Intelligent bottle body blow molding machine based on Internet of Things

Abstract

The invention relates to the technical field of blow molding machines, in particular to an intelligent bottle body blow molding machine based on the Internet of Things, comprising: a bottle preparation module for performing blow molding on a bottle blank to prepare a finished bottle; the data storage module is used for storing a reference cooling curve; comprising a temperature acquisition unit for acquiring the temperature of a plurality of detection points of the prepared bottle and a wall thickness acquisition unit for acquiring the wall thickness values of the detection points; the control module is used for judging whether the preparation of the bottle meets a preset standard or not according to the cooling uniformity characterization value of the bottle and the cooling curve coincidence characterization value of the bottle; and the adjusting module is used for determining a processing strategy according to the wall thickness distribution standard deviation of the bottle. The preparation efficiency of the bottle is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blow molding machines, in particular to an intelligent bottle blow molding machine based on the Internet of Things. BACKGROUND

[0002] Bottle blow molding is one of the key processes in the manufacture of plastic hollow products, and is widely used in the production of packaging containers for beverages, food, pharmaceuticals and chemical products. The typical process is as follows: first, a tubular preform is prepared by an injection molding machine; then the preform is sent to a blow molding machine, heated and tempered, placed in a blow molding mold, and stretched axially and blown radially using compressed air to make it fit the mold cavity, and finally cooled and shaped to obtain a finished bottle.

[0003] In recent years, although some technical solutions have attempted to introduce sensors to monitor the blow molding process, such as monitoring the temperature of the heating furnace or the blowing pressure, these monitoring is often local and isolated, and the data obtained is not used to build a digital model that can fully reflect the quality characteristics of the finished product. More importantly, it has not formed a closed-loop intelligent control system based on real-time data and ideal benchmark model comparison, and automatic adjustment of process parameters accordingly.

[0004] Chinese patent application publication No. CN116100790A discloses a full-automatic intelligent bottle blowing machine based on a cloud platform. The present application adjusts the process parameters of blow molding by calculating the shape feature parameter table representing the shape feature according to the preform standard contour information through the data processing module, and the detection module determines the products with quality problems by calculating the offset, and uploads the production data of the products with quality problems to the cloud data platform. After analyzing the production data, the adjusted process parameters are corrected, considering the influence of shape features on the blow molding process, and automatically adjusting the process parameters.

[0005] As can be seen, the above technical solution only analyzes the finished product contour image, without considering the influence of the blow molding process of the bottle on the cooling of the bottle body, which cannot guarantee the performance reliability of the finished bottle, thereby leading to the problem of poor bottle preparation efficiency. SUMMARY

[0006] Therefore, the present application provides an intelligent bottle blow molding machine based on the Internet of Things to overcome the problem in the prior art that only analyzes the finished product contour image, without considering the influence of the blow molding process of the bottle on the cooling of the bottle body, which cannot guarantee the performance reliability of the finished bottle, thereby leading to the problem of poor bottle preparation efficiency.

[0007] To achieve the above purpose, the present application provides an intelligent bottle blow molding machine based on the Internet of Things, comprising: a bottle preparation module for blow molding and shaping a bottle blank to prepare a finished bottle; a data storage module configured to store a reference cooling curve; a data acquisition module connected to the bottle preparation module, comprising a temperature acquisition unit configured to acquire temperatures of a plurality of detection points of the prepared bottle and a wall thickness acquisition unit configured to acquire wall thickness values of the plurality of detection points; a control module connected to the data storage module, the data acquisition module and the bottle preparation module respectively, configured to determine whether the preparation of the bottle meets a preset standard according to a cooling uniformity representation value of the bottle and a cooling curve coincidence representation value of the bottle; an adjustment module connected to the control module and the bottle preparation module respectively, configured to determine a processing strategy according to a wall thickness distribution standard deviation of the bottle on a condition that the preparation of the bottle is determined not to meet the preset standard according to the cooling uniformity representation value of the bottle, wherein the processing strategy is to increase a blow molding pressure or to issue a blow molding unevenness alarm.

[0008] Further, the control module determines that the preparation of the bottle meets the preset standard according to a comparison result that the cooling uniformity representation value of the bottle is less than a second preset cooling uniformity threshold value. If the cooling uniformity representation value is greater than or equal to a first preset cooling uniformity threshold value and less than the second preset cooling uniformity threshold value, it is determined that the preparation of the bottle meets the preset standard, and the preparation of the bottle is determined again according to the cooling curve coincidence representation value of the bottle whether it meets the preset standard. The first preset cooling uniformity threshold value is less than the second preset cooling uniformity threshold value.

