A hot melt adhesive for heat shrinkable tapes and a method and apparatus for its preparation

Abstract

The application provides a heat-shrinkable tape hot melt adhesive and a preparation method and device, belongs to the field of high polymer materials and preparation technology, and is composed of ethylene-vinyl acetate copolymer, C5 petroleum resin, dioctyl phthalate, hindered phenol 1010 antioxidant and talcum powder. The core purpose of the application is to solve the core pain points of poor raw material adaptation, process disconnection and unstable forming in the preparation of the existing hot melt adhesive, to follow the technical development direction of the high polymer material processing field, and to finally obtain a high-performance hot melt adhesive with a tensile strength of greater than or equal to 15 MPa, a peeling strength of greater than or equal to 8 N / cm, a melting temperature of 85-95 DEG C and a performance qualified rate of greater than or equal to 95%, so as to meet the harsh requirements of the pipeline anticorrosion engineering, and improve the product application range and reliability.

Description

Technical Field

[0001] This invention relates to the field of polymer materials and their preparation technology, and in particular to a heat-shrinkable tape hot melt adhesive and its preparation method and apparatus. Background Technology

[0002] Heat shrinkable tape is a core component of pipeline corrosion protection. Its bonding performance is directly determined by the matching hot melt adhesive. The hot melt adhesive must simultaneously possess high tensile strength, good peel strength (for bonding with the steel pipe substrate), and stable melting temperature in order to resist the risk of corrosion layer detachment caused by soil stress, chemical media erosion, and extreme temperature changes.

[0003] Existing hot melt adhesive preparation technologies are limited by traditional process logic, making it difficult to meet the precision and stability requirements of polymer material processing. The core pain points are as follows: Poor raw material compatibility: Traditional preparation methods only use a fixed ratio to mix polymer base materials and functional ingredients, failing to consider the impact of synergistic deviations in raw material particle size and moisture content on mixing compatibility. This results in uneven system dispersion, making subsequent processing prone to agglomeration. Disconnected processing technology: The entire processing flow lacks dynamic matching with the initial state of the raw materials. Key steps such as mixing and homogenization rely on experience-based parameter settings, leading to large viscosity fluctuations and failing to establish a synergistic control mechanism between raw materials and processes. Poor pretreatment and molding effects: Material pretreatment involves only simple drying without graded activation based on different raw material characteristics. Rapid cooling after molding leads to concentrated internal thermal stress, making the product prone to cracking defects, with a core performance compliance rate of only 85%-90%. Uncontrolled dispersion of functional ingredients: There are no standardized requirements for the order of adding functional ingredients such as antioxidants and fillers, making them prone to adsorption and agglomeration, failing to fully utilize their aging resistance and reinforcing functions, and affecting the overall performance of the product. Lack of a closed-loop control mechanism: When product performance fails to meet standards, it is impossible to accurately determine the direction of adjustment for raw material ratios or processing parameters, requiring repeated trial and error optimization, resulting in low production efficiency and high overall costs. These problems make it difficult for existing hot melt adhesives to meet the stringent application requirements of pipeline corrosion protection projects, limiting their promotion in high-pressure, low-temperature, and complex geological environments, and also failing to adapt to the trend of precision development in the field of polymer material processing. Summary of the Invention

[0004] This invention provides a heat-shrinkable tape hot melt adhesive and its preparation method and apparatus. The core objective is to address the critical pain points in existing hot melt adhesive preparation methods, such as poor raw material compatibility, process disconnect, and unstable molding. Aligning with the technological development trends in the field of polymer material processing, this invention specifically achieves the following goals: Optimizing the composite system of polymer substrate and functional ingredients; establishing a synergistic compatibility mechanism for the multi-dimensional characteristics of raw materials (particle size, moisture content, viscosity) to improve mixing compatibility; innovating the entire processing technology to achieve dynamic parameter matching in material pretreatment, mixing, homogenization, and molding, solving key problems such as agglomeration and stress concentration; constructing a closed-loop control system for raw material composite, processing technology, and product performance to precisely locate the direction of parameter adjustment and avoid blind trial and error; and ultimately obtaining a high-performance hot melt adhesive with tensile strength ≥15MPa, peel strength ≥8N / cm, melting temperature 85-95℃, and performance qualification rate ≥95%, meeting the stringent requirements of pipeline corrosion protection engineering and improving the product's application range and reliability. To achieve the above objectives, this invention adopts the following technical solution: A heat-shrinkable tape hot melt adhesive (prepared using the method described below) is composed of ethylene-vinyl acetate copolymer, C5 petroleum resin, dioctyl phthalate, hindered phenolic 1010 antioxidant, and talc. The ethylene-vinyl acetate copolymer has a moisture content ≤0.1% and a particle size of 100-150 μm, the C5 petroleum resin has a moisture content ≤0.08% and a particle size of 80-120 μm, and the dioctyl phthalate has a moisture content ≤0.08% and a particle size of 80-120 μm. The moisture content of dioctyl phthalate is ≤0.05%, and the viscosity at 25℃ is 200-300 mPa·s; the moisture content of hindered phenolic 1010 antioxidant is ≤0.06%, and the particle size is 50-80 μm; the moisture content of talc is ≤0.03%, and the particle size is 10-30 μm; the tensile strength of the heat shrinkable tape hot melt adhesive is ≥15 MPa, the peel strength to the steel pipe substrate is ≥8 N / cm, and the melting temperature is 85-95℃.

[0005] In this specification, the VA content of the ethylene-vinyl acetate copolymer is 28%-33%, the viscosity of dioctyl phthalate at 25°C is 230-270 mPa·s, and the bubble content of the heat-shrinkable tape hot melt adhesive is ≤0.3% and the dispersion uniformity is ≥95%.

[0006] A method for preparing a heat-shrinkable tape hot melt adhesive includes: S1. Performing differentiated activation treatments on ethylene-vinyl acetate copolymer, C5 petroleum resin, dioctyl phthalate, hindered phenolic 1010 antioxidant, and talc, and recording the measured moisture content, particle size, and measured viscosity of dioctyl phthalate for each raw material; S2. Based on the measured data recorded in S1, determining the mass proportion of each raw material through a combined calculation of a particle size-moisture content co-adaptation algorithm and a multi-factor weight dynamic allocation algorithm; S3. Based on the mass proportion determined in S2, adding materials in stages according to the order of low melting point melting first and high melting point melting later, and monitoring and recording the viscosity of the mixing system at each stage in real time; S4. Based on the viscosity data recorded in S3, adding hindered phenolic 1010 antioxidant and talc in a preset order, adjusting process parameters for directional dispersion, and recording the mixing... S5. Based on the dispersion uniformity and composite viscosity recorded in S4, homogenization parameters are determined through the collaborative calculation of viscosity-dispersion dual-objective optimization algorithm and ultrasonic energy dynamic attenuation compensation algorithm. The mixed system is homogenized, and the viscosity and particle size distribution range of the homogenized system are recorded. S6. Based on the viscosity and particle size distribution range recorded in S5, the vacuum degree and temperature are adjusted to perform gradient degassing, and the bubble content and viscosity of the degassed system are recorded. S7. Based on the bubble content and viscosity recorded in S6, molding parameters are set and gradient cooling and curing are performed. The tensile strength, peel strength and melting temperature of the product are detected and recorded. S8. Based on the tensile strength, peel strength and melting temperature recorded in S7, the proportioning parameters in S2 or the process parameters in S3-S6 are precisely adjusted until the product performance meets the standards.

[0007] In this specification, the working process of the particle size-moisture content co-adaptation algorithm is as follows: obtain the measured particle size and measured moisture content of each raw material in S1, predetermine the standard particle size median and standard moisture content upper limit of each raw material, calculate the relative deviation of particle size and relative deviation of moisture content of each raw material respectively, and calculate the co-adaptation coefficient of each raw material by combining the preset particle size deviation weight coefficient and moisture content deviation weight coefficient. The value range of the co-adaptation coefficient is 0.8-1.2.

[0008] In this specification, the working process of the multi-factor weight dynamic allocation algorithm is as follows: obtain the measured viscosity of dioctyl phthalate in S1 and the synergistic adaptation coefficient of each raw material, calculate the average value of the synergistic adaptation coefficient of all raw materials and the relative viscosity deviation of dioctyl phthalate, combine the preset basic weight coefficient of each raw material, the influence weight of the synergistic adaptation coefficient and the influence weight of the viscosity deviation, adjust to obtain the dynamic weight coefficient of each raw material, convert the dynamic weight coefficient of all raw materials into the corresponding mass proportion, and ensure that the weighted sum of the synergistic adaptation coefficient of each raw material and the corresponding mass proportion meets the preset threshold.

[0009] In this specification, the collaborative process between the particle size-moisture content co-adaptation algorithm and the multi-factor weight dynamic allocation algorithm is as follows: the co-adaptation coefficients of each raw material obtained by the particle size-moisture content co-adaptation algorithm are used as the core input of the multi-factor weight dynamic allocation algorithm. After the multi-factor weight dynamic allocation algorithm calculates the mass proportion of each raw material, it calculates the weighted sum of the co-adaptation coefficients of each raw material and the corresponding mass proportions in reverse. If the weighted sum does not meet the preset threshold, the particle size deviation weight coefficient and the moisture content deviation weight coefficient of the particle size-moisture content co-adaptation algorithm are adjusted, and the co-adaptation coefficients and mass proportions are recalculated until the weighted sum meets the preset threshold.

[0010] In this specification, the working process of the viscosity-dispersion dual-objective optimization algorithm is as follows: obtain the dispersion uniformity and composite viscosity obtained from S4, predetermine the target median viscosity and target dispersion uniformity after homogenization, calculate the relative deviation of viscosity and the relative deviation of dispersion, combine the preset viscosity deviation weighting coefficient, dispersion deviation weighting coefficient and equipment energy consumption penalty coefficient, construct the objective function, and within the equipment parameter constraints, find the ultrasonic power and mechanical stirring speed that minimize the objective function value as the initial homogenization parameters.

[0011] In this specification, the working process of the ultrasonic energy dynamic attenuation compensation algorithm is as follows: obtain the initial homogenization parameters and the composite viscosity obtained by S4, predetermine the homogenization time, calculate the initial output energy and energy attenuation coefficient of the ultrasonic generator, and then obtain the actual ultrasonic energy received inside the material and the ultrasonic energy compensation amount. Based on the initial energy and compensation amount, adjust the compensated ultrasonic power and mechanical stirring speed to ensure that the stirring speed matches the compensated ultrasonic power.

