Method, device, electronic device and storage medium for analyzing abnormal size of waste pipe

Abstract

The present invention relates to the technical field of seamless steel pipe size anomaly analysis, and in particular to a method, device, electronic device, and storage medium for analyzing waste pipe size anomalies. The method of the present invention first obtains multiple waste pipe size queues; then performs frequency domain feature analysis on the multiple waste pipe size queues to obtain multiple main frequency feature values; then, data is reconstructed for each first continuous rolling production parameter queue based on the multiple main frequency feature values to obtain multiple second continuous rolling production parameter queues; finally, based on the correlation between the multiple second continuous rolling production parameter queues and the waste pipe size queues, the cause of the waste pipe size deviation is determined. The present invention performs frequency domain transformation and inverse transformation on the continuous rolling production parameter queues. The length of the continuous rolling production parameter queues is the same as the length of the waste pipe size queues. The method of the present invention integrates the size data and production parameter data to make a cause analysis, so the cause analysis result is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of seamless steel pipe dimensional anomaly analysis, and in particular to a method, device, electronic equipment and storage medium for analyzing the dimensional anomaly of a rough steel pipe. Background Art

[0002] Seamless steel pipe is a long, hollow steel strip with no seams around its perimeter. The most common production process involves heating a solid billet, piercing it with a piercing machine, and then rolling it through a series of processes, ultimately transforming the billet into a seamless steel pipe of a specified size and precision. This method is highly efficient and suitable for producing large-diameter, thick-walled seamless steel pipes.

[0003] Rough pipe refers to semi-finished seamless steel pipe that has undergone preliminary processing such as perforation and rolling but has not yet undergone finishing. It plays a critical transitional role in the entire seamless steel pipe manufacturing process. Common dimensional anomalies in rough seamless steel pipe include outside diameter, wall thickness, and length. The main method currently used to identify the causes of these dimensional anomalies is to classify them and, based on the results of these classifications, to estimate the probability of the cause.

[0004] With the continuous upgrading of production lines, the traceability of seamless steel pipes has become increasingly stronger. For example, the current and torque of the roller motor during the production process of a seamless steel pipe can be traced. However, the various parameters of the production equipment have not played their due role in analyzing the abnormal dimensions of seamless steel pipes. As a result, the analysis results of the causes of pipe shortages are not specific and accurate, and the production guidance is of poor significance.

[0005] Based on this, it is necessary to develop and design a method for analyzing the abnormal size of waste pipes. Summary of the Invention

[0006] The embodiments of the present invention provide a method, device, electronic device and storage medium for analyzing the size anomaly of a waste pipe, which are used to solve the problem in the prior art that the results of the abnormality cause analysis obtained when the size anomaly analysis is performed based on the waste pipe size data itself are inaccurate.

[0007] In a first aspect, an embodiment of the present invention provides a method for analyzing abnormal dimensions of a waste pipe, comprising:

[0008] Acquire multiple waste pipe size queues, where the waste pipe size queues are constructed based on size data obtained from multiple detection positions of the waste pipe, and multiple data in the waste pipe size queues correspond to the same detection item;

[0009] Performing frequency domain feature analysis on the plurality of queues of unused pipe sizes to obtain a plurality of main frequency feature values, wherein the main frequency feature values represent frequency values at which fluctuation characteristics of the queues of unused pipe sizes exist;

[0010] Reconstructing data of each first continuous rolling production parameter queue according to the multiple main frequency characteristic values to obtain multiple second continuous rolling production parameter queues, wherein the length of the second continuous rolling production parameter queue is the same as the length of the rough pipe size queue;

[0011] According to the correlation between the plurality of second continuous rolling production parameter queues and the rough pipe size queues, the cause of the rough pipe size deviation is determined.

[0012] In a possible implementation, obtaining multiple queues of unused pipe sizes includes:

[0013] Get the total length of the wasteland pipe;

[0014] According to the total length, a preset number of detection reference points are set at equal intervals along the axial direction of the rough pipe;

[0015] Acquire multiple dimension arrays based on each detection reference point, wherein each dimension array corresponds to a detection item, each dimension array includes a preset number of dimension data, and the multiple dimension arrays include a wall thickness array and / or an outer diameter array;

[0016] For each detection item, multiple arrays are arranged according to the detection reference points corresponding to the size arrays to construct a rough pipe size queue.

