Abnormal positioning method and device for cutting machine, storage medium and electronic equipment

Abstract

The invention relates to the technical field of data processing, and particularly provides a cutting machine abnormity positioning method and device, a storage medium and electronic device.The method can comprise the steps that abnormal data generated when abnormity happens to multiple cutting machines in the cutting process are obtained, the types of the abnormities comprise at least one of wire jumping abnormities, wire breaking abnormities and wire bow abnormities; performing statistics on the abnormal data to obtain a statistical result; the statistical result is analyzed, an abnormal object with the abnormity is positioned, and the abnormal object is an abnormal cutting machine or an auxiliary material related to at least one cutting machine. According to some embodiments of the invention, accurate positioning of the abnormal object in the cutting machine can be realized, so that the abnormal object can be efficiently processed, and the working efficiency of the cutting machine is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, storage medium and electronic device for abnormal positioning of a cutting machine. Background Art

[0002] Silicon rod cutting machine is a machine commonly used for cutting silicon rods, and diamond wire is often used as a cutting tool. The cutting machine may malfunction due to various reasons during operation. At present, the prior art analyzes the abnormal position information of the new and old guide wheels when cutting silicon wafers and the abnormal position information of the jump, and determines that the cause of the TTV abnormality is a guide wheel problem or a bearing box matching problem, so as to determine the abnormal position. However, this prior art can only realize the abnormal positioning of the guide wheel or the bearing box, and the detection range is limited, and other abnormal problems of the cutting machine cannot be discovered in time.

[0003] Therefore, how to provide a more comprehensive technical solution for the method of locating abnormalities of a cutting machine becomes a technical problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of some embodiments of the present application is to provide a method, device, storage medium and electronic device for locating abnormalities of a cutting machine. Through the technical solutions of the embodiments of the present application, comprehensive detection of the cutting machine can be achieved, and abnormal objects can be located in a timely and accurate manner, so that the abnormalities can be handled efficiently and the working efficiency of the cutting machine can be guaranteed.

[0005] In a first aspect, some embodiments of the present application provide a method for locating abnormalities in a cutting machine, comprising: obtaining abnormal data of multiple cutting machines when abnormalities occur during the cutting process, wherein the types of the abnormalities include: at least one of wire jump abnormality, wire break abnormality and wire bow abnormality; performing statistics on the abnormal data to obtain statistical results; analyzing the statistical results to locate the abnormal object where the abnormality occurs, wherein the abnormal object is an abnormal cutting machine or auxiliary materials related to at least one cutting machine.

[0006] Some embodiments of the present application collect and count abnormal data when multiple cutting machines have abnormalities to obtain statistical results, and then locate the abnormal objects that have abnormalities according to the statistical results. The embodiments of the present application can achieve comprehensive detection of the cutting machine and the auxiliary materials in the cutting machine, and locate the abnormal objects in a timely and accurate manner, so that the abnormalities can be efficiently handled and the working efficiency of the cutting machine can be guaranteed.

[0007] In some embodiments, the obtaining of abnormal data when an abnormality occurs in the cutting process of multiple cutting machines includes: using a control variable method to set cutting parameters of each of the multiple cutting machines, and obtaining the abnormal data when the abnormality occurs under the cutting parameters of each cutting machine.

[0008] Some embodiments of the present application obtain abnormal data after setting the cutting parameters of each cutting machine through the control variable method, which can subsequently realize accurate and efficient positioning of abnormal objects.

[0009] In some embodiments, when the abnormality is the wire jumper abnormality, the abnormal data include: wire jumper position, cutting depth, wire jumper machine, wire jumper times and auxiliary material parameters; when the abnormality is the wire break abnormality, the abnormal data include: seam break position, wire break machine, wire break times and auxiliary material parameters; when the abnormality is the wire bow abnormality, the abnormal data include: wire bow abnormal machine, wire bow value, wire bow abnormal position and auxiliary material parameters.

[0010] Some embodiments of the present application can provide data support for subsequent positioning of abnormal objects by collecting relevant abnormal data when there is an abnormal jump line or a broken line.

[0011] In some embodiments, the statistics of the abnormal data to obtain statistical results include: statistics on the correspondence between various abnormal data in the abnormal data and the cutting parameters to generate the statistical results, wherein the representation method of the statistical results includes at least one of the following methods: a bar chart, a scatter plot, a pie chart and a line chart.

