Automatic foaming machine production control system and operation feedback method

Abstract

The present invention relates to the field of foaming machine control technology, and specifically to an automatic foaming machine production control system and an operation feedback method. The present invention analyzes the correlation between stirring rate data and material viscosity data at each moment, and obtains the indirect influence coefficient of temperature on foam density data at each moment; performs trend analysis on the data within the neighborhood range at each moment, obtains the indirect trend value corresponding to the indirect influence coefficient, and the actual trend value corresponding to the foam density data; and then obtains the direct influence coefficient of temperature on foam density data at the real time moment; adjusts the stirring rate data at the real time moment according to the feedback adjustment factor at the next moment of the real time moment, and obtains the adjusted stirring rate data at the next moment; and performs operation feedback on the mixer. The present invention balances the fluctuation of reaction temperature and improves foaming stability by obtaining accurate stirring rate data at the next moment.

Description

Technical Field

[0001] The invention relates to the technical field of foaming machine control, and in particular to an automatic foaming machine production control system and an operation feedback method. Background Art

[0002] The automatic foaming machine is a device specially used for producing foam products. It generates foam products by mixing the foaming agent and other raw materials and injecting them into the mold. The mixer is one of the key components in the automatic foaming machine. It is responsible for accurately mixing the foaming agent and the base material to ensure a uniform foaming effect. Therefore, it is necessary to feedback and adjust the operating parameters in the mixer to optimize the foaming process and improve product quality.

[0003] In the prior art, a fixed empirical value is used for the stirring rate of materials in a mixer. However, different base materials in the mixer will react with each other after mixing. Different reaction processes will be accompanied by local heat release and heat absorption, which will lead to temperature fluctuations of the materials, which may affect the foam density and cause unstable quality of the molded product. Traditional stirring based on fixed empirical values ​​cannot adapt to the temperature fluctuations caused by the heat changes generated by the reaction, resulting in poor foaming stability at the stirring rate. Summary of the invention

[0004] In order to solve the technical problem that the traditional stirring according to fixed empirical values ​​cannot adapt to temperature fluctuations, resulting in poor foaming stability, the purpose of the present invention is to provide an automatic foaming machine production control system and an operation feedback method. The technical scheme adopted is as follows:

[0005] The present invention proposes a production control operation feedback method for an automatic foaming machine, the method comprising:

[0006] Obtain stirring rate data, material viscosity data and foam density data of the automatic foaming machine mixer at each moment;

[0007] According to the correlation between the stirring rate data and the material viscosity data at each moment, the indirect influence coefficient of the temperature on the foam density data at each moment is obtained; the data within the neighborhood range at each moment are trend analyzed to obtain the indirect trend value corresponding to the indirect influence coefficient and the actual trend value corresponding to the foam density data; according to the difference change trend between the actual trend value and the indirect trend value at each moment within the neighborhood range of the real time moment, the direct influence coefficient of the temperature on the foam density data at the real time moment is obtained;

[0008] According to the indirect influence coefficient and the direct influence coefficient at the real time, a feedback adjustment factor at the next moment of the real time is obtained; according to the feedback adjustment factor at the next moment, the stirring rate data at the real time is adjusted to obtain the adjusted stirring rate data at the next moment;

[0009] The mixer is fed back with operation according to the adjusted stirring rate data at the next moment.

[0010] Furthermore, the method for obtaining the indirect influence coefficient includes:

[0011] For the stirring rate data and material viscosity data, the indirect influence coefficient of temperature on the foam density data at each moment is obtained according to the difference between the preset standard correlation and the actual correlation at each moment.

[0012] Furthermore, the method for obtaining the preset standard relevance and the actual relevance includes:

[0013] Obtaining a ratio between standard stirring rate data and corresponding standard material viscosity data as a preset standard correlation;

[0014] The ratio between the stirring rate data and the material viscosity data at each moment is obtained as the actual correlation at each moment.