[0012] In this specification, the collaborative process between the viscosity-dispersion bi-objective optimization algorithm and the ultrasonic energy dynamic attenuation compensation algorithm is as follows: the initial homogenization parameters obtained by the viscosity-dispersion bi-objective optimization algorithm are used as the initial input of the ultrasonic energy dynamic attenuation compensation algorithm. After the ultrasonic energy dynamic attenuation compensation algorithm calculates the compensated homogenization parameters, it calculates the corresponding viscosity deviation and dispersion deviation based on the compensated parameters and constructs the compensated objective function. If the value of the objective function exceeds the preset upper limit, the weight coefficients and penalty coefficients of the viscosity-dispersion bi-objective optimization algorithm are readjusted, and the initial homogenization parameters and the compensated homogenization parameters are recalculated until the value of the compensated objective function meets the preset requirements.

[0013] An apparatus for preparing heat-shrinkable hot melt adhesive tape according to any one of the above methods includes a raw material pretreatment device, a proportioning calculation module, a mixing device, a directional dispersion device, a synergistic homogenization device, a degassing device, a molding and cooling device, a performance testing device, and a parameter adjustment module.

[0014] In summary, this invention has at least the following beneficial effects: Significantly improved raw material compatibility: Through precise proportioning using dual algorithms, the raw material mixing compatibility error is ≤±0.5%, mixing compatibility is improved by more than 30%, and the dispersion uniformity of functional ingredients is ≥96%, fully leveraging their functions such as aging resistance and reinforcement. Significantly optimized process synergy: The entire processing technology is dynamically matched with the raw material state, viscosity fluctuations in the mixing stage are controlled within ±5%, the raw material compliance rate after pretreatment is 100%, and process stability is significantly improved. Extremely low defect rate in molded products: Gradient cooling technology effectively alleviates internal thermal stress, and gradient degassing reduces the product bubble content to ≤0.1% (as low as 0.08% in the example), and the defect rate of pores, cracks, etc., is reduced to below 1%. Stable and compliant core performance: The product tensile strength is ≥16MPa, peel strength is ≥8.5N / cm, and melting temperature is 85-90℃, with a pass rate of ≥98% for the three core indicators. This represents a significant improvement in stability compared to existing technologies (85%-90%), fully meeting the requirements of pipeline corrosion protection projects. Production efficiency and cost optimization: The closed-loop control mechanism avoids repeated trial and error, shortening the production cycle by 20%, increasing raw material utilization by 15%, and reducing overall production costs by 12%-15%, demonstrating significant industrialization advantages. Expanding application scenarios: The product's high performance and stability allow it to adapt to a wide temperature range of -40℃ to 80℃ and complex geological environments, successfully covering harsh corrosion protection scenarios such as high-pressure pipelines and deep-sea pipelines, significantly broadening its application scope. This invention, through the deep integration of raw material adaptation, process innovation, and closed-loop control, achieves a leap from experience-based preparation to precise and intelligent preparation of hot melt adhesives, providing reliable material support for pipeline corrosion protection engineering and fully aligning with the technological development requirements of the polymer material processing field. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the steps of the preparation method of the heat-shrinkable tape hot melt adhesive involved in this invention.

[0016] Figure 2 This is a schematic diagram of the dual-algorithm matching and interaction process involved in this invention.

[0017] Figure 3 This is a schematic diagram of the interaction process of the ultrasonic-mechanical homogenization algorithm involved in this invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] This embodiment provides a heat-shrinkable tape hot melt adhesive, composed of ethylene-vinyl acetate copolymer, C5 petroleum resin, dioctyl phthalate, hindered phenolic 1010 antioxidant, and talc. The ethylene-vinyl acetate copolymer has a moisture content ≤0.1% and a particle size of 100-150 μm; the C5 petroleum resin has a moisture content ≤0.08% and a particle size of 80-120 μm; the dioctyl phthalate has a moisture content ≤0.05% and a viscosity of 200-300 mPa·s at 25°C; the hindered phenolic 1010 antioxidant has a moisture content ≤0.06% and a particle size of 50-80 μm; and the talc has a moisture content ≤0.03% and a particle size of 10-30 μm. The heat-shrinkable tape hot melt adhesive has a tensile strength ≥15 MPa, a peel strength to the steel pipe substrate ≥8 N / cm, and a melting temperature of 85-95°C.

[0020] In some embodiments, the VA content of the ethylene-vinyl acetate copolymer is 28%-33%, the viscosity of dioctyl phthalate at 25°C is 230-270 mPa·s, and the bubble content of the heat-shrinkable tape hot melt adhesive is ≤0.3% and the dispersion uniformity is ≥95%.

[0021] refer to Figure 1This embodiment provides a method for preparing heat-shrinkable hot melt adhesive tape. It is a hot melt adhesive preparation scheme that combines raw material synergistic adaptation, full-process linkage, and closed-loop control. The core process revolves around the entire chain from raw materials to processing and molding, specifically as follows: Material classification and activation pretreatment: Differential activation treatment is carried out to address the differences in characteristics between the polymer substrate (ethylene-vinyl acetate copolymer, C5 petroleum resin) and functional ingredients (antioxidant, talc), precisely controlling the moisture content and particle size, and recording core data to lay the foundation for subsequent raw material blending and matching. Precise raw material proportioning using dual algorithms: The compatibility of different raw materials is quantified through a particle size-moisture content synergistic adaptation algorithm, combined with a multi-factor weight dynamic allocation algorithm to dynamically adjust the mass ratio of the polymer substrate and functional ingredients, forming a closed-loop calculation logic to ensure the compatibility and functional integrity of the mixed system. Staged Melting and Directional Dispersion: Materials are added in stages, with lower melting points melting first and higher melting points melting later. Stirring speed and temperature are dynamically adjusted based on viscosity data from previous stages. Functional ingredients are added in a directional order, with antioxidants added first and fillers added later, combined with specific stirring parameters to prevent agglomeration. Synergistic Homogenization: Initial homogenization parameters are determined using a viscosity-dispersion dual-objective optimization algorithm, combined with an ultrasonic energy dynamic attenuation compensation algorithm to compensate for energy loss in high-viscosity materials, achieving molecular-level homogenization of the mixture and enhancing structural uniformity. Gradient Degassing and Gradient Cooling: Based on the homogenized viscosity and particle size distribution data, vacuum and temperature are adjusted gradients to thoroughly remove microbubbles from the system. A two-stage gradient cooling solidification method is used to slowly release internal thermal stress, ensuring the structural stability of the molded product. Closed-Loop Feedback Adjustment: Based on the performance test data of the final product, raw material ratios or processing parameters are precisely adjusted in reverse, achieving a closed-loop control from substandard performance to precise parameter adjustment and product qualification, ensuring product consistency.

[0022] I. Preparation Steps

[0023] S1. Raw Material Grading and Activation Pretreatment: The performance of heat-shrinkable tape hot melt adhesive depends on the initial state of the raw materials. Raw materials that have not been precisely activated are prone to problems such as excessive moisture content and uneven particle size distribution, directly leading to poor compatibility and uneven dispersion in the subsequent melt mixing stage, ultimately affecting the product's core properties such as tensile strength and peel strength. Therefore, this step involves grading and activating the core raw material system of the hot melt adhesive. By controlling differentiated temperature, time, and atmosphere conditions, the physical state of each type of raw material is precisely optimized, and key data is recorded to provide a core basis for subsequent proportioning calculations. The selected raw material system includes ethylene-vinyl acetate copolymer, C5 petroleum resin, dioctyl phthalate, hindered phenolic 1010 antioxidant, and talc. The activation treatment process for each raw material is as follows: 1. Activation of Ethylene-Vinyl Acetate Copolymer: The raw material was placed in a vacuum drying oven. To avoid resin molecular chain breakage due to high temperature, the activation temperature range was set to 75 to 85 degrees Celsius, and the activation time was controlled at 40 to 50 minutes, maintaining a vacuum level of -0.07 to -0.06 MPa throughout the process. After treatment, the moisture content was measured using a Karl Fischer moisture analyzer, requiring it to be no more than 0.1%; the particle size was measured using a laser particle size analyzer, requiring it to be controlled within 100 to 150 micrometers. The measured moisture content was recorded as follows. The measured particle size is ,in Characterizing the moisture content percentage of ethylene-vinyl acetate copolymers. The average diameter of the raw material particles is used to characterize the particle size.

[0024] 2. C5 Petroleum Resin Activation: Place the raw material in a forced-air drying oven. To balance drying efficiency and the risk of resin softening, set the activation temperature range to 60-70 degrees Celsius, the activation time to 30-40 minutes, and the forced-air speed to 2-3 meters per second. After processing, the moisture content should not exceed 0.08%, and the particle size should be controlled between 80 and 120 micrometers. Record the measured moisture content. The measured particle size is ,in Characterizing the moisture content percentage of C5 petroleum resin. The average diameter of the raw material particles is used to characterize the particle size.

[0025] 3. Activation of dioctyl phthalate: Place the raw material in a constant temperature bath. To reduce plasticizer volatilization loss, set the activation temperature range to 50-60 degrees Celsius, control the activation time to 20-25 minutes, and maintain the stirring rate at 100-150 rpm. After treatment, the moisture content should not exceed 0.05%, and the viscosity should be controlled at 200-300 mPa·s at 25 degrees Celsius. Record the measured moisture content. The measured viscosity is ,in Characterizing the water content percentage of dioctyl phthalate. Characterizes the flow resistance of the raw material at a set temperature.

[0026] 4. Activation of Hindered Phenolic 1010 Antioxidant: Place the raw material in a vacuum drying oven. To ensure the stability of the active antioxidant groups, set the activation temperature range to 80-90 degrees Celsius, the activation time to 35-45 minutes, and the vacuum level to -0.08-0.07 MPa. After treatment, the moisture content should not exceed 0.06%, and the particle size should be controlled between 50 and 80 micrometers. Record the measured moisture content. The measured particle size is ,in Characterizing the percentage of moisture content in hindered phenolic 1010 antioxidants. The average diameter of the raw material particles is used to characterize the particle size.

[0027] 5. Talc Activation: The raw material is placed in a high-temperature activation furnace. To remove adsorbed water from the filler surface and avoid high-temperature modification, the activation temperature range is set to 110-120 degrees Celsius, and the activation time is controlled at 60-70 minutes. Nitrogen gas is introduced as a protective atmosphere, and the aeration rate is maintained at 1-2 liters per minute. After treatment, the moisture content should not exceed 0.03%, and the particle size should be controlled at 10-30 micrometers. Record the measured moisture content. The measured particle size is ,in Characterizing the percentage of moisture content in talc. The average diameter of the raw material particles is used to characterize the particle size.

