A performance evaluation analysis method and system of an ultra-high molecular fiber hoisting belt

Abstract

The application provides a performance evaluation analysis method and system of an ultrahigh molecular fiber hoisting belt, belongs to the technical field of performance evaluation, and specifically comprises the following steps: a monitoring data acquisition module is responsible for determining the monitoring deviation of the Internet of Things monitoring data of different processing equipment, a deviation identification module is responsible for determining the deviation data in the Internet of Things monitoring data, a test environment matching module is responsible for determining the matching result with different test environments by using the deviation data, and a performance evaluation analysis module is responsible for determining the performance evaluation strategy of the hoisting belt in different test environments by using the matching result with different test environments, thereby improving the accuracy of the performance evaluation result of the hoisting belt.

Description

Technical Field

[0001] This invention belongs to the field of performance evaluation technology, and in particular relates to a performance evaluation and analysis method and system for ultra-high molecular weight fiber lifting slings. Background Technology

[0002] To evaluate the performance of lifting slings, existing technologies often use tensile testing equipment. Specific examples include CN202321591544.0 "A Tensile Testing Device for Lifting Sling Production" and CN202411322908.4 "A Preparation Apparatus and Method for High-Strength Polyester Fiber Lifting Slings." However, these technologies suffer from the following problems: During the production of lifting slings, different production stages are often equipped with IoT monitoring devices. Due to differences in the usage environment and installation location, the monitoring data from these IoT monitoring devices may be inaccurate. When the monitoring data is inaccurate, the processing data in the production stage may also be inaccurate, which in turn will affect the aging performance, corrosion resistance, and abrasion resistance of the lifting slings. Therefore, if the performance evaluation and analysis strategy based on the reliability of the processing data monitoring is not taken into account, the accuracy of the performance evaluation and analysis results cannot be guaranteed.

[0003] To address the aforementioned technical problems, this application provides a method and system for performance evaluation and analysis of ultra-high molecular weight fiber lifting slings. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, in the first aspect, this application provides a performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings, which specifically includes: Monitoring data acquisition module, deviation identification module, test environment matching module, performance evaluation and analysis module; The monitoring data acquisition module is responsible for determining the monitoring deviation of IoT monitoring data for different processing equipment. The deviation identification module is responsible for identifying deviation data in the IoT monitoring data; The test environment matching module is responsible for using the deviation data to determine the matching results with different test environments; The performance evaluation and analysis module is responsible for using the matching results with different test environments to determine the performance evaluation strategy of the lifting sling in different test environments.

[0005] A further technical solution is that the deviation data is IoT monitoring data with monitoring deviations.

[0006] A further technical solution involves using the deviation data to determine the matching results with different testing environments, specifically including: Based on the different types of quality defects in the lifting sling when different deviation data show anomalies, determine the associated defect types of different deviation data; The matching coefficient with the test environment is determined by the proportion of the same quantity of the associated defect type and the quality defect type corresponding to the test environment.

[0007] Secondly, this application provides a performance evaluation and analysis method for ultra-high molecular weight fiber lifting slings, applied to the aforementioned performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings, specifically including: S1 uses the processing data of the ultra-high molecular weight fiber of the hoisting sling to determine the processing equipment of different processing stages, the historical anomalies of the IoT monitoring data, and the monitoring deviation data in the IoT monitoring data based on the historical anomalies. S2 acquires monitoring deviation data from different production stages, and uses the analysis results of the test data of the lifting sling when the monitoring deviation data is abnormal to determine the associated defect type of the monitoring deviation data; S3 When there are associated defect types with multiple monitoring deviation data, determine the overlapping data of different monitoring deviation data in different production batches, and combine the associated defect types of the monitoring deviation data to determine the verification defect type among the associated defect types; S4 constructs a test environment for performance evaluation based on the aforementioned verification defect type. Based on the monitoring deviation of the current IoT monitoring data and the matching results of the test environment, it determines the performance evaluation strategy of the hoisting belt in different test environments for different processing periods.

[0008] The beneficial effects of this invention are as follows: The construction and processing of the test environment for performance evaluation based on the verification defect type not only avoids the technical problem of the large number of test environments for performance evaluation, which leads to greater difficulty in construction and processing, but also ensures the reliability of the verification and processing of quality defect types under the condition of monitoring deviation in IoT monitoring data by further combining the verification defect type, thus ensuring the accuracy of quality defect identification and processing.

