temperature of the autoclaved aerated concrete brick at the beginning of curing, d=0 represents the initial time point of the curing process, d is the number of each curing time point, △t is the set time step, is the reference injection volume of steam per unit time, is the temperature of steam injected into the autoclave, is the temperature in the steam curing kettle at the dth steam curing time point, is the average temperature of the environment from the beginning of steam curing to the dth steam curing time point, is the total heat capacity in the autoclave from the start of steam curing to the dth steam curing time point.

[0013] A second aspect of the present invention provides a digital management device for an autoclaved aerated concrete production line based on a MES system, comprising a processor, a memory and a communication bus; The memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, it is implemented to implement the digital management system of autoclaved aerated concrete production line based on the MES system as described in any one of the present invention.

[0014] As described above, the digital management system for autoclaved aerated concrete production line based on the MES system provided by the present invention has at least the following beneficial effects: The autoclave aerated concrete production line digital management system based on the MES system provided by the present invention can effectively eliminate the influence of measurement errors and local temperature fluctuations on the overall temperature control accuracy by collecting the temperatures of each target position point in the autoclave corresponding to each monitoring time point during the curing process of the autoclave aerated concrete bricks, and independently correcting and calculating them to obtain the corrected average temperature, thereby more accurately reflecting the actual temperature distribution in the autoclave; based on the predicted temperature at each future time point in the autoclave, the working parameters of the autoclave at each future time point are dynamically adjusted, and the curing process can be finely controlled. The advantage of this method is that it can not only improve the uniformity of the temperature in the autoclave, avoid the influence of local overheating or overcooling on the quality of bricks, but also optimize energy consumption, reduce steam waste, and ensure the consistency and stability of product performance. The necessity lies in the fact that the curing process of autoclaved aerated concrete bricks has very strict requirements on the time-space distribution of temperature. Accurate temperature control is the key to ensuring that the strength, density and other physical properties of bricks meet the standards. By real-time monitoring, correction and prediction of temperature and dynamic adjustment of working parameters, the intelligence and automation level of the curing process can be significantly improved to meet the needs of modern industry for high-quality and high-efficiency production. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are 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 creative work.

[0016] Figure 1 It is a schematic diagram of the connection of various modules of the system of the present invention. DETAILED DESCRIPTION

[0017] The above contents in combination with the implementation of the present invention are merely examples and explanations of the concept of the present invention. The technical personnel in the relevant technical field may make various modifications or supplements to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

[0018] Example 1 See also Figure 1 As shown, a digital management system for autoclaved aerated concrete production line based on the MES system includes a temperature data correction module, a temperature trend prediction module and an autoclave parameter control module. The above modules are connected by wired and / or wireless connection to achieve data transmission between the modules; Temperature data correction module: This module collects the temperature of each target position point in the autoclave corresponding to each monitoring time point during the curing process of autoclaved aerated concrete bricks, and performs independent correction calculations on it to obtain the average temperature of each monitoring time point in the autoclave after correction; Based on the above scheme, the logic of autonomous correction calculation is as follows: The temperature of each target position point in the autoclave corresponding to each monitoring time point is recorded as , a is the number of each target location point, c is the number of each monitoring time point; The humidity, pressure and carbon dioxide gas concentration of each target position point in the autoclave corresponding to each monitoring time point are collected synchronously, and the humidity of each target position point in the autoclave corresponding to the last monitoring time point is subtracted from the humidity of the first monitoring time point to obtain the humidity change of each target position point in the autoclave; at the same time, the pressure change and carbon dioxide gas concentration change of each target position point in the autoclave are obtained in the same way; thus, the environmental factor correction value corresponding to each target position point in the autoclave is calculated , β1 is the set pressure correction coefficient, β2 is the set humidity correction coefficient, and β3 is the set gas concentration correction coefficient; are the humidity change, pressure change and carbon dioxide gas concentration change at the ath target position in the autoclave respectively; Considering the influence of heat conduction and heat convection in the autoclave, correction is performed based on the heat conduction equation. The correction value of the heat conduction factor corresponding to each monitoring time point at each target position point in the autoclave is obtained by calculation. , MD, κ, ρ are the specific heat capacity, thermal conductivity and density of the corresponding materials in the autoclave, is the Laplace operator of the temperature corresponding to the cth monitoring time point at the ath target position in the autoclave, which is used to describe the change of temperature distribution in space. is the spatial coordinate of the ath target position point in the autoclave; Thus, the temperature of each target position point in the autoclave corresponding to each monitoring time point after correction is obtained. ,in is the sensor error correction value corresponding to the cth monitoring time point at the ath target position in the autoclave, , They respectively represent the linear error coefficient and fixed offset of the sensor at the ath target position point in the autoclave, which can be obtained by fitting the experimental data.

