A quality tracing method based on waterproof material supply chain collaborative management

Abstract

This invention discloses a quality traceability method based on collaborative management of the waterproof material supply chain, relating to the field of waterproof material technology. The key technical points include the following steps: acquiring raw data from the waterproof material supply chain and dividing the raw data into several data blocks; determining the relationships between data blocks and extracting quality content from the data blocks to generate data feature values; generating profile index factors for the data blocks based on data feature values, identifiers, dynamic index codes, and adjacent tags; comparing the material status information of the profile index factors according to quality evaluation criteria and analyzing the material status information to obtain quality profile labels for the profile data blocks; obtaining quality profile verification labels for the data blocks based on quality compliance, status deviation, and quality feature information; and outputting quality traceability results based on the quality profile verification labels and quality profile labels. The effect is to improve the efficiency of quality control in the supply chain.

Description

Technical Field

[0001] This invention relates to the field of waterproof materials technology, and more specifically, to a quality traceability method based on collaborative management of the waterproof materials supply chain. Background Technology

[0002] The waterproofing material supply chain involves raw material procurement, production and processing, and warehousing and transportation. Due to the dispersed data sources at each stage and the lack of data segmentation and correlation processing in traditional methods, data fragmentation is severe, making it impossible to form a quality traceability chain. This makes it difficult to pinpoint quality issues. Furthermore, traditional quality assessment relies on single-stage indicators, lacking analysis of the relationships between data points and failing to reflect the transmission characteristics of quality impacts throughout the supply chain. This makes it difficult to identify hidden quality risks at different stages. Existing quality label generation methods lack dynamic adaptation and cross-validation, generating labels based solely on single test data without considering dynamic business changes and the quality impacts of upstream and downstream stages, thus hindering quality traceability and collaborative management of the waterproofing material supply chain. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a quality traceability method based on collaborative management of the waterproof material supply chain.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A quality traceability method based on collaborative management of the waterproof material supply chain includes the following steps: Obtain raw data from the waterproofing material supply chain and divide the raw data into several data blocks; Determine the relationships between data blocks and extract the quality content of data blocks to generate data feature values; Based on data feature values, identifiers, dynamic index codes, and adjacent tags, a profile index factor is generated for the data block; the material status information of the profile index factor is compared with the quality evaluation criteria, and the quality profile label of the profile data block is obtained by analyzing the material status information. Based on data blocks, generate profile index verification factors, extract quality attributes of data feature values ​​and the correlation and flow relationships of data blocks; extract quality feature information of data blocks; Based on quality attributes and associated circulation relationships, determine the quality compliance status and status deviation of the profile index verification factors; Based on the quality compliance status, status deviation status, and quality characteristic information, a quality profile verification label is obtained for the data block; The quality traceability results are output based on the quality profile verification label and the quality profile label.

[0005] Preferably, the original data is divided into several data blocks, specifically: Based on the business timeline and quality correlation attributes of the production and circulation of waterproof materials, the original data is hierarchically split into several data blocks.

[0006] Preferably, determining the association between data blocks specifically includes the following steps: By combining the dependency attributes of business operations at each stage of the supply chain and the characteristics of quality impact transmission, we can determine the quality correlation dimensions and business linkage attributes of different data blocks. Based on the quality correlation dimension and business linkage attribute, the linkage connection characteristics are obtained, and the quality transmission correlation and business dependency correlation between data blocks are established based on the linkage connection characteristics. The relationship is obtained by combining quality transmission relationship and business dependency relationship.

[0007] Preferably, the process of extracting quality content from data blocks to generate data feature values ​​specifically includes the following steps: The quality content is normalized and standardized to obtain the target effective content; The degree of influence of the target effective content is obtained based on the quality evaluation criteria for waterproof materials; Quality content is fused and quantified based on its degree of impact to obtain quality characterization information; Integrate quality characterization information to generate data feature values.

[0008] Preferably, the process of generating a profile index factor for a data block based on data feature values, identifiers, dynamic index codes, and adjacent tags specifically includes the following steps: Based on the identifiers, the data feature values ​​are processed for subject ownership confirmation to obtain quality ownership benchmark information; Dynamic quality information is obtained by dynamically adapting and correcting the quality weighting benchmark information based on the dynamic index code. By associating and completing dynamic quality information with adjacent labels, complete quality information can be obtained. Complete quality information is aggregated and standardized in dimensions to generate profile index factors.

[0009] Preferably, the quality profile label for the profile data block is obtained by analyzing the material status information, specifically including the following steps: The inherent quality attributes of materials are obtained based on their status information; Match the actual quality performance characteristics of each link in the supply chain based on the inherent quality attributes of the materials. Define the quality category of the corresponding data block based on the actual quality performance characteristics, and determine the quality profile label of the quality category according to the category classification rules.

[0010] Preferably, generating a profile index verification factor based on data blocks specifically includes the following steps: Use the data feature values ​​and quality profile label information of the data blocks as the basic data; Based on the correlation between data blocks and the preset quality evaluation criteria, the basic data is integrated and summarized to obtain the profile index verification factor.