[0009] Further, the control module determines that the preparation of the bottle does not meet the preset standard according to a comparison result that the cooling uniformity representation value of the bottle is greater than or equal to the second preset cooling uniformity threshold value, and the adjustment module determines a processing strategy of the bottle on a condition that the preparation of the bottle does not meet the preset standard according to a wall thickness distribution standard deviation of the bottle.

[0010] Further, the cooling uniformity representation value of the bottle is a standard deviation of a cooling time length required for the plurality of detection points of the bottle to decrease from a first preset temperature to a second preset temperature.

[0011] Further, the control module determines again whether the preparation of the bottle meets the preset standard according to the cooling curve coincidence representation value of the bottle, wherein, If the cooling curve coincidence representation value is less than a preset cooling curve coincidence representation value, it is determined that the preparation of the bottle meets the preset standard. If the cooling curve coincidence representation value is greater than or equal to the preset cooling curve coincidence representation value, it is determined that the preparation of the bottle does not meet the preset standard, and a blow molding speed of a blow molding machine is reduced according to a difference between the cooling curve coincidence representation value and the preset cooling curve coincidence representation value.

[0012] Further, the cooling curve coincidence value of the bottle is an average distance between cooling curves of a plurality of detection points of the bottle and a reference cooling curve, wherein the cooling curves are cooling curves of the plurality of detection points of the bottle from a first preset temperature to a second preset temperature, and the reference cooling curve is an average curve of the cooling curves of a plurality of qualified bottles.

[0013] Further, the control module is provided with a plurality of rate adjustment modes for the reduction amplitude of the blow molding rate, and each rate adjustment mode is different from the reduction amplitude of the blow molding rate.

[0014] Further, the adjustment module determines a processing strategy of the bottle under the condition that the bottle is not prepared according to the preset standard according to the wall thickness distribution standard deviation, wherein, If the wall thickness distribution standard deviation is less than the preset wall thickness distribution standard deviation, the blow molding pressure of the blow molding machine is increased according to the difference between the preset wall thickness distribution standard deviation and the wall thickness distribution standard deviation. If the wall thickness distribution standard deviation is greater than or equal to the preset wall thickness distribution standard deviation, a blow molding unevenness alarm of the bottle body is issued.

[0015] Further, the wall thickness distribution standard deviation of the bottle is a standard deviation of wall thickness values of a plurality of detection points of the bottle.

[0016] Further, the increase amplitude of the blow molding pressure and the difference between the preset wall thickness distribution standard deviation and the wall thickness distribution standard deviation are positively correlated.

[0017] Compared with the prior art, the beneficial effects of the present application are that the present application constructs a complete closed-loop intelligent control system integrating acquisition, analysis, decision and execution functions, real-time acquisition of the cooling process dynamics behavior (cooling uniformity, cooling curve shape) and product structural index (wall thickness distribution) of the bottle as the core monitoring object through the data acquisition module, quantitative analysis through the cooling time standard deviation and dynamic time warping distance algorithm, intelligent analysis and decision based on the data and the stored reference data by the control module, and finally automatic triggering of the adjustment strategy by the adjustment module to change the characteristics of the traditional blow molding machine relying on manual experience and quality control lag, thereby improving the bottle preparation efficiency.

[0018] Further, the application establishes a three-level judgment mechanism by introducing a first preset cooling uniformity threshold and a second preset cooling uniformity threshold, can effectively distinguish three product states of excellent, critical qualified and unqualified, avoids the misjudgment or omission caused by a single threshold criterion, improves the precision and reliability of quality control, and for the products in the critical qualified state, the system will not simply pass or scrap, but start a more stringent secondary determination, thereby improving the intelligent level of evaluation.

[0019] Further, the application clearly determines that the cooling uniformity representation value greater than or equal to the second preset cooling uniformity threshold is unqualified, provides a clear standard for rapid identification of unqualified products, avoids the flow of unqualified products into subsequent processes due to ambiguous determination standards, and adjusts the processing strategy based on the wall thickness distribution standard deviation of the bottle, rather than directly stopping or uniformly adjusting the parameters, thereby improving the control precision.

