Surface polishing control system for carbon dioxide steel cylinder

Abstract

The invention discloses a surface grinding control system of a carbon dioxide steel cylinder, and relates to the related field of a polishing process, and the system comprises the steps of obtaining target material characteristic data, and obtaining a first grinding strategy; and taking the first polishing strategy as input information of the surface temperature prediction model to obtain a first predicted surface temperature. When the first predicted surface temperature is at a surface temperature limit value, activating ultrasonic detection equipment to perform dynamic detection to obtain a first polishing detection signal; and a grinding fitness evaluation function is called to compare and analyze the first grinding detection signal and a preset signal, and the first grinding fitness is obtained. And iteration is continued until the iteration frequency limit value is reached, and surface grinding control is conducted on the carbon dioxide steel cylinder based on the optimal grinding strategy at the other time. The technical problems that in the prior art, a carbon dioxide steel cylinder surface grinding process is low in grinding efficiency, unstable in grinding quality, inaccurate in surface temperature control and lack of intelligent control are solved.

Description

Technical Field

[0001] The present application relates to the field of polishing technology, and in particular to a surface polishing control system for a carbon dioxide cylinder. Background Art

[0002] During the production and use of carbon dioxide cylinders, strict surface polishing is required to ensure their quality and safety. However, the traditional polishing process mainly relies on manual operation or simple mechanical equipment, which has problems such as low efficiency, uneven polishing, and inaccurate control of the surface temperature of the cylinder. These problems may cause over- or under-polishing of the cylinder surface, which in turn affects the strength and service life of the cylinder. In addition, the manual polishing process is easily affected by human factors, resulting in unstable polishing quality, increasing production costs and the difficulty of quality control.

[0003] Therefore, in the prior art, the surface polishing process of carbon dioxide cylinders has technical problems such as low polishing efficiency, unstable polishing quality, inaccurate surface temperature control and lack of intelligent control. Summary of the invention

[0004] This application provides a surface polishing control system for carbon dioxide cylinders, which solves the technical problems of low polishing efficiency, unstable polishing quality, inaccurate surface temperature control and lack of intelligent control in the surface polishing process of carbon dioxide cylinders in the prior art. By introducing intelligent control and machine learning algorithms, the polishing efficiency is improved, the stability of the polishing quality of carbon dioxide cylinders is ensured, the surface temperature is accurately controlled, and the intelligent control of the polishing process is realized.

[0005] The present application provides a surface polishing control system for a carbon dioxide cylinder, the system comprising: a data acquisition module, used to execute step 1: read a predetermined characteristic dimension, and perform multi-dimensional feature collection on the cylinder material based on the predetermined characteristic dimension to obtain target material characteristic data, wherein the cylinder material is the raw material of the carbon dioxide cylinder. An optimization space construction module, used to execute step 2: randomly obtain a first polishing strategy in the polishing optimization space, wherein the polishing optimization space is a polishing control optimization space formed based on a predetermined polishing parameter threshold of a predetermined polishing index. A surface temperature prediction module, used to execute step 3: use the first polishing strategy as input information of a surface temperature prediction model to obtain a first predicted surface temperature, wherein the surface temperature prediction model is obtained by machine learning data training in the historical polishing control record of the cylinder material. A detection signal acquisition module, used to execute step 4: when the first predicted surface temperature is at the surface temperature limit, activate the ultrasonic detection device to perform dynamic detection on the first polishing test of the carbon dioxide cylinder based on the first polishing strategy to obtain a first polishing detection signal, wherein the surface temperature limit is determined by analyzing the target material chemical characteristic data in the target material characteristic data. A comparison and analysis module is used to execute step five: call the polishing fitness evaluation function to compare and analyze the first polishing detection signal with the predetermined signal to obtain the first polishing fitness. A loop execution module is used to execute step six: repeat steps two to five to obtain the second polishing fitness of the second polishing strategy, and the second polishing strategy is different from the first polishing strategy. An optimization module is used to execute step seven: when the second polishing fitness is greater than the first polishing fitness, the second polishing strategy is used as the optimal polishing strategy; when the second polishing fitness is less than the first polishing fitness, the first polishing strategy is used as the optimal polishing strategy. A polishing control module is used to execute step eight: continue to iterate until the iteration number limit is reached, and control the surface polishing of the carbon dioxide cylinder based on the optimal polishing strategy at that time.

