A data analysis method and system for tracing the origin of production identification based on ultra-thin fabrics

Abstract

The present invention discloses a production identification traceability data analysis method and system based on ultra-thin fabrics, which relates to the technical field of electrical digital data processing. The production identification traceability data analysis method based on ultra-thin fabrics includes the following steps: obtaining a traceability accurate evaluation value; evaluating quality inspection node information; and adjusting node information. By obtaining traceability-related data during the quality inspection of ultra-thin fabrics, and then obtaining the traceability accurate evaluation value of a specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data and determining whether to evaluate the quality inspection node information, and finally determining whether to adjust the node information based on the obtained node processing evaluation value and node information integrity evaluation value, the present invention achieves the effect of improving the accuracy of traceability data analysis during the production identification traceability process. It solves the problem of insufficient traceability data analysis during the production identification traceability process in the prior art.

Description

Technical Field

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

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

[0003] The existing methods for analyzing production identification traceability data mainly rely on tracing each node in each link one by one, that is, tracing from one node to the previous node until the source is traced.

[0004] For example, the method and device for adjusting traceability rules based on network data analysis disclosed in the invention patent announcement with the publication number: CN112307301B, including: obtaining analysis result data; performing traceability analysis on the analysis result data in a sandbox environment based on a preset backtracking engine module; adjusting the analysis rules according to the traceability analysis result to determine the target analysis rules; wherein, the analysis rules correspond to the analysis result data.

[0005] For example, the tracking and tracing system based on big data analysis disclosed in the invention patent announcement with the publication number: CN114780956B, including an acquisition module, a detection module, a processing module, a central control module, a traceability module, and a security protection module. When the terminal is attacked by the network, the central control module scores the attack log and determines whether the attack log constitutes a network attack according to the scoring result. The detection module detects the degree of damage and tampering of the terminal file determined to be attacked. The central control module can quickly and accurately determine the damage level of the terminal according to the detection result and control the processing module to repair the damaged file and generate a log database, and control the traceability module to track and trace the attack address and send the traced suspected IP address to the network security protection module.

[0006] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems:

[0007] In the prior art, generally speaking, after the production of functional ultra-thin fabrics (such as ultra-thin fabrics with breathable, moisture-permeable and heat-dissipating functions), fabric quality inspection is involved. Since it is necessary to trace each node according to the production label one by one, the tracing time is relatively long, which will lead to the problem that the source of the problem cannot be quickly located due to the functional ultra-thin fabric during the quality inspection process, affecting the response speed, and resulting in insufficient analysis of the tracing data during the production label tracing process. Summary of the Invention

[0008] By providing a method and system for analyzing tracing data of production labels based on ultra-thin fabrics in the embodiments of the present application, the problem of insufficient analysis of tracing data during the production label tracing process in the prior art is solved, and the accuracy of tracing data analysis during the production label tracing process is improved.

[0009] The embodiments of the present application provide a method for analyzing tracing data of production labels based on ultra-thin fabrics, including the following steps: S1, obtaining tracing-related data during the quality inspection of ultra-thin fabrics, and obtaining an accurate tracing evaluation value of a specified ultra-thin fabric based on the obtained tracing-related data and reference tracing data, where the accurate tracing evaluation value is used to reflect the accurate tracing situation of the ultra-thin fabric; S2, judging whether to evaluate the quality inspection node information based on the accurate tracing evaluation value of the specified ultra-thin fabric. If the quality inspection node information is evaluated, then obtain a node processing evaluation value and a node information integrity evaluation value based on the tracing-related data and the accurate tracing evaluation value. The node processing evaluation value is used to reflect the processing ability of the quality inspection node during the tracing 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 during the tracing process of the specified ultra-thin fabric; S3, judging whether to adjust the node information based on the node processing evaluation value and the node information integrity evaluation value.

[0010] Further, the tracing-related data includes fabric thickness, fabric density, number of successfully traced nodes, total number of detected nodes, average node tracing time, amount of missing node information, and total node information amount; the number of successfully traced nodes represents the number of nodes traced during the quality inspection of the ultra-thin fabric; the amount of missing node information represents the data amount of the nodes missing during the tracing of the ultra-thin fabric quality inspection; the reference tracing data includes a preset minimum fabric thickness, a preset minimum fabric density, a preset node tracing accuracy threshold, a preset node information amount missing threshold, a preset node tracing time threshold, a preset first tracing accuracy allocation weight, a preset second tracing accuracy allocation weight, and a preset third tracing accuracy allocation weight.

