Rubber vulcanization process control method and system based on intelligent temperature control

Abstract

The present invention discloses a rubber vulcanization process control method and system based on intelligent temperature control, and relates to the technical field of rubber vulcanization. The method comprises: uniformly distributing according to the vulcanization temperature constraint interval to generate a vulcanization temperature set; traversing the vulcanization temperature set to predict the vulcanization time and generate a vulcanization time prediction value set; predicting the flat period time and generating a flat period time prediction value set; predicting the vulcanization temperature coefficient and generating a vulcanization temperature coefficient set; traversing the vulcanization time prediction value set and the flat period time prediction value set to perform fitness evaluation and generate a fitness evaluation value set; when the vulcanization temperature set cannot simultaneously meet the expected vulcanization time and the convergence fitness threshold, optimizing the vulcanization temperature set based on the fitness evaluation value set and the vulcanization temperature coefficient set to generate a recommended vulcanization temperature; and performing process control according to the recommended vulcanization temperature. Thus, the technical effect of improving the adaptability of temperature control and improving product quality is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of rubber vulcanization, and in particular to a rubber vulcanization process control method and system based on intelligent temperature control. Background Art

[0002] Rubber vulcanization is a crucial step in the manufacturing process of rubber products. Through the vulcanization process, the physical and chemical properties of rubber are significantly improved, and excellent elasticity, wear resistance, aging resistance and other characteristics are obtained. The temperature control during the vulcanization process has a decisive influence on the quality of vulcanization. However, due to the complexity of the vulcanization process and the characteristics of rubber materials, the existing rubber vulcanization process that relies on trial production and experience judgment for temperature control has technical problems such as poor adaptability and low product quality stability. Summary of the invention

[0003] The present invention provides a rubber vulcanization process control method and system based on intelligent temperature control to solve the technical problems of poor adaptability and low product performance stability in the prior art, and achieve the technical effect of improving temperature control adaptability and improving product quality.

[0004] In a first aspect, the present invention provides a rubber vulcanization process control method based on intelligent temperature control, wherein the method comprises:

[0005] The vulcanization temperature set is generated by uniformly distributing it according to the vulcanization temperature constraint interval.

[0006] The vulcanization temperature set is traversed to perform vulcanization time prediction, and a vulcanization time prediction value set is generated.

[0007] The vulcanization temperature set is traversed to predict the duration of the flat period, and a set of predicted values ​​of the duration of the flat period is generated.

[0008] The vulcanization temperature set is traversed to predict the vulcanization temperature coefficient and generate a vulcanization temperature coefficient set.

[0009] The vulcanization duration prediction value set and the flat period duration prediction value set are traversed to perform fitness evaluation and generate a fitness evaluation value set.

[0010] When any of the vulcanization temperature sets cannot simultaneously satisfy the expected vulcanization time and the convergence fitness threshold, the vulcanization temperature set is optimized based on the vulcanization temperature coefficient set according to the fitness evaluation value set to generate a recommended vulcanization temperature.

[0011] The rubber vulcanization process is controlled according to the recommended vulcanization temperature.

[0012] In a second aspect, the present invention further provides a rubber vulcanization process control system based on intelligent temperature control, wherein the system comprises:

[0013] A temperature distribution module, wherein the temperature distribution module is used to evenly distribute the temperature according to the curing temperature constraint interval to generate a curing temperature set;

[0014] A duration prediction module, the duration prediction module is used to traverse the vulcanization temperature set to perform vulcanization duration prediction and generate a vulcanization duration prediction value set;

[0015] A flat period prediction module, the flat period prediction module is used to traverse the vulcanization temperature set to predict the flat period duration and generate a flat period duration prediction value set;

[0016] A temperature coefficient prediction module, the temperature coefficient prediction module is used to traverse the vulcanization temperature set to predict the vulcanization temperature coefficient and generate a vulcanization temperature coefficient set;

[0017] An adaptability evaluation module, the adaptability evaluation module is used to traverse the vulcanization duration prediction value set and the flat period duration prediction value set to perform fitness evaluation and generate a fitness evaluation value set;

[0018] A temperature optimization module, wherein when any one of the vulcanization temperature sets cannot simultaneously meet the expected vulcanization time and the convergence fitness threshold, the temperature optimization module is used to optimize the vulcanization temperature set based on the vulcanization temperature coefficient set according to the fitness evaluation value set to generate a recommended vulcanization temperature;

[0019] A control execution module is used to control the rubber vulcanization process according to the recommended vulcanization temperature.

[0020] The present invention discloses a rubber vulcanization process control method and system based on intelligent temperature control, including: uniformly distributing according to the vulcanization temperature constraint interval to generate a vulcanization temperature set. Traversing the vulcanization temperature set to predict the vulcanization time and generate a vulcanization time prediction value set. Traversing the vulcanization temperature set to predict the flat period time and generate a flat period time prediction value set. Traversing the vulcanization temperature set to predict the vulcanization temperature coefficient and generate a vulcanization temperature coefficient set. Traversing the vulcanization time prediction value set and the flat period time prediction value set to perform fitness evaluation and generate a fitness evaluation value set. When any vulcanization temperature set cannot simultaneously meet the expected vulcanization time and the convergence fitness threshold, optimizing the vulcanization temperature set based on the fitness evaluation value set and the vulcanization temperature coefficient set to generate a recommended vulcanization temperature. Controlling the rubber vulcanization process based on the recommended vulcanization temperature. The rubber vulcanization process control method and system based on intelligent temperature control disclosed by the present invention solve the technical problems of poor adaptability and low product performance stability, and achieve the technical effects of improving temperature control adaptability and improving product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1It is a schematic flow chart of the rubber vulcanization process control method based on intelligent temperature control of the present invention.

[0022] Figure 2 It is a structural schematic diagram of the rubber vulcanization process control system based on intelligent temperature control of the present invention.

[0023] Explanation of the reference numerals: temperature distribution module 11 , duration prediction module 12 , flat period prediction module 13 , temperature coefficient prediction module 14 , adaptation evaluation module 15 , temperature optimization module 16 , control execution module 17 . DETAILED DESCRIPTION

[0024] The technical solution provided in the embodiments of the present invention is to solve the technical problems of poor adaptability and low product performance stability in the prior art. The overall idea adopted is as follows:

[0025] First, a uniform distribution is performed according to the vulcanization temperature constraint interval to generate a vulcanization temperature set. Then, the vulcanization temperature set is traversed to predict the vulcanization duration and generate a vulcanization duration prediction value set. Then, the vulcanization temperature set is traversed to predict the flat period duration and generate a flat period duration prediction value set. After that, the vulcanization temperature set is traversed to predict the vulcanization temperature coefficient and generate a vulcanization temperature coefficient set. Then, the vulcanization duration prediction value set and the flat period duration prediction value set are traversed to perform fitness evaluation and generate a fitness evaluation value set. When any of the vulcanization temperature sets cannot simultaneously meet the expected vulcanization duration and the convergence fitness threshold, the vulcanization temperature set is optimized based on the fitness evaluation value set and the vulcanization temperature coefficient set to generate a recommended vulcanization temperature. Finally, the rubber vulcanization process is controlled according to the recommended vulcanization temperature.