[0019] The above calculation formula is mainly reflected in its ability to accurately model and correct the complex thermal field environment in the autoclave in real time, which is of great significance for improving the accuracy and stability of the temperature control system. In the production process of autoclaved aerated concrete, the temperature distribution in the autoclave directly affects the quality and production efficiency of the product. However, due to the complexity of the environment in the autoclave (such as high pressure, high humidity, and the combined effects of heat conduction and heat convection), it is difficult to meet the needs of precise control by relying solely on simple temperature measurement methods. Therefore, the proposed multi-factor correction formula comprehensively solves this problem from a technical perspective; First, the sensor error correction term is an important part of the formula. Since the sensor may produce linear drift, nonlinear error, and environmental interference in long-term use, directly using uncorrected sensor data will cause deviations in the temperature control system, thereby affecting the temperature uniformity in the autoclave. By introducing the linear correction coefficient and fixed offset of the sensor, the inherent error of the sensor itself can be effectively eliminated, ensuring that the collected temperature data is more reliable; Secondly, the introduction of environmental factor correction items is an adaptive adjustment to the complex environmental changes in the autoclave. During the autoclave process, changes in pressure, humidity, and gas concentration in the autoclave will have a significant impact on temperature measurement. For example, pressure fluctuations may change the response characteristics of the sensor, while changes in humidity may cause changes in heat conduction and convection. By introducing correction coefficients for pressure, humidity, and gas concentration, the formula can dynamically adjust the temperature measurement value to adapt to changes in environmental parameters. This correction method not only improves the measurement accuracy, but also enhances the robustness of the system in complex environments; The design of the heat conduction correction term is based on the physical model of the heat conduction equation, which fully considers the influence of heat conduction and heat convection in the autoclave. In the autoclave, due to the uneven distribution of heat sources and the difference in the thermophysical parameters of the materials, the temperature distribution at different locations may be significantly different. This ensures that the temperature in the autoclave is more uniform, thereby improving the quality consistency of the product; through this multi-factor correction method, the performance of the temperature control system can be significantly improved, ensuring the uniformity and stability of the temperature distribution during the production process of autoclaved aerated concrete, thereby improving production efficiency, reducing energy consumption, and improving product quality consistency.

[0020] Based on the above scheme, the logic of obtaining the average temperature at each monitoring time point in the autoclave after correction is as follows: Based on the temperature of each target position point in the autoclave corresponding to each monitoring time point after correction , using the calculation formula , and obtain the average temperature at each monitoring time point in the autoclave after correction , a is the number of each target location point, the value range of a is 1 to A, A is the total number of target location points, is the dynamic weight of the ath target position point in the autoclave.

[0021] Based on the above scheme, the dynamic weight of each target position point in the autoclave is calculated, including the following steps: Step 1: Obtain the spatial coordinates of each target position point in the autoclave, and compare them with the spatial coordinates of each key position point contained in the set key position point area in the autoclave. If the spatial coordinates of a target position point in the autoclave are consistent with the spatial coordinates of a key position point contained in the set key position point area in the autoclave, then the position importance factor of the target position point in the autoclave is recorded as φ1, otherwise, the position importance factor of the target position point in the autoclave is recorded as φ2, thereby comprehensively obtaining the position importance factor of each target position point in the autoclave ,in The value is φ1 or φ2; Step 2: Based on the temperature of each target position point in the autoclave corresponding to each monitoring time point, the temperature change rate of each target position point in the autoclave is calculated, and the absolute value calculation of each target position point in the autoclave is performed simultaneously, and the result after the absolute value calculation is recorded as the thermal field dynamic change factor of each target position point in the autoclave ; Step 3: Calculate the dynamic weight of each target position point in the autoclave .

