Production identification traceability data analysis method and system based on ultrathin fabric

Abstract

The invention discloses a production identification traceability data analysis method and system based on ultrathin fabric, and relates to the technical field of electric digital data processing. The production identification traceability data analysis method based on the ultrathin fabric comprises the following steps: obtaining a traceability accurate evaluation value; evaluating quality detection node information; and adjusting node information. According to the invention, the traceability related data in the ultrathin fabric quality detection process is obtained, then the traceability accurate evaluation value of the specified ultrathin fabric is obtained based on the obtained traceability related data and the reference traceability data, and whether the quality detection node information evaluation is carried out or not is judged; and finally, based on the obtained node processing evaluation value and the node information integrity evaluation value, whether node information adjustment is performed is judged, and the effect of improving the traceability data analysis accuracy in the production identifier traceability process is achieved. The problem of insufficient traceability data analysis in the production identification traceability process in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a production identification traceability data analysis method and system based on ultra-thin fabrics. Background Art

[0002] As consumers pay more attention to environmental protection and sustainable development, the textile industry needs to pay more attention to the sustainability of raw materials, the environmental friendliness of the production process, and the treatment of waste. Traceability data analysis can help companies track the source of raw materials, the production process, and the flow of products, so as to formulate more environmentally friendly and sustainable production strategies. In the global supply chain, ensuring the transparency of the supply chain is crucial to improving production efficiency, reducing risks, and enhancing consumer trust. Through traceability data analysis, companies can grasp the dynamic information of the supply chain in real time, including raw material procurement, production and processing, logistics and transportation, and improve the transparency and traceability of the supply chain. Through production identification traceability data analysis, the production process of ultra-thin fabrics can be monitored and traced in real time to ensure that the products meet quality standards and to promptly discover and resolve potential quality problems.

[0003] The existing production identification traceability data analysis method mainly relies on tracing each node in each link one by one, that is, tracing back from one node to the previous node until tracing back to the source.

[0004] For example, the invention patent with announcement number: CN112307301B discloses a rule adjustment method and device based on network data analysis and tracing, comprising: obtaining analysis result data; performing tracing analysis on the analysis result data in a sandbox environment based on a preset backtracking engine module; adjusting the analysis rules according to the tracing analysis results to determine the target analysis rules; wherein the analysis rules correspond to the analysis result data.

[0005] For example, the invention patent with announcement number CN114780956B announces a tracking and tracing system based on big data analysis, which includes an acquisition module, a detection module, a processing module, a central control module, a tracing module, and a security protection module. When a terminal is attacked by a network, the central control module scores the attack log and determines whether the attack log constitutes a network attack based on the scoring result. The detection module detects the degree of damage and tampering of the terminal files determined to be attacked. The central control module can quickly and accurately determine the damage level of the terminal based on the detection results and control the processing module to repair the damaged files and generate a log database. The control tracing module tracks and traces the attack address and sends the tracked suspected IP address to the network security protection module.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the prior art, generally speaking, functional ultra-thin fabrics (such as breathable, moisture-permeable and heat-dissipating ultra-thin fabrics) involve fabric quality inspection after production. Since it is necessary to trace each node according to the production mark, the traceability time is relatively long, which will lead to the inability to quickly locate the source of the problem during the quality inspection process due to the functional ultra-thin fabrics, affecting the response speed and resulting in insufficient traceability data analysis during the production mark traceability process. Summary of the invention

[0007] The embodiment of the present application solves the problem of insufficient traceability data analysis in the production identification traceability process in the prior art by providing a production identification traceability data analysis method and system based on ultra-thin fabrics, thereby improving the accuracy of traceability data analysis in the production identification traceability process.

[0008] The embodiment of the present application provides a method for analyzing production identification traceability data based on ultra-thin fabrics, comprising the following steps: S1, obtaining traceability-related data in the process of ultra-thin fabric quality inspection, and obtaining a traceability accuracy assessment value of a specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data, wherein the traceability accuracy assessment value is used to reflect the traceability accuracy of the ultra-thin fabric; S2, judging whether to perform quality inspection node information assessment based on the traceability accuracy assessment value of the specified ultra-thin fabric, and if the quality inspection node information assessment is performed, obtaining a node processing assessment value and a node information integrity assessment value based on the traceability-related data and the traceability accuracy assessment value, wherein the node processing assessment value is used to reflect the quality inspection node processing capacity in the traceability process of the specified ultra-thin fabric, and the node information integrity assessment value is used to reflect the completeness of the quality inspection node information in the traceability process of the specified ultra-thin fabric; S3, judging whether to perform node information adjustment based on the node processing assessment value and the node information integrity assessment value.

[0009] Furthermore, the traceability-related data include fabric thickness, fabric density, the number of successful traceability nodes, the total number of detected nodes, the average node traceability time, the amount of missing node information and the total amount of node information; the number of successful traceability nodes represents the number of nodes traced back during the ultra-thin fabric quality inspection process; the amount of missing node information represents the amount of data of the missing nodes traced during the ultra-thin fabric quality inspection process; the reference traceability data includes a preset minimum fabric thickness, a preset minimum fabric density, a preset node traceability accuracy threshold, a preset node information missing threshold, a preset node traceability time threshold, a preset first traceability accurate allocation weight, a preset second traceability accurate allocation weight and a preset third traceability accurate allocation weight.

[0010] Furthermore, the specific process of obtaining the traceability accuracy evaluation value of the specified ultra-thin fabric based on the obtained traceability-related data and the reference traceability data is as follows: the first step is to obtain the fabric thickness ratio, which is represented by the result of the average calculation after the ratio calculation between the fabric thickness and the preset minimum fabric thickness; the second step is to obtain the traceability accuracy value, which is represented by the result of the ratio calculation between the number of successful traceability nodes and the total number of detected nodes; the third step is to obtain the traceability accuracy deviation value, which is represented by the sum of the traceability accuracy value and the preset node traceability accuracy threshold and twice the preset value. The result of ratio operation is represented by setting the node traceability accuracy threshold; the fourth step is to obtain the average traceability accuracy deviation value, which is represented by the result of mean operation of the traceability accuracy deviation value; the fifth step is to obtain the fabric density ratio, which is represented by the result of mean operation after ratio operation between the preset minimum fabric density and the fabric density; the sixth step is to obtain the traceability accuracy assessment value by combining the fabric thickness ratio, fabric density ratio, average traceability accuracy deviation value, the preset first traceability accuracy allocation weight, the preset second traceability accuracy allocation weight and the preset third traceability accuracy allocation weight.