[0026] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them. Embodiment 1

[0027] Figure 1 The present invention is a schematic flow chart of a rubber vulcanization process control method based on intelligent temperature control, which includes:

[0028] The vulcanization temperature set is generated by uniformly distributing it according to the vulcanization temperature constraint interval.

[0029] Optionally, the vulcanization temperature constraint interval refers to a numerical range space of a limited vulcanization temperature value, and the vulcanization temperature constraint interval can be determined based on the temperature control range of the target vulcanization production line, the historical temperature control range of the target vulcanization production line, or the industry commonly used temperature range of vulcanized rubber. Exemplary. The vulcanization temperature constraint interval can be 150°C to 180°C.

[0030] In some embodiments, the curing temperature set is generated by uniformly distributing the curing temperature constraint interval, including:

[0031] The proportion of rubber components, the proportion of vulcanization components, and the preset vulcanization pressure are obtained through the user terminal; local back-tracing distribution is performed based on the vulcanization temperature constraint interval using the proportion of rubber components, the proportion of vulcanization components, and the preset vulcanization pressure to generate a first vulcanization temperature set; based on the vulcanization temperature constraint interval and the preset vulcanization temperature difference, the first vulcanization temperature set is compensated to generate the vulcanization temperature set.

[0032] Specifically, first, the user end of the interactive target vulcanization production line is used to obtain the process parameters of the target vulcanization process. The process parameters include the proportion of rubber components, the proportion of vulcanization components, and the preset vulcanization pressure, which characterize the predetermined process characteristics of the target vulcanized rubber product of the target vulcanization production line.

[0033] Optionally, the proportion of rubber components and the proportion of vulcanized components are determined based on the rubber formula of the target vulcanization process, wherein the rubber formula includes the proportions of various raw materials such as rubber, carbon black, sulfur, accelerators, antioxidants, fillers, etc., and the optimal vulcanization temperature and optimal vulcanization time for rubbers with different formula proportions are also different. The vulcanization temperature and vulcanization time jointly determine the quality of vulcanized rubber products. Specifically, a higher vulcanization temperature can speed up the vulcanization speed, but too high a temperature poses the risk of rubber aging or over-vulcanization, while a vulcanization time that is too short will result in insufficient vulcanization, and a vulcanization time that is too long may result in over-vulcanization, thereby affecting the rubber performance.

[0034] Optionally, the vulcanization preset pressure refers to the amount of pressure applied to the mold while heating. An appropriate vulcanization preset pressure helps to ensure that the rubber fills the mold and eliminates bubbles, thereby increasing the density of the vulcanized rubber product.

[0035] Optionally, based on the rubber component proportion, the vulcanization component proportion and the vulcanization preset pressure, a local back-tracing distribution is performed on multiple local sample vulcanization processes within the vulcanization temperature constraint interval, and based on the distribution results, it is determined whether the multiple sample vulcanization processes have a sufficiently high process similarity with a target vulcanized rubber product of a target vulcanization production line, thereby obtaining sample vulcanization processes that meet the process similarity constraint conditions and generating a first vulcanization temperature set.

[0036] Specifically, since the local sample vulcanization processes obtained during the local back-tracing distribution are random, there may be an uneven distribution within the vulcanization temperature constraint range, which results in the first vulcanization temperature set obtained having some temperature intervals with too large intervals between temperature sample values. It is necessary to compensate the first vulcanization temperature set within the vulcanization temperature constraint range according to the preset vulcanization temperature difference to generate a vulcanization temperature set.

[0037] In some implementations, the first vulcanization temperature set is generated by performing local back-tracing distribution based on the vulcanization temperature constraint interval according to the rubber component ratio, the vulcanization component ratio, and the vulcanization preset pressure, including:

[0038] According to the rubber component ratio, a first evaluation coordinate is constructed, according to the vulcanization component ratio, a second evaluation coordinate is constructed, and according to the vulcanization preset pressure, a third evaluation coordinate is constructed; based on the vulcanization temperature constraint interval, a first retrospective vulcanization process is obtained, wherein the first retrospective vulcanization process has a first retrospective rubber component ratio, a first retrospective vulcanization component ratio, a first vulcanization retrospective pressure, and a first vulcanization retrospective temperature; according to the first retrospective rubber component ratio, a first comparison coordinate is constructed, according to the first retrospective vulcanization component ratio, a second comparison coordinate is constructed, and according to the first vulcanization retrospective pressure, a third comparison coordinate is constructed; when a first Euclidean distance between the first evaluation coordinate and the first comparison coordinate is less than or equal to a first distance threshold, and a second Euclidean distance between the second evaluation coordinate and the second comparison coordinate is less than or equal to a second distance threshold, and a third Euclidean distance between the third evaluation coordinate and the third comparison coordinate is less than or equal to a third distance threshold, the first vulcanization retrospective temperature is added to the first vulcanization temperature set.

[0039] Specifically, based on the proportion of rubber components, the proportion of vulcanized components and the preset vulcanization pressure, a local backtracking distribution is performed in the vulcanization temperature constraint range. First, a multidimensional backtracking space is established, and the proportion of rubber components is used as the first evaluation coordinate, the proportion of vulcanized components is used as the second evaluation coordinate, and the preset vulcanization pressure is used as the third evaluation coordinate. The proportion of rubber components, the proportion of vulcanized components and the preset vulcanization pressure of the target vulcanized rubber product are mapped to the multidimensional backtracking space.

[0040] Furthermore, taking the vulcanization temperature constraint interval as the index constraint, traverse the production log or production record of the target vulcanization production line, screen the historical vulcanization processes whose vulcanization temperature is within the vulcanization temperature constraint interval, and store them as the first retrospective vulcanization process. In other words, the retrospective vulcanization industry is the historical vulcanization process that meets the vulcanization temperature constraint interval.

[0041] Optionally, after obtaining the first retrospective vulcanization process, based on the first retrospective rubber component ratio, the first retrospective vulcanization component ratio, and the first vulcanization retrospective pressure of the first retrospective vulcanization process, the first retrospective vulcanization process is mapped to the above-mentioned multidimensional retrospective space to achieve the local retrospective distribution of the first retrospective vulcanization process, and the result of the local retrospective distribution is represented as a spatial coordinate point in the multidimensional retrospective space. Among them, the first retrospective rubber component ratio, the first retrospective vulcanization component ratio, and the first vulcanization retrospective pressure correspond to the first comparison coordinate, the second comparison coordinate, and the third comparison coordinate of the multidimensional retrospective space, respectively.