[0020] Further, the application uses the cooling time standard deviation as a quantitative index, which can accurately reflect the dispersion degree of the cooling speed at different detection points; the smaller the standard deviation, the closer the cooling time of each detection point, and the better the cooling uniformity; otherwise, the cooling uniformity is worse, which is more objective and repeatable than qualitative description, avoids the determination error caused by experience difference of different operators, ensures the uniformity of quality determination standard, and thereby improves the reliability of the evaluation result.

[0021] Further, the application sets a hierarchical adjustment strategy for the reduction of the blow molding rate, thereby realizing accurate control of the reduction amplitude of the blow molding rate of the blow molding machine. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The module connection schematic diagram of the intelligent bottle body blow molding machine based on the Internet of Things in the embodiment of the application; Figure 2 The flowchart for determining whether the preparation of the bottle meets the preset standard according to the cooling uniformity representation value of the bottle in the embodiment of the application; Figure 3 The flowchart for determining whether the preparation of the bottle meets the preset standard according to the cooling curve coincidence representation value of the bottle in the embodiment of the application; Figure 4 The flowchart for determining the processing strategy under the condition that the preparation of the bottle does not meet the preset standard in the embodiment of the application. DETAILED DESCRIPTION

[0023] In order to make the purpose and advantages of the application more clear and obvious, the application will be further described below in combination with embodiments; it should be understood that the specific embodiments described herein are only used to explain the application, and do not limit the application.

[0024] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will understand that the embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0025] It should be noted that the data in the present embodiment are obtained by comprehensive analysis and evaluation of historical detection data and corresponding historical detection results of the present application in the past three months before the present detection. Those skilled in the art can understand that the determination method of the present application for a single parameter can be to select the value with the highest proportion as the preset standard parameter according to the data distribution, to use weighted summation to obtain the value as the preset standard parameter, to substitute each historical data into a specific formula and to obtain the value by using the formula as the preset standard parameter, or other selection methods, as long as the present application can clearly define different specific situations in the single determination process by using the obtained value.

[0026] Please refer to Figure 1 , Figure 2 , Figure 3 and Figure 4 , which are respectively a module connection diagram of the intelligent bottle blowing machine based on the Internet of Things according to the present embodiment, a flowchart of determining whether the preparation of the bottle conforms to the preset standard according to the cooling uniformity representation value of the bottle according to the present embodiment, a flowchart of twice determining whether the preparation of the bottle conforms to the preset standard according to the cooling curve coincidence representation value of the bottle according to the present embodiment, and a flowchart of the processing strategy under the condition that the preparation of the bottle does not conform to the preset standard according to the present embodiment.

[0027] The present embodiment provides an intelligent bottle blowing machine based on the Internet of Things, which comprises: a bottle preparation module for preparing a finished bottle by blow molding a bottle blank; a data storage module for storing a reference cooling curve and various preset threshold values, wherein the module is a cloud server and performs data interaction with other modules through the Internet of Things technology; a data acquisition module connected to the bottle preparation module, comprising a temperature acquisition unit for acquiring the temperature of a plurality of detection points of the prepared bottle and a wall thickness acquisition unit for acquiring the wall thickness value of a plurality of detection points; a control module connected to the data storage module, the data acquisition module and the bottle preparation module, for determining whether the preparation of the bottle conforms to the preset standard according to the cooling uniformity representation value of the bottle and the cooling curve coincidence representation value of the bottle; an adjusting module connected with the control module and the bottle preparation module respectively, configured to determine a processing strategy according to a wall thickness distribution standard deviation of the bottle under the condition that the preparation of the bottle is determined not to meet the preset standard according to the cooling uniformity characteristic value of the bottle, wherein the processing strategy is to increase the blow molding pressure or to issue a blow molding unevenness alarm.

[0028] It should be noted that the data in the present embodiment are obtained through preliminary experiments before the present detection by the method of the present application, and each preset value can be adjusted according to the specific use, as long as the method of the present application can clearly define different specific conditions in the single determination process by the obtained numerical value. The preset values set in the present embodiment are obtained according to preliminary experiments, and each correction coefficient is also selected through experimental verification.

[0029] In the present embodiment, the bottle body is a PE plastic bottle, the blow molding pressure is 0.6 MPa, and the blow molding rate is 800 bottles / hour.

[0030] In the present embodiment, the temperature acquisition unit is an infrared thermal imager, and the wall thickness acquisition unit is an ultrasonic thickness gauge.