[0012] Some embodiments of the present application obtain statistical results by counting the correspondence between abnormal data and cutting parameters, which can provide a reference for subsequent positioning of abnormal objects.

[0013] In some embodiments, analyzing the statistical results to locate the abnormal object where the abnormality occurs includes: sorting the multiple cutting machines based on the number of occurrences of the abnormality to determine the abnormal cutting machine.

[0014] Some embodiments of the present application sort multiple cutting machines by the number of abnormality occurrences and determine the abnormal cutting machines, thereby obtaining the red line machines and achieving accurate positioning of the abnormal objects.

[0015] In some embodiments, after locating the abnormal object where the abnormality occurs, the method further includes: optimizing a cutting process associated with the abnormal object based on the type of the abnormal object.

[0016] Some embodiments of the present application optimize the cutting process of the cutting machine by the type of abnormal object, so as to achieve timely control of the cutting machine, enable the cutting machine to work normally, and ensure its work efficiency.

[0017] In a second aspect, some embodiments of the present application provide a device for locating abnormalities in a cutting machine, comprising: a data acquisition module, used to acquire abnormal data of multiple cutting machines when abnormalities occur during the cutting process, wherein the types of the abnormalities include: at least one of wire jumper abnormality, wire break abnormality and wire bow abnormality; a data statistics module, used to perform statistics on the abnormal data and obtain statistical results; a data analysis module, used to analyze the statistical results and locate the abnormal object where the abnormality occurs, wherein the abnormal object is an abnormal cutting machine or auxiliary materials related to at least one cutting machine.

[0018] In a third aspect, some embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the method described in any embodiment of the first aspect.

[0019] In a fourth aspect, some embodiments of the present application provide an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement a method as described in any embodiment of the first aspect when executing the program.

[0020] In a fifth aspect, some embodiments of the present application provide a computer program product, wherein the computer program product comprises a computer program, wherein the computer program, when executed by a processor, can implement the method described in any embodiment of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of some embodiments of the present application, the drawings required for use in some embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 System diagram for abnormal positioning of a cutting machine provided for some embodiments of the present application;

[0023] Figure 2 One of the flow charts of the method for locating abnormality of a cutting machine provided in some embodiments of the present application;

[0024] Figure 3 A schematic diagram of statistical results of the number of abnormalities of sheaves provided in some embodiments of the present application;

[0025] Figure 4 A schematic diagram of statistical results of abnormal positions of sheaves provided in some embodiments of the present application;

[0026] Figure 5A second flow chart of a method for locating abnormalities of a cutting machine provided in some embodiments of the present application;

[0027] Figure 6 A block diagram of a device for abnormal positioning of a cutting machine provided in some embodiments of the present application;

[0028] Figure 7 A schematic diagram of an electronic device is provided for some embodiments of the present application. DETAILED DESCRIPTION

[0029] The technical solutions in some embodiments of the present application will be described below in conjunction with the drawings in some embodiments of the present application.

[0030] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0031] In the related technology, when locating the abnormality of the cutting equipment, the defects of the guide wheel itself can be ruled out by the abnormal position information of the TTV of the cutting silicon wafer. Through the abnormal position information of the TTV of the new and old guide wheels for cutting silicon wafers and the abnormal position information of the new and old guide wheels jumping, it can be determined whether the TTV abnormality is caused by the defect of the guide wheel itself or the problem of matching with the corresponding bearing box, so that the abnormal position can be accurately known. Due to the differences in cutting machine manufacturers and batches, as well as the differences in the relevant parameters of the auxiliary materials in the cutting machine, they may all be the cause of the abnormality of the cutting machine. In other words, there are many reasons for the abnormality of the cutting machine during operation, and the current existing technology can only detect the matching problems of the guide wheel and the bearing box. The detection range is limited and the comprehensiveness is poor, and the specific cause of the abnormality of the cutting machine cannot be discovered in time.

[0032] In view of this, some embodiments of the present application provide a method for locating abnormalities of a cutting machine, which can collect and count abnormal data of abnormalities that occur during the cutting process of multiple cutting machines to obtain statistical results; and then locate the abnormal object that has abnormalities by analyzing the statistical results. Some embodiments of the present application can realize abnormal detection of the cutting machine itself and its internal auxiliary materials, with a wide detection range, improving the comprehensiveness and timeliness of detection, and ensuring the working efficiency of the cutting machine.