[0011] Further, the specific process of obtaining the accurate traceability 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 the fabric thickness ratio, which is represented by the result of the average operation after the ratio operation of the fabric thickness and the preset minimum fabric thickness; the second step is to obtain the accurate traceability value, which is represented by the result of the ratio operation of the number of successfully traced nodes and the total number of detected nodes; the third step is to obtain the accurate traceability deviation value, which is represented by the result of the ratio operation of the sum of the accurate traceability value and the preset node traceability accuracy threshold and twice the preset node traceability accuracy threshold; the fourth step is to obtain the average accurate traceability deviation value, which is represented by the result of the average operation of the accurate traceability deviation value; the fifth step is to obtain the fabric density ratio, which is represented by the result of the average operation after the ratio operation of the preset minimum fabric density and the fabric density; the sixth step is to obtain the accurate traceability evaluation value by combining the fabric thickness ratio, the fabric density ratio, the average accurate traceability deviation value, the preset first accurate traceability allocation weight, the preset second accurate traceability allocation weight, and the preset third accurate traceability allocation weight.

[0012] Further, the limit expression of the accurate traceability evaluation value is as follows:

[0013] ;

[0014] ;

[0015] ;

[0016] ;

[0017] In the formula, represents the accurate traceability evaluation value of the specified ultra-thin fabric at the a-th quality inspection node, , a represents the number of the quality inspection node, b represents the total number of quality inspection nodes, represents the fabric thickness ratio of the specified ultra-thin fabric at the a-th quality inspection node, represents the fabric density ratio of the specified ultra-thin fabric at the a-th quality inspection node, represents the average accurate traceability deviation value of the specified ultra-thin fabric, represents the fabric thickness of the x-th ultra-thin fabric sample of the specified ultra-thin fabric at the a-th quality inspection node, , x represents the number of the ultra-thin fabric sample, y represents the total number of ultra-thin fabric samples, represents the fabric density of the x-th ultra-thin fabric sample of the specified ultra-thin fabric at the a-th quality inspection node, Represents the number of successful traceability nodes of the x-th ultra-thin fabric sample of the specified ultra-thin fabric, Represents the total number of detection nodes of the x-th ultra-thin fabric sample of the specified ultra-thin fabric, Represents the preset minimum value of fabric thickness, Represents the preset threshold of node traceability accuracy, Represents the preset minimum value of fabric density, Represents the preset first traceability accuracy allocation weight, Represents the preset second traceability accuracy allocation weight, Represents the preset third traceability accuracy allocation weight, and e represents the natural constant.

[0018] Further, the specific process of obtaining the node processing evaluation value is as follows: JM1, obtain the initial traceability time, which is represented by the result of multiplying the total number of detection nodes by the average node traceability time; JM2, obtain the initial traceability time deviation, which is represented by the result of adding the initial traceability time and the preset node traceability time threshold; JM3, obtain the traceability time ratio, which is represented by the result of dividing the initial traceability time deviation by twice the preset node traceability time threshold; JM4, obtain the average traceability time ratio, which is represented by the result of taking the mean value of the traceability time ratio, and the traceability time ratio is used to reflect the deviation of the traceability time during the quality inspection of the ultra-thin fabric; JM5, combine the average traceability time ratio and the traceability accuracy evaluation value to obtain the node processing evaluation value.

[0019] Further, the specific process of obtaining the node information integrity evaluation value is as follows: Step 1, obtain the initial node information missing rate, which is represented by the result of calculating the ratio of the missing node information amount to the total node information amount, and the initial node information missing rate is used to reflect the missing situation of the quality inspection node information during the quality inspection of the ultra-thin fabric; Step 2, obtain the initial node information missing deviation value, which is represented by the result of adding the initial node information missing rate and the preset node information missing threshold; Step 3, obtain the node information missing deviation value, which is represented by the result of dividing the initial node information missing deviation value by twice the preset node information missing threshold; Step 4, obtain the average node information missing deviation value, which is represented by the result of taking the mean value 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 quality inspection of the ultra-thin fabric; Step 5, combine the average node information missing deviation value and the traceability accuracy evaluation value to obtain the node information integrity evaluation value.