[0042] Optionally, a first Euclidean distance between the first evaluation coordinate and the first comparison coordinate, a second Euclidean distance between the second evaluation coordinate and the second comparison coordinate, and a third Euclidean distance between the third evaluation coordinate and the third comparison coordinate are calculated respectively, and the Euclidean distances are compared with the distance threshold. Specifically, when the first Euclidean distance is less than or equal to the first distance threshold, and the second Euclidean distance is less than or equal to the second distance threshold, and the third Euclidean distance is less than or equal to the third distance threshold, the first vulcanization retrospective temperature is added to the first vulcanization temperature set.

[0043] In other words, if the mapping points of the first retrospective vulcanization process in the multidimensional retrospective space and the mapping points of the preset process of the target vulcanized rubber product in the multidimensional retrospective space meet the corresponding distance thresholds in multiple spatial coordinate dimensions, it can be considered that the first retrospective vulcanization process and the preset process of the target vulcanized rubber product have a high similarity. Then, the first retrospective vulcanization process is stored in the vulcanization temperature set for subsequent vulcanization temperature evaluation and optimization.

[0044] In some implementations, compensating the first vulcanization temperature set based on the vulcanization temperature constraint interval and a preset vulcanization temperature difference to generate the vulcanization temperature set includes:

[0045] According to the preset vulcanization temperature difference, the vulcanization temperature constraint interval is divided to generate a plurality of vulcanization temperature sub-intervals; the first vulcanization temperature set is distributed to the plurality of vulcanization temperature sub-intervals to obtain a set of vacant sub-intervals and a set of filled sub-intervals, wherein the set of vacant sub-intervals is a sub-interval in which the number of distributed first vulcanization temperatures is equal to 0, and the set of filled sub-intervals is a sub-interval in which the number of distributed first vulcanization temperatures is not equal to 0; a temperature value is randomly selected by traversing the vulcanization temperature subset of the filled sub-interval set, and the temperature value is added to the vulcanization temperature set; the set of vacant sub-intervals is traversed to obtain the median of the interval, and the median is added to the vulcanization temperature set.

[0046] Optionally, the preset vulcanization temperature difference is a segmented step size for segmented compensation of the vulcanization temperature constraint interval. The preset vulcanization temperature difference is configured based on the quality control requirements of the target vulcanized rubber product. Specifically, if the quality control requirements of the target vulcanized rubber product are high, a smaller preset vulcanization temperature difference is selected to divide the vulcanization temperature constraint interval into a plurality of more precise sub-intervals, thereby ensuring a more refined analysis and compensation of the first vulcanization temperature set.

[0047] Optionally, the first vulcanization temperature set is distributed to the vulcanization temperature sub-intervals to obtain a set of vacant sub-intervals and a set of filled sub-intervals, wherein the set of vacant sub-intervals is a set of multiple sub-intervals that do not contain temperature values ​​in the first vulcanization temperature set; and the set of filled sub-intervals is a set of multiple sub-intervals that contain temperature values ​​in the first vulcanization temperature set. In particular, if a certain vulcanization temperature value in the first vulcanization temperature set is located at a sub-interval division point, it is considered that both sub-intervals on both sides of the division point do not contain the vulcanization temperature value.

[0048] Optionally, for each subinterval in the filled subinterval set, a vulcanization temperature value is randomly selected and added to the vulcanization temperature set. Through the above random selection process, on the one hand, the reduction of multiple subintervals in the filled subinterval set is achieved, while also ensuring the diversity of the vulcanization temperature set, thereby more comprehensively evaluating the vulcanization process.

[0049] Optionally, for each sub-interval in the set of vacant sub-intervals, the median of the interval is calculated and added to the vulcanization temperature set. By adding the median of the interval, the continuity and integrity of the vulcanization temperature values ​​in the vulcanization temperature set can be ensured.

[0050] Through the above steps, the vulcanization temperature set obtained contains randomly distributed temperature values ​​and the median of the vacant sub-intervals, which has better randomness and more uniform numerical distribution. It combines the local samples with the median points in the standard interval, which helps to better simulate and optimize the vulcanization process and improve material properties.

[0051] The vulcanization temperature set is traversed to perform vulcanization time prediction, and a vulcanization time prediction value set is generated.

[0052] Optionally, the vulcanization time is predicted for the vulcanization temperature set, including record call prediction of the temperature values ​​obtained based on backtracking matching in the vulcanization temperature set and fitting prediction of the temperature values ​​obtained based on difference compensation. By taking corresponding prediction paths for temperature values ​​from different sources, the accuracy of the vulcanization time prediction value set obtained is ensured, while the vulcanization time prediction efficiency is improved.

[0053] In some embodiments, traversing the vulcanization temperature set to perform vulcanization time prediction and generating a vulcanization time prediction value set includes:

[0054] The kth vulcanization temperature of the vulcanization temperature set is obtained, where N≥k≥1, the initial value of k is equal to 1, N is the number of temperatures in the vulcanization temperature set, and k and N are integers; when the kth vulcanization temperature belongs to the backtracking matching temperature, the vulcanization time record value of the kth vulcanization temperature is obtained, set as the kth vulcanization time prediction value, and added to the vulcanization time prediction value set; when the kth vulcanization temperature belongs to the non-backtracking matching temperature, network statistics are performed according to the rubber component ratio, the vulcanization component ratio, the vulcanization preset pressure and the kth vulcanization temperature to obtain a vulcanization time retrieval value set; the vulcanization time retrieval value set is de-extremely fitted to generate a kth vulcanization time prediction value, and added to the vulcanization time prediction value set; when k=N, the vulcanization time prediction value set is output; when k<N, k is increased by one.

[0055] Specifically, first, the initial value of k is set to 1, indicating the start of traversing the vulcanization temperature set. Among them, k is the index of traversing the vulcanization temperature set, which increases from 1 until all temperatures are traversed. Then, based on the k value, the kth vulcanization temperature is obtained from the vulcanization temperature set, and it is checked whether the kth vulcanization temperature belongs to the backtracking matching temperature.

[0056] Optionally, if the kth vulcanization temperature is a backtracking matching temperature, its vulcanization duration record value is directly used as the prediction value. The vulcanization duration record value is a known vulcanization duration and can be directly used as a prediction value. Specifically, the vulcanization duration record value T of the kth vulcanization temperature is obtained. k , added to the vulcanization time prediction value set.

[0057] Optionally, if the kth vulcanization temperature is a non-backtracking matching temperature, network statistics are performed based on the rubber component ratio, the vulcanization component ratio, the vulcanization preset pressure and the kth vulcanization temperature. The vulcanization time of multiple homologous vulcanization schemes is obtained, and a vulcanization time retrieval value set is generated. The rubber component ratio, the vulcanization component ratio and the vulcanization preset pressure of multiple homologous vulcanization schemes are used as independent variables to perform a de-extreme value fitting on the vulcanization time retrieval value set to generate the kth vulcanization time prediction value T k `, add to the vulcanization time prediction value set.