[0009] Based on the current IoT monitoring data and the matching results of the test environment, performance evaluation strategies for hoisting slings in different test environments during different processing periods are determined. This fully considers the differences in the probability of different types of quality defects caused by the differences in monitoring deviations, thereby enabling targeted testing and processing for different processing periods in different test environments based on the differences in the probability of occurrence, ensuring the accuracy and efficiency of performance evaluation.

[0010] A further technical solution is that the processing steps include material pretreatment, fiber preparation, weaving and braiding, multi-layer structure treatment, surface treatment, and marking.

[0011] A further technical solution is that the historical anomalies in the IoT monitoring data include the number of times the monitoring data deviated and the duration of each occurrence.

[0012] A further technical solution is that the monitoring deviation is determined based on whether the monitoring data is within a preset monitoring data range, specifically based on the situation where the monitoring data is inconsistent with the actual operating data.

[0013] A further technical solution is that the method for determining the monitoring deviation data in the IoT monitoring data is as follows: Based on the historical anomalies in the IoT monitoring data, determine the number of times the IoT monitoring data has deviated, and use this number as the monitoring deviation count; Based on the number of monitoring deviations in different production batches, production batches with a number of monitoring deviations exceeding a preset monitoring deviation threshold are identified and designated as monitoring deviation batches. Whether the IoT monitoring data is monitoring deviation data is determined based on the number of monitoring deviations.

[0014] A further technical solution is that when the number of monitored deviation batches is greater than a preset threshold for the number of deviation batches, the IoT monitoring data is determined to be monitoring deviation data.

[0015] A further technical solution is that, when there is no monitoring deviation data, the performance evaluation of the lifting sling is carried out according to a preset tensile test, and the breaking tensile force is taken as the performance evaluation result of the lifting sling.

[0016] A further technical solution involves determining the performance evaluation strategy for the hoisting belt during the processing period in different test environments using the following method: Based on the current monitoring deviation of IoT monitoring data, identify IoT monitoring data with monitoring deviation in different processing periods and use them as deviation data; Based on the associated defect types of different deviation data, determine the same number of quality defect types corresponding to different test environments, and treat them as the same defect type. Based on the proportion of the same defect type in the corresponding quality defect type in the test environment, an evaluation matching coefficient is determined for different test environments. Based on the evaluation matching coefficient, a performance evaluation strategy for the hoisting belt during the processing period in different test environments is determined.

[0017] A further technical solution involves determining a performance evaluation strategy for the hoisting belt during the processing period in different test environments based on the evaluation matching coefficient, specifically including: When the evaluation matching coefficient of the test environment is greater than the preset evaluation matching coefficient threshold, the performance evaluation process in the test environment is performed according to the preset ratio. When the evaluation matching coefficient of the test environment is not greater than the preset evaluation matching coefficient threshold, it is determined whether the evaluation matching coefficient is less than the preset matching coefficient threshold. If yes, no performance evaluation processing is required in the test environment. If no, the performance evaluation processing in the test environment is performed according to the second preset ratio.

[0018] A further technical solution is that the second preset ratio is smaller than the preset ratio.

[0019] A further technical solution involves performing performance evaluation processing in the test environment according to a preset ratio, specifically including: Based on the number of lifting slings generated during the processing period, the number of lifting slings for performance evaluation is determined by multiplying the number of lifting slings by a preset ratio. Based on the number of lifting slings in the performance evaluation, and taking into account the quality defect type corresponding to the test environment, the test results corresponding to different quality defect types are determined. The performance evaluation results in the test environment are determined by the average value of the test results corresponding to different quality defect types for the lifting slings under different performance evaluations.