[0020] The embodiment of the present application provides a production identification traceability data analysis system based on an ultra-thin fabric, including a traceability accurate evaluation module, a quality inspection node information evaluation module, and a node information adjustment module: Among them, the traceability accurate evaluation module is used to obtain traceability-related data in the quality inspection process of the ultra-thin fabric, and obtain the traceability accurate evaluation value of the specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data. The traceability accurate evaluation value is used to reflect the traceability accuracy of the ultra-thin fabric; the quality inspection node information evaluation module is used to judge whether to perform quality inspection node information evaluation based on the traceability accurate evaluation value of the specified ultra-thin fabric. 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 accurate evaluation value. The node processing evaluation value is used to reflect the quality inspection node processing ability 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 judge whether to perform node information adjustment based on the node processing evaluation value and the node information integrity evaluation value.

[0021] One or more technical solutions provided in the embodiment of the present application have at least the following technical effects or advantages:

[0022] 1. By obtaining the traceability-related data in the quality inspection process of the ultra-thin fabric, then obtaining the traceability accurate evaluation 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 evaluation, and finally judging whether to perform node information adjustment based on the obtained node processing evaluation value and node information integrity evaluation value, the dynamic traceability of the ultra-thin fabric is realized, and then the accuracy of traceability data analysis in the production identification traceability process is improved, effectively solving the problem of insufficient traceability data analysis in the production identification traceability process in the prior art.

[0023] 2. The traceability accurate evaluation value is obtained through the fabric thickness ratio, fabric density ratio, average traceability accurate deviation value, preset first traceability accurate allocation weight, preset second traceability accurate allocation weight, and preset third traceability accurate allocation weight. Then, the node processing evaluation value is obtained by combining the average traceability time ratio and the traceability accurate evaluation value. Finally, the node information integrity evaluation value is obtained by combining the average node information missing deviation value and the traceability accurate evaluation value, thereby realizing the quantification of the ultra-thin fabric traceability data analysis process, and then realizing the improvement of the reliability of the ultra-thin fabric traceability data analysis.

[0024] 3. By determining 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 precise quantification of the traceability node processing ability and further improving the reliability of node information adjustment. Description of the Drawings

[0025] Figure 1 It is a flowchart of a method for analyzing production label traceability data based on ultra-thin fabrics provided by an embodiment of the present application;

[0026] Figure 2 It is a statistical chart of the change in the accurate evaluation value of traceability provided by an embodiment of the present application;

[0027] Figure 3 It is a schematic structural diagram of a system for analyzing production label traceability data based on ultra-thin fabrics provided by an embodiment of the present application;

[0028] Figure 4 It is a general flowchart provided by an embodiment of the present application. Detailed Embodiments

[0029] By providing a method and system for analyzing production label traceability data based on ultra-thin fabrics in an embodiment of the present application, the problem of insufficient analysis of traceability data in the production label traceability process in the prior art is solved. By obtaining traceability-related data during the quality inspection process of ultra-thin fabrics, then obtaining the accurate evaluation value of traceability for a specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data, and then determining whether to perform quality inspection node information evaluation based on the accurate evaluation value of traceability for the specified ultra-thin fabric, and finally determining whether to perform node information adjustment based on the obtained node processing evaluation value and node information integrity evaluation value, the accuracy of traceability data analysis in the production label traceability process is improved.

[0030] The technical solution in the embodiment of the present application for solving the problem of insufficient analysis of traceability data in the above-mentioned production label traceability process has the following general idea:

[0031] By obtaining traceability-related data during the quality inspection process of ultra-thin fabrics, then obtaining the accurate evaluation value of traceability for a specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data and determining whether to perform quality inspection node information evaluation, and finally determining whether to perform node information adjustment based on the obtained node processing evaluation value and node information integrity evaluation value, the effect of improving the accuracy of traceability data analysis in the production label traceability process is achieved.

[0032] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0033] As shown in Figure 1 , the figure is a flowchart of a method for analyzing production label traceability data based on an ultra-thin fabric provided by an embodiment of the present application. The method includes the following steps: S1, obtaining a traceability accuracy evaluation value: tracing the production label of the ultra-thin fabric during the quality inspection process of the ultra-thin fabric, obtaining the traceability-related data during the quality inspection process of the ultra-thin fabric, and obtaining the traceability accuracy evaluation value of the specified ultra-thin fabric based on the obtained traceability-related data and the reference traceability data. The traceability accuracy evaluation value is used to reflect the traceability accuracy of the ultra-thin fabric; S2, evaluating the quality inspection node information: judging whether to evaluate the quality inspection node information based on the traceability accuracy evaluation value of the specified ultra-thin fabric. If the quality inspection node information is evaluated, 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 the quality inspection node information is not evaluated, the traceability positioning data is obtained according to the production label. The node processing evaluation value is used to reflect the processing ability of the quality inspection node during 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 during the traceability process of the specified ultra-thin fabric; S3, adjusting the node information: judging 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 at the same time adjust the node information integrity evaluation value to within the preset traceability delay threshold range.