[0011] Furthermore, the limiting expression of the traceability accurate evaluation value is as follows: ; ; ; ; In the formula, It represents the traceability accuracy evaluation value of the specified ultra-thin fabric at the ath quality inspection node. , a represents the number of quality detection nodes, b represents the total number of quality detection nodes, Indicates the thickness ratio of the specified ultra-thin fabric in the ath quality inspection node. Indicates the density ratio of the specified ultra-thin fabric in the ath quality inspection node. Indicates the average traceability accuracy deviation value of the specified ultra-thin fabric. Indicates the fabric thickness of the xth ultra-thin fabric sample of the specified ultra-thin fabric in the ath quality inspection node. , x represents the number of ultra-thin fabric samples, y represents the total number of ultra-thin fabric samples, Indicates the fabric density of the xth ultra-thin fabric sample of the specified ultra-thin fabric in the ath quality inspection node. Indicates the number of successful traceability nodes for the xth ultra-thin fabric sample of the specified ultra-thin fabric. Indicates the total number of detection nodes of the xth ultra-thin fabric sample of the specified ultra-thin fabric, Indicates the preset minimum fabric thickness. Indicates the preset node traceability accuracy threshold. Indicates the preset minimum fabric density. Indicates that the first traceability is preset to accurately allocate weights. Indicates that the second traceability is preset to accurately allocate weights. It indicates that the third traceability is preset to accurately allocate weights, and e represents a natural constant.

[0012] Furthermore, the specific process of obtaining the node processing evaluation value is as follows: JM1, obtaining the initial traceability time, the initial traceability time is represented by the result of multiplying the total number of detected nodes and the average node traceability time; JM2, obtaining the initial traceability time deviation, the initial traceability time deviation is represented by the result of adding the initial traceability time and the preset node traceability time threshold; JM3, obtaining the traceability time ratio, the traceability time ratio is represented by the result of ratio operation between the initial traceability time deviation and twice the preset node traceability time threshold; JM4, obtaining the average traceability time ratio, the average traceability time ratio is represented by the result of mean operation of the traceability time ratio, and the traceability time ratio is used to reflect the deviation of the traceability time in the ultra-thin fabric quality detection process; JM5, combining the average traceability time ratio and the traceability accuracy evaluation value to obtain the node processing evaluation value.

[0013] Furthermore, the specific acquisition process of the node information integrity evaluation value is as follows: step one, obtaining the initial node information missing rate, the initial node information missing rate is represented by the result of ratio calculation of the missing node information amount and the total node information amount, and the initial node information missing rate is used to reflect the missing situation of the quality detection node information in the ultra-thin fabric quality detection process; step two, obtaining the initial node information missing deviation value, the initial node information missing deviation value is represented by the result of addition operation of the initial node information missing rate and the preset node information missing threshold; step three, obtaining the node information missing deviation value, the node information missing deviation value is represented by the result of ratio operation of the initial node information missing deviation value and twice the preset node information missing threshold; step four, obtaining the average node information missing deviation value, the average node information missing deviation value is represented by the result of mean operation of the node information missing deviation value, and the average node information missing deviation value is used to reflect the deviation of the missing quality detection node information in the ultra-thin fabric quality detection process; step five, combining the average node information missing deviation value and the traceability accuracy evaluation value to obtain the node information integrity evaluation value.

[0014] The embodiment of the present application provides a production identification traceability data analysis system based on ultra-thin fabrics, including a traceability accuracy evaluation module, a quality detection node information evaluation module and a node information adjustment module: wherein the traceability accuracy evaluation module is used to obtain traceability-related data in the ultra-thin fabric quality detection process, and obtain a traceability accuracy evaluation value of a specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data, and the traceability accuracy evaluation value is used to reflect the traceability accuracy of the ultra-thin fabric; the quality detection node information evaluation module is used to determine whether to perform quality detection node information evaluation based on the traceability accuracy evaluation value of the specified ultra-thin fabric, and if the quality detection node information evaluation is performed, a node processing evaluation value and a node information integrity evaluation value are obtained based on the traceability-related data and the traceability accuracy evaluation value, the node processing evaluation value is used to reflect the quality detection node processing capacity in the traceability process of the specified ultra-thin fabric, and the node information integrity evaluation value is used to reflect the completeness of the quality detection node information in the traceability process of the specified ultra-thin fabric; the node information adjustment module is used to determine whether to perform node information adjustment based on the node processing evaluation value and the node information integrity evaluation value.

[0015] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By obtaining the traceability-related data in the process of ultra-thin fabric quality inspection, and then obtaining the traceability accurate assessment value of the specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data, and judging whether to perform quality inspection node information assessment, and finally judging whether to adjust the node information based on the obtained node processing assessment value and node information integrity assessment value, the dynamic traceability of ultra-thin fabrics is realized, and then the accuracy of traceability data analysis in the production identification traceability process is improved, which effectively solves the problem of insufficient traceability data analysis in the production identification traceability process in the prior art.

[0016] 2. The traceability accuracy assessment value is obtained through the fabric thickness ratio, fabric density ratio, average traceability accuracy deviation value, preset first traceability accuracy allocation weight, preset second traceability accuracy allocation weight and preset third traceability accuracy allocation weight, and then the node processing assessment value is obtained by combining the average traceability time ratio and the traceability accuracy assessment value. Finally, the node information integrity assessment value is obtained by combining the average node information missing deviation value and the traceability accuracy assessment value, thereby realizing the quantification of the ultra-thin fabric traceability data analysis process, and then realizing the improvement of the reliability of ultra-thin fabric traceability data analysis.

