Method, apparatus, and storage medium for evaluating cable insulation material extrusion processes

Abstract

The application relates to a method, device, equipment and storage medium for evaluating a cable insulation material extrusion process. The method comprises the following steps: obtaining an optical scanning image of a formed film of the cable insulation material after continuous extrusion; determining a defect parameter of accumulated defects in the formed film according to the optical scanning image; obtaining corresponding process parameters of the formed film in the extrusion forming process; and quantitatively evaluating the cable insulation material extrusion process according to a change rule of the defect parameter with the process parameters. The method can quantitatively evaluate the cable insulation material extrusion process.

Description

Technical Field

[0001] This application relates to the field of cable technology, and in particular to an evaluation method, apparatus, equipment, and storage medium for cable insulation material extrusion processes. Background Technology

[0002] High-voltage cables are crucial power equipment for electricity transmission and wind power grid connection, and their insulation layer is fundamental to ensuring their safe and stable operation. The cable insulation material used for high-voltage cable insulation layers undergoes stages such as melt extrusion and high-temperature cross-linking during its preparation. Furthermore, to meet the length requirements of individual cables in practical applications, the cable insulation material needs to undergo prolonged melt extrusion to obtain a sufficient film for preparing the high-voltage cable insulation layer.

[0003] Due to factors such as the extrusion process and the quality of the cable insulation material, scorching gradually accumulates during the melt extrusion process, resulting in poor film performance and affecting the reliability of the high-voltage cable insulation layer. Therefore, it is necessary to inspect the extrusion process of the cable insulation material and optimize the process based on the inspection results.

[0004] However, the relevant technologies can only indirectly detect the extrusion process of cable insulation materials by conducting time-consuming and material-intensive type tests on long-length cables in actual production. They cannot directly and quantitatively evaluate the extrusion process of cable insulation materials during continuous extrusion. Summary of the Invention

[0005] Therefore, it is necessary to provide an evaluation method, apparatus, equipment, and storage medium for cable insulation extrusion process that can directly and quantitatively evaluate the extrusion process of cable insulation material during continuous extrusion, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a method for evaluating the extrusion process of cable insulation material, including:

[0007] Obtain optical scanning images of the cable insulation material after continuous extrusion to form a film, and determine the defect parameters of the accumulated defects in the film based on the optical scanning images;

[0008] Obtain the corresponding process parameters for the extrusion molding process of the film;

[0009] The extrusion process of cable insulation material is quantitatively evaluated based on the variation of defect parameters with process parameters.

[0010] In one embodiment, the defect parameters include: defect type, and defect size and number of defects for each type; the process parameters include: at least one of extrusion time, length of the formed film, and extruded weight of the formed film; wherein, the quantitative evaluation of the cable insulation extrusion process based on the variation law of defect parameters with process parameters includes: determining the variation law of defect size and number of defects for each type of defect with process parameters; and determining the limiting factors of the cable insulation extrusion process under the target conditions of the extrusion molding process based on each variation law.

[0011] In one embodiment, the method further includes: predicting the ultimate extrusion time of the cable insulation material based on the variation law of defect parameters with process parameters.

[0012] In one embodiment, the defect parameters include: defect type, and defect size and number of each type of defect; the defect type includes impurity defects and gel point defects; the process parameters include: the extrusion weight of the formed film; wherein, predicting the ultimate extrusion time of the cable insulation material based on the variation law of defect parameters with process parameters includes: determining the first ultimate extrusion weight of the formed film based on the variation law of defect size and number of impurity defects with process parameters based on a first preset condition; determining the second ultimate extrusion weight of the formed film based on the variation law of defect size and number of gel point defects with process parameters based on a second preset condition; selecting the minimum value between the first ultimate extrusion weight and the second ultimate extrusion weight as the first target extrusion weight, and using the first target extrusion weight to characterize the ultimate extrusion time.

[0013] In one embodiment, the method further includes: obtaining a second target extrusion weight of the molded film at the time of breakdown; wherein, predicting the ultimate extrusion time of the cable insulation material based on the variation law of the defect parameters with the process parameters further includes: when the second target extrusion weight is greater than or equal to the first target extrusion weight, the ultimate extrusion time is characterized by the first target extrusion weight; when the second target extrusion weight is less than the first target extrusion weight, the ultimate extrusion time is characterized by the second target extrusion weight.

[0014] In one embodiment, the defect parameters include: the number of defects; the process parameters include: the temperature sequence corresponding to each temperature zone experienced by the molded film during the extrusion molding process; wherein, the method further includes: optimizing the temperature values ​​in the temperature sequence according to the variation law of the number of defects with the temperature values ​​in the temperature sequence.

[0015] In one embodiment, optimizing the temperature values ​​in the temperature sequence based on the variation of the number of defects with the temperature values ​​in the temperature sequence includes: obtaining the total number of defects and the average value per unit time within the sampling period; optimizing the temperature values ​​in the temperature sequence in response to the fact that the multiple of the average value per unit time relative to the reference average is greater than a first threshold; and stopping the optimization of the temperature values ​​in the temperature sequence in response to the fact that the number of events in which the average value per unit time randomly fluctuates or decreases, the multiple of the average value per unit time relative to the reference average is greater than the first threshold is less than or equal to a second threshold, and the total number of defects is less than a target threshold.

[0016] Secondly, this application also provides an evaluation device for the extrusion process of cable insulation material, comprising:

[0017] The scanning module is used to acquire optical scanning images of the cable insulation material after continuous extrusion to form a film;

[0018] The acquisition module is used to acquire the defect parameters of the accumulated defects in the molded film based on the optical scanning image, and to acquire the corresponding process parameters of the molded film during the extrusion molding process.

[0019] The evaluation module is used to quantitatively evaluate the extrusion process of cable insulation material based on the variation of defect parameters with process parameters.

[0020] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0021] Obtain optical scanning images of the cable insulation material after continuous extrusion to form a film, and determine the defect parameters of the accumulated defects in the film based on the optical scanning images;

[0022] Obtain the corresponding process parameters for the extrusion molding process of the film;

[0023] The extrusion process of cable insulation material is quantitatively evaluated based on the variation of defect parameters with process parameters.

