A method and system for controlling constant temperature of a bicycle saddle foaming mold

Abstract

The present invention relates to the field of temperature control technology, and in particular to a method and system for controlling the constant temperature of a bicycle saddle foaming mold. The method comprises: obtaining temperature data of a bicycle saddle foaming mold during use; determining the degree of fluctuation of the temperature data; determining the classification index of the temperature data in combination with other temperature data that are adjacent in time sequence; constructing a decision tree for the temperature data according to the classification index; determining the degree of diffusion of different nodes in the decision tree; constructing a PID control adjustment function according to the numerical distribution of the degree of diffusion of all temperature data; determining a proportional adjustment coefficient according to the PID control adjustment function, and determining a PID control output according to the proportional adjustment coefficient to achieve control and regulation of the temperature at the current moment. The present application avoids the temperature response delay caused by thermal inertia, which in turn leads to adjustment hysteresis, and at the same time, optimizes the constant temperature control performance and stability of the foaming mold.

Description

Technical Field

[0001] The invention relates to the technical field of temperature control, and in particular to a constant temperature control method and system for a bicycle saddle foaming mould. Background Art

[0002] Bicycle saddles are usually made of foam materials (such as polyurethane foam). The temperature of the foam material needs to be controlled during the foaming process to ensure the quality and performance of the material. PLC control is a common industrial temperature control method that achieves stable control of the temperature in the mold by adjusting the power of the heating element (such as an electric heating rod or heating plate).

[0003] In the prior art, a temperature acquisition device is arranged on the outside of the bicycle saddle foaming mold to achieve a temperature control effect based on a set control coefficient. However, the thermal inertia of the mold may cause a delay in the temperature response, which will affect the real-time adjustment capability of the PLC controller, resulting in a lag in the adjustment and poor constant temperature control performance and stability of the foaming mold. Summary of the invention

[0004] In order to solve the technical problem in the related art that the thermal inertia of the mold may cause a delay in temperature response, which will affect the real-time adjustment capability of the PLC controller and cause hysteresis in the adjustment, the present invention provides a constant temperature control method and system for a bicycle saddle foaming mold, and the technical scheme adopted is as follows:

[0005] The present invention provides a method for controlling the constant temperature of a bicycle saddle foaming mold, the method comprising:

[0006] Periodically obtain temperature data of bicycle saddle foaming molds during use;

[0007] Determine the degree of fluctuation of the temperature data according to the numerical distribution of all temperature data; determine the classification index of each temperature data according to the temperature changes of a preset number of other temperature data closest to the time series and the degree of fluctuation;

[0008] Constructing a decision tree for temperature data according to the classification index; determining the diffusion degree of different nodes in the decision tree according to the number and value of temperature data in different nodes at different levels in the decision tree; constructing a PID control adjustment function according to the numerical distribution of the diffusion degree of all temperature data;

[0009] The proportional adjustment coefficient is determined according to the PID control adjustment function, and the PID control output is determined according to the proportional adjustment coefficient to achieve control and adjustment of the temperature at the current moment.

[0010] Furthermore, the method for obtaining the degree of fluctuation of the temperature data includes:

[0011] Determine the difference between the maximum value and the minimum value of all the temperature data as the extreme value difference;

[0012] Determine the variance of all the temperature data as the degree of dispersion;

[0013] The extreme value difference and the discrete degree are forwardly fused to obtain the change fluctuation degree of the temperature data, wherein the value of the change fluctuation degree is a normalized value.

[0014] Furthermore, the method for obtaining the classification index of the temperature data includes:

[0015] Taking any moment as the moment to be measured, determining the ratio of the temperature data at the moment to be measured to the degree of fluctuation as the fluctuation contribution at the moment to be measured;

[0016] The temperature data of a preset number of other moments closest to the moment to be measured are used as neighborhood data;

[0017] Determine the difference between the temperature data at the time to be measured and the neighborhood data as the neighborhood difference; and take the mean of all neighborhood differences as the neighborhood fluctuation coefficient;

[0018] The fluctuation contribution and the neighborhood fluctuation coefficient are forwardly integrated to determine a classification index for the temperature data.