[0017] 3. By judging whether the node processing evaluation value is within the preset node processing threshold range, when the node processing evaluation value is within the preset node processing threshold range, no node information adjustment is performed and the traceability positioning data is obtained. Otherwise, traceability request allocation and node load migration are performed, thereby achieving accurate quantification of the traceability node processing capacity and improving the reliability of node information adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a method for analyzing production identification traceability data based on ultra-thin fabrics provided in an embodiment of the present application; Figure 2 A statistical chart showing changes in the accurate evaluation value provided by the embodiment of the present application; Figure 3 A schematic diagram of the structure of a production identification traceability data analysis system based on ultra-thin fabrics provided in an embodiment of the present application; Figure 4 An overall flow chart provided for an embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiment of the present application solves the problem of insufficient traceability data analysis in the production identification traceability process in the prior art by providing a production identification traceability data analysis method and system based on ultra-thin fabrics. The method obtains traceability-related data in the ultra-thin fabric quality inspection process, and then obtains the traceability accurate evaluation value of the specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data. Then, based on the traceability accurate evaluation value of the specified ultra-thin fabric, it is determined whether to perform quality inspection node information evaluation. Finally, based on the obtained node processing evaluation value and node information integrity evaluation value, it is determined whether to adjust the node information, thereby improving the accuracy of traceability data analysis in the production identification traceability process.

[0020] The technical solution in the embodiment of the present application is to solve the problem of insufficient traceability data analysis in the above-mentioned production identification traceability process. The overall idea is as follows: By obtaining traceability-related data during the quality inspection of ultra-thin fabrics, and then obtaining the traceability accuracy assessment value of the specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data, and judging whether to conduct quality inspection node information assessment, and finally judging whether to adjust the node information based on the obtained node processing assessment value and node information integrity assessment value, the accuracy of traceability data analysis in the production identification traceability process is improved.

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] like Figure 1As shown, it is a flow chart of a method for analyzing production identification traceability data based on ultra-thin fabrics provided by an embodiment of the present application, the method comprising the following steps: S1, obtaining an accurate traceability evaluation value: tracing the production identification of the ultra-thin fabric during the ultra-thin fabric quality inspection process, obtaining traceability-related data during the ultra-thin fabric quality inspection process, and obtaining an accurate traceability evaluation value of the specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data. The accurate traceability evaluation value is used to reflect the traceability accuracy of the ultra-thin fabric; S2, quality inspection node information evaluation: judging whether to perform quality inspection node information evaluation based on the accurate traceability evaluation value of the specified ultra-thin fabric, and if performing quality inspection node information evaluation, then judging whether to perform quality inspection node information evaluation based on the traceability-related data. The node processing evaluation value and the node information integrity evaluation value are obtained based on the relevant data and traceability accuracy evaluation value. If the quality inspection node information evaluation is not performed, the traceability positioning data is obtained according to the production identification. The node processing evaluation value is used to reflect the quality inspection node processing capacity in the traceability process of the specified ultra-thin fabric, and the node information integrity evaluation value is used to reflect the completeness of the quality inspection node information in the traceability process of the specified ultra-thin fabric; S3, node information adjustment: based on the node processing evaluation value and the node information integrity evaluation value, it is determined whether to adjust the node information. The node information adjustment is used to adjust the node processing evaluation value to within the preset node processing threshold range and adjust the node information integrity evaluation value to within the preset traceability delay threshold range.

[0023] In this embodiment, for a specified ultra-thin fabric, a lot of ultra-thin fabric samples are selected for testing. The traceability accuracy evaluation value, the node processing evaluation value and the node information integrity evaluation value are interdependent in the process. The node processing evaluation value and the node information integrity evaluation value are further refinements and quantifications of the traceability accuracy evaluation value. If the traceability accuracy evaluation value is lower, it means that there may be problems such as information loss, error or inconsistency in the traceability process. If the node processing evaluation value is lower than the preset node processing threshold, it means that the processing capacity of the node is insufficient. For example, when tracing the ultra-thin fabric that is breathable, breathable and heat-dissipating, the quality detection node includes functional detection nodes such as moisture permeability and air permeability. The traceability accuracy evaluation value can quantitatively represent the information accuracy of the fabric at the quality detection node. The higher the traceability accuracy evaluation value means that the quality of the ultra-thin fabric that is breathable, breathable and heat-dissipating is more reliable. The higher the node processing evaluation value means that the quality detection node has higher efficiency and accuracy in processing, storing and transmitting traceability information. The higher the node information integrity evaluation value means that the quality detection node information has higher integrity and consistency in the traceability process; the accuracy of traceability data analysis in the production identification traceability process is improved.

[0024] It should be supplemented that the traceability-related data include fabric thickness, fabric density, number of successful traceability nodes, total number of detection nodes, average node traceability time, missing node information volume and total node information volume; the number of successful traceability nodes indicates the number of nodes traced back during the ultra-thin fabric quality inspection process; the missing node information volume indicates the amount of data of the missing nodes traced during the ultra-thin fabric quality inspection process; the reference traceability data includes the preset minimum fabric thickness, the preset minimum fabric density, the preset node traceability accuracy threshold, the preset node information missing threshold, the preset node traceability time threshold, the preset first traceability accurate allocation weight, the preset second traceability accurate allocation weight and the preset third traceability accurate allocation weight; the preset first traceability accurate allocation weight is used to evaluate the influence of fabric thickness on the traceability accurate assessment value during the ultra-thin fabric quality inspection process; the preset second traceability accurate allocation weight is used to evaluate the influence of the traceability accuracy of the detection node on the traceability accurate assessment value during the ultra-thin fabric quality inspection process, and the preset third traceability accurate allocation weight is used to evaluate the influence of fabric density on the traceability accurate assessment value during the ultra-thin fabric quality inspection process.

[0025] Specifically, the fabric thickness of the ultra-thin fabric samples is measured by an ellipsometer, the fabric density of the ultra-thin fabric samples is measured by a fabric warp and weft density ruler, and the number of successful traceability nodes and the average node traceability time are recorded by the existing traceability system. The existing traceability system has recording and statistical functions. The total number of detected nodes is obtained by the preset number of nodes that need to be detected, and the total node information volume is obtained by calling the preset data volume corresponding to the nodes that need to be detected through an API (Application Programming Interface). The amount of missing node information is obtained by the difference between the preset data volume corresponding to the nodes that need to be detected and the data volume corresponding to the successfully traced nodes.

[0026] It should be understood that the preset minimum fabric thickness is represented by the minimum value of the ultra-thin fabric thickness in the historical time period in the preset database, the preset minimum fabric density is represented by the minimum value of the ultra-thin fabric density in the historical time period in the preset database, the preset node traceability accuracy threshold is represented by the average value of the ultra-thin fabric traceability accuracy in the historical time period in the preset database, the preset node information missing threshold is represented by the average value of the ultra-thin fabric traceability node missing rate in the historical time period in the preset database, and the preset node traceability time threshold is represented by the minimum value of the ultra-thin fabric node traceability time in the historical time period in the preset database.