[0011] Preferably, the quality traceability results are output based on the quality profile verification label and the quality profile label, specifically including the following steps: The first verification result of the data block is obtained by analyzing the quality profile verification label and the quality profile label; the association path information of the quality profile label is extracted, and the second verification result of the data block is obtained by analyzing the quality profile verification label and the association path information. Output the quality traceability results based on the first and second verification results.

[0012] Preferably, the first verification result of the data block is obtained by analyzing the quality profile verification label and the quality profile label; the association path information of the quality profile label is extracted, and the second verification result of the data block is obtained by analyzing the quality profile verification label and the association path information. Specifically, this includes the following steps: Determine the differences in quality judgment between the quality profile verification label and the quality profile label; Based on the differences in quality assessment content and data feature values, the source of the differences is traced and the first verification result corresponding to the data block is obtained. Extract the association relationships between quality profile tags and data blocks; determine the association path information based on the association relationships. Based on the associated path information, determine the quality flow trajectory and the quality judgment result of the quality profile verification label; The second verification result of the data block is obtained based on the quality flow trajectory and the quality judgment result.

[0013] Preferably, the quality traceability result is output based on the first verification result and the second verification result, specifically including the following steps: Determine the matching difference between the first verification result and the second verification result; Determine the quality verification status of the data block based on the matching differences; Output quality traceability results based on the quality verification status.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention generates profile index factors for data blocks by using data feature values, identifiers, dynamic index codes, and adjacent tags. Based on quality evaluation criteria, it compares the material status information of the profile index factors and analyzes the material status information to obtain quality profile labels for the profile data blocks. This enables the classification and labeling of the quality status of data blocks, improving the efficiency of quality identification. A dual verification mechanism ensures the reliability of traceability results. Profile index verification factors are generated based on data feature values ​​and associated flow relationships. The quality profile verification labels are compared with the initial quality profile labels. Furthermore, the quality traceability results provide guidance for quality optimization in the waterproof material supply chain, thereby improving the efficiency of supply chain quality control. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a quality traceability method based on collaborative management of the waterproof material supply chain, as provided in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the process of obtaining correlations in a quality traceability method based on collaborative management of the waterproof material supply chain, as provided in an embodiment of the present invention. Detailed Implementation

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0019] Reference Figures 1-2 As shown.

[0020] The embodiments further illustrate the quality traceability method based on collaborative management of the waterproof material supply chain proposed in this invention.

[0021] A quality traceability method based on collaborative management of the waterproof material supply chain includes the following steps: Obtain raw data from the waterproofing material supply chain and divide the raw data into several data blocks, specifically: Based on the business timeline and quality correlation attributes of the production and circulation of waterproof materials, the original data is hierarchically split into several data blocks.

[0022] The raw data includes raw material batch data, production parameter data, warehouse temperature and humidity data, transportation trajectory data, and quality inspection data. The business timeline dimension refers to the production flow sequence of waterproof materials. For example, the first timeline node is raw material arrival, the second is production and processing, the third is finished product warehousing, the fourth is trunk transportation, the fifth is engineering construction, and the sixth is final acceptance. Each timeline node corresponds to a business link in the supply chain. Preliminary division according to these business sequences ensures that data blocks correspond to the business timeline stage, thus avoiding cross-timeline data mixing. The quality correlation attribute dimension refers to the degree of impact of data from different stages on product quality. For example, raw material performance data affects the waterproof performance of the finished product (strong quality correlation data), warehouse temperature and humidity data affects the aging rate of the material (medium quality correlation data), and transportation trajectory data affects delivery timeliness and storage environment stability (weak quality correlation data).

[0023] The raw data is processed using a hierarchical splitting approach based on business time sequence and quality correlation attributes. The raw data is initially divided into primary data blocks according to business time sequence. The primary data blocks include data from the raw material stage, production stage, warehousing stage, transportation stage, construction stage, and acceptance stage. Within each primary data block, secondary division is performed according to quality correlation attributes, resulting in multiple independent data blocks with both business time sequence and quality correlation attributes.

[0024] Assume the business time series set is T. J Among them, including T1 to T n The set of quality-related attributes for n business time-series nodes is Q. I Including Q1 to Q m If there are m quality association levels, then the set of split data blocks D = D 11 +D 12 +…+D 1m +D 21 +D 22 +…+D 2m +…+D n1 +D n2 +…+D nm , where D ij This represents the data block of the i-th business time sequence node and the j-th quality association level. The + sign indicates that the set of data blocks is merged, that is, all data blocks of different time sequences and different quality association levels together constitute the split data block set.

[0025] Determining the relationships between data blocks involves the following steps: By combining the dependency attributes of business operations at each stage of the supply chain and the characteristics of quality impact transmission, we can determine the quality correlation dimensions and business linkage attributes of different data blocks. Business dependency refers to the process dependence between upstream and downstream business links. For example, the production of waterproof materials may begin after the raw material procurement stage, and warehousing may follow after the production and processing stage. This process dependence is a business dependency attribute. Quality impact transmission characteristics refer to the property that quality status is transmitted to subsequent stages according to the intensity of its impact. For example, if the tensile strength of the raw materials is substandard, it will lead to a decrease in the tensile properties of the produced roll material, thereby affecting the waterproofing effect of the project.