[0028] After all raw materials have been activated, if any test indicator fails to meet the above requirements, the corresponding activation process must be repeated until all indicators meet the specified range. All moisture content and particle size data recorded in this step will serve as core input parameters for subsequent proportioning calculations, directly determining the accuracy of the proportioning results and thus affecting the final product performance of the entire preparation process.

[0029] S2. Precise Proportioning Calculation Based on Dual Algorithm Fusion: Traditional hot melt adhesive proportioning often uses fixed ratios or single index adjustments, failing to consider the synergistic effects of the multi-dimensional physical properties of raw materials. This can easily lead to insufficient compatibility between the proportioned results and subsequent melting and homogenization steps. Therefore, refer to... Figure 2 This step innovatively integrates a particle size-moisture content collaborative adaptation algorithm with a multi-factor weight dynamic allocation algorithm to form a closed-loop interactive logic, ultimately achieving accurate calculation of the raw material mass ratio, and providing a scientific basis for feeding in the subsequent graded melting process.

[0030] (I) Particle size-moisture content co-adaptation algorithm: The core value of this algorithm lies in breaking through the limitation of evaluating the compatibility of raw materials with a single index, quantifying the impact of the co-deviation of particle size and moisture content on the compatibility of raw materials, providing accurate quantitative basis for compatibility in subsequent proportioning, and avoiding unreasonable proportioning caused by deviation of the initial state of raw materials.

[0031] 1. Model Construction: The core logic of model construction is to calculate the relative deviation between the measured particle size and moisture content of the raw materials and the standard values, and combine the weight allocation of the two types of deviations to obtain the co-fit coefficient that characterizes the compatibility of the raw materials.

[0032] First, calculate the relative particle size deviation using the following formula: ; in This is an index for raw material types, with values ​​of 1, 2, 4, and 5, corresponding to ethylene-vinyl acetate copolymer, C5 petroleum resin, hindered phenolic 1010 antioxidant, and talc, respectively. For the first The measured particle size of the raw material is derived from the measured data in step S1; For the first The standard median particle size of the raw materials was determined based on the particle size data of 50 qualified products, including the ethylene-vinyl acetate copolymer. For 125 micrometers, C5 petroleum resin For 100 micrometers, hindered phenolic 1010 antioxidant The micrometer size is 65 micrometers, and the talc content is... It is 20 micrometers; For the first The relative particle size deviation of similar raw materials characterizes the degree of deviation between the measured particle size and the standard median. Dioctyl phthalate has no particle size testing requirements. The value is 0.

[0033] Next, calculate the relative deviation of moisture content using the following formula: ; in The value ranges from 1 to 5, covering all raw material types; For the first The measured moisture content of the raw materials is derived from the measured data in step S1; For the first The upper limit of the standard moisture content for similar raw materials is determined based on the raw material performance threshold, among which the moisture content of ethylene-vinyl acetate copolymers is... The content is 0.08%, of C5 petroleum resin. The content of dioctyl phthalate is 0.06%. The content is 0.03%, hindered phenolic 1010 antioxidant. The content of talc is 0.04%. It is 0.02%; For the first The relative deviation of the moisture content of raw materials characterizes the degree of deviation between the measured moisture content and the upper limit of the standard.

[0034] Finally, the co-adaptation coefficient is calculated using the following formula: ;in This is the particle size deviation weighting coefficient, which characterizes the degree of influence of particle size deviation on fit. Let be the weighting coefficient for moisture content deviation, characterizing the degree of influence of moisture content deviation on fit, and satisfying the following conditions: ; For the first The compatibility coefficient of raw materials is limited to a range of 0.8 to 1.2. The closer the value is to 1, the better the compatibility of the raw materials. If the calculated result is less than 0.8, then take 0.8; if it is greater than 1.2, then take 1.2.

[0035] 2. Model Training Process: The core objective of model training is to achieve a product qualification rate of no less than 95% on the validation set, ensuring that the algorithm's output co-fit coefficients effectively correlate raw material status with product performance. Training data comes from 50 sets of historical raw material grading and activation preprocessing data; each set includes data on various types of raw materials. , The data includes corresponding product qualification data, with qualification criteria being tensile strength not less than 15 MPa, peel strength not less than 8 N / cm, and melt temperature between 85 and 95 degrees Celsius. The 50 data sets are divided into a training set of 40 sets and a validation set of 10 sets. Initial settings... 0.65 The value was set to 0.35, and gradient descent was used for iterative optimization with an iteration step size of 0.01 and 500 iterations. After each iteration, the raw data from the training set was substituted into the algorithm for calculation. And link it to the qualification status of the corresponding products, and adjust accordingly. and The value of is determined when the validation set product pass rate stabilizes at 96% after 320 iterations, meeting the set target. The final value was 0.68. The final value is 0.32.

[0036] 3. Model Application Process: Substitute the measured data of the five types of raw materials from step S1 into the above formula, and calculate the synergistic compatibility coefficient for each. The following is an example: If the ethylene-vinyl acetate copolymer... 120 micrometers If it is 0.07%, then , , If C5 petroleum resin For 90 micrometers, If it is 0.05%, then , , If dioctyl phthalate... If it is 0.02%, then , If hindered by phenolic 1010 antioxidants For 60 micrometers, If it is 0.03%, then , , If talcum powder 15 micrometers If it is 0.01%, then , , Since it is less than 0.8, the final value is taken as... .

[0037] (II) Multi-factor weight dynamic allocation algorithm: The core value of this algorithm lies in dynamically adjusting the weights of various raw materials based on the co-adaptation coefficient and combined with the viscosity deviation of plasticizers, and converting them into mass proportions. At the same time, through closed-loop calibration logic, it ensures that the formulation results meet the product performance requirements, thus solving the problem of fixed weights in traditional formulations that cannot adapt to changes in the state of raw materials.

[0038] 1. Model Construction: The core logic of model construction is to first calculate the plasticizer viscosity deviation, then adjust the basic weights of raw materials based on the synergistic adaptation coefficient, and finally convert it into a mass proportion. The proportioning results are then calibrated using weighted sums and thresholds. First, the relative deviation of the plasticizer viscosity is calculated using the following formula: ; in The measured viscosity of dioctyl phthalate is derived from the measured data in step S1. The standard viscosity of dioctyl phthalate is 250 mPa·s, determined based on melt compatibility experiments. The viscosity relative deviation of dioctyl phthalate characterizes the degree of deviation between the measured viscosity and the standard median. Next, the dynamically adjusted weighting coefficient is calculated. For raw materials other than dioctyl phthalate, the formula is: ; in The values ​​are 1, 2, 4, and 5. For the first The basic weighting coefficients for similar raw materials are initially set to meet the following requirements. Among them, ethylene-vinyl acetate copolymer The value is 0.6, for C5 petroleum resin. The value is 0.23, which is the value of hindered phenolic 1010 antioxidant. The value is 0.015, which is the value of talc. It is 0.045; For the first The synergistic adaptation coefficient of the raw materials is derived from the calculation results of the particle size-moisture content synergistic adaptation algorithm; The average value of the compatibility coefficients of all raw materials is calculated using the following formula: ; The adaptive coefficient influences the weights, representing the degree to which the adaptive coefficient adjusts the weights; For the first The weighting coefficients of raw materials after dynamic adjustment.

[0039] For dioctyl phthalate, the formula is: ; in The basic weighting coefficient for dioctyl phthalate is initially set to 0.1; The weighting of viscosity deviation is applied only to dioctyl phthalate, characterizing the degree to which viscosity deviation adjusts its weighting. The weighting coefficient is the dynamically adjusted value of dioctyl phthalate.

[0040] Then calculate the mass percentage using the following formula: ; in For the first The mass percentage of similar raw materials, meeting the requirements. This provides a direct basis for subsequent material feeding. Finally, an algorithm interaction calibration is performed, using the following formula: ;in The weighted sum threshold for the co-fit coefficient is set at 0.95 based on product performance requirements. If this condition is not met, the weighting coefficients of the particle size-moisture content co-fit algorithm need to be adjusted, and the adjustment rule is as follows: , Recalculate and Continue until the conditions are met.

[0041] 2. Model Training Process: Model training aims to achieve both weighted sum and threshold targets, and a product qualification rate of no less than 95%, ensuring that the algorithm's output formulation results are both adaptable and practical. Based on 50 sets of data from the particle size-moisture content co-adaptation algorithm, 30 sets of formulation and performance correlation data at different viscosities were added to the training data, covering the entire viscosity range of dioctyl phthalate from 200 to 300 mPa·s. Particle swarm optimization was used for training, with a particle dimension set to 2, corresponding to... and The number of particles was set to 30, the number of iterations to 400, and the inertia weight to 0.7. After each iteration, the raw data from the training set was substituted into the algorithm for calculation. The weighted sum threshold and product qualification rate are verified to meet the target. When the iteration reaches 280, both targets are met, at which point the determination is made. The final value was 0.47. The final value is 0.13.

[0042] 3. Model Application Process: Based on the calculation results of the particle size-moisture content co-adaptation algorithm, combined with the viscosity data from step S1, the complete proportioning calculation is performed: First, calculate... Substitute into the example , , , , ,but If dioctyl phthalate... If it is 230 millipascals, then Calculate the dynamically adjusted weighting coefficients one by one: Ethylene-vinyl acetate copolymer: C5 petroleum resin: Dioctyl phthalate: Hindered phenolic 1010 antioxidant: .talcum powder: Calculate the sum of the weighting coefficients: Converted to mass percentage: ethylene-vinyl acetate copolymer: C5 petroleum resin: Dioctyl phthalate: Hindered phenolic 1010 antioxidant: .talcum powder: Perform interactive calibration: ; 0.91 is less than 0.95, which does not meet the condition. Adjust the coefficients of the particle size-moisture content co-fitting algorithm: , Recalculate Ethylene-vinyl acetate copolymer: C5 petroleum resin: Recalculate Calculate again and Finally obtained To meet the threshold requirements, the final quality percentage is determined.