[0027] Specifically, the sum of the preset first traceability accurate allocation weight, the preset second traceability accurate allocation weight and the preset third traceability accurate allocation weight is 1. For example, the preset first traceability accurate allocation weight is 0.6, the preset second traceability accurate allocation weight is 0.2, and the preset third traceability accurate allocation weight is 0.2.

[0028] The preset first traceability accurate allocation weight is the weight corresponding to the preset fabric thickness value in the preset database, which indicates the influence of the fabric thickness value on the traceability accurate assessment value. When used, the weight corresponding to the preset fabric thickness value can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the fabric thickness in the ultra-thin fabric traceability training set and the weight corresponding to the preset fabric thickness value in the preset database form a mapping set, and the real-time fabric thickness is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1]; the accuracy of traceability data analysis in the production identification traceability process is improved.

[0029] Specifically, the third traceability accurate allocation weight is preset as the weight corresponding to the preset fabric density value in the preset database, which indicates the influence of the fabric density value on the traceability accurate assessment value. When used, the weight corresponding to the preset fabric density value can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the fabric density in the ultra-thin fabric traceability training set and the weight corresponding to the preset fabric density value in the preset database form a mapping set, and the real-time fabric density is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1]; the accuracy of traceability data analysis in the production identification traceability process is improved.

[0030] Furthermore, the specific process of obtaining the traceability accurate evaluation value of a specified ultra-thin fabric based on the obtained traceability-related data and the reference traceability data is as follows: Step 1: Obtain the fabric thickness ratio (i.e., the ratio of the thickness of the fabric in the limiting expression of the traceability accurate evaluation value). ), the fabric thickness ratio is represented by the result of the average operation after the ratio operation between the fabric thickness and the preset minimum fabric thickness. The fabric thickness ratio is used to evaluate the deviation of the fabric thickness during the ultra-thin fabric quality inspection process; the second step is to obtain the traceability accuracy value. The traceability accuracy value is represented by the result of the ratio operation between the number of successful traceability nodes and the total number of inspection nodes; the third step is to obtain the traceability accuracy deviation value. The traceability accuracy deviation value is represented by the result of the ratio operation between the sum of the traceability accuracy value and the preset node traceability accuracy threshold and twice the preset node traceability accuracy threshold; the fourth step is to obtain the average traceability accuracy deviation value (that is, the value in the restriction expression of the traceability accuracy evaluation value). ), the average traceability accuracy deviation value is expressed by the result of the mean operation of the traceability accuracy deviation value, and the traceability accuracy deviation value is used to evaluate the traceability accuracy deviation of the detection node of ultra-thin fabrics in the ultra-thin fabric quality detection process; the fifth step is to obtain the fabric density ratio (that is, the value in the limiting expression of the traceability accuracy evaluation value) ), the fabric density ratio is represented by the result of the ratio operation of the preset minimum fabric density and the fabric density and then the average operation; the sixth step is to obtain the traceability accuracy assessment value by combining the fabric thickness ratio, the fabric density ratio, the average traceability accuracy deviation value, the preset first traceability accuracy allocation weight, the preset second traceability accuracy allocation weight and the preset third traceability accuracy allocation weight.

[0031] Among them, the limiting expression of the traceability accurate evaluation value is as follows: ; ; ; ; In the formula, It represents the traceability accuracy evaluation value of the specified ultra-thin fabric at the ath quality inspection node. , a represents the number of quality detection nodes, b represents the total number of quality detection nodes, Indicates the thickness ratio of the specified ultra-thin fabric in the ath quality inspection node. Indicates the density ratio of the specified ultra-thin fabric in the ath quality inspection node. Indicates the average traceability accuracy deviation value of the specified ultra-thin fabric. Indicates the fabric thickness of the xth ultra-thin fabric sample of the specified ultra-thin fabric in the ath quality inspection node. , x represents the number of ultra-thin fabric samples, y represents the total number of ultra-thin fabric samples, Indicates the fabric density of the xth ultra-thin fabric sample of the specified ultra-thin fabric in the ath quality inspection node. Indicates the number of successful traceability nodes for the xth ultra-thin fabric sample of the specified ultra-thin fabric. Indicates the total number of detection nodes of the xth ultra-thin fabric sample of the specified ultra-thin fabric, Indicates the preset minimum fabric thickness. Indicates the preset node traceability accuracy threshold. Indicates the preset minimum fabric density. Indicates that the first traceability is preset to accurately assign weights. Indicates that the second traceability is preset to accurately allocate weights. It indicates that the third traceability is preset to accurately allocate weights, and e represents a natural constant.

[0032] The algorithm of this embodiment combines the traceability-related data for comprehensive analysis to obtain an accurate traceability assessment value. The traceability-related data in the algorithm of this embodiment does not exist independently, but are interrelated. Specified ultra-thin fabrics may be more susceptible to damage or deformation, which may affect the attachment and reading of traceability tags, which may lead to a reduction in the number of successful traceability nodes and affect the accuracy of the traceability system. The higher the density of the fabric, the more yarns per unit area, the smaller the gaps between the yarns, and the relatively reduced contact area between the label and the fabric. This may affect the adhesion of the label. The more total nodes detected, the greater the amount of information that may be contained, but it may also increase the complexity of the traceability system, thereby reducing the accuracy of traceability. The parameters of the algorithm of this embodiment need to consider their impact on the results together.

[0033] Specifically, assuming that the first traceability is accurately assigned weights Fixed to 0.6, preset second traceability to accurately allocate weights Fixed to 0.2, preset third-party traceability to accurately allocate weights Fixed to 0.2, fabric thickness ratio The range is 0.01-0.1, the average traceability accuracy deviation value The range is 0.3-0.6, the fabric density ratio The range is 0.3-0.6, such as Figure 2 As shown, it is a statistical chart of the change of the accurate evaluation value of the traceability provided in the embodiment of the present application, which is composed of Figure 2 It can be seen that when the average traceability accuracy deviation value When fixed at 0.3, the fabric density ratio When fixed at 0.3, as the fabric thickness The gradual increase means that the label adhesion of the fabric is enhanced and the traceability accuracy of the ultra-thin fabric is improved; the accurate quantification of the traceability accuracy of the ultra-thin fabric is achieved, and then the accuracy of the traceability data analysis in the production identification traceability process is improved.