[0006] In a possible implementation, the optimization space construction module is further used to: extract the target material physical property data from the target material property data, analyze the target material physical property data to obtain a first parameter threshold of a first indicator in the predetermined polishing indicators, and construct the predetermined polishing parameter threshold based on the first parameter threshold.

[0007] In a possible implementation, the optimization space construction module is also used to: obtain a first detection time. Combined with a predetermined detection trajectory, obtain a first detection angle of the ultrasonic detection device at the first detection time. Traverse the first detection time in the first grinding detection signal to obtain a first detection signal. Generate a first detection visual graph based on a first correspondence between the first detection angle and the first detection signal, and visually monitor the first grinding strategy through the first detection visual graph. Wherein, the predetermined detection trajectory is a semicircular trajectory with the carbon dioxide cylinder as the center.

[0008] In a possible implementation, the optimization space construction module is further used to: compare the first detection signal with the predetermined signal according to the polishing fitness evaluation function to obtain the first fitness of the first detection angle, and calculate the first polishing fitness based on the first fitness, wherein the expression of the polishing fitness evaluation function is as follows:

[0009]

[0010] Wherein, l(x) refers to the first fitness of the first detection angle x, refers to the total deviation between the first detection signal and the signal time domain features of the predetermined signal at the first detection angle x, i refers to the i-th time domain feature among the m signal time domain features, α i refers to the weight coefficient of the i-th time domain feature, refers to the total deviation between the first detection signal and the signal frequency domain characteristics of the predetermined signal at the first detection angle x, j refers to the jth frequency domain feature among the n signal frequency domain features, β j It refers to the weight coefficient of the j-th frequency domain feature, a and b are the first coefficient and the second coefficient respectively, and the sum of a and b is 1.

[0011] In a possible implementation, the polishing control module is further used to: obtain a first thickness of a first point of the carbon dioxide cylinder and match a first predetermined stress of the first thickness. Generate a stress distribution visual graph according to a second corresponding relationship between the first point and the first predetermined stress. Visually monitor the polishing quality of the carbon dioxide cylinder through the stress distribution visual graph.

[0012] In a possible implementation, the polishing control module is further used to: read predetermined physical properties, and traverse the physical property data of the target material based on the predetermined physical properties to obtain predetermined physical property data. Feedback adjustment is performed on the first predetermined stress based on the predetermined physical property data. The predetermined physical properties include yield strength, tensile strength and toughness.

[0013] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0014] The present application provides a surface polishing control system for a carbon dioxide cylinder, comprising: step 1: reading a predetermined characteristic dimension, and collecting multi-dimensional characteristics of the cylinder material based on the predetermined characteristic dimension to obtain target material characteristic data, wherein the cylinder material is the raw material of the carbon dioxide cylinder. Step 2: randomly obtaining a first polishing strategy in a polishing optimization space, wherein the polishing optimization space is a polishing control optimization space formed by a predetermined polishing parameter threshold based on a predetermined polishing index. Step 3: using the first polishing strategy as input information of a surface temperature prediction model to obtain a first predicted surface temperature, wherein the surface temperature prediction model is obtained by machine learning data training in the historical polishing control record of the cylinder material. Step 4: when the first predicted surface temperature is at the surface temperature limit, activating an ultrasonic detection device to dynamically detect the first polishing test of the carbon dioxide cylinder based on the first polishing strategy to obtain a first polishing detection signal, wherein the surface temperature limit is determined by analyzing the target material chemical characteristic data in the target material characteristic data. Step 5: Retrieve the polishing fitness evaluation function to compare and analyze the first polishing detection signal with the predetermined signal to obtain the first polishing fitness. Step 6: Repeat steps 2 to 5 to obtain the second polishing fitness of the second polishing strategy, which is different from the first polishing strategy. Step 7: When the second polishing fitness is greater than the first polishing fitness, the second polishing strategy is used as the optimal polishing strategy. When the second polishing fitness is less than the first polishing fitness, the first polishing strategy is used as the optimal polishing strategy. Step 8: Continue to iterate until the iteration number limit is reached, and control the surface polishing of the carbon dioxide cylinder based on the optimal polishing strategy at that time. The technical problems of low polishing efficiency, unstable polishing quality, inaccurate surface temperature control and lack of intelligent control in the surface polishing process of carbon dioxide cylinders in the prior art are solved. By introducing intelligent control and machine learning algorithms, the polishing efficiency is improved, the stability of the polishing quality of carbon dioxide cylinders is ensured, the surface temperature is accurately controlled, and the intelligent control of the polishing process is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0016] Figure 1 A schematic diagram of the structure of a surface polishing control system for a carbon dioxide cylinder provided in an embodiment of the present application;