[0026] For example, the business linkage attribute of the raw material data block is that there is no upstream link and the downstream connects to the production link, while the business linkage attribute of the transportation data block is that the upstream connects to the warehousing link and the downstream connects to the construction link.

[0027] To determine the quality correlation dimension, we need to identify which subsequent stages the quality impact of a data block is transmitted to and which preceding stages' quality status influences it. For example, the quality correlation dimension of a raw material data block might be its impact on the quality of the production and construction stages, while the quality correlation dimension of a warehousing data block might be its impact on the aging state of materials in the transportation and construction stages. Assuming the business dependency attribute value of a data block is YB and the quality impact transmission characteristic value is ZA, then the quality correlation dimension value of that data block is MW = YB + ZA. The "+" sign indicates a comprehensive mapping of the two characteristic values, rather than a numerical addition.

[0028] Based on the quality correlation dimension and business linkage attribute, the linkage connection characteristics are obtained, and the quality transmission correlation and business dependency correlation between data blocks are established based on the linkage connection characteristics. Linkage and connection characteristics refer to the way two data blocks are linked in terms of business processes and quality impact. For example, the business linkage attribute between the raw material data block and the production data block is upstream and downstream processes, and the quality correlation dimension is that the quality of raw materials directly affects the quality of production.

[0029] Quality transmission associations and business dependency associations are established based on different linkage and connection characteristics. Quality transmission associations reflect the transmission path and strength of quality impact between data blocks using quality impact transmission characteristics. For example, the quality transmission association strength between the raw material data block and the production data block is set at 80 points, the quality transmission association strength between the production data block and the warehousing data block is set at 50 points, and the quality transmission association strength between the warehousing data block and the transportation data block is set at 40 points. These strength values ​​together constitute the quality transmission association architecture. Assuming the quality transmission association strength is T, the quality transmission association from data block A to data block B is represented as T. AB T ABThe value ranges from 0 to 100. A larger value indicates a stronger influence of the quality status of the preceding data block on the subsequent data block. Business dependency relationships reflect the process dependencies between data blocks using business dependency attributes. For example, the business dependency relationship of a production data block must depend on a raw material data block, and the business dependency relationship of a transportation data block must depend on a warehousing data block. Assuming the business dependency relationship is Y, then the business dependency relationship of data block B on data block A can be represented as Y. BA Y BA The value can be 1 or 0. 1 means that data block B must depend on data block A to exist, and 0 means that there is no business dependency relationship.

[0030] The relationship is obtained by combining quality transmission relationship and business dependency relationship.

[0031] Association is a fusion of quality propagation association and business dependency association, simultaneously containing both process dependency and quality impact information between data blocks. Assuming the final association between data blocks is R, the quality propagation association strength is T, and the business dependency association is Y, then R... AB =T AB ×Y AB The × sign indicates a constraint on the business dependency association strength of quality transmission association. For example, when Y BA =1, and T AB When R is 80, AB =80×1=80, indicating that there is a business dependency relationship between data block A and data block B, and the quality transmission correlation strength is 80; if Y AB Even if T is 0, AB For 80, R AB It is also equal to 0, indicating that there is no actual relationship between the two.

[0032] Extracting quality content from data blocks to generate data feature values ​​includes the following steps: The quality content is normalized and standardized to obtain the target effective content; The quality content includes raw material testing reports, production process records, storage temperature and humidity data, transportation environment logs, and construction acceptance records. These data have different formats, units, and dimensions. Through normalization and standardization processing, these data with different units and magnitudes are uniformly converted into dimensionless values ​​between 0 and 1, eliminating unit differences and magnitude deviations between data.

[0033] The degree of influence of the target effective content is obtained based on the quality evaluation criteria for waterproof materials; Different quality parameters have varying degrees of impact on the quality of waterproofing materials. For example, the tensile strength and elongation at break of the raw materials determine the service life of the waterproofing membrane and have a higher impact. Temperature and humidity deviations during storage affect the aging rate of the material, but the impact is relatively low. The impact of bumps and jostling during transportation on the physical damage of the membrane is a secondary factor. Therefore, a corresponding weight is assigned to each effective parameter. For example, the weight for tensile strength is W1=0.3, for elongation at break W2=0.25, for impermeability W3=0.2, for temperature and humidity control W4=0.15, and for transportation stability W5=0.1. The sum of all weights is 1 to ensure the rationality of the weight allocation. The determination of the degree of impact is based on the quality evaluation standards of the waterproofing material industry. For instance, membranes that fail to meet tensile strength standards are directly judged as substandard products, therefore their weight is set to the highest; while temperature and humidity deviations within a reasonable range have a smaller impact on material quality, therefore their weight is set to a lower value.

[0034] Quality content is fused and quantified based on its degree of impact to obtain quality characterization information; The quality characterization information V = Σ(Ni × Wi), where Ni is the effective content of the i-th objective, Wi is the influence weight of the i-th objective, and Σ represents the summation over all objectives. For example, the normalized value of tensile strength N1 = 0.375, the normalized value of elongation at break N2 = 0.4, and the normalized value of impermeability N3 = 0.5 have corresponding influence weights of W1 = 0.3, W2 = 0.25, and W3 = 0.2, respectively. The quality characterization information V = N1 × W1 + N2 × W2 + N3 × W3. Substituting the values, we get V = 0.375 × 0.3 + 0.4 × 0.25 + 0.5 × 0.2 = 0.3125, which can intuitively reflect the overall quality level of this process.