[0022] By introducing the importance factor of the position in the above calculation formula, the system's focus can be effectively focused on the key positions that have a greater impact on the overall temperature distribution. For example, in an autoclave or other industrial thermal field control scenario, different positions do not contribute equally to the temperature control effect, and some positions may play a decisive role in the uniformity of the overall temperature field or system performance. By assigning higher weights to these key positions, their impact on the system can be more accurately reflected, thereby optimizing the control strategy and improving the efficiency and stability of the temperature control system. At the same time, this method avoids simplistic treatment of all positions, and can allocate resources more scientifically, especially in complex systems with multi-point temperature monitoring; Secondly, the introduction of the dynamic change factor of the thermal field enables the dynamic weight to reflect the dynamic change characteristics of the temperature in real time. The dynamic change of the thermal field is often nonlinear, and the temperature at different locations may fluctuate significantly in a short period of time, and this fluctuation may directly affect the temperature uniformity of the system or the realization of the target temperature. By calculating the dynamic change factor, the intensity and trend of these fluctuations can be captured, so that the weight distribution can be adjusted in real time, giving priority to locations with drastic temperature changes. This dynamic adjustment mechanism can not only improve the system's sensitivity to temperature changes, but also enhance the system's responsiveness, avoiding the decline in overall control effect due to local temperature anomalies; In summary, the weight calculation method based on these two factors can achieve higher control accuracy and stronger adaptability in complex temperature control systems. On the one hand, it reflects the pertinence and scientificity of system design through the importance factor of the position; on the other hand, it introduces real-time and flexibility through the dynamic change factor of the thermal field, allowing the system to adaptively respond to complex thermal field changes.

[0023] Temperature trend prediction module: This module reflects the dynamic thermal field distribution in the autoclave in real time and predicts the predicted temperature in the autoclave at each future time point; Based on the above scheme, the predicted temperature in the autoclave corresponding to each future time point is predicted, including: Get the ambient temperature at each monitoring time point, calculate the average value, and get the average ambient temperature ; Synchronously obtain the average temperature of the autoclave at the last monitoring time point after correction ; Get the time point of each future time point, calculate the difference between it and the time point of the last monitoring time point, and get the difference between each future time point and the last monitoring time point , j is the number of each future time point; Substituting into the formula: ; Calculate the predicted temperature in the autoclave at each future time point , k is the set thermal conductivity coefficient, and e is the natural constant.

[0024] Based on the above scheme, the specific calculation formula of the set thermal conductivity k is as follows: A target position point is randomly selected from each target position point in the autoclave corresponding to each monitoring time point as a reference position point, and the temperature of each monitoring time point corresponding to the reference position point is obtained; The temperatures of the reference position points corresponding to the monitoring time points are combined in pairs according to the order of the monitoring time points to obtain the temperatures of the reference position points corresponding to the monitoring time points in each combination; Sum the ambient temperatures of each monitoring time point in each combination corresponding to the reference position point and divide it by 2 to get the average ambient temperature of each combination corresponding to the reference position point. ; Substituting into the formula: ; Get the thermal conductivity coefficient of each combination corresponding to the reference position point , u is the number of each combination; They represent the time points corresponding to the second monitoring time point and the first monitoring time point in the u-th combination of the reference position point, respectively. are the temperatures of the first monitoring time point and the second monitoring time point in the u-th combination corresponding to the reference position point respectively; The heat conductivity coefficients of the reference position point corresponding to each combination are summed and averaged to obtain the heat conductivity coefficient of the reference position point; The heat conductivity coefficients of other target positions in the autoclave except the reference position are calculated similarly based on the heat conductivity coefficient of the reference position; The thermal conductivity coefficients of the target positions other than the reference position in the autoclave and the thermal conductivity coefficient of the reference position are arranged in ascending order, and the thermal conductivity coefficient whose median is selected is used as the set thermal conductivity coefficient k.

