Continuous toughening improvement method and system for netting production equipment

Abstract

The invention relates to the field of netting toughening, and discloses a continuous toughening improvement method and system for netting production equipment, and the method comprises the following steps: successively carrying out raw material treatment continuous improvement and extrusion molding continuous transformation on the netting production equipment; and carrying out netting surface plasma treatment and coating integrated treatment on the transformed netting production equipment to obtain a netting finished product. Performing overall analysis on the netting production equipment and the netting finished product, and performing defect positioning and dynamic deviation correction on the second-class netting production equipment in combination with an analysis result. The production efficiency can be improved, meanwhile, the product is higher in reliability, toughening improvement of the netting is achieved, and help is provided for adapting to the harsh use environment, prolonging the service life, reducing the breakage rate and improving the replacement efficiency of fishermen.

Description

Technical Field

[0001] The present invention relates to the field of netting toughening, and in particular to a continuous toughening improvement method and system for netting production equipment. Background Art

[0002] Net production equipment is an industrial machinery system specifically designed for net manufacturing. Its core function is to process polymer materials (such as polyethylene and nylon) into mesh fabrics with specific strength, toughness, and weather resistance. Net production equipment comprises a raw material pretreatment system, which removes moisture from the raw materials and uniformly mixes carbon fiber nanofillers; an extrusion unit, which melts the raw materials and forms a continuous film; a stretching and shearing unit, which enhances the film's mechanical properties through longitudinal and transverse stretching; and a surface treatment and shearing system, which applies a wear-resistant and UV-resistant coating to the thin surface and winds it into the finished net. Continuous toughening improvements to net production equipment help improve production efficiency, reduce energy consumption and costs, and ensure consistent quality throughout net production. The core goal of net toughening improvements is to adapt to harsh operating environments and extend its service life, thereby reducing breakage and reducing fisher replacement rates. Continuous toughening improvements to net production equipment, through process integration and material enhancement, improve production efficiency and enhance product reliability. This not only addresses the fishery's demand for high-performance nets but also promotes the upgrading of manufacturing technology towards green and intelligent manufacturing. Therefore, a continuous toughening improvement method and system for net clothing production equipment are proposed. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a continuous toughening improvement method and system for net clothing production equipment.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a continuous toughening improvement method for net production equipment, comprising the following steps:

[0006] Continuously improve the raw material processing of the net clothing production equipment to obtain the net clothing production equipment with improved raw material processing, which is calibrated as a Class I net clothing production equipment;

[0007] The first type of net clothing production equipment is transformed into a continuous extrusion molding equipment to obtain the second type of net clothing production equipment;

[0008] The primary net is subjected to surface plasma treatment and coating integrated treatment by Class II net production equipment to obtain a secondary treated net;

[0009] By connecting the data of Class II net clothing production equipment with the Industrial Internet of Things, a machine learning model is established to locate defects and dynamically correct errors in Class II net clothing production equipment.

[0010] Furthermore, in a preferred embodiment of the present invention, the net clothing production equipment is continuously improved in raw material processing to obtain the net clothing production equipment with improved raw material processing, which is calibrated as a Class I net clothing production equipment, specifically:

[0011] Obtaining a net production equipment, calibrating it as a target net production equipment, and obtaining a loss-in-weight feeder and a twin-screw extruder in the target net production equipment;

[0012] Introducing raw materials for net production, wherein the raw materials for net production include a base material and a toughening agent, and determining a standard ratio of the raw materials for net production and the number of nets to be produced;

[0013] Determining the amount of raw materials for net clothing production based on the number of net clothing produced, and controlling a loss-in-weight feeder to continuously feed the raw materials for net clothing production in an amount equal to the amount of raw materials for net clothing production into the twin-screw extruder according to a standard ratio of the raw materials for net clothing production;

[0014] Introducing an infrared preheating tunnel in series with the feed port of a twin-screw extruder to determine the standard temperature threshold for drying and preheating the raw materials for net clothing production in the twin-screw extruder, and presetting the feeding temperature of each section in the twin-screw extruder based on the standard temperature threshold;

[0015] The raw materials for net clothing production are preheated through an infrared preheating tunnel. At the same time, a closed-loop humidity sensor is installed in the twin-screw extruder to monitor the humidity of the raw materials in real time. The standard humidity threshold is preset and the feeding section temperature of the twin-screw extruder is dynamically adjusted to control the real-time humidity value of the raw materials for net clothing production to be dynamically maintained within the standard humidity threshold.

[0016] At this time, the net clothing production equipment is raw material processing and improvement net clothing production equipment, and is calibrated as a Class I net clothing production equipment.