[0019] Furthermore, a decision tree for temperature data is constructed according to the classification index, including:

[0020] Sort the classification index of each temperature data in descending order to obtain a classification sequence;

[0021] Hierarchical clustering is performed on the classification sequence, and the number of clustering layers is set to 2 to obtain multiple clusters. The number of clusters is used as the number of layers of the decision tree to construct a decision tree for temperature data.

[0022] Furthermore, the method for obtaining the diffusion degree of the node includes:

[0023] A node of any layer in the decision tree is used as a main node, the main node is divided into two branch nodes, any branch node is used as a node to be tested, and the other branch node is used as a comparison node;

[0024] Determine the data change index of the node to be measured according to the quantity and average value of all temperature data in the node to be measured and the average value of all temperature data in the layer where the node to be measured is located;

[0025] Determining the temperature divergence coefficient of the master node according to the difference in the quantity of temperature data between the node to be tested and the comparison node;

[0026] The temperature change index and the temperature divergence coefficient are forwardly integrated to determine the diffusion degree of the node to be measured.

[0027] Furthermore, the method for acquiring the data change index of the node to be tested includes:

[0028] Taking the mean of all temperature data in the node to be tested as the node mean;

[0029] The mean of all temperature data in the layer where the node to be tested is located is taken as the layer mean;

[0030] Calculate the difference between the node mean and the level mean to obtain the temperature distribution difference;

[0031] The data change index of the node to be measured is determined according to the temperature distribution difference and the number of all temperature data in the node to be measured, wherein the temperature distribution difference is positively correlated with the data change index, and the number of all temperature data in the node to be measured is negatively correlated with the data change index.

[0032] Furthermore, the method for obtaining the temperature divergence coefficient of the master node includes:

[0033] The absolute value of the difference between the quantity of temperature data of the node to be tested and the comparison node is used as a quantity distribution difference indicator;

[0034] The ratio of the quantity distribution difference index to the quantity of all temperature data in the master node is used as the temperature divergence coefficient of the master node.

[0035] Furthermore, the construction of the PID control adjustment function includes:

[0036] Based on the variance and mean of the diffusion degree of all node temperature data, a PID control adjustment function is constructed.

[0037] Further, determining the proportional adjustment coefficient according to the PID control adjustment function includes:

[0038] The proportional adjustment coefficient is determined based on the Gaussian distribution principle.

[0039] On the other hand, the present invention also provides a constant temperature control system for a bicycle saddle foaming mold, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of a constant temperature control method for a bicycle saddle foaming mold as described in any one of the preceding items when executing the computer program.

[0040] The present invention has the following beneficial effects:

[0041] The embodiment of the present invention analyzes the abnormal situation of each temperature data itself by analyzing the degree of change and fluctuation of temperature data, combining the degree of change and fluctuation and the numerical difference of other temperature data in the local range of the time series, and obtains the classification index of each temperature data; it can be understood that since the degree of change and fluctuation and the difference of temperature data in the local range can both characterize the numerical fluctuation characteristics of the temperature data itself, when making adjustments, it is necessary to adjust the proportional adjustment in combination with the numerical fluctuation characteristics; then, in the embodiment of the present invention, by constructing a decision tree, the diffusion degree of different nodes in the decision tree is analyzed, so as to realize the construction of the PID control adjustment function according to the diffusion degree, and a more accurate and adaptive PID control adjustment function can be constructed, and then the proportional adjustment coefficient is determined. The decision analysis is performed through the construction of the decision tree, and the adaptive proportional adjustment coefficient characterizes the objective temperature change situation, and then the temperature change situation that best meets the subsequent mold interior is determined, so as to avoid the influence of the temperature response delay caused by thermal inertia and reduce the lag effect of the adjustment; a more timely and accurate PID control output is obtained, and the control and adjustment of the temperature at the current moment is realized. In the embodiment of the present invention, by analyzing the change and distribution characteristics of temperature data, effective analysis of subsequent adjustment is performed in combination with PID control, thereby avoiding temperature response delay caused by thermal inertia, which in turn leads to adjustment lag, and at the same time, optimizing the constant temperature control performance and stability of the foaming mold. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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.

