A method and system for self-detecting errors in underwear fabric knitting process

Abstract

The present invention provides a method and system for self-detecting errors in the weaving process of underwear fabrics, which belongs to the technical field of data processing, and specifically comprises: determining the processing data of different underwear machine control instructions based on the plate-making processing results of the weaving process of the underwear fabrics, and combining the historical error data of the weaving machine under the different underwear machine control instructions to determine when there are underwear machine control instructions with an error probability within a preset interval; obtaining the historical error data of the weaving machine in different similar processing time periods, and combining the underwear machine control instructions with an error probability within a preset interval in the processing time period to determine when there are no processing time periods in which the error abnormality probability does not meet the requirements, based on the timing data of the underwear machine control instructions in the plate-making processing results, determining the historical similar plate-making results of the plate-making processing results, and determining whether to issue a reminder message based on the historical error data of the weaving machine with the historical similar plate-making results, thereby improving the reliability of the error detection processing.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing, and in particular relates to a method and system for self-detecting errors in the knitting process of underwear fabrics. Background Art

[0002] In order to achieve the success rate of plate making in underwear fabric weaving process, an automatic pattern generation algorithm was developed based on machine learning and artificial intelligence technology in the invention patent application CN202410998594.3 "A method and system for automatic clothing pattern making". The algorithm takes into account the different parts and style requirements of clothing, and generates various size patterns that meet the design requirements, which significantly improves the efficiency and accuracy of clothing pattern making. However, there are the following technical problems:

[0003] In the plate-making process of the weaving technology of underwear fabrics, the relevant parameters of the weaving machine are ignored in the existing technical solutions. The seamless underwear machine runs at a high speed and the machine action timing corresponding to the weaving structure is complex. This results in that even if the plate-making result meets the plate-making requirements of the weaving process and the weaving data is not wrong, errors may also occur in the actual weaving process due to the parameter limitations of the weaving machine.

[0004] In view of the above technical problems, the present application specifically provides a method and system for self-detecting errors in the knitting process of underwear fabrics. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a method for self-detecting errors in the knitting process of underwear fabric is provided.

[0007] A method for self-detecting errors in underwear fabric knitting process, specifically comprising:

[0008] S1 determines the processing data of different underwear machine control instructions based on the plate making processing results of the underwear fabric knitting process, and combines the historical error data of the knitting machine under different underwear machine control instructions to determine that there is an underwear machine control instruction with an error probability within a preset range, and then proceeds to the next step;

[0009] S2 divides the underwear machine control instructions into different processing time periods based on the time series data of the underwear machine control instructions in the plate making processing result, and determines similar processing time periods of different processing time periods based on the distribution data of different underwear machine control instructions in different processing time periods;

[0010] S3: acquiring historical error data of the knitting machine in different similar processing periods, and combining the underwear machine control instructions with error probabilities within a preset range in the processing period, when it is determined that there is no processing period in which the error abnormality probability does not meet the requirements, determining the historical similar plate making results of the plate making processing result based on the time series data of the underwear machine control instructions in the plate making processing result;

[0011] S4 determines whether to issue a reminder message based on the historical error data of the knitting machine based on the historical similar plate making results.

[0012] The beneficial effects of the present invention are:

[0013] By utilizing the processing data of underwear machine control instructions and the historical error data of the weaving machine under the underwear machine control instructions, it is determined whether there is an underwear machine control instruction with an error probability within a preset interval. This not only takes into account the difference in the probability of abnormality in plate making processing results caused by the abnormality in the underwear machine control instructions caused by the processing data of the underwear machine control instructions, but also takes into account the historical abnormalities of the underwear machine control instructions in the weaving machine. This realizes the identification of underwear machine control instructions with an error probability within a preset interval from the perspective of the weaving machine, and also lays the foundation for further determination of the identification, processing and measurement of differentiated plate making processing results.

[0014] Whether to issue a reminder message is determined based on the historical error data of the knitting machine based on historical similar plate-making results, thereby avoiding the technical problem of insufficient reliability of abnormal recognition results caused by solely considering underwear machine control instructions, and realizing a comprehensive evaluation of the error probability from the perspective of historical similar plate-making results, thereby improving the accuracy of abnormal recognition processing of plate-making processing results.