[0035] Integrate quality characterization information to generate data feature values; Each data block has corresponding quality characterization information, which represents the quality level of that stage. All quality characterization information within a data block is standardized and aggregated to form a data feature value that represents the overall quality status of the data block. For a single-stage data block, its data feature value is equal to the quality characterization information of that stage. For a data block containing multiple sub-stages, such as a production and processing stage including batching, mixing, and calendering processes, each process corresponds to quality characterization information V1, V2, and V3. The data feature value S is calculated using a weighted average. Assuming the importance weights of each process are A1, A2, and A3, then S = V1 × A1 + V2 × A2 + V3 × A3. For example, if V1 = 0.4, A1 = 0.4, V2 = 0.35, A2 = 0.3, and V3 = 0.45, A3 = 0.3, then S = 0.4 × 0.4 + 0.35 × 0.3 + 0.45 × 0.3 = 0.4, and the data feature value of this production and processing data block is 0.4. The data feature values ​​range from 0 to 1. The closer the value is to 1, the higher the quality level of the data block, and the closer it is to 0, the lower the quality level.

[0036] The process of generating a profile index factor for a data block based on data feature values, identifiers, dynamic index codes, and adjacent labels includes the following steps: Based on the identifiers, the data feature values ​​are processed for subject ownership confirmation to obtain quality ownership benchmark information; The identifier is a unique identifier for a data block. It is linked to the business entity, batch, and process information within the waterproofing material supply chain. Entity ownership verification involves binding data feature values ​​to their corresponding identifiers, thus establishing a relationship. For example, let's assume the data feature value of data block A is S. A The corresponding identifier is ID. A Then, the quality ownership benchmark information KA=S after ownership confirmation A +ID A The plus sign indicates the binding association between data feature values ​​and identifiers, rather than the addition of numerical values. The quality determination benchmark information includes the quantitative quality level of data feature values ​​and the subject attribution information of identifiers.

[0037] Dynamic quality information is obtained by dynamically adapting and correcting the quality weighting benchmark information based on the dynamic index code. Dynamic index codes reflect the dynamic parameters of a data block's business stage and environmental changes. For example, process adjustments in the production stage include temperature and humidity fluctuations in warehousing, timeliness deviations in transportation, and environmental changes in construction. Assuming the data characteristic value of the quality assessment benchmark information is S, and the correction coefficient of the dynamic index code is C, then the quantified value of the corrected dynamic quality information is S' = S × (1 + C), where C ranges from -0.2 to 0.2. When the environmental changes reflected by the dynamic index code are beneficial to quality, C is positive, and the data characteristic value is increased; when the environmental changes are detrimental to quality, C is negative, and the data characteristic value is decreased. For example, in the quality assessment benchmark information of a certain warehousing data block, the data characteristic value is 0.5, and the dynamic index code shows that the warehousing environment humidity exceeds the standard range by 10%, with a corresponding correction coefficient C of -0.1. Therefore, the quantified value of the corrected dynamic quality information is S' = 0.5 × (1 - 0.1) = 0.45.

[0038] By associating and completing dynamic quality information with adjacent labels, complete quality information can be obtained. For example, the adjacent labels of a raw material data block are identifiers of production data blocks, and the adjacent labels of production data blocks are identifiers of warehousing data blocks. These labels record the association path of data blocks in the supply chain. Association completion combines the dynamic quality information of the current data block with the association information of upstream and downstream data blocks to supplement the missing association information of the current data block, thereby forming a quality information chain. Assuming the dynamic quality information of the current data block is MX, the upstream adjacent label is IDprev, and the downstream adjacent label is IDnext, then the completed quality information F = MX + IDprev + IDnext. The "+" sign indicates the association and integration of information. The complete quality information includes the quality level of the current data block, the dynamic correction result, and the identifiers of the upstream and downstream associated data blocks, clearly defining the position of the data block in the supply chain quality chain. For example, the dynamic quality information includes the data feature value 0.45, the identifier production batch 20260101, the upstream adjacent label being raw material batch 20260101, and the downstream adjacent label being warehousing batch 20260101.

[0039] The complete quality information is aggregated and standardized in dimensions to generate profile index factors; Dimensional aggregation and standardization involve standardizing the aggregated information to ensure a unified format. The profile index factor is a composite unit containing multi-dimensional information. Assuming the quantized value in the complete quality information is S', the subject identifier is ID, the correction coefficient of the dynamic index code is C, the upstream adjacent label is IDprev, and the downstream adjacent label is IDnext, the profile index factor I = {S', ID, C, IDprev, IDnext}. Each element corresponds to a different dimension of the complete quality information. After standardization, the profile index factor of each data block has a unified structure, containing all information about the quality level, subject attribution, dynamic correction, and association path. For example, the profile index factor of a certain data block shows a quantized quality value of 0.45, the subject is production batch 20260101, the dynamic correction coefficient is -0.1, the upstream is raw material batch 20260101, and the downstream is warehouse batch 20260101.