[0017] Furthermore, in a preferred embodiment of the present invention, the first type of net clothing production equipment is subjected to continuous extrusion molding transformation to obtain the second type of net clothing production equipment, specifically:

[0018] In a type of net clothing production equipment, independent servo motors are introduced to drive the twin-screw extruder and loss-in-weight feeder respectively. During the driving process, the real-time speed matching accuracy of the twin-screw extruder and loss-in-weight feeder is calculated in real time;

[0019] The standard speed matching accuracy range is preset, and the power of independent servo motors is dynamically adjusted to ensure that the real-time speed matching accuracy of the twin-screw extruder and loss-in-weight feeder is maintained within the standard speed matching accuracy range;

[0020] In a type of net clothing production equipment, a twin-screw extruder is designed with a segmented screw. The screw of the twin-screw extruder is adjusted to a barrier screw, and the segmented screw design is divided into a conveying section, a mixing section, and a homogenizing section.

[0021] In the process of dynamically adjusting the feeding section temperature of the twin-screw extruder, the temperature gradient is controlled in the conveying section, mixing section and homogenizing section of the screw respectively;

[0022] Controlling the twin-screw extruder to perform extrusion molding on the raw materials for net clothing production after the heating treatment, wherein the extrusion molding process includes horizontally and vertically stretching the raw materials for net clothing production, and collecting the waste raw materials for net clothing production produced after the horizontal and vertical stretching;

[0023] The net clothing production raw material waste produced after the transverse and longitudinal stretching is returned to the screw for secondary heating and secondary granulation until the amount of the net clothing production raw material waste produced after the transverse and longitudinal stretching is less than a preset value, and then the extruded net clothing production raw material is output and marked as a preliminary net clothing;

[0024] At this time, the first type of net clothing production equipment is calibrated as the second type of net clothing production equipment, and the second type of net clothing production equipment is the net clothing production equipment with the extrusion molding process adjusted.

[0025] Furthermore, in a preferred embodiment of the present invention, the surface plasma treatment and coating integrated treatment are performed on the preliminary net by the second type of net production equipment to obtain the secondary treated net, specifically:

[0026] Obtain an atmospheric pressure plasma treatment machine, and control the twin-screw extruder of the second-type net clothing production equipment to connect to the atmospheric pressure plasma treatment machine;

[0027] After the preliminary mesh is generated, an atmospheric pressure plasma treatment machine is started to perform plasma activation on the preliminary mesh by the atmospheric pressure plasma treatment machine. During the plasma activation process, a historical data network is introduced to retrieve and output a standard processing speed range and a standard discharge frequency of the plasma activation to obtain a plasma-activated mesh;

[0028] Obtaining a coating device from the second-class net production equipment, wherein the coating device is a gravure coater for continuously coating a plasma-activated net with a coating material;

[0029] Obtaining a coating material for coating a plasma-activated mesh, calibrating it as a target coating material, and continuously coating the plasma-activated mesh using a coating device, while controlling the continuous coating process speed to be maintained within a standard process speed range for plasma activation;

[0030] During the continuous coating process, medium-wave infrared lamps are used to perform infrared curing on the plasma-activated mesh during the continuous coating process, while the coating thickness of the plasma-activated mesh is monitored in real time;

[0031] Among them, the plasma-activated net is scanned horizontally by a laser thickness gauge to monitor the coating thickness of the plasma-activated net in real time. A standard coating thickness threshold is preset. If the coating thickness of the plasma-activated net is less than the standard coating thickness threshold, a coating abnormality warning is triggered in the Class II net production equipment.

[0032] When a coating abnormality warning is triggered in the Class II mesh production equipment, the continuous coating processing speed is dynamically adjusted to ensure that the coating thickness of the plasma-activated mesh is not less than the standard coating thickness threshold, thereby obtaining a secondary processed mesh.

[0033] Furthermore, in a preferred embodiment of the present invention, by connecting the Class II net clothing production equipment with the industrial Internet of Things, a machine learning model is established to locate defects and dynamically correct deviations of the Class II net clothing production equipment, specifically:

[0034] Connecting the Class II net production equipment to the Industrial Internet of Things, where the Industrial Internet of Things can collect real-time operating parameters of the Class II net production equipment and quality parameters of the secondary processed nets;

[0035] The quality parameters of the secondary processed net include surface tension parameters and mesh size parameters, and the surface image of the secondary processed net is collected at the same time;

[0036] The isolation forest algorithm is introduced into the industrial Internet of Things to perform noise identification and noise removal on the quality parameters of the secondary processed mesh, thereby obtaining the improved quality parameters of the secondary processed mesh. At the same time, the threshold segmentation method is used to perform image enhancement and feature parameter extraction on the surface image of the secondary processed mesh, thereby obtaining the surface feature parameters of the secondary processed mesh.

[0037] By combining the operating parameters of the second-class net production equipment, the quality improvement parameters of the secondary processed nets, and the surface characteristic parameters of the secondary processed nets, and retrieving the corresponding historical standard data from the historical data network, the LSTM algorithm was introduced to build a net yield prediction model.

