Processing method and equipment for plastic-lined pipe and medium
By predicting and weighting the temperature data of the outer and inner tubes of the plastic-lined pipe, the problem of unstable and uneven temperature during hot processing was solved, enabling quality inspection and control of the plastic-lined pipe processing process and improving the processing effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HANDAN ZHENGDA PIPE MFG CO LTD
- Filing Date
- 2025-12-26
- Publication Date
- 2026-05-12
AI Technical Summary
During the processing of plastic-lined pipes, the unstable hot working temperature and uneven position lead to unsatisfactory processing results, making it difficult to achieve quality inspection and control.
By predicting the temperature data of each data acquisition point of the outer and inner tubes of the plastic-lined pipe during the hot processing, the first and second prediction models are obtained by training a neural network model, the first and second matching weights are determined, and weighted processing is performed to obtain the processing feature value, thereby judging the normality of the hot processing state.
It enables real-time quality detection and control of the hot processing of plastic-lined pipes, improving the stability and consistency of processing results.
Smart Images
Figure CN122020449A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of pipe processing, and in particular to a processing method, equipment and medium for plastic-lined pipes. Background Technology
[0002] Plastic-lined pipes are composite pipes made of ordinary carbon steel pipes as the base material and lined with thermoplastic plastic through cold drawing or rotational molding. That is, the outer pipe of the plastic-lined pipe is a steel pipe and the inner pipe is a plastic pipe. In the process of manufacturing plastic-lined pipes, the plastic pipe needs to be placed inside the steel pipe first, and then the steel pipe is heat-processed. The plastic pipe is then filled with air to raise its temperature, so that the plastic pipe can adhere to the inner wall of the steel pipe to form a plastic-lined pipe.
[0003] During the processing of plastic-lined pipes, the processing effect may be unsatisfactory due to factors such as unstable heat processing temperature and uneven heat processing position. Therefore, it is necessary to conduct quality inspection on the plastic-lined pipes during the processing to ensure that the processing effect meets the processing standards. Summary of the Invention
[0004] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows: According to one aspect of this application, a method for processing a plastic-lined pipe is provided, comprising: Step S100: For each data collection point on the outer tube of the plastic-lined pipe to be processed, perform prediction processing on several temperature data corresponding to the target time period during the heat treatment process to obtain several first prediction data corresponding to the current moment; the duration of the target time period is a preset duration, and the end time of the target time period is the data collection moment that is before the current moment and has the shortest duration between it and the current moment. Step S200: Perform predictive processing on several temperature data corresponding to each data acquisition point of the inner tube of the plastic-lined pipe to be processed within the target time period to obtain several second predictive data corresponding to the current time. Step S300: Based on the fluctuation characteristics of several temperature data corresponding to each data acquisition point of the inner tube of the plastic-lined pipe to be processed at the current time, determine the first matching weight and the second matching weight. Step S400: Weight the first matching weight, the second matching weight, a number of first prediction data and a number of second prediction data to obtain the processing feature value of the plastic-lined pipe to be processed at the current time. Step S500: If the processing characteristic value is within the preset normal processing characteristic value range, then the heat processing status of the plastic-lined pipe to be processed at the current moment is determined to be normal; otherwise, the heat processing status of the plastic-lined pipe to be processed at the current moment is determined to be abnormal.
[0005] According to another aspect of this application, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored therein, the at least one instruction or the at least one program being loaded and executed by a processor to implement the aforementioned processing method for plastic-lined pipes.
[0006] According to another aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.