[0043] S3. Staged Melting and Co-mixing: After precise proportioning calculations, the mass ratio of raw materials is clear. The core objective of this step is to achieve gradual compatibility of raw materials with different melting points through staged melting, avoiding problems such as localized overheating and uneven mixing caused by one-time feeding. Simultaneously, the viscosity data of the melt system is monitored in real time to provide parameter basis for subsequent directional dispersion. A twin-screw extruder is used as the mixing equipment, which features segmented temperature control and adjustable speed, adapting to the process requirements of staged melting. The specific implementation process is as follows: 1. Equipment Preheating: The twin-screw extruder barrel is divided into three heating zones: zone one at the feed end, zone two in the middle section, and zone three at the discharge end. Zone one is preheated to 90-100 degrees Celsius, a temperature range suitable for the melting requirements of dioctyl phthalate; zone two is preheated to 120-130 degrees Celsius, a temperature range suitable for the melting requirements of C5 petroleum resin; and zone three is preheated to 140-150 degrees Celsius, a temperature range suitable for the melting requirements of ethylene-vinyl acetate copolymer. The screw speed is preset to 200-250 revolutions per minute, and the preheating time is controlled to 30 minutes to ensure uniform and stable temperature in all areas of the barrel, avoiding temperature fluctuations after feeding that could affect the melting effect.

[0044] 2. First-stage melting: Add raw materials according to the mass ratio of dioctyl phthalate determined in step S2. Maintain the temperature in zone one at 90-100°C, zone two at 120-130°C, and zone three at 140-150°C. Keep the screw speed at 200 rpm and the stirring time at 10-15 minutes. After stirring, use an online viscometer to measure the viscosity of the molten dioctyl phthalate in real time and record it. The viscosity should be controlled between 500 and 800 mPa second. If it exceeds this range, the stirring time should be adjusted: if the viscosity is higher than 800 mPa second, extend the stirring time by 5 minutes; if the viscosity is lower than 500 mPa second, shorten the stirring time by 3 minutes until the viscosity meets the standard.

[0045] 3. Second-stage melting: based on Adjust the feeding and mixing parameters according to the test results. Immediately add the raw materials at a concentration of 500 to 650 mPa·s, according to the C5 petroleum resin mass ratio determined in step S2, maintaining a constant temperature in all zones, increasing the screw speed to 220 rpm, and controlling the stirring time to 15 to 20 minutes; if At a speed of 650 to 800 mPa·s, maintaining a screw speed of 200 rpm, the stirring time was extended to 20 to 25 minutes. After stirring, the viscosity of the mixture was measured and recorded. The viscosity should be controlled between 900 and 1200 mPa second. If it exceeds this range, the stirring speed needs to be adjusted: when the viscosity is higher than 1200 mPa second, increase the stirring speed by 20 revolutions per minute; when the viscosity is lower than 900 mPa second, decrease the stirring speed by 20 revolutions per minute and extend the stirring time by 5 minutes until the viscosity meets the standard.

[0046] 4. Third-stage melting: Feed the raw materials according to the mass ratio of the ethylene-vinyl acetate copolymer determined in step S2. Raise the temperature in zone two to 130-140 degrees Celsius and the temperature in zone three to 150-160 degrees Celsius. The purpose of raising the temperature is to ensure that the ethylene-vinyl acetate copolymer melts completely without thermal degradation. Adjust the screw speed based on the test results: If At a speed of 900 to 1050 mPa·s, adjust the rotation speed to 230 rpm and control the stirring time to 25 to 30 minutes; if The stirring speed was adjusted to 250 rpm within the range of 1050 to 1200 mPa·s, and the stirring time was controlled to be 20 to 25 minutes. After stirring, the viscosity of the mixture was measured and recorded. The viscosity should be controlled between 1200 and 1500 mPa second. If it exceeds this range, the temperature needs to be adjusted: when the viscosity is higher than 1500 mPa second, increase the temperature of zone two and zone three by 5 degrees Celsius; when the viscosity is lower than 1200 mPa second, decrease the temperature of zone two and zone three by 5 degrees Celsius and extend the stirring time by 5 minutes until the viscosity meets the standard.

[0047] This step achieves gradual melting and compatibility of different raw materials through graded feeding and dynamic parameter adjustment. The real-time monitored viscosity data provides core parameters for the subsequent directional dispersion of functional additives, ensuring smooth data connection between the preceding and following steps and avoiding process control failures caused by parameter disconnection.

[0048] S4. Directional Dispersion and Mixing of Functional Additives: After the graded melting of the basic raw materials, antioxidants and fillers need to be added. These two types of additives have small particle sizes and are prone to agglomeration. Direct addition will lead to uneven dispersion, affecting the product's anti-aging and mechanical properties. Therefore, this step, based on the viscosity data from step S3, employs directional dispersion to ensure the functional additives are uniformly dispersed in the molten system. Simultaneously, the dispersion effect and composite viscosity are monitored to provide a basis for subsequent homogenization processes. The specific implementation process is as follows: 1. Determining the order of adding materials: First, add the raw materials according to the mass ratio of hindered phenolic 1010 antioxidant determined in step S2, and stir for 5 minutes. Then, add the raw materials according to the mass ratio of talc determined in step S2. The core purpose of this order of adding materials is to allow the antioxidant to fully integrate into the molten system first, to avoid the talc added later adsorbing the antioxidant and causing excessively high local concentrations, and to reduce the probability of talc agglomeration.

[0049] 2. Parameter adjustment rules: based on the records in step S3. Adjust the stirring parameters according to the data. Maintain a barrel temperature of 130-140 degrees Celsius in zone two and 150-160 degrees Celsius in zone three at 1200-1350 mPa·s, adjust the screw speed to 300 rpm, and control the total mixing time to 20 minutes; if Between 1350 and 1500 mPa·s, the temperature in zone two of the barrel is increased to 140 to 150 degrees Celsius, and the temperature in zone three is increased to 160 to 170 degrees Celsius. The screw speed is adjusted to 350 rpm, and the total stirring time is controlled to 25 minutes. The core logic for adjusting the temperature and speed is: the higher the viscosity of the molten system, the higher the temperature needs to be to reduce the viscosity, while increasing the speed enhances the shear force to ensure uniform dispersion of the functional additives.

[0050] 3. Dispersion effect monitoring: After stirring, the dispersion uniformity of the mixture was measured using a laser particle size analyzer and recorded. The requirement is that this index be no less than 95%; the composite viscosity of the mixture is measured using an online viscometer and recorded as follows. This indicator is required to be controlled between 1800 and 2200 millipascals per second. If... If the concentration is below 95%, the screw speed needs to be increased by 50 revolutions per minute, the stirring time extended by 10 minutes, and the mixture redispersed before retesting; if... If the viscosity exceeds the range of 1800 to 2200 mPa·s, the temperature needs to be adjusted: if the viscosity is higher than 2200 mPa·s, increase the temperature by 5 degrees Celsius; if the viscosity is lower than 1800 mPa·s, decrease the temperature by 5 degrees Celsius and extend the stirring time by 5 minutes until both indicators meet the standard.

[0051] This step solves the problem of functional additive agglomeration through directional feeding and dynamic parameter adjustment. The monitored dispersion uniformity and composite viscosity data directly determine the parameter settings for subsequent homogenization processes, ensuring logical connection and data closure between steps.

[0052] S5. Ultrasonic-Mechanical Coordinated Dynamic Homogenization: Even after directional dispersion, the mixed system still suffers from uneven molecular-level dispersion. Traditional single mechanical stirring or ultrasonic homogenization methods cannot simultaneously optimize both viscosity and dispersion. Furthermore, ultrasonic energy is easily attenuated in high-viscosity materials, resulting in poor homogenization effects. Therefore, refer to... Figure 3 This step innovatively integrates a viscosity-dispersion dual-objective optimization algorithm with an ultrasonic energy dynamic attenuation compensation algorithm. The two algorithms form a closed-loop interactive logic: the former outputs the optimal ultrasonic power and stirring speed as the initial input of the latter; the latter calculates the energy attenuation based on the material viscosity, outputs the compensated parameters, and reverse-calibrates the objective function of the former, ultimately achieving molecular-level homogenization and providing a uniform and stable material system for the subsequent degassing process.

[0053] (I) Viscosity-Dispersion Dual-Objective Optimization Algorithm: The core value of this algorithm lies in minimizing both viscosity deviation and dispersion deviation within the constraints of equipment parameters, while also taking into account equipment energy consumption, providing initial optimal parameters for the homogenization process, and solving the problem that traditional homogenization parameters are set based on experience and cannot take into account multiple objectives.

[0054] 1. Model Construction: The core logic of model construction is to find the optimal ultrasonic power and stirring speed under the constraints of equipment parameters by constructing an objective function that includes viscosity deviation, dispersion deviation and energy consumption penalty.

[0055] First, calculate the relative viscosity deviation using the following formula: ; in The composite viscosity output from step S4 is derived from the measured data of this step; The target viscosity range after homogenization is the median, which is determined to be 2000 mPa·s based on product performance requirements; The relative viscosity deviation characterizes the degree of deviation between the measured viscosity and the target median.

[0056] Next, the relative deviation of the dispersion is calculated using the following formula: ; in The dispersion uniformity output by step S4 is derived from the measured data of this step. The target dispersion uniformity is set at 98% based on the requirement of molecular-level dispersion. The relative deviation of the dispersion characterizes the degree of deviation between the measured dispersion and the target value. Then, the objective function is constructed, with the following formula: ; in Ultrasonic power, in watts, is an optimization variable; the equipment's allowable range is 300 to 400 watts. The mechanical stirring speed is expressed in revolutions per minute (rpm). This is an optimized variable, and the equipment's allowable range is 400 to 500 rpm. This is the viscosity deviation weighting coefficient; The dispersion deviation weighting coefficient satisfies ; The energy consumption penalty coefficient represents the impact of energy consumption on the optimization objective. The initial ultrasonic power is set to 300 watts. The initial speed of the mechanical stirrer is set to 400 revolutions per minute. The objective function is optimized for the dual objectives; a smaller value indicates a better overall performance. The model's constraints are: ;in The lower limit of ultrasonic power is set at 300 watts. The upper limit of ultrasonic power is set at 400 watts. The lower limit for stirring speed is set at 400 revolutions per minute; This is the upper limit for the stirring speed, set to 500 revolutions per minute.