[0017] In one possible implementation, performing frequency domain feature analysis on the multiple queues of unused pipe sizes to obtain multiple main frequency feature values includes:

[0018] traversally extracting the queues of the plurality of unused pipe sizes as queues to be analyzed;

[0019] Determine the upper frequency limit of the frequency domain analysis according to the total amount of data in the queue to be analyzed;

[0020] The quotient of the upper frequency limit and the total number of preset frequencies is used as the lower frequency limit;

[0021] Performing frequency domain feature extraction on the queue to be analyzed according to the frequency upper limit value and the frequency lower limit value to obtain a plurality of first frequency domain values;

[0022] Adding frequencies corresponding to multiple maximum values among the multiple first frequency domain values to a main frequency feature array;

[0023] If the traversal of the plurality of unused pipe size queues is not completed, the process jumps to the step of traversing and extracting the unused pipe size queues from the plurality of unused pipe size queues as the queues to be analyzed.

[0024] In one possible implementation, performing frequency domain feature extraction on the queue to be analyzed according to the frequency upper limit value and the frequency lower limit value to obtain multiple frequency domain values includes:

[0025] Frequency domain features are extracted from the queue to be analyzed according to a first formula to obtain a plurality of first frequency domain values, wherein the first formula is:

[0026]

[0027] Where, For the The first frequency domain value, is the total number of data in the queue to be analyzed, The queue to be analyzed data, is a natural constant, is pi, is the lower frequency limit, is the upper frequency limit, is the cardinality number, Is an imaginary unit.

[0028] In one possible implementation, reconstructing data of each first continuous rolling production parameter queue according to the multiple main frequency characteristic values to obtain multiple second continuous rolling production parameter queues includes:

[0029] For each first continuous rolling production parameter queue, perform the following steps respectively:

[0030] Performing frequency domain feature extraction on the first continuous rolling production parameter queue according to the multiple main frequency feature values to obtain multiple second frequency domain values;

[0031] According to the length of the waste pipe size queue, an inverse frequency domain transformation is performed on the multiple second frequency domain values to obtain a second continuous rolling production parameter queue.

[0032] In one possible implementation, performing an inverse frequency domain transformation on the plurality of second frequency domain values according to the length of the waste pipe size queue to obtain the second continuous rolling production parameter queue includes:

[0033] According to the second formula and the length of the waste pipe size queue, the multiple second frequency domain values are subjected to an inverse frequency domain transformation to obtain a second continuous rolling production parameter queue, wherein the second formula is:

[0034]

[0035] Where, The second continuous rolling production parameter queue data, For the The second frequency domain value, is the total number of second frequency domain values, is a natural constant, is pi, is the lower frequency limit for frequency domain main characteristic analysis of the waste pipe size queue. For the The frequency multiplication coefficient corresponding to the second frequency domain value, is the cardinality number when performing frequency domain main feature analysis on the size of the waste pipe queue, Is an imaginary unit.

[0036] In one possible implementation, determining the cause of the waste pipe size deviation based on the correlation between the plurality of second continuous rolling production parameter queues and the waste pipe size queue includes:

[0037] For each queue size, perform the following steps:

[0038] Calculating similarities between the queue of waste pipe sizes and the plurality of queues of second continuous rolling production parameters respectively, and arranging the obtained plurality of similarity values in a predetermined order to construct a similarity vector;

[0039] Finding multiple reference vectors closest to the similar vector from multiple vector classes;

[0040] taking the vector class to which the largest number of the plurality of reference vectors belongs as the target class;

[0041] The deviation causes corresponding to the majority of vectors in the target class are regarded as the causes of the deviation in the size of the waste pipe.