[0034] In this embodiment, for the specified ultra-thin fabric, a large number 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 refinement and quantification of the traceability accuracy evaluation value. If the traceability accuracy evaluation value is lower, it indicates that there may be problems such as information loss, error, or inconsistency during the traceability process. If the node processing evaluation value is lower than the preset node processing threshold, it indicates that the processing ability of the node is insufficient. For example, when tracing the ultra-thin fabric with breathable, moisture-permeable, and heat-dissipating properties, the quality inspection nodes include functional inspection 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 inspection nodes. The higher the traceability accuracy evaluation value, the more reliable the quality of the ultra-thin fabric with breathable, moisture-permeable, and heat-dissipating properties. The higher the node processing evaluation value, the higher the efficiency and accuracy of the quality inspection node in processing, storing, and transmitting traceability information. The higher the node information integrity evaluation value, the higher the integrity and consistency of the quality inspection node information during the traceability process; improving the accuracy of traceability data analysis during the production label traceability process is achieved.

[0035] It should be added that the data related to traceability includes fabric thickness, fabric density, the number of successfully traced nodes, the total number of detected nodes, the average traceability time of nodes, the information volume of missing nodes, and the total information volume of nodes; the number of successfully traced nodes represents the number of nodes traced during the quality inspection of the ultra-thin fabric; the information volume of missing nodes represents the data volume of nodes missing in the traceability during the quality inspection of the ultra-thin fabric; the reference traceability data includes the preset minimum fabric thickness, the preset minimum fabric density, the preset threshold of node traceability accuracy, the preset threshold of missing node information volume, the preset threshold of node traceability time, the preset first traceability accuracy allocation weight, the preset second traceability accuracy allocation weight, and the preset third traceability accuracy allocation weight; the preset first traceability accuracy allocation weight is used to evaluate the influence degree of fabric thickness on the traceability accurate evaluation value during the quality inspection of the ultra-thin fabric; the preset second traceability accuracy allocation weight is used to evaluate the influence degree of the traceability accuracy of detected nodes on the traceability accurate evaluation value during the quality inspection of the ultra-thin fabric, and the preset third traceability accuracy allocation weight is used to evaluate the influence degree of fabric density on the traceability accurate evaluation value during the quality inspection of the ultra-thin fabric.

[0036] Specifically, the fabric thickness of the ultra-thin fabric sample is measured by an ellipsometer, the fabric density of the ultra-thin fabric sample is measured by a fabric warp and weft density scale, the number of successfully traced nodes and the average traceability time of nodes are recorded by the existing traceability system, and the existing traceability system has recording and statistical functions. The total number of detected nodes is obtained through the preset number of nodes to be detected, the total information volume of nodes is obtained by calling the data volume corresponding to the preset nodes to be detected through the API (Application Programming Interface), and the information volume of missing nodes is obtained by the difference between the data volume corresponding to the preset nodes to be detected and the data volume corresponding to the successfully traced nodes.

[0037] 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 threshold of node traceability accuracy is represented by the average value of the ultra-thin fabric traceability accuracy in the historical time period in the preset database, the preset threshold of missing node information volume is represented by the average value of the missing rate of ultra-thin fabric traceability nodes in the historical time period in the preset database, and the preset threshold of node traceability time is represented by the minimum value of the ultra-thin fabric node traceability time in the historical time period in the preset database.

[0038] Specifically, the sum of the preset first traceability accuracy allocation weight, the preset second traceability accuracy allocation weight, and the preset third traceability accuracy allocation weight is 1. For example, the preset first traceability accuracy allocation weight is 0.6, the preset second traceability accuracy allocation weight is 0.2, and the preset third traceability accuracy allocation weight is 0.2.

[0039] The preset first traceability accuracy allocation weight is the weight corresponding to the preset fabric thickness value in the preset database, which represents the degree of influence of the fabric thickness value on the traceability accurate evaluation value. When used, the weight corresponding to the preset fabric thickness value can be directly obtained from the preset database, and its corresponding relationship can be a pre-set mapping relationship. For example, in the fabric thickness of 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, and the mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1]; it realizes the improvement of the accuracy of traceability data analysis in the production identification traceability process.

