Processing temperature optimization control method and system applied to plastic pipe fitting

Abstract

The invention relates to a processing temperature optimization control method and system applied to a plastic pipe fitting, and the method comprises the steps: M1, collecting the change data information of historical temperatures of different sections of a charging barrel of an extruder in the processing process of the plastic pipe fitting in the extruder, the data information of the temperature of each section of a charging barrel of the extruder, the data information of the rotating speed of a screw rod and the data information of the pressure of a machine head are collected in real time; and M2, based on the change data information of the historical temperatures of the different sections of the extruder charging barrel, predicting the temperature of each section of the extruder charging barrel by adopting a BP neural network regression prediction algorithm based on random walk matrix optimization to obtain predicted data information of the temperature of each section of the extruder charging barrel. According to the invention, the temperature of each section of the extruder cylinder can be accurately controlled and adjusted, so that the energy consumption is reduced, and self-adaptive adjustment can be carried out according to different application scenes in the control process, so that the robustness of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of processing of plastic pipe fittings, and in particular to a processing temperature optimization control method and system applied to plastic pipe fittings. Background Art

[0002] With the continuous development of automation technology, it has promoted industrial upgrading and helped enterprises transform from traditional production models to modern production models.

[0003] In the prior art, a Chinese patent (application number: 201410715221.7, publication number: CN 104460764A) discloses a method for controlling the temperature of an extruder barrel based on fuzzy PID with pseudo-removal control, wherein the temperature of the extruder barrel is collected, and the obtained temperature is input into a fuzzy inference controller after differential processing, and the fuzzy inference controller processes the input deviation e and the deviation change rate ec, and outputs the result after fuzzy inference; the result of fuzzy inference is directly input into the pseudo-removal controller, and the PID controller parameters Kp, Ki, and Kd are obtained after processing by the pseudo-removal controller; the PID controller obtains the final control variable u through the pseudo-removal controller output and the actual input, which is used to control the temperature of the extruder barrel. During the control process, the control accuracy of the temperature of each section of the extruder barrel is not high, which leads to increased energy consumption. Summary of the invention

[0004] In view of the above deficiencies in the prior art, the present invention provides a processing temperature optimization control method and system for plastic pipe fittings, which can not only accurately control and adjust the temperature of each section of the extruder barrel, thereby reducing energy consumption, but also can adaptively adjust according to different application scenarios during the control process, thereby improving the robustness of the system.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows:

[0006] A processing temperature optimization control method for plastic pipe fittings, the method comprising:

[0007] M1. During the processing of plastic pipes in the extruder, the historical temperature change data of different sections of the extruder barrel are collected, and the temperature data of each section of the extruder barrel, the screw speed data and the head pressure data are collected in real time;

[0008] M2. Based on the historical temperature change data information of different sections of the extruder barrel, the temperature of each section of the extruder barrel is predicted by using a BP neural network regression prediction algorithm based on random walk matrix optimization to obtain the predicted temperature data information of each section of the extruder barrel;

[0009] M3. Based on the predicted data information of the temperature of each section of the extruder barrel and the data of the temperature of each section of the extruder barrel, the temperature of each section of the extruder barrel is optimized by using an improved adaptive Aurora optimization algorithm based on chaotic mapping to obtain the optimized data information of the temperature of each section of the extruder barrel;

[0010] M4. Based on the data information of the temperature of each section of the optimized extruder barrel, the data information of the screw speed and the data information of the head pressure, a control model of the processing temperature of plastic pipe fittings based on crazy adaptation is constructed to control the processing temperature of plastic pipe fittings and output the control data information of the processing temperature of plastic pipe fittings.

[0011] Furthermore, in step M2, the temperature of each section of the extruder barrel is predicted by using a BP neural network regression prediction algorithm based on random walk matrix optimization, including:

[0012] M21. Inputting the historical temperature change data information of different sections of the extruder barrel into the BP neural network for training and learning, initializing the weights and biases of the BP neural network, and obtaining the weights and biases of the initialized BP neural network;

[0013] M22. Based on the weight and bias data information of the initialized BP neural network, a random walk matrix function Q is established.