[0007] The present invention has at least the following beneficial effects: The processing method for the plastic-lined pipe of the present invention firstly predicts several temperature data points corresponding to each data acquisition point on the outer tube of the plastic-lined pipe during the target time period of the heat treatment process to obtain several first predicted data points corresponding to the current moment. Then, it predicts several temperature data points corresponding to each data acquisition point on the inner tube of the plastic-lined pipe during the target time period to obtain several second predicted data points corresponding to the current moment. The first and second predicted data points are the temperature data under normal conditions at the current moment obtained by predicting the temperature data of the outer and inner tubes of the plastic-lined pipe during the target time period. Finally, based on the fluctuation characteristics of the several temperature data points corresponding to each data acquisition point on the inner tube of the plastic-lined pipe at the current moment, a first matching weight and a second matching weight are determined. The first and second matching weights are used to represent the degree of emphasis of the temperature data of the outer tube and the temperature data of the inner tube on the determination of the subsequent heat treatment state. Then, the first matching weight, the second matching weight, several first prediction data and several second prediction data are weighted to obtain the processing characteristic value of the plastic-lined pipe to be processed at the current moment. The processing characteristic value is used to represent the quality degree corresponding to the heat treatment state of the plastic-lined pipe to be processed at the current moment. If the processing characteristic value is within the normal processing characteristic value range, the heat treatment state of the plastic-lined pipe to be processed at the current moment is determined to be normal. By predicting the data of the outer tube and the inner tube of the plastic-lined pipe to be processed, the heat treatment state of the plastic-lined pipe to be processed at the current moment can be determined, so that the staff can carry out quality control during the processing of the plastic-lined pipe. Attached Figure Description
[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0009] Figure 1 A flowchart illustrating the processing method of a plastic-lined pipe provided in an embodiment of the present invention. Detailed Implementation
[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0011] This application proposes a processing method for plastic-lined pipes, such as... Figure 1 As shown, it includes: Step S100: Perform predictive processing on several temperature data corresponding to each data collection point on the outer tube of the plastic-lined pipe to be processed during the target time period of the heat treatment process to obtain several first predicted data corresponding to the current moment. The outer tube of the plastic-lined pipe to be processed is the steel pipe.
[0012] Several data acquisition points on the outer tube of the plastic-lined pipe to be processed are distributed in a matrix on the outer tube so that the temperature value at each data acquisition point can reflect the overall thermal processing status of the outer tube.
[0013] Temperature data at each data acquisition point can be obtained by setting a temperature sensor at each data acquisition point.
[0014] The target time period is a preset duration, and the end time of the target time period is the data collection time that is before the current time and has the shortest duration between it and the current time.
[0015] Furthermore, step S100 includes steps S110-S120: Step S110: Obtain several temperature data points corresponding to each data acquisition point on the outer tube of the plastic-lined pipe to be processed during the target time period of the heat treatment process, so as to obtain several first temperature data lists A1, A2, ..., A i ,...,A j Where i = 1, 2, ..., j; j is the number of data collection moments included in the target time period; the j-th data collection moment is the data collection moment preceding the current moment; the duration between any two adjacent data collection moments in the target time period is equal; A i This is a list of the first temperature data corresponding to the i-th data acquisition time within the target time period; A i =(A i1 A i2 ,...,A im ,...,A in); m=1,2,...,n; n is the number of data collection points on the outer pipe of the plastic-lined pipe to be processed; A im The temperature data at the i-th data acquisition time within the target time period is the m-th data acquisition point on the outer tube of the plastic-lined pipe to be processed. Step S120: Set A1, A2, ..., A i ,...,A j The data is input into a preset first prediction model to obtain a first prediction data list output by the first prediction model. The first prediction data list includes several first prediction data corresponding to the current time.
[0016] The first prediction model is trained based on several historical temperature data points corresponding to plastic-lined pipes in a normal heat treatment state over a historical period. Specifically, the first prediction model is determined according to steps S121-S124: Step S121: Identify the plastic-lined pipes that have been in normal heat treatment status during the historical time period and have the same specifications as the plastic-lined pipe to be processed as the target plastic-lined pipes. The end point of a historical time period is located before the current point.
[0017] Step S122: Obtain historical temperature data for each data acquisition point on the outer pipe of each target plastic-lined pipe within several corresponding historical sub-time periods, to obtain several first historical temperature data list sets B1, B2, ..., B t ,...,B s Where t = 1, 2, ..., s; s is the target number of plastic-lined pipes; B t This is the first historical temperature data set corresponding to the t-th target plastic-lined pipe; B t =(B t1 B t2 ,...,B te ,...,B tf(t) ); e=1,2,...,f(t); f(t) is the number of historical sub-time periods corresponding to the t-th target plastic-lined pipe; B te This is a list of the first historical temperature data corresponding to the t-th target plastic-lined pipe in its corresponding e-th historical sub-time period; B te =(B te1 B te2 ,...,B tei ,...,B tej );B tei This is the first historical temperature data sub-table corresponding to the i-th data acquisition time within the corresponding e-th historical sub-time period for the t-th target plastic-lined pipe; B tei =(B tei1 B tei2 ,...,B teim ,...,B tein );B teim The historical temperature data corresponding to the m-th data acquisition point on the outer pipe of the t-th target plastic-lined pipe at the i-th data acquisition time within its corresponding e-th historical sub-time period; The arrangement of several data acquisition points on the outer tube of each target plastic-lined pipe is the same as the arrangement of several data acquisition points on the outer tube of the plastic-lined pipe to be processed.