[0015] Furthermore, the trend analysis of the data within the neighborhood at each moment includes:

[0016] Constructing a trend value calculation model, inputting input data of all moments within the neighborhood range of each moment into the trend value calculation model, and obtaining an output trend value at each moment, wherein the output trend value includes an indirect trend value corresponding to an indirect influence coefficient or an actual trend value corresponding to foam density data;

[0017] The calculation method of the trend value calculation model includes:

[0018] The input data includes the indirect influence coefficient or the foam density data;

[0019] Obtain the range values ​​of the input data at all moments within the neighborhood of each moment;

[0020] According to the input data at each moment, the difference between the input data at each moment and other moments within the corresponding neighborhood range, and the extreme value, the output trend value at each moment is obtained, the input data and the difference between the input data are positively correlated with the output trend value, and the extreme value is negatively correlated with the output trend value.

[0021] Furthermore, the method for obtaining the direct influence coefficient includes:

[0022] Obtaining a first difference between the actual trend value and the indirect trend value at each moment within a neighborhood of the real time moment;

[0023] Calculate the mean of the first differences at all other moments in the corresponding neighborhood as the average difference level;

[0024] According to the deviation of the first difference at the real time moment relative to the average difference level, a direct influence coefficient of temperature on foam density data at the real time moment is obtained.

[0025] Furthermore, the indirect influence coefficient and the direct influence coefficient are positively correlated with the feedback adjustment factor.

[0026] Furthermore, the method for obtaining the data of adjusting the stirring rate includes:

[0027] Calculating the sum of a preset constant and the feedback adjustment factor at the next moment as an adjustment weight;

[0028] The stirring rate data at the real time is weighted according to the adjustment weight to obtain the adjusted stirring rate data at the next moment.

[0029] Furthermore, the method for obtaining the neighborhood range at the real time includes:

[0030] The real time is taken as the benchmark, and the range formed by all historical moments is taken as the neighborhood range of the real time.

[0031] Furthermore, the absolute value range of the feedback adjustment factor is between 0 and 1, and the preset constant is a positive integer 1.

[0032] The present invention also proposes an automatic foaming machine production control system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any one of the steps of the automatic foaming machine production control operation feedback method.

[0033] The present invention has the following beneficial effects:

[0034] Since temperature affects material viscosity, and thus affects stirring effect and foam formation, the present invention obtains an indirect influence coefficient of temperature on foam density data at each moment according to the correlation between stirring rate data and material viscosity data at each moment, reflecting the indirect influence of temperature on foam density; performs trend analysis on data within a neighborhood range at each moment, obtains an indirect trend value corresponding to the indirect influence coefficient, and an actual trend value corresponding to the foam density data; in addition to the indirect influence, temperature may also have a direct influence on foam density, and obtains a direct influence coefficient of temperature on foam density data at the real time moment according to a difference change trend between the actual trend value and the indirect trend value at each moment within the neighborhood range at the real time moment; obtains a feedback adjustment factor at the next moment of the real time moment according to the indirect influence coefficient and the direct influence coefficient at the real time moment, and comprehensively considers the two influences to more accurately reflect the actual effect of temperature and better adapt to changes in parameters such as temperature in the production process; adjusts the stirring rate data at the real time moment according to the feedback adjustment factor at the next moment, obtains the adjusted stirring rate data at the next moment, and adjusts the stirring rate data to change the physical and chemical conditions in the mixing process, thereby balancing temperature fluctuations and affecting the formation and stability of foam; and performs operation feedback on the mixer. The present invention obtains accurate stirring rate data of the next moment in the mixer to balance the fluctuation of the reaction temperature and improve the foaming stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions 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 paying creative work.

[0036] Figure 1 A flow chart of a method for production control and operation feedback of an automatic foaming machine provided by one embodiment of the present invention;

[0037] Figure 2 A schematic diagram of a curve of stirring rate and material viscosity provided by one embodiment of the present invention;

[0038] Figure 3 A flow chart of a method for obtaining a direct influence coefficient provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the automatic foaming machine production control system and operation feedback method proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0041] The specific scheme of an automatic foaming machine production control system and operation feedback method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0042] See also Figure 1 , which shows a flow chart of an automatic foaming machine production control operation feedback method provided by an embodiment of the present invention, and the specific method includes:

[0043] Step S1: Obtain stirring rate data, material viscosity data and foam density data of the automatic foaming machine mixer at each moment.