[0034] Furthermore, the specific process of judging whether to conduct quality inspection node information evaluation based on the traceability accuracy evaluation value of the specified ultra-thin fabric is as follows: judging whether the traceability accuracy evaluation value is not lower than the preset accuracy threshold, when the traceability accuracy evaluation value is not lower than the preset accuracy threshold, obtaining the node processing evaluation value and the node information integrity evaluation value; when the traceability accuracy evaluation value is lower than the preset accuracy threshold, it indicates that the traceability accuracy of the specified ultra-thin fabric is high, and the production identification of the specified ultra-thin fabric is obtained through identification recognition to obtain the corresponding traceability positioning data; the traceability positioning data includes quality inspection results and quality inspection reports, and the identification recognition is used to read the production identification of the specified ultra-thin fabric.

[0035] Specifically, the specific process of obtaining the node processing evaluation value is as follows: JM1, obtain the initial traceability time, which is expressed by the product of the total number of detected nodes and the average node traceability time; JM2, obtain the initial traceability time deviation, which is expressed by the result of the addition operation of the initial traceability time and the preset node traceability time threshold; JM3, obtain the traceability time ratio, which is expressed by the result of the ratio operation of the initial traceability time deviation and twice the preset node traceability time threshold; JM4, obtain the average traceability time ratio (that is, the ratio of the initial traceability time deviation in the restriction expression of the node processing evaluation value) ), the average traceability time ratio is expressed by the result of the mean operation of the traceability time ratio, and the traceability time ratio is used to reflect the deviation of the traceability time in the process of ultra-thin fabric quality inspection; JM5, combines the average traceability time ratio and the traceability accuracy evaluation value to obtain the node processing evaluation value.

[0036] The restriction expression of the node processing evaluation value is as follows: ; ; In the formula, It represents the node processing evaluation value of the specified ultra-thin fabric at the ath quality inspection node. , a represents the number of quality detection nodes, b represents the total number of quality detection nodes, Indicates the average traceability time ratio of a specified ultra-thin fabric. Indicates that the traceability accuracy assessment value of the specified ultra-thin fabric at the ath quality inspection node is not lower than the preset accuracy threshold, It represents the average node tracing time of the xth ultra-thin fabric sample of the specified ultra-thin fabric. , x represents the number of ultra-thin fabric samples, y represents the total number of ultra-thin fabric samples, Indicates the total number of detection nodes of the xth ultra-thin fabric sample of the specified ultra-thin fabric, represents the preset node tracing time threshold, and e represents a natural constant.

[0037] In this embodiment, the preset accuracy threshold is represented by the variance of the traceability accuracy of ultra-thin fabrics in the historical time period in the preset database. The identification recognition includes RFID (Radio Frequency Identification), barcode, etc. The production identification of the specified ultra-thin fabric is read through the identification recognition, and the quality inspection result and quality inspection report corresponding to the specified ultra-thin fabric are recorded according to the read production identification. For example, the quality inspection result is the data obtained after the quality test of the specified ultra-thin fabric, such as strength, abrasion resistance, color fastness, etc. The quality inspection report includes information such as the test method, test conditions, test results and conclusions.

[0038] It should be understood that the algorithm of this embodiment combines the traceability-related data and the traceability accurate evaluation value to obtain the node processing evaluation value through comprehensive analysis. The traceability-related data and the traceability accurate evaluation value in the algorithm of this embodiment do not exist independently, but are interrelated. With the increase of the total number of detection nodes, the average node traceability time may increase, because the amount of information to be processed increases, the burden of the traceability system increases, and the shortening of the average node traceability time helps to improve the traceability accurate evaluation value. Because the shorter the average node traceability time means the faster the overall response speed of the system, when multiple traceability requests occur at the same time, the system can respond and process these requests more quickly, thereby improving the overall processing speed. This helps to accurately trace back to the specified node in a shorter time, and at the same time, more detection nodes mean more sources of information. The parameters of the algorithm of this embodiment need to consider the impact on the results together; the precise quantification of the processing capacity of the quality inspection node in the traceability process of the specified ultra-thin fabric is achieved, and the accuracy of the traceability data analysis in the production identification traceability process is improved.

[0039] Furthermore, the specific process of obtaining the node information integrity evaluation value is as follows: the first step is to obtain the initial node information missing rate. The initial node information missing rate is expressed by the result of calculating the ratio of the missing node information amount to the total node information amount. The initial node information missing rate is used to reflect the missing situation of the quality inspection node information in the ultra-thin fabric quality inspection process; the second step is to obtain the initial node information missing deviation value. The initial node information missing deviation value is expressed by the result of adding the initial node information missing rate and the preset node information missing threshold; the third step is to obtain the node information missing deviation value. The node information missing deviation value is expressed by the result of calculating the ratio of the initial node information missing deviation value and twice the preset node information missing threshold; the fourth step is to obtain the average node information missing deviation value (that is, the average node information missing deviation value in the restriction expression of the node information integrity evaluation value). ), the average node information missing deviation value is represented by the result of the mean operation of the node information missing deviation value, and the average node information missing deviation value is used to reflect the deviation of the quality inspection node information missing in the ultra-thin fabric quality inspection process; step five, combine the average node information missing deviation value and the traceability accuracy assessment value to obtain the node information integrity assessment value.

[0040] Among them, the restriction expression of the node information integrity evaluation value is as follows: ; ; In the formula, It represents the node information integrity evaluation value of the specified ultra-thin fabric at the ath quality inspection node. , a represents the number of quality detection nodes, b represents the total number of quality detection nodes, Indicates the average node information missing deviation value of the specified ultra-thin fabric. Indicates that the traceability accuracy assessment value of the specified ultra-thin fabric at the ath quality inspection node is not lower than the preset accuracy threshold, Indicates the amount of missing node information of the xth ultra-thin fabric sample of the specified ultra-thin fabric, , x represents the number of ultra-thin fabric samples, y represents the total number of ultra-thin fabric samples, Indicates the total node information of the xth ultra-thin fabric sample of the specified ultra-thin fabric, represents the preset node information missing threshold, and e represents a natural constant.