[0015] A further technical solution is that the underwear machine control instructions include action angle, action sequence and action position.

[0016] A further technical solution is that the processing data includes the number of times the underwear machine operation instruction is processed at different positions in the plate making processing result.

[0017] A further technical solution is that the historical error data includes the historical error times of the underwear machine control instructions and the operation times corresponding to different historical error times.

[0018] A further technical solution is that the method for determining the error probability of the underwear machine control instruction is:

[0019] The number of operations of the underwear machine control instruction is used as the number of matching operations;

[0020] The number of matching operations in which errors occur in the underwear machine control instruction is taken as the number of error operations, and the error probability of the underwear machine control instruction is determined by using the proportion of the number of error operations in the number of matching operations.

[0021] A further technical solution is to determine whether to issue a reminder message, specifically including:

[0022] Based on the historical error data of the knitting machine in the historical similar plate-making results, the historical similar plate-making results with the historical error data are determined;

[0023] The proportion of the number of historical similar plate-making results with historical error data in the historical similar plate-making results is used as the plate-making error proportion, and the plate-making error proportion is used to determine whether to issue a reminder message.

[0024] A further technical solution is to use the plate making error ratio to determine whether to issue a reminder message, specifically including:

[0025] When the proportion of the plate-making errors is greater than the proportion of the preset plate-making quantity, it is determined that a reminder message needs to be issued.

[0026] A further technical solution is that, when it is necessary to issue a reminder message, a processing period in which the error probability is within a preset error probability interval is used as a reminder output target.

[0027] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned underwear fabric weaving process self-detection method when running the computer program.

[0028] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings;

[0031] Figure 1 It is a flow chart of a method for self-detection of errors in underwear fabric weaving process;

[0032] Figure 2 is a flow chart of a method for determining the error probability of a control instruction of an underwear machine;

[0033] Figure 3 is a flow chart of a method for determining similar processing time periods;

[0034] Figure 4 is a flow chart of a method for determining an error anomaly probability during a processing period;

[0035] Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0037] In the plate-making process of underwear fabric weaving technology, it is necessary to comprehensively consider the relevant parameters of the textile machine and the textile data of the plate-making processing results to identify abnormalities in the plate-making processing results.

[0038] Error probability of underwear machine control instructions: the number of operations in which underwear machine control instructions exist is taken as the number of matching operations, the number of matching operations in which errors occur in underwear machine control instructions is taken as the number of error operations, and the error probability of underwear machine control instructions is determined by using the proportion of the number of error operations in the number of matching operations.

[0039] Similar processing periods of processing periods: According to the underwear machine control instructions corresponding to different times, determine the consistent time of the processing period and the underwear machine control instructions in different historical periods, determine the period similarity coefficient between the historical period and the processing period based on the proportion of the number of consistent times, and use the historical period with a period similarity coefficient greater than 0.6 as a similar processing period.

[0040] Probability of abnormal errors: Based on the historical error data of the knitting machine in different similar processing periods, similar processing periods with historical error data are determined, and the proportion of similar processing periods with historical error data in similar processing periods is taken as the error quantity proportion. According to the distribution data of the underwear machine control instructions with error probability within the preset interval in the processing period, the moment quantity proportion of the underwear machine control instructions with error probability within the preset interval in the processing period is determined. Based on the moment quantity proportion and the average value of the error quantity proportion, the probability of abnormal errors in the processing period is determined.

[0041] The processing period in which the error probability is greater than 0.4 is regarded as the processing period in which the error probability does not meet the requirements.

[0042] Historical similar plate-making results: using underwear machine control instructions at similar moments between the plate-making processing result and different historical plate-making results, the number ratio of similar moments is used as the similar moment coefficient between the underwear machine control instructions in the plate-making processing result and different historical plate-making results, and the historical plate-making results with similar moment coefficients greater than 0.6 are used as historical similar plate-making results.