[0044] In the embodiments of the present invention, the foam density of the automatic foaming machine during the foaming process is an important parameter for measuring the foaming effect, which directly affects the strength, thermal insulation performance and weight of the material; since the mixer mixes and stirs the foaming agent and the substrate, the material mixing is more uniform, but the mutual reaction of different substrates during the mixing process will be accompanied by local heat release and heat absorption, which will lead to material temperature fluctuations, and the foam density is unstable during the foaming process; in order to obtain more stable and higher quality foamed products, it is necessary to feedback adjust the production control of the automatic foaming machine mixer to ensure that a uniform and stable foam density is generated in the mixer; first, the stirring rate data, material viscosity data and foam density data of the automatic foaming machine mixer at each moment are obtained.

[0045] It should be noted that in order to facilitate subsequent data processing and avoid differences in data units and numerical magnitudes, the data is standardized to eliminate the influence of dimensions in data calculations.

[0046] It should be noted that, in one embodiment of the present invention, the time interval is 0.2S; in other embodiments of the present invention, the size of the time interval can be specifically set according to specific circumstances, and is not limited or elaborated herein.

[0047] Step S2: According to the correlation between the stirring rate data and the material viscosity data at each moment, the indirect influence coefficient of the temperature on the foam density data at each moment is obtained; the data within the neighborhood range at each moment are trend analyzed to obtain the indirect trend value corresponding to the indirect influence coefficient and the actual trend value corresponding to the foam density data; according to the difference change trend between the actual trend value and the indirect trend value at each moment in the neighborhood range of the real time moment, the direct influence coefficient of the temperature on the foam density data at the real time moment is obtained.

[0048] Since the reaction between materials will produce temperature fluctuations, the materials entering the foaming mold from the mixer will not only directly affect the stability of the foam density during the foaming process, but the temperature fluctuation will also affect the viscosity of the materials at a specific stirring rate in the mixer, and the change in material viscosity will also indirectly affect the stability of the foam density; therefore, by analyzing the correlation and changes in the data, it is helpful to understand the direct and indirect effects of temperature on foam density data.

[0049] Since the viscosity of the material is mainly determined by the stirring rate of the mixer, when the stirring rate is constant, as the reaction temperature fluctuates, the thermal motion of the material molecules intensifies or weakens, and the interaction between molecules weakens or strengthens, resulting in changes in the viscosity of the material, such as Figure 2 , which shows a curve diagram of stirring rate and material viscosity. Therefore, the change of temperature will directly affect the viscosity of the material, and then indirectly affect the generation and stability of foam; so according to the correlation between the stirring rate data and the material viscosity data at each moment, the indirect influence coefficient of temperature on the foam density data at each moment is obtained.

[0050] Preferably, in one embodiment of the present invention, the method for obtaining the indirect influence coefficient includes:

[0051] For the stirring rate data and material viscosity data, the indirect influence coefficient of temperature on the foam density data at each moment is obtained according to the difference between the preset standard correlation and the actual correlation at each moment.

[0052] Among them, the larger the difference is, the greater the preset standard correlation is than the actual correlation at each moment, and the greater the material viscosity is relative to its standard material viscosity at the stirring rate at the corresponding moment, the greater the indirect influence coefficient on the foam density data.

[0053] Generally, the stirring rate of a mixer is constant. Ideally, the change in material viscosity will be stable as the stirring rate remains constant. The preset standard correlation represents the normal or expected relationship between the stirring rate and the material viscosity, which can reflect the basic connection between the stirring rate and the material viscosity and is used for subsequent comparison and analysis. The actual correlation between the stirring rate and the material viscosity at each moment is analyzed to reflect the dynamic changes in the production process and evaluate deviations or anomalies compared with the preset standard correlation.

[0054] Preferably, in one embodiment of the present invention, the method for obtaining the preset standard correlation and the actual correlation includes:

[0055] Obtaining a ratio between standard stirring rate data and corresponding standard material viscosity data as a preset standard correlation;

[0056] The ratio between the stirring rate data and the material viscosity data at each moment is obtained as the actual correlation at each moment.