[0018] The historical sub-time period is any sub-time period during the heat treatment process of the target plastic-lined pipe within the historical time period; the duration of the historical sub-time period is equal to the duration of the target time period; the number of data acquisition moments within the historical sub-time period is equal to the number of data acquisition moments within the target time period, and the duration between two adjacent data acquisition moments within the historical sub-time period is equal to the duration between two adjacent data acquisition moments within the target time period.
[0019] Step S123: Obtain the first historical temperature data sub-table C corresponding to the first data acquisition time after the e-th historical sub-time period of the t-th target plastic-lined pipe. te1 =(C te11 C te12 ,...,C te1m ,...,C te1n ); where C te1m For the m-th data acquisition point on the outer pipe of the t-th target plastic-lined pipe, the historical temperature data is located at the first data acquisition time after its corresponding e-th historical sub-time period. Step S124, B te1 B te2 ,...,B tei ,...,B tej As input sample, C te1 As the output label, a pre-defined neural network model is subjected to supervised training to obtain the first prediction model.
[0020] The training method for the first prediction model can adopt the existing training methods for neural network models.
[0021] Step S200: Perform predictive processing on several temperature data corresponding to each data acquisition point of the inner tube of the plastic-lined pipe to be processed within the target time period to obtain several second predictive data corresponding to the current time. The inner tube of the plastic-lined pipe to be processed is the plastic tube. When hot processing the plastic-lined pipe, the inner tube needs to be placed inside the outer tube, and air needs to be filled inside the inner tube. The outer tube is heated so that the plastic tube of the inner tube is thermally melted and bonded to the inner wall of the outer tube. The data acquisition points of the inner tube are arranged in a matrix evenly on the inner wall of the inner tube. The temperature data of the data acquisition points of the inner tube can be obtained by setting a temperature sensor at each data acquisition point, or by inserting a roller through the inner tube and setting a temperature sensor at the corresponding position of each data acquisition point on the roller.
[0022] Furthermore, step S200 includes steps S210-S220: Step S210: Obtain several temperature data points corresponding to each data acquisition point on the inner tube of the plastic-lined pipe to be processed during the target time period of the heat treatment process, so as to obtain several second temperature data lists D1, D2, ..., D i ,...,D j ; where D i This is the list of second temperature data corresponding to the i-th data acquisition time within the target time period; D i =(D i1 D i2 ,...,D iy ,...,D iz ); y=1,2,...,z; z is the number of data acquisition points on the inner tube of the plastic-lined pipe to be processed; several data acquisition points on the inner tube are distributed in a matrix on the inner tube; D iy The temperature data corresponding to the y-th data acquisition point on the inner tube of the plastic-lined pipe to be processed at the ith data acquisition time within the target time period; Step S220: Set D1, D2, ..., D i ,...,D j Input the data into the preset second prediction model to obtain the second prediction data list output by the second prediction model; The second prediction data list includes several second prediction data corresponding to the current time.
[0023] The second prediction model is trained based on several historical temperature data points corresponding to plastic-lined pipes in a normal heat treatment state over a historical period. Specifically, the second prediction model is determined according to steps S221-S223: Step S221: Obtain historical temperature data for each data acquisition point on the inner tube of each target plastic-lined pipe within several corresponding historical sub-time periods, to obtain several second historical temperature data list sets E1, E2, ..., E t ,...,E sAmong them, E t This is the second historical temperature data set corresponding to the t-th target plastic-lined pipe; E t =(E t1 E t2 ,...,E te ,...,E tf(t) ); E te This is a list of the second historical temperature data for the t-th target plastic-lined pipe within its corresponding e-th historical sub-time period. E te =(E te1 E te2 ,...,E tei ,...,E tej ); E tei This is the second historical temperature data sub-table corresponding to the i-th data acquisition time within the corresponding e-th historical sub-time period for the t-th target plastic-lined pipe; E tei =(E tei1 E tei2 ,...,E teiy ,...,E teiz ); E teiy The historical temperature data corresponding to the y-th data acquisition point on the inner tube of the t-th target plastic-lined pipe at the ith data acquisition time within its corresponding e-th historical sub-time period; The arrangement of several data acquisition points on the inner tube of each target plastic-lined pipe is the same as the arrangement of several data acquisition points on the inner tube of the plastic-lined pipe to be processed.