[0058] Specifically, the data after de-extreme value fitting is used to generate the predicted value of the kth vulcanization time, wherein the de-extreme value processing can eliminate the influence of outliers, thereby improving the accuracy of the vulcanization time prediction based on fitting.

[0059] The vulcanization temperature set is traversed to predict the duration of the flat period, and a set of predicted values ​​of the duration of the flat period is generated.

[0060] Specifically, at the beginning of the rubber vulcanization process, the vulcanization curve of the rubber first shows an increasing trend. As the vulcanization time increases, the vulcanization curve of the rubber reaches its highest point, and then maintains a relatively flat performance level for a long period of time. The mechanical properties of the rubber in this stage change little, that is, it maintains a high performance level, which is called the flat period. Among them, the vulcanization curve shows the torque or other mechanical properties of the rubber that change over time during the vulcanization process.

[0061] Optionally, based on the same method principle of predicting the vulcanization duration of the vulcanization temperature set to generate a set of vulcanization duration prediction values, the flat period duration prediction of the vulcanization temperature set is performed to obtain a set of flat period duration prediction values. Specifically, for the temperature values ​​obtained based on backtracking matching in the vulcanization temperature set, the local records are called to obtain the flat period duration prediction values, and for the temperature values ​​obtained based on difference compensation, the flat period duration prediction values ​​are obtained by reversely checking the flat period duration fitting curve, wherein the fitting curve is a mapping curve of vulcanization temperature-flat period duration obtained by fitting the flat period duration retrieval value set collected by network statistics. It should be understood that for the sake of brevity of the specification, the specific flat period duration prediction steps will not be further explained here.

[0062] The vulcanization temperature set is traversed to predict the vulcanization temperature coefficient and generate a vulcanization temperature coefficient set.

[0063] Specifically, the vulcanization temperature coefficient is an indicator that reflects the temperature sensitivity of the vulcanization rate or vulcanization degree. During the vulcanization process, increasing the vulcanization temperature can speed up the vulcanization speed and shorten the production cycle. Correspondingly, too high a vulcanization temperature will also cause the rubber molecular chain to break, thereby affecting the performance of the vulcanized rubber product. Furthermore, for vulcanized rubbers with different formulations and different vulcanization temperature ranges, the corresponding vulcanization temperature coefficients are also different. Therefore, it is necessary to predict the vulcanization temperature coefficient in the vicinity of the selected vulcanization temperature.

[0064] Optionally, based on the prediction of the vulcanization duration, a set of vulcanization duration prediction values ​​is generated. The same method principle is used to predict the vulcanization temperature coefficient and obtain the set of vulcanization temperature coefficients. Specifically, for the temperature values ​​obtained based on the backtracking matching in the vulcanization temperature set, the local records are called to obtain the vulcanization temperature coefficients. For the temperature values ​​obtained based on the difference compensation, the vulcanization temperature coefficients are obtained according to the fitting curve of the vulcanization temperature coefficients and the vulcanization temperature. Specifically, based on the set of vulcanization temperature coefficient retrieval values ​​collected through network statistics, the corresponding vulcanization temperatures are fitted to generate the above fitting curve. It should be understood that for the sake of brevity of the specification, the specific steps of vulcanization temperature coefficient prediction will not be further explained here.

[0065] The vulcanization duration prediction value set and the flat period duration prediction value set are traversed to perform fitness evaluation and generate a fitness evaluation value set.

[0066] Optionally, based on the vulcanization time prediction value set and the flat period time prediction value set, the fitness of the vulcanization temperature set is evaluated, that is, based on the vulcanization time prediction values ​​and the flat period time prediction values ​​corresponding to multiple vulcanization temperature values ​​in the vulcanization temperature set, the adaptability of the temperature values ​​to the target vulcanization process is quantitatively evaluated.

[0067] In some embodiments, traversing the vulcanization duration prediction value set and the flat period duration prediction value set to perform fitness evaluation and generate a fitness evaluation value set includes:

[0068] A fitness evaluation factor set is obtained by comparing the flat period duration prediction value set with the vulcanization duration prediction value set; a first fitness evaluation factor of the fitness evaluation factor set is obtained; when the first fitness evaluation factor is greater than or equal to 1, the first fitness evaluation value is set to 0 and added to the fitness evaluation value set; when the first fitness evaluation factor is less than 1, the first vulcanization duration prediction value and the first fitness evaluation factor are fitted to generate a first fitness evaluation value and add it to the fitness evaluation value set.

[0069] Optionally, the ratio of the corresponding flat period duration prediction value to the vulcanization duration prediction value is used as a fitness evaluation factor, and the flat period duration prediction value set and the vulcanization duration prediction value set are traversed to calculate and obtain the fitness evaluation factor set. Specifically, the fitness evaluation factor reflects the ratio of the flat period to the vulcanization duration.

[0070] Optionally, a first evaluation factor is obtained by random selection from the set of fitness evaluation factors. If the first fitness evaluation factor is greater than or equal to 1, it means that the predicted value of the flat period duration corresponding to the first evaluation factor is greater than the predicted value of the vulcanization duration. In this case, it can be considered that there is a large prediction error in this group of prediction values. Accordingly, the first fitness evaluation value is set to 0 to ensure that the first vulcanization temperature corresponding to the first evaluation factor is excluded in subsequent selection and optimization.

[0071] Furthermore, if the first fitness evaluation factor is less than 1, it can be considered that the size relationship between the flat period duration prediction value and the vulcanization duration prediction value corresponding to the first evaluation factor is normal, and then the first fitness evaluation value is further fitted and generated based on the first fitness evaluation factor.

[0072] Further, when the first fitness evaluation factor is less than 1, fitting the first vulcanization time prediction value and the first fitness evaluation factor to generate a first fitness evaluation value, and adding it to the fitness evaluation value set, includes:

[0073] Construct a fitness fitting function:

[0074] ;

[0075] in, Represents the fitness evaluation value, Characterize the fitness evaluation factor, >1, a is the influence weight adjustment parameter of vulcanization time, T Characterizes the vulcanization time.

[0076] The first vulcanization time prediction value and the first fitness evaluation factor are fitted according to the fitness fitting function to generate the first fitness evaluation value.

[0077] Optionally, the influencing weight adjustment parameter a is determined based on production application experience or optimization methods. Through the above-mentioned fitness fitting function, the predicted value of the flat period duration and the predicted value of the vulcanization time are comprehensively considered to evaluate the fitness of the vulcanization temperature. Specifically, the higher the proportion of the flat period duration to the entire vulcanization time and the shorter the vulcanization time, the corresponding vulcanization temperature takes into account both the performance of the vulcanized rubber product (the longer the flat period, the better the physical properties of the rubber) and the vulcanization production efficiency (short vulcanization time), and has a better application effect.