[0040] Specifically, the preset third traceability accuracy allocation weight is the weight corresponding to the preset fabric density value in the preset database, which represents the degree of influence of the fabric density value on the traceability accurate evaluation value. When used, the weight corresponding to the preset fabric density value can be directly obtained from the preset database, and its corresponding relationship can be a pre-set mapping relationship. For example, in the fabric density of 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, and the mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1]; it realizes the improvement of the accuracy of traceability data analysis in the production identification traceability process.

[0041] Furthermore, 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 the fabric thickness ratio (i.e., in the limit expression of the traceability accurate evaluation value), and the fabric thickness ratio is represented by the result of the mean operation after the ratio operation of the fabric thickness and the preset minimum fabric thickness value. The fabric thickness ratio is used to evaluate the deviation of the fabric thickness during the quality inspection of ultra-thin fabrics; the second step is to obtain the traceability accurate value, and the traceability accurate value is represented by the result of the ratio operation of the number of successfully traced nodes and the total number of detected nodes; the third step is to obtain the traceability accurate deviation value, and the traceability accurate deviation value is represented by the result of the ratio operation of the sum of the traceability accurate 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 accurate deviation value (i.e., in the limit expression of the traceability accurate evaluation value), and the average traceability accurate deviation value is represented by the result of the mean operation of the traceability accurate deviation value. The traceability accurate deviation value is used to evaluate the traceability accurate deviation of the detection nodes of ultra-thin fabrics during the quality inspection of ultra-thin fabrics; the fifth step is to obtain the fabric density ratio (i.e., ), the fabric density is represented by the result of the average operation after the ratio operation of the preset minimum fabric density and the fabric density; Step 6, 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 to obtain the traceability accuracy evaluation value.

[0042] Among them, the limiting expression of the traceability accuracy evaluation value is as follows:

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] In the formula, represents the traceability accuracy evaluation value of the specified ultra-thin fabric at the a-th quality inspection node, , a represents the number of the quality inspection node, b represents the total number of quality inspection nodes, represents the fabric thickness ratio of the specified ultra-thin fabric at the a-th quality inspection node, represents the fabric density ratio of the specified ultra-thin fabric at the a-th quality inspection node, represents the average traceability accuracy deviation value of the specified ultra-thin fabric, represents the fabric thickness of the x-th ultra-thin fabric sample of the specified ultra-thin fabric at the a-th quality inspection node, , x represents the number of the ultra-thin fabric sample, y represents the total number of ultra-thin fabric samples, represents the fabric density of the x-th ultra-thin fabric sample of the specified ultra-thin fabric at the a-th quality inspection node, represents the number of successful traceability nodes of the x-th ultra-thin fabric sample of the specified ultra-thin fabric, represents the total number of inspection nodes of the x-th ultra-thin fabric sample of the specified ultra-thin fabric, represents the preset minimum fabric thickness, represents the preset node traceability accuracy threshold, represents the preset minimum fabric density, represents the preset first traceability accuracy allocation weight, represents the preset second traceability accuracy allocation weight, represents the preset third traceability accuracy allocation weight, and e represents the natural constant.

[0048] The algorithm of this embodiment combines relevant traceability data for comprehensive analysis to obtain an accurate traceability evaluation value. In the algorithm of this embodiment, the relevant traceability data do not exist independently and are interrelated. Specified ultra-thin fabrics may be more prone to damage or deformation, thus affecting the attachment and reading of traceability tags, which may lead to a reduction in the number of successfully traced nodes and affect the accuracy of the traceability system. The higher the fabric density, the more yarns per unit area, and the smaller the gaps between the yarns, resulting in a relatively reduced contact area between the tag and the fabric. This may affect the tag's adhesiveness. The larger the total number of detected nodes, the greater the possible amount of information, but it may also increase the complexity of the traceability system, thereby reducing the accuracy of traceability. The parameters of the algorithm in this embodiment need to jointly consider the impact on the results at the same time.