[0057] 2. Model Training Process: The core objective of model training is to minimize the objective function while ensuring that the performance meets the target after averaging, thus ensuring that the parameters output by the algorithm are both optimizable and practical. Training data comes from 40 different sets of data. , Below , Experimental data, each set of data includes the homogenized viscosity. With particle size distribution span The pass / fail criteria are Between 1900 and 2300 millipascals, The value should not exceed 2.0. The 40 datasets were divided into a training set (30 datasets) and a validation set (10 datasets). A non-dominated sorting genetic algorithm was used for training, with a population size of 50, 300 iterations, a crossover probability of 0.8, and a mutation probability of 0.05. Initial settings. 0.5 0.5 The initial value was 0.1. After each iteration, the training set data was substituted into the objective function, and the coefficient values ​​were adjusted. When the iteration reached 210, the objective function value stabilized below 0.05, and the performance after set averaging met the target. At this point, the objective function was determined. The final value is 0.55. The final value was 0.45. The final value is 0.05.

[0058] 3. Model Application Process: Substitute the measured data from step S4 into the algorithm to perform complete parameter optimization. A detailed example is provided below: If For 2000 millipascals, If it is 96%, then , Initial iteration settings 320 watts Given 430 revolutions per minute, substitute the values ​​into the objective function to calculate: After multiple rounds of iterative optimization, the optimal solution was finally obtained. 325 watts At 435 revolutions per minute, at this time The value is 0.0158, which meets the requirement that the objective function value is less than 0.05. This parameter is output as the initial input of the ultrasonic energy dynamic attenuation compensation algorithm.

[0059] (II) Ultrasonic Energy Dynamic Attenuation Compensation Algorithm: The core value of this algorithm lies in compensating for the attenuation of ultrasonic energy in high-viscosity materials, ensuring that the actual ultrasonic energy received inside the material meets the standard, and adjusting the stirring speed to match the compensated ultrasonic power, thus solving the problem of uneven homogenization caused by the energy attenuation of traditional ultrasonic homogenization.

[0060] 1. Model Construction: The core logic of model construction is to first calculate the initial ultrasonic energy, then calculate the energy attenuation coefficient based on the material viscosity, thereby obtaining the actual received energy and compensation amount. Finally, the ultrasonic power and stirring speed are adjusted, and the calibration objective function is fed back. The initial ultrasonic energy is calculated first using the following formula: ;in The optimal ultrasonic power output by the viscosity-dispersion dual-objective optimization algorithm is derived from the calculation results of the algorithm. To equalize the time, it is set to 600 seconds based on the equipment process. The initial energy output to the ultrasonic generator is given, measured in joules. Next, the energy attenuation coefficient is calculated using the following formula: ;in The coefficient of the first term of the attenuation coefficient is the reciprocal of the millipascal-second (mPa·s), which characterizes the degree of linear influence of viscosity on the attenuation coefficient. This is the constant term for the attenuation coefficient; The ultrasonic energy attenuation coefficient characterizes the degree of attenuation of ultrasonic energy in materials. Then, the actual received energy and compensation amount are calculated using the following formula: ; ;in The actual ultrasonic energy received inside the material, measured in joules; Ultrasonic energy compensation, measured in joules, represents the energy required to reach the initial energy level.

[0061] Next, calculate the compensated parameters using the following formula: ; ;in The compensated ultrasonic power is expressed in watts. The compensated mechanical stirring speed, measured in revolutions per minute, ensures that the stirring speed matches the ultrasonic energy, enhancing the homogenization effect. Finally, feedback calibration is performed using the following formula: ; in To compensate for the viscosity deviation corresponding to the parameters, To compensate for the dispersion deviation of the parameters; The objective function value after compensation should not exceed 0.05; otherwise, adjustment is required. and The value of .

[0062] 2. Model Training Process: The core objective of model training is to ensure that the actual received energy deviation does not exceed 3% and that the compensated objective function value meets the target, thereby ensuring the compensation effect and compatibility of the algorithm. Training data comes from 35 different sets of data. The ultrasonic energy input and output data were measured using an ultrasonic energy detector. The target actually receives energy Set as The least squares method was used for fitting. and The linear relationship, initial setting 1×10 -5 The reciprocal of millipascal second The initial value was 0.05, and the coefficients were adjusted through fitting. The final result was... The value is 2×10 -5 The reciprocal of millipascal second When the value is set to 0.1, the actual received energy deviation does not exceed 2.8%, and the objective function value after compensation does not exceed 0.05, thus satisfying the training objective.

[0063] 3. Model Application Process: Based on the output parameters of the viscosity-dispersion dual-objective optimization algorithm, complete energy compensation and parameter adjustment are performed. The following is an example: Given... 325 watts It is 435 revolutions per minute. 2000 millipascals per second The duration is 600 seconds. Calculate the initial ultrasonic energy: Joules. Calculate the energy decay coefficient: Calculate the actual received energy and the compensation amount: joule; Joules. Calculate the compensated parameters: Watts, exceeding the equipment's maximum capacity of 400 watts, take It is 400 watts; The speed exceeds the device's maximum limit of 500 revolutions per minute. The speed is 500 revolutions per minute. Perform feedback calibration: assuming compensation... 0.01 If it is 0.005, then The viscosity was not more than 0.05, meeting the requirements. The final homogenization parameters were determined as follows: ultrasonic power 400 watts, mechanical stirring speed 500 rpm, and homogenization time 10 minutes. The mixture from step S4 was introduced into an ultrasonic homogenizer (ultrasonic homogenizer), and homogenization was performed according to these parameters. After homogenization, the viscosity of the system was measured. With particle size distribution span ,Require Controlled between 1900 and 2300 millipascals per second The value should not exceed 2.0. If the target is not met, the algorithm coefficients need to be readjusted until the averaging effect meets the requirements.

[0064] In some embodiments, the particle size-moisture content co-adaptation algorithm (Algorithm A) and the multi-factor weight dynamic allocation algorithm (Algorithm B) interact bidirectionally: ① Forward interaction: Algorithm A outputs the co-adaptation coefficients of each raw material. As the core input for calculating the dynamic weight coefficients in the B algorithm, The closer the value is to 1 (the better the material compatibility), the closer the dynamic weight of that material in the B algorithm is to the base weight; ② Reverse interaction: The B algorithm is based on After calculating the raw material mass percentage, the compatibility is verified by weighting the compatibility coefficient with the mass percentage. If the weighted sum does not meet the standard, the particle size / moisture content deviation weighting coefficient of Algorithm A is adjusted in reverse to optimize the process. The computational precision improves the matching logic of algorithm A, making the output of algorithm A more suitable for algorithm B.

[0065] Positive interaction formula (dynamic weight calculation using the B algorithm): Reverse interaction formula (A algorithm weight adjustment): ; ; The dynamic weight coefficient for the i-th type of raw material in the B algorithm; This is the basic weighting coefficient for raw materials of type i (e.g., 0.6 for ethylene-vinyl acetate copolymer). Output the synergistic adaptation coefficient of the i-th type of raw material for Algorithm A; The average value of the compatibility coefficient of all raw materials; The weight of the B algorithm's co-fit coefficient is 0.47. The adjusted particle size deviation weighting coefficient for Algorithm A; The initial particle size deviation weighting coefficient for Algorithm A is 0.68. The weighted sum threshold is 0.95. The sum of the co-fit coefficient and the quality ratio; This refers to the weighting coefficient for moisture content deviation in the adjusted Algorithm A.

[0066] Interaction termination condition: ① must be met simultaneously. ② After adjusting Algorithm A ③ The total quality percentage of algorithm B If the percentage fluctuation of a single raw material is ≤±1%, the two-way interaction is considered to have ended; otherwise, the forward calculation and reverse adjustment are repeated until the standard is met.

[0067] In some embodiments, the viscosity-dispersion dual-objective optimization algorithm (C algorithm) and the ultrasonic energy dynamic attenuation compensation algorithm (D algorithm) interact bidirectionally: ① Forward interaction: The C algorithm outputs initial homogenization parameters (ultrasonic power) based on the system dispersion uniformity and composite viscosity. Stirring speed ), serving as the basic input for the D algorithm to calculate energy attenuation compensation; ② Reverse interaction: The D algorithm calculates the compensated ultrasonic power based on the initial homogenization parameters and system viscosity. After homogenization, viscosity deviation and dispersion deviation are detected. If the deviation does not meet the standard, the weight coefficient of the objective function of the C algorithm or the energy consumption penalty coefficient is adjusted in reverse to optimize the calculation logic of the initial homogenization parameters, so that the C algorithm output can adapt to the energy compensation capability of the D algorithm.

[0068] Forward interaction formula (D-algorithm energy compensation calculation): ; ; Reverse interaction formula (adjustment of objective function in C algorithm): ; ; ; The ultrasonic power (W) after D algorithm compensation; The initial ultrasonic energy (J); This is the adjusted energy compensation amount; The averaging time is 600s. The adjusted energy attenuation coefficient is 0.14. The adjusted viscosity deviation weighting coefficient for the C algorithm; The initial viscosity deviation weighting factor is 0.55. The target viscosity deviation is 0.02. This represents the actual viscosity deviation. This is the adjusted dispersion deviation weighting coefficient; This is the adjusted energy consumption penalty coefficient; The initial energy consumption penalty coefficient is 0.05. This represents the actual dispersion deviation.

[0069] Interaction termination condition: Simultaneously satisfying ① viscosity deviation after homogenization And dispersion deviation ② Compensated ultrasonic power (Equipment upper limit); ③ Objective function value If the two-way interaction ends, the positive compensation and reverse adjustment are repeated until the target is met.

[0070] In some embodiments, the particle size-moisture content co-adaptation algorithm and the viscosity-dispersion dual-objective optimization algorithm interact bidirectionally: ① Forward interaction: Obtain the average value of all raw material co-adaptation coefficients output by algorithm A. Dynamically adjust the dispersion deviation weighting coefficient in the C algorithm ; The lower the value, the worse the raw material compatibility. The higher the value, the more priority is given to ensuring the dispersion effect; ② Reverse interaction: based on the adjusted Calculate the objective function of the C algorithm And detect the span of particle size distribution after homogenization. ,like or If the target is not met, adjust the particle size deviation weighting coefficient in Algorithm A in reverse. and moisture content deviation weighting coefficient The logic for calculating the co-fit coefficient of algorithm A is optimized to make the output of algorithm A more consistent with the averaging requirements of algorithm C.

[0071] Positive interaction formula: ; ; Reverse interaction formula: ; ; The adjusted C algorithm dispersion deviation weighting coefficient; Initializing the C algorithm (0.45); The average value of the co-fit coefficients output by algorithm A; The adjusted viscosity deviation weighting coefficient for the C algorithm; The adjusted particle size deviation weighting coefficient for Algorithm A; Initialize Algorithm A (0.6); Adjust the objective function value for the C algorithm; This refers to the weighting coefficient for moisture content deviation in the adjusted Algorithm A.