[0015] Among them, x1 is the data information of the weight of the BP neural network after initialization, x2 is the data information of the bias of the BP neural network after initialization, α1, α2 and α3 are the penalty factors of the random walk matrix, and the random walk matrix of the weight and bias of the BP neural network is characterized to obtain the data information of the random walk matrix of the weight and bias of the BP neural network;

[0016] M23. Based on the data information of the random walk matrix of the weight and bias of the BP neural network, establish the target optimization function W of the random walk matrix,

[0018] Among them, y is the data information of the random walk matrix of the weight and bias of the BP neural network, β1, β2 and β3 are the target optimization factors of the random walk matrix, and the weight and bias of the BP neural network are optimized to obtain the optimized BP neural network;

[0019] M24. Based on the optimized BP neural network, the historical temperature change data information of different sections of the extruder barrel is input, the temperature of each section of the extruder barrel is predicted, and the predicted temperature data information of each section of the extruder barrel is obtained.

[0020] Furthermore, the target optimization factors β1, β2 and β3 of the random walk matrix are,

[0022] Among them, y is the data information of the random walk matrix of the weight and bias of the BP neural network.

[0023] Furthermore, the constraints of the penalty factors α1, α2 and α3 of the random walk matrix are:

[0025] Among them, the value range of the function f(α1,α2,α3) is (0,1).

[0026] Furthermore, in step M3, the optimization of the temperature of each section of the extruder barrel by using the improved adaptive Aurora optimization algorithm based on chaotic mapping includes:

[0027] M31. Based on the predicted data information of the temperature of each section of the extruder barrel and the data of the temperature of each section of the extruder barrel, a chaotic mapping function R of the extruder barrel is established.

[0029] Among them, r1 is the data information of the temperature of each section of the extruder barrel after prediction, r2 is the data information of the temperature of each section of the extruder barrel, δ1, δ2 and δ3 are the chaotic mapping factors of the extruder barrel, and the chaotic sequence of the temperature of the extruder barrel is characterized to obtain the data information of the chaotic sequence of the temperature of the extruder barrel;

[0030] M32. Based on the chaotic sequence data information of the temperature of the extruder barrel, the aurora population is initialized, the population parameters and the maximum number of iterations L of the population are determined, and the data information of the initialized aurora population is obtained;

[0031] M33. Based on the data information of the initialized aurora population, establish the fitness function P of the aurora population individuals,

[0033] Among them, z is the data information of the initialized aurora population, γ1, γ2 and γ3 are the difference factors of the aurora population individuals, and the fitness values ​​of the aurora population individuals are calculated to obtain the data information of the fitness values ​​of the aurora population individuals;

[0034] M34. Based on the data information of the fitness values ​​of the aurora population individuals, establish the target optimization function S of the aurora population,

[0036] Among them, a is the data information of the fitness value of the individual aurora population, η1, η2 and η3 are the weight factors of the aurora population, and the temperature of each section of the extruder barrel is optimized to obtain the data information of the temperature of each section of the extruder barrel after optimization.

[0037] Furthermore, the constraint function g of the weight factor of the aurora population is,

[0039] Among them, the value range of the constraint function g is (1,2).

[0040] Furthermore, the chaotic mapping factors δ1, δ2 and δ3 of the extruder barrel are:

[0043] Among them, r1 is the data information of the temperature of each section of the extruder barrel after prediction, and r2 is the data information of the temperature of each section of the extruder barrel.

[0044] Furthermore, in step M4, the construction of a control model for the processing temperature of the plastic pipe fitting based on crazy self-adaptation and the control of the processing temperature of the plastic pipe fitting include:

[0045] M41. Based on the optimized data information of the temperature of each section of the extruder barrel, the data information of the screw speed and the data information of the head pressure, a relationship function G between the temperature of each section of the extruder barrel and the screw speed and temperature is established,

[0047] Among them, h1 is the data information of the temperature of each section of the extruder barrel after optimization, h2 is the data information of the screw speed, h3 is the data information of the head pressure, λ1, λ2 and λ3 are relationship factors, and the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure is characterized to obtain the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure;