[0024] Step S222: Obtain the second historical temperature data sub-table C corresponding to the first data acquisition time after the e-th historical sub-time period of the t-th target plastic-lined pipe. te2 =(C te21 C te22 ,...,C te2y ,...,C te2z ); where C te2y The historical temperature data corresponding to the y-th data acquisition point on the inner tube of the t-th target plastic-lined pipe at the first data acquisition time after its corresponding e-th historical sub-time period; Step S223, E te1 E te2 ,...,E tei ,...,E tej As input sample, C te2 As the output label, a pre-defined neural network model is subjected to supervised training to obtain a second prediction model.
[0025] Step S300: Based on the fluctuation characteristics of several temperature data corresponding to each data acquisition point of the inner tube of the plastic-lined pipe to be processed at the current time, determine the first matching weight and the second matching weight. Furthermore, step S300 includes steps S310-S340: Step S310: Obtain the temperature data corresponding to each data acquisition point on the inner tube of the plastic-lined pipe to be processed at the current time, so as to obtain the second target data list F2=(F 21 ,F 22 ,...,F 2y ,...,F 2z ); where F 2y The temperature data at the current moment corresponds to the y-th data acquisition point on the inner tube of the plastic-lined pipe to be processed. Step S320, Determine F 21 ,F 22 ,...,F 2y ,...,F 2z The variance is H2; Step S330: Determine the variance interval where H2 is located from the preset variance interval list, and determine the matching weight corresponding to the variance interval as the second matching weight G2. The variance interval list includes several variance intervals and the matching weight corresponding to each variance interval. The average value of the variance interval is directly proportional to the matching weight corresponding to that variance interval. That is, the larger the variance, the more uneven the heating of the inner tube and the more unstable the heat treatment state. Therefore, the corresponding matching weight is increased so that the subsequent heat treatment state detection results focus more on the temperature detection of the inner tube, thereby improving the influence of the heating effect of the inner tube on the heat treatment state of the plastic-lined pipe to be processed, and making the detection results of the heat treatment state of the plastic-lined pipe to be processed more accurate.
[0026] The smallest matching weight among the several matching weights included in the variance interval list is greater than or equal to 0.5, and the largest matching weight among the several matching weights included in the variance interval list is less than 1.
[0027] Step S340: Determine the first matching weight G1 = 1 - G2.
[0028] Step S400: Weight the first matching weight, the second matching weight, a number of first prediction data and a number of second prediction data to obtain the processing feature value of the plastic-lined pipe to be processed at the current time. Furthermore, step S400 includes steps S410-S460: Step S410: Obtain the temperature data corresponding to each data acquisition point on the outer tube of the plastic-lined pipe to be processed at the current time, so as to obtain the first target data list F1=(F 11 ,F 12 ,...,F 1m ,...,F 1n ); where F 1m This is the temperature data at the current moment for the m-th data acquisition point on the outer tube of the plastic-lined pipe to be processed. Step S420: Perform feature encoding on the first target data list and the first prediction data list respectively to obtain the first target data vector corresponding to the first target data list and the first prediction data vector corresponding to the first prediction data list; Step S430: Determine the matching degree between the first target data vector and the first predicted data vector as L1; Step S440: Perform feature encoding on the second target data list and the second prediction data list respectively to obtain the second target data vector corresponding to the second target data list and the second prediction data vector corresponding to the second prediction data list. The existing data vector encoding method can be used to encode the features of the temperature data in the second target data list and the second prediction data list.
[0029] Step S450: Determine the matching degree of the second target data vector and the second predicted data vector as L2; Step S460: Determine the processing characteristic value T = G1×L1 + G2×L2 corresponding to the plastic-lined pipe to be processed at the current moment.
[0030] Step S500: If the processing characteristic value is within the preset normal processing characteristic value range, then the heat processing status of the plastic-lined pipe to be processed at the current moment is determined to be normal; otherwise, the heat processing status of the plastic-lined pipe to be processed at the current moment is determined to be abnormal.
[0031] Furthermore, step S500 includes step S510: Step S510: If T > T0, then the heat treatment status of the plastic-lined pipe to be processed at the current moment is determined to be normal; otherwise, the heat treatment status of the plastic-lined pipe to be processed at the current moment is determined to be abnormal; where T0 is a preset normal processing characteristic threshold.