[0020] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0022] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0023] Figure 1 This is a framework diagram of a performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings. Figure 2 This is a flowchart of a performance evaluation and analysis method for ultra-high molecular weight fiber lifting slings; Figure 3 This is a flowchart illustrating the method for determining monitoring deviation data in IoT monitoring data; Figure 4 This is a flowchart illustrating the method for determining the associated defect types based on monitoring deviation data. Detailed Implementation

[0024] 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 with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0025] In this application, by analyzing the historical deviations of monitoring data of production equipment, monitoring data with deviations is identified, i.e., when the monitoring data fluctuates or fails to accurately reflect the true state. Based on the monitoring data with abnormalities, a differentiated performance evaluation and processing scheme is generated, thereby ensuring the reliability of the performance evaluation and processing. Example

[0026] like Figure 1 As shown, this application provides a first aspect: a performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings, specifically comprising: Monitoring data acquisition module, deviation identification module, test environment matching module, performance evaluation and analysis module; The monitoring data acquisition module is responsible for determining the monitoring deviation of IoT monitoring data for different processing equipment. The deviation identification module is responsible for identifying deviation data in the IoT monitoring data; The test environment matching module is responsible for using the deviation data to determine the matching results with different test environments; The performance evaluation and analysis module is responsible for using the matching results with different test environments to determine the performance evaluation strategy of the lifting sling in different test environments.

[0027] Furthermore, the deviation data refers to IoT monitoring data that exhibits monitoring deviations.

[0028] Specifically, using the aforementioned deviation data, the matching results with different testing environments are determined, including: Based on the different types of quality defects in the lifting sling when different deviation data show anomalies, determine the associated defect types of different deviation data; The matching coefficient with the test environment is determined by the proportion of the same quantity of the associated defect type and the quality defect type corresponding to the test environment. Example

[0029] Secondly, such as Figure 2 As shown, this application provides a performance evaluation and analysis method for ultra-high molecular weight fiber lifting slings, applied to the aforementioned performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings, specifically including: S1 uses the processing data of the ultra-high molecular weight fiber of the hoisting sling to determine the processing equipment of different processing stages, the historical anomalies of the IoT monitoring data, and the monitoring deviation data in the IoT monitoring data based on the historical anomalies. S2 acquires monitoring deviation data from different production stages, and uses the analysis results of the test data of the lifting sling when the monitoring deviation data is abnormal to determine the associated defect type of the monitoring deviation data; S3 When there are associated defect types with multiple monitoring deviation data, determine the overlapping data of different monitoring deviation data in different production batches, and combine the associated defect types of the monitoring deviation data to determine the verification defect type among the associated defect types; S4 constructs a test environment for performance evaluation based on the aforementioned verification defect type. Based on the monitoring deviation of the current IoT monitoring data and the matching results of the test environment, it determines the performance evaluation strategy of the hoisting belt in different test environments for different processing periods.

[0030] Furthermore, the processing steps include material pretreatment, fiber preparation, weaving and braiding, multi-layer structure treatment, surface treatment, and marking.

[0031] Specifically, the historical anomalies in the IoT monitoring data include the number of times monitoring deviations occurred and the duration of each occurrence.

[0032] It should be noted that the monitoring deviation is determined based on whether the monitoring data is within the preset monitoring data range, specifically based on the situation where the monitoring data is inconsistent with the actual operating data.

[0033] Specifically, such as Figure 3 As shown, the method for determining the monitoring deviation data in the IoT monitoring data is as follows: Based on the historical anomalies in the IoT monitoring data, determine the number of times the IoT monitoring data has deviated, and use this number as the monitoring deviation count; Based on the number of monitoring deviations in different production batches, production batches with a number of monitoring deviations exceeding a preset monitoring deviation threshold are identified and designated as monitoring deviation batches. Whether the IoT monitoring data is monitoring deviation data is determined based on the number of batches of the monitoring deviation.

[0034] Furthermore, when the number of monitored deviation batches is greater than a preset threshold for the number of deviation batches, the IoT monitoring data is determined to be monitoring deviation data.

[0035] It is understandable that when there is no monitoring deviation data, the performance evaluation of the lifting sling is carried out according to the preset tensile test, and the breaking tensile force is taken as the performance evaluation result of the lifting sling.

[0036] In another possible embodiment, the method for determining the monitoring deviation data in the IoT monitoring data is as follows: Based on the historical anomalies in the IoT monitoring data, determine the number of times the IoT monitoring data has deviated, and use this number as the monitoring deviation count; The average number of monitoring deviations in different production batches is determined based on the number of monitoring deviations in different production batches; Based on the average number of monitoring deviations in different production batches, it is determined whether the IoT monitoring data is monitoring deviation data.