[0057] In one embodiment of the present invention, the formula of the indirect influence coefficient is expressed as:

[0058] ;

[0059] in, Indicates The indirect influence coefficient of temperature on foam density data at the time; Indicates standard stirring rate data; Indicates the standard material viscosity data corresponding to the standard stirring rate data; Indicates Stirring rate data at the time; Indicates Material viscosity data at the time; represents a non-linear activation function.

[0060] In the formula of the indirect impact coefficient, Indicates preset standard relevance; Indicates The actual relevance at the moment; It means calculating the difference between the preset standard correlation and the actual correlation at each moment, and using The function is mapped to the range of [-1,1], that is, the indirect influence coefficient; the closer the indirect influence coefficient is to 0, the closer the difference is to 0, the closer the actual correlation at each moment is to the preset standard correlation, and the smaller the impact on the material viscosity, that is, the smaller the indirect impact on the foam density; when the indirect influence coefficient is a positive number, the larger the indirect influence coefficient is, the larger the difference is greater than 0, and the smaller the actual correlation at each moment is than the preset standard correlation, that is, the greater the material viscosity is compared with the standard situation, the greater the positive impact on the material viscosity is, that is, the greater the positive indirect impact on the foam density data is; when the indirect influence coefficient is a negative number, the smaller the indirect influence coefficient is, the smaller the difference is less than 0, and the greater the actual correlation at each moment is than the preset standard correlation, that is, the smaller the material viscosity is compared with the standard situation, the greater the negative impact on the material viscosity is, and the greater the negative indirect impact on the foam density data is.

[0061] It should be noted that, in one embodiment of the present invention, the standard stirring rate data and the corresponding standard material viscosity data are pre-acquired by the implementer based on experience, which will not be elaborated herein.

[0062] Data usually changes over time. By performing trend analysis on the data in the neighborhood at each moment, we can capture the details and patterns of data changes, and thus understand the dynamic characteristics of the data more accurately. Therefore, we perform trend analysis on the data in the neighborhood at each moment to obtain the indirect trend value corresponding to the indirect influence coefficient and the actual trend value corresponding to the foam density data.

[0063] Preferably, in one embodiment of the present invention, trend analysis is performed on data within the neighborhood at each moment, including:

[0064] Construct a trend value calculation model, input the input data of all moments within the neighborhood range of each moment into the trend value calculation model, and obtain the output trend value of each moment, the output trend value includes the indirect trend value corresponding to the indirect influence coefficient or the actual trend value corresponding to the foam density data;

[0065] In one embodiment of the present invention, the calculation method of the trend value calculation model includes:

[0066] The input data include indirect influence coefficient or foam density data; the extreme value of the input data at all moments within the neighborhood range of each moment is obtained;

[0067] According to the input data at each moment, the difference between the input data at each moment and other moments in the corresponding neighborhood, and the extreme value, the output trend value at each moment is obtained. The input data and the difference between the input data are positively correlated with the output trend value, and the extreme value is negatively correlated with the output trend value.

[0068] Among them, the positive correlation relationship indicates that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases, that is, the larger the input data at each moment, the greater the difference between the input data at each moment and other moments in the corresponding neighborhood range, the greater the input data at each moment is than the input data at other moments in the neighborhood range, the more the overall upward trend is, and the larger the overall output trend value is; the negative correlation relationship indicates that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases, that is, the larger the range of the extreme value, the larger the fluctuation range of the input data in the neighborhood range, which makes the output trend value corresponding to the input data at each moment relatively smaller.

[0069] In one embodiment of the present invention, the calculation formula of the trend value calculation model is expressed as:

[0070] ;

[0071] in, Indicates Output trend value at each moment; Indicates Input data at the moment; Indicates The neighborhood of the moment Input data at the moment; Indicates The maximum value of the input data corresponding to the neighborhood at the time; Indicates The minimum value of the input data corresponding to the neighborhood at the time; Indicates The number of moments in the neighborhood of a moment; Represents the adjustment parameter and takes 0.01.