[0031] Specifically, the specific structure of the control module and the adjusting module is not limited, and each unit thereof can be composed of a logic component, including a field programmable component, a computer or a microprocessor in a computer.

[0032] Specifically, the control module determines whether the preparation of the bottle meets the preset standard according to the cooling uniformity characteristic value of the bottle, wherein, if the cooling uniformity characteristic value is less than a first preset cooling uniformity threshold 6s, it is determined that the preparation of the bottle meets the preset standard; if the cooling uniformity characteristic value is greater than or equal to the first preset cooling uniformity threshold and less than a second preset cooling uniformity threshold 12s, it is determined that the preparation of the bottle meets the preset standard, and whether the preparation of the bottle meets the preset standard is determined again according to the cooling curve coincidence characteristic value of the bottle; if the cooling uniformity characteristic value is greater than or equal to the second preset cooling uniformity threshold, it is determined that the preparation of the bottle does not meet the preset standard, and the adjusting module determines a processing strategy under the condition that the preparation of the bottle does not meet the preset standard according to the wall thickness distribution standard deviation of the bottle; wherein the first preset cooling uniformity threshold is less than the second preset cooling uniformity threshold.

[0033] Specifically, the essence of the cooling uniformity characteristic value is the degree of data dispersion, and the smaller the cooling uniformity characteristic value, the more consistent the cooling speed of each detection point (the more uniform the cooling); the greater the cooling uniformity characteristic value, the more significant the difference in cooling speed of each part (the more uneven the cooling).

[0034] When the cooling uniformity representation value is in the critical interval, it indicates that the cooling speed of each point of the bottle does indeed have an unignorable difference, which has exceeded the excellent category but has not reached the thoroughly out-of-control level; at this time, the single dispersion index cannot uniquely determine the nature of the difference and its impact on product quality. Therefore, the system starts the secondary judgment based on the dynamic time warping algorithm, aiming to make in-depth diagnosis from the aspect of the morphological similarity of the cooling process, so as to distinguish whether the abnormality is caused by acceptable systemic fluctuation or by unacceptable local process distortion.

[0035] Specifically, the cooling uniformity representation value of the bottle is the standard deviation of the cooling time length required for the detection points of the bottle to drop from the first preset temperature 90°C to the second preset temperature 40°C.

[0036] Specifically, the first preset cooling uniformity threshold value is in the range of [4s, 7s], and the second preset cooling uniformity threshold value is in the range of [11s, 15s], preferably, the first preset cooling uniformity threshold value is 6s, and the second preset cooling uniformity threshold value is 12s.

[0037] Specifically, the control module secondarily judges whether the preparation of the bottle conforms to the preset standard according to the cooling curve coincidence representation value of the bottle, wherein, If the cooling curve coincidence representation value is less than the preset cooling curve coincidence representation value 0.9, it is determined that the preparation of the bottle conforms to the preset standard; If the cooling curve coincidence representation value is greater than or equal to the preset cooling curve coincidence representation value, it is determined that the preparation of the bottle does not conform to the preset standard, and the blow molding speed of the blow molding machine is reduced according to the difference between the cooling curve coincidence representation value and the preset cooling curve coincidence representation value.

[0038] Specifically, the cooling curve coincidence representation value of the bottle is the average distance of dynamic time warping between the cooling curve of all detection points and the reference cooling curve, wherein the cooling curve of the bottle is the cooling curve of the detection points of the bottle from the first preset temperature to the second preset temperature, and the reference cooling curve is the average curve of the cooling curves of the qualified bottles.

[0039] In this embodiment, the process of obtaining the reference cooling curve includes: Using an infrared thermal imager, the complete temperature change data of all preset detection points on the surface of the qualified bottle from the first preset temperature to the second preset temperature is monitored and recorded throughout the process; the data of each detection point forms a time-temperature curve, i.e., the cooling curve of the point; Since there may be slight fluctuations in the total cooling time of each bottle, direct averaging will have errors. Therefore, the dynamic time warping algorithm needs to be used; All the cooling curves of hundreds of qualified bottles after dynamic time warping alignment are averaged (point-by-point arithmetic average) to obtain a reference cooling curve, which is stored in the data storage module.

[0040] In this embodiment, the preset cooling curve coincidence representation value is 0.9, but the above value is not limited thereto, and the value can be adjusted according to actual needs by those skilled in the art.