[0038] The net yield prediction model is run to predict the net yield obtained by the second-class net production equipment, and the net yield is calibrated as the target net yield. According to the target net yield, the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net, the corresponding defect correction is performed on the second-class net production equipment and the secondary processed net.

[0039] Furthermore, in a preferred embodiment of the present invention, the target net yield, the improved quality parameters of the secondary processed net, and the surface characteristic parameters of the secondary processed net are combined to perform corresponding defect correction on the second type of net production equipment, specifically:

[0040] Analyze the target net clothing yield rate. If the target net clothing yield rate is not less than the standard value, the Class II net clothing production equipment is determined to be qualified net clothing production equipment.

[0041] If the target net clothing yield rate is less than the standard value, the second-class net clothing production equipment is judged as unqualified net clothing production equipment;

[0042] If there is unqualified net production equipment, the surface defect position of the secondary processed net is located based on the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net. Based on the surface defect position of the secondary processed net, the process of producing the secondary processed net at the surface defect position on the unqualified net production equipment is determined and marked as the process to be analyzed.

[0043] Based on the working parameters of the second-class net clothing production equipment, the sub-working parameters of the process to be analyzed are determined, and the sub-working parameters of the process to be analyzed are dynamically adjusted to ensure that the sub-working parameters of the process to be analyzed are maintained within the standard threshold;

[0044] After dynamically adjusting the sub-working parameters of the analyzed process, if the target net yield is still lower than the standard value, the grey correlation method is introduced to calculate the grey correlation values ​​of the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net and the surrounding environment parameters.

[0045] If the grey correlation value is greater than the preset value, the surrounding environmental parameters are adjusted until the target net clothing yield is not less than the standard value, and qualified net clothing production equipment is obtained;

[0046] If the grey correlation value is not greater than the preset value, it is determined that the secondary processed mesh has material defects, and the secondary processed mesh with material defects is discarded.

[0047] A second aspect of the present invention further provides a continuous toughening and improvement system for net clothing production equipment, the continuous toughening and improvement system comprising a memory and a processor, the memory storing a continuous toughening and improvement method, and the continuous toughening and improvement method, when executed by the processor, implementing the following steps:

[0048] Continuously improve the raw material processing of the net clothing production equipment to obtain the net clothing production equipment with improved raw material processing, which is calibrated as a Class I net clothing production equipment;

[0049] The first type of net clothing production equipment is transformed into a continuous extrusion molding equipment to obtain the second type of net clothing production equipment;

[0050] The primary net is subjected to surface plasma treatment and coating integrated treatment by Class II net production equipment to obtain a secondary treated net;

[0051] By connecting the data of Class II net clothing production equipment with the Industrial Internet of Things, a machine learning model is established to locate defects and dynamically correct errors in Class II net clothing production equipment.

[0052] This invention addresses the technical deficiencies in the background art and has the following beneficial effects: Net production equipment is modified to continuously process raw materials and then continuously extrusion-molded. The modified net production equipment is then subjected to plasma treatment and integrated coating to produce finished nets. A comprehensive analysis of the net production equipment and finished nets is conducted, and the results of this analysis are used to locate defects and dynamically correct deviations in the two types of net production equipment. This invention improves production efficiency while also enhancing product reliability, achieving tougher nets, helping them adapt to harsh operating environments, extend their service life, reduce breakage rates, and increase fisher replacement efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0054] Figure 1 A flow chart showing a continuous toughening improvement method for net clothing production equipment;

[0055] Figure 2 A flow chart showing a method for defect location and dynamic deviation correction for Class II net clothing production equipment is shown;

[0056] Figure 3 The present invention shows a process diagram of a continuous toughening and improving system of a net clothing production equipment. DETAILED DESCRIPTION

[0057] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0059] Figure 1 A flow chart showing a continuous toughening improvement method for netting production equipment is shown, comprising the following steps:

[0060] Continuously improve the raw material processing of the net clothing production equipment to obtain the net clothing production equipment with improved raw material processing, which is calibrated as a Class I net clothing production equipment;

[0061] The first type of net clothing production equipment is transformed into a continuous extrusion molding equipment to obtain the second type of net clothing production equipment;

[0062] The primary net is subjected to surface plasma treatment and coating integrated treatment by Class II net production equipment to obtain a secondary treated net;

[0063] By connecting the data of Class II net clothing production equipment with the Industrial Internet of Things, a machine learning model is established to locate defects and dynamically correct errors in Class II net clothing production equipment.