[0040] The material status information is compared with the profile index factors according to the quality evaluation criteria; The quality assessment criteria are a pre-defined quality standard system for the entire process of waterproof materials, including raw material performance, production process control, storage environment requirements, transportation conditions, and construction acceptance indicators. The material status information of the profile index factors includes dynamic correction results of quality values ​​and related path information. Each indicator of the material status information is verified against the corresponding standard of the quality assessment criteria. For example, the normalized value of the tensile strength of the raw material data block is compared with the standard requirements, the temperature and humidity fluctuation data of the storage data block is compared with the storage standard, and the bonding strength test value of the construction data block is compared with the acceptance standard. By comparing, the deviation between the material status information and the standard is clarified, providing a basis for attribute extraction and feature matching. Assuming the core quantitative indicator of the material status information belonging to the portrait index factor is G, and the corresponding standard value of the quality judgment criterion is VB, then the deviation value between the two is E=G-VB. The sign and magnitude of the deviation value reflect the direction and degree of deviation between the material status information and the standard. For example, when G=0.8 and VB=0.7, E=0.1, indicating that the indicator is higher than the standard requirement; when G=0.6 and VB=0.7, E=-0.1, indicating that the indicator is lower than the standard requirement.

[0041] The quality profile labels for the profile data blocks obtained from the analysis of material status information include the following steps: The inherent quality attributes of materials are obtained based on their status information; The inherent quality attributes of materials are the fundamental quality characteristics of waterproof materials that are unaffected by the external environment. These include, for example, the purity of raw materials, the basic formula of the waterproofing membrane, and the inherent parameters of the production process. These attributes determine the quality level of the waterproofing material. Inherent quality attributes are extracted from material status information, retaining indicators that reflect the material's inherent quality. For instance, in the material status information of the storage data block, the dynamic correction effects of temperature and humidity fluctuations are filtered out to extract the membrane's aging resistance and physical stability indicators; similarly, in the material status information of the transportation data block, the dynamic effects of transportation bumps are filtered out to extract the membrane's impact resistance and structural strength indicators.

[0042] Match the actual quality performance characteristics of each link in the supply chain based on the inherent quality attributes of the materials. Actual quality performance characteristics refer to the specific quality status of waterproofing materials at different business stages. These include, for example, the purity of raw materials, the stability of the production process, the aging of materials during storage, the physical damage during transportation, and the bonding effect during construction. The matching process maps the inherent quality attributes of materials to the quality performance characteristics at each stage, determining how the material's basic quality is reflected in different business stages. For instance, raw materials with higher inherent quality attribute values ​​exhibit better process stability during production, slower aging during storage, and more stable bonding effects during construction. Conversely, raw materials with lower inherent quality attribute values ​​are more prone to process fluctuations during production, faster aging during storage, and more likely to experience poor bonding during construction. Assuming the inherent quality attribute value of a material is P, the matching degree between the two is M = P × L, where L is the quality performance weight of that stage, reflecting the degree of influence of the inherent quality attribute on the quality performance of that stage. For example, L = 0.3 for the production stage, L = 0.2 for the warehousing stage, and L = 0.3 for the construction stage. When P = 0.5, the matching degree M1 for the production stage is M1 = 0.5 × 0.3 = 0.15, the matching degree M2 for the warehousing stage is M2 = 0.5 × 0.2 = 0.1, and the matching degree M3 for the construction stage is M3 = 0.5 × 0.3 = 0.15. These matching degree values ​​reflect the strength of the inherent quality attribute's quality performance in different stages.

[0043] Define the quality category of the corresponding data block based on the actual quality performance characteristics, and determine the quality profile label of the quality category according to the category classification rules. Quality categories are the classification of data blocks into quality levels based on their actual quality performance characteristics. For example, they can be divided into Excellent, Acceptable, Need Improvement, and Unacceptable, with each level corresponding to a specific range of quality performance characteristics. Category classification rules are pre-defined grading standards. For instance, a total matching degree between 0.4 and 0.5 is Excellent, between 0.3 and 0.4 is Acceptable, between 0.2 and 0.3 is Need Improvement, and below 0.2 is Unacceptable. When defining quality categories, the matching degree values ​​of each stage are added together to obtain the overall matching degree. For example, if the matching degrees for the production, warehousing, and construction stages of a data block are 0.15, 0.1, and 0.15 respectively, the overall matching degree PS = 0.15 + 0.1 + 0.15 = 0.4. Since 0.4 falls within the Excellent range, the quality category of this data block is Excellent. Once the quality category is determined, a corresponding quality profile label can be generated for the data block. For example, it could be a high-quality raw material batch, a qualified production batch, a warehousing batch that needs improvement, or a substandard construction batch. For instance, when the overall matching degree is 0.35, the corresponding quality category is qualified, and the generated quality profile label is qualified raw material batch 20260101; when the overall matching degree is 0.18, the corresponding quality category is substandard, and the generated quality profile label is substandard construction batch 20260101.

[0044] The process of generating profile index verification factors based on data blocks includes the following steps: Use the data feature values ​​and quality profile label information of the data blocks as the basic data; For example, the data feature value of a certain production data block is 0.65, and the corresponding quality profile label is qualified production batch 20260101. These two pieces of information together constitute the basic data of this data block, including the quality quantification level and classification label.