[0032] The processing method for the plastic-lined pipe of the present invention firstly predicts several temperature data points corresponding to each data acquisition point on the outer tube of the plastic-lined pipe during the target time period of the heat treatment process to obtain several first predicted data points corresponding to the current moment. Then, it predicts several temperature data points corresponding to each data acquisition point on the inner tube of the plastic-lined pipe during the target time period to obtain several second predicted data points corresponding to the current moment. The first and second predicted data points are the temperature data under normal conditions at the current moment obtained by predicting the temperature data of the outer and inner tubes of the plastic-lined pipe during the target time period. Finally, based on the fluctuation characteristics of the several temperature data points corresponding to each data acquisition point on the inner tube of the plastic-lined pipe at the current moment, a first matching weight and a second matching weight are determined. The first and second matching weights are used to represent the degree of emphasis of the temperature data of the outer tube and the temperature data of the inner tube on the determination of the subsequent heat treatment state. Then, the first matching weight, the second matching weight, several first prediction data and several second prediction data are weighted to obtain the processing characteristic value of the plastic-lined pipe to be processed at the current moment. The processing characteristic value is used to represent the quality degree corresponding to the heat treatment state of the plastic-lined pipe to be processed at the current moment. If the processing characteristic value is within the normal processing characteristic value range, the heat treatment state of the plastic-lined pipe to be processed at the current moment is determined to be normal. By predicting the data of the outer tube and the inner tube of the plastic-lined pipe to be processed, the heat treatment state of the plastic-lined pipe to be processed at the current moment can be determined, so that the staff can carry out quality control during the processing of the plastic-lined pipe.
[0033] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.
[0034] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.
[0035] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.
[0036] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.
[0037] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”
[0038] An electronic device according to this embodiment of the invention. The electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the invention.
[0039] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).
[0040] The storage device stores program code that can be executed by the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of the present invention.
[0041] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).
[0042] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.
[0043] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.
[0044] Electronic devices can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable users to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed through input / output (I / O) interfaces. Furthermore, electronic devices can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters.
[0045] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.
[0046] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.
[0047] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0048] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.
[0049] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.
[0050] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, and conventional procedural programming languages such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0051] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.
[0052] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.
[0053] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those 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 determined by the scope of the claims.
Claims
1. A method for processing plastic-lined pipes, characterized in that, include: Step S100: For each data acquisition point on the outer tube of the plastic-lined pipe to be processed, perform prediction processing on several temperature data corresponding to the target time period during the heat treatment process to obtain several first prediction data corresponding to the current moment; the duration of the target time period is a preset duration, and the end time of the target time period is the data acquisition moment that is before the current moment and has the shortest duration between it and the current moment. Step S200: Perform predictive processing on several temperature data corresponding to each data acquisition point of the inner tube of the plastic-lined pipe to be processed within the target time period to obtain several second predictive data corresponding to the current time. Step S300: Based on the fluctuation characteristics of several temperature data corresponding to each data acquisition point of the inner tube of the plastic-lined pipe to be processed at the current time, determine the first matching weight and the second matching weight. Step S400: Perform weighted processing on the first matching weight, the second matching weight, a plurality of first prediction data and a plurality of second prediction data to obtain the processing feature value of the plastic-lined pipe to be processed at the current time. Step S500: If the processing characteristic value is within the preset normal processing characteristic value range, then the heat processing status of the plastic-lined pipe to be processed at the current moment is determined to be normal; otherwise, the heat processing status of the plastic-lined pipe to be processed at the current moment is determined to be abnormal.
2. The method according to claim 1, characterized in that, Step S100 includes: Step S110: Obtain several temperature data points corresponding to each data acquisition point on the outer tube of the plastic-lined pipe to be processed during the target time period of the heat treatment process, so as to obtain several first temperature data lists A1, A2, ..., A i ,...,A j Where i = 1, 2, ..., j; j is the number of data collection moments included in the target time period; the j-th data collection moment is the data collection moment preceding the current moment; the duration between any two adjacent data collection moments in the target time period is equal; A i This is a list of first temperature data corresponding to the i-th data acquisition time within the target time period; A i =(A i1 A i2 ,...,A im ,...,A in ); m=1,2,...,n; n is the number of data acquisition points on the outer tube of the plastic-lined pipe to be processed; the data acquisition points on the outer tube are distributed in a matrix on the outer tube; A im The temperature data corresponding to the m-th data acquisition point on the outer tube of the plastic-lined pipe to be processed at the i-th data acquisition time within the target time period; Step S120: Set A1, A2, ..., A i ,...,A j The data is input into a preset first prediction model to obtain a first prediction data list output by the first prediction model; the first prediction data list includes several first prediction data corresponding to the current time; the first prediction model is trained based on several historical temperature data corresponding to the plastic-lined pipe in the historical time period when the heat treatment state is normal.