[0064] Furthermore, in a preferred embodiment of the present invention, the net clothing production equipment is continuously improved in raw material processing to obtain the net clothing production equipment with improved raw material processing, which is calibrated as a Class I net clothing production equipment, specifically:

[0065] Obtaining a net production equipment, calibrating it as a target net production equipment, and obtaining a loss-in-weight feeder and a twin-screw extruder in the target net production equipment;

[0066] Introducing raw materials for net production, wherein the raw materials for net production include a base material and a toughening agent, and determining a standard ratio of the raw materials for net production and the number of nets to be produced;

[0067] Determining the amount of raw materials for net clothing production based on the number of net clothing produced, and controlling a loss-in-weight feeder to continuously feed the raw materials for net clothing production in an amount equal to the amount of raw materials for net clothing production into the twin-screw extruder according to a standard ratio of the raw materials for net clothing production;

[0068] Introducing an infrared preheating tunnel in series with the feed port of a twin-screw extruder to determine the standard temperature threshold for drying and preheating the raw materials for net clothing production in the twin-screw extruder, and presetting the feeding temperature of each section in the twin-screw extruder based on the standard temperature threshold;

[0069] The raw materials for net clothing production are preheated through an infrared preheating tunnel. At the same time, a closed-loop humidity sensor is installed in the twin-screw extruder to monitor the humidity of the raw materials in real time. The standard humidity threshold is preset and the feeding section temperature of the twin-screw extruder is dynamically adjusted to control the real-time humidity value of the raw materials for net clothing production to be dynamically maintained within the standard humidity threshold.

[0070] At this time, the net clothing production equipment is raw material processing and improvement net clothing production equipment, and is calibrated as a Class I net clothing production equipment.

[0071] It should be noted that a loss-in-weight feeder is used to feed the materials used to toughen nets and the materials used to produce them into a twin-screw extruder, and this feeding process requires continuous, proportional feeding. The net production material is the base material, and toughening agents include, but are not limited to, nano-silica and chopped carbon fiber. Compared to conventional feeders, loss-in-weight feeders can improve feeding accuracy and have a positive effect on continuous toughening improvements. The quantity of raw materials used for net production can be determined by determining the standard ratio of raw materials and the number of nets produced. Raw materials must be preheated before feeding to reduce energy consumption during heating and eliminate microporous defects in the net caused by moisture. After preheating, the material is placed into the twin-screw extruder and the feeding temperature is preset in stages. Staged heating allows for the dispersion of the reinforcing phase during net molding, improves the fracture toughness of the material, and avoids uneven dispersion. A closed-loop humidity sensor is used to monitor temperature and humidity in real time.

[0072] Furthermore, in a preferred embodiment of the present invention, the first type of net clothing production equipment is subjected to continuous extrusion molding transformation to obtain the second type of net clothing production equipment, specifically:

[0073] In a type of net clothing production equipment, independent servo motors are introduced to drive the twin-screw extruder and loss-in-weight feeder respectively. During the driving process, the real-time speed matching accuracy of the twin-screw extruder and loss-in-weight feeder is calculated in real time;

[0074] The standard speed matching accuracy range is preset, and the power of independent servo motors is dynamically adjusted to ensure that the real-time speed matching accuracy of the twin-screw extruder and loss-in-weight feeder is maintained within the standard speed matching accuracy range;

[0075] In a type of net clothing production equipment, a twin-screw extruder is designed with a segmented screw. The screw of the twin-screw extruder is adjusted to a barrier screw, and the segmented screw design is divided into a conveying section, a mixing section, and a homogenizing section.

[0076] In the process of dynamically adjusting the feeding section temperature of the twin-screw extruder, the temperature gradient is controlled in the conveying section, mixing section and homogenizing section of the screw respectively;

[0077] Controlling the twin-screw extruder to perform extrusion molding on the raw materials for net clothing production after the heating treatment, wherein the extrusion molding process includes horizontally and vertically stretching the raw materials for net clothing production, and collecting the waste raw materials for net clothing production produced after the horizontal and vertical stretching;

[0078] The net clothing production raw material waste produced after the transverse and longitudinal stretching is returned to the screw for secondary heating and secondary granulation until the amount of the net clothing production raw material waste produced after the transverse and longitudinal stretching is less than a preset value, and then the extruded net clothing production raw material is output and marked as a preliminary net clothing;

[0079] At this time, the first type of net clothing production equipment is calibrated as the second type of net clothing production equipment, and the second type of net clothing production equipment is the net clothing production equipment with the extrusion molding process adjusted.

[0080] It should be noted that the twin-screw extruder is used for continuous extrusion of net fabrics. It utilizes a barrier-type screw with a three-section design: conveying, mixing, and homogenizing. The conveying section features a deep groove to improve material conveying efficiency; the mixing section incorporates a pin element to increase the net fabric shear rate and enhance fiber dispersion; the homogenizing section features a shallow groove and reversed flight to stabilize the net fabric melt pressure. The barrier-type screw improves net fabric melt uniformity, preventing web breakage caused by unmelted particles. Independent servo motors drive the main screw and feeder, respectively, addressing the uneven feeding issues associated with traditional single-motor operation due to load fluctuations. The real-time speed matching accuracy of the twin-screw extruder and loss-in-weight feeder must be maintained within the standard speed matching accuracy range. Each of the three sections requires independent temperature control. The feeder temperature preset within the twin-screw extruder applies to each section to prevent material degradation from overheating. The extrusion process involves stretching the raw materials for netting in both the horizontal and vertical directions. This involves utilizing a three-stage roller system consisting of a preheating roller, a main stretching roller, and a conditioning roller for sequential stretching. This eliminates traditional offline stretching and reduces the intermediate winding process, thereby improving the molecular orientation and toughness of the netting. The resulting netting scraps are then fed back into the screw for secondary heating and pelletization, conserving resources and enabling continuous, zero-waste production to produce the final netting.