[0042] In a second aspect, an embodiment of the present invention provides a device for analyzing the size anomaly of an unused pipe, for implementing the method for analyzing the size anomaly of an unused pipe as described in the first aspect or any possible implementation of the first aspect, the device for analyzing the size anomaly of an unused pipe comprising:

[0043] an unused pipe size data acquisition module, configured to acquire multiple unused pipe size queues, wherein the unused pipe size queues are constructed based on size data obtained from multiple detection positions of the unused pipe, and multiple data in the unused pipe size queues correspond to the same detection item;

[0044] an unused pipe size fluctuation analysis module, configured to perform frequency domain characteristic analysis on the plurality of unused pipe size queues to obtain a plurality of main frequency characteristic values, wherein the main frequency characteristic values represent frequency values at which fluctuation characteristics of the unused pipe size queues are located;

[0045] a production parameter reconstruction module, configured to reconstruct data of each first continuous rolling production parameter queue according to the plurality of main frequency characteristic values to obtain a plurality of second continuous rolling production parameter queues, wherein the length of the second continuous rolling production parameter queue is the same as the length of the rough pipe size queue;

[0046] as well as,

[0047] The size deviation cause analysis module is used to determine the cause of the waste pipe size deviation based on the correlation between the multiple second continuous rolling production parameter queues and the waste pipe size queues.

[0048] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0049] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0050] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0051] An embodiment of the present invention discloses a method for analyzing the abnormality of the size of an uncultivated pipe. The method first obtains multiple uncultivated pipe size queues, wherein the uncultivated pipe size queues are constructed based on the size data obtained from multiple detection positions of the uncultivated pipe, and multiple data in the uncultivated pipe size queues correspond to the same detection item; then, frequency domain feature analysis is performed on the multiple uncultivated pipe size queues to obtain multiple main frequency characteristic values, wherein the main frequency characteristic values represent the frequency values at which the fluctuation characteristics of the uncultivated pipe size queues are located; then, data of each first continuous rolling production parameter queue is reconstructed according to the multiple main frequency characteristic values to obtain multiple second continuous rolling production parameter queues, wherein the length of the second continuous rolling production parameter queue is the same as the length of the uncultivated pipe size queue; finally, based on the correlation between the multiple second continuous rolling production parameter queues and the uncultivated pipe size queues, the cause of the uncultivated pipe size deviation is determined. The embodiment of the present invention performs frequency domain feature analysis on the waste pipe size queue to determine multiple main frequency features, and then performs frequency domain transformation and inverse transformation on the continuous rolling production parameter queue according to the multiple main frequency features to obtain a continuous rolling production parameter queue with the same length as the waste pipe size queue. In this way, the length of the continuous rolling production parameter queue is the same as the length of the waste pipe size queue, and the main frequency features of the waste pipe size are retained. On this basis, the waste pipe size queue and multiple continuous rolling production parameter queues are similarly calculated to construct a similarity vector. The cause of the waste pipe size deviation is judged by referring to the cause of the majority of vectors in the closest class of the similar vector. The method of the present invention integrates size data and production parameter data to make a cause analysis, so the cause analysis result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0053] Figure 1 This is a flow chart of a method for analyzing abnormalities in size of waste pipes provided by an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of a process for determining the cause of the size deviation of a rough pipe according to an embodiment of the present invention;

[0055] Figure 3 This is a functional block diagram of a device for analyzing abnormalities in rough pipe dimensions provided by an embodiment of the present invention;

[0056] Figure 4 This is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in alternative embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clear, the following will be described through specific implementation methods in conjunction with the accompanying drawings.

[0059] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiment.

[0060] Figure 1 This is a flow chart of a method for analyzing abnormalities in rough pipe dimensions provided by an embodiment of the present invention.

[0061] like Figure 1 As shown, it shows a flowchart of the implementation method of the method for analyzing the abnormal size of the waste pipe provided by the embodiment of the present invention, which is detailed as follows:

[0062] In step 101, a plurality of waste pipe size queues are obtained, wherein the waste pipe size queues are constructed based on size data obtained from a plurality of detection positions of the waste pipe, and the plurality of data in the waste pipe size queues correspond to the same detection item.

[0063] In some embodiments, obtaining multiple queues of unused pipe sizes includes:

[0064] Get the total length of the wasteland pipe;

[0065] According to the total length, a preset number of detection reference points are set at equal intervals along the axial direction of the rough pipe;

[0066] Acquire multiple dimension arrays based on each detection reference point, wherein each dimension array corresponds to a detection item, each dimension array includes a preset number of dimension data, and the multiple dimension arrays include a wall thickness array and / or an outer diameter array;

[0067] For each detection item, multiple arrays are arranged according to the detection reference points corresponding to the size arrays to construct a rough pipe size queue.

