High temperature and high pressure condition high wear resistance hose outer film reinforcing method and system

Abstract

The application discloses a high-temperature and high-pressure high-wear-resistance hose outer film reinforcing method and system, relates to the technical field of data processing, and comprises the following steps: collecting environmental parameters of a hose operation area in a chemical preparation process; dividing and determining a plurality of sub-hoses of the hose operation area to be processed, and respectively performing motion characteristic analysis; respectively performing wear-resistance demand analysis according to static operation conditions and a plurality of dynamic operation conditions, and determining a plurality of wear-resistance parameter indexes; respectively performing coating parameter optimization of the hose outer film for the purpose of approximating the plurality of wear-resistance parameter indexes, determining a plurality of optimal coating parameters, and performing outer film reinforcement of the plurality of sub-hoses. The application solves the technical problems that the prior art does not fully consider the differences between the hose operation environment and the motion state, resulting in poor hose local wear, outer film easy to fall off, poor pertinence and precision of hose outer film reinforcement, and achieves the technical effects of improving the pertinence and precision of hose outer film reinforcement and the overall wear resistance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a high-wear-resistance hose outer film strengthening method and system under high temperature and high pressure conditions. BACKGROUND

[0002] As a key transmission component, the high-wear-resistance hose undertakes important tasks such as conveying corrosive chemicals, high-temperature fluids, and high-hardness particulate materials, and its performance directly affects production efficiency, equipment safety, and operating costs. Traditional hose outer film strengthening usually only considers static environmental (temperature, pressure, humidity, ultraviolet light, corrosion) conditions, and does not fully consider the dynamic motion characteristics (bending, shaking, tensioning) of the hose during operation, resulting in insufficient precision in wear resistance demand analysis, poor specificity, precision, and practicality of hose outer film strengthening, premature local wear and tear of the hose, and outer film shedding, which may even cause safety accidents such as leakage, thereby affecting the continuity and stability of industrial production.

[0003] Therefore, in the related art, the differences in operating environment and motion state of the hose are not fully considered, resulting in local wear and tear of the hose, easy shedding of the outer film, and poor specificity and precision of hose outer film strengthening. SUMMARY

[0004] The present application provides a high-wear-resistance hose outer film strengthening method and system under high temperature and high pressure conditions, which solves the technical problem of poor specificity and precision of hose outer film strengthening due to insufficient consideration of the differences in operating environment and motion state of the hose, and achieves the technical effect of improving the specificity and precision of hose outer film strengthening and overall wear resistance.

[0005] The present application provides a high-wear-resistance hose outer film strengthening method under high temperature and high pressure conditions, which includes: collecting environmental parameters of the hose operating area during chemical preparation as static operating conditions; dividing the hose operating area into multiple sub-hoses to determine the multiple sub-hoses, and analyzing the motion characteristics of the multiple sub-hoses to obtain multiple dynamic operating conditions; analyzing the wear resistance demand of the multiple sub-hoses according to the static operating conditions and the multiple dynamic operating conditions to determine multiple wear resistance parameter indicators; optimizing the coating parameters of the hose outer film to approach the multiple wear resistance parameter indicators, determining multiple optimal coating parameters, and executing the outer film strengthening of the multiple sub-hoses.

[0006] In a possible implementation, the high-temperature and high-pressure high-wear-resistance hose outer membrane strengthening method further performs the following processing: according to an environment monitoring log of a hose operation area in a chemical preparation process, collecting a temperature interval, a temperature average, a pressure interval, a pressure average, a humidity interval, a humidity average, an ultraviolet intensity interval, an ultraviolet intensity average, an air corrosion intensity interval, and an air corrosion intensity average of the hose operation area in a preset historical time zone as static operation conditions.

[0007] In a possible implementation, the high-temperature and high-pressure high-wear-resistance hose outer membrane strengthening method further performs the following processing: dividing the hose to be processed in the hose operation area according to a preset length to obtain a plurality of sub-hoses; according to an operation monitoring log of the hose operation area in the chemical preparation process, respectively performing historical motion feature analysis on the plurality of sub-hoses to obtain a plurality of historical high-frequency motion features as a plurality of dynamic operation conditions.

[0008] In a possible implementation, the high-temperature and high-pressure high-wear-resistance hose outer membrane strengthening method further performs the following processing: randomly selecting a first sub-hose and obtaining a first preset hose operation area of the first sub-hose; according to the operation monitoring log, statistically analyzing high-frequency motion features of the hose in the first preset hose operation area in a preset historical time zone to obtain a first historical high-frequency motion feature and add the first historical high-frequency motion feature to the plurality of historical high-frequency motion features, wherein the historical high-frequency motion feature includes a high-frequency bending angle interval, a bending frequency, a high-frequency shaking amplitude interval, a shaking frequency, a high-frequency tension interval, and a tension application frequency.

[0009] In a possible implementation, the high-temperature and high-pressure high-wear-resistance hose outer membrane strengthening method further performs the following processing: combining the static operation conditions and the plurality of dynamic operation conditions respectively to obtain a plurality of comprehensive operation conditions; according to the plurality of comprehensive operation conditions, respectively performing wear-resistance demand analysis on the plurality of sub-hoses to determine a plurality of wear-resistance parameter indexes, with a constraint of meeting an expected service life.

[0010] In a possible implementation, the high-temperature and high-pressure high-wear-resistance hose outer membrane strengthening method further performs the following processing: according to a hose operation and maintenance log of a same type of chemical, collecting a sample comprehensive operation condition set and a sample wear-resistance parameter index set with a constraint of meeting an expected service life; training a generative adversarial network to convergence by using the sample comprehensive operation condition set and the sample wear-resistance parameter index set to obtain a wear-resistance demand analyzer; and using the wear-resistance demand analyzer to respectively perform wear-resistance demand analysis according to the plurality of comprehensive operation conditions to output the plurality of wear-resistance parameter indexes.

[0011] In a possible implementation, the high-wear-resistance outer membrane reinforcement method of the high-temperature and high-pressure resistant hose further performs the following processing: according to a hose operation and maintenance log of a hose operation area, a plurality of high-frequency historical use time interval of the hose in a plurality of preset hose operation areas of the plurality of hoses is obtained by statistics, and a plurality of historical use time mean values is calculated; a plurality of time deviation ratios is obtained by performing maximum deviation amplitude calculation according to the plurality of high-frequency historical use time interval and the plurality of historical use time mean values; and the plurality of wear-resistance parameter indicators are respectively mapped and compensated according to the plurality of time deviation ratios.