[0048] M42. Input the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the die head pressure into the control model of the processing temperature of the plastic pipe fittings based on crazy adaptive training and learning, and determine the control function H of the processing temperature of the plastic pipe fittings,

[0050] Among them, c is the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure, μ1, μ2 and μ3 are the crazy adaptive determining factors of the processing temperature of the plastic pipe fittings, and the trained crazy adaptive control model of the processing temperature of the plastic pipe fittings is obtained;

[0051] M43. Based on the trained crazy adaptive control model of the processing temperature of plastic pipe fittings, the data information of the temperature of each section of the optimized extruder barrel, the data information of the screw speed and the data information of the head pressure are input to control the processing temperature of the plastic pipe fittings, and output the control data information of the processing temperature of the plastic pipe fittings.

[0052] Furthermore, the crazy adaptive determining factors μ1, μ2 and μ3 of the processing temperature of the plastic pipe fitting are,

[0054] Among them, c is the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure.

[0055] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a processing temperature optimization control system applied to plastic pipe fittings, including a computer device, which is programmed or configured to execute any one of the steps of the processing temperature optimization control method applied to plastic pipe fittings.

[0056] The present invention has the following positive effects:

[0057] 1. The present invention predicts the temperature of each section of the extruder barrel by adopting a BP neural network regression prediction algorithm based on random walk matrix optimization, and optimizes the temperature of each section of the extruder barrel by combining an improved adaptive Aurora optimization algorithm based on chaos mapping. It can not only accurately control and adjust the temperature of each section of the extruder barrel, thereby reducing energy consumption, but also can perform adaptive adjustments according to different application scenarios during the control process, thereby improving the robustness of the system.

[0058] 2. The present invention controls the processing temperature of plastic pipe fittings by constructing a control model of the processing temperature of plastic pipe fittings based on crazy adaptation. This not only allows some tedious, repetitive and dangerous work to be completed by robots or mechanical equipment, thereby reducing the labor intensity of workers and reducing labor costs, but also improves the working environment, and realizes the automation and intelligence of the production process. Automation technology can complete tasks quickly and accurately, thereby greatly improving production speed and accuracy, reducing human errors, and improving product quality and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0060] Figure 2 It is a schematic diagram of the process of the BP neural network regression prediction algorithm based on random walk matrix optimization of the present invention;

[0061] Figure 3A schematic diagram of the flow of the improved adaptive aurora optimization algorithm based on chaotic mapping of the present invention;

[0062] Figure 4 A schematic diagram of a process flow of constructing a control model for processing temperature of plastic pipes based on crazy self-adaptation according to the present invention;

[0063] Figure 5 It is a schematic diagram of the system framework of the present invention;

[0064] Figure 6 It is a schematic structural diagram of the extruder of the present invention.

[0065] Explanation of the numbers in the figure: 1- screw speed sensor (taken from the frequency converter), 2- barrel temperature sensor of the extruder, 3- die head pressure sensor. DETAILED DESCRIPTION

[0066] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0067] Example 1: Figure 1 As shown, a processing temperature optimization control method for plastic pipe fittings, the method comprising:

[0068] M1. During the processing of plastic pipes in the extruder, the historical temperature change data of different sections of the extruder barrel are collected, and the temperature data of each section of the extruder barrel, the screw speed data and the head pressure data are collected in real time;

[0069] M2. Based on the historical temperature change data information of different sections of the extruder barrel, the temperature of each section of the extruder barrel is predicted by using a BP neural network regression prediction algorithm based on random walk matrix optimization to obtain the predicted temperature data information of each section of the extruder barrel;

[0070] M3. Based on the predicted data information of the temperature of each section of the extruder barrel and the data of the temperature of each section of the extruder barrel, the temperature of each section of the extruder barrel is optimized by using an improved adaptive Aurora optimization algorithm based on chaotic mapping to obtain the optimized data information of the temperature of each section of the extruder barrel;

[0071] M4. Based on the data information of the temperature of each section of the optimized extruder barrel, the data information of the screw speed and the data information of the head pressure, a control model of the processing temperature of plastic pipe fittings based on crazy adaptation is constructed to control the processing temperature of plastic pipe fittings and output the control data information of the processing temperature of plastic pipe fittings.