[0033] The following is combined with Figure 1 The overall composition structure of the system for abnormal positioning of a cutting machine provided by some embodiments of the present application is exemplified.

[0034] like Figure 1As shown, some embodiments of the present application provide a system for locating abnormalities of a cutting machine, and the system for locating abnormalities of a cutting machine may include: a terminal 100 and a first cutting machine 110, a second cutting machine 120...the Nth cutting machine. The terminal 100 may establish a communication connection with the first cutting machine 110, the second cutting machine 120...the Nth cutting machine to obtain abnormal data of the first cutting machine 110, the second cutting machine 120...the Nth cutting machine when an abnormality occurs. Afterwards, the terminal 100 may perform statistics and analysis on the abnormal data to locate the abnormal object where the abnormality occurs.

[0035] In some embodiments of the present application, the number of cutting machines can be set according to actual needs, and the present application does not make specific restrictions here. The terminal 100 can be a mobile terminal or a non-portable computer terminal, or the terminal 100 can be a monitoring module, unit or controller connected to each cutting machine; as long as the terminal 100 can achieve the above functions, the embodiments of the present application do not make specific restrictions on the type of the terminal 100.

[0036] The following is combined with Figure 2 The implementation process of abnormal location of a cutting machine performed by the terminal 100 provided in some embodiments of the present application is exemplarily described.

[0037] Please see attached Figure 2 , Figure 2 A flow chart of a method for abnormal positioning of a cutting machine is provided for some embodiments of the present application. The method for abnormal positioning of a cutting machine may include:

[0038] S210, acquiring abnormal data of multiple cutting machines when abnormalities occur during the cutting process, wherein the types of the abnormalities include: at least one of: wire jump abnormality, wire break abnormality and wire bow abnormality.

[0039] For example, in some embodiments of the present application, when an abnormality occurs during the operation of the cutting machine, the sensor of the cutting machine can upload the abnormal data collected in real time to the terminal 100 in real time. Among them, the abnormality may include: line jump abnormality (referred to as line jump), line bow abnormality (referred to as line bow), line break (or seam break) abnormality, line parallel abnormality (referred to as line parallel), etc. Among them, the line bow abnormality is determined by collecting the line bow value during the cutting process and comparing it with the line bow threshold. In one embodiment, when the line bow value exceeds the line bow threshold, there is a line bow abnormality.

[0040] In some embodiments of the present application, when the abnormality is the wire jumper abnormality, the abnormal data include: wire jumper position, cutting depth, wire jumper machine, wire jumper times and auxiliary material parameters; when the abnormality is the wire break abnormality, the abnormal data include: seam break position, wire break machine, wire break times and auxiliary material parameters; when the abnormality is the wire bow abnormality, the abnormal data include: wire bow abnormal machine, wire bow value, wire bow abnormal position and auxiliary material parameters.

[0041] For example, in some embodiments of the present application, a wire jumper, a wire breaker, or a wire bow abnormality machine is a cutting machine where wire jumpers, wire breaks, or wire bow abnormalities occur. Auxiliary materials may include steel wires, coolants, resin plates, groove wheel usage time, groove wheel wear, glue, etc. in the cutting machine. Auxiliary material parameters may be the category, source batch, manufacturer, etc. of the auxiliary materials. For example, the batch of steel wire, steel wire category, process, groove wheel manufacturer, etc. It should be noted that the content contained in the above-mentioned abnormal data can be flexibly adjusted according to actual conditions, and the embodiments of the present application are not limited to this.

[0042] In some embodiments of the present application, S210 may include: using a control variable method to set cutting parameters of each of the multiple cutting machines, and obtaining the abnormal data when the abnormality occurs under the cutting parameters of each cutting machine.

[0043] For example, in some embodiments of the present application, the cutting parameters may include: the cutting depth of the silicon rod, the number of cutting knives, which batch of steel wire to use, the type of steel wire, the type of coolant, etc. The cutting parameters in the cutting machine are set by the control variable method, and then a comparative experiment is conducted to obtain abnormal data of each cutting machine under the set cutting parameters. For example, taking three cutting machines as an example, except for the different batches of steel wire in each cutting machine, other cutting parameters are set to the same, so as to conduct a cutting comparison experiment and obtain abnormal data. In this way, it is possible to select which batch of steel wire has a better cutting effect through statistical results.