[0012] In a possible implementation, the high-wear-resistance outer membrane reinforcement method of the high-temperature and high-pressure resistant hose further performs the following processing: an outer membrane wear-resistance coating parameter space is obtained, and a first coating parameter is randomly generated in the coating parameter space, wherein the coating parameter includes a coating thickness and a coating chemical composition and a ratio; a first sub-hose and a first wear-resistance parameter indicator of the first sub-hose are randomly selected; a wear-resistance parameter simulator is pre-trained; a first predicted wear-resistance parameter indicator of the first coating parameter is obtained by using the wear-resistance parameter simulator, and a first deviation value from the first wear-resistance parameter indicator is calculated; a second coating parameter is randomly generated in the coating parameter space again, and a second deviation value is calculated; if the first deviation value is greater than or equal to the second deviation value, the second coating parameter is set as a current optimal coating parameter, and if the first deviation value is less than the second deviation value, the second coating parameter is set as the current optimal coating parameter according to a probability, wherein the probability decreases with an increase in an optimization number; iterative optimization is performed until a preset optimization number is reached, and then the optimization is stopped, and a current optimal coating parameter at the end of the optimization is output as a first optimal coating parameter, and a plurality of optimal coating parameters of a plurality of sub-hoses is sequentially analyzed.

[0013] In a possible implementation, the high-wear-resistance outer membrane reinforcement method of the high-temperature and high-pressure resistant hose further performs the following processing: a sample coating parameter set and a sample wear-resistance parameter indicator set are collected, a generative adversarial network is trained to convergence, and the wear-resistance parameter simulator is obtained.

[0014] The application also provides a high-wear-resistance hose outer film strengthening system under high-temperature and high-pressure conditions, which comprises: an environmental parameter acquisition module, configured to acquire environmental parameters of a hose operation area in a chemical preparation process as static operation conditions; a motion characteristic analysis module, configured to divide a hose to be processed in the hose operation area to determine a plurality of sub-hoses, and perform motion characteristic analysis on the plurality of sub-hoses respectively to obtain a plurality of dynamic operation conditions; a wear-resistance demand analysis module, configured to perform wear-resistance demand analysis on the plurality of sub-hoses respectively according to the static operation conditions and the plurality of dynamic operation conditions, and determine a plurality of wear-resistance parameter indexes; and a coating parameter optimization module, configured to perform coating parameter optimization of the outer film of the hose respectively for the purpose of approximating the plurality of wear-resistance parameter indexes, determine a plurality of optimal coating parameters, and perform outer film strengthening of the plurality of sub-hoses.

[0015] The high-wear-resistance hose outer film strengthening method and system under high-temperature and high-pressure conditions provided by the application can acquire environmental parameters of a hose operation area in a chemical preparation process, divide a hose to be processed in the hose operation area to determine a plurality of sub-hoses, perform motion characteristic analysis on the plurality of sub-hoses respectively, perform wear-resistance demand analysis on the plurality of sub-hoses respectively according to static operation conditions and a plurality of dynamic operation conditions, determine a plurality of wear-resistance parameter indexes, perform coating parameter optimization of the outer film of the hose respectively for the purpose of approximating the plurality of wear-resistance parameter indexes, determine a plurality of optimal coating parameters, and perform outer film strengthening of the plurality of sub-hoses. The technical problems of not fully considering the differences in hose operation environments and motion states, leading to local wear of the hose, easy peeling of the outer film, poor pertinence and precision of hose outer film strengthening, are solved, and the technical effects of improving the pertinence and precision of hose outer film strengthening and the overall wear resistance are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. In the present application, a flowchart is used to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. Meanwhile, other operations can be added to these processes, or a step or several steps can be removed from these processes.

[0017] Figure 1 The high-wear-resistance hose outer film strengthening method under high-temperature and high-pressure conditions provided by the embodiments of the present application is shown in the flowchart.

[0018] Figure 2 The high-wear-resistance hose outer film strengthening system under high-temperature and high-pressure conditions provided by the embodiments of the present application is shown in the structural schematic diagram.

[0019] Explanation of reference signs: environmental parameter acquisition module 10, motion feature analysis module 20, wear resistance requirement analysis module 30, coating parameter optimization module 40. DETAILED DESCRIPTION

[0020] The above description is only a summary of the technical scheme of the present application. In order to make the technical means of the present application more clear, the following specific embodiments of the present application are described in accordance with the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described.

[0021] In order to make the purposes, technical schemes and advantages of the present application more clear, the following will combine the drawings to make a further detailed description of the present application. The described embodiments should not be regarded as a limitation of the present application. All other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present application.

[0022] In the following description, "some embodiments" are related to a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subset of all possible embodiments, and can be combined with each other without conflict. The term "first\second" is only to distinguish similar objects, and does not represent the specific order of the objects. The terms "include" and "have" and any variations, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0023] The embodiments of the present application provide a high-temperature and high-pressure high-wear-resistant hose outer film strengthening method, as shown in the formula: Figure 1 The method comprises the following steps:

[0024] Step S100, collecting the environmental parameters of the hose operation area in the chemical preparation process as the static operation condition.

[0025] Step S100 further comprises: according to the environmental monitoring log of the hose operation area in the chemical preparation process, collecting the temperature interval, temperature average, pressure interval, pressure average, humidity interval, humidity average, ultraviolet intensity interval, ultraviolet intensity average, air corrosion intensity interval and air corrosion intensity average of the hose operation area in the preset historical time zone as the static operation condition.

[0026] Preferably, based on the existing environmental monitoring log in the chemical preparation process (such as historical data recorded in real time by sensors, monitoring systems), the environmental parameters of the hose operation area in the chemical preparation process within the preset historical time zone (such as the last 1 month, 3 months or custom time period) are collected, that is, the statistical characteristic values (interval and mean value) of the environmental parameters are collected, to reflect the long-term trend and fluctuation range of the environment, including the temperature interval, temperature mean value, pressure interval, pressure mean value, humidity interval, humidity mean value, ultraviolet intensity interval, ultraviolet intensity mean value, air corrosion intensity interval and air corrosion intensity mean value of the hose operation area. These environmental parameters are used as static operation conditions, as shown in Table 1:

[0027] Table 1: Hose operation area environmental parameter data table

[0028] Parameter Category Specific Parameter Significance Temperature Temperature Range, Temperature Mean Reflects the thermal environment stability of the operating area, the mean reflects the average temperature level; the range reflects the temperature fluctuation range (such as the influence of high temperature peak on the hose) Pressure Pressure Range, Pressure Mean Characterizes the mechanical load borne by the hose, the mean corresponds to the normal pressure working condition; the range reflects the pressure pulse or extreme high pressure scene (such as the pressure impact when starting and stopping the equipment) Humidity Humidity Range, Humidity Mean Reflects the degree of environmental humidity, high humidity may accelerate the aging or corrosion of the outer membrane of the hose (especially for metal parts or organic materials) Ultraviolet Intensity Ultraviolet Intensity Range, Mean Evaluates the risk of photoaging, long-term ultraviolet radiation may cause the outer membrane material of the hose to degrade and become brittle (such as the oxidation reaction of rubber materials) Air Corrosion Intensity Air Corrosion Intensity Range, Mean Reflects the environmental corrosiveness, chemical preparation processes may release acidic / alkaline gases (such as sulfides, chlorides), which may corrode the outer membrane material of the hose

[0029] Wherein, high temperature can reduce material strength and accelerate molecular chain rupture (such as rubber softening and plastic deformation), high pressure can increase the mechanical stress of the outer membrane of the hose, which may cause micro-crack propagation; when humidity and corrosion intensity are coupled, electrochemical corrosion (such as metal joint rusting) may be induced, and ultraviolet radiation and high temperature together can accelerate material aging (such as surface cracking of the outer membrane), the mean value of the environmental parameters is used to define the conventional working environment reference of the hose (such as an average temperature of 200℃ and an average pressure of 10MPa), and the interval of the environmental parameters is used to identify the boundary of extreme working conditions (such as a temperature of up to 250℃ and a minimum of 150℃), to avoid missing potential risks due to a single extreme value.

[0030] Step S200, the to-be-processed hose in the hose operation area is divided to determine a plurality of sub-hoses, and the plurality of sub-hoses are respectively analyzed for motion characteristics to obtain a plurality of dynamic operation conditions.

[0031] Step S200 further includes step S210, the to-be-processed hose in the hose operation area is divided according to a preset length to obtain a plurality of sub-hoses; step S220, based on the operation monitoring log of the hose operation area in the chemical preparation process, the plurality of sub-hoses are respectively analyzed for historical motion characteristics to obtain a plurality of historical high-frequency motion characteristics as a plurality of dynamic operation conditions.

[0032] Preferably, the to-be-processed hose in the hose operation area is divided, specifically, the whole to-be-processed hose in the hose operation area is divided into a plurality of sub-hoses according to a preset length (such as every 5 meters, 10 meters), so as to facilitate fine analysis of the motion difference of different sections, wherein the preset length is set according to the hose specification (such as the total length), the process equipment layout (such as the distance between pipe bends), etc.; then based on the operation monitoring log of the hose operation area in the chemical preparation process (such as the hose motion data collected by the motion sensor, the visual monitoring system), the historical motion characteristics of the plurality of sub-hoses are analyzed respectively, including extracting the historical high-frequency motion characteristics of each sub-hose, identifying the typical motion mode (such as frequent bending, vibration or movement) in the actual operation, obtaining a plurality of historical high-frequency motion characteristics, and taking these historical high-frequency motion characteristics as a plurality of dynamic operation conditions.

[0033] Preferably, the hose outer membrane wear is usually caused by cumulative damage caused by repeated motion (such as cracks preferentially appearing at high-frequency bending parts), and occasional motion (such as abnormal shaking when the equipment fails) has less effect on long-term wear, which can be excluded by filtering historical data; the high-frequency characteristics reflect the normal working mode of the hose, for example, a sub-hose bends 1000 times a day (high frequency), and the outer membrane thereof needs to be focused on the fatigue resistance performance; another sub-hose moves once a month (low frequency) only during equipment maintenance, and the wear risk is low.

[0034] Further, step S220 further comprises step S221 of randomly selecting a first sub-hose and obtaining a first preset hose operation area of the first sub-hose; and step S222 of, according to the operation monitoring log, counting the high-frequency motion characteristics of the hose in the first preset hose operation area in a preset historical time zone to obtain first historical high-frequency motion characteristics, and adding the first historical high-frequency motion characteristics to the plurality of historical high-frequency motion characteristics, wherein the historical high-frequency motion characteristics include a high-frequency bending angle interval, a bending frequency, a high-frequency shaking amplitude interval, a shaking frequency, a high-frequency tension interval, and a tension application frequency.

[0035] Preferably, one of the divided plurality of sub-hoses is randomly selected as a sample (such as a sub-hose numbered A3), which is used for typical motion characteristic analysis, and the sub-hose is further divided into smaller local areas (such as the middle section of the sub-hose, the connection port, etc.), so as to focus on the wear sensitive parts, for example, the sub-hose is 10 meters long, which can be divided into 5 local areas every 2 meters, the motion difference of each area is analyzed, then according to the operation monitoring log, the high-frequency motion characteristics of the hose in the first preset hose operation area in a preset historical time zone (such as an operation period of about 30 days) are counted, and finally the first historical high-frequency motion characteristics of the sample sub-hose are included in the whole plurality of historical high-frequency motion characteristic sets, so as to exclude accidental abnormal working condition data, and the high-frequency motion characteristic parameter data is as shown in Table 2:

[0036] Table 2: Hose high-frequency motion characteristic parameter data table

[0037] Motion Type Typical Characteristic Parameters Significance Bending Motion High-frequency Bending Angle Range, Bending Frequency The bending angle range reflects the amplitude range of the hose bending in this area (such as 30°~60°), the greater the angle, the greater the tensile stress on the outside of the outer membrane; the bending frequency is the number of bends per unit time (such as 10 times / minute), high-frequency bending is prone to cause material fatigue cracks Shaking Motion High-frequency Shaking Amplitude Range, Shaking Frequency The shaking amplitude range is the displacement range of the hose transverse / longitudinal oscillation (such as ±2cm~±5cm), the greater the amplitude, the higher the risk of collision and wear between the outer membrane and surrounding objects (such as pipe supports, equipment housings); the shaking frequency refers to whether it is in resonance with the equipment vibration frequency (such as pumps, fans), resonance will exacerbate wear Tension Motion High-frequency Tension Range, Tension Application Frequency The tension range is the range of axial tensile force borne by the hose (such as 50N~200N), exceeding the material yield strength may cause the outer membrane to tear; the tension application frequency is the number of tension changes per unit time (such as sudden changes in tension when starting and stopping the equipment), high-frequency tension fluctuations are prone to cause the outer membrane at the interface to delaminate

[0038] In step S300, abrasion resistance requirements of the plurality of sub-hoses are analyzed according to the static working condition and the plurality of dynamic working conditions, and a plurality of abrasion resistance parameter indexes are determined.