[0072] In this embodiment, if Figure 2 As shown, in step M2, the temperature of each section of the extruder barrel is predicted by using a BP neural network regression prediction algorithm based on random walk matrix optimization, including:

[0073] M21. Inputting the historical temperature change data information of different sections of the extruder barrel into the BP neural network for training and learning, initializing the weights and biases of the BP neural network, and obtaining the weights and biases of the initialized BP neural network;

[0074] M22. Based on the weight and bias data information of the initialized BP neural network, a random walk matrix function Q is established.

[0076] Among them, x1 is the data information of the weight of the BP neural network after initialization, x2 is the data information of the bias of the BP neural network after initialization, α1, α2 and α3 are the penalty factors of the random walk matrix, and the random walk matrix of the weight and bias of the BP neural network is characterized to obtain the data information of the random walk matrix of the weight and bias of the BP neural network;

[0077] M23. Based on the data information of the random walk matrix of the weight and bias of the BP neural network, establish the target optimization function W of the random walk matrix,

[0079] Among them, y is the data information of the random walk matrix of the weight and bias of the BP neural network, β1, β2 and β3 are the target optimization factors of the random walk matrix, and the weight and bias of the BP neural network are optimized to obtain the optimized BP neural network;

[0080] M24. Based on the optimized BP neural network, the historical temperature change data information of different sections of the extruder barrel is input, the temperature of each section of the extruder barrel is predicted, and the predicted temperature data information of each section of the extruder barrel is obtained.

[0081] In this embodiment, the target optimization factors β1, β2 and β3 of the random walk matrix are,

[0083] Among them, y is the data information of the random walk matrix of the weight and bias of the BP neural network.

[0084] In this embodiment, the constraints of the penalty factors α1, α2 and α3 of the random walk matrix are:

[0086] Among them, the value range of the function f(α1,α2,α3) is (0,1).

[0087] In this embodiment, if Figure 3 As shown, in step M3, the optimization of the temperature of each section of the extruder barrel by using the improved adaptive Aurora optimization algorithm based on chaos mapping includes:

[0088] M31. Based on the predicted data information of the temperature of each section of the extruder barrel and the data of the temperature of each section of the extruder barrel, a chaotic mapping function R of the extruder barrel is established.

[0090] Among them, r1 is the data information of the temperature of each section of the extruder barrel after prediction, r2 is the data information of the temperature of each section of the extruder barrel, δ1, δ2 and δ3 are the chaotic mapping factors of the extruder barrel, and the chaotic sequence of the temperature of the extruder barrel is characterized to obtain the data information of the chaotic sequence of the temperature of the extruder barrel;

[0091] M32. Based on the chaotic sequence data information of the temperature of the extruder barrel, the aurora population is initialized, the population parameters and the maximum number of iterations L of the population are determined, and the data information of the initialized aurora population is obtained;

[0092] M33. Based on the data information of the initialized aurora population, establish the fitness function P of the aurora population individuals,

[0094] Among them, z is the data information of the initialized aurora population, γ1, γ2 and γ3 are the difference factors of the aurora population individuals, and the fitness values ​​of the aurora population individuals are calculated to obtain the data information of the fitness values ​​of the aurora population individuals;

[0095] M34. Based on the data information of the fitness values ​​of the aurora population individuals, establish the target optimization function S of the aurora population,

[0097] Among them, a is the data information of the fitness value of the individual aurora population, η1, η2 and η3 are the weight factors of the aurora population, and the temperature of each section of the extruder barrel is optimized to obtain the data information of the temperature of each section of the extruder barrel after optimization.

[0098] In this embodiment, the constraint function g of the weight factor of the aurora population is,

[0100] Among them, the value range of the constraint function g is (1,2).

[0101] In this embodiment, the chaotic mapping factors δ1, δ2 and δ3 of the extruder barrel are,

[0103] Among them, r1 is the data information of the temperature of each section of the extruder barrel after prediction, and r2 is the data information of the temperature of each section of the extruder barrel.

[0104] Example 2: Based on the processing temperature control optimization control method applied to plastic pipe fittings in Example 1, the present invention is further illustrated and described below.