[0043] Determine whether to issue a reminder message: the ratio of the number of historical similar plate-making results with historical error data in the historical similar plate-making results is used as the plate-making error ratio, and the plate-making error ratio is used to determine whether to issue a reminder message. Example

[0044] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a first aspect is provided, and the present invention provides a method for self-detecting errors in the knitting process of underwear fabric, specifically comprising:

[0045] S1 determines the processing data of different underwear machine control instructions based on the plate making processing results of the underwear fabric knitting process, and combines the historical error data of the knitting machine under different underwear machine control instructions to determine that there is an underwear machine control instruction with an error probability within a preset range, and then proceeds to the next step;

[0046] Furthermore, the underwear machine control instructions include action angle, action sequence and action position.

[0047] Specifically, the processing data includes the underwear machine operation instruction and the number of times the underwear machine operation instruction is processed at different positions in the plate making processing result.

[0048] Furthermore, the historical error data includes the historical error times of the underwear machine control instructions and the operation times corresponding to different historical error times.

[0049] It is understandable that if Figure 2 As shown, the method for determining the error probability of the underwear machine control instruction is:

[0050] The number of operations of the underwear machine control instruction is used as the number of matching operations;

[0051] The number of matching operations in which errors occur in the underwear machine control instruction is taken as the number of error operations, and the error probability of the underwear machine control instruction is determined by using the proportion of the number of error operations in the number of matching operations.

[0052] Specifically, the error probability of the underwear machine control instruction ranges from 0 to 1, wherein the greater the error probability of the underwear machine control instruction, the higher the probability that the underwear machine control instruction fails in the knitting machine.

[0053] It is understandable that when there is no underwear machine control instruction with an error probability within a preset range, it is determined not to issue a reminder message.

[0054] Optionally, the method for determining the error probability of the underwear machine control instruction is:

[0055] The number of operations in which the underwear machine control instruction exists is regarded as the number of matching operations, and the number of matching operations in which an error occurs in the underwear machine control instruction is regarded as the number of error operations;

[0056] Determining the number of screening operations in the number of error operations according to the number of historical errors of the knitting machine in the different number of error operations;

[0057] Based on the preset error probability corresponding to the number of screening operations, the error probability of the underwear machine control instruction is determined.

[0058] Optionally, the method for determining the error probability of the underwear machine control instruction is:

[0059] S11 determines the number of historical errors of the knitting machine under the underwear machine control instruction based on the historical error data of the knitting machine under the underwear machine control instruction, takes the number of operations of the underwear machine control instruction as the number of matching operations, and determines the basic error probability of the underwear machine control instruction based on the number of historical errors and the number of matching operations;

[0060] Optionally, the above step S11 includes:

[0061] S111 determines the number of historical errors of the knitting machine under the underwear machine control instruction based on the historical error data of the knitting machine under the underwear machine control instruction. When the number of historical errors of the knitting machine under the underwear machine control instruction is less than the preset number of errors, the process proceeds to step S112. When the number of historical errors of the knitting machine under the underwear machine control instruction is not less than the preset number of errors, the process proceeds to step S113.

[0062] S112: the operation number of the underwear machine control instruction is used as the matching operation number. When the matching operation number is less than the preset operation number threshold, the process proceeds to step S113. When the matching operation number is not less than the preset operation number threshold, it is determined that the error probability of the underwear machine control instruction is not within the preset range.

[0063] S113 determines the basic error probability of the underwear machine control instruction based on the historical error count and the matching operation count. When the basic error probability of the underwear machine control instruction is less than the preset error probability threshold, it is determined that the error probability of the underwear machine control instruction is not within the preset interval. When the basic error probability of the underwear machine control instruction is not less than the preset error probability threshold, proceed to step S12.

[0064] S12: the number of matching operations in which errors occur in the underwear machine control instruction is used as the number of error operations, and operation abnormality coefficients of different error operation numbers are determined according to the historical error numbers of the knitting machine in different error operation numbers, and the operation abnormality coefficients are used to determine the screening operation number;

[0065] Optionally, the above step S12 includes:

[0066] S121: the number of matching operations in which errors occur in the underwear machine control instruction is taken as the number of error operations. When the number of error operations is less than the preset operation number threshold, it is determined that the error probability of the underwear machine control instruction is not within the preset interval. When the number of error operations is not less than the preset operation number threshold, the process proceeds to step S122.