[0039] Step S300 further comprises step S310 of combining the static working condition and the plurality of dynamic working conditions to obtain a plurality of comprehensive working conditions; and step S320 of analyzing the abrasion resistance requirements of the plurality of sub-hoses according to the plurality of comprehensive working conditions, with a constraint of meeting an expected service life, to determine a plurality of abrasion resistance parameter indexes.

[0040] Preferably, the abrasion resistance requirements of the plurality of sub-hoses are analyzed in combination with the static working condition and the plurality of dynamic working conditions, and the abrasion resistance requirements of different sub-hoses are quantified, wherein the static working condition (environmental stress) determines the basic durability of the material (such as the thermal degradation rate of the material at high temperature), and the dynamic working condition (mechanical stress) determines the fatigue wear rate of the material (such as crack propagation caused by high-frequency bending). Specifically, the static working condition and the plurality of dynamic working conditions are combined, i.e., the static parameters and the dynamic parameters of each sub-hose are one-to-one combined to form a unique comprehensive working condition (comprehensive working condition) of the sub-hose, and a plurality of comprehensive working conditions are obtained. For example, the static condition of sub-hose A is “high temperature + high corrosion”, the dynamic condition is “high-frequency bending + high vibration”, and the comprehensive working condition is described as “high-frequency bending vibration in a high-temperature and high-corrosion environment”. Then, a constraint condition is set to meet the expected service life, i.e., the target service life of the hose (such as 12 months, 18 months, etc.) is set to inversely deduce the abrasion resistance requirement, avoid over-strengthening (increase cost) or insufficient strengthening (premature failure), and balance reliability and economy.

[0041] Preferably, the abrasion resistance of the plurality of sub-hoses is analyzed according to a plurality of comprehensive operating conditions. Specifically, the abrasion of the outer membrane of the hose is usually caused by environmental erosion and mechanical damage. An environmental erosion model and a mechanical damage model are established respectively. High temperature accelerates material oxidation and thermal cracking, reduces elongation at break, and corrosion medium causes chemical degradation (such as rubber swelling and metal corrosion), which weakens the strength of the material. Bending / tensioning produces periodic stress, which causes fatigue cracks, and vibration / friction causes surface abrasive wear and adhesive wear (such as the friction between the outer membrane and the pipe support). Environmental erosion reduces the material's resistance to mechanical damage (such as corrosion, which makes the outer membrane surface brittle and more prone to cracking due to vibration). Then, by taking the expected service life as a constraint, the abrasion resistance parameter index is calculated by the abrasion rate. Specifically, first, the single stress abrasion is quantified. For example, at a static high temperature (200°C), the annual thermal aging thickness loss of the outer membrane material is 0.1mm, and the annual fatigue abrasion thickness loss is 0.3mm when high-frequency bending (10 times / minute) acts alone. Considering the stress coupling effect (such as high temperature, which increases the bending fatigue abrasion rate by 50%), the comprehensive abrasion rate = 0.1+0.3x1.5=0.55mm / year. If the expected service life is 2 years, the total allowable abrasion thickness is ≤1mm, the initial thickness of the outer membrane is required to be ≥1mm+reserved safety thickness (such as 0.2mm), and the material needs to meet the requirement of "abrasion rate ≤0.6mm / year under comprehensive operating conditions". Similarly, a plurality of abrasion resistance parameter indexes are analyzed and determined, as shown in Table 3:

[0042] Table 3: Abrasion parameter index data table

[0043] Index Category Typical Parameters Meaning Material Mechanical Properties Tensile Strength, Elongation at Break, Hardness Tensile strength is the ability to resist tensile tearing (unit: MPa); elongation at break is the maximum deformation capacity of the material before breaking (%), reflecting the bending fatigue resistance Wear Resistance Wear Amount (mg / 1000 cycles) The unit cycle wear amount measured by wear testing (such as Akron Abrasion Machine), directly related to the service life Environmental Resistance High Temperature Aging Resistance Grade, Corrosion Resistance Grade High temperature aging resistance, such as strength retention rate ≥80% after 1000 hours of hot air aging; corrosion resistance is the mass change rate ≤±2% in a specific corrosive medium Coating Structure Parameters Coating Thickness, Composite Layer Number, Interface Bonding Strength Thickness refers to the minimum design thickness calculated according to the wear rate (such as 0.8mm); interface bonding strength refers to the adhesion between the coating and the substrate (≥5MPa), to avoid delamination and falling off Fatigue Life Index Fatigue Cycle Number (to Crack Initiation) The minimum cycle number for the outer membrane to have no visible cracks under a specific bending / vibration frequency (such as ≥10^6 times)

[0044] Further, step S320 further includes step S321, collecting a sample comprehensive operating condition set and a sample abrasion resistance parameter index set according to the hose operation log of the same type of chemical, taking the expected service life as a constraint; step S322, using the sample comprehensive operating condition set and the sample abrasion resistance parameter index set to train the generative adversarial network to convergence, obtaining an abrasion resistance demand analyzer; step S323, using the abrasion resistance demand analyzer to analyze the abrasion resistance demand according to the plurality of comprehensive operating conditions respectively, and outputting a plurality of abrasion resistance parameter indexes.

[0045] Preferably, the hose operation log of the same type of chemical refers to historical data in the same or similar process scene (such as the same chemical production line, the same type of chemical delivery), which contains verified "working condition-wear resistance index" correspondence, collects sample comprehensive operation condition set and sample wear resistance parameter index set that meet the expected service life requirement (such as actual service life ≥ target length), and ensures the effectiveness of the data label, wherein the sample comprehensive operation condition refers to the combination of static conditions (such as temperature, pressure) and dynamic conditions (such as bending frequency, vibration amplitude), forming a multi-dimensional input feature (such as a 10-dimensional vector), and the sample wear resistance parameter index refers to the wear resistance parameter (such as coating thickness, material hardness) that is actually applied and meets the expected service life as the output label (such as a 5-dimensional vector).