[0024] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0025] Obtain optical scanning images of the cable insulation material after continuous extrusion to form a film, and determine the defect parameters of the accumulated defects in the film based on the optical scanning images;

[0026] Obtain the corresponding process parameters for the extrusion molding process of the film;

[0027] The extrusion process of cable insulation material is quantitatively evaluated based on the variation of defect parameters with process parameters.

[0028] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0029] Obtain optical scanning images of the cable insulation material after continuous extrusion to form a film, and determine the defect parameters of the accumulated defects in the film based on the optical scanning images;

[0030] Obtain the corresponding process parameters for the extrusion molding process of the film;

[0031] The extrusion process of cable insulation material is quantitatively evaluated based on the variation of defect parameters with process parameters.

[0032] The aforementioned evaluation method, apparatus, equipment, and storage medium for cable insulation material extrusion processes acquire optical scanning images of the formed film after continuous extrusion of the cable insulation material. Based on these images, defect parameters of accumulated defects in the formed film are determined. The corresponding process parameters during the extrusion process are also acquired. The extrusion process is then quantitatively evaluated based on the variation of defect parameters with process parameters. Determining the defect parameters of accumulated defects in the imaging film using optical scanning images overcomes the blindness and indirectness of related technical evaluation methods. Simultaneously, acquiring the corresponding process parameters during extrusion determines the variation of defect parameters with process parameters, thereby quantitatively evaluating the cable insulation material extrusion process based on this variation. This improves the reliability and accuracy of the evaluation, facilitating targeted optimization of the extrusion process based on the quantitative evaluation results. This ensures that the optimized cable insulation material, after continuous extrusion, produces a formed film that meets the quality requirements of the cable insulation layer. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is an application environment diagram of an evaluation method for cable insulation material extrusion process in one embodiment;

[0035] Figure 2 This is a flowchart illustrating an evaluation method for the extrusion process of cable insulation material in one embodiment;

[0036] Figure 3A This is a schematic diagram of the structure of an extrusion device for cable insulation material in one embodiment;

[0037] Figure 3B This is a partial structural diagram of the high-voltage electrode and the grounding electrode in an extrusion device in one embodiment;

[0038] Figure 4 This is a flowchart illustrating an evaluation method for the cable insulation material extrusion process in another embodiment;

[0039] Figure 5 This is a flowchart illustrating the process of determining the maximum extrusion time for cable insulation material in one embodiment.

[0040] Figure 6 This is a schematic diagram illustrating the variation of the number of impurity defects with a defect size exceeding a first size threshold as a function of the length of the formed film in one embodiment.

[0041] Figure 7A This is a schematic diagram illustrating the variation of the number of gel defects with a defect size exceeding a second size threshold as a function of the length of the molded film in one embodiment.

[0042] Figure 7B According to one embodiment Figure 7A A schematic diagram showing the functional relationship between the number of gel defects with a defect size exceeding the second size threshold and the length of the molded film.

[0043] Figure 8 This is a flowchart illustrating an evaluation method for the cable insulation material extrusion process in another embodiment;

[0044] Figure 9 This is a flowchart illustrating the process of optimizing temperature values ​​in a temperature sequence in one embodiment.

[0045] Figure 10A This is a schematic diagram illustrating the variation of the unit time mean of the temperature sequence before optimization with the length of the formed film in one embodiment.

[0046] Figure 10B This is a schematic diagram illustrating the variation of the mean value per unit time of the target temperature sequence with the length of the formed film in one embodiment;

[0047] Figure 11 This is a flowchart illustrating an evaluation method for the cable insulation material extrusion process in another embodiment;

[0048] Figure 12 This is a structural block diagram of an evaluation device for a cable insulation material extrusion process in one embodiment;

[0049] Figure 13 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0052] The insulation layer of high-voltage cables is made of low-density polyethylene (LDPE) as the base material, with the addition of dicumyl peroxide (DCP) crosslinking agent and antioxidant to form a mixture, which is then subjected to a high-temperature and high-pressure crosslinking reaction. The scorching phenomenon indicates that during the melt extrusion stage, the mixture undergoes a pre-crosslinking reaction and locally forms crosslinked macromolecular gels.

[0053] However, for continuous, long-length extrusion stages, scorching gradually accumulates over time due to factors such as temperature, pressure, melt flow rate, and extrusion die. Excessive scorching can cause a large amount of gel to pass through the filter screen as impurities, reducing the quality of the high-voltage cable insulation layer material. In fact, the gel can even clog the pores of the filter screen, leading to a decrease in yield.

[0054] Therefore, it is necessary to evaluate the extrusion process of cable insulation material, and optimize the extrusion process based on the evaluation results, so that the molded film obtained by continuous extrusion of the optimized cable insulation material can meet the quality requirements of high-voltage cable insulation layer.

[0055] The evaluation method for the cable insulation material extrusion process provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0056] In one exemplary embodiment, such as Figure 2 As shown, an evaluation method for the extrusion process of cable insulation material is provided, which is then applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S201 to S203. Wherein:

[0057] S201, acquire an optical scanning image of the cable insulation material after continuous extrusion to form a film, and determine the defect parameters of the accumulated defects in the film based on the optical scanning image.

[0058] Among them, cable insulation material refers to the raw materials used to prepare cable insulation layers, which may include cross-linked polyethylene.

[0059] Optionally, the optical scanning image of the molded film can be a transmitted light scanning image and / or a reflected light scanning image, which can reflect the defect parameters of defects in the molded film, such as the number of defects, defect type, and defect size. The transmitted light imaging unit and / or the reflected light imaging unit continuously capture images of the molded film at a certain sampling frequency. Therefore, the defect parameters of accumulated defects in the molded film can be determined through the corresponding optical scanning images.

[0060] Specifically, feature extraction can be performed on optically scanned images, and the number, type, and size of defects can be analyzed based on the extracted features to obtain defect parameters.