[0067] S122 determines the operation abnormality coefficients of different error operation times according to the historical error times of the knitting machine in different error operation times, and when the operation abnormality coefficient does not exist for filtering the operation times, proceeds to step S123, and when the operation abnormality coefficient exists for filtering the operation times, proceeds to step S13;

[0068] S123 obtains the ratio of the number of erroneous operations to the number of matching operations. When the ratio is less than the preset ratio, it is determined that the error probability of the underwear machine control instruction is not within the preset interval. When the ratio is not less than the preset ratio, proceed to step S13.

[0069] S13 determines the error probability of the underwear machine control instruction based on the proportion of the number of screening operations and the basic error probability.

[0070] S2 divides the underwear machine control instructions into different processing time periods based on the time series data of the underwear machine control instructions in the plate making processing result, and determines similar processing time periods of different processing time periods based on the distribution data of different underwear machine control instructions in different processing time periods;

[0071] It should also be noted that the underwear machine control instructions are divided into different processing time periods, specifically including:

[0072] According to the preset interval length, the underwear machine control instructions are divided into different processing time periods.

[0073] Specifically, Figure 3 As shown, the method for determining the similar processing period is:

[0074] Based on the distribution data of different underwear machine control instructions in the processing period, determining the underwear machine control instructions corresponding to different moments in the processing period;

[0075] According to the underwear machine control instructions corresponding to different times, determining the coincidence time of the underwear machine control instructions in the processing time period and different historical time periods;

[0076] The time period similarity coefficient between the historical time period and the processing time period is determined based on the number ratio of the consistent moments, and whether the historical time period is a similar processing time period is determined using the time period similarity coefficient.

[0077] Further, when the period similarity coefficient between the historical period and the processing period is greater than a preset similarity coefficient threshold, the historical period is determined to be a similar processing period.

[0078] Optionally, the method for determining the similar processing period is:

[0079] Based on the distribution data of different underwear machine control instructions in the processing period, determining the underwear machine control instructions corresponding to different moments in the processing period;

[0080] According to the underwear machine control instructions corresponding to different moments, the number of moments when the underwear machine control instructions are consistent in different unit time periods is determined, and the number of moments when the underwear machine control instructions are consistent is used to determine the similar time periods in different unit time periods;

[0081] It is determined whether the historical period is a similar processing period based on the number of similar periods.

[0082] Optionally, the method for determining the similar processing period is:

[0083] S21, based on the distribution data of different underwear machine control instructions in the processing period, determining the underwear machine control instructions corresponding to different moments in the processing period, and determining the consistent moments in the processing period and the historical period by using the underwear machine control instructions;

[0084] Optionally, the above step S21 includes the following contents:

[0085] S211 determines the underwear machine control instructions corresponding to different moments in the processing period based on the distribution data of different underwear machine control instructions in the processing period, and determines the consistent moments in the processing period and the historical period by using the underwear machine control instructions. When the number of the consistent moments does not meet the requirement, it is determined that the historical period does not belong to the similar processing period. When the number of the consistent moments meets the requirement, it proceeds to step S212.

[0086] S212: determining control instruction similarity coefficients of different underwear machine control instructions according to the number of consistent moments of different underwear machine control instructions and in combination with the number of moments of different underwear machine control instructions; when there is an underwear machine control instruction whose control instruction similarity coefficient does not meet the requirement, and when the control instruction similarity coefficients of different underwear machine control instructions all meet the requirement, determining that the historical period belongs to a similar processing period;

[0087] S213 regards the underwear machine control instructions whose control instruction similarity coefficients do not meet the requirements as deviation control instructions. When the number of the deviation control instructions is greater than the preset number of control instructions, it is determined that the historical period does not belong to the similar processing period. When the number of the deviation control instructions is not greater than the preset number of control instructions, it goes to step S22.

[0088] S22: determining the distribution data of the consistent moments in different unit time periods according to the distribution data of the consistent moments, and determining the instruction similarity coefficients of the different unit time periods by using the distribution data of the consistent moments in the different unit time periods;

[0089] Optionally, the above step S22 includes the following contents:

[0090] S221 determines the similarity number of the consistent moments in different unit time periods according to the distribution data of the consistent moments, and when there is a unit time period in which the similarity number does not meet the requirement, proceeds to step S222; when there is no unit time period in which the similarity number does not meet the requirement, proceeds to step S223;

[0091] S222: the unit time period whose similarity quantity does not meet the requirement is regarded as the similarity quantity deviation time period. When the number of the similarity quantity deviation time periods is greater than the preset deviation time period number, it is determined that the historical time period does not belong to the similarity processing time period. When the number of the similarity quantity deviation time periods is not greater than the preset deviation time period number, the process proceeds to step S223.