[0046] Preferably, the sample comprehensive operation condition set and the sample wear resistance parameter index set are used to train the generative adversarial network. The generative adversarial network (GAN) can capture the complex distribution characteristics (such as nonlinear wear mechanism under multivariate coupling) hidden in the data through the adversarial game between the generator and the discriminator. Specifically, the static conditions (such as temperature mean, pressure interval) and the dynamic conditions (such as bending frequency, vibration amplitude) are preprocessed, including extracting mean features and normalizing, and scaling features of different dimensions to [-1, 1] or [0, 1] interval to avoid slow convergence caused by feature scale difference during model training. Then, the generative adversarial network is designed, the comprehensive operation condition is input into the generator of the generative adversarial network, the corresponding wear resistance parameter index is obtained, and then the discriminator is used to judge whether the generated index conforms to the real sample distribution (that is, whether it can meet the expected service life). When the discriminator cannot distinguish between generated samples and real samples, it is considered that the model converges. At this time, the generator can accurately generate reasonable wear resistance parameters according to the input comprehensive operation condition, so as to obtain the wear resistance demand analyzer. The design parameters of the generative adversarial network are shown in Table 4:

[0047] Table 4 Generative adversarial network design parameter table

[0048] Component Structural Design Input / Output Generator (G) Input layer: comprehensive working condition vector (n-dimensional) Input: comprehensive working condition; Output: predicted index Discriminator (D) Input layer: wear-resistant parameter index vector of real sample or generated sample; Hidden layer: 2-3 layers of fully connected layers, activation function LeakyReLU; Output layer: binary classification probability (0-1) Input: index vector; Output: whether it is a real sample

[0049] Preferably, a plurality of comprehensive operation conditions are normalized to obtain corresponding working condition vectors, which are respectively input into the wear resistance demand analyzer for wear resistance demand analysis. Specifically, the generator receives the normalized working condition vectors, calculates after the hidden layer, and outputs the predicted wear resistance parameter indicators, for example, coating thickness = 0.9mm (normalized value 0.9), material tensile strength = 15MPa (normalized value 0.75), and wear amount = 12mg / 1000 cycles (normalized value 0.6); then the discriminator is used for verification, that is, the wear resistance indicators (such as coating thickness 0.8-1.0mm, tensile strength 14-16MPa) under similar working conditions in the historical real samples are compared to determine whether the generated indicators are reasonable, and the indicators are confirmed to meet the expected service life (such as the historical data verification under this indicator, the hose life ≥18 months), and finally a plurality of wear resistance parameter indicators are output, thereby significantly improving the scientificity and efficiency of the hose outer membrane reinforcement under complex working conditions such as high temperature and high pressure.

[0050] Further, step S323 further comprises step a, obtaining a plurality of high-frequency historical use time interval of the plurality of hoses in a plurality of preset hose operation areas according to the hose operation area of the hose operation log, and calculating a plurality of historical use time mean; step b, according to the plurality of high-frequency historical use time interval and the plurality of historical use time mean, the maximum deviation amplitude is calculated, and the plurality of time deviation proportions are obtained; step c, according to the plurality of time deviation proportions, respectively mapping and compensating the plurality of wear resistance parameter indicators.

[0051] Preferably, based on the hose operation log, the historical use records of each hose in its preset hose operation area (such as a specific work station, a device connection point, etc.) are extracted, including statistics of multiple high-frequency historical use time length intervals of the hoses in multiple preset hose operation areas of multiple hoses and calculation of multiple historical use time length means. Specifically, the time period with the highest frequency of hose use time length in multiple operation areas is counted (such as 3-5 hours per day in most cases), and the average daily / each use time length of the hose in the preset historical time zone (such as the past 1 month, 1 year) in the operation area is calculated. Then, the maximum deviation amplitude of multiple high-frequency historical use time length intervals and multiple historical use time length means is calculated, that is, for the use time length data of each operation area, the maximum value (such as suddenly using 8 hours / day for a few days) and the minimum value (such as using only 1 hour / day for a few days) are found, and the deviation amplitude of the maximum value, the minimum value and the mean value is calculated, wherein the maximum value deviation amplitude = maximum value - mean value, and the minimum value deviation amplitude = mean value - minimum value. Then, the absolute value of the deviation amplitude is divided by the mean value to obtain the time length deviation proportion, which reflects the relative degree of deviation. Finally, multiple wear-resistant parameter indicators are respectively mapped and compensated according to multiple time length deviation proportions, that is, a wear-resistant parameter compensation coefficient (for example, 1 + time length deviation proportion) is set based on the time length deviation proportion, which is used to adjust the wear-resistant parameters (such as material thickness, compression resistance grade, etc.), so as to avoid the premature wear or insufficient performance of the hose due to unstable use time length. For example, if the time length deviation proportion is large (such as long-term overloading use), it means that the hose may wear faster, so the wear-resistant parameter indicators are increased, such as increasing the hose wall thickness and selecting a material with higher strength; if the time length deviation proportion is small (such as the use time length is much lower than the mean value), the wear-resistant parameter indicators are appropriately reduced, such as using a lighter material, to optimize the cost, and finally the optimized wear-resistant parameters are obtained.

[0052] Step S400, for the purpose of approaching the multiple wear-resistant parameter indicators, respectively performing coating parameter optimization of the outer membrane of the hose to determine multiple optimal coating parameters, and executing outer membrane reinforcement of the multiple sub-hoses.

[0053] The step S400 further comprises the following steps: S410, obtaining a coating parameter space for enhancing the wear resistance of the outer membrane of the hose, and randomly generating a first coating parameter in the coating parameter space, wherein the coating parameter comprises a coating thickness and a coating chemical composition and proportion; S420, randomly selecting a first sub-hose and a first wear resistance parameter index of the first sub-hose; S430, pre-training a wear resistance parameter simulator; S440, predicting a first predicted wear resistance parameter index of the first coating parameter by using the wear resistance parameter simulator, and calculating a first deviation value of the first wear resistance parameter index; S450, randomly generating a second coating parameter in the coating parameter space again, and calculating a second deviation value; S460, if the first deviation value is greater than or equal to the second deviation value, setting the second coating parameter as a current optimal coating parameter, and if the first deviation value is less than the second deviation value, setting the second coating parameter as the current optimal coating parameter according to a probability, wherein the probability decreases with an increase in the number of optimization times; S470, performing iterative optimization until a preset number of optimization times is reached, then stopping the optimization, and outputting the current optimal coating parameter at the end of the optimization as a first optimal coating parameter, and sequentially analyzing a plurality of optimal coating parameters of a plurality of sub-hoses.

[0054] Preferably, the coating parameter space is constructed with the coating thickness (such as a continuous variable of 0.5-2.0 mm) and the coating chemical composition and proportion (such as a continuous variable of 10%-40% of ceramic particle content in a rubber-based composite material, 5%-15% of curing agent proportion, etc.) as the coating parameters for enhancing the wear resistance of the outer membrane of the hose, the coating parameters are generated iteratively and compared with the predicted deviation, the wear resistance parameter index of each sub-hose is gradually approximated, the combination that minimizes the deviation between the predicted wear resistance parameter and the target index is found, the individualized wear resistance parameter index (such as a wear amount ≤15 mg / 1000 times, a tensile strength ≥12 MPa) of each sub-hose is obtained, the coating parameters are optimized iteratively, the simulation prediction result is as close as possible to the target, and the expected service life constraint is met at the same time.