3. The method according to claim 2, characterized in that, The first prediction model is determined according to the following steps: Step S121: The plastic-lined pipe that has been in a normal state of heat treatment during the historical time period and has the same specifications as the plastic-lined pipe to be processed is identified as the target plastic-lined pipe; the end time of the historical time period is before the current time. Step S122: Obtain historical temperature data for each data acquisition point on the outer tube of each target plastic-lined pipe within several corresponding historical sub-time periods, to obtain several first historical temperature data list sets B1, B2, ..., B t ,...,B s Where t=1,2,...,s; s is the number of target plastic-lined pipes; B t This is the first historical temperature data set corresponding to the t-th target plastic-lined pipe; B t =(B t1 B t2 ,...,B te ,...,B tf(t) ); e=1,2,...,f(t); f(t) is the number of historical sub-time periods corresponding to the t-th target plastic-lined pipe; B te This is a list of the first historical temperature data corresponding to the t-th target plastic-lined pipe in its corresponding e-th historical sub-time period; B te =(B te1 B te2 ,...,B tei ,...,B tej ); B tei This is the first historical temperature data sub-table corresponding to the i-th data acquisition time within the corresponding e-th historical sub-time period of the t-th target plastic-lined pipe; B tei =(B tei1 B tei2 ,...,B teim ,...,B tein ); B teim The historical temperature data corresponding to the m-th data acquisition point on the outer tube of the t-th target plastic-lined pipe at the i-th data acquisition time within its corresponding e-th historical sub-time period; The arrangement of several data acquisition points on the outer tube of each target plastic-lined pipe is the same as the arrangement of several data acquisition points on the outer tube of the plastic-lined pipe to be processed. The historical sub-time period is any sub-time period during the heat treatment process of the target plastic-lined pipe within the historical time period; the duration of the historical sub-time period is equal to the duration of the target time period; the number of data acquisition moments within the historical sub-time period is equal to the number of data acquisition moments within the target time period, and the duration between two adjacent data acquisition moments within the historical sub-time period is equal to the duration between two adjacent data acquisition moments within the target time period. Step S123: Obtain the first historical temperature data sub-table C corresponding to the first data acquisition time after the e-th historical sub-time period of the t-th target plastic-lined pipe. te1 =(C te11 C te12 ,...,C te1m ,...,C te1n ); where C te1m The historical temperature data corresponding to the first data acquisition time after the corresponding e-historical sub-time period is the m-th data acquisition point on the outer tube of the t-th target plastic-lined pipe. Step S124, B te1 B te2 ,...,B tei ,...,B tej As input sample, C te1 As the output label, a pre-defined neural network model is subjected to supervised training to obtain the first prediction model.
4. The method according to claim 3, characterized in that, Step S200 includes: Step S210: Obtain several temperature data points corresponding to each data acquisition point on the inner tube of the plastic-lined pipe to be processed during the target time period of the heat treatment process, so as to obtain several second temperature data lists D1, D2, ..., D i ,...,D j ; where D i This is a list of second temperature data corresponding to the i-th data acquisition time within the target time period; D i =(D i1 D i2 ,...,D iy ,...,D iz ); y=1,2,...,z; z is the number of data acquisition points on the inner tube of the plastic-lined pipe to be processed; the data acquisition points on the inner tube are distributed in a matrix on the inner tube; D iy The temperature data corresponding to the y-th data acquisition point on the inner tube of the plastic-lined pipe to be processed at the i-th data acquisition time within the target time period; Step S220: Set D1, D2, ..., D i ,...,D j The data is input into a preset second prediction model to obtain a second prediction data list output by the second prediction model; the second prediction data list includes several second prediction data corresponding to the current time; the second prediction model is trained based on several historical temperature data corresponding to the plastic-lined pipe in the historical time period when the heat treatment state is normal.