[0105] like Figure 1 As shown, a processing temperature optimization control method for plastic pipe fittings, the method comprising:

[0106] M1. During the processing of plastic pipes in the extruder, the historical temperature change data of different sections of the extruder barrel are collected, and the temperature data of each section of the extruder barrel, the screw speed data and the head pressure data are collected in real time;

[0107] M2. Based on the historical temperature change data information of different sections of the extruder barrel, the temperature of each section of the extruder barrel is predicted by using a BP neural network regression prediction algorithm based on random walk matrix optimization to obtain the predicted temperature data information of each section of the extruder barrel;

[0108] M3. Based on the predicted data information of the temperature of each section of the extruder barrel and the data of the temperature of each section of the extruder barrel, the temperature of each section of the extruder barrel is optimized by using an improved adaptive Aurora optimization algorithm based on chaotic mapping to obtain the optimized data information of the temperature of each section of the extruder barrel;

[0109] M4. Based on the data information of the temperature of each section of the optimized extruder barrel, the data information of the screw speed and the data information of the head pressure, a control model of the processing temperature of plastic pipe fittings based on crazy adaptation is constructed to control the processing temperature of plastic pipe fittings and output the control data information of the processing temperature of plastic pipe fittings.

[0110] In this embodiment, if Figure 4 As shown, in step M4, the construction of a control model for the processing temperature of the plastic pipe fitting based on crazy self-adaptation and the control of the processing temperature of the plastic pipe fitting include:

[0111] M41. Based on the optimized data information of the temperature of each section of the extruder barrel, the data information of the screw speed and the data information of the head pressure, a relationship function G between the temperature of each section of the extruder barrel, the screw speed and the head pressure is established,

[0113] Among them, h1 is the data information of the temperature of each section of the extruder barrel after optimization, h2 is the data information of the screw speed, h3 is the data information of the head pressure, λ1, λ2 and λ3 are relationship factors, and the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure is characterized to obtain the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure;

[0114] M42. Input the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the die head pressure into the control model of the processing temperature of the plastic pipe fittings based on crazy adaptive training and learning, and determine the control function H of the processing temperature of the plastic pipe fittings,

[0116] Among them, c is the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure, μ1, μ2 and μ3 are the crazy adaptive determining factors of the processing temperature of the plastic pipe fittings, and the trained crazy adaptive control model of the processing temperature of the plastic pipe fittings is obtained;

[0117] M43. Based on the trained crazy adaptive control model of the processing temperature of plastic pipe fittings, the data information of the temperature of each section of the optimized extruder barrel, the data information of the screw speed and the data information of the head pressure are input to control the processing temperature of the plastic pipe fittings, and output the control data information of the processing temperature of the plastic pipe fittings.

[0118] In this embodiment, the crazy adaptive determining factors μ1, μ2 and μ3 of the processing temperature of the plastic pipe are,

[0120] Among them, c is the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure.

[0121] In this embodiment, the present invention provides a processing temperature optimization control system applied to plastic pipe fittings, including a computer device, which is programmed or configured to execute any one of the steps of the processing temperature optimization control method applied to plastic pipe fittings.

[0122] like Figure 5 As shown, a processing temperature optimization control system applied to plastic pipe fittings comprises:

[0123] The data acquisition module is used to collect the historical temperature change data information of different sections of the extruder barrel, and collect the temperature data information of each section of the extruder barrel, the screw speed data information and the head pressure data information in real time;

[0124] The temperature prediction module of different sections of the extruder barrel is connected to the data acquisition module, and the temperature of each section of the extruder barrel is predicted by using a BP neural network regression prediction algorithm based on random walk matrix optimization to obtain the predicted temperature data information of each section of the extruder barrel;

[0125] The temperature optimization module of different sections of the extruder barrel is connected to the temperature prediction module of different sections of the extruder barrel, and is used to optimize the temperature of each section of the extruder barrel by adopting an improved adaptive Aurora optimization algorithm based on chaotic mapping, so as to obtain data information of the temperature of each section of the extruder barrel after optimization;

[0126] The temperature control modules of different sections of the extruder barrel are connected to the temperature optimization modules of different sections of the extruder barrel, and are used to construct a control model of the processing temperature of plastic pipe fittings based on crazy adaptation, control the processing temperature of plastic pipe fittings, and output control data information of the processing temperature of plastic pipe fittings.