[0072] In the calculation formula of the trend value calculation model, Indicates the calculation The difference between the maximum and minimum values ​​of the input data in the neighborhood range at the time, that is, the extreme value; In order to avoid the denominator of the formula being 0, the adjustment parameter is set to 0.01; Indicates the calculation The input data at time The ratio between the range values ​​of the corresponding input data in the neighborhood range of the moment. The larger the ratio, the The larger the input data at a moment, the smaller the range value, that is, the input data at the corresponding moment changes more relative to the overall input data, and the larger the output trend value; Indicates the calculation The accumulated value of the difference between the input data at that moment and all other moments in the corresponding neighborhood. The larger the accumulated value of the difference, the higher the value of the first moment. The more the input data at a certain moment tends to be larger than the input data at other moments, The input data at the moment shows an overall upward trend, the larger the output trend value; on the contrary, the smaller the difference accumulation value, the The more the input data at a certain moment tends to be smaller than the input data at other moments, The input data at the moment shows an overall downward trend, and the output trend value is smaller.

[0073] It should be noted that, in other embodiments of the present invention, accomplish The bigger, The larger the value, the larger the output trend value. The correlation can also be achieved by adding the values ​​together. The specific means are well known to those skilled in the art and will not be elaborated here.

[0074] It should be noted that, in one embodiment of the present invention, the domain range of each moment is based on each moment and is composed of all historical moments; in other embodiments of the present invention, the size of the neighborhood range can be set according to specific circumstances, and is not limited or elaborated here.

[0075] Based on this, by inputting the indirect influence coefficient or foam density data of all moments within the neighborhood of each moment into the trend value calculation model, the indirect trend value corresponding to the indirect influence coefficient or the actual trend value corresponding to the foam density data is obtained, reflecting the changing trend of the indirect influence of temperature on the foam density data at each moment and the changing trend of the actual foam density data, which is helpful for analyzing the subsequent direct influence of temperature on the foam density data.

[0076] For the real-time foam density data during the foaming process, temperature fluctuations affect the stability of foam density by direct and indirect superposition. By analyzing the difference changing trend between the actual trend value and the indirect trend value at each moment, the indirect effect of temperature on foam density is eliminated, and the result of the direct effect of temperature fluctuations is obtained more accurately; if the changes in foam density data are all indirectly caused by temperature fluctuations, then the indirect trend value of the indirect influence coefficient corresponding to each moment should be more similar to the actual trend value of the foam density data, that is, the smaller the difference changing trend, the smaller the direct influence; the larger the difference changing trend, the greater the difference between the indirect trend value and the actual trend value, the greater the direct influence in a positive or negative direction, and the larger or smaller the direct influence coefficient; therefore, according to the difference changing trend between the actual trend value and the indirect trend value at each moment within the neighborhood range of the real-time moment, the direct influence coefficient of temperature on the foam density data at the real-time moment is obtained.

[0077] Preferably, in one embodiment of the present invention, the method for obtaining the direct influence coefficient can be found in Figure 3, which shows a flow chart of a method for obtaining a direct influence coefficient provided by an embodiment of the present invention, the method comprising:

[0078] Step S301: Obtain the first difference between the actual trend value and the indirect trend value at each moment within the neighborhood range of the real time moment.

[0079] The first difference is the difference between the actual trend value and the indirect trend value. It reflects the difference between the change in the indirect effect of temperature on foam density and the actual change in foam density at a specific moment, and helps us understand the degree of direct influence of temperature on foam density data. The size of the difference reflects the intensity of the direct influence. The larger the difference, the smaller the indirect trend value is smaller than the actual trend value, and the more positive the direct influence is. On the contrary, the smaller the difference, the larger the indirect trend value is larger than the actual trend value, and the more negative the direct influence is. The more the indirect influence trend is approximately consistent with the actual trend of foam density, the less influence the direct influence trend will have.

[0080] The scope of the real-time moment is the scope consisting of the real-time moment and all historical moments.

[0081] Step S302: Calculate the mean of the first differences at all other moments within the corresponding neighborhood as the average difference level.

[0082] In order to comprehensively reflect the overall situation of the trend difference at other times within the neighborhood, the first difference of all other times within the neighborhood is quantified by averaging to obtain the average difference level, which provides a benchmark level for evaluating the direct impact coefficient of the real-time moment.