[0078] When any of the vulcanization temperature sets cannot simultaneously satisfy the expected vulcanization time and the convergence fitness threshold, the vulcanization temperature set is optimized based on the vulcanization temperature coefficient set according to the fitness evaluation value set to generate a recommended vulcanization temperature.

[0079] Furthermore, based on the expected vulcanization time and the convergence fitness threshold, the vulcanization temperature set is traversed for multi-objective discrimination, and the vulcanization temperature value whose vulcanization time prediction value and fitness evaluation value simultaneously meet the expected vulcanization time and the convergence fitness threshold is obtained and set as the recommended vulcanization temperature.

[0080] Optionally, if there are multiple vulcanization temperature values ​​that simultaneously meet the expected vulcanization time and the convergence fitness threshold, based on the demand preference of the target vulcanization production line, the vulcanization time prediction value or the fitness evaluation value is used to select the multiple vulcanization temperature values ​​that simultaneously meet the expected vulcanization time and the convergence fitness threshold to obtain the recommended vulcanization temperature.

[0081] In some embodiments, according to the fitness evaluation value set, optimizing the vulcanization temperature set based on the vulcanization temperature coefficient set to generate a recommended vulcanization temperature includes:

[0082] According to the fitness evaluation value deviation threshold, based on the fitness evaluation value set, cluster analysis is performed on the vulcanization temperature set to obtain multiple clusters of vulcanization temperatures; loop step one: obtain the vulcanization temperature of the head of the first cluster preset number and the vulcanization temperature of the tail of the first cluster preset number, and sort them from head to tail, from best to worst; loop step two: obtain the vulcanization temperature of the head of the second cluster preset number and the vulcanization temperature of the tail of the second cluster preset number, and sort them from head to tail, from best to worst; loop step three: based on the vulcanization temperature of the tail of the first cluster preset number and the vulcanization temperature coefficient set, search with the vulcanization temperature of the head of the second cluster preset number as the target, and based on the vulcanization temperature of the tail of the second cluster preset number and the vulcanization temperature coefficient set, search with the vulcanization temperature of the head of the first cluster preset number as the target to obtain extended vulcanization temperature; when the vulcanization time prediction value of the extended vulcanization temperature meets the expected vulcanization time, and the fitness evaluation value of the extended vulcanization temperature meets the convergence fitness threshold, the extended vulcanization temperature is set to the recommended vulcanization temperature; otherwise, repeat loop steps one to three.

[0083] Specifically, the differences between multiple fitness evaluation values ​​in the fitness evaluation value set and the convergence fitness threshold are first calculated to obtain a fitness evaluation value deviation set. Then, according to the fitness evaluation value deviation threshold, a cluster analysis based on the fitness evaluation value deviation set is performed on the vulcanization temperature set to divide the vulcanization temperature set into multiple clusters of vulcanization temperatures with different degrees of deviation.

[0084] Furthermore, in each cluster, a preset number of head vulcanization temperatures and tail vulcanization temperatures are obtained, and these vulcanization temperatures are sorted from best to worst according to the fitness evaluation values, wherein the head vulcanization temperature represents the temperature with the best vulcanization performance in the cluster, and the tail vulcanization temperature represents the temperature with the worst performance.

[0085] Optionally, in actual vulcanization production, the settings of vulcanization temperatures are mostly relatively concentrated in several temperature intervals, which results in a certain interval of temperature value vacancy between multiple clusters of vulcanization temperatures. Therefore, based on the vulcanization temperatures at the tail of the preset number of clusters and the set of vulcanization temperature coefficients, the search is carried out with the vulcanization temperatures at the head of the preset number of clusters as the target; based on the vulcanization temperatures at the tail of the preset number of clusters and the set of vulcanization temperature coefficients, the search is carried out with the vulcanization temperatures at the head of the preset number of clusters as the target, which is helpful to find a better vulcanization temperature among multiple clusters of vulcanization temperatures.

[0086] Optionally, check whether the predicted value of the curing time of the extended curing temperature satisfies the expected curing time and whether the fitness evaluation value of the extended curing temperature satisfies the convergence fitness threshold. If both are satisfied, the extended curing temperature is set as the recommended curing temperature. Correspondingly, if one of the predicted value of the curing time of the extended curing temperature and the fitness evaluation value does not meet the above conditions, repeat loop steps 1 to 3 until a satisfactory extended curing temperature is obtained.

[0087] In some implementations, based on the first cluster of preset number of tail vulcanization temperatures and the set of vulcanization temperature coefficients, searching with the second cluster of preset number of head vulcanization temperatures as a target includes:

[0088] Obtain a vulcanization time prediction value deviation of a first tail vulcanization temperature of a preset number of tail vulcanization temperatures of the first cluster and a first head vulcanization temperature of the tail vulcanization temperature; obtain a first matching adjustment step based on the vulcanization time prediction value deviation according to a first tail vulcanization temperature coefficient of the first tail vulcanization temperature; obtain a second matching adjustment step based on the vulcanization time prediction value deviation according to the first head vulcanization temperature coefficient of the first head vulcanization temperature; construct a temperature search interval according to the first matching adjustment step and the second matching adjustment step; and search the first tail vulcanization temperature with the first head vulcanization temperature as a target according to the temperature search interval.

[0089] Optionally, according to the first tail vulcanization temperature coefficient, based on the deviation of the vulcanization time prediction value, a first matching adjustment step is obtained. Optionally, according to the first head vulcanization temperature coefficient, based on the deviation of the vulcanization time prediction value, a second matching adjustment step is obtained. Specifically, the matching adjustment step is used to control the optimization step of the first tail vulcanization temperature. The larger the vulcanization temperature coefficient, the more sensitive the vulcanization speed or vulcanization process is to temperature near the vulcanization temperature, and more refined optimization is required, and the corresponding matching adjustment step is shorter. At the same time, the larger the deviation of the vulcanization time prediction value corresponding to the vulcanization temperature, the faster the optimization convergence is required, and the corresponding matching adjustment step is longer.

[0090] Optionally, a temperature search interval is constructed according to the first matching adjustment step and the second matching adjustment step. The temperature search interval refers to a range for searching and optimizing the vulcanization temperature. Exemplarily, the temperature search interval is a temperature interval formed by deducting the second matching adjustment step from the first head vulcanization temperature and deducting the first matching adjustment step from the first tail vulcanization temperature.

[0091] The rubber vulcanization process is controlled according to the recommended vulcanization temperature.