[0049] Specifically, assume that the preset first traceability accuracy allocation weight is fixed at 0.6, the preset second traceability accuracy allocation weight is fixed at 0.2, and the preset third traceability accuracy allocation weight is fixed at 0.2. The fabric thickness ratio ranges from 0.01 to 0.1, the average traceability accuracy deviation value ranges from 0.3 to 0.6, and the fabric density ratio ranges from 0.3 to 0.6. As Figure 2 shown, it is a statistical chart of the change in the traceability accuracy evaluation value provided by the embodiment of the present application. It can be seen from Figure 2 that when the average traceability accuracy deviation value is fixed at 0.3 and the fabric density ratio is fixed at 0.3, as the fabric thickness ratio gradually increases, it means that the tag adhesiveness of the fabric is enhanced and the traceability accuracy of the ultra-thin fabric is improved; the traceability accuracy of the ultra-thin fabric is accurately quantified, and further, the accuracy of traceability data analysis in the production identification traceability process is improved.

[0050] Furthermore, the specific process of judging whether to perform quality inspection node information evaluation based on the traceability accuracy evaluation value of the specified ultra-thin fabric is as follows: Judge 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, obtain 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 obtain the production identification of the specified ultra-thin fabric through identification to obtain the corresponding traceability positioning data; the traceability positioning data includes quality inspection results and quality inspection reports, and identification is used to read the production identification of the specified ultra-thin fabric.

[0051] Specifically, the specific process of obtaining the node processing evaluation value is as follows: JM1, obtain the initial traceability time, which is represented by the result of multiplying the total number of detected nodes by the average node traceability time; JM2, obtain the initial traceability time deviation, which is represented by the result of adding the initial traceability time and the preset node traceability time threshold; JM3, obtain the traceability time ratio, which is represented by the result of dividing the initial traceability time deviation by twice the preset node traceability time threshold; JM4, obtain the average traceability time ratio (i.e., in the limiting expression of the node processing evaluation value), and the average traceability time ratio is represented by the result of averaging the traceability time ratio. The traceability time ratio is used to reflect the deviation of the traceability time during the quality inspection of the ultra-thin fabric; JM5, combine the average traceability time ratio and the traceability accuracy evaluation value to obtain the node processing evaluation value.

[0052] Among them, the limiting expression of the node processing evaluation value is as follows:

[0053] ;

[0054] ;

[0055] In the formula, represents the node processing evaluation value of the specified ultra-thin fabric at the a-th quality inspection node, , a represents the number of the quality inspection node, b represents the total number of quality inspection nodes, represents the average traceability time ratio of the specified ultra-thin fabric, represents the traceability accuracy evaluation value of the specified ultra-thin fabric at the a-th quality inspection node not lower than the preset accuracy threshold, represents the average node traceability time of the x-th ultra-thin fabric sample of the specified ultra-thin fabric, , x represents the number of the ultra-thin fabric sample, y represents the total number of ultra-thin fabric samples, represents the total number of detected nodes of the x-th ultra-thin fabric sample of the specified ultra-thin fabric, represents the preset node traceability time threshold, and e represents the natural constant.

[0056] 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. Identification includes RFID (Radio Frequency Identification), barcodes, etc. The production identification of the specified ultra-thin fabric is read through identification, and the corresponding quality inspection results and quality inspection reports of the specified ultra-thin fabric are recorded according to the read production identification. For example, the quality inspection results are data obtained after quality testing of the specified ultra-thin fabric, such as strength, abrasion resistance, color fastness, etc. The quality inspection report includes information such as test methods, test conditions, test results, and conclusions.

[0057] It should be understood that the algorithm of this embodiment comprehensively analyzes the traceability-related data and the traceability accuracy evaluation value to obtain the node processing evaluation value. In the algorithm of this embodiment, the traceability-related data and the traceability accuracy evaluation value do not exist independently and are interrelated. As the total number of detection nodes increases, the average node traceability time may increase because the amount of information to be processed increases and the burden on the traceability system becomes heavier. The shortening of the average node traceability time helps to improve the traceability accuracy 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 simultaneously, the system can respond and process these requests more quickly, thus improving the overall processing speed. This helps to accurately trace to the specified node in a shorter time. At the same time, the more detection nodes mean more information sources. The parameters of the algorithm of this embodiment need to jointly consider the impact on the results; the accurate quantification of the processing ability of the quality inspection nodes in the traceability process of the specified ultra-thin fabric is realized, and then the accuracy of the traceability data analysis in the production identification traceability process is improved.