[0072] Interaction termination condition: ① must be met simultaneously. and ② Algorithm A recalculates ③ After two iterations If the fluctuation is ≤±2%, the two-way interaction is considered to have ended; otherwise, the forward + reverse adjustment process is repeated until all conditions are met.

[0073] In some embodiments, the particle size-moisture content co-adaptation algorithm and the ultrasonic energy dynamic attenuation compensation algorithm interact bidirectionally: ① Forward interaction: Obtain the average value of the functional ingredient co-adaptation coefficient output by algorithm A. Dynamically adjust the energy decay coefficient of the D algorithm ; The lower the level (the more easily functional ingredients agglomerate). The higher the value, the greater the ultrasonic energy compensation; ② Reverse interaction: based on the adjusted... Calculate the compensated ultrasonic power Detect the dispersion uniformity after homogenization ,like If the target is not met, the particle size / moisture content standard thresholds for functional ingredients in Algorithm A are adjusted in reverse to adapt the preprocessing requirements of Algorithm A to the energy compensation capability of Algorithm D. Forward interaction formula: ; Reverse interaction formula: ; ; The adjusted energy decay coefficient for the D algorithm; Initialization of the D algorithm (0.14); The average value of the functional ingredient coordination and adaptation coefficients output by algorithm A; The standard particle size (μm) of the adjusted functional ingredients; The initial standard particle size for functional ingredients is 65 μm. This refers to the uniformity of dispersion after homogenization. The upper limit of moisture content (%) of the adjusted functional ingredients standard; The initial standard moisture content of the functional ingredients is set at the upper limit (0.045%).

[0074] Interaction termination condition: ① must be met simultaneously. ② The pre-processing compliance rate of functional ingredients after adjustment using Algorithm A is ≥99%; ③ The energy compensation amount of Algorithm D If the value is ≤250000J (device safety threshold), the two-way interaction is considered to have ended; otherwise, the forward and reverse adjustments are repeated until all conditions are met.

[0075] In some embodiments, the multi-factor weight dynamic allocation algorithm and the viscosity-dispersion bi-objective optimization algorithm interact bidirectionally: ① Forward interaction: Obtain the mass percentage of ethylene-vinyl acetate copolymer output by algorithm B. Dynamically adjust the target viscosity median of the C algorithm ; The higher the viscosity (the higher the basic viscosity of the system). The higher the level, the more it matches the homogenization target; ② Reverse interaction: based on the adjusted Calculate the optimal homogenization parameters using the C algorithm and detect the viscosity of the homogenized system. ,like If the target range is deviated from, the basic weight coefficient of the ethylene-vinyl acetate copolymer in algorithm B is adjusted in reverse to optimize the raw material ratio and adapt it to the homogenization capability of algorithm C. Forward interaction formula: ; Reverse interaction formula: ; The adjusted target viscosity median (mPa·s) for the C algorithm; Initializing the C algorithm (2000 mPa·s); This represents the mass percentage of ethylene-vinyl acetate copolymer output by algorithm B. The basic weighting coefficient of ethylene-vinyl acetate copolymer in the adjusted B algorithm; The initial basic weight coefficient is 0.6. The viscosity of the homogenized system is (mPa·s).

[0076] Interaction termination condition: ① must be met simultaneously. ;② ③ After adjusting the B algorithm (Within the allowable range of the process), determine the end of the two-way interaction; otherwise, repeat the forward + reverse adjustment.

[0077] In some embodiments, the multi-factor weight dynamic allocation algorithm and the ultrasonic energy dynamic attenuation compensation algorithm interact bidirectionally: ① Forward interaction: Obtain the mass percentage of dioctyl phthalate output by algorithm B. Dynamically adjust the ultrasonic energy compensation amount of the D algorithm. ; The lower the viscosity (the higher the system viscosity). The higher the value, the more it compensates for ultrasound energy attenuation; ② Reverse interaction: based on the adjusted... Calculate the compensated ultrasonic power The viscosity fluctuation and bubble content of the homogenized system were detected. If the target is not met, the weighting of the viscosity deviation of dioctyl phthalate in Algorithm B is adjusted in reverse, and the raw material ratio is optimized to reduce system viscosity fluctuations and adapt to the energy compensation effect of Algorithm D. Forward interaction formula: ; Reverse interaction formula: ; The adjusted ultrasonic energy compensation amount (J) for the D algorithm. Initialization of the D algorithm (195000J); The mass percentage of dioctyl phthalate output by algorithm B; The weighting of the effect of dioctyl phthalate viscosity deviation in the adjusted B algorithm; The initial weight is 0.25. The viscosity fluctuation of the homogenized system (%). The percentage of bubbles after homogenization is %.

[0078] Interaction termination condition: ① must be met simultaneously. and ②After adjusting the B algorithm (Process allowable range); ③D algorithm (Device power threshold) determines the end of the two-way interaction; otherwise, repeat the forward + reverse adjustment.

[0079] S6. Vacuum Gradient Degassing Treatment: Microbubbles still exist in the homogenized material system. The presence of these bubbles can lead to porosity in the molded product, reducing tensile and peel strength. Therefore, this step uses gradient degassing based on the viscosity and particle size distribution data after homogenization to precisely remove microbubbles. The degassing effect is monitored simultaneously to provide bubble-free, homogeneous material for subsequent compression molding. The specific implementation process is as follows: 1. Degassing parameter setting: The homogenized material system is introduced into the vacuum degasser, based on the parameters recorded in step S5. and Data setting degassing parameters: If Between 1900 and 2100 millipascals and The vacuum level is adjusted in two stages, not exceeding 1.5 MPa: the first stage lasts 6 minutes, with the vacuum level controlled between -0.07 and -0.06 MPa; the second stage lasts 6 minutes, increasing the vacuum level to between -0.09 and -0.08 MPa. The chamber temperature is maintained between 150 and 160 degrees Celsius throughout the process. This temperature range reduces material viscosity, facilitating bubble escape, and prevents thermal degradation of the material. Between 2100 and 2300 millipascals and The vacuum level is adjusted in two stages, not exceeding 1.5 MPa: the first stage lasts 8 minutes, with the vacuum level controlled between -0.08 and -0.07 MPa; the second stage lasts 10 minutes, increasing the vacuum level to between -0.095 and -0.09 MPa. The chamber temperature is maintained between 155 and 165 degrees Celsius throughout the process; increasing the temperature reduces the flow resistance of high-viscosity materials, facilitating bubble removal. Greater than 1.5 and not exceeding 2.0, regardless of Regardless of the range, based on the corresponding parameters mentioned above, the total degassing time will be extended by 5 minutes and the chamber temperature will be increased by 5 degrees Celsius. The purpose of extending the time and increasing the temperature is to allow materials with a wide particle size distribution sufficient time to expel bubbles.

[0080] 2. Degassing effect monitoring: During the degassing process, a laser bubble detector is used to monitor the bubble content of the system in real time. After degassing is completed, the final bubble content is recorded. The requirement is that this indicator does not exceed 0.3%; simultaneously, the viscosity of the degassed system is measured and recorded as follows. This indicator is required to not exceed 2400 millipascals per second. If... If the deviation exceeds 0.3%, the total degassing time needs to be extended by 10 minutes, the vacuum level increased by 0.005 MPa, and degassing should be repeated before retesting; if... If the speed exceeds 2400 mPa second, the chamber temperature needs to be increased by 5 degrees Celsius, the stirring speed needs to be increased by 50 rpm, and the mixture needs to be stirred for 5 minutes before degassing again until both indicators meet the standard.

[0081] This step involves adjusting the vacuum level and temperature in a gradient manner to adapt to material systems in different states, ensuring that microbubbles are fully expelled. The monitored bubble content and viscosity data provide key parameters for subsequent compression molding, avoiding molding defects caused by bubbles.

[0082] S7. Compression Molding and Gradient Cooling Curing: The degassed material system needs to be molded to obtain the target shape. Traditional rapid cooling methods can easily lead to stress concentration inside the material, reducing the mechanical properties of the product. Therefore, this step sets differentiated molding parameters based on the bubble content and viscosity data after degassed treatment, and uses gradient cooling for curing to ensure stable product structure and meet mechanical property standards. The specific implementation process is as follows: 1. Molding parameter settings: Compression molding is performed using a flat vulcanizing machine, based on the parameters recorded in step S6. and Data setting parameters: If Not exceeding 0.1% and The molding pressure is set to 10 MPa within the range of 1900 to 2200 mPa·s, the mold temperature to 140 degrees Celsius, and the holding time to 5 minutes. This parameter set is suitable for materials with low bubble content and moderate viscosity, ensuring molding results while preventing material overflow due to excessive pressure. Between 0.1% and 0.3% and Between 1900 and 2200 mPa·s, the molding pressure is increased to 12 MPa, the mold temperature is increased to 145 degrees Celsius, and the holding time is extended to 7 minutes. The purpose of increasing the pressure and temperature and extending the holding time is to further compact the material and remove residual micro-air bubbles. If In the range of 2200 to 2400 millipascals and For materials with a viscosity not exceeding 0.3%, the molding pressure is increased to 15 MPa, the mold temperature is increased to 150 degrees Celsius, and the holding time is extended to 8 minutes. High-viscosity materials have poor flowability, requiring increased pressure and temperature to ensure complete mold filling, and extended holding time to ensure full melting and molding.

[0083] 2. Gradient Cooling and Curing: After heat preservation, gradient cooling is performed to avoid stress concentration caused by rapid cooling: First stage cooling: The mold temperature is cooled to 80 degrees Celsius at a rate of 5 degrees Celsius per minute, maintaining a constant molding pressure throughout the cooling process. The cooling time is controlled at 12 minutes. The purpose of slow cooling is to allow the internal temperature of the material to decrease evenly, reducing thermal stress. Second stage cooling: The mold temperature is cooled to room temperature (set to 25 degrees Celsius) at a rate of 3 degrees Celsius per minute. During the cooling process, the pressure is gradually released, with the depressurization rate synchronized with the cooling time, i.e., linearly decreasing from the set molding pressure to 0 MPa. The cooling time is determined based on the cooling rate and is approximately 18 minutes. The combination of slow depressurization and cooling further releases internal stress in the material, ensuring product structural stability.

[0084] 3. Performance Testing: After cooling and curing, samples are taken from the molded product to test core performance indicators, and the tensile strength is recorded. The requirement is not less than 15 MPa; the recorded peel strength is... The requirement is not less than 8 N / cm, and the substrate for testing is steel pipe; the melting temperature is recorded as follows. The temperature must be controlled between 85 and 95 degrees Celsius. If any indicator fails to meet the standard, the specific value must be recorded to provide a basis for subsequent parameter iteration and adjustment.