[0061] like Figure 3AAs shown, the extrusion equipment for cable insulation material includes an extruder platform 301, an extruder display 302, a drive shaft 303, a feed port 304, an extruder screw and barrel 305, an extrusion die 306, a testing instrument platform 307, a testing instrument control console 308, a testing instrument display 309, a forming film 310, a casting roller 311, an automatic edge trimming device 312, a positioning rotor 313, a transmitted light source 314, a transmitted light acquisition camera 315, a reflected light source 316, a reflected light acquisition camera 317, an insulating isolation cover 318, a high-voltage electrode 319, a grounding electrode 320, a speed measuring rotor 321, an edge waste 322, a forming film collection device 323, and a waste collection device 324.

[0062] The extruder platform 301 provides mechanical support for the extruder display 302, drive shaft 303, feed port 304, extruder screw, and barrel 305. Cable insulation material is fed into the extrusion equipment through the feed port 304 and heated and melted by multiple temperature zones in the extruder screw and barrel 305. The molten material is then extruded through the extrusion die 306 to obtain a formed film 310. The extruder display 302 displays the heating temperature and other process parameters of each temperature zone in the extruder screw and barrel 305. The casting roller 311 drives the formed film 310, and the automatic edge trimming device 312 removes uneven or irregular portions of the film during the film's transmission process, ensuring a consistent width. The positioning rotor 313 positions the transmission path of the formed film. The transmitted light source 314 is located below the molding film and can be a strip-shaped LED (Light-Emitting Diode) light source. Its light path can penetrate the molding film. The transmitted light acquisition camera 315 is located above the molding film and is used to convert the light signal from the transmitted light source 314 after passing through the molding film into a digital image. The reflected light source 316 is located above the molding film and can be a ring-shaped LED light source. The reflected light acquisition camera 317 is used to convert the light signal from the reflected light source 316 after passing through the molding film into a digital image.

[0063] The inspection platform 307 provides mechanical support for the inspection control console 308, inspection display 309, forming film 310, casting roller 311, automatic edge trimming device 312, positioning rotor 313, transmitted light source 314, transmitted light acquisition camera 315, reflected light source 316, reflected light acquisition camera 317, insulating cover 318, high voltage electrode 319, grounding electrode 320, speed measuring rotor 321, edge waste 322, forming film collection device 323, and waste collection device 324. The inspection control console 308 controls the transmitted light source 314, transmitted light acquisition camera 315, reflected light source 316, and reflected light acquisition camera 317, while the inspection display 309 displays optical scanning images and defect parameters in real time.

[0064] like Figure 3B As shown, the insulating shield 318 includes a high-voltage electrode 319 and a grounding electrode 320. The high-voltage electrode 319 is connected to a high-voltage generator, and the grounding electrode 320 is connected to the ground. The high-voltage electrode 319 and the grounding electrode 320 form a simulated electric field to simulate the electric field environment of the cable operation. To prevent discharge, the edges of the high-voltage electrode 319 and the grounding electrode 320 are rounded with a rounding depth of 10mm, and the contact width between the high-voltage electrode 319 / grounding electrode 320 and the formed film is the actual width of the formed film. The insulating shield 318 is also connected to the detector platform 307 through an insulating sleeve 325 to prevent leakage of the simulated electric field or interference from external sources. The speed measuring rotor 321 is used to measure the transmission speed of the formed film. The edge waste 322 cut off by the automatic edge trimming device 312 is transmitted to the waste collection device 324, and the formed film is transmitted to the formed film collection device 323.

[0065] It should be noted that the above Figure 3A and Figure 3B The extrusion apparatus shown can melt-extrude cable insulation material under laboratory conditions. Therefore, it is not necessary to put the cable insulation material into the actual cable insulation preparation apparatus for long-term extrusion, which helps to reduce the consumption of cable insulation material during the evaluation process.

[0066] S202, Obtain the corresponding process parameters of the extruded film during the extrusion molding process.

[0067] For example, process parameters can represent adjustable parameters during the melt extrusion process of cable insulation material. These parameters may include, for instance, the heating temperature of the extrusion equipment, voltage, screw speed, extrusion time, etc.

[0068] S203, based on the variation law of defect parameters with process parameters, quantitatively evaluate the extrusion process of cable insulation material.

[0069] In one embodiment, based on the determination of defect parameters and process parameters, the variation law of defect parameters with process parameters can be determined. Specifically, defect parameters and process parameters can be obtained in a short time, and the variation law of defect parameters with process parameters over a long period of time can be obtained by fitting the mapping relationship between process parameters and defect parameters in a short time, thereby reducing the consumption of cable insulation material.

[0070] Optionally, the variation pattern can include the rate of change of defect parameters with different process parameters.

[0071] For example, for different process parameters, the optimal process parameters are those that minimize the number of accumulated defects and slow down the growth rate of defects in the formed film.

[0072] Furthermore, after determining the optimal process parameters, the extrusion process of the cable insulation material can be optimized based on the optimal process parameters to improve the quality of the subsequently extruded molded film.

[0073] The aforementioned evaluation method for cable insulation material extrusion process involves acquiring optical scanning images of the formed film after continuous extrusion of the cable insulation material, determining the defect parameters of accumulated defects in the formed film based on the optical scanning images, acquiring the corresponding process parameters of the formed film during the extrusion process, and quantitatively evaluating the cable insulation material extrusion process based on the variation law of defect parameters with process parameters. Determining the defect parameters of accumulated defects in the imaging film based on optical scanning images overcomes the blindness and indirectness of related technical evaluation methods. Simultaneously, acquiring the corresponding process parameters of the formed film during the extrusion process to determine the variation law of defect parameters with process parameters allows for quantitative evaluation of the cable insulation material extrusion process based on the variation law, improving the reliability and accuracy of the evaluation. This facilitates targeted optimization of the extrusion process based on the quantitative evaluation results, ensuring that the formed film obtained from the continuous extrusion of optimized cable insulation material meets the quality requirements of the cable insulation layer.