[0068] For example, the rough pipe in the embodiment of the present invention is a semi-finished seamless steel pipe that has been rolled and formed by continuous rolling equipment and has not yet been refined. Thanks to technological advances, the production parameters of the continuous rolling equipment during the production process of each rough pipe can be obtained through traceability means.

[0069] As mentioned above, the analysis of abnormalities in the size of abandoned pipes is currently mainly carried out through the size of the abandoned pipes themselves. The cause analysis conclusions given usually point to a certain process, which is not specific and accurate.

[0070] On the other hand, there are many technical difficulties in analyzing the causes of dimensional anomalies through the production parameters of continuous rolling equipment. First, the production parameter data of continuous rolling equipment are complicated and contain a lot of data noise, which affects the results of anomaly analysis; second, the production parameter data vary in length and are difficult to correspond to the rough pipe size data.

[0071] The present invention divides the rough pipe into a predetermined number of detection reference points based on its total length. Multiple dimensional measurement data are then acquired using these detection reference points as a reference. For example, for wall thickness measurement, multiple wall thickness values are acquired circumferentially around the detection reference points. These values are then organized into arrays, resulting in multiple arrays corresponding to the multiple detection reference points. After the measurement is complete, the multiple arrays for each measurement item are arranged based on the positions of the reference points, forming a rough pipe size queue. In this way, the rough pipe dimensional data is arranged in a predetermined order, resulting in a predetermined number of monitoring points.

[0072] In step 102, frequency domain characteristic analysis is performed on the plurality of queues of unused pipe sizes to obtain a plurality of main frequency characteristic values, wherein the main frequency characteristic values represent the frequency values where the fluctuation characteristics of the queues of unused pipe sizes are located.

[0073] In some embodiments, performing frequency domain feature analysis on the multiple queues of unused pipe sizes to obtain multiple main frequency feature values includes:

[0074] traversally extracting the queues of the plurality of unused pipe sizes as queues to be analyzed;

[0075] Determine the upper frequency limit of the frequency domain analysis according to the total amount of data in the queue to be analyzed;

[0076] The quotient of the upper frequency limit and the total number of preset frequencies is used as the lower frequency limit;

[0077] Performing frequency domain feature extraction on the queue to be analyzed according to the frequency upper limit value and the frequency lower limit value to obtain a plurality of first frequency domain values;

[0078] Adding frequencies corresponding to multiple maximum values among the multiple first frequency domain values to a main frequency feature array;

[0079] If the traversal of the plurality of unused pipe size queues is not completed, the process jumps to the step of traversing and extracting the unused pipe size queues from the plurality of unused pipe size queues as the queues to be analyzed.

[0080] In some implementations, performing frequency domain feature extraction on the queue to be analyzed based on the frequency upper limit value and the frequency lower limit value to obtain multiple frequency domain values includes:

[0081] Frequency domain features are extracted from the queue to be analyzed according to a first formula to obtain a plurality of first frequency domain values, wherein the first formula is:

[0082]

[0083] Where, For the The first frequency domain value, is the total number of data in the queue to be analyzed, The queue to be analyzed data, is a natural constant, is pi, is the lower frequency limit, is the upper frequency limit, is the cardinality number, Is an imaginary unit.

[0084] Exemplarily, in order to unify the production parameter data and the dimensional data quantity and eliminate the noise of the production parameter data, the present invention performs frequency domain feature analysis on the dimensional data queue.

[0085] In terms of frequency domain feature analysis, this invention determines an upper frequency limit for frequency analysis based on the total number of data in the empty pipe size queue. This upper frequency limit is typically less than half the total number of data in the empty pipe size queue. Multiple frequency values are then determined based on this upper frequency limit using integer divisors. In other words, the upper frequency limit is an integer multiple of the other frequency values. For example, if the empty pipe size queue is constructed from 10 arrays, each with 13 data items, and the total number of data items in the empty pipe size queue is 130, then the upper frequency limit is less than 65. Since the upper frequency limit is an integer multiple of the lower frequency limit, combined with the number of data items in the arrays, it is determined to be 5 (the lower frequency limit).