[0044] S220, performing statistics on the abnormal data to obtain statistical results.

[0045] For example, in some embodiments of the present application, statistics are collected on the cutting parameters involved in the abnormal data and the situations in which the abnormality occurs, and statistical results are obtained to facilitate subsequent positioning of the abnormality.

[0046] In some embodiments of the present application, S220 may include: performing statistics on the correspondence between various abnormal data in the abnormal data and the cutting parameters to generate the statistical results, wherein the representation method of the statistical results includes at least one of the following methods: a bar chart, a scatter plot, a pie chart and a line chart.

[0047] For example, in some embodiments of the present application, by statistically analyzing the corresponding relationship between the cutting depth and the abnormal data when the abnormality occurs, or statistically analyzing the corresponding relationship between the number of cutting blades and the ratio of the abnormal data when the abnormality occurs, the corresponding statistical results are obtained. Figure 3 In the statistics of the number of abnormalities of the sheave shown in the figure, the horizontal axis is the number of the machine (that is, the cutting machine), and the vertical axis is the abnormality rate. When other cutting parameters in the machine are the same, only the sheave parameter is changed, that is, the sheaves on different machines are from different manufacturers and batches. By counting the number of abnormalities of the sheave, the performance of the sheaves involved in different sheave manufacturers and batches can be distinguished. Figure 4 The statistical results of the abnormal position of the groove wheel shown are used to calculate the corresponding relationship between the groove wheel position and the cutting depth when other cutting parameters are consistent.

[0048] S230, analyzing the statistical results to locate the abnormal object where the abnormality occurs, wherein the abnormal object is an abnormal cutting machine or auxiliary materials related to at least one cutting machine.

[0049] For example, in some embodiments of the present application, Figure 3 Taking the statistical results of the number of abnormalities of the sheave shown in the figure as an example, the statistical results can be used to compare which sheave manufacturer and which batch of sheaves have the lowest abnormality rate, so as to locate which sheave manufacturer and which batch of sheaves (as a specific example of an abnormal object) has the worst performance. On the contrary, high-quality sheave manufacturers and batches can be selected to reduce the probability of abnormalities in the cutting machine. Figure 4 Taking the statistical results of the abnormal position of the groove wheel shown as an example, the corresponding relationship between the groove wheel position and the cutting depth can be used to find out under which circumstances the groove wheel position (as a specific example of an abnormal object) has a low frequency of abnormality; then the relationship between the groove wheel position and the cutting depth of the cutting machine can be adjusted to the most appropriate corresponding relationship to reduce the probability of abnormality. In other words, by statistically analyzing the abnormal data and cutting parameters, the correlation between the abnormality and the auxiliary materials and the machine can be found, and the batch, category, process, manufacturer and other parameters of the machine and auxiliary materials can be distinguished, so as to select high-quality auxiliary materials and machines and reduce the probability of abnormality. Subsequently, IPQC or relevant personnel can determine whether the groove wheel needs to be replaced by locating the groove wheel; or they can query the frequency of abnormality of a certain groove wheel manufacturer, and analyze it in combination with its material and grooving process to reduce the impact of the groove wheel on the abnormality of the cutting machine.

[0050] In some embodiments of the present application, S230 may include: sorting the plurality of cutting machines based on the number of occurrences of the abnormality, and determining the abnormal cutting machine.

[0051] For example, in some embodiments of the present application, machines with abnormalities such as wire jumpers and seam breaks can be sorted according to the number of abnormalities that occur to select red-line machines (as a specific example of an abnormal cutting machine). For example, machines with more than 2 seam breaks or wire jumpers are marked in red and determined as red-line machines. Alternatively, the machine yield of each cutting machine is calculated based on the number of abnormalities, and then the machine yields are sorted to screen out red-line machines with poor machine yields. For example, machines with a machine yield of less than 70% are used as red-line machines. After the red-line machines are determined, subsequent intelligent maintenance or manual maintenance is facilitated. It should be understood that the basis for judging the marking of red-line machines may have other judgment methods in addition to the number of abnormalities and the machine yield, and the embodiments of the present application are not limited to this.