[0043] Figure 1 A flow chart of a method for controlling the constant temperature of a bicycle saddle foaming mold provided by one embodiment of the present invention;

[0044] Figure 2 A schematic flow chart of a method for obtaining the diffusion degree of a node provided by an embodiment of the present invention;

[0045] Figure 3 A structural diagram of a constant temperature control system for a bicycle saddle foaming mold provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0046] 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 a method and system for controlling the constant temperature of a foaming mold for a bicycle saddle according to the present invention, its specific implementation, 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.

[0047] 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.

[0048] The specific scheme of the constant temperature control method of a bicycle saddle foaming mold provided by the present invention is described in detail below with reference to the accompanying drawings.

[0049] See also Figure 1 , which shows a flow chart of a method for controlling the constant temperature of a bicycle saddle foaming mold provided by an embodiment of the present invention, the method comprising:

[0050] S101: Periodically obtain temperature data of a bicycle saddle foaming mold during use.

[0051] One implementation scenario of the present invention is the bicycle saddle foam mold injection molding scenario. Bicycle saddles are usually made of foam materials (such as polyurethane foam). The foaming process of foam materials is more affected by temperature than that of plastic materials. Slight temperature changes can affect the foaming situation, resulting in the produced bicycle saddle being soft or hard and having unstable quality. Therefore, a stable temperature control method is urgently needed to adjust the heating or cooling process in time.

[0052] In an embodiment of the present invention, a temperature sensor can be configured on the inner side of the bicycle saddle foaming mold. Compared with the temperature detection located in the scene in the prior art, the internal temperature sensor can more reliably and timely feedback the temperature changes of the foaming mold itself. The temperature sensor is used to collect temperature data. The collection frequency of the temperature data is 10HZ, that is, the temperature data is collected every 0.1s. Of course, the collection period of the temperature data can also be adjusted according to actual production needs, and there is no restriction on this.

[0053] It should be noted that when the temperature in the bicycle saddle foaming mold is controlled by the PLC control system, real-time feedback control is required according to the temperature in the foaming mold. When the temperature is abnormal, timely adjustment is required to ensure that the temperature of the foaming mold is within the normal temperature range and no large temperature changes occur. In the related art, the temperature data is directly analyzed to realize the PLC control and adjustment process. In this way, due to the rapid change of temperature, the numerical reliability of the proportional adjustment coefficient will be poor during PLC control, which will lead to certain oscillation and delay effects.

[0054] It is understandable that in PLC systems, PID control is usually part of the PLC control function, especially suitable for occasions that require fast response and precise control. The flexibility of PLC allows it to flexibly configure and adjust the PID controller in different application scenarios to meet specific control requirements. When adjusting temperature control through the PID system, it is necessary to determine the appropriate proportional adjustment coefficient. The proportional coefficient determines the linear relationship between the controller output and the error. The proportional control responds to the size of the error. The larger the error, the greater the correction amount of the controller output. If the proportional coefficient is set too high, the system may become too sensitive and cause oscillation; if it is set too low, the system may react slowly and fail to reach the set point quickly. Therefore, in order to reduce the delay effect, it is necessary to reasonably adjust the proportional adjustment coefficient of the PID system in the PLC control to avoid being too large or too small, thereby improving the stability and control efficiency of temperature control.

[0055] S102: Determine the degree of fluctuation of the temperature data according to the numerical distribution of all temperature data; determine the classification index of each temperature data according to the temperature changes of a preset number of other temperature data closest to the time series, and the degree of fluctuation.

[0056] Among them, the degree of fluctuation is the temperature change of the bicycle saddle foam mold during the injection molding process. It can be understood that the process may be directly raised from room temperature to the temperature required for injection molding, or it may be fluctuating up and down around the injection molding temperature. Both situations are different production processes in normal production. Using the same control system will result in only one of the situations being applicable, and the other temperature control situation cannot be adaptively adjusted. Therefore, the embodiment of the present invention analyzes the fluctuation of the overall temperature for the numerical distribution of temperature data in the overall production process, thereby eliminating this influence.

[0057] Furthermore, in some embodiments of the present invention, a method for obtaining the degree of change fluctuation of temperature data includes: determining the difference between the maximum value and the minimum value of all temperature data as the extreme value difference; determining the variance of all temperature data as the degree of discreteness; forwardly fusing the extreme value difference and the degree of discreteness to obtain the degree of change fluctuation of the temperature data, wherein the value of the degree of change fluctuation is a normalized value.