[0037] Furthermore, when the average number of monitoring deviations in different batches of the monitoring data is greater than a preset deviation threshold, the IoT monitoring data is determined to be monitoring deviation data.

[0038] In another possible embodiment, the method for determining the monitoring deviation data in the IoT monitoring data is as follows: Based on the historical anomalies of the IoT monitoring data, the number of times the IoT monitoring data has a monitoring deviation is determined and used as the monitoring deviation count. If the monitoring deviation count of the IoT monitoring data does not meet the requirements, the IoT monitoring data is determined to be monitoring deviation data. When the number of monitoring deviations in the IoT monitoring data meets the requirements: The number of monitoring deviations within a preset duration range is determined by the duration of different monitoring deviation counts. If the number of monitoring deviations within the preset duration range does not meet the requirements, the IoT monitoring data is determined to be monitoring deviation data. When the number of monitoring deviations within the preset duration range meets the requirements: If, based on the number of monitoring deviations in different production batches, there are no production batches where the number of monitoring deviations exceeds a preset threshold and the sum of the durations of the monitoring deviations exceeds a preset duration threshold, then the IoT monitoring data is determined not to be monitoring deviation data. When there is a production batch with a number of monitoring deviations exceeding a preset monitoring deviation number threshold or a sum of the durations of monitoring deviations exceeding a preset duration threshold: Production batches whose number of monitoring deviations exceeds a preset threshold and whose duration of monitoring deviations exceeds a preset duration threshold are designated as monitoring deviation batches. When a monitoring deviation batch does not meet the requirements, the IoT monitoring data is determined to be monitoring deviation data. When the monitored deviation batch meets the requirements: Based on the number of monitoring deviations in different production batches and the duration of different monitoring deviations, a monitoring data deviation factor is determined for the IoT monitoring data. Based on the monitoring data deviation factor, it is determined whether the IoT monitoring data is monitoring deviation data.

[0039] Furthermore, if the monitoring data deviation factor does not meet the requirements, the IoT monitoring data is determined to be monitoring deviation data.

[0040] Specifically, the analysis results of the test data of the lifting slings include the proportion of lifting slings with different quality defect types in different test batches.

[0041] It should be noted that the types of quality defects include wear-resistant quality defects, aging quality defects, hardness defects, and strength defects.

[0042] Specifically, such as Figure 4 As shown, the method for determining the associated defect type of the monitoring deviation data is as follows: Based on the analysis results of the test data of the lifting slings when the monitoring deviation data is abnormal, determine the proportion of the number of lifting slings with different quality defect types in different test batches when the monitoring deviation data is abnormal; Based on the proportion of lifting slings of different quality defect types in different test batches, the defect correlation coefficient of different quality defect types in different test batches is determined; By averaging the defect correlation coefficients of different quality defect types in different test batches, it is determined whether the quality defect type is a related defect type of the monitoring deviation data.

[0043] Furthermore, when the average value of the defect correlation coefficients of different quality defect types in different test batches is greater than the preset defect correlation coefficient threshold, the quality defect type is determined to be the associated defect type of the monitoring deviation data.

[0044] In another possible embodiment, the method for determining the associated defect type of the monitoring deviation data is as follows: Based on the analysis results of the test data of the lifting slings when the monitoring deviation data is abnormal, the number of lifting slings with different quality defect types in different test batches when the monitoring deviation data is abnormal is determined. Based on the number of lifting slings with different quality defect types in different test batches, determine the total number of lifting slings with different quality defect types in different test batches; The total number of lifting slings of the aforementioned quality defect type is used to determine whether the quality defect type is a related defect type to the monitoring deviation data.

[0045] Furthermore, when the total number of lifting slings of the quality defect type is greater than a preset threshold for the number of defective lifting slings, the quality defect type is determined to be the associated defect type of the monitoring deviation data.