[0052] In some embodiments of the present application, after executing S230, the method for locating abnormalities in a cutting machine may further include: optimizing a cutting process associated with the abnormal object based on the type of the abnormal object.

[0053] For example, in some embodiments of the present application, the relevant cutting process can be adjusted according to the type of abnormal object. For example, taking the wire bow abnormality as an example, the cutting process is adjusted according to the set given principle based on the general trend of the wire bow of factors such as the category of steel wire and the process category. For example, when cutting to 30-60mm, the wire bow value exceeds 29mm, and the lifting speed of the silicon rod is automatically reduced from 3.1 to 2.8, and the original cutting process is restored after 3 minutes, so that the existing fixed process can be changed into a variable process to form flexible manufacturing. In other words, corresponding cutting process adjustment methods can be set for different abnormal objects, and the cutting process can be optimized in a targeted manner after locating the abnormality so that the cutting machine can work normally. The adjustment of the cutting process can be adjusted according to actual needs, and the embodiments of the present application are not limited to this.

[0054] The following is combined with Figure 5 The specific process of abnormal positioning of the cutting machine provided by some embodiments of the present application is exemplified.

[0055] Please see attached Figure 5 , Figure 5 A flow chart of a method for abnormal positioning of a cutting machine is provided for some embodiments of the present application.

[0056] The above process is explained below as an example.

[0057] S510, using a control variable method to set cutting parameters of each cutting machine among the multiple cutting machines to perform a comparative experiment.

[0058] S520, obtaining abnormal data when an abnormality occurs under the cutting parameters of each cutting machine.

[0059] S530, statistically analyzing the correspondence between various abnormal data and cutting parameters in the abnormal data to generate statistical results.

[0060] S540, analyzing the statistical results to locate the abnormal object where the abnormality occurs.

[0061] S550: Optimize the cutting process associated with the abnormal object based on the type of the abnormal object.

[0062] It can be understood that the specific implementation process of S510 to S550 can refer to the method embodiment provided above, and in order to avoid repetition, the detailed description is appropriately omitted here.

[0063] It can be seen from some of the above embodiments of the present application that the method for locating anomalies based on abnormal data statistics provided by the present application can quickly and accurately locate abnormal auxiliary materials in the cutting machine with high efficiency and a wide and comprehensive anomaly detection range.

[0064] Please refer to Figure 6 , Figure 6 The block diagram of the device for abnormal positioning of a cutting machine provided in some embodiments of the present application is shown. It should be understood that the device for abnormal positioning of a cutting machine corresponds to the above method embodiment and can execute each step involved in the above method embodiment. The specific functions of the device for abnormal positioning of a cutting machine can be found in the description above. To avoid repetition, the detailed description is appropriately omitted here.

[0065] Figure 6 The device for locating the abnormality of a cutting machine includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the device for locating the abnormality of a cutting machine, and the device for locating the abnormality of a cutting machine includes: a data acquisition module 610, used to obtain abnormal data of multiple cutting machines when abnormalities occur during the cutting process, wherein the types of the abnormalities include: at least one of jumper abnormality, broken wire abnormality and wire bow abnormality; a data statistics module 620, used to count the abnormal data and obtain statistical results; a data analysis module 630, used to analyze the statistical results and locate the abnormal object where the abnormality occurs, wherein the abnormal object is an abnormal cutting machine or auxiliary materials related to at least one cutting machine.

[0066] In some embodiments of the present application, the data acquisition module 610 is used to set the cutting parameters of each of the multiple cutting machines using the control variable method, and obtain the abnormal data when the abnormality occurs under the cutting parameters of each cutting machine.

[0067] In some embodiments of the present application, the data statistics module 620 is used to perform statistics on the correspondence between various abnormal data in the abnormal data and the cutting parameters to generate the statistical results, wherein the representation method of the statistical results includes at least one of the following methods: a bar chart, a scatter plot, a pie chart and a line chart.

[0068] In some embodiments of the present application, the data analysis module 630 is used to sort the multiple cutting machines based on the number of occurrences of the abnormality to determine the abnormal cutting machine.

[0069] In some embodiments of the present application, after the data analysis module 630, a process optimization module (not shown in the figure) is also included, which is used to optimize the cutting process associated with the abnormal object based on the type of the abnormal object.