[0058] The extreme value difference represents the extreme value analysis in the temperature data. The larger the extreme value difference is, the larger the overall variation range of the temperature data is, thereby realizing the overall analysis process for different mold injection molding processes.

[0059] It should be noted that the greater the change in the extreme value difference and the more discrete the numerical distribution of the temperature data, the greater the fluctuation in the corresponding temperature data. The degree of fluctuation is an indicator of the corresponding fluctuation. Therefore, the embodiment of the present invention can positively integrate the extreme value difference and the degree of discreteness to obtain the degree of fluctuation in the temperature data.

[0060] Among them, forward fusion is the numerical fusion of the positive correlation relationship that increases with the increase of the fused data and decreases with the decrease of the fused data. It should be noted that this forward fusion only considers the numerical characteristics and eliminates the dimensional influence caused by the data unit. The specific means of removing the dimensional influence are technical means well known to those skilled in the art and are not limited here.

[0061] Therefore, the present invention can calculate the product of the extreme value difference and the degree of dispersion, and normalize the product value by using the maximum and minimum value normalization method to obtain the degree of change fluctuation. Alternatively, in other embodiments, the sum of the extreme value difference and the degree of dispersion can also be calculated, and the sum can be normalized to obtain the degree of change fluctuation, and the specific analysis of forward fusion is not limited.

[0062] It should be noted that, since the temperature fluctuations may vary due to different working conditions, it is necessary to perform a temperature distribution category analysis on all temperature data, and this category analysis needs to be combined with the waveform of the temperature data itself at any moment in the time series. Among them, the classification index is the index information corresponding to the category analysis. In the embodiment of the present invention, the classification index of each temperature data can be determined by combining other temperature data in the time series neighborhood corresponding to the temperature data and the degree of fluctuation.

[0063] Furthermore, in some embodiments of the present invention, a method for obtaining classification indicators of temperature data includes: taking any moment as a moment to be measured, determining the ratio of the temperature data at the moment to be measured to the degree of fluctuation of the change as the fluctuation contribution of the moment to be measured; taking the temperature data of a preset number of other moments closest to the moment to be measured as neighborhood data; determining the difference between the temperature data at the moment to be measured and the neighborhood data as the neighborhood difference; taking the average of all neighborhood differences as the neighborhood fluctuation coefficient; and forwardly fusing the fluctuation contribution and the neighborhood fluctuation coefficient to determine the classification indicator of the temperature data.

[0064] Among them, the preset number can be specifically 5, for example, that is, the temperature data of the 5 other moments closest to the moment to be measured is used as the neighborhood data. When the moment to be measured is the current moment, it is the 5 moments before the current moment. Of course, the preset number can also be adjusted according to the actual detection situation, and there is no restriction on this.

[0065] Among them, the variation fluctuation degree represents the overall variation fluctuation situation, and each temperature data has a different contribution degree to it. It should be noted that the higher the temperature of the bicycle saddle foam mold during the injection molding process, the greater the impact. Therefore, the ratio of the temperature data at the time to be measured to the variation fluctuation degree is used as the fluctuation contribution at the time to be measured. That is, the higher the temperature, the smaller the overall variation fluctuation degree, and the corresponding high temperature has a greater impact on the overall variation fluctuation, and thus, its fluctuation contribution is higher.

[0066] Among them, the difference between the temperature data at the time to be measured and the neighborhood data is determined as the neighborhood difference; the average of all neighborhood differences is taken as the neighborhood fluctuation coefficient. The greater the neighborhood difference between the temperature data at the time to be measured and the neighborhood data, the higher the overall neighborhood fluctuation coefficient, that is, the higher the temperature mutation shown at the time to be measured.

[0067] Thus, the fluctuation contribution and the neighborhood fluctuation coefficient are forwardly integrated to determine the classification index of the temperature data. This forward integration is the same as the forward integration mentioned above, and the product of the combined fluctuation contribution and the neighborhood fluctuation coefficient can be calculated as the classification index of the temperature data. It is then possible to effectively perform classification analysis based on the classification index of the temperature data, and the specific analysis process can be referred to in the subsequent embodiments.