[0025] It should be added that the thermal conductivity coefficient of each combination corresponding to the reference position point is The rationality of is that it is based on the basic principle of heat conduction, that is, the rate of temperature change is proportional to the temperature difference. By calculating the discrete time points of the actual temperature data, the formula can directly extract the core parameter k of the heat conduction process from the experimental data, reflecting the dynamic characteristics of temperature change.

[0026] Autoclave parameter control module: Based on the predicted temperature in the autoclave at each future time point, the working parameters of the autoclave at each future time point are adjusted.

[0027] Preferably, based on the above scheme, the working parameter of the autoclave at each future time point is the steam injection amount.

[0028] Based on the above scheme, preferably, the process of adjusting the steam injection amount of the autoclave at each future time point includes: Based on analytical model , calculate the steam injection amount of the autoclave at each future time point , is the average temperature of the environment in the autoclave corresponding to the jth future time point, is the expected temperature in the autoclave corresponding to the jth future time point; λ is the heat loss coefficient, which indicates the speed at which the temperature tends to the ambient temperature, reflecting the heat loss of the autoclave. is an equivalent thermal efficiency parameter, indicating the average thermal efficiency at all locations; It should be added that the first term in the above formula Indicates the deviation between the current predicted temperature and the target temperature, which needs to be compensated by steam injection; the second item represents the temperature drop due to heat loss, which requires additional steam injection to compensate; It is a comprehensive thermal efficiency parameter that reflects the actual conversion efficiency of steam injection to temperature change. By taking these factors into consideration, the formula can reasonably calculate the required steam injection amount, thereby achieving dynamic temperature adjustment and ensuring that the system achieves a balance between energy consumption and temperature control accuracy.

[0029] , is the average temperature of the autoclaved aerated concrete brick at the beginning of curing, d=0 represents the initial time point of the curing process, d is the number of each curing time point, △t is the set time step, is the reference injection volume of steam per unit time, is the temperature of steam injected into the autoclave, is the temperature in the steam curing kettle at the dth steam curing time point, is the average temperature of the environment from the beginning of steam curing to the dth steam curing time point, is the total heat capacity in the autoclave from the start of steam curing to the dth steam curing time point, , is the total number of autoclaved aerated concrete bricks in the autoclave, They are respectively the mean heat capacity of the autoclaved aerated concrete bricks in the autoclave at the d-th curing time point and the ability of steam to release heat.

[0030] The above formula decomposes the complex heat transfer process into iterative calculations of multiple time steps by discretizing the heat transfer equation. It can dynamically simulate the temperature change process in the autoclave over time, rather than just calculating the initial and final temperatures. This dynamic calculation method is particularly important for control systems because during the autoclave curing process, the rate of temperature change directly affects the curing quality of the bricks. Through this formula, the temperature at any point in the future can be accurately predicted, thus providing a basis for optimizing the temperature control strategy; The formula takes into account several key factors such as steam injection volume, steam temperature, ambient temperature, and the total number of bricks, while also taking into account heat loss and total heat capacity. These parameters jointly affect the temperature change of the system, and through the iteration of the formula, the complexity of temperature changes during actual operation can be reflected. For example, an increase in the number of bricks will significantly increase the total heat capacity of the system, thereby slowing down the rate of temperature rise; and changes in the heat loss coefficient will affect the thermal equilibrium state of the system. By incorporating these factors into the formula, the thermodynamic behavior of the autoclave can be more realistically reflected; The operating cost of the autoclave is closely related to the amount of steam injected. If too much steam is injected, energy will be wasted; if not enough steam is injected, the temperature may not reach the target value, affecting the quality of the bricks. This formula provides a theoretical basis for optimizing the steam injection strategy by clarifying the impact of the reference injection amount of steam per unit time on the temperature change. By adjusting the size and distribution of the reference injection amount of steam per unit time, precise temperature control can be achieved, thereby reducing energy consumption while ensuring product quality.