[0017] Figure 2 This is a schematic diagram of the process of establishing the predetermined grinding parameter threshold in a surface grinding control system of a carbon dioxide cylinder in the present application.

[0018] Explanation of the reference numerals: data acquisition module 11 , optimization space construction module 12 , surface temperature prediction module 13 , detection signal acquisition module 14 , comparison and analysis module 15 , loop execution module 16 , optimization module 17 , polishing control module 18 . DETAILED DESCRIPTION

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0021] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, systems, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0022] The present application embodiment provides a surface polishing control system for a carbon dioxide cylinder, such as Figure 1 As shown, the system comprises:

[0023] The data acquisition module 11 is used to execute step 1: read the predetermined characteristic dimension, and collect multi-dimensional features of the cylinder material based on the predetermined characteristic dimension to obtain target material characteristic data, wherein the cylinder material is the raw material of the carbon dioxide cylinder.

[0024] The optimization space construction module 12 is used to execute step 2: randomly obtain a first polishing strategy in the polishing optimization space, wherein the polishing optimization space is a polishing control optimization space formed based on a predetermined polishing parameter threshold of a predetermined polishing indicator.

[0025] The surface temperature prediction module 13 is used to execute step three: using the first polishing strategy as input information of a surface temperature prediction model to obtain a first predicted surface temperature, wherein the surface temperature prediction model is trained by machine learning based on data from the historical polishing control records of the cylinder material.

[0026] Read the predetermined characteristic dimensions, which include the chemical composition, physical properties, surface roughness, etc. of the material. Based on these predetermined characteristic dimensions, collect multi-dimensional features of the raw materials of the carbon dioxide cylinder to obtain the target material characteristic data. Subsequently, randomly obtain the first polishing strategy in the polishing optimization space, wherein the polishing optimization space is a polishing control optimization space formed based on the predetermined polishing parameter threshold of the predetermined polishing index, the predetermined polishing index includes polishing particle size, polishing pressure, polishing speed and polishing direction, and the predetermined polishing parameter threshold is the control parameter threshold range corresponding to each polishing index, and the predetermined polishing parameter threshold is obtained based on the target material physical characteristic data in the target material characteristic data. Randomly obtain the polishing strategy of each polishing index in the corresponding polishing parameter threshold in the polishing optimization space to obtain the first polishing strategy. Step three: Use the first polishing strategy as the input information of the surface temperature prediction model to obtain the first predicted surface temperature, wherein the surface temperature prediction model is trained by machine learning the data in the historical polishing control record of the cylinder material. When constructing the surface temperature prediction model, a large number of historical polishing control records are collected from the production line and laboratory. These records should include specific cylinder materials and corresponding polishing strategies, i.e., polishing indicators and specific polishing parameters, and include real-time measured surface temperatures of cylinders under different polishing strategies. Training data is constructed based on the data in the historical polishing control records. The artificial neural network in the machine learning algorithm is selected to conduct supervised training of the model based on the training set, and the accuracy is judged based on multiple predicted surface temperature results output by the model. When the accuracy of the model output meets the preset accuracy, the model training is completed, and the trained surface temperature prediction model is obtained.

[0027] like Figure 2As shown, the optimization space construction module 12 is further used to: extract the physical property data of the target material from the target material property data, analyze the physical property data of the target material to obtain a first parameter threshold of the first indicator in the predetermined polishing indicator, and construct the predetermined polishing parameter threshold based on the first parameter threshold.