[0092] S223 determines the instruction similarity coefficients of different unit time periods by using the distribution data of the coincident time in different unit time periods. When there is no unit time period with an instruction similarity coefficient less than the preset instruction similarity coefficient threshold, the process proceeds to step S224. When there is a unit time period with an instruction similarity coefficient less than the preset instruction similarity coefficient threshold, the process proceeds to step S23.

[0093] S224 When the average value of the instruction similarity coefficients of different unit time periods is greater than the preset similarity coefficient setting value, it is determined that the historical period belongs to a similar processing period. When the average value of the instruction similarity coefficients of different unit time periods is not greater than the preset similarity coefficient setting value, proceed to step S23.

[0094] S23 determines a comprehensive similarity coefficient through instruction similarity coefficients of different unit time periods, and uses the comprehensive similarity coefficient to determine whether the historical time period is a similar processing time period.

[0095] S3: acquiring historical error data of the knitting machine in different similar processing periods, and combining the underwear machine control instructions with error probabilities within a preset range in the processing period, when it is determined that there is no processing period in which the error abnormality probability does not meet the requirements, determining the historical similar plate making results of the plate making processing result based on the time series data of the underwear machine control instructions in the plate making processing result;

[0096] Specifically, Figure 4 As shown, the method for determining the error anomaly probability of the processing period is:

[0097] Determine the similar processing time period with the historical error data of the knitting machine in different similar processing time periods, and take the proportion of the similar processing time periods with the historical error data in the similar processing time periods as the proportion of the number of errors;

[0098] Determine the time quantity ratio of the underwear machine control instructions with error probability within the preset interval in the processing period according to the distribution data of the underwear machine control instructions with error probability within the preset interval in the processing period;

[0099] Based on the average of the time quantity proportion and the error quantity proportion, the error anomaly probability of the processing period is determined.

[0100] Furthermore, the error exception probability of the processing period ranges from 0 to 1, wherein when the error exception probability of the processing period is greater than a preset error probability threshold, it is determined that the error exception probability of the processing period does not meet the requirement.

[0101] It can be understood that the method for determining the historical similar plate-making results is:

[0102] Based on the timing data of the underwear machine control instruction in the plate making process result, determining the similar time of the underwear machine control instruction in the plate making process result and different historical plate making results;

[0103] Determine the similarity moment coefficient of the underwear machine control instruction between the plate making result and different historical plate making results by using the number of similar moments;

[0104] The historical plate-making results whose similar time coefficients are greater than the preset time coefficient threshold are taken as historical similar plate-making results.

[0105] S4 determines whether to issue a reminder message based on the historical error data of the knitting machine based on the historical similar plate making results.

[0106] Further, determining whether to issue a reminder message includes:

[0107] Based on the historical error data of the knitting machine in the historical similar plate-making results, the historical similar plate-making results with the historical error data are determined;

[0108] The proportion of the number of historical similar plate-making results with historical error data in the historical similar plate-making results is used as the plate-making error proportion, and the plate-making error proportion is used to determine whether to issue a reminder message.

[0109] Specifically, using the plate making error ratio to determine whether to issue a reminder message specifically includes:

[0110] When the proportion of the plate-making errors is greater than the proportion of the preset plate-making quantity, it is determined that a reminder message needs to be issued.

[0111] It should be noted that when it is necessary to issue a reminder message, the processing period in which the error probability is within a preset error probability interval is used as the reminder output target. Example

[0112] Second, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned underwear fabric weaving process self-detection method when running the computer program.

[0113] Optionally, the method for determining the error anomaly probability of the processing period is:

[0114] Determine similar processing periods with historical error data of the knitting machine in different similar processing periods, obtain the number of historical errors in different similar processing periods, and when there is a similar processing period with a number of historical errors greater than a preset error number threshold, determine that the error anomaly probability of the processing period does not meet the requirement;

[0115] When there is no similar processing period with a historical error count greater than the preset error count threshold:

[0116] The proportion of similar processing periods with historical error data in similar processing periods is taken as the error proportion. When the error proportion is greater than a preset error proportion threshold, it is determined that the error anomaly probability of the processing period does not meet the requirement.