[0127] In this embodiment, if Figure 6 As shown, a screw speed sensor 1 is provided on the screw drive motor of the extruder, which is used to obtain data information of the screw speed of the extruder in real time; a plurality of groups of barrel temperature sensors 2 of the extruder are provided on the barrel of the extruder, which are used to obtain data information of the temperature of each section of the barrel of the extruder in real time; a die head pressure sensor 3 is provided on the die head of the extruder, which is used to obtain data information of the die head pressure in real time.

[0128] In this embodiment, the present invention provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the processing temperature optimization control methods for plastic pipe fittings.

[0129] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0130] In summary, the present invention can not only accurately control and adjust the temperature of each section of the extruder barrel, thereby reducing energy consumption, but also can adaptively adjust according to different application scenarios during the control process, thereby improving the robustness of the system.

[0131] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A processing temperature optimization control method for plastic pipe fittings, characterized in that: The method comprises: M1. During the processing of plastic pipes in the extruder, the historical temperature change data of different sections of the extruder barrel are collected, and the temperature data of each section of the extruder barrel, the screw speed data and the head pressure data are collected in real time; M2. Based on the historical temperature change data information of different sections of the extruder barrel, the temperature of each section of the extruder barrel is predicted by using a BP neural network regression prediction algorithm based on random walk matrix optimization to obtain the predicted temperature data information of each section of the extruder barrel; M3. Based on the predicted data information of the temperature of each section of the extruder barrel and the data of the temperature of each section of the extruder barrel, the temperature of each section of the extruder barrel is optimized by using an improved adaptive Aurora optimization algorithm based on chaotic mapping to obtain the optimized data information of the temperature of each section of the extruder barrel; M4. Based on the data information of the temperature of each section of the optimized extruder barrel, the data information of the screw speed and the data information of the head pressure, a control model of the processing temperature of plastic pipe fittings based on crazy adaptation is constructed to control the processing temperature of plastic pipe fittings and output the control data information of the processing temperature of plastic pipe fittings.

2. The processing temperature optimization control method for plastic pipe fittings according to claim 1 is characterized in that: In step M2, the prediction of the temperature of each section of the extruder barrel by using the BP neural network regression prediction algorithm based on random walk matrix optimization includes: M21. Inputting the historical temperature change data information of different sections of the extruder barrel into the BP neural network for training and learning, initializing the weights and biases of the BP neural network, and obtaining the weights and biases of the initialized BP neural network; M22. Based on the weight and bias data information of the initialized BP neural network, a random walk matrix function Q is established. Among them, x1 is the data information of the weight of the BP neural network after initialization, x2 is the data information of the bias of the BP neural network after initialization, α1, α2 and α3 are the penalty factors of the random walk matrix, and the random walk matrix of the weight and bias of the BP neural network is characterized to obtain the data information of the random walk matrix of the weight and bias of the BP neural network; M23. Based on the data information of the random walk matrix of the weight and bias of the BP neural network, establish the target optimization function W of the random walk matrix, Among them, y is the data information of the random walk matrix of the weight and bias of the BP neural network, β1, β2 and β3 are the target optimization factors of the random walk matrix, and the weight and bias of the BP neural network are optimized to obtain the optimized BP neural network; M24. Based on the optimized BP neural network, the historical temperature change data information of different sections of the extruder barrel is input, the temperature of each section of the extruder barrel is predicted, and the predicted temperature data information of each section of the extruder barrel is obtained.

3. The processing temperature optimization control method for plastic pipe fittings according to claim 2 is characterized in that: The target optimization factors β1, β2 and β3 of the random walk matrix are, Among them, y is the data information of the random walk matrix of the weight and bias of the BP neural network.

4. The processing temperature optimization control method for plastic pipe fittings according to claim 2 is characterized in that: The constraints of the penalty factors α1, α2 and α3 of the random walk matrix are: Among them, the value range of the function f(α1,α2,α3) is (0,1).