[0058] 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, which is represented 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, which is represented 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, which is represented by the result of calculating the ratio of the initial node information missing deviation value to twice the preset node information missing threshold; the fourth step is to obtain the average node information missing deviation value (i.e., in the limit expression of the node information integrity evaluation value ),(the average node information missing deviation value is represented by the result of taking the mean of the node information missing deviation values, and the average node information missing deviation value is used to reflect the deviation situation of the missing node information in the quality inspection process of the ultra-thin fabric; Step Five, combine the average node information missing deviation value and the traceability accurate evaluation value to obtain the node information integrity evaluation value.

[0059] Among them, the limit expression of the node information integrity evaluation value is as follows:

[0060] ;

[0061] ;

[0062] In the formula, represents the node information integrity evaluation value of the specified ultra-thin fabric at the a-th quality inspection node, , a represents the number of the quality inspection node, b represents the total number of quality inspection nodes, represents the average node information missing deviation value of the specified ultra-thin fabric, represents the traceability accurate evaluation value of the specified ultra-thin fabric at the a-th quality inspection node that is not lower than the preset accurate threshold, represents the amount of missing node information of the x-th ultra-thin fabric sample of the specified ultra-thin fabric, , x represents the number of the ultra-thin fabric sample, y represents the total number of ultra-thin fabric samples, represents the total amount of node information of the x-th ultra-thin fabric sample of the specified ultra-thin fabric, represents the preset threshold for the amount of missing node information, and e represents the natural constant.

[0063] In this embodiment, the algorithm of this embodiment combines the traceability-related data and the traceability accurate evaluation value to comprehensively analyze and obtain the node information integrity evaluation value. In the algorithm of this embodiment, the traceability-related data and the traceability accurate evaluation value do not exist independently and are interrelated. Due to the particularity of the ultra-thin fabric, some node information may be excluded or lost during quality inspection by the traceability system, which may lead 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 the effective information in the total amount of node information. If the amount of missing node information is large, the number of successfully traced nodes may decrease, resulting in a decrease in the traceability accurate evaluation value. The parameters of the algorithm of this embodiment need to jointly consider the influence on the result at the same time.

[0064] Specifically, assume that the range of the average node information missing deviation value is 0.6 - 1, and the traceability accurate evaluation value If the range is 0.1 - 0.5, then as shown in Table 1, it is a statistical table of the changes in the node information integrity evaluation value provided by the embodiments of the present application:

[0065] Table 1 Statistical Table of Changes in Node Information Integrity Evaluation Value

[0066] Average node information missing deviation value Traceability accuracy evaluation value not lower than the preset accurate threshold Node information integrity evaluation value 0.873 0.125 1.7466 0.732 0.371 1.8540 0.661 0.479 1.9060 ... ... ...

[0067] As can be seen from the above table, as the average node information missing deviation value gradually decreases, and the traceability accuracy evaluation value not lower than the preset accurate threshold gradually increases, the node information integrity evaluation value gradually increases, which means that the integrity degree of the quality inspection node information in the process of tracing the specified ultra-thin fabric is gradually improved; the integrity degree of the quality inspection node information in the process of tracing the specified ultra-thin fabric is accurately quantified, and thus the accuracy of the traceability data analysis in the production identification traceability process is improved.

[0068] Further, 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, judge 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, do not adjust the node information and obtain the traceability positioning data. Otherwise, execute VV2; VV2, perform traceability request allocation. When the node processing evaluation value is within the preset node processing threshold range, stop adjusting the node information and obtain the traceability positioning data. Otherwise, execute VV3. Traceability request allocation means allocating the traceability request to multiple servers through the load balancing algorithm; VV3, perform node load migration. When the node processing evaluation value is within the preset node processing threshold range, stop adjusting the node information and obtain the traceability positioning data. Otherwise, send an alarm message to the preset personnel. Node load migration is used to prevent node resource overload and thus improve the node processing ability of the system.

[0069] In this embodiment, the production identification of a specified ultra-thin fabric is traced through radio frequency identification method to obtain corresponding traceability positioning data, including properties of the specified ultra-thin fabric such as strength, abrasion resistance, color fastness, etc. The preset node processing threshold is obtained from a 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 traceability requests to multiple servers to disperse the load and improve the processing efficiency. The load balancing algorithm includes the round-robin method, the least connections method, etc. In this embodiment, the round-robin method is adopted to distribute requests to the preset backend servers in turn. 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.