[0045] Based on the correlation between data blocks and the preset quality evaluation criteria, the basic data is integrated and summarized to obtain the profile index verification factor.

[0046] The relationships between data blocks include business dependency relationships and quality transmission relationships, clarifying the upstream and downstream connection methods and quality impact paths of data blocks in the supply chain. The pre-set quality assessment criteria form a full-process quality standard system, specifying the correspondence between the quality level and label classification of each stage. The relationship links the basic data of the current data block with the information of upstream and downstream data blocks, forming a complete information chain containing the quality transmission path. The consistency of the data feature values ​​and quality profile labels in the chain is verified according to the quality assessment criteria to ensure the matching of quantitative levels and classification labels. Assuming the data feature value of the current data block is S, the theoretical feature value range corresponding to the quality profile label is [Smin, Smax], and the upstream and downstream impact correction coefficient corresponding to the relationship is K, then the core verification value of the profile index verification factor is VH = S × K. The value of K ranges from 0.8 to 1.2. When the quality level of upstream and downstream stages is high, K takes a value greater than 1; when the quality level of upstream and downstream stages is low, K takes a value less than 1.

[0047] The construction of the profile index verification factor enables multi-dimensional integration of data block quality information. When the data feature value of a certain warehouse data block is 0.7 and the quality profile label is high-quality warehouse batch, it is found that the quality level of its upstream production data block is low according to the correlation. The correction coefficient K is 0.8 and the verification value VH=0.7×0.8=0.56. At this time, the verification value does not match the theoretical range corresponding to the label, so it is marked as a verification object.

[0048] Extract the quality attributes of data feature values ​​and the correlation and flow relationships of data blocks; extract the quality feature information of data blocks; Based on quality attributes and associated circulation relationships, determine the quality compliance status and status deviation of the profile index verification factors; Quality attributes include quantitative and categorical information such as data feature values, label levels, and check values, reflecting whether the quality level of the data block itself meets the preset standards. The correlation and flow relationship represents the business dependence and quality transmission path of the upstream and downstream data blocks to which the profile index verification factor belongs, reflecting the consistency and rationality of the current data block's quality status within the supply chain. Quality attributes are compared with preset quality judgment criteria to determine whether they meet the standard requirements. Assuming the core quality attribute value of the profile index verification factor is AS, and the pass threshold of the preset quality judgment criteria is A0, then the compliance degree CH = AS - A0. When CH is greater than or equal to 0, it indicates that the quality attribute of the data block meets the standard requirements, and it is judged as compliant; when CH is less than 0, it indicates that the standard requirements are not met, and it is judged as non-compliant. For example, if the quality attribute value AS of a construction data block is 0.6 and the pass threshold A0 is 0.5, the compliance degree CH = 0.6 - 0.5 = 0.1, and it is judged as compliant; if the quality attribute value AS of a construction data block is 0.45 and the pass threshold A0 is 0.5, then the compliance degree CH = 0.45 - 0.5 = -0.05, and it is judged as non-compliant.

[0049] Based on the quality compliance status, status deviation status, and quality characteristic information, a quality profile verification label is obtained for the data block; For example, if the upstream raw material data block has a high quality level, the downstream production data block should also have a high quality level. Assuming the current data block's quality attribute value is AZ, the upstream data block's quality attribute value is Aup, and the downstream data block's quality attribute value is Adown, with a preset allowable transmission deviation range of ±0.1, then the state deviation degree DZ = AZ - (Aup + Adown) ÷ 2. When the absolute value of DZ is less than or equal to 0.1, it indicates that the current data block's quality state is consistent with the upstream and downstream transmission trend, with no significant deviation; when the absolute value of DZ is greater than 0.1, it indicates the existence of a state deviation.

[0050] The quality profile verification label for a data block is generated based on its quality compliance status, state deviation status, and quality characteristic information. Quality characteristic information refers to the details of specific quality indicators contained within the data block, such as the tensile strength of the raw material, the impermeability of the roll material, and the bonding strength during construction, used to refine the type and degree of deviation. A basic level is determined based on the compliance status, and deviation details are supplemented by the state deviation status. Finally, specific issues are refined based on the quality characteristic information. For example, if a data block has a compliance level CH=0.1 and a state deviation level DZ=-0.075, and the quality characteristic information shows that all indicators meet the standards, the generated verification label is "compliant with no deviation." If the compliance level CH=-0.05 and the state deviation level DZ=-0.175, and the quality characteristic information shows that the bonding strength does not meet the standards, the generated verification label is "non-compliant."