[0068] S103: construct a decision tree for temperature data according to the classification index; determine the diffusion degree of different nodes in the decision tree according to the number and value of temperature data in different nodes at different levels in the decision tree; and construct a PID control adjustment function according to the numerical distribution of the diffusion degree of all temperature data.

[0069] Among them, the decision tree is a binary tree, that is, the binary tree is constructed through classification indicators, so as to realize the analysis process of the overall temperature data change based on the branch characteristics of the binary tree.

[0070] Furthermore, in some embodiments of the present invention, a decision tree for temperature data is constructed based on classification indicators, including: sorting the classification indicators of each temperature data in descending order to obtain a classification sequence; performing hierarchical clustering on the classification sequence, setting the number of clustering layers to 2, obtaining multiple clustering clusters, and using the number of clustering clusters as the number of layers of the decision tree to construct a decision tree for temperature data.

[0071] Among them, the setting of the number of clustering layers corresponding to the hierarchical clustering can be adjusted according to the actual detection needs. The embodiment of the present invention only analyzes with the number of clustering layers being 2, thereby obtaining multiple clustering clusters after 2 layers of clustering. It can be understood that the clustering cluster is the data cluster obtained by effectively classifying and analyzing the corresponding temperature, that is, the number of clustering clusters can further characterize the classification of the corresponding temperature data. Therefore, in the embodiment of the present invention, the number of clustering clusters is used as the number of layers of the decision tree to construct a decision tree for temperature data. If the decision tree is a binary tree, the construction process of the corresponding decision tree is a technology well known to those skilled in the art, and no further limitation or elaboration is made on this.

[0072] In the embodiment of the present invention, after obtaining the decision tree, it is necessary to perform category analysis based on the distribution of temperature data in the decision tree, that is, different branches in the decision tree represent different changes in temperature data. When performing a specific analysis on the changes in temperature data, it is necessary to take into account the data anomalies of the temperature data itself. The data anomalies in the embodiment of the present invention are specifically analyzed based on the diffusion degree of the nodes, that is, the smaller the node diffusion degree, the more stable the temperature data changes, that is, the smaller the possibility of data anomalies, and the larger the node diffusion degree, the more drastic the temperature data changes, that is, the greater the possibility of data anomalies.

[0073] Further, in some embodiments of the present invention, see Figure 2 , Figure 2 A schematic flow chart of a method for obtaining the diffusion degree of a node provided by an embodiment of the present invention includes:

[0074] S201: A node in any layer of the decision tree is used as a main node, and the main node is divided into two branch nodes. Any branch node is used as a node to be tested, and the other branch node is used as a comparison node.

[0075] It should be noted that the main node needs to be divided into two branch nodes, that is, it is not the topmost node in the decision tree. According to the division of the main node, any one of the branch nodes is used as the node to be tested, and the other branch node is used as the comparison node.

[0076] S202: Determine a data change index of the node to be tested according to the quantity and average value of all temperature data in the node to be tested and the average value of all temperature data in the layer where the node to be tested is located.

[0077] Among them, the temperature data in the node to be measured has similar data characteristics, which is obviously different from the comparison node. Therefore, it is necessary to conduct a specific analysis on the data characteristics of the node to be measured itself.

[0078] Furthermore, in some embodiments of the present invention, a method for obtaining a data change index of a node to be measured includes: taking the mean of all temperature data in the node to be measured as the node mean; taking the mean of all temperature data in the layer where the node to be measured is located as the layer mean; calculating the difference between the node mean and the layer mean to obtain a temperature distribution difference; determining the data change index of the node to be measured based on the temperature distribution difference and the number of all temperature data in the node to be measured, wherein the temperature distribution difference is positively correlated with the data change index, and the number of all temperature data in the node to be measured is negatively correlated with the data change index.

[0079] Among them, the node mean is the numerical mean of all temperature data in the node to be tested, and the level mean is the mean of all temperature data in the level where the node to be tested is located. This mean includes not only the node to be tested and the comparison node, but also the nodes in the same level corresponding to other branches of the decision tree, so as to facilitate the overall analysis.