[0092] In summary, the rubber vulcanization process control method based on intelligent temperature control provided by the present invention has the following technical effects:

[0093] The vulcanization temperature set is generated by uniformly distributing according to the vulcanization temperature constraint interval. The vulcanization temperature set is traversed to predict the vulcanization time and generate a vulcanization time prediction value set. The vulcanization temperature set is traversed to predict the flat period duration and generate a flat period duration prediction value set. The vulcanization temperature coefficient is traversed to predict the vulcanization temperature set and generate a vulcanization temperature coefficient set. The vulcanization time prediction value set and the flat period duration prediction value set are traversed to perform fitness evaluation and generate a fitness evaluation value set. When any vulcanization temperature set cannot simultaneously meet the expected vulcanization time and the convergence fitness threshold, the vulcanization temperature set is optimized based on the fitness evaluation value set and the vulcanization temperature coefficient set to generate a recommended vulcanization temperature. The rubber vulcanization process is controlled according to the recommended vulcanization temperature. Thereby, the technical effect of improving the adaptability of temperature control and improving product quality is achieved.

[0094] Embodiment 2

[0095] Figure 2 Schematic diagram of the structure of the rubber vulcanization process control system based on intelligent temperature control of the present invention. Figure 1 The flow chart of the rubber vulcanization process control method based on intelligent temperature control of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0096] Based on the same concept as the rubber vulcanization process control method based on intelligent temperature control in the above embodiment, the present invention also provides a rubber vulcanization process control system based on intelligent temperature control, including:

[0097] The temperature distribution module 11 is used to evenly distribute the temperature according to the curing temperature constraint interval to generate a curing temperature set.

[0098] The duration prediction module 12 is used to traverse the vulcanization temperature set to perform vulcanization duration prediction and generate a vulcanization duration prediction value set.

[0099] The flat period prediction module 13 is used to traverse the vulcanization temperature set to predict the flat period duration and generate a flat period duration prediction value set.

[0100] The temperature coefficient prediction module 14 is used to traverse the vulcanization temperature set to predict the vulcanization temperature coefficient and generate a vulcanization temperature coefficient set.

[0101] The adaptability evaluation module 15 is used to traverse the vulcanization duration prediction value set and the flat period duration prediction value set to perform adaptability evaluation and generate a fitness evaluation value set.

[0102] The temperature optimization module 16 is used to optimize the vulcanization temperature set based on the vulcanization temperature coefficient set according to the fitness evaluation value set to generate a recommended vulcanization temperature when any of the vulcanization temperature sets cannot simultaneously meet the expected vulcanization time and the convergence fitness threshold.

[0103] The control execution module 17 is used to control the rubber vulcanization process according to the recommended vulcanization temperature.

[0104] Wherein, the temperature distribution module 11 comprises:

[0105] The formula acquisition unit is used to obtain the rubber component ratio, the vulcanization component ratio, and the vulcanization preset pressure through the user end.

[0106] The retrospective distribution unit is used to perform local retrospective distribution based on the vulcanization temperature constraint interval according to the rubber component proportion, the vulcanization component proportion and the vulcanization preset pressure, so as to generate a first vulcanization temperature set.

[0107] The interval compensation unit is used to compensate the first vulcanization temperature set based on the vulcanization temperature constraint interval and a preset vulcanization temperature difference to generate the vulcanization temperature set.

[0108] Furthermore, the traceback distribution unit in the temperature distribution module 11 includes:

[0109] The reference coordinate construction unit is used to construct a first evaluation coordinate according to the rubber component ratio, to construct a second evaluation coordinate according to the vulcanization component ratio, and to construct a third evaluation coordinate according to the vulcanization preset pressure.

[0110] The retrospective process extraction unit is used to obtain a first retrospective vulcanization process based on the vulcanization temperature constraint interval, wherein the first retrospective vulcanization process has a first retrospective rubber component ratio, a first retrospective vulcanization component ratio, a first vulcanization retrospective pressure and a first vulcanization retrospective temperature.

[0111] The backtracking process mapping unit is used to construct a first comparison coordinate according to the first backtracking rubber component ratio, to construct a second comparison coordinate according to the first backtracking vulcanization component ratio, and to construct a third comparison coordinate according to the first vulcanization backtracking pressure.

[0112] A distance determination unit is used to add the first vulcanization retrospective temperature into the first vulcanization temperature set when a first Euclidean distance between the first evaluation coordinate and the first comparison coordinate is less than or equal to a first distance threshold, a second Euclidean distance between the second evaluation coordinate and the second comparison coordinate is less than or equal to a second distance threshold, and a third Euclidean distance between the third evaluation coordinate and the third comparison coordinate is less than or equal to a third distance threshold.

[0113] Furthermore, the interval compensation unit in the temperature distribution module 11 includes:

[0114] The interval segmentation unit is used to segment the vulcanization temperature constraint interval according to the preset vulcanization temperature difference to generate a plurality of vulcanization temperature sub-intervals.

[0115] The interval clustering unit is used to distribute the first vulcanization temperature set to the several vulcanization temperature sub-intervals to obtain a set of vacant sub-intervals and a set of filled sub-intervals, wherein the set of vacant sub-intervals is a sub-interval in which the number of distributed first vulcanization temperatures is equal to 0, and the set of filled sub-intervals is a sub-interval in which the number of distributed first vulcanization temperatures is not equal to 0.

[0116] The filling interval compensation unit is used to traverse the vulcanization temperature subset of the filling sub-interval set, randomly select a temperature value, and add it into the vulcanization temperature set.

[0117] The empty interval compensation unit is used to traverse the empty sub-interval set to obtain the median of the interval and add it into the vulcanization temperature set.

[0118] In some implementations, the duration prediction module 12 includes:

[0119] The temperature extraction unit is used to obtain the kth vulcanization temperature of the vulcanization temperature set, N≥k≥1, the initial value of k is equal to 1, N is the number of temperatures in the vulcanization temperature set, and k and N are integers.

[0120] The backtracking record calling unit is used to obtain the vulcanization time record value of the kth vulcanization temperature when the kth vulcanization temperature belongs to the backtracking matching temperature, set it as the kth vulcanization time prediction value, and add it into the vulcanization time prediction value set.

[0121] The networked retrieval unit is used to perform networked statistics according to the rubber component ratio, the vulcanization component ratio, the vulcanization preset pressure and the kth vulcanization temperature to obtain a vulcanization time retrieval value set when the kth vulcanization temperature belongs to a non-backtracking matching temperature.

[0122] The fitting prediction unit is used to perform extreme value fitting on the vulcanization time retrieval value set to generate a kth vulcanization time prediction value and add it into the vulcanization time prediction value set.

[0123] The output determination unit is used to output the vulcanization time prediction value set when k=N. When k<N, k increases by one.

[0124] In some implementations, the adaptation assessment module 15 includes:

[0125] The fitness factor unit is used to compare the flat period duration prediction value set with the vulcanization duration prediction value set to obtain a fitness evaluation factor set.