[0081] Furthermore, in a preferred embodiment of the present invention, the surface plasma treatment and coating integrated treatment are performed on the preliminary net by the second type of net production equipment to obtain the secondary treated net, specifically:

[0082] Obtain an atmospheric pressure plasma treatment machine, and control the twin-screw extruder of the second-type net clothing production equipment to connect to the atmospheric pressure plasma treatment machine;

[0083] After the preliminary mesh is generated, an atmospheric pressure plasma treatment machine is started to perform plasma activation on the preliminary mesh by the atmospheric pressure plasma treatment machine. During the plasma activation process, a historical data network is introduced to retrieve and output a standard processing speed range and a standard discharge frequency of the plasma activation to obtain a plasma-activated mesh;

[0084] Obtaining a coating device from the second-class net production equipment, wherein the coating device is a gravure coater for continuously coating a plasma-activated net with a coating material;

[0085] Obtaining a coating material for coating a plasma-activated mesh, calibrating it as a target coating material, and continuously coating the plasma-activated mesh using a coating device, while controlling the continuous coating process speed to be maintained within a standard process speed range for plasma activation;

[0086] During the continuous coating process, medium-wave infrared lamps are used to perform infrared curing on the plasma-activated mesh during the continuous coating process, while the coating thickness of the plasma-activated mesh is monitored in real time;

[0087] Among them, the plasma-activated net is scanned horizontally by a laser thickness gauge to monitor the coating thickness of the plasma-activated net in real time. A standard coating thickness threshold is preset. If the coating thickness of the plasma-activated net is less than the standard coating thickness threshold, a coating abnormality warning is triggered in the Class II net production equipment.

[0088] When a coating abnormality warning is triggered in the Class II mesh production equipment, the continuous coating processing speed is dynamically adjusted to ensure that the coating thickness of the plasma-activated mesh is not less than the standard coating thickness threshold, thereby obtaining a secondary processed mesh.

[0089] It should be noted that the continuous integration of mesh surface treatment aims to seamlessly integrate plasma activation, coating, and curing into the production line, replacing traditional intermittent operations and improving efficiency and quality consistency. The atmospheric pressure plasma treatment machine is placed immediately after the heat setting and cooling process, before the coating machine, to ensure that the surface activity of the treated surface is not attenuated. By bombarding the surface with plasma, hydroxyl (-OH) and carboxyl (-COOH) groups are generated, reducing the contact angle from 110° to below 40°. This achieves continuous process optimization for surface treatment and contributes to continuous toughening improvements. The gravure coater is suitable for high-precision coating. Coating formulas include but are not limited to water-based polyurethane or fluorocarbon resins, which enhance wear resistance. The speed is synchronized with the plasma treatment section to avoid tension fluctuations. Infrared curing is used in the final curing step to improve curing efficiency. If the coating thickness is too thin during the coating process, it will reduce the toughness of the mesh. Therefore, if the coating thickness of the plasma-activated mesh is less than the standard coating thickness threshold, a coating abnormality warning is triggered in the Class II mesh production equipment, and the coating is controlled to continue thickening to obtain a secondary treated mesh.

[0090] Figure 2 A flow chart of a method for defect location and dynamic deviation correction of Class II net clothing production equipment is shown, including the following steps:

[0091] S202: By connecting the Class II net clothing production equipment with the industrial Internet of Things, a machine learning model is established to locate defects and dynamically correct deviations in the Class II net clothing production equipment;

[0092] S204: Based on the target net yield, the improved quality parameters of the secondary processed net, and the surface characteristic parameters of the secondary processed net, corresponding defect correction is performed on the second-class net production equipment.

[0093] Furthermore, in a preferred embodiment of the present invention, by connecting the Class II net clothing production equipment with the industrial Internet of Things, a machine learning model is established to locate defects and dynamically correct deviations of the Class II net clothing production equipment, specifically:

[0094] Connecting the Class II net production equipment to the Industrial Internet of Things, where the Industrial Internet of Things can collect real-time operating parameters of the Class II net production equipment and quality parameters of the secondary processed nets;

[0095] The quality parameters of the secondary processed net include surface tension parameters and mesh size parameters, and the surface image of the secondary processed net is collected at the same time;

[0096] The isolation forest algorithm is introduced into the industrial Internet of Things to perform noise identification and noise removal on the quality parameters of the secondary processed mesh, thereby obtaining the improved quality parameters of the secondary processed mesh. At the same time, the threshold segmentation method is used to perform image enhancement and feature parameter extraction on the surface image of the secondary processed mesh, thereby obtaining the surface feature parameters of the secondary processed mesh.