[0041] In this embodiment, the algorithm of this embodiment combines the traceability-related data and the traceability accuracy evaluation value to obtain the node information integrity evaluation value through comprehensive analysis. The traceability-related data and the traceability accuracy evaluation value in the algorithm of this embodiment do not exist independently, but are interrelated. Due to the particularity of ultra-thin fabrics, the traceability system may cause some node information to be excluded or lost when conducting quality inspections, which in turn leads to an increase in the amount of missing node information. The larger the amount of missing node information, the more node information that cannot be obtained during the traceability process, which directly leads to a reduction in effective information in the total node information. If the amount of missing node information is large, the number of successfully traced nodes may be reduced, resulting in a decrease in the traceability accuracy evaluation value. The parameters of the algorithm of this embodiment need to be considered together and simultaneously in terms of their impact on the results.

[0042] Specifically, assuming that the average node information missing deviation value The range is 0.6-1, which is not less than the preset accuracy threshold of the retrospective accuracy assessment value. The range is 0.1-0.5, as shown in Table 1, which is a statistical table of changes in the node information integrity evaluation value provided in the embodiment of the present application: Table 1 Statistics of changes in node information integrity evaluation values Average node information missing deviation value Retrospective accurate assessment value not less than the preset accuracy threshold Node information integrity evaluation value 0.873 0.125 1.7466 0.732 0.371 1.8540 0.661 0.479 1.9060 ... ... ... As can be seen from the above table, as the average node information missing deviation value The value of the retrospective accurate assessment is not less than the preset accurate threshold. The node information integrity evaluation value It gradually increases, which means that the completeness of the quality inspection node information in the traceability process of the specified ultra-thin fabric is gradually improved; the accurate quantification of the completeness of the quality inspection node information in the traceability process of the specified ultra-thin fabric is achieved, and then the accuracy of the traceability data analysis in the production identification traceability process is improved.

[0043] Furthermore, the specific process of judging whether to adjust the node information based on the node processing evaluation value and the node information integrity evaluation value is as follows: VV1, judging whether the node processing evaluation value is within the preset node processing threshold range, when the node processing evaluation value is within the preset node processing threshold range, no node information adjustment is performed and the traceability positioning data is obtained, otherwise VV2 is executed; VV2, tracing request allocation is performed, when the node processing evaluation value is within the preset node processing threshold range, node information adjustment is stopped and traceability positioning data is obtained, otherwise VV3 is executed, traceability request allocation means allocating traceability requests to multiple servers through a load balancing algorithm; VV3, node load migration is performed, when the node processing evaluation value is within the preset node processing threshold range, node information adjustment is stopped and traceability positioning data is obtained, otherwise an alarm message is sent to the preset personnel, and node load migration is used to prevent node resource overload and thus improve the node processing capability of the system.

[0044] In this embodiment, the production identification of the specified ultra-thin fabric is tracked by the radio frequency identification method to obtain the corresponding traceability positioning data, including the strength, wear resistance, color fastness, etc. of the specified ultra-thin fabric, and the preset node processing threshold is obtained from the preset database. The preset node processing threshold is represented by the variance of the fabric node traceability time in the historical time period in the preset database. In this embodiment, the maximum value of the preset node processing threshold is used as the upper limit of the preset node processing threshold range, and the minimum value of the preset node processing threshold is used as the lower limit of the preset node processing threshold range; the load balancing algorithm is used to distribute the traceability request to multiple servers to disperse the load and improve the processing efficiency. The load balancing algorithm includes polling method, minimum connection number method, etc. In this embodiment, the polling method is used to distribute the requests to the preset back-end server in sequence. When the node load exceeds the maximum value of the node load in the historical time period in the preset database, the load to be migrated is removed from the IP (Internet Protocol) address or domain name of the source node through the load balancer, and these loads are added to the IP address or domain name of the preset target node; the accuracy of traceability data analysis in the production identification traceability process is improved.

[0045] Furthermore, node information adjustment is performed, followed by node integrity adjustment, and the specific process of node integrity adjustment is as follows: MM1, judging whether the node information integrity evaluation value is within the preset traceability delay threshold range, when the node information integrity evaluation value is within the preset traceability delay threshold range, no node integrity adjustment is performed and traceability positioning data is obtained, otherwise MM2 is executed; MM2, tracking the ultra-thin fabric corresponding to the node information integrity evaluation value not within the preset traceability delay threshold range through a wireless radio frequency identification method, sending a repair prompt to the preset personnel, when the node information integrity evaluation value is within the preset traceability delay threshold range, stopping the node integrity adjustment and obtaining the traceability positioning data, otherwise MM3 is executed; MM3, increasing the node information collection frequency by a preset multiple to the preset frequency maximum value, when the node information integrity evaluation value is within the preset traceability delay threshold range, stopping the node integrity adjustment and obtaining the traceability positioning data, otherwise an alarm message is sent to the preset personnel, and increasing the node information collection frequency is used to prevent node information damage from causing low node information integrity.

[0046] In this embodiment, a preset traceability delay threshold is obtained from a preset database. The preset traceability delay threshold is represented by the variance of the traceability delay time of the fabric node in the historical time period in the preset database. In this embodiment, the maximum value of the preset traceability delay threshold is used as the upper limit of the preset traceability delay threshold range, and the minimum value of the preset traceability delay threshold is used as the lower limit of the preset traceability delay threshold range. A wireless radio frequency identification method is used to track ultra-thin fabrics whose node information integrity assessment values ​​are not within the preset traceability delay threshold range. The wireless radio frequency identification method can track and locate items in real time. The preset frequency maximum value is represented by the maximum value of the fabric traceability collection frequency in the historical time period in the preset database; the accuracy of traceability data analysis in the production identification traceability process is improved.