[0041] Specifically, the cooling curve coincidence representation value measures the difference between the cooling process mode of the current bottle and the cooling process mode of the ideal qualified product.

[0042] Specifically, the traditional comparison method (such as Euclidean distance) requires strict alignment of data points, while the cooling curve may have slight "stretching" or "compression" on the time axis (i.e., overall cooling is slightly faster or slower). Dynamic time warping algorithm can overcome this non-linear deformation on the time axis, find the most matching path between the two curves, and calculate the cumulative distance under the optimal path. It measures the similarity of shape and trend of the two curves.

[0043] Specifically, the control module is provided with several rate adjustment modes for the reduction amplitude of the blow molding rate, wherein, If the cooling curve coincidence difference is less than the first preset cooling curve coincidence difference 0.25, the blow molding rate is reduced to the corresponding value by the first adjustment coefficient 0.98; If the cooling curve coincidence difference is greater than or equal to the first preset cooling curve coincidence difference and less than the second preset cooling curve coincidence difference 0.45, the blow molding rate is reduced to the corresponding value by the second adjustment coefficient 0.96; If the cooling curve coincidence difference is greater than or equal to the second preset cooling curve coincidence difference, the blow molding rate is reduced to the corresponding value by the third adjustment coefficient 0.94; The cooling curve coincidence difference is the difference between the cooling curve coincidence representation value and the preset cooling curve coincidence representation value.

[0044] Specifically, the adjustment module determines the processing strategy of the bottle under the condition that the preparation of the bottle does not meet the preset standard according to the wall thickness distribution standard deviation, wherein, If the wall thickness distribution standard deviation is less than the preset wall thickness distribution standard deviation 0.12 mm, the blow molding pressure of the blow molding machine is increased according to the difference between the preset wall thickness distribution standard deviation and the wall thickness distribution standard deviation; If the wall thickness distribution standard deviation is greater than or equal to the preset wall thickness distribution standard deviation, a bottle blow molding unevenness alarm is issued.

[0045] Specifically, the wall thickness distribution standard deviation is a standard deviation of wall thickness values of a plurality of detection points of the bottle.

[0046] Specifically, if the wall thickness distribution standard deviation is less than the preset wall thickness distribution standard deviation, it indicates that the wall thickness distribution is uniform. Since the material distribution is uniform, but the cooling is uneven, the problem must be in the blow molding. The blow molding pressure is insufficient, resulting in that the softened parison cannot be completely and tightly attached to the inner wall of the mold, thereby leading to low heat exchange efficiency and uneven cooling. If the wall thickness distribution standard deviation is greater than or equal to the preset wall thickness distribution standard deviation, it indicates that the wall thickness distribution itself is uneven. The problem is in the parison preparation or pretreatment stage, including uneven heating furnace temperature, stretching rod action failure, parison defects or mold design defects. These problems belong to equipment failure or incoming material defects, and must be intervened and processed by the operator or higher maintenance system, so the alarm is issued.

[0047] Specifically, the wall thickness distribution standard deviation quantifies the uniformity of the wall thickness of each part of the bottle.

[0048] In this embodiment, the preset wall thickness distribution standard deviation is 0.12 mm, but the above value is not limited thereto, and the person skilled in the art can adjust the value according to actual needs.

[0049] Specifically, when the cooling uniformity representation value is seriously out of standard, the wall thickness distribution standard deviation is introduced to automatically determine the root cause of the problem and adaptively select a processing strategy, rather than blindly taking a single measure.

[0050] Specifically, the increase range of the blow molding pressure and the difference between the preset wall thickness distribution standard deviation and the wall thickness distribution standard deviation are positively correlated, for example, linear positive correlation or nonlinear positive correlation. The linear slope of the linear positive correlation is not specifically limited. It can be understood that the greater the difference between the preset wall thickness distribution standard deviation and the wall thickness distribution standard deviation, the greater the increase range of the blow molding pressure.