[0080] Therefore, the embodiment of the present invention calculates the difference between the node mean and the level mean to obtain the temperature distribution difference, which can specifically represent the absolute value of the difference, that is, the absolute value of the difference between the node mean and the level mean is calculated to obtain the temperature distribution difference. The corresponding temperature distribution difference characterizes the difference between the temperature distribution of the node to be tested and other nodes in the same layer.

[0081] It should be noted that a positive correlation indicates that there is a same-direction change relationship between the independent variable and the dependent variable, that is, the larger the independent variable is, the larger the dependent variable is; a negative correlation indicates that there is an opposite-direction change relationship between the independent variable and the dependent variable, that is, the smaller the independent variable is, the larger the dependent variable is; the specific manifestations of the positive correlation and the negative correlation are determined by actual applications and are not particularly limited in this application.

[0082] Therefore, in the embodiment of the present invention, the ratio of the temperature distribution difference to the number of all temperature data in the node to be measured can be calculated as the data change index. Alternatively, the difference between the temperature distribution difference and the number of all temperature data in the node to be measured can also be calculated to obtain the data change index, which is not limited.

[0083] It should be noted that, in order to facilitate calculation, all indicator data involved in the calculation in the embodiment of the present invention are preprocessed to eliminate the dimension effect. The specific means of eliminating the dimension effect are technical means well known to those skilled in the art and are not limited here.

[0084] That is to say, the smaller the difference between the mean of the node to be tested and the whole, and the more data points there are in the node to be tested, the smaller the difference between its temperature data and the whole, that is, the sensitivity of data change is lower. Therefore, the data change index is obtained by calculation to characterize the diffusion of temperature data in the node to be tested.

[0085] S203: Determine the temperature divergence coefficient of the main node according to the difference in the quantity of temperature data between the node to be tested and the comparison node.

[0086] Among them, it is also necessary to conduct a specific analysis on the divergence of the upper-level node (ie, the main node). The present invention characterizes the divergence of the temperature data of the main node through the temperature divergence coefficient.

[0087] Furthermore, in some embodiments of the present invention, the method for obtaining the temperature divergence coefficient of the main node includes: taking the absolute value of the difference in the number of temperature data between the node to be tested and the comparison node as a quantity distribution difference index; taking the ratio of the quantity distribution difference index to the number of all temperature data in the main node as the temperature divergence coefficient of the main node.

[0088] Among them, the larger the value of the quantity distribution difference index is, the greater the difference in the quantity of temperature data between the node to be tested and the comparison node is. By normalizing it with the quantity of all temperature data in the main node, the temperature divergence coefficient of the main node is obtained. The temperature divergence coefficient can further characterize the data divergence in the branch corresponding to the node to be tested in the binary classification process of the decision tree.

[0089] S204: Forwardly integrate the temperature change index and the temperature divergence coefficient to determine the diffusion degree of the node to be tested.

[0090] Among them, since the data change index can characterize the diffusion of temperature data in the node to be measured, and the temperature divergence coefficient can further characterize the data divergence of the decision tree binary classification process, the diffusion degree of the node to be measured is obtained by forward fusion. For example, the product of the temperature change index and the temperature divergence coefficient can be calculated to obtain the diffusion degree of the node to be measured, or the sum of the temperature change index and the temperature divergence coefficient can be calculated to obtain the diffusion degree of the node to be measured, and there is no restriction on this.

[0091] It should be noted that a PID control adjustment function can be constructed in combination with the numerical distribution of the diffusion degree of all temperature data, thereby performing a specific analysis.

[0092] The construction of the PID control regulation function includes: constructing the PID control regulation function based on the variance and mean of the diffusion degree of all node temperature data.

[0093] The process of obtaining the PID control adjustment function is a process well known to those skilled in the art, and the corresponding PID control adjustment function may be specifically, for example:

[0094]

[0095] In the formula, Represents the PID control regulation function, represents pi, The variance represents the degree of spread of the temperature data, represents the mean value of the spread of temperature data, represents a natural constant, Represents the independent variable in the PID control regulation function.

[0096] It should be noted that the PID control adjustment function is well known to those skilled in the art and will not be described in detail.

[0097] S104: Determine a proportional adjustment coefficient according to a PID control adjustment function, and determine a PID control output according to the proportional adjustment coefficient to achieve control and adjustment of the temperature at the current moment.