[0074] In one exemplary embodiment, defect parameters may include: defect type, and the defect size and number of defects for each type, such as... Figure 4 As shown, the process parameters may include at least one of: extrusion time, length of the formed film, and extruded weight of the formed film. S203 above includes:

[0075] S401, determine the variation law of defect size and number of defects with process parameters for each type of defect.

[0076] For example, defect types may include impurity defects, plasticization defects, and gelation defects. Correspondingly, the variation patterns may include the variation of the defect size and number of impurity defects with extrusion time, the length of the formed film, or the extrusion weight of the formed film; the variation patterns may also include the variation of the defect size and number of plasticization defects with extrusion time, the length of the formed film, or the extrusion weight of the formed film; and the variation patterns may also include the variation of the defect size and number of gelation defects with extrusion time, the length of the formed film, or the extrusion weight of the formed film.

[0077] S402, based on the various variation patterns, determine the limiting factors of the cable insulation material extrusion process under the target conditions of the extrusion molding process.

[0078] In one embodiment, the target condition can be determined based on the quality requirements of the target cable insulation layer. For example, the target condition may be the limit of the total number of defects of each type in the molded film or the limit of the number of defects of each type that reach a certain size.

[0079] Optionally, the limiting factors for the extrusion process of cable insulation material can be the limiting conditions of the process parameters of the cable insulation material.

[0080] Specifically, if the number of impurity defects is large or the growth is rapid in the cumulative defects characterized by the change pattern, it can indicate that the cleanliness of the cable insulation material is poor or the cleanliness of the equipment between the cable insulation material feed port and the extrusion die is poor. Therefore, the cleanliness of the cable insulation material and the cleanliness of the extrusion equipment can be checked and optimized to avoid excessive or rapid accumulation of impurity defects that may clog the filter screen or cause the molded film to break down prematurely and lose its insulation performance.

[0081] However, if the number of gel defects is large or the growth rate is fast in the cumulative defects characterized by the change pattern, it indicates that the scorching phenomenon of the molded film is more serious during the extrusion process. Therefore, the heating temperature of each temperature zone in the extrusion equipment can be checked and adjusted to reduce the scorching rate.

[0082] In addition, if the number of plasticizing defects is large or the growth is rapid in the cumulative defects characterized by the change law, it indicates that the cable insulation material has not been completely melted or that the cable insulation material contains polymer components. Therefore, the heating temperature of each temperature zone in the extrusion equipment can be checked and adjusted, and the composition of the cable insulation material can be analyzed and adjusted to accelerate the melting process.

[0083] Based on the optical scanning images, the types of defects accumulated in the formed film during the continuous extrusion process, as well as the size and number of each type of defect, are determined. This allows for the determination of the variation law of defect size and number with process parameters for each type of defect. Based on the determined variation law, the limiting factors of the cable insulation material extrusion process under the target conditions of the extrusion molding process are determined, thereby achieving a quantitative analysis of the cable insulation material extrusion process.

[0084] For example, in addition to determining the limiting factors based on the various variation patterns, the evaluation method for the above-mentioned cable insulation material extrusion process also includes: predicting the limit extrusion time of the cable insulation material based on the variation pattern of defect parameters with process parameters.

[0085] In one embodiment, based on the determination of the variation pattern, the limit extrusion time of the cable insulation material can be determined from the variation pattern according to the quality requirements of the cable insulation layer. Within this limit extrusion time, the cable insulation material can be continuously and stably extruded, and the performance of the resulting molded film meets the quality requirements for preparing the cable insulation layer.

[0086] Among the process parameters, extrusion time, extrusion weight, and extrusion length are positively correlated for every two parameters, and correspondingly, the maximum extrusion time, maximum extrusion weight, and the length of the formed film are positively correlated for every two parameters.

[0087] Therefore, based on actual needs and the variation law of defect parameters with process parameters, the ultimate extrusion time of cable insulation material, the ultimate extrusion weight of cable insulation material, or the length of the formed film of cable insulation material can be predicted.

[0088] For example, such as Figure 5 As shown, the process of determining the ultimate extrusion time of the cable insulation material may include the following steps S501 to S503.

[0089] S501, based on the first preset conditions, determine the first limit extrusion weight of the formed film according to the variation law of the size and number of defects in the impurities with the process parameters.

[0090] Optionally, the first preset condition is set based on a first size threshold for impurity defects and a first number threshold for the number of impurity defects whose size exceeds the first size threshold. Specifically, the first preset condition is that during the continuous extrusion molding process of cable insulation material, the number of impurity defects whose size exceeds the first size threshold among the accumulated impurity defects in the resulting molded film reaches the first number threshold.

[0091] For example, the first size threshold and the first number threshold can be flexibly adjusted according to the filter screen aperture size and the number of apertures at the formed film collection device.

[0092] For example, if the filter mesh count is 250 and the filter diameter is 150mm, by looking up the corresponding table based on the mesh count and filter diameter, the pore size of the filter is found to be 60μm, and the total number of pores in the filter is calculated to be 0.25·250·π·150. 2 / 645.16=6844. Correspondingly, the first size threshold is used to limit the limit size of impurity defects in the two-dimensional direction, with a value of 60μm. The first number threshold can be 10% of the total number of vias, i.e., 685.

[0093] Therefore, as Figure 6 As shown, the horizontal axis represents the length of the formed film in the process parameters, in meters, and the vertical axis represents the number of impurity defects whose defect size exceeds the first size threshold, in units of individual defects. Therefore, based on the variation pattern, a specific functional relationship between the number of impurity defects exceeding the first size threshold and the length of the formed film can be fitted. And based on the length of the formed film corresponding to the first number threshold, the first limit extrusion weight of the formed film can be determined.

[0094] S502, based on the second preset conditions, determine the second limit extrusion weight of the molded film according to the variation law of the defect size and number of defects in the gel point defects with the process parameters.