[0051] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but the person skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. The person skilled in the art can make equivalent changes or replacements to the related technical features without deviating from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

[0052] The above description is only the preferred embodiments of the present application and is not used to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent bottle blow molding machine based on the Internet of Things, characterized in that, include: The bottle preparation module is used to blow mold preforms to produce finished bottles; Data storage module, used to store the baseline cooling curve; The data acquisition module, which is connected to the bottle preparation module, includes a temperature acquisition unit for acquiring the temperature of several detection points of the prepared bottle and a wall thickness acquisition unit for acquiring the wall thickness values ​​of several detection points. The control module is connected to the data storage module, the data acquisition module and the bottle preparation module respectively, and is used to determine whether the preparation of the bottle meets the preset standard based on the cooling uniformity characterization value and the overlapping characterization value of the cooling curve of the bottle. An adjustment module, which is connected to both the control module and the bottle preparation module, is used to determine a processing strategy based on the standard deviation of the bottle wall thickness distribution when the bottle preparation does not meet the preset standard according to the cooling uniformity characterization value of the bottle. The processing strategy is to increase the blow molding pressure or issue a blow molding unevenness alarm.

2. The intelligent bottle blow molding machine based on the Internet of Things according to claim 1, characterized in that, The control module determines that the bottle's preparation meets a preset standard based on a comparison result showing that the bottle's cooling uniformity characterization value is less than a second preset cooling uniformity threshold. If the cooling uniformity characterization value is greater than or equal to the first preset cooling uniformity threshold and less than the second preset cooling uniformity threshold, then the preparation of the bottle is determined to meet the preset standard, and the preparation of the bottle is further determined based on the overlapping characterization value of the cooling curve of the bottle. Wherein, the first preset uniform cooling threshold is less than the second preset uniform cooling threshold.

3. The intelligent bottle blow molding machine based on the Internet of Things according to claim 1, characterized in that, The control module determines that the preparation of the bottle does not meet the preset standard based on the comparison result of the cooling uniformity characterization value being greater than or equal to the second preset cooling uniformity threshold. The adjustment module determines the processing strategy under the condition that the preparation of the bottle does not meet the preset standard based on the standard deviation of the bottle wall thickness distribution.

4. The intelligent bottle blow molding machine based on the Internet of Things according to claim 3, characterized in that, The uniformity of cooling of the bottle is characterized by the standard deviation of the cooling time required for several detection points of the bottle to drop from a first preset temperature to a second preset temperature.

5. The intelligent bottle blow molding machine based on the Internet of Things according to claim 4, characterized in that, The control module makes a secondary determination of whether the bottle's preparation meets the preset standard based on the coincidence value of the bottle's cooling curve. If the value of the overlapping characterization of the cooling curves is less than the preset value of the overlapping characterization of the cooling curves, then the preparation of the bottle is determined to meet the preset standard. If the overlapping value of the cooling curve is greater than or equal to the preset overlapping value of the cooling curve, it is determined that the preparation of the bottle does not meet the preset standard, and the blow molding rate of the blow molding machine is reduced according to the difference between the overlapping value of the cooling curve and the preset overlapping value of the cooling curve.

6. The intelligent bottle blow molding machine based on the Internet of Things according to claim 5, characterized in that, The coincidence value of the cooling curves of the bottle is the average distance between the cooling curves of all detection points and the reference cooling curve, which is the dynamic time-normalized distance. The cooling curve is the cooling curve of several detection points of the bottle from a first preset temperature to a second preset temperature, and the reference cooling curve is the average curve of the cooling curves of several qualified bottles.

7. The intelligent bottle blow molding machine based on the Internet of Things according to claim 6, characterized in that, The control module has several rate adjustment methods for reducing the blow molding rate, and each rate adjustment method reduces the blow molding rate by a different amount.

8. The intelligent bottle blow molding machine based on the Internet of Things according to claim 7, characterized in that, The adjustment module determines a processing strategy for cases where the bottle's manufacturing process does not meet preset standards based on the standard deviation of the bottle's wall thickness distribution. If the standard deviation of wall thickness distribution is less than the preset standard deviation of wall thickness distribution, the blowing pressure of the blow molding machine is increased according to the difference between the preset standard deviation of wall thickness distribution and the standard deviation of wall thickness distribution. If the standard deviation of the wall thickness distribution is greater than or equal to the preset standard deviation of the wall thickness distribution, an alarm for uneven blow molding of the bottle will be issued.

9. The intelligent bottle blow molding machine based on the Internet of Things according to claim 8, characterized in that, The standard deviation of the bottle's wall thickness distribution is the standard deviation of the wall thickness values ​​at several detection points of the bottle.

10. The intelligent bottle blow molding machine based on the Internet of Things according to claim 9, characterized in that, The increase in blow molding pressure is positively correlated with the difference between the preset wall thickness distribution standard deviation and the wall thickness distribution standard deviation.

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