[0046] In another possible embodiment, the method for determining the associated defect type of the monitoring deviation data is as follows: Based on the analysis results of the test data of the lifting slings when the monitoring deviation data is abnormal, the number of lifting slings with different quality defect types in different test batches when the monitoring deviation data is abnormal is determined. Based on the number of lifting slings of different quality defect types in different test batches, the total number of lifting slings of different quality defect types in different test batches is determined. When the total number of lifting slings of the quality defect type is greater than a preset threshold for the number of defective lifting slings, the quality defect type is determined to be the associated defect type of the monitoring deviation data. When the total number of lifting slings of the aforementioned quality defect type is not greater than a preset defective sling quantity threshold: When the total number of lifting slings of the quality defect type is less than the preset value of the number of defective lifting slings: then it is determined that the quality defect type does not belong to the associated defect type of the monitoring deviation data; When the total number of lifting slings of the aforementioned quality defect type is not less than the preset value for the number of defective lifting slings: Based on the analysis results of the test data of the lifting sling when the monitoring deviation data is abnormal, the proportion of the number of lifting slings with different quality defect types in different test batches when the monitoring deviation data is abnormal is determined. Based on the proportion of the number of lifting slings with different quality defect types in different test batches, the defect correlation coefficient of the quality defect type in different test batches is determined. When there is a test batch whose defect correlation coefficient does not meet the requirements, the quality defect type is determined to be the associated defect type of the monitoring deviation data. When there are no test batches whose defect correlation coefficients do not meet the requirements: When the average value of the defect correlation coefficient of the quality defect type in different test batches is greater than the preset defect correlation coefficient threshold, the quality defect type is determined to be the associated defect type of the monitoring deviation data. When the average value of the defect correlation coefficients for different quality defect types in different test batches is not greater than the preset defect correlation coefficient threshold: The defect correlation value of the quality defect type is determined by using the defect correlation coefficient of the quality defect type in different test batches and the number of lifting straps of the quality defect type. The defect correlation value is then used to determine whether the quality defect type is a related defect type of the monitoring deviation data.

[0047] Furthermore, if the amount of the monitored deviation data is not within the preset range, it is determined that the monitored deviation data is abnormal.

[0048] Specifically, when the defect association value is greater than the preset defect association threshold, the quality defect type is determined to be the associated defect type of the monitoring deviation data.

[0049] It should be noted that when there is no associated defect type with multiple monitoring deviation data, the performance evaluation of the lifting sling is carried out according to the preset tensile test, and the breaking tensile force is taken as the performance evaluation result of the lifting sling.

[0050] It is understood that the method for determining the verification defect type in the associated defect type is as follows: The monitoring deviation data associated with the aforementioned defect type is used as the associated deviation data. The overlapping data of the associated deviation data in different production batches is used to determine the time period in which the associated deviation data and monitoring deviation coexist, and this period is taken as the overlapping time period. The overlap coefficient of different production batches is determined based on the proportion of the overlapping time periods in the generation time period of the production batch. Based on the overlap coefficient of different production batches, it is determined whether the associated defect type is a verification defect type.

[0051] Furthermore, when there is a production batch with an overlap coefficient greater than a preset overlap coefficient threshold, the associated defect type is determined to be a verification defect type.

[0052] It should be noted that when there is no verification defect type, the performance evaluation of the lifting sling is carried out according to the preset tensile test, and the breaking tensile force is taken as the performance evaluation result of the lifting sling.

[0053] Furthermore, the method for determining the verification defect type in the associated defect type is as follows: S31 The monitoring deviation data associated with the associated defect type is used as the associated deviation data. The overlapping data of the associated deviation data in different production batches is used to determine the time period in which the associated deviation data and the monitoring deviation exist simultaneously, and this time period is used as the overlapping time period. S32 determines the overlap anomaly coefficient for different overlapping periods based on the number and duration of associated deviation data that simultaneously exhibit monitoring deviations in different overlapping periods, and determines the batch overlap anomaly coefficient for different production batches based on the overlap anomaly coefficient for different overlapping periods in different production batches. S33 determines the defect anomaly coefficient of the associated defect type based on the matching overlap anomaly coefficient of different production batches, and determines whether the associated defect type is a verification defect type based on the defect anomaly coefficient.