[0028] The step 2 includes: extracting the target material physical property data from the target material property data, the target material physical property data including but not limited to the hardness, density, yield strength, tensile strength and toughness of the material. Analyzing the target material physical property data to obtain the first parameter threshold of the first indicator in the predetermined polishing index, and constructing the predetermined polishing parameter threshold based on the first parameter threshold. For example, if the predetermined polishing index includes polishing speed and polishing pressure, then the appropriate polishing speed and polishing pressure ranges can be determined based on the physical properties of the material such as hardness and density, and the parameter threshold of the indicator is constructed based on the polishing speed and polishing pressure ranges. Finally, construct the predetermined polishing parameter threshold based on the obtained parameter thresholds.

[0029] The detection signal acquisition module 14 is used to execute step four: when the first predicted surface temperature is at the surface temperature limit, the ultrasonic detection equipment is activated to perform dynamic detection on the first grinding test of the carbon dioxide cylinder based on the first grinding strategy to obtain a first grinding detection signal, and the surface temperature limit is determined by analyzing the target material chemical characteristic data in the target material characteristic data.

[0030] The comparison and analysis module 15 is used to execute step five: call the polishing fitness evaluation function to compare and analyze the first polishing detection signal with a predetermined signal to obtain a first polishing fitness.

[0031] The loop execution module 16 is used to execute step six: repeating steps two to five to obtain a second grinding adaptability of a second grinding strategy, where the second grinding strategy is different from the first grinding strategy.

[0032] The optimization module 17 is used to execute step seven: when the second polishing fitness is greater than the first polishing fitness, the second polishing strategy is used as the optimal polishing strategy; when the second polishing fitness is less than the first polishing fitness, the first polishing strategy is used as the optimal polishing strategy.

[0033] The polishing control module 18 is used to execute step eight: continue to iterate until the iteration number limit is reached, and perform surface polishing control on the carbon dioxide cylinder based on the optimal polishing strategy at that time.

[0034] Specifically, after completing the temperature prediction of the target material characteristic data polishing strategy to obtain the first predicted surface temperature, it is determined whether the first predicted surface temperature is within the surface temperature limit. The surface temperature limit is determined by analyzing the target material chemical characteristic data in the target material characteristic data, and is a temperature parameter value that does not affect the performance of the cylinder, ensuring that the material is not damaged by overheating during the polishing process. When it is greater than the temperature surface temperature limit, the shutdown operation is performed. When the first predicted surface temperature is less than or equal to the surface temperature limit, the polishing temperature has no effect on the performance of the cylinder, and the ultrasonic detection device is activated to dynamically detect the first polishing test of the carbon dioxide cylinder based on the first polishing strategy to obtain a first polishing detection signal. The first polishing detection signal includes ultrasonic detection receiving signals at multiple angles. At the 0° position, the ultrasonic transmission signal passes through the cylinder at the 0° position and its two opposite cylinder surfaces, transmits a signal at the 0° position, and receives a signal at the 180° position opposite to the 0° position of the cylinder. Further, the polishing fitness evaluation function is retrieved to compare and analyze the first polishing detection signal with the predetermined signal to obtain the first polishing fitness.

[0035] The first detection signal is compared with the predetermined signal according to the grinding fitness evaluation function to obtain the first fitness of the first detection angle, and the first grinding fitness is calculated based on the first fitness. The expression of the grinding fitness evaluation function is as follows:

[0036]