[0051] Based on the quality profile verification label and the quality profile label, the quality traceability results are output, specifically including the following steps: The first verification result of the data block is obtained by analyzing the quality profile verification tags and the quality profile tags; the association path information of the quality profile tags is extracted, and the second verification result of the data block is obtained by analyzing the quality profile verification tags and the association path information. The specific steps include: Determine the differences in quality judgment between the quality profile verification label and the quality profile label; Based on the differences in quality assessment content and data feature values, the source of the differences is traced and the first verification result corresponding to the data block is obtained. The quality profile label is an initial quality classification identifier generated based on the profile index factor. The quality profile verification label is a secondary classification identifier after compliance and deviation verification. The differences between the two mainly lie in the quality level, deviation, and problem indication. For example, if the initial quality profile label indicates a high-quality raw material batch, while the verification label indicates a qualified raw material batch, the difference in quality judgment is that the quality level drops from high-quality to qualified, and the deviation is that the tensile strength of the raw material is lower than the high-quality standard. The root cause of the difference is determined by combining the specific values ​​of the data feature values. Assuming that the theoretical feature value range corresponding to the quality profile label is [S1, S2], and the actual feature value corresponding to the quality profile verification label is SS, then the difference degree D1 = SS - (S1 + S2) ÷ 2. When the absolute value of D1 is less than or equal to 0.05, it is determined that the label difference is within a reasonable fluctuation range, and the first verification result is no substantial deviation; when the absolute value of D1 is greater than 0.05, it is determined that there is a substantial deviation, and the first verification result is that the deviation needs to be verified. The source of the deviation is located by combining the specific indicators of the data feature values. For example, the quality profile label of a certain raw material data block is "high quality", corresponding to a theoretical range of 0.7 to 0.8. The actual feature value SS corresponding to the verification label is 0.68. By calculation, D1 = 0.68 - (0.7 + 0.8) ÷ 2 = -0.07. The absolute value is greater than 0.05, which is judged to be a substantial deviation.

[0052] Extract the association relationships between quality profile tags and data blocks; determine the association path information based on the association relationships. Based on the associated path information, determine the quality flow trajectory and the quality judgment result of the quality profile verification label; The second verification result of the data block is obtained based on the quality flow trajectory and the quality judgment result; The relationship status records the dependence and quality transmission relationships of data blocks in upstream and downstream business of the supply chain. The relationship path information is a complete chain composed of multiple data block relationship statuses, such as the path of raw material data block, production data block, and warehousing data block. The quality flow trajectory is the process of quality impact transmission reconstructed based on the relationship path information, such as the impact of raw material quality on production process stability, and the impact of production quality on aging rate in storage. The quality profile verification label is used to determine whether the quality status of the current data block is consistent with the transmission trend of the flow trajectory. Assuming the quality profile verification label level of the current data block is LD, the label level of the upstream data block is Lup, and the label level of the downstream data block is Ldown, with a preset transmission consistency threshold of ±1 level, then the flow deviation degree D2 = LD - (Lup + Ldown) ÷ 2. When the absolute value of D2 is less than or equal to 0.5, it is determined that the flow trajectory is consistent with the verification label, and the second verification result is normal flow; when the absolute value of D2 is greater than 0.5, it is determined that there is a deviation between the flow trajectory and the verification label, and the second verification result is abnormal flow.

[0053] The quality traceability results are output based on the first and second verification results, specifically including the following steps: Determine the matching difference between the first verification result and the second verification result; Determine the quality verification status of the data block based on the matching differences; Output quality traceability results based on the quality verification status.

[0054] Assuming the deviation of the first verification result is D1 and the deviation of the second verification result is D2, the matching difference value MP = |D1-D2|, where || represents the absolute value. MP ranges from 0 to 1. The closer MP is to 0, the higher the matching degree between the first and second verification results; conversely, the closer MP is to 1, the lower the matching degree. For example, if the deviation of the first verification result D1 for a construction data block is 0.08 and the deviation of the second verification result D2 is 0.07, the matching difference value MP = |0.08-0.07| = 0.01, indicating a high degree of matching between the two results. However, if the deviation of the first verification result D1 for a construction data block is 0.08 and the deviation of the second verification result D2 is 0.2, the matching difference value MP = |0.08-0.2| = 0.12, indicating a lower matching degree and inconsistencies.

[0055] The quality verification status of data blocks is determined based on matching discrepancies. The quality verification status is a reliability level based on the matching discrepancy value (MP), categorized into Highly Reliable, Basically Reliable, Needs Verification, and Unreliable, each corresponding to a different range of matching discrepancies. When the matching discrepancy value (MP) is less than or equal to 0.05, it is considered Highly Reliable; when it is greater than 0.05 and less than or equal to 0.1, it is considered Basically Reliable; when it is greater than 0.1 and less than or equal to 0.2, it is considered Needs Verification; and when it is greater than 0.2, it is considered Unreliable. Different verification statuses correspond to different quality handling strategies: Highly Reliable data blocks indicate consistent internal labels and normal flow paths, requiring no additional verification; Basically Reliable data blocks indicate deviations within a reasonable range, confirmed through routine sampling; Data blocks needing verification indicate some deviations, requiring investigation; and Unreliable data blocks indicate severe deviations, requiring immediate full-link backtracking.

[0056] The quality traceability results are output based on the quality verification status. These results include the data block's production stage, batch information, quality verification status, deviation information, and traceability recommendations, reflecting the quality status and positioning direction of the data block. Highly reliable data blocks are marked as "quality qualified," indicating normal flow; basically reliable data blocks are marked as "quality qualified," and routine sampling is recommended; data blocks pending verification are marked as having deviations, and the corresponding stage is recommended for investigation; unreliable data blocks are marked as having quality anomalies, and it is recommended to immediately stop flow and trace back upstream and downstream. For example, the traceability result of a production data block shows that the quality verification status of this batch of roll material is "pending verification," with deviations including a slightly lower process parameter in the first verification result and inconsistencies in quality transmission from the raw material stage in the second verification result. The traceability recommendation is to investigate the process stability of the production stage and the compatibility of the raw material batches.