[0055] Preferably, the first coating parameter (such as thickness 1.2mm, ceramic content 25%) is randomly generated in the coating parameter space as the starting point of optimization, and the first sub-hose and its corresponding first wear-resistant parameter index are randomly selected. A machine learning model (such as neural network, random forest) is trained based on historical coating test data to establish a mapping relationship between coating parameters and wear-resistant parameters, and a wear-resistant parameter simulator is obtained to predict the wear-resistant performance of the hose under different coating parameters. Then the first coating parameter is input into the wear-resistant parameter simulator to obtain the first predicted wear-resistant parameter index through the wear-resistant parameter simulator, and the first deviation value between the first predicted wear-resistant parameter index and the first wear-resistant parameter index is calculated. The second coating parameter is randomly generated in the coating parameter space again, and the second deviation value is also calculated. If the first deviation value is greater than or equal to the second deviation value, the second coating parameter is set as the current optimal coating parameter, i.e. the new parameter is accepted as the current optimal (greedy strategy). If the first deviation value is less than the second deviation value, the second coating parameter is set as the current optimal coating parameter according to a probability, i.e. the new parameter is accepted with a certain probability, and the probability decreases with the increase of the number of iterations, allowing the initial solution to be accepted with a higher probability to jump out of the local optimal trap, and the probability is reduced in the later period to focus on local fine optimization, such as the probability being (1-current optimization number) / total optimization number. Iterative optimization is continuously performed until a preset optimization number (such as 500) is reached, the search is stopped, and the current optimal coating parameter is output as the first optimal coating parameter. Multiple optimal coating parameters of multiple sub-hoses are obtained by successive analysis.

[0056] Further, step S430 further comprises collecting a sample coating parameter set and a sample wear-resistant parameter index set, training a generative adversarial network to convergence, and obtaining the wear-resistant parameter simulator.

[0057] Preferably, a sample coating parameter set and a sample wear resistance parameter index set are collected, wherein the sample coating parameter set is a historical coating parameter combination actually used, including coating thickness and coating chemical composition and ratio; the sample wear resistance parameter index set is a corresponding wear resistance performance index obtained through experiments or actual operation data for each coating parameter group, such as wear resistance life (such as 1000 hours, 2000 hours, etc.), wear rate (thickness loss per unit time), each coating parameter group corresponds to a wear resistance parameter index, forming an "input-output" training sample pair; then, the two neural networks (generator and discriminator) included in the generative adversarial network are subjected to adversarial training, specifically, the sample coating parameter set and the sample wear resistance parameter index set are preprocessed (normalized), the coating parameters are input into the generator, and the predicted wear resistance parameter index is output; the real wear resistance parameter index (from the sample set) or the wear resistance parameter index predicted by the generator is input into the discriminator, and a probability value (between 0 and 1) is output, indicating the possibility that the input data is a real sample; during the training process, the generator parameters are adjusted so that the generated wear resistance parameter index is as close as possible to the real sample, so as to reduce the discrimination probability of the discriminator, the discriminator parameters are adjusted, and the ability to distinguish between real samples and generated samples is improved; the two are alternately trained until a convergence state is reached, so that the generator learns to accurately predict the wear resistance parameter index according to the input coating parameters (such as thickness, composition), the predicted value output by the generator is difficult to distinguish from the real sample, and the discriminator cannot accurately judge the difference between the generated data and the real data, and finally the wear resistance parameter simulator is obtained.

[0058] Preferably, according to the determined plurality of optimal coating parameters, the outer film of the plurality of sub-hoses is reinforced, wherein each optimal coating parameter corresponds to a plurality of sub-hoses of the hose to be processed, for example, the coating thickness of the sub-hose A (high-temperature high-frequency bending area) is 1.2 mm, the composition contains 30% ceramic particles + 70% high-temperature resistant resin, the coating thickness of the sub-hose B (high-corrosion low-vibration area) is 0.8 mm, and the composition contains 50% polytetrafluoroethylene + 50% corrosion-resistant adhesive; a segmented coating process is adopted, and the optimized material combination is attached to the outer surface of the hose by a coating device to form a protective layer with specific performance, thereby increasing the thickness and hardness of the outer film, resisting mechanical wear (such as friction and impact), resisting environmental erosion (such as acid / alkali gas and high-temperature oxidation) through corrosion-resistant components (such as fluoroplastic), and improving bending fatigue resistance and tensile performance through composite formula (such as ceramic particle reinforcement); the high-wear-risk sub-hose (such as the bending section) is subjected to multi-layer coating (such as a primer layer + a wear-resistant layer + a protective layer), so as to obtain a reinforced hose and ensure its life and reliability.

[0059] In the foregoing, with reference to Figure 1 The high-temperature high-pressure wear-resistant hose outer film reinforcement method according to the embodiment of the application is described in detail. Next, with reference to Figure 2The application discloses a high-wear-resistance hose outer film reinforcing system under high-temperature and high-pressure conditions.

[0060] The high-wear-resistance hose outer film reinforcing system under high-temperature and high-pressure conditions according to the embodiment of the application is used for solving the technical problem that the difference between the working environment and the motion state of the hose is not fully considered in the prior art, leading to local wear of the hose, easy falling of the outer film, poor pertinence and precision of the hose outer film reinforcing, and achieves the technical effects of improving the pertinence and precision of the hose outer film reinforcing and the overall wear resistance. Figure 2 As shown in the figure, the high-wear-resistance hose outer film reinforcing system under high-temperature and high-pressure conditions comprises an environment parameter acquisition module 10, a motion feature analysis module 20, a wear resistance demand analysis module 30 and a coating parameter optimization module 40.

[0061] The environment parameter acquisition module 10 is used for acquiring the environment parameters of the hose working area in the chemical preparation process as the static working condition; the motion feature analysis module 20 is used for dividing the hose to be processed in the hose working area to determine a plurality of sub-hoses, and performing motion feature analysis on the plurality of sub-hoses respectively to obtain a plurality of dynamic working conditions; the wear resistance demand analysis module 30 is used for performing wear resistance demand analysis on the plurality of sub-hoses respectively according to the static working condition and the plurality of dynamic working conditions to determine a plurality of wear resistance parameter indexes; and the coating parameter optimization module 40 is used for performing coating parameter optimization of the outer film of the hose respectively for the purpose of approximating the plurality of wear resistance parameter indexes to determine a plurality of optimal coating parameters and performing outer film reinforcing of the plurality of sub-hoses.