[0086] Then, the frequency domain features of the empty pipe size queue are extracted using the first formula according to the upper and lower frequency limits to obtain frequency domain values corresponding to multiple frequency values:

[0087]

[0088] Where, For the The first frequency domain value, is the total number of data in the queue to be analyzed, The queue to be analyzed data, is a natural constant, is pi, is the lower frequency limit, is the upper frequency limit, is the cardinality number, Is an imaginary unit.

[0089] For each waste pipe size queue, the above formula yields multiple frequency domain values. These frequency domain values reflect the volatility of waste pipe size data analyzed from the frequency domain. The frequencies corresponding to several major frequency domain values are selected from these frequency domain values as the primary frequency eigenvalues for the waste pipe size queue. For example, one selection method selects the frequencies corresponding to the N largest frequency domain values as the primary frequency eigenvalues. Other methods calculate the sum of all frequency domain values, perform a proportional calculation based on the sum to obtain a reference value, and then sequentially select multiple largest frequency domain values from the frequency domain values. The sum of these largest frequency domain values is greater than the reference value, and the frequencies corresponding to these largest frequency domain values are used as the primary frequency eigenvalues. After multiple waste pipe size queues undergo the above steps, the final multiple primary frequency eigenvalues are obtained.

[0090] In step 103, data of each first continuous rolling production parameter queue is reconstructed according to the multiple main frequency characteristic values to obtain multiple second continuous rolling production parameter queues, wherein the length of the second continuous rolling production parameter queue is the same as the length of the rough pipe size queue.

[0091] In some embodiments, reconstructing data of each first continuous rolling production parameter queue according to the multiple main frequency characteristic values to obtain multiple second continuous rolling production parameter queues includes:

[0092] For each first continuous rolling production parameter queue, perform the following steps respectively:

[0093] Performing frequency domain feature extraction on the first continuous rolling production parameter queue according to the multiple main frequency feature values to obtain multiple second frequency domain values;

[0094] According to the length of the waste pipe size queue, an inverse frequency domain transformation is performed on the multiple second frequency domain values to obtain a second continuous rolling production parameter queue.

[0095] In some embodiments, performing an inverse frequency domain transform on the plurality of second frequency domain values according to the length of the waste pipe size queue to obtain the second continuous rolling production parameter queue includes:

[0096] According to the second formula and the length of the waste pipe size queue, the multiple second frequency domain values are subjected to an inverse frequency domain transformation to obtain a second continuous rolling production parameter queue, wherein the second formula is:

[0097]

[0098] Where, The second continuous rolling production parameter queue data, For the The second frequency domain value, is the total number of second frequency domain values, is a natural constant, is pi, is the lower frequency limit for frequency domain main characteristic analysis of the waste pipe size queue. For the The frequency multiplication coefficient corresponding to the second frequency domain value, is the cardinality number when performing frequency domain main feature analysis on the size of the waste pipe queue, Is an imaginary unit.

[0099] For example, continuous rolling production parameters typically include: continuous rolling inlet temperature, continuous rolling force, roll motor speed, roll motor current, roll motor torque, mandrel trolley speed, mandrel trolley current, and mandrel trolley torque. It can be seen that continuous rolling production parameters are time-varying quantities and are closely related to the size of the rough pipe. The first continuous rolling production parameter queue is a data queue derived from these production parameters. As mentioned above, this data queue is noisy, and the data length is difficult to maintain consistency with the rough pipe size queue, which makes it difficult to analyze rough pipe size anomalies.

[0100] Therefore, in an embodiment of the present invention, frequency domain features are extracted from the first continuous rolling production parameter queue based on the multiple main frequency values obtained in the aforementioned steps to obtain frequency domain values, and then the second continuous rolling production parameter queue is obtained through inverse frequency domain transformation. The number of data in the obtained second continuous rolling production parameter queue is the same as the total number of data in the rough pipe size data queue. In terms of inverse transformation, the present invention applies the second formula:

[0101]

[0102] Where, The second continuous rolling production parameter queue data, For the The second frequency domain value, is the total number of second frequency domain values, is a natural constant, is pi, is the lower frequency limit for frequency domain main characteristic analysis of the waste pipe size queue. For the The frequency multiplication coefficient corresponding to the second frequency domain value, is the cardinality number when performing frequency domain main feature analysis on the size queue of the waste pipe. Is an imaginary unit.