[0054] Optionally, step S31 above includes the following: S311 The monitoring deviation data associated with the associated defect type is used as the associated deviation data. When the number of associated deviation data of the associated defect type is less than the preset associated deviation number threshold, it is determined that the associated defect type does not belong to the verification defect type. When the number of associated deviation data of the associated defect type is not less than the preset associated deviation number threshold, the process proceeds to step S312. S312 uses the overlapping data of the associated deviation data in different production batches to determine the time period in which the associated deviation data and the monitoring deviation coexist, and takes it as the overlapping time period. When the associated deviation data does not have an overlapping time period, it is determined that the associated defect type does not belong to the verification defect type. When the associated deviation data has an overlapping time period, proceed to step S313. S313 Obtain the number of overlapping time periods of the associated deviation data. When the number of overlapping time periods of the associated deviation data is greater than the preset threshold for the number of overlapping time periods, it is determined that the associated defect type belongs to the verification defect type. When the number of overlapping time periods of the associated deviation data is not greater than the preset threshold for the number of overlapping time periods, proceed to step S314. S314 determines the overlapping time periods in which the number of associated deviation data with simultaneous monitoring deviations is greater than a preset threshold based on the number of such data in different overlapping time periods. If the number of overlapping time periods in which the number of associated deviation data with simultaneous monitoring deviations is greater than the preset threshold does not meet the requirement, then the associated defect type is determined to be a verification defect type. If the number of overlapping time periods in which the number of associated deviation data with simultaneous monitoring deviations is greater than the preset threshold meets the requirement, then proceed to step S32.

[0055] Optionally, step S32 above includes the following: S321 determines the overlap anomaly coefficient for different overlapping periods based on the number and duration of associated deviation data that simultaneously exhibit monitoring deviations in different overlapping periods. When there is an overlapping period with an overlap anomaly coefficient greater than the preset overlap anomaly coefficient threshold, proceed to step S322. When there is no overlapping period with an overlap anomaly coefficient greater than the preset overlap anomaly coefficient threshold, proceed to step S324. S322 When the number of production batches in overlapping periods with an overlap anomaly coefficient greater than the preset overlap anomaly coefficient threshold does not meet the requirements, the associated defect type is determined to be a verification defect type. When the number of production batches in overlapping periods with an overlap anomaly coefficient greater than the preset overlap anomaly coefficient threshold meets the requirements, the process proceeds to step S323. S323 When the number of overlapping time periods with an overlap anomaly coefficient greater than the preset overlap anomaly coefficient threshold does not meet the requirements, the associated defect type is determined to be a verification defect type. When the number of overlapping time periods with an overlap anomaly coefficient greater than the preset overlap anomaly coefficient threshold meets the requirements, proceed to step S324. S324 determines the batch overlap anomaly coefficient of different production batches based on the overlap anomaly coefficient of different overlapping time periods in different production batches. When the average value of the batch overlap anomaly coefficient of different production batches does not meet the requirements, it is determined that the associated defect type belongs to the verification defect type. When the average value of the batch overlap anomaly coefficient of different production batches meets the requirements, the process proceeds to step S33.

[0056] Specifically, the construction and processing of the test environment for performance evaluation based on the aforementioned verification defect types includes: The verification defect types can be freely combined to obtain multiple defect type groups; Based on the verification defect types of different defect type groups, and in turn combined with other quality defect types, test environments for performance evaluation corresponding to other quality defect types under different defect type groups are generated.

[0057] It should be noted that the method for determining the performance evaluation strategy of the hoisting belt during the processing period in different test environments is as follows: Based on the current monitoring deviation of IoT monitoring data, identify IoT monitoring data with monitoring deviation in different processing periods and use them as deviation data; Based on the associated defect types of different deviation data, determine the same number of quality defect types corresponding to different test environments, and treat them as the same defect type. Based on the proportion of the same defect type in the corresponding quality defect type in the test environment, an evaluation matching coefficient is determined for different test environments. Based on the evaluation matching coefficient, a performance evaluation strategy for the hoisting belt during the processing period in different test environments is determined.

[0058] Furthermore, based on the evaluation matching coefficient, a performance evaluation strategy for the hoisting sling during the processing period in different test environments is determined, specifically including: When the evaluation matching coefficient of the test environment is greater than the preset evaluation matching coefficient threshold, the performance evaluation process in the test environment is performed according to the preset ratio. When the evaluation matching coefficient of the test environment is not greater than the preset evaluation matching coefficient threshold, it is determined whether the evaluation matching coefficient is less than the preset matching coefficient threshold. If yes, no performance evaluation processing is required in the test environment. If no, the performance evaluation processing in the test environment is performed according to the second preset ratio.

[0059] Furthermore, the second preset ratio is smaller than the preset ratio.