[0070] Further, node information adjustment is performed, and then node integrity adjustment is also included. The specific process of 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, trace the ultra-thin fabric corresponding to the node information integrity evaluation value that is not within the preset traceability delay threshold range through radio frequency identification method, and send a repair prompt to the preset personnel. When the node information integrity evaluation value is within the preset traceability delay threshold range, stop node integrity adjustment and obtain traceability positioning data. Otherwise, MM3 is executed; MM3, increase the node information collection frequency to the maximum value of the preset frequency by a preset multiple. When the node information integrity evaluation value is within the preset traceability delay threshold range, stop node integrity adjustment and obtain traceability positioning data. Otherwise, an alarm message is sent to the preset personnel. Increasing the node information collection frequency is used to prevent low node information integrity caused by damaged node information.

[0071] 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 fabric nodes in a 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. The radio frequency identification method is used to track ultra-thin fabrics whose node information integrity evaluation values are not within the preset traceability delay threshold range. The radio frequency identification method can track and locate items in real time. The maximum value of the preset frequency is represented by the maximum value of the fabric traceability collection frequency in a historical time period in the preset database; the accuracy of traceability data analysis in the production identification traceability process is improved.

[0072] As Figure 3 shown, it is a schematic structural 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: Among them, the traceability accuracy evaluation module is used to trace the production identification of ultra-thin fabrics during the quality inspection process of ultra-thin fabrics, obtain traceability-related data during the quality inspection process of ultra-thin fabrics, and obtain a traceability accuracy evaluation value of a specified ultra-thin fabric based on the obtained traceability-related data and reference traceability data. 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 node information evaluation based on the traceability accuracy evaluation value of the specified ultra-thin fabric. If quality inspection 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. If quality inspection node information evaluation is not performed, traceability positioning data is obtained according to the production identification. The node processing evaluation value is used to reflect the processing ability of the quality inspection node during 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 during 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. The node information adjustment is used to adjust the node processing evaluation value to within the preset node processing threshold range and at the same time adjust the node information integrity evaluation value to within the preset traceability delay threshold range.

[0073] As Figure 4As shown in the figure, it is the overall flowchart provided by the embodiment of the present application. The traceability accurate evaluation module is responsible for tracing the production identification of the ultra-thin fabric and evaluating the accuracy of the traceability during the quality inspection process of the ultra-thin fabric. The quality inspection node information evaluation module is used to ensure the integrity and reliability of tracing the production identification of the ultra-thin fabric. The node information adjustment module aims to improve the accuracy and integrity of the traceability information, and the accuracy of the traceability data analysis during the production identification traceability process is improved.

[0074] In summary, the embodiment of the present application obtains the traceability-related data during the quality inspection process of the ultra-thin fabric, then obtains the traceability accurate evaluation value of the specified ultra-thin fabric based on the obtained traceability-related data and the reference traceability data, and judges whether to perform the quality inspection node information evaluation. Finally, based on the obtained node processing evaluation value and the node information integrity evaluation value, it judges whether to perform the node information adjustment, thereby realizing the dynamic traceability of the ultra-thin fabric, and further improving the accuracy of the traceability data analysis during the production identification traceability process, effectively solving the problem of insufficient traceability data analysis in the production identification traceability process in the prior art.

[0075] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in 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.

[0076] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0077] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocksFigure 1 The functions specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 process or more processes and / or boxes Figure 1 or more boxes.

[0079] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0080] 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 equivalent technologies, 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, judging whether to adjust the node information based on the node processing evaluation value and the node information integrity evaluation value; 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 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.

2. A method for analyzing production identification traceability data based on ultra-thin fabrics as claimed in claim 1, characterized in that: 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 detection process.

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, ZQ a It represents the traceability accuracy evaluation value of the specified ultra-thin fabric at the ath quality inspection node, a=1,2,...,b, a represents the number of the quality inspection node, b represents the total number of quality inspection nodes, Indicates the thickness ratio of the specified ultra-thin fabric in the ath quality inspection node. represents the fabric density ratio of the specified ultra-thin fabric in the ath quality inspection node, SZ′1 represents 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=1,2,...,y, x represents the number of the ultra-thin fabric sample, 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. It represents the preset minimum fabric density, β1 represents the preset first traceability accurate allocation weight, β2 represents the preset second traceability accurate allocation weight, β3 represents the preset third traceability accurate allocation weight, 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, which is represented by the result of adding the initial traceability time and the preset node traceability time threshold; JM3, obtain the traceability time ratio, which is represented by the result of the 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: The method for analyzing production identification traceability data based on ultra-thin fabrics according to any one of claims 1 to 9 comprises a traceability accuracy assessment module, a quality inspection node information assessment module and a 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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