[0097] By combining the operating parameters of the second-class net production equipment, the quality improvement parameters of the secondary processed nets, and the surface characteristic parameters of the secondary processed nets, and retrieving the corresponding historical standard data from the historical data network, the LSTM algorithm was introduced to build a net yield prediction model.

[0098] The net yield prediction model is run to predict the net yield obtained by the second-class net production equipment, and the net yield is calibrated as the target net yield. According to the target net yield, the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net, the corresponding defect correction is performed on the second-class net production equipment and the secondary processed net.

[0099] It's important to note that the Industrial Internet of Things (IIoT) enables real-time collection of operating parameters from Class II net production equipment. This is achieved by deploying sensors that collect key parameters. These sensors monitor these parameters and adjust the equipment accordingly. The Isolation Forest algorithm is used to identify and remove noise, and combined with a threshold segmentation algorithm, image enhancement and feature extraction are performed on the surface image of the secondary processed net, yielding the surface characteristic parameters of the secondary processed net. The purpose of building a yield prediction model is to assess the quality of finished nets produced by the production equipment, determine whether further improvements and toughening are needed, or identify defects such as excessive perforations. The LSTM algorithm combines the operating parameters of the Class II net production equipment, the quality parameters of the secondary processed net, and the surface characteristic parameters of the secondary processed net with historical data to construct a prediction model. The LSTM algorithm is a predictive algorithm. Finally, the target net yield is obtained.

[0100] Furthermore, in a preferred embodiment of the present invention, the target net yield, the improved quality parameters of the secondary processed net, and the surface characteristic parameters of the secondary processed net are combined to perform corresponding defect correction on the second type of net production equipment, specifically:

[0101] Analyze the target net clothing yield rate. If the target net clothing yield rate is not less than the standard value, the Class II net clothing production equipment is determined to be qualified net clothing production equipment.

[0102] If the target net clothing yield rate is less than the standard value, the second-class net clothing production equipment is judged as unqualified net clothing production equipment;

[0103] If there is unqualified net production equipment, the surface defect position of the secondary processed net is located based on the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net. Based on the surface defect position of the secondary processed net, the process of producing the secondary processed net at the surface defect position on the unqualified net production equipment is determined and marked as the process to be analyzed.

[0104] Based on the working parameters of the second-class net clothing production equipment, the sub-working parameters of the process to be analyzed are determined, and the sub-working parameters of the process to be analyzed are dynamically adjusted to ensure that the sub-working parameters of the process to be analyzed are maintained within the standard threshold;

[0105] After dynamically adjusting the sub-working parameters of the analyzed process, if the target net yield is still lower than the standard value, the grey correlation method is introduced to calculate the grey correlation values ​​of the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net and the surrounding environment parameters.

[0106] If the grey correlation value is greater than the preset value, the surrounding environmental parameters are adjusted until the target net clothing yield is not less than the standard value, and qualified net clothing production equipment is obtained;

[0107] If the grey correlation value is not greater than the preset value, it is determined that the secondary processed mesh has material defects, and the secondary processed mesh with material defects is discarded.

[0108] It should be noted that the target net yield rate is used to determine whether production equipment needs to be adjusted and corrected. If substandard net production equipment exists, the improved quality parameters and surface characteristic parameters of the reprocessed nets obtained are analyzed to determine the approximate location of the defect. This allows the corresponding process in the production equipment to be identified and the process parameters analyzed to determine whether a process problem is causing the low net yield rate. If the low yield rate persists after adjustments to the corresponding process, it is necessary to determine whether external factors are affecting equipment production. This involves looking at ambient environmental parameters, such as abnormal ambient temperature, which can cause molecular fragmentation within the net, making it susceptible to breakage. Using the grey correlation method, it is possible to determine whether there is a correlation between the improved quality parameters and surface characteristic parameters of the reprocessed nets and the ambient environmental parameters. If there is a correlation, i.e., a large grey correlation value, adjustments to the ambient environment are necessary to ensure that the environment does not affect net production and to obtain qualified net production equipment. If the grey correlation value is not greater than the preset value, it is determined that the reprocessed net has a material defect, and the net defect is caused by the material, requiring replacement and disposal.

[0109] like Figure 3 As shown, the second aspect of the present invention further provides a continuous toughening improvement system for net clothing production equipment, the continuous toughening improvement system comprising a memory 31 and a processor 32. The memory 31 stores a continuous toughening improvement method. When the continuous toughening improvement method is executed by the processor 32, the following steps are implemented:

[0110] Continuously improve the raw material processing of the net clothing production equipment to obtain the net clothing production equipment with improved raw material processing, which is calibrated as a Class I net clothing production equipment;

[0111] The first type of net clothing production equipment is transformed into a continuous extrusion molding equipment to obtain the second type of net clothing production equipment;

[0112] The primary net is subjected to surface plasma treatment and coating integrated treatment by Class II net production equipment to obtain a secondary treated net;

[0113] By connecting the data of Class II net clothing production equipment with the Industrial Internet of Things, a machine learning model is established to locate defects and dynamically correct errors in Class II net clothing production equipment.