[0083] Step S303: Obtain the direct influence coefficient of temperature on foam density data at the real time according to the deviation of the first difference at the real time relative to the average difference level.

[0084] Among them, the larger the first difference at the real time moment, the larger the difference between the actual trend value and the indirect trend value, the larger the trend generated by the direct influence, and the larger the direct influence coefficient; the larger the average difference level, the larger the difference between the actual trend value and the indirect trend value at other moments in the neighborhood, indicating that there is a more positive direct influence, and the direct influence coefficient at the current moment is relatively smaller; the smaller the average difference level, the smaller the difference between the actual trend value and the indirect trend value at other moments in the neighborhood, indicating that there is a more negative direct influence, and the direct influence coefficient at the current moment is relatively larger.

[0085] In one embodiment of the present invention, the formula of the direct influence coefficient is expressed as:

[0086] ;

[0087] in, Indicates real time The direct influence coefficient of temperature on foam density data; Indicates real time The actual trend value of Indicates real time The interval trend value of Indicates real time The number of moments in the neighborhood of ; Indicates real time In the neighborhood of The actual trend value at the moment; Indicates real time In the neighborhood of Indirect trend value at the moment; represents a non-linear activation function.

[0088] In the formula of direct impact coefficient, The function will Mapped to the range [-1,1], Indicates real time The difference between the actual trend value and the interval trend value, that is, the first difference at the real time, the larger the first difference is greater than 0, the smaller the indirect trend value is, the smaller the indirect influence is, the smaller the actual change is, the greater the direct influence trend is, and the more positive the direct influence is; on the contrary, the smaller the first difference is, the larger the indirect trend value is, the greater the actual trend value is, the greater the indirect influence is, the greater the actual change is, the smaller the direct influence trend is, and the more positive the direct influence is; the closer the first difference is to 0, the closer the indirect trend value is to the actual trend value, and the less direct influence is; It means calculating the mean of the first differences of all other moments within the corresponding neighborhood, that is, the average difference level. The larger the average difference level, the smaller the direct influence coefficient of the current moment is relatively; the smaller the average difference level, the larger the direct influence coefficient of the current moment is relatively; therefore, the larger the first difference of the real-time moment is than the average difference level, the larger the direct influence coefficient of the real-time moment is relatively.

[0089] Step S3: obtaining the feedback adjustment factor at the next moment of the real time according to the indirect influence coefficient and the direct influence coefficient at the real time; adjusting the stirring rate data at the real time according to the feedback adjustment factor at the next moment to obtain the adjusted stirring rate data at the next moment.

[0090] From the perspective of the indirect effect of temperature, since the increase in temperature causes the viscosity of the material to decrease, which in turn causes the foam density to decrease, the smaller the negative number of the indirect effect coefficient is, the greater the degree of negative effect, and the more it is necessary to lower the temperature to increase the viscosity of the material, increase the foam density, and approach a stable state; on the contrary, since the decrease in temperature causes the viscosity of the material to increase, which in turn causes the foam density to increase, the larger the positive number of the indirect effect coefficient is, the greater the degree of positive effect, and the more it is necessary to increase the temperature to reduce the viscosity of the material, reduce the foam density, and approach a stable state; from the perspective of the direct effect of temperature, the increase in temperature can promote the expansion of the material, resulting in a lower density foam product, so the smaller the negative number of the direct effect coefficient is, the greater the degree of negative effect, and the more it is necessary to lower the temperature to increase the foam density; on the contrary, the decrease in temperature can promote the expansion of the material, resulting in a higher density foam product, the larger the positive number of the direct effect coefficient is, the greater the degree of positive effect, and the more it is necessary to increase the temperature to reduce the foam density. Therefore, according to the indirect effect coefficient and the direct effect coefficient at the real time, the feedback adjustment factor at the next moment of the real time is obtained.

[0091] Preferably, in one embodiment of the present invention, the indirect influence coefficient and the direct influence coefficient are positively correlated with the feedback adjustment factor.