[0057] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0058] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A quality traceability method based on collaborative management of the waterproof material supply chain, characterized in that, Includes the following steps: Obtain raw data from the waterproofing material supply chain and divide the raw data into several data blocks; Determine the relationships between data blocks and extract the quality content of data blocks to generate data feature values; The process of generating a profile index factor for a data block based on data feature values, identifiers, dynamic index codes, and adjacent labels includes the following steps: Based on the identifiers, the data feature values ​​are processed for subject ownership confirmation to obtain quality ownership benchmark information; Dynamic quality information is obtained by dynamically adapting and correcting the quality weighting benchmark information based on the dynamic index code. By associating and completing dynamic quality information with adjacent labels, complete quality information can be obtained. The complete quality information is aggregated and standardized in dimensions to generate profile index factors; Based on the quality assessment criteria, the material status information of the profile index factors is compared, and the quality profile labels of the profile data blocks are obtained by analyzing the material status information. The specific steps include: The inherent quality attributes of materials are obtained based on their status information; Match the actual quality performance characteristics of each link in the supply chain based on the inherent quality attributes of the materials. Define the quality category of the corresponding data block based on the actual quality performance characteristics, and determine the quality profile label of the quality category according to the category classification rules. Based on data blocks, generate profile index verification factors, extract quality attributes of data feature values ​​and the correlation and flow relationships of data blocks; extract quality feature information of data blocks; Based on quality attributes and associated circulation relationships, determine the quality compliance status and status deviation of the profile index verification factors; Based on the quality compliance status, status deviation status, and quality characteristic information, a quality profile verification label is obtained for the data block; The quality traceability results are output based on the quality profile verification label and the quality profile label.

2. The quality traceability method based on collaborative management of the waterproof material supply chain according to claim 1, characterized in that, The original data is divided into several data blocks, specifically: Based on the business timeline and quality correlation attributes of the production and circulation of waterproof materials, the original data is hierarchically split into several data blocks.

3. The quality traceability method based on collaborative management of the waterproof material supply chain according to claim 1, characterized in that, Determining the relationships between data blocks involves the following steps: By combining the dependency attributes of business operations at each stage of the supply chain and the characteristics of quality impact transmission, we can determine the quality correlation dimensions and business linkage attributes of different data blocks. Based on the quality correlation dimension and business linkage attribute, the linkage connection characteristics are obtained, and the quality transmission correlation and business dependency correlation between data blocks are established based on the linkage connection characteristics. The relationship is obtained by combining quality transmission relationship and business dependency relationship.

4. The quality traceability method based on collaborative management of the waterproof material supply chain according to claim 1, characterized in that, Extracting quality content from data blocks to generate data feature values ​​includes the following steps: The quality content is normalized and standardized to obtain the target effective content; The degree of influence of the target effective content is obtained based on the quality evaluation criteria for waterproof materials; Quality content is fused and quantified based on its degree of impact to obtain quality characterization information; Integrate quality characterization information to generate data feature values.

5. A quality traceability method based on collaborative management of the waterproof material supply chain according to claim 4, characterized in that, The process of generating profile index verification factors based on data blocks includes the following steps: Use the data feature values ​​and quality profile label information of the data blocks as the basic data; Based on the correlation between data blocks and the preset quality evaluation criteria, the basic data is integrated and summarized to obtain the profile index verification factor.

6. A quality traceability method based on collaborative management of the waterproof material supply chain according to claim 5, characterized in that, Based on the quality profile verification label and the quality profile label, the quality traceability results are output, specifically including the following steps: The first verification result of the data block is obtained by analyzing the quality profile verification label and the quality profile label; the association path information of the quality profile label is extracted, and the second verification result of the data block is obtained by analyzing the quality profile verification label and the association path information. Output the quality traceability results based on the first and second verification results.

7. A quality traceability method based on collaborative management of the waterproof material supply chain according to claim 6, characterized in that, The first verification result of the data block is obtained by analyzing the quality profile verification tags and the quality profile tags; the association path information of the quality profile tags is extracted, and the second verification result of the data block is obtained by analyzing the quality profile verification tags and the association path information. The specific steps include: Determine the differences in quality judgment between the quality profile verification label and the quality profile label; Based on the differences in quality assessment content and data feature values, the source of the differences is traced and the first verification result corresponding to the data block is obtained. Extract the association relationships between quality profile tags and data blocks; determine the association path information based on the association relationships. Based on the associated path information, determine the quality flow trajectory and the quality judgment result of the quality profile verification label; The second verification result of the data block is obtained based on the quality flow trajectory and the quality judgment result.

8. A quality traceability method based on collaborative management of the waterproof material supply chain according to claim 7, characterized in that, The quality traceability results are output based on the first and second verification results, specifically including the following steps: Determine the matching difference between the first verification result and the second verification result; Determine the quality verification status of the data block based on the matching differences; Output quality traceability results based on the quality verification status.

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