[0114] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A continuous toughening improvement method for net clothing production equipment, characterized in that: The following steps are involved: Continuously improve the raw material processing of the net clothing production equipment to obtain the net clothing production equipment with improved raw material processing, which is calibrated as a Class I net clothing production equipment; The first type of net clothing production equipment is transformed into a continuous extrusion molding equipment to obtain the second type of net clothing production equipment; The primary net is subjected to surface plasma treatment and coating integrated treatment by Class II net production equipment to obtain a secondary treated net; By connecting the data of Class II net clothing production equipment with the Industrial Internet of Things, a machine learning model is established to locate defects and dynamically correct errors in Class II net clothing production equipment.

2. The continuous toughening and improving method for net production equipment according to claim 1, characterized in that: The net clothing production equipment is continuously improved by the raw material processing, and the net clothing production equipment with improved raw material processing is calibrated as a Class I net clothing production equipment, specifically: Obtaining a net production equipment, calibrating it as a target net production equipment, and obtaining a loss-in-weight feeder and a twin-screw extruder in the target net production equipment; Introducing raw materials for net production, wherein the raw materials for net production include a base material and a toughening agent, and determining a standard ratio of the raw materials for net production and the number of nets to be produced; Determining the amount of raw materials for net clothing production based on the number of net clothing produced, and controlling a loss-in-weight feeder to continuously feed the raw materials for net clothing production in an amount equal to the amount of raw materials for net clothing production into the twin-screw extruder according to a standard ratio of the raw materials for net clothing production; Introducing an infrared preheating tunnel in series with the feed port of a twin-screw extruder to determine the standard temperature threshold for drying and preheating the raw materials for net clothing production in the twin-screw extruder, and presetting the feeding temperature of each section in the twin-screw extruder based on the standard temperature threshold; The raw materials for net clothing production are preheated through an infrared preheating tunnel. At the same time, a closed-loop humidity sensor is installed in the twin-screw extruder to monitor the humidity of the raw materials in real time. The standard humidity threshold is preset and the feeding section temperature of the twin-screw extruder is dynamically adjusted to control the real-time humidity value of the raw materials for net clothing production to be dynamically maintained within the standard humidity threshold. At this time, the net clothing production equipment is raw material processing and improvement net clothing production equipment, and is calibrated as a Class I net clothing production equipment.

3. The continuous toughening and improving method for net clothing production equipment according to claim 1, characterized in that: The first type of net clothing production equipment is subjected to continuous extrusion molding transformation to obtain the second type of net clothing production equipment, specifically: In a type of net clothing production equipment, independent servo motors are introduced to drive the twin-screw extruder and loss-in-weight feeder respectively. During the driving process, the real-time speed matching accuracy of the twin-screw extruder and loss-in-weight feeder is calculated in real time; The standard speed matching accuracy range is preset, and the power of independent servo motors is dynamically adjusted to ensure that the real-time speed matching accuracy of the twin-screw extruder and loss-in-weight feeder is maintained within the standard speed matching accuracy range; In a type of net clothing production equipment, a twin-screw extruder is designed with a segmented screw. The screw of the twin-screw extruder is adjusted to a barrier screw, and the segmented screw design is divided into a conveying section, a mixing section, and a homogenizing section. In the process of dynamically adjusting the feeding section temperature of the twin-screw extruder, the temperature gradient is controlled in the conveying section, mixing section and homogenizing section of the screw respectively; Controlling the twin-screw extruder to perform extrusion molding on the raw materials for net clothing production after the heating treatment, wherein the extrusion molding process includes horizontally and vertically stretching the raw materials for net clothing production, and collecting the waste raw materials for net clothing production produced after the horizontal and vertical stretching; The net clothing production raw material waste produced after the transverse and longitudinal stretching is returned to the screw for secondary heating and secondary granulation until the amount of the net clothing production raw material waste produced after the transverse and longitudinal stretching is less than a preset value, and then the extruded net clothing production raw material is output and marked as a preliminary net clothing; At this time, the first type of net clothing production equipment is calibrated as the second type of net clothing production equipment, and the second type of net clothing production equipment is the net clothing production equipment with the extrusion molding process adjusted.