[0037] Wherein, l(x) refers to the first fitness of the first detection angle x, refers to the total deviation between the first detection signal and the signal time domain features of the predetermined signal at the first detection angle x, i refers to the i-th time domain feature among the m signal time domain features, α i refers to the weight coefficient of the i-th time domain feature, refers to the total deviation between the first detection signal and the signal frequency domain characteristics of the predetermined signal at the first detection angle x, j refers to the jth frequency domain feature among the n signal frequency domain features, β jRefers to the weight coefficient of the jth frequency domain feature, a and b are the first coefficient and the second coefficient respectively, and the sum of a and b is 1. Step 6: Repeat steps 2 to 5 to obtain the second polishing fitness of the second polishing strategy, the second polishing strategy is different from the first polishing strategy, and the acquisition method of the second polishing strategy is the same as the acquisition method of the first polishing strategy. Step 7: After processing the second polishing strategy in steps 3 to 5, obtain the corresponding second polishing fitness. When the second polishing fitness is greater than the first polishing fitness, the second polishing strategy is used as the optimal polishing strategy, and when the second polishing fitness is less than the first polishing fitness, the first polishing strategy is used as the optimal polishing strategy. Step 8: Continue to iterate until the iteration number limit is reached, the iteration number limit is the preset iteration number, and when the iteration number limit is reached, the optimization iteration is completed, and the current optimal polishing strategy is obtained to control the surface polishing of the carbon dioxide cylinder. The technical problems of low polishing efficiency, unstable polishing quality, inaccurate surface temperature control and lack of intelligent control in the surface polishing process of carbon dioxide cylinders in the prior art are solved. By introducing intelligent control and machine learning algorithms, the grinding efficiency is improved, the stability of the grinding quality of CO2 cylinders is ensured, the surface temperature is accurately controlled, and intelligent control of the grinding process is achieved.

[0038] The optimization space construction module 14 is also used to: obtain a first detection time. Combined with a predetermined detection trajectory, obtain a first detection angle of the ultrasonic detection device at the first detection time. Traverse the first detection time in the first grinding detection signal to obtain a first detection signal. Generate a first detection visual graph based on a first correspondence between the first detection angle and the first detection signal, and visually monitor the first grinding strategy through the first detection visual graph. Among them, the predetermined detection trajectory is a semicircular trajectory with the carbon dioxide cylinder as the center.

[0039] During the grinding test, the current detection time is recorded as the first detection time. This time point is the starting time for ultrasonic detection. The first detection angle of the ultrasonic detection device at the first detection time is obtained in combination with the predetermined detection trajectory. The predetermined detection trajectory is a semicircular trajectory with the carbon dioxide cylinder as the center. Based on the first detection time, the first grinding detection signal is traversed to obtain the first detection signal. The first detection signal is the signal received by the ultrasonic receiver at the first detection time. Furthermore, a first detection visual graph is generated based on the first corresponding relationship between the first detection angle and the first detection signal, and the first grinding strategy is visually monitored through the first detection visual graph.

[0040] The polishing control module 18 is also used to: obtain a first thickness of a first point of the carbon dioxide cylinder and match a first predetermined stress of the first thickness. Generate a stress distribution visual graph according to a second corresponding relationship between the first point and the first predetermined stress. Visually monitor the polishing quality of the carbon dioxide cylinder through the stress distribution visual graph.

[0041] During the grinding process, a key point on the carbon dioxide cylinder is selected and recorded as the first point. The thickness of the point is measured in real time using a thickness measuring device (such as an ultrasonic thickness gauge) to obtain the first thickness of the first point. According to the pre-established stress-thickness correspondence table, the first predetermined stress corresponding to the first thickness of the first point is matched, and the first predetermined stress at this time does not consider the physical properties of the specific material. The predetermined stress is obtained through experimental data and theoretical analysis based on the stress distribution law of the material at different thicknesses. Further, according to the correspondence between the first point and the first predetermined stress, a stress distribution visual graph is generated. The stress distribution visual graph graphically displays the stress distribution on the surface of the cylinder, reflecting the stress changes and distribution laws of the material during the grinding process. Finally, the carbon dioxide cylinder is visually monitored for grinding quality through the stress distribution visual graph. During the monitoring process, the stress state of each point on the surface of the cylinder can be intuitively understood through the stress distribution graph, and the stress concentration or excessive stress area that may occur during the grinding process can be discovered and handled in time. During the grinding process, the first point of the cylinder is selected, and the thickness of the point is measured using an ultrasonic thickness gauge. For example, the thickness of the first point is measured to be 15 mm. According to the measured thickness of 15mm, the corresponding predetermined stress is matched from the stress-thickness correspondence table. For example, the predetermined stress corresponding to the thickness of 15mm is 150MPa. The first point and the corresponding predetermined stress are input into the stress distribution generation module to generate a stress distribution visual graph, and multiple points and the corresponding predetermined stresses are acquired in a loop and displayed in the stress distribution visual graph. The visual graph shows the stress distribution at different points on the surface of the cylinder, and intuitively displays the stress state of the material during the grinding process. Through the stress distribution visual graph, the grinding quality can be monitored in real time, which helps to detect stress concentration or excessive stress areas during the grinding process, and timely adjust the grinding position to ensure uniform stress on the surface of the cylinder and guarantee the grinding quality.