[0092] Among them, the positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases, that is, the larger the indirect influence coefficient, the larger the direct influence coefficient, the greater the positive impact of the foam density data, the more it is necessary to increase the temperature to reduce the foam density, and the more the feedback adjustment factor needs to be increased.

[0093] In one embodiment of the present invention, the formula of the feedback adjustment factor is expressed as:

[0094] ;

[0095] in, Indicates the next moment of the real time Feedback regulators; Indicates real time The indirect impact coefficient of Indicates real time The direct impact coefficient of represents a non-linear activation function.

[0096] In the formula of the feedback adjustment factor, The function will Mapped to the range of [-1,1], the larger the direct influence coefficient, the larger the indirect influence coefficient, the greater the positive influence on the foam density data, and the larger the feedback adjustment factor; the smaller the direct influence coefficient, the smaller the indirect influence coefficient, the greater the negative influence on the foam density data, and the smaller the feedback adjustment factor.

[0097] When the mixer blades are in contact with the material during high-speed rotation, friction will be generated to convert mechanical energy into heat energy, causing the material temperature to rise. That is, the higher the stirring rate of the mixer, the more obvious the temperature rise; the slower the stirring rate, the faster the heat dissipation rate, and the temperature is relatively lower. Therefore, by analyzing the feedback adjustment factor to adjust the stirring rate of the mixer in real time, the purpose of regulating the material temperature is achieved. The stirring rate data at the real time is adjusted according to the feedback adjustment factor at the next moment to obtain the adjusted stirring rate data at the next moment.

[0098] Preferably, in one embodiment of the present invention, the method for obtaining the stirring rate data includes:

[0099] The sum of the preset constant and the feedback adjustment factor at the next moment is calculated as the adjustment weight; the stirring rate data at the real time moment is weighted according to the adjustment weight to obtain the adjusted stirring rate data at the next moment.

[0100] In one embodiment of the present invention, the formula for adjusting the stirring rate data is expressed as:

[0101] ;

[0102] in, Indicates the next moment of the real time Adjust the stirring rate data; Indicates real time Stirring rate data; Indicates the next moment of the real time Feedback regulators; Indicates a preset constant.

[0103] In the formula for adjusting the stirring rate data, It means calculating the sum of the preset constant and the feedback adjustment factor of the next moment, that is, the adjustment weight, the next moment of the real time The larger the feedback adjustment factor, the larger the direct influence coefficient and the indirect influence coefficient, the larger the adjustment weight, and the greater the real-time The greater the stirring speed data adjustment, the greater the stirring speed data adjustment, which increases the temperature and reduces the foam density data; on the contrary, the next moment of the real time The smaller the feedback adjustment factor, the smaller the direct influence coefficient and the indirect influence coefficient, the smaller the adjustment weight, and the smaller the real-time The smaller the stirring rate data is adjusted, the smaller the stirring rate data is adjusted, so that the temperature is reduced and the foam density data is increased.

[0104] It should be noted that, in one embodiment of the present invention, the absolute value range of the feedback adjustment factor is between 0 and 1, and the preset constant is a positive integer 1.

[0105] Step S4: providing operation feedback to the mixer according to the stirring rate data adjusted at the next moment.

[0106] After obtaining the data for adjusting the stirring rate, the relevant control instructions can be sent to the stirring controller of the mixer through the PLC system, so as to realize the real-time feedback control of the stirring rate, control the mixing effect between materials, ensure the real-time stability of the foam density during the foaming process, and ensure the quality of the foamed product. The operation feedback of the mixer is carried out according to the stirring rate data adjusted at the next moment.

[0107] In summary, the present invention obtains the indirect influence coefficient of temperature on foam density data at each moment according to the correlation between stirring rate data and material viscosity data at each moment; performs trend analysis on the data within the neighborhood range at each moment to obtain the indirect trend value corresponding to the indirect influence coefficient and the actual trend value corresponding to the foam density data; obtains the direct influence coefficient of temperature on foam density data at the real time moment according to the difference change trend between the actual trend value and the indirect trend value at each moment within the neighborhood range of the real time moment; obtains the feedback adjustment factor at the next moment of the real time moment according to the indirect influence coefficient and the direct influence coefficient at the real time moment; adjusts the stirring rate data at the real time moment to obtain the adjusted stirring rate data at the next moment; and performs operation feedback on the mixer. The present invention balances the fluctuation of reaction temperature and improves foaming stability by obtaining accurate stirring rate data at the next moment in the mixer.