[0103] In step 104, the cause of the waste pipe size deviation is determined based on the correlation between the plurality of second continuous rolling production parameter queues and the waste pipe size queues.

[0104] In some embodiments, determining the cause of the waste pipe size deviation based on the correlation between the plurality of second continuous rolling production parameter queues and the waste pipe size queues includes:

[0105] For each queue size, perform the following steps:

[0106] Calculating similarities between the queue of waste pipe sizes and the plurality of queues of second continuous rolling production parameters respectively, and arranging the obtained plurality of similarity values in a predetermined order to construct a similarity vector;

[0107] Finding multiple reference vectors closest to the similar vector from multiple vector classes;

[0108] taking the vector class to which the largest number of the plurality of reference vectors belongs as the target class;

[0109] The deviation causes corresponding to the majority of vectors in the target class are regarded as the causes of the deviation in the size of the waste pipe.

[0110] For example, similarity calculation is performed on the waste pipe size queue and multiple second continuous rolling production parameter queues, and the obtained similarity values are constructed as similarity vectors, which can reflect the correlation between the continuous rolling production parameters and the waste pipe size anomaly.

[0111] like Figure 2 As shown, the present invention first obtains some sample similarity vectors 202. The process of obtaining these sample similarity vectors 202 is the same as the process of obtaining similarity vectors described above. These sample similarity vectors 202 are then clustered to obtain multiple vector classes 201. For a certain vector class 201, the majority of the sample similarity vectors 202 correspond to the same anomaly cause. For example, through clustering, vector classes C1 to Cn are obtained. Among them, the majority of sample similarity vectors 201 in vector class C1 are caused by core rod skew, and the majority of sample similarity vectors 202 in vector class C2 are caused by equipment misalignment. For similarity vectors 203 obtained by similarity calculation with the waste pipe size queue, a predetermined number of sample similarity vectors 202 with the closest distance are found from these vector classes 201. For example, the three closest sample similarity vectors 202 are found. Then, the class to which the most of these three sample similarity vectors 202 belong is used as the target class, and the cause of the target class is used as the cause of the size anomaly.

[0112] The implementation method of the method for analyzing the abnormality of the size of the waste pipe of the present invention first obtains multiple waste pipe size queues, wherein the waste pipe size queues are constructed based on the size data obtained from multiple detection positions of the waste pipe, and multiple data in the waste pipe size queues correspond to the same detection item; then, the frequency domain feature analysis is performed on the multiple waste pipe size queues to obtain multiple main frequency characteristic values, wherein the main frequency characteristic values represent the frequency values at which the fluctuation characteristics of the waste pipe size queues are located; then, data of each first continuous rolling production parameter queue is reconstructed according to the multiple main frequency characteristic values to obtain multiple second continuous rolling production parameter queues, wherein the length of the second continuous rolling production parameter queue is the same as the length of the waste pipe size queue; finally, based on the correlation between the multiple second continuous rolling production parameter queues and the waste pipe size queues, the cause of the waste pipe size deviation is determined. The embodiment of the present invention performs frequency domain feature analysis on the waste pipe size queue to determine multiple main frequency features, and then performs frequency domain transformation and inverse transformation on the continuous rolling production parameter queue according to the multiple main frequency features to obtain a continuous rolling production parameter queue with the same length as the waste pipe size queue. In this way, the length of the continuous rolling production parameter queue is the same as the length of the waste pipe size queue, and the main frequency features of the waste pipe size are retained. On this basis, the waste pipe size queue and multiple continuous rolling production parameter queues are similarly calculated to construct a similarity vector. The cause of the waste pipe size deviation is judged by referring to the cause of the majority of vectors in the closest class of the similar vector. The method of the present invention integrates size data and production parameter data to make a cause analysis, so the cause analysis result is more accurate.

[0113] It should be understood that the size of the serial numbers of each step in the above embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0114] The following is an embodiment of the device of the present invention. For details not described in detail, please refer to the corresponding method embodiment described above.