5. The processing temperature optimization control method for plastic pipe fittings according to claim 1, characterized in that: In step M3, the optimization of the temperature of each section of the extruder barrel by using the improved adaptive Aurora optimization algorithm based on chaos mapping includes: M31. Based on the predicted data information of the temperature of each section of the extruder barrel and the data of the temperature of each section of the extruder barrel, a chaotic mapping function R of the extruder barrel is established. Among them, r1 is the data information of the temperature of each section of the extruder barrel after prediction, r2 is the data information of the temperature of each section of the extruder barrel, δ1, δ2 and δ3 are the chaotic mapping factors of the extruder barrel, and the chaotic sequence of the temperature of the extruder barrel is characterized to obtain the data information of the chaotic sequence of the temperature of the extruder barrel; M32. Based on the chaotic sequence data information of the temperature of the extruder barrel, the aurora population is initialized, the population parameters and the maximum number of iterations L of the population are determined, and the data information of the initialized aurora population is obtained; M33. Based on the data information of the initialized aurora population, establish the fitness function P of the aurora population individuals, Among them, z is the data information of the initialized aurora population, γ1, γ2 and γ3 are the difference factors of the aurora population individuals, and the fitness values ​​of the aurora population individuals are calculated to obtain the data information of the fitness values ​​of the aurora population individuals; M34. Based on the data information of the fitness values ​​of the aurora population individuals, establish the target optimization function S of the aurora population, Among them, a is the data information of the fitness value of the individual aurora population, η1, η2 and η3 are the weight factors of the aurora population, and the temperature of each section of the extruder barrel is optimized to obtain the data information of the temperature of each section of the extruder barrel after optimization.

6. The processing temperature optimization control method for plastic pipe fittings according to claim 5 is characterized in that: The constraint function g of the weight factor of the aurora population is, Among them, the value range of the constraint function g is (1,2).

7. The processing temperature optimization control method for plastic pipe fittings according to claim 5 is characterized in that: The chaotic mapping factors δ1, δ2 and δ3 of the extruder barrel are, Among them, r1 is the data information of the temperature of each section of the extruder barrel after prediction, and r2 is the data information of the temperature of each section of the extruder barrel.

8. The processing temperature optimization control method for plastic pipe fittings according to claim 1, characterized in that: In step M4, the construction of a control model for the processing temperature of the plastic pipe fitting based on crazy self-adaptation and the control of the processing temperature of the plastic pipe fitting include: M41. Based on the optimized data information of the temperature of each section of the extruder barrel, the data information of the screw speed and the data information of the head pressure, a relationship function G between the temperature of each section of the extruder barrel and the screw speed and temperature is established, Among them, h1 is the data information of the temperature of each section of the extruder barrel after optimization, h2 is the data information of the screw speed, h3 is the data information of the head pressure, λ1, λ2 and λ3 are relationship factors, and the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure is characterized to obtain the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure; M42. Input the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the die head pressure into the control model of the processing temperature of the plastic pipe fittings based on crazy adaptive training and learning, and determine the control function H of the processing temperature of the plastic pipe fittings, Among them, c is the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure, μ1, μ2 and μ3 are the crazy adaptive determining factors of the processing temperature of the plastic pipe fittings, and the trained crazy adaptive control model of the processing temperature of the plastic pipe fittings is obtained; M43. Based on the trained crazy adaptive control model of the processing temperature of plastic pipe fittings, the data information of the temperature of each section of the optimized extruder barrel, the data information of the screw speed and the data information of the head pressure are input to control the processing temperature of the plastic pipe fittings, and output the control data information of the processing temperature of the plastic pipe fittings.

9. The processing temperature optimization control method for plastic pipe fittings according to claim 8, characterized in that: The crazy adaptive determining factors μ1, μ2 and μ3 of the processing temperature of the plastic pipe fitting are, Among them, c is the data information of the relationship between the temperature of each section of the extruder barrel, the screw speed and the head pressure.

10. A processing temperature optimization control system for plastic pipe fittings, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the processing temperature optimization control method for plastic pipes as described in any one of claims 1 to 9.

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