[0098] It should be noted that the function value distribution corresponding to the PID control adjustment function can be similar to the state of Gaussian distribution. Therefore, in the embodiment of the present invention, the " " principle to determine the proportional adjustment coefficient.

[0099] Specifically, the corresponding proportional adjustment coefficient acquisition formula includes:

[0100]

[0101] In the formula, represents the proportional adjustment coefficient, Represents the PID control regulation function, The variance represents the degree of spread of the temperature data, represents the mean value of the spread of temperature data, Indicates that the adjustment function is in the interval The integral on Indicates that the adjustment function is in the interval The integral on Indicates that the adjustment function is in the interval The points on .

[0102] In the formula, Indicates that the temperature data is in the range The distribution on , The ratio of the distribution on , The greater the abnormality of the upper temperature data, the smaller the ratio, which means the larger the proportional adjustment coefficient. Indicates that the temperature data is in the range The distribution on , The ratio of the distribution on is also positively correlated with the proportional adjustment coefficient.

[0103] Therefore, the proportional adjustment coefficient is calculated in the embodiment of the present invention, and then the proportional adjustment coefficient is used as the proportional term in the PID control adjustment. Thus, the integral term and the differential term are calculated, and then a complete PID control system is constructed. Based on the PID control system, the control and adjustment of the temperature at the current moment are realized. It should be noted that the specific process of this scheme to specifically analyze the proportional adjustment coefficient, construct a PID control system, and control and adjust the temperature at the current moment is a process well known to those skilled in the art, and will not be elaborated on.

[0104] The embodiment of the present invention analyzes the abnormal situation of each temperature data itself by analyzing the fluctuation degree of the temperature data, combining the fluctuation degree of the temperature data and the numerical difference of other temperature data in the local range of the time series, and obtains the classification index of each temperature data; it can be understood that since the fluctuation degree of the temperature data and the difference of the temperature data in the local range can both characterize the numerical fluctuation characteristics of the temperature data itself, it is necessary to adjust the proportional adjustment in combination with the numerical fluctuation characteristics when adjusting; the embodiment of the present invention constructs a decision tree to analyze the diffusion degree of different nodes in the decision tree, so as to realize the construction of the PID control adjustment function according to the diffusion degree, and can construct a more accurate and adaptive PID control adjustment function, and then determine the proportional adjustment coefficient, and characterize the objective temperature change through the adaptive proportional adjustment coefficient, so as to avoid the influence of the temperature response delay caused by thermal inertia, and reduce the lag effect of the adjustment; obtain a more timely and accurate PID control output, and realize the control and adjustment of the temperature at the current moment. In the embodiment of the present invention, by analyzing the change and distribution characteristics of the temperature data, the effective analysis of the subsequent adjustment combined with the PID control is avoided, the temperature response delay caused by thermal inertia is avoided, and the adjustment hysteresis is caused, and at the same time, the PID control performance and stability are optimized.

[0105] Some other embodiments of the present invention also provide a bicycle saddle foaming mold constant temperature control system, see Figure 3 , Figure 3This is a structural diagram of a bicycle saddle foaming mold constant temperature control system provided by an embodiment of the present invention. The system 600 includes a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and executable on the processor 602. When the processor 602 executes the computer program 603, the steps of the aforementioned bicycle saddle foaming mold constant temperature control method are implemented.

[0106] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is run on a computer, the computer executes the above-mentioned related method steps to implement a constant temperature control method for a bicycle saddle foaming mold provided in the above embodiment.

[0107] This embodiment also provides a computer program product. When the computer program product is run on a computer, the computer is enabled to execute the above-mentioned related steps to implement a constant temperature control method for a bicycle saddle foaming mold provided in the above embodiment.

[0108] Among them, the system, computer-readable storage medium or computer program product provided in this embodiment is used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here.