[0062] In the following, the specific configuration of the environment parameter acquisition module 10 will be described in detail. The environment parameter acquisition module 10 further comprises: according to the environment monitoring log of the hose working area in the chemical preparation process, collecting the temperature interval, the temperature average value, the pressure interval, the pressure average value, the humidity interval, the humidity average value, the ultraviolet intensity interval, the ultraviolet intensity average value, the air corrosion intensity interval and the air corrosion intensity average value of the hose working area in the preset historical time zone as the static working condition.

[0063] In the following, the specific configuration of the motion feature analysis module 20 will be described in detail. The motion feature analysis module 20 further comprises: dividing the hose to be processed in the hose working area according to a preset length to obtain a plurality of sub-hoses; and according to the operation monitoring log of the hose working area in the chemical preparation process, performing historical motion feature analysis on the plurality of sub-hoses respectively to obtain a plurality of historical high-frequency motion features as a plurality of dynamic working conditions.

[0064] Next, the specific configuration of the motion feature analysis module 20 will be described in detail. The motion feature analysis module 20 further comprises: randomly selecting a first sub-hose, and obtaining a first preset hose operation area of the first sub-hose; according to the operation monitoring log, counting the high-frequency motion features of the hose in the first preset hose operation area in the preset historical time zone to obtain the first historical high-frequency motion feature, and adding it to the plurality of historical high-frequency motion features, wherein the historical high-frequency motion feature includes a high-frequency bending angle interval, a bending frequency, a high-frequency shaking amplitude interval, a shaking frequency, a high-frequency tension interval, and a tension application frequency.

[0065] Next, the specific configuration of the wear-resistant demand analysis module 30 will be described in detail. The wear-resistant demand analysis module 30 further comprises: combining the static operation condition and the plurality of dynamic operation conditions respectively to obtain a plurality of comprehensive operation conditions; according to the plurality of comprehensive operation conditions, performing wear-resistant demand analysis on the plurality of sub-hoses respectively to determine a plurality of wear-resistant parameter indicators, with the constraint of meeting the expected use duration.

[0066] Next, the specific configuration of the wear-resistant demand analysis module 30 will be described in detail. The wear-resistant demand analysis module 30 further comprises: according to the hose operation log of the same type of chemical, collecting a sample comprehensive operation condition set and a sample wear-resistant parameter indicator set with the constraint of meeting the expected use duration; training the generative adversarial network to convergence using the sample comprehensive operation condition set and the sample wear-resistant parameter indicator set to obtain a wear-resistant demand analyzer; and using the wear-resistant demand analyzer to perform wear-resistant demand analysis according to the plurality of comprehensive operation conditions to output a plurality of wear-resistant parameter indicators.

[0067] Next, the specific configuration of the wear-resistant demand analysis module 30 will be described in detail. The wear-resistant demand analysis module 30 further comprises: according to the hose operation log of the hose operation area, counting a plurality of high-frequency historical use duration intervals of the hose in a plurality of preset hose operation areas of a plurality of hoses, and calculating a plurality of historical use duration means; performing maximum deviation amplitude calculation according to the plurality of high-frequency historical use duration intervals and the plurality of historical use duration means to obtain a plurality of duration deviation proportions; and mapping and compensating the plurality of wear-resistant parameter indicators respectively according to the plurality of duration deviation proportions.

[0068] The specific configuration of the coating parameter optimization module 40 will be described in detail below. The coating parameter optimization module 40 further comprises: obtaining a coating parameter space for enhancing the wear resistance of the outer membrane of the hose, and randomly generating a first coating parameter in the coating parameter space, wherein the coating parameter comprises a coating thickness and a coating chemical composition and ratio; randomly selecting a first sub-hose and a first wear resistance parameter index of the first sub-hose; pre-training a wear resistance parameter simulator; using the wear resistance parameter simulator, predicting a first predicted wear resistance parameter index of the first coating parameter, and calculating a first deviation value of the first wear resistance parameter index; randomly generating a second coating parameter in the coating parameter space again, and calculating a second deviation value; if the first deviation value is greater than or equal to the second deviation value, setting the second coating parameter as the current optimal coating parameter, and if the first deviation value is less than the second deviation value, setting the second coating parameter as the current optimal coating parameter according to a probability, wherein the probability decreases as the number of optimization increases; performing iterative optimization until a preset number of optimization is reached, then stopping optimization, and outputting the current optimal coating parameter at the end of optimization as the first optimal coating parameter, and sequentially analyzing a plurality of optimal coating parameters of a plurality of sub-hoses.

[0069] The specific configuration of the coating parameter optimization module 40 will be described in detail below. The coating parameter optimization module 40 further comprises: obtaining a coating parameter space for enhancing the wear resistance of the outer membrane of the hose, and randomly generating a first coating parameter in the coating parameter space, wherein the coating parameter comprises a coating thickness and a coating chemical composition and ratio; randomly selecting a first sub-hose and a first wear resistance parameter index of the first sub-hose; pre-training a wear resistance parameter simulator; using the wear resistance parameter simulator, predicting a first predicted wear resistance parameter index of the first coating parameter, and calculating a first deviation value of the first wear resistance parameter index; randomly generating a second coating parameter in the coating parameter space again, and calculating a second deviation value; if the first deviation value is greater than or equal to the second deviation value, setting the second coating parameter as the current optimal coating parameter, and if the first deviation value is less than the second deviation value, setting the second coating parameter as the current optimal coating parameter according to a probability, wherein the probability decreases as the number of optimization increases; performing iterative optimization until a preset number of optimization is reached, then stopping optimization, and outputting the current optimal coating parameter at the end of optimization as the first optimal coating parameter, and sequentially analyzing a plurality of optimal coating parameters of a plurality of sub-hoses.

[0070] The high-temperature and high-pressure wear-resistant hose outer membrane strengthening system provided by the embodiments of the present application can perform the high-temperature and high-pressure wear-resistant hose outer membrane strengthening method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0071] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, and each unit and module included is only divided according to the functional logic, but is not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation, and do not limit the protection scope of the present application.

[0072] The specific embodiments described above do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for strengthening the outer membrane of a high-wear-resistant flexible hose under high temperature and high pressure conditions, characterized in that the method... include: Collect environmental parameters of the hose operating area during the chemical preparation process as static operating conditions; The hose to be processed in the hose operation area is divided into multiple sub-hose, and the motion characteristics of each sub-hose are analyzed to obtain multiple dynamic operation conditions. Based on the static operating conditions and multiple dynamic operating conditions, the wear resistance requirements of the multiple sub-hose are analyzed to determine multiple wear resistance parameter indicators. With the aim of approximating the multiple wear resistance parameters, the coating parameters of the hose outer membrane are optimized to determine multiple optimal coating parameters, and the outer membrane reinforcement of the multiple sub-hose is performed. Based on the static operating conditions and multiple dynamic operating conditions, the wear resistance requirements of the multiple sub-hose are analyzed to determine multiple wear resistance parameters, including: The static operating conditions and multiple dynamic operating conditions are combined to obtain multiple comprehensive operating conditions; Based on the aforementioned comprehensive operating conditions and constrained by the expected service life, the wear resistance requirements of the multiple sub-hose are analyzed to determine multiple wear resistance parameters.