[0031] Example 2 A digital management device for autoclaved aerated concrete production line based on the MES system, including a processor, a memory and a communication bus; The memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, it is implemented to implement the digital management system of autoclaved aerated concrete production line based on the MES system as described in any one of the present invention.

[0032] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0033] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0034] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0035] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The digital management system of autoclaved aerated concrete production line based on MES system is characterized by: It includes a temperature data correction module, a temperature trend prediction module and an autoclave parameter control module, and the above modules are connected by wired and / or wireless connection to achieve data transmission between the modules; Temperature data correction module: This module collects the temperature of each target position point in the autoclave corresponding to each monitoring time point during the curing process of autoclaved aerated concrete bricks, and performs independent correction calculations on it to obtain the average temperature of each monitoring time point in the autoclave after correction; Temperature trend prediction module: predict the predicted temperature in the autoclave at each future time point; Autoclave parameter control module: Based on the predicted temperature in the autoclave at each future time point, the working parameters of the autoclave at each future time point are adjusted.

2. The digital management system for autoclaved aerated concrete production line based on the MES system according to claim 1 is characterized in that: The logic for autonomous correction calculation is: The temperature of each target position point in the autoclave corresponding to each monitoring time point is recorded as , a is the number of each target location point, c is the number of each monitoring time point; The humidity, pressure and carbon dioxide gas concentration of each target position point in the autoclave corresponding to each monitoring time point are collected synchronously, and the humidity of each target position point in the autoclave corresponding to the last monitoring time point is subtracted from the humidity of the first monitoring time point to obtain the humidity change of each target position point in the autoclave; at the same time, the pressure change and carbon dioxide gas concentration change of each target position point in the autoclave are obtained in the same way; thus, the environmental factor correction value corresponding to each target position point in the autoclave is calculated , β1 is the set pressure correction coefficient, β2 is the set humidity correction coefficient, and β3 is the set gas concentration correction coefficient; are the humidity change, pressure change and carbon dioxide gas concentration change at the ath target position in the autoclave respectively; Considering the influence of heat conduction and heat convection in the autoclave, correction is performed based on the heat conduction equation. The correction value of the heat conduction factor corresponding to each monitoring time point at each target position point in the autoclave is obtained by calculation. , MD, κ, ρ are the specific heat capacity, thermal conductivity and density of the corresponding materials in the autoclave, is the Laplace operator of the temperature corresponding to the cth monitoring time point at the ath target position in the autoclave, which is used to describe the change of temperature distribution in space; Thus, the temperature of each target position point in the autoclave corresponding to each monitoring time point after correction is obtained. ,in is the sensor error correction value corresponding to the cth monitoring time point at the ath target position in the autoclave, , They respectively represent the linear error coefficient and fixed offset of the sensor at the ath target position point in the autoclave.

3. The digital management system for autoclaved aerated concrete production line based on the MES system according to claim 1 is characterized in that: The logic for obtaining the average temperature at each monitoring time point in the autoclave after correction is: Based on the temperature of each target position point in the autoclave corresponding to each monitoring time point after correction , using the calculation formula , and obtain the average temperature at each monitoring time point in the autoclave after correction , a is the number of each target location point, the value range of a is 1 to A, A is the total number of target location points, is the dynamic weight of the ath target position point in the autoclave.

4. The digital management system for autoclaved aerated concrete production line based on the MES system according to claim 3 is characterized in that: Calculating the dynamic weight of each target position point in the autoclave includes the following steps: Step 1: Obtain the spatial coordinates of each target position point in the autoclave, and compare them with the spatial coordinates of each key position point contained in the set key position point area in the autoclave. If the spatial coordinates of a target position point in the autoclave are consistent with the spatial coordinates of a key position point contained in the set key position point area in the autoclave, then the position importance factor of the target position point in the autoclave is recorded as φ1, otherwise, the position importance factor of the target position point in the autoclave is recorded as φ2, thereby comprehensively obtaining the position importance factor of each target position point in the autoclave ,in The value is φ1 or φ2; Step 2: Based on the temperature of each target position point in the autoclave corresponding to each monitoring time point, the temperature change rate of each target position point in the autoclave is calculated, and the absolute value calculation of each target position point in the autoclave is performed simultaneously, and the result after the absolute value calculation is recorded as the thermal field dynamic change factor of each target position point in the autoclave ; Step 3: Calculate the dynamic weight of each target position point in the autoclave .