[0047] like Figure 3As shown, it is a structural schematic diagram of a production identification traceability data analysis system based on ultra-thin fabrics provided by an embodiment of the present application. A production identification traceability data analysis system based on ultra-thin fabrics provided by an embodiment of the present application includes a traceability accuracy evaluation module, a quality inspection node information evaluation module and a node information adjustment module: wherein the traceability accuracy evaluation module is used to trace the production identification of ultra-thin fabrics during the ultra-thin fabric quality inspection process, obtain traceability related data during the ultra-thin fabric quality inspection process, obtain the traceability accuracy evaluation value of the specified ultra-thin fabric based on the obtained traceability related data and the reference traceability data, and the traceability accuracy evaluation value is used to reflect the traceability accuracy of the ultra-thin fabric; the quality inspection node information evaluation module is used to determine whether to perform quality inspection based on the traceability accuracy evaluation value of the specified ultra-thin fabric Node information evaluation is performed. If quality inspection node information evaluation is performed, the node processing evaluation value and the node information integrity evaluation value are obtained based on the traceability-related data and the traceability accuracy evaluation value. If quality inspection node information evaluation is not performed, the traceability positioning data is obtained according to the production identification. The node processing evaluation value is used to reflect the quality inspection node processing capacity in the traceability process of the specified ultra-thin fabric, and the node information integrity evaluation value is used to reflect the completeness of the quality inspection node information in the traceability process of the specified ultra-thin fabric; the node information adjustment module is used to determine whether to adjust the node information based on the node processing evaluation value and the node information integrity evaluation value. The node information adjustment is used to adjust the node processing evaluation value to within the preset node processing threshold range and adjust the node information integrity evaluation value to within the preset traceability delay threshold range.

[0048] like Figure 4 As shown, it is an overall flow chart provided in an embodiment of the present application. The traceability accuracy assessment module is responsible for tracing the production identification of ultra-thin fabrics and evaluating the traceability accuracy during the ultra-thin fabric quality inspection process. The quality inspection node information assessment module is used to ensure the integrity and reliability of the traceability of the production identification of ultra-thin fabrics. The node information adjustment module aims to improve the accuracy and integrity of the traceability information; the accuracy of traceability data analysis is improved during the production identification traceability process.

[0049] To summarize, the embodiment of the present application obtains traceability-related data in the process of ultra-thin fabric quality inspection, and then obtains the traceability accuracy assessment value of the specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data and determines whether to perform quality inspection node information assessment, and finally determines whether to adjust the node information based on the obtained node processing assessment value and node information integrity assessment value, thereby realizing dynamic traceability of ultra-thin fabrics, and further realizing the improvement of the accuracy of traceability data analysis in the production identification traceability process, effectively solving the problem of insufficient traceability data analysis in the production identification traceability process in the prior art.

[0050] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0052] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0054] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0055] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for analyzing production identification traceability data based on ultra-thin fabrics, characterized in that: The following steps are involved: S1, obtaining traceability-related data in the process of ultra-thin fabric quality inspection, and obtaining a traceability accuracy evaluation value of a specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data, wherein the traceability accuracy evaluation value is used to reflect the traceability accuracy of the ultra-thin fabric; S2, judging whether to perform quality inspection node information evaluation based on the traceability accuracy evaluation value of the specified ultra-thin fabric, and if performing quality inspection node information evaluation, obtaining a node processing evaluation value and a node information integrity evaluation value based on the traceability-related data and the traceability accuracy evaluation value, wherein the node processing evaluation value is used to reflect the quality inspection node processing capability in the traceability process of the specified ultra-thin fabric, and the node information integrity evaluation value is used to reflect the completeness of the quality inspection node information in the traceability process of the specified ultra-thin fabric; S3: Determine whether to adjust the node information based on the node processing evaluation value and the node information integrity evaluation value.

2. A method for analyzing production identification traceability data based on ultra-thin fabrics as claimed in claim 1, characterized in that: The traceability-related data include fabric thickness, fabric density, number of successfully traced nodes, total number of detected nodes, average node traceability time, amount of missing node information, and total amount of node information; The number of successful traceability nodes refers to the number of nodes traced during the ultra-thin fabric quality inspection process; The amount of missing node information represents the amount of data of missing nodes traced back during the ultra-thin fabric quality inspection process; The reference traceability data includes a preset minimum fabric thickness, a preset minimum fabric density, a preset node traceability accuracy threshold, a preset node information missing threshold, a preset node traceability time threshold, a preset first traceability accurate allocation weight, a preset second traceability accurate allocation weight, and a preset third traceability accurate allocation weight.

3. A method for analyzing production identification traceability data based on ultra-thin fabrics as claimed in claim 2, characterized in that: The specific process of obtaining the traceability accurate evaluation value of the specified ultra-thin fabric based on the obtained traceability related data and reference traceability data is as follows: The first step is to obtain a fabric thickness ratio, which is represented by a result of performing a ratio operation on the fabric thickness and a preset minimum fabric thickness value and then performing an average operation; The second step is to obtain the traceability accuracy value, which is represented by the result of the ratio operation between the number of successfully traced nodes and the total number of detected nodes; The third step is to obtain a traceability accuracy deviation value, which is represented by the result of a ratio operation between the sum of the traceability accuracy value and the preset node traceability accuracy threshold and twice the preset node traceability accuracy threshold; The fourth step is to obtain an average traceability accurate deviation value, wherein the average traceability accurate deviation value is represented by a result of performing a mean operation on the traceability accurate deviation values; The fifth step is to obtain a fabric density ratio, which is represented by a result of a ratio calculation between a preset minimum fabric density and a fabric density and then an average calculation; In the sixth step, the traceability accuracy assessment value is obtained by combining the fabric thickness ratio, fabric density ratio, average traceability accuracy deviation value, preset first traceability accuracy allocation weight, preset second traceability accuracy allocation weight and preset third traceability accuracy allocation weight.

4. A method for analyzing production identification traceability data based on ultra-thin fabrics as claimed in claim 3, characterized in that: The limiting expression of the traceability accurate evaluation value is as follows: ； ； ； ； In the formula, It represents the traceability accuracy evaluation value of the specified ultra-thin fabric at the ath quality inspection node. , a represents the number of quality detection nodes, b represents the total number of quality detection nodes, Indicates the thickness ratio of the specified ultra-thin fabric in the ath quality inspection node. Indicates the density ratio of the specified ultra-thin fabric in the ath quality inspection node. Indicates the average traceability accuracy deviation value of the specified ultra-thin fabric. Indicates the fabric thickness of the xth ultra-thin fabric sample of the specified ultra-thin fabric in the ath quality inspection node. , x represents the number of ultra-thin fabric samples, y represents the total number of ultra-thin fabric samples, Indicates the fabric density of the xth ultra-thin fabric sample of the specified ultra-thin fabric in the ath quality inspection node. Indicates the number of successful traceability nodes for the xth ultra-thin fabric sample of the specified ultra-thin fabric. Indicates the total number of detection nodes of the xth ultra-thin fabric sample of the specified ultra-thin fabric, Indicates the preset minimum fabric thickness. Indicates the preset node traceability accuracy threshold. Indicates the preset minimum fabric density. Indicates that the first traceability is preset to accurately allocate weights. Indicates that the second traceability is preset to accurately allocate weights. It indicates that the third traceability is preset to accurately allocate weights, and e represents a natural constant.