[0042] The grinding control module 18 is also used to: read predetermined physical properties, and traverse the physical property data of the target material based on the predetermined physical properties to obtain predetermined physical property data. Feedback adjustment is performed on the first predetermined stress based on the predetermined physical property data. The predetermined physical properties include yield strength, tensile strength and toughness.

[0043] Read the predetermined physical properties, which are parameters in the target material property data, including yield strength, tensile strength and toughness, to obtain the predetermined physical property data. Since the acquisition of the first predetermined stress is based on the thickness-stress correspondence of the general material and does not take into account the specific material properties. By acquiring the predetermined physical property data, the first predetermined stress is feedback-adjusted based on the predetermined physical property data. When performing the feedback adjustment, a preset adjustment factor is obtained based on the parameter difference between the material properties of the general material and the predetermined physical property data, and the differences generated by the physical property data correspond to specific adjustment factors. Feedback adjustment of the first predetermined stress is performed based on the adjustment factor. For example, if the first predetermined stress is 150MPa and the adjustment factor is 1.05, the adjusted predetermined stress is 157.5MPa.

[0044] The embodiment of the present application performs step 1: reading a predetermined characteristic dimension, and collecting multi-dimensional characteristics of the cylinder material based on the predetermined characteristic dimension to obtain target material characteristic data, wherein the cylinder material is the raw material of the carbon dioxide cylinder. Step 2: randomly obtaining a first polishing strategy in the polishing optimization space, wherein the polishing optimization space is a polishing control optimization space formed by a predetermined polishing parameter threshold based on a predetermined polishing index. Step 3: using the first polishing strategy as input information of a surface temperature prediction model to obtain a first predicted surface temperature, wherein the surface temperature prediction model is obtained by machine learning data training in the historical polishing control record of the cylinder material. Step 4: when the first predicted surface temperature is at the surface temperature limit, activating an ultrasonic detection device to dynamically detect the first polishing test of the carbon dioxide cylinder based on the first polishing strategy, and obtaining a first polishing detection signal, wherein the surface temperature limit is determined by analyzing the target material chemical characteristic data in the target material characteristic data. Step 5: calling a polishing fitness evaluation function to compare and analyze the first polishing detection signal with a predetermined signal, and obtaining a first polishing fitness. Step six: Repeat steps two to five to obtain a second polishing fitness of a second polishing strategy, where the second polishing strategy is different from the first polishing strategy. Step seven: When the second polishing fitness is greater than the first polishing fitness, the second polishing strategy is used as the optimal polishing strategy; when the second polishing fitness is less than the first polishing fitness, the first polishing strategy is used as the optimal polishing strategy. Step eight: Continue to iterate until the iteration limit is reached, and perform surface polishing control on the carbon dioxide cylinder based on the optimal polishing strategy at that time. The technical problems of low polishing efficiency, unstable polishing quality, inaccurate surface temperature control and lack of intelligent control in the surface polishing process of carbon dioxide cylinders in the prior art are solved. By introducing intelligent control and machine learning algorithms, the polishing efficiency is improved, the stability of the polishing quality of carbon dioxide cylinders is ensured, the surface temperature is accurately controlled, and intelligent control of the polishing process is realized.