[0126] The factor extraction unit is used to obtain the first fitness evaluation factor of the fitness evaluation factor set.

[0127] The error elimination unit is used to set the first fitness evaluation value to 0 and add it to the fitness evaluation value set when the first fitness evaluation factor is greater than or equal to 1.

[0128] The fitness evaluation unit is used to fit the first vulcanization time prediction value and the first fitness evaluation factor when the first fitness evaluation factor is less than 1, generate a first fitness evaluation value, and add it into the fitness evaluation value set.

[0129] Furthermore, the fitness evaluation unit in the fitness evaluation module 15 includes:

[0130] Fitting function unit, used to construct fitness fitting function:

[0131] .

[0132] in, Represents the fitness evaluation value, Characterize the fitness evaluation factor, >1, a is the influence weight adjustment parameter of vulcanization time, T Characterizes the vulcanization time.

[0133] A fitting evaluation unit is used to fit the first vulcanization time prediction value and the first fitness evaluation factor according to the fitness fitting function to generate the first fitness evaluation value.

[0134] In some implementations, the temperature optimization module 16 includes:

[0135] The cluster analysis unit is used to perform cluster analysis on the vulcanization temperature set based on the fitness evaluation value set according to the fitness evaluation value deviation threshold, so as to obtain multiple clusters of vulcanization temperatures.

[0136] The first circulation unit is used to execute circulation step 1: obtaining the vulcanization temperature of the head of the first cluster preset number and the vulcanization temperature of the tail of the first cluster preset number, and sorting them from the head to the tail, from the best to the worst.

[0137] The second circulation unit is used to execute circulation step 2: obtaining the vulcanization temperature of the head of the second cluster preset number and the vulcanization temperature of the tail of the second cluster preset number, and sorting them from the head to the tail, from the best to the worst.

[0138] The third loop unit is used to execute loop step three: based on the tail vulcanization temperature of the first cluster preset number and the vulcanization temperature coefficient set, searching with the head vulcanization temperature of the second cluster preset number as the target; based on the tail vulcanization temperature of the second cluster preset number and the vulcanization temperature coefficient set, searching with the head vulcanization temperature of the first cluster preset number as the target to obtain the expanded vulcanization temperature.

[0139] The optimized temperature output unit is used to set the expanded vulcanization temperature to the recommended vulcanization temperature when the predicted vulcanization time of the expanded vulcanization temperature meets the expected vulcanization time and the fitness evaluation value of the expanded vulcanization temperature meets the convergence fitness threshold. Otherwise, the loop step 1 to the loop step 3 are repeatedly executed.

[0140] In some implementations, the temperature optimization module 16 includes:

[0141] The temperature and deviation acquisition unit is used to obtain the deviation of the predicted value of the vulcanization time of the first tail vulcanization temperature of the first preset number of tail vulcanization temperatures and the first head vulcanization temperature of the tail vulcanization temperature.

[0142] The first step length unit is used to obtain a first matching adjustment step length according to a first tail vulcanization temperature coefficient of the first tail vulcanization temperature and based on the vulcanization time prediction value deviation.

[0143] The second step length unit is used to obtain a second matching adjustment step length according to a first head vulcanization temperature coefficient of the first head vulcanization temperature and based on the vulcanization time prediction value deviation.

[0144] The search interval configuration unit is used to construct a temperature search interval according to the first matching adjustment step and the second matching adjustment step.

[0145] The interval search unit is used to search the first tail vulcanization temperature according to the temperature search interval and take the first head vulcanization temperature as a target.

[0146] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the rubber vulcanization process control system based on intelligent temperature control described in embodiment two. For the sake of brevity of the specification, no further elaboration is given here.

[0147] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A rubber vulcanization process control method based on intelligent temperature control, characterized in that: include: Uniformly distribute according to the curing temperature constraint interval to generate a curing temperature set; Traversing the vulcanization temperature set to predict the vulcanization time and generate a vulcanization time prediction value set; Traversing the vulcanization temperature set to predict the duration of the flat period, and generating a set of predicted values ​​of the duration of the flat period; Traversing the vulcanization temperature set to predict the vulcanization temperature coefficient and generate a vulcanization temperature coefficient set; Traversing the vulcanization duration prediction value set and the flat period duration prediction value set to perform fitness evaluation and generate a fitness evaluation value set; When any of the vulcanization temperature sets cannot simultaneously satisfy the expected vulcanization time and the convergence fitness threshold, optimizing the vulcanization temperature set based on the vulcanization temperature coefficient set according to the fitness evaluation value set to generate a recommended vulcanization temperature; Controlling the rubber vulcanization process according to the recommended vulcanization temperature; Among them, the vulcanization temperature set is generated by uniformly distributing the vulcanization temperature constraint interval, including: Through the user end, obtain the rubber component ratio, vulcanization component ratio, and vulcanization preset pressure; Based on the vulcanization temperature constraint interval, local back-tracing distribution is performed based on the rubber component ratio, the vulcanization component ratio and the vulcanization preset pressure to generate a first vulcanization temperature set; Based on the vulcanization temperature constraint interval and the preset vulcanization temperature difference, compensating the first vulcanization temperature set to generate the vulcanization temperature set; The step of traversing the vulcanization duration prediction value set and the flat period duration prediction value set to perform fitness evaluation and generate a fitness evaluation value set includes: Comparing the flat period duration prediction value set with the vulcanization duration prediction value set respectively to obtain a fitness evaluation factor set; Obtaining a first fitness evaluation factor of the fitness evaluation factor set; When the first fitness evaluation factor is greater than or equal to 1, the first fitness evaluation value is set to 0 and added to the fitness evaluation value set; When the first fitness evaluation factor is less than 1, the first vulcanization time prediction value and the first fitness evaluation factor are fitted to generate a first fitness evaluation value, which is added to the fitness evaluation value set.

2. The method according to claim 1, characterized in that The first vulcanization temperature set is generated by performing local back-tracing distribution based on the vulcanization temperature constraint interval according to the rubber component ratio, the vulcanization component ratio and the vulcanization preset pressure, including: According to the rubber component ratio, a first evaluation coordinate is constructed, according to the vulcanization component ratio, a second evaluation coordinate is constructed, and according to the vulcanization preset pressure, a third evaluation coordinate is constructed; Based on the vulcanization temperature constraint interval, a first retroactive vulcanization process is obtained, wherein the first retroactive vulcanization process has a first retroactive rubber component ratio, a first retroactive vulcanization component ratio, a first vulcanization retroactive pressure, and a first vulcanization retroactive temperature; According to the first retrospective rubber component ratio, a first comparison coordinate is constructed, according to the first retrospective vulcanization component ratio, a second comparison coordinate is constructed, and according to the first vulcanization retrospective pressure, a third comparison coordinate is constructed; When the first Euclidean distance between the first evaluation coordinate and the first comparison coordinate is less than or equal to a first distance threshold, and the second Euclidean distance between the second evaluation coordinate and the second comparison coordinate is less than or equal to a second distance threshold, and the third Euclidean distance between the third evaluation coordinate and the third comparison coordinate is less than or equal to a third distance threshold, the first vulcanization retrospective temperature is added to the first vulcanization temperature set.