4. The continuous toughening and improving method for net production equipment according to claim 1, characterized in that: The secondary treated net is obtained by subjecting the primary net to surface plasma treatment and coating integrated treatment using the second type of net production equipment, specifically: Obtain an atmospheric pressure plasma treatment machine, and control the twin-screw extruder of the second-type net clothing production equipment to connect to the atmospheric pressure plasma treatment machine; After the preliminary mesh is generated, an atmospheric pressure plasma treatment machine is started to perform plasma activation on the preliminary mesh by the atmospheric pressure plasma treatment machine. During the plasma activation process, a historical data network is introduced to retrieve and output a standard processing speed range and a standard discharge frequency of the plasma activation to obtain a plasma-activated mesh; Obtaining a coating device from the second-class net production equipment, wherein the coating device is a gravure coater for continuously coating a plasma-activated net with a coating material; Obtaining a coating material for coating a plasma-activated mesh, calibrating it as a target coating material, and continuously coating the plasma-activated mesh using a coating device, while controlling the continuous coating process speed to be maintained within a standard process speed range for plasma activation; During the continuous coating process, medium-wave infrared lamps are used to perform infrared curing on the plasma-activated mesh during the continuous coating process, while the coating thickness of the plasma-activated mesh is monitored in real time; Among them, the plasma-activated net is scanned horizontally by a laser thickness gauge to monitor the coating thickness of the plasma-activated net in real time. A standard coating thickness threshold is preset. If the coating thickness of the plasma-activated net is less than the standard coating thickness threshold, a coating abnormality warning is triggered in the Class II net production equipment. When a coating abnormality warning is triggered in the Class II mesh production equipment, the continuous coating processing speed is dynamically adjusted to ensure that the coating thickness of the plasma-activated mesh is not less than the standard coating thickness threshold, thereby obtaining a secondary processed mesh.

5. The continuous toughening and improving method for net production equipment according to claim 1, characterized in that: By connecting the Class II net clothing production equipment with the industrial Internet of Things, a machine learning model is established to locate defects and dynamically correct deviations of the Class II net clothing production equipment. Specifically: Connecting the Class II net production equipment to the Industrial Internet of Things, where the Industrial Internet of Things can collect real-time operating parameters of the Class II net production equipment and quality parameters of the secondary processed nets; The quality parameters of the secondary processed net include surface tension parameters and mesh size parameters, and the surface image of the secondary processed net is collected at the same time; The isolation forest algorithm is introduced into the industrial Internet of Things to perform noise identification and noise removal on the quality parameters of the secondary processed mesh, thereby obtaining the improved quality parameters of the secondary processed mesh. At the same time, the threshold segmentation method is used to perform image enhancement and feature parameter extraction on the surface image of the secondary processed mesh, thereby obtaining the surface feature parameters of the secondary processed mesh. By combining the operating parameters of the second-class net production equipment, the quality improvement parameters of the secondary processed nets, and the surface characteristic parameters of the secondary processed nets, and retrieving the corresponding historical standard data from the historical data network, the LSTM algorithm was introduced to build a net yield prediction model. The net yield prediction model is run to predict the net yield obtained by the second-class net production equipment, and the net yield is calibrated as the target net yield. According to the target net yield, the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net, the corresponding defect correction is performed on the second-class net production equipment and the secondary processed net.

6. The continuous toughening and improving method for net production equipment according to claim 5, characterized in that: The target net yield, the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net are combined to perform corresponding defect correction on the second type of net production equipment, specifically: Analyze the target net clothing yield rate. If the target net clothing yield rate is not less than the standard value, the Class II net clothing production equipment is determined to be qualified net clothing production equipment. If the target net clothing yield rate is less than the standard value, the second-class net clothing production equipment is judged as unqualified net clothing production equipment; If there is unqualified net production equipment, the surface defect position of the secondary processed net is located based on the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net. Based on the surface defect position of the secondary processed net, the process of producing the secondary processed net at the surface defect position on the unqualified net production equipment is determined and marked as the process to be analyzed. Based on the working parameters of the second-class net clothing production equipment, the sub-working parameters of the process to be analyzed are determined, and the sub-working parameters of the process to be analyzed are dynamically adjusted to ensure that the sub-working parameters of the process to be analyzed are maintained within the standard threshold; After dynamically adjusting the sub-working parameters of the analyzed process, if the target net yield is still lower than the standard value, the grey correlation method is introduced to calculate the grey correlation values ​​of the improved quality parameters of the secondary processed net and the surface characteristic parameters of the secondary processed net and the surrounding environment parameters. If the grey correlation value is greater than the preset value, the surrounding environmental parameters are adjusted until the target net clothing yield is not less than the standard value, and qualified net clothing production equipment is obtained; If the grey correlation value is not greater than the preset value, it is determined that the secondary processed mesh has material defects, and the secondary processed mesh with material defects is discarded.

7. A continuous toughening and improvement system for net clothing production equipment, characterized in that: The continuous toughening improvement system includes a memory and a processor. The memory stores a continuous toughening improvement method program. When the continuous toughening improvement method program is executed by the processor, the continuous toughening improvement method steps as described in any one of claims 1 to 6 are implemented.

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