[0060] It is understood that the performance evaluation process in the test environment is performed according to a preset ratio, specifically including: Based on the number of lifting slings generated during the processing period, the number of lifting slings for performance evaluation is determined by multiplying the number of lifting slings by a preset ratio. Based on the number of lifting slings in the performance evaluation, and taking into account the quality defect type corresponding to the test environment, the test results corresponding to different quality defect types are determined. The performance evaluation results in the test environment are determined by the average value of the test results corresponding to different quality defect types for the lifting slings under different performance evaluations.

[0061] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0062] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0063] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings, characterized in that, Specifically, it includes: Monitoring data acquisition module, deviation identification module, test environment matching module, performance evaluation and analysis module; The monitoring data acquisition module is responsible for determining the monitoring deviation of IoT monitoring data for different processing equipment. Using the processing data of ultra-high molecular weight fiber in the hoisting sling, the processing equipment for different processing stages is determined, historical anomalies in the IoT monitoring data are identified, and monitoring deviation data in the IoT monitoring data is determined based on the historical anomalies. The deviation identification module is responsible for identifying deviation data in the IoT monitoring data; The test environment matching module is responsible for using the deviation data to determine the matching results with different test environments; Acquire monitoring deviation data from different production stages, and determine the associated defect type of the monitoring deviation data based on the analysis results of the test data of the lifting sling when the monitoring deviation data is abnormal; When there are associated defect types with multiple monitoring deviation data, the overlapping data of different monitoring deviation data in different production batches are identified, and the verification defect type in the associated defect type is determined by combining the associated defect types of the monitoring deviation data. The performance evaluation and analysis module is responsible for constructing and processing the test environment for performance evaluation based on the verification defect type. Based on the monitoring deviation of the current IoT monitoring data and the matching result of the test environment, it determines the performance evaluation strategy of the hoisting belt in different test environments for different processing periods. The method for determining the performance evaluation strategy of the hoisting belt during the processing period in different test environments is as follows: Based on the current monitoring deviation of IoT monitoring data, identify IoT monitoring data with monitoring deviation in different processing periods and use them as deviation data; Based on the associated defect types of different deviation data, determine the same number of quality defect types corresponding to different test environments, and treat them as the same defect type. Based on the proportion of the same defect type in the corresponding quality defect type in the test environment, an evaluation matching coefficient is determined for different test environments. Based on the evaluation matching coefficient, a performance evaluation strategy for the hoisting belt during the processing period in different test environments is determined. The deviation data refers to IoT monitoring data that exhibits monitoring deviations.

2. The performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings as described in claim 1, characterized in that, Using the aforementioned deviation data, the matching results with different testing environments are determined, specifically including: Based on the different types of quality defects in the lifting sling when different deviation data show anomalies, determine the associated defect types of different deviation data; The matching coefficient with the test environment is determined by the proportion of the same quantity of the associated defect type and the quality defect type corresponding to the test environment.

3. The performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings as described in claim 1, characterized in that, The processing steps include material pretreatment, fiber preparation, weaving and braiding, multi-layer structure treatment, surface treatment, and marking.

4. The performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings as described in claim 1, characterized in that, The historical anomalies in the IoT monitoring data include the number of times monitoring deviations occurred and the duration of each occurrence.

5. The performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings as described in claim 1, characterized in that, The monitoring deviation is determined based on whether the monitoring data is within the preset monitoring data range, specifically based on the situation where the monitoring data is inconsistent with the actual operating data.

6. The performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings as described in claim 1, characterized in that, The method for determining the monitoring deviation data in the IoT monitoring data is as follows: Based on the historical anomalies in the IoT monitoring data, determine the number of times the IoT monitoring data has deviated, and use this number as the monitoring deviation count; Based on the number of monitoring deviations in different production batches, production batches with a number of monitoring deviations exceeding a preset monitoring deviation threshold are identified and designated as monitoring deviation batches. Whether the IoT monitoring data is monitoring deviation data is determined based on the number of monitoring deviations.

7. The performance evaluation and analysis system for ultra-high molecular weight fiber lifting slings as described in claim 1, characterized in that, When there is no monitoring deviation data, the performance evaluation of the lifting sling is carried out according to the preset tensile test, and the breaking tensile force is taken as the performance evaluation result of the lifting sling.

Images