2. The method for strengthening the outer membrane of a high-wear-resistant flexible hose under high temperature and high pressure conditions according to claim 1, characterized in that, Environmental parameters of the hose operating area during chemical preparation are collected as static operating conditions, including: Based on the environmental monitoring logs of the hose operation area during the chemical preparation process, the temperature range, average temperature, pressure range, average pressure, humidity range, average humidity, ultraviolet intensity range, average ultraviolet intensity, air corrosion intensity range, and average air corrosion intensity of the hose operation area within a preset historical time zone are collected as static operating conditions.

3. The method for strengthening the outer membrane of a high-wear-resistant flexible hose under high temperature and high pressure conditions according to claim 1, characterized in that, The hose to be processed in the hose operation area is divided into multiple sub-hose sections. Motion characteristic analysis is performed on each of these sub-hose sections to obtain multiple dynamic operating conditions, including: The hoses to be processed in the hose operation area are divided according to a preset length to obtain multiple sub-hose; Based on the operation monitoring logs of the hose operation area during the chemical preparation process, the historical motion characteristics of the multiple sub-hose were analyzed to obtain multiple historical high-frequency motion characteristics, which were used as multiple dynamic operation conditions.

4. The method for strengthening the outer membrane of a high-wear-resistant hose under high temperature and high pressure conditions according to claim 3, characterized in that, Historical motion feature analysis was performed on the multiple sub-tubes to obtain multiple historical high-frequency motion features, including: Randomly select the first sub-hose and obtain the first preset hose operation area of ​​the first sub-hose; Based on the operation monitoring log, the high-frequency motion characteristics of the hose within the first preset hose operation area in the preset historical time zone are statistically analyzed to obtain the first historical high-frequency motion characteristics, which are then added to the plurality of historical high-frequency motion characteristics. The historical high-frequency motion characteristics include the high-frequency bending angle range, bending frequency, high-frequency swaying amplitude range, swaying frequency, high-frequency tension range, and tension application frequency.

5. The method for strengthening the outer membrane of a high-wear-resistant hose under high temperature and high pressure conditions according to claim 1, characterized in that, Based on the aforementioned comprehensive operating conditions and constrained by the expected service life, the wear resistance requirements of the multiple sub-hose are analyzed to determine several wear resistance parameters, including: Based on the hose maintenance logs of similar chemicals, and constrained by the expected service life, a comprehensive set of sample operating conditions and a set of sample abrasion resistance parameters were collected. Using the sample integrated operating condition set and the sample wear resistance parameter index set, an adversarial network is trained until convergence to obtain a wear resistance demand parser. Using the wear resistance requirement analyzer, wear resistance requirements are analyzed according to the multiple comprehensive operating conditions, and multiple wear resistance parameter indicators are output.

6. The method for strengthening the outer membrane of a high-wear-resistant flexible hose under high temperature and high pressure conditions according to claim 1, characterized in that, After determining multiple wear resistance parameters, the following also includes: Based on the hose operation and maintenance logs of the hose operation area, multiple high-frequency historical usage time intervals of hoses in multiple preset hose operation areas are statistically obtained, and multiple historical usage time averages are calculated. The maximum deviation amplitude is calculated based on the multiple high-frequency historical usage duration intervals and multiple historical usage duration averages to obtain multiple duration deviation ratios; Based on the multiple time deviation ratios, the multiple wear resistance parameters are mapped and compensated respectively.

7. The method for strengthening the outer membrane of a high-wear-resistant hose under high temperature and high pressure conditions according to claim 1, characterized in that, With the aim of approximating the aforementioned multiple wear resistance parameters, the coating parameters of the hose outer membrane are optimized to determine multiple optimal coating parameters, including: Obtain a coating parameter space for enhancing the wear resistance of the outer membrane of the hose, and randomly generate a first coating parameter within the coating parameter space, wherein the coating parameter includes coating thickness and coating chemical composition and ratio; Randomly select the first sub-hose and the first wear resistance parameter index of the first sub-hose; Pre-trained wear resistance parameter simulator; Using the wear resistance parameter simulator, a first predicted wear resistance parameter index of the first coating parameter is predicted and obtained, and a first deviation value from the first wear resistance parameter index is calculated. A second coating parameter is randomly generated again within the coating parameter space, and a second deviation value is calculated. If the first deviation value is greater than or equal to the second deviation value, then the second coating parameter is set as the current optimal coating parameter; if the first deviation value is less than the second deviation value, then the second coating parameter is set as the current optimal coating parameter according to probability, wherein the probability decreases as the number of optimization attempts increases. The optimization process is iterated until the preset number of optimization attempts is reached. Then the optimization stops, and the current optimal coating parameter at the end of the optimization is output as the first optimal coating parameter. Multiple optimal coating parameters for multiple sub-tubes are obtained by sequential analysis.

8. The method for strengthening the outer membrane of a high-wear-resistant hose under high temperature and high pressure conditions according to claim 7, characterized in that, Collect sample coating parameter set and sample wear resistance parameter index set, train the adversarial network until convergence, and obtain the wear resistance parameter simulator.

9. A high-wear-resistant hose outer membrane reinforcement system under high temperature and high pressure conditions, characterized in that, The system is used to implement the high wear-resistant hose outer membrane reinforcement method under high temperature and high pressure conditions as described in any one of claims 1 to 8, the system comprising: The environmental parameter acquisition module is used to collect environmental parameters in the hose operation area during the chemical preparation process, as static operating conditions. The motion feature analysis module is used to divide the hose to be processed in the hose operation area into multiple sub-hose, and perform motion feature analysis on the multiple sub-hose to obtain multiple dynamic operation conditions. The wear resistance requirement analysis module is used to analyze the wear resistance requirements of the multiple sub-hoops according to the static operating conditions and multiple dynamic operating conditions, and determine multiple wear resistance parameter indicators. The coating parameter optimization module is used to optimize the coating parameters of the hose outer membrane with the aim of approximating the multiple wear resistance parameters, determine multiple optimal coating parameters, and perform outer membrane reinforcement of the multiple sub-hose.

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