[0085] This step addresses stress concentration and bubble residue issues during the molding process by using differentiated molding parameters and gradient cooling. The performance data obtained from the tests provides a core basis for the closed-loop feedback process, ensuring that the product performance meets the requirements.

[0086] S8. Performance Feedback and Parameter Iterative Adjustment: To ensure the consistency and stability of product performance, this step establishes a closed-loop feedback mechanism based on the performance test data from step S7. For different performance failures, the parameters of previous steps are precisely adjusted until all product performance meets the standards, forming a complete process closed loop. The specific implementation process is as follows: 1. Qualification Criteria: When Not less than 15 MPa Not less than 8 N / cm The product is deemed qualified when the temperature is between 85 and 95 degrees Celsius, and then proceeds to the packaging and warehousing process.

[0087] 2. Non-conforming adjustment logic: If Below 15 MPa and , If all parameters are met, it indicates that the proportion of the base resin or the melting effect is insufficient. The process needs to return to step S2 to adjust the base weighting coefficient of the ethylene-vinyl acetate copolymer. Increase the concentration by 0.02 to 0.03, and repeat steps S2 to S7 to increase the base resin content and enhance the tensile properties of the product. Less than 8 N / cm and , If all parameters are met, it indicates that the proportion or dispersion effect of the tackifier is insufficient, and it is necessary to return to step S2 to adjust the basic weighting coefficient of C5 petroleum resin. Increase the concentration by 0.01 to 0.02, and repeat steps S2 to S7 to increase the tackifier ratio and enhance the product's peel performance. If Below 85 degrees Celsius and , If all parameters are met, it indicates that insufficient melting temperature has resulted in a low product melting point. It is necessary to return to step S3, raising the temperature of the third-stage melting zone by 5 to 10 degrees Celsius, and then repeating steps S3 to S7 to increase the melting temperature and raise the product melting point. If... Above 95 degrees Celsius and , If all parameters are met, it indicates that insufficient plasticizer content leads to a high product melt point. Therefore, it is necessary to return to step S2 and adjust the basic weighting coefficient of dioctyl phthalate. Increase by 0.01 and repeat steps S2 to S7 to increase the plasticizer ratio and lower the product melting point. If two or more indicators fail to meet the standards, it indicates a deviation in the core parameters of the raw material ratio. Return to step S2 and retrain the coefficients of the particle size-moisture content co-adaptation algorithm and the multi-factor weight dynamic allocation algorithm based on the preprocessed data from step S1. Adjust the coefficients by ±0.01, recalculate the ratio, and then execute the subsequent steps sequentially until all product performance meets the standards.

[0088] This step, through precise closed-loop feedback adjustment, solves the problem of substandard performance caused by single batches or raw material fluctuations, ensuring that the entire preparation process has self-calibration capabilities and that product performance is stable and controllable.

[0089] II. Specific Examples of Hot Melt Adhesive Preparation for Heat Shrinkable Tape

[0090] Raw material and equipment preparation: (I) Raw material list and initial information: Ethylene-vinyl acetate copolymer: VA content 30%, initial particle size 110-160μm, initial moisture content 0.15%; C5 petroleum resin: initial particle size 70-130μm, initial moisture content 0.12%; Dioctyl phthalate: initial viscosity 280mPa·s (25℃), initial moisture content 0.08%; Hindered phenolic 1010 antioxidant: initial particle size 40-90μm, initial moisture content 0.09%; Talc: initial particle size 5-35μm, initial moisture content 0.06%.

[0091] (II) Core equipment: vacuum drying oven, forced air drying oven, constant temperature bath, high temperature activation furnace, Karl Fischer moisture analyzer, laser particle size analyzer, online viscometer, twin-screw extruder, ultrasonic homogenizer, vacuum degasser, flat vulcanizing machine, laser bubble detector, tensile testing machine, peel strength tester, differential scanning calorimeter (DSC).

[0092] Step-by-step implementation process: S1. Raw material classification and activation pretreatment; activation parameters were set according to the technical plan, and the actual measured data after completion are as follows: 1. Ethylene-vinyl acetate copolymer: activation temperature of vacuum drying oven 80℃, activation time 45 minutes, vacuum degree -0.065MPa; measured moisture content (≤0.1%), measured particle size (100-150μm), data meets standards. 2. C5 petroleum resin: activation temperature in a forced-air drying oven 65℃, activation time 35 minutes, forced-air speed 2.5m / s; measured moisture content (≤0.08%), measured particle size (80-120μm), data meets standards. 3. Dioctyl phthalate: Activation bath temperature 55℃, activation time 22 minutes, stirring rate 120r / min; measured moisture content (≤0.05%), measured viscosity (25℃, 200-300mPa·s), data meets standards. 4. Hindered phenolic 1010 antioxidant: activation temperature in vacuum drying oven 85℃, activation time 40 minutes, vacuum degree -0.075MPa; measured moisture content (≤0.06%), measured particle size (50-80μm), data meets standards. 5. Talc powder: High-temperature activation furnace activation temperature 115℃, activation time 65 minutes, nitrogen gas flow rate 1.5L / min; measured moisture content (≤0.03%), measured particle size (10-30μm), data met standards. All raw materials met the requirements after activation; records were kept. , , , which serves as the input data for S2.

[0093] S2. Precise proportion calculation based on dual-algorithm fusion; (I) Application of particle size-moisture content co-adaptation algorithm: Substitute the measured data of S1 and calculate according to the formula: 1. Relative particle size deviation: ethylene-vinyl acetate copolymer ( ): , C5 petroleum resin ( ): , Hindered phenolic 1010 antioxidants ( ): , ;talcum powder( ): , Dioctyl phthalate ( ): 2. Relative deviation in moisture content: : , ; : , ; : , ; : , ; : , 3. Coordination adaptation coefficient ( , ): ; ; ; ; ,Pick .

[0094] (II) Application of Multi-Factor Weight Dynamic Allocation Algorithm: 1. Basic Data Calculation: Dioctyl phthalate viscosity deviation: , 2. Dynamic weight adjustment ( , Basic weights , , , , ): ; ; ; ; 3. Calculation of mass percentage: ; , , , , 4. Interactive Calibration: Initial ,Adjustment , Recalculate , ,final Calibration passed. The final mass percentages are determined as follows: ethylene-vinyl acetate copolymer 61.5%, C5 petroleum resin 22.9%, dioctyl phthalate 9.9%, hindered phenolic 1010 antioxidant 1.5%, talc 4.2% (total 100%).

[0095] S3. Staged melt synergistic mixing; 1. Equipment preheating: Twin-screw extruder, zone 1 95℃, zone 2 125℃, zone 3 145℃, screw speed preset 220r / min, preheat for 30 minutes. 2. First stage melting: Add dioctyl phthalate (9.9%), keep the temperature constant, screw speed 200r / min, stir for 12 minutes; Actual measurement (500-800 mPa·s), meets the standard.

[0096] 3. Second stage of melting: Add C5 petroleum resin (22.9%) to a pressure of 500-650 mPa·s, increase screw speed to 220 r / min, and stir for 18 minutes; actual measurement (900-1200 mPa·s), meets the standard. 4. Third stage melting: Add ethylene-vinyl acetate copolymer (61.5%), increase the temperature in zone two to 135℃ and in zone three to 155℃; (900-1050 mPa·s), screw speed 230 r / min, stirring for 28 minutes; actual measurement (1200-1500 mPa·s), meets the standard.

[0097] S4. Directional dispersion and mixing of functional additives; 1. Feeding sequence: First add hindered phenolic 1010 antioxidant (1.5%), stir for 5 minutes, then add talc powder (4.2%). 2. Parameter adjustment: (1200-1350 mPa·s), Zone II 135℃, Zone III 155℃, screw speed 300 r / min, total stirring time 20 minutes. 3. Measured results: Dispersion uniformity (≥95%) Composite viscosity (1800-2200 mPa·s), both indicators meet the standards.

[0098] S5. Ultrasonic-mechanical synergistic dynamic homogenization treatment; (I) Application of the viscosity-dispersion bi-objective optimization algorithm: Substituting , , , , : , After iterative optimization, the optimal parameters are... , , .

[0099] (II) Application of Ultrasonic Energy Dynamic Attenuation Compensation Algorithm: 1. Energy Calculation: , ; ; , 2. Compensated parameters: (Device limit) (Equipment upper limit); Feedback calibration The standard was met. 3. Homogenization implementation: The ultrasonic-assisted homogenizer chamber temperature was 150℃, the ultrasonic power was 400W, the stirring speed was 500r / min, and the homogenization time was 10 minutes; actual measurement... (1900-2300 mPa·s), particle size distribution range (≤2.0), meets the standard.

[0100] S6. Vacuum gradient degassing treatment; 1. Parameter settings: (1900-2100 mPa·s) (≤1.5), vacuum level: first stage (0-6 minutes) -0.065MPa, second stage (6-12 minutes) -0.085MPa, chamber temperature 155℃. 2. Measured results: bubble content after degassing. (≤0.3%) viscosity (≤2400mPa·s), both indicators meet the standards.

[0101] S7. Compression molding and gradient cooling curing; 1. Compression parameters: (≤0.1%) (1900-2200 mPa·s), molding pressure 10 MPa, mold temperature 140℃, holding time 5 minutes. 2. Gradient cooling: First stage: cooling to 80℃ at 5℃ / min, maintaining pressure 10 MPa, cooling for 12 minutes; Second stage: cooling to 25℃ at 3℃ / min, simultaneously linearly depressurizing to 0 MPa, cooling for 18 minutes. 3. Performance testing: measured tensile strength. (≥15MPa), peel strength (≥8N / cm), melting temperature (85-95℃), all three indicators met the standards.

[0102] S8. Performance feedback and parameter iterative adjustment; due to , , All products meet the qualification standards and are deemed qualified. They can then proceed directly to the packaging and warehousing process without any parameter adjustments.

[0103] Example Results: The heat-shrinkable tape hot melt adhesive prepared in this example has a uniform appearance, free of bubbles and particle agglomeration. Key performance indicators are as follows: Tensile strength: 16 MPa (≥15 MPa); Peel strength (with steel pipe substrate): 8.5 N / cm (≥8 N / cm); Melting temperature: 90℃ (85-95℃); Moisture content: ≤0.05%; Dispersion uniformity: 96%; Bubble content: 0.08%. All indicators meet the requirements set in the technical solution, verifying the feasibility and stability of this preparation method.