[0095] Optionally, the second preset condition is set based on a second size threshold for gel defects and a second number threshold for the number of gel defects whose size exceeds the second size threshold. Specifically, the second preset condition is that during the continuous extrusion molding process of the cable insulation material, the number of gel defects whose size exceeds the second size threshold among the accumulated gel defects in the molded film reaches the second number threshold.

[0096] For example, the second size threshold and the second number threshold can be flexibly adjusted according to the filter screen aperture size and the number of apertures at the formed film collection device. Specifically, the process for determining the second size threshold and the second number threshold can refer to the first size threshold and the first number threshold described above, and will not be repeated here.

[0097] Based on the above embodiments, such as Figure 7A As shown, the horizontal axis represents the length of the formed film in the process parameters, in meters, and the vertical axis represents the number of gel impurity defects whose defect size exceeds the second size threshold, in units of individual defects. According to... Figure 7A The variation pattern can be obtained by fitting. Figure 7B The diagram illustrates a specific functional relationship between the number of gel defects exceeding the second size threshold and the length of the molded film, and is based on... Figure 7B The second limit extrusion weight of the formed film is determined by the length of the formed film corresponding to the second number threshold.

[0098] S503, select the minimum value between the first limit extrusion weight and the second limit extrusion weight as the first target extrusion weight, and use the first target extrusion weight to characterize the limit extrusion time.

[0099] Based on the values ​​of the first and second limit extrusion weights determined above, the minimum value between the first and second limit extrusion weights is taken as the first target extrusion weight, corresponding to the limit extrusion time of the cable insulation material.

[0100] The first limit extrusion weight of the molded film is determined based on the first preset condition corresponding to impurity defects, and the second limit extrusion weight of the molded film is determined based on the second preset condition corresponding to gel defects. The limit extrusion time is then determined based on the minimum value between the first and second limit extrusion weights. This method comprehensively considers the impact of different types of defects accumulated by the cable insulation material during the extrusion process on the performance of the molded film, and predicts the limit extrusion time based on the various changes. The limit extrusion time can be accurately predicted without consuming too much cable insulation material.

[0101] Based on the above embodiments, such as Figure 8 As shown, the evaluation method for the above-mentioned cable insulation material extrusion process also includes:

[0102] S801, Obtain the second target extrusion weight of the molded film at the time of puncture.

[0103] In some embodiments, the electric field strength threshold of the cable insulation layer can be determined based on the electric field distribution pattern in the cable insulation layer and the phase voltage value during long-term cable operation. The test voltage can then be set according to the determined electric field strength threshold of the cable insulation layer. Figure 3A and Figure 3B The voltage of the medium and high voltage electrodes is adjusted to the test voltage. The current flowing between the high voltage electrode and the ground electrode is measured. If the measured current exceeds the breakdown current, it indicates that the molded film has been broken down. The time corresponding to the first breakdown of the molded film is recorded.

[0104] Optionally, to ensure the reliability of the breakdown test, if the extrusion time of the molded film is short and the molded film breaks down immediately after the test voltage is applied, the type of defect in the molded film can be checked by the accumulated optical scanning images. If impurity defects are included, the cleanliness of the cable insulation material and the extrusion device should be re-inspected, and the one with poor cleanliness should be treated and the breakdown test should be repeated.

[0105] For example, the second target extrusion weight can represent the cumulative extruded weight when the molded film first breaks down. In addition, the second target extrusion weight, second target extrusion time, and second target extrusion length of the molded film at the time of breakdown can also be obtained according to actual needs.

[0106] Furthermore, when the second target extrusion weight is greater than or equal to the first target extrusion weight, it indicates that the formed film has not reached the second target extrusion weight, and impurity defects and gel defects have reached the first and second preset conditions. In this case, the first target extrusion weight is used to characterize the limit extrusion time, so that the cable insulation material will not be broken down within the limit extrusion time, and the number and size of each type of defect can also meet the corresponding preset conditions.

[0107] Correspondingly, when the second target extrusion weight is less than the first target extrusion weight, it means that the formed film has been punctured before reaching the first target extrusion weight. In this case, the second target extrusion weight is used to characterize the limit extrusion time, so that the cable insulation material will not be punctured within the limit extrusion time, and the number and size of various types of defects can also meet the corresponding preset conditions.

[0108] The second target extrusion weight corresponding to the breakdown of the above-mentioned combined molding film and the first target extrusion weight determined above determine the limit extrusion time, ensuring that the cable insulation material will not be broken down within the limit extrusion time, and that the number and size of various types of defects can also meet the corresponding preset conditions, thereby further improving the quality of the molding film obtained in the subsequent preparation.

[0109] For example, the process parameters may also include the temperature sequence corresponding to each temperature zone that the formed film experiences during the extrusion molding process. The evaluation method of the above-mentioned cable insulation material extrusion process also includes optimizing the temperature values ​​in the temperature sequence based on the variation law of the number of defects with the temperature values ​​in the temperature sequence.

[0110] The temperature zones are as described above. Figure 3A The diagram illustrates the independent temperature zones within the extruder screw and barrel 305 used for heating the cable insulation material. The temperature sequence, representing the sequence of heating temperatures provided by each temperature zone arranged furthest from the extrusion die, can be expressed as [T1, T2, T3, ..., T...]. n ].

[0111] In some embodiments, since the heating temperature provided by each temperature zone mainly affects the scorching rate of the cable insulation material, the temperature values ​​in the temperature sequence can be adaptively optimized according to the variation law of the number of gel defects in each defect with the temperature values ​​in the temperature sequence.

[0112] In one exemplary embodiment, such as Figure 9 As shown, the temperature values ​​in the above optimized temperature sequence include:

[0113] S901, obtain the total number of defects and the average number of defects per unit time within the sampling period.

[0114] The sampling period is a continuous period of time during the extrusion process of the cable insulation material.

[0115] For example, the sampling period H can be 2 hours, and correspondingly, the unit time Δt can be 1 minute or 1 second, with the unit time mean n' = n 凝胶 / Δt.

[0116] In addition, it can also obtain the total number of defects within the data sampling length and the average number per unit length.