[0115] Figure 3 This is a functional block diagram of the device for analyzing the abnormal size of waste pipes provided by the embodiment of the present invention, referring to Figure 3 The device for analyzing the abnormal size of waste pipes includes: a waste pipe size data acquisition module 301, a waste pipe size fluctuation analysis module 302, a production parameter reconstruction module 303, and a size deviation cause analysis module 304, wherein:

[0116] The unused pipe size data acquisition module 301 is configured to acquire multiple unused pipe size queues, wherein the unused pipe size queues are constructed based on size data obtained from multiple detection positions of the unused pipe, and multiple data in the unused pipe size queues correspond to the same detection item;

[0117] The unused pipe size fluctuation analysis module 302 is configured to perform frequency domain characteristic analysis on the plurality of unused pipe size queues to obtain a plurality of main frequency characteristic values, wherein the main frequency characteristic values represent the frequency values at which the fluctuation characteristics of the unused pipe size queues are located;

[0118] The production parameter reconstruction module 303 is used to reconstruct data of each first continuous rolling production parameter queue according to the multiple main frequency characteristic values to obtain multiple second continuous rolling production parameter queues, wherein the length of the second continuous rolling production parameter queue is the same as the length of the waste pipe size queue;

[0119] The size deviation cause analysis module 304 is used to determine the cause of the waste pipe size deviation based on the correlation between the plurality of second continuous rolling production parameter queues and the waste pipe size queues.

[0120] Figure 4 : is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 400 and a memory 401, wherein the memory 401 stores a computer program 402 that can be run on the processor 400. When the processor 400 executes the computer program 402, the steps in the above-mentioned waste pipe size abnormality analysis method and embodiment are implemented, such as Figure 1 Steps 101 to 104 are shown.

[0121] Illustratively, the computer program 402 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 401 and executed by the processor 400 to implement the present invention.

[0122] The electronic device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will understand that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 4 may also include input and output devices, network access devices, buses, etc.

[0123] The processor 400 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0124] The memory 401 may be an internal storage unit of the electronic device 4, such as a hard drive or memory of the electronic device 4. The memory 401 may also be an external storage device of the electronic device 4, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 4. Furthermore, the memory 401 may include both an internal storage unit of the electronic device 4 and an external storage device. The memory 401 is used to store the computer program 402 and other programs and data required by the electronic device 4. The memory 401 may also be used to temporarily store data that has been output or is about to be output.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, and will not be repeated here.

[0126] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0127] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0128] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0129] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of this embodiment.

[0130] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0131] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the processes in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-mentioned method and device embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium.

[0132] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for analyzing abnormal size of waste pipes, characterized in that: include: Acquire multiple waste pipe size queues, where the waste pipe size queues are constructed based on size data obtained from multiple detection positions of the waste pipe, and multiple data in the waste pipe size queues correspond to the same detection item; Performing frequency domain feature analysis on the plurality of queues of unused pipe sizes to obtain a plurality of main frequency feature values, wherein the main frequency feature values represent frequency values at which fluctuation characteristics of the queues of unused pipe sizes exist; Reconstructing data of each first continuous rolling production parameter queue according to the multiple main frequency characteristic values to obtain multiple second continuous rolling production parameter queues, wherein the length of the second continuous rolling production parameter queue is the same as the length of the rough pipe size queue; determining the cause of the waste pipe size deviation based on the correlation between the plurality of second continuous rolling production parameter queues and the waste pipe size queue; The step of reconstructing data of each first continuous rolling production parameter queue according to the multiple main frequency characteristic values to obtain multiple second continuous rolling production parameter queues includes: For each first continuous rolling production parameter queue, perform the following steps respectively: Performing frequency domain feature extraction on the first continuous rolling production parameter queue according to the multiple main frequency feature values to obtain multiple second frequency domain values; According to the second formula and the length of the waste pipe size queue, the multiple second frequency domain values are subjected to an inverse frequency domain transformation to obtain a second continuous rolling production parameter queue, wherein the second formula is: Where, The second continuous rolling production parameter queue data, For the The second frequency domain value, is the total number of second frequency domain values, is a natural constant, is pi, is the lower frequency limit for frequency domain main characteristic analysis of the waste pipe size queue. For the The frequency multiplication coefficient corresponding to the second frequency domain value, is the cardinality number when performing frequency domain main feature analysis on the size of the waste pipe queue, Is an imaginary unit.