[0104] This embodiment provides an apparatus for preparing the heat-shrinkable tape hot melt adhesive as described in any one of the above-mentioned methods, including a raw material pretreatment device, a proportioning calculation module, a mixing device, a directional dispersion device, a synergistic homogenization device, a degassing device, a molding and cooling device, a performance testing device, and a parameter adjustment module. The raw material pretreatment device is used to perform differentiated activation treatment on each raw material and obtain measured moisture content, particle size, and viscosity data. The proportioning calculation module is used to execute a particle size-moisture content synergistic adaptation algorithm and a multi-factor weight dynamic allocation algorithm to determine the mass ratio of raw materials. The mixing device is used to achieve graded melting and synergistic mixing and monitor viscosity. The directional dispersion device is used to achieve directional dispersion and mixing of functional additives and monitor dispersion uniformity and composite viscosity. The synergistic homogenization device is used to execute a viscosity-dispersion dual-objective optimization algorithm and an ultrasonic energy dynamic attenuation compensation algorithm to achieve homogenization treatment. The degassing device is used to achieve vacuum gradient degassing treatment and monitor bubble content and viscosity. The molding and cooling device is used to achieve compression molding and gradient cooling curing. The performance testing device is used to test the tensile strength, peel strength, and melting temperature of the product and record the data. The parameter adjustment module is used to iteratively adjust the proportioning parameters or process parameters based on the performance testing data.

Claims

1. A method for preparing a heat-shrinkable tape hot melt adhesive, characterized in that, include: S1. Differential activation treatments were performed on ethylene-vinyl acetate copolymer, C5 petroleum resin, dioctyl phthalate, hindered phenolic 1010 antioxidant and talc, and the measured moisture content, particle size and measured viscosity of dioctyl phthalate of each raw material were recorded. S2. Based on the measured data recorded in S1, the mass proportion of each raw material is determined through the collaborative calculation of the particle size-moisture content co-adaptation algorithm and the multi-factor weight dynamic allocation algorithm. S3. Based on the mass ratio determined in S2, feed the materials in stages according to the order of low melting point melting first and high melting point melting later, and monitor and record the viscosity of the mixing system at each stage in real time; S4. Based on the viscosity data recorded in S3, add hindered phenolic 1010 antioxidant and talc in a preset order, adjust the process parameters to perform directional dispersion, and record the dispersion uniformity and composite viscosity of the mixed system. S5. Based on the dispersion uniformity and composite viscosity recorded in S4, the homogenization parameters are determined by the combined calculation of the viscosity-dispersion dual-objective optimization algorithm and the ultrasonic energy dynamic attenuation compensation algorithm. The mixed system is then homogenized, and the viscosity and particle size distribution span of the homogenized system are recorded. S6. Based on the viscosity and particle size distribution range recorded in S5, adjust the vacuum and temperature to perform gradient degassing, and record the bubble content and viscosity of the system after degassing; S7. Based on the bubble content and viscosity recorded in S6, set the molding parameters and perform gradient cooling curing. Detect and record the tensile strength, peel strength, and melt temperature of the product. S8. Based on the tensile strength, peel strength and melt temperature recorded in S7, precisely adjust the proportioning parameters of S2 or the process parameters of S3-S6 until the product performance meets the standards.

2. The method for preparing the heat-shrinkable tape hot melt adhesive according to claim 1, characterized in that, The working process of the particle size-moisture content co-adaptation algorithm is as follows: obtain the measured particle size and measured moisture content of each raw material in S1, predetermine the standard particle size median and standard moisture content upper limit of each raw material, calculate the relative deviation of particle size and relative deviation of moisture content of each raw material respectively, and calculate the co-adaptation coefficient of each raw material by combining the preset particle size deviation weight coefficient and moisture content deviation weight coefficient. The value range of the co-adaptation coefficient is 0.8-1.

2.

3. The method for preparing the heat-shrinkable tape hot melt adhesive according to claim 2, characterized in that, The working process of the multi-factor weight dynamic allocation algorithm is as follows: obtain the measured viscosity of dioctyl phthalate in S1 and the synergistic adaptation coefficient of each raw material, calculate the average value of the synergistic adaptation coefficient of all raw materials and the relative viscosity deviation of dioctyl phthalate, combine the preset basic weight coefficient of each raw material, the influence weight of the synergistic adaptation coefficient and the influence weight of the viscosity deviation, adjust to obtain the dynamic weight coefficient of each raw material, convert the dynamic weight coefficient of all raw materials into the corresponding mass proportion, and ensure that the weighted sum of the synergistic adaptation coefficient of each raw material and the corresponding mass proportion meets the preset threshold.

4. The method for preparing the heat-shrinkable tape hot melt adhesive according to claim 3, characterized in that, The collaborative process of the particle size-moisture content co-adaptation algorithm and the multi-factor weight dynamic allocation algorithm is as follows: the co-adaptation coefficients of each raw material obtained by the particle size-moisture content co-adaptation algorithm are used as the core input of the multi-factor weight dynamic allocation algorithm. After the multi-factor weight dynamic allocation algorithm calculates the mass proportion of each raw material, it calculates the weighted sum of the co-adaptation coefficients of each raw material and the corresponding mass proportions in reverse. If the weighted sum does not meet the preset threshold, the particle size deviation weight coefficient and the moisture content deviation weight coefficient of the particle size-moisture content co-adaptation algorithm are adjusted, and the co-adaptation coefficients and mass proportions are recalculated until the weighted sum meets the preset threshold.

5. The method for preparing the heat-shrinkable tape hot melt adhesive according to claim 1, characterized in that, The working process of the viscosity-dispersion dual-objective optimization algorithm is as follows: obtain the dispersion uniformity and composite viscosity obtained from S4, predetermine the target median viscosity and target dispersion uniformity after homogenization, calculate the relative viscosity deviation and relative dispersion deviation, combine the preset viscosity deviation weight coefficient, dispersion deviation weight coefficient and equipment energy consumption penalty coefficient to construct the objective function, and within the equipment parameter constraint range, find the ultrasonic power and mechanical stirring speed that minimize the objective function value as the initial homogenization parameters.

6. The method for preparing the heat-shrinkable tape hot melt adhesive according to claim 5, characterized in that, The working process of the ultrasonic energy dynamic attenuation compensation algorithm is as follows: obtain the initial homogenization parameters and the composite viscosity obtained by S4, predetermine the homogenization time, calculate the initial energy output by the ultrasonic generator and the energy attenuation coefficient, and then obtain the actual ultrasonic energy received inside the material and the ultrasonic energy compensation amount. Based on the initial energy and the compensation amount, adjust the compensated ultrasonic power and mechanical stirring speed to ensure that the stirring speed matches the compensated ultrasonic power.

7. The method for preparing the heat-shrinkable tape hot melt adhesive according to claim 6, characterized in that, The collaborative process of the viscosity-dispersion dual-objective optimization algorithm and the ultrasonic energy dynamic attenuation compensation algorithm is as follows: the initial homogenization parameters obtained by the viscosity-dispersion dual-objective optimization algorithm are used as the initial input of the ultrasonic energy dynamic attenuation compensation algorithm. After the ultrasonic energy dynamic attenuation compensation algorithm calculates the compensated homogenization parameters, it calculates the corresponding viscosity deviation and dispersion deviation based on the compensated parameters and constructs the compensated objective function. If the objective function value exceeds the preset upper limit, the weight coefficients and penalty coefficients of the viscosity-dispersion bi-objective optimization algorithm are readjusted, and the initial homogenization parameters and the compensated homogenization parameters are recalculated until the compensated objective function value meets the preset requirements.

8. A heat-shrinkable tape hot melt adhesive, characterized in that, The heat shrinkable tape hot melt adhesive is prepared using the preparation method of any one of claims 1-7; the heat shrinkable tape hot melt adhesive is composed of ethylene-vinyl acetate copolymer, C5 petroleum resin, dioctyl phthalate, hindered phenolic 1010 antioxidant, and talc, wherein the ethylene-vinyl acetate copolymer has a water content ≤0.1% and a particle size of 100-150 μm, and the C5 petroleum resin has a water content ≤0.08% and a particle size of 80-120 μm. m, wherein the dioctyl phthalate has a water content ≤0.05% and a viscosity of 200-300 mPa·s at 25℃; the hindered phenolic 1010 antioxidant has a water content ≤0.06% and a particle size of 50-80 μm; the talc has a water content ≤0.03% and a particle size of 10-30 μm; the heat-shrinkable tape hot melt adhesive has a tensile strength ≥15 MPa, a peel strength to the steel pipe substrate ≥8 N / cm, and a melting temperature of 85-95℃.

9. The heat-shrinkable tape hot melt adhesive according to claim 8, characterized in that, The VA content of the ethylene-vinyl acetate copolymer is 28%-33%, the viscosity of the dioctyl phthalate at 25°C is 230-270 mPa·s, and the bubble content of the heat-shrinkable tape hot melt adhesive is ≤0.3% and the dispersion uniformity is ≥95%.

10. An apparatus for implementing the method for preparing heat-shrinkable tape hot melt adhesive according to any one of claims 1-7, characterized in that, The system includes raw material pretreatment equipment, a proportioning calculation module, mixing equipment, directional dispersion equipment, synergistic homogenization equipment, degassing equipment, molding and cooling equipment, performance testing equipment, and a parameter adjustment module. The raw material pretreatment equipment is used to perform differentiated activation treatment on each raw material and obtain measured moisture content, particle size, and viscosity data. The proportioning calculation module is used to execute a particle size-moisture content synergistic adaptation algorithm and a multi-factor weight dynamic allocation algorithm to determine the raw material mass ratio. The mixing equipment is used to achieve graded melt synergistic mixing and monitor viscosity. The directional dispersion equipment is used to achieve directional dispersion mixing of functional additives and monitor dispersion uniformity and composite viscosity. The synergistic homogenization equipment is used to execute a viscosity-dispersion dual-objective optimization algorithm and an ultrasonic energy dynamic attenuation compensation algorithm to achieve homogenization. The degassing equipment is used to achieve vacuum gradient degassing and monitor bubble content and viscosity. The molding and cooling equipment is used to achieve compression molding and gradient cooling solidification. The performance testing equipment is used to test and record the tensile strength, peel strength, and melt temperature of the product. The parameter adjustment module is used to iteratively adjust the proportioning parameters or process parameters based on the performance testing data.

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