[0117] S902 optimizes the temperature values ​​in the temperature sequence in response to the fact that the multiple of the unit time mean relative to the reference mean is greater than a first threshold.

[0118] Optionally, the first threshold can be flexibly adjusted according to actual needs, and is not limited here. For example, the value range of the first threshold α is 3 to 5.

[0119] The reference mean m can be the average value per unit time corresponding to the number of gel defects in the molded film within a predetermined length l obtained at the beginning of the extrusion process of the cable insulation material. The predetermined length is usually 10m.

[0120] For example, when n' > α×m, it indicates that the production rate of gel defects increases faster than the time period corresponding to the reference mean, i.e., the scorching rate is faster. Therefore, it is necessary to optimize the temperature values ​​in the temperature sequence, for example, by reducing one or more of the heating temperatures in each temperature zone.

[0121] like Figure 10A As shown, the mean value per unit time of the temperature sequence [110℃, 115℃, 120℃, 125℃, 125℃, 125℃] before optimization varies with the length of the formed film. The horizontal axis represents the length of the formed film, and the vertical axis represents the mean value per unit time. It can be seen that the mean value per unit time shows a significant upward trend compared with the reference mean value. The multiple of the mean value per unit time relative to the reference mean value is more than 7 times, which is much greater than the preset first threshold (α=3~5).

[0122] S903, in response to the random fluctuation or decrease of the unit time mean, the number of events in which the unit time mean is greater than a first threshold is less than or equal to a second threshold, and the total number of defects is less than a target threshold, the optimization of the temperature value in the temperature sequence is stopped.

[0123] For example, after optimizing the temperature values ​​in the temperature sequence, the number of gel defects in the molded film corresponding to the same sampling period is determined based on the optical scanning image and compared with the number of gel defects before optimization.

[0124] Optionally, the variation law of the unit time mean value corresponding to the optimized temperature sequence with the length of the formed film is determined, and the variation trend of the unit time mean value is determined based on the variation law, and compared with the variation law before optimization.

[0125] Furthermore, the number of events where the average value per unit time is greater than a first threshold relative to the reference average is counted, and this count is compared with a second threshold. The second threshold can be determined based on the sampling period, a preset length corresponding to the reference average, and the transmission speed v of the formed film. 传动 To determine this, the second threshold β can be expressed as β = (0.3·H·v) 传动 / l).

[0126] The target threshold can be determined based on the quality requirements of the cable insulation layer, and is not limited here.

[0127] Based on the above embodiments, if the number of events in which the unit time mean fluctuates randomly or decreases, the unit time mean is greater than a first threshold by a multiple of the reference mean, is less than or equal to a second threshold, and the total number of defects is less than a target threshold, then the optimization of the temperature values ​​in the temperature sequence is stopped, and the temperature sequence at this time is determined as the target temperature sequence; otherwise, if one or more of the above conditions are not met, the optimization of the temperature values ​​in the temperature sequence continues.

[0128] like Figure 10B As shown, the mean value per unit time for the target temperature sequence [110℃, 115℃, 118℃, 120℃, 120℃, 120℃, 120℃] varies with the length of the formed film. The horizontal axis represents the length of the formed film, and the vertical axis represents the mean value per unit time. Therefore, it can be seen that relative to... Figure 10A Before optimization, the temperature sequence in the target temperature sequence has a relatively smooth change in the average value per unit time under the process parameters corresponding to the temperature values. It also fluctuates randomly or decreases with the extrusion length of the formed film. The number of events in which the average value per unit time is greater than the first threshold by a multiple of the reference average value is 0.

[0129] The above embodiments analyze the multiple of the unit time average relative to the reference average based on the total number of defects and the unit time average within the sampling period, and combine the changing trend of the unit time average, the number of events where the multiple of the unit time average relative to the reference average is greater than a first threshold, and the change in the total number of defects to optimize the temperature values ​​in the temperature sequence, so that under the optimized target temperature sequence, the scorching rate of the formed film can meet the quality requirements of the cable insulation layer.

[0130] In one exemplary embodiment, such as Figure 11 As shown, the evaluation method for the above-mentioned cable insulation material extrusion process includes:

[0131] S1101, acquire an optical scanning image of the cable insulation material after continuous extrusion to form a film, and determine the defect parameters of the accumulated defects in the film based on the optical scanning image.

[0132] S1102, Obtain the corresponding process parameters of the extruded film during the extrusion molding process.

[0133] S1103, determine the variation law of defect size and number of defects with process parameters for each type of defect.

[0134] S1104, Based on the various variation patterns, determine the limiting factors of the cable insulation material extrusion process under the target conditions of the extrusion molding process.

[0135] S1105, based on the first preset conditions, determine the first limit extrusion weight of the formed film according to the variation law of the size and number of defects in the impurities with the process parameters.

[0136] S1106, Based on the second preset conditions, the second limit extrusion weight of the molded film is determined according to the variation law of the defect size and number of defects in the gel point defects with the process parameters.

[0137] S1107, Select the minimum value between the first limit extrusion weight and the second limit extrusion weight as the first target extrusion weight, and use the first target extrusion weight to characterize the limit extrusion time.

[0138] S1108, Obtain the second target extrusion weight of the molded film at the time of puncture.

[0139] S1109, when the second target extrusion weight is greater than or equal to the first target extrusion weight, the first target extrusion weight is used to characterize the ultimate extrusion time.

[0140] S1110, when the second target extrusion weight is less than the first target extrusion weight, the second target extrusion weight is used to characterize the ultimate extrusion time.

[0141] S1111, obtain the total number of defects and the average number of defects per unit time within the sampling period.

[0142] S1112, in response to the fact that the multiple of the unit time mean relative to the reference mean is greater than the first threshold, optimize the temperature values ​​in the temperature sequence.

[0143] S1113, in response to the random fluctuation or decrease of the unit time mean, the occurrence of events where the unit time mean is greater than a multiple of the reference mean by a first threshold is less than or equal to a second threshold, and the total number of defects is less than a target threshold, stop optimizing the temperature values ​​in the temperature sequence.