5. The method according to claim 4, characterized in that, The second prediction model is determined according to the following steps: Step S221: Obtain historical temperature data for each data acquisition point on the inner tube of each target plastic-lined pipe within several corresponding historical sub-time periods, to obtain several second historical temperature data list sets E1, E2, ..., E t ,...,E s ; Among them, E t This is the second historical temperature data set corresponding to the t-th target plastic-lined pipe; E t =(E t1 E t2 ,...,E te ,...,E tf(t) ); E te This is a list of the second historical temperature data corresponding to the t-th target plastic-lined pipe in its corresponding e-th historical sub-time period; E te =(E te1 E te2 ,...,E tei ,...,E tej ); E tei This is the second historical temperature data sub-table corresponding to the i-th data acquisition time within the corresponding e-th historical sub-time period of the t-th target plastic-lined pipe; E tei =(E tei1 E tei2 ,...,E teiy ,...,E teiz ); E teiy The historical temperature data corresponding to the y-th data acquisition point on the inner tube of the t-th target plastic-lined pipe at the ith data acquisition time within its corresponding e-th historical sub-time period; The arrangement of several data acquisition points on the inner tube of each target plastic-lined pipe is the same as the arrangement of several data acquisition points on the inner tube of the plastic-lined pipe to be processed. Step S222: Obtain the second historical temperature data sub-table C corresponding to the first data acquisition time after the e-th historical sub-time period of the t-th target plastic-lined pipe. te2 =(C te21 C te22 ,...,C te2y ,...,C te2z ); where C te2y The historical temperature data corresponding to the y-th data acquisition point on the inner tube of the t-th target plastic-lined pipe at the first data acquisition time after its corresponding e-th historical sub-time period; Step S223, E te1 E te2 ,...,E tei ,...,E tej As input sample, C te2 As the output label, a pre-defined neural network model is subjected to supervised training to obtain a second prediction model.
6. The method according to claim 5, characterized in that, Step S300 includes: Step S310: Obtain the temperature data corresponding to each data acquisition point on the inner tube of the plastic-lined pipe to be processed at the current time, so as to obtain the second target data list F2=(F 21 ,F 22 ,...,F 2y ,...,F 2z ); where F 2y The temperature data corresponding to the y-th data acquisition point on the inner tube of the plastic-lined pipe to be processed at the current time; Step S320, Determine F 21 ,F 22 ,...,F 2y ,...,F 2z The variance is H2; Step S330: Determine the variance interval where H2 is located from the preset variance interval list, and determine the matching weight corresponding to the variance interval as the second matching weight G2. The variance interval list includes several variance intervals and a matching weight corresponding to each variance interval, and the average value of the variance interval is directly proportional to the matching weight corresponding to the variance interval. The smallest matching weight among the several matching weights included in the variance interval list is greater than or equal to 0.5, and the largest matching weight among the several matching weights included in the variance interval list is less than 1. Step S340: Determine the first matching weight G1 = 1 - G2.
7. The method according to claim 6, characterized in that, Step S400 includes: Step S410: Obtain the temperature data corresponding to each data acquisition point on the outer tube of the plastic-lined pipe to be processed at the current time, so as to obtain the first target data list F1=(F 11 ,F 12 ,...,F 1m ,...,F 1n ); where F 1m The temperature data at the current moment corresponds to the m-th data acquisition point on the outer tube of the plastic-lined pipe to be processed. Step S420: Perform feature encoding on the first target data list and the first prediction data list respectively to obtain the first target data vector corresponding to the first target data list and the first prediction data vector corresponding to the first prediction data list; Step S430: Determine the matching degree between the first target data vector and the first predicted data vector as L1; Step S440: Perform feature encoding on the second target data list and the second prediction data list respectively to obtain the second target data vector corresponding to the second target data list and the second prediction data vector corresponding to the second prediction data list; Step S450: Determine the matching degree between the second target data vector and the second predicted data vector as L2; Step S460: Determine the processing characteristic value T = G1×L1 + G2×L2 corresponding to the plastic-lined pipe to be processed at the current moment.
8. The method according to claim 7, characterized in that, Step S500 includes: Step S510: If T > T0, then the heat treatment status of the plastic-lined pipe to be processed at the current moment is determined to be normal; otherwise, the heat treatment status of the plastic-lined pipe to be processed at the current moment is determined to be abnormal; where T0 is a preset normal processing characteristic threshold.
9. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, characterized in that the at least one instruction or the at least one program segment is loaded and executed by a processor to implement the method as described in any one of claims 1-8.
10. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 9.