5. The digital management system for autoclaved aerated concrete production line based on MES system according to claim 1, characterized in that: Predict the predicted temperature in the autoclave at each future time point, including: Get the ambient temperature at each monitoring time point, calculate the average value, and get the average ambient temperature ; Synchronously obtain the average temperature of the autoclave at the last monitoring time point after correction ; Get the time point of each future time point, calculate the difference between it and the time point of the last monitoring time point, and get the difference between each future time point and the last monitoring time point , j is the number of each future time point; Substituting into the formula: ； Calculate the predicted temperature in the autoclave at each future time point , k is the set thermal conductivity coefficient, and e is the natural constant.

6. The digital management system for autoclaved aerated concrete production line based on the MES system according to claim 5 is characterized in that: The specific calculation formula of the set thermal conductivity coefficient k is as follows: A target position point is randomly selected from each target position point in the autoclave corresponding to each monitoring time point as a reference position point, and the temperature of each monitoring time point corresponding to the reference position point is obtained; The temperatures of the reference position points corresponding to the monitoring time points are combined in pairs according to the order of the monitoring time points to obtain the temperatures of the reference position points corresponding to the monitoring time points in each combination; Sum the ambient temperatures of each monitoring time point in each combination corresponding to the reference position point and divide it by 2 to get the average ambient temperature of each combination corresponding to the reference position point. ; Substituting into the formula: ； Get the thermal conductivity coefficient of each combination corresponding to the reference position point , u is the number of each combination; They represent the time points corresponding to the second monitoring time point and the first monitoring time point in the u-th combination of the reference position point, respectively. are the temperatures of the first monitoring time point and the second monitoring time point in the u-th combination corresponding to the reference position point respectively; The heat conductivity coefficients of the reference position point corresponding to each combination are summed and averaged to obtain the heat conductivity coefficient of the reference position point; The heat conductivity coefficients of other target positions in the autoclave except the reference position are calculated similarly based on the heat conductivity coefficient of the reference position; The thermal conductivity coefficients of the target positions other than the reference position in the autoclave and the thermal conductivity coefficient of the reference position are arranged in ascending order, and the thermal conductivity coefficient whose median is selected is used as the set thermal conductivity coefficient k.

7. The digital management system for autoclaved aerated concrete production line based on the MES system according to claim 1 is characterized in that: The operating parameter of the autoclave at each future time point is the steam injection rate.

8. The digital management system for autoclaved aerated concrete production line based on the MES system according to claim 7 is characterized by: The process of adjusting the steam injection rate of the autoclave at various future time points includes: Based on analytical model , calculate the steam injection amount of the autoclave at each future time point , is the average temperature of the environment in the autoclave corresponding to the jth future time point, is the expected temperature in the autoclave corresponding to the jth future time point; λ is the heat loss coefficient, which indicates the speed at which the temperature tends to the ambient temperature, reflecting the heat loss of the autoclave. is an equivalent thermal efficiency parameter, indicating the average thermal efficiency at all locations; , is the average temperature of the autoclaved aerated concrete brick at the beginning of curing, d=0 represents the initial time point of the curing process, d is the number of each curing time point, △t is the set time step, is the reference injection volume of steam per unit time, is the temperature of steam injected into the autoclave, is the temperature in the steam curing kettle at the dth steam curing time point, is the average temperature of the environment from the beginning of steam curing to the dth steam curing time point, is the total heat capacity in the autoclave from the start of steam curing to the dth steam curing time point.

9. The digital management device for autoclaved aerated concrete production line based on the MES system is characterized by: It is implemented based on the digital management system of autoclaved aerated concrete production line based on the MES system as described in any one of claims 1 to 8, and includes a processor, a memory and a communication bus; The memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, it is implemented to implement the digital management system of the autoclaved aerated concrete production line based on the MES system as described in any one of claims 1 to 8.

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