[0045] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art 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. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A surface polishing control system for a carbon dioxide cylinder, characterized in that: include: The data acquisition module is used to execute step 1: read the predetermined characteristic dimension, and collect multi-dimensional characteristics of the cylinder material based on the predetermined characteristic dimension to obtain target material characteristic data, wherein the cylinder material is the raw material of the carbon dioxide cylinder; An optimization space construction module is used to execute step 2: randomly obtain a first polishing strategy in a polishing optimization space, wherein the polishing optimization space is a polishing control optimization space formed based on a predetermined polishing parameter threshold of a predetermined polishing indicator; A surface temperature prediction module is used to execute step three: using the first grinding strategy as input information of a surface temperature prediction model to obtain a first predicted surface temperature, wherein the surface temperature prediction model is trained by machine learning data in a historical grinding control record of the cylinder material; a detection signal acquisition module, configured to execute step 4: when the first predicted surface temperature is at a surface temperature limit, activating an ultrasonic detection device to dynamically detect a first grinding test of the carbon dioxide cylinder based on the first grinding strategy to obtain a first grinding detection signal, wherein the surface temperature limit is determined by analyzing target material chemical characteristic data in the target material characteristic data; The comparison and analysis module is used to execute step five: call a grinding fitness evaluation function to compare and analyze the first grinding detection signal with a predetermined signal to obtain a first grinding fitness; A loop execution module, configured to execute step six: repeating steps two to five to obtain a second grinding fitness of a second grinding strategy, wherein the second grinding strategy is different from the first grinding strategy; An optimization module, used to execute step seven: when the second polishing fitness is greater than the first polishing fitness, using the second polishing strategy as the optimal polishing strategy; when the second polishing fitness is less than the first polishing fitness, using the first polishing strategy as the optimal polishing strategy; The polishing control module is used to execute step eight: continue to iterate until the iteration number limit is reached, and control the surface polishing of the carbon dioxide cylinder based on the optimal polishing strategy at that time.

2. According to claim 1, a surface polishing control system for a carbon dioxide cylinder is characterized in that: The predetermined grinding indicators include grinding particle size, grinding pressure, grinding speed and grinding direction.

3. A surface polishing control system for a carbon dioxide cylinder according to claim 1, characterized in that: The optimization space construction module is also used for: Extracting target material physical property data from the target material property data; Analyzing the physical property data of the target material to obtain a first parameter threshold of a first indicator in the predetermined polishing indicators; The predetermined polishing parameter threshold is established based on the first parameter threshold.

4. A surface polishing control system for a carbon dioxide cylinder according to claim 1, characterized in that: The optimization space construction module is also used for: Get the first detection time; obtaining a first detection angle of the ultrasonic detection device at the first detection time in combination with a predetermined detection trajectory; Traversing the first detection time in the first polishing detection signal to obtain a first detection signal; generating a first detection visual graph according to a first corresponding relationship between the first detection angle and the first detection signal, and visually monitoring the first grinding strategy through the first detection visual graph; Wherein, the predetermined detection trajectory is a semicircular trajectory with the carbon dioxide cylinder as the center.

5. A surface polishing control system for a carbon dioxide cylinder according to claim 4, characterized in that: The optimization space construction module is further used to: compare the first detection signal with the predetermined signal according to the polishing fitness evaluation function to obtain the first fitness of the first detection angle, and calculate the first polishing fitness based on the first fitness, wherein the expression of the polishing fitness evaluation function is as follows: Wherein, l(x) refers to the first fitness of the first detection angle x, refers to the total deviation between the first detection signal and the signal time domain features of the predetermined signal at the first detection angle x, i refers to the i-th time domain feature among the m signal time domain features, α i refers to the weight coefficient of the i-th time domain feature, refers to the total deviation between the first detection signal and the signal frequency domain characteristics of the predetermined signal at the first detection angle x, j refers to the jth frequency domain feature among the n signal frequency domain features, β j It refers to the weight coefficient of the j-th frequency domain feature, a and b are the first coefficient and the second coefficient respectively, and the sum of a and b is 1.

6. A surface polishing control system for a carbon dioxide cylinder according to claim 1, characterized in that: The grinding control module is also used for: Obtaining a first thickness at a first point of the carbon dioxide cylinder and matching a first predetermined stress of the first thickness; Generate a visual graph of stress distribution according to a second corresponding relationship between the first point and the first predetermined stress; The polishing quality of the carbon dioxide cylinder can be visually monitored through the stress distribution visual diagram.

7. A surface polishing control system for a carbon dioxide cylinder according to claim 6, characterized in that: The grinding control module is also used for: Reading a predetermined physical property, and traversing the physical property data of the target material based on the predetermined physical property to obtain the predetermined physical property data; Performing feedback adjustment on the first predetermined stress based on the predetermined physical property data; Wherein, the predetermined physical properties include yield strength, tensile strength and toughness.

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