[0070] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method, and will not be described in detail here.

[0071] Some embodiments of the present application further provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the operations of the method corresponding to any of the above methods provided in the above embodiments.

[0072] Some embodiments of the present application further provide a computer program product, which includes a computer program, wherein when the computer program is executed by a processor, it can implement the operations corresponding to any of the above methods provided in the above embodiments.

[0073] like Figure 7 As shown, some embodiments of the present application provide an electronic device 700, which includes: a memory 710, a processor 720, and a computer program stored in the memory 710 and executable on the processor 720, wherein the processor 720 can implement a method as described in any of the above embodiments when reading the program from the memory 710 through a bus 730 and executing the program.

[0074] Processor 720 can process digital signals and can include various computing structures, such as complex instruction set computer structure, reduced instruction set computer structure, or a structure that implements a combination of multiple instruction sets. In some examples, processor 720 can be a microprocessor.

[0075] The memory 710 may be used to store instructions executed by the processor 720 or data related to the execution of instructions. These instructions and / or data may include codes for implementing some or all functions of one or more modules described in the embodiments of the present application. The processor 720 of the disclosed embodiment may be used to execute instructions in the memory 710 to implement the method shown above. The memory 710 includes a dynamic random access memory, a static random access memory, a flash memory, an optical memory, or other memory known to those skilled in the art.

[0076] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0077] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0078] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for abnormal positioning of a cutting machine, characterized in that: include: Acquire abnormal data of multiple cutting machines when abnormalities occur during the cutting process, wherein the types of the abnormalities include: at least one of wire jump abnormality, wire break abnormality and wire bow abnormality; Performing statistics on the abnormal data to obtain statistical results; The statistical results are analyzed to locate the abnormal object where the abnormality occurs, wherein the abnormal object is an abnormal cutting machine or auxiliary materials related to at least one cutting machine.

2. The method according to claim 1, characterized in that The obtaining of abnormal data when abnormalities occur in the cutting process of the plurality of cutting machines includes: The cutting parameters of each cutting machine among the plurality of cutting machines are set by using a control variable method, and the abnormal data when the abnormality occurs under the cutting parameters of each cutting machine is obtained.

3. The method according to claim 1 or 2, characterized in that When the abnormality is the wire jump abnormality, the abnormal data includes: wire jump position, cutting depth, wire jump machine, wire jump times and auxiliary material parameters; When the abnormality is the thread break abnormality, the abnormal data includes: the break position, the thread break machine, the number of thread breaks and auxiliary material parameters; When the abnormality is the wire bow abnormality, the abnormal data includes: wire bow abnormal machine, wire bow value, wire bow abnormal position and auxiliary material parameters.

4. The method according to claim 2, characterized in that The performing statistics on the abnormal data to obtain statistical results includes: The corresponding relationship between various abnormal data in the abnormal data and the cutting parameters is counted to generate the statistical result, wherein the representation method of the statistical result includes at least one of the following methods: a bar chart, a scatter plot, a pie chart and a line chart.

5. The method according to claim 1 or 2, characterized in that: The analyzing the statistical results to locate the abnormal object where the abnormality occurs includes: Based on the number of occurrences of the abnormality, the plurality of cutting machines are sorted to determine the abnormal cutting machine.

6. The method according to claim 3, characterized in that After locating the abnormal object where the abnormality occurs, the method further includes: Based on the type of the abnormal object, a cutting process associated with the abnormal object is optimized.

7. A device for abnormal positioning of a cutting machine, characterized in that: include: A data acquisition module, used to acquire abnormal data of multiple cutting machines when abnormalities occur during the cutting process, wherein the types of the abnormalities include: at least one of wire jump abnormality, wire break abnormality and wire bow abnormality; A data statistics module is used to collect statistics on the abnormal data and obtain statistical results; The data analysis module is used to analyze the statistical results and locate the abnormal object where the abnormality occurs, wherein the abnormal object is an abnormal cutting machine or auxiliary materials related to at least one cutting machine.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program executes the method according to any one of claims 1 to 6 when executed by a processor.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the computer program executes the method according to any one of claims 1 to 6 when being run by the processor.

10. A computer program product, characterized in that The computer program product comprises a computer program, wherein the computer program executes the method according to any one of claims 1 to 6 when executed by a processor.

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