3. The method according to claim 1, characterized in that Based on the vulcanization temperature constraint interval and the preset vulcanization temperature difference, compensating the first vulcanization temperature set to generate the vulcanization temperature set includes: According to the preset vulcanization temperature difference, the vulcanization temperature constraint interval is divided to generate a plurality of vulcanization temperature sub-intervals; Distributing the first vulcanization temperature set to the plurality of vulcanization temperature subintervals to obtain a set of vacant subintervals and a set of filled subintervals, wherein the set of vacant subintervals is a subinterval in which the number of distributed first vulcanization temperatures is equal to 0, and the set of filled subintervals is a subinterval in which the number of distributed first vulcanization temperatures is not equal to 0; Traversing the vulcanization temperature subset of the filling sub-interval set, randomly selecting a temperature value, and adding it to the vulcanization temperature set; The missing sub-interval set is traversed to obtain the median value of the interval and add it into the vulcanization temperature set.

4. The method according to claim 3, characterized in that Traversing the vulcanization temperature set to predict the vulcanization time, generating a vulcanization time prediction value set, including: Obtaining the kth vulcanization temperature of the vulcanization temperature set, where N≥k≥1, the initial value of k is equal to 1, N is the number of temperatures in the vulcanization temperature set, and k and N are integers; When the k-th vulcanization temperature belongs to the backtracking matching temperature, the vulcanization duration record value of the k-th vulcanization temperature is obtained, set as the k-th vulcanization duration prediction value, and added into the vulcanization duration prediction value set; When the kth vulcanization temperature belongs to a non-backtracking matching temperature, online statistics are performed according to the rubber component ratio, the vulcanization component ratio, the vulcanization preset pressure and the kth vulcanization temperature to obtain a vulcanization time retrieval value set; De-extreme value fitting is performed on the vulcanization time retrieval value set to generate a k-th vulcanization time prediction value, and the k-th vulcanization time prediction value is added to the vulcanization time prediction value set; When k=N, output the vulcanization time prediction value set; When k<N, k increases by one.

5. The method according to claim 1, characterized in that When the first fitness evaluation factor is less than 1, fitting the first vulcanization time prediction value and the first fitness evaluation factor to generate a first fitness evaluation value, and adding the first fitness evaluation value to the fitness evaluation value set, including: Construct a fitness fitting function: ； in, Represents the fitness evaluation value, Characterize the fitness evaluation factor, >1, a is the influence weight adjustment parameter of vulcanization time, T Characterize the vulcanization time; The first vulcanization time prediction value and the first fitness evaluation factor are fitted according to the fitness fitting function to generate the first fitness evaluation value.

6. The method according to claim 1, characterized in that According to the fitness evaluation value set, based on the vulcanization temperature coefficient set, the vulcanization temperature set is optimized to generate a recommended vulcanization temperature, including: According to a fitness evaluation value deviation threshold, based on the fitness evaluation value set, cluster analysis is performed on the vulcanization temperature set to obtain multiple clusters of vulcanization temperatures; Cycle step 1: obtaining the vulcanization temperature of the head of the first cluster of preset quantity and the vulcanization temperature of the tail of the first cluster of preset quantity, and sorting them from the head to the tail, from the best to the worst; Cycle step 2: obtaining the vulcanization temperature of the head of the second cluster of preset quantity and the vulcanization temperature of the tail of the second cluster of preset quantity, and sorting them from the head to the tail, from the best to the worst; Loop step three: based on the first cluster preset number of tail vulcanization temperatures and the vulcanization temperature coefficient set, searching with the second cluster preset number of head vulcanization temperatures as the target, based on the second cluster preset number of tail vulcanization temperatures and the vulcanization temperature coefficient set, searching with the first cluster preset number of head vulcanization temperatures as the target, to obtain expanded vulcanization temperatures; When the predicted value of the vulcanization time of the extended vulcanization temperature satisfies the expected vulcanization time and the fitness evaluation value of the extended vulcanization temperature satisfies the convergence fitness threshold, setting the extended vulcanization temperature as the recommended vulcanization temperature; Otherwise, repeat loop step 1 to loop step 3.

7. The method according to claim 6, characterized in that Based on the first cluster of preset number of tail vulcanization temperatures and the vulcanization temperature coefficient set, searching with the second cluster of preset number of head vulcanization temperatures as a target includes: Obtaining a deviation of a predicted value of a vulcanization time between a first tail vulcanization temperature of the first preset number of tail vulcanization temperatures and a first head vulcanization temperature of the tail vulcanization temperature; Obtaining a first matching adjustment step length according to a first tail vulcanization temperature coefficient of the first tail vulcanization temperature and based on the vulcanization time prediction value deviation; Obtaining a second matching adjustment step length according to a first head vulcanization temperature coefficient of the first head vulcanization temperature and based on the vulcanization time prediction value deviation; Constructing a temperature search interval according to the first matching adjustment step and the second matching adjustment step; According to the temperature search interval, the first tail vulcanization temperature is searched with the first head vulcanization temperature as a target.

8. Rubber vulcanization process control system based on intelligent temperature control, characterized in that: The system is used to execute the rubber vulcanization process control method based on intelligent temperature control according to any one of claims 1 to 7, and the system comprises: A temperature distribution module, wherein the temperature distribution module is used to evenly distribute the temperature according to the curing temperature constraint interval to generate a curing temperature set; A duration prediction module, the duration prediction module is used to traverse the vulcanization temperature set to perform vulcanization duration prediction and generate a vulcanization duration prediction value set; A flat period prediction module, the flat period prediction module is used to traverse the vulcanization temperature set to predict the flat period duration and generate a flat period duration prediction value set; A temperature coefficient prediction module, the temperature coefficient prediction module is used to traverse the vulcanization temperature set to predict the vulcanization temperature coefficient and generate a vulcanization temperature coefficient set; An adaptability evaluation module, the adaptability evaluation module is used to traverse the vulcanization duration prediction value set and the flat period duration prediction value set to perform fitness evaluation and generate a fitness evaluation value set; A temperature optimization module, wherein when any one of the vulcanization temperature sets cannot simultaneously meet the expected vulcanization time and the convergence fitness threshold, the temperature optimization module is used to optimize the vulcanization temperature set based on the vulcanization temperature coefficient set according to the fitness evaluation value set to generate a recommended vulcanization temperature; A control execution module is used to control the rubber vulcanization process according to the recommended vulcanization temperature.

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