2. The method for analyzing the abnormal size of waste pipes according to claim 1, characterized in that: The step of obtaining multiple queues of waste pipe sizes includes: Get the total length of the wasteland pipe; According to the total length, a preset number of detection reference points are set at equal intervals along the axial direction of the rough pipe; Acquire multiple dimension arrays based on each detection reference point, wherein each dimension array corresponds to a detection item, each dimension array includes a preset number of dimension data, and the multiple dimension arrays include a wall thickness array and / or an outer diameter array; For each detection item, multiple arrays are arranged according to the detection reference points corresponding to the size arrays to construct a rough pipe size queue.

3. The method for analyzing abnormal size of waste pipes according to claim 1, characterized in that: The frequency domain characteristic analysis is performed on the plurality of queues of waste pipe sizes to obtain a plurality of main frequency characteristic values, including: traversally extracting the queues of the plurality of unused pipe sizes as queues to be analyzed; Determine the upper frequency limit of the frequency domain analysis according to the total amount of data in the queue to be analyzed; The quotient of the upper frequency limit and the total number of preset frequencies is used as the lower frequency limit; Performing frequency domain feature extraction on the queue to be analyzed according to the frequency upper limit value and the frequency lower limit value to obtain a plurality of first frequency domain values; Adding frequencies corresponding to multiple maximum values among the multiple first frequency domain values to a main frequency feature array; If the traversal of the plurality of unused pipe size queues is not completed, the process jumps to the step of traversing and extracting the unused pipe size queues from the plurality of unused pipe size queues as the queues to be analyzed.

4. The method for analyzing the abnormal size of waste pipes according to claim 3, characterized in that: The extracting frequency domain features of the queue to be analyzed according to the frequency upper limit value and the frequency lower limit value to obtain a plurality of first frequency domain values includes: Frequency domain features are extracted from the queue to be analyzed according to a first formula to obtain a plurality of first frequency domain values, wherein the first formula is: Where, For the The first frequency domain value, is the total number of data in the queue to be analyzed, The queue to be analyzed data, is a natural constant, is pi, is the lower frequency limit, is the upper frequency limit, is the cardinality number, Is an imaginary unit.

5. The method for analyzing the abnormal size of waste pipes according to any one of claims 1 to 4, characterized in that: The determining the cause of the waste pipe size deviation based on the correlation between the plurality of second continuous rolling production parameter queues and the waste pipe size queues includes: For each queue size, perform the following steps: Calculating similarities between the queue of waste pipe sizes and the plurality of queues of second continuous rolling production parameters respectively, and arranging the obtained plurality of similarity values in a predetermined order to construct a similarity vector; Finding multiple reference vectors closest to the similar vector from multiple vector classes; taking the vector class to which the largest number of the plurality of reference vectors belongs as the target class; The deviation causes corresponding to the majority of vectors in the target class are regarded as the causes of the deviation in the size of the waste pipe.

6. A device for analyzing abnormal size of waste pipes, characterized in that: For implementing the method for analyzing the abnormal size of waste pipes according to any one of claims 1 to 5, the device for analyzing the abnormal size of waste pipes comprises: an unused pipe size data acquisition module, configured to acquire multiple unused pipe size queues, wherein the unused pipe size queues are constructed based on size data obtained from multiple detection positions of the unused pipe, and multiple data in the unused pipe size queues correspond to the same detection item; an unused pipe size fluctuation analysis module, configured to perform frequency domain characteristic analysis on the plurality of unused pipe size queues to obtain a plurality of main frequency characteristic values, wherein the main frequency characteristic values represent frequency values at which fluctuation characteristics of the unused pipe size queues are located; a production parameter reconstruction module, configured to reconstruct data of each first continuous rolling production parameter queue according to the plurality of main frequency characteristic values to obtain a plurality of second continuous rolling production parameter queues, wherein the length of the second continuous rolling production parameter queue is the same as the length of the rough pipe size queue; as well as, The size deviation cause analysis module is used to determine the cause of the waste pipe size deviation based on the correlation between the multiple second continuous rolling production parameter queues and the waste pipe size queues.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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