5. A method for analyzing production identification traceability data based on ultra-thin fabrics as claimed in claim 4, characterized in that: The specific process of judging whether to conduct quality inspection node information evaluation based on the traceability accurate evaluation value of the specified ultra-thin fabric is as follows: Determine whether the traceability accuracy evaluation value is not lower than a preset accuracy threshold, and when the traceability accuracy evaluation value is not lower than the preset accuracy threshold, obtain the node processing evaluation value and the node information integrity evaluation value; When the traceability accuracy assessment value is lower than the preset accuracy threshold, the production mark of the specified ultra-thin fabric is obtained through mark recognition to obtain the corresponding traceability positioning data; The traceability and positioning data includes quality inspection results and quality inspection reports.

6. A method for analyzing production identification traceability data based on ultra-thin fabrics as claimed in claim 2, characterized in that: The specific process of obtaining the node processing evaluation value is as follows: JM1, obtain the initial tracing time, which is represented by the product of the total number of detected nodes and the average node tracing time; JM2, obtain the initial traceability time deviation, where the initial traceability time deviation is represented by the result of adding the initial traceability time to the preset node traceability time threshold; JM3, obtain the traceability time ratio, which is represented by the result of a ratio operation between the initial traceability time deviation and twice the preset node traceability time threshold; JM4, obtaining an average tracing time ratio, wherein the average tracing time ratio is represented by the result of the mean operation of the tracing time ratio, and the tracing time ratio is used to reflect the deviation of the tracing time in the ultra-thin fabric quality detection process; JM5,combines the average tracing time ratio and the tracing accuracy evaluation value to obtain the node processing evaluation value.

7. A method for analyzing production identification traceability data based on ultra-thin fabrics as claimed in claim 2, characterized in that: The specific process of obtaining the node information integrity evaluation value is as follows: Step 1, obtaining the initial node information missing rate, the initial node information missing rate is represented by the result of calculating the ratio of the amount of missing node information to the total amount of node information, and the initial node information missing rate is used to reflect the missing state of quality inspection node information in the process of ultra-thin fabric quality inspection; Step 2: obtaining an initial node information missing deviation value, wherein the initial node information missing deviation value is represented by the result of adding the initial node information missing rate and a preset node information missing threshold; Step 3, obtaining a node information missing deviation value, wherein the node information missing deviation value is represented by a result of a ratio operation between the initial node information missing deviation value and twice a preset node information missing threshold value; Step 4, obtaining an average node information missing deviation value, wherein the average node information missing deviation value is represented by a result of a mean operation of the node information missing deviation value, and the average node information missing deviation value is used to reflect the deviation of the quality inspection node information missing during the ultra-thin fabric quality inspection process; Step 5: Combine the average node information missing deviation value and the traceability accuracy assessment value to obtain the node information integrity assessment value.

8. A method for analyzing production identification traceability data based on ultra-thin fabrics as claimed in claim 1, characterized in that: The specific process of judging whether to adjust the node information based on the node processing evaluation value and the node information integrity evaluation value is as follows: VV1, determine whether the node processing evaluation value is within the preset node processing threshold range. When the node processing evaluation value is within the preset node processing threshold range, no node information adjustment is performed and the traceability positioning data is obtained. Otherwise, VV2 is executed; VV2, perform traceability request allocation. When the node processing evaluation value is within the preset node processing threshold range, stop node information adjustment and obtain traceability positioning data. Otherwise, execute VV3; VV3 performs node load migration. When the node processing evaluation value is within the preset node processing threshold, it stops adjusting the node information and obtains the traceability positioning data. Otherwise, it sends an alarm message to the preset personnel.

9. A method for analyzing production identification traceability data based on ultra-thin fabrics as claimed in claim 8, characterized in that: The node information adjustment further includes node integrity adjustment, and the specific process of the node integrity adjustment is as follows: MM1, determine whether the node information integrity evaluation value is within the preset traceability delay threshold range. When the node information integrity evaluation value is within the preset traceability delay threshold range, no node integrity adjustment is performed and traceability positioning data is obtained. Otherwise, MM2 is executed; MM2, using the radio frequency identification method to track the corresponding ultra-thin fabric when the node information integrity assessment value is not within the preset traceability delay threshold, and send a repair prompt to the preset personnel. When the node information integrity assessment value is within the preset traceability delay threshold, stop the node integrity adjustment and obtain the traceability positioning data, otherwise execute MM3; MM3, increases the node information collection frequency by a preset multiple to the preset maximum frequency. When the node information integrity assessment value is within the preset traceability delay threshold, stops the node integrity adjustment and obtains the traceability positioning data. Otherwise, sends an alarm message to the preset personnel.

10. A production identification traceability data analysis system based on ultra-thin fabrics, characterized in that: Including traceability accuracy assessment module, quality inspection node information assessment module and node information adjustment module: The traceability accuracy assessment module is used to obtain traceability-related data in the ultra-thin fabric quality inspection process, and obtain the traceability accuracy assessment value of the specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data. The traceability accuracy assessment value is used to reflect the traceability accuracy of the ultra-thin fabric. The quality inspection node information evaluation module is used to determine whether to perform quality inspection node information evaluation based on the traceability accuracy evaluation value of the specified ultra-thin fabric. If the quality inspection node information evaluation is performed, the node processing evaluation value and the node information integrity evaluation value are obtained based on the traceability related data and the traceability accuracy evaluation value. The node processing evaluation value is used to reflect the quality inspection node processing capacity in the traceability process of the specified ultra-thin fabric, and the node information integrity evaluation value is used to reflect the completeness of the quality inspection node information in the traceability process of the specified ultra-thin fabric; The node information adjustment module is used to determine whether to adjust the node information based on the node processing evaluation value and the node information integrity evaluation value.

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