[0117] When the error ratio is not greater than the preset error ratio threshold:

[0118] Obtaining the number of historical errors in different similar processing periods with historical error data, taking the similar processing period with historical error data as the error processing period, and when there is an error processing period with a number of historical errors not less than a preset number of setting values, taking the error processing period with a number of historical errors not less than the preset number of setting values ​​as a screening processing period, and when the number of the screening processing periods does not meet the requirement, determining that the error anomaly probability of the processing period does not meet the requirement;

[0119] When there is no error processing period with a historical error count not less than a preset number of error processing periods or the number of the screening processing periods meets the requirement:

[0120] Determine the time quantity ratio of the underwear machine control instructions with error probability within the preset interval in the processing period according to the distribution data of the underwear machine control instructions with error probability within the preset interval in the processing period;

[0121] Based on the average of the time quantity proportion and the error quantity proportion, the error anomaly probability of the processing period is determined.

[0122] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0123] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0124] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for self-detecting errors in underwear fabric knitting process, characterized in that: Specifically include: Determine the processing data of different underwear machine control instructions based on the plate making processing result of the underwear fabric knitting process, and combine the historical error data of the knitting machine under different underwear machine control instructions to determine that there is an underwear machine control instruction with an error probability within a preset range, and then proceed to the next step; Based on the timing data of the underwear machine control instructions in the plate making processing result, the underwear machine control instructions are divided into different processing time periods, and based on the distribution data of different underwear machine control instructions in different processing time periods, similar processing time periods of different processing time periods are determined; Acquire historical error data of the knitting machine in different similar processing periods, and combine the underwear machine control instructions with error probability within a preset interval in the processing period to determine that there is no processing period in which the error abnormality probability does not meet the requirements, and determine the historical similar plate making results of the plate making processing result based on the time series data of the underwear machine control instructions in the plate making processing result; Whether to issue a reminder message is determined based on the historical error data of the knitting machine based on the historical similar pattern making results.

2. The underwear fabric knitting process self-detection method according to claim 1, characterized in that: The underwear machine control instructions include action angle, action sequence and action position.

3. The underwear fabric knitting process self-detection method according to claim 1, characterized in that: The processing data includes the number of times the underwear machine operation instruction is processed at different positions in the plate making processing result.

4. The underwear fabric knitting process self-detection method according to claim 1, characterized in that: The historical error data includes the historical error times of the underwear machine control instructions and the operation times corresponding to different historical error times.

5. The underwear fabric knitting process self-detection method according to claim 1, characterized in that: The method for determining the error probability of the underwear machine control instruction is: The number of operations of the underwear machine control instruction is used as the number of matching operations; The number of matching operations in which errors occur in the underwear machine control instruction is taken as the number of error operations, and the error probability of the underwear machine control instruction is determined by using the proportion of the number of error operations in the number of matching operations.

6. The underwear fabric knitting process self-detection method according to claim 1, characterized in that: When there is no underwear machine control instruction with an error probability within a preset range, it is determined not to issue a reminder message.

7. The underwear fabric knitting process self-detection method according to claim 1, characterized in that: The underwear machine control instructions are divided into different processing time periods, specifically including: According to the preset interval length, the underwear machine control instructions are divided into different processing time periods.

8. The underwear fabric knitting process self-detection method according to claim 1, characterized in that: Determine whether to issue a reminder message, including: Based on the historical error data of the knitting machine in the historical similar plate-making results, the historical similar plate-making results with the historical error data are determined; The proportion of the number of historical similar plate-making results with historical error data in the historical similar plate-making results is used as the plate-making error proportion, and the plate-making error proportion is used to determine whether to issue a reminder message.

9. The underwear fabric knitting process self-detection method according to claim 8, characterized in that: When it is necessary to issue a reminder message, the processing period in which the error probability is within the preset error probability interval is used as the reminder output target.

10. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes a method for self-detecting errors in the knitting process of underwear fabric as described in any one of claims 1 to 9 when running the computer program.

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