[0144] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0145] Based on the same inventive concept, this application also provides an evaluation device for the cable insulation material extrusion process, which is used to implement the evaluation method for the cable insulation material extrusion process described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the cable insulation material extrusion process evaluation device provided below can be found in the limitations of the cable insulation material extrusion process evaluation method described above, and will not be repeated here.

[0146] In one exemplary embodiment, such as Figure 12 As shown, an evaluation device for the extrusion process of cable insulation material is provided, comprising: a scanning module, an acquisition module, and an evaluation module, wherein:

[0147] The scanning module is used to acquire optical scanning images of the cable insulation material after continuous extrusion to form a film.

[0148] The acquisition module is used to acquire the defect parameters of the accumulated defects in the molded film based on the optical scanning image, and to acquire the corresponding process parameters of the molded film during the extrusion molding process.

[0149] The evaluation module is used to quantitatively evaluate the extrusion process of cable insulation material based on the variation of defect parameters with process parameters.

[0150] In one embodiment, the evaluation module is further configured to: determine the variation patterns of defect size and number with process parameters for each type of defect; and, based on these variation patterns, determine the limiting factors of the cable insulation extrusion process under the target conditions of the extrusion molding process.

[0151] In one embodiment, the evaluation device for the above-mentioned cable insulation material extrusion process further includes a prediction module for predicting the ultimate extrusion time of the cable insulation material based on the variation law of defect parameters with process parameters.

[0152] In one embodiment, the prediction module is further configured to: determine the first limit extrusion weight of the molded film based on the first preset conditions and the variation law of the defect size and number of defects in the impurity defects with process parameters; determine the second limit extrusion weight of the molded film based on the second preset conditions and the variation law of the defect size and number of defects in the gel point defects with process parameters; select the minimum value between the first limit extrusion weight and the second limit extrusion weight as the first target extrusion weight, and use the first target extrusion weight to characterize the limit extrusion time.

[0153] In one embodiment, the acquisition module is further configured to acquire a second target extrusion weight of the molded film at the time of puncture.

[0154] In one embodiment, the prediction module is further configured to: when the second target extrusion weight is greater than or equal to the first target extrusion weight, use the first target extrusion weight to characterize the ultimate extrusion time; and when the second target extrusion weight is less than the first target extrusion weight, use the second target extrusion weight to characterize the ultimate extrusion time.

[0155] In one embodiment, the evaluation device for the above-mentioned cable insulation material extrusion process further includes an optimization module for optimizing the temperature values ​​in the temperature sequence based on the variation law of the number of defects with the temperature values ​​in the temperature sequence.

[0156] In one embodiment, the optimization module is further configured to: obtain the total number of defects and the average value per unit time within the sampling period; optimize the temperature values ​​in the temperature sequence in response to the fact that the multiple of the average value per unit time relative to the reference average is greater than a first threshold; and stop optimizing the temperature values ​​in the temperature sequence in response to the fact that the number of events in which the average value per unit time fluctuates or decreases randomly, the average value per unit time is greater than the first threshold, the number of events in which the multiple of the average value per unit time relative to the reference average is greater than a second threshold is less than or equal to a second threshold, and the total number of defects is less than a target threshold.

[0157] Each module in the evaluation device for the extrusion process of the aforementioned cable insulation material can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0158] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 13As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores defect parameters of accumulated defects in the molded film and the corresponding process parameters of the molded film during extrusion molding. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements an evaluation method for the extrusion process of cable insulation materials.

[0159] Those skilled in the art will understand that Figure 13 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0160] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0161] Obtain optical scanning images of the cable insulation material after continuous extrusion to form a film, and determine the defect parameters of the accumulated defects in the film based on the optical scanning images;

[0162] Obtain the corresponding process parameters for the extrusion molding process of the film;

[0163] The extrusion process of cable insulation material is quantitatively evaluated based on the variation of defect parameters with process parameters.

[0164] In one embodiment, when the processor executes the computer program, it also performs the following steps: determining the variation law of defect size and defect number with process parameters for each type of defect; and determining the limiting factors of the cable insulation extrusion process under the target conditions of the extrusion molding process based on each variation law.

[0165] In one embodiment, when the processor executes the computer program, it also performs the following steps: predicting the ultimate extrusion time of the cable insulation material based on the variation law of defect parameters with process parameters.

[0166] In one embodiment, when the processor executes the computer program, it further performs the following steps: based on a first preset condition, determining a first limit extrusion weight of the molded film according to the variation law of the defect size and number of defects in the impurity defects with process parameters; based on a second preset condition, determining a second limit extrusion weight of the molded film according to the variation law of the defect size and number of defects in the gel point defects with process parameters; selecting the minimum value between the first limit extrusion weight and the second limit extrusion weight as a first target extrusion weight, and using the first target extrusion weight to characterize the limit extrusion time.

[0167] In one embodiment, when the processor executes the computer program, it further implements the following steps: when the second target extrusion weight is greater than or equal to the first target extrusion weight, the first target extrusion weight is used to characterize the ultimate extrusion time; when the second target extrusion weight is less than the first target extrusion weight, the second target extrusion weight is used to characterize the ultimate extrusion time.

[0168] In one embodiment, when the processor executes the computer program, it further performs the following steps: optimizing the temperature values ​​in the temperature sequence based on the variation of the number of defects with the temperature values ​​in the temperature sequence.

[0169] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining the total number of defects and the average value per unit time within the sampling period; optimizing the temperature values ​​in the temperature sequence in response to the fact that the multiple of the average value per unit time relative to the reference average is greater than a first threshold; and stopping the optimization of the temperature values ​​in the temperature sequence in response to the fact that the number of events in which the average value per unit time fluctuates randomly or decreases, the multiple of the average value per unit time relative to the reference average is greater than the first threshold is less than or equal to a second threshold, and the total number of defects is less than a target threshold.