[0109] It should be noted that the sequence of the above embodiments of the present invention is only for description 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 controlling the constant temperature of a bicycle saddle foaming mold, characterized in that: The method comprises: Periodically obtain temperature data of bicycle saddle foaming molds during use; Determine the degree of fluctuation of the temperature data according to the numerical distribution of all temperature data; determine the classification index of each temperature data according to the temperature changes of a preset number of other temperature data closest to the time series and the degree of fluctuation; Constructing a decision tree for temperature data according to the classification index; determining the diffusion degree of different nodes in the decision tree according to the number and value of temperature data in different nodes at different levels in the decision tree; constructing a PID control adjustment function according to the numerical distribution of the diffusion degree of all temperature data; Determine a proportional adjustment coefficient according to the PID control adjustment function, determine a PID control output according to the proportional adjustment coefficient, and realize control and adjustment of the temperature at the current moment; The method for obtaining the diffusion degree of the node includes: A node of any layer in the decision tree is used as a main node, the main node is divided into two branch nodes, any branch node is used as a node to be tested, and the other branch node is used as a comparison node; Determine the data change index of the node to be measured according to the quantity and average value of all temperature data in the node to be measured and the average value of all temperature data in the layer where the node to be measured is located; Determining the temperature divergence coefficient of the master node according to the difference in the quantity of temperature data between the node to be tested and the comparison node; Forwardly integrating the data change index and the temperature divergence coefficient to determine the diffusion degree of the node to be tested; The method for obtaining the data change index of the node to be tested includes: Taking the mean of all temperature data in the node to be tested as the node mean; The mean of all temperature data in the layer where the node to be tested is located is taken as the layer mean; Calculate the difference between the node mean and the level mean to obtain the temperature distribution difference; Determine a data change index of the node to be measured according to the temperature distribution difference and the number of all temperature data in the node to be measured, wherein the temperature distribution difference is positively correlated with the data change index, and the number of all temperature data in the node to be measured is negatively correlated with the data change index; The method for obtaining the temperature divergence coefficient of the master node includes: The absolute value of the difference between the quantity of temperature data of the node to be tested and the comparison node is used as a quantity distribution difference indicator; The ratio of the quantity distribution difference index to the quantity of all temperature data in the master node is used as the temperature divergence coefficient of the master node.

2. A method for controlling the constant temperature of a bicycle saddle foaming mold according to claim 1, characterized in that: The method for obtaining the degree of fluctuation of the temperature data includes: Determine the difference between the maximum value and the minimum value of all the temperature data as the extreme value difference; Determine the variance of all the temperature data as the degree of dispersion; The extreme value difference and the discrete degree are forwardly fused to obtain the change fluctuation degree of the temperature data, wherein the value of the change fluctuation degree is a normalized value.

3. A method for controlling the constant temperature of a bicycle saddle foaming mold according to claim 1, characterized in that: The method for obtaining the classification index of the temperature data includes: Taking any moment as the moment to be measured, determining the ratio of the temperature data at the moment to be measured to the degree of fluctuation as the fluctuation contribution at the moment to be measured; The temperature data of a preset number of other moments closest to the moment to be measured are used as neighborhood data; Determine the difference between the temperature data at the time to be measured and the neighborhood data as the neighborhood difference; and take the mean of all neighborhood differences as the neighborhood fluctuation coefficient; The fluctuation contribution and the neighborhood fluctuation coefficient are forwardly integrated to determine a classification index for the temperature data.

4. A method for controlling the constant temperature of a bicycle saddle foaming mold according to claim 1, characterized in that: Constructing a decision tree for temperature data according to the classification index includes: Sort the classification index of each temperature data in descending order to obtain a classification sequence; Hierarchical clustering is performed on the classification sequence, and the number of clustering layers is set to 2 to obtain multiple clusters. The number of clusters is used as the number of layers of the decision tree to construct a decision tree for temperature data.

5. A method for controlling the constant temperature of a bicycle saddle foaming mold according to claim 1, characterized in that: The construction of the PID control adjustment function includes: Based on the variance and mean of the diffusion degree of all node temperature data, a PID control adjustment function is constructed.

6. A method for controlling the constant temperature of a bicycle saddle foaming mold according to claim 1, characterized in that: Determining a proportional adjustment coefficient according to the PID control adjustment function includes: The proportional adjustment coefficient is determined based on the Gaussian distribution principle.

7. A constant temperature control system for a bicycle saddle foaming mold, 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 method according to any one of claims 1 to 6 are implemented.

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