[0108] The present invention also proposes an automatic foaming machine production control system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any one of the steps of an automatic foaming machine production control operation feedback method.

[0109] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0110] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for production control and operation feedback of an automatic foaming machine, characterized in that: The method comprises: Obtain stirring rate data, material viscosity data and foam density data of the automatic foaming machine mixer at each moment; According to the correlation between the stirring rate data and the material viscosity data at each moment, the indirect influence coefficient of the temperature on the foam density data at each moment is obtained; the data within the neighborhood range at each moment are trend analyzed to obtain the indirect trend value corresponding to the indirect influence coefficient and the actual trend value corresponding to the foam density data; according to the difference change trend between the actual trend value and the indirect trend value at each moment within the neighborhood range of the real time moment, the direct influence coefficient of the temperature on the foam density data at the real time moment is obtained; According to the indirect influence coefficient and the direct influence coefficient at the real time, a feedback adjustment factor at the next moment of the real time is obtained; according to the feedback adjustment factor at the next moment, the stirring rate data at the real time is adjusted to obtain the adjusted stirring rate data at the next moment; Provide operation feedback to the mixer according to the adjusted stirring rate data at the next moment; The method for obtaining the indirect influence coefficient includes: For the stirring rate data and the material viscosity data, the indirect influence coefficient of the temperature on the foam density data at each moment is obtained according to the difference between the preset standard correlation and the actual correlation at each moment; The method for obtaining the preset standard correlation and the actual correlation includes: Obtaining a ratio between standard stirring rate data and corresponding standard material viscosity data as a preset standard correlation; The ratio between the stirring rate data and the material viscosity data at each moment is obtained as the actual correlation at each moment; The trend analysis of the data within the neighborhood at each moment includes: Constructing a trend value calculation model, inputting input data of all moments within the neighborhood range of each moment into the trend value calculation model, and obtaining an output trend value at each moment, wherein the output trend value includes an indirect trend value corresponding to an indirect influence coefficient or an actual trend value corresponding to foam density data; The calculation method of the trend value calculation model includes: The input data includes the indirect influence coefficient or the foam density data; Obtain the range values ​​of the input data at all moments within the neighborhood of each moment; According to the input data at each moment, the difference between the input data at each moment and other moments within the corresponding neighborhood, and the extreme value, the output trend value at each moment is obtained, wherein the input data and the difference between the input data are both positively correlated with the output trend value, and the extreme value is negatively correlated with the output trend value; The method for obtaining the direct influence coefficient includes: Obtaining a first difference between the actual trend value and the indirect trend value at each moment within a neighborhood of the real time moment; Calculate the mean of the first differences at all other moments in the corresponding neighborhood as the average difference level; According to the deviation of the first difference value at the real time moment relative to the average difference value level, obtaining a direct influence coefficient of temperature on foam density data at the real time moment; The indirect influence coefficient and the direct influence coefficient are positively correlated with the feedback adjustment factor.

2. The automatic foaming machine production control operation feedback method according to claim 1, characterized in that: The method for obtaining the data of adjusting the stirring rate comprises: Calculating the sum of a preset constant and the feedback adjustment factor at the next moment as an adjustment weight; The stirring rate data at the real time is weighted according to the adjustment weight to obtain the adjusted stirring rate data at the next moment.

3. The automatic foaming machine production control operation feedback method according to claim 1, characterized in that: The method for obtaining the neighborhood range at the real time includes: The real time is taken as the benchmark, and the range formed by all historical moments is taken as the neighborhood range of the real time.

4. The automatic foaming machine production control operation feedback method according to claim 2, characterized in that: The absolute value range of the feedback adjustment factor is between 0 and 1, and the preset constant is a positive integer 1.

5. An automatic foaming machine production control system, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the automatic foaming machine production control operation feedback method as described in any one of claims 1 to 4 are implemented.

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