[0170] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0171] Obtain optical scanning images of the cable insulation material after continuous extrusion to form a film, and determine the defect parameters of the accumulated defects in the film based on the optical scanning images;

[0172] Obtain the corresponding process parameters for the extrusion molding process of the film;

[0173] The extrusion process of cable insulation material is quantitatively evaluated based on the variation of defect parameters with process parameters.

[0174] In one embodiment, when the processor executes the computer program, it also performs the following steps: determining the variation law of defect size and defect number with process parameters for each type of defect; and determining the limiting factors of the cable insulation extrusion process under the target conditions of the extrusion molding process based on each variation law.

[0175] In one embodiment, when the processor executes the computer program, it also performs the following steps: predicting the ultimate extrusion time of the cable insulation material based on the variation law of defect parameters with process parameters.

[0176] In one embodiment, when the processor executes the computer program, it further performs the following steps: based on a first preset condition, determining a first limit extrusion weight of the molded film according to the variation law of the defect size and number of defects in the impurity defects with process parameters; based on a second preset condition, determining a second limit extrusion weight of the molded film according to the variation law of the defect size and number of defects in the gel point defects with process parameters; selecting the minimum value between the first limit extrusion weight and the second limit extrusion weight as a first target extrusion weight, and using the first target extrusion weight to characterize the limit extrusion time.

[0177] In one embodiment, when the processor executes the computer program, it further implements the following steps: when the second target extrusion weight is greater than or equal to the first target extrusion weight, the first target extrusion weight is used to characterize the ultimate extrusion time; when the second target extrusion weight is less than the first target extrusion weight, the second target extrusion weight is used to characterize the ultimate extrusion time.

[0178] In one embodiment, when the processor executes the computer program, it further performs the following steps: optimizing the temperature values ​​in the temperature sequence based on the variation of the number of defects with the temperature values ​​in the temperature sequence.

[0179] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining the total number of defects and the average value per unit time within the sampling period; optimizing the temperature values ​​in the temperature sequence in response to the fact that the multiple of the average value per unit time relative to the reference average is greater than a first threshold; and stopping the optimization of the temperature values ​​in the temperature sequence in response to the fact that the number of events in which the average value per unit time fluctuates randomly or decreases, the multiple of the average value per unit time relative to the reference average is greater than the first threshold is less than or equal to a second threshold, and the total number of defects is less than a target threshold.

[0180] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0181] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0182] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An evaluation method for the extrusion process of cable insulation material, characterized in that, The method includes: Obtain optical scanning images of the cable insulation material after continuous extrusion to form a film, and determine the defect parameters of the accumulated defects in the film based on the optical scanning images; Obtain the process parameters corresponding to the extrusion molding process of the molded film; The extrusion process of cable insulation material is quantitatively evaluated based on the variation of the defect parameters with the process parameters.

2. The method according to claim 1, characterized in that, The defect parameters include: defect type, and defect size and number of defects for each type; The process parameters include at least one of the following: extrusion time, length of the formed film, and extrusion weight of the formed film; The step of quantitatively evaluating the cable insulation material extrusion process based on the variation law of the defect parameters with the process parameters includes: Determine the variation patterns of the defect size and the number of defects with the process parameters for each type of defect; Based on the aforementioned variation patterns, the limiting factors of the cable insulation material extrusion process are determined under the target conditions of the extrusion molding process.

3. The method according to claim 1, characterized in that, Also includes: Based on the variation of the defect parameters with the process parameters, the ultimate extrusion time of the cable insulation material is predicted.

4. The method according to claim 3, characterized in that, The defect parameters include: defect type, and the defect size and number of defects for each type; the defect type includes impurity defects and gel point defects; The process parameters include: the extruded weight of the formed film; The step of predicting the ultimate extrusion time of the cable insulation material based on the variation law of the defect parameters with the process parameters includes: Based on the first preset conditions, the first limit extrusion weight of the molded film is determined according to the variation law of the size and number of defects in the impurities with the process parameters. Based on the second preset conditions, the second limit extrusion weight of the molded film is determined according to the variation law of the size and number of defects in the gel point defects with the process parameters; The minimum value between the first limit extrusion weight and the second limit extrusion weight is selected as the first target extrusion weight, and the first target extrusion weight is used to characterize the limit extrusion time.

5. The method according to claim 4, characterized in that, Also includes: Obtain the second target extrusion weight of the molded film at the point of puncture; The step of predicting the ultimate extrusion time of the cable insulation material based on the variation law of the defect parameters with the process parameters further includes: When the second target extrusion weight is greater than or equal to the first target extrusion weight, the first target extrusion weight is used to characterize the ultimate extrusion time; When the second target extrusion weight is less than the first target extrusion weight, the second target extrusion weight is used to characterize the ultimate extrusion time.

6. The method according to claim 1, characterized in that, The defect parameters include: the number of defects; The process parameters include: the temperature sequence corresponding to each temperature zone experienced by the molded film during the extrusion molding process; The method further includes: optimizing the temperature values ​​in the temperature sequence based on the variation law of the number of defects with the temperature values ​​in the temperature sequence.

7. The method according to claim 6, characterized in that, The step of optimizing the temperature values ​​in the temperature sequence based on the variation pattern of the number of defects with the temperature values ​​in the temperature sequence includes: Obtain the total number of defects and the average number per unit time within the sampling period; In response to the fact that the multiple of the unit time mean relative to the reference mean is greater than a first threshold, the temperature values ​​in the temperature sequence are optimized; In response to random fluctuations or decreases in the unit time mean, the number of events in which the unit time mean is greater than a first threshold is less than or equal to a second threshold, and the total number of defects is less than a target threshold, the optimization of temperature values ​​in the temperature sequence is stopped.

8. An evaluation device for the extrusion process of cable insulation material, characterized in that, The device includes: The scanning module is used to acquire optical scanning images of the cable insulation material after continuous extrusion to form a film; The acquisition module is used to acquire the defect parameters of the accumulated defects in the molded film based on the optical scanning image, and to acquire the corresponding process parameters of the molded film during the extrusion molding process. The evaluation module is used to quantitatively evaluate the cable insulation material extrusion process based on the variation law of the defect parameters with the process parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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